Remove remaining references to ACL

ML-Frameworks/pytorch-aarch64/CHANGELOG.md

@@ -45,11 +45,11 @@ where `YY` is the year, and `MM` the month of the increment.
    - `IDEEP_HASH=e087b6e4b32a7ba684db82231d1558123968ac1d`, from ideep_pytorch, May 11th, 2026.
    - `ONEDNN_HASH=3004f0a1d9cf92c06eaaca57840aaa2149ebba85`, from main, May 27th, 2026.
    - `KLEIDIAI_HASH=5866364d3bc079d2d6cae5f0acf6d076594bc7a7`, v1.25.0 from main, May 28th, 2026.
- - Replaces `ACL_VERSION=v52.8.0` with `ACL_VERSION=v53.1.0`, from main, May 18th.
  - Updates `OPENBLAS_VERSION` from `d26960a21ec5da7f77377f28bd6e230060841ae0` to v0.3.33, from main, Apr 23rd.
  - Updates `torchvision` from 0.26.0.dev20260329 to 0.28.0.dev20260527.
 
 ### Removed
+ - Removes `ACL_VERSION` which references library that is no longer present.
  - Disables PyTorch [PR #182655](https://github.com/pytorch/pytorch/pull/182655), to update the PyTorch CI build scripts.
  - Disables PyTorch [PR #170600](https://github.com/pytorch/pytorch/pull/170600), to gate deletion of clean-up steps in build_common.sh.
  - Disables PyTorch [PR #167328](https://github.com/pytorch/pytorch/pull/167328), to build cpuinfo into c10 shared library.

ML-Frameworks/pytorch-aarch64/README.md

@@ -109,10 +109,7 @@ Note: use the `CommitDate` for the trailing comments unless otherwise specified
 
 #### Tags
 
-For these dependencies, you should assign the latest tag from the releases to the appropriate variable in `versions.sh` (e.g. assign the latest tag for `ComputeLibrary` to `ACL_VERSION`).
-
-- ComputeLibrary: https://github.com/ARM-software/ComputeLibrary/tags
-  - Pick the newest release tag.
+For these dependencies, you should assign the latest tag from the releases to the appropriate variable in `versions.sh` (e.g. assign the latest tag for `OpenBLAS` to `OPENBLAS_VERSION`).
 
 - OpenBLAS: https://github.com/OpenMathLib/OpenBLAS/tags
   - Pick the newest release tag.

ML-Frameworks/pytorch-aarch64/build-wheel.sh

@@ -94,10 +94,9 @@ mkdir -p "${OUTPUT_LOCAL_DIR}"
 
 trap cleanup EXIT
 
-echo "Building local manywheel builder image with ACL_VERSION=${ACL_VERSION} and OPENBLAS_VERSION=${OPENBLAS_VERSION}"
+echo "Building local manywheel builder image with OPENBLAS_VERSION=${OPENBLAS_VERSION}"
 (
     cd "${PYTORCH_LOCAL_DIR}"
-    ACL_VERSION="${ACL_VERSION}" \
     OPENBLAS_VERSION="${OPENBLAS_VERSION}" \
     MAX_JOBS="${MAX_JOBS}" \
     USE_CCACHE="${USE_CCACHE}" \

ML-Frameworks/pytorch-aarch64/examples/README.md

@@ -1,5 +1,5 @@
 <!--
-SPDX-FileCopyrightText: Copyright 2021-2025 Arm Limited and affiliates.
+SPDX-FileCopyrightText: Copyright 2021-2026 Arm Limited and affiliates.
 
