Update base for Update on "refactor bucketing"

Preparatory refactory

cc voznesenskym penguinwu EikanWang jgong5 Guobing-Chen XiaobingSuper zhuhaozhe blzheng wenzhe-nrv jiayisunx ipiszy chenyang78 kadeng muchulee8 amjames chauhang aakhundov coconutruben

[ghstack-poisoned]

.ci/aarch64_linux/build_aarch64_wheel.py

@@ -241,7 +241,7 @@ def wait_for_connection(addr, port, timeout=15, attempt_cnt=5):
         try:
             with socket.create_connection((addr, port), timeout=timeout):
                 return
-        except (ConnectionRefusedError, TimeoutError):  # noqa: PERF203
+        except (ConnectionRefusedError, socket.timeout):  # noqa: PERF203
             if i == attempt_cnt - 1:
                 raise
             time.sleep(timeout)
@@ -1004,7 +1004,7 @@ if __name__ == "__main__":
         install_condaforge_python(host, args.python_version)
         sys.exit(0)
 
-    python_version = args.python_version if args.python_version is not None else "3.10"
+    python_version = args.python_version if args.python_version is not None else "3.9"
 
     if args.use_torch_from_pypi:
         configure_system(host, compiler=args.compiler, python_version=python_version)

.ci/docker/build.sh

@@ -262,10 +262,13 @@ case "$tag" in
     TRITON_CPU=yes
     ;;
   pytorch-linux-jammy-linter)
-    PYTHON_VERSION=3.10
+    # TODO: Use 3.9 here because of this issue https://github.com/python/mypy/issues/13627.
+    # We will need to update mypy version eventually, but that's for another day. The task
+    # would be to upgrade mypy to 1.0.0 with Python 3.11
+    PYTHON_VERSION=3.9
     ;;
-  pytorch-linux-jammy-cuda12.8-cudnn9-py3.10-linter)
-    PYTHON_VERSION=3.10
+  pytorch-linux-jammy-cuda12.8-cudnn9-py3.9-linter)
+    PYTHON_VERSION=3.9
     CUDA_VERSION=12.8.1
     ;;
   pytorch-linux-jammy-aarch64-py3.10-gcc11)

.ci/docker/ci_commit_pins/triton.txt

@@ -1 +1 @@
-bbb06c0334a6772b92d24bde54956e675c8c6604
+5ae38bdb0dc066c5823e34dc9797afb9de42c866

.ci/docker/requirements-ci.txt

@@ -112,6 +112,8 @@ ninja==1.11.1.3
 #Pinned versions: 1.11.1.3
 #test that import: run_test.py, test_cpp_extensions_aot.py,test_determination.py
 
+numba==0.49.0 ; python_version < "3.9" and platform_machine != "s390x"
+numba==0.55.2 ; python_version == "3.9" and platform_machine != "s390x"
 numba==0.55.2 ; python_version == "3.10" and platform_machine != "s390x"
 numba==0.60.0 ; python_version == "3.12" and platform_machine != "s390x"
 #Description: Just-In-Time Compiler for Numerical Functions
@@ -132,7 +134,7 @@ numba==0.60.0 ; python_version == "3.12" and platform_machine != "s390x"
 #test_nn.py, test_namedtensor.py, test_linalg.py, test_jit_cuda_fuser.py,
 #test_jit.py, test_indexing.py, test_datapipe.py, test_dataloader.py,
 #test_binary_ufuncs.py
-numpy==1.22.4; python_version == "3.10"
+numpy==1.22.4; python_version == "3.9" or python_version == "3.10"
 numpy==1.26.2; python_version == "3.11" or python_version == "3.12"
 numpy==2.1.2; python_version >= "3.13"
 
@@ -324,6 +326,8 @@ pywavelets==1.7.0 ; python_version >= "3.12"
 lxml==5.3.0
 #Description: This is a requirement of unittest-xml-reporting
 
+# Python-3.9 binaries
+
 PyGithub==2.3.0
 
 sympy==1.13.3

.ci/docker/requirements-docs.txt

@@ -1,7 +1,7 @@
 sphinx==5.3.0
 #Description: This is used to generate PyTorch docs
 #Pinned versions: 5.3.0
--e git+https://github.com/pytorch/pytorch_sphinx_theme.git@d53b0ffb9b1cda68260693ea98f3483823c88d8e#egg=pytorch_sphinx_theme2
+-e git+https://github.com/pytorch/pytorch_sphinx_theme.git@1657ad2fc1acdc98aa719eebecbb0128a7c13ce4#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

.ci/lumen_cli/cli/lib/core/vllm/lib.py

@@ -72,7 +72,7 @@ def sample_vllm_test_library():
                     ]
                 ),
                 "pytest -v -s entrypoints/llm/test_generate.py",
-                "pytest -v -s entrypoints/offline_mode",
+                "VLLM_USE_V1=0 pytest -v -s entrypoints/offline_mode",
             ],
         },
         "vllm_regression_test": {

.ci/pytorch/macos-build.sh

@@ -35,11 +35,10 @@ fi
 
 print_cmake_info
 if [[ ${BUILD_ENVIRONMENT} == *"distributed"* ]]; then
-  # Needed for inductor benchmarks, as lots of HF networks make `torch.distribtued` calls
-  USE_DISTRIBUTED=1 USE_OPENMP=1 WERROR=1 python setup.py bdist_wheel
+  USE_OPENMP=1 WERROR=1 python setup.py bdist_wheel
 else
-  # Explicitly set USE_DISTRIBUTED=0 to align with the default build config on mac. This also serves as the sole CI config that tests
-  # that building with USE_DISTRIBUTED=0 works at all. See https://github.com/pytorch/pytorch/issues/86448
+  # NB: we always build with distributed; USE_DISTRIBUTED turns off all
+  # backends (specifically the gloo backend), so test that this case works too
   USE_DISTRIBUTED=0 USE_OPENMP=1 MACOSX_DEPLOYMENT_TARGET=11.0 WERROR=1 BUILD_TEST=OFF USE_PYTORCH_METAL=1 python setup.py bdist_wheel --plat-name macosx_11_0_arm64
 fi
 if which sccache > /dev/null; then

.ci/pytorch/macos-test.sh

@@ -13,9 +13,13 @@ if [[ ! $(python -c "import torch; print(int(torch.backends.openmp.is_available(
 fi
 popd
 
+python -mpip install -r requirements.txt
+
 # enable debug asserts in serialization
 export TORCH_SERIALIZATION_DEBUG=1
 
+python -mpip install --no-input -r requirements.txt
+
 setup_test_python() {
   # The CircleCI worker hostname doesn't resolve to an address.
   # This environment variable makes ProcessGroupGloo default to
@@ -55,7 +59,7 @@ test_python_shard() {
 
   setup_test_python
 
-  time python test/run_test.py --verbose --exclude-jit-executor --exclude-distributed-tests --exclude-quantization-tests --shard "$1" "$NUM_TEST_SHARDS"
+  time python test/run_test.py --verbose --exclude-jit-executor --exclude-distributed-tests --shard "$1" "$NUM_TEST_SHARDS"
 
   assert_git_not_dirty
 }

.ci/pytorch/multigpu-test.sh

@@ -26,6 +26,7 @@ if [[ "${SHARD_NUMBER:-2}" == "2" ]]; then
     time python test/run_test.py --verbose -i distributed/test_c10d_spawn_gloo
     time python test/run_test.py --verbose -i distributed/test_c10d_spawn_nccl
     time python test/run_test.py --verbose -i distributed/test_compute_comm_reordering
+    time python test/run_test.py --verbose -i distributed/test_aten_comm_compute_reordering
     time python test/run_test.py --verbose -i distributed/test_store
     time python test/run_test.py --verbose -i distributed/test_symmetric_memory
     time python test/run_test.py --verbose -i distributed/test_pg_wrapper

.ci/pytorch/test.sh

@@ -322,29 +322,23 @@ test_python_shard() {
 
   # modify LD_LIBRARY_PATH to ensure it has the conda env.
   # This set of tests has been shown to be buggy without it for the split-build
-  time python test/run_test.py --exclude-jit-executor --exclude-distributed-tests --exclude-quantization-tests $INCLUDE_CLAUSE --shard "$1" "$NUM_TEST_SHARDS" --verbose $PYTHON_TEST_EXTRA_OPTION --upload-artifacts-while-running
+  time python test/run_test.py --exclude-jit-executor --exclude-distributed-tests $INCLUDE_CLAUSE --shard "$1" "$NUM_TEST_SHARDS" --verbose $PYTHON_TEST_EXTRA_OPTION --upload-artifacts-while-running
 
   assert_git_not_dirty
 }
 
 test_python() {
   # shellcheck disable=SC2086
-  time python test/run_test.py --exclude-jit-executor --exclude-distributed-tests --exclude-quantization-tests $INCLUDE_CLAUSE --verbose $PYTHON_TEST_EXTRA_OPTION
+  time python test/run_test.py --exclude-jit-executor --exclude-distributed-tests $INCLUDE_CLAUSE --verbose $PYTHON_TEST_EXTRA_OPTION
   assert_git_not_dirty
 }
 
 test_python_smoke() {
-  # Smoke tests for H100/B200
+  # Smoke tests for H100
   time python test/run_test.py --include test_matmul_cuda inductor/test_fp8 inductor/test_max_autotune $PYTHON_TEST_EXTRA_OPTION --upload-artifacts-while-running
   assert_git_not_dirty
 }
 
-test_python_smoke_b200() {
-  # Targeted smoke tests for B200 - staged approach to avoid too many failures
-  time python test/run_test.py --include test_matmul_cuda inductor/test_fp8 $PYTHON_TEST_EXTRA_OPTION --upload-artifacts-while-running
-  assert_git_not_dirty
-}
-
 test_h100_distributed() {
   # Distributed tests at H100
   time python test/run_test.py --include distributed/_composable/test_composability/test_pp_composability.py  $PYTHON_TEST_EXTRA_OPTION --upload-artifacts-while-running
@@ -390,7 +384,6 @@ test_dynamo_wrapped_shard() {
     --exclude-distributed-tests \
     --exclude-torch-export-tests \
     --exclude-aot-dispatch-tests \
-    --exclude-quantization-tests \
     --shard "$1" "$NUM_TEST_SHARDS" \
     --verbose \
     --upload-artifacts-while-running
@@ -435,7 +428,7 @@ test_inductor_distributed() {
 
   # this runs on both single-gpu and multi-gpu instance. It should be smart about skipping tests that aren't supported
   # with if required # gpus aren't available
-  python test/run_test.py --include distributed/test_dynamo_distributed distributed/test_inductor_collectives distributed/test_compute_comm_reordering --verbose
+  python test/run_test.py --include distributed/test_dynamo_distributed distributed/test_inductor_collectives distributed/test_aten_comm_compute_reordering distributed/test_compute_comm_reordering --verbose
   assert_git_not_dirty
 }
 
@@ -1163,12 +1156,6 @@ test_distributed() {
   fi
 }
 
-test_quantization() {
-  echo "Testing quantization"
-
-  python test/test_quantization.py
-}
-
 test_rpc() {
   echo "Testing RPC C++ tests"
   # NB: the ending test_rpc must match the current function name for the current
@@ -1586,7 +1573,7 @@ test_executorch() {
 test_linux_aarch64() {
   python test/run_test.py --include test_modules test_mkldnn test_mkldnn_fusion test_openmp test_torch test_dynamic_shapes \
         test_transformers test_multiprocessing test_numpy_interop test_autograd test_binary_ufuncs test_complex test_spectral_ops \
-        test_foreach test_reductions test_unary_ufuncs test_tensor_creation_ops test_ops profiler/test_memory_profiler \
+        test_foreach test_reductions test_unary_ufuncs test_tensor_creation_ops test_ops \
         distributed/elastic/timer/api_test distributed/elastic/timer/local_timer_example distributed/elastic/timer/local_timer_test \
         --shard "$SHARD_NUMBER" "$NUM_TEST_SHARDS" --verbose
 
@@ -1662,8 +1649,6 @@ elif [[ "${TEST_CONFIG}" == *executorch* ]]; then
   test_executorch
 elif [[ "$TEST_CONFIG" == 'jit_legacy' ]]; then
   test_python_legacy_jit
-elif [[ "$TEST_CONFIG" == 'quantization' ]]; then
-  test_quantization
 elif [[ "${BUILD_ENVIRONMENT}" == *libtorch* ]]; then
   # TODO: run some C++ tests
   echo "no-op at the moment"
@@ -1788,8 +1773,6 @@ elif [[ "${BUILD_ENVIRONMENT}" == *xpu* ]]; then
   test_xpu_bin
 elif [[ "${TEST_CONFIG}" == smoke ]]; then
   test_python_smoke
-elif [[ "${TEST_CONFIG}" == smoke_b200 ]]; then
-  test_python_smoke_b200
 elif [[ "${TEST_CONFIG}" == h100_distributed ]]; then
   test_h100_distributed
 elif [[ "${TEST_CONFIG}" == "h100-symm-mem" ]]; then

.ci/pytorch/win-test-helpers/test_python_shard.bat

@@ -25,7 +25,7 @@ echo Copying over test times file
 robocopy /E "%PYTORCH_FINAL_PACKAGE_DIR_WIN%\.additional_ci_files" "%PROJECT_DIR_WIN%\.additional_ci_files"
 
 echo Run nn tests
-python run_test.py --exclude-jit-executor --exclude-distributed-tests --exclude-quantization-tests --shard "%SHARD_NUMBER%" "%NUM_TEST_SHARDS%" --verbose
+python run_test.py --exclude-jit-executor --exclude-distributed-tests --shard "%SHARD_NUMBER%" "%NUM_TEST_SHARDS%" --verbose
 if ERRORLEVEL 1 goto fail
 
 popd

.ci/wheel/build_wheel.sh

@@ -177,7 +177,8 @@ source ~/${desired_python}-build/bin/activate
 retry pip install "${PINNED_PACKAGES[@]}" -r "${pytorch_rootdir}/requirements.txt"
 retry brew install libomp
 
-# For USE_DISTRIBUTED=1 on macOS, need libuv, which is build as part of tensorpipe submodule
+# For USE_DISTRIBUTED=1 on macOS, this enables gloo, which needs libuv, which
+# is build as part of tensorpipe submodule
 export USE_DISTRIBUTED=1
 
 export USE_MKLDNN=OFF

.github/actions/setup-win/action.yml

@@ -59,7 +59,7 @@ runs:
         set -x
 
         # Create new py_tmp env with python-version
-        ${CONDA} create -y -n py_tmp python=${PYTHON_VERSION} intel-openmp libuv
+        ${CONDA} create -y -n py_tmp python=${PYTHON_VERSION} intel-openmp
 
         PYTHON3=$(${CONDA_RUN} -n py_tmp which python3)
         EXIT_CODE=$?

.github/ci_commit_pins/vllm.txt

@@ -1 +1 @@
-1983609239caaab24ab1ed2bfa2aa92e8c76c1b1
+367a480bd3534edf27a8dac3c6f7ea8af9d1ed45

.github/merge_rules.yaml

@@ -525,21 +525,6 @@
   - Lint
   - pull
 
-- name: typechecking
-  patterns:
-  - 'pyrefly.toml'
-  - 'mypy.ini'
-  - 'mypy-strict.ini'
-  approved_by:
-  - lolpack
-  - maggiemoss
-  - ndmitchell
-  - kinto0
-  mandatory_checks_name:
-  - EasyCLA
-  - Lint
-  - pull
-
 - name: superuser
   patterns:
   - '*'

.github/pytorch-probot.yml

@@ -19,7 +19,6 @@ ciflow_push_tags:
 - ciflow/nightly
 - ciflow/periodic
 - ciflow/periodic-rocm-mi300
-- ciflow/quantization-periodic
 - ciflow/rocm
 - ciflow/rocm-mi300
 - ciflow/s390
@@ -37,7 +36,6 @@ ciflow_push_tags:
 - ciflow/win-arm64
 - ciflow/h100-symm-mem
 - ciflow/h100-cutlass-backend
-- ciflow/b200
 retryable_workflows:
 - pull
 - trunk

.github/scripts/generate_ci_workflows.py

@@ -155,7 +155,7 @@ LINUX_BINARY_SMOKE_WORKFLOWS = [
         package_type="manywheel",
         build_configs=generate_binary_build_matrix.generate_wheels_matrix(
             OperatingSystem.LINUX,
-            arches=["13.0"],
+            arches=["12.8"],
             python_versions=["3.12"],
         ),
         branches="main",

.github/templates/linux_binary_build_workflow.yml.j2

@@ -71,15 +71,12 @@ jobs:
     with:!{{ upload.binary_env_as_input(config) }}
       {%- if "aarch64" in build_environment %}
       runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
-      runs_on: linux.arm64.r7g.12xlarge.memory
+      runs_on: linux.arm64.m7g.4xlarge.ephemeral
       ALPINE_IMAGE: "arm64v8/alpine"
       {%- elif "s390x" in build_environment %}
       runs_on: linux.s390x
       ALPINE_IMAGE: "docker.io/s390x/alpine"
       timeout-minutes: 420
-      {%- elif config["gpu_arch_type"] == "rocm" %}
-      runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
-      timeout-minutes: 300
       {%- elif "conda" in build_environment and config["gpu_arch_type"] == "cuda" %}
       runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
       runs_on: linux.24xlarge.ephemeral

.github/workflows/_get-changed-files.yml

@@ -2,12 +2,6 @@ name: Get Changed Files
 
 on:
   workflow_call:
-    inputs:
-      all_files:
-        description: "Whether to return all files instead of just changed files"
-        required: false
-        type: boolean
-        default: false
     outputs:
       changed-files:
         description: "List of changed files (space-separated) or '*' if not in a PR"
@@ -32,23 +26,17 @@ jobs:
             # Get the PR number from the github context
             PR_NUMBER="${{ github.event.number }}"
 
-            # Check if all_files is requested
-            if [ "${{ inputs.all_files }}" = "true" ]; then
-              echo "all_files input is true, returning all files"
-              echo "changed-files=*" >> "$GITHUB_OUTPUT"
-            else
-              # Use gh CLI to get changed files in the PR with explicit repo
-              CHANGED_FILES=$(gh api repos/${{ github.repository }}/pulls/$PR_NUMBER/files --paginate --jq '.[] | select(.status != "removed") | .filename' | tr '\n' ' ' | sed 's/ $//')
+            # Use gh CLI to get changed files in the PR with explicit repo
+            CHANGED_FILES=$(gh api repos/${{ github.repository }}/pulls/$PR_NUMBER/files --paginate --jq '.[] | select(.status != "removed") | .filename' | tr '\n' ' ' | sed 's/ $//')
 
-              if [ -z "$CHANGED_FILES" ]; then
-                echo "No changed files found, setting to '*'"
-                CHANGED_FILES="*"
-              fi
-
-              echo "Changed files: $CHANGED_FILES"
-              echo "changed-files=$CHANGED_FILES" >> "$GITHUB_OUTPUT"
+            if [ -z "$CHANGED_FILES" ]; then
+              echo "No changed files found, setting to '*'"
+              CHANGED_FILES="*"
             fi
 
+            echo "Changed files: $CHANGED_FILES"
+            echo "changed-files=$CHANGED_FILES" >> "$GITHUB_OUTPUT"
+
           else
             echo "Not in PR context, setting changed files to '*'"
             echo "changed-files=*" >> "$GITHUB_OUTPUT"

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

@@ -50,7 +50,7 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        py_vers: [ "3.10", "3.11", "3.12", "3.13", "3.13t", "3.14", "3.14t" ]
+        py_vers: [ "3.9", "3.10", "3.11", "3.12", "3.13", "3.13t", "3.14", "3.14t" ]
         device: ["cuda", "rocm", "xpu", "aarch64"]
         docker-image: ["pytorch/manylinux2_28-builder:cpu"]
         include:
@@ -108,6 +108,9 @@ jobs:
 
           # Determine python executable for given version
           case $PY_VERS in
+          3.9)
+            PYTHON_EXECUTABLE=/opt/python/cp39-cp39/bin/python
+            ;;
           3.10)
             PYTHON_EXECUTABLE=/opt/python/cp310-cp310/bin/python
             ;;
@@ -191,7 +194,7 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        py_vers: [ "3.10", "3.11", "3.12", "3.13", "3.13t", "3.14", "3.14t" ]
+        py_vers: [ "3.9", "3.10", "3.11", "3.12", "3.13", "3.13t", "3.14", "3.14t" ]
         device: ["xpu"]
     timeout-minutes: 40
     env:

.github/workflows/create_release.yml

@@ -35,7 +35,6 @@ jobs:
       contents: write
     outputs:
       pt_release_name: ${{ steps.release_name.outputs.pt_release_name }}
-      pt_pep517_release_name: ${{ steps.release_name.outputs.pt_pep517_release_name }}
     steps:
       - uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
         with:
@@ -54,12 +53,8 @@ jobs:
           tag_or_branch="${tag_or_branch#refs/heads/}"
           # replace directory separators with _ in branch name
           tag_or_branch="${tag_or_branch//\//_}"
-          torch_version="$(python -c 'from tools.generate_torch_version import get_torch_version; print(get_torch_version())')"
-          {
-            echo "PT_RELEASE_NAME=pytorch-$tag_or_branch";
-            echo "PT_RELEASE_FILE=pytorch-$tag_or_branch.tar.gz";
-            echo "PT_PEP517_RELEASE_FILE=torch-${torch_version}.tar.gz";
-          } >> "$GITHUB_ENV"
+          echo "PT_RELEASE_NAME=pytorch-$tag_or_branch" >> "$GITHUB_ENV"
+          echo "PT_RELEASE_FILE=pytorch-$tag_or_branch.tar.gz" >> "$GITHUB_ENV"
       - name: Checkout optional submodules
         run: python3 tools/optional_submodules.py
       - name: Copy docs requirements for inclusion
@@ -69,47 +64,30 @@ jobs:
           cp .ci/docker/requirements-docs.txt docs/requirements.txt
       - name: Create source distribution
         run: |
-          # Create new folder with specified name so extracting the archive yields that
-          rm -rf "/tmp/$PT_RELEASE_NAME"
-          cp -r "$PWD" "/tmp/$PT_RELEASE_NAME"
-          mv "/tmp/$PT_RELEASE_NAME" .
-          # Cleanup
-          rm -rf "$PT_RELEASE_NAME"/{.circleci,.ci}
-          find "$PT_RELEASE_NAME" -name '.git*' -exec rm -rv {} \; || true
-          # Create archive
-          tar -czf "$PT_RELEASE_FILE" "$PT_RELEASE_NAME"
-          echo "Created source archive $PT_RELEASE_FILE with content: $(ls -a "$PT_RELEASE_NAME")"
-      - name: Create PEP 517 compatible source distribution
-        run: |
-          pip install build==1.2.2.post1 || exit 1
-          python -m build --sdist || exit 1
-          cd dist || exit 1
+            # Create new folder with specified name so extracting the archive yields that
+            rm -rf "/tmp/$PT_RELEASE_NAME"
+            cp -r "$PWD" "/tmp/$PT_RELEASE_NAME"
+            mv "/tmp/$PT_RELEASE_NAME" .
+            # Cleanup
+            rm -rf "$PT_RELEASE_NAME"/{.circleci,.ci}
+            find "$PT_RELEASE_NAME" -name '.git*' -exec rm -rv {} \; || true
+            # Create archive
+            tar -czf "$PT_RELEASE_FILE" "$PT_RELEASE_NAME"
+            echo "Created source archive $PT_RELEASE_FILE with content: $(ls -a "$PT_RELEASE_NAME")"
       - name: Upload source distribution for release
         if: ${{ github.event_name == 'release' }}
         uses: softprops/action-gh-release@da05d552573ad5aba039eaac05058a918a7bf631 # v2.2.2
         with:
-          files: |
-            ${{ env.PT_RELEASE_FILE }}
-            ${{ env.PT_PEP517_RELEASE_FILE }}
-      - name: Upload source distribution to GHA artifacts  # for release tags
+          files: ${{env.PT_RELEASE_FILE}}
+      - name: Upload source distribution to GHA artifacts for release tags
         if: ${{ github.event_name == 'push' && startsWith(github.ref, 'refs/tags/v') && contains(github.ref, 'rc') }}
         uses: actions/upload-artifact@50769540e7f4bd5e21e526ee35c689e35e0d6874 # v4.4.0
         with:
           name: ${{ env.PT_RELEASE_FILE }}
           path: ${{ env.PT_RELEASE_FILE }}
-      - name: Upload PEP 517 source distribution to GHA artifacts  # for release tags
-        if: ${{ github.event_name == 'push' && startsWith(github.ref, 'refs/tags/v') && contains(github.ref, 'rc') }}
-        uses: actions/upload-artifact@50769540e7f4bd5e21e526ee35c689e35e0d6874 # v4.4.0
-        with:
-          name: ${{ env.PT_PEP517_RELEASE_FILE }}
-          path: dist/${{ env.PT_PEP517_RELEASE_FILE }}
       - name: Set output
         id: release_name
-        run: |
-          {
-            echo "pt_release_name=${{ env.PT_RELEASE_FILE }}";
-            echo "pt_pep517_release_name=${{ env.PT_PEP517_RELEASE_FILE }}";
-          } >> "${GITHUB_OUTPUT}"
+        run: echo "pt_release_name=${{ env.PT_RELEASE_NAME }}.tar.gz" >> "${GITHUB_OUTPUT}"
 
   upload_source_code_to_s3:
     if: ${{ github.repository == 'pytorch/pytorch' && github.event_name == 'push' && startsWith(github.ref, 'refs/tags/v') && contains(github.ref, 'rc') }}
@@ -125,9 +103,6 @@ jobs:
       - uses: actions/download-artifact@65a9edc5881444af0b9093a5e628f2fe47ea3b2e # v4.1.7
         with:
           name: ${{ needs.release.outputs.pt_release_name }}
-      - uses: actions/download-artifact@65a9edc5881444af0b9093a5e628f2fe47ea3b2e # v4.1.7
-        with:
-          name: ${{ needs.release.outputs.pt_pep517_release_name }}
       - name: Configure AWS credentials(PyTorch account)
         uses: aws-actions/configure-aws-credentials@ececac1a45f3b08a01d2dd070d28d111c5fe6722 # v4.1.0
         with:
@@ -138,9 +113,7 @@ jobs:
           s3-bucket: pytorch
           s3-prefix: source_code/test
           if-no-files-found: warn
-          path: |
-            ${{ needs.release.outputs.pt_release_name }}
-            ${{ needs.release.outputs.pt_pep517_release_name }}
+          path: ${{ needs.release.outputs.pt_release_name }}
 
 concurrency:
   group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.sha }}-${{ github.event_name }}

