compile_kernel remove cp from graph test

.ci/docker/ci_commit_pins/nccl-cu12.txt

@@ -1 +1 @@
-v2.28.3-1
+v2.27.5-1

.ci/docker/ci_commit_pins/nccl-cu13.txt

@@ -1 +1 @@
-v2.28.3-1
+v2.27.7-1

.ci/docker/common/install_rocm_magma.sh

@@ -12,8 +12,8 @@ function do_install() {
 
     rocm_version_nodot=${rocm_version//./}
 
-    # https://github.com/icl-utk-edu/magma/pull/65
-    MAGMA_VERSION=d6e4117bc88e73f06d26c6c2e14f064e8fc3d1ec
+    # Version 2.7.2 + ROCm related updates
+    MAGMA_VERSION=a1625ff4d9bc362906bd01f805dbbe12612953f6
     magma_archive="magma-rocm${rocm_version_nodot}-${MAGMA_VERSION}-1.tar.bz2"
 
     rocm_dir="/opt/rocm"

.ci/docker/requirements-docs.txt

@@ -1,15 +1,8 @@
 sphinx==5.3.0
 #Description: This is used to generate PyTorch docs
 #Pinned versions: 5.3.0
-
-standard-imghdr==3.13.0; python_version >= "3.13"
-#Description: This is needed by Sphinx, so it needs to be added here.
-# The reasons are as follows:
-# 1) This module has been removed from the Python standard library since Python 3.13(https://peps.python.org/pep-0594/#imghdr);
-# 2) The current version of Sphinx (5.3.0) is not compatible with Python 3.13.
-# Once Sphinx is upgraded to a version compatible with Python 3.13 or later, we can remove this dependency.
-
 -e git+https://github.com/pytorch/pytorch_sphinx_theme.git@d53b0ffb9b1cda68260693ea98f3483823c88d8e#egg=pytorch_sphinx_theme2
+
 # TODO: sphinxcontrib.katex 0.9.0 adds a local KaTeX server to speed up pre-rendering
 # but it doesn't seem to work and hangs around idly. The initial thought that it is probably
 # something related to Docker setup. We can investigate this later.

.ci/manywheel/build_libtorch.sh

@@ -104,7 +104,7 @@ if [[ "$DESIRED_CUDA" == *"rocm"* ]]; then
     export ROCclr_DIR=/opt/rocm/rocclr/lib/cmake/rocclr
 fi
 
-echo "Calling -m pip install . -v --no-build-isolation at $(date)"
+echo "Calling 'python -m pip install .' at $(date)"
 
 if [[ $LIBTORCH_VARIANT = *"static"* ]]; then
     STATIC_CMAKE_FLAG="-DTORCH_STATIC=1"

.ci/pytorch/test.sh

@@ -1617,7 +1617,7 @@ test_operator_benchmark() {
   test_inductor_set_cpu_affinity
 
   cd benchmarks/operator_benchmark/pt_extension
-  python -m pip install . -v --no-build-isolation
+  python -m pip install .
 
   cd "${TEST_DIR}"/benchmarks/operator_benchmark
   $TASKSET python -m benchmark_all_test --device "$1" --tag-filter "$2" \
@@ -1630,25 +1630,6 @@ test_operator_benchmark() {
       --expected "expected_ci_operator_benchmark_eager_float32_cpu.csv"
 }
 
-test_operator_microbenchmark() {
-  TEST_REPORTS_DIR=$(pwd)/test/test-reports
-  mkdir -p "$TEST_REPORTS_DIR"
-  TEST_DIR=$(pwd)
-
-  cd benchmarks/operator_benchmark/pt_extension
-  python -m pip install .
-
-  cd "${TEST_DIR}"/benchmarks/operator_benchmark
-
-  for OP_BENCHMARK_TESTS in matmul mm addmm bmm; do
-    $TASKSET python -m pt.${OP_BENCHMARK_TESTS}_test --tag-filter long \
-      --output-json-for-dashboard "${TEST_REPORTS_DIR}/operator_microbenchmark_${OP_BENCHMARK_TESTS}_compile.json" \
-      --benchmark-name "PyTorch operator microbenchmark" --use-compile
-    $TASKSET python -m pt.${OP_BENCHMARK_TESTS}_test --tag-filter long \
-      --output-json-for-dashboard "${TEST_REPORTS_DIR}/operator_microbenchmark_${OP_BENCHMARK_TESTS}.json" \
-      --benchmark-name "PyTorch operator microbenchmark"
-  done
-}
 
 if ! [[ "${BUILD_ENVIRONMENT}" == *libtorch* || "${BUILD_ENVIRONMENT}" == *-bazel-* ]]; then
   (cd test && python -c "import torch; print(torch.__config__.show())")
@@ -1705,8 +1686,6 @@ elif [[ "${TEST_CONFIG}" == *operator_benchmark* ]]; then
     test_operator_benchmark cpu ${TEST_MODE}
 
   fi
-elif [[ "${TEST_CONFIG}" == *operator_microbenchmark* ]]; then
-  test_operator_microbenchmark
 elif [[ "${TEST_CONFIG}" == *inductor_distributed* ]]; then
   test_inductor_distributed
 elif [[ "${TEST_CONFIG}" == *inductor-halide* ]]; then
@@ -1815,8 +1794,6 @@ elif [[ "${TEST_CONFIG}" == h100_distributed ]]; then
   test_h100_distributed
 elif [[ "${TEST_CONFIG}" == "h100-symm-mem" ]]; then
   test_h100_symm_mem
-elif [[ "${TEST_CONFIG}" == "b200-symm-mem" ]]; then
-  test_h100_symm_mem
 elif [[ "${TEST_CONFIG}" == h100_cutlass_backend ]]; then
   test_h100_cutlass_backend
 else

.ci/pytorch/windows/internal/static_lib_test.bat

@@ -63,7 +63,7 @@ if errorlevel 1 exit /b 1
 call %CONDA_HOME%\condabin\activate.bat testenv
 if errorlevel 1 exit /b 1
 
-call conda install  -y -q -c conda-forge libuv=1.51
+call conda install  -y -q -c conda-forge libuv=1.39
 call conda install -y -q intel-openmp
 
 echo "install and test libtorch"

.circleci/scripts/functorch_doc_push_script.sh

@@ -0,0 +1,47 @@
+#!/bin/bash
+# =================== The following code **should** be executed inside Docker container ===================
+
+# Install dependencies
+sudo apt-get -y update
+sudo apt-get -y install expect-dev
+
+# This is where the local pytorch install in the docker image is located
+pt_checkout="/var/lib/jenkins/workspace"
+source "$pt_checkout/.ci/pytorch/common_utils.sh"
+echo "functorch_doc_push_script.sh: Invoked with $*"
+
+set -ex
+
+version=${DOCS_VERSION:-nightly}
+echo "version: $version"
+
+# Build functorch docs
+pushd $pt_checkout/functorch/docs
+pip -q install -r requirements.txt
+make html
+popd
+
+git clone https://github.com/pytorch/functorch -b gh-pages --depth 1 functorch_ghpages
+pushd functorch_ghpages
+
+if [ $version == "main" ]; then
+  version=nightly
+fi
+
+git rm -rf "$version" || true
+mv "$pt_checkout/functorch/docs/build/html" "$version"
+
+git add "$version" || true
+git status
+git config user.email "soumith+bot@pytorch.org"
+git config user.name "pytorchbot"
+# If there aren't changes, don't make a commit; push is no-op
+git commit -m "Generate Python docs from pytorch/pytorch@${GITHUB_SHA}" || true
+git status
+
+if [[ "${WITH_PUSH:-}" == true ]]; then
+  git push -u origin gh-pages
+fi
+
+popd
+# =================== The above code **should** be executed inside Docker container ===================

.clang-tidy

@@ -69,8 +69,6 @@ readability-string-compare,
 '
 HeaderFilterRegex: '^(aten/|c10/|torch/).*$'
 WarningsAsErrors: '*'
-LineFilter:
-  - name: '/usr/include/.*'
 CheckOptions:
   cppcoreguidelines-special-member-functions.AllowSoleDefaultDtor: true
   cppcoreguidelines-special-member-functions.AllowImplicitlyDeletedCopyOrMove: true

.github/ISSUE_TEMPLATE/ci-sev.md

@@ -1,10 +1,6 @@
 ---
 name: "⚠️ CI SEV"
 about: Tracking incidents for PyTorch's CI infra.
-title: ''
-labels: ''
-assignees: ''
-
 ---
 
 > NOTE: Remember to label this issue with "`ci: sev`"

.github/ISSUE_TEMPLATE/disable-autorevert.md

@@ -1,18 +0,0 @@
----
-name: DISABLE AUTOREVERT
-about: Disables autorevert when open
-title: "❌​\U0001F519​ [DISABLE AUTOREVERT]"
-labels: 'ci: disable-autorevert'
-assignees: ''
-
----
-
-This issue, while open, disables the autorevert functionality.
-
-More details can be found [here](https://github.com/pytorch/test-infra/blob/main/aws/lambda/pytorch-auto-revert/README.md)
-
-
-## Why are you disabling autorevert?
-
-
-## Links to any issues/commits/errors that shows the source of problem

.github/ISSUE_TEMPLATE/disable-ci-jobs.md

@@ -1,10 +1,8 @@
 ---
 name: Disable CI jobs (PyTorch Dev Infra only)
 about: Use this template to disable CI jobs
-title: DISABLED [WORKFLOW_NAME] / [PLATFORM_NAME] / [JOB_NAME]
-labels: 'module: ci'
-assignees: ''
-
+title: "DISABLED [WORKFLOW_NAME] / [PLATFORM_NAME] / [JOB_NAME]"
+labels: "module: ci"
 ---
 
 > For example, DISABLED pull / win-vs2022-cpu-py3 / test (default). Once

.github/ci_commit_pins/vllm.txt

@@ -1 +1 @@
-0307428d65acf5cf1a73a70a7722e076bbb83f22
+9fe4c2bdb9859c14ad7f7479e1db7e01083bada3

.github/ci_commit_pins/xla.txt

@@ -1 +1 @@
-0fc62aa26a30ed7ca419d285f285cb5ba02c4394
+c77852e117bdf056c8e9a087e51d6f65cf6ba53d

.github/pytorch-probot.yml

@@ -1,44 +1,43 @@
 tracking_issue: 24422
 ciflow_tracking_issue: 64124
 ciflow_push_tags:
-- ciflow/b200
-- ciflow/b200-symm-mem
 - ciflow/binaries
 - ciflow/binaries_libtorch
 - ciflow/binaries_wheel
-- ciflow/h100
-- ciflow/h100-cutlass-backend
-- ciflow/h100-distributed
-- ciflow/h100-symm-mem
+- ciflow/triton_binaries
 - ciflow/inductor
-- ciflow/inductor-cu126
-- ciflow/inductor-micro-benchmark
-- ciflow/inductor-micro-benchmark-cpu-x86
-- ciflow/inductor-perf-compare
-- ciflow/inductor-perf-test-nightly-rocm
-- ciflow/inductor-perf-test-nightly-x86-zen
 - ciflow/inductor-periodic
 - ciflow/inductor-rocm
+- ciflow/inductor-perf-test-nightly-rocm
+- ciflow/inductor-perf-compare
+- ciflow/inductor-micro-benchmark
+- ciflow/inductor-micro-benchmark-cpu-x86
+- ciflow/inductor-perf-test-nightly-x86-zen
+- ciflow/inductor-cu126
 - ciflow/linux-aarch64
 - ciflow/mps
 - ciflow/nightly
-- ciflow/op-benchmark
 - ciflow/periodic
 - ciflow/periodic-rocm-mi300
-- ciflow/pull
 - ciflow/quantization-periodic
-- ciflow/riscv64
 - ciflow/rocm
 - ciflow/rocm-mi300
 - ciflow/s390
+- ciflow/riscv64
 - ciflow/slow
-- ciflow/torchbench
-- ciflow/triton_binaries
 - ciflow/trunk
 - ciflow/unstable
-- ciflow/vllm
-- ciflow/win-arm64
 - ciflow/xpu
+- ciflow/vllm
+- ciflow/torchbench
+- ciflow/op-benchmark
+- ciflow/pull
+- ciflow/h100
+- ciflow/h100-distributed
+- ciflow/win-arm64
+- ciflow/h100-symm-mem
+- ciflow/h100-cutlass-backend
+- ciflow/b200
 retryable_workflows:
 - pull
 - trunk
@@ -47,4 +46,4 @@ retryable_workflows:
 - inductor-A100-perf-nightly
 labeler_config: labeler.yml
 label_to_label_config: label_to_label.yml
-mergebot: true
+mergebot: True

.github/scripts/generate_binary_build_matrix.py

@@ -30,7 +30,7 @@ CUDA_ARCHES_CUDNN_VERSION = {
 }
 
 # NOTE: Please also update the ROCm sources in `PIP_SOURCES` in tools/nightly.py when changing this
-ROCM_ARCHES = ["6.4", "7.0"]
+ROCM_ARCHES = ["6.3", "6.4"]
 
 XPU_ARCHES = ["xpu"]
 
@@ -53,7 +53,7 @@ PYTORCH_EXTRA_INSTALL_REQUIREMENTS = {
         "nvidia-cusolver-cu12==11.7.1.2; platform_system == 'Linux' | "
         "nvidia-cusparse-cu12==12.5.4.2; platform_system == 'Linux' | "
         "nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' | "
-        "nvidia-nccl-cu12==2.28.3; platform_system == 'Linux' | "
+        "nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' | "
         "nvidia-nvshmem-cu12==3.3.24; platform_system == 'Linux' | "
         "nvidia-nvtx-cu12==12.6.77; platform_system == 'Linux' | "
         "nvidia-nvjitlink-cu12==12.6.85; platform_system == 'Linux' | "
@@ -70,7 +70,7 @@ PYTORCH_EXTRA_INSTALL_REQUIREMENTS = {
         "nvidia-cusolver-cu12==11.7.3.90; platform_system == 'Linux' | "
         "nvidia-cusparse-cu12==12.5.8.93; platform_system == 'Linux' | "
         "nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' | "
-        "nvidia-nccl-cu12==2.28.3; platform_system == 'Linux' | "
+        "nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' | "
         "nvidia-nvshmem-cu12==3.3.24; platform_system == 'Linux' | "
         "nvidia-nvtx-cu12==12.8.90; platform_system == 'Linux' | "
         "nvidia-nvjitlink-cu12==12.8.93; platform_system == 'Linux' | "
@@ -87,7 +87,7 @@ PYTORCH_EXTRA_INSTALL_REQUIREMENTS = {
         "nvidia-cusolver==12.0.3.29; platform_system == 'Linux' | "
         "nvidia-cusparse==12.6.2.49; platform_system == 'Linux' | "
         "nvidia-cusparselt-cu13==0.8.0; platform_system == 'Linux' | "
-        "nvidia-nccl-cu13==2.28.3; platform_system == 'Linux' | "
+        "nvidia-nccl-cu13==2.27.7; platform_system == 'Linux' | "
         "nvidia-nvshmem-cu13==3.3.24; platform_system == 'Linux' | "
         "nvidia-nvtx==13.0.39; platform_system == 'Linux' | "
         "nvidia-nvjitlink==13.0.39; platform_system == 'Linux' | "

.github/workflows/_linux-test.yml

@@ -273,8 +273,6 @@ jobs:
           TEST_CONFIG: ${{ matrix.config }}
           SHARD_NUMBER: ${{ matrix.shard }}
           NUM_TEST_SHARDS: ${{ matrix.num_shards }}
-          EXTRA_FLAGS: ${{ matrix.extra_flags || '' }}
-          OP_BENCHMARK_TESTS: ${{ matrix.op_benchmark_tests }}
           REENABLED_ISSUES: ${{ steps.keep-going.outputs.reenabled-issues }}
           CONTINUE_THROUGH_ERROR: ${{ steps.keep-going.outputs.keep-going }}
           VERBOSE_TEST_LOGS: ${{ steps.keep-going.outputs.ci-verbose-test-logs }}

.github/workflows/b200-symm-mem.yml

@@ -1,60 +0,0 @@
-name: Limited CI for symmetric memory tests on B200
-
-on:
-  pull_request:
-    paths:
-      - .github/workflows/b200-symm-mem.yml
-  workflow_dispatch:
-  push:
-    tags:
-      - ciflow/b200-symm-mem/*
-  schedule:
-    - cron: 22 8 * * *  # about 1:22am PDT
-
-concurrency:
-  group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.sha }}-${{ github.event_name == 'workflow_dispatch' }}-${{ github.event_name == 'schedule' }}
-  cancel-in-progress: true
-
-permissions:
-  id-token: write
-  contents: read
-
-jobs:
-
-  get-label-type:
-    if: github.repository_owner == 'pytorch'
-    name: get-label-type
-    uses: pytorch/pytorch/.github/workflows/_runner-determinator.yml@main
-    with:
-      triggering_actor: ${{ github.triggering_actor }}
-      issue_owner: ${{ github.event.pull_request.user.login || github.event.issue.user.login }}
-      curr_branch: ${{ github.head_ref || github.ref_name }}
-      curr_ref_type: ${{ github.ref_type }}
-
-  linux-jammy-cuda12_8-py3_10-gcc11-sm100-build-symm:
-    name: linux-jammy-cuda12.8-py3.10-gcc11-sm100-symm
-    uses: ./.github/workflows/_linux-build.yml
-    needs: get-label-type
-    with:
-      runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
-      runner: linux.12xlarge.memory
-      build-environment: linux-jammy-cuda12.8-py3.10-gcc11-sm100-symm
-      docker-image-name: ci-image:pytorch-linux-jammy-cuda12.8-cudnn9-py3-gcc11
-      cuda-arch-list: '10.0'
-      test-matrix: |
-        { include: [
-          { config: "b200-symm-mem", shard: 1, num_shards: 1, runner: "linux.dgx.b200.8" },
-        ]}
-    secrets: inherit
-
-  linux-jammy-cuda12_8-py3_10-gcc11-sm100-test:
-    name: linux-jammy-cuda12.8-py3.10-gcc11-sm100-symm
-    uses: ./.github/workflows/_linux-test.yml
-    needs:
-      - linux-jammy-cuda12_8-py3_10-gcc11-sm100-build-symm
-    with:
-      build-environment: linux-jammy-cuda12.8-py3.10-gcc11-sm100-symm
-      docker-image: ${{ needs.linux-jammy-cuda12_8-py3_10-gcc11-sm100-build-symm.outputs.docker-image }}
-      test-matrix: ${{ needs.linux-jammy-cuda12_8-py3_10-gcc11-sm100-build-symm.outputs.test-matrix }}
-      aws-role-to-assume: arn:aws:iam::308535385114:role/gha_workflow_s3_and_ecr_read_only
-    secrets: inherit

.github/workflows/build-libtorch-images.yml

@@ -52,6 +52,7 @@ jobs:
           { tag: "cuda12.9" },
           { tag: "cuda12.8" },
           { tag: "cuda12.6" },
+          { tag: "rocm6.3"  },
           { tag: "rocm6.4"  },
           { tag: "rocm7.0"  },
           { tag: "cpu"      },

.github/workflows/build-magma-rocm-linux.yml

@@ -34,7 +34,7 @@ jobs:
       id-token: write
     strategy:
       matrix:
-        rocm_version: ["70", "64"]
+        rocm_version: ["70", "64", "63"]
     steps:
       - name: Checkout PyTorch
         uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2

.github/workflows/build-manywheel-images.yml

@@ -52,6 +52,7 @@ jobs:
           { name: "manylinuxaarch64-builder",       tag: "cuda13.0",          runner: "linux.arm64.2xlarge.ephemeral" },
           { name: "manylinuxaarch64-builder",       tag: "cuda12.8",          runner: "linux.arm64.2xlarge.ephemeral" },
           { name: "manylinuxaarch64-builder",       tag: "cuda12.6",          runner: "linux.arm64.2xlarge.ephemeral" },
+          { name: "manylinux2_28-builder",          tag: "rocm6.3",           runner: "linux.9xlarge.ephemeral" },
           { name: "manylinux2_28-builder",          tag: "rocm6.4",           runner: "linux.9xlarge.ephemeral" },
           { name: "manylinux2_28-builder",          tag: "rocm7.0",           runner: "linux.9xlarge.ephemeral" },
           { name: "manylinux2_28-builder",          tag: "cpu",               runner: "linux.9xlarge.ephemeral" },

.github/workflows/build-triton-wheel.yml

@@ -55,7 +55,7 @@ jobs:
         docker-image: ["pytorch/manylinux2_28-builder:cpu"]
         include:
           - device: "rocm"
-            rocm_version: "7.0"
+            rocm_version: "6.4"
             runs_on: "${{ needs.get-label-type.outputs.label-type }}linux.4xlarge"
           - device: "cuda"
             rocm_version: ""

