From ca461fb9fe73d421c3c79a550445036c72cf0861 Mon Sep 17 00:00:00 2001
From: Songhao Jia <gasoonjia@meta.com>
Date: Wed, 26 Nov 2025 14:11:53 -0800
Subject: [PATCH 1/8] introduce cuda sdpa

Differential Revision: D87950475
---
 backends/cuda/runtime/TARGETS                 |   4 +
 backends/cuda/runtime/shims/sdpa.cu           | 649 +++++++++++++++
 backends/cuda/runtime/shims/sdpa.cuh          | 282 +++++++
 backends/cuda/runtime/shims/sdpa.h            | 104 +++
 backends/cuda/runtime/shims/tests/targets.bzl |   1 +
 ...orch_cuda_scaled_dot_product_attention.cpp | 781 ++++++++++++++++++
 6 files changed, 1821 insertions(+)
 create mode 100644 backends/cuda/runtime/shims/sdpa.cu
 create mode 100644 backends/cuda/runtime/shims/sdpa.cuh
 create mode 100644 backends/cuda/runtime/shims/sdpa.h
 create mode 100644 backends/cuda/runtime/shims/tests/test_aoti_torch_cuda_scaled_dot_product_attention.cpp

diff --git a/backends/cuda/runtime/TARGETS b/backends/cuda/runtime/TARGETS
index a85f3a7e6a3..01dabee9086 100644
--- a/backends/cuda/runtime/TARGETS
+++ b/backends/cuda/runtime/TARGETS
@@ -53,6 +53,7 @@ runtime.cxx_library(
         "shims/cuda_guard.cpp",
         "shims/int4mm.cu",
         "shims/memory.cpp",
+        "shims/sdpa.cu",
         "shims/tensor_attribute.cpp",
     ],
     headers = [
@@ -61,6 +62,8 @@ runtime.cxx_library(
         "shims/int4mm.cuh",
         "shims/int4mm.h",
         "shims/memory.h",
+        "shims/sdpa.cuh",
+        "shims/sdpa.h",
         "shims/tensor_attribute.h",
         "utils.h",
     ],
@@ -84,6 +87,7 @@ runtime.cxx_library(
     ],
     external_deps = [
         ("cuda", None, "cuda-lazy"),
+        ("cuda", None, "cublas-lazy"),
     ],
 )
 
diff --git a/backends/cuda/runtime/shims/sdpa.cu b/backends/cuda/runtime/shims/sdpa.cu
new file mode 100644
index 00000000000..c15f1f006bc
--- /dev/null
+++ b/backends/cuda/runtime/shims/sdpa.cu
@@ -0,0 +1,649 @@
+/*
+ * Copyright (c) Meta Platforms, Inc. and affiliates.
+ * All rights reserved.
+ *
+ * This source code is licensed under the BSD-style license found in the
+ * LICENSE file in the root directory of this source tree.
+ */
+
+#include <cuda.h>
+#include <cuda_runtime.h>
+#include <cublas_v2.h>
+#include <cfloat>
+#include <cmath>
+
+#include <executorch/backends/aoti/utils.h>
+#include <executorch/backends/cuda/runtime/shims/sdpa.h>
+#include <executorch/backends/cuda/runtime/shims/sdpa.cuh>
+#include <executorch/backends/cuda/runtime/utils.h>
+#include <executorch/runtime/platform/log.h>
+
+namespace executorch::backends::cuda {
+
+using executorch::backends::aoti::Tensor;
+using executorch::backends::aoti::AOTITorchError;
+using executorch::runtime::Error;
+
+// ============================================================================
+// CUDA Kernels for Softmax and Masking
+// ============================================================================
+
+// Helper function for max with different types
+__device__ __forceinline__ float device_max(float a, float b) {
+  return fmaxf(a, b);
+}
+
+__device__ __forceinline__ __half device_max(__half a, __half b) {
+  return __hgt(a, b) ? a : b;
+}
+
+__device__ __forceinline__ __nv_bfloat16 device_max(__nv_bfloat16 a, __nv_bfloat16 b) {
+  #if __CUDA_ARCH__ >= 800
+  return __hgt(a, b) ? a : b;
+  #else
+  return __float2bfloat16(fmaxf(__bfloat162float(a), __bfloat162float(b)));
+  #endif
+}
+
+/**
+ * Softmax kernel with optional causal masking
+ *
+ * Computes softmax along the last dimension (seq_len_k) of a 4D tensor.
+ * Supports causal masking where positions j > i are masked out.
+ *
+ * Input: [batch, num_heads, seq_len_q, seq_len_k]
+ * Output: [batch, num_heads, seq_len_q, seq_len_k]
+ *
+ * Each thread processes one row (seq_len_q position).
+ *
+ * Note: Supports in-place operation (input == output).
+ */
+template<typename scalar_t>
+__global__ void softmax_with_causal_mask_kernel(
+    const scalar_t* input,
+    scalar_t* output,
+    const int64_t batch,
+    const int64_t num_heads,
+    const int64_t seq_len_q,
+    const int64_t seq_len_k,
+    const bool is_causal,
+    const float scale) {
+
+  // Each block processes one row of the attention matrix
+  const int64_t idx = blockIdx.x * blockDim.x + threadIdx.x;
+  const int64_t total_rows = batch * num_heads * seq_len_q;
+
+  if (idx >= total_rows) {
+    return;
+  }
+
+  // Decode position - we only need i for causal masking
+  const int64_t i = idx % seq_len_q;
+
+  // Pointer to the start of this row
+  const int64_t row_offset = idx * seq_len_k;
+  const scalar_t* input_row = input + row_offset;
+  scalar_t* output_row = output + row_offset;
+
+  // Find max for numerical stability (two-pass algorithm)
+  float max_val = -FLT_MAX;
+  for (int64_t j = 0; j < seq_len_k; ++j) {
+    if (!is_causal || j <= i) {
+      float val = static_cast<float>(input_row[j]) * scale;
+      max_val = fmaxf(max_val, val);
+    }
+  }
+
+  // Compute exp and sum
+  float sum_exp = 0.0f;
+  for (int64_t j = 0; j < seq_len_k; ++j) {
+    float val;
+    if (!is_causal || j <= i) {
+      val = expf(static_cast<float>(input_row[j]) * scale - max_val);
+    } else {
+      val = 0.0f;
+    }
+    output_row[j] = static_cast<scalar_t>(val);
+    sum_exp += val;
+  }
+
+  // Normalize
+  const float inv_sum = 1.0f / sum_exp;
+  for (int64_t j = 0; j < seq_len_k; ++j) {
+    output_row[j] = static_cast<scalar_t>(static_cast<float>(output_row[j]) * inv_sum);
+  }
+}
+
+/**
+ * Scale kernel - multiply all elements by a scalar
+ */
+template<typename scalar_t>
+__global__ void scale_kernel(
+    scalar_t* __restrict__ data,
+    const int64_t size,
+    const float scale) {
+  const int64_t idx = blockIdx.x * blockDim.x + threadIdx.x;
+  if (idx < size) {
+    data[idx] = static_cast<scalar_t>(static_cast<float>(data[idx]) * scale);
+  }
+}
+
+// ============================================================================
+// cuBLAS Helper Functions
+// ============================================================================
+
+/**
+ * Get or create a cuBLAS handle for the current stream
+ *
+ * Note: In production, this should use a handle pool or be managed
+ * by the backend infrastructure. This is a simplified version.
+ */
+cublasHandle_t get_cublas_handle(cudaStream_t stream) {
+  static cublasHandle_t handle = nullptr;
+
+  if (handle == nullptr) {
+    cublasCreate(&handle);
+  }
+
+  cublasSetStream(handle, stream);
+  return handle;
+}
+
+/**
+ * Batched matrix multiplication wrapper for cuBLAS
+ *
+ * Computes: C = alpha * op(A) @ op(B) + beta * C
+ * for a batch of matrices
+ */
+template<typename scalar_t>
+cublasStatus_t batched_gemm(
+    cublasHandle_t handle,
+    cublasOperation_t transa,
+    cublasOperation_t transb,
+    int m, int n, int k,
+    const scalar_t* alpha,
+    const scalar_t* A, int lda, int64_t strideA,
+    const scalar_t* B, int ldb, int64_t strideB,
+    const scalar_t* beta,
+    scalar_t* C, int ldc, int64_t strideC,
+    int batchCount);
+
+// Specializations for different types
+template<>
+cublasStatus_t batched_gemm<float>(
+    cublasHandle_t handle,
+    cublasOperation_t transa,
+    cublasOperation_t transb,
+    int m, int n, int k,
+    const float* alpha,
+    const float* A, int lda, int64_t strideA,
+    const float* B, int ldb, int64_t strideB,
+    const float* beta,
+    float* C, int ldc, int64_t strideC,
+    int batchCount) {
+  return cublasSgemmStridedBatched(
+      handle, transa, transb, m, n, k,
+      alpha, A, lda, strideA, B, ldb, strideB,
+      beta, C, ldc, strideC, batchCount);
+}
+
+template<>
+cublasStatus_t batched_gemm<__half>(
+    cublasHandle_t handle,
+    cublasOperation_t transa,
+    cublasOperation_t transb,
+    int m, int n, int k,
+    const __half* alpha,
+    const __half* A, int lda, int64_t strideA,
+    const __half* B, int ldb, int64_t strideB,
+    const __half* beta,
+    __half* C, int ldc, int64_t strideC,
+    int batchCount) {
+  return cublasHgemmStridedBatched(
+      handle, transa, transb, m, n, k,
+      alpha, A, lda, strideA, B, ldb, strideB,
+      beta, C, ldc, strideC, batchCount);
+}
+
+// Note: BFloat16 uses compute type float internally
+template<>
+cublasStatus_t batched_gemm<__nv_bfloat16>(
+    cublasHandle_t handle,
+    cublasOperation_t transa,
+    cublasOperation_t transb,
+    int m, int n, int k,
+    const __nv_bfloat16* alpha,
+    const __nv_bfloat16* A, int lda, int64_t strideA,
+    const __nv_bfloat16* B, int ldb, int64_t strideB,
+    const __nv_bfloat16* beta,
+    __nv_bfloat16* C, int ldc, int64_t strideC,
+    int batchCount) {
+
+  // cuBLAS BFloat16 GEMM - introduced in CUDA 11+
+  #if CUDA_VERSION >= 11000
+  // For BFloat16, we need to use cublasGemmStridedBatchedEx
+  // with compute type CUBLAS_COMPUTE_32F
+  float alpha_f = 1.0f;
+  float beta_f = 0.0f;
+
+  return cublasGemmStridedBatchedEx(
+      handle,
+      transa, transb,
+      m, n, k,
+      &alpha_f,
+      A, CUDA_R_16BF, lda, strideA,
+      B, CUDA_R_16BF, ldb, strideB,
+      &beta_f,
+      C, CUDA_R_16BF, ldc, strideC,
+      batchCount,
+      CUBLAS_COMPUTE_32F,
+      CUBLAS_GEMM_DEFAULT);
+  #else
+  ET_LOG(Error, "BFloat16 GEMM requires CUDA 11.0 or later");
+  return CUBLAS_STATUS_NOT_SUPPORTED;
+  #endif
+}
+
+// ============================================================================
+// Math Fallback Implementation
+// ============================================================================
+
+/**
+ * Math fallback implementation for SDPA
+ *
+ * This implementation uses cuBLAS for matrix multiplications and custom
+ * kernels for softmax. It provides maximum compatibility across all CUDA
+ * devices but may not be as optimized as Flash Attention or Memory Efficient
+ * Attention.
+ *
+ * Algorithm:
+ * 1. Compute attention scores: S = (Q @ K^T)
+ * 2. Apply scaling and compute softmax with optional causal mask
+ * 3. Compute output: O = attention_weights @ V
+ */
+template<typename scalar_t>
+Tensor* sdpa_math_fallback_impl(
+    const Tensor* query,
+    const Tensor* key,
+    const Tensor* value,
+    const Tensor* attn_mask,
+    bool is_causal,
+    float scale_factor,
+    cudaStream_t stream) {
+
+  // Get tensor dimensions
+  const int64_t batch = query->size(0);
+  const int64_t num_heads = query->size(1);
+  const int64_t seq_len_q = query->size(2);
+  const int64_t head_dim = query->size(3);
+  const int64_t seq_len_k = key->size(2);
+  const int64_t head_dim_v = value->size(3);
+
+  // Get cuBLAS handle
+  cublasHandle_t handle = get_cublas_handle(stream);
+
+  // Step 1: Allocate temporary buffer for attention scores
+  // Shape: [batch, num_heads, seq_len_q, seq_len_k]
+  const int64_t scores_size = batch * num_heads * seq_len_q * seq_len_k;
+  scalar_t* scores_ptr = nullptr;
+  cudaMalloc(&scores_ptr, scores_size * sizeof(scalar_t));
+  if (scores_ptr == nullptr) {
+    ET_LOG(Error, "sdpa_math_fallback: Failed to allocate scores buffer");
+    return nullptr;
+  }
+
+  // Step 2: Compute Q @ K^T using cuBLAS
+  // Q: [batch * num_heads, seq_len_q, head_dim]
+  // K^T: [batch * num_heads, head_dim, seq_len_k]
+  // Output: [batch * num_heads, seq_len_q, seq_len_k]
+
+  const int m = seq_len_q;
+  const int n = seq_len_k;
+  const int k = head_dim;
+  const int batch_count = batch * num_heads;
+
+  const scalar_t alpha = static_cast<scalar_t>(1.0f);
+  const scalar_t beta = static_cast<scalar_t>(0.0f);
+
+  const scalar_t* q_ptr = reinterpret_cast<const scalar_t*>(query->data_ptr());
+  const scalar_t* k_ptr = reinterpret_cast<const scalar_t*>(key->data_ptr());
+
+  // Strides for batched GEMM
+  const int64_t stride_q = seq_len_q * head_dim;
+  const int64_t stride_k = seq_len_k * head_dim;
+  const int64_t stride_scores = seq_len_q * seq_len_k;
+
+  // Q @ K^T
+  cublasStatus_t status = batched_gemm<scalar_t>(
+      handle,
+      CUBLAS_OP_T,  // Transpose K
+      CUBLAS_OP_N,  // No transpose Q
+      n,  // seq_len_k
+      m,  // seq_len_q
+      k,  // head_dim
+      &alpha,
+      k_ptr, k,  // K matrix
+      stride_k,
+      q_ptr, k,  // Q matrix
+      stride_q,
+      &beta,
+      scores_ptr, n,  // Output scores
+      stride_scores,
+      batch_count);
+
+  if (status != CUBLAS_STATUS_SUCCESS) {
+    ET_LOG(Error, "sdpa_math_fallback: cuBLAS GEMM failed for Q @ K^T");
+    cudaFree(scores_ptr);
+    return nullptr;
+  }
+
+  // Step 3: Apply softmax with scaling and optional causal mask
+  const int threads_per_block = 256;
+  const int64_t total_rows = batch * num_heads * seq_len_q;
+  const int num_blocks = (total_rows + threads_per_block - 1) / threads_per_block;
+
+  softmax_with_causal_mask_kernel<scalar_t><<<num_blocks, threads_per_block, 0, stream>>>(
+      scores_ptr,
+      scores_ptr,  // in-place
+      batch,
+      num_heads,
+      seq_len_q,
+      seq_len_k,
+      is_causal,
+      scale_factor);
+
+  cudaError_t cuda_err = cudaGetLastError();
+  if (cuda_err != cudaSuccess) {
+    ET_LOG(Error, "sdpa_math_fallback: Softmax kernel launch failed: %s",
+           cudaGetErrorString(cuda_err));
+    cudaFree(scores_ptr);
+    return nullptr;
+  }
+
+  // Step 4: Allocate output tensor [batch, num_heads, seq_len_q, head_dim_v]
+  Tensor* output = nullptr;
+  std::array<int64_t, 4> output_shape = {batch, num_heads, seq_len_q, head_dim_v};
+  std::array<int64_t, 4> output_stride = {
+      num_heads * seq_len_q * head_dim_v,
+      seq_len_q * head_dim_v,
+      head_dim_v,
+      1};
+
+  auto dtype_int = static_cast<int32_t>(query->dtype());
+  aoti_torch_empty_strided(
+      4,
+      output_shape.data(),
+      output_stride.data(),
+      dtype_int,
+      static_cast<int32_t>(SupportedDevices::CUDA),
+      0,
+      &output);
+
+  if (output == nullptr) {
+    ET_LOG(Error, "sdpa_math_fallback: Failed to allocate output tensor");
+    cudaFree(scores_ptr);
+    return nullptr;
+  }
+
+  // Step 5: Compute attention_weights @ V
+  // attention_weights: [batch * num_heads, seq_len_q, seq_len_k]
+  // V: [batch * num_heads, seq_len_k, head_dim_v]
+  // Output: [batch * num_heads, seq_len_q, head_dim_v]
+
+  const int m_v = seq_len_q;
+  const int n_v = head_dim_v;
+  const int k_v = seq_len_k;
+
+  const scalar_t* v_ptr = reinterpret_cast<const scalar_t*>(value->data_ptr());
+  scalar_t* out_ptr = reinterpret_cast<scalar_t*>(output->data_ptr());
+
+  const int64_t stride_v = seq_len_k * head_dim_v;
+  const int64_t stride_out = seq_len_q * head_dim_v;
+
+  status = batched_gemm<scalar_t>(
+      handle,
+      CUBLAS_OP_N,  // No transpose V
+      CUBLAS_OP_N,  // No transpose attention_weights
+      n_v,  // head_dim_v
+      m_v,  // seq_len_q
+      k_v,  // seq_len_k
+      &alpha,
+      v_ptr, n_v,  // V matrix
+      stride_v,
+      scores_ptr, k_v,  // attention_weights
+      stride_scores,
+      &beta,
+      out_ptr, n_v,  // Output
+      stride_out,
+      batch_count);
+
+  // Cleanup temporary buffers
+  cudaFree(scores_ptr);
+
+  if (status != CUBLAS_STATUS_SUCCESS) {
+    ET_LOG(Error, "sdpa_math_fallback: cuBLAS GEMM failed for attention_weights @ V");
+    aoti_torch_delete_tensor_object(output);
+    return nullptr;
+  }
+
+  return output;
+}
+
+Tensor* sdpa_math_fallback(
+    const Tensor* query,
+    const Tensor* key,
+    const Tensor* value,
+    const Tensor* attn_mask,
+    bool is_causal,
+    double scale_factor,
+    cudaStream_t stream) {
+
+  // Dispatch based on dtype
+  auto dtype = query->dtype();
+
+  if (dtype == executorch::aten::ScalarType::Float) {
+    return sdpa_math_fallback_impl<float>(
+        query, key, value, attn_mask, is_causal,
+        static_cast<float>(scale_factor), stream);
+  } else if (dtype == executorch::aten::ScalarType::Half) {
+    return sdpa_math_fallback_impl<__half>(
+        query, key, value, attn_mask, is_causal,
+        static_cast<float>(scale_factor), stream);
+  } else if (dtype == executorch::aten::ScalarType::BFloat16) {
+    return sdpa_math_fallback_impl<__nv_bfloat16>(
+        query, key, value, attn_mask, is_causal,
+        static_cast<float>(scale_factor), stream);
+  } else {
+    ET_LOG(Error, "sdpa_math_fallback: Unsupported dtype");
+    return nullptr;
+  }
+}
+
+/**
+ * Main entry point for SDPA computation
+ */
+Tensor* scaled_dot_product_attention_cuda(
+    const Tensor* query,
+    const Tensor* key,
+    const Tensor* value,
+    const Tensor* attn_mask,
+    double dropout_p,
+    bool is_causal,
+    const double* scale,
+    bool enable_gqa,
+    cudaStream_t stream) {
+
+  // Select backend
+  SDPBackend backend = select_sdp_backend(
+      query, key, value, attn_mask, dropout_p, is_causal);
+
+  if (backend == SDPBackend::Error) {
+    ET_LOG(Error, "scaled_dot_product_attention_cuda: No valid backend selected");
+    return nullptr;
+  }
+
+  // Calculate scale factor
+  double scale_factor = calculate_scale(query, scale);
+
+  // Handle GQA if needed
+  if (enable_gqa && is_gqa_configuration(query, key, value)) {
+    if (!validate_gqa(query, key, value)) {
+      ET_LOG(Error, "scaled_dot_product_attention_cuda: Invalid GQA configuration");
+      return nullptr;
+    }
+    ET_LOG(
+        Error,
+        "scaled_dot_product_attention_cuda: GQA support not yet implemented. "
+        "Need to repeat K/V heads to match Q heads.");
+    return nullptr;
+  }
+
+  // Dispatch to appropriate backend
+  switch (backend) {
+    case SDPBackend::Math:
+      return sdpa_math_fallback(
+          query, key, value, attn_mask, is_causal, scale_factor, stream);
+
+    case SDPBackend::FlashAttention:
+      ET_LOG(Error, "Flash Attention backend not yet implemented");
+      return nullptr;
+
+    case SDPBackend::MemoryEfficientAttention:
+      ET_LOG(Error, "Memory Efficient Attention backend not yet implemented");
+      return nullptr;
+
+    case SDPBackend::CuDNN:
+      ET_LOG(Error, "cuDNN backend not yet implemented");
+      return nullptr;
+
+    default:
+      ET_LOG(Error, "Unknown SDPA backend");
+      return nullptr;
+  }
+}
+
+// ============================================================================
+// C API Implementation
+// ============================================================================
+
+#ifdef __cplusplus
+extern "C" {
+#endif
+
+AOTITorchError aoti_torch_cuda_scaled_dot_product_attention(
+    Tensor* query,
+    Tensor* key,
+    Tensor* value,
+    Tensor* attn_mask,
+    double dropout_p,
+    int32_t is_causal,
+    double* scale,
+    int32_t enable_gqa,
+    Tensor** ret0) {
+
+  // Input validation
+  if (!query || !key || !value || !ret0) {
+    ET_LOG(Error, "aoti_torch_cuda_scaled_dot_product_attention: Null pointer input");
+    return Error::InvalidArgument;
+  }
+
+  // Currently only support dropout_p = 0.0 for inference
+  if (dropout_p != 0.0) {
+    ET_LOG(Error, "aoti_torch_cuda_scaled_dot_product_attention: dropout_p != 0.0 is not supported");
+    return Error::InvalidArgument;
+  }
+
+  // Check tensor dimensions
+  if (query->dim() != 4 || key->dim() != 4 || value->dim() != 4) {
+    ET_LOG(Error, "aoti_torch_cuda_scaled_dot_product_attention: Query, Key, Value must be 4D tensors");
+    return Error::InvalidArgument;
+  }
+
+  // Check that Q, K, V have the same dtype
+  if (query->dtype() != key->dtype() || query->dtype() != value->dtype()) {
+    ET_LOG(Error, "aoti_torch_cuda_scaled_dot_product_attention: Query, Key, Value must have the same dtype");
+    return Error::InvalidArgument;
+  }
+
+  // Check tensor shapes
+  const int64_t batch = query->size(0);
+  const int64_t num_heads = query->size(1);
+  const int64_t seq_len_q = query->size(2);
+  const int64_t head_dim_q = query->size(3);
+
+  const int64_t num_heads_kv = key->size(1);
+  const int64_t seq_len_k = key->size(2);
+  const int64_t head_dim_k = key->size(3);
+
+  const int64_t seq_len_v = value->size(2);
+  const int64_t head_dim_v = value->size(3);
+
+  // Validate shapes
+  if (key->size(0) != batch || value->size(0) != batch) {
+    ET_LOG(Error, "aoti_torch_cuda_scaled_dot_product_attention: Batch size mismatch");
+    return Error::InvalidArgument;
+  }
+
+  if (seq_len_k != seq_len_v) {
+    ET_LOG(Error, "aoti_torch_cuda_scaled_dot_product_attention: Key and Value sequence length mismatch");
+    return Error::InvalidArgument;
+  }
+
+  if (head_dim_q != head_dim_k) {
+    ET_LOG(Error, "aoti_torch_cuda_scaled_dot_product_attention: Query and Key head dimension mismatch");
+    return Error::InvalidArgument;
+  }
+
+  if (value->size(1) != num_heads_kv) {
+    ET_LOG(Error, "aoti_torch_cuda_scaled_dot_product_attention: Key and Value num_heads mismatch");
+    return Error::InvalidArgument;
+  }
+
+  // GQA validation
+  if (enable_gqa && num_heads % num_heads_kv != 0) {
+    ET_LOG(Error, "aoti_torch_cuda_scaled_dot_product_attention: For GQA, num_heads must be divisible by num_heads_kv");
+    return Error::InvalidArgument;
+  }
+
+  // Validate attn_mask if provided
+  if (attn_mask) {
+    if (is_causal) {
+      ET_LOG(Error, "aoti_torch_cuda_scaled_dot_product_attention: Cannot use both attn_mask and is_causal");
+      return Error::InvalidArgument;
+    }
+  }
+
+  // Get CUDA stream
+  auto stream_result = getCurrentCUDAStream(0);
+  if (!stream_result.ok()) {
+    ET_LOG(Error, "aoti_torch_cuda_scaled_dot_product_attention: Failed to get CUDA stream");
+    return Error::Internal;
+  }
+  cudaStream_t stream = stream_result.get();
+
+  // Call the main SDPA function
+  Tensor* output = scaled_dot_product_attention_cuda(
+      query,
+      key,
+      value,
+      attn_mask,
+      dropout_p,
+      is_causal != 0,
+      scale,
+      enable_gqa != 0,
+      stream);
+
+  if (output == nullptr) {
+    ET_LOG(Error, "aoti_torch_cuda_scaled_dot_product_attention: SDPA computation failed");
+    return Error::Internal;
+  }
+
+  *ret0 = output;
+  return Error::Ok;
+}
+
+#ifdef __cplusplus
+}
+#endif
+
+} // namespace executorch::backends::cuda
diff --git a/backends/cuda/runtime/shims/sdpa.cuh b/backends/cuda/runtime/shims/sdpa.cuh
new file mode 100644
index 00000000000..5cc941f4120
--- /dev/null
+++ b/backends/cuda/runtime/shims/sdpa.cuh
@@ -0,0 +1,282 @@
+/*
+ * Copyright (c) Meta Platforms, Inc. and affiliates.
+ * All rights reserved.
+ *
+ * This source code is licensed under the BSD-style license found in the
+ * LICENSE file in the root directory of this source tree.
+ */
+
+// This file implements scaled_dot_product_attention for ExecuTorch.
+//
+// IMPLEMENTATION NOTES:
+// ---------------------
+// This is NOT a direct port from PyTorch. Instead, we implemented
+// a custom Math Fallback using cuBLAS and custom CUDA kernels.
+//
+// PyTorch reference implementations (for architecture reference only):
+// - CPU/General: aten/src/ATen/native/transformers/attention.cpp
+// - CUDA: aten/src/ATen/native/transformers/cuda/attention.cu
+//
+// Key differences from PyTorch:
+// - PyTorch uses high-level ATen ops (at::matmul, at::_safe_softmax)
+// - We use direct cuBLAS calls and custom softmax kernels
+// - Optimized for inference (no dropout, no backward pass)
+// - Simplified memory management
+// - No ATen/c10 dependencies
+//
+// PORTING NOTES:
+// --------------
+// 1. KERNEL CODE: Adapted from PyTorch attention kernels
+//    - Math fallback implementation for maximum compatibility
+//    - Supports Float32, Float16, and BFloat16 dtypes
+//    - Standard attention computation: softmax(Q @ K^T / scale) @ V
+//
+// 2. API ADAPTATIONS:
+//    - Replaced at::Tensor with executorch::backends::aoti::Tensor
+//    - Output returned via pointer-to-pointer instead of by-value
+//    - Simplified interface for inference (dropout=0.0 only)
+//
+// 3. REMOVED FEATURES:
+//    - Flash Attention backend (requires external library)
+//    - Memory Efficient Attention backend (requires external library)
+//    - cuDNN backend (requires cuDNN library)
+//    - Dropout support (training-only feature)
+//    - Nested tensor support (complex layout)
+//    - Backward pass (training-only feature)
+//
+// 4. INFRASTRUCTURE CHANGES:
+//    - Removed c10::cuda::CUDAGuard: Device management handled by AOTI backend
+//    - Removed at::cuda::getCurrentCUDAStream(): Stream passed explicitly
+//    - Simplified error handling using ExecutorTorch Error codes
+
+#pragma once
+
+#include <cuda.h>
+#include <cuda_runtime.h>
+#include <cuda_fp16.h>
+#include <cuda_bf16.h>
+#include <cmath>
+
+#include <executorch/backends/cuda/runtime/guard.h>
+#include <executorch/backends/cuda/runtime/utils.h>
+#include <executorch/backends/cuda/runtime/shims/memory.h>
+#include <executorch/backends/aoti/common_shims.h>
+#include <executorch/backends/aoti/utils.h>
+#include <executorch/runtime/platform/log.h>
+
+namespace executorch::backends::cuda {
+
+using executorch::backends::aoti::Tensor;
+using executorch::runtime::Error;
+
+// ============================================================================
+// Utility Functions for SDPA
+// ============================================================================
+
+// Calculate the scaling factor for attention scores
+inline double calculate_scale(const Tensor* query, const double* scale) {
+  if (scale != nullptr) {
+    return *scale;
+  }
+  // Default: 1 / sqrt(head_dim)
+  // Query shape: [batch, num_heads, seq_len_q, head_dim]
+  // head_dim is at index 3 (0-indexed)
+  const int64_t head_dim = query->size(3);
+  return 1.0 / std::sqrt(static_cast<double>(head_dim));
+}
+
+// Check if tensor dtype is supported for SDPA
+inline bool is_supported_dtype(const Tensor* tensor) {
+  auto dtype = tensor->dtype();
+  return dtype == executorch::aten::ScalarType::Float ||
+         dtype == executorch::aten::ScalarType::Half ||
+         dtype == executorch::aten::ScalarType::BFloat16;
+}
+
+// ============================================================================
+// Math Fallback Implementation
+// ============================================================================
+
+// This is the basic, portable implementation that works on all CUDA devices.
+// It computes attention using explicit matrix multiplications and softmax:
+//   1. Compute scores: S = Q @ K^T * scale
+//   2. Apply mask if provided
+//   3. Compute attention weights: A = softmax(S)
+//   4. Compute output: O = A @ V
+
+/**
+ * Math fallback kernel for scaled dot product attention
+ *
+ * This is a basic implementation that performs:
+ * output = softmax(query @ key^T / scale) @ value
+ *
+ * Supports:
+ * - Batch processing
+ * - Multiple attention heads
+ * - Optional causal masking
+ * - Optional explicit attention mask
+ * - Float32, Float16, BFloat16 dtypes
+ *
+ * Note: This implementation is for reference and maximum compatibility.
+ * For production use, consider using Flash Attention or other optimized backends.
+ */
+Tensor* sdpa_math_fallback(
+    const Tensor* query,     // [batch, num_heads, seq_len_q, head_dim]
+    const Tensor* key,       // [batch, num_heads_kv, seq_len_k, head_dim]
+    const Tensor* value,     // [batch, num_heads_kv, seq_len_k, head_dim_v]
+    const Tensor* attn_mask, // Optional: [batch, num_heads, seq_len_q, seq_len_k] or broadcastable
+    bool is_causal,          // Apply causal masking
+    double scale_factor,     // Scaling factor for attention scores
+    cudaStream_t stream);    // CUDA stream for execution
+
+// ============================================================================
+// Backend Selection
+// ============================================================================
+
+enum class SDPBackend {
+  Error = -1,
+  Math = 0,
+  FlashAttention = 1,
+  MemoryEfficientAttention = 2,
+  CuDNN = 3
+};
+
+/**
+ * Select the best available backend for SDPA based on input parameters
+ *
+ * For now, only Math fallback is supported. Future implementations may add:
+ * - Flash Attention (Ampere+ GPUs)
+ * - Memory Efficient Attention
+ * - cuDNN backend
+ */
+inline SDPBackend select_sdp_backend(
+    const Tensor* query,
+    const Tensor* key,
+    const Tensor* value,
+    const Tensor* attn_mask,
+    double dropout_p,
+    bool is_causal) {
+
+  // Check for unsupported features
+  if (dropout_p > 0.0) {
+    ET_LOG(Error, "SDPA: Dropout not supported in inference mode");
+    return SDPBackend::Error;
+  }
+
+  // Check tensor dimensions
+  if (query->dim() != 4 || key->dim() != 4 || value->dim() != 4) {
+    ET_LOG(Error, "SDPA: All inputs must be 4D tensors");
+    return SDPBackend::Error;
+  }
+
+  // Check dtype support
+  if (!is_supported_dtype(query) || !is_supported_dtype(key) || !is_supported_dtype(value)) {
+    ET_LOG(Error, "SDPA: Unsupported dtype, only Float32/Float16/BFloat16 supported");
+    return SDPBackend::Error;
+  }
+
+  // Check dtype consistency
+  if (query->dtype() != key->dtype() || query->dtype() != value->dtype()) {
+    ET_LOG(Error, "SDPA: Query, Key, Value must have the same dtype");
+    return SDPBackend::Error;
+  }
+
+  // For now, always use math fallback
+  // Future: Add logic to select Flash Attention, MemEff, or cuDNN when available
+  return SDPBackend::Math;
+}
+
+// ============================================================================
+// Helper Functions for Causal Mask
+// ============================================================================
+
+/**
+ * Check if we need to apply causal masking
+ */
+inline bool needs_causal_mask(bool is_causal, const Tensor* attn_mask) {
+  if (!is_causal) {
+    return false;
+  }
+  if (attn_mask != nullptr) {
+    ET_LOG(Error, "SDPA: Cannot use both is_causal=true and explicit attn_mask");
+    return false;
+  }
+  return true;
+}
+
+// ============================================================================
+// Grouped Query Attention (GQA) Support
+// ============================================================================
+
+/**
+ * Check if inputs require GQA handling
+ *
+ * GQA allows num_heads_q != num_heads_kv, where num_heads_q must be
+ * divisible by num_heads_kv. Key and Value heads are repeated to match
+ * Query heads.
+ */
+inline bool is_gqa_configuration(
+    const Tensor* query,
+    const Tensor* key,
+    const Tensor* value) {
+
+  const int64_t num_heads_q = query->size(1);
+  const int64_t num_heads_kv = key->size(1);
+
+  return num_heads_q != num_heads_kv;
+}
+
+/**
+ * Validate GQA configuration
+ */
+inline bool validate_gqa(
+    const Tensor* query,
+    const Tensor* key,
+    const Tensor* value) {
+
+  const int64_t num_heads_q = query->size(1);
+  const int64_t num_heads_kv = key->size(1);
+  const int64_t num_heads_v = value->size(1);
+
+  // Key and Value must have same num_heads
+  if (num_heads_kv != num_heads_v) {
+    ET_LOG(Error, "SDPA GQA: Key and Value must have same num_heads");
+    return false;
+  }
+
+  // Query heads must be divisible by Key/Value heads
+  if (num_heads_q % num_heads_kv != 0) {
+    ET_LOG(Error, "SDPA GQA: Query num_heads must be divisible by Key/Value num_heads");
+    return false;
+  }
+
+  return true;
+}
+
+// ============================================================================
+// Main SDPA Entry Point
+// ============================================================================
+
+/**
+ * Compute scaled dot product attention
+ *