 SPDX-License-Identifier: Apache-2.0
 -->
@@ -9,25 +9,23 @@ SPDX-License-Identifier: Apache-2.0
 <!-- Generated with VS Code's 'Markdown All in One' extension. -->
 <!-- Regenerate with: 'Markdown All in One: Update Table of Contents'. -->
 
-- [Examples](#examples)
-  - [Description](#description)
-  - [Vision](#vision)
-    - [Image classification](#image-classification)
-    - [Object detection](#object-detection)
-  - [Natural Language Processing (NLP)](#natural-language-processing-nlp)
-    - [Question answering](#question-answering)
-  - [Dynamic quantization](#dynamic-quantization)
-  - [General optimization guidelines](#general-optimization-guidelines)
-    - [Weight prepacking](#weight-prepacking)
-    - [General flags](#general-flags)
-    - [Compiled mode flags](#compiled-mode-flags)
-    - [Eager mode flags](#eager-mode-flags)
-  - [Generative AI](#generative-ai)
-    - [4 bit Dynamic Quantization](#4-bit-dynamic-quantization)
-    - [Vision](#vision-1)
-      - [Command-Line Options](#command-line-options)
-    - [Text Generation](#text-generation)
-      - [Command-Line Options](#command-line-options-1)
+- [Description](#description)
+- [Vision](#vision)
+  - [Image classification](#image-classification)
+  - [Object detection](#object-detection)
+- [Natural Language Processing (NLP)](#natural-language-processing-nlp)
+  - [Question answering](#question-answering)
+- [Dynamic quantization](#dynamic-quantization)
+- [General optimization guidelines](#general-optimization-guidelines)
+  - [General flags](#general-flags)
+  - [Compiled mode flags](#compiled-mode-flags)
+  - [Eager mode flags](#eager-mode-flags)
+- [Generative AI](#generative-ai)
+  - [4 bit Dynamic Quantization](#4-bit-dynamic-quantization)
+  - [Vision](#vision-1)
+    - [Command-Line Options](#command-line-options)
+  - [Text Generation](#text-generation)
+    - [Command-Line Options](#command-line-options-1)
 
 ## Description
 
@@ -56,7 +54,7 @@ The file [`resnet_v1-50.yml`](resnet_v1-50.yml) provides, in [YAML format](https
 
 ### Object detection
 
-The script [`detect_objects.py`](detect_object.py) demonstrates how to run object detection using SSD-ResNet-34.
+The script [`detect_objects.py`](detect_objects.py) demonstrates how to run object detection using SSD-ResNet-34.
 
 The SSD-ResNet-34 model is trained from the Common Object in Context (COCO) image dataset.
 This is a multiscale SSD (Single Shot Detection) model based on the ResNet-34 backbone network that performs object detection.
@@ -198,15 +196,11 @@ Note that in the above data we used the `--warmup` flag to run the model once be
 
 ## General optimization guidelines
 
-### Weight prepacking
-
-`Linear` layers calling [Arm ComputeLibrary](https://github.com/ARM-software/ComputeLibrary) (ACL) matmuls reorder weights during runtime by default. These reorders can be eliminated by calling `pack_linear_weights` as shown in `pack_linear_weights.py`. This improves the performance of any models calling a `Linear` layer multiple times.
-
 ### General flags
 
 There are several flags which typically improve the performance of PyTorch.
 
-`DNNL_DEFAULT_FPMATH_MODE`: setting the environment variable `DNNL_DEFAULT_FPMATH_MODE` to `BF16` or `ANY` will instruct ACL to dispatch fp32 workloads to bfloat16 kernels where hardware support permits. _Note: this may introduce a drop in accuracy._
+`DNNL_DEFAULT_FPMATH_MODE`: setting the environment variable `DNNL_DEFAULT_FPMATH_MODE` to `BF16` or `ANY` will result in fp32 workloads being dispatched to bfloat16 kernels where hardware support permits. _Note: this may introduce a drop in accuracy._
 
 You can control the number of threads with `OMP_NUM_THREADS`, smaller models may perform better with fewer threads.
 

ML-Frameworks/pytorch-aarch64/versions.sh

@@ -15,7 +15,6 @@ ONEDNN_HASH=3004f0a1d9cf92c06eaaca57840aaa2149ebba85    # From main, May 27th, 2
 KLEIDIAI_HASH=5866364d3bc079d2d6cae5f0acf6d076594bc7a7  # v1.25.0 from main, May 28th, 2026
 
 # build-wheel.sh deps
-ACL_VERSION="v53.1.0"   # May 18th
 OPENBLAS_VERSION="v0.3.33"  # Apr 23rd
 
 # Dockerfile deps