.github/workflows/generated-linux-aarch64-binary-manywheel-nightly.yml

@@ -132,7 +132,7 @@ jobs:
       ALPINE_IMAGE: "arm64v8/alpine"
       build_name: manywheel-py3_10-cuda-aarch64-12_6
       build_environment: linux-aarch64-binary-manywheel
-      PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.6.77; platform_system == 'Linux' | nvidia-cuda-runtime-cu12==12.6.77; platform_system == 'Linux' | nvidia-cuda-cupti-cu12==12.6.80; platform_system == 'Linux' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' | nvidia-cublas-cu12==12.6.4.1; platform_system == 'Linux' | nvidia-cufft-cu12==11.3.0.4; platform_system == 'Linux' | nvidia-curand-cu12==10.3.7.77; platform_system == 'Linux' | nvidia-cusolver-cu12==11.7.1.2; platform_system == 'Linux' | nvidia-cusparse-cu12==12.5.4.2; platform_system == 'Linux' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' | nvidia-nccl-cu12==2.28.3; platform_system == 'Linux' | nvidia-nvshmem-cu12==3.3.24; platform_system == 'Linux' | nvidia-nvtx-cu12==12.6.77; platform_system == 'Linux' | nvidia-nvjitlink-cu12==12.6.85; platform_system == 'Linux' | nvidia-cufile-cu12==1.11.1.6; platform_system == 'Linux'
+      PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.6.77; platform_system == 'Linux' | nvidia-cuda-runtime-cu12==12.6.77; platform_system == 'Linux' | nvidia-cuda-cupti-cu12==12.6.80; platform_system == 'Linux' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' | nvidia-cublas-cu12==12.6.4.1; platform_system == 'Linux' | nvidia-cufft-cu12==11.3.0.4; platform_system == 'Linux' | nvidia-curand-cu12==10.3.7.77; platform_system == 'Linux' | nvidia-cusolver-cu12==11.7.1.2; platform_system == 'Linux' | nvidia-cusparse-cu12==12.5.4.2; platform_system == 'Linux' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' | nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' | nvidia-nvshmem-cu12==3.3.24; platform_system == 'Linux' | nvidia-nvtx-cu12==12.6.77; platform_system == 'Linux' | nvidia-nvjitlink-cu12==12.6.85; platform_system == 'Linux' | nvidia-cufile-cu12==1.11.1.6; platform_system == 'Linux'
       timeout-minutes: 420
     secrets:
       github-token: ${{ secrets.GITHUB_TOKEN }}
@@ -178,7 +178,7 @@ jobs:
       ALPINE_IMAGE: "arm64v8/alpine"
       build_name: manywheel-py3_10-cuda-aarch64-12_8
       build_environment: linux-aarch64-binary-manywheel
-      PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.8.93; platform_system == 'Linux' | nvidia-cuda-runtime-cu12==12.8.90; platform_system == 'Linux' | nvidia-cuda-cupti-cu12==12.8.90; platform_system == 'Linux' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' | nvidia-cublas-cu12==12.8.4.1; platform_system == 'Linux' | nvidia-cufft-cu12==11.3.3.83; platform_system == 'Linux' | nvidia-curand-cu12==10.3.9.90; platform_system == 'Linux' | nvidia-cusolver-cu12==11.7.3.90; platform_system == 'Linux' | nvidia-cusparse-cu12==12.5.8.93; platform_system == 'Linux' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' | nvidia-nccl-cu12==2.28.3; platform_system == 'Linux' | nvidia-nvshmem-cu12==3.3.24; platform_system == 'Linux' | nvidia-nvtx-cu12==12.8.90; platform_system == 'Linux' | nvidia-nvjitlink-cu12==12.8.93; platform_system == 'Linux' | nvidia-cufile-cu12==1.13.1.3; platform_system == 'Linux'
+      PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.8.93; platform_system == 'Linux' | nvidia-cuda-runtime-cu12==12.8.90; platform_system == 'Linux' | nvidia-cuda-cupti-cu12==12.8.90; platform_system == 'Linux' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' | nvidia-cublas-cu12==12.8.4.1; platform_system == 'Linux' | nvidia-cufft-cu12==11.3.3.83; platform_system == 'Linux' | nvidia-curand-cu12==10.3.9.90; platform_system == 'Linux' | nvidia-cusolver-cu12==11.7.3.90; platform_system == 'Linux' | nvidia-cusparse-cu12==12.5.8.93; platform_system == 'Linux' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' | nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' | nvidia-nvshmem-cu12==3.3.24; platform_system == 'Linux' | nvidia-nvtx-cu12==12.8.90; platform_system == 'Linux' | nvidia-nvjitlink-cu12==12.8.93; platform_system == 'Linux' | nvidia-cufile-cu12==1.13.1.3; platform_system == 'Linux'
       timeout-minutes: 420
     secrets:
       github-token: ${{ secrets.GITHUB_TOKEN }}
@@ -224,7 +224,7 @@ jobs:
       ALPINE_IMAGE: "arm64v8/alpine"
       build_name: manywheel-py3_10-cuda-aarch64-13_0
       build_environment: linux-aarch64-binary-manywheel
-      PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc==13.0.48; platform_system == 'Linux' | nvidia-cuda-runtime==13.0.48; platform_system == 'Linux' | nvidia-cuda-cupti==13.0.48; platform_system == 'Linux' | nvidia-cudnn-cu13==9.13.0.50; platform_system == 'Linux' | nvidia-cublas==13.0.0.19; platform_system == 'Linux' | nvidia-cufft==12.0.0.15; platform_system == 'Linux' | nvidia-curand==10.4.0.35; platform_system == 'Linux' | nvidia-cusolver==12.0.3.29; platform_system == 'Linux' | nvidia-cusparse==12.6.2.49; platform_system == 'Linux' | nvidia-cusparselt-cu13==0.8.0; platform_system == 'Linux' | nvidia-nccl-cu13==2.28.3; platform_system == 'Linux' | nvidia-nvshmem-cu13==3.3.24; platform_system == 'Linux' | nvidia-nvtx==13.0.39; platform_system == 'Linux' | nvidia-nvjitlink==13.0.39; platform_system == 'Linux' | nvidia-cufile==1.15.0.42; platform_system == 'Linux'
+      PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc==13.0.48; platform_system == 'Linux' | nvidia-cuda-runtime==13.0.48; platform_system == 'Linux' | nvidia-cuda-cupti==13.0.48; platform_system == 'Linux' | nvidia-cudnn-cu13==9.13.0.50; platform_system == 'Linux' | nvidia-cublas==13.0.0.19; platform_system == 'Linux' | nvidia-cufft==12.0.0.15; platform_system == 'Linux' | nvidia-curand==10.4.0.35; platform_system == 'Linux' | nvidia-cusolver==12.0.3.29; platform_system == 'Linux' | nvidia-cusparse==12.6.2.49; platform_system == 'Linux' | nvidia-cusparselt-cu13==0.8.0; platform_system == 'Linux' | nvidia-nccl-cu13==2.27.7; platform_system == 'Linux' | nvidia-nvshmem-cu13==3.3.24; platform_system == 'Linux' | nvidia-nvtx==13.0.39; platform_system == 'Linux' | nvidia-nvjitlink==13.0.39; platform_system == 'Linux' | nvidia-cufile==1.15.0.42; platform_system == 'Linux'
       timeout-minutes: 420
     secrets:
       github-token: ${{ secrets.GITHUB_TOKEN }}
@@ -335,7 +335,7 @@ jobs:
       ALPINE_IMAGE: "arm64v8/alpine"
       build_name: manywheel-py3_11-cuda-aarch64-12_6
       build_environment: linux-aarch64-binary-manywheel
-      PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.6.77; platform_system == 'Linux' | nvidia-cuda-runtime-cu12==12.6.77; platform_system == 'Linux' | nvidia-cuda-cupti-cu12==12.6.80; platform_system == 'Linux' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' | nvidia-cublas-cu12==12.6.4.1; platform_system == 'Linux' | nvidia-cufft-cu12==11.3.0.4; platform_system == 'Linux' | nvidia-curand-cu12==10.3.7.77; platform_system == 'Linux' | nvidia-cusolver-cu12==11.7.1.2; platform_system == 'Linux' | nvidia-cusparse-cu12==12.5.4.2; platform_system == 'Linux' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' | nvidia-nccl-cu12==2.28.3; platform_system == 'Linux' | nvidia-nvshmem-cu12==3.3.24; platform_system == 'Linux' | nvidia-nvtx-cu12==12.6.77; platform_system == 'Linux' | nvidia-nvjitlink-cu12==12.6.85; platform_system == 'Linux' | nvidia-cufile-cu12==1.11.1.6; platform_system == 'Linux'
+      PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.6.77; platform_system == 'Linux' | nvidia-cuda-runtime-cu12==12.6.77; platform_system == 'Linux' | nvidia-cuda-cupti-cu12==12.6.80; platform_system == 'Linux' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' | nvidia-cublas-cu12==12.6.4.1; platform_system == 'Linux' | nvidia-cufft-cu12==11.3.0.4; platform_system == 'Linux' | nvidia-curand-cu12==10.3.7.77; platform_system == 'Linux' | nvidia-cusolver-cu12==11.7.1.2; platform_system == 'Linux' | nvidia-cusparse-cu12==12.5.4.2; platform_system == 'Linux' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' | nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' | nvidia-nvshmem-cu12==3.3.24; platform_system == 'Linux' | nvidia-nvtx-cu12==12.6.77; platform_system == 'Linux' | nvidia-nvjitlink-cu12==12.6.85; platform_system == 'Linux' | nvidia-cufile-cu12==1.11.1.6; platform_system == 'Linux'
       timeout-minutes: 420
     secrets:
       github-token: ${{ secrets.GITHUB_TOKEN }}
@@ -381,7 +381,7 @@ jobs:
       ALPINE_IMAGE: "arm64v8/alpine"
       build_name: manywheel-py3_11-cuda-aarch64-12_8
       build_environment: linux-aarch64-binary-manywheel
-      PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.8.93; platform_system == 'Linux' | nvidia-cuda-runtime-cu12==12.8.90; platform_system == 'Linux' | nvidia-cuda-cupti-cu12==12.8.90; platform_system == 'Linux' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' | nvidia-cublas-cu12==12.8.4.1; platform_system == 'Linux' | nvidia-cufft-cu12==11.3.3.83; platform_system == 'Linux' | nvidia-curand-cu12==10.3.9.90; platform_system == 'Linux' | nvidia-cusolver-cu12==11.7.3.90; platform_system == 'Linux' | nvidia-cusparse-cu12==12.5.8.93; platform_system == 'Linux' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' | nvidia-nccl-cu12==2.28.3; platform_system == 'Linux' | nvidia-nvshmem-cu12==3.3.24; platform_system == 'Linux' | nvidia-nvtx-cu12==12.8.90; platform_system == 'Linux' | nvidia-nvjitlink-cu12==12.8.93; platform_system == 'Linux' | nvidia-cufile-cu12==1.13.1.3; platform_system == 'Linux'
+      PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.8.93; platform_system == 'Linux' | nvidia-cuda-runtime-cu12==12.8.90; platform_system == 'Linux' | nvidia-cuda-cupti-cu12==12.8.90; platform_system == 'Linux' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' | nvidia-cublas-cu12==12.8.4.1; platform_system == 'Linux' | nvidia-cufft-cu12==11.3.3.83; platform_system == 'Linux' | nvidia-curand-cu12==10.3.9.90; platform_system == 'Linux' | nvidia-cusolver-cu12==11.7.3.90; platform_system == 'Linux' | nvidia-cusparse-cu12==12.5.8.93; platform_system == 'Linux' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' | nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' | nvidia-nvshmem-cu12==3.3.24; platform_system == 'Linux' | nvidia-nvtx-cu12==12.8.90; platform_system == 'Linux' | nvidia-nvjitlink-cu12==12.8.93; platform_system == 'Linux' | nvidia-cufile-cu12==1.13.1.3; platform_system == 'Linux'
       timeout-minutes: 420
     secrets:
       github-token: ${{ secrets.GITHUB_TOKEN }}
@@ -427,7 +427,7 @@ jobs:
       ALPINE_IMAGE: "arm64v8/alpine"
       build_name: manywheel-py3_11-cuda-aarch64-13_0
       build_environment: linux-aarch64-binary-manywheel
-      PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc==13.0.48; platform_system == 'Linux' | nvidia-cuda-runtime==13.0.48; platform_system == 'Linux' | nvidia-cuda-cupti==13.0.48; platform_system == 'Linux' | nvidia-cudnn-cu13==9.13.0.50; platform_system == 'Linux' | nvidia-cublas==13.0.0.19; platform_system == 'Linux' | nvidia-cufft==12.0.0.15; platform_system == 'Linux' | nvidia-curand==10.4.0.35; platform_system == 'Linux' | nvidia-cusolver==12.0.3.29; platform_system == 'Linux' | nvidia-cusparse==12.6.2.49; platform_system == 'Linux' | nvidia-cusparselt-cu13==0.8.0; platform_system == 'Linux' | nvidia-nccl-cu13==2.28.3; platform_system == 'Linux' | nvidia-nvshmem-cu13==3.3.24; platform_system == 'Linux' | nvidia-nvtx==13.0.39; platform_system == 'Linux' | nvidia-nvjitlink==13.0.39; platform_system == 'Linux' | nvidia-cufile==1.15.0.42; platform_system == 'Linux'
+      PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc==13.0.48; platform_system == 'Linux' | nvidia-cuda-runtime==13.0.48; platform_system == 'Linux' | nvidia-cuda-cupti==13.0.48; platform_system == 'Linux' | nvidia-cudnn-cu13==9.13.0.50; platform_system == 'Linux' | nvidia-cublas==13.0.0.19; platform_system == 'Linux' | nvidia-cufft==12.0.0.15; platform_system == 'Linux' | nvidia-curand==10.4.0.35; platform_system == 'Linux' | nvidia-cusolver==12.0.3.29; platform_system == 'Linux' | nvidia-cusparse==12.6.2.49; platform_system == 'Linux' | nvidia-cusparselt-cu13==0.8.0; platform_system == 'Linux' | nvidia-nccl-cu13==2.27.7; platform_system == 'Linux' | nvidia-nvshmem-cu13==3.3.24; platform_system == 'Linux' | nvidia-nvtx==13.0.39; platform_system == 'Linux' | nvidia-nvjitlink==13.0.39; platform_system == 'Linux' | nvidia-cufile==1.15.0.42; platform_system == 'Linux'
       timeout-minutes: 420
     secrets:
       github-token: ${{ secrets.GITHUB_TOKEN }}
@@ -538,7 +538,7 @@ jobs:
       ALPINE_IMAGE: "arm64v8/alpine"
       build_name: manywheel-py3_12-cuda-aarch64-12_6
       build_environment: linux-aarch64-binary-manywheel
-      PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.6.77; platform_system == 'Linux' | nvidia-cuda-runtime-cu12==12.6.77; platform_system == 'Linux' | nvidia-cuda-cupti-cu12==12.6.80; platform_system == 'Linux' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' | nvidia-cublas-cu12==12.6.4.1; platform_system == 'Linux' | nvidia-cufft-cu12==11.3.0.4; platform_system == 'Linux' | nvidia-curand-cu12==10.3.7.77; platform_system == 'Linux' | nvidia-cusolver-cu12==11.7.1.2; platform_system == 'Linux' | nvidia-cusparse-cu12==12.5.4.2; platform_system == 'Linux' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' | nvidia-nccl-cu12==2.28.3; platform_system == 'Linux' | nvidia-nvshmem-cu12==3.3.24; platform_system == 'Linux' | nvidia-nvtx-cu12==12.6.77; platform_system == 'Linux' | nvidia-nvjitlink-cu12==12.6.85; platform_system == 'Linux' | nvidia-cufile-cu12==1.11.1.6; platform_system == 'Linux'
+      PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.6.77; platform_system == 'Linux' | nvidia-cuda-runtime-cu12==12.6.77; platform_system == 'Linux' | nvidia-cuda-cupti-cu12==12.6.80; platform_system == 'Linux' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' | nvidia-cublas-cu12==12.6.4.1; platform_system == 'Linux' | nvidia-cufft-cu12==11.3.0.4; platform_system == 'Linux' | nvidia-curand-cu12==10.3.7.77; platform_system == 'Linux' | nvidia-cusolver-cu12==11.7.1.2; platform_system == 'Linux' | nvidia-cusparse-cu12==12.5.4.2; platform_system == 'Linux' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' | nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' | nvidia-nvshmem-cu12==3.3.24; platform_system == 'Linux' | nvidia-nvtx-cu12==12.6.77; platform_system == 'Linux' | nvidia-nvjitlink-cu12==12.6.85; platform_system == 'Linux' | nvidia-cufile-cu12==1.11.1.6; platform_system == 'Linux'
       timeout-minutes: 420
     secrets:
       github-token: ${{ secrets.GITHUB_TOKEN }}
@@ -584,7 +584,7 @@ jobs:
       ALPINE_IMAGE: "arm64v8/alpine"
       build_name: manywheel-py3_12-cuda-aarch64-12_8
       build_environment: linux-aarch64-binary-manywheel
-      PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.8.93; platform_system == 'Linux' | nvidia-cuda-runtime-cu12==12.8.90; platform_system == 'Linux' | nvidia-cuda-cupti-cu12==12.8.90; platform_system == 'Linux' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' | nvidia-cublas-cu12==12.8.4.1; platform_system == 'Linux' | nvidia-cufft-cu12==11.3.3.83; platform_system == 'Linux' | nvidia-curand-cu12==10.3.9.90; platform_system == 'Linux' | nvidia-cusolver-cu12==11.7.3.90; platform_system == 'Linux' | nvidia-cusparse-cu12==12.5.8.93; platform_system == 'Linux' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' | nvidia-nccl-cu12==2.28.3; platform_system == 'Linux' | nvidia-nvshmem-cu12==3.3.24; platform_system == 'Linux' | nvidia-nvtx-cu12==12.8.90; platform_system == 'Linux' | nvidia-nvjitlink-cu12==12.8.93; platform_system == 'Linux' | nvidia-cufile-cu12==1.13.1.3; platform_system == 'Linux'
+      PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.8.93; platform_system == 'Linux' | nvidia-cuda-runtime-cu12==12.8.90; platform_system == 'Linux' | nvidia-cuda-cupti-cu12==12.8.90; platform_system == 'Linux' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' | nvidia-cublas-cu12==12.8.4.1; platform_system == 'Linux' | nvidia-cufft-cu12==11.3.3.83; platform_system == 'Linux' | nvidia-curand-cu12==10.3.9.90; platform_system == 'Linux' | nvidia-cusolver-cu12==11.7.3.90; platform_system == 'Linux' | nvidia-cusparse-cu12==12.5.8.93; platform_system == 'Linux' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' | nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' | nvidia-nvshmem-cu12==3.3.24; platform_system == 'Linux' | nvidia-nvtx-cu12==12.8.90; platform_system == 'Linux' | nvidia-nvjitlink-cu12==12.8.93; platform_system == 'Linux' | nvidia-cufile-cu12==1.13.1.3; platform_system == 'Linux'
       timeout-minutes: 420
     secrets:
       github-token: ${{ secrets.GITHUB_TOKEN }}
@@ -630,7 +630,7 @@ jobs:
       ALPINE_IMAGE: "arm64v8/alpine"
       build_name: manywheel-py3_12-cuda-aarch64-13_0
       build_environment: linux-aarch64-binary-manywheel
-      PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc==13.0.48; platform_system == 'Linux' | nvidia-cuda-runtime==13.0.48; platform_system == 'Linux' | nvidia-cuda-cupti==13.0.48; platform_system == 'Linux' | nvidia-cudnn-cu13==9.13.0.50; platform_system == 'Linux' | nvidia-cublas==13.0.0.19; platform_system == 'Linux' | nvidia-cufft==12.0.0.15; platform_system == 'Linux' | nvidia-curand==10.4.0.35; platform_system == 'Linux' | nvidia-cusolver==12.0.3.29; platform_system == 'Linux' | nvidia-cusparse==12.6.2.49; platform_system == 'Linux' | nvidia-cusparselt-cu13==0.8.0; platform_system == 'Linux' | nvidia-nccl-cu13==2.28.3; platform_system == 'Linux' | nvidia-nvshmem-cu13==3.3.24; platform_system == 'Linux' | nvidia-nvtx==13.0.39; platform_system == 'Linux' | nvidia-nvjitlink==13.0.39; platform_system == 'Linux' | nvidia-cufile==1.15.0.42; platform_system == 'Linux'
+      PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc==13.0.48; platform_system == 'Linux' | nvidia-cuda-runtime==13.0.48; platform_system == 'Linux' | nvidia-cuda-cupti==13.0.48; platform_system == 'Linux' | nvidia-cudnn-cu13==9.13.0.50; platform_system == 'Linux' | nvidia-cublas==13.0.0.19; platform_system == 'Linux' | nvidia-cufft==12.0.0.15; platform_system == 'Linux' | nvidia-curand==10.4.0.35; platform_system == 'Linux' | nvidia-cusolver==12.0.3.29; platform_system == 'Linux' | nvidia-cusparse==12.6.2.49; platform_system == 'Linux' | nvidia-cusparselt-cu13==0.8.0; platform_system == 'Linux' | nvidia-nccl-cu13==2.27.7; platform_system == 'Linux' | nvidia-nvshmem-cu13==3.3.24; platform_system == 'Linux' | nvidia-nvtx==13.0.39; platform_system == 'Linux' | nvidia-nvjitlink==13.0.39; platform_system == 'Linux' | nvidia-cufile==1.15.0.42; platform_system == 'Linux'
       timeout-minutes: 420
     secrets:
       github-token: ${{ secrets.GITHUB_TOKEN }}
@@ -741,7 +741,7 @@ jobs:
       ALPINE_IMAGE: "arm64v8/alpine"
       build_name: manywheel-py3_13-cuda-aarch64-12_6
       build_environment: linux-aarch64-binary-manywheel
-      PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.6.77; platform_system == 'Linux' | nvidia-cuda-runtime-cu12==12.6.77; platform_system == 'Linux' | nvidia-cuda-cupti-cu12==12.6.80; platform_system == 'Linux' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' | nvidia-cublas-cu12==12.6.4.1; platform_system == 'Linux' | nvidia-cufft-cu12==11.3.0.4; platform_system == 'Linux' | nvidia-curand-cu12==10.3.7.77; platform_system == 'Linux' | nvidia-cusolver-cu12==11.7.1.2; platform_system == 'Linux' | nvidia-cusparse-cu12==12.5.4.2; platform_system == 'Linux' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' | nvidia-nccl-cu12==2.28.3; platform_system == 'Linux' | nvidia-nvshmem-cu12==3.3.24; platform_system == 'Linux' | nvidia-nvtx-cu12==12.6.77; platform_system == 'Linux' | nvidia-nvjitlink-cu12==12.6.85; platform_system == 'Linux' | nvidia-cufile-cu12==1.11.1.6; platform_system == 'Linux'
+      PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.6.77; platform_system == 'Linux' | nvidia-cuda-runtime-cu12==12.6.77; platform_system == 'Linux' | nvidia-cuda-cupti-cu12==12.6.80; platform_system == 'Linux' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' | nvidia-cublas-cu12==12.6.4.1; platform_system == 'Linux' | nvidia-cufft-cu12==11.3.0.4; platform_system == 'Linux' | nvidia-curand-cu12==10.3.7.77; platform_system == 'Linux' | nvidia-cusolver-cu12==11.7.1.2; platform_system == 'Linux' | nvidia-cusparse-cu12==12.5.4.2; platform_system == 'Linux' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' | nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' | nvidia-nvshmem-cu12==3.3.24; platform_system == 'Linux' | nvidia-nvtx-cu12==12.6.77; platform_system == 'Linux' | nvidia-nvjitlink-cu12==12.6.85; platform_system == 'Linux' | nvidia-cufile-cu12==1.11.1.6; platform_system == 'Linux'
       timeout-minutes: 420
     secrets:
       github-token: ${{ secrets.GITHUB_TOKEN }}
@@ -787,7 +787,7 @@ jobs:
       ALPINE_IMAGE: "arm64v8/alpine"
       build_name: manywheel-py3_13-cuda-aarch64-12_8
       build_environment: linux-aarch64-binary-manywheel
-      PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.8.93; platform_system == 'Linux' | nvidia-cuda-runtime-cu12==12.8.90; platform_system == 'Linux' | nvidia-cuda-cupti-cu12==12.8.90; platform_system == 'Linux' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' | nvidia-cublas-cu12==12.8.4.1; platform_system == 'Linux' | nvidia-cufft-cu12==11.3.3.83; platform_system == 'Linux' | nvidia-curand-cu12==10.3.9.90; platform_system == 'Linux' | nvidia-cusolver-cu12==11.7.3.90; platform_system == 'Linux' | nvidia-cusparse-cu12==12.5.8.93; platform_system == 'Linux' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' | nvidia-nccl-cu12==2.28.3; platform_system == 'Linux' | nvidia-nvshmem-cu12==3.3.24; platform_system == 'Linux' | nvidia-nvtx-cu12==12.8.90; platform_system == 'Linux' | nvidia-nvjitlink-cu12==12.8.93; platform_system == 'Linux' | nvidia-cufile-cu12==1.13.1.3; platform_system == 'Linux'
