Update (base update)

[ghstack-poisoned]

.ci/manywheel/build_cuda.sh

@@ -187,22 +187,19 @@ if [[ $CUDA_VERSION == 12* || $CUDA_VERSION == 13* ]]; then
             export USE_CUFILE=0
         else
             DEPS_LIST+=(
+                "/usr/local/cuda/lib64/libnvToolsExt.so.1"
                 "/usr/local/cuda/lib64/libcublas.so.12"
                 "/usr/local/cuda/lib64/libcublasLt.so.12"
                 "/usr/local/cuda/lib64/libcudart.so.12"
                 "/usr/local/cuda/lib64/libnvrtc.so.12"
                 "/usr/local/cuda/extras/CUPTI/lib64/libcupti.so.12")
             DEPS_SONAME+=(
+                "libnvToolsExt.so.1"
                 "libcublas.so.12"
                 "libcublasLt.so.12"
                 "libcudart.so.12"
                 "libnvrtc.so.12"
                 "libcupti.so.12")
-
-            if [[ $CUDA_VERSION != 12.9* ]]; then
-                DEPS_LIST+=("/usr/local/cuda/lib64/libnvToolsExt.so.1")
-                DEPS_SONAME+=("libnvToolsExt.so.1")
-            fi
         fi
     else
         echo "Using nvidia libs from pypi."

.github/ISSUE_TEMPLATE/ci-sev.md

@@ -8,7 +8,6 @@ assignees: ''
 ---
 
 > NOTE: Remember to label this issue with "`ci: sev`"
->       If you want autorevert to be disabled, keep the ci: disable-autorevert label
 
  <!-- Add the `merge blocking` label to this PR to prevent PRs from being merged while this issue is open -->
 

.github/ISSUE_TEMPLATE/disable-autorevert.md

@@ -1,7 +1,7 @@
 ---
-name: "D❌​\U0001F519​ ISABLE AUTOREVERT"
+name: DISABLE AUTOREVERT
 about: Disables autorevert when open
-title: "[DISABLE AUTOREVERT]"
+title: "❌​\U0001F519​ [DISABLE AUTOREVERT]"
 labels: 'ci: disable-autorevert'
 assignees: ''
 

.github/actions/build-external-packages/action.yml

@@ -65,7 +65,7 @@ runs:
           cd .ci/lumen_cli
           python3 -m pip install -e .
         )
-        MAX_JOBS="$(nproc --ignore=10)"
+        MAX_JOBS="$(nproc --ignore=6)"
         export MAX_JOBS
 
         # Split the comma-separated list and build each target

.github/ci_commit_pins/audio.txt

@@ -1 +1 @@
-1b013f5b5a87a1882eb143c26d79d091150d6a37
+8ad2aa5d354d1bf432339113860185d5a5d1abbd

.github/ci_commit_pins/vision.txt

@@ -1 +1 @@
-faffd5cf673615583da6517275e361cb3dbc77e6
+f5c6c2ec6490455e86f67b2a25c10390d60a27f7

.github/pytorch-probot.yml

@@ -3,7 +3,6 @@ ciflow_tracking_issue: 64124
 ciflow_push_tags:
 - ciflow/b200
 - ciflow/b200-symm-mem
-- ciflow/b200-distributed
 - ciflow/binaries
 - ciflow/binaries_libtorch
 - ciflow/binaries_wheel
@@ -16,8 +15,7 @@ ciflow_push_tags:
 - ciflow/inductor-micro-benchmark
 - ciflow/inductor-micro-benchmark-cpu-x86
 - ciflow/inductor-perf-compare
-- ciflow/inductor-perf-test-nightly-rocm-mi300
-- ciflow/inductor-perf-test-nightly-rocm-mi355
+- ciflow/inductor-perf-test-nightly-rocm
 - ciflow/inductor-perf-test-nightly-x86-zen
 - ciflow/inductor-periodic
 - ciflow/inductor-rocm

.github/workflows/b200-distributed.yml

@@ -1,62 +0,0 @@
-name: CI for distributed tests on B200
-
-on:
-  pull_request:
-    paths:
-      - .github/workflows/b200-distributed.yml
-  workflow_dispatch:
-  push:
-    tags:
-      - ciflow/b200-distributed/*
-  schedule:
-    - cron: 46 8 * * *  # about 1:46am PDT
-
-concurrency:
-  group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.sha }}-${{ github.event_name == 'workflow_dispatch' }}-${{ github.event_name == 'schedule' }}
-  cancel-in-progress: true
-
-permissions:
-  id-token: write
-  contents: read
-
-jobs:
-
-  get-label-type:
-    if: github.repository_owner == 'pytorch'
-    name: get-label-type
-    uses: pytorch/pytorch/.github/workflows/_runner-determinator.yml@main
-    with:
-      triggering_actor: ${{ github.triggering_actor }}
-      issue_owner: ${{ github.event.pull_request.user.login || github.event.issue.user.login }}
-      curr_branch: ${{ github.head_ref || github.ref_name }}
-      curr_ref_type: ${{ github.ref_type }}
-
-  linux-jammy-cuda12_8-py3_10-gcc11-build-distributed-b200:
-    name: linux-jammy-cuda12.8-py3.10-gcc11-build-distributed-b200
-    uses: ./.github/workflows/_linux-build.yml
-    needs: get-label-type
-    with:
-      runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
-      runner: linux.12xlarge.memory
-      build-environment: linux-jammy-cuda12.8-py3.10-gcc11-distributed-b200
-      docker-image-name: ci-image:pytorch-linux-jammy-cuda12.8-cudnn9-py3-gcc11
-      cuda-arch-list: '10.0'
-      test-matrix: |
-        { include: [
-          { config: "distributed", shard: 1, num_shards: 2, runner: "linux.dgx.b200.8" },
-          { config: "distributed", shard: 2, num_shards: 2, runner: "linux.dgx.b200.8" },
-        ]}
-    secrets: inherit
-
-  linux-jammy-cuda12_8-py3_10-gcc11-test-distributed-b200:
-    name: linux-jammy-cuda12.8-py3.10-gcc11-test-b200
-    uses: ./.github/workflows/_linux-test.yml
-    needs:
-      - linux-jammy-cuda12_8-py3_10-gcc11-build-distributed-b200
-    with:
-      timeout-minutes: 1200
-      build-environment: linux-jammy-cuda12.8-py3.10-gcc11-distributed-b200
-      docker-image: ${{ needs.linux-jammy-cuda12_8-py3_10-gcc11-build-distributed-b200.outputs.docker-image }}
-      test-matrix: ${{ needs.linux-jammy-cuda12_8-py3_10-gcc11-build-distributed-b200.outputs.test-matrix }}
-      aws-role-to-assume: arn:aws:iam::308535385114:role/gha_workflow_s3_and_ecr_read_only
-    secrets: inherit

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

@@ -27,8 +27,9 @@ jobs:
       fail-fast: false
       matrix:
         python-version: [ '3.12' ]
+        # TODO (huydhn): Add cu130 after https://github.com/vllm-project/vllm/issues/24464 is resolved
         platform: [ 'manylinux_2_28_x86_64', 'manylinux_2_28_aarch64' ]
-        device: [ 'cu128', 'cu129', 'cu130' ]
+        device: [ 'cu128', 'cu129' ]
         include:
           - platform: manylinux_2_28_x86_64
             device: cu128
@@ -38,10 +39,6 @@ jobs:
             device: cu129
             manylinux-image: 'pytorch/manylinux2_28-builder:cuda12.9'
             runner: linux.12xlarge.memory
-          - platform: manylinux_2_28_x86_64
-            device: cu130
-            manylinux-image: 'pytorch/manylinux2_28-builder:cuda13.0'
-            runner: linux.12xlarge.memory
           - platform: manylinux_2_28_aarch64
             device: cu128
             manylinux-image: 'pytorch/manylinuxaarch64-builder:cuda12.8'
@@ -50,11 +47,6 @@ jobs:
             device: cu129
             manylinux-image: 'pytorch/manylinuxaarch64-builder:cuda12.9'
             runner: linux.arm64.r7g.12xlarge.memory
-        exclude:
-          # TODO (huydhn): Add cu130 aarch64 once PyTorch is on 2.9+ and
-          # xformers is update to support 13.0
-          - platform: manylinux_2_28_aarch64
-            device: cu130
     name: "Build ${{ matrix.device }} vLLM wheel on ${{ matrix.platform }}"
     runs-on: ${{ matrix.runner }}
     timeout-minutes: 480
@@ -177,12 +169,7 @@ jobs:
       fail-fast: false
       matrix:
         platform: [ 'manylinux_2_28_x86_64', 'manylinux_2_28_aarch64' ]
-        device: [ 'cu128', 'cu129', 'cu130' ]
-        exclude:
-          # TODO (huydhn): Add cu130 aarch64 once PyTorch is on 2.9+ and
-          # xformers is update to support 13.0
-          - platform: manylinux_2_28_aarch64
-            device: cu130
+        device: [ 'cu128', 'cu129' ]
     env:
       PLATFORM: ${{ matrix.platform }}
       BUILD_DEVICE: ${{ matrix.device }}

.github/workflows/inductor-perf-test-nightly-rocm-mi300.yml

@@ -1,132 +0,0 @@
-name: inductor-perf-nightly-rocm-mi300
-
-on:
-  push:
-    tags:
-      - ciflow/inductor-perf-test-nightly-rocm-mi300/*
-  schedule:
-    - cron: 15 0 * * *
-  # NB: GitHub has an upper limit of 10 inputs here, so before we can sort it
-  # out, let try to run torchao cudagraphs_low_precision as part of cudagraphs
-  workflow_dispatch:
-    inputs:
-      training:
-        description: Run training (on by default)?
-        required: false
-        type: boolean
-        default: true
-      inference:
-        description: Run inference (on by default)?
-        required: false
-        type: boolean
-        default: true
-      default:
-        description: Run inductor_default?
-        required: false
-        type: boolean
-        default: false
-      dynamic:
-        description: Run inductor_dynamic_shapes?
-        required: false
-        type: boolean
-        default: false
-      cppwrapper:
-        description: Run inductor_cpp_wrapper?
-        required: false
-        type: boolean
-        default: false
-      cudagraphs:
-        description: Run inductor_cudagraphs?
-        required: false
-        type: boolean
-        default: true
-      freezing_cudagraphs:
-        description: Run inductor_cudagraphs with freezing for inference?
-        required: false
-        type: boolean
-        default: false
-      aotinductor:
-        description: Run aot_inductor for inference?
-        required: false
-        type: boolean
-        default: false
-      maxautotune:
-        description: Run inductor_max_autotune?
-        required: false
-        type: boolean
-        default: false
-      benchmark_configs:
-        description: The list of configs used the benchmark
-        required: false
-        type: string
-        default: inductor_huggingface_perf_rocm_mi300,inductor_timm_perf_rocm_mi300,inductor_torchbench_perf_rocm_mi300
-
-concurrency:
-  group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.ref_name }}-${{ github.ref_type == 'branch' && github.sha }}-${{ github.event_name == 'workflow_dispatch' }}-${{ github.event_name == 'schedule' }}
-  cancel-in-progress: true
-
-permissions: read-all
-
-jobs:
-  get-label-type:
-    name: get-label-type
-    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
-
-  linux-jammy-rocm-py3_10-inductor-benchmark-build:
-    if: github.repository_owner == 'pytorch'
-    name: rocm-py3_10-inductor-benchmark-build
-    uses: ./.github/workflows/_linux-build.yml
-    with:
-      build-environment: linux-jammy-rocm-py3_10
-      docker-image-name: ci-image:pytorch-linux-jammy-rocm-n-py3-benchmarks
-      test-matrix: |
-        { include: [
-          { config: "inductor_huggingface_perf_rocm_mi300", shard: 1, num_shards: 5, runner: "linux.rocm.gpu.gfx942.1" },
-          { config: "inductor_huggingface_perf_rocm_mi300", shard: 2, num_shards: 5, runner: "linux.rocm.gpu.gfx942.1" },
-          { config: "inductor_huggingface_perf_rocm_mi300", shard: 3, num_shards: 5, runner: "linux.rocm.gpu.gfx942.1" },
-          { config: "inductor_huggingface_perf_rocm_mi300", shard: 4, num_shards: 5, runner: "linux.rocm.gpu.gfx942.1" },
-          { config: "inductor_huggingface_perf_rocm_mi300", shard: 5, num_shards: 5, runner: "linux.rocm.gpu.gfx942.1" },
-          { config: "inductor_timm_perf_rocm_mi300", shard: 1, num_shards: 7, runner: "linux.rocm.gpu.gfx942.1" },
-          { config: "inductor_timm_perf_rocm_mi300", shard: 2, num_shards: 7, runner: "linux.rocm.gpu.gfx942.1" },
-          { config: "inductor_timm_perf_rocm_mi300", shard: 3, num_shards: 7, runner: "linux.rocm.gpu.gfx942.1" },
-          { config: "inductor_timm_perf_rocm_mi300", shard: 4, num_shards: 7, runner: "linux.rocm.gpu.gfx942.1" },
-          { config: "inductor_timm_perf_rocm_mi300", shard: 5, num_shards: 7, runner: "linux.rocm.gpu.gfx942.1" },
-          { config: "inductor_timm_perf_rocm_mi300", shard: 6, num_shards: 7, runner: "linux.rocm.gpu.gfx942.1" },
-          { config: "inductor_timm_perf_rocm_mi300", shard: 7, num_shards: 7, runner: "linux.rocm.gpu.gfx942.1" },
-          { config: "inductor_torchbench_perf_rocm_mi300", shard: 1, num_shards: 9, runner: "linux.rocm.gpu.gfx942.1" },
-          { config: "inductor_torchbench_perf_rocm_mi300", shard: 2, num_shards: 9, runner: "linux.rocm.gpu.gfx942.1" },
-          { config: "inductor_torchbench_perf_rocm_mi300", shard: 3, num_shards: 9, runner: "linux.rocm.gpu.gfx942.1" },
-          { config: "inductor_torchbench_perf_rocm_mi300", shard: 4, num_shards: 9, runner: "linux.rocm.gpu.gfx942.1" },
-          { config: "inductor_torchbench_perf_rocm_mi300", shard: 5, num_shards: 9, runner: "linux.rocm.gpu.gfx942.1" },
-          { config: "inductor_torchbench_perf_rocm_mi300", shard: 6, num_shards: 9, runner: "linux.rocm.gpu.gfx942.1" },
-          { config: "inductor_torchbench_perf_rocm_mi300", shard: 7, num_shards: 9, runner: "linux.rocm.gpu.gfx942.1" },
-          { config: "inductor_torchbench_perf_rocm_mi300", shard: 8, num_shards: 9, runner: "linux.rocm.gpu.gfx942.1" },
-          { config: "inductor_torchbench_perf_rocm_mi300", shard: 9, num_shards: 9, runner: "linux.rocm.gpu.gfx942.1" },
-        ]}
-    secrets: inherit
-
-  linux-jammy-rocm-py3_10-inductor-benchmark-test:
-    permissions:
-      id-token: write
-      contents: read
-    name: rocm-py3_10-inductor-benchmark-test
-    uses: ./.github/workflows/_rocm-test.yml
-    needs: linux-jammy-rocm-py3_10-inductor-benchmark-build
-    with:
-      build-environment: linux-jammy-rocm-py3_10
-      dashboard-tag: training-true-inference-true-default-true-dynamic-true-cudagraphs-true-cppwrapper-true-aotinductor-true-freezing_cudagraphs-true-cudagraphs_low_precision-true
-      docker-image: ${{ needs.linux-jammy-rocm-py3_10-inductor-benchmark-build.outputs.docker-image }}
-      test-matrix: ${{ needs.linux-jammy-rocm-py3_10-inductor-benchmark-build.outputs.test-matrix }}
-      timeout-minutes: 720
-      # Disable monitor in perf tests for more investigation
-      disable-monitor: true
-      monitor-log-interval: 10
-      monitor-data-collect-interval: 2
-    secrets: inherit

.github/workflows/inductor-perf-test-nightly-rocm.yml

@@ -1,11 +1,11 @@
-name: inductor-perf-nightly-rocm-mi355
+name: inductor-perf-nightly-rocm
 
 on:
   push:
     tags:
-      - ciflow/inductor-perf-test-nightly-rocm-mi355/*
+      - ciflow/inductor-perf-test-nightly-rocm/*
   schedule:
-    - cron: 15 0 * * *
+    - cron: 0 7 * * 0,3
   # NB: GitHub has an upper limit of 10 inputs here, so before we can sort it
   # out, let try to run torchao cudagraphs_low_precision as part of cudagraphs
   workflow_dispatch:
@@ -59,7 +59,7 @@ on:
         description: The list of configs used the benchmark
         required: false
         type: string
-        default: inductor_huggingface_perf_rocm_mi355,inductor_timm_perf_rocm_mi355,inductor_torchbench_perf_rocm_mi355
+        default: inductor_huggingface_perf_rocm,inductor_timm_perf_rocm,inductor_torchbench_perf_rocm
 
 concurrency:
   group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.ref_name }}-${{ github.ref_type == 'branch' && github.sha }}-${{ github.event_name == 'workflow_dispatch' }}-${{ github.event_name == 'schedule' }}
@@ -88,27 +88,23 @@ jobs:
       docker-image-name: ci-image:pytorch-linux-jammy-rocm-n-py3-benchmarks
       test-matrix: |
         { include: [
-          { config: "inductor_huggingface_perf_rocm_mi355", shard: 1, num_shards: 5, runner: "linux.rocm.gpu.mi355.2" },
-          { config: "inductor_huggingface_perf_rocm_mi355", shard: 2, num_shards: 5, runner: "linux.rocm.gpu.mi355.2" },
-          { config: "inductor_huggingface_perf_rocm_mi355", shard: 3, num_shards: 5, runner: "linux.rocm.gpu.mi355.2" },
-          { config: "inductor_huggingface_perf_rocm_mi355", shard: 4, num_shards: 5, runner: "linux.rocm.gpu.mi355.2" },
-          { config: "inductor_huggingface_perf_rocm_mi355", shard: 5, num_shards: 5, runner: "linux.rocm.gpu.mi355.2" },
-          { config: "inductor_timm_perf_rocm_mi355", shard: 1, num_shards: 7, runner: "linux.rocm.gpu.mi355.2" },
-          { config: "inductor_timm_perf_rocm_mi355", shard: 2, num_shards: 7, runner: "linux.rocm.gpu.mi355.2" },
-          { config: "inductor_timm_perf_rocm_mi355", shard: 3, num_shards: 7, runner: "linux.rocm.gpu.mi355.2" },
-          { config: "inductor_timm_perf_rocm_mi355", shard: 4, num_shards: 7, runner: "linux.rocm.gpu.mi355.2" },
-          { config: "inductor_timm_perf_rocm_mi355", shard: 5, num_shards: 7, runner: "linux.rocm.gpu.mi355.2" },
-          { config: "inductor_timm_perf_rocm_mi355", shard: 6, num_shards: 7, runner: "linux.rocm.gpu.mi355.2" },
-          { config: "inductor_timm_perf_rocm_mi355", shard: 7, num_shards: 7, runner: "linux.rocm.gpu.mi355.2" },
-          { config: "inductor_torchbench_perf_rocm_mi355", shard: 1, num_shards: 9, runner: "linux.rocm.gpu.mi355.2" },
-          { config: "inductor_torchbench_perf_rocm_mi355", shard: 2, num_shards: 9, runner: "linux.rocm.gpu.mi355.2" },
-          { config: "inductor_torchbench_perf_rocm_mi355", shard: 3, num_shards: 9, runner: "linux.rocm.gpu.mi355.2" },
-          { config: "inductor_torchbench_perf_rocm_mi355", shard: 4, num_shards: 9, runner: "linux.rocm.gpu.mi355.2" },
-          { config: "inductor_torchbench_perf_rocm_mi355", shard: 5, num_shards: 9, runner: "linux.rocm.gpu.mi355.2" },
-          { config: "inductor_torchbench_perf_rocm_mi355", shard: 6, num_shards: 9, runner: "linux.rocm.gpu.mi355.2" },
-          { config: "inductor_torchbench_perf_rocm_mi355", shard: 7, num_shards: 9, runner: "linux.rocm.gpu.mi355.2" },
-          { config: "inductor_torchbench_perf_rocm_mi355", shard: 8, num_shards: 9, runner: "linux.rocm.gpu.mi355.2" },
-          { config: "inductor_torchbench_perf_rocm_mi355", shard: 9, num_shards: 9, runner: "linux.rocm.gpu.mi355.2" },
+          { config: "inductor_huggingface_perf_rocm", shard: 1, num_shards: 4, runner: "linux.rocm.gpu.gfx942.1" },
+          { config: "inductor_huggingface_perf_rocm", shard: 2, num_shards: 4, runner: "linux.rocm.gpu.gfx942.1" },
+          { config: "inductor_huggingface_perf_rocm", shard: 3, num_shards: 4, runner: "linux.rocm.gpu.gfx942.1" },
+          { config: "inductor_huggingface_perf_rocm", shard: 4, num_shards: 4, runner: "linux.rocm.gpu.gfx942.1" },
+          { config: "inductor_timm_perf_rocm", shard: 1, num_shards: 5, runner: "linux.rocm.gpu.gfx942.1" },
+          { config: "inductor_timm_perf_rocm", shard: 2, num_shards: 5, runner: "linux.rocm.gpu.gfx942.1" },
+          { config: "inductor_timm_perf_rocm", shard: 3, num_shards: 5, runner: "linux.rocm.gpu.gfx942.1" },
+          { config: "inductor_timm_perf_rocm", shard: 4, num_shards: 5, runner: "linux.rocm.gpu.gfx942.1" },
+          { config: "inductor_timm_perf_rocm", shard: 5, num_shards: 5, runner: "linux.rocm.gpu.gfx942.1" },
+          { config: "inductor_torchbench_perf_rocm", shard: 1, num_shards: 8, runner: "linux.rocm.gpu.gfx942.1" },
+          { config: "inductor_torchbench_perf_rocm", shard: 2, num_shards: 8, runner: "linux.rocm.gpu.gfx942.1" },
+          { config: "inductor_torchbench_perf_rocm", shard: 3, num_shards: 8, runner: "linux.rocm.gpu.gfx942.1" },
+          { config: "inductor_torchbench_perf_rocm", shard: 4, num_shards: 8, runner: "linux.rocm.gpu.gfx942.1" },
+          { config: "inductor_torchbench_perf_rocm", shard: 5, num_shards: 8, runner: "linux.rocm.gpu.gfx942.1" },
+          { config: "inductor_torchbench_perf_rocm", shard: 6, num_shards: 8, runner: "linux.rocm.gpu.gfx942.1" },
+          { config: "inductor_torchbench_perf_rocm", shard: 7, num_shards: 8, runner: "linux.rocm.gpu.gfx942.1" },
+          { config: "inductor_torchbench_perf_rocm", shard: 8, num_shards: 8, runner: "linux.rocm.gpu.gfx942.1" },
         ]}
     secrets: inherit
 

.github/workflows/operator_benchmark.yml

@@ -7,11 +7,9 @@ on:
   workflow_dispatch:
     inputs:
       test_mode:
-        type: choice
-        options:
-          - 'short'
-          - 'long'
-          - 'all'
+        required: false
+        type: string
+        default: 'short'
         description: tag filter for operator benchmarks, options from long, short, all
   schedule:
     # Run at 07:00 UTC every Sunday
@@ -39,7 +37,20 @@ jobs:
       docker-image-name: ci-image:pytorch-linux-jammy-py3-gcc11-inductor-benchmarks
       test-matrix: |
         { include: [
-          { config: "cpu_operator_benchmark_${{ inputs.test_mode || 'short' }}", shard: 1, num_shards: 1, runner: "linux.12xlarge" },
+          { config: "cpu_operator_benchmark_short", shard: 1, num_shards: 1, runner: "linux.12xlarge" },
+        ]}
+    secrets: inherit
+
+  opbenchmark-on-demand-build:
+    if: ${{ github.event_name == 'workflow_dispatch' && github.repository_owner == 'pytorch' }}
+    name: opbenchmark-on-demand-build
+    uses: ./.github/workflows/_linux-build.yml
+    with:
+      build-environment: linux-jammy-py3.10-gcc11-build
+      docker-image-name: ci-image:pytorch-linux-jammy-py3-gcc11-inductor-benchmarks
+      test-matrix: |
+        { include: [
+          { config: "cpu_operator_benchmark_${{ inputs.test_mode }}", shard: 1, num_shards: 1, runner: "linux.12xlarge" },
         ]}
     secrets: inherit
 

aten/src/ATen/CMakeLists.txt

@@ -256,7 +256,6 @@ endif()
 IF(USE_FBGEMM_GENAI)
   set(FBGEMM_THIRD_PARTY ${PROJECT_SOURCE_DIR}/third_party/fbgemm/external/)
   set(FBGEMM_GENAI_SRCS ${PROJECT_SOURCE_DIR}/third_party/fbgemm/fbgemm_gpu/experimental/gen_ai/src/quantize)
-
   if(USE_CUDA)
     # To avoid increasing the build time/binary size unnecessarily, use an allow-list of kernels to build.
     # If you want to integrate a kernel from FBGEMM into torch, you have to add it here.
@@ -293,64 +292,58 @@ IF(USE_FBGEMM_GENAI)
       "${FBGEMM_GENAI_SRCS}/cutlass_extensions/mx8mx8bf16_grouped/"
     )
 
-    target_include_directories(fbgemm_genai PRIVATE
+    target_include_directories(fbgemm_genai PUBLIC
       ${FBGEMM_THIRD_PARTY}/cutlass/include
       ${FBGEMM_THIRD_PARTY}/cutlass/tools/util/include
       ${fbgemm_genai_mx8mx8bf16_grouped}
       ${FBGEMM_GENAI_SRCS}/common/include/   # includes fbgemm_gpu/quantize/utils.h, fbgemm_gpu/quantize/tuning_cache.hpp
       ${FBGEMM_GENAI_SRCS}/include/          # includes fbgemm_gpu/torch_ops.h
     )
+  else()
+    if(USE_ROCM)
+      # Only include the kernels we want to build to avoid increasing binary size.
+      file(GLOB_RECURSE fbgemm_genai_native_rocm_hip
+        "${FBGEMM_GENAI_SRCS}/ck_extensions/fp8_rowwise_grouped/kernels/fp8_rowwise_grouped*.hip"
+        "${FBGEMM_GENAI_SRCS}/ck_extensions/fp8_rowwise_grouped/fp8_rowwise_grouped_gemm.hip")
+      set_source_files_properties(${fbgemm_genai_native_rocm_hip} PROPERTIES HIP_SOURCE_PROPERTY_FORMAT 1)
 