.github/workflows/docker-builds.yml

@@ -70,7 +70,7 @@ jobs:
           pytorch-linux-jammy-py3-clang18-asan,
           pytorch-linux-jammy-py3-clang12-onnx,
           pytorch-linux-jammy-linter,
-          pytorch-linux-jammy-cuda12.8-cudnn9-py3.10-linter,
+          pytorch-linux-jammy-cuda12.8-cudnn9-py3.9-linter,
           pytorch-linux-jammy-py3-clang12-executorch,
           pytorch-linux-jammy-py3.12-triton-cpu,
           pytorch-linux-noble-riscv64-py3.12-gcc14

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

@@ -62,7 +62,7 @@ jobs:
       DOCKER_IMAGE_TAG_PREFIX: cpu-aarch64
       DESIRED_PYTHON: "3.10"
       runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
-      runs_on: linux.arm64.r7g.12xlarge.memory
+      runs_on: linux.arm64.m7g.4xlarge.ephemeral
       ALPINE_IMAGE: "arm64v8/alpine"
       build_name: manywheel-py3_10-cpu-aarch64
       build_environment: linux-aarch64-binary-manywheel
@@ -128,7 +128,7 @@ jobs:
       DOCKER_IMAGE_TAG_PREFIX: cuda12.6
       DESIRED_PYTHON: "3.10"
       runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
-      runs_on: linux.arm64.r7g.12xlarge.memory
+      runs_on: linux.arm64.m7g.4xlarge.ephemeral
       ALPINE_IMAGE: "arm64v8/alpine"
       build_name: manywheel-py3_10-cuda-aarch64-12_6
       build_environment: linux-aarch64-binary-manywheel
@@ -174,7 +174,7 @@ jobs:
       DOCKER_IMAGE_TAG_PREFIX: cuda12.8
       DESIRED_PYTHON: "3.10"
       runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
-      runs_on: linux.arm64.r7g.12xlarge.memory
+      runs_on: linux.arm64.m7g.4xlarge.ephemeral
       ALPINE_IMAGE: "arm64v8/alpine"
       build_name: manywheel-py3_10-cuda-aarch64-12_8
       build_environment: linux-aarch64-binary-manywheel
@@ -220,7 +220,7 @@ jobs:
       DOCKER_IMAGE_TAG_PREFIX: cuda13.0
       DESIRED_PYTHON: "3.10"
       runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
-      runs_on: linux.arm64.r7g.12xlarge.memory
+      runs_on: linux.arm64.m7g.4xlarge.ephemeral
       ALPINE_IMAGE: "arm64v8/alpine"
       build_name: manywheel-py3_10-cuda-aarch64-13_0
       build_environment: linux-aarch64-binary-manywheel
@@ -265,7 +265,7 @@ jobs:
       DOCKER_IMAGE_TAG_PREFIX: cpu-aarch64
       DESIRED_PYTHON: "3.11"
       runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
-      runs_on: linux.arm64.r7g.12xlarge.memory
+      runs_on: linux.arm64.m7g.4xlarge.ephemeral
       ALPINE_IMAGE: "arm64v8/alpine"
       build_name: manywheel-py3_11-cpu-aarch64
       build_environment: linux-aarch64-binary-manywheel
@@ -331,7 +331,7 @@ jobs:
       DOCKER_IMAGE_TAG_PREFIX: cuda12.6
       DESIRED_PYTHON: "3.11"
       runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
-      runs_on: linux.arm64.r7g.12xlarge.memory
+      runs_on: linux.arm64.m7g.4xlarge.ephemeral
       ALPINE_IMAGE: "arm64v8/alpine"
       build_name: manywheel-py3_11-cuda-aarch64-12_6
       build_environment: linux-aarch64-binary-manywheel
@@ -377,7 +377,7 @@ jobs:
       DOCKER_IMAGE_TAG_PREFIX: cuda12.8
       DESIRED_PYTHON: "3.11"
       runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
-      runs_on: linux.arm64.r7g.12xlarge.memory
+      runs_on: linux.arm64.m7g.4xlarge.ephemeral
       ALPINE_IMAGE: "arm64v8/alpine"
       build_name: manywheel-py3_11-cuda-aarch64-12_8
       build_environment: linux-aarch64-binary-manywheel
@@ -423,7 +423,7 @@ jobs:
       DOCKER_IMAGE_TAG_PREFIX: cuda13.0
       DESIRED_PYTHON: "3.11"
       runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
-      runs_on: linux.arm64.r7g.12xlarge.memory
+      runs_on: linux.arm64.m7g.4xlarge.ephemeral
       ALPINE_IMAGE: "arm64v8/alpine"
       build_name: manywheel-py3_11-cuda-aarch64-13_0
       build_environment: linux-aarch64-binary-manywheel
@@ -468,7 +468,7 @@ jobs:
       DOCKER_IMAGE_TAG_PREFIX: cpu-aarch64
       DESIRED_PYTHON: "3.12"
       runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
-      runs_on: linux.arm64.r7g.12xlarge.memory
+      runs_on: linux.arm64.m7g.4xlarge.ephemeral
       ALPINE_IMAGE: "arm64v8/alpine"
       build_name: manywheel-py3_12-cpu-aarch64
       build_environment: linux-aarch64-binary-manywheel
@@ -534,7 +534,7 @@ jobs:
       DOCKER_IMAGE_TAG_PREFIX: cuda12.6
       DESIRED_PYTHON: "3.12"
       runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
-      runs_on: linux.arm64.r7g.12xlarge.memory
+      runs_on: linux.arm64.m7g.4xlarge.ephemeral
       ALPINE_IMAGE: "arm64v8/alpine"
       build_name: manywheel-py3_12-cuda-aarch64-12_6
       build_environment: linux-aarch64-binary-manywheel
@@ -580,7 +580,7 @@ jobs:
       DOCKER_IMAGE_TAG_PREFIX: cuda12.8
       DESIRED_PYTHON: "3.12"
       runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
-      runs_on: linux.arm64.r7g.12xlarge.memory
+      runs_on: linux.arm64.m7g.4xlarge.ephemeral
       ALPINE_IMAGE: "arm64v8/alpine"
       build_name: manywheel-py3_12-cuda-aarch64-12_8
       build_environment: linux-aarch64-binary-manywheel
@@ -626,7 +626,7 @@ jobs:
       DOCKER_IMAGE_TAG_PREFIX: cuda13.0
       DESIRED_PYTHON: "3.12"
       runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
-      runs_on: linux.arm64.r7g.12xlarge.memory
+      runs_on: linux.arm64.m7g.4xlarge.ephemeral
       ALPINE_IMAGE: "arm64v8/alpine"
       build_name: manywheel-py3_12-cuda-aarch64-13_0
       build_environment: linux-aarch64-binary-manywheel
@@ -671,7 +671,7 @@ jobs:
       DOCKER_IMAGE_TAG_PREFIX: cpu-aarch64
       DESIRED_PYTHON: "3.13"
       runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
-      runs_on: linux.arm64.r7g.12xlarge.memory
+      runs_on: linux.arm64.m7g.4xlarge.ephemeral
       ALPINE_IMAGE: "arm64v8/alpine"
       build_name: manywheel-py3_13-cpu-aarch64
       build_environment: linux-aarch64-binary-manywheel
@@ -737,7 +737,7 @@ jobs:
       DOCKER_IMAGE_TAG_PREFIX: cuda12.6
       DESIRED_PYTHON: "3.13"
       runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
-      runs_on: linux.arm64.r7g.12xlarge.memory
+      runs_on: linux.arm64.m7g.4xlarge.ephemeral
       ALPINE_IMAGE: "arm64v8/alpine"
       build_name: manywheel-py3_13-cuda-aarch64-12_6
       build_environment: linux-aarch64-binary-manywheel
@@ -783,7 +783,7 @@ jobs:
       DOCKER_IMAGE_TAG_PREFIX: cuda12.8
       DESIRED_PYTHON: "3.13"
       runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
-      runs_on: linux.arm64.r7g.12xlarge.memory
+      runs_on: linux.arm64.m7g.4xlarge.ephemeral
       ALPINE_IMAGE: "arm64v8/alpine"
       build_name: manywheel-py3_13-cuda-aarch64-12_8
       build_environment: linux-aarch64-binary-manywheel
@@ -829,7 +829,7 @@ jobs:
       DOCKER_IMAGE_TAG_PREFIX: cuda13.0
       DESIRED_PYTHON: "3.13"
       runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
-      runs_on: linux.arm64.r7g.12xlarge.memory
+      runs_on: linux.arm64.m7g.4xlarge.ephemeral
       ALPINE_IMAGE: "arm64v8/alpine"
       build_name: manywheel-py3_13-cuda-aarch64-13_0
       build_environment: linux-aarch64-binary-manywheel
@@ -874,7 +874,7 @@ jobs:
       DOCKER_IMAGE_TAG_PREFIX: cpu-aarch64
       DESIRED_PYTHON: "3.13t"
       runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
-      runs_on: linux.arm64.r7g.12xlarge.memory
+      runs_on: linux.arm64.m7g.4xlarge.ephemeral
       ALPINE_IMAGE: "arm64v8/alpine"
       build_name: manywheel-py3_13t-cpu-aarch64
       build_environment: linux-aarch64-binary-manywheel
@@ -940,7 +940,7 @@ jobs:
       DOCKER_IMAGE_TAG_PREFIX: cuda12.6
       DESIRED_PYTHON: "3.13t"
       runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
-      runs_on: linux.arm64.r7g.12xlarge.memory
+      runs_on: linux.arm64.m7g.4xlarge.ephemeral
       ALPINE_IMAGE: "arm64v8/alpine"
       build_name: manywheel-py3_13t-cuda-aarch64-12_6
       build_environment: linux-aarch64-binary-manywheel
@@ -986,7 +986,7 @@ jobs:
       DOCKER_IMAGE_TAG_PREFIX: cuda12.8
       DESIRED_PYTHON: "3.13t"
       runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
-      runs_on: linux.arm64.r7g.12xlarge.memory
+      runs_on: linux.arm64.m7g.4xlarge.ephemeral
       ALPINE_IMAGE: "arm64v8/alpine"
       build_name: manywheel-py3_13t-cuda-aarch64-12_8
       build_environment: linux-aarch64-binary-manywheel
@@ -1032,7 +1032,7 @@ jobs:
       DOCKER_IMAGE_TAG_PREFIX: cuda13.0
       DESIRED_PYTHON: "3.13t"
       runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
-      runs_on: linux.arm64.r7g.12xlarge.memory
+      runs_on: linux.arm64.m7g.4xlarge.ephemeral
       ALPINE_IMAGE: "arm64v8/alpine"
       build_name: manywheel-py3_13t-cuda-aarch64-13_0
       build_environment: linux-aarch64-binary-manywheel
@@ -1077,7 +1077,7 @@ jobs:
       DOCKER_IMAGE_TAG_PREFIX: cpu-aarch64
       DESIRED_PYTHON: "3.14"
       runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
-      runs_on: linux.arm64.r7g.12xlarge.memory
+      runs_on: linux.arm64.m7g.4xlarge.ephemeral
       ALPINE_IMAGE: "arm64v8/alpine"
       build_name: manywheel-py3_14-cpu-aarch64
       build_environment: linux-aarch64-binary-manywheel
@@ -1143,7 +1143,7 @@ jobs:
       DOCKER_IMAGE_TAG_PREFIX: cuda12.6
       DESIRED_PYTHON: "3.14"
       runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
-      runs_on: linux.arm64.r7g.12xlarge.memory
+      runs_on: linux.arm64.m7g.4xlarge.ephemeral
       ALPINE_IMAGE: "arm64v8/alpine"
       build_name: manywheel-py3_14-cuda-aarch64-12_6
       build_environment: linux-aarch64-binary-manywheel
@@ -1189,7 +1189,7 @@ jobs:
       DOCKER_IMAGE_TAG_PREFIX: cuda12.8
       DESIRED_PYTHON: "3.14"
       runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
-      runs_on: linux.arm64.r7g.12xlarge.memory
+      runs_on: linux.arm64.m7g.4xlarge.ephemeral
       ALPINE_IMAGE: "arm64v8/alpine"
       build_name: manywheel-py3_14-cuda-aarch64-12_8
       build_environment: linux-aarch64-binary-manywheel
@@ -1235,7 +1235,7 @@ jobs:
       DOCKER_IMAGE_TAG_PREFIX: cuda13.0
       DESIRED_PYTHON: "3.14"
       runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
-      runs_on: linux.arm64.r7g.12xlarge.memory
+      runs_on: linux.arm64.m7g.4xlarge.ephemeral
       ALPINE_IMAGE: "arm64v8/alpine"
       build_name: manywheel-py3_14-cuda-aarch64-13_0
       build_environment: linux-aarch64-binary-manywheel
@@ -1280,7 +1280,7 @@ jobs:
       DOCKER_IMAGE_TAG_PREFIX: cpu-aarch64
       DESIRED_PYTHON: "3.14t"
       runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
-      runs_on: linux.arm64.r7g.12xlarge.memory
+      runs_on: linux.arm64.m7g.4xlarge.ephemeral
       ALPINE_IMAGE: "arm64v8/alpine"
       build_name: manywheel-py3_14t-cpu-aarch64
       build_environment: linux-aarch64-binary-manywheel
@@ -1346,7 +1346,7 @@ jobs:
       DOCKER_IMAGE_TAG_PREFIX: cuda12.6
       DESIRED_PYTHON: "3.14t"
       runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
-      runs_on: linux.arm64.r7g.12xlarge.memory
+      runs_on: linux.arm64.m7g.4xlarge.ephemeral
       ALPINE_IMAGE: "arm64v8/alpine"
       build_name: manywheel-py3_14t-cuda-aarch64-12_6
       build_environment: linux-aarch64-binary-manywheel
@@ -1392,7 +1392,7 @@ jobs:
       DOCKER_IMAGE_TAG_PREFIX: cuda12.8
       DESIRED_PYTHON: "3.14t"
       runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
-      runs_on: linux.arm64.r7g.12xlarge.memory
+      runs_on: linux.arm64.m7g.4xlarge.ephemeral
       ALPINE_IMAGE: "arm64v8/alpine"
       build_name: manywheel-py3_14t-cuda-aarch64-12_8
       build_environment: linux-aarch64-binary-manywheel
@@ -1438,7 +1438,7 @@ jobs:
       DOCKER_IMAGE_TAG_PREFIX: cuda13.0
       DESIRED_PYTHON: "3.14t"
       runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
-      runs_on: linux.arm64.r7g.12xlarge.memory
+      runs_on: linux.arm64.m7g.4xlarge.ephemeral
       ALPINE_IMAGE: "arm64v8/alpine"
       build_name: manywheel-py3_14t-cuda-aarch64-13_0
       build_environment: linux-aarch64-binary-manywheel

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

@@ -333,7 +333,6 @@ jobs:
       LIBTORCH_CONFIG: release
       LIBTORCH_VARIANT: shared-with-deps
       runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
-      timeout-minutes: 300
       build_name: libtorch-rocm6_3-shared-with-deps-release
       build_environment: linux-binary-libtorch
     secrets:
@@ -448,7 +447,6 @@ jobs:
       LIBTORCH_CONFIG: release
       LIBTORCH_VARIANT: shared-with-deps
       runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
-      timeout-minutes: 300
       build_name: libtorch-rocm6_4-shared-with-deps-release
       build_environment: linux-binary-libtorch
     secrets:

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

@@ -42,7 +42,7 @@ jobs:
       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 }}
-  manywheel-py3_12-cuda13_0-build:
+  manywheel-py3_12-cuda12_8-build:
     if: ${{ github.repository_owner == 'pytorch' }}
     uses: ./.github/workflows/_binary-build-linux.yml
     needs: get-label-type
@@ -51,22 +51,22 @@ jobs:
       PACKAGE_TYPE: manywheel
       # TODO: This is a legacy variable that we eventually want to get rid of in
       #       favor of GPU_ARCH_VERSION
-      DESIRED_CUDA: cu130
-      GPU_ARCH_VERSION: "13.0"
+      DESIRED_CUDA: cu128
+      GPU_ARCH_VERSION: "12.8"
       GPU_ARCH_TYPE: cuda
       DOCKER_IMAGE: manylinux2_28-builder
-      DOCKER_IMAGE_TAG_PREFIX: cuda13.0
+      DOCKER_IMAGE_TAG_PREFIX: cuda12.8
       DESIRED_PYTHON: "3.12"
       runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
-      build_name: manywheel-py3_12-cuda13_0
+      build_name: manywheel-py3_12-cuda12_8
       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==2.27.7; 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'
+      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'
     secrets:
       github-token: ${{ secrets.GITHUB_TOKEN }}
-  manywheel-py3_12-cuda13_0-test:  # Testing
+  manywheel-py3_12-cuda12_8-test:  # Testing
     if: ${{ github.repository_owner == 'pytorch' }}
     needs:
-      - manywheel-py3_12-cuda13_0-build
+      - manywheel-py3_12-cuda12_8-build
       - get-label-type
     uses: ./.github/workflows/_binary-test-linux.yml
     with:
@@ -74,13 +74,13 @@ jobs:
       PACKAGE_TYPE: manywheel
       # TODO: This is a legacy variable that we eventually want to get rid of in
       #       favor of GPU_ARCH_VERSION
-      DESIRED_CUDA: cu130
-      GPU_ARCH_VERSION: "13.0"
+      DESIRED_CUDA: cu128
+      GPU_ARCH_VERSION: "12.8"
       GPU_ARCH_TYPE: cuda
       DOCKER_IMAGE: manylinux2_28-builder
-      DOCKER_IMAGE_TAG_PREFIX: cuda13.0
+      DOCKER_IMAGE_TAG_PREFIX: cuda12.8
       DESIRED_PYTHON: "3.12"
-      build_name: manywheel-py3_12-cuda13_0
+      build_name: manywheel-py3_12-cuda12_8
       build_environment: linux-binary-manywheel
       runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
       runs_on: linux.g4dn.4xlarge.nvidia.gpu # 12.8+ builds need sm_70+ runner

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

@@ -323,7 +323,6 @@ jobs:
       DOCKER_IMAGE_TAG_PREFIX: rocm6.3
       DESIRED_PYTHON: "3.10"
       runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
-      timeout-minutes: 300
       build_name: manywheel-py3_10-rocm6_3
       build_environment: linux-binary-manywheel
     secrets:
@@ -435,7 +434,6 @@ jobs:
       DOCKER_IMAGE_TAG_PREFIX: rocm6.4
       DESIRED_PYTHON: "3.10"
       runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
-      timeout-minutes: 300
       build_name: manywheel-py3_10-rocm6_4
       build_environment: linux-binary-manywheel
     secrets:
@@ -917,7 +915,6 @@ jobs:
       DOCKER_IMAGE_TAG_PREFIX: rocm6.3
       DESIRED_PYTHON: "3.11"
       runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
-      timeout-minutes: 300
       build_name: manywheel-py3_11-rocm6_3
       build_environment: linux-binary-manywheel
     secrets:
@@ -1029,7 +1026,6 @@ jobs:
       DOCKER_IMAGE_TAG_PREFIX: rocm6.4
       DESIRED_PYTHON: "3.11"
       runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
-      timeout-minutes: 300
       build_name: manywheel-py3_11-rocm6_4
       build_environment: linux-binary-manywheel
     secrets:
@@ -1511,7 +1507,6 @@ jobs:
       DOCKER_IMAGE_TAG_PREFIX: rocm6.3
       DESIRED_PYTHON: "3.12"
       runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
-      timeout-minutes: 300
       build_name: manywheel-py3_12-rocm6_3
       build_environment: linux-binary-manywheel
     secrets:
@@ -1623,7 +1618,6 @@ jobs:
       DOCKER_IMAGE_TAG_PREFIX: rocm6.4
       DESIRED_PYTHON: "3.12"
       runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
-      timeout-minutes: 300
       build_name: manywheel-py3_12-rocm6_4
       build_environment: linux-binary-manywheel
     secrets:
@@ -2105,7 +2099,6 @@ jobs:
       DOCKER_IMAGE_TAG_PREFIX: rocm6.3
       DESIRED_PYTHON: "3.13"
       runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
-      timeout-minutes: 300
       build_name: manywheel-py3_13-rocm6_3
       build_environment: linux-binary-manywheel
     secrets:
@@ -2217,7 +2210,6 @@ jobs:
       DOCKER_IMAGE_TAG_PREFIX: rocm6.4
       DESIRED_PYTHON: "3.13"
       runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
-      timeout-minutes: 300
       build_name: manywheel-py3_13-rocm6_4
       build_environment: linux-binary-manywheel
     secrets:
@@ -2699,7 +2691,6 @@ jobs:
       DOCKER_IMAGE_TAG_PREFIX: rocm6.3
       DESIRED_PYTHON: "3.13t"
       runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
-      timeout-minutes: 300
       build_name: manywheel-py3_13t-rocm6_3
       build_environment: linux-binary-manywheel
     secrets:
@@ -2811,7 +2802,6 @@ jobs:
       DOCKER_IMAGE_TAG_PREFIX: rocm6.4
       DESIRED_PYTHON: "3.13t"
       runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
-      timeout-minutes: 300
       build_name: manywheel-py3_13t-rocm6_4
       build_environment: linux-binary-manywheel
     secrets:
@@ -3293,7 +3283,6 @@ jobs:
       DOCKER_IMAGE_TAG_PREFIX: rocm6.3
       DESIRED_PYTHON: "3.14"
       runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
-      timeout-minutes: 300
       build_name: manywheel-py3_14-rocm6_3
       build_environment: linux-binary-manywheel
     secrets:
@@ -3405,7 +3394,6 @@ jobs:
       DOCKER_IMAGE_TAG_PREFIX: rocm6.4
       DESIRED_PYTHON: "3.14"
       runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
-      timeout-minutes: 300
       build_name: manywheel-py3_14-rocm6_4
       build_environment: linux-binary-manywheel
     secrets:
@@ -3887,7 +3875,6 @@ jobs:
       DOCKER_IMAGE_TAG_PREFIX: rocm6.3
       DESIRED_PYTHON: "3.14t"
       runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
-      timeout-minutes: 300
       build_name: manywheel-py3_14t-rocm6_3
       build_environment: linux-binary-manywheel
     secrets:
@@ -3999,7 +3986,6 @@ jobs:
       DOCKER_IMAGE_TAG_PREFIX: rocm6.4
       DESIRED_PYTHON: "3.14t"
       runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
-      timeout-minutes: 300
       build_name: manywheel-py3_14t-rocm6_4
       build_environment: linux-binary-manywheel
     secrets:

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

@@ -60,7 +60,6 @@ jobs:
       DOCKER_IMAGE_TAG_PREFIX: rocm6.4
       DESIRED_PYTHON: "3.10"
       runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
-      timeout-minutes: 300
       build_name: manywheel-py3_10-rocm6_4
       build_environment: linux-binary-manywheel-rocm
     secrets:

.github/workflows/lint.yml

@@ -31,8 +31,6 @@ jobs:
     if: github.repository_owner == 'pytorch'
     name: Get changed files
     uses: ./.github/workflows/_get-changed-files.yml
-    with:
-      all_files: ${{ contains(github.event.pull_request.labels.*.name, 'lint-all-files') || contains(github.event.pull_request.labels.*.name, 'Reverted') }}
 
   lintrunner-clang:
     uses: pytorch/test-infra/.github/workflows/linux_job_v2.yml@main
@@ -55,7 +53,7 @@ jobs:
     with:
       timeout: 120
       runner: "${{ needs.get-label-type.outputs.label-type }}linux.2xlarge"
-      docker-image: ci-image:pytorch-linux-jammy-cuda12.8-cudnn9-py3.10-linter
+      docker-image: ci-image:pytorch-linux-jammy-cuda12.8-cudnn9-py3.9-linter
       # NB: A shallow checkout won't work here because calculate-docker-image requires a full checkout
       # to run git rev-parse HEAD~:.ci/docker when a new image is needed
       fetch-depth: 0
@@ -266,10 +264,10 @@ jobs:
         with:
           submodules: false
           fetch-depth: 1
-      - name: Setup Python 3.10
+      - name: Setup Python 3.9
         uses: actions/setup-python@a26af69be951a213d495a4c3e4e4022e16d87065 # v5.6.0
         with:
-          python-version: '3.10'
+          python-version: '3.9'
           architecture: x64
           cache: pip
       - name: Install dependencies