+      PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.8.93; platform_system == 'Linux' | nvidia-cuda-runtime-cu12==12.8.90; platform_system == 'Linux' | nvidia-cuda-cupti-cu12==12.8.90; platform_system == 'Linux' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' | nvidia-cublas-cu12==12.8.4.1; platform_system == 'Linux' | nvidia-cufft-cu12==11.3.3.83; platform_system == 'Linux' | nvidia-curand-cu12==10.3.9.90; platform_system == 'Linux' | nvidia-cusolver-cu12==11.7.3.90; platform_system == 'Linux' | nvidia-cusparse-cu12==12.5.8.93; platform_system == 'Linux' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' | nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' | nvidia-nvshmem-cu12==3.3.24; platform_system == 'Linux' | nvidia-nvtx-cu12==12.8.90; platform_system == 'Linux' | nvidia-nvjitlink-cu12==12.8.93; platform_system == 'Linux' | nvidia-cufile-cu12==1.13.1.3; platform_system == 'Linux'
       timeout-minutes: 420
     secrets:
       github-token: ${{ secrets.GITHUB_TOKEN }}
@@ -833,7 +833,7 @@ jobs:
       ALPINE_IMAGE: "arm64v8/alpine"
       build_name: manywheel-py3_13-cuda-aarch64-13_0
       build_environment: linux-aarch64-binary-manywheel
-      PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc==13.0.48; platform_system == 'Linux' | nvidia-cuda-runtime==13.0.48; platform_system == 'Linux' | nvidia-cuda-cupti==13.0.48; platform_system == 'Linux' | nvidia-cudnn-cu13==9.13.0.50; platform_system == 'Linux' | nvidia-cublas==13.0.0.19; platform_system == 'Linux' | nvidia-cufft==12.0.0.15; platform_system == 'Linux' | nvidia-curand==10.4.0.35; platform_system == 'Linux' | nvidia-cusolver==12.0.3.29; platform_system == 'Linux' | nvidia-cusparse==12.6.2.49; platform_system == 'Linux' | nvidia-cusparselt-cu13==0.8.0; platform_system == 'Linux' | nvidia-nccl-cu13==2.28.3; platform_system == 'Linux' | nvidia-nvshmem-cu13==3.3.24; platform_system == 'Linux' | nvidia-nvtx==13.0.39; platform_system == 'Linux' | nvidia-nvjitlink==13.0.39; platform_system == 'Linux' | nvidia-cufile==1.15.0.42; platform_system == 'Linux'
+      PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc==13.0.48; platform_system == 'Linux' | nvidia-cuda-runtime==13.0.48; platform_system == 'Linux' | nvidia-cuda-cupti==13.0.48; platform_system == 'Linux' | nvidia-cudnn-cu13==9.13.0.50; platform_system == 'Linux' | nvidia-cublas==13.0.0.19; platform_system == 'Linux' | nvidia-cufft==12.0.0.15; platform_system == 'Linux' | nvidia-curand==10.4.0.35; platform_system == 'Linux' | nvidia-cusolver==12.0.3.29; platform_system == 'Linux' | nvidia-cusparse==12.6.2.49; platform_system == 'Linux' | nvidia-cusparselt-cu13==0.8.0; platform_system == 'Linux' | nvidia-nccl-cu13==2.27.7; platform_system == 'Linux' | nvidia-nvshmem-cu13==3.3.24; platform_system == 'Linux' | nvidia-nvtx==13.0.39; platform_system == 'Linux' | nvidia-nvjitlink==13.0.39; platform_system == 'Linux' | nvidia-cufile==1.15.0.42; platform_system == 'Linux'
       timeout-minutes: 420
     secrets:
       github-token: ${{ secrets.GITHUB_TOKEN }}
@@ -944,7 +944,7 @@ jobs:
       ALPINE_IMAGE: "arm64v8/alpine"
       build_name: manywheel-py3_13t-cuda-aarch64-12_6
       build_environment: linux-aarch64-binary-manywheel
-      PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.6.77; platform_system == 'Linux' | nvidia-cuda-runtime-cu12==12.6.77; platform_system == 'Linux' | nvidia-cuda-cupti-cu12==12.6.80; platform_system == 'Linux' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' | nvidia-cublas-cu12==12.6.4.1; platform_system == 'Linux' | nvidia-cufft-cu12==11.3.0.4; platform_system == 'Linux' | nvidia-curand-cu12==10.3.7.77; platform_system == 'Linux' | nvidia-cusolver-cu12==11.7.1.2; platform_system == 'Linux' | nvidia-cusparse-cu12==12.5.4.2; platform_system == 'Linux' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' | nvidia-nccl-cu12==2.28.3; platform_system == 'Linux' | nvidia-nvshmem-cu12==3.3.24; platform_system == 'Linux' | nvidia-nvtx-cu12==12.6.77; platform_system == 'Linux' | nvidia-nvjitlink-cu12==12.6.85; platform_system == 'Linux' | nvidia-cufile-cu12==1.11.1.6; platform_system == 'Linux'
+      PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.6.77; platform_system == 'Linux' | nvidia-cuda-runtime-cu12==12.6.77; platform_system == 'Linux' | nvidia-cuda-cupti-cu12==12.6.80; platform_system == 'Linux' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' | nvidia-cublas-cu12==12.6.4.1; platform_system == 'Linux' | nvidia-cufft-cu12==11.3.0.4; platform_system == 'Linux' | nvidia-curand-cu12==10.3.7.77; platform_system == 'Linux' | nvidia-cusolver-cu12==11.7.1.2; platform_system == 'Linux' | nvidia-cusparse-cu12==12.5.4.2; platform_system == 'Linux' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' | nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' | nvidia-nvshmem-cu12==3.3.24; platform_system == 'Linux' | nvidia-nvtx-cu12==12.6.77; platform_system == 'Linux' | nvidia-nvjitlink-cu12==12.6.85; platform_system == 'Linux' | nvidia-cufile-cu12==1.11.1.6; platform_system == 'Linux'
       timeout-minutes: 420
     secrets:
       github-token: ${{ secrets.GITHUB_TOKEN }}
@@ -990,7 +990,7 @@ jobs:
       ALPINE_IMAGE: "arm64v8/alpine"
       build_name: manywheel-py3_13t-cuda-aarch64-12_8
       build_environment: linux-aarch64-binary-manywheel
-      PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.8.93; platform_system == 'Linux' | nvidia-cuda-runtime-cu12==12.8.90; platform_system == 'Linux' | nvidia-cuda-cupti-cu12==12.8.90; platform_system == 'Linux' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' | nvidia-cublas-cu12==12.8.4.1; platform_system == 'Linux' | nvidia-cufft-cu12==11.3.3.83; platform_system == 'Linux' | nvidia-curand-cu12==10.3.9.90; platform_system == 'Linux' | nvidia-cusolver-cu12==11.7.3.90; platform_system == 'Linux' | nvidia-cusparse-cu12==12.5.8.93; platform_system == 'Linux' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' | nvidia-nccl-cu12==2.28.3; platform_system == 'Linux' | nvidia-nvshmem-cu12==3.3.24; platform_system == 'Linux' | nvidia-nvtx-cu12==12.8.90; platform_system == 'Linux' | nvidia-nvjitlink-cu12==12.8.93; platform_system == 'Linux' | nvidia-cufile-cu12==1.13.1.3; platform_system == 'Linux'
+      PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.8.93; platform_system == 'Linux' | nvidia-cuda-runtime-cu12==12.8.90; platform_system == 'Linux' | nvidia-cuda-cupti-cu12==12.8.90; platform_system == 'Linux' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' | nvidia-cublas-cu12==12.8.4.1; platform_system == 'Linux' | nvidia-cufft-cu12==11.3.3.83; platform_system == 'Linux' | nvidia-curand-cu12==10.3.9.90; platform_system == 'Linux' | nvidia-cusolver-cu12==11.7.3.90; platform_system == 'Linux' | nvidia-cusparse-cu12==12.5.8.93; platform_system == 'Linux' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' | nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' | nvidia-nvshmem-cu12==3.3.24; platform_system == 'Linux' | nvidia-nvtx-cu12==12.8.90; platform_system == 'Linux' | nvidia-nvjitlink-cu12==12.8.93; platform_system == 'Linux' | nvidia-cufile-cu12==1.13.1.3; platform_system == 'Linux'
       timeout-minutes: 420
     secrets:
       github-token: ${{ secrets.GITHUB_TOKEN }}
@@ -1036,7 +1036,7 @@ jobs:
       ALPINE_IMAGE: "arm64v8/alpine"
       build_name: manywheel-py3_13t-cuda-aarch64-13_0
       build_environment: linux-aarch64-binary-manywheel
-      PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc==13.0.48; platform_system == 'Linux' | nvidia-cuda-runtime==13.0.48; platform_system == 'Linux' | nvidia-cuda-cupti==13.0.48; platform_system == 'Linux' | nvidia-cudnn-cu13==9.13.0.50; platform_system == 'Linux' | nvidia-cublas==13.0.0.19; platform_system == 'Linux' | nvidia-cufft==12.0.0.15; platform_system == 'Linux' | nvidia-curand==10.4.0.35; platform_system == 'Linux' | nvidia-cusolver==12.0.3.29; platform_system == 'Linux' | nvidia-cusparse==12.6.2.49; platform_system == 'Linux' | nvidia-cusparselt-cu13==0.8.0; platform_system == 'Linux' | nvidia-nccl-cu13==2.28.3; platform_system == 'Linux' | nvidia-nvshmem-cu13==3.3.24; platform_system == 'Linux' | nvidia-nvtx==13.0.39; platform_system == 'Linux' | nvidia-nvjitlink==13.0.39; platform_system == 'Linux' | nvidia-cufile==1.15.0.42; platform_system == 'Linux'
+      PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc==13.0.48; platform_system == 'Linux' | nvidia-cuda-runtime==13.0.48; platform_system == 'Linux' | nvidia-cuda-cupti==13.0.48; platform_system == 'Linux' | nvidia-cudnn-cu13==9.13.0.50; platform_system == 'Linux' | nvidia-cublas==13.0.0.19; platform_system == 'Linux' | nvidia-cufft==12.0.0.15; platform_system == 'Linux' | nvidia-curand==10.4.0.35; platform_system == 'Linux' | nvidia-cusolver==12.0.3.29; platform_system == 'Linux' | nvidia-cusparse==12.6.2.49; platform_system == 'Linux' | nvidia-cusparselt-cu13==0.8.0; platform_system == 'Linux' | nvidia-nccl-cu13==2.27.7; platform_system == 'Linux' | nvidia-nvshmem-cu13==3.3.24; platform_system == 'Linux' | nvidia-nvtx==13.0.39; platform_system == 'Linux' | nvidia-nvjitlink==13.0.39; platform_system == 'Linux' | nvidia-cufile==1.15.0.42; platform_system == 'Linux'
       timeout-minutes: 420
     secrets:
       github-token: ${{ secrets.GITHUB_TOKEN }}
@@ -1147,7 +1147,7 @@ jobs:
       ALPINE_IMAGE: "arm64v8/alpine"
       build_name: manywheel-py3_14-cuda-aarch64-12_6
       build_environment: linux-aarch64-binary-manywheel
-      PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.6.77; platform_system == 'Linux' | nvidia-cuda-runtime-cu12==12.6.77; platform_system == 'Linux' | nvidia-cuda-cupti-cu12==12.6.80; platform_system == 'Linux' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' | nvidia-cublas-cu12==12.6.4.1; platform_system == 'Linux' | nvidia-cufft-cu12==11.3.0.4; platform_system == 'Linux' | nvidia-curand-cu12==10.3.7.77; platform_system == 'Linux' | nvidia-cusolver-cu12==11.7.1.2; platform_system == 'Linux' | nvidia-cusparse-cu12==12.5.4.2; platform_system == 'Linux' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' | nvidia-nccl-cu12==2.28.3; platform_system == 'Linux' | nvidia-nvshmem-cu12==3.3.24; platform_system == 'Linux' | nvidia-nvtx-cu12==12.6.77; platform_system == 'Linux' | nvidia-nvjitlink-cu12==12.6.85; platform_system == 'Linux' | nvidia-cufile-cu12==1.11.1.6; platform_system == 'Linux'
+      PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.6.77; platform_system == 'Linux' | nvidia-cuda-runtime-cu12==12.6.77; platform_system == 'Linux' | nvidia-cuda-cupti-cu12==12.6.80; platform_system == 'Linux' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' | nvidia-cublas-cu12==12.6.4.1; platform_system == 'Linux' | nvidia-cufft-cu12==11.3.0.4; platform_system == 'Linux' | nvidia-curand-cu12==10.3.7.77; platform_system == 'Linux' | nvidia-cusolver-cu12==11.7.1.2; platform_system == 'Linux' | nvidia-cusparse-cu12==12.5.4.2; platform_system == 'Linux' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' | nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' | nvidia-nvshmem-cu12==3.3.24; platform_system == 'Linux' | nvidia-nvtx-cu12==12.6.77; platform_system == 'Linux' | nvidia-nvjitlink-cu12==12.6.85; platform_system == 'Linux' | nvidia-cufile-cu12==1.11.1.6; platform_system == 'Linux'
       timeout-minutes: 420
     secrets:
       github-token: ${{ secrets.GITHUB_TOKEN }}
@@ -1193,7 +1193,7 @@ jobs:
       ALPINE_IMAGE: "arm64v8/alpine"
       build_name: manywheel-py3_14-cuda-aarch64-12_8
       build_environment: linux-aarch64-binary-manywheel
-      PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.8.93; platform_system == 'Linux' | nvidia-cuda-runtime-cu12==12.8.90; platform_system == 'Linux' | nvidia-cuda-cupti-cu12==12.8.90; platform_system == 'Linux' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' | nvidia-cublas-cu12==12.8.4.1; platform_system == 'Linux' | nvidia-cufft-cu12==11.3.3.83; platform_system == 'Linux' | nvidia-curand-cu12==10.3.9.90; platform_system == 'Linux' | nvidia-cusolver-cu12==11.7.3.90; platform_system == 'Linux' | nvidia-cusparse-cu12==12.5.8.93; platform_system == 'Linux' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' | nvidia-nccl-cu12==2.28.3; platform_system == 'Linux' | nvidia-nvshmem-cu12==3.3.24; platform_system == 'Linux' | nvidia-nvtx-cu12==12.8.90; platform_system == 'Linux' | nvidia-nvjitlink-cu12==12.8.93; platform_system == 'Linux' | nvidia-cufile-cu12==1.13.1.3; platform_system == 'Linux'
+      PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.8.93; platform_system == 'Linux' | nvidia-cuda-runtime-cu12==12.8.90; platform_system == 'Linux' | nvidia-cuda-cupti-cu12==12.8.90; platform_system == 'Linux' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' | nvidia-cublas-cu12==12.8.4.1; platform_system == 'Linux' | nvidia-cufft-cu12==11.3.3.83; platform_system == 'Linux' | nvidia-curand-cu12==10.3.9.90; platform_system == 'Linux' | nvidia-cusolver-cu12==11.7.3.90; platform_system == 'Linux' | nvidia-cusparse-cu12==12.5.8.93; platform_system == 'Linux' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' | nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' | nvidia-nvshmem-cu12==3.3.24; platform_system == 'Linux' | nvidia-nvtx-cu12==12.8.90; platform_system == 'Linux' | nvidia-nvjitlink-cu12==12.8.93; platform_system == 'Linux' | nvidia-cufile-cu12==1.13.1.3; platform_system == 'Linux'
       timeout-minutes: 420
     secrets:
       github-token: ${{ secrets.GITHUB_TOKEN }}
@@ -1239,7 +1239,7 @@ jobs:
       ALPINE_IMAGE: "arm64v8/alpine"
       build_name: manywheel-py3_14-cuda-aarch64-13_0
       build_environment: linux-aarch64-binary-manywheel
-      PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc==13.0.48; platform_system == 'Linux' | nvidia-cuda-runtime==13.0.48; platform_system == 'Linux' | nvidia-cuda-cupti==13.0.48; platform_system == 'Linux' | nvidia-cudnn-cu13==9.13.0.50; platform_system == 'Linux' | nvidia-cublas==13.0.0.19; platform_system == 'Linux' | nvidia-cufft==12.0.0.15; platform_system == 'Linux' | nvidia-curand==10.4.0.35; platform_system == 'Linux' | nvidia-cusolver==12.0.3.29; platform_system == 'Linux' | nvidia-cusparse==12.6.2.49; platform_system == 'Linux' | nvidia-cusparselt-cu13==0.8.0; platform_system == 'Linux' | nvidia-nccl-cu13==2.28.3; platform_system == 'Linux' | nvidia-nvshmem-cu13==3.3.24; platform_system == 'Linux' | nvidia-nvtx==13.0.39; platform_system == 'Linux' | nvidia-nvjitlink==13.0.39; platform_system == 'Linux' | nvidia-cufile==1.15.0.42; platform_system == 'Linux'
+      PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc==13.0.48; platform_system == 'Linux' | nvidia-cuda-runtime==13.0.48; platform_system == 'Linux' | nvidia-cuda-cupti==13.0.48; platform_system == 'Linux' | nvidia-cudnn-cu13==9.13.0.50; platform_system == 'Linux' | nvidia-cublas==13.0.0.19; platform_system == 'Linux' | nvidia-cufft==12.0.0.15; platform_system == 'Linux' | nvidia-curand==10.4.0.35; platform_system == 'Linux' | nvidia-cusolver==12.0.3.29; platform_system == 'Linux' | nvidia-cusparse==12.6.2.49; platform_system == 'Linux' | nvidia-cusparselt-cu13==0.8.0; platform_system == 'Linux' | nvidia-nccl-cu13==2.27.7; platform_system == 'Linux' | nvidia-nvshmem-cu13==3.3.24; platform_system == 'Linux' | nvidia-nvtx==13.0.39; platform_system == 'Linux' | nvidia-nvjitlink==13.0.39; platform_system == 'Linux' | nvidia-cufile==1.15.0.42; platform_system == 'Linux'
       timeout-minutes: 420
     secrets:
       github-token: ${{ secrets.GITHUB_TOKEN }}
@@ -1350,7 +1350,7 @@ jobs:
       ALPINE_IMAGE: "arm64v8/alpine"
       build_name: manywheel-py3_14t-cuda-aarch64-12_6
       build_environment: linux-aarch64-binary-manywheel
-      PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.6.77; platform_system == 'Linux' | nvidia-cuda-runtime-cu12==12.6.77; platform_system == 'Linux' | nvidia-cuda-cupti-cu12==12.6.80; platform_system == 'Linux' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' | nvidia-cublas-cu12==12.6.4.1; platform_system == 'Linux' | nvidia-cufft-cu12==11.3.0.4; platform_system == 'Linux' | nvidia-curand-cu12==10.3.7.77; platform_system == 'Linux' | nvidia-cusolver-cu12==11.7.1.2; platform_system == 'Linux' | nvidia-cusparse-cu12==12.5.4.2; platform_system == 'Linux' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' | nvidia-nccl-cu12==2.28.3; platform_system == 'Linux' | nvidia-nvshmem-cu12==3.3.24; platform_system == 'Linux' | nvidia-nvtx-cu12==12.6.77; platform_system == 'Linux' | nvidia-nvjitlink-cu12==12.6.85; platform_system == 'Linux' | nvidia-cufile-cu12==1.11.1.6; platform_system == 'Linux'
+      PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.6.77; platform_system == 'Linux' | nvidia-cuda-runtime-cu12==12.6.77; platform_system == 'Linux' | nvidia-cuda-cupti-cu12==12.6.80; platform_system == 'Linux' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' | nvidia-cublas-cu12==12.6.4.1; platform_system == 'Linux' | nvidia-cufft-cu12==11.3.0.4; platform_system == 'Linux' | nvidia-curand-cu12==10.3.7.77; platform_system == 'Linux' | nvidia-cusolver-cu12==11.7.1.2; platform_system == 'Linux' | nvidia-cusparse-cu12==12.5.4.2; platform_system == 'Linux' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' | nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' | nvidia-nvshmem-cu12==3.3.24; platform_system == 'Linux' | nvidia-nvtx-cu12==12.6.77; platform_system == 'Linux' | nvidia-nvjitlink-cu12==12.6.85; platform_system == 'Linux' | nvidia-cufile-cu12==1.11.1.6; platform_system == 'Linux'
       timeout-minutes: 420
     secrets:
       github-token: ${{ secrets.GITHUB_TOKEN }}
@@ -1396,7 +1396,7 @@ jobs:
       ALPINE_IMAGE: "arm64v8/alpine"
       build_name: manywheel-py3_14t-cuda-aarch64-12_8
       build_environment: linux-aarch64-binary-manywheel
-      PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.8.93; platform_system == 'Linux' | nvidia-cuda-runtime-cu12==12.8.90; platform_system == 'Linux' | nvidia-cuda-cupti-cu12==12.8.90; platform_system == 'Linux' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' | nvidia-cublas-cu12==12.8.4.1; platform_system == 'Linux' | nvidia-cufft-cu12==11.3.3.83; platform_system == 'Linux' | nvidia-curand-cu12==10.3.9.90; platform_system == 'Linux' | nvidia-cusolver-cu12==11.7.3.90; platform_system == 'Linux' | nvidia-cusparse-cu12==12.5.8.93; platform_system == 'Linux' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' | nvidia-nccl-cu12==2.28.3; platform_system == 'Linux' | nvidia-nvshmem-cu12==3.3.24; platform_system == 'Linux' | nvidia-nvtx-cu12==12.8.90; platform_system == 'Linux' | nvidia-nvjitlink-cu12==12.8.93; platform_system == 'Linux' | nvidia-cufile-cu12==1.13.1.3; platform_system == 'Linux'
+      PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.8.93; platform_system == 'Linux' | nvidia-cuda-runtime-cu12==12.8.90; platform_system == 'Linux' | nvidia-cuda-cupti-cu12==12.8.90; platform_system == 'Linux' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' | nvidia-cublas-cu12==12.8.4.1; platform_system == 'Linux' | nvidia-cufft-cu12==11.3.3.83; platform_system == 'Linux' | nvidia-curand-cu12==10.3.9.90; platform_system == 'Linux' | nvidia-cusolver-cu12==11.7.3.90; platform_system == 'Linux' | nvidia-cusparse-cu12==12.5.8.93; platform_system == 'Linux' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' | nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' | nvidia-nvshmem-cu12==3.3.24; platform_system == 'Linux' | nvidia-nvtx-cu12==12.8.90; platform_system == 'Linux' | nvidia-nvjitlink-cu12==12.8.93; platform_system == 'Linux' | nvidia-cufile-cu12==1.13.1.3; platform_system == 'Linux'
       timeout-minutes: 420
     secrets:
       github-token: ${{ secrets.GITHUB_TOKEN }}
@@ -1442,7 +1442,7 @@ jobs:
       ALPINE_IMAGE: "arm64v8/alpine"
       build_name: manywheel-py3_14t-cuda-aarch64-13_0
       build_environment: linux-aarch64-binary-manywheel
-      PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc==13.0.48; platform_system == 'Linux' | nvidia-cuda-runtime==13.0.48; platform_system == 'Linux' | nvidia-cuda-cupti==13.0.48; platform_system == 'Linux' | nvidia-cudnn-cu13==9.13.0.50; platform_system == 'Linux' | nvidia-cublas==13.0.0.19; platform_system == 'Linux' | nvidia-cufft==12.0.0.15; platform_system == 'Linux' | nvidia-curand==10.4.0.35; platform_system == 'Linux' | nvidia-cusolver==12.0.3.29; platform_system == 'Linux' | nvidia-cusparse==12.6.2.49; platform_system == 'Linux' | nvidia-cusparselt-cu13==0.8.0; platform_system == 'Linux' | nvidia-nccl-cu13==2.28.3; platform_system == 'Linux' | nvidia-nvshmem-cu13==3.3.24; platform_system == 'Linux' | nvidia-nvtx==13.0.39; platform_system == 'Linux' | nvidia-nvjitlink==13.0.39; platform_system == 'Linux' | nvidia-cufile==1.15.0.42; platform_system == 'Linux'
+      PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc==13.0.48; platform_system == 'Linux' | nvidia-cuda-runtime==13.0.48; platform_system == 'Linux' | nvidia-cuda-cupti==13.0.48; platform_system == 'Linux' | nvidia-cudnn-cu13==9.13.0.50; platform_system == 'Linux' | nvidia-cublas==13.0.0.19; platform_system == 'Linux' | nvidia-cufft==12.0.0.15; platform_system == 'Linux' | nvidia-curand==10.4.0.35; platform_system == 'Linux' | nvidia-cusolver==12.0.3.29; platform_system == 'Linux' | nvidia-cusparse==12.6.2.49; platform_system == 'Linux' | nvidia-cusparselt-cu13==0.8.0; platform_system == 'Linux' | nvidia-nccl-cu13==2.27.7; platform_system == 'Linux' | nvidia-nvshmem-cu13==3.3.24; platform_system == 'Linux' | nvidia-nvtx==13.0.39; platform_system == 'Linux' | nvidia-nvjitlink==13.0.39; platform_system == 'Linux' | nvidia-cufile==1.15.0.42; platform_system == 'Linux'
       timeout-minutes: 420
     secrets:
       github-token: ${{ secrets.GITHUB_TOKEN }}