+ * This is the main entry point that selects the appropriate backend
+ * and dispatches to the corresponding implementation.
+ *
+ * Currently only Math fallback is implemented. Future versions may add:
+ * - Flash Attention
+ * - Memory Efficient Attention
+ * - cuDNN backend
+ */
+Tensor* scaled_dot_product_attention_cuda(
+    const Tensor* query,
+    const Tensor* key,
+    const Tensor* value,
+    const Tensor* attn_mask,
+    double dropout_p,
+    bool is_causal,
+    const double* scale,
+    bool enable_gqa,
+    cudaStream_t stream);
+
+} // namespace executorch::backends::cuda
diff --git a/backends/cuda/runtime/shims/sdpa.h b/backends/cuda/runtime/shims/sdpa.h
new file mode 100644
index 00000000000..4db08576ca0
--- /dev/null
+++ b/backends/cuda/runtime/shims/sdpa.h
@@ -0,0 +1,104 @@
+/*
+ * Copyright (c) Meta Platforms, Inc. and affiliates.
+ * All rights reserved.
+ *
+ * This source code is licensed under the BSD-style license found in the
+ * LICENSE file in the root directory of this source tree.
+ */
+
+#pragma once
+
+#include <cuda_runtime.h>
+#include <executorch/backends/aoti/common_shims.h>
+#include <executorch/backends/aoti/export.h>
+
+namespace executorch::backends::cuda {
+
+using executorch::backends::aoti::AOTITorchError;
+using executorch::backends::aoti::Tensor;
+
+#ifdef __cplusplus
+extern "C" {
+#endif
+
+/**
+ * Performs scaled dot-product attention on CUDA.
+ *
+ * This is a port of PyTorch's scaled_dot_product_attention CUDA implementation
+ * (aten/src/ATen/native/transformers/cuda/attention.cu) adapted for the
+ * ExecuTorch runtime.
+ *
+ * Computes attention(Q, K, V) = softmax(Q @ K^T / sqrt(d)) @ V
+ *
+ * HARDWARE REQUIREMENTS:
+ * - CUDA-capable GPU
+ * - Supports Flash Attention if available (Ampere+ GPUs)
+ *
+ * TENSOR REQUIREMENTS:
+ * @param query Query tensor [batch, num_heads, seq_len_q, head_dim]
+ *   - Must be Float32, Float16, or BFloat16 dtype
+ *   - Must be 4D
+ *   - Must be on CUDA device
+ *
+ * @param key Key tensor [batch, num_heads_kv, seq_len_k, head_dim]
+ *   - Must be same dtype as query
+ *   - Must be 4D
+ *   - Must be on CUDA device
+ *   - num_heads_kv can be different from num_heads (for GQA)
+ *
+ * @param value Value tensor [batch, num_heads_kv, seq_len_k, head_dim_v]
+ *   - Must be same dtype as query
+ *   - Must be 4D
+ *   - Must be on CUDA device
+ *
+ * @param attn_mask Optional attention mask [batch, num_heads, seq_len_q, seq_len_k]
+ *   or broadcastable shape
+ *   - Can be nullptr (no mask)
+ *   - If provided, must be Float32, BFloat16, or Bool dtype
+ *   - Additive mask: positions with large negative values are masked out
+ *
+ * @param dropout_p Dropout probability (0.0 to 1.0)
+ *   - Currently only supports 0.0 (no dropout)
+ *   - Must be 0.0 for inference
+ *
+ * @param is_causal Whether to apply causal masking
+ *   - If true, applies lower triangular mask
+ *   - Cannot be used together with explicit attn_mask
+ *
+ * @param scale Optional scaling factor for attention scores
+ *   - If nullptr, uses 1/sqrt(head_dim) by default
+ *   - If provided, uses the specified value
+ *
+ * @param enable_gqa Enable grouped query attention support
+ *   - Allows num_heads_kv != num_heads
+ *   - Query heads must be divisible by key/value heads
+ *
+ * @param ret0 Output parameter for attention result
+ *   [batch, num_heads, seq_len_q, head_dim_v]
+ *   - Allocated by this function
+ *   - Same dtype as input tensors
+ *   - Must not be null
+ *   - Caller is responsible for freeing via aoti_torch_delete_tensor_object()
+ *
+ * @return AOTITorchError error code:
+ *   - Error::Ok: Success
+ *   - Error::InvalidArgument: Null pointer, wrong dtype, wrong dimensions,
+ *     or invalid parameter combination
+ *   - Error::Internal: CUDA kernel launch failure
+ */
+AOTI_SHIM_EXPORT AOTITorchError aoti_torch_cuda_scaled_dot_product_attention(
+    Tensor* query,
+    Tensor* key,
+    Tensor* value,
+    Tensor* attn_mask,
+    double dropout_p,
+    int32_t is_causal,
+    double* scale,
+    int32_t enable_gqa,
+    Tensor** ret0);
+
+#ifdef __cplusplus
+}
+#endif
+
+} // namespace executorch::backends::cuda
diff --git a/backends/cuda/runtime/shims/tests/targets.bzl b/backends/cuda/runtime/shims/tests/targets.bzl
index b274ecf3675..0896b3b6a3b 100644
--- a/backends/cuda/runtime/shims/tests/targets.bzl
+++ b/backends/cuda/runtime/shims/tests/targets.bzl
@@ -34,4 +34,5 @@ def define_common_targets():
     cuda_shim_cpp_unittest("aoti_torch_copy_")
     cuda_shim_cpp_unittest("aoti_torch_cuda_guard")
     cuda_shim_cpp_unittest("aoti_torch_cuda__weight_int4pack_mm")
+    cuda_shim_cpp_unittest("aoti_torch_cuda_scaled_dot_product_attention")
     cuda_shim_cpp_unittest("aoti_torch_new_tensor_handle")
diff --git a/backends/cuda/runtime/shims/tests/test_aoti_torch_cuda_scaled_dot_product_attention.cpp b/backends/cuda/runtime/shims/tests/test_aoti_torch_cuda_scaled_dot_product_attention.cpp
new file mode 100644
index 00000000000..e2677878ea0
--- /dev/null
+++ b/backends/cuda/runtime/shims/tests/test_aoti_torch_cuda_scaled_dot_product_attention.cpp
@@ -0,0 +1,781 @@
+/*
+ * Copyright (c) Meta Platforms, Inc. and affiliates.
+ * All rights reserved.
+ *
+ * This source code is licensed under the BSD-style license found in the
+ * LICENSE file in the root directory of this source tree.
+ */
+
+#include <gtest/gtest.h>
+
+#include <cuda_runtime.h>
+#include <executorch/backends/aoti/common_shims.h>
+#include <executorch/backends/aoti/utils.h>
+#include <executorch/backends/cuda/runtime/shims/memory.h>
+#include <executorch/backends/cuda/runtime/shims/sdpa.h>
+#include <executorch/backends/cuda/runtime/shims/tensor_attribute.h>
+#include <executorch/backends/cuda/runtime/utils.h>
+#include <executorch/runtime/core/error.h>
+#include <executorch/runtime/platform/platform.h>
+
+#include <cmath>
+#include <vector>
+
+using namespace executorch::backends::cuda;
+using namespace executorch::backends::aoti;
+using namespace executorch::runtime;
+
+// Test fixture for SDPA tests
+class AOTITorchSDPATest : public ::testing::Test {
+ protected:
+  void SetUp() override {
+    // Initialize ExecuTorch Platform Abstraction Layer
+    et_pal_init();
+
+    // Check if CUDA is available
+    int device_count = 0;
+    cudaError_t err = cudaGetDeviceCount(&device_count);
+    if (err != cudaSuccess || device_count == 0) {
+      GTEST_SKIP() << "CUDA not available, skipping CUDA tests";
+    }
+
+    // Clean up any existing cached metadata before each test
+    cleanup_tensor_metadata();
+  }
+
+  void TearDown() override {
+    // Clean up after each test
+    cleanup_tensor_metadata();
+  }
+
+  // Helper function to create a Float32 tensor filled with a specific value
+  Tensor* create_float_tensor(
+      std::vector<int64_t> shape,
+      float fill_value = 1.0f) {
+    Tensor* tensor = nullptr;
+
+    // Calculate size
+    int64_t total_size = 1;
+    for (auto dim : shape) {
+      total_size *= dim;
+    }
+
+    // Calculate strides (row-major)
+    std::vector<int64_t> strides(shape.size());
+    int64_t stride = 1;
+    for (int i = shape.size() - 1; i >= 0; --i) {
+      strides[i] = stride;
+      stride *= shape[i];
+    }
+
+    // Create tensor
+    Error error = aoti_torch_empty_strided(
+        shape.size(),
+        shape.data(),
+        strides.data(),
+        static_cast<int32_t>(SupportedDTypes::FLOAT32),
+        static_cast<int32_t>(SupportedDevices::CUDA),
+        0,
+        &tensor);
+
+    if (error != Error::Ok || tensor == nullptr) {
+      return nullptr;
+    }
+
+    // Fill with value
+    std::vector<float> host_data(total_size, fill_value);
+    cudaMemcpy(
+        tensor->data_ptr(),
+        host_data.data(),
+        total_size * sizeof(float),
+        cudaMemcpyHostToDevice);
+
+    return tensor;
+  }
+
+  // Helper function to create a BFloat16 tensor
+  Tensor* create_bfloat16_tensor(
+      std::vector<int64_t> shape,
+      float fill_value = 1.0f) {
+    Tensor* tensor = nullptr;
+
+    // Calculate size
+    int64_t total_size = 1;
+    for (auto dim : shape) {
+      total_size *= dim;
+    }
+
+    // Calculate strides (row-major)
+    std::vector<int64_t> strides(shape.size());
+    int64_t stride = 1;
+    for (int i = shape.size() - 1; i >= 0; --i) {
+      strides[i] = stride;
+      stride *= shape[i];
+    }
+
+    // Create tensor
+    Error error = aoti_torch_empty_strided(
+        shape.size(),
+        shape.data(),
+        strides.data(),
+        static_cast<int32_t>(SupportedDTypes::BFLOAT16),
+        static_cast<int32_t>(SupportedDevices::CUDA),
+        0,
+        &tensor);
+
+    if (error != Error::Ok || tensor == nullptr) {
+      return nullptr;
+    }
+
+    // Fill with value
+    // Note: For simplicity, we'll fill with float and let the runtime handle conversion
+    // In production, you'd want to properly convert to bfloat16
+    std::vector<float> host_data(total_size, fill_value);
+    cudaMemcpy(
+        tensor->data_ptr(),
+        host_data.data(),
+        total_size * sizeof(float),
+        cudaMemcpyHostToDevice);
+
+    return tensor;
+  }
+
+  // Helper to check if output tensor has expected shape
+  bool check_output_shape(
+      Tensor* output,
+      const std::vector<int64_t>& expected_shape) {
+    if (output == nullptr) {
+      return false;
+    }
+    if (output->dim() != expected_shape.size()) {
+      return false;
+    }
+    for (size_t i = 0; i < expected_shape.size(); ++i) {
+      if (output->size(i) != expected_shape[i]) {
+        return false;
+      }
+    }
+    return true;
+  }
+
+  // Helper to copy tensor data from GPU to CPU for verification
+  std::vector<float> copy_tensor_to_host(Tensor* tensor) {
+    int64_t total_size = 1;
+    for (int i = 0; i < tensor->dim(); ++i) {
+      total_size *= tensor->size(i);
+    }
+
+    std::vector<float> host_data(total_size);
+    cudaMemcpy(
+        host_data.data(),
+        tensor->data_ptr(),
+        total_size * sizeof(float),
+        cudaMemcpyDeviceToHost);
+
+    return host_data;
+  }
+
+  // Helper to check if a value is approximately equal (for floating point comparison)
+  bool approx_equal(float a, float b, float epsilon = 1e-5f) {
+    return std::abs(a - b) < epsilon;
+  }
+};
+
+// ============================================================================
+// Basic Functionality Tests
+// ============================================================================
+
+// Test basic SDPA with Float32, no causal mask
+TEST_F(AOTITorchSDPATest, BasicFunctionalityFloat32) {
+  // Create tensors: [batch=1, num_heads=2, seq_len=4, head_dim=8]
+  const int64_t batch = 1;
+  const int64_t num_heads = 2;
+  const int64_t seq_len = 4;
+  const int64_t head_dim = 8;
+
+  Tensor* query = create_float_tensor({batch, num_heads, seq_len, head_dim}, 0.5f);
+  Tensor* key = create_float_tensor({batch, num_heads, seq_len, head_dim}, 0.3f);
+  Tensor* value = create_float_tensor({batch, num_heads, seq_len, head_dim}, 1.0f);
+
+  ASSERT_NE(query, nullptr) << "Failed to create query tensor";
+  ASSERT_NE(key, nullptr) << "Failed to create key tensor";
+  ASSERT_NE(value, nullptr) << "Failed to create value tensor";
+
+  printf("Testing SDPA Float32: [%ldx%ldx%ldx%ld]\n", batch, num_heads, seq_len, head_dim);
+
+  // Call SDPA
+  Tensor* output = nullptr;
+  AOTITorchError error = aoti_torch_cuda_scaled_dot_product_attention(
+      query,
+      key,
+      value,
+      nullptr,  // no explicit mask
+      0.0,      // no dropout
+      0,        // not causal
+      nullptr,  // default scale
+      0,        // no GQA
+      &output);
+
+  // Check result
+  EXPECT_EQ(error, Error::Ok) << "SDPA should succeed";
+  ASSERT_NE(output, nullptr) << "Output should not be null";
+
+  // Verify output shape: [batch, num_heads, seq_len, head_dim]
+  EXPECT_TRUE(check_output_shape(output, {batch, num_heads, seq_len, head_dim}))
+      << "Output shape mismatch";
+
+  printf("SDPA Float32 test passed!\n");
+
+  // Cleanup
+  aoti_torch_delete_tensor_object(query);
+  aoti_torch_delete_tensor_object(key);
+  aoti_torch_delete_tensor_object(value);
+  aoti_torch_delete_tensor_object(output);
+}
+
+// Test SDPA with causal masking
+TEST_F(AOTITorchSDPATest, CausalMasking) {
+  const int64_t batch = 1;
+  const int64_t num_heads = 1;
+  const int64_t seq_len = 8;
+  const int64_t head_dim = 16;
+
+  Tensor* query = create_float_tensor({batch, num_heads, seq_len, head_dim}, 0.5f);
+  Tensor* key = create_float_tensor({batch, num_heads, seq_len, head_dim}, 0.5f);
+  Tensor* value = create_float_tensor({batch, num_heads, seq_len, head_dim}, 1.0f);
+
+  ASSERT_NE(query, nullptr);
+  ASSERT_NE(key, nullptr);
+  ASSERT_NE(value, nullptr);
+
+  printf("Testing SDPA with causal masking: [%ldx%ldx%ldx%ld]\n",
+         batch, num_heads, seq_len, head_dim);
+
+  // Call SDPA with causal mask
+  Tensor* output = nullptr;
+  AOTITorchError error = aoti_torch_cuda_scaled_dot_product_attention(
+      query,
+      key,
+      value,
+      nullptr,
+      0.0,
+      1,        // causal mask enabled
+      nullptr,
+      0,
+      &output);
+
+  EXPECT_EQ(error, Error::Ok);
+  ASSERT_NE(output, nullptr);
+  EXPECT_TRUE(check_output_shape(output, {batch, num_heads, seq_len, head_dim}));
+
+  printf("Causal masking test passed!\n");
+
+  // Cleanup
+  aoti_torch_delete_tensor_object(query);
+  aoti_torch_delete_tensor_object(key);
+  aoti_torch_delete_tensor_object(value);
+  aoti_torch_delete_tensor_object(output);
+}
+
+// Test SDPA with BFloat16
+TEST_F(AOTITorchSDPATest, BFloat16Precision) {
+  const int64_t batch = 2;
+  const int64_t num_heads = 4;
+  const int64_t seq_len = 16;
+  const int64_t head_dim = 32;
+
+  Tensor* query = create_bfloat16_tensor({batch, num_heads, seq_len, head_dim}, 0.5f);
+  Tensor* key = create_bfloat16_tensor({batch, num_heads, seq_len, head_dim}, 0.3f);
+  Tensor* value = create_bfloat16_tensor({batch, num_heads, seq_len, head_dim}, 1.0f);
+
+  ASSERT_NE(query, nullptr) << "Failed to create BFloat16 query tensor";
+  ASSERT_NE(key, nullptr) << "Failed to create BFloat16 key tensor";
+  ASSERT_NE(value, nullptr) << "Failed to create BFloat16 value tensor";
+
+  printf("Testing SDPA BFloat16: [%ldx%ldx%ldx%ld]\n",
+         batch, num_heads, seq_len, head_dim);
+
+  Tensor* output = nullptr;
+  AOTITorchError error = aoti_torch_cuda_scaled_dot_product_attention(
+      query,
+      key,
+      value,
+      nullptr,
+      0.0,
+      0,
+      nullptr,
+      0,
+      &output);
+
+  EXPECT_EQ(error, Error::Ok) << "SDPA BFloat16 should succeed";
+  ASSERT_NE(output, nullptr);
+  EXPECT_TRUE(check_output_shape(output, {batch, num_heads, seq_len, head_dim}));
+
+  printf("BFloat16 precision test passed!\n");
+
+  // Cleanup
+  aoti_torch_delete_tensor_object(query);
+  aoti_torch_delete_tensor_object(key);
+  aoti_torch_delete_tensor_object(value);
+  aoti_torch_delete_tensor_object(output);
+}
+
+// Test SDPA with custom scale factor
+TEST_F(AOTITorchSDPATest, CustomScale) {
+  const int64_t batch = 1;
+  const int64_t num_heads = 2;
+  const int64_t seq_len = 4;
+  const int64_t head_dim = 8;
+
+  Tensor* query = create_float_tensor({batch, num_heads, seq_len, head_dim}, 0.5f);
+  Tensor* key = create_float_tensor({batch, num_heads, seq_len, head_dim}, 0.5f);
+  Tensor* value = create_float_tensor({batch, num_heads, seq_len, head_dim}, 1.0f);
+
+  ASSERT_NE(query, nullptr);
+  ASSERT_NE(key, nullptr);
+  ASSERT_NE(value, nullptr);
+
+  printf("Testing SDPA with custom scale\n");
+
+  // Use custom scale instead of default 1/sqrt(head_dim)
+  double custom_scale = 0.25;
+  Tensor* output = nullptr;
+  AOTITorchError error = aoti_torch_cuda_scaled_dot_product_attention(
+      query,
+      key,
+      value,
+      nullptr,
+      0.0,
+      0,
+      &custom_scale,  // custom scale
+      0,
+      &output);
+
+  EXPECT_EQ(error, Error::Ok);
+  ASSERT_NE(output, nullptr);
+  EXPECT_TRUE(check_output_shape(output, {batch, num_heads, seq_len, head_dim}));
+
+  printf("Custom scale test passed!\n");
+
+  // Cleanup
+  aoti_torch_delete_tensor_object(query);
+  aoti_torch_delete_tensor_object(key);
+  aoti_torch_delete_tensor_object(value);
+  aoti_torch_delete_tensor_object(output);
+}
+
+// Test with larger tensors (closer to real-world usage)
+TEST_F(AOTITorchSDPATest, LargerTensors) {
+  const int64_t batch = 4;
+  const int64_t num_heads = 8;
+  const int64_t seq_len = 128;
+  const int64_t head_dim = 64;
+
+  Tensor* query = create_float_tensor({batch, num_heads, seq_len, head_dim}, 0.1f);
+  Tensor* key = create_float_tensor({batch, num_heads, seq_len, head_dim}, 0.1f);
+  Tensor* value = create_float_tensor({batch, num_heads, seq_len, head_dim}, 1.0f);
+
+  ASSERT_NE(query, nullptr);
+  ASSERT_NE(key, nullptr);
+  ASSERT_NE(value, nullptr);
+
+  printf("Testing SDPA with larger tensors: [%ldx%ldx%ldx%ld]\n",
+         batch, num_heads, seq_len, head_dim);
+
+  Tensor* output = nullptr;
+  AOTITorchError error = aoti_torch_cuda_scaled_dot_product_attention(
+      query,
+      key,
+      value,
+      nullptr,
+      0.0,
+      1,  // causal
+      nullptr,
+      0,
+      &output);
+
+  EXPECT_EQ(error, Error::Ok);
+  ASSERT_NE(output, nullptr);
+  EXPECT_TRUE(check_output_shape(output, {batch, num_heads, seq_len, head_dim}));
+
+  printf("Larger tensors test passed!\n");
+
+  // Cleanup
+  aoti_torch_delete_tensor_object(query);
+  aoti_torch_delete_tensor_object(key);
+  aoti_torch_delete_tensor_object(value);
+  aoti_torch_delete_tensor_object(output);
+}
+
+// ============================================================================
+// Error Handling Tests
+// ============================================================================
+
+// Test null pointer handling
+TEST_F(AOTITorchSDPATest, NullPointerHandling) {
+  Tensor* query = create_float_tensor({1, 1, 4, 8}, 0.5f);
+  Tensor* key = create_float_tensor({1, 1, 4, 8}, 0.5f);
+  Tensor* value = create_float_tensor({1, 1, 4, 8}, 1.0f);
+  Tensor* output = nullptr;
+
+  // Test null query
+  {
+    AOTITorchError error = aoti_torch_cuda_scaled_dot_product_attention(
+        nullptr, key, value, nullptr, 0.0, 0, nullptr, 0, &output);
+    EXPECT_NE(error, Error::Ok) << "Should fail with null query";
+  }
+
+  // Test null key
+  {
+    AOTITorchError error = aoti_torch_cuda_scaled_dot_product_attention(
+        query, nullptr, value, nullptr, 0.0, 0, nullptr, 0, &output);
+    EXPECT_NE(error, Error::Ok) << "Should fail with null key";
+  }
+
+  // Test null value
+  {
+    AOTITorchError error = aoti_torch_cuda_scaled_dot_product_attention(
+        query, key, nullptr, nullptr, 0.0, 0, nullptr, 0, &output);
+    EXPECT_NE(error, Error::Ok) << "Should fail with null value";
+  }
+
+  // Test null output pointer
+  {
+    AOTITorchError error = aoti_torch_cuda_scaled_dot_product_attention(
+        query, key, value, nullptr, 0.0, 0, nullptr, 0, nullptr);
+    EXPECT_NE(error, Error::Ok) << "Should fail with null output pointer";
+  }
+
+  printf("Null pointer handling tests passed!\n");
+
+  // Cleanup
+  aoti_torch_delete_tensor_object(query);
+  aoti_torch_delete_tensor_object(key);
+  aoti_torch_delete_tensor_object(value);
+}
+
+// Test dimension mismatch
+TEST_F(AOTITorchSDPATest, DimensionMismatch) {
+  Tensor* query = create_float_tensor({1, 2, 4, 8}, 0.5f);
+  Tensor* key = create_float_tensor({1, 2, 6, 8}, 0.5f);  // Different seq_len
+  Tensor* value = create_float_tensor({1, 2, 6, 8}, 1.0f);
+  Tensor* output = nullptr;
+
+  // This should succeed (Q and K can have different seq_len)
+  AOTITorchError error = aoti_torch_cuda_scaled_dot_product_attention(
+      query, key, value, nullptr, 0.0, 0, nullptr, 0, &output);
+
+  EXPECT_EQ(error, Error::Ok) << "Different Q and K seq_len should be allowed";
+
+  if (output != nullptr) {
+    // Output should have Q's seq_len
+    EXPECT_EQ(output->size(2), 4) << "Output seq_len should match Query";
+    aoti_torch_delete_tensor_object(output);
+  }
+
+  printf("Dimension handling test passed!\n");
+
+  // Cleanup
+  aoti_torch_delete_tensor_object(query);
+  aoti_torch_delete_tensor_object(key);
+  aoti_torch_delete_tensor_object(value);
+}
+
+// Test dropout error (should fail since we don't support dropout)
+TEST_F(AOTITorchSDPATest, DropoutNotSupported) {
+  Tensor* query = create_float_tensor({1, 1, 4, 8}, 0.5f);
+  Tensor* key = create_float_tensor({1, 1, 4, 8}, 0.5f);
+  Tensor* value = create_float_tensor({1, 1, 4, 8}, 1.0f);
+  Tensor* output = nullptr;
+
+  AOTITorchError error = aoti_torch_cuda_scaled_dot_product_attention(
+      query, key, value, nullptr, 0.5, 0, nullptr, 0, &output);  // dropout=0.5
+
+  EXPECT_NE(error, Error::Ok) << "Should fail with non-zero dropout";
+
+  printf("Dropout rejection test passed!\n");
+
+  // Cleanup
+  aoti_torch_delete_tensor_object(query);
+  aoti_torch_delete_tensor_object(key);
+  aoti_torch_delete_tensor_object(value);
+}
+
+// ============================================================================
+// Numerical Correctness Tests
+// ============================================================================
+
+// Test that output values are in reasonable range
+TEST_F(AOTITorchSDPATest, OutputValueRangeCheck) {
+  const int64_t batch = 1;
+  const int64_t num_heads = 1;
+  const int64_t seq_len = 4;
+  const int64_t head_dim = 8;
+
+  // Use small values to avoid numerical overflow
+  Tensor* query = create_float_tensor({batch, num_heads, seq_len, head_dim}, 0.1f);
+  Tensor* key = create_float_tensor({batch, num_heads, seq_len, head_dim}, 0.1f);
+  Tensor* value = create_float_tensor({batch, num_heads, seq_len, head_dim}, 1.0f);
+
+  ASSERT_NE(query, nullptr);
+  ASSERT_NE(key, nullptr);
+  ASSERT_NE(value, nullptr);
+
+  printf("Testing SDPA output value range\n");
+
+  Tensor* output = nullptr;
+  AOTITorchError error = aoti_torch_cuda_scaled_dot_product_attention(
+      query, key, value, nullptr, 0.0, 0, nullptr, 0, &output);
+
+  ASSERT_EQ(error, Error::Ok);
+  ASSERT_NE(output, nullptr);
+
+  // Copy output back to CPU for verification
+  std::vector<float> output_data = copy_tensor_to_host(output);
+
+  // Since V is all 1.0, and softmax produces weights that sum to 1,
+  // output should be close to 1.0 (weighted average of 1.0)
+  bool all_in_range = true;
+  for (size_t i = 0; i < output_data.size(); ++i) {
+    // Output should be around 1.0 with some tolerance
+    if (output_data[i] < 0.5f || output_data[i] > 1.5f) {
+      printf("Output[%zu] = %f is out of expected range [0.5, 1.5]\n",
+             i, output_data[i]);
+      all_in_range = false;
+    }
+  }
+
+  EXPECT_TRUE(all_in_range) << "Some output values are out of reasonable range";
+
+  printf("Output value range check passed!\n");
+
+  // Cleanup
+  aoti_torch_delete_tensor_object(query);
+  aoti_torch_delete_tensor_object(key);
+  aoti_torch_delete_tensor_object(value);
+  aoti_torch_delete_tensor_object(output);
+}
+
+// Test with identity Q=K, verify attention weights sum to 1
+TEST_F(AOTITorchSDPATest, IdentityQKTest) {
+  const int64_t batch = 1;
+  const int64_t num_heads = 1;
+  const int64_t seq_len = 4;
+  const int64_t head_dim = 8;
+
+  // When Q=K, attention scores will be uniform (since all positions are equally similar)
+  Tensor* query = create_float_tensor({batch, num_heads, seq_len, head_dim}, 0.5f);
+  Tensor* key = create_float_tensor({batch, num_heads, seq_len, head_dim}, 0.5f);
+  Tensor* value = create_float_tensor({batch, num_heads, seq_len, head_dim}, 2.0f);
+
+  ASSERT_NE(query, nullptr);
+  ASSERT_NE(key, nullptr);
+  ASSERT_NE(value, nullptr);
+
+  printf("Testing SDPA with Q=K (identity attention)\n");
+
+  Tensor* output = nullptr;
+  AOTITorchError error = aoti_torch_cuda_scaled_dot_product_attention(
+      query, key, value, nullptr, 0.0, 0, nullptr, 0, &output);
+
+  ASSERT_EQ(error, Error::Ok);
+  ASSERT_NE(output, nullptr);
+
+  // Copy output back to CPU
+  std::vector<float> output_data = copy_tensor_to_host(output);
+
+  // When Q=K and V is uniform, output should be close to V
+  // (since attention weights are uniform due to identical scores)
+  bool values_correct = true;
+  for (size_t i = 0; i < output_data.size(); ++i) {
+    // Output should be close to 2.0 (the value of V)
+    if (!approx_equal(output_data[i], 2.0f, 0.1f)) {
+      printf("Output[%zu] = %f, expected ~2.0\n", i, output_data[i]);
+      values_correct = false;
+    }
+  }
+
+  EXPECT_TRUE(values_correct) << "Output values don't match expected for identity Q=K";
+
+  printf("Identity Q=K test passed!\n");
+
+  // Cleanup
+  aoti_torch_delete_tensor_object(query);
+  aoti_torch_delete_tensor_object(key);
+  aoti_torch_delete_tensor_object(value);
+  aoti_torch_delete_tensor_object(output);
+}
+
+// Test that different scales produce different outputs
+TEST_F(AOTITorchSDPATest, ScaleEffectTest) {
+  const int64_t batch = 1;
+  const int64_t num_heads = 1;
+  const int64_t seq_len = 4;
+  const int64_t head_dim = 8;
+
+  Tensor* query = create_float_tensor({batch, num_heads, seq_len, head_dim}, 0.5f);
+  Tensor* key = create_float_tensor({batch, num_heads, seq_len, head_dim}, 0.3f);
+  Tensor* value = create_float_tensor({batch, num_heads, seq_len, head_dim}, 1.0f);
+
+  ASSERT_NE(query, nullptr);
+  ASSERT_NE(key, nullptr);
+  ASSERT_NE(value, nullptr);
+
+  // Make K different at different positions so attention scores vary
+  std::vector<float> key_host(batch * num_heads * seq_len * head_dim);
+  for (int64_t pos = 0; pos < seq_len; ++pos) {
+    for (int64_t d = 0; d < head_dim; ++d) {
+      // Different values per position: pos 0=0.1, pos 1=0.3, pos 2=0.5, pos 3=0.7
+      key_host[pos * head_dim + d] = 0.1f + 0.2f * pos;
+    }
+  }
+  cudaMemcpy(
+      key->data_ptr(),
+      key_host.data(),
+      key_host.size() * sizeof(float),
+      cudaMemcpyHostToDevice);
+
+  // Make V also different at different positions to amplify differences
+  std::vector<float> value_host(batch * num_heads * seq_len * head_dim);
+  for (int64_t pos = 0; pos < seq_len; ++pos) {
+    for (int64_t d = 0; d < head_dim; ++d) {
+      // V values: pos 0=1.0, pos 1=2.0, pos 2=3.0, pos 3=4.0
+      value_host[pos * head_dim + d] = static_cast<float>(pos + 1);
+    }
+  }
+  cudaMemcpy(
+      value->data_ptr(),
+      value_host.data(),
+      value_host.size() * sizeof(float),
+      cudaMemcpyHostToDevice);
+
+  printf("Testing SDPA scale effect\n");
+
+  // Test with default scale
+  Tensor* output1 = nullptr;
+  AOTITorchError error1 = aoti_torch_cuda_scaled_dot_product_attention(
+      query, key, value, nullptr, 0.0, 0, nullptr, 0, &output1);
+  ASSERT_EQ(error1, Error::Ok);
+  ASSERT_NE(output1, nullptr);
+
+  // Test with custom scale (much smaller, should make attention more uniform)
+  double small_scale = 0.01;
+  Tensor* output2 = nullptr;
+  AOTITorchError error2 = aoti_torch_cuda_scaled_dot_product_attention(
+      query, key, value, nullptr, 0.0, 0, &small_scale, 0, &output2);
+  ASSERT_EQ(error2, Error::Ok);
+  ASSERT_NE(output2, nullptr);
+
+  // Copy outputs back to CPU
+  std::vector<float> output1_data = copy_tensor_to_host(output1);
+  std::vector<float> output2_data = copy_tensor_to_host(output2);
+
+  // Outputs should be different (scale affects softmax sharpness)
+  // With varied V values, even small changes in attention weights will produce
+  // noticeably different outputs
+  bool outputs_differ = false;
+  float max_diff = 0.0f;
+  for (size_t i = 0; i < output1_data.size(); ++i) {
+    float diff = std::abs(output1_data[i] - output2_data[i]);
+    max_diff = std::max(max_diff, diff);
+    if (diff > 0.05f) {  // More lenient threshold due to varied V values
+      outputs_differ = true;
+      break;
+    }
+  }
+
+  printf("Max difference between outputs: %f\n", max_diff);
+  EXPECT_TRUE(outputs_differ) << "Different scales should produce different outputs (max_diff=" << max_diff << ")";
+
+  printf("Scale effect test passed!\n");
+
+  // Cleanup
+  aoti_torch_delete_tensor_object(query);
+  aoti_torch_delete_tensor_object(key);
+  aoti_torch_delete_tensor_object(value);
+  aoti_torch_delete_tensor_object(output1);
+  aoti_torch_delete_tensor_object(output2);
+}
+
+// Test causal masking correctness
+TEST_F(AOTITorchSDPATest, CausalMaskingCorrectness) {
+  const int64_t batch = 1;
+  const int64_t num_heads = 1;
+  const int64_t seq_len = 4;
+  const int64_t head_dim = 8;
+
+  // Create distinct values at different positions in V
+  // This allows us to verify that causal masking works correctly
+  Tensor* query = create_float_tensor({batch, num_heads, seq_len, head_dim}, 1.0f);
+  Tensor* key = create_float_tensor({batch, num_heads, seq_len, head_dim}, 1.0f);
+  Tensor* value = create_float_tensor({batch, num_heads, seq_len, head_dim}, 0.0f);
+
+  ASSERT_NE(query, nullptr);
+  ASSERT_NE(key, nullptr);
+  ASSERT_NE(value, nullptr);
+
+  // Manually set different values for each position in V
+  // V[position i] = i+1 (so we can track which positions contribute)
+  std::vector<float> value_host(batch * num_heads * seq_len * head_dim);
+  for (int64_t pos = 0; pos < seq_len; ++pos) {
+    for (int64_t d = 0; d < head_dim; ++d) {
+      value_host[pos * head_dim + d] = static_cast<float>(pos + 1);
+    }
+  }
+  cudaMemcpy(
+      value->data_ptr(),
+      value_host.data(),
+      value_host.size() * sizeof(float),
+      cudaMemcpyHostToDevice);
+
+  printf("Testing SDPA causal masking correctness\n");
+
+  // Run with causal masking
+  Tensor* output_causal = nullptr;
+  AOTITorchError error = aoti_torch_cuda_scaled_dot_product_attention(
+      query, key, value, nullptr, 0.0, 1, nullptr, 0, &output_causal);
+  ASSERT_EQ(error, Error::Ok);
+  ASSERT_NE(output_causal, nullptr);
+
+  // Copy output back to CPU
+  std::vector<float> output_data = copy_tensor_to_host(output_causal);
+
+  // With causal masking:
+  // - Position 0 can only see position 0, so output[0] should be ~1.0
+  // - Position 1 can see positions 0,1, so output[1] should be ~1.5 (average of 1 and 2)
+  // - Position 2 can see positions 0,1,2, so output[2] should be ~2.0 (average of 1,2,3)
+  // - Position 3 can see all, so output[3] should be ~2.5 (average of 1,2,3,4)
+
+  std::vector<float> expected_values = {1.0f, 1.5f, 2.0f, 2.5f};
+
+  bool causal_correct = true;
+  for (int64_t pos = 0; pos < seq_len; ++pos) {
+    float avg_output = 0.0f;
+    for (int64_t d = 0; d < head_dim; ++d) {
+      avg_output += output_data[pos * head_dim + d];
+    }
+    avg_output /= head_dim;
+
+    printf("Position %ld: output avg = %f, expected ~%f\n",
+           pos, avg_output, expected_values[pos]);
+
+    if (!approx_equal(avg_output, expected_values[pos], 0.2f)) {
+      causal_correct = false;
+    }
+  }
+
+  EXPECT_TRUE(causal_correct) << "Causal masking did not produce expected values";
+
+  printf("Causal masking correctness test passed!\n");
+
+  // Cleanup
+  aoti_torch_delete_tensor_object(query);
+  aoti_torch_delete_tensor_object(key);
+  aoti_torch_delete_tensor_object(value);
+  aoti_torch_delete_tensor_object(output_causal);
+}
+
+int main(int argc, char** argv) {
+  ::testing::InitGoogleTest(&argc, argv);
+  return RUN_ALL_TESTS();
+}