.github/workflows/quantization-periodic.yml

@@ -1,54 +0,0 @@
-name: quantization-periodic
-
-on:
-  push:
-    tags:
-      - ciflow/quantization-periodic/*
-  workflow_dispatch:
-  schedule:
-    # run weekly
-    - cron: "45 0 * * 0"
-
-concurrency:
-  group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.ref_name }}-${{ github.ref_type == 'branch' && github.sha }}-${{ github.event_name == 'workflow_dispatch' }}
-  cancel-in-progress: true
-
-permissions:
-  id-token: write
-  contents: read
-
-jobs:
-  get-default-label-prefix:
-    name: get-default-label-prefix
-    uses: pytorch/pytorch/.github/workflows/_runner-determinator.yml@main
-    if: ${{ (github.event_name != 'schedule' || github.repository == 'pytorch/pytorch') && github.repository_owner == 'pytorch' }}
-    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: ${{ github.ref_type }}
-      opt_out_experiments: lf
-
-  periodic-quantization-build:
-    name: periodic-quantization-build
-    uses: ./.github/workflows/_linux-build.yml
-    needs: get-default-label-prefix
-    with:
-      runner_prefix: "${{ needs.get-default-label-prefix.outputs.label-type }}"
-      build-environment: linux-jammy-cuda12.8-cudnn9-py3-gcc11
-      docker-image-name: ci-image:pytorch-linux-jammy-cuda12.8-cudnn9-py3-gcc11
-      cuda-arch-list: '8.9'
-      test-matrix: |
-        { include: [
-          { config: "quantization", shard: 1, num_shards: 1, runner: "${{ needs.get-default-label-prefix.outputs.label-type }}linux.g6.4xlarge.experimental.nvidia.gpu" },
-        ]}
-    secrets: inherit
-  periodic-test-quantization:
-    name: periodic-test-quantization
-    uses: ./.github/workflows/_linux-test.yml
-    needs: periodic-quantization-build
-    with:
-      build-environment: linux-jammy-cuda12.8-cudnn9-py3-gcc11
-      docker-image: ${{ needs.periodic-quantization-build.outputs.docker-image }}
-      test-matrix: ${{ needs.periodic-quantization-build.outputs.test-matrix }}
-    secrets: inherit

.github/workflows/test-b200.yml

@@ -1,76 +0,0 @@
-# B200 Smoke Tests CI Workflow
-#
-# This workflow runs smoke tests on B200 hardware
-#
-# Flow:
-# 1. Builds PyTorch with CUDA 12.8+ and sm100 architecture for B200
-# 2. Runs smoke tests on linux.dgx.b200 runner
-# 3. Tests executed are defined in .ci/pytorch/test.sh -> test_python_smoke() function
-#
-# Triggered by:
-# - Pull requests modifying this workflow file
-# - Manual dispatch
-# - Schedule (every 6 hours)
-# - Adding ciflow/b200 label to a PR (creates ciflow/b200/* tag)
-
-name: B200 Smoke Tests
-
-on:
-  pull_request:
-    paths:
-      - .github/workflows/test-b200.yml
-  workflow_dispatch:
-  schedule:
-    - cron: 0 4,10,16,22 * * *  # every 6 hours
-  push:
-    tags:
-      - ciflow/b200/*
-
-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: ${{ github.ref_type }}
-
-  linux-jammy-cuda12_8-py3_10-gcc11-sm100-build:
-    name: linux-jammy-cuda12.8-py3.10-gcc11-sm100
-    uses: ./.github/workflows/_linux-build.yml
-    needs: get-label-type
-    with:
-      runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
-      runner: linux.12xlarge.memory
-      build-environment: linux-jammy-cuda12.8-py3.10-gcc11-sm100
-      docker-image-name: ci-image:pytorch-linux-jammy-cuda12.8-cudnn9-py3-gcc11
-      cuda-arch-list: '10.0'
-      test-matrix: |
-        { include: [
-          { config: "smoke_b200", shard: 1, num_shards: 1, runner: "linux.dgx.b200" },
-        ]}
-      # config: "smoke_b200" maps to test_python_smoke_b200() in .ci/pytorch/test.sh
-    secrets: inherit
-
-  linux-jammy-cuda12_8-py3_10-gcc11-sm100-test:
-    name: linux-jammy-cuda12.8-py3.10-gcc11-sm100
-    uses: ./.github/workflows/_linux-test.yml
-    needs:
-      - linux-jammy-cuda12_8-py3_10-gcc11-sm100-build
-    with:
-      build-environment: linux-jammy-cuda12.8-py3.10-gcc11-sm100
-      docker-image: ${{ needs.linux-jammy-cuda12_8-py3_10-gcc11-sm100-build.outputs.docker-image }}
-      test-matrix: ${{ needs.linux-jammy-cuda12_8-py3_10-gcc11-sm100-build.outputs.test-matrix }}
-      aws-role-to-assume: arn:aws:iam::308535385114:role/gha_workflow_s3_and_ecr_read_only
-    secrets: inherit

.github/workflows/unstable.yml

@@ -53,3 +53,27 @@ jobs:
       issue_owner: ${{ github.event.pull_request.user.login || github.event.issue.user.login }}
       curr_branch: ${{ github.head_ref || github.ref_name }}
       curr_ref_type: ${{ github.ref_type }}
+
+  linux-jammy-py3_9-clang9-xla-build:
+    name: linux-jammy-py3_9-clang9-xla
+    uses: ./.github/workflows/_linux-build.yml
+    needs: get-label-type
+    with:
+      runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
+      build-environment: linux-jammy-py3.9-clang9-xla
+      docker-image-name: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/xla_base:v1.3-lite
+      test-matrix: |
+        { include: [
+          { config: "xla", shard: 1, num_shards: 1, runner: "${{ needs.get-label-type.outputs.label-type }}linux.12xlarge" },
+        ]}
+    secrets: inherit
+
+  linux-jammy-py3_9-clang9-xla-test:
+    name: linux-jammy-py3_9-clang9-xla
+    uses: ./.github/workflows/_linux-test.yml
+    needs: linux-jammy-py3_9-clang9-xla-build
+    with:
+      build-environment: linux-jammy-py3.9-clang9-xla
+      docker-image: ${{ needs.linux-jammy-py3_9-clang9-xla-build.outputs.docker-image }}
+      test-matrix: ${{ needs.linux-jammy-py3_9-clang9-xla-build.outputs.test-matrix }}
+    secrets: inherit

.gitignore

@@ -82,7 +82,6 @@ torch/return_types.pyi
 torch/nn/functional.pyi
 torch/utils/data/datapipes/datapipe.pyi
 torch/csrc/autograd/generated/*
-torch/csrc/functionalization/generated/*
 torch/csrc/lazy/generated/*.[!m]*
 torch_compile_debug/
 # Listed manually because some files in this directory are not generated

.lintrunner.toml

@@ -49,7 +49,7 @@ init_command = [
     'mccabe==0.7.0',
     'pycodestyle==2.14.0',
     'pyflakes==3.4.0',
-    'torchfix==0.4.0 ; python_version >= "3.10" and python_version < "3.13"',
+    'torchfix==0.4.0 ; python_version >= "3.9" and python_version < "3.13"',
 ]
 
 
@@ -153,7 +153,7 @@ init_command = [
     'python3',
     'tools/linter/adapters/pip_init.py',
     '--dry-run={{DRYRUN}}',
-    'numpy==1.26.4 ; python_version >= "3.10" and python_version <= "3.11"',
+    'numpy==1.26.4 ; python_version >= "3.9" and python_version <= "3.11"',
     'numpy==2.1.0 ; python_version >= "3.12"',
     'expecttest==0.3.0',
     'mypy==1.16.0',
@@ -196,7 +196,6 @@ exclude_patterns = [
     'tools/test/gen_operators_yaml_test.py',
     'tools/test/gen_oplist_test.py',
     'tools/test/test_selective_build.py',
-    'tools/experimental/dynamic_shapes/torchfuzz/**',
 ]
 command = [
     'python3',

BUILD.bazel

@@ -22,7 +22,6 @@ COMMON_COPTS = [
     "-DHAVE_SHM_UNLINK=1",
     "-D_FILE_OFFSET_BITS=64",
     "-DUSE_FBGEMM",
-    "-DUSE_DISTRIBUTED",
     "-DAT_PER_OPERATOR_HEADERS",
     "-DATEN_THREADING=NATIVE",
     "-DNO_CUDNN_DESTROY_HANDLE",
@@ -91,8 +90,6 @@ generated_cpu_cpp = [
     "aten/src/ATen/NativeMetaFunctions.h",
     "aten/src/ATen/RegistrationDeclarations.h",
     "aten/src/ATen/VmapGeneratedPlumbing.h",
-    "aten/src/ATen/ViewMetaClasses.h",
-    "aten/src/ATen/ViewMetaClasses.cpp",
     "aten/src/ATen/core/aten_interned_strings.h",
     "aten/src/ATen/core/enum_tag.h",
     "aten/src/ATen/core/TensorBody.h",
@@ -813,7 +810,7 @@ cc_library(
     name = "torch_python",
     srcs = libtorch_python_core_sources
         + if_cuda(libtorch_python_cuda_sources)
-        + if_cuda(libtorch_python_distributed_sources)
+        + libtorch_python_distributed_sources
         + GENERATED_AUTOGRAD_PYTHON,
     hdrs = glob([
         "torch/csrc/generic/*.cpp",
@@ -1077,7 +1074,6 @@ test_suite(
         "aten/src/ATen/templates/LazyNonNativeIr.h",
         "aten/src/ATen/templates/RegisterDispatchKey.cpp",
         "aten/src/ATen/templates/RegisterDispatchDefinitions.ini",
-        "aten/src/ATen/templates/ViewMetaClassesPythonBinding.cpp",
         "aten/src/ATen/native/native_functions.yaml",
         "aten/src/ATen/native/tags.yaml",
         "aten/src/ATen/native/ts_native_functions.yaml",

CMakeLists.txt

@@ -1,4 +1,5 @@
 cmake_minimum_required(VERSION 3.27 FATAL_ERROR)
+# cmake_policy(SET CMP0022 NEW) cmake_policy(SET CMP0023 NEW)
 
 # Use compiler ID "AppleClang" instead of "Clang" for XCode. Not setting this
 # sometimes makes XCode C compiler gets detected as "Clang", even when the C++
@@ -180,8 +181,9 @@ elseif(CMAKE_SYSTEM_PROCESSOR MATCHES "^(ppc64le)")
   set(CPU_POWER ON)
 endif()
 
-# For non-supported platforms, turn USE_DISTRIBUTED off by default. It is not
-# tested and likely won't work without additional changes.
+# For non-supported platforms, turn USE_DISTRIBUTED off by default.
+# NB: USE_DISTRIBUTED simply disables the backend; distributed code
+# still gets built
 if(NOT LINUX AND NOT WIN32)
   set(USE_DISTRIBUTED
       OFF
@@ -261,11 +263,11 @@ option(USE_PYTORCH_METAL "Use Metal for PyTorch iOS build" OFF)
 option(USE_PYTORCH_METAL_EXPORT "Export Metal models on MacOSX desktop" OFF)
 option(USE_NATIVE_ARCH "Use -march=native" OFF)
 cmake_dependent_option(USE_MPS "Use MPS for macOS build" ON "MPS_FOUND" OFF)
-option(USE_DISTRIBUTED "Use distributed" ON)
+option(USE_DISTRIBUTED "Enable default distributed backends" ON)
 cmake_dependent_option(USE_NCCL "Use NCCL" ON
                        "USE_DISTRIBUTED;USE_CUDA OR USE_ROCM;UNIX;NOT APPLE" OFF)
 cmake_dependent_option(USE_XCCL "Use XCCL" ON
-                       "USE_XPU;UNIX;NOT APPLE" OFF)
+                       "USE_DISTRIBUTED;USE_XPU;UNIX;NOT APPLE" OFF)
 cmake_dependent_option(USE_RCCL "Use RCCL" ON USE_NCCL OFF)
 cmake_dependent_option(USE_RCCL "Use RCCL" ON "USE_NCCL;NOT WIN32" OFF)
 cmake_dependent_option(USE_STATIC_NCCL "Use static NCCL" OFF "USE_NCCL" OFF)
@@ -437,11 +439,10 @@ if(WIN32)
       PATH_SUFFIXES lib
       NO_DEFAULT_PATH)
     if(NOT libuv_tmp_LIBRARY)
-      set(USE_DISTRIBUTED OFF)
       set(USE_GLOO OFF)
       message(
         WARNING
-          "Libuv is not installed in current conda env. Set USE_DISTRIBUTED to OFF. "
+          "Libuv is not installed in current conda env. Set USE_GLOO to OFF. "
           "Please run command 'conda install -c conda-forge libuv=1.39' to install libuv."
       )
     else()
@@ -1481,4 +1482,4 @@ else()
     To do so please export USE_PRIORITIZED_TEXT_FOR_LD=1
     ]])
   endif()
-endif()
+endif()

MANIFEST.in

@@ -1,61 +1,20 @@
 # Reference: https://setuptools.pypa.io/en/latest/userguide/miscellaneous.html
 
-# Include individual top-level files
-include CITATION.cff
-include CODEOWNERS
-include Dockerfile
-include LICENSE
-include MANIFEST.in
-include Makefile
-include NOTICE
-include .bc-linter.yml
-include .clang-format .clang-tidy
-include .cmakelintrc
-include .coveragerc
-include .dockerignore
-include .editorconfig
-include .flake8
-include .gdbinit
-include .lintrunner.toml
-include .lldbinit
-include codex_setup.sh
-include docker.Makefile
-include pyrefly.toml
-include ubsan.supp
-
-# Include bazel and BUCK related files
-include BUILD.bazel BUCK.oss
-include WORKSPACE
-include *.bzl
-include .bazelignore .bazelrc .bazelversion
-
-# Include general configuration files
-include *.ini
-# Include important top-level information
-include *.md
-# Include technical text files at the moment, comprises
-# version.txt, CMakeLists.txt, requirements.txt
-include *.txt
-
-# Include ctags configuration
-include .ctags.d/*.ctags
-
-# Include subfolders completely
-graft .devcontainer
-graft .vscode
+# Include source files in SDist
+include CMakeLists.txt
+include *.bzl *.bazel .bazel* BUILD *.BUILD BUILD.* WORKSPACE
+include BUCK BUCK.*
+include requirements*.txt
+include version.txt
+include [Mm]akefile *.[Mm]akefile [Mm]akefile.*
+include [Dd]ockerfile *.[Dd]ockerfile [Dd]ockerfile.* .dockerignore
 graft android
 graft aten
-graft benchmarks
 graft binaries
 graft c10
 graft caffe2
 graft cmake
-graft docs
 graft functorch
-graft ios
-graft mypy_plugins
-graft scripts
-graft test
 graft third_party
 graft tools
 graft torch
@@ -63,37 +22,29 @@ graft torchgen
 # FIXME: torch-xla build during codegen will fail if include this file in wheel
 exclude torchgen/BUILD.bazel
 
-# The following exclusions omit parts from third-party dependencies that
-# contain invalid symlinks[1] and that are not needed for pytorch, such as
-# bindings for unused languages
-prune third_party/flatbuffers/java
-prune third_party/flatbuffers/kotlin
-prune third_party/ittapi/rust
-prune third_party/nccl/pkg/debian
-prune third_party/opentelemetry-cpp/third_party/prometheus-cpp/cmake/project-import-*
-
-# The following document is also an invalid symlink[1] and superfluous
-exclude third_party/flatbuffers/docs/source/CONTRIBUTING.md
-
-# Omit autogenerated code
-prune torchgen/packaged
-
-# Omit caches, compiled, and scm related content
-prune */__pycache__
-prune **/.github
-prune **/.gitlab
-global-exclude *.o *.obj *.so *.dylib *.a *.pxd *.dll *.lib
-global-exclude *.py[cod] *.swp *~
-global-exclude .git .git-blame-ignore-revs .gitattributes .gitignore .gitmodules
-global-exclude .gitlab-ci.yml
+# Misc files and directories in SDist
+include *.md
+include CITATION.cff
+include LICENSE NOTICE
+include mypy*.ini
+graft benchmarks
+graft docs
+graft mypy_plugins
+graft scripts
 
 # Misc files needed for custom setuptools command
 include .gitignore
 include .gitmodules
 
-# [1] Invalid symlinks for the purposes of Python source distributions are,
-# according to the source distribution format[2] links pointing outside the
-# destination directory or links with a `..` component, which is those of
-# concern here.
+# Include test suites in SDist
+graft test
+include pytest.ini
+include .coveragerc
 
-# [2] https://packaging.python.org/en/latest/specifications/source-distribution-format/#source-distribution-archive-features
+# Prune generated/compiled files
+prune torchgen/packaged
+prune */__pycache__
+global-exclude *.o *.obj *.so *.a *.dylib *.pxd *.dll *.lib *.py[cod]
+
+prune */.git
+global-exclude .git *~ *.swp

README.md

@@ -161,7 +161,7 @@ They require JetPack 4.2 and above, and [@dusty-nv](https://github.com/dusty-nv)
 
 #### Prerequisites
 If you are installing from source, you will need:
-- Python 3.10 or later
+- Python 3.9 or later
 - A compiler that fully supports C++17, such as clang or gcc (gcc 9.4.0 or newer is required, on Linux)
 - Visual Studio or Visual Studio Build Tool (Windows only)
 

aten/src/ATen/CMakeLists.txt

@@ -317,20 +317,10 @@ IF(USE_FBGEMM_GENAI)
         -greedy-reverse-local-assignment=1
         -fhip-new-launch-api)
 
-      # Only compile for gfx942 for now.
-      # This is rather hacky, I could not figure out a clean solution :(
-      set(HIP_CLANG_FLAGS_ORIGINAL ${HIP_CLANG_FLAGS})
-      string(REGEX REPLACE "--offload-arch=[^ ]*" "" FILTERED_HIP_CLANG_FLAGS "${HIP_CLANG_FLAGS}")
-      if("gfx942" IN_LIST PYTORCH_ROCM_ARCH)
-        list(APPEND FILTERED_HIP_CLANG_FLAGS --offload-arch=gfx942;)
-      endif()
-      set(HIP_CLANG_FLAGS ${FILTERED_HIP_CLANG_FLAGS})
-
       hip_add_library(
         fbgemm_genai STATIC
         ${fbgemm_genai_native_rocm_hip}
         HIPCC_OPTIONS ${HIP_HCC_FLAGS} ${FBGEMM_GENAI_EXTRA_HIPCC_FLAGS})
-      set(HIP_CLANG_FLAGS ${HIP_CLANG_FLAGS_ORIGINAL})
       set_target_properties(fbgemm_genai PROPERTIES POSITION_INDEPENDENT_CODE ON)
       target_compile_definitions(fbgemm_genai PRIVATE FBGEMM_GENAI_NO_EXTENDED_SHAPES)
 

aten/src/ATen/DLConvertor.cpp

@@ -401,13 +401,30 @@ T* toDLPackImpl(const Tensor& src) {
   // The following code detects whether the src follows
   // a continuous pattern. If the src follows such pattern (common-case)
   // then we do not need to normalize the strides.
-  bool need_normalize_strides = src.dim() == 1 && src.size(0) == 1 && src.stride(0) != 1;
+  bool need_normalize_strides = false;
+  int64_t expected_stride = 1;
+  for (int i = src.dim() - 1; i >= 0; i--) {
+    // detect if we do not meet continuous pattern
+    // and the size is 1, so there is opportunity to normalize
+    if (src.stride(i) != expected_stride && src.size(i) == 1) {
+      need_normalize_strides = true;
+      break;
+    }
+    expected_stride *= src.size(i);
+  }
+
   // less common case, try normalizing the strides
   if (need_normalize_strides) {
     // create a new tensor with possibly normalized strides
     // gh-83069
     auto shape = src.sizes();
-    view = src.as_strided(shape, {1}, src.storage_offset());
+    auto strides = src.strides().vec();
+    for (int i = 0; i < src.dim(); i++) {
+      if (shape[i] < 2) {
+        strides[i] = 1;
+      }
+    }
+    view = src.as_strided(shape, strides, src.storage_offset());
   }
 
   ATenDLMTensor<T>* atDLMTensor(new ATenDLMTensor<T>);

aten/src/ATen/FunctionalStorageImpl.cpp

@@ -9,6 +9,11 @@
 
 namespace at::functionalization {
 
+ViewMeta ViewMeta::to_out_idx(int64_t out_idx) {
+  if (out_idx == this->out_index) return *this;
+  return ViewMeta(forward_fn, reverse_fn, has_symbolic_inputs, is_multi_output, is_as_strided, out_idx);
+}
+
 // Note [Functionalization: Alias Removal Part 2]
 // See Note [Functionalization: Alias Removal] for more details.
 // This function applies a single update from one of the views to the StorageImpl.
@@ -37,12 +42,12 @@ namespace at::functionalization {
 static const Tensor apply_update(const FunctionalStorageImpl::Update& update, const Tensor& base) {
   at::Tensor t = update.new_val;
   TORCH_INTERNAL_ASSERT(!at::functionalization::impl::isFunctionalTensor(t));
-  if (update.view_metas.empty()) { return t; }
+  if (update.view_metas.empty()) return t;
 
   std::vector<at::Tensor> tmp_values({base});
   tmp_values.reserve(update.view_metas.size());
   for (size_t i = 0; i < update.view_metas.size() - 1; ++i) {
-    at::Tensor next_view = update.view_metas[i]->forward(tmp_values.back());
+    at::Tensor next_view = update.view_metas[i].forward_fn(tmp_values.back(), update.view_metas[i].out_index);
     // NB: We only actually need tmp_values for ops like select/slice/diagonal/squeeze/as_strided
     // All of these ops require additional information to recover the sizes of the original tensor.
     // If need to, we could probably apply this optimization and only bother computing tmp_values
@@ -50,8 +55,9 @@ static const Tensor apply_update(const FunctionalStorageImpl::Update& update, co
     tmp_values.push_back(std::move(next_view));
   }
   for(int64_t i = static_cast<int64_t>(update.view_metas.size()) - 1; i >= 0; --i) {
+    int64_t out_idx = update.view_metas[i].out_index;
     // Each view inverse is implemented in ViewInverses.cpp.
-    t = update.view_metas[i]->reverse(tmp_values[i], t);
+    t = update.view_metas[i].reverse_fn(tmp_values[i], t, out_idx);
   }
   TORCH_INTERNAL_ASSERT(!at::functionalization::impl::isFunctionalTensor(t));
   return t;
@@ -105,13 +111,13 @@ FunctionalStorageImpl::FunctionalStorageImpl(const Tensor& base)
   TORCH_INTERNAL_ASSERT(!at::functionalization::impl::isFunctionalTensor(base_));
 }
 
-void FunctionalStorageImpl::add_update(const Tensor& updated_val, const std::vector<std::shared_ptr<ViewMeta>>& metas) {
+void FunctionalStorageImpl::add_update(const Tensor& updated_val, const std::vector<ViewMeta>& metas) {
   TORCH_CHECK(!frozen_, "cannot mutate tensors with frozen storage");
 
   if (metas.size() > 1) {
     for (size_t i = 1; i < metas.size(); ++i) {
       // Skipping this check for XLA. Would be good to add it back, but it is failing XLA CI
-      TORCH_CHECK(updated_val.device().type() == c10::DeviceType::XLA || !metas[i]->is_as_strided,
+      TORCH_CHECK(updated_val.device().type() == c10::DeviceType::XLA || !metas[i].is_as_strided,
 "During torch.compile, encountered a mutation on a view chain of length ", metas.size(), ", where view ", i,
 " was an as_strided() call. as_strided() is non-compositional, and therefore is not possible to functionalize properly today,"
 "so this behavior is banned in compile. As a workaround, you can either remove the mutation from the model code, or you "

aten/src/ATen/FunctionalStorageImpl.h

@@ -8,89 +8,44 @@ namespace at::functionalization {
 
 // See Note [Functionalization Pass In Core]
 
-enum class InverseReturnMode {
-  /// Specifies that functional inverses should always return a view.
-  AlwaysView,
-  /// Specifies that functional inverses should always return a non-view / copy.
-  NeverView,
-  /// Specifies that functional inverses should return a view unless a (copying)
-  /// scatter
-  /// inverse exists, in which case that will be used instead.
-  /// This avoids as_strided() calls that can be difficult for subclasses to
-  /// handle.
-  ViewOrScatterInverse,
-};
-
-#define FUNCTIONALIZATION_VIEWMETA_NAME(TYPE) \
-  static const char* name() {                 \
-    return #TYPE;                             \
-  }
-
-#define FUNCTIONALIZATION_VIEWMETA_SERIALIZABLE_TUPLE(...) \
-  using SerializableTuple = std::tuple<__VA_ARGS__>
-
 // ViewMeta is a class used by the functionalization pass to navigate between
 // a base tensor and a view tensor.
 // For example, if I call `b = a.view1(...)`
-// the functionalization pass will generate and store a ViewMeta specialization
-// for `view1` operation on b that looks like:
+// the functionalization pass will generate and store a ViewMeta on b that looks
+// like:
 //
-// struct TORCH_API view1_ViewMeta : public ViewMeta {
-//   FUNCTIONALIZATION_VIEWMETA_NAME(view1_ViewMeta);
-//   FUNCTIONALIZATION_VIEWMETA_SERIALIZABLE_TUPLE(
-//       bool /* reapply_views */,
-//       const std::vector<int64_t>&);
-//
-//   view1_ViewMeta(const SerializableTuple& tpl)
-//       : view1_ViewMeta(std::get<0>(tpl), std::get<1>(tpl)) {}
-//
-//   view1_ViewMeta(bool reapply_views, const std::vector<int64_t>& size)
-//       : ViewMeta(/*has_symbolic_inputs=*/false),
-//         reapply_views(reapply_views),
-//         size(size) {}
-//
-//   Tensor forward(const Tensor& base) override {
-//       return base.view1(...);
+// ViewMeta(
+//   [<captures>](const Tensor& base, int64_t mutated_view_idx) {
+//     return base.view1(...);
+//   },
+//   [<captures>](const at::Tensor& base, const at::Tensor& mutated_view,
+//   int64_t mutated_view_idx) -> at::Tensor {
+//     return at::functionalization::impl::view1_inverse(base, mutated_view,
+//     ...);
 //   }
 //
-//   Tensor reverse(const Tensor& base, const Tensor& mutated_view) override {
-//       return at::functionalization::impl::view1_inverse(base, mutated_view,
-//       ...);
-//   }
+// The forward_fn lambda describes how to replay view1 on a tensor.
 //
-//   SerializableTuple to_serializable_tuple() {
-//     return std::make_tuple(reapply_views, size);
-//   }
-//
-//   bool reapply_views;
-//   std::vector<int64_t> size;
-// };
-//
-// The forward function describes how to replay view1 on a tensor.
-//
-// The reverse function describes how, given a tensor that is already a view,
+// The reverse_fn lambda describes how, given a tensor that is already a view,
 // how to get the corresponding base tensor. See Note [Functionalization Pass:
 // View Inverses] for details.
-//
-// `SerializedTuple` is a typedef that defines an `std::tuple<...>` type
-// representing the `ViewMeta` instance state. Methods that take in/return such
-// a type are used for supporting pickle serialization.
 struct ViewMeta {
   ViewMeta(
+      std::function<Tensor(const Tensor&, int64_t)> forward,
+      std::function<Tensor(const Tensor&, const Tensor&, int64_t)> reverse,
       bool has_symbolic_inputs,
       bool is_multi_output = false,
       bool is_as_strided = false,
       int64_t out_idx = 0)
-      : out_index(out_idx),
+      : forward_fn(std::move(forward)),
+        reverse_fn(std::move(reverse)),
+        out_index(out_idx),
         is_multi_output(is_multi_output),
         is_as_strided(is_as_strided),
         has_symbolic_inputs(has_symbolic_inputs) {}
 