.github/workflows/generated-linux-binary-libtorch-nightly.yml

@@ -316,6 +316,121 @@ jobs:
       github-token: ${{ secrets.GITHUB_TOKEN }}
     uses: ./.github/workflows/_binary-upload.yml
 
+  libtorch-rocm6_3-shared-with-deps-release-build:
+    if: ${{ github.repository_owner == 'pytorch' }}
+    uses: ./.github/workflows/_binary-build-linux.yml
+    needs: get-label-type
+    with:
+      PYTORCH_ROOT: /pytorch
+      PACKAGE_TYPE: libtorch
+      # TODO: This is a legacy variable that we eventually want to get rid of in
+      #       favor of GPU_ARCH_VERSION
+      DESIRED_CUDA: rocm6.3
+      GPU_ARCH_VERSION: "6.3"
+      GPU_ARCH_TYPE: rocm
+      DOCKER_IMAGE: libtorch-cxx11-builder
+      DOCKER_IMAGE_TAG_PREFIX: rocm6.3
+      LIBTORCH_CONFIG: release
+      LIBTORCH_VARIANT: shared-with-deps
+      runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
+      timeout-minutes: 300
+      build_name: libtorch-rocm6_3-shared-with-deps-release
+      build_environment: linux-binary-libtorch
+    secrets:
+      github-token: ${{ secrets.GITHUB_TOKEN }}
+  libtorch-rocm6_3-shared-with-deps-release-test:  # Testing
+    if: ${{ github.repository_owner == 'pytorch' }}
+    needs:
+      - libtorch-rocm6_3-shared-with-deps-release-build
+      - get-label-type
+    runs-on: linux.rocm.gpu.mi250
+    timeout-minutes: 240
+    env:
+      PYTORCH_ROOT: /pytorch
+      PACKAGE_TYPE: libtorch
+      # TODO: This is a legacy variable that we eventually want to get rid of in
+      #       favor of GPU_ARCH_VERSION
+      DESIRED_CUDA: rocm6.3
+      GPU_ARCH_VERSION: "6.3"
+      GPU_ARCH_TYPE: rocm
+      SKIP_ALL_TESTS: 1
+      DOCKER_IMAGE: libtorch-cxx11-builder
+      DOCKER_IMAGE_TAG_PREFIX: rocm6.3
+      LIBTORCH_CONFIG: release
+      LIBTORCH_VARIANT: shared-with-deps
+    steps:
+      - name: Setup ROCm
+        uses: ./.github/actions/setup-rocm
+      - uses: actions/download-artifact@v4.1.7
+        name: Download Build Artifacts
+        with:
+          name: libtorch-rocm6_3-shared-with-deps-release
+          path: "${{ runner.temp }}/artifacts/"
+      - name: Checkout PyTorch
+        uses: actions/checkout@v4
+        with:
+          ref: ${{ github.event_name == 'pull_request' && github.event.pull_request.head.sha || github.sha }}
+          submodules: recursive
+          path: pytorch
+          show-progress: false
+      - name: Clean PyTorch checkout
+        run: |
+          # Remove any artifacts from the previous checkouts
+          git clean -fxd
+        working-directory: pytorch
+      - name: ROCm set GPU_FLAG
+        run: |
+          echo "GPU_FLAG=--device=/dev/mem --device=/dev/kfd --device=/dev/dri --group-add video --group-add daemon" >> "${GITHUB_ENV}"
+      - name: configure aws credentials
+        id: aws_creds
+        if: ${{ startsWith(github.event.ref, 'refs/tags/ciflow/') }}
+        uses: aws-actions/configure-aws-credentials@v4
+        with:
+          role-to-assume: arn:aws:iam::308535385114:role/gha_workflow_s3_and_ecr_read_only
+          aws-region: us-east-1
+          role-duration-seconds: 18000
+      - name: Calculate docker image
+        id: calculate-docker-image
+        uses: pytorch/test-infra/.github/actions/calculate-docker-image@main
+        with:
+          docker-registry: ${{ startsWith(github.event.ref, 'refs/tags/ciflow/') && '308535385114.dkr.ecr.us-east-1.amazonaws.com' || 'docker.io' }}
+          docker-image-name: libtorch-cxx11-builder
+          custom-tag-prefix: rocm6.3
+          docker-build-dir: .ci/docker
+          working-directory: pytorch
+      - name: Pull Docker image
+        uses: pytorch/test-infra/.github/actions/pull-docker-image@main
+        with:
+          docker-image: ${{ steps.calculate-docker-image.outputs.docker-image }}
+      - name: Test Pytorch binary
+        uses: ./pytorch/.github/actions/test-pytorch-binary
+        env:
+          DOCKER_IMAGE: ${{ steps.calculate-docker-image.outputs.docker-image }}
+      - name: Teardown ROCm
+        uses: ./.github/actions/teardown-rocm
+  libtorch-rocm6_3-shared-with-deps-release-upload:  # Uploading
+    if: ${{ github.repository_owner == 'pytorch' }}
+    permissions:
+      id-token: write
+      contents: read
+    needs: libtorch-rocm6_3-shared-with-deps-release-test
+    with:
+      PYTORCH_ROOT: /pytorch
+      PACKAGE_TYPE: libtorch
+      # TODO: This is a legacy variable that we eventually want to get rid of in
+      #       favor of GPU_ARCH_VERSION
+      DESIRED_CUDA: rocm6.3
+      GPU_ARCH_VERSION: "6.3"
+      GPU_ARCH_TYPE: rocm
+      DOCKER_IMAGE: libtorch-cxx11-builder
+      DOCKER_IMAGE_TAG_PREFIX: rocm6.3
+      LIBTORCH_CONFIG: release
+      LIBTORCH_VARIANT: shared-with-deps
+      build_name: libtorch-rocm6_3-shared-with-deps-release
+    secrets:
+      github-token: ${{ secrets.GITHUB_TOKEN }}
+    uses: ./.github/workflows/_binary-upload.yml
+
   libtorch-rocm6_4-shared-with-deps-release-build:
     if: ${{ github.repository_owner == 'pytorch' }}
     uses: ./.github/workflows/_binary-build-linux.yml
@@ -430,118 +545,3 @@ jobs:
     secrets:
       github-token: ${{ secrets.GITHUB_TOKEN }}
     uses: ./.github/workflows/_binary-upload.yml
-
-  libtorch-rocm7_0-shared-with-deps-release-build:
-    if: ${{ github.repository_owner == 'pytorch' }}
-    uses: ./.github/workflows/_binary-build-linux.yml
-    needs: get-label-type
-    with:
-      PYTORCH_ROOT: /pytorch
-      PACKAGE_TYPE: libtorch
-      # TODO: This is a legacy variable that we eventually want to get rid of in
-      #       favor of GPU_ARCH_VERSION
-      DESIRED_CUDA: rocm7.0
-      GPU_ARCH_VERSION: "7.0"
-      GPU_ARCH_TYPE: rocm
-      DOCKER_IMAGE: libtorch-cxx11-builder
-      DOCKER_IMAGE_TAG_PREFIX: rocm7.0
-      LIBTORCH_CONFIG: release
-      LIBTORCH_VARIANT: shared-with-deps
-      runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
-      timeout-minutes: 300
-      build_name: libtorch-rocm7_0-shared-with-deps-release
-      build_environment: linux-binary-libtorch
-    secrets:
-      github-token: ${{ secrets.GITHUB_TOKEN }}
-  libtorch-rocm7_0-shared-with-deps-release-test:  # Testing
-    if: ${{ github.repository_owner == 'pytorch' }}
-    needs:
-      - libtorch-rocm7_0-shared-with-deps-release-build
-      - get-label-type
-    runs-on: linux.rocm.gpu.mi250
-    timeout-minutes: 240
-    env:
-      PYTORCH_ROOT: /pytorch
-      PACKAGE_TYPE: libtorch
-      # TODO: This is a legacy variable that we eventually want to get rid of in
-      #       favor of GPU_ARCH_VERSION
-      DESIRED_CUDA: rocm7.0
-      GPU_ARCH_VERSION: "7.0"
-      GPU_ARCH_TYPE: rocm
-      SKIP_ALL_TESTS: 1
-      DOCKER_IMAGE: libtorch-cxx11-builder
-      DOCKER_IMAGE_TAG_PREFIX: rocm7.0
-      LIBTORCH_CONFIG: release
-      LIBTORCH_VARIANT: shared-with-deps
-    steps:
-      - name: Setup ROCm
-        uses: ./.github/actions/setup-rocm
-      - uses: actions/download-artifact@v4.1.7
-        name: Download Build Artifacts
-        with:
-          name: libtorch-rocm7_0-shared-with-deps-release
-          path: "${{ runner.temp }}/artifacts/"
-      - name: Checkout PyTorch
-        uses: actions/checkout@v4
-        with:
-          ref: ${{ github.event_name == 'pull_request' && github.event.pull_request.head.sha || github.sha }}
-          submodules: recursive
-          path: pytorch
-          show-progress: false
-      - name: Clean PyTorch checkout
-        run: |
-          # Remove any artifacts from the previous checkouts
-          git clean -fxd
-        working-directory: pytorch
-      - name: ROCm set GPU_FLAG
-        run: |
-          echo "GPU_FLAG=--device=/dev/mem --device=/dev/kfd --device=/dev/dri --group-add video --group-add daemon" >> "${GITHUB_ENV}"
-      - name: configure aws credentials
-        id: aws_creds
-        if: ${{ startsWith(github.event.ref, 'refs/tags/ciflow/') }}
-        uses: aws-actions/configure-aws-credentials@v4
-        with:
-          role-to-assume: arn:aws:iam::308535385114:role/gha_workflow_s3_and_ecr_read_only
-          aws-region: us-east-1
-          role-duration-seconds: 18000
-      - name: Calculate docker image
-        id: calculate-docker-image
-        uses: pytorch/test-infra/.github/actions/calculate-docker-image@main
-        with:
-          docker-registry: ${{ startsWith(github.event.ref, 'refs/tags/ciflow/') && '308535385114.dkr.ecr.us-east-1.amazonaws.com' || 'docker.io' }}
-          docker-image-name: libtorch-cxx11-builder
-          custom-tag-prefix: rocm7.0
-          docker-build-dir: .ci/docker
-          working-directory: pytorch
-      - name: Pull Docker image
-        uses: pytorch/test-infra/.github/actions/pull-docker-image@main
-        with:
-          docker-image: ${{ steps.calculate-docker-image.outputs.docker-image }}
-      - name: Test Pytorch binary
-        uses: ./pytorch/.github/actions/test-pytorch-binary
-        env:
-          DOCKER_IMAGE: ${{ steps.calculate-docker-image.outputs.docker-image }}
-      - name: Teardown ROCm
-        uses: ./.github/actions/teardown-rocm
-  libtorch-rocm7_0-shared-with-deps-release-upload:  # Uploading
-    if: ${{ github.repository_owner == 'pytorch' }}
-    permissions:
-      id-token: write
-      contents: read
-    needs: libtorch-rocm7_0-shared-with-deps-release-test
-    with:
-      PYTORCH_ROOT: /pytorch
-      PACKAGE_TYPE: libtorch
-      # TODO: This is a legacy variable that we eventually want to get rid of in
-      #       favor of GPU_ARCH_VERSION
-      DESIRED_CUDA: rocm7.0
-      GPU_ARCH_VERSION: "7.0"
-      GPU_ARCH_TYPE: rocm
-      DOCKER_IMAGE: libtorch-cxx11-builder
-      DOCKER_IMAGE_TAG_PREFIX: rocm7.0
-      LIBTORCH_CONFIG: release
-      LIBTORCH_VARIANT: shared-with-deps
-      build_name: libtorch-rocm7_0-shared-with-deps-release
-    secrets:
-      github-token: ${{ secrets.GITHUB_TOKEN }}
-    uses: ./.github/workflows/_binary-upload.yml

.github/workflows/generated-linux-binary-manywheel-main.yml

@@ -60,7 +60,7 @@ jobs:
       runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
       build_name: manywheel-py3_12-cuda13_0
       build_environment: linux-binary-manywheel
-      PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc==13.0.48; platform_system == 'Linux' | nvidia-cuda-runtime==13.0.48; platform_system == 'Linux' | nvidia-cuda-cupti==13.0.48; platform_system == 'Linux' | nvidia-cudnn-cu13==9.13.0.50; platform_system == 'Linux' | nvidia-cublas==13.0.0.19; platform_system == 'Linux' | nvidia-cufft==12.0.0.15; platform_system == 'Linux' | nvidia-curand==10.4.0.35; platform_system == 'Linux' | nvidia-cusolver==12.0.3.29; platform_system == 'Linux' | nvidia-cusparse==12.6.2.49; platform_system == 'Linux' | nvidia-cusparselt-cu13==0.8.0; platform_system == 'Linux' | nvidia-nccl-cu13==2.28.3; platform_system == 'Linux' | nvidia-nvshmem-cu13==3.3.24; platform_system == 'Linux' | nvidia-nvtx==13.0.39; platform_system == 'Linux' | nvidia-nvjitlink==13.0.39; platform_system == 'Linux' | nvidia-cufile==1.15.0.42; platform_system == 'Linux'
+      PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc==13.0.48; platform_system == 'Linux' | nvidia-cuda-runtime==13.0.48; platform_system == 'Linux' | nvidia-cuda-cupti==13.0.48; platform_system == 'Linux' | nvidia-cudnn-cu13==9.13.0.50; platform_system == 'Linux' | nvidia-cublas==13.0.0.19; platform_system == 'Linux' | nvidia-cufft==12.0.0.15; platform_system == 'Linux' | nvidia-curand==10.4.0.35; platform_system == 'Linux' | nvidia-cusolver==12.0.3.29; platform_system == 'Linux' | nvidia-cusparse==12.6.2.49; platform_system == 'Linux' | nvidia-cusparselt-cu13==0.8.0; platform_system == 'Linux' | nvidia-nccl-cu13==2.27.7; platform_system == 'Linux' | nvidia-nvshmem-cu13==3.3.24; platform_system == 'Linux' | nvidia-nvtx==13.0.39; platform_system == 'Linux' | nvidia-nvjitlink==13.0.39; platform_system == 'Linux' | nvidia-cufile==1.15.0.42; platform_system == 'Linux'
     secrets:
       github-token: ${{ secrets.GITHUB_TOKEN }}
   manywheel-py3_12-cuda13_0-test:  # Testing

.github/workflows/operator_microbenchmark.yml

@@ -1,46 +0,0 @@
-name: operator_microbenchmark
-
-on:
-  push:
-    tags:
-      - ciflow/op-benchmark/*
-  workflow_dispatch:
-  schedule:
-    # Run at 06:00 UTC everyday
-    - cron: 0 6 * * *
-
-concurrency:
-  group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.ref_name }}-${{ github.ref_type == 'branch' && github.sha }}-${{ github.event_name == 'workflow_dispatch' }}-${{ github.event_name == 'schedule' }}
-  cancel-in-progress: true
-
-permissions:
-  id-token: write
-  contents: read
-
-jobs:
-  opmicrobenchmark-build:
-    if: github.repository_owner == 'pytorch'
-    name: opmicrobenchmark-build
-    uses: ./.github/workflows/_linux-build.yml
-    with:
-      runner: linux.12xlarge.memory
-      build-environment: linux-jammy-cuda12.8-py3.10-gcc9-sm80
-      docker-image-name: ci-image:pytorch-linux-jammy-cuda12.8-cudnn9-py3-gcc11
-      cuda-arch-list: '8.0 9.0'
-      test-matrix: |
-        { include: [
-          { config: "operator_microbenchmark_test", shard: 1, num_shards: 1, runner: "linux.aws.h100" },
-          { config: "operator_microbenchmark_test", shard: 1, num_shards: 1, runner: "linux.aws.a100" },
-        ]}
-    secrets: inherit
-
-  opmicrobenchmark-test:
-    name: opmicrobenchmark-test
-    uses: ./.github/workflows/_linux-test.yml
-    needs: opmicrobenchmark-build
-    with:
-      timeout-minutes: 500
-      build-environment: linux-jammy-cuda12.8-py3.10-gcc9-sm80
-      docker-image: ${{ needs.opmicrobenchmark-build.outputs.docker-image }}
-      test-matrix: ${{ needs.opmicrobenchmark-build.outputs.test-matrix }}
-    secrets: inherit

.github/workflows/periodic.yml

@@ -59,14 +59,13 @@ jobs:
       runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
       build-environment: linux-jammy-cuda12.4-py3.10-gcc11
       docker-image-name: ci-image:pytorch-linux-jammy-cuda12.4-cudnn9-py3-gcc11
-      cuda-arch-list: 7.5
       test-matrix: |
         { include: [
-          { config: "legacy_nvidia_driver", shard: 1, num_shards: 5, runner: "${{ needs.get-label-type.outputs.label-type }}linux.g4dn.4xlarge.nvidia.gpu" },
-          { config: "legacy_nvidia_driver", shard: 2, num_shards: 5, runner: "${{ needs.get-label-type.outputs.label-type }}linux.g4dn.4xlarge.nvidia.gpu" },
-          { config: "legacy_nvidia_driver", shard: 3, num_shards: 5, runner: "${{ needs.get-label-type.outputs.label-type }}linux.g4dn.4xlarge.nvidia.gpu" },
-          { config: "legacy_nvidia_driver", shard: 4, num_shards: 5, runner: "${{ needs.get-label-type.outputs.label-type }}linux.g4dn.4xlarge.nvidia.gpu" },
-          { config: "legacy_nvidia_driver", shard: 5, num_shards: 5, runner: "${{ needs.get-label-type.outputs.label-type }}linux.g4dn.4xlarge.nvidia.gpu" },
+          { config: "legacy_nvidia_driver", shard: 1, num_shards: 5, runner: "${{ needs.get-label-type.outputs.label-type }}linux.4xlarge.nvidia.gpu" },
+          { config: "legacy_nvidia_driver", shard: 2, num_shards: 5, runner: "${{ needs.get-label-type.outputs.label-type }}linux.4xlarge.nvidia.gpu" },
+          { config: "legacy_nvidia_driver", shard: 3, num_shards: 5, runner: "${{ needs.get-label-type.outputs.label-type }}linux.4xlarge.nvidia.gpu" },
+          { config: "legacy_nvidia_driver", shard: 4, num_shards: 5, runner: "${{ needs.get-label-type.outputs.label-type }}linux.4xlarge.nvidia.gpu" },
+          { config: "legacy_nvidia_driver", shard: 5, num_shards: 5, runner: "${{ needs.get-label-type.outputs.label-type }}linux.4xlarge.nvidia.gpu" },
         ]}
     secrets: inherit
 
@@ -113,13 +112,13 @@ jobs:
       test-matrix: ${{ needs.linux-jammy-cuda12_8-py3_10-gcc11-build.outputs.test-matrix }}
     secrets: inherit
 
-  linux-jammy-cuda12_8-py3_10-gcc9-build:
-    name: linux-jammy-cuda12.8-py3.10-gcc9
+  linux-jammy-cuda12_8-py3_9-gcc9-build:
+    name: linux-jammy-cuda12.8-py3.9-gcc9
     uses: ./.github/workflows/_linux-build.yml
     needs: get-label-type
     with:
       runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
-      build-environment: linux-jammy-cuda12.8-py3.10-gcc9
+      build-environment: linux-jammy-cuda12.8-py3.9-gcc9
       docker-image-name: ci-image:pytorch-linux-jammy-cuda12.8-cudnn9-py3-gcc9
       cuda-arch-list: 8.6
       test-matrix: |
@@ -129,14 +128,14 @@ jobs:
         ]}
     secrets: inherit
 
-  linux-jammy-cuda12_8-py3_10-gcc9-test:
-    name: linux-jammy-cuda12.8-py3.10-gcc9
+  linux-jammy-cuda12_8-py3_9-gcc9-test:
+    name: linux-jammy-cuda12.8-py3.9-gcc9
     uses: ./.github/workflows/_linux-test.yml
-    needs: linux-jammy-cuda12_8-py3_10-gcc9-build
+    needs: linux-jammy-cuda12_8-py3_9-gcc9-build
     with:
-      build-environment: linux-jammy-cuda12.8-py3.10-gcc9
-      docker-image: ${{ needs.linux-jammy-cuda12_8-py3_10-gcc9-build.outputs.docker-image }}
-      test-matrix: ${{ needs.linux-jammy-cuda12_8-py3_10-gcc9-build.outputs.test-matrix }}
+      build-environment: linux-jammy-cuda12.8-py3.9-gcc9
+      docker-image: ${{ needs.linux-jammy-cuda12_8-py3_9-gcc9-build.outputs.docker-image }}
+      test-matrix: ${{ needs.linux-jammy-cuda12_8-py3_9-gcc9-build.outputs.test-matrix }}
     secrets: inherit
 
   linux-jammy-cuda12_8-py3_10-gcc9-debug-build:

.github/workflows/pull.yml

@@ -343,14 +343,14 @@ jobs:
       test-matrix: ${{ needs.linux-jammy-cuda12_8-py3_10-gcc9-inductor-build.outputs.test-matrix }}
     secrets: inherit
 
-  linux-jammy-xpu-n-py3_10-build:
-    name: linux-jammy-xpu-n-py3.10
+  linux-jammy-xpu-n-py3_9-build:
+    name: linux-jammy-xpu-n-py3.9
     uses: ./.github/workflows/_linux-build.yml
     needs: get-label-type
     with:
       sync-tag: linux-xpu-n-build
       runner_prefix: ${{ needs.get-label-type.outputs.label-type }}
-      build-environment: linux-jammy-xpu-n-py3.10
+      build-environment: linux-jammy-xpu-n-py3.9
       docker-image-name: ci-image:pytorch-linux-jammy-xpu-n-py3
       test-matrix: |
         { include: [

CMakeLists.txt

@@ -442,7 +442,7 @@ if(WIN32)
       message(
         WARNING
           "Libuv is not installed in current conda env. Set USE_DISTRIBUTED to OFF. "
-          "Please run command 'conda install -c conda-forge libuv=1.51' to install libuv."
+          "Please run command 'conda install -c conda-forge libuv=1.39' to install libuv."
       )
     else()
       set(ENV{libuv_ROOT} ${libuv_tmp_LIBRARY}/../../)