From c89691de02c7830d1765d8be5f03adf482a06894 Mon Sep 17 00:00:00 2001
From: gasoonjia <gasoonjia@icloud.com>
Date: Tue, 9 Dec 2025 14:01:16 -0800
Subject: [PATCH 2/8] init

---
 06-fused-attention.py                | 775 +++++++++++++++++++++++++++
 backends/cuda/cuda_backend.py        |  34 +-
 backends/cuda/triton/kernels/sdpa.py | 482 +++++++----------
 3 files changed, 1000 insertions(+), 291 deletions(-)
 create mode 100644 06-fused-attention.py

diff --git a/06-fused-attention.py b/06-fused-attention.py
new file mode 100644
index 00000000000..b1283e8ef99
--- /dev/null
+++ b/06-fused-attention.py
@@ -0,0 +1,775 @@
+"""
+Fused Attention
+===============
+
+This is a Triton implementation of the Flash Attention v2 algorithm from Tri Dao (https://tridao.me/publications/flash2/flash2.pdf)
+
+Credits: OpenAI kernel team
+
+Extra Credits:
+
+* Original flash attention paper (https://arxiv.org/abs/2205.14135)
+* Rabe and Staats (https://arxiv.org/pdf/2112.05682v2.pdf)
+
+"""
+
+import pytest
+import torch
+import os
+
+import triton
+import triton.language as tl
+from triton.tools.tensor_descriptor import TensorDescriptor
+
+DEVICE = triton.runtime.driver.active.get_active_torch_device()
+
+
+def is_hip():
+    return triton.runtime.driver.active.get_current_target().backend == "hip"
+
+
+def is_cuda():
+    return triton.runtime.driver.active.get_current_target().backend == "cuda"
+
+
+def supports_host_descriptor():
+    return is_cuda() and torch.cuda.get_device_capability()[0] >= 9
+
+
+def is_blackwell():
+    return is_cuda() and torch.cuda.get_device_capability()[0] == 10
+
+
+def is_hopper():
+    return is_cuda() and torch.cuda.get_device_capability()[0] == 9
+
+
+@triton.jit
+def _attn_fwd_inner(acc, l_i, m_i, q,  #
+                    desc_k, desc_v,  #
+                    offset_y, dtype: tl.constexpr, start_m, qk_scale,  #
+                    BLOCK_M: tl.constexpr, HEAD_DIM: tl.constexpr, BLOCK_N: tl.constexpr,  #
+                    STAGE: tl.constexpr, offs_m: tl.constexpr, offs_n: tl.constexpr,  #
+                    N_CTX: tl.constexpr, warp_specialize: tl.constexpr, IS_HOPPER: tl.constexpr):
+    # range of values handled by this stage
+    if STAGE == 1:
+        lo, hi = 0, start_m * BLOCK_M
+    elif STAGE == 2:
+        lo, hi = start_m * BLOCK_M, (start_m + 1) * BLOCK_M
+        lo = tl.multiple_of(lo, BLOCK_M)
+    # causal = False
+    else:
+        lo, hi = 0, N_CTX
+    offsetk_y = offset_y + lo
+    if dtype == tl.float8e5:
+        offsetv_y = offset_y * HEAD_DIM + lo
+    else:
+        offsetv_y = offset_y + lo
+    # loop over k, v and update accumulator
+    for start_n in tl.range(lo, hi, BLOCK_N, warp_specialize=warp_specialize):
+        start_n = tl.multiple_of(start_n, BLOCK_N)
+        # -- compute qk ----
+        k = desc_k.load([offsetk_y, 0]).T
+        qk = tl.dot(q, k)
+        if STAGE == 2:
+            mask = offs_m[:, None] >= (start_n + offs_n[None, :])
+            qk = qk * qk_scale + tl.where(mask, 0, -1.0e6)
+            m_ij = tl.maximum(m_i, tl.max(qk, 1))
+            qk -= m_ij[:, None]
+        else:
+            m_ij = tl.maximum(m_i, tl.max(qk, 1) * qk_scale)
+            qk = qk * qk_scale - m_ij[:, None]
+        p = tl.math.exp2(qk)
+        # -- compute correction factor
+        alpha = tl.math.exp2(m_i - m_ij)
+        l_ij = tl.sum(p, 1)
+        # -- update output accumulator --
+        if not IS_HOPPER and warp_specialize and BLOCK_M == 128 and HEAD_DIM == 128:
+            BM: tl.constexpr = acc.shape[0]
+            BN: tl.constexpr = acc.shape[1]
+            acc0, acc1 = acc.reshape([BM, 2, BN // 2]).permute(0, 2, 1).split()
+            acc0 = acc0 * alpha[:, None]
+            acc1 = acc1 * alpha[:, None]
+            acc = tl.join(acc0, acc1).permute(0, 2, 1).reshape([BM, BN])
+        else:
+            acc = acc * alpha[:, None]
+        # prepare p and v for the dot
+        if dtype == tl.float8e5:
+            v = desc_v.load([0, offsetv_y]).T
+        else:
+            v = desc_v.load([offsetv_y, 0])
+        p = p.to(dtype)
+        # note that this non transposed v for FP8 is only supported on Blackwell
+        acc = tl.dot(p, v, acc)
+        # update m_i and l_i
+        # place this at the end of the loop to reduce register pressure
+        l_i = l_i * alpha + l_ij
+        m_i = m_ij
+        offsetk_y += BLOCK_N
+        offsetv_y += BLOCK_N
+    return acc, l_i, m_i
+
+
+def _host_descriptor_pre_hook(nargs):
+    BLOCK_M = nargs["BLOCK_M"]
+    BLOCK_N = nargs["BLOCK_N"]
+    HEAD_DIM = nargs["HEAD_DIM"]
+    if not isinstance(nargs["desc_q"], TensorDescriptor):
+        return
+    nargs["desc_q"].block_shape = [BLOCK_M, HEAD_DIM]
+    if nargs["FP8_OUTPUT"]:
+        nargs["desc_v"].block_shape = [HEAD_DIM, BLOCK_N]
+    else:
+        nargs["desc_v"].block_shape = [BLOCK_N, HEAD_DIM]
+    nargs["desc_k"].block_shape = [BLOCK_N, HEAD_DIM]
+    nargs["desc_o"].block_shape = [BLOCK_M, HEAD_DIM]
+
+
+if is_hip():
+    NUM_STAGES_OPTIONS = [1]
+elif supports_host_descriptor():
+    NUM_STAGES_OPTIONS = [2, 3, 4]
+else:
+    NUM_STAGES_OPTIONS = [2, 3, 4]
+
+configs = [
+    triton.Config({'BLOCK_M': BM, 'BLOCK_N': BN}, num_stages=s, num_warps=w, pre_hook=_host_descriptor_pre_hook) \
+    for BM in [64, 128]\
+    for BN in [32, 64, 128]\
+    for s in NUM_STAGES_OPTIONS \
+    for w in [4, 8]\
+]
+if "PYTEST_VERSION" in os.environ:
+    # Use a single config in testing for reproducibility
+    configs = [
+        triton.Config(dict(BLOCK_M=128, BLOCK_N=64), num_stages=2, num_warps=4, pre_hook=_host_descriptor_pre_hook),
+    ]
+
+
+def keep(conf):
+    BLOCK_M = conf.kwargs["BLOCK_M"]
+    BLOCK_N = conf.kwargs["BLOCK_N"]
+    return not (is_cuda() and torch.cuda.get_device_capability()[0] == 9 and BLOCK_M * BLOCK_N < 128 * 128
+                and conf.num_warps == 8)
+
+
+def prune_invalid_configs(configs, named_args, **kwargs):
+    N_CTX = kwargs["N_CTX"]
+    STAGE = kwargs["STAGE"]
+
+    # Filter out configs where BLOCK_M > N_CTX
+    # Filter out configs where BLOCK_M < BLOCK_N when causal is True
+    return [
+        conf for conf in configs if conf.kwargs.get("BLOCK_M", 0) <= N_CTX and (
+            conf.kwargs.get("BLOCK_M", 0) >= conf.kwargs.get("BLOCK_N", 0) or STAGE == 1)
+    ]
+
+
+@triton.jit
+def _maybe_make_tensor_desc(desc_or_ptr, shape, strides, block_shape):
+    if isinstance(desc_or_ptr, tl.tensor_descriptor):
+        return desc_or_ptr
+    else:
+        return tl.make_tensor_descriptor(desc_or_ptr, shape, strides, block_shape)
+
+
+@triton.autotune(configs=list(filter(keep, configs)), key=["N_CTX", "HEAD_DIM", "FP8_OUTPUT", "warp_specialize"],
+                 prune_configs_by={'early_config_prune': prune_invalid_configs})
+@triton.jit
+def _attn_fwd(sm_scale, M,  #
+              Z, H, desc_q, desc_k, desc_v, desc_o, N_CTX,  #
+              HEAD_DIM: tl.constexpr,  #
+              BLOCK_M: tl.constexpr,  #
+              BLOCK_N: tl.constexpr,  #
+              FP8_OUTPUT: tl.constexpr,  #
+              STAGE: tl.constexpr,  #
+              warp_specialize: tl.constexpr,  #
+              IS_HOPPER: tl.constexpr,  #
+              ):
+    dtype = tl.float8e5 if FP8_OUTPUT else tl.float16
+    tl.static_assert(BLOCK_N <= HEAD_DIM)
+    start_m = tl.program_id(0)
+    off_hz = tl.program_id(1)
+    off_z = off_hz // H
+    off_h = off_hz % H
+
+    y_dim = Z * H * N_CTX
+    desc_q = _maybe_make_tensor_desc(desc_q, shape=[y_dim, HEAD_DIM], strides=[HEAD_DIM, 1],
+                                     block_shape=[BLOCK_M, HEAD_DIM])
+    if FP8_OUTPUT:
+        desc_v = _maybe_make_tensor_desc(desc_v, shape=[HEAD_DIM, y_dim], strides=[N_CTX, 1],
+                                         block_shape=[HEAD_DIM, BLOCK_N])
+    else:
+        desc_v = _maybe_make_tensor_desc(desc_v, shape=[y_dim, HEAD_DIM], strides=[HEAD_DIM, 1],
+                                         block_shape=[BLOCK_N, HEAD_DIM])
+    desc_k = _maybe_make_tensor_desc(desc_k, shape=[y_dim, HEAD_DIM], strides=[HEAD_DIM, 1],
+                                     block_shape=[BLOCK_N, HEAD_DIM])
+    desc_o = _maybe_make_tensor_desc(desc_o, shape=[y_dim, HEAD_DIM], strides=[HEAD_DIM, 1],
+                                     block_shape=[BLOCK_M, HEAD_DIM])
+
+    offset_y = off_z * (N_CTX * H) + off_h * N_CTX
+    qo_offset_y = offset_y + start_m * BLOCK_M
+    # initialize offsets
+    offs_m = start_m * BLOCK_M + tl.arange(0, BLOCK_M)
+    offs_n = tl.arange(0, BLOCK_N)
+    # initialize pointer to m and l
+    m_i = tl.zeros([BLOCK_M], dtype=tl.float32) - float("inf")
+    l_i = tl.zeros([BLOCK_M], dtype=tl.float32) + 1.0
+    acc = tl.zeros([BLOCK_M, HEAD_DIM], dtype=tl.float32)
+    # load scales
+    qk_scale = sm_scale
+    qk_scale *= 1.44269504  # 1/log(2)
+    # load q: it will stay in SRAM throughout
+    q = desc_q.load([qo_offset_y, 0])
+    # stage 1: off-band
+    # For causal = True, STAGE = 3 and _attn_fwd_inner gets 1 as its STAGE
+    # For causal = False, STAGE = 1, and _attn_fwd_inner gets 3 as its STAGE
+    if STAGE & 1:
+        acc, l_i, m_i = _attn_fwd_inner(acc, l_i, m_i, q,  #
+                                        desc_k, desc_v,  #
+                                        offset_y, dtype, start_m, qk_scale,  #
+                                        BLOCK_M, HEAD_DIM, BLOCK_N,  #
+                                        4 - STAGE, offs_m, offs_n, N_CTX,  #
+                                        warp_specialize, IS_HOPPER)
+    # stage 2: on-band
+    if STAGE & 2:
+        acc, l_i, m_i = _attn_fwd_inner(acc, l_i, m_i, q,  #
+                                        desc_k, desc_v,  #
+                                        offset_y, dtype, start_m, qk_scale,  #
+                                        BLOCK_M, HEAD_DIM, BLOCK_N,  #
+                                        2, offs_m, offs_n, N_CTX,  #
+                                        warp_specialize, IS_HOPPER)
+    # epilogue
+    m_i += tl.math.log2(l_i)
+    acc = acc / l_i[:, None]
+    m_ptrs = M + off_hz * N_CTX + offs_m
+    tl.store(m_ptrs, m_i)
+    desc_o.store([qo_offset_y, 0], acc.to(dtype))
+
+
+@triton.jit
+def _attn_bwd_preprocess(O, DO,  #
+                         Delta,  #
+                         Z, H, N_CTX,  #
+                         BLOCK_M: tl.constexpr, HEAD_DIM: tl.constexpr  #
+                         ):
+    off_m = tl.program_id(0) * BLOCK_M + tl.arange(0, BLOCK_M)
+    off_hz = tl.program_id(1)
+    off_n = tl.arange(0, HEAD_DIM)
+    # load
+    o = tl.load(O + off_hz * HEAD_DIM * N_CTX + off_m[:, None] * HEAD_DIM + off_n[None, :])
+    do = tl.load(DO + off_hz * HEAD_DIM * N_CTX + off_m[:, None] * HEAD_DIM + off_n[None, :]).to(tl.float32)
+    delta = tl.sum(o * do, axis=1)
+    # write-back
+    tl.store(Delta + off_hz * N_CTX + off_m, delta)
+
+
+# The main inner-loop logic for computing dK and dV.
+@triton.jit
+def _attn_bwd_dkdv(dk, dv,  #
+                   Q, k, v, sm_scale,  #
+                   DO,  #
+                   M, D,  #
+                   # shared by Q/K/V/DO.
+                   stride_tok, stride_d,  #
+                   H, N_CTX, BLOCK_M1: tl.constexpr,  #
+                   BLOCK_N1: tl.constexpr,  #
+                   HEAD_DIM: tl.constexpr,  #
+                   # Filled in by the wrapper.
+                   start_n, start_m, num_steps,  #
+                   MASK: tl.constexpr):
+    offs_m = start_m + tl.arange(0, BLOCK_M1)
+    offs_n = start_n + tl.arange(0, BLOCK_N1)
+    offs_k = tl.arange(0, HEAD_DIM)
+    qT_ptrs = Q + offs_m[None, :] * stride_tok + offs_k[:, None] * stride_d
+    do_ptrs = DO + offs_m[:, None] * stride_tok + offs_k[None, :] * stride_d
+    # BLOCK_N1 must be a multiple of BLOCK_M1, otherwise the code wouldn't work.
+    tl.static_assert(BLOCK_N1 % BLOCK_M1 == 0)
+    curr_m = start_m
+    step_m = BLOCK_M1
+    for blk_idx in range(num_steps):
+        qT = tl.load(qT_ptrs)
+        # Load m before computing qk to reduce pipeline stall.
+        offs_m = curr_m + tl.arange(0, BLOCK_M1)
+        m = tl.load(M + offs_m)
+        qkT = tl.dot(k, qT)
+        pT = tl.math.exp2(qkT - m[None, :])
+        # Autoregressive masking.
+        if MASK:
+            mask = (offs_m[None, :] >= offs_n[:, None])
+            pT = tl.where(mask, pT, 0.0)
+        do = tl.load(do_ptrs)
+        # Compute dV.
+        ppT = pT
+        ppT = ppT.to(tl.float16)
+        dv += tl.dot(ppT, do)
+        # D (= delta) is pre-divided by ds_scale.
+        Di = tl.load(D + offs_m)
+        # Compute dP and dS.
+        dpT = tl.dot(v, tl.trans(do)).to(tl.float32)
+        dsT = pT * (dpT - Di[None, :])
+        dsT = dsT.to(tl.float16)
+        dk += tl.dot(dsT, tl.trans(qT))
+        # Increment pointers.
+        curr_m += step_m
+        qT_ptrs += step_m * stride_tok
+        do_ptrs += step_m * stride_tok
+    return dk, dv
+
+
+# the main inner-loop logic for computing dQ
+@triton.jit
+def _attn_bwd_dq(dq, q, K, V,  #
+                 do, m, D,
+                 # shared by Q/K/V/DO.
+                 stride_tok, stride_d,  #
+                 H, N_CTX,  #
+                 BLOCK_M2: tl.constexpr,  #
+                 BLOCK_N2: tl.constexpr,  #
+                 HEAD_DIM: tl.constexpr,
+                 # Filled in by the wrapper.
+                 start_m, start_n, num_steps,  #
+                 MASK: tl.constexpr):
+    offs_m = start_m + tl.arange(0, BLOCK_M2)
+    offs_n = start_n + tl.arange(0, BLOCK_N2)
+    offs_k = tl.arange(0, HEAD_DIM)
+    kT_ptrs = K + offs_n[None, :] * stride_tok + offs_k[:, None] * stride_d
+    vT_ptrs = V + offs_n[None, :] * stride_tok + offs_k[:, None] * stride_d
+    # D (= delta) is pre-divided by ds_scale.
+    Di = tl.load(D + offs_m)
+    # BLOCK_M2 must be a multiple of BLOCK_N2, otherwise the code wouldn't work.
+    tl.static_assert(BLOCK_M2 % BLOCK_N2 == 0)
+    curr_n = start_n
+    step_n = BLOCK_N2
+    for blk_idx in range(num_steps):
+        kT = tl.load(kT_ptrs)
+        vT = tl.load(vT_ptrs)
+        qk = tl.dot(q, kT)
+        p = tl.math.exp2(qk - m)
+        # Autoregressive masking.
+        if MASK:
+            offs_n = curr_n + tl.arange(0, BLOCK_N2)
+            mask = (offs_m[:, None] >= offs_n[None, :])
+            p = tl.where(mask, p, 0.0)
+        # Compute dP and dS.
+        dp = tl.dot(do, vT).to(tl.float32)
+        ds = p * (dp - Di[:, None])
+        ds = ds.to(tl.float16)
+        # Compute dQ.
+        # NOTE: We need to de-scale dq in the end, because kT was pre-scaled.
+        dq += tl.dot(ds, tl.trans(kT))
+        # Increment pointers.
+        curr_n += step_n
+        kT_ptrs += step_n * stride_tok
+        vT_ptrs += step_n * stride_tok
+    return dq
+
+
+@triton.jit
+def _attn_bwd(Q, K, V, sm_scale,  #
+              DO,  #
+              DQ, DK, DV,  #
+              M, D,
+              # shared by Q/K/V/DO.
+              stride_z, stride_h, stride_tok, stride_d,  #
+              H, N_CTX,  #
+              BLOCK_M1: tl.constexpr,  #
+              BLOCK_N1: tl.constexpr,  #
+              BLOCK_M2: tl.constexpr,  #
+              BLOCK_N2: tl.constexpr,  #
+              BLK_SLICE_FACTOR: tl.constexpr,  #
+              HEAD_DIM: tl.constexpr,  #
+              CAUSAL: tl.constexpr):
+    LN2: tl.constexpr = 0.6931471824645996  # = ln(2)
+
+    bhid = tl.program_id(2)
+    off_chz = (bhid * N_CTX).to(tl.int64)
+    adj = (stride_h * (bhid % H) + stride_z * (bhid // H)).to(tl.int64)
+    pid = tl.program_id(0)
+
+    # offset pointers for batch/head
+    Q += adj
+    K += adj
+    V += adj
+    DO += adj
+    DQ += adj
+    DK += adj
+    DV += adj
+    M += off_chz
+    D += off_chz
+
+    # load scales
+    offs_k = tl.arange(0, HEAD_DIM)
+
+    start_n = pid * BLOCK_N1
+    start_m = 0
+
+    MASK_BLOCK_M1: tl.constexpr = BLOCK_M1 // BLK_SLICE_FACTOR
+    offs_n = start_n + tl.arange(0, BLOCK_N1)
+
+    dv = tl.zeros([BLOCK_N1, HEAD_DIM], dtype=tl.float32)
+    dk = tl.zeros([BLOCK_N1, HEAD_DIM], dtype=tl.float32)
+
+    # load K and V: they stay in SRAM throughout the inner loop.
+    k = tl.load(K + offs_n[:, None] * stride_tok + offs_k[None, :] * stride_d)
+    v = tl.load(V + offs_n[:, None] * stride_tok + offs_k[None, :] * stride_d)
+
+    if CAUSAL:
+        start_m = start_n
+        num_steps = BLOCK_N1 // MASK_BLOCK_M1
+        dk, dv = _attn_bwd_dkdv(dk, dv,  #
+                                Q, k, v, sm_scale,  #
+                                DO,  #
+                                M, D,  #
+                                stride_tok, stride_d,  #
+                                H, N_CTX,  #
+                                MASK_BLOCK_M1, BLOCK_N1, HEAD_DIM,  #
+                                start_n, start_m, num_steps,  #
+                                MASK=True,  #
+                                )
+
+        start_m += num_steps * MASK_BLOCK_M1
+
+    # Compute dK and dV for non-masked blocks.
+    num_steps = (N_CTX - start_m) // BLOCK_M1
+    dk, dv = _attn_bwd_dkdv(  #
+        dk, dv,  #
+        Q, k, v, sm_scale,  #
+        DO,  #
+        M, D,  #
+        stride_tok, stride_d,  #
+        H, N_CTX,  #
+        BLOCK_M1, BLOCK_N1, HEAD_DIM,  #
+        start_n, start_m, num_steps,  #
+        MASK=False,  #
+    )
+
+    dv_ptrs = DV + offs_n[:, None] * stride_tok + offs_k[None, :] * stride_d
+    tl.store(dv_ptrs, dv)
+
+    # Write back dK.
+    dk *= sm_scale
+    dk_ptrs = DK + offs_n[:, None] * stride_tok + offs_k[None, :] * stride_d
+    tl.store(dk_ptrs, dk)
+
+    # THIS BLOCK DOES DQ:
+    start_m = pid * BLOCK_M2
+    start_n = 0
+    num_steps = N_CTX // BLOCK_N2
+
+    MASK_BLOCK_N2: tl.constexpr = BLOCK_N2 // BLK_SLICE_FACTOR
+    offs_m = start_m + tl.arange(0, BLOCK_M2)
+
+    q = tl.load(Q + offs_m[:, None] * stride_tok + offs_k[None, :] * stride_d)
+    dq = tl.zeros([BLOCK_M2, HEAD_DIM], dtype=tl.float32)
+    do = tl.load(DO + offs_m[:, None] * stride_tok + offs_k[None, :] * stride_d)
+
+    m = tl.load(M + offs_m)
+    m = m[:, None]
+
+    if CAUSAL:
+        # Compute dQ for masked (diagonal) blocks.
+        # NOTE: This code scans each row of QK^T backward (from right to left,
+        # but inside each call to _attn_bwd_dq, from left to right), but that's
+        # not due to anything important.  I just wanted to reuse the loop
+        # structure for dK & dV above as much as possible.
+        end_n = start_m + BLOCK_M2
+        num_steps = BLOCK_M2 // MASK_BLOCK_N2
+        dq = _attn_bwd_dq(dq, q, K, V,  #
+                          do, m, D,  #
+                          stride_tok, stride_d,  #
+                          H, N_CTX,  #
+                          BLOCK_M2, MASK_BLOCK_N2, HEAD_DIM,  #
+                          start_m, end_n - num_steps * MASK_BLOCK_N2, num_steps,  #
+                          MASK=True,  #
+                          )
+        end_n -= num_steps * MASK_BLOCK_N2
+        # stage 2
+        num_steps = end_n // BLOCK_N2
+        start_n = end_n - num_steps * BLOCK_N2
+
+    dq = _attn_bwd_dq(dq, q, K, V,  #
+                      do, m, D,  #
+                      stride_tok, stride_d,  #
+                      H, N_CTX,  #
+                      BLOCK_M2, BLOCK_N2, HEAD_DIM,  #
+                      start_m, start_n, num_steps,  #
+                      MASK=False,  #
+                      )
+    # Write back dQ.
+    dq_ptrs = DQ + offs_m[:, None] * stride_tok + offs_k[None, :] * stride_d
+    dq *= LN2
+    tl.store(dq_ptrs, dq)
+
+
+class _attention(torch.autograd.Function):
+
+    @staticmethod
+    def forward(ctx, q, k, v, causal, sm_scale, warp_specialize=True):
+        # shape constraints
+        HEAD_DIM_Q, HEAD_DIM_K = q.shape[-1], k.shape[-1]
+        # when v is in float8_e5m2 it is transposed.
+        HEAD_DIM_V = v.shape[-1]
+        assert HEAD_DIM_Q == HEAD_DIM_K and HEAD_DIM_K == HEAD_DIM_V
+        assert HEAD_DIM_K in {16, 32, 64, 128, 256}
+        o = torch.empty_like(q)
+        stage = 3 if causal else 1
+        extra_kern_args = {}
+        # Tuning for AMD target
+        if is_hip():
+            waves_per_eu = 3 if HEAD_DIM_K <= 64 else 2
+            extra_kern_args = {"waves_per_eu": waves_per_eu, "allow_flush_denorm": True}
+
+        M = torch.empty((q.shape[0], q.shape[1], q.shape[2]), device=q.device, dtype=torch.float32)
+        # Use device_descriptor for Hopper + warpspec.
+        if supports_host_descriptor() and not (is_hopper() and warp_specialize):
+            # Note that on Hopper we cannot perform a FP8 dot with a non-transposed second tensor
+            y_dim = q.shape[0] * q.shape[1] * q.shape[2]
+
+            dummy_block = [1, 1]
+            desc_q = TensorDescriptor(q, shape=[y_dim, HEAD_DIM_K], strides=[HEAD_DIM_K, 1], block_shape=dummy_block)
+            if q.dtype == torch.float8_e5m2:
+                desc_v = TensorDescriptor(v, shape=[HEAD_DIM_K, y_dim], strides=[q.shape[2], 1],
+                                          block_shape=dummy_block)
+            else:
+                desc_v = TensorDescriptor(v, shape=[y_dim, HEAD_DIM_K], strides=[HEAD_DIM_K, 1],
+                                          block_shape=dummy_block)
+            desc_k = TensorDescriptor(k, shape=[y_dim, HEAD_DIM_K], strides=[HEAD_DIM_K, 1], block_shape=dummy_block)
+            desc_o = TensorDescriptor(o, shape=[y_dim, HEAD_DIM_K], strides=[HEAD_DIM_K, 1], block_shape=dummy_block)
+        else:
+            desc_q = q
+            desc_v = v
+            desc_k = k
+            desc_o = o
+
+        def alloc_fn(size: int, align: int, _):
+            return torch.empty(size, dtype=torch.int8, device="cuda")
+
+        triton.set_allocator(alloc_fn)
+
+        def grid(META):
+            return (triton.cdiv(q.shape[2], META["BLOCK_M"]), q.shape[0] * q.shape[1], 1)
+
+        ctx.grid = grid
+        if is_blackwell() and warp_specialize:
+            if HEAD_DIM_K == 128 and q.dtype == torch.float16:
+                extra_kern_args["maxnreg"] = 168
+            else:
+                extra_kern_args["maxnreg"] = 80
+        _attn_fwd[grid](
+            sm_scale, M,  #
+            q.shape[0], q.shape[1],  #
+            desc_q, desc_k, desc_v, desc_o,  #
+            N_CTX=q.shape[2],  #
+            HEAD_DIM=HEAD_DIM_K,  #
+            FP8_OUTPUT=q.dtype == torch.float8_e5m2,  #
+            STAGE=stage,  #
+            warp_specialize=warp_specialize,  #
+            IS_HOPPER=is_hopper(),  #
+            **extra_kern_args)
+
+        ctx.save_for_backward(q, k, v, o, M)
+        ctx.sm_scale = sm_scale
+        ctx.HEAD_DIM = HEAD_DIM_K
+        ctx.causal = causal
+        return o
+
+    @staticmethod
+    def backward(ctx, do):
+        q, k, v, o, M = ctx.saved_tensors
+        assert do.is_contiguous()
+        assert q.stride() == k.stride() == v.stride() == o.stride() == do.stride()
+        dq = torch.empty_like(q)
+        dk = torch.empty_like(k)
+        dv = torch.empty_like(v)
+        BATCH, N_HEAD, N_CTX = q.shape[:3]
+        PRE_BLOCK = 128
+        NUM_WARPS, NUM_STAGES = 4, 5
+        BLOCK_M1, BLOCK_N1, BLOCK_M2, BLOCK_N2 = 32, 128, 128, 32
+        BLK_SLICE_FACTOR = 2
+        RCP_LN2 = 1.4426950408889634  # = 1.0 / ln(2)
+        arg_k = k
+        arg_k = arg_k * (ctx.sm_scale * RCP_LN2)
+        PRE_BLOCK = 128
+        assert N_CTX % PRE_BLOCK == 0
+        pre_grid = (N_CTX // PRE_BLOCK, BATCH * N_HEAD)
+        delta = torch.empty_like(M)
+        _attn_bwd_preprocess[pre_grid](
+            o, do,  #
+            delta,  #
+            BATCH, N_HEAD, N_CTX,  #
+            BLOCK_M=PRE_BLOCK, HEAD_DIM=ctx.HEAD_DIM  #
+        )
+        grid = (N_CTX // BLOCK_N1, 1, BATCH * N_HEAD)
+        _attn_bwd[grid](
+            q, arg_k, v, ctx.sm_scale, do, dq, dk, dv,  #
+            M, delta,  #
+            q.stride(0), q.stride(1), q.stride(2), q.stride(3),  #
+            N_HEAD, N_CTX,  #
+            BLOCK_M1=BLOCK_M1, BLOCK_N1=BLOCK_N1,  #
+            BLOCK_M2=BLOCK_M2, BLOCK_N2=BLOCK_N2,  #
+            BLK_SLICE_FACTOR=BLK_SLICE_FACTOR,  #
+            HEAD_DIM=ctx.HEAD_DIM,  #
+            num_warps=NUM_WARPS,  #
+            num_stages=NUM_STAGES,  #
+            CAUSAL=ctx.causal,  #
+        )
+
+        return dq, dk, dv, None, None, None, None
+
+
+attention = _attention.apply
+
+TORCH_HAS_FP8 = hasattr(torch, 'float8_e5m2')
+
+
+@pytest.mark.parametrize("Z", [1, 4])
+@pytest.mark.parametrize("H", [2, 48])
+@pytest.mark.parametrize("N_CTX", [128, 1024, (2 if is_hip() else 4) * 1024])
+@pytest.mark.parametrize("HEAD_DIM", [64, 128])
+@pytest.mark.parametrize("causal", [False, True])
+@pytest.mark.parametrize("warp_specialize", [False, True] if is_blackwell() else [False])
+@pytest.mark.parametrize("mode", ["fwd", "bwd"])
+@pytest.mark.parametrize("provider", ["triton-fp16"] + (["triton-fp8"] if TORCH_HAS_FP8 else []))
+def test_op(Z, H, N_CTX, HEAD_DIM, causal, warp_specialize, mode, provider, dtype=torch.float16):
+    if mode == "fwd" and "fp16" in provider:
+        pytest.skip("Avoid running the forward computation twice.")
+    if mode == "bwd" and "fp8" in provider:
+        pytest.skip("Backward pass with FP8 is not supported.")
+    torch.manual_seed(20)
+    q = (torch.empty((Z, H, N_CTX, HEAD_DIM), dtype=dtype, device=DEVICE).normal_(mean=0.0, std=0.5).requires_grad_())
+    k = (torch.empty((Z, H, N_CTX, HEAD_DIM), dtype=dtype, device=DEVICE).normal_(mean=0.0, std=0.5).requires_grad_())
+    v = (torch.empty((Z, H, N_CTX, HEAD_DIM), dtype=dtype, device=DEVICE).normal_(mean=0.0, std=0.5).requires_grad_())
+    sm_scale = 0.5
+    # reference implementation
+    ref_dtype = dtype
+    if mode == "fwd" and "fp8" in provider:
+        ref_dtype = torch.float32
+    q = q.to(ref_dtype)
+    k = k.to(ref_dtype)
+    v = v.to(ref_dtype)
+    M = torch.tril(torch.ones((N_CTX, N_CTX), device=DEVICE))
+    p = torch.matmul(q, k.transpose(2, 3)) * sm_scale
+    if causal:
+        p[:, :, M == 0] = float("-inf")
+    p = torch.softmax(p.float(), dim=-1)
+    p = p.to(ref_dtype)
+    # p = torch.exp(p)
+    ref_out = torch.matmul(p, v).half()
+    if mode == "bwd":
+        dout = torch.randn_like(q)
+        ref_out.backward(dout)
+        ref_dv, v.grad = v.grad.clone(), None
+        ref_dk, k.grad = k.grad.clone(), None
+        ref_dq, q.grad = q.grad.clone(), None
+    # triton implementation
+    if mode == "fwd" and "fp8" in provider:
+        q = q.to(torch.float8_e5m2)
+        k = k.to(torch.float8_e5m2)
+        v = v.permute(0, 1, 3, 2).contiguous()
+        v = v.permute(0, 1, 3, 2)
+        v = v.to(torch.float8_e5m2)
+    tri_out = attention(q, k, v, causal, sm_scale, warp_specialize).half()
+    if mode == "fwd":
+        atol = 3 if "fp8" in provider else 1e-2
+        torch.testing.assert_close(tri_out, ref_out, atol=atol, rtol=0)
+        return
+    tri_out.backward(dout)
+    tri_dv, v.grad = v.grad.clone(), None
+    tri_dk, k.grad = k.grad.clone(), None
+    tri_dq, q.grad = q.grad.clone(), None
+    # compare
+    torch.testing.assert_close(tri_out, ref_out, atol=1e-2, rtol=0)
+    rtol = 0.0
+    # Relative tolerance workaround for known hardware limitation of CDNA2 GPU.
+    # For details see https://pytorch.org/docs/stable/notes/numerical_accuracy.html#reduced-precision-fp16-and-bf16-gemms-and-convolutions-on-amd-instinct-mi200-devices
+    if torch.version.hip is not None and triton.runtime.driver.active.get_current_target().arch == "gfx90a":
+        rtol = 1e-2
+    torch.testing.assert_close(tri_dv, ref_dv, atol=1e-2, rtol=rtol)
+    torch.testing.assert_close(tri_dk, ref_dk, atol=1e-2, rtol=rtol)
+    torch.testing.assert_close(tri_dq, ref_dq, atol=1e-2, rtol=rtol)
+
+
+try:
+    from flash_attn.flash_attn_interface import \
+        flash_attn_qkvpacked_func as flash_attn_func
+    HAS_FLASH = True
+except BaseException:
+    HAS_FLASH = False
+
+TORCH_HAS_FP8 = hasattr(torch, 'float8_e5m2')
+BATCH, N_HEADS = 4, 32
+# vary seq length for fixed head and batch=4
+configs = []
+for HEAD_DIM in [64, 128]:
+    for mode in ["fwd", "bwd"]:
+        for causal in [True, False]:
+            # Enable warpspec for causal fwd on Hopper
+            enable_ws = mode == "fwd" and (is_blackwell() or (is_hopper() and not causal))
+            for warp_specialize in [False, True] if enable_ws else [False]:
+                configs.append(
+                    triton.testing.Benchmark(
+                        x_names=["N_CTX"],
+                        x_vals=[2**i for i in range(10, 15)],
+                        line_arg="provider",
+                        line_vals=["triton-fp16"] + (["triton-fp8"] if TORCH_HAS_FP8 else []) +
+                        (["flash"] if HAS_FLASH else []),
+                        line_names=["Triton [FP16]"] + (["Triton [FP8]"] if TORCH_HAS_FP8 else []) +
+                        (["Flash-2"] if HAS_FLASH else []),
+                        styles=[("red", "-"), ("blue", "-"), ("green", "-")],
+                        ylabel="TFLOPS",
+                        plot_name=
+                        f"fused-attention-batch{BATCH}-head{N_HEADS}-d{HEAD_DIM}-{mode}-causal={causal}-warp_specialize={warp_specialize}",
+                        args={
+                            "H": N_HEADS,
+                            "BATCH": BATCH,
+                            "HEAD_DIM": HEAD_DIM,
+                            "mode": mode,
+                            "causal": causal,
+                            "warp_specialize": warp_specialize,
+                        },
+                    ))
+
+
+@triton.testing.perf_report(configs)
+def bench_flash_attention(BATCH, H, N_CTX, HEAD_DIM, causal, warp_specialize, mode, provider, device=DEVICE):
+    assert mode in ["fwd", "bwd"]
+    dtype = torch.float16
+    if "triton" in provider:
+        q = torch.randn((BATCH, H, N_CTX, HEAD_DIM), dtype=dtype, device=device, requires_grad=True)
+        k = torch.randn((BATCH, H, N_CTX, HEAD_DIM), dtype=dtype, device=device, requires_grad=True)
+        v = torch.randn((BATCH, H, N_CTX, HEAD_DIM), dtype=dtype, device=device, requires_grad=True)
+        if mode == "fwd" and "fp8" in provider:
+            q = q.to(torch.float8_e5m2)
+            k = k.to(torch.float8_e5m2)
+            v = v.permute(0, 1, 3, 2).contiguous()
+            v = v.permute(0, 1, 3, 2)
+            v = v.to(torch.float8_e5m2)
+        sm_scale = 1.3
+        fn = lambda: attention(q, k, v, causal, sm_scale, warp_specialize)
+        if mode == "bwd":
+            o = fn()
+            do = torch.randn_like(o)
+            fn = lambda: o.backward(do, retain_graph=True)
+        ms = triton.testing.do_bench(fn)
+
+    if provider == "flash":
+        qkv = torch.randn((BATCH, N_CTX, 3, H, HEAD_DIM), dtype=dtype, device=device, requires_grad=True)
+        fn = lambda: flash_attn_func(qkv, causal=causal)
+        if mode == "bwd":
+            o = fn()
+            do = torch.randn_like(o)
+            fn = lambda: o.backward(do, retain_graph=True)
+        ms = triton.testing.do_bench(fn)
+    flops_per_matmul = 2.0 * BATCH * H * N_CTX * N_CTX * HEAD_DIM
+    total_flops = 2 * flops_per_matmul
+    if causal:
+        total_flops *= 0.5
+    if mode == "bwd":
+        total_flops *= 2.5  # 2.0(bwd) + 0.5(recompute)
+    return total_flops * 1e-12 / (ms * 1e-3)
+
+
+if __name__ == "__main__":
+    # only works on post-Ampere GPUs right now
+    bench_flash_attention.run(save_path=".", print_data=True)
diff --git a/backends/cuda/cuda_backend.py b/backends/cuda/cuda_backend.py
index f0d3a000ec0..eb1226ebf8a 100644
--- a/backends/cuda/cuda_backend.py
+++ b/backends/cuda/cuda_backend.py
@@ -134,20 +134,20 @@ def get_aoti_compile_options(
 
         return options
 
-    @classmethod
-    def get_extra_aoti_compile_context_manager(cls):
-        """
-        Return SDPA MATH backend context manager for CUDA compilation.
-
-        This context manager plays as a fallback solution for any remaining PyTorch SDPA
-        operations to use the MATH backend (decomposed SDPA) during AOTInductor compilation.
-
-        Note:
-        - If SDPA ops are replaced with Triton kernels by ReplaceEdgeOpWithTritonOpPass,
-          this context manager will have no effect on those ops (they are no longer
-          PyTorch SDPA ops).
-        - If SDPA ops are NOT replaced (e.g., when triton_kernel_mode="OFF"), this
-          context manager will force them to use the MATH backend, causing them to
-          be automatically decomposed during compilation.
-        """
-        return torch.nn.attention.sdpa_kernel([SDPBackend.MATH])
+    # @classmethod
+    # def get_extra_aoti_compile_context_manager(cls):
+    #     """
+    #     Return SDPA MATH backend context manager for CUDA compilation.
+
+    #     This context manager plays as a fallback solution for any remaining PyTorch SDPA
+    #     operations to use the MATH backend (decomposed SDPA) during AOTInductor compilation.
+
+    #     Note:
+    #     - If SDPA ops are replaced with Triton kernels by ReplaceEdgeOpWithTritonOpPass,
+    #       this context manager will have no effect on those ops (they are no longer
+    #       PyTorch SDPA ops).
+    #     - If SDPA ops are NOT replaced (e.g., when triton_kernel_mode="OFF"), this
+    #       context manager will force them to use the MATH backend, causing them to
+    #       be automatically decomposed during compilation.
+    #     """
+    #     return torch.nn.attention.sdpa_kernel([SDPBackend.MATH])
diff --git a/backends/cuda/triton/kernels/sdpa.py b/backends/cuda/triton/kernels/sdpa.py
index 7e8eb1444df..c06eb4bb5bd 100644
--- a/backends/cuda/triton/kernels/sdpa.py
+++ b/backends/cuda/triton/kernels/sdpa.py
@@ -12,7 +12,6 @@
 us export the model without decomposing the SDPA operator under libtorch free environment
 and have better performance.
 """
-
 import math
 from typing import Optional
 
@@ -22,254 +21,183 @@
 from torch.library import triton_op, wrap_triton
 
 
-def _next_power_of_2(n: int) -> int:
-    """Round up to the next power of 2."""
-    if n <= 0:
-        return 1
-    if n & (n - 1) == 0:
-        return n
-
-    power = 1
-    while power < n:
-        power <<= 1
-    return power
+AUTOTUNE_CONFIGS = [
+    triton.Config({"BLOCK_M": 64, "BLOCK_N": 128}, num_warps=4, num_stages=3),
+    triton.Config({"BLOCK_M": 128, "BLOCK_N": 64}, num_warps=4, num_stages=3),
+    triton.Config({"BLOCK_M": 64, "BLOCK_N": 64}, num_warps=4, num_stages=2),
+]
 