-  virtual ~ViewMeta() = default;
-
-  virtual Tensor forward(const Tensor& base) = 0;
-  virtual Tensor reverse(const Tensor& base, const Tensor& mutated_view) = 0;
-
+  std::function<Tensor(const Tensor&, int64_t)> forward_fn;
+  std::function<Tensor(const Tensor&, const Tensor&, int64_t)> reverse_fn;
   // See Note [out_idx in ViewMeta]
   int64_t out_index;
 
@@ -102,17 +57,10 @@ struct ViewMeta {
   // Tells us if this view operation has any symbolic inputs
   bool has_symbolic_inputs;
 
-  // Returns a new ViewMeta with the same forward/reverse
+  // Returns a copy of the current ViewMeta, if out_idx matches the current
+  // out_index. Otherwise, returns a new ViewMeta with the same forward/reverse
   // functions, but a new out index.
-  //
-  // This method should be implemented by those `ViewMeta` that have more than
-  // one output.
-  virtual std::shared_ptr<ViewMeta> to_out_index(int64_t out_index) {
-    TORCH_CHECK_NOT_IMPLEMENTED(
-        false,
-        "ViewMeta::to_out_index not implemented. ",
-        "Likely because there's only one output.");
-  }
+  ViewMeta to_out_idx(int64_t out_idx);
 };
 
 // FunctionalStorageImpl is a subclass of StorageImpl used by the
@@ -145,14 +93,14 @@ struct TORCH_API FunctionalStorageImpl : public c10::StorageImpl {
     // NOLINTNEXTLINE(cppcoreguidelines-avoid-const-or-ref-data-members)
     const at::Tensor new_val;
     // NOLINTNEXTLINE(cppcoreguidelines-avoid-const-or-ref-data-members)
-    const std::vector<std::shared_ptr<ViewMeta>> view_metas;
+    const std::vector<ViewMeta> view_metas;
   };
 
   explicit FunctionalStorageImpl(const Tensor& value);
 
   void add_update(
       const Tensor& updated_val,
-      const std::vector<std::shared_ptr<ViewMeta>>& view_metas);
+      const std::vector<ViewMeta>& view_metas);
   bool apply_updates();
   const Tensor& base() {
     return base_;

aten/src/ATen/FunctionalTensorWrapper.cpp

@@ -129,19 +129,17 @@ void FunctionalTensorWrapper::freeze_storage() const {
 // - view_value: The output tensor that we need to wrap.
 // - base: The "base" of the view that `view_value` was generated from.
 // See Note [Functionalization: Alias Removal Part 2] for more details on the mutation replay logic.
-FunctionalTensorWrapper::FunctionalTensorWrapper(
-    const Tensor& view_value,
-    const FunctionalTensorWrapper* base,
-    const std::shared_ptr<functionalization::ViewMeta>& meta)
-    : c10::TensorImpl(
-          c10::DispatchKeySet(DispatchKey::Functionalize),
-          view_value.dtype(),
-          base->storage().data_ptr().device()),
-      value_(view_value),
-      is_multi_output_view_(
-          base->is_multi_output_view_ || meta->is_multi_output),
-      was_storage_changed_(base->was_storage_changed_),
-      is_symbolic_(base->is_symbolic_) {
+FunctionalTensorWrapper::FunctionalTensorWrapper(const Tensor& view_value, const FunctionalTensorWrapper* base, const functionalization::ViewMeta& meta)
+  : c10::TensorImpl(
+      c10::DispatchKeySet(DispatchKey::Functionalize),
+      view_value.dtype(),
+      base->storage().data_ptr().device()
+    ),
+    value_(view_value),
+    is_multi_output_view_(base->is_multi_output_view_ || meta.is_multi_output),
+    was_storage_changed_(base->was_storage_changed_),
+    is_symbolic_(base->is_symbolic_)
+{
   TORCH_INTERNAL_ASSERT(!at::functionalization::impl::isFunctionalTensor(value_));
   TORCH_INTERNAL_ASSERT(!value_.key_set().has(c10::DispatchKey::Functionalize));
   set_constructor_metadata();
@@ -150,10 +148,11 @@ FunctionalTensorWrapper::FunctionalTensorWrapper(
       view_metas_ = base->view_metas_;  // copy
   }
   view_metas_.push_back(meta);
-  maybe_mark_symbolic(meta.get());
+  maybe_mark_symbolic(meta);
   storage_ = base->storage_; // alias this tensor's storage with the base tensor's
 }
 
+
 functionalization::FunctionalStorageImpl* FunctionalTensorWrapper::functional_storage_impl() const {
   return static_cast<functionalization::FunctionalStorageImpl*>(storage_.unsafeGetStorageImpl());
 }
@@ -177,18 +176,18 @@ bool FunctionalTensorWrapper::is_up_to_date() const {
 }
 
 // See Note [Functionalization Pass - Inplace View Ops]
-void FunctionalTensorWrapper::mutate_view_meta(const std::shared_ptr<at::functionalization::ViewMeta>& meta) {
+void FunctionalTensorWrapper::mutate_view_meta(const at::functionalization::ViewMeta& meta) {
   view_metas_.push_back(meta);
   // Manually track the fact that this tensor received a metadata mutation!
   has_metadata_mutation_ = true;
   // Mark this tensor as being symbolic if there are any symbolic inputs used by the view operation.
-  maybe_mark_symbolic(meta.get());
+  maybe_mark_symbolic(meta);
   // Note [Functionalization Pass - Inplace View Ops]
   // So, these ops are special - they're mutation AND view ops. They get special codegen.
   // An example is transpose_, e.g. `a.transpose_()`
   // Calling transpose_() should ensure that a gets an alias, and append the new ViewMeta to a's current list of ViewMetas.
   at::AutoDispatchSkipFunctionalize guard;
-  value_ = meta->forward(value_);
+  value_ = meta.forward_fn(value_, meta.out_index);
   TORCH_INTERNAL_ASSERT(!value_.key_set().has(c10::DispatchKey::Functionalize));
 }
 
@@ -369,8 +368,15 @@ void FunctionalTensorWrapper::sync_() {
   regenerate_from_base();
 }
 
-const std::vector<std::shared_ptr<functionalization::ViewMeta>>& FunctionalTensorWrapper::view_metas() const {
-  return view_metas_;
+Tensor FunctionalTensorWrapper::apply_view_metas(const Tensor& base) {
+  auto t = base;
+
+  // Reapply views to get the viewed tensor from the base in alias_
+  for (auto& view_meta: view_metas_) {
+    t = view_meta.forward_fn(t, view_meta.out_index);
+  }
+
+  return t;
 }
 
 void FunctionalTensorWrapper::regenerate_from_base() {
@@ -379,7 +385,7 @@ void FunctionalTensorWrapper::regenerate_from_base() {
   auto t = storage_impl->base();
 
   TORCH_INTERNAL_ASSERT(!at::functionalization::impl::isFunctionalTensor(t));
-  t = at::functionalization::impl::apply_view_meta_sequence(t, view_metas_);
+  t = apply_view_metas(t);
   TORCH_INTERNAL_ASSERT(!at::functionalization::impl::isFunctionalTensor(t));
 
   replace_(t, /*from_lazy_regenerate=*/true);
@@ -721,11 +727,11 @@ bool isFunctionalTensor(const std::optional<Tensor>& t) {
 }
 
 bool isFunctionalTensor(const c10::List<::std::optional<Tensor>>& t_list) {
-  if (t_list.empty()) { return false; }
+  if (t_list.empty()) return false;
   auto functional_count = 0;
   for (const auto i : c10::irange(t_list.size())) {
     auto const & e= t_list[i];
-    if (!e.has_value() || !e->defined()) { continue; }
+    if (!e.has_value() || !e->defined()) continue;
     if (isFunctionalTensor(e)) {
       ++functional_count;
     }
@@ -735,10 +741,10 @@ bool isFunctionalTensor(const c10::List<::std::optional<Tensor>>& t_list) {
 
 template <typename T>
 static bool isFunctionalTensorIListRef(c10::IListRef<T> list) {
-  if (list.size() == 0) { return false; }
+  if (list.size() == 0) return false;
   auto functional_count = 0;
   for (const auto& tensor : list) {
-    if (!tensor.defined()) { continue; }
+    if (!tensor.defined()) continue;
     if (isFunctionalTensor(tensor)) {
       ++functional_count;
     }
@@ -756,28 +762,20 @@ void freeze_functional_tensor(const Tensor& tensor) {
   functional_base_impl->freeze_storage();
 }
 
-Tensor create_functional_tensor_with_view_meta(
-    const at::Tensor& view_to_wrap,
-    const at::Tensor& base,
-    const std::shared_ptr<functionalization::ViewMeta>& meta,
-    int64_t out_idx) {
+Tensor create_functional_tensor_with_view_meta(const at::Tensor& view_to_wrap, const at::Tensor& base, functionalization::ViewMeta meta, int64_t out_idx) {
   TORCH_INTERNAL_ASSERT(!at::functionalization::impl::isFunctionalTensor(view_to_wrap));
   TORCH_INTERNAL_ASSERT(at::functionalization::impl::isFunctionalTensor(base));
   auto functional_base_impl = at::functionalization::impl::unsafeGetFunctionalWrapper(base);
-  auto meta_ = meta;
   if (out_idx != 0) {
     // Note [out_idx in ViewMeta]
     // When a view op outputs multiple tensors, each output needs its own separate ViewMeta.
     // Each ViewMeta also tracks the index of the particular output tensor, which is needed in the reverse function.
-    meta_ = meta->to_out_index(out_idx);
+    meta = meta.to_out_idx(out_idx);
   }
-  return at::detail::make_tensor<FunctionalTensorWrapper>(view_to_wrap, functional_base_impl, meta_);
+  return at::detail::make_tensor<FunctionalTensorWrapper>(view_to_wrap, functional_base_impl, meta);
 }
 
-std::vector<Tensor> create_functional_tensor_with_view_meta(
-    ITensorListRef view_to_wrap,
-    const at::Tensor& base,
-    const std::shared_ptr<functionalization::ViewMeta>& meta) {
+std::vector<Tensor> create_functional_tensor_with_view_meta(ITensorListRef view_to_wrap, const at::Tensor& base, const functionalization::ViewMeta& meta) {
   std::vector<Tensor> outputs(view_to_wrap.size());
   int64_t i = 0;
   for (const auto& tensor : view_to_wrap) {
@@ -787,22 +785,12 @@ std::vector<Tensor> create_functional_tensor_with_view_meta(
   return outputs;
 }
 
-void mutate_view_meta(const at::Tensor& self, const std::shared_ptr<functionalization::ViewMeta>& meta) {
+void mutate_view_meta(const at::Tensor& self, const functionalization::ViewMeta& meta) {
   TORCH_INTERNAL_ASSERT(at::functionalization::impl::isFunctionalTensor(self));
   auto self_impl = at::functionalization::impl::unsafeGetFunctionalWrapper(self);
   self_impl->mutate_view_meta(meta);
 }
 
-Tensor apply_view_meta_sequence(
-    const Tensor& base,
-    const std::vector<std::shared_ptr<functionalization::ViewMeta>>& sequence) {
-  Tensor r = base;
-  for (auto& vm : sequence) {
-    r = vm->forward(r);
-  }
-  return r;
-}
-
 // Note [Propagating strides in the functionalization pass]
 // In order to properly compute stride information, the functionalization pass
 // calls each {view} reference implementations with meta tensors.
@@ -896,7 +884,7 @@ void functionalize_op_helper(const c10::OperatorHandle& op, torch::jit::Stack* s
     const auto& ivalue = returns[idx];
     if (ivalue.isTensor()) {
       const auto& t = ivalue.toTensor();
-      if (!t.defined()) { continue; }
+      if (!t.defined()) continue;
       at::functionalization::impl::sync(t);
       auto t_new = c10::IValue(at::functionalization::impl::from_functional_tensor(t));
       (*stack)[returns_begin + idx] = t_new;

aten/src/ATen/FunctionalTensorWrapper.h

@@ -56,7 +56,7 @@ struct TORCH_API FunctionalTensorWrapper : public c10::TensorImpl {
   explicit FunctionalTensorWrapper(
       const Tensor& view_value,
       const FunctionalTensorWrapper* base,
-      const std::shared_ptr<functionalization::ViewMeta>& meta);
+      const functionalization::ViewMeta& meta);
 
   // Get the underlying, actual tensor, that doesn't know anything about
   // functionalization.
@@ -99,17 +99,17 @@ struct TORCH_API FunctionalTensorWrapper : public c10::TensorImpl {
         ->are_all_mutations_under_no_grad_or_inference_mode();
   }
 
-  void maybe_mark_symbolic(functionalization::ViewMeta* meta) {
-    is_symbolic_ = is_symbolic_ | meta->has_symbolic_inputs;
+  void maybe_mark_symbolic(const functionalization::ViewMeta& meta) {
+    is_symbolic_ = is_symbolic_ | meta.has_symbolic_inputs;
   }
 
   bool is_symbolic() const {
     return is_symbolic_;
   }
 
-  // Retrieves the ViewMeta sequence of this tensor.
-  const std::vector<std::shared_ptr<functionalization::ViewMeta>>& view_metas()
-      const;
+  // Runs the forward_fn of every ViewMeta collected in the current instance
+  // to some other base.
+  Tensor apply_view_metas(const Tensor& base);
 
   // Sync's the underlying tensor with its alias, if it's out of date. This
   // involves two steps: 1) Apply any pending updates/mutations to the alias 2)
@@ -146,8 +146,7 @@ struct TORCH_API FunctionalTensorWrapper : public c10::TensorImpl {
   // from the base tensor. This method is used by inplace-view ops like
   // transpose_. It appends a ViewMeta to the existing stack, and refreshes the
   // tensor by replaying the views off of the alias.
-  void mutate_view_meta(
-      const std::shared_ptr<at::functionalization::ViewMeta>& meta);
+  void mutate_view_meta(const at::functionalization::ViewMeta& meta);
 
   // Custom implementation of self.set_(src)
   void set__impl(const FunctionalTensorWrapper* other);
@@ -286,7 +285,7 @@ struct TORCH_API FunctionalTensorWrapper : public c10::TensorImpl {
   bool is_symbolic_ = false;
 
   size_t generation_ = 0;
-  std::vector<std::shared_ptr<at::functionalization::ViewMeta>> view_metas_;
+  std::vector<at::functionalization::ViewMeta> view_metas_;
 
  protected:
   static void copy_tensor_metadata(
@@ -378,20 +377,16 @@ TORCH_API void propagate_xla_data_direct(
 Tensor create_functional_tensor_with_view_meta(
     const Tensor& view_to_wrap,
     const Tensor& base,
-    const std::shared_ptr<functionalization::ViewMeta>& meta,
+    functionalization::ViewMeta meta,
     int64_t out_idx = 0);
 std::vector<Tensor> create_functional_tensor_with_view_meta(
     ITensorListRef view_to_wrap,
     const Tensor& base,
-    const std::shared_ptr<functionalization::ViewMeta>& meta);
+    const functionalization::ViewMeta& meta);
 
 void mutate_view_meta(
     const Tensor& self,
-    const std::shared_ptr<functionalization::ViewMeta>& meta);
-
-TORCH_API Tensor apply_view_meta_sequence(
-    const Tensor& base,
-    const std::vector<std::shared_ptr<functionalization::ViewMeta>>& sequence);
+    const functionalization::ViewMeta& meta);
 
 void set_sizes_strides_offset(const Tensor& out, const Tensor& meta_out);
 void set_sizes_strides_offset(

aten/src/ATen/FunctionalizeFallbackKernel.cpp

@@ -1,5 +1,3 @@
-#include <ATen/FunctionalizeFallbackKernel.h>
-
 #include <ATen/core/dispatch/Dispatcher.h>
 #include <ATen/core/LegacyTypeDispatch.h>
 #include <ATen/EmptyTensor.h>
@@ -9,6 +7,7 @@
 #include <torch/library.h>
 #include <c10/util/irange.h>
 #include <c10/util/strides.h>
+#include <ATen/EmptyTensor.h>
 
 #ifndef AT_PER_OPERATOR_HEADERS
 #include <ATen/ATen.h>
@@ -29,31 +28,6 @@
 #include <utility>
 #endif
 
-namespace at::functionalization {
-
-Tensor resize__ViewMeta::forward(const Tensor& base) {
-  if (reapply_views) {
-    return base.as_strided(size, c10::contiguous_strides(size));
-  } else {
-    return at::as_strided_copy(base, size, c10::contiguous_strides(size));
-  }
-}
-
-Tensor resize__ViewMeta::reverse(const Tensor& base, const Tensor& mutated_view) {
-  return base.as_strided_scatter(
-      mutated_view, size, c10::contiguous_strides(size));
-}
-
-Tensor _unsafe_view_ViewMeta::forward(const Tensor& base) {
-  return at::_unsafe_view_symint(base, size);
-}
-
-Tensor _unsafe_view_ViewMeta::reverse(const Tensor& base, const Tensor& mutated_view) {
-  return at::_unsafe_view_symint(mutated_view, base.sym_sizes());
-}
-
-} // namespace at::functionalization
-
 namespace {
   void functionalizeFallback(const c10::OperatorHandle& op, c10::DispatchKeySet dispatchKeySet [[maybe_unused]], torch::jit::Stack* stack) {
     const auto& schema = op.schema();
@@ -132,9 +106,7 @@ namespace {
       const auto& ivalue = returns[idx];
       if (ivalue.isTensor() && should_wrap_outputs) {
         const auto& t = ivalue.toTensor();
-        if (!t.defined()) {
-          continue;
-        }
+        if (!t.defined()) continue;
         auto t_new = c10::IValue(at::functionalization::impl::to_functional_tensor(t));
         (*stack)[returns_begin + idx] = t_new;
       } else if (ivalue.isTensorList() && should_wrap_outputs) {
@@ -197,8 +169,19 @@ static const at::Tensor & resize__functionalization(c10::DispatchKeySet dispatch
   // The output of resizing is equivalent to taking a slice of a larger tensor.
   // We have to emulate this "slicing" with an as_strided call.
   auto reapply_views = at::functionalization::impl::getFunctionalizationReapplyViewsTLS();
-  auto view_meta = std::make_shared<at::functionalization::resize__ViewMeta>(
-      reapply_views, size.vec());
+  at::functionalization::ViewMeta view_meta = at::functionalization::ViewMeta(
+    [reapply_views = reapply_views, size = size.vec()](const at::Tensor & base, int64_t mutated_view_idx [[maybe_unused]]) -> at::Tensor {
+      if (reapply_views) {
+        return base.as_strided(size, c10::contiguous_strides(size));
+      } else {
+        return at::as_strided_copy(base, size, c10::contiguous_strides(size));
+      }
+    },
+    [size = size.vec()](const at::Tensor & base, const at::Tensor & mutated_view, int64_t mutated_view_idx [[maybe_unused]]) -> at::Tensor {
+      return base.as_strided_scatter(mutated_view, size, c10::contiguous_strides(size));
+    },
+    /*has_symbolic_inputs=*/false
+  );
   at::functionalization::impl::mutate_view_meta(self, view_meta);
   return self;
 }
@@ -317,11 +300,17 @@ static at::Tensor _unsafe_view_functionalize(const at::Tensor & self, at::SymInt
     tmp_output = at::_unsafe_view_symint(self_, size);
   }
 
-  bool has_symbolic_inputs = std::any_of(
-      size.begin(), size.end(), [=](auto& s) { return s.is_symbolic(); });
-  auto view_meta =
-      std::make_shared<at::functionalization::_unsafe_view_ViewMeta>(
-          has_symbolic_inputs, size.vec());
+  bool has_symbolic_inputs = std::any_of(size.begin(), size.end(), [=](auto& s) { return s.is_symbolic(); });
+
+  at::functionalization::ViewMeta view_meta = at::functionalization::ViewMeta(
+    [size = size.vec()](const at::Tensor & base, int64_t mutated_view_idx [[maybe_unused]]) -> at::Tensor {
+      return at::_unsafe_view_symint(base, size);
+    },
+    [size = size.vec()](const at::Tensor & base, const at::Tensor & mutated_view, int64_t mutated_view_idx [[maybe_unused]]) -> at::Tensor {
+      return at::_unsafe_view_symint(mutated_view, base.sym_sizes());
+    },
+    /*has_symbolic_inputs=*/has_symbolic_inputs
+  );
 
   auto out = at::functionalization::impl::create_functional_tensor_with_view_meta(tmp_output, self, std::move(view_meta));
   // See  Note [Propagating strides in the functionalization pass]

aten/src/ATen/FunctionalizeFallbackKernel.h

@@ -1,58 +0,0 @@
-#pragma once
-
-#include <ATen/FunctionalStorageImpl.h>
-
-namespace at::functionalization {
-
-// `ViewMeta` implementation for `resize_` operation.
-struct TORCH_API resize__ViewMeta : public ViewMeta {
-  FUNCTIONALIZATION_VIEWMETA_NAME(resize__ViewMeta)
-  FUNCTIONALIZATION_VIEWMETA_SERIALIZABLE_TUPLE(
-      bool /* reapply_views */,
-      const std::vector<int64_t>&);
-
-  resize__ViewMeta(const SerializableTuple& tpl)
-      : resize__ViewMeta(std::get<0>(tpl), std::get<1>(tpl)) {}
-
-  resize__ViewMeta(bool reapply_views, const std::vector<int64_t>& size)
-      : ViewMeta(/*has_symbolic_inputs=*/false),
-        reapply_views(reapply_views),
-        size(size) {}
-
-  Tensor forward(const Tensor& base) override;
-  Tensor reverse(const Tensor& base, const Tensor& mutated_view) override;
-
-  SerializableTuple to_serializable_tuple() {
-    return std::make_tuple(reapply_views, size);
-  }
-
-  bool reapply_views;
-  std::vector<int64_t> size;
-};
-
-// `ViewMeta` implementation for `_unsafe_view` operation.
-struct TORCH_API _unsafe_view_ViewMeta : public ViewMeta {
-  FUNCTIONALIZATION_VIEWMETA_NAME(_unsafe_view_ViewMeta)
-  FUNCTIONALIZATION_VIEWMETA_SERIALIZABLE_TUPLE(
-      bool /* has_symbolic_inputs */,
-      const std::vector<c10::SymInt>&);
-
-  _unsafe_view_ViewMeta(const SerializableTuple& tpl)
-      : _unsafe_view_ViewMeta(std::get<0>(tpl), std::get<1>(tpl)) {}
-
-  _unsafe_view_ViewMeta(
-      bool has_symbolic_inputs,
-      const std::vector<c10::SymInt>& size)
-      : ViewMeta(has_symbolic_inputs), size(size) {}
-
-  Tensor forward(const Tensor& base) override;
-  Tensor reverse(const Tensor& base, const Tensor& mutated_view) override;
-
-  SerializableTuple to_serializable_tuple() {
-    return std::make_tuple(has_symbolic_inputs, size);
-  }
-
-  std::vector<c10::SymInt> size;
-};
-
-} // namespace at::functionalization

aten/src/ATen/cuda/CUDABlas.cpp

@@ -1637,7 +1637,9 @@ bool gemm_and_bias(
   if (activation == GEMMAndBiasActivationEpilogue::RELU) {
     epilogue = CUBLASLT_EPILOGUE_RELU_BIAS;
   } else if (activation == GEMMAndBiasActivationEpilogue::GELU) {
+#if CUDA_VERSION >= 11040 || defined(USE_ROCM)
     epilogue = CUBLASLT_EPILOGUE_GELU_BIAS;
+#endif
   }
 
   if (bias != nullptr) {
@@ -1929,6 +1931,7 @@ void scaled_gemm(
     bool use_fast_accum) {
   // Note: see `cublasCommonArgs` for various non-intuitive manupulations
   // of input arguments to this function.
+#if CUDA_VERSION >= 11080 || defined(USE_ROCM)
   const auto computeType = CUBLAS_COMPUTE_32F;
   const auto scaleType = CUDA_R_32F;
   const float alpha_val = 1.0;
@@ -2130,6 +2133,8 @@ void scaled_gemm(
       " scaleType ",
       scaleType);
   return;
+#endif // if CUDA_VERSION >= 11080 || defined(USE_ROCM)
+  TORCH_CHECK(false, "scaled_gemm is only supported for CUDA 11.8 and above");
 }
 
 void int8_gemm(

aten/src/ATen/native/Fill.cpp

@@ -97,38 +97,43 @@ Tensor& fill_diagonal_(Tensor& self, const Scalar& fill_value, bool wrap) {
   int64_t nDims = self.dim();
   TORCH_CHECK(nDims >= 2, "dimensions must larger than 1");
 