README.md

@@ -275,7 +275,7 @@ conda install pkg-config libuv
 pip install mkl-static mkl-include
 # Add these packages if torch.distributed is needed.
 # Distributed package support on Windows is a prototype feature and is subject to changes.
-conda install -c conda-forge libuv=1.51
+conda install -c conda-forge libuv
 ```
 
 #### Install PyTorch

aten/src/ATen/InferSize.h

@@ -45,39 +45,7 @@ inline void infer_size_impl(
     }
   }
 
-  if (infer_dim) {
-    // numel is the product of known sizes, it has to be divisible by newsize.
-    // and newsize should be positive unless newsize == numel (we throw
-    // different) error message in that case.
-    if constexpr (std::is_same_v<NumelType, c10::SymInt>) {
-      auto v = newsize.maybe_as_int();
-      if (v and *v == 0) {
-        // Avoid div by 0 when sym_eq(numel % newsize, 0) is constructed!
-        // which may happen when newsize is not a symbol! if its a symbol
-        // division won't happen anyway during compile.
-        TORCH_MAYBE_SYM_CHECK(
-            numel == newsize,
-            "shape '",
-            shape,
-            "' is invalid for input of size ",
-            numel);
-      } else {
-        auto cond = sym_gt(newsize, 0)
-                        .sym_and(sym_eq(numel % newsize, 0))
-                        .sym_or(sym_eq(numel, newsize));
-        TORCH_MAYBE_SYM_CHECK(
-            cond, "shape '", shape, "' is invalid for input of size ", numel);
-      }
-
-    } else {
-      TORCH_CHECK(
-          (newsize > 0 && (numel % newsize == 0)) || numel == newsize,
-          "shape '",
-          shape,
-          "' is invalid for input of size ",
-          numel);
-    }
-
+  auto set_infer_dim = [&]() {
     // We have a degree of freedom here to select the dimension size; follow
     // NumPy semantics and just bail.  However, a nice error message is needed
     // because users often use `view` as a way to flatten & unflatten
@@ -86,15 +54,19 @@ inline void infer_size_impl(
     // works yet
     //   empty_tensor.view(-1, 0)
     // doesn't.
-    TORCH_MAYBE_SYM_CHECK(
+    TORCH_CHECK(
         newsize != 0,
         "cannot reshape tensor of 0 elements into shape ",
         shape,
         " because the unspecified dimension size -1 can be any "
         "value and is ambiguous");
-
     res[*infer_dim] = numel / newsize;
     return;
+  };
+
+  if (infer_dim && newsize > 0 && numel % newsize == 0) {
+    set_infer_dim();
+    return;
   }
 
   TORCH_MAYBE_SYM_CHECK(
@@ -103,6 +75,9 @@ inline void infer_size_impl(
       shape,
       "' is invalid for input of size ",
       numel);
+  if (infer_dim) {
+    set_infer_dim();
+  }
 }
 
 inline std::vector<int64_t> infer_size(IntArrayRef shape, int64_t numel) {

aten/src/ATen/Version.cpp

@@ -103,9 +103,7 @@ std::string get_cpu_capability() {
 #elif defined(HAVE_ZVECTOR_CPU_DEFINITION)
     case native::CPUCapability::ZVECTOR:
       return "Z VECTOR";
-#elif defined(HAVE_SVE_CPU_DEFINITION) && defined(HAVE_ARM_BF16_CPU_DEFINITION)
-    case native::CPUCapability::SVE128:
-      return "SVE128";
+#elif defined(HAVE_SVE256_CPU_DEFINITION) && defined(HAVE_ARM_BF16_CPU_DEFINITION)
     case native::CPUCapability::SVE256:
       return "SVE256";
 #else

aten/src/ATen/core/jit_type.h

@@ -1234,7 +1234,7 @@ struct TORCH_API TupleType : public NamedType {
   std::shared_ptr<FunctionSchema> schema_;
 };
 
-// the common supertype of all Enums, only used in operator registration.
+// the common supertype of all Enums, only used in operator registraion.
 // EnumType <: AnyEnumType for all Enums
 struct AnyEnumType;
 using AnyEnumTypePtr = SingletonTypePtr<AnyEnumType>;

aten/src/ATen/cpu/vec/functional_base.h

@@ -102,31 +102,8 @@ struct VecReduceAllSIMD<float, Op> {
 #endif // defined(__GNUC__) && (__GNUC__ > 5) && !defined(_MSC_VER) &&
        // !defined(C10_MOBILE)
 
-#if defined(__aarch64__) && !defined(C10_MOBILE) && !defined(__CUDACC__)
-#if defined(CPU_CAPABILITY_SVE256)
-template <typename Op>
-struct VecReduceAllSIMD<float, Op> {
-  static inline float apply(
-      const Op& vec_fun,
-      const Vectorized<float>& acc_vec) {
-    using Vec = Vectorized<float>;
-    Vec v = acc_vec;
-    // 128-bit shuffle
-    svuint32_t ind = svdupq_n_u32(4, 5, 6, 7);
-    Vec v1 = svtbl_f32(v, ind);
-    v = vec_fun(v, v1);
-    // 64-bit shuffle
-    ind = svdupq_n_u32(2, 3, 0, 1);
-    v1 = svtbl_f32(v, ind);
-    v = vec_fun(v, v1);
-    // 32-bit shuffle
-    ind = svdupq_n_u32(1, 0, 2, 3);
-    v1 = svtbl_f32(v, ind);
-    v = vec_fun(v, v1);
-    return svlasta(svpfalse(), v);
-  }
-};
-#else
+#if defined(__aarch64__) && !defined(C10_MOBILE) && !defined(__CUDACC__) && \
+    !defined(CPU_CAPABILITY_SVE)
 template <typename Op>
 struct VecReduceAllSIMD<float, Op> {
   static inline float apply(
@@ -163,8 +140,35 @@ struct VecReduceAllSIMD<float, std::plus<Vectorized<float>>> {
     return vaddvq_f32(acc_vec);
   }
 };
-#endif // defined(CPU_CAPABILITY_SVE256)
 #endif // defined(__aarch64__) && !defined(C10_MOBILE) && !defined(__CUDACC__)
+       // && !defined(CPU_CAPABILITY_SVE)
+
+#if defined(__aarch64__) && !defined(C10_MOBILE) && !defined(__CUDACC__) && \
+    defined(CPU_CAPABILITY_SVE256)
+template <typename Op>
+struct VecReduceAllSIMD<float, Op> {
+  static inline float apply(
+      const Op& vec_fun,
+      const Vectorized<float>& acc_vec) {
+    using Vec = Vectorized<float>;
+    Vec v = acc_vec;
+    // 128-bit shuffle
+    svuint32_t ind = svdupq_n_u32(4, 5, 6, 7);
+    Vec v1 = svtbl_f32(v, ind);
+    v = vec_fun(v, v1);
+    // 64-bit shuffle
+    ind = svdupq_n_u32(2, 3, 0, 1);
+    v1 = svtbl_f32(v, ind);
+    v = vec_fun(v, v1);
+    // 32-bit shuffle
+    ind = svdupq_n_u32(1, 0, 2, 3);
+    v1 = svtbl_f32(v, ind);
+    v = vec_fun(v, v1);
+    return svlasta(svpfalse(), v);
+  }
+};
+#endif // defined(__aarch64__) && !defined(C10_MOBILE) && !defined(__CUDACC__)
+       // && defined(CPU_CAPABILITY_SVE256)
 
 template <typename scalar_t, typename Op>
 inline scalar_t vec_reduce_all(

aten/src/ATen/cpu/vec/sve/sve_helper.h

@@ -1,21 +1,9 @@
 #pragma once
 
 #include <ATen/cpu/vec/intrinsics.h>
-#include <c10/macros/Macros.h>
-#include <cstdint>
 
 #include <ATen/cpu/vec/vec_base.h>
 
-#if defined(__aarch64__) &&                     \
-    (defined(AT_BUILD_ARM_VEC256_WITH_SLEEF) || \
-     defined(AT_BUILD_ARM_VECSVE_WITH_SLEEF))
-#define SLEEF_STATIC_LIBS
-#include <sleef.h>
-#define USE_SLEEF(sleef_code, non_sleef_code) sleef_code
-#else
-#define USE_SLEEF(sleef_code, non_sleef_code) non_sleef_code
-#endif
-
 #if defined(CPU_CAPABILITY_SVE)
 
 // Define the data type of VLS(vector-length specific).

aten/src/ATen/cpu/vec/sve/vec_bfloat16.h

@@ -2,6 +2,7 @@
 
 #include <ATen/cpu/vec/intrinsics.h>
 #include <ATen/cpu/vec/sve/sve_helper.h>
+#include <ATen/cpu/vec/sve/vec_common_sve.h>
 #include <ATen/cpu/vec/sve/vec_float.h>
 #include <ATen/cpu/vec/vec_base.h>
 #include <c10/util/bit_cast.h>

aten/src/ATen/cpu/vec/sve/vec_common_sve.h

@@ -8,48 +8,13 @@
 #include <ATen/cpu/vec/sve/sve_helper.h>
 #include <ATen/cpu/vec/vec_base.h>
 
-#ifdef CPU_CAPABILITY_SVE128
-
-#include <ATen/cpu/vec/vec128/vec128_float_neon.h>
-
-#include <ATen/cpu/vec/vec128/vec128_bfloat16_neon.h>
-
-#include <ATen/cpu/vec/vec128/vec128_half_neon.h>
-
-#include <ATen/cpu/vec/vec128/vec128_convert.h>
-
-#include <ATen/cpu/vec/sve/vec_qint.h>
-
-#elif defined(CPU_CAPABILITY_SVE)
-
-#include <ATen/cpu/vec/sve/vec_float.h>
-
+#if defined(CPU_CAPABILITY_SVE)
 #include <ATen/cpu/vec/sve/vec_bfloat16.h>
-
 #include <ATen/cpu/vec/sve/vec_double.h>
+#include <ATen/cpu/vec/sve/vec_float.h>
 #include <ATen/cpu/vec/sve/vec_int.h>
-
 #include <ATen/cpu/vec/sve/vec_qint.h>
-
-#include <ATen/cpu/vec/vec256/vec256_half.h>
-
-#include <ATen/cpu/vec/vec256/vec256_convert.h>
-
-#else // NEON
-
-#include <ATen/cpu/vec/vec128/vec128_float_neon.h>
-
-#include <ATen/cpu/vec/vec128/vec128_half_neon.h>
-
-#include <ATen/cpu/vec/vec128/vec128_bfloat16_neon.h>
-
-#include <ATen/cpu/vec/vec128/vec128_convert.h>
-
-#include <ATen/cpu/vec/vec256/vec256_qint.h>
-
-#endif // defined(CPU_CAPABILITY_SVE128)
-
-#include <ATen/cpu/vec/functional.h>
+#endif
 
 namespace at::vec {
 // Note [CPU_CAPABILITY namespace]
@@ -83,6 +48,12 @@ DEFINE_SVE_CAST(int32_t, s32, float, f32)
 DEFINE_SVE_CAST(int16_t, s16, float, f32)
 DEFINE_SVE_CAST(float, f32, double, f64)
 
+#ifdef __ARM_FEATURE_BF16
+DEFINE_SVE_CAST(int64_t, s64, c10::BFloat16, bf16)
+DEFINE_SVE_CAST(int32_t, s32, c10::BFloat16, bf16)
+DEFINE_SVE_CAST(int16_t, s16, c10::BFloat16, bf16)
+#endif // __ARM_FEATURE_BF16
+
 // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ GATHER ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 template <int64_t scale = 1>
@@ -202,11 +173,9 @@ std::pair<
   // group cols crossing lanes:
   //   return {a0, b0, a1, b1, a2, b2, a3, b3}
   //          {a4, b4, a5, b5, a6, b6, a7, b7}
-  svbfloat16_t aReg = a;
-  svbfloat16_t bReg = b;
-  Vectorized<c10::BFloat16> c = svzip1_bf16(aReg, bReg);
-  Vectorized<c10::BFloat16> d = svzip2_bf16(aReg, bReg);
-  return std::make_pair(c, d);
+  return std::make_pair(
+      Vectorized<c10::BFloat16>(svzip1_bf16(a, b)),
+      Vectorized<c10::BFloat16>(svzip2_bf16(a, b)));
 }
 #endif // __ARM_FEATURE_BF16
 
@@ -255,27 +224,12 @@ std::pair<
   // swap lanes:
   //   return {a0, a1, a2, a3, a4, a5, a6, a7}
   //          {b0, b1, b2, b3, b4, b5, b6, b7}
-  svbfloat16_t aReg = a;
-  svbfloat16_t bReg = b;
-  Vectorized<c10::BFloat16> c = svuzp1_bf16(aReg, bReg);
-  Vectorized<c10::BFloat16> d = svuzp2_bf16(aReg, bReg);
-  return std::make_pair(c, d);
+  return std::make_pair(
+      Vectorized<c10::BFloat16>(svuzp1_bf16((svbfloat16_t)a, (svbfloat16_t)b)),
+      Vectorized<c10::BFloat16>(svuzp2_bf16((svbfloat16_t)a, (svbfloat16_t)b)));
 }
 #endif // __ARM_FEATURE_BF16
 
-// ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ FLIP ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-#define DEFINE_FLIP_FUNC(type, sve_func)                    \
-  inline Vectorized<type> flip(const Vectorized<type>& v) { \
-    return Vectorized<type>(sve_func(v));                   \
-  }
-// Use the macro to define the flip functions
-DEFINE_FLIP_FUNC(float, svrev_f32)
-DEFINE_FLIP_FUNC(double, svrev_f64)
-DEFINE_FLIP_FUNC(int64_t, svrev_s64)
-DEFINE_FLIP_FUNC(int32_t, svrev_s32)
-DEFINE_FLIP_FUNC(int16_t, svrev_s16)
-DEFINE_FLIP_FUNC(int8_t, svrev_s8)
-
 #endif // defined(CPU_CAPABILITY_SVE)
 
 } // namespace CPU_CAPABILITY

aten/src/ATen/cpu/vec/vec.h

@@ -1,8 +1,6 @@
 #pragma once
 
-#if defined(__aarch64__)
-#include <ATen/cpu/vec/vec_common_aarch64.h>
-#elif defined(CPU_CAPABILITY_AVX512)
+#if defined(CPU_CAPABILITY_AVX512)
 #include <ATen/cpu/vec/vec512/vec512.h>
 #else
 #include <ATen/cpu/vec/vec128/vec128.h>
@@ -13,34 +11,6 @@ namespace at::vec {
 // See Note [CPU_CAPABILITY namespace]
 inline namespace CPU_CAPABILITY {
 
-inline std::ostream& operator<<(std::ostream& stream, const c10::qint32& val) {
-  stream << val.val_;
-  return stream;
-}
-inline std::ostream& operator<<(std::ostream& stream, const c10::qint8& val) {
-  stream << static_cast<int>(val.val_);
-  return stream;
-}
-inline std::ostream& operator<<(std::ostream& stream, const c10::quint8& val) {
-  stream << static_cast<unsigned int>(val.val_);
-  return stream;
-}
-
-template <typename T>
-std::ostream& operator<<(std::ostream& stream, const Vectorized<T>& vec) {
-  T buf[Vectorized<T>::size()];
-  vec.store(buf);
-  stream << "vec[";
-  for (int i = 0; i != Vectorized<T>::size(); i++) {
-    if (i != 0) {
-      stream << ", ";
-    }
-    stream << buf[i];
-  }
-  stream << "]";
-  return stream;
-}
-
 inline Vectorized<bool> convert_to_bool(Vectorized<int8_t> x) {
   __at_align__ bool buffer[x.size()];
   x.ne(Vectorized<int8_t>(0)).store(buffer);

aten/src/ATen/cpu/vec/vec128/vec128_bfloat16_neon.h

@@ -2,7 +2,6 @@
 
 // DO NOT DEFINE STATIC DATA IN THIS HEADER!
 // See Note [Do not compile initializers with AVX]
-#include <ATen/cpu/vec/sve/sve_helper.h>
 #include <ATen/cpu/vec/vec128/vec128_float_neon.h>
 #include <ATen/cpu/vec/vec128/vec128_reduced_precision_common_neon.h>
 #include <ATen/cpu/vec/vec_base.h>
@@ -263,13 +262,6 @@ class Vectorized<c10::BFloat16> : public Vectorized16<
             c10::bit_cast<at_bfloat16_t>(val6.x),
             c10::bit_cast<at_bfloat16_t>(val7.x)}) {}
 
-#ifdef CPU_CAPABILITY_SVE128
-  Vectorized(svbfloat16_t v) : Vectorized16(svget_neonq(v)) {}
-  operator svbfloat16_t() const {
-    return svset_neonq(svundef_bf16(), values);
-  }
-#endif
-
   static Vectorized<c10::BFloat16> blendv(
       const Vectorized<c10::BFloat16>& a,
       const Vectorized<c10::BFloat16>& b,
@@ -382,23 +374,6 @@ class Vectorized<c10::BFloat16> : public Vectorized16<
   Vectorized ge(const Vectorized& other) const;
   Vectorized lt(const Vectorized& other) const;
   Vectorized le(const Vectorized& other) const;
-
-#ifdef CPU_CAPABILITY_SVE128
-
-  template <typename step_t>
-  static Vectorized<BFloat16> arange(
-      BFloat16 base = 0.f,
-      step_t step = static_cast<step_t>(1)) {
-    __at_align__ BFloat16 buffer[size()];
-    for (int64_t i = 0; i < size(); i++) {
-      buffer[i] = base + i * step;
-    }
-    return svget_neonq(
-        svld1_bf16(ptrue, reinterpret_cast<bfloat16_t*>(buffer)));
-  }
-
-#endif // CPU_CAPABILITY_SVE128
-
 }; // Vectorized<c10::BFloat16>
 
 inline std::tuple<Vectorized<float>, Vectorized<float>> convert_bfloat16_float(
@@ -422,24 +397,6 @@ inline Vectorized<c10::BFloat16> convert_float_bfloat16(
   return Vectorized<c10::BFloat16>(at_vcombine_bf16(x1, x2));
 }
 
-inline void load_fp32_from_bf16(const BFloat16* data, Vectorized<float>& out) {
-  __at_align__ float values[Vectorized<float>::size()];
-  for (const auto k : c10::irange(Vectorized<float>::size())) {
-    values[k] = data[k];
-  }
-  out = Vectorized<float>::loadu(values);
-}
-
-inline void load_fp32_from_bf16(
-    const BFloat16* data,
-    Vectorized<float>& out1,
-    Vectorized<float>& out2) {
-  Vectorized<BFloat16> bf16_vec = Vectorized<BFloat16>::loadu(data);
-  auto floats = convert_bfloat16_float(bf16_vec);
-  out1 = std::get<0>(floats);
-  out2 = std::get<1>(floats);
-}
-
 template <typename Op>
 Vectorized<c10::BFloat16> binary_operator_via_float(
     Op op,
@@ -622,12 +579,6 @@ Vectorized<c10::BFloat16> inline fnmsub(
   return -a * b - c;
 }
 
-#else //
-
-CONVERT_NON_VECTORIZED_INIT(BFloat16, bfloat16)
-
-LOAD_FP32_NON_VECTORIZED_INIT(BFloat16, bf16)
-
 #endif // !defined(C10_MOBILE) && defined(__aarch64__)
 
 } // namespace CPU_CAPABILITY

aten/src/ATen/cpu/vec/vec128/vec128_convert.h

@@ -4,7 +4,7 @@
 
 namespace at::vec {
 inline namespace CPU_CAPABILITY {
-#if defined(__aarch64__) && !defined(CPU_CAPABILITY_SVE256)
+#if (defined(__aarch64__) && !defined(CPU_CAPABILITY_SVE256))
 template <typename src_t>
 struct VecConvert<
     float,
@@ -60,7 +60,6 @@ struct VecConvert<float, 1, BFloat16, 1> {
   }
 };
 
-#endif // defined(__aarch64__) && (!defined(CPU_CAPABILITY_SVE) ||
-       // defined(CPU_CAPABILITY_SVE128))
+#endif // defined(__aarch64__) && !defined(CPU_CAPABILITY_SVE256)
 } // namespace CPU_CAPABILITY
 } // namespace at::vec

aten/src/ATen/cpu/vec/vec128/vec128_float_neon.h

@@ -4,10 +4,13 @@
 // See Note [Do not compile initializers with AVX]
 
 #include <ATen/cpu/vec/intrinsics.h>
-#include <ATen/cpu/vec/sve/sve_helper.h>
 #include <ATen/cpu/vec/vec_base.h>
 #include <c10/util/irange.h>
 
+#if defined(__aarch64__) && defined(AT_BUILD_ARM_VEC256_WITH_SLEEF)
+#include <sleef.h>
+#endif
+
 // Sleef offers vectorized versions of some transcedentals
 // such as sin, cos, tan etc..
 // However for now opting for STL, since we are not building
@@ -32,6 +35,12 @@ inline namespace CPU_CAPABILITY {
 #error "Big endian is not supported."
 #endif
 
+#if defined(AT_BUILD_ARM_VEC256_WITH_SLEEF)
+#define USE_SLEEF(sleef_code, non_sleef_code) sleef_code
+#else
+#define USE_SLEEF(sleef_code, non_sleef_code) non_sleef_code
+#endif
+
 template <int index, bool mask_val>
 struct BlendRegs {
   static float32x4_t impl(
@@ -85,12 +94,6 @@ class Vectorized<float> {
   operator float32x4_t() const {
     return values;
   }
-#ifdef CPU_CAPABILITY_SVE128
-  Vectorized(svfloat32_t v) : values(svget_neonq(v)) {}
-  operator svfloat32_t() const {
-    return svset_neonq(svundef_f32(), values);
-  }
-#endif
   template <int64_t mask>
   static Vectorized<float> blend(
       const Vectorized<float>& a,

aten/src/ATen/cpu/vec/vec128/vec128_half_neon.h

@@ -4,6 +4,7 @@
 // See Note [Do not compile initializers with AVX]
 
 #include <ATen/cpu/vec/intrinsics.h>
+#include <ATen/cpu/vec/vec128/vec128_convert.h>
 #include <ATen/cpu/vec/vec128/vec128_float_neon.h>
 #include <ATen/cpu/vec/vec128/vec128_reduced_precision_common_neon.h>
 #include <ATen/cpu/vec/vec_base.h>
@@ -24,6 +25,7 @@ inline namespace CPU_CAPABILITY {
 //    https://bugs.llvm.org/show_bug.cgi?id=45824
 // Most likely we will do aarch32 support with inline asm.
 #if !defined(C10_MOBILE) && defined(__aarch64__)
+
 #ifdef __BIG_ENDIAN__
 #error "Big endian is not supported."
 #endif
@@ -419,24 +421,6 @@ Vectorized<c10::Half> inline operator+(
 #endif
 }
 
-inline void load_fp32_from_fp16(const c10::Half* data, Vectorized<float>& out) {
-  __at_align__ float values[Vectorized<float>::size()];
-  for (const auto k : c10::irange(Vectorized<float>::size())) {
-    values[k] = data[k];
-  }
-  out = Vectorized<float>::loadu(values);
-}
-
-inline void load_fp32_from_fp16(
-    const c10::Half* data,
-    Vectorized<float>& out1,
-    Vectorized<float>& out2) {
-  Vectorized<c10::Half> f16_vec = Vectorized<c10::Half>::loadu(data);
-  auto floats = convert_half_float(f16_vec);
-  out1 = std::get<0>(floats);
-  out2 = std::get<1>(floats);
-}
-
 template <>
 Vectorized<c10::Half> inline operator-(
     const Vectorized<c10::Half>& a,
@@ -672,53 +656,6 @@ Vectorized<c10::Half> inline fnmsub(
   return -a * b - c;
 #endif
 }
-
-#else
-
-#define CONVERT_NON_VECTORIZED_INIT(type, name)                     \
-  inline std::tuple<Vectorized<float>, Vectorized<float>>           \
-      convert_##name##_float(const Vectorized<type>& a) {           \
-    constexpr int64_t K = Vectorized<type>::size();                 \
-    __at_align__ float arr[K];                                      \
-    __at_align__ type arr2[K];                                      \
-    a.store(arr2);                                                  \
-    convert(arr2, arr, K);                                          \
-    return std::make_tuple(                                         \
-        Vectorized<float>::loadu(arr),                              \
-        Vectorized<float>::loadu(arr + Vectorized<float>::size())); \
-  }                                                                 \
-  inline Vectorized<type> convert_float_##name(                     \
-      const Vectorized<float>& a, const Vectorized<float>& b) {     \
-    constexpr int64_t K = Vectorized<type>::size();                 \
-    __at_align__ float arr[K];                                      \
-    __at_align__ type arr2[K];                                      \
-    a.store(arr);                                                   \
-    b.store(arr + Vectorized<float>::size());                       \
-    convert(arr, arr2, K);                                          \
-    return Vectorized<type>::loadu(arr2);                           \
-  }
-
-#define LOAD_FP32_NON_VECTORIZED_INIT(type, name)                           \
-  inline void load_fp32_from_##name(                                        \
-      const type* data, Vectorized<float>& out) {                           \
-    __at_align__ float values[Vectorized<float>::size()];                   \
-    for (const auto k : c10::irange(Vectorized<float>::size())) {           \
-      values[k] = data[k];                                                  \
-    }                                                                       \
-    out = Vectorized<float>::loadu(values);                                 \
-  }                                                                         \
-                                                                            \
-  inline void load_fp32_from_##name(                                        \
-      const type* data, Vectorized<float>& out1, Vectorized<float>& out2) { \
-    load_fp32_from_##name(data, out1);                                      \
-    data += Vectorized<float>::size();                                      \
-    load_fp32_from_##name(data, out2);                                      \
-  }
-
-CONVERT_NON_VECTORIZED_INIT(Half, half)
-
-LOAD_FP32_NON_VECTORIZED_INIT(Half, fp16)
-
 #endif // !defined(C10_MOBILE) && defined(__aarch64__)
 
 } // namespace CPU_CAPABILITY

aten/src/ATen/cpu/vec/vec256/vec256.h

@@ -9,16 +9,21 @@
 #if !(                                                 \
     defined(__VSX__) || defined(CPU_CAPABILITY_VSX) || \
     defined(CPU_CAPABILITY_ZVECTOR))
-#include <ATen/cpu/vec/vec256/vec256_double.h>
+#if defined(CPU_CAPABILITY_SVE256)
+#include <ATen/cpu/vec/sve/vec_common_sve.h>