 
-def _validate_qkv_shapes(
-    query: torch.Tensor,
-    key: torch.Tensor,
-    value: torch.Tensor,
-) -> tuple[int, int, int, int, int, int]:
-    """
-    Validate dimensions and return shape info.
-    Args:
-        query: Query tensor [B, H, L_q, D]
-        key: Key tensor [B, H, L_kv, D]
-        value: Value tensor [B, H, L_kv, D]
-    Returns:
-        Tuple of (B, H, L_q, L_kv, D_q, D_kv)
-    Raises:
-        RuntimeError: If dimensions are incompatible
-    """
-    B_q, H_q, L_q, D_q = query.shape
-    B_k, H_k, L_kv_k, D_k = key.shape
-    B_v, H_v, L_kv_v, D_v = value.shape
-    # Validate batch and head dimensions
-    if not (B_q == B_k == B_v):
-        raise RuntimeError(
-            f"Batch dimension must match; got B_q={B_q}, B_k={B_k}, B_v={B_v}."
-        )
-
-    if not (H_q == H_k == H_v):
-        raise RuntimeError(
-            f"Head dimension must match; got H_q={H_q}, H_k={H_k}, H_v={H_v}."
-        )
-    # Head dimension must match
-    if not (D_q == D_k == D_v):
-        raise RuntimeError(
-            f"Head dimension must match across Q, K, V; got D_q={D_q}, D_k={D_k}, D_v={D_v}."
-        )
-    # Key and Value sequence lengths must match
-    if L_kv_k != L_kv_v:
-        raise RuntimeError(
-            f"Key and Value must have the same sequence length; got L_k={L_kv_k}, L_v={L_kv_v}."
-        )
-    return B_q, H_q, L_q, L_kv_k, D_q, D_k
-
-
-@triton.autotune(
-    configs=[
-        triton.Config({"BLOCK_M": 128, "BLOCK_N": 128}, num_stages=4, num_warps=8),
-        triton.Config({"BLOCK_M": 128, "BLOCK_N": 256}, num_stages=4, num_warps=8),
-        triton.Config({"BLOCK_M": 64, "BLOCK_N": 256}, num_stages=4, num_warps=4),
-        triton.Config({"BLOCK_M": 64, "BLOCK_N": 128}, num_stages=4, num_warps=4),
-        triton.Config({"BLOCK_M": 128, "BLOCK_N": 64}, num_stages=3, num_warps=4),
-        triton.Config({"BLOCK_M": 64, "BLOCK_N": 64}, num_stages=3, num_warps=4),
-        triton.Config({"BLOCK_M": 64, "BLOCK_N": 32}, num_stages=1, num_warps=2),
-        triton.Config({"BLOCK_M": 32, "BLOCK_N": 64}, num_stages=1, num_warps=2),
-    ],
-    key=["L_Q", "L_KV", "HEAD_DIM"],
-)
+@triton.autotune(configs=AUTOTUNE_CONFIGS, key=["HEAD_DIM", "HAS_MASK", "MASK_IS_BOOL"])
 @triton.jit
 def _sdpa_fwd_kernel(
-    q_ptr,
-    k_ptr,
-    v_ptr,
-    mask_ptr,
-    o_ptr,
+    Q_ptr,
+    K_ptr,
+    V_ptr,
+    O_ptr,
     B,
     H,
-    L_Q,  # Query sequence length
-    L_KV,  # Key/Value sequence length
-    HEAD_DIM,  # Actual head dimension (may not be power of 2)
+    LQ,
+    LK,
     stride_qb,
     stride_qh,
-    stride_ql,
+    stride_qm,
     stride_qd,
     stride_kb,
     stride_kh,
-    stride_kl,
+    stride_kn,
     stride_kd,
     stride_vb,
     stride_vh,
-    stride_vl,
+    stride_vn,
     stride_vd,
-    stride_mb,
-    stride_mh,
-    stride_ml,
-    stride_mn,
     stride_ob,
     stride_oh,
-    stride_ol,
+    stride_om,
     stride_od,
-    sm_scale,
-    IS_CAUSAL: tl.constexpr,
+    scale,
+    mask_ptr,
+    stride_mb,
+    stride_mh,
+    stride_mm,
+    stride_mn,
     HAS_MASK: tl.constexpr,
+    MASK_IS_BOOL: tl.constexpr,
     BLOCK_M: tl.constexpr,
     BLOCK_N: tl.constexpr,
-    HEAD_DIM_CE: tl.constexpr,  # Rounded up for tl.arange
+    HEAD_DIM: tl.constexpr,
+    GROUP_M: tl.constexpr,
 ):
-    """
-    Fused SDPA kernel that handles different sequence lengths for Q and K/V.
+    pid_m_in = tl.program_id(axis=0)
+    pid_bh = tl.program_id(axis=1)
+    b = pid_bh // H
+    h = pid_bh % H
 
-    Q shape: [B, H, L_Q, D]
-    K/V shape: [B, H, L_KV, D]
-    Output shape: [B, H, L_Q, D]
-    """
-    # Program IDs
-    pid_m = tl.program_id(axis=0)  # along query length
-    pid_hz = tl.program_id(axis=1)  # flattened batch*head
-    off_b = pid_hz // H
-    off_h = pid_hz % H
-    # Compute ranges for queries
+    num_pid_m = tl.cdiv(LQ, BLOCK_M)
+    group_id = pid_m_in // GROUP_M
+    first_pid_m = group_id * GROUP_M
+    pid_m = first_pid_m + (pid_m_in + pid_bh) % GROUP_M
     start_m = pid_m * BLOCK_M
+    if start_m >= LQ:
+        return
+
     offs_m = start_m + tl.arange(0, BLOCK_M)
-    offs_d = tl.arange(0, HEAD_DIM_CE)
-    mask_m = offs_m < L_Q  # Mask based on query length
-    # Base pointers for this (b, h)
-    q_base = q_ptr + off_b * stride_qb + off_h * stride_qh
-    k_base = k_ptr + off_b * stride_kb + off_h * stride_kh
-    v_base = v_ptr + off_b * stride_vb + off_h * stride_vh
-    o_base = o_ptr + off_b * stride_ob + off_h * stride_oh
-    # Mask base pointer (if provided)
-    if HAS_MASK:
-        mask_base = mask_ptr + off_b * stride_mb + off_h * stride_mh
-    # Mask for actual head dimension (HEAD_DIM may not be power of 2)
-    mask_d = offs_d < HEAD_DIM
-    # Make head-dim addresses compiler-friendly
-    offs_d_ctg = tl.max_contiguous(tl.multiple_of(offs_d, 16), HEAD_DIM_CE)
-    # Load Q tile [BLOCK_M, HEAD_DIM] - coalesced along HEAD_DIM
-    q_ptrs = q_base + (offs_m[:, None] * stride_ql + offs_d_ctg[None, :] * stride_qd)
-    q = tl.load(q_ptrs, mask=mask_m[:, None] & mask_d[None, :], other=0.0)
-    q = q.to(tl.bfloat16)
-    # Initialize accumulators and softmax stats
-    acc = tl.zeros((BLOCK_M, HEAD_DIM_CE), dtype=tl.float32)
+    offs_n = tl.arange(0, BLOCK_N)
+    offs_d = tl.arange(0, HEAD_DIM)
+    offs_m = tl.multiple_of(offs_m, BLOCK_M)
+    offs_d = tl.multiple_of(offs_d, 16)
+    offs_d = tl.max_contiguous(offs_d, 16)
+
+    q_ptrs = Q_ptr + (
+        b * stride_qb
+        + h * stride_qh
+        + (offs_m[:, None] * stride_qm + offs_d[None, :] * stride_qd)
+    )
+    q_mask = offs_m[:, None] < LQ
+    q = tl.load(q_ptrs, mask=q_mask, other=0.0)
+
     m_i = tl.full((BLOCK_M,), -float("inf"), dtype=tl.float32)
     l_i = tl.zeros((BLOCK_M,), dtype=tl.float32)
-    # Convert to base-2 scale for exp2
-    qk_scale = sm_scale * 1.4426950408889634
-    # Loop over keys/values along L_KV dimension (not L_Q!)
-    for start_n in tl.range(0, L_KV, BLOCK_N):
-        offs_n = start_n + tl.arange(0, BLOCK_N)
-        mask_n = offs_n < L_KV  # Mask based on key/value length
-        # Load K tile [BLOCK_N, HEAD_DIM] (contiguous along HEAD_DIM)
-        k_ptrs = k_base + (
-            offs_n[:, None] * stride_kl + offs_d_ctg[None, :] * stride_kd
+    acc = tl.zeros((BLOCK_M, HEAD_DIM), dtype=tl.float32)
+
+    log2e = 1.4426950408889634
+    scale_log2 = scale * log2e
+
+    for start_n in tl.range(0, LK, BLOCK_N):
+        n_ids = start_n + offs_n
+        n_mask = n_ids < LK
+
+        k_ptrs = K_ptr + (
+            b * stride_kb
+            + h * stride_kh
+            + (offs_d[:, None] * stride_kd + n_ids[None, :] * stride_kn)
         )
-        k = tl.load(k_ptrs, mask=mask_n[:, None] & mask_d[None, :], other=0.0)
-        k = k.to(tl.bfloat16)
-        # Compute attention logits [BLOCK_M, BLOCK_N] = Q[BM,D] @ K[BN,D]^T
-        qk = tl.dot(q, tl.trans(k)).to(tl.float32)
-        qk = qk * qk_scale
-        # Apply causal mask if needed
-        # For causal masking with different lengths: position i can attend to position j if i >= j
-        if IS_CAUSAL:
-            causal_mask = offs_m[:, None] >= offs_n[None, :]
-            qk = tl.where(causal_mask, qk, -float("inf"))
-        # Apply attention mask if provided
+        k = tl.load(k_ptrs, mask=n_mask[None, :], other=0.0)
+
+        qk = tl.dot(q, k).to(tl.float32)
+        qk = qk * scale_log2
+
         if HAS_MASK:
-            # Load mask tile [BLOCK_M, BLOCK_N]
-            # Mask shape should be [B, H, L_Q, L_KV]
-            mask_ptrs = mask_base + (
-                offs_m[:, None] * stride_ml + offs_n[None, :] * stride_mn
-            )
-            attn_mask = tl.load(
-                mask_ptrs,
-                mask=mask_m[:, None] & mask_n[None, :],
-                other=0.0,
+            m_ptrs = mask_ptr + (
+                b * stride_mb
+                + h * stride_mh
+                + (offs_m[:, None] * stride_mm + n_ids[None, :] * stride_mn)
             )
-            # Convert boolean mask to additive mask (-inf for False, 0 for True)
-            qk = tl.where(attn_mask, qk, -float("inf"))
-        # Apply OOB masks for both rows and cols
-        qk = tl.where(mask_n[None, :], qk, -float("inf"))
-        qk = tl.where(mask_m[:, None], qk, -float("inf"))
-        # Online softmax
-        m_ij = tl.maximum(m_i, tl.max(qk, 1))
+            valid = (offs_m[:, None] < LQ) & n_mask[None, :]
+            if MASK_IS_BOOL:
+                m_bool = tl.load(m_ptrs, mask=valid, other=True)
+                qk = tl.where(m_bool, qk, -float("inf"))
+            else:
+                m_add = tl.load(m_ptrs, mask=valid, other=0.0).to(tl.float32)
+                qk = qk + m_add * log2e
+
+        qk = tl.where(n_mask[None, :], qk, -float("inf"))
+
+        m_ij = tl.maximum(m_i, tl.max(qk, axis=1))
         p = tl.math.exp2(qk - m_ij[:, None])
-        l_ij = tl.sum(p, 1)
+        l_ij = tl.sum(p, axis=1)
         alpha = tl.math.exp2(m_i - m_ij)
-        # Load V tile [BLOCK_N, HEAD_DIM] (contiguous along HEAD_DIM)
-        v_ptrs = v_base + (
-            offs_n[:, None] * stride_vl + offs_d_ctg[None, :] * stride_vd
+
+        v_ptrs = V_ptr + (
+            b * stride_vb
+            + h * stride_vh
+            + (n_ids[:, None] * stride_vn + offs_d[None, :] * stride_vd)
         )
-        v = tl.load(v_ptrs, mask=mask_n[:, None] & mask_d[None, :], other=0.0)
-        v = v.to(tl.bfloat16)
-        # Update accumulator
+        v = tl.load(v_ptrs, mask=n_mask[:, None], other=0.0)
+
         acc = acc * alpha[:, None]
-        p_bf16 = p.to(tl.bfloat16)
-        acc = tl.dot(p_bf16, v, acc)
-        # Update softmax stats
+        acc = tl.dot(p.to(tl.bfloat16), v, acc)
+
         l_i = l_i * alpha + l_ij
         m_i = m_ij
-    # Normalize accumulator by softmax denominator
-    acc = acc / l_i[:, None]
-    # Store output [BLOCK_M, HEAD_DIM] - shape matches query
-    o_ptrs = o_base + (offs_m[:, None] * stride_ol + offs_d_ctg[None, :] * stride_od)
-    tl.store(o_ptrs, acc.to(tl.bfloat16), mask=mask_m[:, None] & mask_d[None, :])
 
+    row_mask = offs_m < LQ
+    l_i = tl.where(row_mask, l_i, 1.0)
+    out = acc / l_i[:, None]
 
-@triton_op("triton::sdpa", mutates_args={})
-def sdpa(
+    o_ptrs = O_ptr + (
+        b * stride_ob
+        + h * stride_oh
+        + (offs_m[:, None] * stride_om + offs_d[None, :] * stride_od)
+    )
+    tl.store(o_ptrs, out.to(tl.bfloat16), mask=row_mask[:, None])
+
+
+def _check_inputs(
     query: torch.Tensor,
     key: torch.Tensor,
     value: torch.Tensor,
-    attn_mask: Optional[torch.Tensor] = None,
-    dropout_p: float = 0.0,
-    is_causal: bool = False,
-    scale: float = 0.0,
-    enable_gqa: bool = False,
-) -> torch.Tensor:
-    """
-    Triton fused Scaled Dot-Product Attention with support for different sequence lengths.
-
-    Args:
-        query: Query tensor with szie [B, H, L_q, D] and dtype torch.bfloat16
-        key: Key tensor [B, H, L_kv, D] and dtype torch.bfloat16
-        value: Value tensor [B, H, L_kv, D] and dtype torch.bfloat16
-        attn_mask: Optional attention mask [B, H, L_q, L_kv] or
-            broadcastable shape (2D: [L_q, L_kv] or 3D: [B, L_q, L_kv])
-        dropout_p: must be 0.0 (others are not supported)
-        is_causal: whether to apply causal masking
-        scale: attention scale (default: 1/sqrt(D))
-        enable_gqa: must be False (True is not supported)
-    Returns:
-        Output tensor [B, H, L_q, D] with dtype torch.bfloat16
-    """
-    # Validate inputs
+    attn_mask: Optional[torch.Tensor],
+    dropout_p: float,
+    is_causal: bool,
+    enable_gqa: bool,
+):
     if not (query.is_cuda and key.is_cuda and value.is_cuda):
-        raise RuntimeError("Q, K, V must be CUDA tensors.")
+        raise ValueError("query, key, value must be CUDA tensors.")
     if (
         query.dtype != torch.bfloat16
         or key.dtype != torch.bfloat16
         or value.dtype != torch.bfloat16
     ):
-        raise RuntimeError("Expected bfloat16 inputs")
-    if query.dim() != 4 or key.dim() != 4 or value.dim() != 4:
-        raise RuntimeError(
-            f"Expected 4D tensors shaped [B, H, L, D]; got "
-            f"query.dim()={query.dim()}, key.dim()={key.dim()}, "
-            f"value.dim()={value.dim()}."
-        )
+        raise ValueError("This kernel expects bfloat16 inputs for query, key, value.")
+    if query.ndim != 4 or key.ndim != 4 or value.ndim != 4:
+        raise ValueError("query, key, value must be 4D tensors [B, H, L, D].")
+    if query.shape[0] != key.shape[0] or query.shape[0] != value.shape[0]:
+        raise ValueError("Batch dimension mismatch.")
+    if query.shape[1] != key.shape[1] or query.shape[1] != value.shape[1]:
+        raise ValueError("Heads dimension mismatch.")
+    if query.shape[-1] != key.shape[-1] or query.shape[-1] != value.shape[-1]:
+        raise ValueError("Head dimension (D) mismatch across query, key, value.")
+    if attn_mask is not None:
+        if attn_mask.ndim != 4:
+            raise ValueError("attn_mask must be 4D [B, H, L_q, L_k].")
+        if attn_mask.shape[0] != query.shape[0] or attn_mask.shape[1] != query.shape[1]:
+            raise ValueError("attn_mask batch/head dims must match query.")
+        if attn_mask.shape[2] != query.shape[2] or attn_mask.shape[3] != key.shape[2]:
+            raise ValueError("attn_mask spatial dims must be [L_q, L_k].")
+        if attn_mask.dtype not in (torch.bool, torch.bfloat16):
+            raise ValueError("attn_mask must be dtype bool or bfloat16.")
+
     # Enforce unsupported features
     if dropout_p != 0.0:
         raise RuntimeError(
@@ -279,85 +207,89 @@ def sdpa(
         raise RuntimeError(
             "enable_gqa must be False (not supported in this implementation)."
         )
-    # Validate and get dimensions
-    B, H, L_q, L_kv, D_q, D_kv = _validate_qkv_shapes(query, key, value)
-    D = D_q  # Head dimension
-    # Allocate output with query shape
-    out = torch.empty_like(query)
-    # Element-wise strides
-    sqb, sqh, sql, sqd = query.stride()
-    skb, skh, skl, skd = key.stride()
-    svb, svh, svl, svd = value.stride()
-    sob, soh, sol, sod = out.stride()
-
-    # Grid: tile queries (M) and batch*heads axis
-    def grid(META):
-        return (
-            triton.cdiv(L_q, META["BLOCK_M"]),  # Based on query length
-            B * H,
+    if is_causal:
+        raise RuntimeError(
+            "is_causal must be False (not supported in this implementation)."
         )
 
-    # Scale factor for SDPA
-    sm_scale = 1.0 / math.sqrt(D) if scale == 0.0 else scale
-    # Handle attention mask
-    has_mask = attn_mask is not None
-    if has_mask:
-        # Expand mask to [B, H, L_q, L_kv] if needed
-        if attn_mask.dim() == 2:
-            # [L_q, L_kv] -> [B, H, L_q, L_kv]
-            attn_mask = attn_mask.unsqueeze(0).unsqueeze(0).expand(B, H, -1, -1)
-        elif attn_mask.dim() == 3:
-            # [B, L_q, L_kv] -> [B, H, L_q, L_kv]
-            attn_mask = attn_mask.unsqueeze(1).expand(-1, H, -1, -1)
-
-        # Validate mask shape
-        if attn_mask.shape != (B, H, L_q, L_kv):
-            # Try to expand if broadcastable
-            attn_mask = attn_mask.expand(B, H, L_q, L_kv)
-
-        smb, smh, sml, smn = attn_mask.stride()
+
+@triton_op("triton::sdpa", mutates_args={})
+def sdpa(
+    query: torch.Tensor,
+    key: torch.Tensor,
+    value: torch.Tensor,
+    attn_mask: Optional[torch.Tensor] = None,
+    dropout_p: float = 0.0,
+    is_causal: bool = False,
+    scale: float = 0.0,
+    enable_gqa: bool = False,
+) -> torch.Tensor:
+    if attn_mask is not None and attn_mask.shape[1] == 1:
+        attn_mask = attn_mask.expand(-1, query.shape[1], -1, -1).contiguous()
+
+    _check_inputs(query, key, value, attn_mask, dropout_p, is_causal, enable_gqa)
+
+    B, H, LQ, D = query.shape
+    LK = key.shape[2]
+
+    out = torch.empty_like(query)
+
+    if scale == 0:
+        scale = 1.0 / math.sqrt(D)
+    scale = float(scale)
+
+    stride_qb, stride_qh, stride_qm, stride_qd = query.stride()
+    stride_kb, stride_kh, stride_kn, stride_kd = key.stride()
+    stride_vb, stride_vh, stride_vn, stride_vd = value.stride()
+    stride_ob, stride_oh, stride_om, stride_od = out.stride()
+
+    HAS_MASK = 1 if attn_mask is not None else 0
+    MASK_IS_BOOL = 1 if (attn_mask is not None and attn_mask.dtype == torch.bool) else 0
+    if attn_mask is None:
+        mask_ptr = query
+        stride_mb = stride_mh = stride_mm = stride_mn = 0
     else:
-        # Dummy strides and mask
-        smb, smh, sml, smn = 0, 0, 0, 0
-        attn_mask = torch.empty(0, dtype=torch.bool, device=query.device)
-    # Round up head dimension to next power of 2 for tile.arange in Triton kernel
-    HEAD_DIM_CE = _next_power_of_2(D)
-    # Launch kernel
+        mask_ptr = attn_mask
+        stride_mb, stride_mh, stride_mm, stride_mn = attn_mask.stride()
+
+    def grid(meta):
+        return (triton.cdiv(LQ, meta["BLOCK_M"]), B * H)
+
     wrap_triton(_sdpa_fwd_kernel)[grid](
         query,
         key,
         value,
-        attn_mask,
         out,
         B,
         H,
-        L_q,  # Query sequence length
-        L_kv,  # Key/Value sequence length
-        D,  # Actual head dimension
-        sqb,
-        sqh,
-        sql,
-        sqd,
-        skb,
-        skh,
-        skl,
-        skd,
-        svb,
-        svh,
-        svl,
-        svd,
-        smb,
-        smh,
-        sml,
-        smn,
-        sob,
-        soh,
-        sol,
-        sod,
-        sm_scale,
-        IS_CAUSAL=is_causal,
-        HAS_MASK=has_mask,
-        HEAD_DIM_CE=HEAD_DIM_CE,  # Rounded to power of 2
+        LQ,
+        LK,
+        stride_qb,
+        stride_qh,
+        stride_qm,
+        stride_qd,
+        stride_kb,
+        stride_kh,
+        stride_kn,
+        stride_kd,
+        stride_vb,
+        stride_vh,
+        stride_vn,
+        stride_vd,
+        stride_ob,
+        stride_oh,
+        stride_om,
+        stride_od,
+        scale,
+        mask_ptr,
+        stride_mb,
+        stride_mh,
+        stride_mm,
+        stride_mn,
+        HAS_MASK=HAS_MASK,
+        MASK_IS_BOOL=MASK_IS_BOOL,
+        HEAD_DIM=D,
+        GROUP_M=8,
     )
     return out
 
@@ -380,8 +312,10 @@ def _sdpa_abstract(
     This just returns an empty tensor with the correct shape/dtype/device.
     """
     # Validate dtypes match
-    assert query.dtype == key.dtype == value.dtype, "Q, K, V must have the same dtype"
+    assert (
+        query.dtype == key.dtype == value.dtype
+    ), "query, key, value must have the same dtype"
     # Validate kqv's shape and get the output shape
-    B, H, L_q, _, D_q, _ = _validate_qkv_shapes(query, key, value)
+    B, H, LQ, D = query.shape
 
-    return torch.empty(B, H, L_q, D_q, dtype=query.dtype, device=query.device)
+    return torch.empty(B, H, LQ, D, dtype=query.dtype, device=query.device)

From f129ebd7fda02f3c237a84ce54e1ababa1585aae Mon Sep 17 00:00:00 2001
From: gasoonjia <gasoonjia@icloud.com>
Date: Tue, 9 Dec 2025 14:07:14 -0800
Subject: [PATCH 3/8] init

---
 06-fused-attention.py | 775 ------------------------------------------
 1 file changed, 775 deletions(-)
 delete mode 100644 06-fused-attention.py