-  auto height = self.sym_size(0);
-  auto width = self.sym_size(1);
+  int64_t height = self.size(0);
+  int64_t width = self.size(1);
 
   if (nDims > 2) {
+    int64_t dim1 = height;
     for (const auto i : c10::irange(1, nDims)) {
-      if (self.sym_size(i) != height) {
+      if (self.size(i) != dim1) {
         TORCH_CHECK(false, "all dimensions of input must be of equal length");
       }
     }
   }
 
-  auto storage_offset = self.sym_storage_offset();
-  auto size = std::min(height, width);
+  int64_t storage_offset = self.storage_offset();
+  std::vector<int64_t> sizes;
+  std::vector<int64_t> strides;
+  int64_t size = std::min(height, width);
 
   int64_t stride = 0;
   for (const auto i : c10::irange(nDims)) {
     stride += self.stride(i);
   }
-  std::vector<SymInt> strides{stride};
-  std::vector<SymInt> sizes{size};
+  strides.push_back(stride);
+  sizes.push_back(size);
 
-  auto main_diag = self.as_strided_symint(sizes, strides, storage_offset);
+  auto main_diag = self.as_strided(sizes, strides, storage_offset);
   main_diag.fill_(fill_value);
 
   if (wrap && nDims == 2 && height > width + 1) {
-    auto step = width + 1;
-    auto wrap_size = ((self.numel() + step - 1) / step) - size;
-    std::vector<SymInt> wrap_sizes{wrap_size};
+    std::vector<int64_t> wrap_sizes;
 
-    auto offset = self.stride(0) * (width + 1);
+    int64_t step = width + 1;
+    int64_t wrap_size = ((self.numel() + step - 1) / step) - size;
+    wrap_sizes.push_back(wrap_size);
 
-    auto wrap_diag = self.as_strided_symint(wrap_sizes, strides, storage_offset + offset);
+    int64_t offset = self.stride(0) * (width + 1);
+
+    auto wrap_diag = self.as_strided(wrap_sizes, strides, storage_offset + offset);
     wrap_diag.fill_(fill_value);
   }
 

aten/src/ATen/native/MaxUnpooling.cpp

@@ -23,6 +23,8 @@ Tensor& max_unpooling2d_forward_out_cpu(
   // Nondeterministic with duplicate indices
   at::globalContext().alertNotDeterministic("max_unpooling2d_forward_out");
 
+  auto oheight = output_size[0];
+  auto owidth = output_size[1];
   TORCH_CHECK(
       indices_.scalar_type() == at::ScalarType::Long,
       "elements in indices should be type int64 but got: ", indices_.scalar_type());
@@ -43,9 +45,6 @@ Tensor& max_unpooling2d_forward_out_cpu(
                 self_.sizes(), " with dimension ", i , " being empty.");
   }
 
-  auto oheight = output_size[0];
-  auto owidth = output_size[1];
-
   auto memory_format = self_.suggest_memory_format();
   auto self = self_.contiguous(memory_format);
   auto indices = indices_.contiguous(memory_format);

aten/src/ATen/native/TensorShape.cpp

@@ -1,5 +1,3 @@
-#include <ATen/core/ATen_fwd.h>
-#include <c10/core/ScalarType.h>
 #define TORCH_ASSERT_ONLY_METHOD_OPERATORS
 #include <ATen/AccumulateType.h>
 #include <ATen/Dispatch.h>
@@ -1880,18 +1878,19 @@ Tensor repeat(const Tensor& self, IntArrayRef repeats) {
 
   Tensor xtensor = self.expand(padded_size);
 
-  Tensor urtensor;
+  Tensor result;
   if (self.is_quantized()) {
-    urtensor = at::empty_quantized(target_size, self);
+    result = at::empty_quantized(target_size, self);
   } else {
-    urtensor = at::empty(target_size, self.options());
+    result = at::empty(target_size, self.options());
   }
 
   // return an empty tensor if one of the repeat dimensions is zero
   if (zero_tensor) {
-    return urtensor;
+    return result;
   }
 
+  Tensor urtensor = at::alias(result);
   for (const auto i : c10::irange(xtensor.dim())) {
     // can't unfold with step 0, so make sure step is at least 1
     // (it doesn't matter what it is in that case, because the size is 0).
@@ -1901,22 +1900,7 @@ Tensor repeat(const Tensor& self, IntArrayRef repeats) {
 
   urtensor.copy_(xtensor.expand_as(urtensor));
 
-  // Combine the dimensions to produce the target_size.
-  // xtensor dims: [a0, ..., ad-1]
-  // urtensor dims: [a0, ..., ad-1, b0, ..., bd-1]
-  // b dims are produced by unfold.
-  // Transform urtensor to [a0 * b0, ..., ad-1 * bd-1]
-  const int64_t n_dims = xtensor.dim();
-  auto range_a = at::arange(xtensor.dim(), at::TensorOptions(at::kLong));
-  auto range_b = range_a + n_dims;
-  auto stacked = stack({std::move(range_a), std::move(range_b)}, 1).flatten();
-  auto permutation = IntArrayRef(stacked.data_ptr<int64_t>(), n_dims * 2);
-  // Permute from [a0, ..., ad-1, b0, ..., bd-1] to [a0, b0, ..., ad-1, bd-1]
-  urtensor = urtensor.permute(permutation);
-  // Reshape from [a0, b0, ..., ad-1, bd-1] to [a0 * b0, ..., ad-1 * bd-1]
-  urtensor = urtensor.reshape(target_size);
-
-  return urtensor;
+  return result;
 }
 
 Tensor tile_symint(const Tensor& self, SymIntArrayRef reps) {

aten/src/ATen/native/cuda/CUDALoops.cuh

@@ -999,41 +999,12 @@ void gpu_kernel_impl(TensorIteratorBase& iter, const func_t& f) {
       dtypes[i] = iter.dtype(i);
     }
     auto offset_calc = ::make_offset_calculator<traits::arity + 1>(iter);
-#ifdef USE_ROCM
-    constexpr int grp_sz = 128;
-    launch_legacy_kernel_manual_unroll<grp_sz, 4>(numel, [=] GPU_LAMBDA(int idx, bool unrl) {
-      if (unrl) {
-        auto offsets0 = offset_calc.get(idx);
-        auto offsets1 = offset_calc.get(idx + grp_sz);
-        auto offsets2 = offset_calc.get(idx + grp_sz * 2);
-        auto offsets3 = offset_calc.get(idx + grp_sz * 3);
-        void* out0 = data[0] + offsets0[0];
-        void* out1 = data[0] + offsets1[0];
-        void* out2 = data[0] + offsets2[0];
-        void* out3 = data[0] + offsets3[0];
-        arg0_t result0 = invoke(f, &data[1], &offsets0[1], &dtypes[1], 1);
-        arg0_t result1 = invoke(f, &data[1], &offsets1[1], &dtypes[1], 1);
-        arg0_t result2 = invoke(f, &data[1], &offsets2[1], &dtypes[1], 1);
-        arg0_t result3 = invoke(f, &data[1], &offsets3[1], &dtypes[1], 1);
-        c10::cast_and_store<arg0_t>(dtypes[0], out0, result0);
-        c10::cast_and_store<arg0_t>(dtypes[0], out1, result1);
-        c10::cast_and_store<arg0_t>(dtypes[0], out2, result2);
-        c10::cast_and_store<arg0_t>(dtypes[0], out3, result3);
-      } else {
-        auto offsets = offset_calc.get(idx);
-        void* out = data[0] + offsets[0];
-        arg0_t result = invoke(f, &data[1], &offsets[1], &dtypes[1], 1);
-        c10::cast_and_store<arg0_t>(dtypes[0], out, result);
-      }
-    });
-#else
     launch_legacy_kernel<128, 4>(numel, [=] GPU_LAMBDA(int idx) {
       auto offsets = offset_calc.get(idx);
       void* out = data[0] + offsets[0];
       arg0_t result = invoke(f, &data[1], &offsets[1], &dtypes[1], 1);
       c10::cast_and_store<arg0_t>(dtypes[0], out, result);
     });
-#endif
   }
 }
 

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

@@ -125,6 +125,8 @@ Tensor& max_unpooling2d_forward_out_cuda(const Tensor& self_,
   TORCH_CHECK(
       indices_.scalar_type() == at::ScalarType::Long,
       "elements in indices should be type int64 but got: ", indices_.scalar_type());
+  auto oheight = output_size[0];
+  auto owidth = output_size[1];
 
   TensorArg output_arg{output, "output", 1}, self_arg{self_, "self_", 2},
       indices_arg{indices_, "indices_", 3};
@@ -147,9 +149,6 @@ Tensor& max_unpooling2d_forward_out_cuda(const Tensor& self_,
       output_size.size() == 2,
       "There should be exactly two elements (height, width) in output_size, but got ", output_size.size(), " elements.");
 
-  auto oheight = output_size[0];
-  auto owidth = output_size[1];
-
   int64_t dimw = 2;
   int64_t dimh = 1;
   int64_t numBatch = 1;
@@ -218,6 +217,9 @@ static void max_unpooling3d_shape_check(
     IntArrayRef stride,
     IntArrayRef padding,
     const char *fn_name) {
+  int64_t oT = output_size[0];
+  int64_t oH = output_size[1];
+  int64_t oW = output_size[2];
   TORCH_CHECK(
       indices.scalar_type() == at::ScalarType::Long,
       "elements in indices should be type int64 but got: ", indices.scalar_type());
@@ -248,10 +250,6 @@ static void max_unpooling3d_shape_check(
       "strides should be greater than zero, but got stride: ",
       stride);
 
-  int64_t oT = output_size[0];
-  int64_t oH = output_size[1];
-  int64_t oW = output_size[2];
-
   int dimw = 3;
   int dimh = 2;
   int dimt = 1;
@@ -404,6 +402,8 @@ at::Tensor& max_unpooling2d_backward_out_cuda(const Tensor& grad_output_,
     const Tensor& indices_,
     IntArrayRef output_size,
     Tensor& grad_input) {
+  int64_t oheight = output_size[0];
+  int64_t owidth = output_size[1];
   TORCH_CHECK(grad_input.is_contiguous(), "grad_input must be contiguous");
   TORCH_CHECK(
       indices_.scalar_type() == at::ScalarType::Long,
@@ -426,9 +426,6 @@ at::Tensor& max_unpooling2d_backward_out_cuda(const Tensor& grad_output_,
 
   TORCH_CHECK(output_size.size() == 2, "output_size must have two elements, got size: ", output_size.size());
 
-  int64_t oheight = output_size[0];
-  int64_t owidth = output_size[1];
-
   int64_t nInputCols, nInputRows, nInputPlane;
 
   int dimw = 2;
@@ -508,14 +505,13 @@ at::Tensor& max_unpooling3d_backward_out_cuda(const Tensor& grad_output_,
     IntArrayRef padding,
     Tensor& grad_input) {
   TORCH_CHECK(grad_input.is_contiguous(), "grad_input must be contiguous");
-
-  max_unpooling3d_shape_check(
-    self_, grad_output_, indices_, output_size, stride, padding, "max_unpooling3d_backward_out_cuda()");
-
   int64_t oT = output_size[0];
   int64_t oH = output_size[1];
   int64_t oW = output_size[2];
 
+  max_unpooling3d_shape_check(
+    self_, grad_output_, indices_, output_size, stride, padding, "max_unpooling3d_backward_out_cuda()");
+
   int batchSize = 0;
   int inputSlices = 0;
   int inputTime = 0;

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

@@ -300,6 +300,8 @@ void nonzero_static_cuda_out_impl(
     int64_t size,
     int64_t fill_value,
     Tensor& out) {
+#if defined(CUDA_VERSION) || defined(USE_ROCM)
+
   Tensor self_contiguous_ = self.contiguous();
   // see comment in nonzero_cuda_out_impl on reqs for out
   bool out_correct_size =
@@ -375,6 +377,9 @@ void nonzero_static_cuda_out_impl(
   if (need_to_copy) {
     out.copy_(out_temp);
   }
+#else
+  TORCH_CHECK(false, "Nonzero_static is not supported for cuda <= 11.4");
+#endif
 }
 
 Tensor& nonzero_out_cuda(const Tensor& self, Tensor& out) {

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

@@ -221,9 +221,22 @@ static const Tensor& _exec_fft(Tensor& out, const Tensor& self, IntArrayRef out_
   std::optional<CuFFTConfig> uncached_plan;
   const CuFFTConfig * config = nullptr;
 
+  // Workaround for gh-63152, gh-58724
+  // Bluestein plans in CUDA 11.1 (cufft 10.3) cannot be re-used
   // Bluestein's algorithm is only used when a size has large prime factors,
   // sizes with only small prime factors can still be cached
-  if (plan_cache.max_size() > 0) {
+  bool use_caching = true;
+#ifdef CUFFT_VERSION
+  if constexpr (10300 <= CUFFT_VERSION && CUFFT_VERSION < 10400) {
+    // Only cache plans for transforms with small prime factors
+    use_caching = std::none_of(
+        signal_size.begin() + 1, signal_size.end(), [](int64_t dim_size) {
+      return has_large_prime_factor(dim_size);
+    });
+  }
+#endif
+
+  if (use_caching && plan_cache.max_size() > 0) {
     guard.lock();
     if (plan_cache.max_size() > 0) {  // check again after acquiring the lock
       config = &plan_cache.lookup(Params);

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

@@ -14,7 +14,7 @@ struct EmbeddingBagParams {
   ::c10::metal::array<idx_type_t, 2> output_strides;
   ::c10::metal::array<idx_type_t, 2> max_indices_strides;
 
-  idx_type_t per_sample_weights_stride;
+  idx_type_t per_sample_weights_strides;
 
   idx_type_t num_indices;
   idx_type_t num_bags;

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

@@ -23,72 +23,54 @@ struct ReductionOpInit<EmbeddingBagMode::MAX, T> {
 template <EmbeddingBagMode M, typename T>
 struct ReductionOp {
   inline opmath_t<T> operator()(
-      opmath_t<T> weight_val,
+      T weight_val,
       opmath_t<T> out_val,
-      bool is_first) {
-    return weight_val + out_val;
+      uint32_t per_sample_weights_index,
+      constant T* per_sample_weights,
+      uint32_t per_sample_weights_strides);
+};
+
+template <typename T>
+struct ReductionOp<EmbeddingBagMode::SUM, T> {
+  inline opmath_t<T> operator()(
+      T weight_val,
+      opmath_t<T> out_val,
+      uint32_t per_sample_weights_index,
+      constant T* per_sample_weights,
+      uint32_t per_sample_weights_strides) {
+    if (per_sample_weights_strides) {
+      T per_sample_weight = per_sample_weights
+          [per_sample_weights_strides * per_sample_weights_index];
+      return static_cast<opmath_t<T>>(per_sample_weight) *
+          static_cast<opmath_t<T>>(weight_val) +
+          out_val;
+    } else {
+      return static_cast<opmath_t<T>>(weight_val) + out_val;
+    }
+  }
+};
+
+template <typename T>
+struct ReductionOp<EmbeddingBagMode::MEAN, T> {
+  inline opmath_t<T> operator()(
+      T weight_val,
+      opmath_t<T> out_val,
+      uint32_t,
+      constant T*,
+      uint32_t) {
+    return static_cast<opmath_t<T>>(weight_val) + out_val;
   }
 };
 
 template <typename T>
 struct ReductionOp<EmbeddingBagMode::MAX, T> {
   inline opmath_t<T> operator()(
-      opmath_t<T> weight_val,
+      T weight_val,
       opmath_t<T> out_val,
-      bool is_first) {
-    return (is_first || weight_val > out_val) ? weight_val : out_val;
-  }
-};
-
-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) {
-    return weight_val;
-  }
-};
-
-template <typename T>
-struct MaybeApplyPerSampleWeight<EmbeddingBagMode::SUM, T> {
-  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) {
-    if (per_sample_weights_stride) {
-      T per_sample_weight = per_sample_weights
-          [per_sample_weights_stride * per_sample_weights_index];
-      return static_cast<opmath_t<T>>(per_sample_weight) * weight_val;
-    } else {
-      return weight_val;
-    }
-  }
-};
-
-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) {}
-};
-
-template <typename T, typename I>
-struct MaybeCalcMaxIndex<EmbeddingBagMode::MAX, T, I> {
-  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) {
-    max_idx = !pad && (is_first || weight_val > out_val) ? weight_idx : max_idx;
+      uint32_t,
+      constant T*,
+      uint32_t) {
+    return max(static_cast<opmath_t<T>>(weight_val), out_val);
   }
 };
 
@@ -114,30 +96,6 @@ struct ReductionOpFinal<EmbeddingBagMode::MAX, T> {
   }
 };
 
-template <EmbeddingBagMode M, typename I>
-struct MaybeWriteMaxIndex {
-  inline void operator()(
-      device I*,
-      const constant ::c10::metal::array<uint32_t, 2>&,
-      uint32_t,
-      uint32_t,
-      I) {}
-};
-
-template <typename I>
-struct MaybeWriteMaxIndex<EmbeddingBagMode::MAX, I> {
-  inline void operator()(
-      device I* max_indices,
-      const constant ::c10::metal::array<uint32_t, 2>& max_indices_strides,
-      uint32_t bag_idx,
-      uint32_t feature_idx,
-      I max_idx) {
-    max_indices
-        [bag_idx * max_indices_strides[0] +
-         feature_idx * max_indices_strides[1]] = max_idx;
-  }
-};
-
 template <EmbeddingBagMode M, typename T, typename I>
 void embedding_bag_impl(
     constant T* weight,
@@ -154,7 +112,7 @@ void embedding_bag_impl(
   auto num_bags = params.num_bags;
   auto feature_size = params.feature_size;
   auto padding_idx = params.padding_idx;
-  auto per_sample_weights_stride = params.per_sample_weights_stride;
+  auto per_sample_weights_strides = params.per_sample_weights_strides;
   constant auto& output_strides = params.output_strides;
   constant auto& weight_strides = params.weight_strides;
   constant auto& max_indices_strides = params.max_indices_strides;
@@ -162,6 +120,8 @@ void embedding_bag_impl(
   auto bag_idx = tid / feature_size;
   auto feature_idx = tid % feature_size;
 
+  output += bag_idx * output_strides[0] + feature_idx * output_strides[1];
+
   uint32_t offsets_end = min(bag_idx + 1, num_bags - 1);
   bool is_last_bag = bag_idx + 1 == num_bags;
   uint32_t indices_start = static_cast<uint32_t>(offsets[bag_idx]);
@@ -171,37 +131,28 @@ void embedding_bag_impl(
   auto out_val = ReductionOpInit<M, T>()();
 
   uint32_t bag_size_ = 0;
-  I max_idx = 0;
 
   for (uint32_t indices_idx = indices_start; indices_idx < indices_end;
        indices_idx++) {
     I weight_idx = indices[indices_idx];
     bool pad = (weight_idx == padding_idx);
-    auto weight_val = static_cast<opmath_t<T>>(
-        weight
-            [static_cast<uint32_t>(weight_idx) * weight_strides[0] +
-             feature_idx * weight_strides[1]]);
+    T weight_val = weight
+        [static_cast<uint32_t>(weight_idx) * weight_strides[0] +
+         feature_idx * weight_strides[1]];
 
-    weight_val = MaybeApplyPerSampleWeight<M, T>()(
-        weight_val, indices_idx, per_sample_weights, per_sample_weights_stride);
-
-    auto new_out_val = ReductionOp<M, T>()(weight_val, out_val, bag_size_ == 0);
-
-    MaybeCalcMaxIndex<M, T, I>()(
-        weight_val, out_val, bag_size_ == 0, max_idx, weight_idx, pad);
-
-    out_val = pad ? out_val : new_out_val;
-    offset2bag[indices_idx] = bag_idx;
     bag_size_ += static_cast<uint32_t>(!pad);
+
+    auto tmp_val = ReductionOp<M, T>()(
+        weight_val,
+        out_val,
+        indices_idx,
+        per_sample_weights,
+        per_sample_weights_strides);
+
+    out_val = pad ? out_val : tmp_val;
   }
 
-  output[bag_idx * output_strides[0] + feature_idx * output_strides[1]] =
-      ReductionOpFinal<M, T>()(out_val, bag_size_);
-
-  bag_size[bag_idx] = bag_size_;
-
-  MaybeWriteMaxIndex<M, I>()(
-      max_indices, max_indices_strides, bag_idx, feature_idx, max_idx);
+  *output = ReductionOpFinal<M, T>()(out_val, bag_size_);
 }
 
 #define DISPATCH_IMPL(MODE)        \

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

@@ -198,7 +198,7 @@ static Tensor _mps_convolution_impl(const Tensor& input_t_,
 
     if (input_t.is_contiguous(memory_format) && output_t.is_contiguous(memory_format) && is_macOS_15_0_or_newer) {
       inputNDArray = getMPSNDArray(input_t, inputShape);
-      outputNDArray = getMPSNDArray(output_t, outputShape);
+      outputNDArray = getMPSNDArray(*output, outputShape);
     }
 
     auto cachedGraph = LookUpOrCreateCachedGraph<CachedGraph>(key, [&](auto mpsGraph, auto newCachedGraph) {
@@ -302,7 +302,7 @@ static Tensor _mps_convolution_impl(const Tensor& input_t_,
       }
     }
     auto outputPlaceholder = outputNDArray ? Placeholder(cachedGraph->outputTensor_, outputNDArray)
-                                           : Placeholder(cachedGraph->outputTensor_, output_t);
+                                           : Placeholder(cachedGraph->outputTensor_, *output);
 
     NSMutableDictionary<MPSGraphTensor*, MPSGraphTensorData*>* feeds =
         [[[NSMutableDictionary alloc] initWithCapacity:3] autorelease];
@@ -315,7 +315,7 @@ static Tensor _mps_convolution_impl(const Tensor& input_t_,
     runMPSGraph(stream, cachedGraph->graph(), feeds, outputPlaceholder);
   }
 
-  return output_t;
+  return *output;
 }
 
 Tensor _mps_convolution(const Tensor& input_t,

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

@@ -66,12 +66,11 @@ static std::tuple<Tensor, Tensor, Tensor, Tensor> _embedding_bag_mps_impl(
   int64_t num_indices = indices.size(0);
   int64_t num_bags = offsets.size(0);
   if (include_last_offset) {
-    TORCH_CHECK(num_bags >= 1, "include_last_offset: number of offsets should be at least 1");
     num_bags -= 1;
   }
   int64_t feature_size = weight.size(1);
 
-  auto bag_size = at::empty({num_bags}, indices.options());
+  auto bag_size = at::empty(offsets.sizes(), indices.options());
   auto offset2bag = at::empty({indices.size(0)}, indices.options());
   auto output = at::empty({num_bags, feature_size}, weight.options());
 
@@ -95,7 +94,7 @@ static std::tuple<Tensor, Tensor, Tensor, Tensor> _embedding_bag_mps_impl(
   }
 
   bool use_per_sample_weights = per_sample_weights_opt.has_value() && per_sample_weights_opt->defined();
-  params.per_sample_weights_stride = use_per_sample_weights ? per_sample_weights_opt->stride(0) : 0;
+  params.per_sample_weights_strides = use_per_sample_weights ? per_sample_weights_opt->stride(0) : 0;
 
   params.num_indices = num_indices;
   params.num_bags = num_bags;

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

@@ -20,7 +20,6 @@
 #include <ATen/ops/baddbmm_native.h>
 #include <ATen/ops/bmm_native.h>
 #include <ATen/ops/cholesky_native.h>
-#include <ATen/ops/eye_native.h>
 #include <ATen/ops/linalg_cholesky_ex_native.h>
 #include <ATen/ops/linalg_inv_ex_native.h>
 #include <ATen/ops/linalg_lu_factor_ex_native.h>
@@ -497,24 +496,26 @@ static void linalg_inv_ex_out_mps_impl(const Tensor& A, bool check_errors, const
   using namespace mps;
   TORCH_CHECK(result.is_mps(), "Output tensor is not MPS");
   TORCH_CHECK(!A.is_complex(), "linalg_inv: not supported for complex types yet!");
+  using CachedGraph = MPSUnaryCachedGraph;
 
+  MPSStream* stream = getCurrentMPSStream();
   info.zero_();
+
   if (A.numel() == 0) {
     return;
   }
 
+  if (!result.is_contiguous()) {
+    result.unsafeGetTensorImpl()->empty_tensor_restride(MemoryFormat::Contiguous);
+  }
   auto A_sizes = A.sizes();
   int ndim = A.dim();
 
-  Tensor LU = empty_like(A, MemoryFormat::Contiguous);
-  Tensor identity = eye(A.size(-2), A.size(-1), A.scalar_type(), A.options().layout(), A.device()).expand_as(A);
+  Tensor LU = empty_like(A);
+  Tensor identity = zeros_like(A);
   Tensor pivots = empty({A_sizes.begin(), A_sizes.end() - 1}, A.options().dtype(kInt));
-  // need to do this to keep the strides of the result tensor
-  // mps's solve expects row major layout, while inductor
-  // expects result to be column major
-  Tensor tmp = empty_like(A, MemoryFormat::Contiguous);
-  linalg_solve_out_mps_impl(A, identity, true, check_errors, tmp, LU, pivots, info);
-  result.copy_(tmp);
+  (ndim == 2 ? identity.diagonal() : identity.diagonal(0, -2, -1)).fill_(1);
+  linalg_solve_out_mps_impl(A, identity, true, check_errors, result, LU, pivots, info);
 }
 
 static Tensor& mm_out_mps_impl(const Tensor& self, const Tensor& other, Tensor& output) {