-    # Add FBGEMM_GENAI include directories for torch_ops.h
-    list(APPEND ATen_CUDA_INCLUDE ${PROJECT_SOURCE_DIR}/third_party/fbgemm/fbgemm_gpu/experimental/gen_ai/src/quantize/include)
-    list(APPEND ATen_CUDA_INCLUDE ${PROJECT_SOURCE_DIR}/third_party/fbgemm/fbgemm_gpu/experimental/gen_ai/src/quantize/common/include)
-  elseif(USE_ROCM)
-    # Only include the kernels we want to build to avoid increasing binary size.
-    file(GLOB_RECURSE fbgemm_genai_native_rocm_hip
-      "${FBGEMM_GENAI_SRCS}/ck_extensions/fp8_rowwise_grouped/kernels/fp8_rowwise_grouped*.hip"
-      "${FBGEMM_GENAI_SRCS}/ck_extensions/fp8_rowwise_grouped/fp8_rowwise_grouped_gemm.hip")
-    set_source_files_properties(${fbgemm_genai_native_rocm_hip} PROPERTIES HIP_SOURCE_PROPERTY_FORMAT 1)
+      # Add additional HIPCC compiler flags for performance
+      set(FBGEMM_GENAI_EXTRA_HIPCC_FLAGS
+        -mllvm
+        -amdgpu-coerce-illegal-types=1
+        -mllvm
+        -enable-post-misched=0
+        -mllvm
+        -greedy-reverse-local-assignment=1
+        -fhip-new-launch-api)
 
-    # Add additional HIPCC compiler flags for performance
-    set(FBGEMM_GENAI_EXTRA_HIPCC_FLAGS
-      -mllvm
-      -amdgpu-coerce-illegal-types=1
-      -mllvm
-      -enable-post-misched=0
-      -mllvm
-      -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})
 
-    # 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;)
+      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)
+
+      target_include_directories(fbgemm_genai PUBLIC
+        # FBGEMM version of Composable Kernel is used due to some customizations
+        ${FBGEMM_THIRD_PARTY}/composable_kernel/include
+        ${FBGEMM_THIRD_PARTY}/composable_kernel/library/include
+        ${FBGEMM_THIRD_PARTY}/cutlass/include
+        ${FBGEMM_THIRD_PARTY}/cutlass/tools/util/include
+        ${FBGEMM_GENAI_SRCS}/common/include/   # includes fbgemm_gpu/quantize/utils.h, fbgemm_gpu/quantize/tuning_cache.hpp
+        ${FBGEMM_GENAI_SRCS}/include/          # includes fbgemm_gpu/torch_ops.h
+      )
     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)
-
-    target_include_directories(fbgemm_genai PRIVATE
-      # FBGEMM version of Composable Kernel is used due to some customizations
-      ${FBGEMM_THIRD_PARTY}/composable_kernel/include
-      ${FBGEMM_THIRD_PARTY}/composable_kernel/library/include
-      ${FBGEMM_THIRD_PARTY}/cutlass/include
-      ${FBGEMM_THIRD_PARTY}/cutlass/tools/util/include
-      ${FBGEMM_GENAI_SRCS}/common/include/   # includes fbgemm_gpu/quantize/utils.h, fbgemm_gpu/quantize/tuning_cache.hpp
-      ${FBGEMM_GENAI_SRCS}/include/          # includes fbgemm_gpu/torch_ops.h
-    )
-
-    # Add FBGEMM_GENAI include directories for torch_ops.h
-    list(APPEND ATen_HIP_INCLUDE ${PROJECT_SOURCE_DIR}/third_party/fbgemm/fbgemm_gpu/experimental/gen_ai/src/quantize/include)
-    list(APPEND ATen_HIP_INCLUDE ${PROJECT_SOURCE_DIR}/third_party/fbgemm/fbgemm_gpu/experimental/gen_ai/src/quantize/common/include)
   endif()
 endif()
 
@@ -699,6 +692,12 @@ if(USE_CUDA AND NOT USE_ROCM)
   list(APPEND ATen_CUDA_INCLUDE ${CMAKE_CURRENT_SOURCE_DIR}/../../../third_party/cutlass/include)
   list(APPEND ATen_CUDA_INCLUDE ${CMAKE_CURRENT_SOURCE_DIR}/../../../third_party/cutlass/tools/util/include)
 
+  # Add FBGEMM_GENAI include directories for torch_ops.h
+  if(USE_FBGEMM_GENAI)
+    list(APPEND ATen_CUDA_INCLUDE ${CMAKE_CURRENT_SOURCE_DIR}/../../../third_party/fbgemm/fbgemm_gpu/experimental/gen_ai/src/quantize/include)
+    list(APPEND ATen_CUDA_INCLUDE ${CMAKE_CURRENT_SOURCE_DIR}/../../../third_party/fbgemm/fbgemm_gpu/experimental/gen_ai/src/quantize/common/include)
+  endif()
+
   if($ENV{ATEN_STATIC_CUDA})
     if(CUDA_VERSION VERSION_LESS_EQUAL 12.9)
       list(APPEND ATen_CUDA_DEPENDENCY_LIBS

aten/src/ATen/DLConvertor.cpp

@@ -389,16 +389,37 @@ void fillVersion<DLManagedTensorVersioned>(
 // constructed out of ATen tensor
 template <class T>
 T* toDLPackImpl(const Tensor& src) {
+  auto view = src;
+
+  // Detect whether there is need to normalize the strides
+  // Background: gh-83069
+  //
+  // However, normalizing strides can come at a high-cost
+  // to slow down toDLPack conversion 3x, so we
+  // only normalize if needed.
+  //
+  // 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;
+  // 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());
+  }
+
   ATenDLMTensor<T>* atDLMTensor(new ATenDLMTensor<T>);
-  atDLMTensor->handle = src;
+  atDLMTensor->handle = view;
   atDLMTensor->tensor.manager_ctx = atDLMTensor;
   atDLMTensor->tensor.deleter = &deleter<T>;
-  atDLMTensor->tensor.dl_tensor.data = src.data_ptr();
+  atDLMTensor->tensor.dl_tensor.data = view.data_ptr();
   atDLMTensor->tensor.dl_tensor.device = torchDeviceToDLDevice(src.device());
   atDLMTensor->tensor.dl_tensor.ndim = static_cast<int32_t>(src.dim());
   atDLMTensor->tensor.dl_tensor.dtype = getDLDataType(src);
-  atDLMTensor->tensor.dl_tensor.shape = const_cast<int64_t*>(src.sizes().data());
-  atDLMTensor->tensor.dl_tensor.strides = const_cast<int64_t*>(src.strides().data());
+  atDLMTensor->tensor.dl_tensor.shape = const_cast<int64_t*>(view.sizes().data());
+  atDLMTensor->tensor.dl_tensor.strides = const_cast<int64_t*>(view.strides().data());
   atDLMTensor->tensor.dl_tensor.byte_offset = 0;
   fillVersion(&atDLMTensor->tensor);
 

aten/src/ATen/core/ivalue.h

@@ -624,14 +624,7 @@ struct TORCH_API IValue final {
   IValue(const c10::SymBool& i) {
     if (auto mi = i.maybe_as_bool()) {
       tag = Tag::Bool;
-#if __BYTE_ORDER__ == __ORDER_LITTLE_ENDIAN__
       payload.u.as_int = *mi;
-#elif __BYTE_ORDER__ == __ORDER_BIG_ENDIAN__
-      /* due to byteorder if value assigned as_int, as_bool actually is not set correctly */
-      payload.u.as_bool = *mi;
-#else
-#error Unexpected or undefined __BYTE_ORDER__
-#endif
     } else {
       tag = Tag::SymBool;
       payload.u.as_intrusive_ptr = i.toSymNodeImpl().release();

aten/src/ATen/cuda/tunable/GemmCommon.h

@@ -13,7 +13,6 @@
 #include <c10/core/ScalarType.h>
 
 #include <ATen/cuda/tunable/TunableOp.h>
-#include <ATen/cuda/tunable/Tunable.h>
 #include <ATen/cuda/CUDABlas.h>
 #include <ATen/cuda/Exceptions.h>
 #include <c10/util/StringUtil.h>
@@ -151,7 +150,6 @@ inline std::string ScalarTypeToBLASType(c10::ScalarType scalar_type) {
       BLASType = "unknown";
   }
   return BLASType;
-
 }
 
 // Similar to Compute Type in GemmRocblas.h
@@ -246,25 +244,33 @@ inline std::string to_string_epilogue(const at::cuda::blas::GEMMAndBiasActivatio
 
 namespace detail {
 
-static bool NumericalCheck(ScalarType dtype, void* c, void* other_c, int64_t size, const NumericalCheckConfig& config) {
-
-  if (!config.enabled) {
-    return true; // skip when disabled
-  }
-
+static bool NumericalCheck(ScalarType dtype, void* c, void* other_c, int64_t size) {
   auto options = at::TensorOptions().dtype(dtype).device(at::kCUDA);
+  // comparison done as 1D tensor
   at::Tensor ref = at::from_blob(c,       {size}, options);
   at::Tensor oth = at::from_blob(other_c, {size}, options);
   at::Tensor ref_float = ref.to(at::kFloat);
   at::Tensor oth_float = oth.to(at::kFloat);
-
-  const bool ok = at::allclose(ref_float, oth_float, config.rtol, config.atol);
-  if (ok) {
-    TUNABLE_LOG3("├──verify numerics: PASSED with atol=", config.atol, ", rtol=", config.rtol);
-  } else {
-    TUNABLE_LOG3("├──verify numerics: FAILED with atol=", config.atol, ", rtol=", config.rtol);
+  std::vector<double> atols{1e-1, 1e-2, 1e-3, 1e-4, 1e-5};
+  std::vector<double> rtols{1e-1, 1e-2, 1e-3, 1e-4, 1e-5};
+  double last_succeed_atol = 1;
+  double last_succeed_rtol = 1;
+  for (auto& atol : atols) {
+    for (auto& rtol : rtols) {
+      if (at::allclose(ref_float, oth_float, rtol, atol)) {
+        last_succeed_atol = atol;
+        last_succeed_rtol = rtol;
+      }
+    }
   }
-  return ok;
+  if (last_succeed_atol == 1) {
+    return false;
+  }
+  else {
+    TUNABLE_LOG3("├──verify numerics: atol=", last_succeed_atol, ", rtol=", last_succeed_rtol);
+  }
+
+  return true;
 }
 
 }
@@ -349,10 +355,8 @@ struct GemmParams : OpParams {
   }
 
   TuningStatus NumericalCheck(GemmParams<T> *other) {
-    auto* ctx = getTuningContext();
-    auto cfg = ctx->GetNumericalCheckConfig();
     auto c_dtype = c10::CppTypeToScalarType<T>::value;
-    return detail::NumericalCheck(c_dtype, c, other->c, GetSizeC()/sizeof(T), cfg) ? OK : FAIL;
+    return detail::NumericalCheck(c_dtype, c, other->c, GetSizeC()/sizeof(T)) ? OK : FAIL;
   }
 
   char transa{};
@@ -445,10 +449,8 @@ struct GemmAndBiasParams : OpParams {
   }
 
   TuningStatus NumericalCheck(GemmAndBiasParams<T> *other) {
-    auto* ctx = getTuningContext();
-    auto cfg = ctx->GetNumericalCheckConfig();
     auto c_dtype = c10::CppTypeToScalarType<T>::value;
-    return detail::NumericalCheck(c_dtype, c, other->c, GetSizeC()/sizeof(T), cfg) ? OK : FAIL;
+    return detail::NumericalCheck(c_dtype, c, other->c, GetSizeC()/sizeof(T)) ? OK : FAIL;
   }
 
   char transa{};
@@ -544,10 +546,8 @@ struct GemmStridedBatchedParams : OpParams {
   }
 
   TuningStatus NumericalCheck(GemmStridedBatchedParams<T> *other) {
-    auto* ctx = getTuningContext();
-    auto cfg = ctx->GetNumericalCheckConfig();
     auto c_dtype = c10::CppTypeToScalarType<C_Dtype>::value;
-    return detail::NumericalCheck(c_dtype, c, other->c, GetSizeC()/sizeof(T), cfg) ? OK : FAIL;
+    return detail::NumericalCheck(c_dtype, c, other->c, GetSizeC()/sizeof(T)) ? OK : FAIL;
   }
 
   char transa{};
@@ -663,9 +663,7 @@ struct ScaledGemmParams : OpParams {
   }
 
   TuningStatus NumericalCheck(ScaledGemmParams<T> *other) {
-    auto* ctx = getTuningContext();
-    auto cfg = ctx->GetNumericalCheckConfig();
-    return detail::NumericalCheck(c_dtype, c, other->c, GetSizeC()/sizeof(T), cfg) ? OK : FAIL;
+    return detail::NumericalCheck(c_dtype, c, other->c, GetSizeC()/sizeof(T)) ? OK : FAIL;
   }
 
   char transa{};

aten/src/ATen/cuda/tunable/README.md

@@ -145,7 +145,7 @@ programmatically since the settings become fixed. Use the C++ or Python APIs ins
 | PYTORCH_TUNABLEOP_VERBOSE | Default is 0. Set to 1 to enable basic logging. 2 for basic tuning status. 3 for full trace. |
 | PYTORCH_TUNABLEOP_VERBOSE_FILENAME | Default is "err" for stderr. Set to "out" for stdout or a filename for capturing verbose logging. |
 | PYTORCH_TUNABLEOP_FILENAME | Default is 'tunableop_results.csv'. |
-| PYTORCH_TUNABLEOP_NUMERICAL_CHECK | Default is off. Set 'atol_rtol' to enable, for example "1e-5_1e-5". |
+| PYTORCH_TUNABLEOP_NUMERICAL_CHECK | Default is 0. Set to 1 to enable. |
 | PYTORCH_TUNABLEOP_ROCBLAS_ENABLED | Default is 1. Set to 0 to disable rocblas being considered during tuning. |
 | PYTORCH_TUNABLEOP_HIPBLASLT_ENABLED | Default is 1. Set to 0 to disable hipblaslt being considered during tuning. |
 | PYTORCH_TUNABLEOP_MAX_TUNING_DURATION_MS | Default is 30. Unit is milliseconds. |
@@ -173,9 +173,10 @@ All python APIs exist in the `torch.cuda.tunable` module.
 | get_max_tuning_iterations() -> int | |
 | set_filename(filename: str, insert_device_ordinal: bool = False) -> None | |
 | get_filename() -> str | |
-| set_numerical_check_tolerances(enable: bool, atol: float, rtol: float) -> None | Enable or disable numerical checking; atol and rtol default to 1e-5.
 | get_results() -> Tuple[str, str, str, float] | |
 | get_validators() -> Tuple[str, str] | |
+| write_file_on_exit(val: bool) -> None | Default is True. |
+| write_file(filename: Optional[str] = None) -> None | If filename not given, it will call get_filename(). |
 | read_file(filename: Optional[str] = None) -> None | If filename not given, it will call get_filename(). |
 | tune_gemm_in_file(filename: str) -> None | read an untuned file and tune GEMMs in it. |
 | mgpu_tune_gemm_in_file(filename_pattern: str, num_gpus: int) -> None: -> None | read one or more untuned files and tune all unique GEMMs on one or more GPUs. |

aten/src/ATen/cuda/tunable/Tunable.cpp

@@ -107,30 +107,14 @@ void TuningResultsManager::AddImpl(const std::string& op_signature,
 }
 
 void TuningResultsManager::Add(const std::string& op_signature, const std::string& params_signature, ResultEntry best) {
-  bool is_new = false;
-  ResultEntry inserted = ResultEntry::Null();
+  std::scoped_lock l{lock_};
 
-  // ---- mutate maps under results lock ----
-  {
-    std::scoped_lock l{lock_};
-    auto& km = results_[op_signature];  // creates if missing
-    is_new = (km.find(params_signature) == km.end());
-    AddImpl(op_signature, params_signature, std::move(best), km);
-    if (is_new) {
-      inserted = km.at(params_signature);  // snapshot for I/O after unlocking
-    }
-  }
-   if (!is_new) return;  // only write once per unique (op, params)
-
-   TuningContext* ctx = getTuningContext();
-  if (ctx->IsTuningEnabled() && !ctx->IsRecordUntunedEnabled()) {
-    InitRealtimeAppend(ctx->GetFilename(), ctx->GetTuningResultsValidator().GetAllValidators());
-
-    if (is_new && realtime_out_ && realtime_out_->good()) {
-      AppendResultLine(op_signature, params_signature, inserted);
-    }
+  auto it = results_.find(op_signature);
+  if (it == results_.end()) {
+    it = results_.insert({op_signature, {}}).first;
   }
 
+  AddImpl(op_signature, params_signature, std::move(best), it->second);
 }
 
 void TuningResultsManager::RecordUntuned( std::ofstream& untuned_file, const std::string& op_signature,
@@ -166,77 +150,6 @@ void TuningResultsManager::RecordUntuned( std::ofstream& untuned_file, const std
   }
 }
 
-void TuningResultsManager::InitRealtimeAppend(const std::string& filename, const std::unordered_map<std::string, std::string>& validators) {
-  std::scoped_lock fl{realtime_file_mutex_};
-
-  if (realtime_out_ && realtime_out_->good() && realtime_filename_ == filename) {
-    return;
-  }
-
-  if (realtime_out_ && realtime_filename_ != filename) {
-    realtime_out_->flush();
-    realtime_out_->close();
-    realtime_out_.reset();
-    validators_written_ = false;
-  }
-
-  bool file_exists = false;
-  bool file_empty = true;
-
-  {
-    std::ifstream check_file(filename);
-    if (check_file.good()) {
-      file_exists = true;
-      file_empty = (check_file.peek() == std::ifstream::traits_type::eof());
-    }
-  }
-
-  realtime_out_ = std::make_unique<std::ofstream>(filename, std::ios::out | std::ios::app);
-
-  if (!realtime_out_->good()) {
-    TORCH_WARN("TunableOp realtime append: failed to open '", filename,"'");
-    realtime_out_.reset();
-    return;
-  }
-
-  if(!file_exists || file_empty) {
-    for(const auto& [key, val] : validators) {
-      (*realtime_out_) << "Validator," << key << "," << val << std::endl;
-      realtime_out_->flush();
-    }
-    validators_written_ = true;
-
-    TUNABLE_LOG2("Wrote validators to realtime output file");
-  }
-
-  realtime_filename_ = filename;
-}
-
-void TuningResultsManager::AppendResultLine(const std::string& op_sig, const std::string& param_sig, const ResultEntry& result) {
-  std::scoped_lock fl{realtime_file_mutex_};
-
-  if(!realtime_out_ || !realtime_out_->good()) {
-    return;
-  }
-
-  (*realtime_out_) << op_sig << "," << param_sig << "," << result << std::endl;
-  realtime_out_->flush(); //ensure immediate write to disk
-
-  TUNABLE_LOG3("Realtime append: ", op_sig, "(", param_sig, ") -> ", result);
-}
-
-void TuningResultsManager::CloseRealtimeAppend() {
-  std::scoped_lock fl{realtime_file_mutex_};
-
-
-  if(realtime_out_) {
-    realtime_out_->flush();
-    realtime_out_->close();
-    realtime_out_.reset();
-    TUNABLE_LOG2("Closed realtime output file");
-  }
-}
-
 void TuningResultsManager::Delete(const std::string& op_signature, const std::string& params_signature) {
   std::scoped_lock l{lock_};
 
@@ -483,6 +396,7 @@ TuningContext::TuningContext() :
     tuning_enable_{true},
     record_untuned_enable_{false},
     manager_initialized_{false},
+    write_file_on_exit_{true},
     numerics_check_enable_{false},
     max_tuning_duration_ms_{30},
     max_tuning_iterations_{100},
@@ -503,8 +417,20 @@ TuningContext::~TuningContext() {
     // but doesn't do any computation itself.
     return;
   }
-  TUNABLE_LOG1("Closing File");
-  GetTuningResultsManager().CloseRealtimeAppend(); // Since, we do instant logging by default now.
+  auto filename = GetFilename();
+  if (IsTunableOpEnabled() && IsTuningEnabled() && !filename.empty() && write_file_on_exit_) {
+    if (results_count_from_input_file_ < GetTuningResultsManager().GetSize()) {
+      if (results_count_from_input_file_ > 0) {
+        TUNABLE_LOG1("additional tuning results available, rewriting file ", filename);
+      }
+      else {
+        TUNABLE_LOG1("writing file ", filename);
+      }
+      if (!WriteFile(filename)) {
+        TUNABLE_LOG1("failed to write file ", filename);
+      }
+    }
+  }
 
   if (untuned_file_.good()) {
     untuned_file_.close();
@@ -585,54 +511,20 @@ std::ofstream& TuningContext::GetUntunedFile(){
   return untuned_file_;
 }
 
+void TuningContext::WriteFileOnExit(bool value) {
+  write_file_on_exit_ = value;
+}
 
 void TuningContext::EnableNumericsCheck(bool value) {
   numerics_check_enable_ = value;
 }
 
-NumericalCheckConfig TuningContext::GetNumericalCheckConfig() const {
-  const auto env_opt = c10::utils::get_env("PYTORCH_TUNABLEOP_NUMERICAL_CHECK");
-
-  if (!env_opt.has_value()) {
-    return numerics_cfg_;
-  }
-
-  const std::string& env = env_opt.value();
-
-  if (env == "0") {
-    return NumericalCheckConfig(false, 1e-5, 1e-5);
-  }
-
-  const size_t underscore = env.find('_');
-
-  TORCH_CHECK(
-      underscore != std::string::npos,
-      "Invalid PYTORCH_TUNABLEOP_NUMERICAL_CHECK format. "
-      "Expected 'atol_rtol', got: ",
-      env);
-
-  double atol = 0.0;
-  double rtol = 0.0;
-
-  try {
-    atol = std::stod(env.substr(0, underscore));
-    rtol = std::stod(env.substr(underscore + 1));
-  } catch (const std::exception& e) {
-    TORCH_CHECK(false, "Failed to parse PYTORCH_TUNABLEOP_NUMERICAL_CHECK: ", e.what());
-  }
-
-  TORCH_CHECK( atol > 0.0 && rtol > 0.0, "Tolerance values must be positive. atol=", atol, ", rtol=", rtol);
-  return NumericalCheckConfig(true, atol, rtol);
-}
-
-void TuningContext::SetNumericalCheckConfig(bool enabled, double atol, double rtol) {
-  TORCH_CHECK(atol > 0.0 && rtol > 0.0, "Numerical check tolerances must be positive");
-  numerics_cfg_ = {enabled, atol, rtol};
-}
-
 bool TuningContext::IsNumericsCheckEnabled() const {
-  const auto cfg = GetNumericalCheckConfig();
-  return cfg.enabled || numerics_check_enable_;
+  const auto env = c10::utils::get_env("PYTORCH_TUNABLEOP_NUMERICAL_CHECK");
+  if (env == "1") {
+    return true;
+  }
+  return numerics_check_enable_;
 }
 
 void TuningContext::SetMaxTuningDurationMs(int max_duration_ms) {
@@ -742,6 +634,11 @@ TuningResultsManager& TuningContext::GetTuningResultsManager() {
     auto filename = GetFilename();
     if (!filename.empty() && !IsRecordUntunedEnabled()) {
       ReadFile(filename);
+      // attempt immediately to open file for writing to catch errors early
+      std::ofstream file(filename, std::ios::out | std::ios::app);
+      if (!file.good()) {
+        TORCH_WARN("failed to open file '", filename, "' for writing; your tuning results will not be saved");
+      }
     }
   });
   return manager_;
@@ -847,6 +744,27 @@ bool TuningContext::ReadFile(const std::string& filename_) {
   return true;
 }
 
+bool TuningContext::WriteFile(const std::string& filename_) {
+  std::string filename = filename_.empty() ? GetFilename() : filename_;
+  std::ofstream file(filename, std::ios::out | std::ios::trunc);
+  if (!file.good()) {
+    TUNABLE_LOG1("error opening tuning results file for writing ", filename);
+    return false;
+  }
+  auto validators = GetTuningResultsValidator().GetAllValidators();
+  for (const auto& [key, val] : validators) {
+    file << "Validator," << key << "," << val << std::endl;
+  }
+  auto results = GetTuningResultsManager().Dump();
+  for (const auto& [op_sig, kernelmap] : results) {
+    for (const auto& [param_sig, result] : kernelmap) {
+      file << op_sig << "," << param_sig << "," << result << std::endl;
+    }
+  }
+  file.close();
+  return true;
+}
+
 namespace {
 
 struct MaybeDelete {

aten/src/ATen/cuda/tunable/Tunable.h

@@ -103,24 +103,10 @@ class TORCH_CUDA_CPP_API TuningResultsManager {
 
     void RecordUntuned( std::ofstream& untuned_file, const std::string& op_signature,
       const std::string& params_signature, const std::string& blas_signature);
-
-    void InitRealtimeAppend(
-        const std::string& filename,
-        const std::unordered_map<std::string, std::string>& validators);
-
-    void AppendResultLine(const std::string& op_sig,
-                         const std::string& param_sig,
-                         const ResultEntry& result);
-
-    void CloseRealtimeAppend();  // For clean shutdown
   private:
     std::mutex lock_;
-    std::mutex realtime_file_mutex_;
-    std::unique_ptr<std::ofstream> realtime_out_;
-    std::string realtime_filename_;
     ResultsMap results_;
     UntunedMap untuned_results_;
-    bool validators_written_ = false;
 
 };
 
@@ -148,16 +134,6 @@ class TORCH_CUDA_CPP_API TuningResultsValidator {
     GetValidateFuncs validators_;
 };
 
-struct NumericalCheckConfig {
-  bool   enabled{false};
-  double atol{1e-5};
-  double rtol{1e-5};
-
-  NumericalCheckConfig() = default;
-  NumericalCheckConfig(bool e, double a, double r) : enabled(e), atol(a), rtol(r) {}
-};
-
-
 class TORCH_CUDA_CPP_API TuningContext {
   public:
     TuningContext();
@@ -179,8 +155,6 @@ class TORCH_CUDA_CPP_API TuningContext {
 
     void EnableNumericsCheck(bool value);
     bool IsNumericsCheckEnabled() const;
-    void SetNumericalCheckConfig(bool enabled, double atol, double rtol);
-    NumericalCheckConfig GetNumericalCheckConfig() const;
 
     void SetMaxTuningDurationMs(int max_duration_ms);
     int GetMaxTuningDurationMs() const;
@@ -211,7 +185,10 @@ class TORCH_CUDA_CPP_API TuningContext {
     void SetFilename(const std::string& filename, bool insert_device_ordinal=false);
     std::string GetFilename() const;
 