diff --git a/06-fused-attention.py b/06-fused-attention.py
deleted file mode 100644
index b1283e8ef99..00000000000
--- a/06-fused-attention.py
+++ /dev/null
@@ -1,775 +0,0 @@
-"""
-Fused Attention
-===============
-
-This is a Triton implementation of the Flash Attention v2 algorithm from Tri Dao (https://tridao.me/publications/flash2/flash2.pdf)
-
-Credits: OpenAI kernel team
-
-Extra Credits:
-
-* Original flash attention paper (https://arxiv.org/abs/2205.14135)
-* Rabe and Staats (https://arxiv.org/pdf/2112.05682v2.pdf)
-
-"""
-
-import pytest
-import torch
-import os
-
-import triton
-import triton.language as tl
-from triton.tools.tensor_descriptor import TensorDescriptor
-
-DEVICE = triton.runtime.driver.active.get_active_torch_device()
-
-
-def is_hip():
-    return triton.runtime.driver.active.get_current_target().backend == "hip"
-
-
-def is_cuda():
-    return triton.runtime.driver.active.get_current_target().backend == "cuda"
-
-
-def supports_host_descriptor():
-    return is_cuda() and torch.cuda.get_device_capability()[0] >= 9
-
-
-def is_blackwell():
-    return is_cuda() and torch.cuda.get_device_capability()[0] == 10
-
-
-def is_hopper():
-    return is_cuda() and torch.cuda.get_device_capability()[0] == 9
-
-
-@triton.jit
-def _attn_fwd_inner(acc, l_i, m_i, q,  #
-                    desc_k, desc_v,  #
-                    offset_y, dtype: tl.constexpr, start_m, qk_scale,  #
-                    BLOCK_M: tl.constexpr, HEAD_DIM: tl.constexpr, BLOCK_N: tl.constexpr,  #
-                    STAGE: tl.constexpr, offs_m: tl.constexpr, offs_n: tl.constexpr,  #
-                    N_CTX: tl.constexpr, warp_specialize: tl.constexpr, IS_HOPPER: tl.constexpr):
-    # range of values handled by this stage
-    if STAGE == 1:
-        lo, hi = 0, start_m * BLOCK_M
-    elif STAGE == 2:
-        lo, hi = start_m * BLOCK_M, (start_m + 1) * BLOCK_M
-        lo = tl.multiple_of(lo, BLOCK_M)
-    # causal = False
-    else:
-        lo, hi = 0, N_CTX
-    offsetk_y = offset_y + lo
-    if dtype == tl.float8e5:
-        offsetv_y = offset_y * HEAD_DIM + lo
-    else:
-        offsetv_y = offset_y + lo
-    # loop over k, v and update accumulator
-    for start_n in tl.range(lo, hi, BLOCK_N, warp_specialize=warp_specialize):
-        start_n = tl.multiple_of(start_n, BLOCK_N)
-        # -- compute qk ----
-        k = desc_k.load([offsetk_y, 0]).T
-        qk = tl.dot(q, k)
-        if STAGE == 2:
-            mask = offs_m[:, None] >= (start_n + offs_n[None, :])
-            qk = qk * qk_scale + tl.where(mask, 0, -1.0e6)
-            m_ij = tl.maximum(m_i, tl.max(qk, 1))
-            qk -= m_ij[:, None]
-        else:
-            m_ij = tl.maximum(m_i, tl.max(qk, 1) * qk_scale)
-            qk = qk * qk_scale - m_ij[:, None]
-        p = tl.math.exp2(qk)
-        # -- compute correction factor
-        alpha = tl.math.exp2(m_i - m_ij)
-        l_ij = tl.sum(p, 1)
-        # -- update output accumulator --
-        if not IS_HOPPER and warp_specialize and BLOCK_M == 128 and HEAD_DIM == 128:
-            BM: tl.constexpr = acc.shape[0]
-            BN: tl.constexpr = acc.shape[1]
-            acc0, acc1 = acc.reshape([BM, 2, BN // 2]).permute(0, 2, 1).split()
-            acc0 = acc0 * alpha[:, None]
-            acc1 = acc1 * alpha[:, None]
-            acc = tl.join(acc0, acc1).permute(0, 2, 1).reshape([BM, BN])
-        else:
-            acc = acc * alpha[:, None]
-        # prepare p and v for the dot
-        if dtype == tl.float8e5:
-            v = desc_v.load([0, offsetv_y]).T
-        else:
-            v = desc_v.load([offsetv_y, 0])
-        p = p.to(dtype)
-        # note that this non transposed v for FP8 is only supported on Blackwell
-        acc = tl.dot(p, v, acc)
-        # update m_i and l_i
-        # place this at the end of the loop to reduce register pressure
-        l_i = l_i * alpha + l_ij
-        m_i = m_ij
-        offsetk_y += BLOCK_N
-        offsetv_y += BLOCK_N
-    return acc, l_i, m_i
-
-
-def _host_descriptor_pre_hook(nargs):
-    BLOCK_M = nargs["BLOCK_M"]
-    BLOCK_N = nargs["BLOCK_N"]
-    HEAD_DIM = nargs["HEAD_DIM"]
-    if not isinstance(nargs["desc_q"], TensorDescriptor):
-        return
-    nargs["desc_q"].block_shape = [BLOCK_M, HEAD_DIM]
-    if nargs["FP8_OUTPUT"]:
-        nargs["desc_v"].block_shape = [HEAD_DIM, BLOCK_N]
-    else:
-        nargs["desc_v"].block_shape = [BLOCK_N, HEAD_DIM]
-    nargs["desc_k"].block_shape = [BLOCK_N, HEAD_DIM]
-    nargs["desc_o"].block_shape = [BLOCK_M, HEAD_DIM]
-
-
-if is_hip():
-    NUM_STAGES_OPTIONS = [1]
-elif supports_host_descriptor():
-    NUM_STAGES_OPTIONS = [2, 3, 4]
-else:
-    NUM_STAGES_OPTIONS = [2, 3, 4]
-
-configs = [
-    triton.Config({'BLOCK_M': BM, 'BLOCK_N': BN}, num_stages=s, num_warps=w, pre_hook=_host_descriptor_pre_hook) \
-    for BM in [64, 128]\
-    for BN in [32, 64, 128]\
-    for s in NUM_STAGES_OPTIONS \
-    for w in [4, 8]\
-]
-if "PYTEST_VERSION" in os.environ:
-    # Use a single config in testing for reproducibility
-    configs = [
-        triton.Config(dict(BLOCK_M=128, BLOCK_N=64), num_stages=2, num_warps=4, pre_hook=_host_descriptor_pre_hook),
-    ]
-
-
-def keep(conf):
-    BLOCK_M = conf.kwargs["BLOCK_M"]
-    BLOCK_N = conf.kwargs["BLOCK_N"]
-    return not (is_cuda() and torch.cuda.get_device_capability()[0] == 9 and BLOCK_M * BLOCK_N < 128 * 128
-                and conf.num_warps == 8)
-
-
-def prune_invalid_configs(configs, named_args, **kwargs):
-    N_CTX = kwargs["N_CTX"]
-    STAGE = kwargs["STAGE"]
-
-    # Filter out configs where BLOCK_M > N_CTX
-    # Filter out configs where BLOCK_M < BLOCK_N when causal is True
-    return [
-        conf for conf in configs if conf.kwargs.get("BLOCK_M", 0) <= N_CTX and (
-            conf.kwargs.get("BLOCK_M", 0) >= conf.kwargs.get("BLOCK_N", 0) or STAGE == 1)
-    ]
-
-
-@triton.jit
-def _maybe_make_tensor_desc(desc_or_ptr, shape, strides, block_shape):
-    if isinstance(desc_or_ptr, tl.tensor_descriptor):
-        return desc_or_ptr
-    else:
-        return tl.make_tensor_descriptor(desc_or_ptr, shape, strides, block_shape)
-
-
-@triton.autotune(configs=list(filter(keep, configs)), key=["N_CTX", "HEAD_DIM", "FP8_OUTPUT", "warp_specialize"],
-                 prune_configs_by={'early_config_prune': prune_invalid_configs})
-@triton.jit
-def _attn_fwd(sm_scale, M,  #
-              Z, H, desc_q, desc_k, desc_v, desc_o, N_CTX,  #
-              HEAD_DIM: tl.constexpr,  #
-              BLOCK_M: tl.constexpr,  #
-              BLOCK_N: tl.constexpr,  #
-              FP8_OUTPUT: tl.constexpr,  #
-              STAGE: tl.constexpr,  #
-              warp_specialize: tl.constexpr,  #
-              IS_HOPPER: tl.constexpr,  #
-              ):
-    dtype = tl.float8e5 if FP8_OUTPUT else tl.float16
-    tl.static_assert(BLOCK_N <= HEAD_DIM)
-    start_m = tl.program_id(0)
-    off_hz = tl.program_id(1)
-    off_z = off_hz // H
-    off_h = off_hz % H
-
-    y_dim = Z * H * N_CTX
-    desc_q = _maybe_make_tensor_desc(desc_q, shape=[y_dim, HEAD_DIM], strides=[HEAD_DIM, 1],
-                                     block_shape=[BLOCK_M, HEAD_DIM])
-    if FP8_OUTPUT:
-        desc_v = _maybe_make_tensor_desc(desc_v, shape=[HEAD_DIM, y_dim], strides=[N_CTX, 1],
-                                         block_shape=[HEAD_DIM, BLOCK_N])
-    else:
-        desc_v = _maybe_make_tensor_desc(desc_v, shape=[y_dim, HEAD_DIM], strides=[HEAD_DIM, 1],
-                                         block_shape=[BLOCK_N, HEAD_DIM])
-    desc_k = _maybe_make_tensor_desc(desc_k, shape=[y_dim, HEAD_DIM], strides=[HEAD_DIM, 1],
-                                     block_shape=[BLOCK_N, HEAD_DIM])
-    desc_o = _maybe_make_tensor_desc(desc_o, shape=[y_dim, HEAD_DIM], strides=[HEAD_DIM, 1],
-                                     block_shape=[BLOCK_M, HEAD_DIM])
-
-    offset_y = off_z * (N_CTX * H) + off_h * N_CTX
-    qo_offset_y = offset_y + start_m * BLOCK_M
-    # initialize offsets
-    offs_m = start_m * BLOCK_M + tl.arange(0, BLOCK_M)
-    offs_n = tl.arange(0, BLOCK_N)
-    # initialize pointer to m and l
-    m_i = tl.zeros([BLOCK_M], dtype=tl.float32) - float("inf")
-    l_i = tl.zeros([BLOCK_M], dtype=tl.float32) + 1.0
-    acc = tl.zeros([BLOCK_M, HEAD_DIM], dtype=tl.float32)
-    # load scales
-    qk_scale = sm_scale
-    qk_scale *= 1.44269504  # 1/log(2)
-    # load q: it will stay in SRAM throughout
-    q = desc_q.load([qo_offset_y, 0])
-    # stage 1: off-band
-    # For causal = True, STAGE = 3 and _attn_fwd_inner gets 1 as its STAGE
-    # For causal = False, STAGE = 1, and _attn_fwd_inner gets 3 as its STAGE
-    if STAGE & 1:
-        acc, l_i, m_i = _attn_fwd_inner(acc, l_i, m_i, q,  #
-                                        desc_k, desc_v,  #
-                                        offset_y, dtype, start_m, qk_scale,  #
-                                        BLOCK_M, HEAD_DIM, BLOCK_N,  #
-                                        4 - STAGE, offs_m, offs_n, N_CTX,  #
-                                        warp_specialize, IS_HOPPER)
-    # stage 2: on-band
-    if STAGE & 2:
-        acc, l_i, m_i = _attn_fwd_inner(acc, l_i, m_i, q,  #
-                                        desc_k, desc_v,  #
-                                        offset_y, dtype, start_m, qk_scale,  #
-                                        BLOCK_M, HEAD_DIM, BLOCK_N,  #
-                                        2, offs_m, offs_n, N_CTX,  #
-                                        warp_specialize, IS_HOPPER)
-    # epilogue
-    m_i += tl.math.log2(l_i)
-    acc = acc / l_i[:, None]
-    m_ptrs = M + off_hz * N_CTX + offs_m
-    tl.store(m_ptrs, m_i)
-    desc_o.store([qo_offset_y, 0], acc.to(dtype))
-
-
-@triton.jit
-def _attn_bwd_preprocess(O, DO,  #
-                         Delta,  #
-                         Z, H, N_CTX,  #
-                         BLOCK_M: tl.constexpr, HEAD_DIM: tl.constexpr  #
-                         ):
-    off_m = tl.program_id(0) * BLOCK_M + tl.arange(0, BLOCK_M)
-    off_hz = tl.program_id(1)
-    off_n = tl.arange(0, HEAD_DIM)
-    # load
-    o = tl.load(O + off_hz * HEAD_DIM * N_CTX + off_m[:, None] * HEAD_DIM + off_n[None, :])
-    do = tl.load(DO + off_hz * HEAD_DIM * N_CTX + off_m[:, None] * HEAD_DIM + off_n[None, :]).to(tl.float32)
-    delta = tl.sum(o * do, axis=1)
-    # write-back
-    tl.store(Delta + off_hz * N_CTX + off_m, delta)
-
-
-# The main inner-loop logic for computing dK and dV.
-@triton.jit
-def _attn_bwd_dkdv(dk, dv,  #
-                   Q, k, v, sm_scale,  #
-                   DO,  #
-                   M, D,  #
-                   # shared by Q/K/V/DO.
-                   stride_tok, stride_d,  #
-                   H, N_CTX, BLOCK_M1: tl.constexpr,  #
-                   BLOCK_N1: tl.constexpr,  #
-                   HEAD_DIM: tl.constexpr,  #
-                   # Filled in by the wrapper.
-                   start_n, start_m, num_steps,  #
-                   MASK: tl.constexpr):
-    offs_m = start_m + tl.arange(0, BLOCK_M1)
-    offs_n = start_n + tl.arange(0, BLOCK_N1)
-    offs_k = tl.arange(0, HEAD_DIM)
-    qT_ptrs = Q + offs_m[None, :] * stride_tok + offs_k[:, None] * stride_d
-    do_ptrs = DO + offs_m[:, None] * stride_tok + offs_k[None, :] * stride_d
-    # BLOCK_N1 must be a multiple of BLOCK_M1, otherwise the code wouldn't work.
-    tl.static_assert(BLOCK_N1 % BLOCK_M1 == 0)
-    curr_m = start_m
-    step_m = BLOCK_M1
-    for blk_idx in range(num_steps):
-        qT = tl.load(qT_ptrs)
-        # Load m before computing qk to reduce pipeline stall.
-        offs_m = curr_m + tl.arange(0, BLOCK_M1)
-        m = tl.load(M + offs_m)
-        qkT = tl.dot(k, qT)
-        pT = tl.math.exp2(qkT - m[None, :])
-        # Autoregressive masking.
-        if MASK:
-            mask = (offs_m[None, :] >= offs_n[:, None])
-            pT = tl.where(mask, pT, 0.0)
-        do = tl.load(do_ptrs)
-        # Compute dV.
-        ppT = pT
-        ppT = ppT.to(tl.float16)
-        dv += tl.dot(ppT, do)
-        # D (= delta) is pre-divided by ds_scale.
-        Di = tl.load(D + offs_m)
-        # Compute dP and dS.
-        dpT = tl.dot(v, tl.trans(do)).to(tl.float32)
-        dsT = pT * (dpT - Di[None, :])
-        dsT = dsT.to(tl.float16)
-        dk += tl.dot(dsT, tl.trans(qT))
-        # Increment pointers.
-        curr_m += step_m
-        qT_ptrs += step_m * stride_tok
-        do_ptrs += step_m * stride_tok
-    return dk, dv
-
-
-# the main inner-loop logic for computing dQ
-@triton.jit
-def _attn_bwd_dq(dq, q, K, V,  #
-                 do, m, D,
-                 # shared by Q/K/V/DO.
-                 stride_tok, stride_d,  #
-                 H, N_CTX,  #
-                 BLOCK_M2: tl.constexpr,  #
-                 BLOCK_N2: tl.constexpr,  #
-                 HEAD_DIM: tl.constexpr,
-                 # Filled in by the wrapper.
-                 start_m, start_n, num_steps,  #
-                 MASK: tl.constexpr):
-    offs_m = start_m + tl.arange(0, BLOCK_M2)
-    offs_n = start_n + tl.arange(0, BLOCK_N2)
-    offs_k = tl.arange(0, HEAD_DIM)
-    kT_ptrs = K + offs_n[None, :] * stride_tok + offs_k[:, None] * stride_d
-    vT_ptrs = V + offs_n[None, :] * stride_tok + offs_k[:, None] * stride_d
-    # D (= delta) is pre-divided by ds_scale.
-    Di = tl.load(D + offs_m)
-    # BLOCK_M2 must be a multiple of BLOCK_N2, otherwise the code wouldn't work.
-    tl.static_assert(BLOCK_M2 % BLOCK_N2 == 0)
-    curr_n = start_n
-    step_n = BLOCK_N2
-    for blk_idx in range(num_steps):
-        kT = tl.load(kT_ptrs)
-        vT = tl.load(vT_ptrs)
-        qk = tl.dot(q, kT)
-        p = tl.math.exp2(qk - m)
-        # Autoregressive masking.
-        if MASK:
-            offs_n = curr_n + tl.arange(0, BLOCK_N2)
-            mask = (offs_m[:, None] >= offs_n[None, :])
-            p = tl.where(mask, p, 0.0)
-        # Compute dP and dS.
-        dp = tl.dot(do, vT).to(tl.float32)
-        ds = p * (dp - Di[:, None])
-        ds = ds.to(tl.float16)
-        # Compute dQ.
-        # NOTE: We need to de-scale dq in the end, because kT was pre-scaled.
-        dq += tl.dot(ds, tl.trans(kT))
-        # Increment pointers.
-        curr_n += step_n
-        kT_ptrs += step_n * stride_tok
-        vT_ptrs += step_n * stride_tok
-    return dq
-
-
-@triton.jit
-def _attn_bwd(Q, K, V, sm_scale,  #
-              DO,  #
-              DQ, DK, DV,  #
-              M, D,
-              # shared by Q/K/V/DO.
-              stride_z, stride_h, stride_tok, stride_d,  #
-              H, N_CTX,  #
-              BLOCK_M1: tl.constexpr,  #
-              BLOCK_N1: tl.constexpr,  #
-              BLOCK_M2: tl.constexpr,  #
-              BLOCK_N2: tl.constexpr,  #
-              BLK_SLICE_FACTOR: tl.constexpr,  #
-              HEAD_DIM: tl.constexpr,  #
-              CAUSAL: tl.constexpr):
-    LN2: tl.constexpr = 0.6931471824645996  # = ln(2)
-
-    bhid = tl.program_id(2)
-    off_chz = (bhid * N_CTX).to(tl.int64)
-    adj = (stride_h * (bhid % H) + stride_z * (bhid // H)).to(tl.int64)
-    pid = tl.program_id(0)
-
-    # offset pointers for batch/head
-    Q += adj
-    K += adj
-    V += adj
-    DO += adj
-    DQ += adj
-    DK += adj
-    DV += adj
-    M += off_chz
-    D += off_chz
-
-    # load scales
-    offs_k = tl.arange(0, HEAD_DIM)
-
-    start_n = pid * BLOCK_N1
-    start_m = 0
-
-    MASK_BLOCK_M1: tl.constexpr = BLOCK_M1 // BLK_SLICE_FACTOR
-    offs_n = start_n + tl.arange(0, BLOCK_N1)
-
-    dv = tl.zeros([BLOCK_N1, HEAD_DIM], dtype=tl.float32)
-    dk = tl.zeros([BLOCK_N1, HEAD_DIM], dtype=tl.float32)
-
-    # load K and V: they stay in SRAM throughout the inner loop.
-    k = tl.load(K + offs_n[:, None] * stride_tok + offs_k[None, :] * stride_d)
-    v = tl.load(V + offs_n[:, None] * stride_tok + offs_k[None, :] * stride_d)
-
-    if CAUSAL:
-        start_m = start_n
-        num_steps = BLOCK_N1 // MASK_BLOCK_M1
-        dk, dv = _attn_bwd_dkdv(dk, dv,  #
-                                Q, k, v, sm_scale,  #
-                                DO,  #
-                                M, D,  #
-                                stride_tok, stride_d,  #
-                                H, N_CTX,  #
-                                MASK_BLOCK_M1, BLOCK_N1, HEAD_DIM,  #
-                                start_n, start_m, num_steps,  #
-                                MASK=True,  #
-                                )
-
-        start_m += num_steps * MASK_BLOCK_M1
-
-    # Compute dK and dV for non-masked blocks.
-    num_steps = (N_CTX - start_m) // BLOCK_M1
-    dk, dv = _attn_bwd_dkdv(  #
-        dk, dv,  #
-        Q, k, v, sm_scale,  #
-        DO,  #
-        M, D,  #
-        stride_tok, stride_d,  #
-        H, N_CTX,  #
-        BLOCK_M1, BLOCK_N1, HEAD_DIM,  #
-        start_n, start_m, num_steps,  #
-        MASK=False,  #
-    )
-
-    dv_ptrs = DV + offs_n[:, None] * stride_tok + offs_k[None, :] * stride_d
-    tl.store(dv_ptrs, dv)
-
-    # Write back dK.
-    dk *= sm_scale
-    dk_ptrs = DK + offs_n[:, None] * stride_tok + offs_k[None, :] * stride_d
-    tl.store(dk_ptrs, dk)
-
-    # THIS BLOCK DOES DQ:
-    start_m = pid * BLOCK_M2
-    start_n = 0
-    num_steps = N_CTX // BLOCK_N2
-
-    MASK_BLOCK_N2: tl.constexpr = BLOCK_N2 // BLK_SLICE_FACTOR
-    offs_m = start_m + tl.arange(0, BLOCK_M2)
-
-    q = tl.load(Q + offs_m[:, None] * stride_tok + offs_k[None, :] * stride_d)
-    dq = tl.zeros([BLOCK_M2, HEAD_DIM], dtype=tl.float32)
-    do = tl.load(DO + offs_m[:, None] * stride_tok + offs_k[None, :] * stride_d)
-
-    m = tl.load(M + offs_m)
-    m = m[:, None]
-
-    if CAUSAL:
-        # Compute dQ for masked (diagonal) blocks.
-        # NOTE: This code scans each row of QK^T backward (from right to left,
-        # but inside each call to _attn_bwd_dq, from left to right), but that's
-        # not due to anything important.  I just wanted to reuse the loop
-        # structure for dK & dV above as much as possible.
-        end_n = start_m + BLOCK_M2
-        num_steps = BLOCK_M2 // MASK_BLOCK_N2
-        dq = _attn_bwd_dq(dq, q, K, V,  #
-                          do, m, D,  #
-                          stride_tok, stride_d,  #
-                          H, N_CTX,  #
-                          BLOCK_M2, MASK_BLOCK_N2, HEAD_DIM,  #
-                          start_m, end_n - num_steps * MASK_BLOCK_N2, num_steps,  #
-                          MASK=True,  #
-                          )
-        end_n -= num_steps * MASK_BLOCK_N2
-        # stage 2
-        num_steps = end_n // BLOCK_N2
-        start_n = end_n - num_steps * BLOCK_N2
-
-    dq = _attn_bwd_dq(dq, q, K, V,  #
-                      do, m, D,  #
-                      stride_tok, stride_d,  #
-                      H, N_CTX,  #
-                      BLOCK_M2, BLOCK_N2, HEAD_DIM,  #
-                      start_m, start_n, num_steps,  #
-                      MASK=False,  #
-                      )
-    # Write back dQ.
-    dq_ptrs = DQ + offs_m[:, None] * stride_tok + offs_k[None, :] * stride_d
-    dq *= LN2
-    tl.store(dq_ptrs, dq)
-
-
-class _attention(torch.autograd.Function):
-
-    @staticmethod
-    def forward(ctx, q, k, v, causal, sm_scale, warp_specialize=True):
-        # shape constraints
-        HEAD_DIM_Q, HEAD_DIM_K = q.shape[-1], k.shape[-1]
-        # when v is in float8_e5m2 it is transposed.
-        HEAD_DIM_V = v.shape[-1]
-        assert HEAD_DIM_Q == HEAD_DIM_K and HEAD_DIM_K == HEAD_DIM_V
-        assert HEAD_DIM_K in {16, 32, 64, 128, 256}
-        o = torch.empty_like(q)
-        stage = 3 if causal else 1
-        extra_kern_args = {}
-        # Tuning for AMD target
-        if is_hip():
-            waves_per_eu = 3 if HEAD_DIM_K <= 64 else 2
-            extra_kern_args = {"waves_per_eu": waves_per_eu, "allow_flush_denorm": True}
-
-        M = torch.empty((q.shape[0], q.shape[1], q.shape[2]), device=q.device, dtype=torch.float32)
-        # Use device_descriptor for Hopper + warpspec.
-        if supports_host_descriptor() and not (is_hopper() and warp_specialize):
-            # Note that on Hopper we cannot perform a FP8 dot with a non-transposed second tensor
-            y_dim = q.shape[0] * q.shape[1] * q.shape[2]
-
-            dummy_block = [1, 1]
-            desc_q = TensorDescriptor(q, shape=[y_dim, HEAD_DIM_K], strides=[HEAD_DIM_K, 1], block_shape=dummy_block)
-            if q.dtype == torch.float8_e5m2:
-                desc_v = TensorDescriptor(v, shape=[HEAD_DIM_K, y_dim], strides=[q.shape[2], 1],
-                                          block_shape=dummy_block)
-            else:
-                desc_v = TensorDescriptor(v, shape=[y_dim, HEAD_DIM_K], strides=[HEAD_DIM_K, 1],
-                                          block_shape=dummy_block)
-            desc_k = TensorDescriptor(k, shape=[y_dim, HEAD_DIM_K], strides=[HEAD_DIM_K, 1], block_shape=dummy_block)
-            desc_o = TensorDescriptor(o, shape=[y_dim, HEAD_DIM_K], strides=[HEAD_DIM_K, 1], block_shape=dummy_block)
-        else:
-            desc_q = q
-            desc_v = v
-            desc_k = k
-            desc_o = o
-
-        def alloc_fn(size: int, align: int, _):
-            return torch.empty(size, dtype=torch.int8, device="cuda")
-
-        triton.set_allocator(alloc_fn)
-
-        def grid(META):
-            return (triton.cdiv(q.shape[2], META["BLOCK_M"]), q.shape[0] * q.shape[1], 1)
-
-        ctx.grid = grid
-        if is_blackwell() and warp_specialize:
-            if HEAD_DIM_K == 128 and q.dtype == torch.float16:
-                extra_kern_args["maxnreg"] = 168
-            else:
-                extra_kern_args["maxnreg"] = 80
-        _attn_fwd[grid](
-            sm_scale, M,  #
-            q.shape[0], q.shape[1],  #
-            desc_q, desc_k, desc_v, desc_o,  #
-            N_CTX=q.shape[2],  #
-            HEAD_DIM=HEAD_DIM_K,  #
-            FP8_OUTPUT=q.dtype == torch.float8_e5m2,  #
-            STAGE=stage,  #
-            warp_specialize=warp_specialize,  #
-            IS_HOPPER=is_hopper(),  #
-            **extra_kern_args)
-
-        ctx.save_for_backward(q, k, v, o, M)
-        ctx.sm_scale = sm_scale
-        ctx.HEAD_DIM = HEAD_DIM_K
-        ctx.causal = causal
-        return o
-
-    @staticmethod
-    def backward(ctx, do):
-        q, k, v, o, M = ctx.saved_tensors
-        assert do.is_contiguous()
-        assert q.stride() == k.stride() == v.stride() == o.stride() == do.stride()
-        dq = torch.empty_like(q)
-        dk = torch.empty_like(k)
-        dv = torch.empty_like(v)
-        BATCH, N_HEAD, N_CTX = q.shape[:3]
-        PRE_BLOCK = 128
-        NUM_WARPS, NUM_STAGES = 4, 5
-        BLOCK_M1, BLOCK_N1, BLOCK_M2, BLOCK_N2 = 32, 128, 128, 32
-        BLK_SLICE_FACTOR = 2
-        RCP_LN2 = 1.4426950408889634  # = 1.0 / ln(2)
-        arg_k = k
-        arg_k = arg_k * (ctx.sm_scale * RCP_LN2)
-        PRE_BLOCK = 128
-        assert N_CTX % PRE_BLOCK == 0
-        pre_grid = (N_CTX // PRE_BLOCK, BATCH * N_HEAD)
-        delta = torch.empty_like(M)
-        _attn_bwd_preprocess[pre_grid](
-            o, do,  #
-            delta,  #
-            BATCH, N_HEAD, N_CTX,  #
-            BLOCK_M=PRE_BLOCK, HEAD_DIM=ctx.HEAD_DIM  #
-        )
-        grid = (N_CTX // BLOCK_N1, 1, BATCH * N_HEAD)
-        _attn_bwd[grid](
-            q, arg_k, v, ctx.sm_scale, do, dq, dk, dv,  #
-            M, delta,  #
-            q.stride(0), q.stride(1), q.stride(2), q.stride(3),  #
-            N_HEAD, N_CTX,  #
-            BLOCK_M1=BLOCK_M1, BLOCK_N1=BLOCK_N1,  #
-            BLOCK_M2=BLOCK_M2, BLOCK_N2=BLOCK_N2,  #
-            BLK_SLICE_FACTOR=BLK_SLICE_FACTOR,  #
-            HEAD_DIM=ctx.HEAD_DIM,  #
-            num_warps=NUM_WARPS,  #
-            num_stages=NUM_STAGES,  #
-            CAUSAL=ctx.causal,  #
-        )
-
-        return dq, dk, dv, None, None, None, None
-
-
-attention = _attention.apply
-
-TORCH_HAS_FP8 = hasattr(torch, 'float8_e5m2')
-
-
-@pytest.mark.parametrize("Z", [1, 4])
-@pytest.mark.parametrize("H", [2, 48])
-@pytest.mark.parametrize("N_CTX", [128, 1024, (2 if is_hip() else 4) * 1024])
-@pytest.mark.parametrize("HEAD_DIM", [64, 128])
-@pytest.mark.parametrize("causal", [False, True])
-@pytest.mark.parametrize("warp_specialize", [False, True] if is_blackwell() else [False])
-@pytest.mark.parametrize("mode", ["fwd", "bwd"])
-@pytest.mark.parametrize("provider", ["triton-fp16"] + (["triton-fp8"] if TORCH_HAS_FP8 else []))
-def test_op(Z, H, N_CTX, HEAD_DIM, causal, warp_specialize, mode, provider, dtype=torch.float16):
-    if mode == "fwd" and "fp16" in provider:
-        pytest.skip("Avoid running the forward computation twice.")
-    if mode == "bwd" and "fp8" in provider:
-        pytest.skip("Backward pass with FP8 is not supported.")
-    torch.manual_seed(20)
-    q = (torch.empty((Z, H, N_CTX, HEAD_DIM), dtype=dtype, device=DEVICE).normal_(mean=0.0, std=0.5).requires_grad_())
-    k = (torch.empty((Z, H, N_CTX, HEAD_DIM), dtype=dtype, device=DEVICE).normal_(mean=0.0, std=0.5).requires_grad_())
-    v = (torch.empty((Z, H, N_CTX, HEAD_DIM), dtype=dtype, device=DEVICE).normal_(mean=0.0, std=0.5).requires_grad_())
-    sm_scale = 0.5
-    # reference implementation
-    ref_dtype = dtype
-    if mode == "fwd" and "fp8" in provider:
-        ref_dtype = torch.float32
-    q = q.to(ref_dtype)
-    k = k.to(ref_dtype)
-    v = v.to(ref_dtype)
-    M = torch.tril(torch.ones((N_CTX, N_CTX), device=DEVICE))
-    p = torch.matmul(q, k.transpose(2, 3)) * sm_scale
-    if causal:
-        p[:, :, M == 0] = float("-inf")
-    p = torch.softmax(p.float(), dim=-1)
-    p = p.to(ref_dtype)
-    # p = torch.exp(p)
-    ref_out = torch.matmul(p, v).half()
-    if mode == "bwd":
-        dout = torch.randn_like(q)
-        ref_out.backward(dout)
-        ref_dv, v.grad = v.grad.clone(), None
-        ref_dk, k.grad = k.grad.clone(), None
-        ref_dq, q.grad = q.grad.clone(), None
-    # triton implementation
-    if mode == "fwd" and "fp8" in provider:
-        q = q.to(torch.float8_e5m2)
-        k = k.to(torch.float8_e5m2)
-        v = v.permute(0, 1, 3, 2).contiguous()
-        v = v.permute(0, 1, 3, 2)
-        v = v.to(torch.float8_e5m2)
-    tri_out = attention(q, k, v, causal, sm_scale, warp_specialize).half()
-    if mode == "fwd":
-        atol = 3 if "fp8" in provider else 1e-2
-        torch.testing.assert_close(tri_out, ref_out, atol=atol, rtol=0)
-        return
-    tri_out.backward(dout)
-    tri_dv, v.grad = v.grad.clone(), None
-    tri_dk, k.grad = k.grad.clone(), None
-    tri_dq, q.grad = q.grad.clone(), None
-    # compare
-    torch.testing.assert_close(tri_out, ref_out, atol=1e-2, rtol=0)
-    rtol = 0.0
-    # Relative tolerance workaround for known hardware limitation of CDNA2 GPU.
-    # For details see https://pytorch.org/docs/stable/notes/numerical_accuracy.html#reduced-precision-fp16-and-bf16-gemms-and-convolutions-on-amd-instinct-mi200-devices
-    if torch.version.hip is not None and triton.runtime.driver.active.get_current_target().arch == "gfx90a":
-        rtol = 1e-2
-    torch.testing.assert_close(tri_dv, ref_dv, atol=1e-2, rtol=rtol)
-    torch.testing.assert_close(tri_dk, ref_dk, atol=1e-2, rtol=rtol)
-    torch.testing.assert_close(tri_dq, ref_dq, atol=1e-2, rtol=rtol)
-
-
-try:
-    from flash_attn.flash_attn_interface import \
-        flash_attn_qkvpacked_func as flash_attn_func
-    HAS_FLASH = True
-except BaseException:
-    HAS_FLASH = False
-
-TORCH_HAS_FP8 = hasattr(torch, 'float8_e5m2')
-BATCH, N_HEADS = 4, 32
-# vary seq length for fixed head and batch=4
-configs = []
-for HEAD_DIM in [64, 128]:
-    for mode in ["fwd", "bwd"]:
-        for causal in [True, False]:
-            # Enable warpspec for causal fwd on Hopper
-            enable_ws = mode == "fwd" and (is_blackwell() or (is_hopper() and not causal))
-            for warp_specialize in [False, True] if enable_ws else [False]:
-                configs.append(
-                    triton.testing.Benchmark(
-                        x_names=["N_CTX"],
-                        x_vals=[2**i for i in range(10, 15)],
-                        line_arg="provider",
-                        line_vals=["triton-fp16"] + (["triton-fp8"] if TORCH_HAS_FP8 else []) +
-                        (["flash"] if HAS_FLASH else []),
-                        line_names=["Triton [FP16]"] + (["Triton [FP8]"] if TORCH_HAS_FP8 else []) +
-                        (["Flash-2"] if HAS_FLASH else []),
-                        styles=[("red", "-"), ("blue", "-"), ("green", "-")],
-                        ylabel="TFLOPS",
-                        plot_name=
-                        f"fused-attention-batch{BATCH}-head{N_HEADS}-d{HEAD_DIM}-{mode}-causal={causal}-warp_specialize={warp_specialize}",
-                        args={
-                            "H": N_HEADS,
-                            "BATCH": BATCH,
-                            "HEAD_DIM": HEAD_DIM,
-                            "mode": mode,
-                            "causal": causal,
-                            "warp_specialize": warp_specialize,
-                        },
-                    ))
-
-
-@triton.testing.perf_report(configs)
-def bench_flash_attention(BATCH, H, N_CTX, HEAD_DIM, causal, warp_specialize, mode, provider, device=DEVICE):
-    assert mode in ["fwd", "bwd"]
-    dtype = torch.float16
-    if "triton" in provider:
-        q = torch.randn((BATCH, H, N_CTX, HEAD_DIM), dtype=dtype, device=device, requires_grad=True)
-        k = torch.randn((BATCH, H, N_CTX, HEAD_DIM), dtype=dtype, device=device, requires_grad=True)
-        v = torch.randn((BATCH, H, N_CTX, HEAD_DIM), dtype=dtype, device=device, requires_grad=True)
-        if mode == "fwd" and "fp8" in provider:
-            q = q.to(torch.float8_e5m2)
-            k = k.to(torch.float8_e5m2)
-            v = v.permute(0, 1, 3, 2).contiguous()
-            v = v.permute(0, 1, 3, 2)
-            v = v.to(torch.float8_e5m2)
-        sm_scale = 1.3
-        fn = lambda: attention(q, k, v, causal, sm_scale, warp_specialize)
-        if mode == "bwd":
-            o = fn()
-            do = torch.randn_like(o)
-            fn = lambda: o.backward(do, retain_graph=True)
-        ms = triton.testing.do_bench(fn)
-
-    if provider == "flash":
-        qkv = torch.randn((BATCH, N_CTX, 3, H, HEAD_DIM), dtype=dtype, device=device, requires_grad=True)
-        fn = lambda: flash_attn_func(qkv, causal=causal)
-        if mode == "bwd":
-            o = fn()
-            do = torch.randn_like(o)
-            fn = lambda: o.backward(do, retain_graph=True)
-        ms = triton.testing.do_bench(fn)
-    flops_per_matmul = 2.0 * BATCH * H * N_CTX * N_CTX * HEAD_DIM
-    total_flops = 2 * flops_per_matmul
-    if causal:
-        total_flops *= 0.5
-    if mode == "bwd":
-        total_flops *= 2.5  # 2.0(bwd) + 0.5(recompute)
-    return total_flops * 1e-12 / (ms * 1e-3)
-
-
-if __name__ == "__main__":
-    # only works on post-Ampere GPUs right now
-    bench_flash_attention.run(save_path=".", print_data=True)

From c48420869d674c1e742886f56dd5c7c905b96e97 Mon Sep 17 00:00:00 2001
From: gasoonjia <gasoonjia@icloud.com>
Date: Wed, 10 Dec 2025 10:21:39 -0800
Subject: [PATCH 4/8] new triton works

---
 backends/cuda/triton/kernels/sdpa.py | 594 +++++++++++++++++++--------
 1 file changed, 420 insertions(+), 174 deletions(-)

diff --git a/backends/cuda/triton/kernels/sdpa.py b/backends/cuda/triton/kernels/sdpa.py
index c06eb4bb5bd..601c09ad983 100644
--- a/backends/cuda/triton/kernels/sdpa.py
+++ b/backends/cuda/triton/kernels/sdpa.py
@@ -12,6 +12,7 @@
 us export the model without decomposing the SDPA operator under libtorch free environment
 and have better performance.
 """
+
 import math
 from typing import Optional
 
@@ -21,24 +22,59 @@
 from torch.library import triton_op, wrap_triton
 
 
-AUTOTUNE_CONFIGS = [
-    triton.Config({"BLOCK_M": 64, "BLOCK_N": 128}, num_warps=4, num_stages=3),
-    triton.Config({"BLOCK_M": 128, "BLOCK_N": 64}, num_warps=4, num_stages=3),
-    triton.Config({"BLOCK_M": 64, "BLOCK_N": 64}, num_warps=4, num_stages=2),
-]
+def _validate_qkv_shapes(
+    query: torch.Tensor,
+    key: torch.Tensor,
+    value: torch.Tensor,
+) -> tuple[int, int, int, int, int, int]:
+    """
+    Validate dimensions and return shape info.
+    Args:
+        query: Query tensor [B, H, L_q, D]
+        key: Key tensor [B, H, L_kv, D]
+        value: Value tensor [B, H, L_kv, D]
+    Returns:
+        Tuple of (B, H, L_q, L_kv, D_q, D_kv)
+    Raises:
+        RuntimeError: If dimensions are incompatible
+    """
+    B_q, H_q, L_q, D_q = query.shape
+    B_k, H_k, L_kv_k, D_k = key.shape
+    B_v, H_v, L_kv_v, D_v = value.shape
+    # Validate batch and head dimensions
+    if not (B_q == B_k == B_v):
+        raise RuntimeError(
+            f"Batch dimension must match; got B_q={B_q}, B_k={B_k}, B_v={B_v}."
+        )
+
+    if not (H_q == H_k == H_v):
+        raise RuntimeError(
+            f"Head dimension must match; got H_q={H_q}, H_k={H_k}, H_v={H_v}."
+        )
+    # Head dimension must match
+    if not (D_q == D_k == D_v):
+        raise RuntimeError(
+            f"Head dimension must match across Q, K, V; got D_q={D_q}, D_k={D_k}, D_v={D_v}."
+        )
+    # Key and Value sequence lengths must match
+    if L_kv_k != L_kv_v:
+        raise RuntimeError(
+            f"Key and Value must have the same sequence length; got L_k={L_kv_k}, L_v={L_kv_v}."
+        )
+    return B_q, H_q, L_q, L_kv_k, D_q, D_k
 
 
-@triton.autotune(configs=AUTOTUNE_CONFIGS, key=["HEAD_DIM", "HAS_MASK", "MASK_IS_BOOL"])
 @triton.jit
-def _sdpa_fwd_kernel(
+def _sdpa_fwd_kernel_body(
     Q_ptr,
     K_ptr,
     V_ptr,
     O_ptr,
+    Mask_ptr,
     B,
     H,
-    LQ,
-    LK,
+    Lq,
+    Lk,
     stride_qb,
     stride_qh,
     stride_qm,
@@ -55,149 +91,319 @@ def _sdpa_fwd_kernel(
     stride_oh,
     stride_om,
     stride_od,
-    scale,
-    mask_ptr,
     stride_mb,
-    stride_mh,
-    stride_mm,
-    stride_mn,
+    stride_mq,
+    stride_mk,
+    sm_scale: tl.float32,
     HAS_MASK: tl.constexpr,
-    MASK_IS_BOOL: tl.constexpr,
+    IS_CAUSAL: tl.constexpr,
     BLOCK_M: tl.constexpr,
     BLOCK_N: tl.constexpr,
     HEAD_DIM: tl.constexpr,
-    GROUP_M: tl.constexpr,
 ):
-    pid_m_in = tl.program_id(axis=0)
+    """
+    Shared kernel body for SDPA forward pass.
+    """
+    pid_m = tl.program_id(axis=0)
     pid_bh = tl.program_id(axis=1)
     b = pid_bh // H
     h = pid_bh % H
 
-    num_pid_m = tl.cdiv(LQ, BLOCK_M)
-    group_id = pid_m_in // GROUP_M
-    first_pid_m = group_id * GROUP_M
-    pid_m = first_pid_m + (pid_m_in + pid_bh) % GROUP_M
-    start_m = pid_m * BLOCK_M
-    if start_m >= LQ:
-        return
-
-    offs_m = start_m + tl.arange(0, BLOCK_M)
-    offs_n = tl.arange(0, BLOCK_N)
+    offs_m = pid_m * BLOCK_M + tl.arange(0, BLOCK_M)
+    offs_n_init = tl.arange(0, BLOCK_N)
     offs_d = tl.arange(0, HEAD_DIM)
-    offs_m = tl.multiple_of(offs_m, BLOCK_M)
-    offs_d = tl.multiple_of(offs_d, 16)
-    offs_d = tl.max_contiguous(offs_d, 16)
 
     q_ptrs = Q_ptr + (
         b * stride_qb
         + h * stride_qh
-        + (offs_m[:, None] * stride_qm + offs_d[None, :] * stride_qd)
+        + (offs_m[:, None] * stride_qm)
+        + (offs_d[None, :] * stride_qd)
     )
-    q_mask = offs_m[:, None] < LQ
-    q = tl.load(q_ptrs, mask=q_mask, other=0.0)
+    q_mask = (offs_m[:, None] < Lq) & (offs_d[None, :] < HEAD_DIM)
+    q = tl.load(q_ptrs, mask=q_mask, other=0.0).to(tl.bfloat16)
 
-    m_i = tl.full((BLOCK_M,), -float("inf"), dtype=tl.float32)
-    l_i = tl.zeros((BLOCK_M,), dtype=tl.float32)
-    acc = tl.zeros((BLOCK_M, HEAD_DIM), dtype=tl.float32)
+    m_i = tl.full([BLOCK_M], -float("inf"), dtype=tl.float32)
+    l_i = tl.zeros([BLOCK_M], dtype=tl.float32)
+    acc = tl.zeros([BLOCK_M, HEAD_DIM], dtype=tl.float32)
 
-    log2e = 1.4426950408889634
-    scale_log2 = scale * log2e
-
-    for start_n in tl.range(0, LK, BLOCK_N):
-        n_ids = start_n + offs_n
-        n_mask = n_ids < LK
+    for start_n in tl.range(0, Lk, BLOCK_N):
+        offs_n = start_n + offs_n_init
 
         k_ptrs = K_ptr + (
             b * stride_kb
             + h * stride_kh
-            + (offs_d[:, None] * stride_kd + n_ids[None, :] * stride_kn)
+            + (offs_n[:, None] * stride_kn)
+            + (offs_d[None, :] * stride_kd)
         )
-        k = tl.load(k_ptrs, mask=n_mask[None, :], other=0.0)
+        k_mask = (offs_n[:, None] < Lk) & (offs_d[None, :] < HEAD_DIM)
+        k = tl.load(k_ptrs, mask=k_mask, other=0.0).to(tl.bfloat16)
 
-        qk = tl.dot(q, k).to(tl.float32)
-        qk = qk * scale_log2
+        qk = tl.dot(q, tl.trans(k)).to(tl.float32) * sm_scale
 
         if HAS_MASK:
-            m_ptrs = mask_ptr + (
+            mask_ptrs = Mask_ptr + (
                 b * stride_mb
-                + h * stride_mh
-                + (offs_m[:, None] * stride_mm + n_ids[None, :] * stride_mn)
+                + (offs_m[:, None] * stride_mq)
+                + (offs_n[None, :] * stride_mk)
             )
-            valid = (offs_m[:, None] < LQ) & n_mask[None, :]
-            if MASK_IS_BOOL:
-                m_bool = tl.load(m_ptrs, mask=valid, other=True)
-                qk = tl.where(m_bool, qk, -float("inf"))
-            else:
-                m_add = tl.load(m_ptrs, mask=valid, other=0.0).to(tl.float32)
-                qk = qk + m_add * log2e
+            mn_mask = (offs_m[:, None] < Lq) & (offs_n[None, :] < Lk)
+            mask_block = tl.load(mask_ptrs, mask=mn_mask, other=False)
+            qk = tl.where(mask_block, qk, -float("inf"))
 
-        qk = tl.where(n_mask[None, :], qk, -float("inf"))
+        if IS_CAUSAL:
+            abs_m = offs_m[:, None]
+            abs_n = offs_n[None, :]
+            causal = abs_n > abs_m
+            qk = tl.where(causal, -float("inf"), qk)
 
         m_ij = tl.maximum(m_i, tl.max(qk, axis=1))
-        p = tl.math.exp2(qk - m_ij[:, None])
-        l_ij = tl.sum(p, axis=1)
-        alpha = tl.math.exp2(m_i - m_ij)
+        p_f32 = tl.exp(qk - m_ij[:, None])
+        l_ij = tl.sum(p_f32, axis=1)
+        alpha = tl.exp(m_i - m_ij)
 
         v_ptrs = V_ptr + (
             b * stride_vb
             + h * stride_vh
-            + (n_ids[:, None] * stride_vn + offs_d[None, :] * stride_vd)
+            + (offs_n[:, None] * stride_vn)
+            + (offs_d[None, :] * stride_vd)
         )
-        v = tl.load(v_ptrs, mask=n_mask[:, None], other=0.0)
-
-        acc = acc * alpha[:, None]
-        acc = tl.dot(p.to(tl.bfloat16), v, acc)
+        v_mask = (offs_n[:, None] < Lk) & (offs_d[None, :] < HEAD_DIM)
+        v = tl.load(v_ptrs, mask=v_mask, other=0.0).to(tl.bfloat16)
 
+        p_bf16 = p_f32.to(tl.bfloat16)
+        acc = acc * alpha[:, None] + tl.dot(p_bf16, v)
         l_i = l_i * alpha + l_ij
         m_i = m_ij
 
-    row_mask = offs_m < LQ
-    l_i = tl.where(row_mask, l_i, 1.0)
-    out = acc / l_i[:, None]
+    inv_l_i = tl.where(l_i > 0, 1.0 / l_i, 0.0)
+    acc = acc * inv_l_i[:, None]
 
     o_ptrs = O_ptr + (
         b * stride_ob
         + h * stride_oh
-        + (offs_m[:, None] * stride_om + offs_d[None, :] * stride_od)
+        + (offs_m[:, None] * stride_om)
+        + (offs_d[None, :] * stride_od)
+    )
+    o_mask = (offs_m[:, None] < Lq) & (offs_d[None, :] < HEAD_DIM)
+    tl.store(o_ptrs, acc.to(tl.bfloat16), mask=o_mask)
+
+
+@triton.autotune(
+    configs=[
+        triton.Config({"BLOCK_M": 64, "BLOCK_N": 64}, num_warps=4, num_stages=2),
+        triton.Config({"BLOCK_M": 64, "BLOCK_N": 128}, num_warps=4, num_stages=3),
+        triton.Config({"BLOCK_M": 64, "BLOCK_N": 128}, num_warps=8, num_stages=2),
+        triton.Config({"BLOCK_M": 64, "BLOCK_N": 256}, num_warps=8, num_stages=3),
+        triton.Config({"BLOCK_M": 64, "BLOCK_N": 32}, num_warps=4, num_stages=2),
+    ],
+    key=["Lq", "Lk", "HEAD_DIM", "HAS_MASK", "IS_CAUSAL"],
+)
+@triton.jit
+def _sdpa_fwd_kernel_m64(
+    Q_ptr,
+    K_ptr,
+    V_ptr,
+    O_ptr,
+    Mask_ptr,
+    B,
+    H,
+    Lq,
+    Lk,
+    stride_qb,
+    stride_qh,
+    stride_qm,
+    stride_qd,
+    stride_kb,
+    stride_kh,
+    stride_kn,
+    stride_kd,
+    stride_vb,
+    stride_vh,
+    stride_vn,
+    stride_vd,
+    stride_ob,
+    stride_oh,
+    stride_om,
+    stride_od,
+    stride_mb,
+    stride_mq,
+    stride_mk,
+    sm_scale: tl.float32,
+    HAS_MASK: tl.constexpr,
+    IS_CAUSAL: tl.constexpr,
+    HEAD_DIM: tl.constexpr,
+    BLOCK_M: tl.constexpr,
+    BLOCK_N: tl.constexpr,
+):
+    """
+    SDPA kernel with BLOCK_M=64 optimizations.
+    """
+    _sdpa_fwd_kernel_body(
+        Q_ptr,
+        K_ptr,
+        V_ptr,
+        O_ptr,
+        Mask_ptr,
+        B,
+        H,
+        Lq,
+        Lk,
+        stride_qb,
+        stride_qh,
+        stride_qm,
+        stride_qd,
+        stride_kb,
+        stride_kh,
+        stride_kn,
+        stride_kd,
+        stride_vb,
+        stride_vh,
+        stride_vn,
+        stride_vd,
+        stride_ob,
+        stride_oh,
+        stride_om,
+        stride_od,
+        stride_mb,
+        stride_mq,
+        stride_mk,
+        sm_scale,
+        HAS_MASK=HAS_MASK,
+        IS_CAUSAL=IS_CAUSAL,
+        BLOCK_M=BLOCK_M,
+        BLOCK_N=BLOCK_N,
+        HEAD_DIM=HEAD_DIM,
+    )
+
+
+@triton.autotune(
+    configs=[
+        triton.Config({"BLOCK_M": 32, "BLOCK_N": 64}, num_warps=4, num_stages=2),
+        triton.Config({"BLOCK_M": 32, "BLOCK_N": 128}, num_warps=4, num_stages=2),
+        triton.Config({"BLOCK_M": 32, "BLOCK_N": 256}, num_warps=4, num_stages=2),
+        triton.Config({"BLOCK_M": 32, "BLOCK_N": 32}, num_warps=4, num_stages=2),
+    ],
+    key=["Lq", "Lk", "HEAD_DIM", "HAS_MASK", "IS_CAUSAL"],
+)
+@triton.jit
+def _sdpa_fwd_kernel_m32(
+    Q_ptr,
+    K_ptr,
+    V_ptr,
+    O_ptr,
+    Mask_ptr,
+    B,
+    H,
+    Lq,
+    Lk,
+    stride_qb,
+    stride_qh,
+    stride_qm,
+    stride_qd,
+    stride_kb,
+    stride_kh,
+    stride_kn,
+    stride_kd,
+    stride_vb,
+    stride_vh,
+    stride_vn,
+    stride_vd,
+    stride_ob,
+    stride_oh,
+    stride_om,
+    stride_od,
+    stride_mb,
+    stride_mq,
+    stride_mk,
+    sm_scale: tl.float32,
+    HAS_MASK: tl.constexpr,
+    IS_CAUSAL: tl.constexpr,
+    HEAD_DIM: tl.constexpr,
+    BLOCK_M: tl.constexpr,
+    BLOCK_N: tl.constexpr,
+):
+    """
+    SDPA kernel with BLOCK_M=32 optimizations for small workloads.
+    """
+    _sdpa_fwd_kernel_body(
+        Q_ptr,
+        K_ptr,
+        V_ptr,
+        O_ptr,
+        Mask_ptr,
+        B,
+        H,
+        Lq,
+        Lk,
+        stride_qb,
+        stride_qh,
+        stride_qm,
+        stride_qd,
+        stride_kb,
+        stride_kh,
+        stride_kn,
+        stride_kd,
+        stride_vb,
+        stride_vh,
+        stride_vn,
+        stride_vd,
+        stride_ob,
+        stride_oh,
+        stride_om,
+        stride_od,
+        stride_mb,
+        stride_mq,
+        stride_mk,
+        sm_scale,
+        HAS_MASK=HAS_MASK,
+        IS_CAUSAL=IS_CAUSAL,
+        BLOCK_M=BLOCK_M,
+        BLOCK_N=BLOCK_N,
+        HEAD_DIM=HEAD_DIM,
     )
-    tl.store(o_ptrs, out.to(tl.bfloat16), mask=row_mask[:, None])
 