+#else
+// clang-format off
 #include <ATen/cpu/vec/vec256/vec256_float.h>
+#include <ATen/cpu/vec/vec256/vec256_double.h>
 #include <ATen/cpu/vec/vec256/vec256_int.h>
 #include <ATen/cpu/vec/vec256/vec256_qint.h>
+#endif
 #if !defined(CPU_CAPABILITY_SVE256) || !defined(__ARM_FEATURE_BF16)
 #include <ATen/cpu/vec/vec256/vec256_bfloat16.h>
 #endif
-#include <ATen/cpu/vec/vec256/vec256_complex_double.h>
-#include <ATen/cpu/vec/vec256/vec256_complex_float.h>
 #include <ATen/cpu/vec/vec256/vec256_half.h>
+#include <ATen/cpu/vec/vec256/vec256_complex_float.h>
+#include <ATen/cpu/vec/vec256/vec256_complex_double.h>
 // clang-format on
 #elif defined(__VSX__) || defined(CPU_CAPABILITY_VSX)
 #include <ATen/cpu/vec/vec256/vsx/vec256_common_vsx.h>
@@ -51,6 +56,34 @@ namespace at::vec {
 // accessed as `at::vec`.
 inline namespace CPU_CAPABILITY {
 
+inline std::ostream& operator<<(std::ostream& stream, const c10::qint32& val) {
+  stream << val.val_;
+  return stream;
+}
+inline std::ostream& operator<<(std::ostream& stream, const c10::qint8& val) {
+  stream << static_cast<int>(val.val_);
+  return stream;
+}
+inline std::ostream& operator<<(std::ostream& stream, const c10::quint8& val) {
+  stream << static_cast<unsigned int>(val.val_);
+  return stream;
+}
+
+template <typename T>
+std::ostream& operator<<(std::ostream& stream, const Vectorized<T>& vec) {
+  T buf[Vectorized<T>::size()];
+  vec.store(buf);
+  stream << "vec[";
+  for (int i = 0; i != Vectorized<T>::size(); i++) {
+    if (i != 0) {
+      stream << ", ";
+    }
+    stream << buf[i];
+  }
+  stream << "]";
+  return stream;
+}
+
 #if defined(CPU_CAPABILITY_AVX2)
 
 // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ CAST (AVX2) ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

aten/src/ATen/cpu/vec/vec256/vec256_bfloat16.h

@@ -268,7 +268,9 @@ LOAD_FP32_VECTORIZED_INIT(BFloat16, bf16)
 
 #else // defined(CPU_CAPABILITY_AVX2)
 
-#if !(defined(__aarch64__))
+#if !(                                                                      \
+    defined(__aarch64__) && !defined(C10_MOBILE) && !defined(__CUDACC__) && \
+    !defined(CPU_CAPABILITY_SVE256))
 CONVERT_NON_VECTORIZED_INIT(BFloat16, bfloat16)
 #endif
 

aten/src/ATen/cpu/vec/vec256/vec256_half.h

@@ -268,7 +268,9 @@ LOAD_FP32_VECTORIZED_INIT(Half, fp16)
 
 #else // defined(CPU_CAPABILITY_AVX2)
 
-#if !defined(__aarch64__) || defined(CPU_CAPABILITY_SVE256)
+#if !(                                                                      \
+    defined(__aarch64__) && !defined(C10_MOBILE) && !defined(__CUDACC__) && \
+    !defined(CPU_CAPABILITY_SVE256))
 CONVERT_NON_VECTORIZED_INIT(Half, half)
 #endif
 

aten/src/ATen/cpu/vec/vec256/vec256_qint.h

@@ -5,13 +5,6 @@
 
 #include <ATen/cpu/vec/intrinsics.h>
 #include <ATen/cpu/vec/vec_base.h>
-
-#ifdef __aarch64__
-#if defined(CPU_CAPABILITY_SVE128) || !defined(CPU_CAPABILITY_SVE)
-#include <ATen/cpu/vec/vec128/vec128_float_neon.h>
-#endif
-#endif
-
 #include <ATen/native/quantized/AffineQuantizerBase.h>
 
 #include <c10/util/irange.h>
@@ -922,7 +915,7 @@ Vectorized<c10::quint8> inline maximum(
   return a.maximum(b);
 }
 
-#else
+#elif !defined(CPU_CAPABILITY_SVE256)
 
 // NOTE: These are low-performance implementations that we fall back on
 // if we are not building with AVX2. This may not be an issue, because
@@ -1379,18 +1372,12 @@ Vectorized<c10::quint8> inline maximum(
   return a.maximum(b);
 }
 
-#if defined(__aarch64__) && \
-    (defined(CPU_CAPABILITY_SVE128) || !defined(CPU_CAPABILITY_SVE))
+#endif // if defined(CPU_CAPABILITY_AVX2)
+
+#if (defined(__aarch64__) && !defined(CPU_CAPABILITY_SVE256))
 std::pair<Vectorized<float>, Vectorized<float>> inline convert_int8_to_float(
     at::vec::Vectorized<int8_t> src) {
-
-#ifdef CPU_CAPABILITY_SVE
-  svint8_t x = src;
-  auto s8x8 = vget_low_s8(svget_neonq(x));
-#else
   auto s8x8 = vld1_s8(src.operator const int8_t*());
-#endif
-
   auto s16x8 = vmovl_s8(s8x8);
 
   auto s32x4_hi = vmovl_s16(vget_high_s16(s16x8));
@@ -1415,14 +1402,7 @@ std::pair<Vectorized<float>, Vectorized<float>> inline convert_int8_to_float(
 
 Vectorized<float> inline convert_int8_half_register_to_float(
     at::vec::Vectorized<int8_t> src) {
-
-#ifdef CPU_CAPABILITY_SVE
-  svint8_t x = src;
-  auto s8x8 = vget_low_s8(svget_neonq(x));
-#else
   auto s8x8 = vld1_s8(src.operator const int8_t*());
-#endif
-
   auto s16x8 = vmovl_s8(s8x8);
 
   auto s32x4_lo = vmovl_s16(vget_low_s16(s16x8));
@@ -1440,8 +1420,5 @@ Vectorized<float> inline convert_int8_half_register_to_float(
 }
 
 #endif
-
-#endif // if defined(CPU_CAPABILITY_AVX2)
-
 } // namespace CPU_CAPABILITY
 } // namespace at::vec

aten/src/ATen/cpu/vec/vec512/vec512.h

@@ -31,6 +31,34 @@ namespace vec {
 // See Note [CPU_CAPABILITY namespace]
 inline namespace CPU_CAPABILITY {
 
+inline std::ostream& operator<<(std::ostream& stream, const c10::qint32& val) {
+  stream << val.val_;
+  return stream;
+}
+inline std::ostream& operator<<(std::ostream& stream, const c10::qint8& val) {
+  stream << static_cast<int>(val.val_);
+  return stream;
+}
+inline std::ostream& operator<<(std::ostream& stream, const c10::quint8& val) {
+  stream << static_cast<unsigned int>(val.val_);
+  return stream;
+}
+
+template <typename T>
+std::ostream& operator<<(std::ostream& stream, const Vectorized<T>& vec) {
+  T buf[Vectorized<T>::size()];
+  vec.store(buf);
+  stream << "vec[";
+  for (int i = 0; i != Vectorized<T>::size(); i++) {
+    if (i != 0) {
+      stream << ", ";
+    }
+    stream << buf[i];
+  }
+  stream << "]";
+  return stream;
+}
+
 #if defined(CPU_CAPABILITY_AVX512)
 
 // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ CAST (AVX512)

aten/src/ATen/cpu/vec/vec_base.h

@@ -67,7 +67,18 @@ Windows llvm will not have this definition.
 #endif
 #define VECTOR_WIDTH 64
 #define int_vector __m512i
-#elif defined(CPU_CAPABILITY_AVX2) || defined(CPU_CAPABILITY_SVE256)
+#elif defined(__aarch64__) && \
+    !defined(CPU_CAPABILITY_SVE) // CPU_CAPABILITY_AVX512
+// SVE code expects 256-vectors; leave that set for SVE?
+#if defined(__GNUC__)
+#define __at_align__ __attribute__((aligned(16)))
+#elif defined(_WIN32)
+#define __at_align__ __declspec(align(16))
+#else
+#define __at_align__
+#endif
+#define VECTOR_WIDTH 16
+#else // CPU_CAPABILITY_AVX512
 #if defined(__GNUC__)
 #define __at_align__ __attribute__((aligned(32)))
 #elif defined(_WIN32)
@@ -77,27 +88,7 @@ Windows llvm will not have this definition.
 #endif
 #define VECTOR_WIDTH 32
 #define int_vector __m256i
-#elif defined(__aarch64__)
-// Define alignment and vector width for SVE128/Default (e.g., NEON)
-#if defined(__GNUC__)
-#define __at_align__ __attribute__((aligned(16)))
-#elif defined(_WIN32)
-#define __at_align__ __declspec(align(16))
-#else
-#define __at_align__
-#endif
-#define VECTOR_WIDTH 16
-#else
-// Fallback: define default alignment and vector width
-#if defined(__GNUC__)
-#define __at_align__ __attribute__((aligned(32)))
-#elif defined(_WIN32)
-#define __at_align__ __declspec(align(32))
-#else
-#define __at_align__
-#endif
-#define VECTOR_WIDTH 32
-#endif
+#endif // CPU_CAPABILITY_AVX512
 
 namespace at::vec {
 // See Note [CPU_CAPABILITY namespace]

aten/src/ATen/mps/MPSHooks.mm

@@ -70,10 +70,7 @@ void MPSHooks::commitStream() const {
 }
 
 void* MPSHooks::getCommandBuffer() const {
-  auto stream = at::mps::getDefaultMPSStream();
-  // Release pending computeCommandEncoder, as extensions is likely to allocate new one
-  stream->endKernelCoalescing();
-  return stream->commandBuffer();
+  return at::mps::getDefaultMPSStream()->commandBuffer();
 }
 
 void* MPSHooks::getDispatchQueue() const {

aten/src/ATen/mps/MPSStream.mm

@@ -158,18 +158,7 @@ void MPSStream::fill(id<MTLBuffer> buffer, uint8_t value, size_t length, size_t
       endKernelCoalescing();
       id<MTLBlitCommandEncoder> blitEncoder = [commandBuffer() blitCommandEncoder];
 
-      // For some reason fillBufferfor stopped working for lengh > 4Gb on MacOS 26
-      // See https://github.com/pytorch/pytorch/issues/163962
-      // Workaround by batching copy commands into 4Gb chunks
-      constexpr size_t max_copy_size = 0x100000000; // 4GB
-      size_t bytes_filled = 0;
-      size_t bytes_remains = length;
-      while (bytes_remains > 0) {
-        NSUInteger bytes_to_copy = std::min(max_copy_size, bytes_remains);
-        [blitEncoder fillBuffer:buffer range:NSMakeRange(offset + bytes_filled, bytes_to_copy) value:value];
-        bytes_filled += bytes_to_copy;
-        bytes_remains -= bytes_to_copy;
-      }
+      [blitEncoder fillBuffer:buffer range:NSMakeRange(offset, length) value:value];
       [blitEncoder endEncoding];
       synchronize(syncType);
     }

aten/src/ATen/native/BatchLinearAlgebraKernel.cpp

@@ -1157,103 +1157,103 @@ REGISTER_AVX512_DISPATCH(cholesky_stub, &cholesky_kernel)
 REGISTER_AVX2_DISPATCH(cholesky_stub, &cholesky_kernel)
 REGISTER_VSX_DISPATCH(cholesky_stub, &cholesky_kernel)
 REGISTER_ZVECTOR_DISPATCH(cholesky_stub, &cholesky_kernel)
-REGISTER_SVE_DISPATCH(cholesky_stub, &cholesky_kernel)
+REGISTER_SVE256_DISPATCH(cholesky_stub, &cholesky_kernel)
 
 REGISTER_ARCH_DISPATCH(cholesky_inverse_stub, DEFAULT, &cholesky_inverse_kernel_impl)
 REGISTER_AVX512_DISPATCH(cholesky_inverse_stub, &cholesky_inverse_kernel_impl)
 REGISTER_AVX2_DISPATCH(cholesky_inverse_stub, &cholesky_inverse_kernel_impl)
 REGISTER_VSX_DISPATCH(cholesky_inverse_stub, &cholesky_inverse_kernel_impl)
 REGISTER_ZVECTOR_DISPATCH(cholesky_inverse_stub, &cholesky_inverse_kernel_impl)
-REGISTER_SVE_DISPATCH(cholesky_inverse_stub, &cholesky_inverse_kernel_impl)
+REGISTER_SVE256_DISPATCH(cholesky_inverse_stub, &cholesky_inverse_kernel_impl)
 
 REGISTER_ARCH_DISPATCH(linalg_eig_stub, DEFAULT, &linalg_eig_kernel)
 REGISTER_AVX512_DISPATCH(linalg_eig_stub, &linalg_eig_kernel)
 REGISTER_AVX2_DISPATCH(linalg_eig_stub, &linalg_eig_kernel)
 REGISTER_VSX_DISPATCH(linalg_eig_stub, &linalg_eig_kernel)
 REGISTER_ZVECTOR_DISPATCH(linalg_eig_stub, &linalg_eig_kernel)
-REGISTER_SVE_DISPATCH(linalg_eig_stub, &linalg_eig_kernel)
+REGISTER_SVE256_DISPATCH(linalg_eig_stub, &linalg_eig_kernel)
 
 REGISTER_ARCH_DISPATCH(linalg_eigh_stub, DEFAULT, &linalg_eigh_kernel)
 REGISTER_AVX512_DISPATCH(linalg_eigh_stub, &linalg_eigh_kernel)
 REGISTER_AVX2_DISPATCH(linalg_eigh_stub, &linalg_eigh_kernel)
 REGISTER_VSX_DISPATCH(linalg_eigh_stub, &linalg_eigh_kernel)
 REGISTER_ZVECTOR_DISPATCH(linalg_eigh_stub, &linalg_eigh_kernel)
-REGISTER_SVE_DISPATCH(linalg_eigh_stub, &linalg_eigh_kernel)
+REGISTER_SVE256_DISPATCH(linalg_eigh_stub, &linalg_eigh_kernel)
 
 REGISTER_ARCH_DISPATCH(geqrf_stub, DEFAULT, &geqrf_kernel)
 REGISTER_AVX512_DISPATCH(geqrf_stub, &geqrf_kernel)
 REGISTER_AVX2_DISPATCH(geqrf_stub, &geqrf_kernel)
 REGISTER_VSX_DISPATCH(geqrf_stub, &geqrf_kernel)
 REGISTER_ZVECTOR_DISPATCH(geqrf_stub, &geqrf_kernel)
-REGISTER_SVE_DISPATCH(geqrf_stub, &geqrf_kernel)
+REGISTER_SVE256_DISPATCH(geqrf_stub, &geqrf_kernel)
 
 REGISTER_ARCH_DISPATCH(orgqr_stub, DEFAULT, &orgqr_kernel_impl)
 REGISTER_AVX512_DISPATCH(orgqr_stub, &orgqr_kernel_impl)
 REGISTER_AVX2_DISPATCH(orgqr_stub, &orgqr_kernel_impl)
 REGISTER_VSX_DISPATCH(orgqr_stub, &orgqr_kernel_impl)
 REGISTER_ZVECTOR_DISPATCH(orgqr_stub, &orgqr_kernel_impl)
-REGISTER_SVE_DISPATCH(orgqr_stub, &orgqr_kernel_impl)
+REGISTER_SVE256_DISPATCH(orgqr_stub, &orgqr_kernel_impl)
 
 REGISTER_ARCH_DISPATCH(ormqr_stub, DEFAULT, &ormqr_kernel)
 REGISTER_AVX512_DISPATCH(ormqr_stub, &ormqr_kernel)
 REGISTER_AVX2_DISPATCH(ormqr_stub, &ormqr_kernel)
 REGISTER_VSX_DISPATCH(ormqr_stub, &ormqr_kernel)
 REGISTER_ZVECTOR_DISPATCH(ormqr_stub, &ormqr_kernel)
-REGISTER_SVE_DISPATCH(ormqr_stub, &ormqr_kernel)
+REGISTER_SVE256_DISPATCH(ormqr_stub, &ormqr_kernel)
 
 REGISTER_ARCH_DISPATCH(lstsq_stub, DEFAULT, &lstsq_kernel)
 REGISTER_AVX512_DISPATCH(lstsq_stub, &lstsq_kernel)
 REGISTER_AVX2_DISPATCH(lstsq_stub, &lstsq_kernel)
 REGISTER_VSX_DISPATCH(lstsq_stub, &lstsq_kernel)
 REGISTER_ZVECTOR_DISPATCH(lstsq_stub, &lstsq_kernel)
-REGISTER_SVE_DISPATCH(lstsq_stub, &lstsq_kernel)
+REGISTER_SVE256_DISPATCH(lstsq_stub, &lstsq_kernel)
 
 REGISTER_ARCH_DISPATCH(triangular_solve_stub, DEFAULT, &triangular_solve_kernel)
 REGISTER_AVX512_DISPATCH(triangular_solve_stub, &triangular_solve_kernel)
 REGISTER_AVX2_DISPATCH(triangular_solve_stub, &triangular_solve_kernel)
 REGISTER_VSX_DISPATCH(triangular_solve_stub, &triangular_solve_kernel)
 REGISTER_ZVECTOR_DISPATCH(triangular_solve_stub, &triangular_solve_kernel)
-REGISTER_SVE_DISPATCH(triangular_solve_stub, &triangular_solve_kernel)
+REGISTER_SVE256_DISPATCH(triangular_solve_stub, &triangular_solve_kernel)
 
 REGISTER_ARCH_DISPATCH(lu_factor_stub, DEFAULT, &lu_factor_kernel)
 REGISTER_AVX512_DISPATCH(lu_factor_stub, &lu_factor_kernel)
 REGISTER_AVX2_DISPATCH(lu_factor_stub, &lu_factor_kernel)
 REGISTER_VSX_DISPATCH(lu_factor_stub, &lu_factor_kernel)
 REGISTER_ZVECTOR_DISPATCH(lu_factor_stub, &lu_factor_kernel)
-REGISTER_SVE_DISPATCH(lu_factor_stub, &lu_factor_kernel)
+REGISTER_SVE256_DISPATCH(lu_factor_stub, &lu_factor_kernel)
 
 REGISTER_ARCH_DISPATCH(ldl_factor_stub, DEFAULT, &ldl_factor_kernel)
 REGISTER_AVX512_DISPATCH(ldl_factor_stub, &ldl_factor_kernel)
 REGISTER_AVX2_DISPATCH(ldl_factor_stub, &ldl_factor_kernel)
 REGISTER_VSX_DISPATCH(ldl_factor_stub, &ldl_factor_kernel)
 REGISTER_ZVECTOR_DISPATCH(ldl_factor_stub, &ldl_factor_kernel)
-REGISTER_SVE_DISPATCH(ldl_factor_stub, &ldl_factor_kernel)
+REGISTER_SVE256_DISPATCH(ldl_factor_stub, &ldl_factor_kernel)
 
 REGISTER_ARCH_DISPATCH(ldl_solve_stub, DEFAULT, &ldl_solve_kernel)
 REGISTER_AVX512_DISPATCH(ldl_solve_stub, &ldl_solve_kernel)
 REGISTER_AVX2_DISPATCH(ldl_solve_stub, &ldl_solve_kernel)
 REGISTER_VSX_DISPATCH(ldl_solve_stub, &ldl_solve_kernel)
 REGISTER_ZVECTOR_DISPATCH(ldl_solve_stub, &ldl_solve_kernel)
-REGISTER_SVE_DISPATCH(ldl_solve_stub, &ldl_solve_kernel)
+REGISTER_SVE256_DISPATCH(ldl_solve_stub, &ldl_solve_kernel)
 
 REGISTER_ARCH_DISPATCH(lu_solve_stub, DEFAULT, &lu_solve_kernel)
 REGISTER_AVX512_DISPATCH(lu_solve_stub, &lu_solve_kernel)
 REGISTER_AVX2_DISPATCH(lu_solve_stub, &lu_solve_kernel)
 REGISTER_VSX_DISPATCH(lu_solve_stub, &lu_solve_kernel)
 REGISTER_ZVECTOR_DISPATCH(lu_solve_stub, &lu_solve_kernel)
-REGISTER_SVE_DISPATCH(lu_solve_stub, &lu_solve_kernel)
+REGISTER_SVE256_DISPATCH(lu_solve_stub, &lu_solve_kernel)
 
 REGISTER_ARCH_DISPATCH(svd_stub, DEFAULT, &svd_kernel)
 REGISTER_AVX512_DISPATCH(svd_stub, &svd_kernel)
 REGISTER_AVX2_DISPATCH(svd_stub, &svd_kernel)
 REGISTER_VSX_DISPATCH(svd_stub, &svd_kernel)
 REGISTER_ZVECTOR_DISPATCH(svd_stub, &svd_kernel)
-REGISTER_SVE_DISPATCH(svd_stub, &svd_kernel)
+REGISTER_SVE256_DISPATCH(svd_stub, &svd_kernel)
 
 REGISTER_ARCH_DISPATCH(unpack_pivots_stub, DEFAULT, &unpack_pivots_cpu_kernel)
 REGISTER_AVX512_DISPATCH(unpack_pivots_stub, &unpack_pivots_cpu_kernel)
 REGISTER_AVX2_DISPATCH(unpack_pivots_stub, &unpack_pivots_cpu_kernel)
 REGISTER_VSX_DISPATCH(unpack_pivots_stub, &unpack_pivots_cpu_kernel)
 REGISTER_ZVECTOR_DISPATCH(unpack_pivots_stub, &unpack_pivots_cpu_kernel)
-REGISTER_SVE_DISPATCH(unpack_pivots_stub, &unpack_pivots_cpu_kernel)
+REGISTER_SVE256_DISPATCH(unpack_pivots_stub, &unpack_pivots_cpu_kernel)
 } // namespace at::native

aten/src/ATen/native/Convolution.cpp

@@ -406,23 +406,11 @@ struct ConvParams {
   // cudnn and miopen are guaranteed not to be on mobile, and T102591915 / T110194934 suggest
   // that maybe the compiledWithCuDNN() check sometimes segfaults (though I can't imagine how)
 #if !defined(C10_MOBILE)
-    if (!detail::getCUDAHooks().compiledWithCuDNN() || !input.is_cuda() || !cudnn_enabled) {
+    if (!detail::getCUDAHooks().compiledWithCuDNN()) {
       return false;
     }
-    static long cudnn_version = detail::getCUDAHooks().versionCuDNN();
-    // broken on cuDNN 9.8
-    if (cudnn_version >= 90800) {
-      if (cudnn_conv_suggest_memory_format(input, weight) == at::MemoryFormat::Contiguous &&
-          (input.scalar_type() == at::kBFloat16 || input.scalar_type() == at::kHalf) &&
-          weight.dim() == 5) {
-        for (int i = 2; i < weight.dim(); i++) {
-          if (weight.size(i) != 1) {
-            return false;
-          }
-        }
-      }
-    }
     if (needs_64bit_indexing_no_split(input, weight)) {
+      static long cudnn_version = detail::getCUDAHooks().versionCuDNN();
       if (!(cudnn_version >= 90300 && at::native::cudnnv8_enabled_check_debug())) {
         TORCH_WARN_ONCE("cuDNN cannot be used for large non-batch-splittable convolutions"
                         " if the V8 API is not enabled or before cuDNN version 9.3+."
@@ -430,6 +418,9 @@ struct ConvParams {
         return false;
       }
     }
+    if (!input.is_cuda() || !cudnn_enabled) {
+      return false;
+    }
     if (input.scalar_type() == at::kBFloat16 || weight.scalar_type() == at::kBFloat16) {
       if (!(detail::getCUDAHooks().supportsBFloat16ConvolutionWithCuDNNv8() && at::native::cudnnv8_enabled_check_debug())) {
         return false;
@@ -448,19 +439,16 @@ struct ConvParams {
 
   // Use cudnn for FP16 depthwise convolutions
   bool use_cudnn_depthwise(const at::Tensor& input, const at::Tensor& weight) const  {
-    if (!cudnn_enabled || !detail::getCUDAHooks().compiledWithCuDNN() || !input.is_cuda()) {
+    if (!detail::getCUDAHooks().compiledWithCuDNN()) {
       return false;
     }
+    if (cudnn_conv_suggest_memory_format(input, weight) != at::MemoryFormat::Contiguous && use_cudnn(input, weight)) {
+      // always use cudnn_depthwise for channels_last format
+      return true;
+    }
     // native kernel doesn't support 64-bit non-splittable case
-    if (!(canUse32BitIndexMath(input) && canUse32BitIndexMath(weight))) {
+    if (cudnn_enabled && !(canUse32BitIndexMath(input) && canUse32BitIndexMath(weight))) {
       static long cudnn_version = detail::getCUDAHooks().compiledWithCuDNN() ? detail::getCUDAHooks().versionCuDNN() : -1;
-      // TODO(eqy): remove this once cuDNN fixes 64-bit depthwise support, first broken in 9.11x
-      if (cudnn_conv_suggest_memory_format(input, weight) != at::MemoryFormat::Contiguous) {
-        if (cudnn_version < 0 || cudnn_version > 91000) {
-          return false;
-        }
-      }
-
       if (!(cudnn_version >= 90300 && at::native::cudnnv8_enabled_check_debug())) {
         TORCH_WARN_ONCE("cuDNN cannot be used for large non-batch-splittable convolutions"
                         " if the V8 API is not enabled or before cuDNN version 9.3+."
@@ -470,10 +458,6 @@ struct ConvParams {
         return true;
       }
     }
-    if (cudnn_conv_suggest_memory_format(input, weight) != at::MemoryFormat::Contiguous) {
-      // always use cudnn_depthwise for channels_last format
-      return true;
-    }
     if (detail::getCUDAHooks().supportsDepthwiseConvolutionWithCuDNN()) {
       bool kernel_cond =  (use_cudnn(input, weight) &&
                            input.scalar_type() == kHalf && // only for FP16

aten/src/ATen/native/DispatchStub.cpp

@@ -39,21 +39,19 @@ static CPUCapability compute_cpu_capability() {
     }
 #elif defined(HAVE_SVE_CPU_DEFINITION)
     int sve_vl = cpuinfo_get_max_arm_sve_length(); //Returns maximum SVE VL supported by your HW.
-    if (envar == "sve") {
-      // Select SVE capability based on the maximum SVE VL supported by the HW.
+#ifdef HAVE_SVE256_CPU_DEFINITION
+    if (envar == "sve256") {
       if (sve_vl == 256) {
+#ifdef HAVE_ARM_BF16_CPU_DEFINITION
         if (cpuinfo_has_arm_bf16()) {
           return CPUCapability::SVE256;
         }
-      } else if (sve_vl == 128) {
-        if (cpuinfo_has_arm_bf16()) {
-          return CPUCapability::SVE128;
-        }
-      } else {
-        TORCH_WARN("SVE capability not available on hardware. Falling back to DEFAULT");
-        return CPUCapability::DEFAULT;
+#endif
       }
+      TORCH_WARN("SVE256 capability not available on hardware. Falling back to DEFAULT");
+      return CPUCapability::DEFAULT;
     }
+#endif
 #else
 #ifdef HAVE_AVX512_CPU_DEFINITION
     if (envar == "avx512") {
@@ -115,11 +113,6 @@ static CPUCapability compute_cpu_capability() {
         #endif
         }
     #endif
-    #ifdef HAVE_SVE128_CPU_DEFINITION
-        if (sve_vl == 128) { // Check for SVE128
-            return CPUCapability::SVE128;
-        }
-    #endif
     // Return the default CPU capability.
     return CPUCapability::DEFAULT;
   }
@@ -154,9 +147,6 @@ DispatchResult DispatchStubImpl::try_get_call_ptr(
 #ifdef HAVE_SVE256_CPU_DEFINITION
   , void *SVE256
 #endif
-#ifdef HAVE_SVE128_CPU_DEFINITION
-  , void *SVE128
-#endif
 ) {
   constexpr auto supported_devices = c10::array_of<c10::DeviceType>(
         c10::DeviceType::CPU,
@@ -194,9 +184,6 @@ DispatchResult DispatchStubImpl::try_get_call_ptr(
 #endif
 #ifdef HAVE_SVE256_CPU_DEFINITION
           , SVE256
-#endif
-#ifdef HAVE_SVE128_CPU_DEFINITION
-          , SVE128
 #endif
         );
         if (!std::holds_alternative<ErrorType>(result)) {
@@ -255,9 +242,6 @@ void* DispatchStubImpl::get_call_ptr(
 #ifdef HAVE_SVE256_CPU_DEFINITION
   , void *SVE256
 #endif
-#ifdef HAVE_SVE128_CPU_DEFINITION
-  , void *SVE128
-#endif
 ) {
 
   auto result = try_get_call_ptr(