+    void WriteFileOnExit(bool value);
+
     bool ReadFile(const std::string& filename={});
+    bool WriteFile(const std::string& filename={});
 
     template<class... Types>
     void Log(int level, Types... args) {
@@ -230,6 +207,7 @@ class TORCH_CUDA_CPP_API TuningContext {
     bool tuning_enable_;
     bool record_untuned_enable_;
     bool manager_initialized_;
+    bool write_file_on_exit_;
     bool numerics_check_enable_;
     int max_tuning_duration_ms_;
     int max_tuning_iterations_;
@@ -244,8 +222,6 @@ class TORCH_CUDA_CPP_API TuningContext {
     std::ofstream untuned_file_;
     size_t results_count_from_input_file_;
     bool is_shutting_down_;
-
-    NumericalCheckConfig numerics_cfg_{};
 };
 
 TORCH_CUDA_CPP_API TuningContext* getTuningContext();

aten/src/ATen/cuda/tunable/TunableOp.h

@@ -267,10 +267,27 @@ class TunableOp {
       for (size_t i = 0; i < op_names_.size(); i++) {
         auto* candidate = ops_[op_names_[i]].get(); // borrow pointer
 
-        auto status = candidate->Call(reusable_params[0]);
-        if (status != OK) {
-          TUNABLE_LOG3("├──unsupported id=", i, ", ", op_sig, '(', params_sig, ") ", op_names_[i]);
-          continue;
+        if (do_numerics_check) {
+          ParamsT* numerical_params = params->DeepCopy(false);
+          auto status = candidate->Call(numerical_params);
+          if (status != OK) {
+            numerical_params->Delete();
+            TUNABLE_LOG3("├──unsupported id=", i, ", ", op_sig, '(', params_sig, ") ", op_names_[i]);
+            continue;
+          }
+          status = reference_params->NumericalCheck(numerical_params);
+          numerical_params->Delete();
+          if (status != OK) {
+            TUNABLE_LOG3("├──numerics check failed for id=", i, ", ", op_sig, '(', params_sig, ") ", op_names_[i]);
+            continue;
+          }
+        }
+        else {
+          auto status = candidate->Call(reusable_params[0]);
+          if (status != OK) {
+            TUNABLE_LOG3("├──unsupported id=", i, ", ", op_sig, '(', params_sig, ") ", op_names_[i]);
+            continue;
+          }
         }
 
         // collect a small profile
@@ -293,22 +310,6 @@ class TunableOp {
           continue;
         }
 
-        if (do_numerics_check) {
-          ParamsT* numerical_params = params->DeepCopy(false);
-          auto status = candidate->Call(numerical_params);
-          if (status != OK) {
-            numerical_params->Delete();
-            TUNABLE_LOG3("├──unsupported id=", i, ", ", op_sig, '(', params_sig, ") ", op_names_[i]);
-            continue;
-          }
-          status = reference_params->NumericalCheck(numerical_params);
-          numerical_params->Delete();
-          if (status != OK) {
-            TUNABLE_LOG3("├──numerics check failed for id=", i, ", ", op_sig, '(', params_sig, ") ", op_names_[i]);
-            continue;
-          }
-        }
-
         // for warmup does user set max duration, max iters, or both?
         // warmup is skipped by default, i.e. warmup_iter = 0
         // warmup will be set to the non-zero value of max_warmup_duration

aten/src/ATen/functorch/BatchRulesModules.cpp

@@ -213,22 +213,40 @@ static cudnn_grid_sample_backward_batch_rule(
   return grid_sample_backward_helper_out(std::move(bw_out), 0, 0, bdim_size);
 }
 
-// uses functional formulation for one_hot under vmap to be compatible with
-// fakeTensor/dynamic shapes and compiled functorch transforms.
-// mirrors the meta path in aten/src/ATen/native/Onehot.cpp,
-// but requires explicit positive num_classes under vmap to avoid
-// data-dependent output shapes.
+// TODO: replace with targetable functionalization
 static Tensor one_hot_decomposition_hack(const Tensor &self, int64_t num_classes) {
     TORCH_CHECK(self.dtype() == kLong, "one_hot is only applicable to index tensor.");
+    auto shape = self.sym_sizes().vec();
+
+    // empty tensor could be converted to one hot representation,
+    // but shape inference is not possible.
+    if (self.sym_numel() == 0) {
+        if (num_classes <= 0) {
+            TORCH_CHECK(false, "Can not infer total number of classes from empty tensor.");
+        } else {
+            shape.emplace_back(num_classes);
+            return at::empty_symint(shape, self.options());
+        }
+    }
 
-    // disallow implicit inference under vmap; this would be data-dependent
-    // and is intentionally guarded by Dynamo in torch/_dynamo/variables/torch.py.
     TORCH_CHECK(num_classes > 0, "When vmap-ing torch.nn.functional.one_hot, please "
         "provide an explicit positive num_classes argument.");
 
-    const auto options = self.options();
-    at::Tensor index = at::arange(num_classes, options);
-    return at::eq(self.unsqueeze(-1), index).to(at::kLong);
+    // Disabling all of the following checks. This is OK because scatter has checks too.
+    // Maybe one_hot should be a primitive wrt autograd so we don't have to deal with this.
+    // // non-empty tensor
+    // if (self.device().type() != at::kCUDA) {
+    //   //for cuda, rely on device assert thrown by scatter
+    //   TORCH_CHECK(self.min().item().toLong() >= 0, "Class values must be non-negative.");
+    // }
+    // if (self.device().type() != at::kCUDA) {
+    //   //rely on device asserts from scatter to avoid sync here
+    //   TORCH_CHECK(num_classes > self.max().item().toLong(), "Class values must be smaller than num_classes.");
+    // }
+
+    shape.emplace_back(num_classes);
+    Tensor ret = at::zeros_symint(shape, self.options());
+    return ret.scatter(-1, self.unsqueeze(-1), 1);
 }
 
 template <typename A, A a, typename C>

aten/src/ATen/native/Onehot.cpp

@@ -34,16 +34,16 @@ Tensor one_hot(const Tensor &self, int64_t num_classes) {
         }
     }
 
-    auto shape = self.sym_sizes().vec();
+    auto shape = self.sizes().vec();
 
     // empty tensor could be converted to one hot representation,
     // but shape inference is not possible.
-    if (self.sym_numel() == 0) {
+    if (self.numel() == 0) {
         if (num_classes <= 0) {
             TORCH_CHECK(false, "Can not infer total number of classes from empty tensor.");
         } else {
-            shape.emplace_back(num_classes);
-            return at::empty_symint(shape, self.options());
+            shape.push_back(num_classes);
+            return at::empty(shape, self.options());
         }
     }
 
@@ -66,8 +66,8 @@ Tensor one_hot(const Tensor &self, int64_t num_classes) {
         }
     }
 
-    shape.emplace_back(num_classes);
-    Tensor ret = at::zeros_symint(shape, self.options());
+    shape.push_back(num_classes);
+    Tensor ret = at::zeros(shape, self.options());
     ret.scatter_(-1, self.unsqueeze(-1), 1);
     return ret;
 }

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

@@ -120,7 +120,7 @@ static void pow_tensor_scalar_kernel(
   } else if (dtype == ScalarType::Half) {
     [&]() {
       using scalar_t =
-          c10::impl::ScalarTypeToCPPTypeT<ScalarType::Half>;
+          decltype(c10::impl::ScalarTypeToCPPType<ScalarType::Half>::t);
       const auto exp = exp_scalar.to<scalar_t>();
       using Vec = Vectorized<scalar_t>;
       cpu_kernel_vec(iter,

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

@@ -1230,205 +1230,8 @@ std::pair<ScalingType, ScalingType> get_joint_scaling(
   );
 }
 
-Tensor&
-_tunable_scaled_gemm_rocm(
-          cublasCommonArgs& args,
-          const Tensor& mat1, const Tensor& mat2,
-          const Tensor& scale_a, const Tensor& scale_b,
-          const ScalingType scaling_choice_a, const ScalingType scaling_choice_b,
-          const std::optional<Tensor>& bias,
-          const bool use_fast_accum,
-          const at::ScalarType out_dtype,
-          Tensor& out) {
-#ifdef USE_ROCM
-#define TUNABLE_DISPATCH(BLASOP_A, BLASOP_B)                            \
-      if (mat1.scalar_type() == ScalarType::Float8_e4m3fnuz) {        \
-        if (mat2.scalar_type() == ScalarType::Float8_e4m3fnuz) {      \
-          static at::cuda::tunable::ScaledGemmTunableOp<              \
-              at::Float8_e4m3fnuz, at::Float8_e4m3fnuz, scalar_t,     \
-              BLASOP_A, BLASOP_B> scaledgemm{};                       \
-          scaledgemm(&params);                                        \
-        }                                                             \
-        else if (mat2.scalar_type() == ScalarType::Float8_e5m2fnuz) { \
-          static at::cuda::tunable::ScaledGemmTunableOp<              \
-              at::Float8_e4m3fnuz, at::Float8_e5m2fnuz, scalar_t,     \
-              BLASOP_A, BLASOP_B> scaledgemm{};                       \
-          scaledgemm(&params);                                        \
-        }                                                             \
-      }                                                               \
-      else if (mat1.scalar_type() == ScalarType::Float8_e5m2fnuz) {   \
-        if (mat2.scalar_type() == ScalarType::Float8_e4m3fnuz) {      \
-          static at::cuda::tunable::ScaledGemmTunableOp<              \
-              at::Float8_e5m2fnuz, at::Float8_e4m3fnuz, scalar_t,     \
-              BLASOP_A, BLASOP_B> scaledgemm{};                       \
-          scaledgemm(&params);                                        \
-        }                                                             \
-        else if (mat2.scalar_type() == ScalarType::Float8_e5m2fnuz) { \
-          static at::cuda::tunable::ScaledGemmTunableOp<              \
-              at::Float8_e5m2fnuz, at::Float8_e5m2fnuz, scalar_t,     \
-              BLASOP_A, BLASOP_B> scaledgemm{};                       \
-          scaledgemm(&params);                                        \
-        }                                                             \
-      }                                                               \
-      else if (mat1.scalar_type() == ScalarType::Float8_e4m3fn) {     \
-        if (mat2.scalar_type() == ScalarType::Float8_e4m3fn) {        \
-          static at::cuda::tunable::ScaledGemmTunableOp<              \
-              at::Float8_e4m3fn, at::Float8_e4m3fn, scalar_t,         \
-              BLASOP_A, BLASOP_B> scaledgemm{};                       \
-          scaledgemm(&params);                                        \
-        }                                                             \
-        else if (mat2.scalar_type() == ScalarType::Float8_e5m2) {     \
-          static at::cuda::tunable::ScaledGemmTunableOp<              \
-              at::Float8_e4m3fn, at::Float8_e5m2, scalar_t,           \
-              BLASOP_A, BLASOP_B> scaledgemm{};                       \
-          scaledgemm(&params);                                        \
-        }                                                             \
-      }                                                               \
-      else if (mat1.scalar_type() == ScalarType::Float8_e5m2) {       \
-        if (mat2.scalar_type() == ScalarType::Float8_e4m3fn) {        \
-          static at::cuda::tunable::ScaledGemmTunableOp<              \
-              at::Float8_e5m2, at::Float8_e4m3fn, scalar_t,           \
-              BLASOP_A, BLASOP_B> scaledgemm{};                       \
-          scaledgemm(&params);                                        \
-        }                                                             \
-        else if (mat2.scalar_type() == ScalarType::Float8_e5m2) {     \
-          static at::cuda::tunable::ScaledGemmTunableOp<              \
-              at::Float8_e5m2, at::Float8_e5m2, scalar_t,             \
-              BLASOP_A, BLASOP_B> scaledgemm{};                       \
-          scaledgemm(&params);                                        \
-        }                                                             \
-      }
-  AT_DISPATCH_V2(out_dtype, "_tunable_scaled_gemm", AT_WRAP([&] {
-    bool transa_ = ((args.transa != 'n') && (args.transa != 'N'));
-    bool transb_ = ((args.transb != 'n') && (args.transb != 'N'));
-    at::cuda::tunable::ScaledGemmParams<scalar_t> params;
-    params.transa = args.transa;
-    params.transb = args.transb;
-    params.m = args.m;
-    params.n = args.n;
-    params.k = args.k;
-    params.a = args.mata->data_ptr();
-    params.a_scale_ptr = args.scale_mata_ptr;
-    params.a_scale_dtype = args.scale_mata_dtype.value();
-    params.lda = args.lda;
-    params.a_dtype = args.mata->scalar_type();
-    params.a_scale_dtype = args.scale_mata_dtype.value();
-    params.a_scaling_type = args.scaling_mata_type.value();
-    params.b = args.matb->data_ptr();
-    params.b_scale_ptr = args.scale_matb_ptr;
-    params.b_scale_dtype = args.scale_matb_dtype.value();
-    params.ldb = args.ldb;
-    params.b_dtype = args.matb->scalar_type();
-    params.b_scale_dtype = args.scale_matb_dtype.value();
-    params.b_scaling_type = args.scaling_matb_type.value();
-    params.bias_ptr = bias ? bias->data_ptr(): nullptr;
-    params.bias_dtype = bias ? bias->scalar_type() : isFloat8Type(out_dtype) ? at::ScalarType::Half : out_dtype;
-    params.c = args.result->data_ptr();
-    params.c_scale_ptr = args.scale_result_ptr;
-    params.ldc = args.result_ld;
-    params.c_dtype = out_dtype;
-    params.use_fast_accum = use_fast_accum;
-    if (transa_ && transb_) {
-      TUNABLE_DISPATCH(at::cuda::tunable::BlasOp::T, at::cuda::tunable::BlasOp::T)
-    }
-    else if (transa_ && !transb_) {
-      TUNABLE_DISPATCH(at::cuda::tunable::BlasOp::T, at::cuda::tunable::BlasOp::N)
-    }
-    else if (!transa_ && transb_) {
-      TUNABLE_DISPATCH(at::cuda::tunable::BlasOp::N, at::cuda::tunable::BlasOp::T)
-    }
-    else if (!transa_ && !transb_) {
-      TUNABLE_DISPATCH(at::cuda::tunable::BlasOp::N, at::cuda::tunable::BlasOp::N)
-    }
-    else {
-      TORCH_CHECK(false, "unreachable");
-    }
-  }),
-  kHalf, kBFloat16, AT_EXPAND(AT_FLOAT8_TYPES), AT_EXPAND(AT_FLOATING_TYPES));
-#undef TUNABLE_DISPATCH
-  return out;
-#else
-  TORCH_CHECK_NOT_IMPLEMENTED(false, "_scaled_gemm_rocm only callable on ROCM devices");
-#endif
-}
-
-Tensor&
-_scaled_gemm(
-          const Tensor& mat1, const Tensor& mat2,
-          const Tensor& scale_a, const Tensor& scale_b,
-          const ScalingType scaling_choice_a, const ScalingType scaling_choice_b,
-          const std::optional<Tensor>& bias,
-          const bool use_fast_accum,
-          Tensor& out) {
-  cublasCommonArgs args(mat1, mat2, out, scale_a, scale_b, std::nullopt, scaling_choice_a, scaling_choice_b);
-  const auto out_dtype_ = args.result->scalar_type();
-  TORCH_CHECK(args.transa == 't' && args.transb == 'n', "Only multiplication of row-major and column-major matrices is supported by cuBLASLt");
-
-// ROCM enables the TunableOp path only
-// but can fallback to at::cuda::blas::scaled_gemm
-#ifdef USE_ROCM
-  auto tuning_ctx = at::cuda::tunable::getTuningContext();
-  bool tunable_op_enabled = tuning_ctx->IsTunableOpEnabled();
-#else
-  bool tunable_op_enabled = false;
-#endif
-  if (tunable_op_enabled) {
-      // Only available on ROCM
-      return _tunable_scaled_gemm_rocm(
-          args,
-          mat1, mat2,
-          scale_a, scale_b,
-          scaling_choice_a, scaling_choice_b,
-          bias,
-          use_fast_accum,
-          out_dtype_,
-          out);
-  }
-  else
-  {
-      at::cuda::blas::scaled_gemm(
-          args.transa,
-          args.transb,
-          args.m,
-          args.n,
-          args.k,
-          args.mata->data_ptr(),
-          args.scale_mata_ptr,
-          args.lda,
-          args.mata->scalar_type(),
-          args.scale_mata_dtype.value(),
-          args.scaling_mata_type.value(),
-          args.matb->data_ptr(),
-          args.scale_matb_ptr,
-          args.ldb,
-          args.matb->scalar_type(),
-          args.scale_matb_dtype.value(),
-          args.scaling_matb_type.value(),
-          bias ? bias->data_ptr(): nullptr,
-          bias ? bias->scalar_type() : isFloat8Type(out_dtype_) ? at::ScalarType::Half : out_dtype_,
-          args.result->data_ptr(),
-          args.scale_result_ptr,
-          args.result_ld,
-          out_dtype_,
-          use_fast_accum);
-      return out;
-  }
-}
-
 } // namespace
 
-// NOTE(slayton58): This is defined as part of the _v2 code (way) below - declare the signature here
-//                  to help cleanup v1 call structure.
-Tensor&
-_scaled_rowwise_rowwise(
-          const Tensor&, const Tensor&,
-          const Tensor&, const Tensor&,
-          const std::optional<Tensor>&,
-          const c10::ScalarType,
-          bool,
-          Tensor&);
-
-
 // Computes matrix multiply + bias while applying scaling to input and output matrices
 // Scales are only applicable when matrices are of Float8 type and assumed to be equal to 1.0 by default.
 // If output matrix type is 16 or 32-bit type, scale_result is not applied.
@@ -1470,10 +1273,6 @@ _scaled_mm_out_cuda(const Tensor& mat1, const Tensor& mat2,
   // by decreasing priority. We prefer "simpler" schemes as they are supported
   // more broadly (more GPU archs, more CUDA versions) and because they are more
   // efficient. This tends to matter only for small matmuls (e.g., 1x1x128).
-
-  // List of supported BlockWise pairs for FP8:
-  // https://docs.nvidia.com/cuda/cublas/#element-1d-and-128x128-2d-block-scaling-for-fp8-data-types
-
   auto [scaling_choice_a, scaling_choice_b] = get_joint_scaling(
     {
       std::make_pair(ScalingType::TensorWise, ScalingType::TensorWise),
@@ -1506,7 +1305,7 @@ _scaled_mm_out_cuda(const Tensor& mat1, const Tensor& mat2,
   TORCH_CHECK(isFloat8Type(mat2.scalar_type()) || mat2.scalar_type() == ScalarType::Float4_e2m1fn_x2, "Expected mat2 to be Float8 or Float4_x2 matrix got ", mat2.scalar_type());
 #ifndef USE_ROCM
   // Type restrictions imposed by CuBLASLt as of CUDA-12.1
-  TORCH_CHECK_VALUE(mat1.scalar_type() != ScalarType::Float8_e5m2 || mat2.scalar_type() != ScalarType::Float8_e5m2,
+  TORCH_CHECK(mat1.scalar_type() != ScalarType::Float8_e5m2 || mat2.scalar_type() != ScalarType::Float8_e5m2,
         "Multiplication of two Float8_e5m2 matrices is not supported");
 #endif
   if (use_fast_accum) {
@@ -1572,44 +1371,41 @@ _scaled_mm_out_cuda(const Tensor& mat1, const Tensor& mat2,
 
   // NVIDIA's cuBLAS only started supporting row-wise scaling in version 12.9,
   // and only for compute capability 9.0+. In other cases we use CUTLASS.
-  // We are doing row-wise scaling
-  if (scaling_choice_a == ScalingType::RowWise && scaling_choice_b == ScalingType::RowWise) {
 #ifndef USE_ROCM
-    auto dprops = at::cuda::getCurrentDeviceProperties();
-    if ((dprops->major < 9 || CUBLAS_VERSION < 120900 || cublasLtGetVersion() < 120900)
-        // cuBLAS only supports tiled 1D factor layout for 1D block scaling, no 2D block scales
-        ||  (dprops->major >= 10 && (!scale_a.sizes().empty() || !scale_b.sizes().empty()))) {
-      TORCH_CHECK_VALUE(out.dtype() == kBFloat16, "Only bf16 high precision output types are supported for row-wise scaling.");
-      return _scaled_rowwise_rowwise(
-          mat1,
-          mat2,
-          scale_a,
-          scale_b,
-          bias,
-          out.scalar_type(),
-          use_fast_accum,
-          out);
-    }
+  // We are doing row-wise scaling
+  auto dprops = at::cuda::getCurrentDeviceProperties();
+  if (scaling_choice_a == ScalingType::RowWise && scaling_choice_b == ScalingType::RowWise
+      && ((dprops->major < 9 || CUBLAS_VERSION < 120900 || cublasLtGetVersion() < 120900)
+      // cuBLAS only supports tiled 1D factor layout for 1D block scaling, no 2D block scales
+      ||  (dprops->major >= 10 && (!scale_a.sizes().empty() || !scale_b.sizes().empty())))) {
+    TORCH_CHECK(out.dtype() == kBFloat16, "Only bf16 high precision output types are supported for row-wise scaling.");
+    at::cuda::detail::f8f8bf16_rowwise(
+        mat1,
+        mat2,
+        scale_a,
+        scale_b,
+        bias,
+        use_fast_accum,
+        out);
+    return out;
+  }
 #else
+  if (scaling_choice_a == ScalingType::RowWise && scaling_choice_b == ScalingType::RowWise) {
     // For ROCm, match behavior of f8f8bf16_rowwise type checking, for unit test purposes.
     Tensor b = mat2;
     if (_scaled_mm_is_fnuz()) {
-      TORCH_CHECK_VALUE(b.dtype() == at::kFloat8_e4m3fnuz,
-          "Expected b.dtype() == at::kFloat8_e4m3fnuz, got: ", b.dtype());
+      TORCH_CHECK(b.dtype() == at::kFloat8_e4m3fnuz);
     }
     else {
-      TORCH_CHECK_VALUE(b.dtype() == at::kFloat8_e4m3fn,
-          "Expected b.dtype() == at::kFloat8_e4m3fn, got: ", b.dtype());
+      TORCH_CHECK(b.dtype() == at::kFloat8_e4m3fn);
     }
     // Until more than bf16 is supported.
-    TORCH_CHECK_VALUE(out.scalar_type() == ScalarType::BFloat16,
+    TORCH_CHECK(out.scalar_type() == ScalarType::BFloat16,
          "hipblaslt rowwise _scaled_mm only supports BFloat16 output but got ", out.scalar_type());
-#endif
   }
   else if (scaling_choice_a == ScalingType::BlockWise1x32 && scaling_choice_b == ScalingType::BlockWise1x32) {
-#ifdef USE_ROCM
     #if ROCM_VERSION >= 70000
-    TORCH_CHECK_NOT_IMPLEMENTED(at::detail::getCUDAHooks().isGPUArch({"gfx950"}),
+    TORCH_CHECK(at::detail::getCUDAHooks().isGPUArch({"gfx950"}),
                 "Block-wise scaling for Float8_e8m0fnu is only supported on gfx950");
 
     int packed_factor = 1;
@@ -1618,20 +1414,163 @@ _scaled_mm_out_cuda(const Tensor& mat1, const Tensor& mat2,
       // effectively packing two elements into one byte.
       packed_factor = 2;
     }
-    TORCH_CHECK_VALUE(mat1.size(0) % 16 == 0 && (mat1.size(1) * packed_factor) % 128 == 0 &&
+    TORCH_CHECK(mat1.size(0) % 16 == 0 && (mat1.size(1) * packed_factor) % 128 == 0 &&
                 mat2.size(1) % 16 == 0,
                 "M, N must be multiples of 16 and K must be multiple of 128 for block-wise scaling");
 
-    TORCH_CHECK_VALUE(out.scalar_type() == ScalarType::BFloat16 ||
+    TORCH_CHECK(out.scalar_type() == ScalarType::BFloat16 ||
                 out.scalar_type() == ScalarType::Half,
                 "Block-wise scaling only supports BFloat16 or Half output types");
 #else
-    TORCH_CHECK_NOT_IMPLEMENTED(false, "Block-wise scaling for Float8_e8m0fnu requires ROCm 7.0 or later");
-#endif
+    TORCH_CHECK(false, "Block-wise scaling for Float8_e8m0fnu requires ROCm 7.0 or later");
 #endif
   }
+#endif
 