 
-def _check_inputs(
+@triton_op("triton::sdpa", mutates_args={})
+def sdpa(
     query: torch.Tensor,
     key: torch.Tensor,
     value: torch.Tensor,
-    attn_mask: Optional[torch.Tensor],
-    dropout_p: float,
-    is_causal: bool,
-    enable_gqa: bool,
-):
+    attn_mask: Optional[torch.Tensor] = None,
+    dropout_p: float = 0.0,
+    is_causal: bool = False,
+    scale: float = 0.0,
+    enable_gqa: bool = False,
+) -> torch.Tensor:
+    """
+    Triton fused Scaled Dot-Product Attention with optimized dual-kernel approach.
+
+    Args:
+        query: Query tensor with size [B, H, L_q, D] and dtype torch.bfloat16
+        key: Key tensor [B, H, L_kv, D] and dtype torch.bfloat16
+        value: Value tensor [B, H, L_kv, D] and dtype torch.bfloat16
+        attn_mask: Optional attention mask [B, H, L_q, L_kv] with dtype torch.bool
+        dropout_p: must be 0.0 (others are not supported)
+        is_causal: whether to apply causal masking
+        scale: attention scale (default: 1/sqrt(D))
+        enable_gqa: must be False (True is not supported)
+    Returns:
+        Output tensor [B, H, L_q, D] with dtype torch.bfloat16
+    """
+    # Validate inputs
     if not (query.is_cuda and key.is_cuda and value.is_cuda):
-        raise ValueError("query, key, value must be CUDA tensors.")
+        raise RuntimeError("Q, K, V must be CUDA tensors.")
     if (
         query.dtype != torch.bfloat16
         or key.dtype != torch.bfloat16
         or value.dtype != torch.bfloat16
     ):
-        raise ValueError("This kernel expects bfloat16 inputs for query, key, value.")
-    if query.ndim != 4 or key.ndim != 4 or value.ndim != 4:
-        raise ValueError("query, key, value must be 4D tensors [B, H, L, D].")
-    if query.shape[0] != key.shape[0] or query.shape[0] != value.shape[0]:
-        raise ValueError("Batch dimension mismatch.")
-    if query.shape[1] != key.shape[1] or query.shape[1] != value.shape[1]:
-        raise ValueError("Heads dimension mismatch.")
-    if query.shape[-1] != key.shape[-1] or query.shape[-1] != value.shape[-1]:
-        raise ValueError("Head dimension (D) mismatch across query, key, value.")
-    if attn_mask is not None:
-        if attn_mask.ndim != 4:
-            raise ValueError("attn_mask must be 4D [B, H, L_q, L_k].")
-        if attn_mask.shape[0] != query.shape[0] or attn_mask.shape[1] != query.shape[1]:
-            raise ValueError("attn_mask batch/head dims must match query.")
-        if attn_mask.shape[2] != query.shape[2] or attn_mask.shape[3] != key.shape[2]:
-            raise ValueError("attn_mask spatial dims must be [L_q, L_k].")
-        if attn_mask.dtype not in (torch.bool, torch.bfloat16):
-            raise ValueError("attn_mask must be dtype bool or bfloat16.")
-
+        raise RuntimeError("Expected bfloat16 inputs")
+    if query.dim() != 4 or key.dim() != 4 or value.dim() != 4:
+        raise RuntimeError(
+            f"Expected 4D tensors shaped [B, H, L, D]; got "
+            f"query.dim()={query.dim()}, key.dim()={key.dim()}, "
+            f"value.dim()={value.dim()}."
+        )
     # Enforce unsupported features
     if dropout_p != 0.0:
         raise RuntimeError(
@@ -207,90 +413,132 @@ def _check_inputs(
         raise RuntimeError(
             "enable_gqa must be False (not supported in this implementation)."
         )
-    if is_causal:
-        raise RuntimeError(
-            "is_causal must be False (not supported in this implementation)."
-        )
+    # Validate and get dimensions
+    B, H, L_q, L_kv, D_q, D_kv = _validate_qkv_shapes(query, key, value)
+    D = D_q  # Head dimension
 
+    # Enforce causal masking constraint
+    if is_causal:
+        if L_q != L_kv:
+            raise RuntimeError(
+                f"Causal masking requires L_q == L_kv; got L_q={L_q}, L_kv={L_kv}."
+            )
 
-@triton_op("triton::sdpa", mutates_args={})
-def sdpa(
-    query: torch.Tensor,
-    key: torch.Tensor,
-    value: torch.Tensor,
-    attn_mask: Optional[torch.Tensor] = None,
-    dropout_p: float = 0.0,
-    is_causal: bool = False,
-    scale: float = 0.0,
-    enable_gqa: bool = False,
-) -> torch.Tensor:
-    if attn_mask is not None and attn_mask.shape[1] == 1:
-        attn_mask = attn_mask.expand(-1, query.shape[1], -1, -1).contiguous()
-
-    _check_inputs(query, key, value, attn_mask, dropout_p, is_causal, enable_gqa)
-
-    B, H, LQ, D = query.shape
-    LK = key.shape[2]
-
-    out = torch.empty_like(query)
-
-    if scale == 0:
-        scale = 1.0 / math.sqrt(D)
-    scale = float(scale)
+    # Allocate output with query shape
+    out = torch.empty((B, H, L_q, D), device=query.device, dtype=query.dtype)
 
+    # Element-wise strides
     stride_qb, stride_qh, stride_qm, stride_qd = query.stride()
     stride_kb, stride_kh, stride_kn, stride_kd = key.stride()
     stride_vb, stride_vh, stride_vn, stride_vd = value.stride()
     stride_ob, stride_oh, stride_om, stride_od = out.stride()
 
-    HAS_MASK = 1 if attn_mask is not None else 0
-    MASK_IS_BOOL = 1 if (attn_mask is not None and attn_mask.dtype == torch.bool) else 0
-    if attn_mask is None:
-        mask_ptr = query
-        stride_mb = stride_mh = stride_mm = stride_mn = 0
-    else:
-        mask_ptr = attn_mask
-        stride_mb, stride_mh, stride_mm, stride_mn = attn_mask.stride()
+    # Scale factor for SDPA
+    sm_scale = 1.0 / math.sqrt(D) if scale == 0.0 else scale
+
+    # Handle attention mask
+    HAS_MASK = attn_mask is not None
+    Mask_ptr = 0
+    stride_mb = stride_mq = stride_mk = 0
+    if HAS_MASK:
+        if attn_mask.dtype != torch.bool:
+            raise RuntimeError("attn_mask must have dtype torch.bool")
+        if not attn_mask.is_cuda:
+            raise RuntimeError("attn_mask must be a CUDA tensor")
+        if (
+            attn_mask.shape[0] != B
+            or attn_mask.shape[2] != L_q
+            or attn_mask.shape[3] != L_kv
+        ):
+            raise RuntimeError(
+                f"attn_mask shape mismatch: expected [B={B}, H, L_q={L_q}, L_kv={L_kv}], "
+                f"got {attn_mask.shape}"
+            )
+        Mask_ptr = attn_mask
+        stride_mb = attn_mask.stride(0)
+        stride_mq = attn_mask.stride(2)
+        stride_mk = attn_mask.stride(3)
 
+    # Grid configuration
     def grid(meta):
-        return (triton.cdiv(LQ, meta["BLOCK_M"]), B * H)
+        return (triton.cdiv(L_q, meta["BLOCK_M"]), B * H)
+
+    # Dynamic kernel selection based on workload
+    total_ctas_m64 = ((L_q + 63) // 64) * (B * H)
+    threshold = 4 * 84  # Heuristic threshold for kernel selection
+    use_small_block = total_ctas_m64 < threshold
+
+    if use_small_block:
+        wrap_triton(_sdpa_fwd_kernel_m32)[grid](
+            query,
+            key,
+            value,
+            out,
+            Mask_ptr if HAS_MASK else 0,
+            B,
+            H,
+            L_q,
+            L_kv,
+            stride_qb,
+            stride_qh,
+            stride_qm,
+            stride_qd,
+            stride_kb,
+            stride_kh,
+            stride_kn,
+            stride_kd,
+            stride_vb,
+            stride_vh,
+            stride_vn,
+            stride_vd,
+            stride_ob,
+            stride_oh,
+            stride_om,
+            stride_od,
+            stride_mb,
+            stride_mq,
+            stride_mk,
+            sm_scale,
+            HAS_MASK=HAS_MASK,
+            IS_CAUSAL=is_causal,
+            HEAD_DIM=D,
+        )
+    else:
+        wrap_triton(_sdpa_fwd_kernel_m64)[grid](
+            query,
+            key,
+            value,
+            out,
+            Mask_ptr if HAS_MASK else 0,
+            B,
+            H,
+            L_q,
+            L_kv,
+            stride_qb,
+            stride_qh,
+            stride_qm,
+            stride_qd,
+            stride_kb,
+            stride_kh,
+            stride_kn,
+            stride_kd,
+            stride_vb,
+            stride_vh,
+            stride_vn,
+            stride_vd,
+            stride_ob,
+            stride_oh,
+            stride_om,
+            stride_od,
+            stride_mb,
+            stride_mq,
+            stride_mk,
+            sm_scale,
+            HAS_MASK=HAS_MASK,
+            IS_CAUSAL=is_causal,
+            HEAD_DIM=D,
+        )
 
-    wrap_triton(_sdpa_fwd_kernel)[grid](
-        query,
-        key,
-        value,
-        out,
-        B,
-        H,
-        LQ,
-        LK,
-        stride_qb,
-        stride_qh,
-        stride_qm,
-        stride_qd,
-        stride_kb,
-        stride_kh,
-        stride_kn,
-        stride_kd,
-        stride_vb,
-        stride_vh,
-        stride_vn,
-        stride_vd,
-        stride_ob,
-        stride_oh,
-        stride_om,
-        stride_od,
-        scale,
-        mask_ptr,
-        stride_mb,
-        stride_mh,
-        stride_mm,
-        stride_mn,
-        HAS_MASK=HAS_MASK,
-        MASK_IS_BOOL=MASK_IS_BOOL,
-        HEAD_DIM=D,
-        GROUP_M=8,
-    )
     return out
 
 
@@ -312,10 +560,8 @@ def _sdpa_abstract(
     This just returns an empty tensor with the correct shape/dtype/device.
     """
     # Validate dtypes match
-    assert (
-        query.dtype == key.dtype == value.dtype
-    ), "query, key, value must have the same dtype"
+    assert query.dtype == key.dtype == value.dtype, "Q, K, V must have the same dtype"
     # Validate kqv's shape and get the output shape
-    B, H, LQ, D = query.shape
+    B, H, L_q, _, D_q, _ = _validate_qkv_shapes(query, key, value)
 
-    return torch.empty(B, H, LQ, D, dtype=query.dtype, device=query.device)
+    return torch.empty(B, H, L_q, D_q, dtype=query.dtype, device=query.device)

From d3e602fd8f38b9d8d25182645106b3ba42648ad5 Mon Sep 17 00:00:00 2001
From: gasoonjia <gasoonjia@icloud.com>
Date: Wed, 10 Dec 2025 11:02:18 -0800
Subject: [PATCH 5/8] Revert "introduce cuda sdpa"

This reverts commit ca461fb9fe73d421c3c79a550445036c72cf0861.
---
 backends/cuda/runtime/TARGETS                 |   4 -
 backends/cuda/runtime/shims/sdpa.cu           | 649 ---------------
 backends/cuda/runtime/shims/sdpa.cuh          | 282 -------
 backends/cuda/runtime/shims/sdpa.h            | 104 ---
 backends/cuda/runtime/shims/tests/targets.bzl |   1 -
 ...orch_cuda_scaled_dot_product_attention.cpp | 781 ------------------
 6 files changed, 1821 deletions(-)
 delete mode 100644 backends/cuda/runtime/shims/sdpa.cu
 delete mode 100644 backends/cuda/runtime/shims/sdpa.cuh
 delete mode 100644 backends/cuda/runtime/shims/sdpa.h
 delete mode 100644 backends/cuda/runtime/shims/tests/test_aoti_torch_cuda_scaled_dot_product_attention.cpp

diff --git a/backends/cuda/runtime/TARGETS b/backends/cuda/runtime/TARGETS
index 01dabee9086..a85f3a7e6a3 100644
--- a/backends/cuda/runtime/TARGETS
+++ b/backends/cuda/runtime/TARGETS
@@ -53,7 +53,6 @@ runtime.cxx_library(
         "shims/cuda_guard.cpp",
         "shims/int4mm.cu",
         "shims/memory.cpp",
-        "shims/sdpa.cu",
         "shims/tensor_attribute.cpp",
     ],
     headers = [
@@ -62,8 +61,6 @@ runtime.cxx_library(
         "shims/int4mm.cuh",
         "shims/int4mm.h",
         "shims/memory.h",
-        "shims/sdpa.cuh",
-        "shims/sdpa.h",
         "shims/tensor_attribute.h",
         "utils.h",
     ],
@@ -87,7 +84,6 @@ runtime.cxx_library(
     ],
     external_deps = [
         ("cuda", None, "cuda-lazy"),
-        ("cuda", None, "cublas-lazy"),
     ],
 )
 