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

@@ -519,13 +519,6 @@ static void max_unpool_out_mps_template(const Tensor& input,
                                         Tensor& output,
                                         const int32_t pooling_dims,
                                         const std::string& op_name) {
-  TORCH_CHECK(output_size_.size() == static_cast<size_t>(pooling_dims),
-              op_name,
-              "There should be exactly ",
-              pooling_dims,
-              " elements but got ",
-              output_size_.size());
-
   auto dims = input.dim();
   auto leading_dims = input.dim() - pooling_dims;
 

aten/src/ATen/native/native_functions.yaml

@@ -3858,7 +3858,7 @@
   device_check: NoCheck   # TensorIterator
   structured: True
   dispatch:
-    CPU, CUDA, MTIA: aminmax_out
+    CPU, CUDA: aminmax_out
     MPS: aminmax_out_mps
 
 - func: _compute_linear_combination(Tensor input, Tensor coefficients) -> Tensor
@@ -3909,7 +3909,7 @@
 - func: amax.out(Tensor self, int[1] dim=[], bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!)
   structured: True
   dispatch:
-    CPU, CUDA, MTIA: amax_out
+    CPU, CUDA: amax_out
     MPS: amax_out_mps
 
 # Return: (Tensor output, Tensor indices)
@@ -4090,7 +4090,7 @@
 - func: amin.out(Tensor self, int[1] dim=[], bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!)
   structured: True
   dispatch:
-    CPU, CUDA, MTIA: amin_out
+    CPU, CUDA: amin_out
     MPS: amin_out_mps
 
 # TODO: Add this function to MPS dispatch key so that we avoid declaring it in

aten/src/ATen/native/quantized/cpu/qembeddingbag_prepack.cpp

@@ -158,46 +158,12 @@ c10::intrusive_ptr<EmbeddingPackedParamsBase> PackedEmbeddingBagWeight::prepack(
   return packed_ptr;
 }
 
-#ifdef USE_FBGEMM
-namespace {
-/// Number of columns in the rowwise min/max buffer passed to the quantization function(s)
-constexpr int kRowwiseMinMaxNumCols = 2;
-
-bool _validate_rowwise_min_max(
-  const at::Tensor& weight,
-  const std::optional<at::Tensor>& rowwise_min_max_opt) {
-  const auto is_valid_rowwise_min_max = rowwise_min_max_opt.has_value();
-
-  if (is_valid_rowwise_min_max) {
-      TORCH_CHECK(
-        (rowwise_min_max_opt->dim() == 2 &&
-        rowwise_min_max_opt->size(0) == weight.size(0) &&
-        rowwise_min_max_opt->size(1) == kRowwiseMinMaxNumCols),
-        "'rowwise_min_max' must be a 2D tensor with shape [num_rows(weight), 2].");
-  }
-
-  return is_valid_rowwise_min_max;
-}
-
-auto _get_rowwise_min_max_contig(
-  const std::optional<at::Tensor>& rowwise_min_max_opt) {
-    return rowwise_min_max_opt.has_value()
-      ? rowwise_min_max_opt->expect_contiguous(rowwise_min_max_opt->suggest_memory_format())
-      : at::borrow_from_optional_tensor(rowwise_min_max_opt);
-}
-}
-#endif // USE_FBGEMM
-
 namespace at::native {
 
 // Note - This is a temporary pack function for embedding bag which quantizes
 // and packs the float weight tensor. In the next step it will be replaced by a
 // quantize and pack function once we support FP scale and FP zero_point
 //
-// The optional rowwise_min_max argument is to support callers to pass in the min/max
-// values of the weight tensor. If the rowwise_min_max is not provided, the min/max
-// values will be computed from the weight tensor.
-//
 // Python example examining a packed 8bit zero_point and scale:
 //
 // >> x = torch.from_numpy(np.array([[[10, 20], [30, 40]],[[50, 60], [70, 80]]],
@@ -255,10 +221,7 @@ namespace at::native {
 //
 //        [[50.        , 60.00000035],
 //         [70.        , 80.00000035]]])
-Tensor& qembeddingbag_byte_prepack_out(
-    Tensor& output,
-    const Tensor& weight,
-    const std::optional<Tensor>& rowwise_min_max_opt) {
+Tensor& qembeddingbag_byte_prepack_out(Tensor& output, const Tensor& weight) {
   // The "last" dimension of an N-Dimensioned batch of embedding bags is
   // quantization channel. E.g. for a 2D embedding bag, this has
   // [ row, col ] dimensions, for batched of embedding bags, dimensions might be
@@ -293,16 +256,9 @@ Tensor& qembeddingbag_byte_prepack_out(
   auto* output_data = output.data_ptr<uint8_t>();
 
 #ifdef USE_FBGEMM
-  // Move these outside of the ifdef when we support non-FBGEMM flow.
-  const auto is_valid_rowwise_min_max = _validate_rowwise_min_max(weight, rowwise_min_max_opt);
-  const auto rowwise_min_max_contig = _get_rowwise_min_max_contig(rowwise_min_max_opt);
-
   if (weight_contig->scalar_type() == at::ScalarType::Half) {
     const auto weight_data =
         static_cast<fbgemm::float16*>(weight_contig->data_ptr());
-    const auto rowwise_min_max_data = is_valid_rowwise_min_max
-        ? static_cast<fbgemm::float16*>(rowwise_min_max_contig->data_ptr())
-        : nullptr;
     at::parallel_for(
         0, embedding_rows, 1, [&](int64_t start_idx, int64_t end_idx) {
           fbgemm::FloatOrHalfToFused8BitRowwiseQuantizedSBFloat<
@@ -310,21 +266,17 @@ Tensor& qembeddingbag_byte_prepack_out(
               weight_data + start_idx * embedding_cols,
               end_idx - start_idx,
               embedding_cols,
-              output_data + start_idx * output_columns,
-              (is_valid_rowwise_min_max ? (rowwise_min_max_data + start_idx * kRowwiseMinMaxNumCols) : nullptr));
+              output_data + start_idx * output_columns);
         });
   } else {
     const auto weight_data = weight_contig->data_ptr<float>();
-    const auto rowwise_min_max_data =
-        is_valid_rowwise_min_max ? rowwise_min_max_contig->data_ptr<float>() : nullptr;
     at::parallel_for(
         0, embedding_rows, 1, [&](int64_t start_idx, int64_t end_idx) {
           fbgemm::FloatOrHalfToFused8BitRowwiseQuantizedSBFloat<float>(
               weight_data + start_idx * embedding_cols,
               end_idx - start_idx,
               embedding_cols,
-              output_data + start_idx * output_columns,
-              (is_valid_rowwise_min_max ? (rowwise_min_max_data + start_idx * kRowwiseMinMaxNumCols) : nullptr));
+              output_data + start_idx * output_columns);
         });
   }
 
@@ -374,22 +326,6 @@ Tensor qembeddingbag_byte_prepack(const Tensor& weight) {
   return output;
 }
 
-static Tensor qembeddingbag_byte_prepack_with_rowwise_min_max(
-    const Tensor& weight,
-    const Tensor& rowwise_min_max) {
-  const auto weight_contig =
-      weight.expect_contiguous(weight.suggest_memory_format());
-  Tensor output = at::detail::empty_cpu(
-      {0},
-      at::kByte,
-      weight_contig->layout(),
-      weight_contig->device(),
-      std::nullopt,
-      std::nullopt);
-  qembeddingbag_byte_prepack_out(output, weight, rowwise_min_max);
-  return output;
-}
-
 Tensor qembeddingbag_byte_prepack_meta(const Tensor& weight) {
   const auto weight_contig =
       weight.expect_contiguous(weight.suggest_memory_format());
@@ -399,7 +335,7 @@ Tensor qembeddingbag_byte_prepack_meta(const Tensor& weight) {
       "'embedding_bag_byte_prepack' only support float32 or float16.");
   const auto weight_sizes = weight.sym_sizes();
   const auto cols_dim = weight.ndimension() - 1;
-  const auto& embedding_cols = weight_sizes[cols_dim];
+  const auto embedding_cols = weight_sizes[cols_dim];
   // Add 8 bytes per column to store FP32 scale and zero_point per row.
   const auto output_columns = embedding_cols + 2 * sizeof(float);
 
@@ -423,8 +359,7 @@ Tensor _qembeddingbag_nbit_prepack_helper(
     int bit_width,
     const bool optimized_qparams,
     const int64_t nbins,
-    const double ratio,
-    const std::optional<Tensor>& rowwise_min_max_opt = std::nullopt) {
+    const double ratio) {
   TORCH_CHECK(
       weight.scalar_type() == at::ScalarType::Float ||
           weight.scalar_type() == at::ScalarType::Half,
@@ -466,17 +401,10 @@ Tensor _qembeddingbag_nbit_prepack_helper(
   auto* output_data = output.data_ptr<uint8_t>();
 
 #ifdef USE_FBGEMM
-  // Move these outside of the ifdef when we support non-FBGEMM flow.
-  const auto is_valid_rowwise_min_max = _validate_rowwise_min_max(weight, rowwise_min_max_opt);
-  const auto rowwise_min_max_contig = _get_rowwise_min_max_contig(rowwise_min_max_opt);
-
   if (!optimized_qparams) {
     if (weight_contig.scalar_type() == at::ScalarType::Half) {
       const auto weight_data =
           static_cast<fbgemm::float16*>(weight_contig.data_ptr());
-      const auto rowwise_min_max_data = is_valid_rowwise_min_max
-          ? static_cast<fbgemm::float16*>(rowwise_min_max_contig->data_ptr())
-          : nullptr;
       at::parallel_for(
           0, embedding_rows, 1, [&](int64_t start_idx, int64_t end_idx) {
             fbgemm::FloatOrHalfToFusedNBitRowwiseQuantizedSBHalf<
@@ -485,13 +413,10 @@ Tensor _qembeddingbag_nbit_prepack_helper(
                 weight_data + start_idx * embedding_cols,
                 end_idx - start_idx,
                 static_cast<int>(embedding_cols),
-                output_data + start_idx * output_shape[1],
-                (is_valid_rowwise_min_max ? (rowwise_min_max_data + start_idx * kRowwiseMinMaxNumCols) : nullptr));
+                output_data + start_idx * output_shape[1]);
           });
     } else {
       const auto weight_data = weight_contig.data_ptr<float>();
-      const auto rowwise_min_max_data =
-          is_valid_rowwise_min_max ? rowwise_min_max_contig->data_ptr<float>() : nullptr;
       at::parallel_for(
           0, embedding_rows, 1, [&](int64_t start_idx, int64_t end_idx) {
             fbgemm::FloatOrHalfToFusedNBitRowwiseQuantizedSBHalf<float>(
@@ -499,8 +424,7 @@ Tensor _qembeddingbag_nbit_prepack_helper(
                 weight_data + start_idx * embedding_cols,
                 end_idx - start_idx,
                 static_cast<int>(embedding_cols),
-                output_data + start_idx * output_shape[1],
-                (is_valid_rowwise_min_max ? (rowwise_min_max_data + start_idx * kRowwiseMinMaxNumCols) : nullptr));
+                output_data + start_idx * output_shape[1]);
           });
     }
   } else {
@@ -590,16 +514,6 @@ Tensor qembeddingbag_4bit_prepack(
       weight, 4 /*bit_width*/, optimized_qparams, nbins, ratio);
 }
 
-Tensor qembeddingbag_4bit_prepack_with_rowwise_min_max(
-    const Tensor& weight,
-    const Tensor& rowwise_min_max,
-    const bool optimized_qparams,
-    const int64_t nbins,
-    const double ratio) {
-  return _qembeddingbag_nbit_prepack_helper(
-      weight, 4 /*bit_width*/, optimized_qparams, nbins, ratio, rowwise_min_max);
-}
-
 // Applies 2-bit row-wise quantization by determining the range
 // (maximum - minimum) and bias (minimum value) of each row in the input
 // matrix, and then scaling each element to an 2-bit number between 0 and
@@ -617,16 +531,6 @@ Tensor qembeddingbag_2bit_prepack(
       weight, 2 /*bit_width*/, optimized_qparams, nbins, ratio);
 }
 
-Tensor qembeddingbag_2bit_prepack_with_rowwise_min_max(
-    const Tensor& weight,
-    const Tensor& rowwise_min_max,
-    const bool optimized_qparams,
-    const int64_t nbins,
-    const double ratio) {
-  return _qembeddingbag_nbit_prepack_helper(
-      weight, 2 /*bit_width*/, optimized_qparams, nbins, ratio, rowwise_min_max);
-}
-
 class QEmbeddingPackWeights final {
  public:
   static c10::intrusive_ptr<EmbeddingPackedParamsBase> run(const at::Tensor& weight) {
@@ -638,21 +542,12 @@ TORCH_LIBRARY_IMPL(quantized, CPU, m) {
   m.impl(
       TORCH_SELECTIVE_NAME("quantized::embedding_bag_byte_prepack"),
       TORCH_FN(qembeddingbag_byte_prepack));
-  m.impl(
-      TORCH_SELECTIVE_NAME("quantized::embedding_bag_byte_prepack_with_rowwise_min_max"),
-      TORCH_FN(qembeddingbag_byte_prepack_with_rowwise_min_max));
   m.impl(
       TORCH_SELECTIVE_NAME("quantized::embedding_bag_4bit_prepack"),
       TORCH_FN(qembeddingbag_4bit_prepack));
-  m.impl(
-      TORCH_SELECTIVE_NAME("quantized::embedding_bag_4bit_prepack_with_rowwise_min_max"),
-      TORCH_FN(qembeddingbag_4bit_prepack_with_rowwise_min_max));
   m.impl(
       TORCH_SELECTIVE_NAME("quantized::embedding_bag_2bit_prepack"),
       TORCH_FN(qembeddingbag_2bit_prepack));
-  m.impl(
-      TORCH_SELECTIVE_NAME("quantized::embedding_bag_2bit_prepack_with_rowwise_min_max"),
-      TORCH_FN(qembeddingbag_2bit_prepack_with_rowwise_min_max));
 }
 
 TORCH_LIBRARY_IMPL(quantized, QuantizedCPU, m) {

aten/src/ATen/native/quantized/cpu/qembeddingbag_prepack.h

@@ -3,10 +3,7 @@
 
 namespace at::native {
 
-Tensor& qembeddingbag_byte_prepack_out(
-    Tensor& output,
-    const Tensor& weight,
-    const std::optional<Tensor>& rowwise_min_max_opt = std::nullopt);
+Tensor& qembeddingbag_byte_prepack_out(Tensor& output, const Tensor& weight);
 
 Tensor qembeddingbag_byte_prepack(const Tensor& weight);
 

aten/src/ATen/native/quantized/library.cpp

@@ -121,12 +121,9 @@ TORCH_LIBRARY(quantized, m) {
   m.def(TORCH_SELECTIVE_SCHEMA("quantized::embedding_bag_unpack(__torch__.torch.classes.quantized.EmbeddingPackedParamsBase W_prepack) -> Tensor W_origin"), {at::Tag::pt2_compliant_tag});
   m.def(TORCH_SELECTIVE_SCHEMA("quantized::embedding_bag_byte_prepack(Tensor weight) -> Tensor"), {at::Tag::pt2_compliant_tag});
   m.def(TORCH_SELECTIVE_SCHEMA("quantized::embedding_bag_byte_unpack(Tensor weight) -> Tensor"), {at::Tag::pt2_compliant_tag});
-  m.def(TORCH_SELECTIVE_SCHEMA("quantized::embedding_bag_byte_prepack_with_rowwise_min_max(Tensor weight, Tensor rowwise_min_max) -> Tensor"), {at::Tag::pt2_compliant_tag});
   m.def(TORCH_SELECTIVE_SCHEMA("quantized::embedding_bag_4bit_prepack(Tensor weight, bool optimized_qparams=False, int nbins=200, float ratio=0.16) -> Tensor"), {at::Tag::pt2_compliant_tag});
-  m.def(TORCH_SELECTIVE_SCHEMA("quantized::embedding_bag_4bit_prepack_with_rowwise_min_max(Tensor weight, Tensor rowwise_min_max, bool optimized_qparams=False, int nbins=200, float ratio=0.16) -> Tensor"), {at::Tag::pt2_compliant_tag});
   m.def(TORCH_SELECTIVE_SCHEMA("quantized::embedding_bag_4bit_unpack(Tensor weight) -> Tensor"), {at::Tag::pt2_compliant_tag});
   m.def(TORCH_SELECTIVE_SCHEMA("quantized::embedding_bag_2bit_prepack(Tensor weight, bool optimized_qparams=False, int nbins=200, float ratio=0.16) -> Tensor"), {at::Tag::pt2_compliant_tag});
-  m.def(TORCH_SELECTIVE_SCHEMA("quantized::embedding_bag_2bit_prepack_with_rowwise_min_max(Tensor weight, Tensor rowwise_min_max, bool optimized_qparams=False, int nbins=200, float ratio=0.16) -> Tensor"), {at::Tag::pt2_compliant_tag});
   m.def(TORCH_SELECTIVE_SCHEMA("quantized::embedding_bag_2bit_unpack(Tensor weight) -> Tensor"), {at::Tag::pt2_compliant_tag});
   m.def(TORCH_SELECTIVE_SCHEMA("quantized::embedding_bag_byte_rowwise_offsets(Tensor weight, Tensor indices, Tensor? offsets=None, bool scale_grad_by_freq=False, int mode=0, bool pruned_weights=False, Tensor? per_sample_weights=None, Tensor? compressed_indices_mapping=None, bool include_last_offset=False) -> Tensor"), {at::Tag::pt2_compliant_tag});
   m.def(TORCH_SELECTIVE_SCHEMA("quantized::embedding_bag_4bit_rowwise_offsets(Tensor weight, Tensor indices, Tensor? offsets=None, bool scale_grad_by_freq=False, int mode=0, bool pruned_weights=False, Tensor? per_sample_weights=None, Tensor? compressed_indices_mapping=None, bool include_last_offset=False) -> Tensor"), {at::Tag::pt2_compliant_tag});

aten/src/ATen/native/sparse/cuda/cuSPARSELtOps.cpp

@@ -120,7 +120,7 @@ at::Tensor _cslt_compress(const Tensor& sparse_input) {
   // buffer (in bytes)
   size_t orig_m = sparse_input.size(0);
   size_t div = orig_m * sparse_input.itemsize();
-  size_t new_n = (compressed_size + div - 1) / div; // ceil(s,d) = (s+d-1)/d
+  size_t new_n = (compressed_size + div - 1) / div; // floor
   auto compressed_tensor = sparse_input.new_empty({(int64_t)orig_m, (int64_t)new_n});
 
   auto& allocator = *::c10::cuda::CUDACachingAllocator::get();
@@ -155,7 +155,7 @@ std::tuple<at::Tensor, int64_t, int64_t, int64_t, int64_t> _cslt_sparse_mm_impl(
     TORCH_CUDASPARSE_CHECK(cusparseLtInit(&handle));
     handle_initialized = true;
   }
-  // cuSPARSELt constructs
+  // cupsarselt constructs
   cusparseLtMatmulDescriptor_t matmul;
   cusparseLtMatmulPlan_t plan;
   cusparseLtMatmulAlgSelection_t alg_sel;

aten/src/ATen/templates/FunctionalInverses.h

@@ -2,12 +2,22 @@
 
 // ${generated_comment}
 
-#include <ATen/FunctionalStorageImpl.h>
 #include <ATen/Tensor.h>
 
 namespace at {
 namespace functionalization {
 
+enum class InverseReturnMode {
+  /// Specifies that functional inverses should always return a view.
+  AlwaysView,
+  /// Specifies that functional inverses should always return a non-view / copy.
+  NeverView,
+  /// Specifies that functional inverses should return a view unless a (copying) scatter
+  /// inverse exists, in which case that will be used instead.
+  /// This avoids as_strided() calls that can be difficult for subclasses to handle.
+  ViewOrScatterInverse,
+};
+
 struct FunctionalInverses {
 
 ${view_inverse_declarations}

aten/src/ATen/templates/RegisterFunctionalization.cpp

@@ -4,7 +4,7 @@
 #include <ATen/core/LegacyTypeDispatch.h>
 #include <ATen/EmptyTensor.h>
 #include <ATen/FunctionalTensorWrapper.h>
-#include <ATen/ViewMetaClasses.h>
+#include <ATen/FunctionalInverses.h>
 #include <ATen/MemoryOverlap.h>
 #include <torch/library.h>
 

aten/src/ATen/templates/ViewMetaClasses.cpp

@@ -1,19 +0,0 @@
-// ${generated_comment}
-
-#include <ATen/FunctionalInverses.h>
-#include <ATen/ViewMetaClasses.h>
-
-#ifndef AT_PER_OPERATOR_HEADERS
-#include <ATen/Operators.h>
-#include <ATen/NativeFunctions.h>
-#else
-${op_headers}
-#endif
-
-namespace at {
-namespace functionalization {
-
-${view_meta_implementations}
-
-} // namespace functionalization
-} // namespace at

aten/src/ATen/templates/ViewMetaClasses.h

@@ -1,12 +0,0 @@
-#define TORCH_ASSERT_ONLY_METHOD_OPERATORS
-// ${generated_comment}
-
-#include <ATen/FunctionalStorageImpl.h>
-
-namespace at {
-namespace functionalization {
-
-${view_meta_declarations}
-
-} // namespace functionalization
-} // namespace at

aten/src/ATen/templates/ViewMetaClassesPythonBinding.cpp

@@ -1,11 +0,0 @@
-#include <ATen/ViewMetaClasses.h>
-#include <torch/csrc/functionalization/Module.h>
-
-namespace torch::functionalization {
-
-void initGenerated(PyObject* module) {
-  auto functionalization = py::handle(module).cast<py::module>();
-  $view_meta_bindings
-}
-
-} // namespace torch::functionalization

benchmarks/dynamo/check_accuracy.py

@@ -78,8 +78,6 @@ def check_accuracy(actual_csv, expected_csv, expected_filename):
                 "google/gemma-3-4b-it",
                 "openai/whisper-tiny",
                 "Qwen/Qwen3-0.6B",
-                "mistralai/Mistral-7B-Instruct-v0.3",
-                "openai/gpt-oss-20b",
             }
         )
 

benchmarks/dynamo/check_graph_breaks.py

@@ -61,8 +61,6 @@ def check_graph_breaks(actual_csv, expected_csv, expected_filename):
                 "google/gemma-3-4b-it",
                 "openai/whisper-tiny",
                 "Qwen/Qwen3-0.6B",
-                "mistralai/Mistral-7B-Instruct-v0.3",
-                "openai/gpt-oss-20b",
             }
         )
 

benchmarks/dynamo/ci_expected_accuracy/aot_eager_huggingface_inference.csv

@@ -191,11 +191,3 @@ openai/whisper-tiny,pass,0
 
 
 Qwen/Qwen3-0.6B,pass,0
-
-
-
-mistralai/Mistral-7B-Instruct-v0.3,pass,0
-
-
-
-openai/gpt-oss-20b,pass,0

benchmarks/dynamo/ci_expected_accuracy/aot_inductor_huggingface_inference.csv

@@ -187,11 +187,3 @@ openai/whisper-tiny,fail_to_run,0
 
 
 Qwen/Qwen3-0.6B,fail_to_run,0
-
-
-
-mistralai/Mistral-7B-Instruct-v0.3,fail_to_run,0
-
-
-
-openai/gpt-oss-20b,fail_to_run,0

benchmarks/dynamo/ci_expected_accuracy/cpu_inductor_amp_freezing_huggingface_inference.csv

@@ -191,11 +191,3 @@ openai/whisper-tiny,pass_due_to_skip,0
 
 
 Qwen/Qwen3-0.6B,pass_due_to_skip,0
-
-
-
-mistralai/Mistral-7B-Instruct-v0.3,pass_due_to_skip,0
-
-
-
-openai/gpt-oss-20b,pass_due_to_skip,0

benchmarks/dynamo/ci_expected_accuracy/cpu_inductor_freezing_huggingface_inference.csv

@@ -191,11 +191,3 @@ openai/whisper-tiny,pass_due_to_skip,0
 
 
 Qwen/Qwen3-0.6B,pass_due_to_skip,0
-
-
-
-mistralai/Mistral-7B-Instruct-v0.3,pass_due_to_skip,0
-
-
-
-openai/gpt-oss-20b,pass_due_to_skip,0

benchmarks/dynamo/ci_expected_accuracy/cpu_inductor_huggingface_inference.csv

@@ -191,11 +191,3 @@ openai/whisper-tiny,pass_due_to_skip,0
 
 
 Qwen/Qwen3-0.6B,pass_due_to_skip,0
-
-
-
-mistralai/Mistral-7B-Instruct-v0.3,pass_due_to_skip,0
-
-
-
-openai/gpt-oss-20b,pass_due_to_skip,0

benchmarks/dynamo/ci_expected_accuracy/dynamic_aot_eager_huggingface_inference.csv

@@ -191,11 +191,3 @@ openai/whisper-tiny,pass,0
 
 
 Qwen/Qwen3-0.6B,pass,0
-
-
-
-mistralai/Mistral-7B-Instruct-v0.3,pass,0
-
-
-
-openai/gpt-oss-20b,pass,0

benchmarks/dynamo/ci_expected_accuracy/dynamic_cpu_inductor_huggingface_inference.csv