-  return _scaled_gemm(mat1, mat2, scale_a, scale_b, scaling_choice_a, scaling_choice_b, bias, use_fast_accum, out);
+  cublasCommonArgs args(mat1, mat2, out, scale_a, scale_b, scale_result, scaling_choice_a, scaling_choice_b);
+  const auto out_dtype_ = args.result->scalar_type();
+  TORCH_CHECK(args.transa == 't' && args.transb == 'n', "Only multiplication of row-major and column-major matrices is supported by cuBLASLt");
+
+#ifdef USE_ROCM
+  auto tuning_ctx = at::cuda::tunable::getTuningContext();
+  if (tuning_ctx->IsTunableOpEnabled()) {
+#define TUNABLE_DISPATCH(BLASOP_A, BLASOP_B)                            \
+        if (mat1.scalar_type() == ScalarType::Float8_e4m3fnuz) {        \
+          if (mat2.scalar_type() == ScalarType::Float8_e4m3fnuz) {      \
+            static at::cuda::tunable::ScaledGemmTunableOp<              \
+                at::Float8_e4m3fnuz, at::Float8_e4m3fnuz, scalar_t,     \
+                BLASOP_A, BLASOP_B> scaledgemm{};                       \
+            scaledgemm(&params);                                        \
+          }                                                             \
+          else if (mat2.scalar_type() == ScalarType::Float8_e5m2fnuz) { \
+            static at::cuda::tunable::ScaledGemmTunableOp<              \
+                at::Float8_e4m3fnuz, at::Float8_e5m2fnuz, scalar_t,     \
+                BLASOP_A, BLASOP_B> scaledgemm{};                       \
+            scaledgemm(&params);                                        \
+          }                                                             \
+        }                                                               \
+        else if (mat1.scalar_type() == ScalarType::Float8_e5m2fnuz) {   \
+          if (mat2.scalar_type() == ScalarType::Float8_e4m3fnuz) {      \
+            static at::cuda::tunable::ScaledGemmTunableOp<              \
+                at::Float8_e5m2fnuz, at::Float8_e4m3fnuz, scalar_t,     \
+                BLASOP_A, BLASOP_B> scaledgemm{};                       \
+            scaledgemm(&params);                                        \
+          }                                                             \
+          else if (mat2.scalar_type() == ScalarType::Float8_e5m2fnuz) { \
+            static at::cuda::tunable::ScaledGemmTunableOp<              \
+                at::Float8_e5m2fnuz, at::Float8_e5m2fnuz, scalar_t,     \
+                BLASOP_A, BLASOP_B> scaledgemm{};                       \
+            scaledgemm(&params);                                        \
+          }                                                             \
+        }                                                               \
+        else if (mat1.scalar_type() == ScalarType::Float8_e4m3fn) {     \
+          if (mat2.scalar_type() == ScalarType::Float8_e4m3fn) {        \
+            static at::cuda::tunable::ScaledGemmTunableOp<              \
+                at::Float8_e4m3fn, at::Float8_e4m3fn, scalar_t,         \
+                BLASOP_A, BLASOP_B> scaledgemm{};                       \
+            scaledgemm(&params);                                        \
+          }                                                             \
+          else if (mat2.scalar_type() == ScalarType::Float8_e5m2) {     \
+            static at::cuda::tunable::ScaledGemmTunableOp<              \
+                at::Float8_e4m3fn, at::Float8_e5m2, scalar_t,           \
+                BLASOP_A, BLASOP_B> scaledgemm{};                       \
+            scaledgemm(&params);                                        \
+          }                                                             \
+        }                                                               \
+        else if (mat1.scalar_type() == ScalarType::Float8_e5m2) {       \
+          if (mat2.scalar_type() == ScalarType::Float8_e4m3fn) {        \
+            static at::cuda::tunable::ScaledGemmTunableOp<              \
+                at::Float8_e5m2, at::Float8_e4m3fn, scalar_t,           \
+                BLASOP_A, BLASOP_B> scaledgemm{};                       \
+            scaledgemm(&params);                                        \
+          }                                                             \
+          else if (mat2.scalar_type() == ScalarType::Float8_e5m2) {     \
+            static at::cuda::tunable::ScaledGemmTunableOp<              \
+                at::Float8_e5m2, at::Float8_e5m2, scalar_t,             \
+                BLASOP_A, BLASOP_B> scaledgemm{};                       \
+            scaledgemm(&params);                                        \
+          }                                                             \
+        }
+    AT_DISPATCH_V2(out_dtype_, "_tunable_scaled_gemm", AT_WRAP([&] {
+      bool transa_ = ((args.transa != 'n') && (args.transa != 'N'));
+      bool transb_ = ((args.transb != 'n') && (args.transb != 'N'));
+      at::cuda::tunable::ScaledGemmParams<scalar_t> params;
+      params.transa = args.transa;
+      params.transb = args.transb;
+      params.m = args.m;
+      params.n = args.n;
+      params.k = args.k;
+      params.a = args.mata->data_ptr();
+      params.a_scale_ptr = args.scale_mata_ptr;
+      params.a_scale_dtype = args.scale_mata_dtype.value();
+      params.lda = args.lda;
+      params.a_dtype = args.mata->scalar_type();
+      params.a_scale_dtype = args.scale_mata_dtype.value();
+      params.a_scaling_type = args.scaling_mata_type.value();
+      params.b = args.matb->data_ptr();
+      params.b_scale_ptr = args.scale_matb_ptr;
+      params.b_scale_dtype = args.scale_matb_dtype.value();
+      params.ldb = args.ldb;
+      params.b_dtype = args.matb->scalar_type();
+      params.b_scale_dtype = args.scale_matb_dtype.value();
+      params.b_scaling_type = args.scaling_matb_type.value();
+      params.bias_ptr = bias ? bias->data_ptr(): nullptr;
+      params.bias_dtype = bias ? bias->scalar_type() : isFloat8Type(out_dtype_) ? at::ScalarType::Half : out_dtype_;
+      params.c = args.result->data_ptr();
+      params.c_scale_ptr = args.scale_result_ptr;
+      params.ldc = args.result_ld;
+      params.c_dtype = out_dtype_;
+      params.use_fast_accum = use_fast_accum;
+      if (transa_ && transb_) {
+        TUNABLE_DISPATCH(at::cuda::tunable::BlasOp::T, at::cuda::tunable::BlasOp::T)
+      }
+      else if (transa_ && !transb_) {
+        TUNABLE_DISPATCH(at::cuda::tunable::BlasOp::T, at::cuda::tunable::BlasOp::N)
+      }
+      else if (!transa_ && transb_) {
+        TUNABLE_DISPATCH(at::cuda::tunable::BlasOp::N, at::cuda::tunable::BlasOp::T)
+      }
+      else if (!transa_ && !transb_) {
+        TUNABLE_DISPATCH(at::cuda::tunable::BlasOp::N, at::cuda::tunable::BlasOp::N)
+      }
+      else {
+        TORCH_CHECK(false, "unreachable");
+      }
+    }),
+    kHalf, kBFloat16, AT_EXPAND(AT_FLOAT8_TYPES), AT_EXPAND(AT_FLOATING_TYPES));
+#undef TUNABLE_DISPATCH
+  }
+  else
+#endif
+ {
+    at::cuda::blas::scaled_gemm(
+        args.transa,
+        args.transb,
+        args.m,
+        args.n,
+        args.k,
+        args.mata->data_ptr(),
+        args.scale_mata_ptr,
+        args.lda,
+        args.mata->scalar_type(),
+        args.scale_mata_dtype.value(),
+        args.scaling_mata_type.value(),
+        args.matb->data_ptr(),
+        args.scale_matb_ptr,
+        args.ldb,
+        args.matb->scalar_type(),
+        args.scale_matb_dtype.value(),
+        args.scaling_matb_type.value(),
+        bias ? bias->data_ptr(): nullptr,
+        bias ? bias->scalar_type() : isFloat8Type(out_dtype_) ? at::ScalarType::Half : out_dtype_,
+        args.result->data_ptr(),
+        args.scale_result_ptr,
+        args.result_ld,
+        out_dtype_,
+        use_fast_accum);
+  }
+
+  return out;
 }
 
 namespace {
@@ -1971,6 +1910,159 @@ std::array<std::tuple<std::string, acceptance_fn, ScaledGemmImplementation>, 8>
   { "nvfp4_nvfp4_single_scale", check_nvfp4_recipe_single_scale, ScaledGemmImplementation::NVFP4_NVFP4_SINGLE_SCALE },
   { "mxfp8_mxfp8", check_mxfp8_recipe, ScaledGemmImplementation::MXFP8_MXFP8}}};
 
+Tensor&
+_cutlass_scaled_gemm(
+          const Tensor& mat1, const Tensor& mat2,
+          const Tensor& scale_a, const Tensor& scale_b,
+          const ScalingType scaling_choice_a, const ScalingType scaling_choice_b,
+          const std::optional<Tensor>& bias,
+          const bool use_fast_accum,
+          Tensor& out) {
+  cublasCommonArgs args(mat1, mat2, out, scale_a, scale_b, std::nullopt, scaling_choice_a, scaling_choice_b);
+  const auto out_dtype_ = args.result->scalar_type();
+  TORCH_CHECK(args.transa == 't' && args.transb == 'n', "Only multiplication of row-major and column-major matrices is supported by cuBLASLt");
+
+#ifdef USE_ROCM
+  auto tuning_ctx = at::cuda::tunable::getTuningContext();
+  if (tuning_ctx->IsTunableOpEnabled()) {
+#define TUNABLE_DISPATCH(BLASOP_A, BLASOP_B)                            \
+        if (mat1.scalar_type() == ScalarType::Float8_e4m3fnuz) {        \
+          if (mat2.scalar_type() == ScalarType::Float8_e4m3fnuz) {      \
+            static at::cuda::tunable::ScaledGemmTunableOp<              \
+                at::Float8_e4m3fnuz, at::Float8_e4m3fnuz, scalar_t,     \
+                BLASOP_A, BLASOP_B> scaledgemm{};                       \
+            scaledgemm(&params);                                        \
+          }                                                             \
+          else if (mat2.scalar_type() == ScalarType::Float8_e5m2fnuz) { \
+            static at::cuda::tunable::ScaledGemmTunableOp<              \
+                at::Float8_e4m3fnuz, at::Float8_e5m2fnuz, scalar_t,     \
+                BLASOP_A, BLASOP_B> scaledgemm{};                       \
+            scaledgemm(&params);                                        \
+          }                                                             \
+        }                                                               \
+        else if (mat1.scalar_type() == ScalarType::Float8_e5m2fnuz) {   \
+          if (mat2.scalar_type() == ScalarType::Float8_e4m3fnuz) {      \
+            static at::cuda::tunable::ScaledGemmTunableOp<              \
+                at::Float8_e5m2fnuz, at::Float8_e4m3fnuz, scalar_t,     \
+                BLASOP_A, BLASOP_B> scaledgemm{};                       \
+            scaledgemm(&params);                                        \
+          }                                                             \
+          else if (mat2.scalar_type() == ScalarType::Float8_e5m2fnuz) { \
+            static at::cuda::tunable::ScaledGemmTunableOp<              \
+                at::Float8_e5m2fnuz, at::Float8_e5m2fnuz, scalar_t,     \
+                BLASOP_A, BLASOP_B> scaledgemm{};                       \
+            scaledgemm(&params);                                        \
+          }                                                             \
+        }                                                               \
+        else if (mat1.scalar_type() == ScalarType::Float8_e4m3fn) {     \
+          if (mat2.scalar_type() == ScalarType::Float8_e4m3fn) {        \
+            static at::cuda::tunable::ScaledGemmTunableOp<              \
+                at::Float8_e4m3fn, at::Float8_e4m3fn, scalar_t,         \
+                BLASOP_A, BLASOP_B> scaledgemm{};                       \
+            scaledgemm(&params);                                        \
+          }                                                             \
+          else if (mat2.scalar_type() == ScalarType::Float8_e5m2) {     \
+            static at::cuda::tunable::ScaledGemmTunableOp<              \
+                at::Float8_e4m3fn, at::Float8_e5m2, scalar_t,           \
+                BLASOP_A, BLASOP_B> scaledgemm{};                       \
+            scaledgemm(&params);                                        \
+          }                                                             \
+        }                                                               \
+        else if (mat1.scalar_type() == ScalarType::Float8_e5m2) {       \
+          if (mat2.scalar_type() == ScalarType::Float8_e4m3fn) {        \
+            static at::cuda::tunable::ScaledGemmTunableOp<              \
+                at::Float8_e5m2, at::Float8_e4m3fn, scalar_t,           \
+                BLASOP_A, BLASOP_B> scaledgemm{};                       \
+            scaledgemm(&params);                                        \
+          }                                                             \
+          else if (mat2.scalar_type() == ScalarType::Float8_e5m2) {     \
+            static at::cuda::tunable::ScaledGemmTunableOp<              \
+                at::Float8_e5m2, at::Float8_e5m2, scalar_t,             \
+                BLASOP_A, BLASOP_B> scaledgemm{};                       \
+            scaledgemm(&params);                                        \
+          }                                                             \
+        }
+    AT_DISPATCH_V2(out_dtype_, "_tunable_scaled_gemm", AT_WRAP([&] {
+      bool transa_ = ((args.transa != 'n') && (args.transa != 'N'));
+      bool transb_ = ((args.transb != 'n') && (args.transb != 'N'));
+      at::cuda::tunable::ScaledGemmParams<scalar_t> params;
+      params.transa = args.transa;
+      params.transb = args.transb;
+      params.m = args.m;
+      params.n = args.n;
+      params.k = args.k;
+      params.a = args.mata->data_ptr();
+      params.a_scale_ptr = args.scale_mata_ptr;
+      params.a_scale_dtype = args.scale_mata_dtype.value();
+      params.lda = args.lda;
+      params.a_dtype = args.mata->scalar_type();
+      params.a_scale_dtype = args.scale_mata_dtype.value();
+      params.a_scaling_type = args.scaling_mata_type.value();
+      params.b = args.matb->data_ptr();
+      params.b_scale_ptr = args.scale_matb_ptr;
+      params.b_scale_dtype = args.scale_matb_dtype.value();
+      params.ldb = args.ldb;
+      params.b_dtype = args.matb->scalar_type();
+      params.b_scale_dtype = args.scale_matb_dtype.value();
+      params.b_scaling_type = args.scaling_matb_type.value();
+      params.bias_ptr = bias ? bias->data_ptr(): nullptr;
+      params.bias_dtype = bias ? bias->scalar_type() : isFloat8Type(out_dtype_) ? at::ScalarType::Half : out_dtype_;
+      params.c = args.result->data_ptr();
+      params.c_scale_ptr = args.scale_result_ptr;
+      params.ldc = args.result_ld;
+      params.c_dtype = out_dtype_;
+      params.use_fast_accum = use_fast_accum;
+      if (transa_ && transb_) {
+        TUNABLE_DISPATCH(at::cuda::tunable::BlasOp::T, at::cuda::tunable::BlasOp::T)
+      }
+      else if (transa_ && !transb_) {
+        TUNABLE_DISPATCH(at::cuda::tunable::BlasOp::T, at::cuda::tunable::BlasOp::N)
+      }
+      else if (!transa_ && transb_) {
+        TUNABLE_DISPATCH(at::cuda::tunable::BlasOp::N, at::cuda::tunable::BlasOp::T)
+      }
+      else if (!transa_ && !transb_) {
+        TUNABLE_DISPATCH(at::cuda::tunable::BlasOp::N, at::cuda::tunable::BlasOp::N)
+      }
+      else {
+        TORCH_CHECK(false, "unreachable");
+      }
+    }),
+    kHalf, kBFloat16, AT_EXPAND(AT_FLOAT8_TYPES), AT_EXPAND(AT_FLOATING_TYPES));
+#undef TUNABLE_DISPATCH
+  }
+  else
+#endif
+ {
+    at::cuda::blas::scaled_gemm(
+        args.transa,
+        args.transb,
+        args.m,
+        args.n,
+        args.k,
+        args.mata->data_ptr(),
+        args.scale_mata_ptr,
+        args.lda,
+        args.mata->scalar_type(),
+        args.scale_mata_dtype.value(),
+        args.scaling_mata_type.value(),
+        args.matb->data_ptr(),
+        args.scale_matb_ptr,
+        args.ldb,
+        args.matb->scalar_type(),
+        args.scale_matb_dtype.value(),
+        args.scaling_matb_type.value(),
+        bias ? bias->data_ptr(): nullptr,
+        bias ? bias->scalar_type() : isFloat8Type(out_dtype_) ? at::ScalarType::Half : out_dtype_,
+        args.result->data_ptr(),
+        args.scale_result_ptr,
+        args.result_ld,
+        out_dtype_,
+        use_fast_accum);
+  }
+  return out;
+}
+
 Tensor&
 _scaled_tensorwise_tensorwise(
           const Tensor& mat_a, const Tensor& mat_b,
@@ -1990,7 +2082,7 @@ _scaled_tensorwise_tensorwise(
   auto scaling_choice_a = ScalingType::TensorWise;
   auto scaling_choice_b = ScalingType::TensorWise;
 
-  _scaled_gemm(mat_a, mat_b, scale_a, scale_b, scaling_choice_a, scaling_choice_b, bias, use_fast_accum, out);
+  _cutlass_scaled_gemm(mat_a, mat_b, scale_a, scale_b, scaling_choice_a, scaling_choice_b, bias, use_fast_accum, out);
 
   return out;
 }
@@ -2026,7 +2118,7 @@ _scaled_rowwise_rowwise(
   if (((dprops->major < 9 || CUBLAS_VERSION < 120900 || cublasLtGetVersion() < 120900)
       // cuBLAS only supports tiled 1D factor layout for 1D block scaling, no 2D block scales
       ||  (dprops->major == 10 && (scale_a.sizes().size() || scale_b.sizes().size())))) {
-    TORCH_CHECK_VALUE(out.dtype() == kBFloat16, "Only bf16 high precision output types are supported for row-wise scaling.");
+    TORCH_CHECK(out.dtype() == kBFloat16, "Only bf16 high precision output types are supported for row-wise scaling.");
     at::cuda::detail::f8f8bf16_rowwise(
         mat_a,
         mat_b,
@@ -2052,38 +2144,11 @@ _scaled_rowwise_rowwise(
        "hipblaslt rowwise _scaled_mm only supports BFloat16 output but got ", out.scalar_type());
 #endif
 
-  _scaled_gemm(mat_a, mat_b, scale_a, scale_b, scaling_choice_a, scaling_choice_b, bias, use_fast_accum, out);
+  _cutlass_scaled_gemm(mat_a, mat_b, scale_a, scale_b, scaling_choice_a, scaling_choice_b, bias, use_fast_accum, out);
 
   return out;
 }
 
-// Check the shapes & sizes of scales for deepseek-style (1x128, 128x128) scaling.
-// Wraps check_size_stride for easier integration, correctly handles cases where a dimension of the scale == 1,
-// and strides become somewhat meaningless
-void _check_deepseek_scale_stride(const Tensor& scale, const Tensor& t, const ScalingType scale_type) {
-  if (scale_type == ScalingType::BlockWise1x128) {
-    TORCH_CHECK_VALUE(check_size_stride(scale, 0, t.size(0), 1),
-        "at dim=0 scale should have ", t.size(0), "elements and stride(0) ", 1, "if ", t.size(0), " > 1 - Got: ",
-        "shape=", scale.sizes(), ", stride=", scale.strides());
-    auto expected_size = ceil_div<int64_t>(t.size(1), 128);
-    TORCH_CHECK_VALUE(check_size_stride(scale, 1, expected_size, t.size(0)),
-        "at dim=1 scale should have ", expected_size, "elements and stride ", t.size(0), "if ", expected_size, " > 1 - Got: ",
-        "shape=", scale.sizes(), ", stride=", scale.strides());
-  } else if (scale_type == ScalingType::BlockWise128x128) {
-      TORCH_CHECK_VALUE(check_size_stride(
-          scale,
-          0,
-          ceil_div<int64_t>(t.size(0), 128),
-          ceil_div<int64_t>(t.size(1), 128)),
-        "at dim=0 scale should have ", ceil_div<int64_t>(t.size(0), 128), "elements and stride(0) ", ceil_div<int64_t>(t.size(1), 128), "if ", ceil_div<int64_t>(t.size(0), 128), " > 1 - Got: ",
-        "shape=", scale.sizes(), ", stride=", scale.strides());
-      TORCH_CHECK(check_size_stride(
-          scale, 1, ceil_div<int64_t>(t.size(1), 128), 1),
-        "at dim=1 scale should have ", ceil_div<int64_t>(t.size(1), 128), "elements and stride(1) ", 1, "if ", ceil_div<int64_t>(t.size(1), 128), " > 1 - Got: ",
-        "shape=", scale.sizes(), ", stride=", scale.strides());
-  }
-}
-
 Tensor&
 _scaled_block1x128_block1x128(
           const Tensor& mat_a, const Tensor& mat_b,
@@ -2101,14 +2166,15 @@ _scaled_block1x128_block1x128(
   TORCH_CHECK_VALUE(scale_b.sizes()[0] == ceil_div<int64_t>(mat_b.sizes()[0], 128) && scale_b.sizes()[1] == mat_b.sizes()[1] && scale_b.scalar_type() == kFloat,
       "scale_b must have shape ", ceil_div<int64_t>(mat_b.sizes()[0], 128), " x ", mat_b.sizes()[1], " Float elements, got ", scale_b.sizes())
 
+  TORCH_CHECK(scale_a.stride(0) == 1, "expected scale_a.stride(0) to be 1, but got ", scale_a.stride(0));
+  TORCH_CHECK(scale_b.stride(1) == 1, "expected scale_b.stride(1) to be 1, but got ", scale_b.stride(1));
+  TORCH_CHECK(scale_b.stride(0) == scale_b.size(1),
+      "expected scale_b.stride(0) to be ", scale_b.size(1), ", but got ", scale_b.size(1));
+
   auto scaling_choice_a = ScalingType::BlockWise1x128;
   auto scaling_choice_b = ScalingType::BlockWise1x128;
 
-  // Check scale strides (including stride=1 small cases)
-  _check_deepseek_scale_stride(scale_a, mat_a, scaling_choice_a);
-  _check_deepseek_scale_stride(scale_b.t(), mat_b.t(), scaling_choice_b);
-
-  _scaled_gemm(mat_a, mat_b, scale_a, scale_b, scaling_choice_a, scaling_choice_b, bias, use_fast_accum, out);
+  _cutlass_scaled_gemm(mat_a, mat_b, scale_a, scale_b, scaling_choice_a, scaling_choice_b, bias, use_fast_accum, out);
 
   return out;
 }
@@ -2123,8 +2189,6 @@ _scaled_block128x128_block1x128(
           Tensor& out) {
   // Restrictions:
   // A, B are FP8, scales are fp32, shape K//128
-  std::cout << "mat_b: " << mat_b.dim() << ", " << mat_b.sizes() << ", " << mat_b.strides() << std::endl;
-  std::cout << "scale_b: " << scale_b.dim() << ", " << scale_b.sizes() << ", " << scale_b.strides() << std::endl;
   TORCH_CHECK_VALUE(isFloat8Type(mat_a.scalar_type()) && isFloat8Type(mat_b.scalar_type()), "mat_a and mat_b must be fp8 types, got: ",
       mat_a.scalar_type(), mat_b.scalar_type());
   TORCH_CHECK_VALUE(scale_a.sizes()[0] == ceil_div<int64_t>(mat_a.sizes()[0], 128) && scale_a.sizes()[1] == ceil_div<int64_t>(mat_a.sizes()[1], 128) && scale_a.scalar_type() == kFloat,
@@ -2132,14 +2196,15 @@ _scaled_block128x128_block1x128(
   TORCH_CHECK_VALUE(scale_b.sizes()[0] == ceil_div<int64_t>(mat_b.sizes()[0], 128) && scale_b.sizes()[1] == mat_b.sizes()[1] && scale_b.scalar_type() == kFloat,
       "scale_b must have shape ", ceil_div<int64_t>(mat_b.sizes()[0], 128), " x ", mat_b.sizes()[1], " Float elements, got ", scale_b.sizes())
 
+  TORCH_CHECK_VALUE(scale_a.stride(1) == 1, "expected scale_a.stride(1) to be 1, but got ", scale_a.stride(1));
+  TORCH_CHECK_VALUE(scale_b.stride(1) == 1, "expected scale_b.stride(1) to be 1, but got ", scale_b.stride(1));
+  TORCH_CHECK_VALUE(scale_b.stride(0) == scale_b.size(1),
+      "expected scale_b.stride(0) to be ", scale_b.size(1), ", but got ", scale_b.stride(0));
+
   auto scaling_choice_a = ScalingType::BlockWise128x128;
   auto scaling_choice_b = ScalingType::BlockWise1x128;
 
-  // Check scale strides (including stride=1 small cases)
-  _check_deepseek_scale_stride(scale_a, mat_a, scaling_choice_a);
-  _check_deepseek_scale_stride(scale_b.t(), mat_b.t(), scaling_choice_b);
-
-  _scaled_gemm(mat_a, mat_b, scale_a, scale_b, scaling_choice_a, scaling_choice_b, bias, use_fast_accum, out);
+  _cutlass_scaled_gemm(mat_a, mat_b, scale_a, scale_b, scaling_choice_a, scaling_choice_b, bias, use_fast_accum, out);
 
   return out;
 }
@@ -2161,14 +2226,15 @@ _scaled_block1x128_block128x128(
   TORCH_CHECK_VALUE(scale_b.sizes()[0] == mat_b.sizes()[0] / 128 && scale_b.sizes()[1] == mat_b.sizes()[1] / 128 && scale_b.scalar_type() == kFloat,
       "scale_b must have shape ", mat_b.sizes()[0] / 128, " x ", mat_b.sizes()[1] / 128, " Float elements, got ", scale_b.sizes())
 
+  TORCH_CHECK_VALUE(scale_a.stride(0) == 1, "expected scale_a.stride(0) to be 1, but got ", scale_a.stride(0));
+  TORCH_CHECK_VALUE(scale_b.stride(0) == 1, "expected scale_b.stride(0) to be 1, but got ", scale_b.stride(0));
+  TORCH_CHECK_VALUE(scale_b.stride(1) == scale_b.size(0),
+      "expected scale_b.stride(1) to be ", scale_b.size(0), ", but got ", scale_b.stride(1));
+
   auto scaling_choice_a = ScalingType::BlockWise1x128;
   auto scaling_choice_b = ScalingType::BlockWise128x128;
 
-  // Check scale strides (including stride=1 small cases)
-  _check_deepseek_scale_stride(scale_a, mat_a, scaling_choice_a);
-  _check_deepseek_scale_stride(scale_b.t(), mat_b.t(), scaling_choice_b);
-
-  _scaled_gemm(mat_a, mat_b, scale_a, scale_b, scaling_choice_a, scaling_choice_b, bias, use_fast_accum, out);
+  _cutlass_scaled_gemm(mat_a, mat_b, scale_a, scale_b, scaling_choice_a, scaling_choice_b, bias, use_fast_accum, out);
 
   return out;
 }
@@ -2222,7 +2288,7 @@ _scaled_mxfp8_mxfp8(
 #endif
 #endif
 
-  return _scaled_gemm(mat_a, mat_b, scale_a, scale_b, scaling_choice_a, scaling_choice_b, bias, false /* use_fast_accum */, out);
+  return _cutlass_scaled_gemm(mat_a, mat_b, scale_a, scale_b, scaling_choice_a, scaling_choice_b, bias, false /* use_fast_accum */, out);
 }
 
 Tensor&
@@ -2259,7 +2325,7 @@ _scaled_nvfp4_nvfp4(
 
   auto scaling_choice_a = ScalingType::BlockWise1x16;
   auto scaling_choice_b = ScalingType::BlockWise1x16;
-  return _scaled_gemm(mat_a, mat_b, scale_a, scale_b, scaling_choice_a, scaling_choice_b, bias, false /* use_fast_accum */, out);
+  return _cutlass_scaled_gemm(mat_a, mat_b, scale_a, scale_b, scaling_choice_a, scaling_choice_b, bias, false /* use_fast_accum */, out);
 }
 