@@ -282,10 +266,6 @@ void* DispatchStubImpl::get_call_ptr(
 #ifdef HAVE_SVE256_CPU_DEFINITION
       ,
       SVE256
-#endif
-#ifdef HAVE_SVE128_CPU_DEFINITION
-      ,
-      SVE128
 #endif
   );
   if (std::holds_alternative<ErrorType>(result)) {
@@ -320,9 +300,6 @@ DispatchResult DispatchStubImpl::try_choose_cpu_impl(
 #endif
 #ifdef HAVE_SVE256_CPU_DEFINITION
     , void *SVE256
-#endif
-#ifdef HAVE_SVE128_CPU_DEFINITION
-    , void *SVE128
 #endif
   ){
 
@@ -365,16 +342,6 @@ DispatchResult DispatchStubImpl::try_choose_cpu_impl(
       return DispatchResult(SVE256);
     }
   }
-#endif
-#ifdef HAVE_SVE128_CPU_DEFINITION
-  if (capability >= static_cast<int>(CPUCapability::SVE128)) {
-    if (C10_UNLIKELY(!SVE128)) {
-      // dispatch to DEFAULT, since the SVE kernel is missing
-      return DEFAULT != nullptr ? DispatchResult(DEFAULT) : ErrorType::MissingDeviceKernel;
-    } else {
-      return DispatchResult(SVE128);
-    }
-  }
 #endif
   return DEFAULT != nullptr ? DispatchResult(DEFAULT) : ErrorType::MissingDeviceKernel;
 }
@@ -396,9 +363,6 @@ void* DispatchStubImpl::choose_cpu_impl(
 #ifdef HAVE_SVE256_CPU_DEFINITION
   , void *SVE256
 #endif
-#ifdef HAVE_SVE128_CPU_DEFINITION
-  , void *SVE128
-#endif
 ) {
   auto capability = static_cast<int>(get_cpu_capability());
   (void)capability;
@@ -444,17 +408,6 @@ void* DispatchStubImpl::choose_cpu_impl(
       return SVE256;
     }
   }
-#endif
-#ifdef HAVE_SVE128_CPU_DEFINITION
-  if (capability >= static_cast<int>(CPUCapability::SVE128)) {
-    if (C10_UNLIKELY(!SVE128)) {
-      // dispatch to DEFAULT, since the SVE kernel is missing
-      TORCH_INTERNAL_ASSERT(DEFAULT, "DispatchStub: missing default kernel");
-      return DEFAULT;
-    } else {
-      return SVE128;
-    }
-  }
 #endif
   TORCH_INTERNAL_ASSERT(DEFAULT, "DispatchStub: missing default kernel");
   return DEFAULT;

aten/src/ATen/native/DispatchStub.h

@@ -64,9 +64,8 @@ enum class CPUCapability {
   VSX = 1,
 #elif defined(HAVE_ZVECTOR_CPU_DEFINITION)
   ZVECTOR = 1,
-#elif defined(HAVE_SVE_CPU_DEFINITION) && defined(HAVE_ARM_BF16_CPU_DEFINITION)
+#elif defined(HAVE_SVE256_CPU_DEFINITION) && defined(HAVE_ARM_BF16_CPU_DEFINITION)
   SVE256 = 1,
-  SVE128 = 2,
 #else
   AVX2 = 1,
   AVX512 = 2,
@@ -118,9 +117,6 @@ struct TORCH_API DispatchStubImpl {
 #endif
 #ifdef HAVE_SVE256_CPU_DEFINITION
       , void *SVE256
-#endif
-#ifdef HAVE_SVE128_CPU_DEFINITION
-      , void *SVE128
 #endif
   );
 
@@ -142,9 +138,6 @@ struct TORCH_API DispatchStubImpl {
 #endif
 #ifdef HAVE_SVE256_CPU_DEFINITION
     , void *SVE256
-#endif
-#ifdef HAVE_SVE128_CPU_DEFINITION
-    , void *SVE128
 #endif
   );
 
@@ -166,9 +159,6 @@ struct TORCH_API DispatchStubImpl {
 #endif
 #ifdef HAVE_SVE256_CPU_DEFINITION
       , void *SVE256
-#endif
-#ifdef HAVE_SVE128_CPU_DEFINITION
-      , void *SVE128
 #endif
   );
 
@@ -193,9 +183,6 @@ struct TORCH_API DispatchStubImpl {
 #endif
 #ifdef HAVE_SVE256_CPU_DEFINITION
     , void *SVE256
-#endif
-#ifdef HAVE_SVE128_CPU_DEFINITION
-    , void *SVE128
 #endif
   );
 
@@ -253,9 +240,6 @@ private:
 #endif
 #ifdef HAVE_SVE256_CPU_DEFINITION
       , reinterpret_cast<void*>(SVE256)
-#endif
-#ifdef HAVE_SVE128_CPU_DEFINITION
-      , reinterpret_cast<void*>(SVE128)
 #endif
       )
     );
@@ -317,9 +301,6 @@ public:
 #endif
 #ifdef HAVE_SVE256_CPU_DEFINITION
       , reinterpret_cast<void*>(SVE256)
-#endif
-#ifdef HAVE_SVE128_CPU_DEFINITION
-      , reinterpret_cast<void*>(SVE128)
 #endif
       );
     if (std::holds_alternative<ErrorType>(result)){
@@ -344,9 +325,6 @@ public:
 #ifdef HAVE_SVE256_CPU_DEFINITION
   static TORCH_API FnPtr SVE256;
 #endif
-#ifdef HAVE_SVE128_CPU_DEFINITION
-  static TORCH_API FnPtr SVE128;
-#endif
 private:
   DispatchStubImpl impl;
 };
@@ -454,12 +432,6 @@ struct RegisterPRIVATEUSE1Dispatch {
 #define REGISTER_SVE256_DISPATCH(name, fn)
 #endif
 
-#ifdef HAVE_SVE128_CPU_DEFINITION
-#define REGISTER_SVE128_DISPATCH(name, fn) REGISTER_ARCH_DISPATCH(name, SVE128, fn)
-#else
-#define REGISTER_SVE128_DISPATCH(name, fn)
-#endif
-
 // Macro to register the same kernel for all CPU arch types. This is useful
 // if a kernel does not benefit from being recompiled across different arch types.
 #define REGISTER_ALL_CPU_DISPATCH(name, fn)                                    \
@@ -468,11 +440,6 @@ struct RegisterPRIVATEUSE1Dispatch {
   REGISTER_AVX2_DISPATCH(name, fn)                                             \
   REGISTER_VSX_DISPATCH(name, fn)                                              \
   REGISTER_ZVECTOR_DISPATCH(name, fn)                                          \
-  REGISTER_SVE256_DISPATCH(name, fn)                                           \
-  REGISTER_SVE128_DISPATCH(name, fn)
-
-#define REGISTER_SVE_DISPATCH(name, fn)                                        \
-  REGISTER_SVE128_DISPATCH(name, fn)                                           \
   REGISTER_SVE256_DISPATCH(name, fn)
 
 #define REGISTER_NO_CPU_DISPATCH(name)                                         \
@@ -515,7 +482,6 @@ struct RegisterPRIVATEUSE1Dispatch {
 // REGISTER_DISPATCH now dispatches an AVX512 kernel to nullptr but registers other dispatches.
 // ALSO_REGISTER_AVX512_DISPATCH should be used for ensuring AVX512 dispatch, among others.
 // ALSO_REGISTER_SVE256_DISPATCH should be used for ensuring SVE256 dispatch, among others.
-// ALSO_REGISTER_SVE128_DISPATCH should be used for ensuring SVE128 dispatch, among others.
 #ifdef CPU_CAPABILITY_AVX512
 #define REGISTER_DISPATCH(name, fn) REGISTER_ARCH_DISPATCH(name, CPU_CAPABILITY, ((void*)(fn) ? nullptr : nullptr))
 #else
@@ -523,7 +489,6 @@ struct RegisterPRIVATEUSE1Dispatch {
 #endif
 #define ALSO_REGISTER_AVX512_DISPATCH(name, fn) REGISTER_ARCH_DISPATCH(name, CPU_CAPABILITY, fn)
 #define ALSO_REGISTER_SVE256_DISPATCH(name, fn) REGISTER_ARCH_DISPATCH(name, CPU_CAPABILITY, fn)
-#define ALSO_REGISTER_SVE128_DISPATCH(name, fn) REGISTER_ARCH_DISPATCH(name, CPU_CAPABILITY, fn)
 #endif
 } // namespace at::native
 

aten/src/ATen/native/SegmentReduce.cpp

@@ -466,7 +466,7 @@ REGISTER_AVX2_DISPATCH(_segment_reduce_lengths_stub, &_segment_reduce_lengths_cp
 REGISTER_AVX512_DISPATCH(_segment_reduce_lengths_stub, &_segment_reduce_lengths_cpu_kernel)
 REGISTER_VSX_DISPATCH(_segment_reduce_lengths_stub, &_segment_reduce_lengths_cpu_kernel)
 REGISTER_ZVECTOR_DISPATCH(_segment_reduce_lengths_stub, &_segment_reduce_lengths_cpu_kernel)
-REGISTER_SVE_DISPATCH(_segment_reduce_lengths_stub, &_segment_reduce_lengths_cpu_kernel)
+REGISTER_SVE256_DISPATCH(_segment_reduce_lengths_stub, &_segment_reduce_lengths_cpu_kernel)
 
 // offsets dispatches
 REGISTER_ARCH_DISPATCH(
@@ -477,7 +477,7 @@ REGISTER_AVX2_DISPATCH(_segment_reduce_offsets_stub, &_segment_reduce_offsets_cp
 REGISTER_AVX512_DISPATCH(_segment_reduce_offsets_stub, &_segment_reduce_offsets_cpu_kernel)
 REGISTER_VSX_DISPATCH(_segment_reduce_offsets_stub, &_segment_reduce_offsets_cpu_kernel)
 REGISTER_ZVECTOR_DISPATCH(_segment_reduce_offsets_stub, &_segment_reduce_offsets_cpu_kernel)
-REGISTER_SVE_DISPATCH(_segment_reduce_offsets_stub, &_segment_reduce_offsets_cpu_kernel)
+REGISTER_SVE256_DISPATCH(_segment_reduce_offsets_stub, &_segment_reduce_offsets_cpu_kernel)
 
 // Currently some computation is being duplicated across forward and backward.
 // TODO: Cache indices in forward pass to reuse in backward
@@ -548,7 +548,7 @@ REGISTER_VSX_DISPATCH(
 REGISTER_ZVECTOR_DISPATCH(
     _segment_reduce_lengths_backward_stub,
     &_segment_reduce_cpu_lengths_backward_kernel)
-REGISTER_SVE_DISPATCH(
+REGISTER_SVE256_DISPATCH(
     _segment_reduce_lengths_backward_stub,
     &_segment_reduce_cpu_lengths_backward_kernel)
 
@@ -568,7 +568,7 @@ REGISTER_VSX_DISPATCH(
 REGISTER_ZVECTOR_DISPATCH(
     _segment_reduce_offsets_backward_stub,
     &_segment_reduce_cpu_offsets_backward_kernel)
-REGISTER_SVE_DISPATCH(
+REGISTER_SVE256_DISPATCH(
     _segment_reduce_offsets_backward_stub,
     &_segment_reduce_cpu_offsets_backward_kernel)
 

aten/src/ATen/native/cpu/ReducedPrecisionFloatGemvFastPathKernel.cpp

@@ -212,7 +212,7 @@ std::pair<vec::Vectorized<float>, vec::Vectorized<float>> fmadd(
     const vec::Vectorized<c10::Half>& b,
     const vec::Vectorized<float>& acc_low,
     const vec::Vectorized<float>& acc_high) {
-#if defined(__aarch64__) && ((defined(__ARM_FEATURE_FP16_FML) && !defined(__ARM_FEATURE_SVE)) || (defined(CPU_CAPABILITY_SVE128)))
+#if defined(__ARM_FEATURE_FP16_FML) && !defined(CPU_CAPABILITY_SVE)
   return std::make_pair(vfmlalq_low_f16(acc_low, a, b), vfmlalq_high_f16(acc_high, a, b));
 #else
   const auto [a_float_low, a_float_high] = convert_half_float(a);
@@ -233,7 +233,7 @@ std::pair<vec::Vectorized<float>, vec::Vectorized<float>> fmadd(
 
 // Return a + b_low * c_low + b_high * c_high
 vec::Vectorized<float> fmadd(vec::Vectorized<float> a, vec::Vectorized<Half> b, vec::Vectorized<Half> c) {
-#if defined(__aarch64__) && ((defined(__ARM_FEATURE_FP16_FML) && !defined(__ARM_FEATURE_SVE)) || (defined(CPU_CAPABILITY_SVE128)))
+#if defined(__aarch64__) && defined(__ARM_FEATURE_FP16_FML) && !defined(__ARM_FEATURE_SVE)
   // NOTE: this instruction is an optional instruction in ARM v8.2 and
   // v8.3, but mandatory in v8.4 per
   // https://developer.arm.com/documentation/ddi0596/2021-03/SIMD-FP-Instructions/FMLAL--FMLAL2--vector---Floating-point-fused-Multiply-Add-Long-to-accumulator--vector--?lang=en

aten/src/ATen/native/cuda/Blas.cpp

@@ -1124,17 +1124,6 @@ bool is_rowwise_scaling(const at::Tensor& t, const at::Tensor& scale) {
       && scale.is_contiguous());
 }
 
-bool check_size_stride(const at::Tensor& scale, int dim, int size, int stride) {
-  // For Blockwise1x128 and Blockwise128x128,
-  // when the scale tensor has a dimension of size 1, the stride is effectively
-  // "meaningless", i.e. PyTorch decides to use a stride of 1. Thus, the regular
-  // stride check fails. Here, we relax the stride check when the effective
-  // stride is 1.
-
-  return (
-      scale.size(dim) == size && (size <= 1 || scale.stride(dim) == stride));
-}
-
 // 1x16 blocks for packed nvfp4 data and fp8_e4m3fn scales
 bool is_blockwise_1x16_scaling(const at::Tensor& t, const at::Tensor& scale) {
   // Multiply t.size(1) by 2 to adjust for fp4x2 packing
@@ -1160,24 +1149,15 @@ bool is_blockwise_1x32_scaling(const at::Tensor& t, const at::Tensor& scale) {
 }
 
 bool is_blockwise_1x128_scaling(const at::Tensor& t, const at::Tensor& scale) {
-  return (
-      isFloat8Type(t.scalar_type()) && scale.scalar_type() == kFloat &&
-      scale.dim() == 2 && check_size_stride(scale, 0, t.size(0), 1) &&
-      check_size_stride(
-          scale, 1, ceil_div<int64_t>(t.size(1), 128), t.size(0)));
+  return (isFloat8Type(t.scalar_type()) && scale.scalar_type() == kFloat && scale.dim() == 2
+      && scale.size(0) == t.size(0) && scale.size(1) == ceil_div<int64_t>(t.size(1), 128)
+      && scale.stride(0) == 1 && scale.stride(1) == t.size(0));
 }
 
 bool is_blockwise_128x128_scaling(const at::Tensor& t, const at::Tensor& scale) {
-  return (
-      isFloat8Type(t.scalar_type()) && scale.scalar_type() == kFloat &&
-      scale.dim() == 2 &&
-      check_size_stride(
-          scale,
-          0,
-          ceil_div<int64_t>(t.size(0), 128),
-          round_up<int64_t>(ceil_div<int64_t>(t.size(1), 128), 4)) &&
-      check_size_stride(
-          scale, 1, ceil_div<int64_t>(t.size(1), 128), 1));
+  return (isFloat8Type(t.scalar_type()) && scale.scalar_type() == kFloat && scale.dim() == 2
+      && scale.size(0) == ceil_div<int64_t>(t.size(0), 128) && scale.size(1) == ceil_div<int64_t>(t.size(1), 128)
+      && scale.stride(0) == round_up<int64_t>(ceil_div<int64_t>(t.size(1), 128), 4) && scale.stride(1) == 1);
 }
 
 bool is_desired_scaling(const at::Tensor& t, const at::Tensor& scale, ScalingType desired_scaling) {

aten/src/ATen/native/cuda/IndexKernel.cu

@@ -5,7 +5,6 @@
 #include <array>
 #include <type_traits>
 #include <ATen/core/TensorBase.h>
-#include <ATen/ceil_div.h>
 #include <ATen/Dispatch.h>
 #include <ATen/Dispatch_v2.h>
 #include <ATen/cuda/CUDAContext.h>
@@ -84,17 +83,11 @@ void gpu_index_kernel(TensorIteratorBase& iter, const IntArrayRef index_size, co
         auto ind_dim_size = index_size[0];
         auto inp_stride_bytes = index_stride[0];
         auto out_stride_bytes = iter.strides(0)[1];
-        // avoid grid overflow in the fast kernel
-        const int64_t vec_chunks = ceil_div(slice_size, alignment);
-        const int64_t blocks_per_slice_upper = ceil_div(vec_chunks, (int64_t)launch_size_nd);
-        const int max_grid_y = at::cuda::getCurrentDeviceProperties()->maxGridSize[1];
-        // if it's an eligible grid we use the fast path, otherwise default to slower path
-        if (blocks_per_slice_upper <= max_grid_y) {
-          at::native::vectorized_gather_kernel_launch<alignment, int64_t>(out_ptr, in_ptr, (int64_t*)iter.data_ptr(2), num_ind,
-          slice_size, ind_dim_size, inp_stride_bytes, out_stride_bytes, /*allow_neg_indices*/true);
-          return;
-        }
-    }
+        if (iter.numel() == 0) return;
+        at::native::vectorized_gather_kernel_launch<alignment, int64_t>(out_ptr, in_ptr, (int64_t*)iter.data_ptr(2), num_ind,
+        slice_size, ind_dim_size, inp_stride_bytes, out_stride_bytes, /*allow_neg_indices*/true);
+        return;
+      }
   }
 
   auto sizes = std::array<int64_t, MAX_DIMS>{};

aten/src/ATen/native/cuda/linalg/BatchLinearAlgebra.cpp

@@ -1238,7 +1238,7 @@ Tensor _cholesky_solve_helper_cuda_magma(const Tensor& self, const Tensor& A, bo
 // Todo: cusolverDn<T>potrsBatched only supports nrhs == 1 and does not have good performance.
 //     Batched cholesky_solve is dispatched to magma.
 Tensor _cholesky_solve_helper_cuda(const Tensor& self, const Tensor& A, bool upper) {
-#if defined(USE_LINALG_SOLVER)
+#if defined(USE_LINALG_SOLVER) && !defined(USE_ROCM)
   auto preferred_backend = at::globalContext().linalgPreferredBackend();
   switch (preferred_backend) {
     case at::LinalgBackend::Cusolver:
@@ -1352,7 +1352,7 @@ void cholesky_helper_magma(const Tensor& input, bool upper, const Tensor& info)
 }
 
 static void cholesky_kernel(const Tensor& input, const Tensor& info, bool upper) {
-#if defined(USE_LINALG_SOLVER)
+#if defined(USE_LINALG_SOLVER) && !defined(USE_ROCM)
   auto preferred_backend = at::globalContext().linalgPreferredBackend();
   switch (preferred_backend) {
     case at::LinalgBackend::Cusolver:
@@ -2709,7 +2709,7 @@ void linalg_lstsq_gels(const Tensor& A, const Tensor& B, const Tensor& /*infos*/
 }
 
 void gels_looped(const Tensor& a, Tensor& b, Tensor& infos) {
-#if defined(USE_LINALG_SOLVER)
+#if defined(USE_LINALG_SOLVER) && !defined(USE_ROCM)
   auto preferred_backend = at::globalContext().linalgPreferredBackend();
   switch (preferred_backend) {
     case at::LinalgBackend::Magma:
@@ -2733,7 +2733,7 @@ void lstsq_kernel(const Tensor& a, Tensor& b, Tensor& /*rank*/, Tensor& /*singul
   // first handle the underdetermined case (m < n)
   // this case is not supported by MAGMA or cuBLAS
   if (m < n) {
-#if defined(USE_LINALG_SOLVER)
+#if defined(USE_LINALG_SOLVER) && !defined(USE_ROCM)
     linalg_lstsq_gels(a, b, infos);
 #else
     TORCH_CHECK(

aten/src/ATen/native/mkl/SpectralOps.cpp

@@ -165,7 +165,7 @@ REGISTER_AVX2_DISPATCH(fft_fill_with_conjugate_symmetry_stub, &_fft_fill_with_co
 REGISTER_AVX512_DISPATCH(fft_fill_with_conjugate_symmetry_stub, &_fft_fill_with_conjugate_symmetry_cpu_)
 REGISTER_ZVECTOR_DISPATCH(fft_fill_with_conjugate_symmetry_stub, &_fft_fill_with_conjugate_symmetry_cpu_)
 REGISTER_VSX_DISPATCH(fft_fill_with_conjugate_symmetry_stub, &_fft_fill_with_conjugate_symmetry_cpu_)
-REGISTER_SVE_DISPATCH(fft_fill_with_conjugate_symmetry_stub, &_fft_fill_with_conjugate_symmetry_cpu_)
+REGISTER_SVE256_DISPATCH(fft_fill_with_conjugate_symmetry_stub, &_fft_fill_with_conjugate_symmetry_cpu_)
 
 // _out variants can be shared between PocketFFT and MKL
 Tensor& _fft_r2c_mkl_out(const Tensor& self, IntArrayRef dim, int64_t normalization,

aten/src/ATen/native/mps/kernels/Attention.metal

@@ -14,8 +14,8 @@ template <typename T, int D, int V = D>
     device T* out [[buffer(3)]],
     const constant uint& gqa_factor [[buffer(4)]],
     const constant uint& N [[buffer(5)]],
-    const constant uint3& qkv_head_strides [[buffer(6)]],
-    const constant uint3& qkv_seq_strides [[buffer(7)]],
+    const constant uint2& k_head_seq_stride [[buffer(6)]],
+    const constant uint2& v_head_seq_stride [[buffer(7)]],
     const constant float& scale [[buffer(8)]],
     const device bool* mask [[buffer(9)]],
     const constant uint3& mask_strides [[buffer(10)]],
@@ -28,12 +28,10 @@ template <typename T, int D, int V = D>
   constexpr uint BD = 32;
   constexpr uint qk_per_thread = D / BD;
   constexpr uint v_per_thread = V / BD;
-  const uint q_head_stride = qkv_head_strides.x;
-  const uint q_seq_stride = qkv_seq_strides.x;
-  const uint k_head_stride = qkv_head_strides.y;
-  const uint k_seq_stride = qkv_seq_strides.y;
-  const uint v_head_stride = qkv_head_strides.z;
-  const uint v_seq_stride = qkv_seq_strides.z;
+  const uint k_head_stride = k_head_seq_stride.x;
+  const uint k_seq_stride = k_head_seq_stride.y;
+  const uint v_head_stride = v_head_seq_stride.x;
+  const uint v_seq_stride = v_head_seq_stride.y;
   const uint mask_head_stride = mask_strides.x;
   const uint mask_kv_seq_stride = mask_strides.y;
   const uint mask_q_seq_stride = mask_strides.z;
@@ -56,9 +54,9 @@ template <typename T, int D, int V = D>
   const int kv_head_idx = head_idx / gqa_factor;
   const int Q = tpg.y;
   const int group_offset = head_idx * Q + q_seq_idx;
+  const int q_offset = group_offset;
   const int o_offset = group_offset;
-  queries += head_idx * q_head_stride + q_seq_idx * q_seq_stride +
-      simd_lid * qk_per_thread;
+  queries += q_offset * D + simd_lid * qk_per_thread;
   keys += kv_head_idx * k_head_stride + simd_gid * k_seq_stride +
       simd_lid * qk_per_thread;
   values += kv_head_idx * v_head_stride + simd_gid * v_seq_stride +
@@ -158,8 +156,8 @@ template <typename T, int D, int V = D>
     device float* maxs [[buffer(5)]],
     const constant uint& gqa_factor [[buffer(6)]],
     const constant uint& N [[buffer(7)]],
-    const constant uint3& qkv_head_strides [[buffer(8)]],
-    const constant uint3& qkv_seq_strides [[buffer(9)]],
+    const constant uint2& k_head_seq_stride [[buffer(8)]],
+    const constant uint2& v_head_seq_stride [[buffer(9)]],
     const constant float& scale [[buffer(10)]],
     const device bool* mask [[buffer(11)]],
     const constant uint3& mask_strides [[buffer(12)]],
@@ -172,12 +170,10 @@ template <typename T, int D, int V = D>
   constexpr int BD = 32;
   constexpr int qk_per_thread = D / BD;
   constexpr int v_per_thread = V / BD;
-  const int q_head_stride = qkv_head_strides.x;
-  const int q_seq_stride = qkv_seq_strides.x;
-  const int k_head_stride = qkv_head_strides.y;
-  const int k_seq_stride = qkv_seq_strides.y;
-  const int v_head_stride = qkv_head_strides.z;
-  const int v_seq_stride = qkv_seq_strides.z;
+  const int k_head_stride = k_head_seq_stride.x;
+  const int k_seq_stride = k_head_seq_stride.y;
+  const int v_head_stride = v_head_seq_stride.x;
+  const int v_seq_stride = v_head_seq_stride.y;
   const int mask_kv_seq_stride = mask_strides.x;
   const int mask_q_seq_stride = mask_strides.y;
   const int mask_head_stride = mask_strides.z;
@@ -200,10 +196,10 @@ template <typename T, int D, int V = D>
   const int head_idx = tid.x;
   const int q_seq_idx = tid.y;
   const int o_offset = head_idx * tpg.y + q_seq_idx;
+  const int q_offset = o_offset;
   const int kv_head_idx = head_idx / gqa_factor;
 
-  queries += head_idx * q_head_stride + q_seq_idx * q_seq_stride +
-      simd_lid * qk_per_thread;
+  queries += q_offset * D + simd_lid * qk_per_thread;
   keys += kv_head_idx * k_head_stride +
       (block_idx * BN + simd_gid) * k_seq_stride + simd_lid * qk_per_thread;
   values += kv_head_idx * v_head_stride +
@@ -524,25 +520,25 @@ kernel void attention(
   }
 }
 
-#define INSTANTIATE_SDPA_VECTOR(DTYPE, QK_DIM, VALUE_DIM)   \
-  template [[host_name("sdpa_vector_" #DTYPE "_" #QK_DIM    \
-                       "_" #VALUE_DIM)]] kernel void        \
-  sdpa_vector<DTYPE, QK_DIM, VALUE_DIM>(                    \
-      const device DTYPE* queries [[buffer(0)]],            \
-      const device DTYPE* keys [[buffer(1)]],               \
-      const device DTYPE* values [[buffer(2)]],             \
-      device DTYPE* out [[buffer(3)]],                      \
-      const constant uint& gqa_factor [[buffer(4)]],        \
-      const constant uint& N [[buffer(5)]],                 \
-      const constant uint3& qkv_head_strides [[buffer(6)]], \
-      const constant uint3& qkv_seq_strides [[buffer(7)]],  \
-      const constant float& scale [[buffer(8)]],            \
-      const device bool* mask [[buffer(9)]],                \
-      const constant uint3& mask_strides [[buffer(10)]],    \
-      const constant bool& has_mask [[buffer(11)]],         \
-      uint3 tid [[threadgroup_position_in_grid]],           \
-      uint3 tpg [[threadgroups_per_grid]],                  \