@@ -191,11 +191,3 @@ openai/whisper-tiny,pass,0
 
 
 Qwen/Qwen3-0.6B,pass,0
-
-
-
-mistralai/Mistral-7B-Instruct-v0.3,pass,0
-
-
-
-openai/gpt-oss-20b,pass,0

benchmarks/dynamo/ci_expected_accuracy/dynamic_inductor_huggingface_inference.csv

@@ -191,11 +191,3 @@ openai/whisper-tiny,pass,0
 
 
 Qwen/Qwen3-0.6B,pass,0
-
-
-
-mistralai/Mistral-7B-Instruct-v0.3,pass,0
-
-
-
-openai/gpt-oss-20b,pass,0

benchmarks/dynamo/ci_expected_accuracy/dynamo_eager_huggingface_inference.csv

@@ -191,11 +191,3 @@ openai/whisper-tiny,pass,0
 
 
 Qwen/Qwen3-0.6B,pass,0
-
-
-
-mistralai/Mistral-7B-Instruct-v0.3,pass,0
-
-
-
-openai/gpt-oss-20b,pass,0

benchmarks/dynamo/ci_expected_accuracy/inductor_huggingface_inference.csv

@@ -191,11 +191,3 @@ openai/whisper-tiny,pass,0
 
 
 Qwen/Qwen3-0.6B,pass,0
-
-
-
-mistralai/Mistral-7B-Instruct-v0.3,pass,0
-
-
-
-openai/gpt-oss-20b,pass,0

benchmarks/dynamo/huggingface.yaml

@@ -11,8 +11,6 @@ skip:
     - GPTJForQuestionAnswering
     # Model too big
     - google/gemma-3-4b-it
-    - openai/gpt-oss-20b
-    - mistralai/Mistral-7B-Instruct-v0.3
 
   device:
     cpu:
@@ -21,8 +19,6 @@ skip:
       - google/gemma-3-4b-it
       - openai/whisper-tiny
       - Qwen/Qwen3-0.6B
-      - mistralai/Mistral-7B-Instruct-v0.3
-      - openai/gpt-oss-20b
 
   control_flow:
     - AllenaiLongformerBase
@@ -83,8 +79,6 @@ batch_size:
     google/gemma-3-4b-it: 8
     openai/whisper-tiny: 8
     Qwen/Qwen3-0.6B: 8
-    mistralai/Mistral-7B-Instruct-v0.3: 8
-    openai/gpt-oss-20b: 8
 
 
 tolerance:

benchmarks/dynamo/huggingface_llm_models.py

@@ -99,6 +99,4 @@ HF_LLM_MODELS: dict[str, Benchmark] = {
     "google/gemma-3-4b-it": TextGenerationBenchmark,
     "openai/whisper-tiny": WhisperBenchmark,
     "Qwen/Qwen3-0.6B": TextGenerationBenchmark,
-    "mistralai/Mistral-7B-Instruct-v0.3": TextGenerationBenchmark,
-    "openai/gpt-oss-20b": TextGenerationBenchmark,
 }

benchmarks/dynamo/huggingface_models_list.txt

@@ -51,5 +51,3 @@ google/gemma-2-2b,8
 google/gemma-3-4b-it,8
 openai/whisper-tiny,8
 Qwen/Qwen3-0.6B,8
-mistralai/Mistral-7B-Instruct-v0.3, 8
-openai/gpt-oss-20b, 8

buckbuild.bzl

@@ -156,7 +156,7 @@ ROOT = "//" if IS_OSS else "//xplat/caffe2"
 # for targets in subfolders
 ROOT_PATH = "//" if IS_OSS else "//xplat/caffe2/"
 
-C10 = "//c10:c10" if IS_OSS else "//xplat/caffe2/c10:c10"
+C10 = "//c10:c10" if IS_OSS else ("//xplat/caffe2/c10:c10_ovrsource" if is_arvr_mode() else "//xplat/caffe2/c10:c10")
 
 # a dictionary maps third party library name to fbsource and oss target
 THIRD_PARTY_LIBS = {
@@ -391,8 +391,6 @@ def get_aten_generated_files(enabled_backends):
         "CompositeExplicitAutogradFunctions_inl.h",
         "CompositeExplicitAutogradNonFunctionalFunctions.h",
         "CompositeExplicitAutogradNonFunctionalFunctions_inl.h",
-        "ViewMetaClasses.h",
-        "ViewMetaClasses.cpp",
         "VmapGeneratedPlumbing.h",
         "core/ATenOpList.cpp",
         "core/TensorBody.h",
@@ -950,6 +948,7 @@ def define_buck_targets(
             [
                 ("torch/csrc/api/include", "torch/**/*.h"),
                 ("", "torch/csrc/**/*.h"),
+                ("", "torch/csrc/**/*.hpp"),
                 ("", "torch/nativert/**/*.h"),
                 ("", "torch/headeronly/**/*.h"),
                 ("", "torch/script.h"),
@@ -1194,7 +1193,6 @@ def define_buck_targets(
             "NativeMetaFunctions.h": ":gen_aten[NativeMetaFunctions.h]",
             "Operators.h": ":gen_aten[Operators.h]",
             "RedispatchFunctions.h": ":gen_aten[RedispatchFunctions.h]",
-            "ViewMetaClasses.h": ":gen_aten[ViewMetaClasses.h]",
             "core/TensorBody.h": ":gen_aten[core/TensorBody.h]",
             "core/aten_interned_strings.h": ":gen_aten[core/aten_interned_strings.h]",
             "core/enum_tag.h": ":gen_aten[core/enum_tag.h]",
@@ -2050,6 +2048,7 @@ def define_buck_targets(
                 ("", "caffe2/utils/*.h"),
                 ("", "caffe2/core/*.h"),
                 ("", "torch/csrc/*.h"),
+                ("", "torch/csrc/*.hpp"),
                 ("", "torch/csrc/api/include/torch/*.h"),
                 ("", "torch/csrc/autograd/*.h"),
                 ("", "torch/csrc/autograd/*/*.h"),

build.bzl

@@ -118,9 +118,6 @@ def define_targets(rules):
             ":LazyNonNativeIr.h",
             ":RegisterDispatchDefinitions.ini",
             ":RegisterDispatchKey.cpp",
-            ":ViewMetaClassesPythonBinding.cpp",
-            ":ViewMetaClasses.cpp",
-            ":ViewMetaClasses.h",
             ":native_functions.yaml",
             ":shape_inference.h",
             ":tags.yaml",
@@ -173,7 +170,6 @@ GENERATED_H = [
     "FunctionalInverses.h",
     "RedispatchFunctions.h",
     "RegistrationDeclarations.h",
-    "ViewMetaClasses.h",
     "VmapGeneratedPlumbing.h",
 ]
 
@@ -250,7 +246,6 @@ GENERATED_CPP = [
     "RegisterFunctionalization_1.cpp",
     "RegisterFunctionalization_2.cpp",
     "RegisterFunctionalization_3.cpp",
-    "ViewMetaClasses.cpp",
 ]
 
 GENERATED_CPP_CORE = [
@@ -312,7 +307,6 @@ _GENERATED_AUTOGRAD_PYTHON_CPP = [
     "torch/csrc/autograd/generated/python_torch_functions_1.cpp",
     "torch/csrc/autograd/generated/python_torch_functions_2.cpp",
     "torch/csrc/autograd/generated/python_variable_methods.cpp",
-    "torch/csrc/functionalization/generated/ViewMetaClassesPythonBinding.cpp"
 ]
 
 GENERATED_AUTOGRAD_PYTHON = _GENERATED_AUTOGRAD_PYTHON_HEADERS + _GENERATED_AUTOGRAD_PYTHON_CPP

build_variables.bzl

@@ -1010,7 +1010,6 @@ libtorch_python_core_sources = [
     "torch/csrc/utils/disable_torch_function.cpp",
     "torch/csrc/utils/verbose.cpp",
     "torch/csrc/cpu/Module.cpp",
-    "torch/csrc/functionalization/Module.cpp",
     "torch/csrc/instruction_counter/Module.cpp",
     "torch/nativert/python/Bindings.cpp",
 ] + lazy_tensor_core_python_sources
@@ -1053,7 +1052,6 @@ def glob_libtorch_python_sources(gencode_pattern = ":generate-code[{}]"):
         "torch/csrc/autograd/generated/python_torch_functions_1.cpp",
         "torch/csrc/autograd/generated/python_torch_functions_2.cpp",
         "torch/csrc/autograd/generated/python_variable_methods.cpp",
-        "torch/csrc/functionalization/generated/ViewMetaClassesPythonBinding.cpp",
     ]]
 
     _libtorch_python_sources.extend(libtorch_python_core_sources)

c10/core/TensorImpl.h

@@ -3244,7 +3244,7 @@ class C10_TensorImpl_Size_Check_Dummy_Class : private TensorImpl {
     are_equal<sizeof(autograd_meta_),      4,  FieldNameEnum::autograd_meta_>();
     are_equal<sizeof(extra_meta_),         4,  FieldNameEnum::extra_meta_>();
     are_equal<sizeof(version_counter_),    4,  FieldNameEnum::version_counter_>();
-    are_equal<sizeof(pyobj_slot_),         4,  FieldNameEnum::pyobj_slot_>();
+    are_equal<sizeof(pyobj_slot_),    8,  FieldNameEnum::pyobj_slot_>();
     is_le<sizeof(sizes_and_strides_),     88, FieldNameEnum::sizes_and_strides_>();
     are_equal<sizeof(storage_offset_),     8,  FieldNameEnum::storage_offset_>();
     are_equal<sizeof(numel_),              8,  FieldNameEnum::numel_>();

c10/cuda/CUDAMallocAsyncAllocator.cpp

@@ -14,6 +14,7 @@ namespace c10::cuda::CUDACachingAllocator::CudaMallocAsync {
 using namespace c10::CachingAllocator;
 using namespace c10::CachingDeviceAllocator;
 
+#if CUDA_VERSION >= 11040 || defined(USE_ROCM)
 // CUDA device allocator that uses cudaMallocAsync to implement
 // the same interface as CUDACachingAllocator.cpp.
 
@@ -925,4 +926,13 @@ CUDAAllocator* allocator() {
   return &device_allocator;
 }
 
+#else
+// NOLINTNEXTLINE(misc-use-internal-linkage)
+CUDAAllocator* allocator() {
+  TORCH_CHECK(false, "Cannot use CudaMallocAsyncAllocator with cuda < 11.4.");
+  return nullptr;
+}
+
+#endif
+
 } // namespace c10::cuda::CUDACachingAllocator::CudaMallocAsync

c10/ovrsource_defs.bzl

@@ -18,9 +18,9 @@ cuda_supported_platforms = [
 
 def define_c10_ovrsource(name, is_mobile):
     if is_mobile:
-        pp_flags = ["-DC10_MOBILE=1"]
+        pp_flags = ["-DC10_MOBILE=1", "-DC10_USE_GLOG"]
     else:
-        pp_flags = []
+        pp_flags = ["-DC10_USE_GLOG"]
 
     oxx_static_library(
         name = name,

c10/util/ExclusivelyOwnedTensorTraits.h

@@ -35,26 +35,26 @@ struct ExclusivelyOwnedTensorTraits {
     // incremented.
     const bool isUndefined = toDestroy == UndefinedTensorImpl::singleton();
     TORCH_INTERNAL_ASSERT_DEBUG_ONLY(
-        toDestroy->refcount() == 1 ||
-            (toDestroy->refcount() == 0 && isUndefined),
+        toDestroy->refcount_ == 1 || (toDestroy->refcount_ == 0 && isUndefined),
         "ExclusivelyOwned<Tensor> destroyed with isUndefined ",
         isUndefined,
         " and refcount ",
-        toDestroy->refcount(),
+        toDestroy->refcount_,
         ", expected 1 or, if isUndefined, 0!");
     TORCH_INTERNAL_ASSERT_DEBUG_ONLY(
-        toDestroy->weakcount() == 1 ||
-            (toDestroy->weakcount() == 0 &&
+        toDestroy->weakcount_ == 1 ||
+            (toDestroy->weakcount_ == 0 &&
              toDestroy == UndefinedTensorImpl::singleton()),
         "ExclusivelyOwned<Tensor> destroyed with isUndefined ",
         isUndefined,
         " and weakcount ",
-        toDestroy->weakcount(),
+        toDestroy->weakcount_,
         ", expected 1 or, if isUndefined, 0!");
     if (!isUndefined) {
 #ifndef NDEBUG
       // Needed to pass the debug assertions in ~intrusive_ptr_target.
-      toDestroy->combined_refcount_.store(0, std::memory_order_relaxed);
+      toDestroy->refcount_ = 0;
+      toDestroy->weakcount_ = 0;
 #endif
       delete toDestroy;
     }

c10/util/intrusive_ptr.h

@@ -27,78 +27,7 @@ struct DontIncreaseRefcount {};
 } // namespace raw
 
 namespace detail {
-constexpr uint64_t kImpracticallyHugeReferenceCount = 0x0FFFFFFF;
-constexpr uint64_t kImpracticallyHugeWeakReferenceCount =
-    (kImpracticallyHugeReferenceCount << 32);
-constexpr uint64_t kReferenceCountOne = 1;
-constexpr uint64_t kWeakReferenceCountOne = (kReferenceCountOne << 32);
-constexpr uint64_t kUniqueRef = (kReferenceCountOne | kWeakReferenceCountOne);
-
-template <class TTarget>
-struct intrusive_target_default_null_type final {
-  static constexpr TTarget* singleton() noexcept {
-    return nullptr;
-  }
-};
-
-template <class TTarget, class ToNullType, class FromNullType>
-TTarget* assign_ptr_(TTarget* rhs) {
-  if (FromNullType::singleton() == rhs) {
-    return ToNullType::singleton();
-  } else {
-    return rhs;
-  }
-}
-
-inline uint32_t refcount(uint64_t combined_refcount) {
-  return static_cast<uint32_t>(combined_refcount);
-}
-
-inline uint32_t weakcount(uint64_t combined_refcount) {
-  return static_cast<uint32_t>(combined_refcount >> 32);
-}
-
-// The only requirement for refcount increment is that it happens-before
-// decrement, so no additional memory ordering is needed.
-inline uint64_t atomic_combined_refcount_increment(
-    std::atomic<uint64_t>& combined_refcount,
-    uint64_t inc) {
-  return combined_refcount.fetch_add(inc, std::memory_order_relaxed) + inc;
-}
-
-inline uint32_t atomic_refcount_increment(
-    std::atomic<uint64_t>& combined_refcount) {
-  return detail::refcount(atomic_combined_refcount_increment(
-      combined_refcount, kReferenceCountOne));
-}
-
-inline uint32_t atomic_weakcount_increment(
-    std::atomic<uint64_t>& combined_refcount) {
-  return detail::weakcount(atomic_combined_refcount_increment(
-      combined_refcount, kWeakReferenceCountOne));
-}
-
-// The requirement is that all modifications to the managed object happen-before
-// invocation of the managed object destructor, and that allocation of the
-// managed object storage happens-before deallocation of the storage.
-//
-// To get this ordering, all non-final decrements must synchronize-with the
-// final decrement. So all non-final decrements have to store-release while the
-// final decrement has to load-acquire, either directly or with the help of
-// fences. But it's easiest just to have all decrements be acq-rel. And it turns
-// out, on modern architectures and chips, it's also fastest.
-inline uint64_t atomic_combined_refcount_decrement(
-    std::atomic<uint64_t>& combined_refcount,
-    uint64_t dec) {
-  return combined_refcount.fetch_sub(dec, std::memory_order_acq_rel) - dec;
-}
-
-inline uint32_t atomic_weakcount_decrement(
-    std::atomic<uint64_t>& combined_refcount) {
-  return detail::weakcount(atomic_combined_refcount_decrement(
-      combined_refcount, kWeakReferenceCountOne));
-}
-
+constexpr uint32_t kImpracticallyHugeReferenceCount = 0x0FFFFFFF;
 } // namespace detail
 
 /**
@@ -151,14 +80,8 @@ class C10_API intrusive_ptr_target {
   //    atomically increment the use count, if it is greater than 0.
   //    If it is not, you must report that the storage is dead.
   //
-  //.We use a single combined count for refcount and weakcount so that
-  // we can atomically operate on both at the same time for performance
-  // and defined behaviors.
-  //
-  mutable std::atomic<uint64_t> combined_refcount_;
-  static_assert(sizeof(std::atomic<uint64_t>) == 8);
-  static_assert(alignof(std::atomic<uint64_t>) == 8);
-  static_assert(std::atomic<uint64_t>::is_always_lock_free);
+  mutable std::atomic<uint32_t> refcount_;
+  mutable std::atomic<uint32_t> weakcount_;
 
   template <typename T, typename NullType>
   friend class intrusive_ptr;
@@ -203,16 +126,16 @@ class C10_API intrusive_ptr_target {
         // caller of unsafe_adapt_non_heap_allocated wanted to
         // use). We choose our reference count such that the count
         // will not dip below kImpracticallyHugeReferenceCount regardless.
-        refcount() == 0 ||
-            refcount() >= detail::kImpracticallyHugeReferenceCount,
+        refcount_.load() == 0 ||
+            refcount_.load() >= detail::kImpracticallyHugeReferenceCount,
         "Tried to destruct an intrusive_ptr_target that still has intrusive_ptr to it; refcount was ",
-        refcount());
+        refcount_.load());
     TORCH_INTERNAL_ASSERT_DEBUG_ONLY(
         // See ~intrusive_ptr for optimization that will frequently result in 1
         // at destruction time.
-        weakcount() == 1 || weakcount() == 0 ||
-            weakcount() == detail::kImpracticallyHugeReferenceCount - 1 ||
-            weakcount() == detail::kImpracticallyHugeReferenceCount,
+        weakcount_.load() == 1 || weakcount_.load() == 0 ||
+            weakcount_.load() == detail::kImpracticallyHugeReferenceCount - 1 ||
+            weakcount_.load() == detail::kImpracticallyHugeReferenceCount,
         "Tried to destruct an intrusive_ptr_target that still has weak_intrusive_ptr to it");
 #if defined(_MSC_VER) && !defined(__clang__)
 #pragma warning(pop)
@@ -221,7 +144,7 @@ class C10_API intrusive_ptr_target {
 #endif
   }
 
-  constexpr intrusive_ptr_target() noexcept : combined_refcount_(0) {}
+  constexpr intrusive_ptr_target() noexcept : refcount_(0), weakcount_(0) {}
 
   // intrusive_ptr_target supports copy and move: but refcount and weakcount
   // don't participate (since they are intrinsic properties of the memory
@@ -254,17 +177,54 @@ class C10_API intrusive_ptr_target {
    * destructed), this function WILL NOT be called.
    */
   virtual void release_resources() {}
+};
 
-  uint32_t refcount(std::memory_order order = std::memory_order_relaxed) const {
-    return detail::refcount(combined_refcount_.load(order));
-  }
-
-  uint32_t weakcount(
-      std::memory_order order = std::memory_order_relaxed) const {
-    return detail::weakcount(combined_refcount_.load(order));
+namespace detail {
+template <class TTarget>
+struct intrusive_target_default_null_type final {
+  static constexpr TTarget* singleton() noexcept {
+    return nullptr;
   }
 };
 
+template <class TTarget, class ToNullType, class FromNullType>
+TTarget* assign_ptr_(TTarget* rhs) {
+  if (FromNullType::singleton() == rhs) {
+    return ToNullType::singleton();
+  } else {
+    return rhs;
+  }
+}
+
+// The only requirement for refcount increment is that it happens-before
+// decrement, so no additional memory ordering is needed.
+inline uint32_t atomic_refcount_increment(std::atomic<uint32_t>& refcount) {
+  return refcount.fetch_add(1, std::memory_order_relaxed) + 1;
+}
+
+inline uint32_t atomic_weakcount_increment(std::atomic<uint32_t>& weakcount) {
+  return weakcount.fetch_add(1, std::memory_order_relaxed) + 1;
+}
+
+// The requirement is that all modifications to the managed object happen-before
+// invocation of the managed object destructor, and that allocation of the
+// managed object storage happens-before deallocation of the storage.
+//
+// To get this ordering, all non-final decrements must synchronize-with the
+// final decrement. So all non-final decrements have to store-release while the
+// final decrement has to load-acquire, either directly or with the help of
+// fences. But it's easiest just to have all decrements be acq-rel. And it turns
+// out, on modern architectures and chips, it's also fastest.
+inline uint32_t atomic_refcount_decrement(std::atomic<uint32_t>& refcount) {
+  return refcount.fetch_sub(1, std::memory_order_acq_rel) - 1;
+}
+
+inline uint32_t atomic_weakcount_decrement(std::atomic<uint32_t>& weakcount) {
+  return weakcount.fetch_sub(1, std::memory_order_acq_rel) - 1;
+}
+
+} // namespace detail
+
 template <class TTarget, class NullType>
 class weak_intrusive_ptr;
 
@@ -315,7 +275,7 @@ class intrusive_ptr final {
   void retain_() {
     if (target_ != NullType::singleton()) {
       uint32_t new_refcount =
-          detail::atomic_refcount_increment(target_->combined_refcount_);
+          detail::atomic_refcount_increment(target_->refcount_);
       TORCH_INTERNAL_ASSERT_DEBUG_ONLY(
           new_refcount != 1,
           "intrusive_ptr: Cannot increase refcount after it reached zero.");
@@ -324,25 +284,41 @@ class intrusive_ptr final {
 
   void reset_() noexcept {
     if (target_ != NullType::singleton()) {
-      if (target_->combined_refcount_.load(std::memory_order_acquire) ==
-          detail::kUniqueRef) {
-        // Both counts are 1, so there are no weak references and
-        // we are releasing the last strong reference. No other
-        // threads can observe the effects of this target_ deletion
-        // call (e.g. calling use_count()) without a data race.
-        target_->combined_refcount_.store(0, std::memory_order_relaxed);
-        delete target_;
-        return;
+#if defined(__linux__) && (defined(__aarch64__) || defined(__x86_64__))
+      if constexpr (
+          std::atomic<uint64_t>::is_always_lock_free &&
+          std::atomic<uint32_t>::is_always_lock_free &&
+          sizeof(std::atomic<uint64_t>) == 8 &&
+          sizeof(std::atomic<uint32_t>) == 4) {
+        auto both_counts_ =
+            reinterpret_cast<std::atomic<uint64_t>*>(&target_->refcount_);
+        TORCH_INTERNAL_ASSERT_DEBUG_ONLY(
+            (reinterpret_cast<std::uintptr_t>(both_counts_) %
+             sizeof(std::atomic<uint64_t>)) == 0 &&
+            (reinterpret_cast<std::uintptr_t>(&target_->weakcount_) -
+             reinterpret_cast<std::uintptr_t>(both_counts_)) ==
+                sizeof(std::atomic<uint32_t>));
+        // 0x100000001ULL is a 64-bit number combination of both the refcount_
+        // and weakcount_ being 1.
+        constexpr uint64_t unique_ref_ = 0x100000001ULL;
+        if (both_counts_->load(std::memory_order_acquire) == unique_ref_) {
+          // Both counts are 1, so there are no weak references and
+          // we are releasing the last strong reference. No other
+          // threads can observe the effects of this target_ deletion
+          // call (e.g. calling use_count()) without a data race.
+          target_->refcount_.store(0, std::memory_order_relaxed);
+          delete target_;
+          return;
+        }
       }
+#endif
 
-      auto combined_refcount = detail::atomic_combined_refcount_decrement(
-          target_->combined_refcount_, detail::kReferenceCountOne);
-      if (detail::refcount(combined_refcount) == 0) {
-        bool should_delete =
-            (combined_refcount == detail::kWeakReferenceCountOne);
+      if (detail::atomic_refcount_decrement(target_->refcount_) == 0) {
         // See comment above about weakcount. As long as refcount>0,
         // weakcount is one larger than the actual number of weak references.
         // So we need to decrement it here.
+        bool should_delete =
+            target_->weakcount_.load(std::memory_order_acquire) == 1;
         if (!should_delete) {
           // justification for const_cast: release_resources is basically a
           // destructor and a destructor always mutates the object, even for
@@ -350,8 +326,8 @@ class intrusive_ptr final {
           // NOLINTNEXTLINE(cppcoreguidelines-pro-type-const-cast)
           const_cast<std::remove_const_t<TTarget>*>(target_)
               ->release_resources();
-          should_delete = detail::atomic_weakcount_decrement(
-                              target_->combined_refcount_) == 0;
+          should_delete =
+              detail::atomic_weakcount_decrement(target_->weakcount_) == 0;
         }
         if (should_delete) {
           delete target_;
@@ -378,12 +354,12 @@ class intrusive_ptr final {
       // `mov`, whereas an atomic increment does a lock-prefixed `add`, which is
       // much more expensive: https://godbolt.org/z/eKPzj8.)
       TORCH_INTERNAL_ASSERT_DEBUG_ONLY(
-          target_->combined_refcount_.load(std::memory_order_relaxed) == 0,
+          target_->refcount_ == 0 && target_->weakcount_ == 0,
           "intrusive_ptr: Newly-created target had non-zero refcounts. Does its "
           "constructor do something strange like incref or create an "
           "intrusive_ptr from `this`?");
-      target_->combined_refcount_.store(
-          detail::kUniqueRef, std::memory_order_relaxed);
+      target_->refcount_.store(1, std::memory_order_relaxed);
+      target_->weakcount_.store(1, std::memory_order_relaxed);
     }
   }
 