 
@@ -2508,9 +2574,7 @@ _mx8_mx8_bf16_grouped_mm_fbgemm(
         const Tensor& mat_a,
         const Tensor& mat_b,
         const Tensor& scale_a,
-        const SwizzleType& swizzle_a,
         const Tensor& scale_b,
-        const SwizzleType& swizzle_b,
         const std::optional<at::Tensor>& offs,
         Tensor& out) {
     const bool a_is_2d = mat_a.dim() == 2;
@@ -2521,16 +2585,6 @@ _mx8_mx8_bf16_grouped_mm_fbgemm(
     TORCH_CHECK_VALUE(is_2d_2d || is_2d_3d, "MXFP8 grouped GEMM currently only supports 2d-2d and 2d-3d cases");
     TORCH_CHECK_VALUE(offs.has_value(), "MXFP8 2d-2d and 2d-3d grouped GEMMs requires offsets");
     TORCH_CHECK_VALUE(out.scalar_type() == at::kBFloat16, "Only bf16 out_dtype is supported for MXFP8 grouped gemm");
-    // MXFP8 expects float8_e8m0fnu scales.
-    TORCH_CHECK_VALUE(scale_a.scalar_type() == at::kFloat8_e8m0fnu && scale_b.scalar_type() == at::kFloat8_e8m0fnu,
-        "For MXFP8 grouped gemm, both scales must be float8_e8m0fnu tensors.");
-#ifdef USE_ROCM
-    TORCH_CHECK_VALUE(swizzle_a == SwizzleType::NO_SWIZZLE && swizzle_b == SwizzleType::NO_SWIZZLE,
-        "For ROCM MXFP8 grouped gemm, both scale swizzle types must be SWIZZLE_NONE");
-#else
-    TORCH_CHECK_VALUE(swizzle_a == SwizzleType::SWIZZLE_32_4_4 && swizzle_b == SwizzleType::SWIZZLE_32_4_4,
-        "For CUDA MXFP8 grouped gemm, both scale swizzle types must be SWIZZLE_32_4_4");
-#endif
 
 #if defined(USE_FBGEMM_GENAI) and !defined(USE_ROCM)
     fbgemm_gpu::mx8mx8bf16_grouped_mm(
@@ -2615,9 +2669,6 @@ _f8_f8_bf16_rowwise_grouped_mm(
       const std::optional<Tensor>& bias,
       bool use_fast_accum,
       Tensor& out) {
-  // FP8 per-tensor and per-row scaling expect fp32 scales.
-  TORCH_CHECK_VALUE(scale_a.scalar_type() == kFloat && scale_b.scalar_type() == kFloat,
-      "For grouped FP8 rowwise, both scales must be float32 tensors");
 #ifndef USE_ROCM
   return _f8_f8_bf16_rowwise_grouped_mm_cuda(
       mat_a,
@@ -2717,15 +2768,11 @@ _scaled_grouped_mm_cuda(
 #endif
 
   if (is_mx8mx8bf16) {
-    // Note: Passing implied SwizzleType here, correctness of scale previously checked
-    //       in `check_scale` call
     return _mx8_mx8_bf16_grouped_mm_fbgemm(
         mat_a,
         mat_b,
         scale_a,
-        SwizzleType::SWIZZLE_32_4_4,
         scale_b,
-        SwizzleType::SWIZZLE_32_4_4,
         offs.value(),
         out);
   }
@@ -2742,140 +2789,6 @@ _scaled_grouped_mm_cuda(
       out);
 }
 
-namespace {
-
-std::array<std::tuple<std::string, acceptance_fn, ScaledGemmImplementation>, 2> scale_grouped_kernel_dispatch = {{
-  { "rowwise_rowwise", check_rowwise_recipe, ScaledGemmImplementation::ROWWISE_ROWWISE},
-  { "mxfp8_mxfp8", check_mxfp8_recipe, ScaledGemmImplementation::MXFP8_MXFP8}}};
-
-} // anonymous namespace
-
-Tensor
-_scaled_grouped_mm_cuda_v2(
-          const Tensor& mat_a, const Tensor& mat_b,
-          ArrayRef<Tensor> scale_a,
-          IntArrayRef scale_recipe_a,
-          IntArrayRef swizzle_a,
-          ArrayRef<Tensor> scale_b,
-          IntArrayRef scale_recipe_b,
-          IntArrayRef swizzle_b,
-          const std::optional<Tensor>& offs,
-          const std::optional<Tensor>& bias,
-          const std::optional<c10::ScalarType> out_dtype,
-          IntArrayRef contraction_dim,
-          bool use_fast_accum) {
-  bool allowed_device = _scaled_mm_allowed_device(/*sm90_only*/true, /*sm100_only*/true);
-  TORCH_CHECK_VALUE(allowed_device, "torch._scaled_grouped_mm is only supported on CUDA devices with compute capability = [9.0, 10.0], or ROCm MI300+");
-
-  TORCH_CHECK_VALUE(!check_valid_strides_and_return_transposed(mat_a), "Expected mat1 to not be transposed");
-  TORCH_CHECK_VALUE(check_valid_strides_and_return_transposed(mat_b), "Expected mat2 to be transposed");
-  TORCH_CHECK_VALUE(mat_a.dim() == 2 || mat_a.dim() == 3, "mat_a has to be 2 or 3d");
-  TORCH_CHECK_VALUE(mat_b.dim() == 2 || mat_b.dim() == 3, "mat_b has to be 2 or 3d");
-  const bool a_is_2d = mat_a.dim() == 2;
-  const bool b_is_2d = mat_b.dim() == 2;
-
-  // NOTE(slayton): For sub-1B formats want contraction_dim argument?
-  if (!a_is_2d || !b_is_2d) {
-    if (contraction_dim.size() > 0) {
-      const int dim_a = contraction_dim[0], dim_b = mat_b.size(contraction_dim[1]);
-      TORCH_CHECK_VALUE(mat_a.size(dim_a) == mat_b.size(dim_b),
-          "Contraction dimensions (", dim_a, ",", dim_b, ") of mat_a and mat_b must match, got: ", mat_a.size(dim_a), " and ",
-          mat_b.size(dim_b));
-      // Note: only (-1, -2) is currently supported
-      TORCH_CHECK_VALUE(dim_a == -1 && dim_b == -2, "Curently contraction dims must be (-1, -2) only");
-    } else {
-      TORCH_CHECK_VALUE(mat_a.size(-1) == mat_b.size(-2), "contraction dimension of mat_a and mat_b must match");
-    }
-  }
-  TORCH_CHECK_VALUE(
-    mat_a.size(-1) % 16 == 0,
-    "Expected trailing dimension of mat_a to be divisible by 16 ",
-    "but got mat1 shape: (",
-    mat_a.sizes(),
-    ").");
-  TORCH_CHECK_VALUE(mat_b.size(-2) % 16 == 0 && mat_b.size(-1) % 16 == 0,
-    "Expected mat_b shape to be divisible by 16 ",
-    "but got mat_b shape: (",
-    mat_b.sizes(),
-    ").");
-
-  TORCH_CHECK_VALUE(!bias.has_value(), "Bias not supported yet");
-  TORCH_CHECK_VALUE(offs.has_value() ==  (a_is_2d || b_is_2d), "Have to provide offsets if there is a 2d matrix");
-
-  // NOTE: mxfp8 x mxfp8 requires (and asserts later) that offsets is present.
-  //       for rowwise, no offsets implies 3d-3d and is handled by lower-level
-  //       routines
-  if (offs.has_value()) {
-    TORCH_CHECK_VALUE(offs->dim() == 1, "offs has to be 1D");
-    TORCH_CHECK_VALUE(offs->dtype() == at::kInt, "Offsets have to be int32");
-  }
-
-  const auto out_dtype_ = out_dtype.value_or(kBFloat16);
-  TORCH_CHECK_VALUE(out_dtype_ == kBFloat16, "Only bf16 high precision output types are supported for grouped gemm");
-
-  Tensor out = create_grouped_gemm_output_tensor(mat_a, mat_b, offs, out_dtype_);
-
-  // Conversion of implicitly-defined enums to explicit
-  auto scale_recipe_a_enum = convert_int_to_enum<ScalingType>(scale_recipe_a);
-  auto swizzle_a_enum = convert_int_to_enum<SwizzleType>(swizzle_a);
-  auto scale_recipe_b_enum = convert_int_to_enum<ScalingType>(scale_recipe_b);
-  auto swizzle_b_enum = convert_int_to_enum<SwizzleType>(swizzle_b);
-
-  // at this point we can start working out what we want to be doing
-  // Try to do as few steps as possible.
-  // NOTE: support is deliberately sparse, can explicitly enumerate all combinations allowed.
-  // Do this via a list of defined (name, acceptance, concrete_impl) tuples.
-  ScaledGemmImplementation gemm_impl = ScaledGemmImplementation::NONE;
-  for (const auto& fn_entry : scale_grouped_kernel_dispatch) {
-    const auto [name, accept_fn, scaled_gemm_impl] = fn_entry;
-    bool ok = accept_fn(mat_a.scalar_type(),
-                        scale_recipe_a_enum,
-                        scale_a,
-                        mat_b.scalar_type(),
-                        scale_recipe_b_enum,
-                        scale_b);
-    if (ok) {
-      gemm_impl = scaled_gemm_impl;
-      break;
-    }
-  }
-  TORCH_CHECK_VALUE(gemm_impl != ScaledGemmImplementation::NONE,
-      "No gemm implementation was found");
-
-  switch (gemm_impl) {
-    case ScaledGemmImplementation::ROWWISE_ROWWISE: {
-      const int scale_multiplier = (mat_a.dim() == 2 && mat_b.dim() == 2) ? offs->size(0) : 1;
-      _check_scales_fp8_rowwise(mat_a, scale_a[0], 0 /* dim */ , 0 /* arg_idx */, scale_multiplier);
-      _check_scales_fp8_rowwise(mat_b, scale_b[0], 1 /* dim */ , 1 /* arg_idx */, scale_multiplier);
-      return _f8_f8_bf16_rowwise_grouped_mm(
-          mat_a,
-          mat_b,
-          scale_a[0],
-          scale_b[0],
-          offs,
-          bias,
-          use_fast_accum,
-          out);
-    }
-    case ScaledGemmImplementation::MXFP8_MXFP8: {
-      _check_scales_mxfp8(mat_a, scale_a[0], 0 /* dim */, 0 /* arg_idx */);
-      _check_scales_mxfp8(mat_b, scale_b[0], 1 /* dim */, 1 /* arg_idx */);
-      return _mx8_mx8_bf16_grouped_mm_fbgemm(
-          mat_a,
-          mat_b,
-          scale_a[0],
-          swizzle_a_enum[0],
-          scale_b[0],
-          swizzle_b_enum[0],
-          offs.value(),
-          out);
-    }
-    default:
-      TORCH_CHECK_NOT_IMPLEMENTED(false,
-          "_scaled_grouped_mm_cuda_v2 is in an inconsistent state - should never reach here");
-  }
-}
-
 Tensor _grouped_mm_cuda(const Tensor& mat_a, const Tensor& mat_b,
 const std::optional<at::Tensor>& offs,
 const std::optional<at::Tensor>& bias,

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

@@ -856,13 +856,9 @@ struct type_specialized_kernel_launcher {
       out_calc_t output_offset_calculator,
       loader_t loader,
       storer_t storer) {
-    constexpr ScalarType sret_t = rt_binary_specializations[arg_index][0];
-    constexpr ScalarType sarg0_t = rt_binary_specializations[arg_index][1];
-    constexpr ScalarType sarg1_t = rt_binary_specializations[arg_index][2];
-    if (ret_t == sret_t && arg0_t == sarg0_t && arg1_t == sarg1_t) {
-      using cret_t = c10::impl::ScalarTypeToCPPTypeT<sret_t>;
-      using carg0_t = c10::impl::ScalarTypeToCPPTypeT<sarg0_t>;
-      using carg1_t = c10::impl::ScalarTypeToCPPTypeT<sarg1_t>;
+    if (ret_t == rt_binary_specializations[arg_index][0] &&
+        arg0_t == rt_binary_specializations[arg_index][1] &&
+        arg1_t == rt_binary_specializations[arg_index][2])
       launch_vectorized_templated_kernel<
           func_t,
           array_t,
@@ -870,9 +866,12 @@ struct type_specialized_kernel_launcher {
           out_calc_t,
           loader_t,
           storer_t,
-          cret_t,
-          carg0_t,
-          carg1_t>(
+          decltype(c10::impl::ScalarTypeToCPPType<
+                   rt_binary_specializations[arg_index][0]>::t),
+          decltype(c10::impl::ScalarTypeToCPPType<
+                   rt_binary_specializations[arg_index][1]>::t),
+          decltype(c10::impl::ScalarTypeToCPPType<
+                   rt_binary_specializations[arg_index][2]>::t)>(
           numel,
           f,
           data,
@@ -880,7 +879,6 @@ struct type_specialized_kernel_launcher {
           output_offset_calculator,
           loader,
           storer);
-    }
   }
 };
 

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

@@ -655,14 +655,8 @@ struct ReduceOp {
     }
 
     __syncthreads();
-    // Intra-warp reduction, fix CUDA to have offset decreasing for better numerics
-    // matching Triton, etc.
-    // todo for AMD
-    #ifdef USE_ROCM
+
     for (int offset = 1; offset < dim_x; offset <<= 1) {
-    #else
-    for (int offset = dim_x >> 1; offset > 0; offset >>= 1) {
-    #endif
       #pragma unroll
       for (int i = 0; i < output_vec_size; i++) {
         arg_t other = ops.warp_shfl_down(value[i], offset);

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

@@ -77,8 +77,8 @@ struct nansum_functor_complex {
 #if AT_USE_JITERATOR()
   void operator()(TensorIterator& iter) {
     std::string func = jiterator_stringify(
-        arg_t combine(arg_t a, arg_t b) {
-          return a + (std::isnan(b) ? arg_t{0.} : b);
+        arg_t combine(arg_t a, scalar_t b) {
+          return a + (std::isnan(b) ? arg_t{0.} : arg_t{b});
         }
     );
     jitted_gpu_reduce_kernel<nansum_name, scalar_t, scalar_t>(

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

@@ -464,7 +464,6 @@ void parallel_cat(const Tensor &out, const MaterializedITensorListRef& inputs, i
     }
 #endif
     int32_t trailingSize;
-    int nDimsLocal = nDims;
     TensorSizeStride<unsigned int, CAT_ARRAY_MAX_INPUT_DIMS> kernelOutputParam;
     if (isInOutAligned) {
       // in this case we can and should flatten the tensors after the cat dim
@@ -478,7 +477,7 @@ void parallel_cat(const Tensor &out, const MaterializedITensorListRef& inputs, i
       // and divide all strides except last by elems_per_vec (last stride is 1 always)
       // for input, we will fix up the sizes and strides in the kernel directly
       kernelOutputParam = outputParam;
-      nDimsLocal = dimension + 1;
+      nDims = dimension + 1;
       constexpr auto elems_per_vec = alignment / sizeof(scalar_t);
       auto out_size = dimension == 0 ? out.numel() : kernelOutputParam.tensorStride[dimension-1];
       kernelOutputParam.tensorSize[dimension] = out_size / elems_per_vec;
@@ -495,7 +494,7 @@ void parallel_cat(const Tensor &out, const MaterializedITensorListRef& inputs, i
       case 0:
         break;
       case 1:
-        cat_dim = nDimsLocal - cat_dim;
+        cat_dim = nDims - cat_dim;
         break;
       default:
         cat_dim--;
@@ -526,7 +525,7 @@ void parallel_cat(const Tensor &out, const MaterializedITensorListRef& inputs, i
               data, catMetaData, outputParam, cat_dim, outputParam.tensorStride[cat_dim]);\
     }\
     C10_CUDA_KERNEL_LAUNCH_CHECK();
-    switch (nDimsLocal) {
+    switch (nDims) {
       case 1:
         HANDLE_CASE(1);
         break;

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

@@ -21,15 +21,9 @@ namespace {
 struct offset_t {
   int stride;
   int begin;
-  __device__ int operator[](int i) const {
+  __device__ int operator[](int i) {
     return stride * (begin + i);
   }
-#if CCCL_VERSION >= 3001000
-  __device__ offset_t& operator+=(int i) {
-    begin += i;
-    return *this;
-  }
-#endif
 };
 // Segmented sort by full sort algorithm:.
 // Say we are sorting a (2, 3) tensor. We have in flattened form:

aten/src/ATen/native/cuda/UpSampleBilinear2d.cu

@@ -127,29 +127,6 @@ __global__ void upsample_bilinear2d_nhwc_out_frame(
   }
 }
 
-#ifdef USE_ROCM
-// Helper function to compute output pixel range that can contribute to input pixel
-template <typename accscalar_t>
-__device__ __forceinline__ void compute_output_range(
-    int input_pos,
-    accscalar_t scale,
-    int output_size,
-    bool align_corners,
-    int& min_output,
-    int& max_output) {
-  accscalar_t lo, hi;
-  if (align_corners) {
-      lo = static_cast<accscalar_t>(input_pos - 1) / scale;
-      hi = static_cast<accscalar_t>(input_pos + 1) / scale;
-  } else {
-      lo = (input_pos - static_cast<accscalar_t>(0.5)) / scale - static_cast<accscalar_t>(0.5);
-      hi = (input_pos + static_cast<accscalar_t>(1.5)) / scale - static_cast<accscalar_t>(0.5);
-  }
-  min_output = max(0, static_cast<int>(ceil(lo)));
-  max_output = min(output_size - 1, static_cast<int>(floor(hi)));
-}
-#endif
-
 // Backward (adjoint) operation 1 <- 2 (accumulates)
 template <typename scalar_t, typename accscalar_t>
 C10_LAUNCH_BOUNDS_1(1024)
@@ -164,74 +141,8 @@ __global__ void upsample_bilinear2d_backward_out_frame(
     const bool align_corners,
     scalar_t* __restrict__ idata,
     const scalar_t* __restrict__ odata) {
-  // In C++, integer multiplication, like in standard arithmetic, is generally commutative.
-  const size_t i_numel = nc * width1 * height1;
-#ifdef USE_ROCM
-  for (size_t index = blockDim.x * blockIdx.x + threadIdx.x; index < i_numel;
-       index += blockDim.x * gridDim.x) {
-    // Decode input pixel coordinates
-    size_t index_temp = index;
-    const int w1 = index_temp % width1;
-    index_temp /= width1;
-    const int h1 = index_temp % height1;
-    const size_t nc_idx = index_temp / height1;
-
-    accscalar_t grad_sum = 0;
-
-    // Find range of output pixels that could interpolate from this input pixel
-    int h2_min, h2_max, w2_min, w2_max;
-    compute_output_range<accscalar_t>(h1, rheight, height2, align_corners, h2_min, h2_max);
-    compute_output_range<accscalar_t>(w1, rwidth, width2, align_corners, w2_min, w2_max);
-
-    // Iterate over potential output pixels
-    for (int h2 = h2_min; h2 <= h2_max; h2++) {
-      for (int w2 = w2_min; w2 <= w2_max; w2++) {
-        // Compute source coordinates for this output pixel
-        const accscalar_t h1r = area_pixel_compute_source_index<accscalar_t>(
-            rheight, h2, align_corners, /*cubic=*/false);
-        const int h1_base = (int)h1r;
-        const int h1p = (h1_base < height1 - 1) ? 1 : 0;
-        const accscalar_t h1lambda = h1r - h1_base;
-        const accscalar_t h0lambda = static_cast<accscalar_t>(1) - h1lambda;
-
-        const accscalar_t w1r = area_pixel_compute_source_index<accscalar_t>(
-            rwidth, w2, align_corners, /*cubic=*/false);
-        const int w1_base = (int)w1r;
-        const int w1p = (w1_base < width1 - 1) ? 1 : 0;
-        const accscalar_t w1lambda = w1r - w1_base;
-        const accscalar_t w0lambda = static_cast<accscalar_t>(1) - w1lambda;
-
-        // Check if our input pixel participates in this interpolation and accumulate all weights
-        // At boundaries, h1p=0 or w1p=0 causes some sampling positions to collapse
-        // to the same pixel, so we need to accumulate weights from all matching positions
-        accscalar_t weight = 0;
-
-        // Check all four interpolation positions and accumulate weights
-        if (h1 == h1_base && w1 == w1_base) {
-          weight += h0lambda * w0lambda;  // top-left
-        }
-        if (h1 == h1_base && w1 == w1_base + w1p) {
-          weight += h0lambda * w1lambda;  // top-right (may be same as top-left if w1p=0)
-        }
-        if (h1 == h1_base + h1p && w1 == w1_base) {
-          weight += h1lambda * w0lambda;  // bottom-left (may be same as top-left if h1p=0)
-        }
-        if (h1 == h1_base + h1p && w1 == w1_base + w1p) {
-          weight += h1lambda * w1lambda;  // bottom-right (may collapse to other positions)
-        }
-
-        if (weight > 0) {
-          const size_t output_idx = nc_idx * height2 * width2 + h2 * width2 + w2;
-          grad_sum += weight * static_cast<accscalar_t>(odata[output_idx]);
-        }
-      }
-    }
-
-    // Write accumulated gradient (no atomics needed)
-    idata[index] = static_cast<scalar_t>(grad_sum);
-  }
-#else
   const size_t o_numel = nc * width2 * height2;
+  const size_t i_numel = nc * width1 * height1;
   for (size_t index = blockDim.x * blockIdx.x + threadIdx.x; index < o_numel;
        index += blockDim.x * gridDim.x) {
     size_t index_temp = index;
@@ -280,7 +191,6 @@ __global__ void upsample_bilinear2d_backward_out_frame(
         static_cast<scalar_t>(h1lambda * w1lambda * d2val),
         true);
   }
-#endif
 }
 
 template <typename scalar_t, typename accscalar_t>
@@ -477,6 +387,7 @@ static void upsample_bilinear2d_backward_out_cuda_template(
   // threads are not covering the whole input tensor.
   grad_input.zero_();
 
+  const size_t num_kernels = nbatch * channels * output_height * output_width;
   const int num_threads = std::min(
       at::cuda::getCurrentDeviceProperties()->maxThreadsPerBlock, 1024);
   cudaStream_t stream = at::cuda::getCurrentCUDAStream();
@@ -486,12 +397,6 @@ static void upsample_bilinear2d_backward_out_cuda_template(
     return;
   }
 
-#ifdef USE_ROCM
-  constexpr bool use_input = true;
-#else
-  constexpr bool use_input = false;
-#endif
-
   AT_DISPATCH_FLOATING_TYPES_AND2(
       at::ScalarType::Half, at::ScalarType::BFloat16,
       grad_output_.scalar_type(), "upsample_bilinear2d_backward_out_frame", [&] {
@@ -509,8 +414,6 @@ static void upsample_bilinear2d_backward_out_cuda_template(
       const accscalar_t rwidth = area_pixel_compute_scale<accscalar_t>(
           input_width, output_width, align_corners, scales_w);
 
-      const size_t num_kernels = nbatch * channels * output_height * output_width;
-
       upsample_bilinear2d_backward_nhwc_out_frame<scalar_t, accscalar_t>
           <<<ceil_div(num_kernels, static_cast<size_t>(num_threads)), num_threads, 0, stream>>>(
               input_height,
@@ -541,8 +444,6 @@ static void upsample_bilinear2d_backward_out_cuda_template(
       const accscalar_t rwidth = area_pixel_compute_scale<accscalar_t>(
           input_width, output_width, align_corners, scales_w);
 
-      const size_t num_kernels = nbatch * channels * (use_input ? input_height * input_width : output_height * output_width);
-
       upsample_bilinear2d_backward_out_frame<scalar_t, accscalar_t>
           <<<ceil_div(num_kernels, static_cast<size_t>(num_threads)),
              num_threads,

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

@@ -466,11 +466,7 @@ struct ReduceJitOp {
 
     __syncthreads();
 
-    #ifdef USE_ROCM
     for (int offset = 1; offset < dim_x; offset <<= 1) {
-    #else
-    for (int offset = dim_x >> 1; offset > 0; offset >>= 1) {
-    #endif
       #pragma unroll
       for (int i = 0; i < output_vec_size; i++) {
         arg_t other = reducer::warp_shfl_down(value[i], offset);

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

@@ -512,7 +512,7 @@ TORCH_IMPL_FUNC(threshold_backward_out_mps)
 }
 
 static MPSGraphTensor* normcdf(MPSGraph* mpsGraph, MPSGraphTensor* inputTensor) {
-  // (1.0f + erf(x*SQRT1_2)) * 0.5f;
+  // (1.0f + erf(x*SQRT1_2)) * 0.5f * x;
   auto dataType = [inputTensor dataType];
   const float SQRT1_2 = 0.707106781186547524400844362104849039f;
   MPSGraphTensor* sqrt1_2 = [mpsGraph constantWithScalar:SQRT1_2 shape:@[ @1 ] dataType:dataType];

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

@@ -54,10 +54,6 @@ Tensor dot_mps(const Tensor& self, const Tensor& other) {
   using namespace mps;
   using CachedGraph = MPSBinaryCachedGraph;
 
-  if (self.numel() == 0 & other.numel() == 0) {
-    return zeros({}, self.options());
-  }
-
   dot_check(self, other);
 
   auto output = at::empty({}, self.scalar_type(), std::nullopt, kMPS, std::nullopt, std::nullopt);

aten/src/ATen/native/native_functions.yaml

@@ -7183,12 +7183,6 @@
     CUDA: _scaled_grouped_mm_cuda
   tags: needs_exact_strides
 
-- func: _scaled_grouped_mm_v2(Tensor self, Tensor mat2, Tensor[] scale_a, int[] recipe_a, int[] swizzle_a, Tensor[] scale_b, int[] recipe_b, int[] swizzle_b, Tensor? offs=None, Tensor? bias=None, ScalarType? out_dtype=None, int[] contraction_dim=[], bool use_fast_accum=False) -> Tensor
-  variants: function
-  dispatch:
-    CUDA: _scaled_grouped_mm_cuda_v2
-  tags: needs_exact_strides
-
 - func: _grouped_mm(Tensor self, Tensor mat2, Tensor? offs=None, Tensor? bias=None, ScalarType? out_dtype=None) -> Tensor
   variants: function
   dispatch:

benchmarks/dynamo/ci_expected_accuracy/aot_eager_timm_inference.csv

@@ -10,18 +10,10 @@ beit_base_patch16_224,pass,0
 
 
 