-      uint simd_gid [[simdgroup_index_in_threadgroup]],     \
+#define INSTANTIATE_SDPA_VECTOR(DTYPE, QK_DIM, VALUE_DIM)    \
+  template [[host_name("sdpa_vector_" #DTYPE "_" #QK_DIM     \
+                       "_" #VALUE_DIM)]] kernel void         \
+  sdpa_vector<DTYPE, QK_DIM, VALUE_DIM>(                     \
+      const device DTYPE* queries [[buffer(0)]],             \
+      const device DTYPE* keys [[buffer(1)]],                \
+      const device DTYPE* values [[buffer(2)]],              \
+      device DTYPE* out [[buffer(3)]],                       \
+      const constant uint& gqa_factor [[buffer(4)]],         \
+      const constant uint& N [[buffer(5)]],                  \
+      const constant uint2& k_head_seq_stride [[buffer(6)]], \
+      const constant uint2& v_head_seq_stride [[buffer(7)]], \
+      const constant float& scale [[buffer(8)]],             \
+      const device bool* mask [[buffer(9)]],                 \
+      const constant uint3& mask_strides [[buffer(10)]],     \
+      const constant bool& has_mask [[buffer(11)]],          \
+      uint3 tid [[threadgroup_position_in_grid]],            \
+      uint3 tpg [[threadgroups_per_grid]],                   \
+      uint simd_gid [[simdgroup_index_in_threadgroup]],      \
       uint simd_lid [[thread_index_in_simdgroup]]);
 
 #define INSTANTIATE_SDPA_VECTOR_2PASS_1(DTYPE, QK_DIM, VALUE_DIM) \
@@ -557,8 +553,8 @@ kernel void attention(
       device float* maxs [[buffer(5)]],                           \
       const constant uint& gqa_factor [[buffer(6)]],              \
       const constant uint& N [[buffer(7)]],                       \
-      const constant uint3& qkv_head_strides [[buffer(8)]],       \
-      const constant uint3& qkv_seq_strides [[buffer(9)]],        \
+      const constant uint2& k_head_seq_stride [[buffer(8)]],      \
+      const constant uint2& v_head_seq_stride [[buffer(9)]],      \
       const constant float& scale [[buffer(10)]],                 \
       const device bool* mask [[buffer(11)]],                     \
       const constant uint3& mask_strides [[buffer(12)]],          \

aten/src/ATen/native/mps/kernels/EmbeddingBag.metal

@@ -25,7 +25,7 @@ struct ReductionOp {
   inline opmath_t<T> operator()(
       opmath_t<T> weight_val,
       opmath_t<T> out_val,
-      bool /*is_first*/) {
+      bool is_first) {
     return weight_val + out_val;
   }
 };
@@ -44,9 +44,9 @@ template <EmbeddingBagMode M, typename T>
 struct MaybeApplyPerSampleWeight {
   inline opmath_t<T> operator()(
       opmath_t<T> weight_val,
-      uint32_t /*per_sample_weights_index*/,
-      constant T* /*per_sample_weights*/,
-      uint32_t /*per_sample_weights_stride*/) {
+      uint32_t per_sample_weights_index,
+      constant T* per_sample_weights,
+      uint32_t per_sample_weights_stride) {
     return weight_val;
   }
 };
@@ -71,12 +71,12 @@ struct MaybeApplyPerSampleWeight<EmbeddingBagMode::SUM, T> {
 template <EmbeddingBagMode M, typename T, typename I>
 struct MaybeCalcMaxIndex {
   inline void operator()(
-      opmath_t<T> /*weight_val*/,
-      opmath_t<T> /*out_val*/,
-      bool /*is_first*/,
-      thread I& /*max_idx*/,
-      I /*weight_idx*/,
-      bool /*pad*/) {}
+      opmath_t<T> weight_val,
+      opmath_t<T> out_val,
+      bool is_first,
+      thread I& max_idx,
+      I weight_idx,
+      bool pad) {}
 };
 
 template <typename T, typename I>

aten/src/ATen/native/mps/kernels/GridSampler.metal

@@ -223,6 +223,9 @@ void grid_sampler_single_element(
     auto input_size = input_sizes[input_dim];
     auto coord = static_cast<opmath_t<T>>(coords[coord_dim]);
 
+    // Interpret nan as -1
+    coord = isnan(coord) ? -1 : coord;
+
     if (!align_corners) {
       // Map unaligned grid space to aligned grid space
       auto corner_alignment_factor = static_cast<opmath_t<T>>(input_size) /

aten/src/ATen/native/mps/operations/Attention.mm

@@ -182,8 +182,6 @@ static std::tuple<Tensor, Tensor> sdpa_vector_fast_mps(const Tensor& q_,
   uint maxSeqLength = k_.size(2);
   uint N = k_.size(2);
   uint B = q_.size(0) * q_.size(1);
-  uint q_head_stride = q_.stride(1);
-  uint q_seq_stride = q_.stride(2);
   uint k_head_stride = k_.stride(1);
   uint k_seq_stride = k_.stride(2);
   uint v_head_stride = v_.stride(1);
@@ -211,8 +209,8 @@ static std::tuple<Tensor, Tensor> sdpa_vector_fast_mps(const Tensor& q_,
                   out,
                   1,
                   N,
-                  std::array<uint32_t, 3>{q_head_stride, k_head_stride, v_head_stride},
-                  std::array<uint32_t, 3>{q_seq_stride, k_seq_stride, v_seq_stride},
+                  std::array<uint32_t, 2>{k_head_stride, k_seq_stride},
+                  std::array<uint32_t, 2>{v_head_stride, v_seq_stride},
                   scale_factor);
 
       if (has_mask) {
@@ -259,8 +257,6 @@ static std::tuple<Tensor, Tensor> sdpa_vector_2pass_mps(const Tensor& q_,
   uint B = batchSize * num_heads;
   uint gqa_factor = q_.size(1) / k_.size(1);
 
-  uint q_head_stride = q_.stride(1);
-  uint q_seq_stride = q_.stride(2);
   uint k_head_stride = k_.stride(1);
   uint k_seq_stride = k_.stride(2);
   uint v_head_stride = v_.stride(1);
@@ -298,8 +294,8 @@ static std::tuple<Tensor, Tensor> sdpa_vector_2pass_mps(const Tensor& q_,
                   maxs,
                   gqa_factor,
                   N,
-                  std::array<uint32_t, 3>{q_head_stride, k_head_stride, v_head_stride},
-                  std::array<uint32_t, 3>{q_seq_stride, k_seq_stride, v_seq_stride},
+                  std::array<uint32_t, 2>{k_head_stride, k_seq_stride},
+                  std::array<uint32_t, 2>{v_head_stride, v_seq_stride},
                   scale_factor);
 
       if (has_mask) {

aten/src/ATen/native/mps/operations/Unique.mm

@@ -9,22 +9,11 @@
 #else
 #include <ATen/ops/_unique2.h>
 #include <ATen/ops/_unique2_native.h>
-#include <ATen/ops/arange.h>
-#include <ATen/ops/argsort.h>
-#include <ATen/ops/cat.h>
-#include <ATen/ops/cumsum.h>
-#include <ATen/ops/full.h>
-#include <ATen/ops/masked_select.h>
-#include <ATen/ops/nonzero.h>
-#include <ATen/ops/ones.h>
-#include <ATen/ops/ones_like.h>
 #include <ATen/ops/slice.h>
 #include <ATen/ops/unique_consecutive.h>
 #include <ATen/ops/unique_consecutive_native.h>
 #include <ATen/ops/unique_dim_consecutive.h>
 #include <ATen/ops/unique_dim_consecutive_native.h>
-#include <ATen/ops/unique_dim_native.h>
-#include <ATen/ops/zeros.h>
 #endif
 
 namespace at::native {
@@ -316,85 +305,4 @@ std::tuple<Tensor, Tensor, Tensor> _unique2_mps(const Tensor& self,
   return _unique_impl_mps(self, return_inverse, return_counts, false, std::nullopt);
 }
 
-static Tensor lexsort_rows_perm_mps(const Tensor& mat_2d) {
-  const auto rows = mat_2d.size(0), cols = mat_2d.size(1);
-  if (rows <= 1 || cols == 0) {
-    return arange(rows, mat_2d.options().dtype(kLong));
-  }
-
-  auto perm = arange(rows, mat_2d.options().dtype(kLong));
-  for (auto c = cols - 1; c >= 0; --c) {
-    auto keys = mat_2d.select(1, c).index_select(0, perm);
-    const auto idx = argsort(keys, /*dim=*/0, /*descending=*/false);
-    perm = perm.index_select(0, idx);
-  }
-  return perm;
-}
-
-static std::tuple<Tensor, Tensor, Tensor> unique_dim_sorted_mps_impl(const Tensor& self,
-                                                                     int64_t dim,
-                                                                     bool return_inverse,
-                                                                     bool return_counts) {
-  dim = maybe_wrap_dim(dim, self.dim());
-
-  auto sizes = self.sizes().vec();
-  auto num_zero_dims = std::count(sizes.begin(), sizes.end(), (int64_t)0);
-  if (self.size(dim) == 0) {
-    auto output = at::empty(sizes, self.options());
-    auto inverse_indices = at::empty({0}, self.options().dtype(kLong));
-    auto counts = at::empty({0}, self.options().dtype(kLong));
-    return {output, inverse_indices, counts};
-  }
-
-  auto transposed = self.moveaxis(dim, 0);
-  auto orig_sizes = transposed.sizes().vec();
-  auto rows = transposed.size(0);
-  auto input_flat = transposed.contiguous().view({rows, -1});
-
-  auto perm = lexsort_rows_perm_mps(input_flat);
-  auto input_sorted = input_flat.index_select(0, perm);
-
-  Tensor is_unique = at::zeros({rows}, self.options().dtype(kBool));
-  if (rows > 0) {
-    is_unique.narrow(0, 0, 1).fill_(true);
-  }
-  if (rows > 1) {
-    auto a = input_sorted.narrow(0, 1, rows - 1);
-    auto b = input_sorted.narrow(0, 0, rows - 1);
-    auto row_changed = a.ne(b).any(1);
-    is_unique.narrow(0, 1, rows - 1).copy_(row_changed);
-  }
-
-  auto unique_pos = nonzero(is_unique).squeeze(1);
-  auto group_id = cumsum(is_unique.to(kLong), 0).sub(1);
-
-  auto unique_rows_2d = input_sorted.index_select(0, unique_pos);
-
-  Tensor inverse_indices = empty({0}, self.options().dtype(kLong));
-  if (return_inverse) {
-    inverse_indices = empty({rows}, self.options().dtype(kLong));
-    inverse_indices.index_copy_(0, perm, group_id);
-  }
-
-  Tensor counts = empty({0}, self.options().dtype(kLong));
-  if (return_counts) {
-    const auto num_unique = unique_pos.size(0);
-    counts = zeros({num_unique}, self.options().dtype(kLong));
-    counts.scatter_add_(0, group_id, ones_like(group_id, group_id.options().dtype(kLong)));
-  }
-
-  orig_sizes[0] = unique_rows_2d.size(0);
-  auto output = unique_rows_2d.view(orig_sizes).moveaxis(0, dim);
-
-  return std::make_tuple(std::move(output), std::move(inverse_indices), std::move(counts));
-}
-
-std::tuple<Tensor, Tensor, Tensor> unique_dim_mps(const Tensor& self,
-                                                  int64_t dim,
-                                                  const bool /*sorted*/,
-                                                  const bool return_inverse,
-                                                  const bool return_counts) {
-  return unique_dim_sorted_mps_impl(self, dim, return_inverse, return_counts);
-}
-
 } // namespace at::native

aten/src/ATen/native/native_functions.yaml

@@ -1409,7 +1409,7 @@
 - func: _sparse_broadcast_to(Tensor(a) self, int[] size) -> Tensor(a)
   variants: function
   dispatch:
-    SparseCPU, SparseCUDA, SparseMPS: sparse_broadcast_to
+    SparseCPU, SparseCUDA: sparse_broadcast_to
 
 - func: cat(Tensor[] tensors, int dim=0) -> Tensor
   structured_delegate: cat.out
@@ -6450,7 +6450,6 @@
   dispatch:
     CPU: unique_dim_cpu
     CUDA: unique_dim_cuda
-    MPS: unique_dim_mps
   tags: dynamic_output_shape
   autogen: unique_dim.out
 

aten/src/ATen/native/sparse/FlattenIndicesKernel.cpp

@@ -27,6 +27,6 @@ REGISTER_AVX512_DISPATCH(flatten_indices_stub, &flatten_indices_cpu_kernel)
 REGISTER_AVX2_DISPATCH(flatten_indices_stub, &flatten_indices_cpu_kernel)
 REGISTER_VSX_DISPATCH(flatten_indices_stub, &flatten_indices_cpu_kernel)
 REGISTER_ZVECTOR_DISPATCH(flatten_indices_stub, &flatten_indices_cpu_kernel)
-REGISTER_SVE_DISPATCH(flatten_indices_stub, &flatten_indices_cpu_kernel)
+REGISTER_SVE256_DISPATCH(flatten_indices_stub, &flatten_indices_cpu_kernel)
 
 } // namespace at::native

aten/src/ATen/native/sparse/SparseBinaryOpIntersectionKernel.cpp

@@ -161,19 +161,19 @@ REGISTER_AVX512_DISPATCH(mul_sparse_sparse_out_stub, &mul_sparse_sparse_out_cpu_
 REGISTER_AVX2_DISPATCH(mul_sparse_sparse_out_stub, &mul_sparse_sparse_out_cpu_kernel)
 REGISTER_VSX_DISPATCH(mul_sparse_sparse_out_stub, &mul_sparse_sparse_out_cpu_kernel)
 REGISTER_ZVECTOR_DISPATCH(mul_sparse_sparse_out_stub, &mul_sparse_sparse_out_cpu_kernel)
-REGISTER_SVE_DISPATCH(mul_sparse_sparse_out_stub, &mul_sparse_sparse_out_cpu_kernel)
+REGISTER_SVE256_DISPATCH(mul_sparse_sparse_out_stub, &mul_sparse_sparse_out_cpu_kernel)
 
 REGISTER_ARCH_DISPATCH(sparse_mask_intersection_out_stub, DEFAULT, &sparse_mask_intersection_out_cpu_kernel)
 REGISTER_AVX512_DISPATCH(sparse_mask_intersection_out_stub, &sparse_mask_intersection_out_cpu_kernel)
 REGISTER_AVX2_DISPATCH(sparse_mask_intersection_out_stub, &sparse_mask_intersection_out_cpu_kernel)
 REGISTER_VSX_DISPATCH(sparse_mask_intersection_out_stub, &sparse_mask_intersection_out_cpu_kernel)
 REGISTER_ZVECTOR_DISPATCH(sparse_mask_intersection_out_stub, &sparse_mask_intersection_out_cpu_kernel)
-REGISTER_SVE_DISPATCH(sparse_mask_intersection_out_stub, &sparse_mask_intersection_out_cpu_kernel)
+REGISTER_SVE256_DISPATCH(sparse_mask_intersection_out_stub, &sparse_mask_intersection_out_cpu_kernel)
 
 REGISTER_ARCH_DISPATCH(sparse_mask_projection_out_stub, DEFAULT, &sparse_mask_projection_out_cpu_kernel)
 REGISTER_AVX512_DISPATCH(sparse_mask_projection_out_stub, &sparse_mask_projection_out_cpu_kernel)
 REGISTER_AVX2_DISPATCH(sparse_mask_projection_out_stub, &sparse_mask_projection_out_cpu_kernel)
 REGISTER_VSX_DISPATCH(sparse_mask_projection_out_stub, &sparse_mask_projection_out_cpu_kernel)
 REGISTER_ZVECTOR_DISPATCH(sparse_mask_projection_out_stub, &sparse_mask_projection_out_cpu_kernel)
-REGISTER_SVE_DISPATCH(sparse_mask_projection_out_stub, &sparse_mask_projection_out_cpu_kernel)
+REGISTER_SVE256_DISPATCH(sparse_mask_projection_out_stub, &sparse_mask_projection_out_cpu_kernel)
 }

aten/src/ATen/native/transformers/attention.cpp

@@ -448,7 +448,7 @@ REGISTER_AVX2_DISPATCH(_fused_sdp_choice_stub, &_fused_sdp_choice_cpp)
 REGISTER_AVX512_DISPATCH(_fused_sdp_choice_stub, &_fused_sdp_choice_cpp)
 REGISTER_VSX_DISPATCH(_fused_sdp_choice_stub, &_fused_sdp_choice_cpp)
 REGISTER_ZVECTOR_DISPATCH(_fused_sdp_choice_stub, &_fused_sdp_choice_cpp)
-REGISTER_SVE_DISPATCH(_fused_sdp_choice_stub, &_fused_sdp_choice_cpp)
+REGISTER_SVE256_DISPATCH(_fused_sdp_choice_stub, &_fused_sdp_choice_cpp)
 REGISTER_HPU_DISPATCH(_fused_sdp_choice_stub, &_fused_sdp_choice_meta)
 
 int64_t _fused_sdp_choice_meta(

aten/src/ATen/native/transformers/cuda/sdp_utils.cpp

@@ -688,15 +688,6 @@ bool can_use_cudnn_attention(const sdp_params& params, bool debug) {
     TORCH_WARN(CUDNN_VERSION, " cuDNN version too old to use cuDNN Attention (< v9.0.0)");
   }
   return false;
-#endif
-#if defined(CUDNN_VERSION)
-  static auto cudnn_version = cudnnGetVersion();
-  if (params.dropout > 0.0 && cudnn_version > 91100 && cudnn_version < 91400) {
-    if (debug) {
-      TORCH_WARN(CUDNN_VERSION, " cuDNN version does not support droppout in SDPA (9.11 - 9.13).");
-    }
-    return false;
-  }
 #endif
   // Define gate functions that determine if a flash kernel can be ran
   // Replace with std::to_array when we migrate to c++20

benchmarks/dynamo/ci_expected_accuracy/aot_eager_torchbench_inference.csv

@@ -218,7 +218,7 @@ maml_omniglot,pass,0
 
 
 
-microbench_unbacked_tolist_sum,pass,2
+microbench_unbacked_tolist_sum,pass,1
 
 
 

benchmarks/dynamo/ci_expected_accuracy/aot_eager_torchbench_training.csv

@@ -146,7 +146,7 @@ maml_omniglot,pass,7
 
 
 
-microbench_unbacked_tolist_sum,pass,9
+microbench_unbacked_tolist_sum,pass,8
 
 
 

benchmarks/dynamo/ci_expected_accuracy/cpu_aot_inductor_amp_freezing_torchbench_inference.csv

@@ -182,7 +182,7 @@ maml_omniglot,pass,0
 
 
 
-microbench_unbacked_tolist_sum,pass,1
+microbench_unbacked_tolist_sum,pass,0
 
 
 

benchmarks/dynamo/ci_expected_accuracy/cpu_aot_inductor_freezing_torchbench_inference.csv

@@ -182,7 +182,7 @@ maml_omniglot,pass,0
 
 
 
-microbench_unbacked_tolist_sum,pass,1
+microbench_unbacked_tolist_sum,pass,0
 
 
 

benchmarks/dynamo/ci_expected_accuracy/cpu_inductor_amp_freezing_torchbench_inference.csv

@@ -198,7 +198,7 @@ maml_omniglot,pass,0
 
 
 
-microbench_unbacked_tolist_sum,pass,2
+microbench_unbacked_tolist_sum,pass,1
 
 
 

benchmarks/dynamo/ci_expected_accuracy/cpu_inductor_freezing_torchbench_inference.csv

@@ -198,7 +198,7 @@ maml_omniglot,pass,0
 
 
 
-microbench_unbacked_tolist_sum,pass,2
+microbench_unbacked_tolist_sum,pass,1
 
 
 

benchmarks/dynamo/ci_expected_accuracy/cpu_inductor_torchbench_inference.csv

@@ -198,7 +198,7 @@ maml_omniglot,pass,0
 
 
 
-microbench_unbacked_tolist_sum,pass,2
+microbench_unbacked_tolist_sum,pass,1
 
 
 

benchmarks/dynamo/ci_expected_accuracy/dynamic_aot_eager_torchbench_inference.csv

@@ -218,7 +218,7 @@ maml_omniglot,pass,0
 
 
 
-microbench_unbacked_tolist_sum,pass,2
+microbench_unbacked_tolist_sum,pass,1
 
 
 

benchmarks/dynamo/ci_expected_accuracy/dynamic_aot_eager_torchbench_training.csv

@@ -146,7 +146,7 @@ maml_omniglot,pass,7
 
 
 
-microbench_unbacked_tolist_sum,pass,9
+microbench_unbacked_tolist_sum,pass,8
 
 
 

benchmarks/dynamo/ci_expected_accuracy/dynamic_cpu_aot_inductor_amp_freezing_torchbench_inference.csv

@@ -166,7 +166,7 @@ maml_omniglot,pass,0
 
 
 
-microbench_unbacked_tolist_sum,pass,1
+microbench_unbacked_tolist_sum,pass,0
 
 
 

benchmarks/dynamo/ci_expected_accuracy/dynamic_cpu_aot_inductor_freezing_torchbench_inference.csv

@@ -166,7 +166,7 @@ maml_omniglot,pass,0
 
 
 
-microbench_unbacked_tolist_sum,pass,1
+microbench_unbacked_tolist_sum,pass,0
 
 
 

benchmarks/dynamo/ci_expected_accuracy/dynamic_cpu_inductor_torchbench_inference.csv

@@ -182,7 +182,7 @@ maml_omniglot,pass,0
 
 
 
-microbench_unbacked_tolist_sum,pass,2
+microbench_unbacked_tolist_sum,pass,1
 
 
 

benchmarks/dynamo/ci_expected_accuracy/dynamic_cpu_max_autotune_inductor_amp_freezing_torchbench_inference.csv

@@ -198,7 +198,7 @@ maml_omniglot,pass,0
 
 
 
-microbench_unbacked_tolist_sum,pass,2
+microbench_unbacked_tolist_sum,pass,1
 
 
 

benchmarks/dynamo/ci_expected_accuracy/dynamic_inductor_torchbench_inference.csv

@@ -218,7 +218,7 @@ maml_omniglot,pass,0
 
 
 
-microbench_unbacked_tolist_sum,pass,2
+microbench_unbacked_tolist_sum,pass,1
 
 
 

benchmarks/dynamo/ci_expected_accuracy/dynamic_inductor_torchbench_training.csv

@@ -146,7 +146,7 @@ maml_omniglot,pass,7
 
 
 
-microbench_unbacked_tolist_sum,pass,9
+microbench_unbacked_tolist_sum,pass,8
 
 
 

benchmarks/dynamo/ci_expected_accuracy/dynamo_eager_torchbench_inference.csv

@@ -218,7 +218,7 @@ maml_omniglot,pass,0
 
 
 
-microbench_unbacked_tolist_sum,pass,2
+microbench_unbacked_tolist_sum,pass,1
 
 
 

benchmarks/dynamo/ci_expected_accuracy/dynamo_eager_torchbench_training.csv

@@ -146,7 +146,7 @@ maml_omniglot,pass,7
 
 
 
-microbench_unbacked_tolist_sum,pass,9
+microbench_unbacked_tolist_sum,pass,8
 
 
 

benchmarks/dynamo/ci_expected_accuracy/inductor_torchbench_inference.csv

@@ -218,7 +218,7 @@ maml_omniglot,pass,0
 
 
 
-microbench_unbacked_tolist_sum,pass,2
+microbench_unbacked_tolist_sum,pass,1
 
 
 

benchmarks/dynamo/ci_expected_accuracy/inductor_torchbench_training.csv

@@ -146,7 +146,7 @@ maml_omniglot,pass,7
 
 
 
-microbench_unbacked_tolist_sum,pass,9
+microbench_unbacked_tolist_sum,pass,8
 
 
 

benchmarks/dynamo/ci_expected_accuracy/rocm/aot_eager_torchbench_inference.csv

@@ -221,7 +221,7 @@ maml_omniglot,pass,0
 
 
 
-microbench_unbacked_tolist_sum,pass,2
+microbench_unbacked_tolist_sum,pass,1
 
 
 

benchmarks/dynamo/ci_expected_accuracy/rocm/aot_eager_torchbench_training.csv

@@ -154,7 +154,7 @@ maml_omniglot,pass,7
 
 
 
-microbench_unbacked_tolist_sum,pass,9
+microbench_unbacked_tolist_sum,pass,8
 
 
 

benchmarks/dynamo/ci_expected_accuracy/rocm/dynamic_aot_eager_torchbench_inference.csv

@@ -221,7 +221,7 @@ maml_omniglot,pass,0
 
 
 
-microbench_unbacked_tolist_sum,pass,2
+microbench_unbacked_tolist_sum,pass,1
 
 
 

benchmarks/dynamo/ci_expected_accuracy/rocm/dynamic_aot_eager_torchbench_training.csv

@@ -154,7 +154,7 @@ maml_omniglot,pass,7
 
 
 
-microbench_unbacked_tolist_sum,pass,9
+microbench_unbacked_tolist_sum,pass,8
 
 
 

benchmarks/dynamo/ci_expected_accuracy/rocm/dynamic_inductor_torchbench_training.csv

@@ -142,7 +142,7 @@ maml_omniglot,pass,7
 
 
 
-microbench_unbacked_tolist_sum,pass,9
+microbench_unbacked_tolist_sum,pass,8
 
 
 

benchmarks/dynamo/ci_expected_accuracy/rocm/dynamo_eager_torchbench_inference.csv

@@ -221,7 +221,7 @@ maml_omniglot,pass,0
 
 
 
-microbench_unbacked_tolist_sum,pass,2
+microbench_unbacked_tolist_sum,pass,1
 
 
 

benchmarks/dynamo/ci_expected_accuracy/rocm/dynamo_eager_torchbench_training.csv

@@ -154,7 +154,7 @@ maml_omniglot,pass,7
 
 
 
-microbench_unbacked_tolist_sum,pass,9
+microbench_unbacked_tolist_sum,pass,8
 
 
 

benchmarks/dynamo/ci_expected_accuracy/rocm/inductor_torchbench_training.csv

@@ -142,7 +142,7 @@ maml_omniglot,pass,7
 
 
 
-microbench_unbacked_tolist_sum,pass,9
+microbench_unbacked_tolist_sum,pass,8
 
 
 

benchmarks/dynamo/pr_time_benchmarks/README.md

@@ -6,4 +6,4 @@
 4. (Optional) flip a flag that you know will change the benchmark and run again with b.txt `PYTHONPATH=./ python benchmarks/[YOUR_BENCHMARK].py a.txt`
 5. Compare `a.txt` and `b.txt` located within the `benchmarks/dynamo/pr_time_benchmarks` folder to make sure things look as you expect
 6. Check in your new benchmark file and submit a new PR
-7. In a few days, if your benchmark is stable, bug Laith Sakka to enable running your benchmark on all PRs. If you are a meta employee, you can find the dashboard here: https://internalfb.com/intern/unidash/dashboard/pt2_diff_time_metrics
+7. In a few days, if your benchmark is stable, bug Laith Sakka to enable running your benchmark on all PRs. If your a meta employee, you can find the dashboard here: internalfb.com/intern/unidash/dashboard/pt2_diff_time_metrics