@@ -506,14 +482,14 @@ class intrusive_ptr final {
     if (target_ == NullType::singleton()) {
       return 0;
     }
-    return target_->refcount(std::memory_order_relaxed);
+    return target_->refcount_.load(std::memory_order_relaxed);
   }
 
   uint32_t weak_use_count() const noexcept {
     if (target_ == NullType::singleton()) {
       return 0;
     }
-    return target_->weakcount(std::memory_order_relaxed);
+    return target_->weakcount_.load(std::memory_order_relaxed);
   }
 
   bool unique() const noexcept {
@@ -542,8 +518,8 @@ class intrusive_ptr final {
    */
   static intrusive_ptr reclaim(TTarget* owning_ptr) {
     TORCH_INTERNAL_ASSERT_DEBUG_ONLY(
-        owning_ptr == NullType::singleton() || owning_ptr->refcount() == 0 ||
-            owning_ptr->weakcount(),
+        owning_ptr == NullType::singleton() ||
+            owning_ptr->refcount_.load() == 0 || owning_ptr->weakcount_.load(),
         "TTarget violates the invariant that refcount > 0  =>  weakcount > 0");
     return intrusive_ptr(owning_ptr, raw::DontIncreaseRefcount{});
   }
@@ -614,11 +590,11 @@ class intrusive_ptr final {
 #ifdef NDEBUG
     expected_decrefs = 0;
 #endif
-    result.target_->combined_refcount_.store(
-        detail::refcount(
-            detail::kImpracticallyHugeReferenceCount + expected_decrefs) |
-            detail::kImpracticallyHugeWeakReferenceCount,
+    result.target_->refcount_.store(
+        detail::kImpracticallyHugeReferenceCount + expected_decrefs,
         std::memory_order_relaxed);
+    result.target_->weakcount_.store(
+        detail::kImpracticallyHugeReferenceCount, std::memory_order_relaxed);
     return result;
   }
 
@@ -635,7 +611,7 @@ class intrusive_ptr final {
   static intrusive_ptr unsafe_reclaim_from_nonowning(TTarget* raw_ptr) {
     // See Note [Stack allocated intrusive_ptr_target safety]
     TORCH_INTERNAL_ASSERT_DEBUG_ONLY(
-        raw_ptr == NullType::singleton() || raw_ptr->refcount() > 0,
+        raw_ptr == NullType::singleton() || raw_ptr->refcount_.load() > 0,
         "intrusive_ptr: Can only reclaim pointers that are owned by someone");
     auto ptr = reclaim(raw_ptr); // doesn't increase refcount
     ptr.retain_();
@@ -769,7 +745,7 @@ class weak_intrusive_ptr final {
   void retain_() {
     if (target_ != NullType::singleton()) {
       uint32_t new_weakcount =
-          detail::atomic_weakcount_increment(target_->combined_refcount_);
+          detail::atomic_weakcount_increment(target_->weakcount_);
       TORCH_INTERNAL_ASSERT_DEBUG_ONLY(
           new_weakcount != 1,
           "weak_intrusive_ptr: Cannot increase weakcount after it reached zero.");
@@ -778,7 +754,7 @@ class weak_intrusive_ptr final {
 
   void reset_() noexcept {
     if (target_ != NullType::singleton() &&
-        detail::atomic_weakcount_decrement(target_->combined_refcount_) == 0) {
+        detail::atomic_weakcount_decrement(target_->weakcount_) == 0) {
       // NOLINTNEXTLINE(clang-analyzer-cplusplus.NewDelete)
       delete target_;
     }
@@ -911,7 +887,7 @@ class weak_intrusive_ptr final {
     if (target_ == NullType::singleton()) {
       return 0;
     }
-    return target_->refcount(
+    return target_->refcount_.load(
         std::memory_order_relaxed); // refcount, not weakcount!
   }
 
@@ -919,7 +895,7 @@ class weak_intrusive_ptr final {
     if (target_ == NullType::singleton()) {
       return 0;
     }
-    return target_->weakcount(std::memory_order_relaxed);
+    return target_->weakcount_.load(std::memory_order_relaxed);
   }
 
   bool expired() const noexcept {
@@ -930,17 +906,16 @@ class weak_intrusive_ptr final {
     if (target_ == NullType::singleton()) {
       return intrusive_ptr<TTarget, NullType>();
     } else {
-      auto combined_refcount =
-          target_->combined_refcount_.load(std::memory_order_relaxed);
+      auto refcount = target_->refcount_.load(std::memory_order_relaxed);
       do {
-        if (detail::refcount(combined_refcount) == 0) {
+        if (refcount == 0) {
           // Object already destructed, no strong references left anymore.
           // Return nullptr.
           return intrusive_ptr<TTarget, NullType>();
         }
-      } while (!target_->combined_refcount_.compare_exchange_weak(
-          combined_refcount,
-          combined_refcount + detail::kReferenceCountOne,
+      } while (!target_->refcount_.compare_exchange_weak(
+          refcount,
+          refcount + 1,
           std::memory_order_acquire,
           std::memory_order_relaxed));
 
@@ -977,9 +952,9 @@ class weak_intrusive_ptr final {
     // if refcount == 0, weakcount only must be >0.
     TORCH_INTERNAL_ASSERT_DEBUG_ONLY(
         owning_weak_ptr == NullType::singleton() ||
-            owning_weak_ptr->weakcount() > 1 ||
-            (owning_weak_ptr->refcount() == 0 &&
-             owning_weak_ptr->weakcount() > 0),
+            owning_weak_ptr->weakcount_.load() > 1 ||
+            (owning_weak_ptr->refcount_.load() == 0 &&
+             owning_weak_ptr->weakcount_.load() > 0),
         "weak_intrusive_ptr: Can only weak_intrusive_ptr::reclaim() owning pointers that were created using weak_intrusive_ptr::release().");
     return weak_intrusive_ptr(owning_weak_ptr);
   }
@@ -1058,7 +1033,7 @@ namespace intrusive_ptr {
 // NullType::singleton to this function
 inline void incref(intrusive_ptr_target* self) {
   if (self) {
-    detail::atomic_refcount_increment(self->combined_refcount_);
+    detail::atomic_refcount_increment(self->refcount_);
   }
 }
 
@@ -1092,7 +1067,7 @@ inline uint32_t use_count(intrusive_ptr_target* self) {
 namespace weak_intrusive_ptr {
 
 inline void incref(weak_intrusive_ptr_target* self) {
-  detail::atomic_weakcount_increment(self->combined_refcount_);
+  detail::atomic_weakcount_increment(self->weakcount_);
 }
 
 inline void decref(weak_intrusive_ptr_target* self) {

caffe2/CMakeLists.txt

@@ -316,7 +316,6 @@ set(GENERATED_CXX_PYTHON
   "${TORCH_SRC_DIR}/csrc/autograd/generated/python_special_functions.cpp"
   "${TORCH_SRC_DIR}/csrc/autograd/generated/python_return_types.cpp"
   "${TORCH_SRC_DIR}/csrc/autograd/generated/python_enum_tag.cpp"
-  "${TORCH_SRC_DIR}/csrc/functionalization/generated/ViewMetaClassesPythonBinding.cpp"
   )
 
 set(GENERATED_H_PYTHON
@@ -380,9 +379,6 @@ add_custom_command(
     "${TORCH_ROOT}/aten/src/ATen/templates/LazyIr.h"
     "${TORCH_ROOT}/aten/src/ATen/templates/LazyNonNativeIr.h"
     "${TORCH_ROOT}/aten/src/ATen/templates/RegisterDispatchKey.cpp"
-    "${TORCH_ROOT}/aten/src/ATen/templates/ViewMetaClasses.h"
-    "${TORCH_ROOT}/aten/src/ATen/templates/ViewMetaClasses.cpp"
-    "${TORCH_ROOT}/aten/src/ATen/templates/ViewMetaClassesPythonBinding.cpp"
     ${autograd_python}
     ${autograd_yaml}
     ${autograd_templates}
@@ -544,11 +540,9 @@ if(NOT INTERN_BUILD_MOBILE AND NOT BUILD_LITE_INTERPRETER)
     ${TORCH_SRC_DIR}/csrc/utils/byte_order.cpp
   )
 
-  if(USE_DISTRIBUTED)
-    append_filelist("libtorch_distributed_base_sources" TORCH_SRCS)
-    if(NOT WIN32)
-      append_filelist("libtorch_distributed_extra_sources" TORCH_SRCS)
-    endif()
+  append_filelist("libtorch_distributed_base_sources" TORCH_SRCS)
+  if(NOT WIN32)
+    append_filelist("libtorch_distributed_extra_sources" TORCH_SRCS)
   endif()
 endif()
 
@@ -579,32 +573,30 @@ if(USE_CUDA)
     list(APPEND Caffe2_GPU_SRCS
       ${TORCH_SRC_DIR}/csrc/cuda/nccl.cpp)
   endif()
-  if(USE_DISTRIBUTED)
-    append_filelist("libtorch_cuda_distributed_base_sources" Caffe2_GPU_SRCS)
-    if(NOT WIN32)
-      append_filelist("libtorch_cuda_distributed_extra_sources" Caffe2_GPU_SRCS)
-      set_source_files_properties(
-        ${TORCH_SRC_DIR}/csrc/distributed/c10d/ProcessGroupNCCL.cpp
-        ${TORCH_SRC_DIR}/csrc/distributed/c10d/cuda/utils.cpp
-        ${TORCH_SRC_DIR}/csrc/distributed/c10d/intra_node_comm.cpp
-        ${TORCH_SRC_DIR}/csrc/distributed/c10d/symm_mem/CudaDMAConnectivity.cpp
-        ${TORCH_SRC_DIR}/csrc/distributed/c10d/symm_mem/CUDASymmetricMemory.cu
-        ${TORCH_SRC_DIR}/csrc/distributed/c10d/symm_mem/CUDASymmetricMemoryOps.cu
-        ${TORCH_SRC_DIR}/csrc/distributed/c10d/symm_mem/CUDASymmetricMemoryUtils.cpp
-        ${TORCH_SRC_DIR}/csrc/distributed/c10d/symm_mem/NCCLSymmetricMemory.cu
-        ${TORCH_SRC_DIR}/csrc/distributed/c10d/symm_mem/cuda_mem_pool.cpp
-        PROPERTIES COMPILE_FLAGS "-DPYTORCH_C10_DRIVER_API_SUPPORTED=1"
-      )
-    endif()
+  append_filelist("libtorch_cuda_distributed_base_sources" Caffe2_GPU_SRCS)
+  if(NOT WIN32)
+    append_filelist("libtorch_cuda_distributed_extra_sources" Caffe2_GPU_SRCS)
+    set_source_files_properties(
+      ${TORCH_SRC_DIR}/csrc/distributed/c10d/ProcessGroupNCCL.cpp
+      ${TORCH_SRC_DIR}/csrc/distributed/c10d/cuda/utils.cpp
+      ${TORCH_SRC_DIR}/csrc/distributed/c10d/intra_node_comm.cpp
+      ${TORCH_SRC_DIR}/csrc/distributed/c10d/symm_mem/CudaDMAConnectivity.cpp
+      ${TORCH_SRC_DIR}/csrc/distributed/c10d/symm_mem/CUDASymmetricMemory.cu
+      ${TORCH_SRC_DIR}/csrc/distributed/c10d/symm_mem/CUDASymmetricMemoryOps.cu
+      ${TORCH_SRC_DIR}/csrc/distributed/c10d/symm_mem/CUDASymmetricMemoryUtils.cpp
+      ${TORCH_SRC_DIR}/csrc/distributed/c10d/symm_mem/NCCLSymmetricMemory.cu
+      ${TORCH_SRC_DIR}/csrc/distributed/c10d/symm_mem/cuda_mem_pool.cpp
+      PROPERTIES COMPILE_FLAGS "-DPYTORCH_C10_DRIVER_API_SUPPORTED=1"
+    )
+  endif()
 
-    set(ASYNC_MM_FILE "${TORCH_SRC_DIR}/csrc/distributed/c10d/cuda/AsyncMM.cu")
-    # Disable the warning to make cutlass warp-specialized cooperative kernel build for gcc-9
-    if(CMAKE_COMPILER_IS_GNUCXX)
-      set_source_files_properties(${ASYNC_MM_FILE} PROPERTIES COMPILE_FLAGS "-Wno-unused-but-set-variable")
-    endif()
-    if(CMAKE_CUDA_COMPILER_VERSION VERSION_GREATER_EQUAL 12.0 AND CUDA_NVCC_FLAGS MATCHES ".*compute_90.*")
-      set_source_files_properties(${ASYNC_MM_FILE} PROPERTIES COMPILE_FLAGS "-gencode arch=compute_90a,code=sm_90a")
-    endif()
+  set(ASYNC_MM_FILE "${TORCH_SRC_DIR}/csrc/distributed/c10d/cuda/AsyncMM.cu")
+  # Disable the warning to make cutlass warp-specialized cooperative kernel build for gcc-9
+  if(CMAKE_COMPILER_IS_GNUCXX)
+    set_source_files_properties(${ASYNC_MM_FILE} PROPERTIES COMPILE_FLAGS "-Wno-unused-but-set-variable")
+  endif()
+  if(CMAKE_CUDA_COMPILER_VERSION VERSION_GREATER_EQUAL 12.0 AND CUDA_NVCC_FLAGS MATCHES ".*compute_90.*")
+    set_source_files_properties(${ASYNC_MM_FILE} PROPERTIES COMPILE_FLAGS "-gencode arch=compute_90a,code=sm_90a")
   endif()
   set_source_files_properties(
     ${TORCH_ROOT}/aten/src/ATen/cuda/detail/LazyNVRTC.cpp
@@ -637,11 +629,9 @@ if(USE_ROCM)
     list(APPEND Caffe2_HIP_SRCS
       ${TORCH_SRC_DIR}/csrc/cuda/nccl.cpp)
   endif()
-  if(USE_DISTRIBUTED)
-    append_filelist("libtorch_cuda_distributed_base_sources" Caffe2_HIP_SRCS)
-    if(NOT WIN32)
-      append_filelist("libtorch_cuda_distributed_extra_sources" Caffe2_HIP_SRCS)
-    endif()
+  append_filelist("libtorch_cuda_distributed_base_sources" Caffe2_HIP_SRCS)
+  if(NOT WIN32)
+    append_filelist("libtorch_cuda_distributed_extra_sources" Caffe2_HIP_SRCS)
   endif()
   # caffe2_nvrtc's stubs to driver APIs are useful for HIP.
   # See NOTE [ ATen NVRTC Stub and HIP ]
@@ -1362,12 +1352,10 @@ if(BUILD_TEST)
     add_subdirectory(${TORCH_ROOT}/test/cpp/jit ${CMAKE_BINARY_DIR}/test_jit)
     add_subdirectory(${TORCH_ROOT}/test/cpp/nativert ${CMAKE_BINARY_DIR}/test_nativert)
     add_subdirectory(${TORCH_ROOT}/test/inductor ${CMAKE_BINARY_DIR}/test_inductor)
-    if(USE_DISTRIBUTED)
-      add_subdirectory(${TORCH_ROOT}/test/cpp/c10d ${CMAKE_BINARY_DIR}/test_cpp_c10d)
-      if(NOT WIN32)
-        add_subdirectory(${TORCH_ROOT}/test/cpp/dist_autograd ${CMAKE_BINARY_DIR}/dist_autograd)
-        add_subdirectory(${TORCH_ROOT}/test/cpp/rpc ${CMAKE_BINARY_DIR}/test_cpp_rpc)
-      endif()
+    add_subdirectory(${TORCH_ROOT}/test/cpp/c10d ${CMAKE_BINARY_DIR}/test_cpp_c10d)
+    if(NOT WIN32)
+      add_subdirectory(${TORCH_ROOT}/test/cpp/dist_autograd ${CMAKE_BINARY_DIR}/dist_autograd)
+      add_subdirectory(${TORCH_ROOT}/test/cpp/rpc ${CMAKE_BINARY_DIR}/test_cpp_rpc)
     endif()
     if(NOT NO_API)
       add_subdirectory(${TORCH_ROOT}/test/cpp/api ${CMAKE_BINARY_DIR}/test_api)
@@ -1472,46 +1460,40 @@ if(BUILD_LITE_INTERPRETER)
   endif()
 endif()
 
-
-# Pass USE_DISTRIBUTED to torch_cpu, as some codes in jit/pickler.cpp and
-# jit/unpickler.cpp need to be compiled only when USE_DISTRIBUTED is set
-if(USE_DISTRIBUTED)
-  target_compile_definitions(torch_cpu PUBLIC USE_DISTRIBUTED)
-  if(USE_GLOO AND USE_C10D_GLOO)
-    target_compile_definitions(torch_cpu PUBLIC USE_C10D_GLOO)
+if(USE_GLOO AND USE_C10D_GLOO)
+  target_compile_definitions(torch_cpu PUBLIC USE_C10D_GLOO)
+endif()
+if(USE_UCC AND USE_C10D_UCC)
+  target_compile_definitions(torch_cpu PUBLIC USE_C10D_UCC)
+  if(USE_CUDA)
+    target_compile_definitions(torch_cuda PUBLIC USE_C10D_UCC)
   endif()
-  if(USE_UCC AND USE_C10D_UCC)
-    target_compile_definitions(torch_cpu PUBLIC USE_C10D_UCC)
-    if(USE_CUDA)
-      target_compile_definitions(torch_cuda PUBLIC USE_C10D_UCC)
-    endif()
+endif()
+if(USE_NCCL AND USE_C10D_NCCL)
+  if(USE_ROCM)
+    target_compile_definitions(torch_hip PUBLIC USE_C10D_NCCL)
+  else()
+    target_compile_definitions(torch_cuda PUBLIC USE_C10D_NCCL)
   endif()
-  if(USE_NCCL AND USE_C10D_NCCL)
-    if(USE_ROCM)
-      target_compile_definitions(torch_hip PUBLIC USE_C10D_NCCL)
-    else()
-      target_compile_definitions(torch_cuda PUBLIC USE_C10D_NCCL)
-    endif()
-  endif()
-  if(USE_MPI AND USE_C10D_MPI)
-    if(CMAKE_CXX_COMPILER_ID MATCHES "Clang" OR CMAKE_CXX_COMPILER_ID STREQUAL "GNU")
-      set_source_files_properties(
-        "${TORCH_SRC_DIR}/csrc/distributed/c10d/ProcessGroupMPI.cpp"
-        PROPERTIES COMPILE_FLAGS -Wno-deprecated-declarations)
-    endif()
-    target_compile_definitions(torch_cpu PUBLIC USE_C10D_MPI)
-  endif()
-  # Pass USE_RPC in order to reduce use of
-  # #if defined(USE_DISTRIBUTED) && !defined(_WIN32)
-  # need to be removed when RPC is supported
-  if(NOT WIN32)
-    target_compile_definitions(torch_cpu PUBLIC USE_RPC)
-  endif()
-  # Pass USE_TENSORPIPE to torch_cpu as some parts of rpc/utils.cpp
-  # can only be compiled with USE_TENSORPIPE is set.
-  if(USE_TENSORPIPE)
-    target_compile_definitions(torch_cpu PUBLIC USE_TENSORPIPE)
+endif()
+if(USE_MPI AND USE_C10D_MPI)
+  if(CMAKE_CXX_COMPILER_ID MATCHES "Clang" OR CMAKE_CXX_COMPILER_ID STREQUAL "GNU")
+    set_source_files_properties(
+      "${TORCH_SRC_DIR}/csrc/distributed/c10d/ProcessGroupMPI.cpp"
+      PROPERTIES COMPILE_FLAGS -Wno-deprecated-declarations)
   endif()
+  target_compile_definitions(torch_cpu PUBLIC USE_C10D_MPI)
+endif()
+# Pass USE_RPC in order to reduce use of
+# #if defined(USE_DISTRIBUTED) && !defined(_WIN32)
+# need to be removed when RPC is supported
+if(NOT WIN32)
+  target_compile_definitions(torch_cpu PUBLIC USE_RPC)
+endif()
+# Pass USE_TENSORPIPE to torch_cpu as some parts of rpc/utils.cpp
+# can only be compiled with USE_TENSORPIPE is set.
+if(USE_TENSORPIPE)
+  target_compile_definitions(torch_cpu PUBLIC USE_TENSORPIPE)
 endif()
 
 if(NOT INTERN_BUILD_MOBILE)

cmake/Dependencies.cmake

@@ -821,9 +821,9 @@ if(NOT Python_Interpreter_FOUND)
   message(FATAL_ERROR "Python3 could not be found.")
 endif()
 
-if(${Python_VERSION} VERSION_LESS 3.10)
+if(${Python_VERSION} VERSION_LESS 3.9)
   message(FATAL_ERROR
-    "Found Python libraries version ${Python_VERSION}. Python < 3.10 is no longer supported by PyTorch.")
+    "Found Python libraries version ${Python_VERSION}. Python < 3.9 is no longer supported by PyTorch.")
 endif()
 
 # ---[ Python + Numpy
@@ -1134,7 +1134,7 @@ if(USE_CUDA AND CUDA_VERSION VERSION_LESS 13.0)
   include_directories(SYSTEM ${CUB_INCLUDE_DIRS})
 endif()
 
-if(USE_DISTRIBUTED AND USE_TENSORPIPE)
+if(USE_TENSORPIPE)
   if(MSVC)
     message(WARNING "Tensorpipe cannot be used on Windows.")
   else()

cmake/External/aotriton.cmake

@@ -46,10 +46,9 @@ if(NOT __AOTRITON_INCLUDED)
   set(__AOTRITON_BASE_URL "https://github.com/ROCm/aotriton/releases/download/")  # @lint-ignore
   set(__AOTRITON_Z "gz")
   # Set the default __AOTRITON_LIB path
-  if(NOT WIN32)
-    set(__AOTRITON_LIB "lib/libaotriton_v2.so")
-  else()
-    set(__AOTRITON_LIB "lib/aotriton_v2.lib")
+  set(__AOTRITON_LIB "${__AOTRITON_INSTALL_DIR}/lib/libaotriton_v2.so")
+  if(WIN32)
+    set(__AOTRITON_LIB "${__AOTRITON_INSTALL_DIR}/lib/aotriton_v2.lib")
   endif()
 
   function(aotriton_build_windows_dependencies dlfcn-win32_external xz_external dlfcn-win32_DIR liblzma_DIR)
@@ -144,7 +143,8 @@ if(NOT __AOTRITON_INCLUDED)
       -DHIP_PLATFORM=amd
       $<$<BOOL:${WIN32}>:-Ddlfcn-win32_DIR=${dlfcn-win32_DIR}>
       $<$<BOOL:${WIN32}>:-Dliblzma_DIR=${liblzma_DIR}>
-      BUILD_BYPRODUCTS "${__AOTRITON_INSTALL_DIR}/${__AOTRITON_LIB}"
+      BUILD_BYPRODUCTS
+        "${__AOTRITON_LIB}"
       USES_TERMINAL_DOWNLOAD TRUE
       USES_TERMINAL_CONFIGURE TRUE
       USES_TERMINAL_BUILD TRUE
@@ -177,7 +177,7 @@ if(NOT __AOTRITON_INCLUDED)
       INSTALL_COMMAND ${CMAKE_COMMAND} -E copy_directory
       "${CMAKE_CURRENT_BINARY_DIR}/aotriton_runtime"
       "${__AOTRITON_INSTALL_DIR}"
-      BUILD_BYPRODUCTS "${__AOTRITON_INSTALL_DIR}/${__AOTRITON_LIB}"
+      BUILD_BYPRODUCTS "${__AOTRITON_LIB}"
     )
     message(STATUS "Using AOTriton Runtime from pre-compiled binary ${__AOTRITON_URL}.\
     Set env variables AOTRITON_INSTALL_FROM_SOURCE=1 to build from source.")
@@ -267,7 +267,7 @@ if(NOT __AOTRITON_INCLUDED)
       endforeach()
     endforeach()
   endif()
-  target_link_libraries(__caffe2_aotriton INTERFACE "${__AOTRITON_INSTALL_DIR}/${__AOTRITON_LIB}")
+  target_link_libraries(__caffe2_aotriton INTERFACE ${__AOTRITON_LIB})
   target_include_directories(__caffe2_aotriton INTERFACE ${__AOTRITON_INSTALL_DIR}/include)
   set(AOTRITON_FOUND TRUE)
 endif() # __AOTRITON_INCLUDED

cmake/Summary.cmake

@@ -193,13 +193,11 @@ function(caffe2_print_configuration_summary)
   message(STATUS "  USE_PYTORCH_QNNPACK   : ${USE_PYTORCH_QNNPACK}")
   message(STATUS "  USE_XNNPACK           : ${USE_XNNPACK}")
   message(STATUS "  USE_DISTRIBUTED       : ${USE_DISTRIBUTED}")
-  if(${USE_DISTRIBUTED})
-    message(STATUS "    USE_MPI               : ${USE_MPI}")
-    message(STATUS "    USE_GLOO              : ${USE_GLOO}")
-    message(STATUS "    USE_GLOO_WITH_OPENSSL : ${USE_GLOO_WITH_OPENSSL}")
-    message(STATUS "    USE_GLOO_IBVERBS      : ${USE_GLOO_IBVERBS}")
-    message(STATUS "    USE_TENSORPIPE        : ${USE_TENSORPIPE}")
-  endif()
+  message(STATUS "    USE_MPI               : ${USE_MPI}")
+  message(STATUS "    USE_GLOO              : ${USE_GLOO}")
+  message(STATUS "    USE_GLOO_WITH_OPENSSL : ${USE_GLOO_WITH_OPENSSL}")
+  message(STATUS "    USE_GLOO_IBVERBS      : ${USE_GLOO_IBVERBS}")
+  message(STATUS "    USE_TENSORPIPE        : ${USE_TENSORPIPE}")
   if(NOT "${SELECTED_OP_LIST}" STREQUAL "")
     message(STATUS "  SELECTED_OP_LIST    : ${SELECTED_OP_LIST}")
   endif()