-convnextv2_nano.fcmae_ft_in22k_in1k,pass,0
-
-
-
 deit_base_distilled_patch16_224,pass,0
 
 
 
-deit_tiny_patch16_224.fb_in1k,pass,0
-
-
-
 dm_nfnet_f0,pass,0
 
 
@@ -63,11 +55,3 @@ tf_efficientnet_b0,pass,0
 
 
 visformer_small,pass,0
-
-
-
-vit_base_patch14_dinov2.lvd142m,pass,0
-
-
-
-vit_base_patch16_siglip_256,pass,0

benchmarks/dynamo/ci_expected_accuracy/aot_eager_timm_training.csv

@@ -10,18 +10,10 @@ beit_base_patch16_224,pass,7
 
 
 
-convnextv2_nano.fcmae_ft_in22k_in1k,pass,7
-
-
-
 deit_base_distilled_patch16_224,pass,7
 
 
 
-deit_tiny_patch16_224.fb_in1k,pass,7
-
-
-
 dm_nfnet_f0,pass,6
 
 
@@ -63,11 +55,3 @@ tf_efficientnet_b0,pass,6
 
 
 visformer_small,pass,7
-
-
-
-vit_base_patch14_dinov2.lvd142m,pass,7
-
-
-
-vit_base_patch16_siglip_256,pass,7

benchmarks/dynamo/ci_expected_accuracy/aot_inductor_timm_inference.csv

@@ -10,18 +10,10 @@ beit_base_patch16_224,pass,0
 
 
 
-convnextv2_nano.fcmae_ft_in22k_in1k,pass,0
-
-
-
 deit_base_distilled_patch16_224,pass,0
 
 
 
-deit_tiny_patch16_224.fb_in1k,pass,0
-
-
-
 dm_nfnet_f0,pass,0
 
 
@@ -63,11 +55,3 @@ tf_efficientnet_b0,pass,0
 
 
 visformer_small,pass,0
-
-
-
-vit_base_patch14_dinov2.lvd142m,pass,0
-
-
-
-vit_base_patch16_siglip_256,pass,0

benchmarks/dynamo/ci_expected_accuracy/cpu_aot_inductor_freezing_timm_inference.csv

@@ -10,18 +10,10 @@ beit_base_patch16_224,pass,0
 
 
 
-convnextv2_nano.fcmae_ft_in22k_in1k,pass,0
-
-
-
 deit_base_distilled_patch16_224,pass,0
 
 
 
-deit_tiny_patch16_224.fb_in1k,pass,0
-
-
-
 dm_nfnet_f0,pass,0
 
 
@@ -63,11 +55,3 @@ tf_efficientnet_b0,pass,0
 
 
 visformer_small,pass,0
-
-
-
-vit_base_patch14_dinov2.lvd142m,pass,0
-
-
-
-vit_base_patch16_siglip_256,pass,0

benchmarks/dynamo/ci_expected_accuracy/cpu_inductor_amp_freezing_timm_inference.csv

@@ -10,18 +10,10 @@ beit_base_patch16_224,pass,0
 
 
 
-convnextv2_nano.fcmae_ft_in22k_in1k,pass,0
-
-
-
 deit_base_distilled_patch16_224,pass,0
 
 
 
-deit_tiny_patch16_224.fb_in1k,pass,0
-
-
-
 dm_nfnet_f0,pass,0
 
 
@@ -63,11 +55,3 @@ tf_efficientnet_b0,pass,0
 
 
 visformer_small,pass,0
-
-
-
-vit_base_patch14_dinov2.lvd142m,pass,0
-
-
-
-vit_base_patch16_siglip_256,pass,0

benchmarks/dynamo/ci_expected_accuracy/cpu_inductor_freezing_timm_inference.csv

@@ -10,18 +10,10 @@ beit_base_patch16_224,pass,0
 
 
 
-convnextv2_nano.fcmae_ft_in22k_in1k,pass,0
-
-
-
 deit_base_distilled_patch16_224,pass,0
 
 
 
-deit_tiny_patch16_224.fb_in1k,pass,0
-
-
-
 dm_nfnet_f0,pass,0
 
 
@@ -63,11 +55,3 @@ tf_efficientnet_b0,pass,0
 
 
 visformer_small,pass,0
-
-
-
-vit_base_patch14_dinov2.lvd142m,pass,0
-
-
-
-vit_base_patch16_siglip_256,pass,0

benchmarks/dynamo/ci_expected_accuracy/cpu_inductor_timm_inference.csv

@@ -10,18 +10,10 @@ beit_base_patch16_224,pass,0
 
 
 
-convnextv2_nano.fcmae_ft_in22k_in1k,pass,0
-
-
-
 deit_base_distilled_patch16_224,pass,0
 
 
 
-deit_tiny_patch16_224.fb_in1k,pass,0
-
-
-
 dm_nfnet_f0,pass,0
 
 
@@ -63,11 +55,3 @@ tf_efficientnet_b0,pass,0
 
 
 visformer_small,pass,0
-
-
-
-vit_base_patch14_dinov2.lvd142m,pass,0
-
-
-
-vit_base_patch16_siglip_256,pass,0

benchmarks/dynamo/ci_expected_accuracy/dynamic_aot_eager_timm_inference.csv

@@ -10,18 +10,10 @@ beit_base_patch16_224,pass,0
 
 
 
-convnextv2_nano.fcmae_ft_in22k_in1k,pass,0
-
-
-
 deit_base_distilled_patch16_224,pass,0
 
 
 
-deit_tiny_patch16_224.fb_in1k,pass,0
-
-
-
 dm_nfnet_f0,pass,0
 
 
@@ -63,11 +55,3 @@ tf_efficientnet_b0,pass,0
 
 
 visformer_small,pass,0
-
-
-
-vit_base_patch14_dinov2.lvd142m,pass,0
-
-
-
-vit_base_patch16_siglip_256,pass,0

benchmarks/dynamo/ci_expected_accuracy/dynamic_aot_eager_timm_training.csv

@@ -10,18 +10,10 @@ beit_base_patch16_224,pass,7
 
 
 
-convnextv2_nano.fcmae_ft_in22k_in1k,pass,7
-
-
-
 deit_base_distilled_patch16_224,pass,7
 
 
 
-deit_tiny_patch16_224.fb_in1k,pass,7
-
-
-
 dm_nfnet_f0,pass,6
 
 
@@ -63,11 +55,3 @@ tf_efficientnet_b0,pass,6
 
 
 visformer_small,pass,7
-
-
-
-vit_base_patch14_dinov2.lvd142m,pass,7
-
-
-
-vit_base_patch16_siglip_256,pass,7

benchmarks/dynamo/ci_expected_accuracy/dynamic_cpu_inductor_timm_inference.csv

@@ -10,18 +10,10 @@ beit_base_patch16_224,pass,0
 
 
 
-convnextv2_nano.fcmae_ft_in22k_in1k,pass,0
-
-
-
 deit_base_distilled_patch16_224,pass,0
 
 
 
-deit_tiny_patch16_224.fb_in1k,pass,0
-
-
-
 dm_nfnet_f0,pass,0
 
 
@@ -63,11 +55,3 @@ tf_efficientnet_b0,pass,0
 
 
 visformer_small,pass,0
-
-
-
-vit_base_patch14_dinov2.lvd142m,pass,0
-
-
-
-vit_base_patch16_siglip_256,pass,0

benchmarks/dynamo/ci_expected_accuracy/dynamic_cpu_max_autotune_inductor_amp_freezing_timm_inference.csv

@@ -10,18 +10,10 @@ beit_base_patch16_224,pass,0
 
 
 
-convnextv2_nano.fcmae_ft_in22k_in1k,pass,0
-
-
-
 deit_base_distilled_patch16_224,pass,0
 
 
 
-deit_tiny_patch16_224.fb_in1k,pass,0
-
-
-
 dm_nfnet_f0,pass,0
 
 
@@ -63,11 +55,3 @@ tf_efficientnet_b0,pass,0
 
 
 visformer_small,pass,0
-
-
-
-vit_base_patch14_dinov2.lvd142m,pass,0
-
-
-
-vit_base_patch16_siglip_256,pass,0

benchmarks/dynamo/ci_expected_accuracy/dynamic_inductor_timm_inference.csv

@@ -10,18 +10,10 @@ beit_base_patch16_224,pass,0
 
 
 
-convnextv2_nano.fcmae_ft_in22k_in1k,pass,0
-
-
-
 deit_base_distilled_patch16_224,pass,0
 
 
 
-deit_tiny_patch16_224.fb_in1k,pass,0
-
-
-
 dm_nfnet_f0,pass,0
 
 
@@ -63,11 +55,3 @@ tf_efficientnet_b0,pass,0
 
 
 visformer_small,pass,0
-
-
-
-vit_base_patch14_dinov2.lvd142m,pass,0
-
-
-
-vit_base_patch16_siglip_256,pass,0

benchmarks/dynamo/ci_expected_accuracy/dynamic_inductor_timm_training.csv

@@ -10,18 +10,10 @@ beit_base_patch16_224,pass,7
 
 
 
-convnextv2_nano.fcmae_ft_in22k_in1k,pass,7
-
-
-
 deit_base_distilled_patch16_224,pass,7
 
 
 
-deit_tiny_patch16_224.fb_in1k,pass,7
-
-
-
 dm_nfnet_f0,pass,6
 
 
@@ -63,11 +55,3 @@ tf_efficientnet_b0,pass,6
 
 
 visformer_small,pass,7
-
-
-
-vit_base_patch14_dinov2.lvd142m,pass,7
-
-
-
-vit_base_patch16_siglip_256,pass,7

benchmarks/dynamo/ci_expected_accuracy/dynamo_eager_timm_inference.csv

@@ -10,18 +10,10 @@ beit_base_patch16_224,pass,0
 
 
 
-convnextv2_nano.fcmae_ft_in22k_in1k,pass,0
-
-
-
 deit_base_distilled_patch16_224,pass,0
 
 
 
-deit_tiny_patch16_224.fb_in1k,pass,0
-
-
-
 dm_nfnet_f0,pass,0
 
 
@@ -63,11 +55,3 @@ tf_efficientnet_b0,pass,0
 
 
 visformer_small,pass,0
-
-
-
-vit_base_patch14_dinov2.lvd142m,pass,0
-
-
-
-vit_base_patch16_siglip_256,pass,0

benchmarks/dynamo/ci_expected_accuracy/dynamo_eager_timm_training.csv

@@ -10,18 +10,10 @@ beit_base_patch16_224,pass,7
 
 
 
-convnextv2_nano.fcmae_ft_in22k_in1k,pass,7
-
-
-
 deit_base_distilled_patch16_224,pass,7
 
 
 
-deit_tiny_patch16_224.fb_in1k,pass,7
-
-
-
 dm_nfnet_f0,pass,6
 
 
@@ -63,11 +55,3 @@ tf_efficientnet_b0,pass,6
 
 
 visformer_small,pass,7
-
-
-
-vit_base_patch14_dinov2.lvd142m,pass,7
-
-
-
-vit_base_patch16_siglip_256,pass,7

benchmarks/dynamo/ci_expected_accuracy/inductor_timm_inference.csv

@@ -10,18 +10,10 @@ beit_base_patch16_224,pass,0
 
 
 
-convnextv2_nano.fcmae_ft_in22k_in1k,pass,0
-
-
-
 deit_base_distilled_patch16_224,pass,0
 
 
 
-deit_tiny_patch16_224.fb_in1k,pass,0
-
-
-
 dm_nfnet_f0,pass,0
 
 
@@ -63,11 +55,3 @@ tf_efficientnet_b0,pass,0
 
 
 visformer_small,pass,0
-
-
-
-vit_base_patch14_dinov2.lvd142m,pass,0
-
-
-
-vit_base_patch16_siglip_256,pass,0

benchmarks/dynamo/ci_expected_accuracy/inductor_timm_training.csv

@@ -10,18 +10,10 @@ beit_base_patch16_224,pass,7
 
 
 
-convnextv2_nano.fcmae_ft_in22k_in1k,fail_accuracy,7
-
-
-
 deit_base_distilled_patch16_224,pass,7
 
 
 
-deit_tiny_patch16_224.fb_in1k,pass,7
-
-
-
 dm_nfnet_f0,pass,6
 
 
@@ -63,11 +55,3 @@ tf_efficientnet_b0,pass,6
 
 
 visformer_small,pass,7
-
-
-
-vit_base_patch14_dinov2.lvd142m,fail_accuracy,7
-
-
-
-vit_base_patch16_siglip_256,pass,7

benchmarks/dynamo/ci_expected_accuracy/rocm/aot_eager_timm_inference.csv

@@ -10,18 +10,10 @@ beit_base_patch16_224,pass,0
 
 
 
-convnextv2_nano.fcmae_ft_in22k_in1k,pass,0
-
-
-
 deit_base_distilled_patch16_224,pass,0
 
 
 
-deit_tiny_patch16_224.fb_in1k,pass,0
-
-
-
 dm_nfnet_f0,pass,0
 
 
@@ -63,11 +55,3 @@ tf_efficientnet_b0,pass,0
 
 
 visformer_small,pass,0
-
-
-
-vit_base_patch14_dinov2.lvd142m,pass,0
-
-
-
-vit_base_patch16_siglip_256,pass,0

benchmarks/dynamo/ci_expected_accuracy/rocm/aot_eager_timm_training.csv

@@ -10,18 +10,10 @@ beit_base_patch16_224,pass,7
 
 
 
-convnextv2_nano.fcmae_ft_in22k_in1k,pass,7
-
-
-
 deit_base_distilled_patch16_224,pass,7
 
 
 
-deit_tiny_patch16_224.fb_in1k,pass,7
-
-
-
 dm_nfnet_f0,pass,6
 
 
@@ -63,11 +55,3 @@ tf_efficientnet_b0,pass,6
 
 
 visformer_small,pass,7
-
-
-
-vit_base_patch14_dinov2.lvd142m,pass,7
-
-
-
-vit_base_patch16_siglip_256,pass,7

benchmarks/dynamo/ci_expected_accuracy/rocm/aot_inductor_timm_inference.csv

@@ -10,18 +10,10 @@ beit_base_patch16_224,pass,0
 
 
 
-convnextv2_nano.fcmae_ft_in22k_in1k,pass,0
-
-
-
 deit_base_distilled_patch16_224,pass,0
 
 
 
-deit_tiny_patch16_224.fb_in1k,pass,0
-
-
-
 dm_nfnet_f0,pass,0
 
 
@@ -63,11 +55,3 @@ tf_efficientnet_b0,pass,0
 
 
 visformer_small,pass,0
-
-
-
-vit_base_patch14_dinov2.lvd142m,pass,0
-
-
-
-vit_base_patch16_siglip_256,pass,0

benchmarks/dynamo/ci_expected_accuracy/rocm/dynamic_aot_eager_timm_inference.csv

@@ -10,18 +10,10 @@ beit_base_patch16_224,pass,0
 
 
 
-convnextv2_nano.fcmae_ft_in22k_in1k,pass,0
-
-
-
 deit_base_distilled_patch16_224,pass,0
 
 
 
-deit_tiny_patch16_224.fb_in1k,pass,0
-
-
-
 dm_nfnet_f0,pass,0
 
 
@@ -63,11 +55,3 @@ tf_efficientnet_b0,pass,0
 
 
 visformer_small,pass,0
-
-
-
-vit_base_patch14_dinov2.lvd142m,pass,0
-
-
-
-vit_base_patch16_siglip_256,pass,0

benchmarks/dynamo/ci_expected_accuracy/rocm/dynamic_aot_eager_timm_training.csv

@@ -10,18 +10,10 @@ beit_base_patch16_224,pass,7
 
 
 
-convnextv2_nano.fcmae_ft_in22k_in1k,pass,7
-
-
-
 deit_base_distilled_patch16_224,pass,7
 
 
 
-deit_tiny_patch16_224.fb_in1k,pass,7
-
-
-
 dm_nfnet_f0,pass,6
 
 
@@ -63,11 +55,3 @@ tf_efficientnet_b0,pass,6
 
 
 visformer_small,pass,7
-
-
-
-vit_base_patch14_dinov2.lvd142m,pass,7
-
-
-
-vit_base_patch16_siglip_256,pass,7

benchmarks/dynamo/ci_expected_accuracy/rocm/dynamic_inductor_timm_inference.csv

@@ -10,18 +10,10 @@ beit_base_patch16_224,pass,0
 
 
 
-convnextv2_nano.fcmae_ft_in22k_in1k,pass,0
-
-
-
 deit_base_distilled_patch16_224,pass,0
 
 
 
-deit_tiny_patch16_224.fb_in1k,pass,0
-
-
-
 dm_nfnet_f0,pass,0
 
 
@@ -63,11 +55,3 @@ tf_efficientnet_b0,pass,0
 
 
 visformer_small,pass,0
-
-
-
-vit_base_patch14_dinov2.lvd142m,pass,0
-
-
-
-vit_base_patch16_siglip_256,pass,0

benchmarks/dynamo/ci_expected_accuracy/rocm/dynamic_inductor_timm_training.csv

@@ -10,18 +10,10 @@ beit_base_patch16_224,pass,7
 
 
 
-convnextv2_nano.fcmae_ft_in22k_in1k,pass,7
-
-
-
 deit_base_distilled_patch16_224,pass,7
 
 
 
-deit_tiny_patch16_224.fb_in1k,pass,7
-
-
-
 dm_nfnet_f0,pass,6
 
 
@@ -63,11 +55,3 @@ tf_efficientnet_b0,pass,6
 
 
 visformer_small,fail_accuracy,7
-
-
-
-vit_base_patch14_dinov2.lvd142m,pass,7
-
-
-
-vit_base_patch16_siglip_256,pass,7

benchmarks/dynamo/ci_expected_accuracy/rocm/dynamo_eager_timm_inference.csv

@@ -10,18 +10,10 @@ beit_base_patch16_224,pass,0
 
 
 
-convnextv2_nano.fcmae_ft_in22k_in1k,pass,0
-
-
-
 deit_base_distilled_patch16_224,pass,0
 
 
 
-deit_tiny_patch16_224.fb_in1k,pass,0
-
-
-
 dm_nfnet_f0,pass,0
 
 
@@ -63,11 +55,3 @@ tf_efficientnet_b0,pass,0
 
 
 visformer_small,pass,0
-
-
-
-vit_base_patch14_dinov2.lvd142m,pass,0
-
-
-
-vit_base_patch16_siglip_256,pass,0

benchmarks/dynamo/ci_expected_accuracy/rocm/dynamo_eager_timm_training.csv

@@ -10,18 +10,10 @@ beit_base_patch16_224,pass,7
 
 
 
-convnextv2_nano.fcmae_ft_in22k_in1k,pass,7
-
-
-
 deit_base_distilled_patch16_224,pass,7
 
 
 
-deit_tiny_patch16_224.fb_in1k,pass,7
-
-
-
 dm_nfnet_f0,pass,6
 
 
@@ -63,11 +55,3 @@ tf_efficientnet_b0,pass,6
 
 
 visformer_small,pass,7
-
-
-
-vit_base_patch14_dinov2.lvd142m,pass,7
-
-
-
-vit_base_patch16_siglip_256,pass,7

benchmarks/dynamo/ci_expected_accuracy/rocm/inductor_timm_inference.csv

@@ -10,18 +10,10 @@ beit_base_patch16_224,pass,0
 
 
 
-convnextv2_nano.fcmae_ft_in22k_in1k,pass,0
-
-
-
 deit_base_distilled_patch16_224,pass,0
 
 
 
-deit_tiny_patch16_224.fb_in1k,pass,0
-
-
-
 dm_nfnet_f0,pass,0
 
 
@@ -63,11 +55,3 @@ tf_efficientnet_b0,pass,0
 
 
 visformer_small,pass,0
-
-
-
-vit_base_patch14_dinov2.lvd142m,pass,0
-
-
-
-vit_base_patch16_siglip_256,pass,0

benchmarks/dynamo/ci_expected_accuracy/rocm/inductor_timm_training.csv

@@ -10,18 +10,10 @@ beit_base_patch16_224,pass,7
 
 
 
-convnextv2_nano.fcmae_ft_in22k_in1k,fail_accuracy,7
-
-
-
 deit_base_distilled_patch16_224,pass,7
 
 
 
-deit_tiny_patch16_224.fb_in1k,pass,7
-
-
-
 dm_nfnet_f0,pass,6
 
 
@@ -63,11 +55,3 @@ tf_efficientnet_b0,pass,6
 
 
 visformer_small,pass,7
-
-
-
-vit_base_patch14_dinov2.lvd142m,fail_accuracy,7
-
-
-
-vit_base_patch16_siglip_256,pass,7

benchmarks/dynamo/common.py

@@ -1060,8 +1060,6 @@ def speedup_experiment(args, model_iter_fn, model, example_inputs, **kwargs):
             frozen_model_iter_fn = export_nativert(model, example_inputs)
         elif args.torchscript_jit_trace:
             frozen_model_iter_fn = torchscript_jit_trace(model, example_inputs)
-        elif args.aot_precompile:
-            frozen_model_iter_fn = aot_precompile(model, example_inputs)
         else:
             if kwargs["hf_llm"]:
                 # If it's an llm, we want to optimize model.forward, and use
@@ -1497,37 +1495,6 @@ def export(model, example_inputs):
     return opt_export
 
 
-def aot_precompile(model, example_inputs):
-    example_args, example_kwargs = _normalize_bench_inputs(example_inputs)
-
-    with tempfile.NamedTemporaryFile(suffix=".pt", delete=False) as f:
-        save_path = f.name
-
-    with fresh_cache(), torch._dynamo.config.patch("enable_aot_compile", True):
-        compiled_fn = torch.compile(
-            model,
-            fullgraph=True,
-            options={"guard_filter_fn": lambda guards: [False for _ in guards]},
-        ).forward.aot_compile((example_args, example_kwargs))
-
-        compiled_fn.save_compiled_function(save_path)
-
-        torch._dynamo.reset()
-        with open(save_path, "rb") as f:
-            load_start_time = time.perf_counter()
-            loaded_fn = torch.compiler.load_compiled_function(f)
-            load_end_time = time.perf_counter()
-            print(
-                f"AOT Precompile loading time: {load_end_time - load_start_time} seconds"
-            )
-
-            def opt_aot_precompile(_, example_inputs, collect_outputs=False):
-                example_args, example_kwargs = _normalize_bench_inputs(example_inputs)
-                return loaded_fn(model, *example_args, **example_kwargs)
-
-            return opt_aot_precompile
-
-
 def export_nativert(model, example_inputs):
     optimized = NativeRTCache.load(model, example_inputs)
 
@@ -2307,7 +2274,6 @@ class BenchmarkRunner:
                     or self.args.export_aot_inductor
                     or self.args.export_nativert
                     or self.args.torchscript_jit_trace
-                    or self.args.aot_precompile
                 ):
                     # apply export on module directly
                     # no need for n iterations
@@ -2763,7 +2729,6 @@ class BenchmarkRunner:
                 self.args.export_aot_inductor
                 or self.args.export_nativert
                 or self.args.torchscript_jit_trace
-                or self.args.aot_precompile
             ):
                 optimized_model_iter_fn = optimize_ctx
             else:
@@ -3540,11 +3505,6 @@ def parse_args(args=None):
         action="store_true",
         help="Measure pass rate with Export+AOTInductor",
     )
-    group.add_argument(
-        "--aot-precompile",
-        action="store_true",
-        help="Measure pass rate with AOT Precompile",
-    )
     group.add_argument(
         "--export-nativert",
         action="store_true",
@@ -3975,10 +3935,6 @@ def run(runner, args, original_dir=None):
         optimize_ctx = export
         experiment = speedup_experiment
         output_filename = "export.csv"
-    elif args.aot_precompile:
-        optimize_ctx = aot_precompile
-        experiment = speedup_experiment
-        output_filename = "aot_precompile.csv"
     elif args.export_nativert:
         optimize_ctx = export_nativert
         experiment = speedup_experiment

benchmarks/dynamo/timm_models.py

@@ -271,6 +271,8 @@ class TimmRunner(BenchmarkRunner):
             memory_format=torch.channels_last if channels_last else None,
         )
 
+        self.num_classes = model.num_classes
+
         data_config = resolve_data_config(
             vars(self._args) if timmversion >= "0.8.0" else self._args,
             model=model,
@@ -300,6 +302,7 @@ class TimmRunner(BenchmarkRunner):
         example_inputs = [
             example_inputs,
         ]
+        self.target = self._gen_target(batch_size, device)
 
         self.loss = torch.nn.CrossEntropyLoss().to(device)
 
@@ -367,6 +370,11 @@ class TimmRunner(BenchmarkRunner):
                 tolerance = 1e-2
         return tolerance, cosine
 
+    def _gen_target(self, batch_size, device):
+        return torch.empty((batch_size,) + (), device=device, dtype=torch.long).random_(
+            self.num_classes
+        )
+
     def compute_loss(self, pred):
         # High loss values make gradient checking harder, as small changes in
         # accumulation order upsets accuracy checks.