diff --git a/backends/cuda/runtime/shims/sdpa.cu b/backends/cuda/runtime/shims/sdpa.cu
deleted file mode 100644
index c15f1f006bc..00000000000
--- a/backends/cuda/runtime/shims/sdpa.cu
+++ /dev/null
@@ -1,649 +0,0 @@
-/*
- * Copyright (c) Meta Platforms, Inc. and affiliates.
- * All rights reserved.
- *
- * This source code is licensed under the BSD-style license found in the
- * LICENSE file in the root directory of this source tree.
- */
-
-#include <cuda.h>
-#include <cuda_runtime.h>
-#include <cublas_v2.h>
-#include <cfloat>
-#include <cmath>
-
-#include <executorch/backends/aoti/utils.h>
-#include <executorch/backends/cuda/runtime/shims/sdpa.h>
-#include <executorch/backends/cuda/runtime/shims/sdpa.cuh>
-#include <executorch/backends/cuda/runtime/utils.h>
-#include <executorch/runtime/platform/log.h>
-
-namespace executorch::backends::cuda {
-
-using executorch::backends::aoti::Tensor;
-using executorch::backends::aoti::AOTITorchError;
-using executorch::runtime::Error;
-
-// ============================================================================
-// CUDA Kernels for Softmax and Masking
-// ============================================================================
-
-// Helper function for max with different types
-__device__ __forceinline__ float device_max(float a, float b) {
-  return fmaxf(a, b);
-}
-
-__device__ __forceinline__ __half device_max(__half a, __half b) {
-  return __hgt(a, b) ? a : b;
-}
-
-__device__ __forceinline__ __nv_bfloat16 device_max(__nv_bfloat16 a, __nv_bfloat16 b) {
-  #if __CUDA_ARCH__ >= 800
-  return __hgt(a, b) ? a : b;
-  #else
-  return __float2bfloat16(fmaxf(__bfloat162float(a), __bfloat162float(b)));
-  #endif
-}
-
-/**
- * Softmax kernel with optional causal masking
- *
- * Computes softmax along the last dimension (seq_len_k) of a 4D tensor.
- * Supports causal masking where positions j > i are masked out.
- *
- * Input: [batch, num_heads, seq_len_q, seq_len_k]
- * Output: [batch, num_heads, seq_len_q, seq_len_k]
- *
- * Each thread processes one row (seq_len_q position).
- *
- * Note: Supports in-place operation (input == output).
- */
-template<typename scalar_t>
-__global__ void softmax_with_causal_mask_kernel(
-    const scalar_t* input,
-    scalar_t* output,
-    const int64_t batch,
-    const int64_t num_heads,
-    const int64_t seq_len_q,
-    const int64_t seq_len_k,
-    const bool is_causal,
-    const float scale) {
-
-  // Each block processes one row of the attention matrix
-  const int64_t idx = blockIdx.x * blockDim.x + threadIdx.x;
-  const int64_t total_rows = batch * num_heads * seq_len_q;
-
-  if (idx >= total_rows) {
-    return;
-  }
-
-  // Decode position - we only need i for causal masking
-  const int64_t i = idx % seq_len_q;
-
-  // Pointer to the start of this row
-  const int64_t row_offset = idx * seq_len_k;
-  const scalar_t* input_row = input + row_offset;
-  scalar_t* output_row = output + row_offset;
-
-  // Find max for numerical stability (two-pass algorithm)
-  float max_val = -FLT_MAX;
-  for (int64_t j = 0; j < seq_len_k; ++j) {
-    if (!is_causal || j <= i) {
-      float val = static_cast<float>(input_row[j]) * scale;
-      max_val = fmaxf(max_val, val);
-    }
-  }
-
-  // Compute exp and sum
-  float sum_exp = 0.0f;
-  for (int64_t j = 0; j < seq_len_k; ++j) {
-    float val;
-    if (!is_causal || j <= i) {
-      val = expf(static_cast<float>(input_row[j]) * scale - max_val);
-    } else {
-      val = 0.0f;
-    }
-    output_row[j] = static_cast<scalar_t>(val);
-    sum_exp += val;
-  }
-
-  // Normalize
-  const float inv_sum = 1.0f / sum_exp;
-  for (int64_t j = 0; j < seq_len_k; ++j) {
-    output_row[j] = static_cast<scalar_t>(static_cast<float>(output_row[j]) * inv_sum);
-  }
-}
-
-/**
- * Scale kernel - multiply all elements by a scalar
- */
-template<typename scalar_t>
-__global__ void scale_kernel(
-    scalar_t* __restrict__ data,
-    const int64_t size,
-    const float scale) {
-  const int64_t idx = blockIdx.x * blockDim.x + threadIdx.x;
-  if (idx < size) {
-    data[idx] = static_cast<scalar_t>(static_cast<float>(data[idx]) * scale);
-  }
-}
-
-// ============================================================================
-// cuBLAS Helper Functions
-// ============================================================================
-
-/**
- * Get or create a cuBLAS handle for the current stream
- *
- * Note: In production, this should use a handle pool or be managed
- * by the backend infrastructure. This is a simplified version.
- */
-cublasHandle_t get_cublas_handle(cudaStream_t stream) {
-  static cublasHandle_t handle = nullptr;
-
-  if (handle == nullptr) {
-    cublasCreate(&handle);
-  }
-
-  cublasSetStream(handle, stream);
-  return handle;
-}
-
-/**
- * Batched matrix multiplication wrapper for cuBLAS
- *
- * Computes: C = alpha * op(A) @ op(B) + beta * C
- * for a batch of matrices
- */
-template<typename scalar_t>
-cublasStatus_t batched_gemm(
-    cublasHandle_t handle,
-    cublasOperation_t transa,
-    cublasOperation_t transb,
-    int m, int n, int k,
-    const scalar_t* alpha,
-    const scalar_t* A, int lda, int64_t strideA,
-    const scalar_t* B, int ldb, int64_t strideB,
-    const scalar_t* beta,
-    scalar_t* C, int ldc, int64_t strideC,
-    int batchCount);
-
-// Specializations for different types
-template<>
-cublasStatus_t batched_gemm<float>(
-    cublasHandle_t handle,
-    cublasOperation_t transa,
-    cublasOperation_t transb,
-    int m, int n, int k,
-    const float* alpha,
-    const float* A, int lda, int64_t strideA,
-    const float* B, int ldb, int64_t strideB,
-    const float* beta,
-    float* C, int ldc, int64_t strideC,
-    int batchCount) {
-  return cublasSgemmStridedBatched(
-      handle, transa, transb, m, n, k,
-      alpha, A, lda, strideA, B, ldb, strideB,
-      beta, C, ldc, strideC, batchCount);
-}
-
-template<>
-cublasStatus_t batched_gemm<__half>(
-    cublasHandle_t handle,
-    cublasOperation_t transa,
-    cublasOperation_t transb,
-    int m, int n, int k,
-    const __half* alpha,
-    const __half* A, int lda, int64_t strideA,
-    const __half* B, int ldb, int64_t strideB,
-    const __half* beta,
-    __half* C, int ldc, int64_t strideC,
-    int batchCount) {
-  return cublasHgemmStridedBatched(
-      handle, transa, transb, m, n, k,
-      alpha, A, lda, strideA, B, ldb, strideB,
-      beta, C, ldc, strideC, batchCount);
-}
-
-// Note: BFloat16 uses compute type float internally
-template<>
-cublasStatus_t batched_gemm<__nv_bfloat16>(
-    cublasHandle_t handle,
-    cublasOperation_t transa,
-    cublasOperation_t transb,
-    int m, int n, int k,
-    const __nv_bfloat16* alpha,
-    const __nv_bfloat16* A, int lda, int64_t strideA,
-    const __nv_bfloat16* B, int ldb, int64_t strideB,
-    const __nv_bfloat16* beta,
-    __nv_bfloat16* C, int ldc, int64_t strideC,
-    int batchCount) {
-
-  // cuBLAS BFloat16 GEMM - introduced in CUDA 11+
-  #if CUDA_VERSION >= 11000
-  // For BFloat16, we need to use cublasGemmStridedBatchedEx
-  // with compute type CUBLAS_COMPUTE_32F
-  float alpha_f = 1.0f;
-  float beta_f = 0.0f;
-
-  return cublasGemmStridedBatchedEx(
-      handle,
-      transa, transb,
-      m, n, k,
-      &alpha_f,
-      A, CUDA_R_16BF, lda, strideA,
-      B, CUDA_R_16BF, ldb, strideB,
-      &beta_f,
-      C, CUDA_R_16BF, ldc, strideC,
-      batchCount,
-      CUBLAS_COMPUTE_32F,
-      CUBLAS_GEMM_DEFAULT);
-  #else
-  ET_LOG(Error, "BFloat16 GEMM requires CUDA 11.0 or later");
-  return CUBLAS_STATUS_NOT_SUPPORTED;
-  #endif
-}
-
-// ============================================================================
-// Math Fallback Implementation
-// ============================================================================
-
-/**
- * Math fallback implementation for SDPA
- *
- * This implementation uses cuBLAS for matrix multiplications and custom
- * kernels for softmax. It provides maximum compatibility across all CUDA
- * devices but may not be as optimized as Flash Attention or Memory Efficient
- * Attention.
- *
- * Algorithm:
- * 1. Compute attention scores: S = (Q @ K^T)
- * 2. Apply scaling and compute softmax with optional causal mask
- * 3. Compute output: O = attention_weights @ V
- */
-template<typename scalar_t>
-Tensor* sdpa_math_fallback_impl(
-    const Tensor* query,
-    const Tensor* key,
-    const Tensor* value,
-    const Tensor* attn_mask,
-    bool is_causal,
-    float scale_factor,
-    cudaStream_t stream) {
-
-  // Get tensor dimensions
-  const int64_t batch = query->size(0);
-  const int64_t num_heads = query->size(1);
-  const int64_t seq_len_q = query->size(2);
-  const int64_t head_dim = query->size(3);
-  const int64_t seq_len_k = key->size(2);
-  const int64_t head_dim_v = value->size(3);
-
-  // Get cuBLAS handle
-  cublasHandle_t handle = get_cublas_handle(stream);
-
-  // Step 1: Allocate temporary buffer for attention scores
-  // Shape: [batch, num_heads, seq_len_q, seq_len_k]
-  const int64_t scores_size = batch * num_heads * seq_len_q * seq_len_k;
-  scalar_t* scores_ptr = nullptr;
-  cudaMalloc(&scores_ptr, scores_size * sizeof(scalar_t));
-  if (scores_ptr == nullptr) {
-    ET_LOG(Error, "sdpa_math_fallback: Failed to allocate scores buffer");
-    return nullptr;
-  }
-
-  // Step 2: Compute Q @ K^T using cuBLAS
-  // Q: [batch * num_heads, seq_len_q, head_dim]
-  // K^T: [batch * num_heads, head_dim, seq_len_k]
-  // Output: [batch * num_heads, seq_len_q, seq_len_k]
-
-  const int m = seq_len_q;
-  const int n = seq_len_k;
-  const int k = head_dim;
-  const int batch_count = batch * num_heads;
-
-  const scalar_t alpha = static_cast<scalar_t>(1.0f);
-  const scalar_t beta = static_cast<scalar_t>(0.0f);
-
-  const scalar_t* q_ptr = reinterpret_cast<const scalar_t*>(query->data_ptr());
-  const scalar_t* k_ptr = reinterpret_cast<const scalar_t*>(key->data_ptr());
-
-  // Strides for batched GEMM
-  const int64_t stride_q = seq_len_q * head_dim;
-  const int64_t stride_k = seq_len_k * head_dim;
-  const int64_t stride_scores = seq_len_q * seq_len_k;
-
-  // Q @ K^T
-  cublasStatus_t status = batched_gemm<scalar_t>(
-      handle,
-      CUBLAS_OP_T,  // Transpose K
-      CUBLAS_OP_N,  // No transpose Q
-      n,  // seq_len_k
-      m,  // seq_len_q
-      k,  // head_dim
-      &alpha,
-      k_ptr, k,  // K matrix
-      stride_k,
-      q_ptr, k,  // Q matrix
-      stride_q,
-      &beta,
-      scores_ptr, n,  // Output scores
-      stride_scores,
-      batch_count);
-
-  if (status != CUBLAS_STATUS_SUCCESS) {
-    ET_LOG(Error, "sdpa_math_fallback: cuBLAS GEMM failed for Q @ K^T");
-    cudaFree(scores_ptr);
-    return nullptr;
-  }
-
-  // Step 3: Apply softmax with scaling and optional causal mask
-  const int threads_per_block = 256;
-  const int64_t total_rows = batch * num_heads * seq_len_q;
-  const int num_blocks = (total_rows + threads_per_block - 1) / threads_per_block;
-
-  softmax_with_causal_mask_kernel<scalar_t><<<num_blocks, threads_per_block, 0, stream>>>(
-      scores_ptr,
-      scores_ptr,  // in-place
-      batch,
-      num_heads,
-      seq_len_q,
-      seq_len_k,
-      is_causal,
-      scale_factor);
-
-  cudaError_t cuda_err = cudaGetLastError();
-  if (cuda_err != cudaSuccess) {
-    ET_LOG(Error, "sdpa_math_fallback: Softmax kernel launch failed: %s",
-           cudaGetErrorString(cuda_err));
-    cudaFree(scores_ptr);
-    return nullptr;
-  }
-
-  // Step 4: Allocate output tensor [batch, num_heads, seq_len_q, head_dim_v]
-  Tensor* output = nullptr;
-  std::array<int64_t, 4> output_shape = {batch, num_heads, seq_len_q, head_dim_v};
-  std::array<int64_t, 4> output_stride = {
-      num_heads * seq_len_q * head_dim_v,
-      seq_len_q * head_dim_v,
-      head_dim_v,
-      1};
-
-  auto dtype_int = static_cast<int32_t>(query->dtype());
-  aoti_torch_empty_strided(
-      4,
-      output_shape.data(),
-      output_stride.data(),
-      dtype_int,
-      static_cast<int32_t>(SupportedDevices::CUDA),
-      0,
-      &output);
-
-  if (output == nullptr) {
-    ET_LOG(Error, "sdpa_math_fallback: Failed to allocate output tensor");
-    cudaFree(scores_ptr);
-    return nullptr;
-  }
-
-  // Step 5: Compute attention_weights @ V
-  // attention_weights: [batch * num_heads, seq_len_q, seq_len_k]
-  // V: [batch * num_heads, seq_len_k, head_dim_v]
-  // Output: [batch * num_heads, seq_len_q, head_dim_v]
-
-  const int m_v = seq_len_q;
-  const int n_v = head_dim_v;
-  const int k_v = seq_len_k;
-
-  const scalar_t* v_ptr = reinterpret_cast<const scalar_t*>(value->data_ptr());
-  scalar_t* out_ptr = reinterpret_cast<scalar_t*>(output->data_ptr());
-
-  const int64_t stride_v = seq_len_k * head_dim_v;
-  const int64_t stride_out = seq_len_q * head_dim_v;
-
-  status = batched_gemm<scalar_t>(
-      handle,
-      CUBLAS_OP_N,  // No transpose V
-      CUBLAS_OP_N,  // No transpose attention_weights
-      n_v,  // head_dim_v
-      m_v,  // seq_len_q
-      k_v,  // seq_len_k
-      &alpha,
-      v_ptr, n_v,  // V matrix
-      stride_v,
-      scores_ptr, k_v,  // attention_weights
-      stride_scores,
-      &beta,
-      out_ptr, n_v,  // Output
-      stride_out,
-      batch_count);
-
-  // Cleanup temporary buffers
-  cudaFree(scores_ptr);
-
-  if (status != CUBLAS_STATUS_SUCCESS) {
-    ET_LOG(Error, "sdpa_math_fallback: cuBLAS GEMM failed for attention_weights @ V");
-    aoti_torch_delete_tensor_object(output);
-    return nullptr;
-  }
-
-  return output;
-}
-
-Tensor* sdpa_math_fallback(
-    const Tensor* query,
-    const Tensor* key,
-    const Tensor* value,
-    const Tensor* attn_mask,
-    bool is_causal,
-    double scale_factor,
-    cudaStream_t stream) {
-
-  // Dispatch based on dtype
-  auto dtype = query->dtype();
-
-  if (dtype == executorch::aten::ScalarType::Float) {
-    return sdpa_math_fallback_impl<float>(
-        query, key, value, attn_mask, is_causal,
-        static_cast<float>(scale_factor), stream);
-  } else if (dtype == executorch::aten::ScalarType::Half) {
-    return sdpa_math_fallback_impl<__half>(
-        query, key, value, attn_mask, is_causal,
-        static_cast<float>(scale_factor), stream);
-  } else if (dtype == executorch::aten::ScalarType::BFloat16) {
-    return sdpa_math_fallback_impl<__nv_bfloat16>(
-        query, key, value, attn_mask, is_causal,
-        static_cast<float>(scale_factor), stream);
-  } else {
-    ET_LOG(Error, "sdpa_math_fallback: Unsupported dtype");
-    return nullptr;
-  }
-}
-
-/**
- * Main entry point for SDPA computation
- */
-Tensor* scaled_dot_product_attention_cuda(
-    const Tensor* query,
-    const Tensor* key,
-    const Tensor* value,
-    const Tensor* attn_mask,
-    double dropout_p,
-    bool is_causal,
-    const double* scale,
-    bool enable_gqa,
-    cudaStream_t stream) {
-
-  // Select backend
-  SDPBackend backend = select_sdp_backend(
-      query, key, value, attn_mask, dropout_p, is_causal);
-
-  if (backend == SDPBackend::Error) {
-    ET_LOG(Error, "scaled_dot_product_attention_cuda: No valid backend selected");
-    return nullptr;
-  }
-
-  // Calculate scale factor
-  double scale_factor = calculate_scale(query, scale);
-
-  // Handle GQA if needed
-  if (enable_gqa && is_gqa_configuration(query, key, value)) {
-    if (!validate_gqa(query, key, value)) {
-      ET_LOG(Error, "scaled_dot_product_attention_cuda: Invalid GQA configuration");
-      return nullptr;
-    }
-    ET_LOG(
-        Error,
-        "scaled_dot_product_attention_cuda: GQA support not yet implemented. "
-        "Need to repeat K/V heads to match Q heads.");
-    return nullptr;
-  }
-
-  // Dispatch to appropriate backend
-  switch (backend) {
-    case SDPBackend::Math:
-      return sdpa_math_fallback(
-          query, key, value, attn_mask, is_causal, scale_factor, stream);
-
-    case SDPBackend::FlashAttention:
-      ET_LOG(Error, "Flash Attention backend not yet implemented");
-      return nullptr;
-
-    case SDPBackend::MemoryEfficientAttention:
-      ET_LOG(Error, "Memory Efficient Attention backend not yet implemented");
-      return nullptr;
-
-    case SDPBackend::CuDNN:
-      ET_LOG(Error, "cuDNN backend not yet implemented");
-      return nullptr;
-
-    default:
-      ET_LOG(Error, "Unknown SDPA backend");
-      return nullptr;
-  }
-}
-
-// ============================================================================
-// C API Implementation
-// ============================================================================
-
-#ifdef __cplusplus
-extern "C" {
-#endif
-
-AOTITorchError aoti_torch_cuda_scaled_dot_product_attention(
-    Tensor* query,
-    Tensor* key,
-    Tensor* value,
-    Tensor* attn_mask,
-    double dropout_p,
-    int32_t is_causal,
-    double* scale,
-    int32_t enable_gqa,
-    Tensor** ret0) {
-
-  // Input validation
-  if (!query || !key || !value || !ret0) {
-    ET_LOG(Error, "aoti_torch_cuda_scaled_dot_product_attention: Null pointer input");
-    return Error::InvalidArgument;
-  }
-
-  // Currently only support dropout_p = 0.0 for inference
-  if (dropout_p != 0.0) {
-    ET_LOG(Error, "aoti_torch_cuda_scaled_dot_product_attention: dropout_p != 0.0 is not supported");
-    return Error::InvalidArgument;
-  }
-
-  // Check tensor dimensions
-  if (query->dim() != 4 || key->dim() != 4 || value->dim() != 4) {
-    ET_LOG(Error, "aoti_torch_cuda_scaled_dot_product_attention: Query, Key, Value must be 4D tensors");
-    return Error::InvalidArgument;
-  }
-
-  // Check that Q, K, V have the same dtype
-  if (query->dtype() != key->dtype() || query->dtype() != value->dtype()) {
-    ET_LOG(Error, "aoti_torch_cuda_scaled_dot_product_attention: Query, Key, Value must have the same dtype");
-    return Error::InvalidArgument;
-  }
-
-  // Check tensor shapes
-  const int64_t batch = query->size(0);
-  const int64_t num_heads = query->size(1);
-  const int64_t seq_len_q = query->size(2);
-  const int64_t head_dim_q = query->size(3);
-
-  const int64_t num_heads_kv = key->size(1);
-  const int64_t seq_len_k = key->size(2);
-  const int64_t head_dim_k = key->size(3);
-
-  const int64_t seq_len_v = value->size(2);
-  const int64_t head_dim_v = value->size(3);
-
-  // Validate shapes
-  if (key->size(0) != batch || value->size(0) != batch) {
-    ET_LOG(Error, "aoti_torch_cuda_scaled_dot_product_attention: Batch size mismatch");
-    return Error::InvalidArgument;
-  }
-
-  if (seq_len_k != seq_len_v) {
-    ET_LOG(Error, "aoti_torch_cuda_scaled_dot_product_attention: Key and Value sequence length mismatch");
-    return Error::InvalidArgument;
-  }
-
-  if (head_dim_q != head_dim_k) {
-    ET_LOG(Error, "aoti_torch_cuda_scaled_dot_product_attention: Query and Key head dimension mismatch");
-    return Error::InvalidArgument;
-  }
-
-  if (value->size(1) != num_heads_kv) {
-    ET_LOG(Error, "aoti_torch_cuda_scaled_dot_product_attention: Key and Value num_heads mismatch");
-    return Error::InvalidArgument;
-  }
-
-  // GQA validation
-  if (enable_gqa && num_heads % num_heads_kv != 0) {
-    ET_LOG(Error, "aoti_torch_cuda_scaled_dot_product_attention: For GQA, num_heads must be divisible by num_heads_kv");
-    return Error::InvalidArgument;
-  }
-
-  // Validate attn_mask if provided
-  if (attn_mask) {
-    if (is_causal) {
-      ET_LOG(Error, "aoti_torch_cuda_scaled_dot_product_attention: Cannot use both attn_mask and is_causal");
-      return Error::InvalidArgument;
-    }
-  }
-
-  // Get CUDA stream
-  auto stream_result = getCurrentCUDAStream(0);
-  if (!stream_result.ok()) {
-    ET_LOG(Error, "aoti_torch_cuda_scaled_dot_product_attention: Failed to get CUDA stream");
-    return Error::Internal;
-  }
-  cudaStream_t stream = stream_result.get();
-
-  // Call the main SDPA function
-  Tensor* output = scaled_dot_product_attention_cuda(
-      query,
-      key,
-      value,
-      attn_mask,
-      dropout_p,
-      is_causal != 0,
-      scale,
-      enable_gqa != 0,
-      stream);
-
-  if (output == nullptr) {
-    ET_LOG(Error, "aoti_torch_cuda_scaled_dot_product_attention: SDPA computation failed");
-    return Error::Internal;
-  }
-
-  *ret0 = output;
-  return Error::Ok;
-}
-
-#ifdef __cplusplus
-}
-#endif
-
-} // namespace executorch::backends::cuda
diff --git a/backends/cuda/runtime/shims/sdpa.cuh b/backends/cuda/runtime/shims/sdpa.cuh
deleted file mode 100644
index 5cc941f4120..00000000000
--- a/backends/cuda/runtime/shims/sdpa.cuh
+++ /dev/null
@@ -1,282 +0,0 @@
-/*
- * Copyright (c) Meta Platforms, Inc. and affiliates.
- * All rights reserved.
- *
- * This source code is licensed under the BSD-style license found in the
- * LICENSE file in the root directory of this source tree.
- */
-
-// This file implements scaled_dot_product_attention for ExecuTorch.
-//
-// IMPLEMENTATION NOTES:
-// ---------------------
-// This is NOT a direct port from PyTorch. Instead, we implemented
-// a custom Math Fallback using cuBLAS and custom CUDA kernels.
-//
-// PyTorch reference implementations (for architecture reference only):
-// - CPU/General: aten/src/ATen/native/transformers/attention.cpp
-// - CUDA: aten/src/ATen/native/transformers/cuda/attention.cu
-//
-// Key differences from PyTorch:
-// - PyTorch uses high-level ATen ops (at::matmul, at::_safe_softmax)
-// - We use direct cuBLAS calls and custom softmax kernels
-// - Optimized for inference (no dropout, no backward pass)
-// - Simplified memory management
-// - No ATen/c10 dependencies
-//
-// PORTING NOTES:
-// --------------
-// 1. KERNEL CODE: Adapted from PyTorch attention kernels
-//    - Math fallback implementation for maximum compatibility
-//    - Supports Float32, Float16, and BFloat16 dtypes
-//    - Standard attention computation: softmax(Q @ K^T / scale) @ V
-//
-// 2. API ADAPTATIONS:
-//    - Replaced at::Tensor with executorch::backends::aoti::Tensor
-//    - Output returned via pointer-to-pointer instead of by-value
-//    - Simplified interface for inference (dropout=0.0 only)
-//
-// 3. REMOVED FEATURES:
-//    - Flash Attention backend (requires external library)
-//    - Memory Efficient Attention backend (requires external library)
-//    - cuDNN backend (requires cuDNN library)
-//    - Dropout support (training-only feature)
-//    - Nested tensor support (complex layout)
-//    - Backward pass (training-only feature)
-//
-// 4. INFRASTRUCTURE CHANGES:
-//    - Removed c10::cuda::CUDAGuard: Device management handled by AOTI backend
-//    - Removed at::cuda::getCurrentCUDAStream(): Stream passed explicitly
-//    - Simplified error handling using ExecutorTorch Error codes
-
-#pragma once
-
-#include <cuda.h>
-#include <cuda_runtime.h>
-#include <cuda_fp16.h>
-#include <cuda_bf16.h>
-#include <cmath>
-
-#include <executorch/backends/cuda/runtime/guard.h>
-#include <executorch/backends/cuda/runtime/utils.h>
-#include <executorch/backends/cuda/runtime/shims/memory.h>
-#include <executorch/backends/aoti/common_shims.h>
-#include <executorch/backends/aoti/utils.h>
-#include <executorch/runtime/platform/log.h>
-
-namespace executorch::backends::cuda {
-
-using executorch::backends::aoti::Tensor;
-using executorch::runtime::Error;
-
-// ============================================================================
-// Utility Functions for SDPA
-// ============================================================================
-
-// Calculate the scaling factor for attention scores
-inline double calculate_scale(const Tensor* query, const double* scale) {
-  if (scale != nullptr) {
-    return *scale;
-  }
-  // Default: 1 / sqrt(head_dim)
-  // Query shape: [batch, num_heads, seq_len_q, head_dim]
-  // head_dim is at index 3 (0-indexed)
-  const int64_t head_dim = query->size(3);
-  return 1.0 / std::sqrt(static_cast<double>(head_dim));
-}
-
-// Check if tensor dtype is supported for SDPA
-inline bool is_supported_dtype(const Tensor* tensor) {
-  auto dtype = tensor->dtype();
-  return dtype == executorch::aten::ScalarType::Float ||
-         dtype == executorch::aten::ScalarType::Half ||
-         dtype == executorch::aten::ScalarType::BFloat16;
-}
-
-// ============================================================================
-// Math Fallback Implementation
-// ============================================================================
-
-// This is the basic, portable implementation that works on all CUDA devices.
-// It computes attention using explicit matrix multiplications and softmax:
-//   1. Compute scores: S = Q @ K^T * scale
-//   2. Apply mask if provided
-//   3. Compute attention weights: A = softmax(S)
-//   4. Compute output: O = A @ V
-
-/**
- * Math fallback kernel for scaled dot product attention
- *
- * This is a basic implementation that performs:
- * output = softmax(query @ key^T / scale) @ value
- *
- * Supports:
- * - Batch processing
- * - Multiple attention heads
- * - Optional causal masking
- * - Optional explicit attention mask
- * - Float32, Float16, BFloat16 dtypes
- *
- * Note: This implementation is for reference and maximum compatibility.
- * For production use, consider using Flash Attention or other optimized backends.
- */
-Tensor* sdpa_math_fallback(
-    const Tensor* query,     // [batch, num_heads, seq_len_q, head_dim]
-    const Tensor* key,       // [batch, num_heads_kv, seq_len_k, head_dim]
-    const Tensor* value,     // [batch, num_heads_kv, seq_len_k, head_dim_v]
-    const Tensor* attn_mask, // Optional: [batch, num_heads, seq_len_q, seq_len_k] or broadcastable
-    bool is_causal,          // Apply causal masking
-    double scale_factor,     // Scaling factor for attention scores
-    cudaStream_t stream);    // CUDA stream for execution
-
-// ============================================================================
-// Backend Selection
-// ============================================================================
-
-enum class SDPBackend {
-  Error = -1,
-  Math = 0,
-  FlashAttention = 1,
-  MemoryEfficientAttention = 2,
-  CuDNN = 3
-};
-
-/**
- * Select the best available backend for SDPA based on input parameters
- *
- * For now, only Math fallback is supported. Future implementations may add:
- * - Flash Attention (Ampere+ GPUs)
- * - Memory Efficient Attention
- * - cuDNN backend
- */
-inline SDPBackend select_sdp_backend(
-    const Tensor* query,
-    const Tensor* key,
-    const Tensor* value,
-    const Tensor* attn_mask,
-    double dropout_p,
-    bool is_causal) {
-
-  // Check for unsupported features
-  if (dropout_p > 0.0) {
-    ET_LOG(Error, "SDPA: Dropout not supported in inference mode");
-    return SDPBackend::Error;
-  }
-
-  // Check tensor dimensions
-  if (query->dim() != 4 || key->dim() != 4 || value->dim() != 4) {
-    ET_LOG(Error, "SDPA: All inputs must be 4D tensors");
-    return SDPBackend::Error;
-  }
-
-  // Check dtype support
-  if (!is_supported_dtype(query) || !is_supported_dtype(key) || !is_supported_dtype(value)) {
-    ET_LOG(Error, "SDPA: Unsupported dtype, only Float32/Float16/BFloat16 supported");
-    return SDPBackend::Error;
-  }
-
-  // Check dtype consistency
-  if (query->dtype() != key->dtype() || query->dtype() != value->dtype()) {
-    ET_LOG(Error, "SDPA: Query, Key, Value must have the same dtype");
-    return SDPBackend::Error;
-  }
-
-  // For now, always use math fallback
-  // Future: Add logic to select Flash Attention, MemEff, or cuDNN when available
-  return SDPBackend::Math;
-}
-
-// ============================================================================
-// Helper Functions for Causal Mask
-// ============================================================================
-
-/**
- * Check if we need to apply causal masking
- */
-inline bool needs_causal_mask(bool is_causal, const Tensor* attn_mask) {
-  if (!is_causal) {
-    return false;
-  }
-  if (attn_mask != nullptr) {
-    ET_LOG(Error, "SDPA: Cannot use both is_causal=true and explicit attn_mask");
-    return false;
-  }
-  return true;
-}
-
-// ============================================================================
-// Grouped Query Attention (GQA) Support
-// ============================================================================
-
-/**
- * Check if inputs require GQA handling
- *
- * GQA allows num_heads_q != num_heads_kv, where num_heads_q must be
- * divisible by num_heads_kv. Key and Value heads are repeated to match
- * Query heads.
- */
-inline bool is_gqa_configuration(
-    const Tensor* query,
-    const Tensor* key,
-    const Tensor* value) {
-
-  const int64_t num_heads_q = query->size(1);
-  const int64_t num_heads_kv = key->size(1);
-
-  return num_heads_q != num_heads_kv;
-}
-
-/**
- * Validate GQA configuration
- */
-inline bool validate_gqa(
-    const Tensor* query,
-    const Tensor* key,
-    const Tensor* value) {
-
-  const int64_t num_heads_q = query->size(1);
-  const int64_t num_heads_kv = key->size(1);
-  const int64_t num_heads_v = value->size(1);
-
-  // Key and Value must have same num_heads
-  if (num_heads_kv != num_heads_v) {
-    ET_LOG(Error, "SDPA GQA: Key and Value must have same num_heads");
-    return false;
-  }
-
-  // Query heads must be divisible by Key/Value heads
-  if (num_heads_q % num_heads_kv != 0) {
-    ET_LOG(Error, "SDPA GQA: Query num_heads must be divisible by Key/Value num_heads");
-    return false;
-  }
-
-  return true;
-}
-
-// ============================================================================
-// Main SDPA Entry Point
-// ============================================================================
-
-/**
- * Compute scaled dot product attention
- *
- * This is the main entry point that selects the appropriate backend
- * and dispatches to the corresponding implementation.
- *
- * Currently only Math fallback is implemented. Future versions may add:
- * - Flash Attention
- * - Memory Efficient Attention
- * - cuDNN backend
- */
-Tensor* scaled_dot_product_attention_cuda(
-    const Tensor* query,
-    const Tensor* key,
-    const Tensor* value,
-    const Tensor* attn_mask,
-    double dropout_p,
-    bool is_causal,
-    const double* scale,
-    bool enable_gqa,
-    cudaStream_t stream);
-
-} // namespace executorch::backends::cuda
diff --git a/backends/cuda/runtime/shims/sdpa.h b/backends/cuda/runtime/shims/sdpa.h
deleted file mode 100644
index 4db08576ca0..00000000000
--- a/backends/cuda/runtime/shims/sdpa.h
+++ /dev/null
@@ -1,104 +0,0 @@
-/*
- * Copyright (c) Meta Platforms, Inc. and affiliates.
- * All rights reserved.
- *
- * This source code is licensed under the BSD-style license found in the
- * LICENSE file in the root directory of this source tree.
- */
-
-#pragma once
-
-#include <cuda_runtime.h>
-#include <executorch/backends/aoti/common_shims.h>
-#include <executorch/backends/aoti/export.h>
-
-namespace executorch::backends::cuda {
-
-using executorch::backends::aoti::AOTITorchError;
-using executorch::backends::aoti::Tensor;
-
-#ifdef __cplusplus
-extern "C" {
-#endif
-
-/**
- * Performs scaled dot-product attention on CUDA.
- *
- * This is a port of PyTorch's scaled_dot_product_attention CUDA implementation
- * (aten/src/ATen/native/transformers/cuda/attention.cu) adapted for the
- * ExecuTorch runtime.
- *
- * Computes attention(Q, K, V) = softmax(Q @ K^T / sqrt(d)) @ V
- *
- * HARDWARE REQUIREMENTS:
- * - CUDA-capable GPU
- * - Supports Flash Attention if available (Ampere+ GPUs)
- *
- * TENSOR REQUIREMENTS:
- * @param query Query tensor [batch, num_heads, seq_len_q, head_dim]
- *   - Must be Float32, Float16, or BFloat16 dtype
- *   - Must be 4D
- *   - Must be on CUDA device
- *
- * @param key Key tensor [batch, num_heads_kv, seq_len_k, head_dim]
- *   - Must be same dtype as query
- *   - Must be 4D
- *   - Must be on CUDA device
- *   - num_heads_kv can be different from num_heads (for GQA)
- *
- * @param value Value tensor [batch, num_heads_kv, seq_len_k, head_dim_v]
- *   - Must be same dtype as query
- *   - Must be 4D
- *   - Must be on CUDA device
- *
- * @param attn_mask Optional attention mask [batch, num_heads, seq_len_q, seq_len_k]
- *   or broadcastable shape
- *   - Can be nullptr (no mask)
- *   - If provided, must be Float32, BFloat16, or Bool dtype
- *   - Additive mask: positions with large negative values are masked out
- *
- * @param dropout_p Dropout probability (0.0 to 1.0)
- *   - Currently only supports 0.0 (no dropout)
- *   - Must be 0.0 for inference
- *
- * @param is_causal Whether to apply causal masking
- *   - If true, applies lower triangular mask
- *   - Cannot be used together with explicit attn_mask
- *
- * @param scale Optional scaling factor for attention scores
- *   - If nullptr, uses 1/sqrt(head_dim) by default
- *   - If provided, uses the specified value
- *
- * @param enable_gqa Enable grouped query attention support
- *   - Allows num_heads_kv != num_heads
- *   - Query heads must be divisible by key/value heads
- *
- * @param ret0 Output parameter for attention result
- *   [batch, num_heads, seq_len_q, head_dim_v]
- *   - Allocated by this function
- *   - Same dtype as input tensors
- *   - Must not be null
- *   - Caller is responsible for freeing via aoti_torch_delete_tensor_object()
- *
- * @return AOTITorchError error code:
- *   - Error::Ok: Success
- *   - Error::InvalidArgument: Null pointer, wrong dtype, wrong dimensions,
- *     or invalid parameter combination
- *   - Error::Internal: CUDA kernel launch failure
- */
-AOTI_SHIM_EXPORT AOTITorchError aoti_torch_cuda_scaled_dot_product_attention(
-    Tensor* query,
-    Tensor* key,
-    Tensor* value,
-    Tensor* attn_mask,
-    double dropout_p,
-    int32_t is_causal,
-    double* scale,
-    int32_t enable_gqa,
-    Tensor** ret0);
-
-#ifdef __cplusplus
-}
-#endif
-
-} // namespace executorch::backends::cuda
diff --git a/backends/cuda/runtime/shims/tests/targets.bzl b/backends/cuda/runtime/shims/tests/targets.bzl
index 0896b3b6a3b..b274ecf3675 100644
--- a/backends/cuda/runtime/shims/tests/targets.bzl
+++ b/backends/cuda/runtime/shims/tests/targets.bzl
@@ -34,5 +34,4 @@ def define_common_targets():
     cuda_shim_cpp_unittest("aoti_torch_copy_")
     cuda_shim_cpp_unittest("aoti_torch_cuda_guard")
     cuda_shim_cpp_unittest("aoti_torch_cuda__weight_int4pack_mm")
-    cuda_shim_cpp_unittest("aoti_torch_cuda_scaled_dot_product_attention")
     cuda_shim_cpp_unittest("aoti_torch_new_tensor_handle")
diff --git a/backends/cuda/runtime/shims/tests/test_aoti_torch_cuda_scaled_dot_product_attention.cpp b/backends/cuda/runtime/shims/tests/test_aoti_torch_cuda_scaled_dot_product_attention.cpp
deleted file mode 100644
index e2677878ea0..00000000000
--- a/backends/cuda/runtime/shims/tests/test_aoti_torch_cuda_scaled_dot_product_attention.cpp
+++ /dev/null
@@ -1,781 +0,0 @@
-/*
- * Copyright (c) Meta Platforms, Inc. and affiliates.
- * All rights reserved.
- *
- * This source code is licensed under the BSD-style license found in the
- * LICENSE file in the root directory of this source tree.
- */
-
-#include <gtest/gtest.h>
-
-#include <cuda_runtime.h>
-#include <executorch/backends/aoti/common_shims.h>
-#include <executorch/backends/aoti/utils.h>
-#include <executorch/backends/cuda/runtime/shims/memory.h>
-#include <executorch/backends/cuda/runtime/shims/sdpa.h>
-#include <executorch/backends/cuda/runtime/shims/tensor_attribute.h>
-#include <executorch/backends/cuda/runtime/utils.h>
-#include <executorch/runtime/core/error.h>
-#include <executorch/runtime/platform/platform.h>
-
-#include <cmath>
-#include <vector>
-
-using namespace executorch::backends::cuda;
-using namespace executorch::backends::aoti;
-using namespace executorch::runtime;
-
-// Test fixture for SDPA tests
-class AOTITorchSDPATest : public ::testing::Test {
- protected:
-  void SetUp() override {
-    // Initialize ExecuTorch Platform Abstraction Layer
-    et_pal_init();
-
-    // Check if CUDA is available
-    int device_count = 0;
-    cudaError_t err = cudaGetDeviceCount(&device_count);
-    if (err != cudaSuccess || device_count == 0) {
-      GTEST_SKIP() << "CUDA not available, skipping CUDA tests";
-    }
-
-    // Clean up any existing cached metadata before each test
-    cleanup_tensor_metadata();
-  }
-
-  void TearDown() override {
-    // Clean up after each test
-    cleanup_tensor_metadata();
-  }
-
-  // Helper function to create a Float32 tensor filled with a specific value
-  Tensor* create_float_tensor(
-      std::vector<int64_t> shape,
-      float fill_value = 1.0f) {
-    Tensor* tensor = nullptr;
-
-    // Calculate size
-    int64_t total_size = 1;
-    for (auto dim : shape) {
-      total_size *= dim;
-    }
-
-    // Calculate strides (row-major)
-    std::vector<int64_t> strides(shape.size());
-    int64_t stride = 1;
-    for (int i = shape.size() - 1; i >= 0; --i) {
-      strides[i] = stride;
-      stride *= shape[i];
-    }
-
-    // Create tensor
-    Error error = aoti_torch_empty_strided(
-        shape.size(),
-        shape.data(),
-        strides.data(),
-        static_cast<int32_t>(SupportedDTypes::FLOAT32),
-        static_cast<int32_t>(SupportedDevices::CUDA),
-        0,
-        &tensor);
-
-    if (error != Error::Ok || tensor == nullptr) {
-      return nullptr;
-    }
-
-    // Fill with value
-    std::vector<float> host_data(total_size, fill_value);
-    cudaMemcpy(
-        tensor->data_ptr(),
-        host_data.data(),
-        total_size * sizeof(float),
-        cudaMemcpyHostToDevice);
-
-    return tensor;
-  }
-
-  // Helper function to create a BFloat16 tensor
-  Tensor* create_bfloat16_tensor(
-      std::vector<int64_t> shape,
-      float fill_value = 1.0f) {
-    Tensor* tensor = nullptr;
-
-    // Calculate size
-    int64_t total_size = 1;
-    for (auto dim : shape) {
-      total_size *= dim;
-    }
-
-    // Calculate strides (row-major)
-    std::vector<int64_t> strides(shape.size());
-    int64_t stride = 1;
-    for (int i = shape.size() - 1; i >= 0; --i) {
-      strides[i] = stride;
-      stride *= shape[i];
-    }
-
-    // Create tensor
-    Error error = aoti_torch_empty_strided(
-        shape.size(),
-        shape.data(),
-        strides.data(),
-        static_cast<int32_t>(SupportedDTypes::BFLOAT16),
-        static_cast<int32_t>(SupportedDevices::CUDA),
-        0,
-        &tensor);
-
-    if (error != Error::Ok || tensor == nullptr) {
-      return nullptr;
-    }
-
-    // Fill with value
-    // Note: For simplicity, we'll fill with float and let the runtime handle conversion
-    // In production, you'd want to properly convert to bfloat16
-    std::vector<float> host_data(total_size, fill_value);
-    cudaMemcpy(
-        tensor->data_ptr(),
-        host_data.data(),
-        total_size * sizeof(float),
-        cudaMemcpyHostToDevice);
-
-    return tensor;
-  }
-
-  // Helper to check if output tensor has expected shape
-  bool check_output_shape(
-      Tensor* output,
-      const std::vector<int64_t>& expected_shape) {
-    if (output == nullptr) {
-      return false;
-    }
-    if (output->dim() != expected_shape.size()) {
-      return false;
-    }
-    for (size_t i = 0; i < expected_shape.size(); ++i) {
-      if (output->size(i) != expected_shape[i]) {
-        return false;
-      }
-    }
-    return true;
-  }
-
-  // Helper to copy tensor data from GPU to CPU for verification
-  std::vector<float> copy_tensor_to_host(Tensor* tensor) {
-    int64_t total_size = 1;
-    for (int i = 0; i < tensor->dim(); ++i) {
-      total_size *= tensor->size(i);
-    }
-
-    std::vector<float> host_data(total_size);
-    cudaMemcpy(
-        host_data.data(),
-        tensor->data_ptr(),
-        total_size * sizeof(float),
-        cudaMemcpyDeviceToHost);
-
-    return host_data;
-  }
-
-  // Helper to check if a value is approximately equal (for floating point comparison)
-  bool approx_equal(float a, float b, float epsilon = 1e-5f) {
-    return std::abs(a - b) < epsilon;
-  }
-};
-
-// ============================================================================
-// Basic Functionality Tests
-// ============================================================================
-
-// Test basic SDPA with Float32, no causal mask
-TEST_F(AOTITorchSDPATest, BasicFunctionalityFloat32) {
-  // Create tensors: [batch=1, num_heads=2, seq_len=4, head_dim=8]
-  const int64_t batch = 1;
-  const int64_t num_heads = 2;
-  const int64_t seq_len = 4;
-  const int64_t head_dim = 8;
-
-  Tensor* query = create_float_tensor({batch, num_heads, seq_len, head_dim}, 0.5f);
-  Tensor* key = create_float_tensor({batch, num_heads, seq_len, head_dim}, 0.3f);
-  Tensor* value = create_float_tensor({batch, num_heads, seq_len, head_dim}, 1.0f);
-
-  ASSERT_NE(query, nullptr) << "Failed to create query tensor";
-  ASSERT_NE(key, nullptr) << "Failed to create key tensor";
-  ASSERT_NE(value, nullptr) << "Failed to create value tensor";
-
-  printf("Testing SDPA Float32: [%ldx%ldx%ldx%ld]\n", batch, num_heads, seq_len, head_dim);
-
-  // Call SDPA
-  Tensor* output = nullptr;
-  AOTITorchError error = aoti_torch_cuda_scaled_dot_product_attention(
-      query,
-      key,
-      value,
-      nullptr,  // no explicit mask
-      0.0,      // no dropout
-      0,        // not causal
-      nullptr,  // default scale
-      0,        // no GQA
-      &output);
-
-  // Check result
-  EXPECT_EQ(error, Error::Ok) << "SDPA should succeed";
-  ASSERT_NE(output, nullptr) << "Output should not be null";
-
-  // Verify output shape: [batch, num_heads, seq_len, head_dim]
-  EXPECT_TRUE(check_output_shape(output, {batch, num_heads, seq_len, head_dim}))
-      << "Output shape mismatch";
-
-  printf("SDPA Float32 test passed!\n");
-
-  // Cleanup
-  aoti_torch_delete_tensor_object(query);
-  aoti_torch_delete_tensor_object(key);
-  aoti_torch_delete_tensor_object(value);
-  aoti_torch_delete_tensor_object(output);
-}
-
-// Test SDPA with causal masking
-TEST_F(AOTITorchSDPATest, CausalMasking) {
-  const int64_t batch = 1;
-  const int64_t num_heads = 1;
-  const int64_t seq_len = 8;
-  const int64_t head_dim = 16;
-
-  Tensor* query = create_float_tensor({batch, num_heads, seq_len, head_dim}, 0.5f);
-  Tensor* key = create_float_tensor({batch, num_heads, seq_len, head_dim}, 0.5f);
-  Tensor* value = create_float_tensor({batch, num_heads, seq_len, head_dim}, 1.0f);
-
-  ASSERT_NE(query, nullptr);
-  ASSERT_NE(key, nullptr);
-  ASSERT_NE(value, nullptr);
-
-  printf("Testing SDPA with causal masking: [%ldx%ldx%ldx%ld]\n",
-         batch, num_heads, seq_len, head_dim);
-
-  // Call SDPA with causal mask
-  Tensor* output = nullptr;
-  AOTITorchError error = aoti_torch_cuda_scaled_dot_product_attention(
-      query,
-      key,
-      value,
-      nullptr,
-      0.0,
-      1,        // causal mask enabled
-      nullptr,
-      0,
-      &output);
-
-  EXPECT_EQ(error, Error::Ok);
-  ASSERT_NE(output, nullptr);
-  EXPECT_TRUE(check_output_shape(output, {batch, num_heads, seq_len, head_dim}));
-
-  printf("Causal masking test passed!\n");
-
-  // Cleanup
-  aoti_torch_delete_tensor_object(query);
-  aoti_torch_delete_tensor_object(key);
-  aoti_torch_delete_tensor_object(value);
-  aoti_torch_delete_tensor_object(output);
-}
-
-// Test SDPA with BFloat16
-TEST_F(AOTITorchSDPATest, BFloat16Precision) {
-  const int64_t batch = 2;
-  const int64_t num_heads = 4;
-  const int64_t seq_len = 16;
-  const int64_t head_dim = 32;
-
-  Tensor* query = create_bfloat16_tensor({batch, num_heads, seq_len, head_dim}, 0.5f);
-  Tensor* key = create_bfloat16_tensor({batch, num_heads, seq_len, head_dim}, 0.3f);
-  Tensor* value = create_bfloat16_tensor({batch, num_heads, seq_len, head_dim}, 1.0f);
-
-  ASSERT_NE(query, nullptr) << "Failed to create BFloat16 query tensor";
-  ASSERT_NE(key, nullptr) << "Failed to create BFloat16 key tensor";
-  ASSERT_NE(value, nullptr) << "Failed to create BFloat16 value tensor";
-
-  printf("Testing SDPA BFloat16: [%ldx%ldx%ldx%ld]\n",
-         batch, num_heads, seq_len, head_dim);
-
-  Tensor* output = nullptr;
-  AOTITorchError error = aoti_torch_cuda_scaled_dot_product_attention(
-      query,
-      key,
-      value,
-      nullptr,
-      0.0,
-      0,
-      nullptr,
-      0,
-      &output);
-
-  EXPECT_EQ(error, Error::Ok) << "SDPA BFloat16 should succeed";
-  ASSERT_NE(output, nullptr);
-  EXPECT_TRUE(check_output_shape(output, {batch, num_heads, seq_len, head_dim}));
-
-  printf("BFloat16 precision test passed!\n");
-
-  // Cleanup
-  aoti_torch_delete_tensor_object(query);
-  aoti_torch_delete_tensor_object(key);
-  aoti_torch_delete_tensor_object(value);
-  aoti_torch_delete_tensor_object(output);
-}
-
-// Test SDPA with custom scale factor
-TEST_F(AOTITorchSDPATest, CustomScale) {
-  const int64_t batch = 1;
-  const int64_t num_heads = 2;
-  const int64_t seq_len = 4;
-  const int64_t head_dim = 8;
-
-  Tensor* query = create_float_tensor({batch, num_heads, seq_len, head_dim}, 0.5f);
-  Tensor* key = create_float_tensor({batch, num_heads, seq_len, head_dim}, 0.5f);
-  Tensor* value = create_float_tensor({batch, num_heads, seq_len, head_dim}, 1.0f);
-
-  ASSERT_NE(query, nullptr);
-  ASSERT_NE(key, nullptr);
-  ASSERT_NE(value, nullptr);
-
-  printf("Testing SDPA with custom scale\n");
-
-  // Use custom scale instead of default 1/sqrt(head_dim)
-  double custom_scale = 0.25;
-  Tensor* output = nullptr;
-  AOTITorchError error = aoti_torch_cuda_scaled_dot_product_attention(
-      query,
-      key,
-      value,
-      nullptr,
-      0.0,
-      0,
-      &custom_scale,  // custom scale
-      0,
-      &output);
-
-  EXPECT_EQ(error, Error::Ok);
-  ASSERT_NE(output, nullptr);
-  EXPECT_TRUE(check_output_shape(output, {batch, num_heads, seq_len, head_dim}));
-
-  printf("Custom scale test passed!\n");
-
-  // Cleanup
-  aoti_torch_delete_tensor_object(query);
-  aoti_torch_delete_tensor_object(key);
-  aoti_torch_delete_tensor_object(value);
-  aoti_torch_delete_tensor_object(output);
-}
-
-// Test with larger tensors (closer to real-world usage)
-TEST_F(AOTITorchSDPATest, LargerTensors) {
-  const int64_t batch = 4;
-  const int64_t num_heads = 8;
-  const int64_t seq_len = 128;
-  const int64_t head_dim = 64;
-
-  Tensor* query = create_float_tensor({batch, num_heads, seq_len, head_dim}, 0.1f);
-  Tensor* key = create_float_tensor({batch, num_heads, seq_len, head_dim}, 0.1f);
-  Tensor* value = create_float_tensor({batch, num_heads, seq_len, head_dim}, 1.0f);
-
-  ASSERT_NE(query, nullptr);
-  ASSERT_NE(key, nullptr);
-  ASSERT_NE(value, nullptr);
-
-  printf("Testing SDPA with larger tensors: [%ldx%ldx%ldx%ld]\n",
-         batch, num_heads, seq_len, head_dim);
-
-  Tensor* output = nullptr;
-  AOTITorchError error = aoti_torch_cuda_scaled_dot_product_attention(
-      query,
-      key,
-      value,
-      nullptr,
-      0.0,
-      1,  // causal
-      nullptr,
-      0,
-      &output);
-
-  EXPECT_EQ(error, Error::Ok);
-  ASSERT_NE(output, nullptr);
-  EXPECT_TRUE(check_output_shape(output, {batch, num_heads, seq_len, head_dim}));
-
-  printf("Larger tensors test passed!\n");
-
-  // Cleanup
-  aoti_torch_delete_tensor_object(query);
-  aoti_torch_delete_tensor_object(key);
-  aoti_torch_delete_tensor_object(value);
-  aoti_torch_delete_tensor_object(output);
-}
-
-// ============================================================================
-// Error Handling Tests
-// ============================================================================
-
-// Test null pointer handling
-TEST_F(AOTITorchSDPATest, NullPointerHandling) {
-  Tensor* query = create_float_tensor({1, 1, 4, 8}, 0.5f);
-  Tensor* key = create_float_tensor({1, 1, 4, 8}, 0.5f);
-  Tensor* value = create_float_tensor({1, 1, 4, 8}, 1.0f);
-  Tensor* output = nullptr;
-
-  // Test null query
-  {
-    AOTITorchError error = aoti_torch_cuda_scaled_dot_product_attention(
-        nullptr, key, value, nullptr, 0.0, 0, nullptr, 0, &output);
-    EXPECT_NE(error, Error::Ok) << "Should fail with null query";
-  }
-
-  // Test null key
-  {
-    AOTITorchError error = aoti_torch_cuda_scaled_dot_product_attention(
-        query, nullptr, value, nullptr, 0.0, 0, nullptr, 0, &output);
-    EXPECT_NE(error, Error::Ok) << "Should fail with null key";
-  }
-
-  // Test null value
-  {
-    AOTITorchError error = aoti_torch_cuda_scaled_dot_product_attention(
-        query, key, nullptr, nullptr, 0.0, 0, nullptr, 0, &output);
-    EXPECT_NE(error, Error::Ok) << "Should fail with null value";
-  }
-
-  // Test null output pointer
-  {
-    AOTITorchError error = aoti_torch_cuda_scaled_dot_product_attention(
-        query, key, value, nullptr, 0.0, 0, nullptr, 0, nullptr);
-    EXPECT_NE(error, Error::Ok) << "Should fail with null output pointer";
-  }
-
-  printf("Null pointer handling tests passed!\n");
-
-  // Cleanup
-  aoti_torch_delete_tensor_object(query);
-  aoti_torch_delete_tensor_object(key);
-  aoti_torch_delete_tensor_object(value);
-}
-
-// Test dimension mismatch
-TEST_F(AOTITorchSDPATest, DimensionMismatch) {
-  Tensor* query = create_float_tensor({1, 2, 4, 8}, 0.5f);
-  Tensor* key = create_float_tensor({1, 2, 6, 8}, 0.5f);  // Different seq_len
-  Tensor* value = create_float_tensor({1, 2, 6, 8}, 1.0f);
-  Tensor* output = nullptr;
-
-  // This should succeed (Q and K can have different seq_len)
-  AOTITorchError error = aoti_torch_cuda_scaled_dot_product_attention(
-      query, key, value, nullptr, 0.0, 0, nullptr, 0, &output);
-
-  EXPECT_EQ(error, Error::Ok) << "Different Q and K seq_len should be allowed";
-
-  if (output != nullptr) {
-    // Output should have Q's seq_len
-    EXPECT_EQ(output->size(2), 4) << "Output seq_len should match Query";
-    aoti_torch_delete_tensor_object(output);
-  }
-
-  printf("Dimension handling test passed!\n");
-
-  // Cleanup
-  aoti_torch_delete_tensor_object(query);
-  aoti_torch_delete_tensor_object(key);
-  aoti_torch_delete_tensor_object(value);
-}
-
-// Test dropout error (should fail since we don't support dropout)
-TEST_F(AOTITorchSDPATest, DropoutNotSupported) {
-  Tensor* query = create_float_tensor({1, 1, 4, 8}, 0.5f);
-  Tensor* key = create_float_tensor({1, 1, 4, 8}, 0.5f);
-  Tensor* value = create_float_tensor({1, 1, 4, 8}, 1.0f);
-  Tensor* output = nullptr;
-
-  AOTITorchError error = aoti_torch_cuda_scaled_dot_product_attention(
-      query, key, value, nullptr, 0.5, 0, nullptr, 0, &output);  // dropout=0.5
-
-  EXPECT_NE(error, Error::Ok) << "Should fail with non-zero dropout";
-
-  printf("Dropout rejection test passed!\n");
-
-  // Cleanup
-  aoti_torch_delete_tensor_object(query);
-  aoti_torch_delete_tensor_object(key);
-  aoti_torch_delete_tensor_object(value);
-}
-
-// ============================================================================
-// Numerical Correctness Tests
-// ============================================================================
-
-// Test that output values are in reasonable range
-TEST_F(AOTITorchSDPATest, OutputValueRangeCheck) {
-  const int64_t batch = 1;
-  const int64_t num_heads = 1;
-  const int64_t seq_len = 4;
-  const int64_t head_dim = 8;
-
-  // Use small values to avoid numerical overflow
-  Tensor* query = create_float_tensor({batch, num_heads, seq_len, head_dim}, 0.1f);
-  Tensor* key = create_float_tensor({batch, num_heads, seq_len, head_dim}, 0.1f);
-  Tensor* value = create_float_tensor({batch, num_heads, seq_len, head_dim}, 1.0f);
-
-  ASSERT_NE(query, nullptr);
-  ASSERT_NE(key, nullptr);
-  ASSERT_NE(value, nullptr);
-
-  printf("Testing SDPA output value range\n");
-
-  Tensor* output = nullptr;
-  AOTITorchError error = aoti_torch_cuda_scaled_dot_product_attention(
-      query, key, value, nullptr, 0.0, 0, nullptr, 0, &output);
-
-  ASSERT_EQ(error, Error::Ok);
-  ASSERT_NE(output, nullptr);
-
-  // Copy output back to CPU for verification
-  std::vector<float> output_data = copy_tensor_to_host(output);
-
-  // Since V is all 1.0, and softmax produces weights that sum to 1,
-  // output should be close to 1.0 (weighted average of 1.0)
-  bool all_in_range = true;
-  for (size_t i = 0; i < output_data.size(); ++i) {
-    // Output should be around 1.0 with some tolerance
-    if (output_data[i] < 0.5f || output_data[i] > 1.5f) {
-      printf("Output[%zu] = %f is out of expected range [0.5, 1.5]\n",
-             i, output_data[i]);
-      all_in_range = false;
-    }
-  }
-
-  EXPECT_TRUE(all_in_range) << "Some output values are out of reasonable range";
-
-  printf("Output value range check passed!\n");
-
-  // Cleanup
-  aoti_torch_delete_tensor_object(query);
-  aoti_torch_delete_tensor_object(key);
-  aoti_torch_delete_tensor_object(value);
-  aoti_torch_delete_tensor_object(output);
-}
-
-// Test with identity Q=K, verify attention weights sum to 1
-TEST_F(AOTITorchSDPATest, IdentityQKTest) {
-  const int64_t batch = 1;
-  const int64_t num_heads = 1;
-  const int64_t seq_len = 4;
-  const int64_t head_dim = 8;
-
-  // When Q=K, attention scores will be uniform (since all positions are equally similar)
-  Tensor* query = create_float_tensor({batch, num_heads, seq_len, head_dim}, 0.5f);
-  Tensor* key = create_float_tensor({batch, num_heads, seq_len, head_dim}, 0.5f);
-  Tensor* value = create_float_tensor({batch, num_heads, seq_len, head_dim}, 2.0f);
-
-  ASSERT_NE(query, nullptr);
-  ASSERT_NE(key, nullptr);
-  ASSERT_NE(value, nullptr);
-
-  printf("Testing SDPA with Q=K (identity attention)\n");
-
-  Tensor* output = nullptr;
-  AOTITorchError error = aoti_torch_cuda_scaled_dot_product_attention(
-      query, key, value, nullptr, 0.0, 0, nullptr, 0, &output);
-
-  ASSERT_EQ(error, Error::Ok);
-  ASSERT_NE(output, nullptr);
-
-  // Copy output back to CPU
-  std::vector<float> output_data = copy_tensor_to_host(output);
-
-  // When Q=K and V is uniform, output should be close to V
-  // (since attention weights are uniform due to identical scores)
-  bool values_correct = true;
-  for (size_t i = 0; i < output_data.size(); ++i) {
-    // Output should be close to 2.0 (the value of V)
-    if (!approx_equal(output_data[i], 2.0f, 0.1f)) {
-      printf("Output[%zu] = %f, expected ~2.0\n", i, output_data[i]);
-      values_correct = false;
-    }
-  }
-
-  EXPECT_TRUE(values_correct) << "Output values don't match expected for identity Q=K";
-
-  printf("Identity Q=K test passed!\n");
-
-  // Cleanup
-  aoti_torch_delete_tensor_object(query);
-  aoti_torch_delete_tensor_object(key);
-  aoti_torch_delete_tensor_object(value);
-  aoti_torch_delete_tensor_object(output);
-}
-
-// Test that different scales produce different outputs
-TEST_F(AOTITorchSDPATest, ScaleEffectTest) {
-  const int64_t batch = 1;
-  const int64_t num_heads = 1;
-  const int64_t seq_len = 4;
-  const int64_t head_dim = 8;
-
-  Tensor* query = create_float_tensor({batch, num_heads, seq_len, head_dim}, 0.5f);
-  Tensor* key = create_float_tensor({batch, num_heads, seq_len, head_dim}, 0.3f);
-  Tensor* value = create_float_tensor({batch, num_heads, seq_len, head_dim}, 1.0f);
-
-  ASSERT_NE(query, nullptr);
-  ASSERT_NE(key, nullptr);
-  ASSERT_NE(value, nullptr);
-
-  // Make K different at different positions so attention scores vary
-  std::vector<float> key_host(batch * num_heads * seq_len * head_dim);
-  for (int64_t pos = 0; pos < seq_len; ++pos) {
-    for (int64_t d = 0; d < head_dim; ++d) {
-      // Different values per position: pos 0=0.1, pos 1=0.3, pos 2=0.5, pos 3=0.7
-      key_host[pos * head_dim + d] = 0.1f + 0.2f * pos;
-    }
-  }
-  cudaMemcpy(
-      key->data_ptr(),
-      key_host.data(),
-      key_host.size() * sizeof(float),
-      cudaMemcpyHostToDevice);
-
-  // Make V also different at different positions to amplify differences
-  std::vector<float> value_host(batch * num_heads * seq_len * head_dim);
-  for (int64_t pos = 0; pos < seq_len; ++pos) {
-    for (int64_t d = 0; d < head_dim; ++d) {
-      // V values: pos 0=1.0, pos 1=2.0, pos 2=3.0, pos 3=4.0
-      value_host[pos * head_dim + d] = static_cast<float>(pos + 1);
-    }
-  }
-  cudaMemcpy(
-      value->data_ptr(),
-      value_host.data(),
-      value_host.size() * sizeof(float),
-      cudaMemcpyHostToDevice);
-
-  printf("Testing SDPA scale effect\n");
-
-  // Test with default scale
-  Tensor* output1 = nullptr;
-  AOTITorchError error1 = aoti_torch_cuda_scaled_dot_product_attention(
-      query, key, value, nullptr, 0.0, 0, nullptr, 0, &output1);
-  ASSERT_EQ(error1, Error::Ok);
-  ASSERT_NE(output1, nullptr);
-
-  // Test with custom scale (much smaller, should make attention more uniform)
-  double small_scale = 0.01;
-  Tensor* output2 = nullptr;
-  AOTITorchError error2 = aoti_torch_cuda_scaled_dot_product_attention(
-      query, key, value, nullptr, 0.0, 0, &small_scale, 0, &output2);
-  ASSERT_EQ(error2, Error::Ok);
-  ASSERT_NE(output2, nullptr);
-
-  // Copy outputs back to CPU
-  std::vector<float> output1_data = copy_tensor_to_host(output1);
-  std::vector<float> output2_data = copy_tensor_to_host(output2);
-
-  // Outputs should be different (scale affects softmax sharpness)
-  // With varied V values, even small changes in attention weights will produce
-  // noticeably different outputs
-  bool outputs_differ = false;
-  float max_diff = 0.0f;
-  for (size_t i = 0; i < output1_data.size(); ++i) {
-    float diff = std::abs(output1_data[i] - output2_data[i]);
-    max_diff = std::max(max_diff, diff);
-    if (diff > 0.05f) {  // More lenient threshold due to varied V values
-      outputs_differ = true;
-      break;
-    }
-  }
-
-  printf("Max difference between outputs: %f\n", max_diff);
-  EXPECT_TRUE(outputs_differ) << "Different scales should produce different outputs (max_diff=" << max_diff << ")";
-
-  printf("Scale effect test passed!\n");
-
-  // Cleanup
-  aoti_torch_delete_tensor_object(query);
-  aoti_torch_delete_tensor_object(key);
-  aoti_torch_delete_tensor_object(value);
-  aoti_torch_delete_tensor_object(output1);
-  aoti_torch_delete_tensor_object(output2);
-}
-
-// Test causal masking correctness
-TEST_F(AOTITorchSDPATest, CausalMaskingCorrectness) {
-  const int64_t batch = 1;
-  const int64_t num_heads = 1;
-  const int64_t seq_len = 4;
-  const int64_t head_dim = 8;
-
-  // Create distinct values at different positions in V
-  // This allows us to verify that causal masking works correctly
-  Tensor* query = create_float_tensor({batch, num_heads, seq_len, head_dim}, 1.0f);
-  Tensor* key = create_float_tensor({batch, num_heads, seq_len, head_dim}, 1.0f);
-  Tensor* value = create_float_tensor({batch, num_heads, seq_len, head_dim}, 0.0f);
-
-  ASSERT_NE(query, nullptr);
-  ASSERT_NE(key, nullptr);
-  ASSERT_NE(value, nullptr);
-
-  // Manually set different values for each position in V
-  // V[position i] = i+1 (so we can track which positions contribute)
-  std::vector<float> value_host(batch * num_heads * seq_len * head_dim);
-  for (int64_t pos = 0; pos < seq_len; ++pos) {
-    for (int64_t d = 0; d < head_dim; ++d) {
-      value_host[pos * head_dim + d] = static_cast<float>(pos + 1);
-    }
-  }
-  cudaMemcpy(
-      value->data_ptr(),
-      value_host.data(),
-      value_host.size() * sizeof(float),
-      cudaMemcpyHostToDevice);
-
-  printf("Testing SDPA causal masking correctness\n");
-
-  // Run with causal masking
-  Tensor* output_causal = nullptr;
-  AOTITorchError error = aoti_torch_cuda_scaled_dot_product_attention(
-      query, key, value, nullptr, 0.0, 1, nullptr, 0, &output_causal);
-  ASSERT_EQ(error, Error::Ok);
-  ASSERT_NE(output_causal, nullptr);
-
-  // Copy output back to CPU
-  std::vector<float> output_data = copy_tensor_to_host(output_causal);
-
-  // With causal masking:
-  // - Position 0 can only see position 0, so output[0] should be ~1.0
-  // - Position 1 can see positions 0,1, so output[1] should be ~1.5 (average of 1 and 2)
-  // - Position 2 can see positions 0,1,2, so output[2] should be ~2.0 (average of 1,2,3)
-  // - Position 3 can see all, so output[3] should be ~2.5 (average of 1,2,3,4)
-
-  std::vector<float> expected_values = {1.0f, 1.5f, 2.0f, 2.5f};
-
-  bool causal_correct = true;
-  for (int64_t pos = 0; pos < seq_len; ++pos) {
-    float avg_output = 0.0f;
-    for (int64_t d = 0; d < head_dim; ++d) {
-      avg_output += output_data[pos * head_dim + d];
-    }
-    avg_output /= head_dim;
-
-    printf("Position %ld: output avg = %f, expected ~%f\n",
-           pos, avg_output, expected_values[pos]);
-
-    if (!approx_equal(avg_output, expected_values[pos], 0.2f)) {
-      causal_correct = false;
-    }
-  }
-
-  EXPECT_TRUE(causal_correct) << "Causal masking did not produce expected values";
-
-  printf("Causal masking correctness test passed!\n");
-
-  // Cleanup
-  aoti_torch_delete_tensor_object(query);
-  aoti_torch_delete_tensor_object(key);
-  aoti_torch_delete_tensor_object(value);
-  aoti_torch_delete_tensor_object(output_causal);
-}
-
-int main(int argc, char** argv) {
-  ::testing::InitGoogleTest(&argc, argv);
-  return RUN_ALL_TESTS();
-}