benchmarks/dynamo/timm_models_list.txt

@@ -1,8 +1,6 @@
 adv_inception_v3 128
 beit_base_patch16_224 128
-convnextv2_nano.fcmae_ft_in22k_in1k 128
 deit_base_distilled_patch16_224 128
-deit_tiny_patch16_224.fb_in1k 128
 dm_nfnet_f0 128
 ghostnet_100 512
 inception_v3 128
@@ -14,5 +12,3 @@ repvgg_a2 128
 swin_base_patch4_window7_224 128
 tf_efficientnet_b0 128
 visformer_small 128
-vit_base_patch14_dinov2.lvd142m 128
-vit_base_patch16_siglip_256 128

benchmarks/dynamo/timm_models_list_cpu.txt

@@ -1,8 +1,6 @@
 adv_inception_v3,128
 beit_base_patch16_224,64
-convnextv2_nano.fcmae_ft_in22k_in1k,128
 deit_base_distilled_patch16_224,64
-deit_tiny_patch16_224.fb_in1k,128
 dm_nfnet_f0,128
 ghostnet_100,128
 inception_v3,128
@@ -14,5 +12,3 @@ repvgg_a2,128
 swin_base_patch4_window7_224,64
 tf_efficientnet_b0,128
 visformer_small,128
-vit_base_patch14_dinov2.lvd142m,128
-ViT-B-16-SigLIP-i18n-256,128

c10/core/ScalarType.h

@@ -28,8 +28,101 @@
 
 namespace c10 {
 
-// See [dtype Macros note] in torch/headeronly/core/ScalarType.h
-// regarding macros.
+// [dtype Macros note] For the macros below:
+//
+// For users: If you want to macro some code for all non-QInt scalar types
+// (i.e. types with complete information, you probably want one of the
+// AT_FORALL_SCALAR_TYPES / AT_FORALL_SCALAR_TYPES_AND macros below, which are
+// designed to behave similarly to the Dispatch macros with the same name.
+//
+// For adding a new dtype: In the beginning, we had an idea that there was a
+// list of all scalar types, and you could use AT_FORALL_SCALAR_TYPES to
+// iterate over them.  But over the years we added weird types which couldn't
+// be handled uniformly everywhere and so in the end we ended up with some
+// mish-mosh of some helper macros, but mostly use sites making a call about
+// what dtypes they can or can't support.  So if you want to add a new dtype,
+// the preferred resolution is to find a dtype similar to what you want,
+// grep for it and edit all the sites you find this way.  If you need to add
+// a completely new kind of dtype, you're going to have to laboriously audit
+// all of the sites everywhere to figure out how it should work.  Consulting
+// some old PRs where we added new dtypes (check history of this file) can
+// help give you an idea where to start.
+
+// If you want to support ComplexHalf for real, add ComplexHalf
+// into this macro (and change the name).  But beware: convert()
+// doesn't work for all the conversions you need...
+//
+// TODO: To add unsigned int types here, we must define accumulate type.
+// But uint8 currently accumulates into int64, so we would have to make
+// an inconsistent choice for the larger types.  Difficult.
+#define AT_FORALL_SCALAR_TYPES_WITH_COMPLEX_EXCEPT_COMPLEX_HALF_F8NZ(_) \
+  _(uint8_t, Byte)                                                      \
+  _(int8_t, Char)                                                       \
+  _(int16_t, Short)                                                     \
+  _(int, Int)                                                           \
+  _(int64_t, Long)                                                      \
+  _(at::Half, Half)                                                     \
+  _(float, Float)                                                       \
+  _(double, Double)                                                     \
+  _(c10::complex<float>, ComplexFloat)                                  \
+  _(c10::complex<double>, ComplexDouble)                                \
+  _(bool, Bool)                                                         \
+  _(at::BFloat16, BFloat16)                                             \
+  _(at::Float8_e5m2, Float8_e5m2)                                       \
+  _(at::Float8_e4m3fn, Float8_e4m3fn)
+
+// This macro controls many of our C++ APIs, including constructors
+// for Scalar as well as the data() and item() accessors on Tensor
+#define AT_FORALL_SCALAR_TYPES_WITH_COMPLEX(_) \
+  _(uint8_t, Byte)                             \
+  _(int8_t, Char)                              \
+  _(int16_t, Short)                            \
+  _(int, Int)                                  \
+  _(int64_t, Long)                             \
+  _(at::Half, Half)                            \
+  _(float, Float)                              \
+  _(double, Double)                            \
+  _(c10::complex<c10::Half>, ComplexHalf)      \
+  _(c10::complex<float>, ComplexFloat)         \
+  _(c10::complex<double>, ComplexDouble)       \
+  _(bool, Bool)                                \
+  _(at::BFloat16, BFloat16)                    \
+  _(at::Float8_e5m2, Float8_e5m2)              \
+  _(at::Float8_e4m3fn, Float8_e4m3fn)          \
+  _(at::Float8_e5m2fnuz, Float8_e5m2fnuz)      \
+  _(at::Float8_e4m3fnuz, Float8_e4m3fnuz)      \
+  _(at::Float8_e8m0fnu, Float8_e8m0fnu)
+
+namespace impl {
+
+// These are used to map ScalarTypes to C++ types.
+
+template <c10::ScalarType N>
+struct ScalarTypeToCPPType;
+
+#define SPECIALIZE_ScalarTypeToCPPType(cpp_type, scalar_type)                \
+  template <>                                                                \
+  struct ScalarTypeToCPPType<c10::ScalarType::scalar_type> {                 \
+    using type = cpp_type;                                                   \
+                                                                             \
+    /* This is a workaround for the CUDA bug which prevents */               \
+    /* ::detail::ScalarTypeToCType<T>::type being used directly due to */    \
+    /* ambiguous reference which can't to be resolved. For some reason it */ \
+    /* can't pick between at::detail and at::cuda::detail. */                \
+    /* For repro example, please see: */                                     \
+    /* https://gist.github.com/izdeby/952ae7cf256ddb740a73776d39a7e7ba */    \
+    /* TODO: remove once the bug is fixed. */                                \
+    static type t;                                                           \
+  };
+
+AT_FORALL_SCALAR_TYPES_WITH_COMPLEX_AND_QINTS(SPECIALIZE_ScalarTypeToCPPType)
+
+#undef SPECIALIZE_ScalarTypeToCPPType
+
+template <c10::ScalarType N>
+using ScalarTypeToCPPTypeT = typename ScalarTypeToCPPType<N>::type;
+
+} // namespace impl
 
 template <typename T>
 struct CppTypeToScalarType;
@@ -45,6 +138,130 @@ AT_FORALL_SCALAR_TYPES_WITH_COMPLEX_AND_QINTS(SPECIALIZE_CppTypeToScalarType)
 
 #undef SPECIALIZE_CppTypeToScalarType
 
+// NB: despite its generic sounding name, the macros that don't take _AND
+// are mostly only used by tensorexpr
+#define AT_FORALL_INT_TYPES(_) \
+  _(uint8_t, Byte)             \
+  _(int8_t, Char)              \
+  _(int16_t, Short)            \
+  _(int, Int)                  \
+  _(int64_t, Long)
+
+#define AT_FORALL_SCALAR_TYPES(_) \
+  _(uint8_t, Byte)                \
+  _(int8_t, Char)                 \
+  _(int16_t, Short)               \
+  _(int, Int)                     \
+  _(int64_t, Long)                \
+  _(float, Float)                 \
+  _(double, Double)
+
+// These macros are often controlling how many template instantiations we
+// create for kernels.  It is typically inappropriate to add new dtypes here,
+// instead, new types should be added to use sites on a case-by-case basis.
+// We generally are not accepting new dtypes due to binary size concerns.
+
+#define AT_FORALL_SCALAR_TYPES_AND(SCALARTYPE, _) \
+  _(uint8_t, Byte)                                \
+  _(int8_t, Char)                                 \
+  _(int16_t, Short)                               \
+  _(int, Int)                                     \
+  _(int64_t, Long)                                \
+  _(float, Float)                                 \
+  _(double, Double)                               \
+  _(decltype(::c10::impl::ScalarTypeToCPPType<    \
+             ::c10::ScalarType::SCALARTYPE>::t),  \
+    SCALARTYPE)
+
+#define AT_FORALL_SCALAR_TYPES_AND2(SCALARTYPE1, SCALARTYPE2, _) \
+  _(uint8_t, Byte)                                               \
+  _(int8_t, Char)                                                \
+  _(int16_t, Short)                                              \
+  _(int, Int)                                                    \
+  _(int64_t, Long)                                               \
+  _(float, Float)                                                \
+  _(double, Double)                                              \
+  _(decltype(::c10::impl::ScalarTypeToCPPType<                   \
+             ::c10::ScalarType::SCALARTYPE1>::t),                \
+    SCALARTYPE1)                                                 \
+  _(decltype(::c10::impl::ScalarTypeToCPPType<                   \
+             ::c10::ScalarType::SCALARTYPE2>::t),                \
+    SCALARTYPE2)
+
+#define AT_FORALL_SCALAR_TYPES_AND3(SCALARTYPE1, SCALARTYPE2, SCALARTYPE3, _) \
+  _(uint8_t, Byte)                                                            \
+  _(int8_t, Char)                                                             \
+  _(int16_t, Short)                                                           \
+  _(int, Int)                                                                 \
+  _(int64_t, Long)                                                            \
+  _(float, Float)                                                             \
+  _(double, Double)                                                           \
+  _(decltype(::c10::impl::ScalarTypeToCPPType<                                \
+             ::c10::ScalarType::SCALARTYPE1>::t),                             \
+    SCALARTYPE1)                                                              \
+  _(decltype(::c10::impl::ScalarTypeToCPPType<                                \
+             ::c10::ScalarType::SCALARTYPE2>::t),                             \
+    SCALARTYPE2)                                                              \
+  _(decltype(::c10::impl::ScalarTypeToCPPType<                                \
+             ::c10::ScalarType::SCALARTYPE3>::t),                             \
+    SCALARTYPE3)
+
+#define AT_FORALL_SCALAR_TYPES_AND7(              \
+    SCALARTYPE1,                                  \
+    SCALARTYPE2,                                  \
+    SCALARTYPE3,                                  \
+    SCALARTYPE4,                                  \
+    SCALARTYPE5,                                  \
+    SCALARTYPE6,                                  \
+    SCALARTYPE7,                                  \
+    _)                                            \
+  _(uint8_t, Byte)                                \
+  _(int8_t, Char)                                 \
+  _(int16_t, Short)                               \
+  _(int, Int)                                     \
+  _(int64_t, Long)                                \
+  _(float, Float)                                 \
+  _(double, Double)                               \
+  _(decltype(::c10::impl::ScalarTypeToCPPType<    \
+             ::c10::ScalarType::SCALARTYPE1>::t), \
+    SCALARTYPE1)                                  \
+  _(decltype(::c10::impl::ScalarTypeToCPPType<    \
+             ::c10::ScalarType::SCALARTYPE2>::t), \
+    SCALARTYPE2)                                  \
+  _(decltype(::c10::impl::ScalarTypeToCPPType<    \
+             ::c10::ScalarType::SCALARTYPE3>::t), \
+    SCALARTYPE3)                                  \
+  _(decltype(::c10::impl::ScalarTypeToCPPType<    \
+             ::c10::ScalarType::SCALARTYPE4>::t), \
+    SCALARTYPE4)                                  \
+  _(decltype(::c10::impl::ScalarTypeToCPPType<    \
+             ::c10::ScalarType::SCALARTYPE5>::t), \
+    SCALARTYPE5)                                  \
+  _(decltype(::c10::impl::ScalarTypeToCPPType<    \
+             ::c10::ScalarType::SCALARTYPE6>::t), \
+    SCALARTYPE6)                                  \
+  _(decltype(::c10::impl::ScalarTypeToCPPType<    \
+             ::c10::ScalarType::SCALARTYPE7>::t), \
+    SCALARTYPE7)
+
+#define AT_FORALL_QINT_TYPES(_) \
+  _(c10::qint8, QInt8)          \
+  _(c10::quint8, QUInt8)        \
+  _(c10::qint32, QInt32)        \
+  _(c10::quint4x2, QUInt4x2)    \
+  _(c10::quint2x4, QUInt2x4)
+
+#define AT_FORALL_FLOAT8_TYPES(_)         \
+  _(at::Float8_e5m2, Float8_e5m2)         \
+  _(at::Float8_e4m3fn, Float8_e4m3fn)     \
+  _(at::Float8_e5m2fnuz, Float8_e5m2fnuz) \
+  _(at::Float8_e4m3fnuz, Float8_e4m3fnuz) \
+  _(at::Float8_e8m0fnu, Float8_e8m0fnu)
+
+#define AT_FORALL_COMPLEX_TYPES(_)     \
+  _(c10::complex<float>, ComplexFloat) \
+  _(c10::complex<double>, ComplexDouble)
+
 #define DEFINE_CONSTANT(_, name) \
   constexpr ScalarType k##name = ScalarType::name;
 
@@ -52,6 +269,19 @@ AT_FORALL_SCALAR_TYPES_WITH_COMPLEX_AND_QINTS(SPECIALIZE_CppTypeToScalarType)
 AT_FORALL_SCALAR_TYPES_WITH_COMPLEX_AND_QINTS(DEFINE_CONSTANT)
 #undef DEFINE_CONSTANT
 
+inline const char* toString(ScalarType t) {
+#define DEFINE_CASE(_, name) \
+  case ScalarType::name:     \
+    return #name;
+
+  switch (t) {
+    AT_FORALL_SCALAR_TYPES_WITH_COMPLEX_AND_QINTS(DEFINE_CASE)
+    default:
+      return "UNKNOWN_SCALAR";
+  }
+#undef DEFINE_CASE
+}
+
 inline size_t elementSize(ScalarType t) {
 #define CASE_ELEMENTSIZE_CASE(ctype, name) \
   case ScalarType::name:                   \
@@ -295,6 +525,12 @@ inline bool canCast(const ScalarType from, const ScalarType to) {
 
 C10_API ScalarType promoteTypes(ScalarType a, ScalarType b);
 
+inline std::ostream& operator<<(
+    std::ostream& stream,
+    at::ScalarType scalar_type) {
+  return stream << toString(scalar_type);
+}
+
 // Returns a pair of strings representing the names for each dtype.
 // The returned pair is (name, legacy_name_if_applicable)
 C10_API std::pair<std::string, std::string> getDtypeNames(

c10/util/strong_type.h

@@ -65,7 +65,7 @@ struct default_constructible
 
 namespace impl {
   template <typename T>
-  constexpr bool supports_default_construction(const ::strong::default_constructible::modifier<T>* /*unused*/)
+  constexpr bool supports_default_construction(const ::strong::default_constructible::modifier<T>*)
   {
     return true;
   }
@@ -76,7 +76,7 @@ class type : public modifier<M, type<T, Tag, M...>>...
 {
 public:
   template <typename TT = T, typename = std::enable_if_t<std::is_trivially_constructible<TT>{}>>
-  explicit type(uninitialized_t /*unused*/)
+  explicit type(uninitialized_t)
     noexcept
   {
   }
@@ -138,7 +138,7 @@ private:
 
 namespace impl {
   template <typename T, typename Tag, typename ... Ms>
-  constexpr bool is_strong_type_func(const strong::type<T, Tag, Ms...>* /*unused*/) { return true;}
+  constexpr bool is_strong_type_func(const strong::type<T, Tag, Ms...>*) { return true;}
   constexpr bool is_strong_type_func(...) { return false;}
   template <typename T, typename Tag, typename ... Ms>
   constexpr T underlying_type(strong::type<T, Tag, Ms...>*);

docs/source/cuda.tunable.md

@@ -68,6 +68,14 @@
 .. autofunction:: get_validators
 ```
 
+```{eval-rst}
+.. autofunction:: write_file_on_exit
+```
+
+```{eval-rst}
+.. autofunction:: write_file
+```
+
 ```{eval-rst}
 .. autofunction:: read_file
 ```
@@ -87,7 +95,3 @@
 ```{eval-rst}
 .. autofunction:: get_rotating_buffer_size
 ```
-
-```{eval-rst}
-.. autofunction:: set_numerical_check_tolerances
-```

docs/source/distributed.fsdp.fully_shard.md

@@ -123,7 +123,3 @@ The frontend API is `fully_shard` that can be called on a `module`:
 .. autoclass:: CPUOffloadPolicy
     :members:
 ```
-
-```{eval-rst}
-.. autofunction:: share_comm_ctx
-```

docs/source/distributed.md

@@ -51,7 +51,7 @@ MPI supports CUDA only if the implementation used to build PyTorch supports it.
 +----------------+-----+-----+-----+-----+-----+-----+-----+-----+
 | reduce_scatter | ✓   | ✓   | ✘   | ✘   | ✘   | ✓   | ✘   | ✓   |
 +----------------+-----+-----+-----+-----+-----+-----+-----+-----+
-| all_to_all     | ✘   | ✘   | ✓   | ?   | ✘   | ✓   | ✘   | ✓   |
+| all_to_all     | ✓   | ✓   | ✓   | ?   | ✘   | ✓   | ✘   | ✓   |
 +----------------+-----+-----+-----+-----+-----+-----+-----+-----+
 | barrier        | ✓   | ✘   | ✓   | ?   | ✘   | ✓   | ✘   | ✓   |
 +----------------+-----+-----+-----+-----+-----+-----+-----+-----+

docs/source/nn.attention.rst

@@ -23,7 +23,6 @@ Submodules
     flex_attention
     bias
     experimental
-    varlen
 
 .. toctree::
     :hidden:
@@ -31,4 +30,3 @@ Submodules
     nn.attention.flex_attention
     nn.attention.bias
     nn.attention.experimental
-    nn.attention.varlen

docs/source/nn.attention.varlen.md

@@ -1,17 +0,0 @@
-```{eval-rst}
-.. role:: hidden
-    :class: hidden-section
-```
-
-# torch.nn.attention.varlen
-
-```{eval-rst}
-.. automodule:: torch.nn.attention.varlen
-.. currentmodule:: torch.nn.attention.varlen
-```
-```{eval-rst}
-.. autofunction:: varlen_attn
-```
-```{eval-rst}
-.. autoclass:: AuxRequest
-```

docs/source/nn.functional.rst

@@ -228,4 +228,3 @@ Low-Precision functions
     ScalingType
     SwizzleType
     scaled_mm
-    scaled_grouped_mm

docs/source/torch.compiler.config.md

@@ -1,12 +1,14 @@
 ```{eval-rst}
 .. currentmodule:: torch.compiler.config
+
 ```
 
 # torch.compiler.config
 
 ```{eval-rst}
 .. automodule:: torch.compiler.config
-   :members:
-   :undoc-members:
-   :show-inheritance:
+```
+
+```{eval-rst}
+.. autodata:: torch.compiler.config.job_id
 ```

docs/source/torch.rst

@@ -816,10 +816,6 @@ Operator Tags
 .. py:module:: torch.types
 .. py:module:: torch.version
 
-.. Compiler configuration module - documented in torch.compiler.config.md
-.. py:module:: torch.compiler.config
-   :noindex:
-
 .. Hidden aliases (e.g. torch.functional.broadcast_tensors()). We want `torch.broadcast_tensors()` to
    be visible only.
 .. toctree::

test/cpp/aoti_abi_check/CMakeLists.txt

@@ -10,7 +10,6 @@ set(AOTI_ABI_CHECK_TEST_SRCS
   ${AOTI_ABI_CHECK_TEST_ROOT}/test_macros.cpp
   ${AOTI_ABI_CHECK_TEST_ROOT}/test_math.cpp
   ${AOTI_ABI_CHECK_TEST_ROOT}/test_rand.cpp
-  ${AOTI_ABI_CHECK_TEST_ROOT}/test_scalartype.cpp
   ${AOTI_ABI_CHECK_TEST_ROOT}/test_vec.cpp
   ${AOTI_ABI_CHECK_TEST_ROOT}/test_vec_half.cpp
 )

test/cpp/aoti_abi_check/test_scalartype.cpp

@@ -1,76 +0,0 @@
-#include <gtest/gtest.h>
-
-#include <torch/headeronly/core/ScalarType.h>
-
-TEST(TestScalarType, ScalarTypeToCPPTypeT) {
-  using torch::headeronly::ScalarType;
-  using torch::headeronly::impl::ScalarTypeToCPPTypeT;
-
-#define DEFINE_CHECK(TYPE, SCALARTYPE) \
-  EXPECT_EQ(typeid(ScalarTypeToCPPTypeT<ScalarType::SCALARTYPE>), typeid(TYPE));
-
-  AT_FORALL_SCALAR_TYPES_WITH_COMPLEX_AND_QINTS(DEFINE_CHECK);
-#undef DEFINE_CHECK
-}
-
-#define DEFINE_CHECK(TYPE, SCALARTYPE)                                       \
-  {                                                                          \
-    EXPECT_EQ(                                                               \
-        typeid(ScalarTypeToCPPTypeT<ScalarType::SCALARTYPE>), typeid(TYPE)); \
-    count++;                                                                 \
-  }
-
-#define TEST_FORALL(M, EXPECTEDCOUNT, ...)               \
-  TEST(TestScalarType, M) {                              \
-    using torch::headeronly::ScalarType;                 \
-    using torch::headeronly::impl::ScalarTypeToCPPTypeT; \
-    int8_t count = 0;                                    \
-    M(__VA_ARGS__ DEFINE_CHECK);                         \
-    EXPECT_EQ(count, EXPECTEDCOUNT);                     \
-  }
-
-TEST_FORALL(AT_FORALL_SCALAR_TYPES_WITH_COMPLEX_EXCEPT_COMPLEX_HALF_F8NZ, 14)
-TEST_FORALL(AT_FORALL_SCALAR_TYPES_WITH_COMPLEX, 18)
-TEST_FORALL(AT_FORALL_SCALAR_TYPES_WITH_COMPLEX_AND_QINTS, 46)
-TEST_FORALL(AT_FORALL_INT_TYPES, 5)
-TEST_FORALL(AT_FORALL_SCALAR_TYPES, 7)
-TEST_FORALL(AT_FORALL_SCALAR_TYPES_AND, 8, Bool, )
-TEST_FORALL(AT_FORALL_SCALAR_TYPES_AND2, 9, Bool, Half, )
-TEST_FORALL(AT_FORALL_SCALAR_TYPES_AND3, 10, Bool, Half, ComplexFloat, )
-TEST_FORALL(
-    AT_FORALL_SCALAR_TYPES_AND7,
-    14,
-    Bool,
-    Half,
-    ComplexHalf,
-    ComplexFloat,
-    ComplexDouble,
-    UInt16,
-    UInt32, )
-TEST_FORALL(AT_FORALL_QINT_TYPES, 5)
-TEST_FORALL(AT_FORALL_FLOAT8_TYPES, 5)
-TEST_FORALL(AT_FORALL_COMPLEX_TYPES, 2)
-
-#undef DEFINE_CHECK
-#undef TEST_FORALL
-
-TEST(TestScalarType, toString) {
-  using torch::headeronly::ScalarType;
-
-#define DEFINE_CHECK(_, name) EXPECT_EQ(toString(ScalarType::name), #name);
-  AT_FORALL_SCALAR_TYPES_WITH_COMPLEX_AND_QINTS(DEFINE_CHECK);
-#undef DEFINE_CHECK
-}
-
-TEST(TestScalarType, operator_left_shift) {
-  using torch::headeronly::ScalarType;
-
-#define DEFINE_CHECK(_, name)   \
-  {                             \
-    std::stringstream ss;       \
-    ss << ScalarType::name;     \
-    EXPECT_EQ(ss.str(), #name); \
-  }
-  AT_FORALL_SCALAR_TYPES_WITH_COMPLEX_AND_QINTS(DEFINE_CHECK);
-#undef DEFINE_CHECK
-}

test/distributed/_composable/fsdp/test_fully_shard_training.py

@@ -6,7 +6,7 @@ import functools
 import itertools
 import unittest
 from collections import defaultdict
-from collections.abc import Callable, Iterable
+from collections.abc import Iterable
 from typing import Any, Optional, Union
 
 import torch
@@ -24,11 +24,6 @@ from torch.distributed.fsdp import (
     fully_shard,
     OffloadPolicy,
     register_fsdp_forward_method,
-    share_comm_ctx,
-)
-from torch.distributed.fsdp._fully_shard._fsdp_collectives import (
-    foreach_all_gather,
-    foreach_reduce,
 )
 from torch.distributed.tensor import DTensor, init_device_mesh, Shard
 from torch.distributed.tensor.debug import CommDebugMode
@@ -44,8 +39,6 @@ from torch.testing._internal.common_fsdp import (
     MLP,
     MLPStack,
     patch_all_gather,
-    patch_foreach_all_gather,
-    patch_foreach_reduce,
     patch_reduce_scatter,
 )
 from torch.testing._internal.common_utils import (
@@ -1494,116 +1487,6 @@ class TestFullyShardCustomForwardMethod(FSDPTest):
         check_sharded_parity(self, ref_model, model)
 