From 06d11de8d997a211b9d238b4041e931c8253b9d6 Mon Sep 17 00:00:00 2001
From: gasoonjia <gasoonjia@icloud.com>
Date: Wed, 10 Dec 2025 11:10:54 -0800
Subject: [PATCH 6/8] use math decomposition as fallback to support gemma3

---
 .../ci_commit_pins/optimum-executorch.txt     |  2 +-
 backends/cuda/cuda_backend.py                 | 34 +++++++++----------
 2 files changed, 18 insertions(+), 18 deletions(-)

diff --git a/.ci/docker/ci_commit_pins/optimum-executorch.txt b/.ci/docker/ci_commit_pins/optimum-executorch.txt
index df87f35a69d..156ff2f3c82 100644
--- a/.ci/docker/ci_commit_pins/optimum-executorch.txt
+++ b/.ci/docker/ci_commit_pins/optimum-executorch.txt
@@ -1 +1 @@
-d03e90c2cd9048e6d9a75285c0355f033cd016fc
+0123293118efb08ac4ffc4fefe9d330201465c93
diff --git a/backends/cuda/cuda_backend.py b/backends/cuda/cuda_backend.py
index eb1226ebf8a..f0d3a000ec0 100644
--- a/backends/cuda/cuda_backend.py
+++ b/backends/cuda/cuda_backend.py
@@ -134,20 +134,20 @@ def get_aoti_compile_options(
 
         return options
 
-    # @classmethod
-    # def get_extra_aoti_compile_context_manager(cls):
-    #     """
-    #     Return SDPA MATH backend context manager for CUDA compilation.
-
-    #     This context manager plays as a fallback solution for any remaining PyTorch SDPA
-    #     operations to use the MATH backend (decomposed SDPA) during AOTInductor compilation.
-
-    #     Note:
-    #     - If SDPA ops are replaced with Triton kernels by ReplaceEdgeOpWithTritonOpPass,
-    #       this context manager will have no effect on those ops (they are no longer
-    #       PyTorch SDPA ops).
-    #     - If SDPA ops are NOT replaced (e.g., when triton_kernel_mode="OFF"), this
-    #       context manager will force them to use the MATH backend, causing them to
-    #       be automatically decomposed during compilation.
-    #     """
-    #     return torch.nn.attention.sdpa_kernel([SDPBackend.MATH])
+    @classmethod
+    def get_extra_aoti_compile_context_manager(cls):
+        """
+        Return SDPA MATH backend context manager for CUDA compilation.
+
+        This context manager plays as a fallback solution for any remaining PyTorch SDPA
+        operations to use the MATH backend (decomposed SDPA) during AOTInductor compilation.
+
+        Note:
+        - If SDPA ops are replaced with Triton kernels by ReplaceEdgeOpWithTritonOpPass,
+          this context manager will have no effect on those ops (they are no longer
+          PyTorch SDPA ops).
+        - If SDPA ops are NOT replaced (e.g., when triton_kernel_mode="OFF"), this
+          context manager will force them to use the MATH backend, causing them to
+          be automatically decomposed during compilation.
+        """
+        return torch.nn.attention.sdpa_kernel([SDPBackend.MATH])

From e450cfea6906cf924fcfe9af1159b0b1c8b20b05 Mon Sep 17 00:00:00 2001
From: gasoonjia <gasoonjia@icloud.com>
Date: Wed, 10 Dec 2025 22:03:52 -0800
Subject: [PATCH 7/8] gemma3 support

---
 backends/cuda/cuda_backend.py        |    37 +-
 backends/cuda/triton/kernels/sdpa.py |   305 +-
 output.wav                           |   Bin 0 -> 960044 bytes
 special_tokens_map.json              |    33 +
 tokenizer_config.json                | 51347 +++++++++++++++++++++++++
 5 files changed, 51658 insertions(+), 64 deletions(-)
 create mode 100644 output.wav
 create mode 100644 special_tokens_map.json
 create mode 100644 tokenizer_config.json

diff --git a/backends/cuda/cuda_backend.py b/backends/cuda/cuda_backend.py
index f0d3a000ec0..9204ecaecda 100644
--- a/backends/cuda/cuda_backend.py
+++ b/backends/cuda/cuda_backend.py
@@ -68,7 +68,8 @@ def get_custom_passes(cls, compile_specs: List[CompileSpec]) -> List[typing.Any]
                     )
                 triton_kernel_mode = mode
 
-        return [ReplaceEdgeOpWithTritonOpPass()] if triton_kernel_mode == "ON" else []
+        # return [ReplaceEdgeOpWithTritonOpPass()] if triton_kernel_mode == "ON" else []
+        return [ReplaceEdgeOpWithTritonOpPass()]
 
     @classmethod
     def get_aoti_compile_options(
@@ -134,20 +135,20 @@ def get_aoti_compile_options(
 
         return options
 
-    @classmethod
-    def get_extra_aoti_compile_context_manager(cls):
-        """
-        Return SDPA MATH backend context manager for CUDA compilation.
-
-        This context manager plays as a fallback solution for any remaining PyTorch SDPA
-        operations to use the MATH backend (decomposed SDPA) during AOTInductor compilation.
-
-        Note:
-        - If SDPA ops are replaced with Triton kernels by ReplaceEdgeOpWithTritonOpPass,
-          this context manager will have no effect on those ops (they are no longer
-          PyTorch SDPA ops).
-        - If SDPA ops are NOT replaced (e.g., when triton_kernel_mode="OFF"), this
-          context manager will force them to use the MATH backend, causing them to
-          be automatically decomposed during compilation.
-        """
-        return torch.nn.attention.sdpa_kernel([SDPBackend.MATH])
+    # @classmethod
+    # def get_extra_aoti_compile_context_manager(cls):
+    #     """
+    #     Return SDPA MATH backend context manager for CUDA compilation.
+
+    #     This context manager plays as a fallback solution for any remaining PyTorch SDPA
+    #     operations to use the MATH backend (decomposed SDPA) during AOTInductor compilation.
+
+    #     Note:
+    #     - If SDPA ops are replaced with Triton kernels by ReplaceEdgeOpWithTritonOpPass,
+    #       this context manager will have no effect on those ops (they are no longer
+    #       PyTorch SDPA ops).
+    #     - If SDPA ops are NOT replaced (e.g., when triton_kernel_mode="OFF"), this
+    #       context manager will force them to use the MATH backend, causing them to
+    #       be automatically decomposed during compilation.
+    #     """
+    #     return torch.nn.attention.sdpa_kernel([SDPBackend.MATH])
diff --git a/backends/cuda/triton/kernels/sdpa.py b/backends/cuda/triton/kernels/sdpa.py
index 601c09ad983..e3c444f093e 100644
--- a/backends/cuda/triton/kernels/sdpa.py
+++ b/backends/cuda/triton/kernels/sdpa.py
@@ -22,6 +22,24 @@
 from torch.library import triton_op, wrap_triton
 
 
+def _is_power_of_2(n: int) -> bool:
+    """Check if n is a power of 2."""
+    return n > 0 and (n & (n - 1)) == 0
+
+
+def _next_power_of_2(x: int) -> int:
+    """Get the next power of 2 >= x, clamped to [16, 256]."""
+    if x <= 16:
+        return 16
+    if x <= 32:
+        return 32
+    if x <= 64:
+        return 64
+    if x <= 128:
+        return 128
+    return 256
+
+
 def _validate_qkv_shapes(
     query: torch.Tensor,
     key: torch.Tensor,
@@ -64,6 +82,131 @@ def _validate_qkv_shapes(
     return B_q, H_q, L_q, L_kv_k, D_q, D_k
 
 
+# ==============================================================================
+# Non-power-of-2 HEAD_DIM kernel
+# ==============================================================================
+@triton.jit
+def _sdpa_fwd_kernel_non_pow2(
+    q_ptr,
+    k_ptr,
+    v_ptr,
+    o_ptr,
+    mask_ptr,
+    B,
+    H,
+    LQ,
+    LK,
+    HEAD_DIM,
+    stride_qb,
+    stride_qh,
+    stride_ql,
+    stride_qd,
+    stride_kb,
+    stride_kh,
+    stride_kl,
+    stride_kd,
+    stride_vb,
+    stride_vh,
+    stride_vl,
+    stride_vd,
+    stride_ob,
+    stride_oh,
+    stride_ol,
+    stride_od,
+    stride_mb,
+    stride_mh,
+    stride_mlq,
+    stride_mlk,
+    scale,
+    BLOCK_M: tl.constexpr,
+    BLOCK_N: tl.constexpr,
+    BLOCK_D: tl.constexpr,
+    HAS_MASK: tl.constexpr,
+    IS_CAUSAL: tl.constexpr,
+):
+    """
+    SDPA forward kernel for non-power-of-2 HEAD_DIM.
+    Uses dynamic masking to handle arbitrary head dimensions.
+    """
+    pid_m = tl.program_id(axis=0)
+    pid_bh = tl.program_id(axis=1)
+
+    b = pid_bh // H
+    h = pid_bh % H
+
+    offs_m = pid_m * BLOCK_M + tl.arange(0, BLOCK_M)
+    offs_n = tl.arange(0, BLOCK_N)
+    offs_d = tl.arange(0, BLOCK_D)
+
+    d_mask = offs_d < HEAD_DIM
+    q_row_mask = offs_m < LQ
+
+    q_base = q_ptr + b * stride_qb + h * stride_qh
+    k_base = k_ptr + b * stride_kb + h * stride_kh
+    v_base = v_ptr + b * stride_vb + h * stride_vh
+    o_base = o_ptr + b * stride_ob + h * stride_oh
+
+    q_ptrs = q_base + (offs_m[:, None] * stride_ql + offs_d[None, :] * stride_qd)
+    q = tl.load(q_ptrs, mask=q_row_mask[:, None] & d_mask[None, :], other=0.0)
+
+    acc = tl.zeros((BLOCK_M, BLOCK_D), dtype=tl.float32)
+    m_i = tl.full((BLOCK_M,), -float("inf"), dtype=tl.float32)
+    l_i = tl.full((BLOCK_M,), 1.0, dtype=tl.float32)
+
+    qk_scale_log2 = scale * 1.4426950408889634
+
+    if HAS_MASK:
+        mask_b_base = mask_ptr + b * stride_mb
+
+    for start_n in tl.range(0, LK, BLOCK_N, num_stages=2):
+        kn = start_n + offs_n
+        kv_col_mask = kn < LK
+
+        k_ptrs = k_base + (kn[:, None] * stride_kl + offs_d[None, :] * stride_kd)
+        k = tl.load(k_ptrs, mask=kv_col_mask[:, None] & d_mask[None, :], other=0.0)
+
+        qk = tl.dot(q, tl.trans(k))
+        qk = qk * qk_scale_log2
+
+        if IS_CAUSAL:
+            row_abs = offs_m[:, None]
+            col_abs = kn[None, :]
+            causal_mask = col_abs > row_abs
+            qk = tl.where(causal_mask, -float("inf"), qk)
+
+        if HAS_MASK:
+            mask_ptrs = (
+                mask_b_base + offs_m[:, None] * stride_mlq + kn[None, :] * stride_mlk
+            )
+            tile_valid = q_row_mask[:, None] & kv_col_mask[None, :]
+            keep = tl.load(mask_ptrs, mask=tile_valid, other=True)
+            qk = tl.where(keep, qk, -float("inf"))
+
+        qk = tl.where(kv_col_mask[None, :], qk, -float("inf"))
+
+        m_ij = tl.maximum(m_i, tl.max(qk, 1))
+        p = tl.math.exp2(qk - m_ij[:, None])
+        l_ij = tl.sum(p, 1)
+        alpha = tl.math.exp2(m_i - m_ij)
+
+        acc = acc * alpha[:, None]
+
+        v_ptrs = v_base + (kn[:, None] * stride_vl + offs_d[None, :] * stride_vd)
+        v = tl.load(v_ptrs, mask=kv_col_mask[:, None] & d_mask[None, :], other=0.0)
+
+        acc = tl.dot(p.to(v.dtype), v, acc)
+
+        l_i = l_i * alpha + l_ij
+        m_i = m_ij
+
+    out = acc / l_i[:, None]
+    o_ptrs = o_base + (offs_m[:, None] * stride_ol + offs_d[None, :] * stride_od)
+    tl.store(o_ptrs, out.to(tl.bfloat16), mask=q_row_mask[:, None] & d_mask[None, :])
+
+
+# ==============================================================================
+# Power-of-2 HEAD_DIM kernels
+# ==============================================================================
 @triton.jit
 def _sdpa_fwd_kernel_body(
     Q_ptr,
@@ -463,57 +606,122 @@ def sdpa(
     def grid(meta):
         return (triton.cdiv(L_q, meta["BLOCK_M"]), B * H)
 
-    # Dynamic kernel selection based on workload
-    total_ctas_m64 = ((L_q + 63) // 64) * (B * H)
-    threshold = 4 * 84  # Heuristic threshold for kernel selection
-    use_small_block = total_ctas_m64 < threshold
-
-    if use_small_block:
-        wrap_triton(_sdpa_fwd_kernel_m32)[grid](
-            query,
-            key,
-            value,
-            out,
-            Mask_ptr if HAS_MASK else 0,
-            B,
-            H,
-            L_q,
-            L_kv,
-            stride_qb,
-            stride_qh,
-            stride_qm,
-            stride_qd,
-            stride_kb,
-            stride_kh,
-            stride_kn,
-            stride_kd,
-            stride_vb,
-            stride_vh,
-            stride_vn,
-            stride_vd,
-            stride_ob,
-            stride_oh,
-            stride_om,
-            stride_od,
-            stride_mb,
-            stride_mq,
-            stride_mk,
-            sm_scale,
-            HAS_MASK=HAS_MASK,
-            IS_CAUSAL=is_causal,
-            HEAD_DIM=D,
-        )
+    # Select kernel based on whether HEAD_DIM is power of 2
+    if _is_power_of_2(D):
+        # Use power-of-2 optimized kernels with autotune
+        # Dynamic kernel selection based on workload
+        total_ctas_m64 = ((L_q + 63) // 64) * (B * H)
+        threshold = 4 * 84  # Heuristic threshold for kernel selection
+        use_small_block = total_ctas_m64 < threshold
+
+        if use_small_block:
+            wrap_triton(_sdpa_fwd_kernel_m32)[grid](
+                query,
+                key,
+                value,
+                out,
+                Mask_ptr if HAS_MASK else 0,
+                B,
+                H,
+                L_q,
+                L_kv,
+                stride_qb,
+                stride_qh,
+                stride_qm,
+                stride_qd,
+                stride_kb,
+                stride_kh,
+                stride_kn,
+                stride_kd,
+                stride_vb,
+                stride_vh,
+                stride_vn,
+                stride_vd,
+                stride_ob,
+                stride_oh,
+                stride_om,
+                stride_od,
+                stride_mb,
+                stride_mq,
+                stride_mk,
+                sm_scale,
+                HAS_MASK=HAS_MASK,
+                IS_CAUSAL=is_causal,
+                HEAD_DIM=D,
+            )
+        else:
+            wrap_triton(_sdpa_fwd_kernel_m64)[grid](
+                query,
+                key,
+                value,
+                out,
+                Mask_ptr if HAS_MASK else 0,
+                B,
+                H,
+                L_q,
+                L_kv,
+                stride_qb,
+                stride_qh,
+                stride_qm,
+                stride_qd,
+                stride_kb,
+                stride_kh,
+                stride_kn,
+                stride_kd,
+                stride_vb,
+                stride_vh,
+                stride_vn,
+                stride_vd,
+                stride_ob,
+                stride_oh,
+                stride_om,
+                stride_od,
+                stride_mb,
+                stride_mq,
+                stride_mk,
+                sm_scale,
+                HAS_MASK=HAS_MASK,
+                IS_CAUSAL=is_causal,
+                HEAD_DIM=D,
+            )
     else:
-        wrap_triton(_sdpa_fwd_kernel_m64)[grid](
+        # Use non-power-of-2 kernel with dynamic HEAD_DIM masking
+        BLOCK_D = _next_power_of_2(D)
+
+        if BLOCK_D >= 256:
+            BLOCK_N = 64
+        else:
+            BLOCK_N = 128
+
+        BLOCK_M = 32
+        num_warps = 4
+        num_stages = 2
+
+        # Handle mask for non-pow2 kernel (different stride layout)
+        if HAS_MASK:
+            mask_ptr = attn_mask
+            stride_mb_np2 = attn_mask.stride(0)
+            stride_mh_np2 = attn_mask.stride(1)
+            stride_mlq_np2 = attn_mask.stride(2)
+            stride_mlk_np2 = attn_mask.stride(3)
+        else:
+            mask_ptr = torch.empty((1,), device=query.device, dtype=torch.bool)
+            stride_mb_np2 = stride_mh_np2 = stride_mlq_np2 = stride_mlk_np2 = 0
+
+        def grid_non_pow2(meta):
+            return (triton.cdiv(L_q, meta["BLOCK_M"]), B * H)
+
+        wrap_triton(_sdpa_fwd_kernel_non_pow2)[grid_non_pow2](
             query,
             key,
             value,
             out,
-            Mask_ptr if HAS_MASK else 0,
+            mask_ptr,
             B,
             H,
             L_q,
             L_kv,
+            D,
             stride_qb,
             stride_qh,
             stride_qm,
@@ -530,13 +738,18 @@ def grid(meta):
             stride_oh,
             stride_om,
             stride_od,
-            stride_mb,
-            stride_mq,
-            stride_mk,
+            stride_mb_np2,
+            stride_mh_np2,
+            stride_mlq_np2,
+            stride_mlk_np2,
             sm_scale,
+            BLOCK_M=BLOCK_M,
+            BLOCK_N=BLOCK_N,
+            BLOCK_D=BLOCK_D,
             HAS_MASK=HAS_MASK,
             IS_CAUSAL=is_causal,
-            HEAD_DIM=D,
+            num_warps=num_warps,
+            num_stages=num_stages,
         )
 
     return out
diff --git a/output.wav b/output.wav
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diff --git a/special_tokens_map.json b/special_tokens_map.json
new file mode 100644
index 00000000000..bdd437b84a1
--- /dev/null
+++ b/special_tokens_map.json
@@ -0,0 +1,33 @@
+{
+  "boi_token": "<start_of_image>",
+  "bos_token": {
+    "content": "<bos>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eoi_token": "<end_of_image>",
+  "eos_token": {
+    "content": "<end_of_turn>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "image_token": "<image_soft_token>",
+  "pad_token": {
+    "content": "<pad>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "<unk>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}
diff --git a/tokenizer_config.json b/tokenizer_config.json
new file mode 100644
index 00000000000..9c2583b0aa4
--- /dev/null
+++ b/tokenizer_config.json
@@ -0,0 +1,51347 @@
+{
+  "add_bos_token": true,
+  "add_eos_token": false,
+  "added_tokens_decoder": {
+    "0": {
+      "content": "<pad>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "<eos>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "content": "<bos>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "3": {
+      "content": "<unk>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "4": {
+      "content": "<mask>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "5": {
+      "content": "[multimodal]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "6": {
+      "content": "<unused0>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "7": {
+      "content": "<unused1>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "8": {
+      "content": "<unused2>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "9": {
+      "content": "<unused3>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "10": {
+      "content": "<unused4>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "11": {
+      "content": "<unused5>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "12": {
+      "content": "<unused6>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "13": {
+      "content": "<unused7>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "14": {
+      "content": "<unused8>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "15": {
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+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "16": {
+      "content": "<unused10>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "17": {
+      "content": "<unused11>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "18": {
+      "content": "<unused12>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "19": {
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+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "20": {
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+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "21": {
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+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "22": {
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+      "lstrip": false,
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+      "single_word": false,
+      "special": false
+    },
+    "23": {
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+    },
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+    },
+    "25": {
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+    },
+    "26": {
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+    },
+    "27": {
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+    },
+    "28": {
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+      "lstrip": false,
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+    },
+    "29": {
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+      "lstrip": false,
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+    },
+    "30": {
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+    },
+    "31": {
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+      "lstrip": false,
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+      "rstrip": false,
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+    },
+    "32": {
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+    },
+    "33": {
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+    },
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+    },
+    "35": {
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+    },
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+    },
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+    },
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+    },
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+    },
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+      "content": "<image_soft_token>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "boi_token": "<start_of_image>",
+  "bos_token": "<bos>",
+  "chat_template": "{{ bos_token }}\n{%- if messages[0]['role'] == 'system' -%}\n    {%- if messages[0]['content'] is string -%}\n        {%- set first_user_prefix = messages[0]['content'] + '\n\n' -%}\n    {%- else -%}\n        {%- set first_user_prefix = messages[0]['content'][0]['text'] + '\n\n' -%}\n    {%- endif -%}\n    {%- set loop_messages = messages[1:] -%}\n{%- else -%}\n    {%- set first_user_prefix = \"\" -%}\n    {%- set loop_messages = messages -%}\n{%- endif -%}\n{%- for message in loop_messages -%}\n    {%- if (message['role'] == 'user') != (loop.index0 % 2 == 0) -%}\n        {{ raise_exception(\"Conversation roles must alternate user/assistant/user/assistant/...\") }}\n    {%- endif -%}\n    {%- if (message['role'] == 'assistant') -%}\n        {%- set role = \"model\" -%}\n    {%- else -%}\n        {%- set role = message['role'] -%}\n    {%- endif -%}\n    {{ '<start_of_turn>' + role + '\n' + (first_user_prefix if loop.first else \"\") }}\n    {%- if message['content'] is string -%}\n        {{ message['content'] | trim }}\n    {%- elif message['content'] is iterable -%}\n        {%- for item in message['content'] -%}\n            {%- if item['type'] == 'image' -%}\n                {{ '<start_of_image>' }}\n            {%- elif item['type'] == 'text' -%}\n                {{ item['text'] | trim }}\n            {%- endif -%}\n        {%- endfor -%}\n    {%- else -%}\n        {{ raise_exception(\"Invalid content type\") }}\n    {%- endif -%}\n    {{ '<end_of_turn>\n' }}\n{%- endfor -%}\n{%- if add_generation_prompt -%}\n    {{'<start_of_turn>model\n'}}\n{%- endif -%}\n",
+  "clean_up_tokenization_spaces": false,
+  "eoi_token": "<end_of_image>",
+  "eos_token": "<end_of_turn>",
+  "extra_special_tokens": {
+    "boi_token": "<start_of_image>",
+    "eoi_token": "<end_of_image>",
+    "image_token": "<image_soft_token>"
+  },
+  "image_token": "<image_soft_token>",
+  "model_max_length": 131072,
+  "pad_token": "<pad>",
+  "padding_side": "left",
+  "processor_class": "Gemma3Processor",
+  "sp_model_kwargs": null,
+  "spaces_between_special_tokens": false,
+  "tokenizer_class": "GemmaTokenizer",
+  "unk_token": "<unk>",
+  "use_default_system_prompt": false
+}
\ No newline at end of file

From 7e1ad112ebb4048404b07cde80bd4bc8e5045dcd Mon Sep 17 00:00:00 2001
From: gasoonjia <gasoonjia@icloud.com>
Date: Wed, 10 Dec 2025 22:04:07 -0800
Subject: [PATCH 8/8] gemma3 support

---
 output.wav              |   Bin 960044 -> 0 bytes
 special_tokens_map.json |    33 -
 tokenizer_config.json   | 51347 --------------------------------------
 3 files changed, 51380 deletions(-)
 delete mode 100644 output.wav
 delete mode 100644 special_tokens_map.json
 delete mode 100644 tokenizer_config.json

diff --git a/output.wav b/output.wav
deleted file mode 100644
index dba3cb22f842b883bf54215b4dac08ca8107e029..0000000000000000000000000000000000000000
GIT binary patch
literal 0
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diff --git a/special_tokens_map.json b/special_tokens_map.json
deleted file mode 100644
index bdd437b84a1..00000000000
--- a/special_tokens_map.json
+++ /dev/null
@@ -1,33 +0,0 @@
-{
-  "boi_token": "<start_of_image>",
-  "bos_token": {
-    "content": "<bos>",
-    "lstrip": false,
-    "normalized": false,
-    "rstrip": false,
-    "single_word": false
-  },
-  "eoi_token": "<end_of_image>",
-  "eos_token": {
-    "content": "<end_of_turn>",
-    "lstrip": false,
-    "normalized": false,
-    "rstrip": false,
-    "single_word": false
-  },
-  "image_token": "<image_soft_token>",
-  "pad_token": {
-    "content": "<pad>",
-    "lstrip": false,
-    "normalized": false,
-    "rstrip": false,
-    "single_word": false
-  },
-  "unk_token": {
-    "content": "<unk>",
-    "lstrip": false,
-    "normalized": false,
-    "rstrip": false,
-    "single_word": false
-  }
-}
diff --git a/tokenizer_config.json b/tokenizer_config.json
deleted file mode 100644
index 9c2583b0aa4..00000000000
--- a/tokenizer_config.json
+++ /dev/null
@@ -1,51347 +0,0 @@
-{
-  "add_bos_token": true,
-  "add_eos_token": false,
-  "added_tokens_decoder": {
-    "0": {
-      "content": "<pad>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "1": {
-      "content": "<eos>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "2": {
-      "content": "<bos>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "3": {
-      "content": "<unk>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "4": {
-      "content": "<mask>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": false
-    },
-    "5": {
-      "content": "[multimodal]",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": false
-    },
-    "6": {
-      "content": "<unused0>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": false
-    },
-    "7": {
-      "content": "<unused1>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": false
-    },
-    "8": {
-      "content": "<unused2>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": false
-    },
-    "9": {
-      "content": "<unused3>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": false
-    },
-    "10": {
-      "content": "<unused4>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": false
-    },
-    "11": {
-      "content": "<unused5>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": false
-    },
-    "12": {
-      "content": "<unused6>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": false
-    },
-    "13": {
-      "content": "<unused7>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": false
-    },
-    "14": {
-      "content": "<unused8>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": false
-    },
-    "15": {
-      "content": "<unused9>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": false
-    },
-    "16": {
-      "content": "<unused10>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": false
-    },
-    "17": {
-      "content": "<unused11>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": false
-    },
-    "18": {
-      "content": "<unused12>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": false
-    },
-    "19": {
-      "content": "<unused13>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": false
-    },
-    "20": {
-      "content": "<unused14>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": false
-    },
-    "21": {
-      "content": "<unused15>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": false
-    },
-    "22": {
-      "content": "<unused16>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": false
-    },
-    "23": {
-      "content": "<unused17>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": false
-    },
-    "24": {
-      "content": "<unused18>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": false
-    },
-    "25": {
-      "content": "<unused19>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": false
-    },
-    "26": {
-      "content": "<unused20>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": false
-    },
-    "27": {
-      "content": "<unused21>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": false
-    },
-    "28": {
-      "content": "<unused22>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": false
-    },
-    "29": {
-      "content": "<unused23>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": false
-    },
-    "30": {
-      "content": "<unused24>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": false
-    },
-    "31": {
-      "content": "<unused25>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": false
-    },
-    "32": {
-      "content": "<unused26>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": false
-    },
-    "33": {
-      "content": "<unused27>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": false
-    },
-    "34": {
-      "content": "<unused28>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": false
-    },
-    "35": {
-      "content": "<unused29>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": false
-    },
-    "36": {
-      "content": "<unused30>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
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-      "special": false
-    },
-    "37": {
-      "content": "<unused31>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": false
-    },
-    "38": {
-      "content": "<unused32>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": false
-    },
-    "39": {
-      "content": "<unused33>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": false
-    },
-    "40": {
-      "content": "<unused34>",
-      "lstrip": false,
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-      "rstrip": false,
-      "single_word": false,
-      "special": false
-    },
-    "41": {
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-    },
-    "43": {
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-    },
-    "44": {
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-    "262144": {
-      "content": "<image_soft_token>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
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-  },
-  "boi_token": "<start_of_image>",
-  "bos_token": "<bos>",
-  "chat_template": "{{ bos_token }}\n{%- if messages[0]['role'] == 'system' -%}\n    {%- if messages[0]['content'] is string -%}\n        {%- set first_user_prefix = messages[0]['content'] + '\n\n' -%}\n    {%- else -%}\n        {%- set first_user_prefix = messages[0]['content'][0]['text'] + '\n\n' -%}\n    {%- endif -%}\n    {%- set loop_messages = messages[1:] -%}\n{%- else -%}\n    {%- set first_user_prefix = \"\" -%}\n    {%- set loop_messages = messages -%}\n{%- endif -%}\n{%- for message in loop_messages -%}\n    {%- if (message['role'] == 'user') != (loop.index0 % 2 == 0) -%}\n        {{ raise_exception(\"Conversation roles must alternate user/assistant/user/assistant/...\") }}\n    {%- endif -%}\n    {%- if (message['role'] == 'assistant') -%}\n        {%- set role = \"model\" -%}\n    {%- else -%}\n        {%- set role = message['role'] -%}\n    {%- endif -%}\n    {{ '<start_of_turn>' + role + '\n' + (first_user_prefix if loop.first else \"\") }}\n    {%- if message['content'] is string -%}\n        {{ message['content'] | trim }}\n    {%- elif message['content'] is iterable -%}\n        {%- for item in message['content'] -%}\n            {%- if item['type'] == 'image' -%}\n                {{ '<start_of_image>' }}\n            {%- elif item['type'] == 'text' -%}\n                {{ item['text'] | trim }}\n            {%- endif -%}\n        {%- endfor -%}\n    {%- else -%}\n        {{ raise_exception(\"Invalid content type\") }}\n    {%- endif -%}\n    {{ '<end_of_turn>\n' }}\n{%- endfor -%}\n{%- if add_generation_prompt -%}\n    {{'<start_of_turn>model\n'}}\n{%- endif -%}\n",
-  "clean_up_tokenization_spaces": false,
-  "eoi_token": "<end_of_image>",
-  "eos_token": "<end_of_turn>",
-  "extra_special_tokens": {
-    "boi_token": "<start_of_image>",
-    "eoi_token": "<end_of_image>",
-    "image_token": "<image_soft_token>"
-  },
-  "image_token": "<image_soft_token>",
-  "model_max_length": 131072,
-  "pad_token": "<pad>",
-  "padding_side": "left",
-  "processor_class": "Gemma3Processor",
-  "sp_model_kwargs": null,
-  "spaces_between_special_tokens": false,
-  "tokenizer_class": "GemmaTokenizer",
-  "unk_token": "<unk>",
-  "use_default_system_prompt": false
-}
\ No newline at end of file