 
-class TestFullyShardShareCommContext(FSDPTest):
-    @property
-    def world_size(self) -> int:
-        return min(torch.get_device_module(device_type).device_count(), 2)
-
-    @skip_if_lt_x_gpu(2)
-    def test_share_comm_context(self):
-        torch.manual_seed(42)
-        n_layers = 3
-        lin_dim = 16
-        model = nn.Sequential(
-            *[MLP(lin_dim, torch.device("cpu")) for _ in range(n_layers)]
-        )
-        ref_model = copy.deepcopy(model).to(device_type)
-        for layer in model:
-            fully_shard(layer)
-            layer._get_fsdp_state()._lazy_init()
-        share_comm_ctx(list(model))
-
-        torch.manual_seed(42 + self.rank + 1)
-        inp = torch.randn(4, 3, lin_dim, device=device_type.type)
-        ref_loss = ref_model(inp).sum()
-
-        all_gather_streams = set()
-        reduce_scatter_streams = set()
-
-        from torch.distributed.fsdp._fully_shard._fsdp_api import (
-            AllGather,
-            ReduceScatter,
-        )
-        from torch.distributed.fsdp._fully_shard._fsdp_param import FSDPParam
-
-        orig_foreach_all_gather = foreach_all_gather
-
-        def foreach_all_gather_with_assert(
-            fsdp_params: list[FSDPParam],
-            group: dist.ProcessGroup,
-            async_op: bool,
-            all_gather_copy_in_stream: torch.Stream,
-            all_gather_stream: torch.Stream,
-            device: torch.device,
-            all_gather_comm: AllGather,
-        ):
-            nonlocal all_gather_streams
-            all_gather_streams.add(all_gather_stream)
-            return orig_foreach_all_gather(
-                fsdp_params,
-                group,
-                async_op,
-                all_gather_copy_in_stream,
-                all_gather_stream,
-                device,
-                all_gather_comm,
-            )
-
-        orig_foreach_reduce = foreach_reduce
-
-        @torch.no_grad()
-        def foreach_reduce_with_assert(
-            fsdp_params: list[FSDPParam],
-            unsharded_grads: list[torch.Tensor],
-            reduce_scatter_group: dist.ProcessGroup,
-            reduce_scatter_stream: torch.Stream,
-            reduce_scatter_comm: ReduceScatter,
-            orig_dtype: Optional[torch.dtype],
-            reduce_dtype: Optional[torch.dtype],
-            device: torch.device,
-            gradient_divide_factor: Optional[float],
-            all_reduce_group: Optional[dist.ProcessGroup],  # not `None` iff HSDP
-            all_reduce_stream: torch.Stream,
-            all_reduce_grads: bool,
-            partial_reduce_output: Optional[torch.Tensor],  # only used for HSDP
-            all_reduce_hook: Optional[Callable[[torch.Tensor], None]],
-            force_sum_reduction_for_comms: bool = False,
-        ):
-            nonlocal reduce_scatter_streams
-            reduce_scatter_streams.add(reduce_scatter_stream)
-            return orig_foreach_reduce(
-                fsdp_params,
-                unsharded_grads,
-                reduce_scatter_group,
-                reduce_scatter_stream,
-                reduce_scatter_comm,
-                orig_dtype,
-                reduce_dtype,
-                device,
-                gradient_divide_factor,
-                all_reduce_group,
-                all_reduce_stream,
-                all_reduce_grads,
-                partial_reduce_output,
-                all_reduce_hook,
-                force_sum_reduction_for_comms,
-            )
-
-        with (
-            patch_foreach_all_gather(foreach_all_gather_with_assert),
-            patch_foreach_reduce(foreach_reduce_with_assert),
-        ):
-            loss = model(inp).sum()
-            self.assertEqual(ref_loss, loss)
-            ref_loss.backward()
-            loss.backward()
-            for param in ref_model.parameters():
-                dist.all_reduce(param.grad, op=dist.ReduceOp.AVG)
-        self.assertEqual(len(all_gather_streams), 1)
-        self.assertEqual(len(reduce_scatter_streams), 1)
-        check_sharded_parity(self, ref_model, model)
-
-
 class TestFullyShardWorldSize1(FSDPTest):
     @property
     def world_size(self) -> int:

test/distributed/elastic/multiprocessing/api_test.py

@@ -559,7 +559,7 @@ if not (TEST_WITH_DEV_DBG_ASAN or IS_WINDOWS or IS_MACOS):
             FAIL = 138
             pc = start_processes(
                 name="echo",
-                entrypoint=bin("echo4.py"),
+                entrypoint=bin("echo1.py"),
                 args={0: ("--exitcode", FAIL, "foo"), 1: ("--exitcode", 0, "bar")},
                 envs={0: {"RANK": "0"}, 1: {"RANK": "1"}},
                 logs_specs=DefaultLogsSpecs(

test/distributed/elastic/multiprocessing/bin/echo1.py

@@ -9,6 +9,7 @@
 import argparse
 import os
 import sys
+import time
 
 
 if __name__ == "__main__":
@@ -23,5 +24,6 @@ if __name__ == "__main__":
         print(f"exit {exitcode} from {rank}", file=sys.stderr)
         sys.exit(exitcode)
     else:
+        time.sleep(1000)
         print(f"{args.msg} stdout from {rank}")
         print(f"{args.msg} stderr from {rank}", file=sys.stderr)

test/distributed/elastic/multiprocessing/bin/echo4.py

@@ -1,29 +0,0 @@
-#!/usr/bin/env python3
-
-# Copyright (c) Facebook, Inc. and its affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the BSD-style license found in the
-# LICENSE file in the root directory of this source tree.
-
-import argparse
-import os
-import sys
-import time
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser(description="test binary, exits with exitcode")
-    parser.add_argument("--exitcode", type=int, default=0)
-    parser.add_argument("msg", type=str)
-    args = parser.parse_args()
-
-    rank = int(os.environ["RANK"])
-    exitcode = args.exitcode
-    if exitcode != 0:
-        print(f"exit {exitcode} from {rank}", file=sys.stderr)
-        sys.exit(exitcode)
-    else:
-        time.sleep(1000)
-        print(f"{args.msg} stdout from {rank}")
-        print(f"{args.msg} stderr from {rank}", file=sys.stderr)

test/distributed/pipelining/test_schedule.py

@@ -536,23 +536,6 @@ class TestScheduleLowering(TestCase):
                 "compute": ["0F0", "0F1", "   ", "0B0", "0B1"],
                 "comms": ["0UNSHARD", "0F0", "0F1", "0B0", "0B1", "0RESHARD"],
             },
-            {
-                "compute": ["0F0", "0F1", "1F0", "1F1", "1B0", "1B1", "0B0", "0B1"],
-                "comms": [
-                    "0UNSHARD",
-                    "1UNSHARD",
-                    "0F0",
-                    "0F1",
-                    "1F0",
-                    "1F1",
-                    "1B0",
-                    "1B1",
-                    "1RESHARD",
-                    "0B0",
-                    "0B1",
-                    "0RESHARD",
-                ],
-            },
         ],
     )
     def test_unshard_reshard(self, test_info):

test/distributed/tensor/test_dtensor.py

@@ -1020,19 +1020,6 @@ class DTensorMeshTest(DTensorTestBase):
             self.fail("Unexpected ValueError raised with run_check=False")
 
 
-DTensorMeshTestWithLocalTensor = create_local_tensor_test_class(
-    DTensorMeshTest,
-    skipped_tests=[
-        # Submeshes are not supported by local tensor mode
-        "test_from_local_sub_mesh",
-        "test_default_value_sub_mesh",
-        "test_redistribute_sub_mesh",
-        # Local tensor mode doesn't support tensors of different types on different ranks
-        "test_metadata_consistency_check",
-    ],
-)
-
-
 class TestDTensorPlacementTypes(DTensorTestBase):
     @property
     def world_size(self):
@@ -1099,11 +1086,6 @@ class TestDTensorPlacementTypes(DTensorTestBase):
                 assert_array_equal(expected_is_tensor_empty, is_tensor_empty)
 
 
-TestDTensorPlacementTypesWithLocalTensor = create_local_tensor_test_class(
-    TestDTensorPlacementTypes,
-)
-
-
 class TestDTensorSpec(DTensorTestBase):
     @property
     def world_size(self):
@@ -1283,9 +1265,5 @@ class TestDTensorSpec(DTensorTestBase):
         )
 
 
-TestDTensorSpecWithLocalTensor = create_local_tensor_test_class(
-    TestDTensorSpec,
-)
-
 if __name__ == "__main__":
     run_tests()

test/distributed/tensor/test_dtensor_export.py

@@ -239,7 +239,9 @@ class DTensorExportTest(TestCase):
                 "view_9",
                 "t_15",
                 "detach",
-                "detach_3",
+                "detach_1",
+                "detach_6",
+                "detach_7",
                 "threshold_backward_1",
                 "t_16",
                 "mm_6",
@@ -257,8 +259,10 @@ class DTensorExportTest(TestCase):
                 "sum_1",
                 "view_7",
                 "t_7",
-                "detach_1",
                 "detach_2",
+                "detach_3",
+                "detach_4",
+                "detach_5",
                 "threshold_backward",
                 "mm_2",
                 "t_9",

test/distributed/tensor/test_redistribute.py

@@ -20,7 +20,6 @@ from torch.distributed.tensor._collective_utils import shard_dim_alltoall
 from torch.distributed.tensor._dtensor_spec import ShardOrderEntry
 from torch.distributed.tensor._redistribute import redistribute_local_tensor
 from torch.distributed.tensor.debug import CommDebugMode
-from torch.distributed.tensor.placement_types import _StridedShard
 from torch.testing._internal.common_utils import (
     instantiate_parametrized_tests,
     parametrize,
@@ -1146,22 +1145,6 @@ class DistributeWithDeviceOrderTest(DTensorTestBase):
             sharded_dt, mesh, tgt_placement, shard_order=None
         )
 
-    @with_comms
-    def test_shard_order_same_data_as_strided_shard(self):
-        device_mesh = init_device_mesh(self.device_type, (4, 2))
-        x = torch.randn(8, 4, device=self.device_type)
-        # specify right-to-left order use _StridedShard
-        strided_placement = [_StridedShard(-2, split_factor=2), Shard(-2)]
-        x_strided_dt = distribute_tensor(x, device_mesh, strided_placement)
-        # specify right-to-left order use ordered shard
-        x_ordered_dt = self.distribute_tensor(
-            x,
-            device_mesh,
-            placements=[Shard(0), Shard(0)],
-            shard_order=(ShardOrderEntry(tensor_dim=0, mesh_dims=(1, 0)),),
-        )
-        self.assertEqual(x_ordered_dt.to_local(), x_strided_dt.to_local())
-
 
 if __name__ == "__main__":
     run_tests()

test/distributed/test_aten_comm_compute_reordering.py

@@ -70,8 +70,6 @@ def get_patches():
         "force_disable_caches": True,
         # Messes up existing test strings
         "test_configs.aten_fx_overlap_insert_overlap_deps": False,
-        # interferes with testing, / custom estimation
-        "test_configs.assume_bucketing_reduces_latency": False,
     }
 
 
@@ -366,8 +364,6 @@ def get_bucket_patches(compute_multiplier=1.0):
         "force_disable_caches": True,
         # messes up test strings
         "test_configs.aten_fx_overlap_insert_overlap_deps": False,
-        # interferes with testing, / custom estimation
-        "test_configs.assume_bucketing_reduces_latency": False,
     }
 
 
@@ -583,7 +579,7 @@ class TestComputeCommReorderingBucketing(TestComputeCommReorderingMultiProc):
 
     @unittest.skipIf(not HAS_GPU, "Inductor+gpu needs triton and recent GPU arch")
     @torch._inductor.config.patch(get_bucket_patches(2.0))
-    def test_bucketing_split_for_overlap_blocking_no_deps(self):
+    def test_bucketing_split_for_overlap_blocking(self):
         """Test that 4 independent all-gathers split into 2+2 buckets for better overlap with compute."""
 
         def func(a, b, c, d, *, ranks):

test/distributed/test_cupy_as_tensor.py

@@ -7,13 +7,8 @@ from dataclasses import dataclass
 
 import torch
 from torch.multiprocessing.reductions import reduce_tensor
-from torch.testing._internal.common_cuda import SM100OrLater
 from torch.testing._internal.common_distributed import MultiProcContinuousTest
-from torch.testing._internal.common_utils import (
-    requires_cuda_p2p_access,
-    run_tests,
-    skip_but_pass_in_sandcastle_if,
-)
+from torch.testing._internal.common_utils import requires_cuda_p2p_access, run_tests
 
 
 # So that tests are written in device-agnostic way
@@ -64,10 +59,6 @@ class CupyAsTensorTest(MultiProcContinuousTest):
     def device(self) -> torch.device:
         return torch.device(device_type, self.rank)
 
-    @skip_but_pass_in_sandcastle_if(
-        SM100OrLater,
-        "Fails if ran in docker environment without privileged access (https://github.com/pytorch/pytorch/issues/165170)",
-    )
     def test_cupy_as_tensor(self) -> None:
         """
         Test that torch.as_tensor works for cupy array interface

test/distributed/test_device_mesh.py

@@ -2,7 +2,6 @@
 # Owner(s): ["oncall: distributed"]
 import os
 import unittest
-from datetime import timedelta
 
 import torch
 import torch.distributed as dist
@@ -41,13 +40,6 @@ from torch.utils._typing_utils import not_none
 device_type = acc.type if (acc := torch.accelerator.current_accelerator()) else "cpu"
 device_count = torch.accelerator.device_count()
 
-try:
-    import torch._C._distributed_c10d.ProcessGroupNCCL
-
-    _NCCL_AVAILABLE = True
-except ImportError:
-    _NCCL_AVAILABLE = False
-
 
 def _set_env_var(addr="localhost", port="25364", world_size=1, rank=0, local_rank=-1):
     os.environ["MASTER_ADDR"] = addr
@@ -970,85 +962,6 @@ class TestDeviceMeshGetItem(DTensorTestBase):
         # check flattened mesh dependency
         self.assertEqual(dp_cp_mesh._get_root_mesh(), mesh_4d)
 
-    @with_comms
-    def test_unflatten_mesh_2d(self):
-        mesh_shape = (4, 2)
-        mesh_dim_names = ("dp", "tp")
-        mesh_2d = init_device_mesh(
-            self.device_type, mesh_shape, mesh_dim_names=mesh_dim_names
-        )
-        unflatten_mesh = mesh_2d._unflatten(0, (2, 2), ("dp_shard", "dp_replicate"))
-        self.assertEqual(
-            unflatten_mesh.mesh_dim_names, ["dp_shard", "dp_replicate", "tp"]
-        )
-        self.assertEqual(mesh_2d["tp"].mesh, unflatten_mesh["tp"].mesh)
-        self.assertEqual(mesh_2d["tp"].get_group(), unflatten_mesh["tp"].get_group())
-
-        # Not supporting slicing out unflatten dim name from root mesh.
-        with self.assertRaises(KeyError):
-            self.assertEqual(mesh_2d["dp_shard"].mesh, unflatten_mesh["dp_shard"].mesh)
-
-    @with_comms
-    def test_unflatten_mesh_3d(self):
-        # Test unflatten from a dummy world mesh, which is the case we need for Expert Parallelism(EP).
-        global_mesh = init_device_mesh(
-            self.device_type,
-            (8,),
-            mesh_dim_names=("world",),
-        )
-        non_ep_mesh = global_mesh._unflatten(0, (2, 2, 2), ("dp", "cp", "tp"))
-        ep_mesh = global_mesh._unflatten(0, (2, 2, 2), ("dp", "ep", "ep_tp"))
-        self.assertEqual(non_ep_mesh["cp"].mesh, ep_mesh["ep"].mesh)
-        self.assertEqual(non_ep_mesh["tp"].mesh, ep_mesh["ep_tp"].mesh)
-        mesh_3d = global_mesh._unflatten(0, (4, 2, 1), ("dp", "cp", "tp"))
-        unflatten_mesh = mesh_3d._unflatten(0, (2, 2), ("dp_shard", "dp_replicate"))
-        self.assertEqual(
-            unflatten_mesh.mesh_dim_names, ["dp_shard", "dp_replicate", "cp", "tp"]
-        )
-        self.assertEqual(mesh_3d["tp"].mesh, unflatten_mesh["tp"].mesh)
-        self.assertEqual(mesh_3d["tp"].get_group(), unflatten_mesh["tp"].get_group())
-        self.assertEqual(mesh_3d["cp"].mesh, unflatten_mesh["cp"].mesh)
-        self.assertEqual(mesh_3d["cp"].get_group(), unflatten_mesh["cp"].get_group())
-
-        # Test unflatten with backend override set.
-        if not _NCCL_AVAILABLE:
-            return
-        opts = dist.ProcessGroupNCCL.Options()
-        opts._timeout = timedelta(seconds=30)
-        mesh_2d = global_mesh._unflatten(
-            0,
-            (1, 8),
-            ("pp", "spmd"),
-            backend_override={"pp": "fake", "spmd": ("nccl", opts)},
-        )
-        opts = dist.ProcessGroupNCCL.Options()
-        opts._timeout = timedelta(seconds=60)
-        mesh_4d = mesh_2d._unflatten(
-            1,
-            (2, 2, 2),
-            ("dp", "cp", "tp"),
-            backend_override={"dp": "nccl", "cp": "nccl", "tp": ("nccl", opts)},
-        )
-        self.assertEqual(mesh_4d["pp"].get_group()._get_backend_name(), "custom")
-        spmd_pg = mesh_2d["spmd"].get_group()
-        self.assertEqual(spmd_pg._get_backend_name(), "nccl")
-        w = spmd_pg.allreduce(torch.rand(10).cuda(self.rank))
-        self.assertTrue(
-            spmd_pg._get_backend(
-                torch.device(f"cuda:{self.rank}")
-            )._verify_work_timeout(w, timedelta(seconds=30))
-        )
-        w.wait()
-        tp_pg = mesh_4d["tp"].get_group()
-        self.assertEqual(tp_pg._get_backend_name(), "nccl")
-        w = tp_pg.allreduce(torch.rand(10).cuda(self.rank))
-        self.assertTrue(
-            tp_pg._get_backend(torch.device(f"cuda:{self.rank}"))._verify_work_timeout(
-                w, timedelta(seconds=60)
-            )
-        )
-        w.wait()
-
     @with_comms
     def test_reconstruct_mesh_with_flatten_dim(self):
         mesh_3d = init_device_mesh(

test/distributed/test_fake_pg.py

@@ -273,7 +273,12 @@ class TestFakePG(TestCase):
                     kwargs = {}
                 return func(*args, **kwargs)
 
-        with self.assertRaisesRegex(TypeError, r"No constructor defined"):
+        with self.assertRaisesRegex(
+            RuntimeError,
+            r"FakeProcessGroup cannot be constructed directly\. "
+            r"Use torch\.distributed\.init_process_group\(backend='fake'\) instead to ensure "
+            r"proper dispatch system integration\.",
+        ):
             fake_pg = FakeProcessGroup(rank=0, world_size=3)
 
             with SimpleTensorMode():

test/distributed/test_nvshmem_triton.py

@@ -12,7 +12,6 @@ import torch.distributed._symmetric_memory as symm_mem
 import torch.distributed._symmetric_memory._nvshmem_triton as nvshmem
 from torch._inductor.runtime.triton_compat import triton
 from torch.distributed._symmetric_memory._nvshmem_triton import requires_nvshmem
-from torch.testing._internal.common_cuda import SM100OrLater
 from torch.testing._internal.common_distributed import MultiProcContinuousTest
 from torch.testing._internal.common_utils import (
     instantiate_parametrized_tests,
@@ -265,10 +264,6 @@ def my_reduce_kernel(
     nvshmem.reduce(team_handle, dest_tensor, source_tensor, nreduce, operation)
 
 
-@skip_but_pass_in_sandcastle_if(
-    SM100OrLater,
-    "Skipping all NVSHMEM Triton tests due to https://github.com/pytorch/pytorch/issues/162897",
-)
 @instantiate_parametrized_tests
 class NVSHMEMTritonTest(MultiProcContinuousTest):
     def _init_device(self) -> None:

test/distributed/test_symmetric_memory.py

@@ -52,9 +52,6 @@ from torch.testing._internal.common_utils import (
 
 test_contexts = [nullcontext, _test_mode]
 
-# Set environment variable to disable multicast for all tests in this module
-os.environ["TORCH_SYMM_MEM_DISABLE_MULTICAST"] = "1"
-
 # So that tests are written in device-agnostic way
 device_type = "cuda"
 device_module = torch.get_device_module(device_type)
@@ -549,10 +546,6 @@ class AsyncTPTest(MultiProcContinuousTest):
     @skipUnless(SM89OrLater, "Requires compute capability >= 8.9")
     @parametrize("scatter_dim", [0, 1])
     @parametrize("rowwise", [True, False])
-    @skipIf(
-        SM100OrLater,
-        "https://github.com/pytorch/pytorch/issues/162940",
-    )
     def test_fused_scaled_matmul_reduce_scatter(
         self, scatter_dim: int, rowwise: bool
     ) -> None:

test/dynamo/cpython/3_13/test_baseexception.diff

@@ -1,5 +1,5 @@
 diff --git a/test/dynamo/cpython/3_13/test_baseexception.py b/test/dynamo/cpython/3_13/test_baseexception.py
-index e599b02c17d..057b6ec01b9 100644
+index e599b02c17d..750d7a84fb4 100644
 --- a/test/dynamo/cpython/3_13/test_baseexception.py
 +++ b/test/dynamo/cpython/3_13/test_baseexception.py
 @@ -1,10 +1,64 @@
@@ -78,27 +78,7 @@ index e599b02c17d..057b6ec01b9 100644
          self.assertEqual(len(exc_set), 0, "%s not accounted for" % exc_set)
  
      interface_tests = ("length", "args", "str", "repr")
-@@ -122,12 +173,13 @@ class ExceptionClassTests(unittest.TestCase):
-         # in PyObject_SetAttr.
-         import gc
-         d = {}
--        class HashThisKeyWillClearTheDict(str):
--            def __hash__(self) -> int:
--                d.clear()
--                return super().__hash__()
--        class Value(str):
--            pass
-+        with torch._dynamo.error_on_graph_break(False):
-+            class HashThisKeyWillClearTheDict(str):
-+                def __hash__(self) -> int:
-+                    d.clear()
-+                    return super().__hash__()
-+            class Value(str):
-+                pass
-         exc = Exception()
- 
-         d[HashThisKeyWillClearTheDict()] = Value()  # refcount of Value() is 1 now
-@@ -142,7 +194,7 @@ class ExceptionClassTests(unittest.TestCase):
+@@ -142,7 +193,7 @@ class ExceptionClassTests(unittest.TestCase):
          gc.collect()
  
  
@@ -107,31 +87,7 @@ index e599b02c17d..057b6ec01b9 100644
  
      """Test usage of exceptions"""
  
-@@ -182,8 +234,9 @@ class UsageTests(unittest.TestCase):
-         # BaseException; the ability was not possible until BaseException's
-         # introduction so no need to support new-style objects that do not
-         # inherit from it.
--        class NewStyleClass(object):
--            pass
-+        with torch._dynamo.error_on_graph_break(False):
-+            class NewStyleClass(object):
-+                pass
-         self.raise_fails(NewStyleClass)
-         self.raise_fails(NewStyleClass())
- 
-@@ -194,8 +247,9 @@ class UsageTests(unittest.TestCase):
-     def test_catch_non_BaseException(self):
-         # Trying to catch an object that does not inherit from BaseException
-         # is not allowed.
--        class NonBaseException(object):
--            pass
-+        with torch._dynamo.error_on_graph_break(False):
-+            class NonBaseException(object):
-+                pass
-         self.catch_fails(NonBaseException)
-         self.catch_fails(NonBaseException())
- 
-@@ -208,5 +262,5 @@ class UsageTests(unittest.TestCase):
+@@ -208,5 +259,5 @@ class UsageTests(unittest.TestCase):
          self.catch_fails("spam")
  
  

test/dynamo/cpython/3_13/test_baseexception.py

@@ -173,13 +173,12 @@ class ExceptionClassTests(__TestCase):
         # in PyObject_SetAttr.
         import gc
         d = {}
-        with torch._dynamo.error_on_graph_break(False):
-            class HashThisKeyWillClearTheDict(str):
-                def __hash__(self) -> int:
-                    d.clear()
-                    return super().__hash__()
-            class Value(str):
-                pass
+        class HashThisKeyWillClearTheDict(str):
+            def __hash__(self) -> int:
+                d.clear()
+                return super().__hash__()
+        class Value(str):
+            pass
         exc = Exception()
 
         d[HashThisKeyWillClearTheDict()] = Value()  # refcount of Value() is 1 now
@@ -234,9 +233,8 @@ class UsageTests(__TestCase):
         # BaseException; the ability was not possible until BaseException's
         # introduction so no need to support new-style objects that do not
         # inherit from it.
-        with torch._dynamo.error_on_graph_break(False):
-            class NewStyleClass(object):
-                pass
+        class NewStyleClass(object):
+            pass
         self.raise_fails(NewStyleClass)
         self.raise_fails(NewStyleClass())
 
@@ -247,9 +245,8 @@ class UsageTests(__TestCase):
     def test_catch_non_BaseException(self):
         # Trying to catch an object that does not inherit from BaseException
         # is not allowed.
-        with torch._dynamo.error_on_graph_break(False):
-            class NonBaseException(object):
-                pass
+        class NonBaseException(object):
+            pass
         self.catch_fails(NonBaseException)
         self.catch_fails(NonBaseException())
 

test/dynamo/cpython/3_13/test_exceptions.diff

@@ -1,5 +1,5 @@
 diff --git a/test/dynamo/cpython/3_13/test_exceptions.py b/test/dynamo/cpython/3_13/test_exceptions.py
-index c91f6662948..3a62dec411c 100644
+index c91f6662948..0ded70db3c7 100644
 --- a/test/dynamo/cpython/3_13/test_exceptions.py
 +++ b/test/dynamo/cpython/3_13/test_exceptions.py
 @@ -1,3 +1,59 @@
@@ -71,305 +71,7 @@ index c91f6662948..3a62dec411c 100644
  
      def raise_catch(self, exc, excname):
          with self.subTest(exc=exc, excname=excname):
-@@ -343,12 +399,13 @@ class ExceptionTests(unittest.TestCase):
-         # test that setting an exception at the C level works even if the
-         # exception object can't be constructed.
- 
--        class BadException(Exception):
--            def __init__(self_):
--                raise RuntimeError("can't instantiate BadException")
-+        with torch._dynamo.error_on_graph_break(False):
-+            class BadException(Exception):
-+                def __init__(self_):
-+                    raise RuntimeError("can't instantiate BadException")
- 
--        class InvalidException:
--            pass
-+            class InvalidException:
-+                pass
- 
-         @unittest.skipIf(_testcapi is None, "requires _testcapi")
-         def test_capi1():
-@@ -636,8 +693,9 @@ class ExceptionTests(unittest.TestCase):
-         self.assertIsInstance(e, IndexError)
-         self.assertEqual(e.__traceback__, tb)
- 
--        class MyException(Exception):
--            pass
-+        with torch._dynamo.error_on_graph_break(False):
-+            class MyException(Exception):
-+                pass
- 
-         e = MyException().with_traceback(tb)
-         self.assertIsInstance(e, MyException)
-@@ -696,8 +754,9 @@ class ExceptionTests(unittest.TestCase):
-         self.assertIsNone(e.__context__)
-         self.assertIsNone(e.__cause__)
- 
--        class MyException(OSError):
--            pass
-+        with torch._dynamo.error_on_graph_break(False):
-+            class MyException(OSError):
-+                pass
- 
-         e = MyException()
-         self.assertIsNone(e.__context__)
-@@ -726,10 +785,11 @@ class ExceptionTests(unittest.TestCase):
-         # but user-defined subclasses can if they want
-         self.assertRaises(TypeError, BaseException, a=1)
- 
--        class DerivedException(BaseException):
--            def __init__(self, fancy_arg):
--                BaseException.__init__(self)
--                self.fancy_arg = fancy_arg
-+        with torch._dynamo.error_on_graph_break(False):
-+            class DerivedException(BaseException):
-+                def __init__(self, fancy_arg):
-+                    BaseException.__init__(self)
-+                    self.fancy_arg = fancy_arg
- 
-         x = DerivedException(fancy_arg=42)
-         self.assertEqual(x.fancy_arg, 42)
-@@ -779,11 +839,12 @@ class ExceptionTests(unittest.TestCase):
-         # Make sure exception state is cleaned up as soon as the except
-         # block is left. See #2507
- 
--        class MyException(Exception):
--            def __init__(self, obj):
--                self.obj = obj
--        class MyObj:
--            pass
-+        with torch._dynamo.error_on_graph_break(False):
-+            class MyException(Exception):
-+                def __init__(self, obj):
-+                    self.obj = obj
-+            class MyObj:
-+                pass
- 
-         def inner_raising_func():
-             # Create some references in exception value and traceback
-@@ -881,11 +942,12 @@ class ExceptionTests(unittest.TestCase):
-         self.assertIsNone(obj)
- 
-         # Inside an exception-silencing "with" block
--        class Context:
--            def __enter__(self):
--                return self
--            def __exit__ (self, exc_type, exc_value, exc_tb):
--                return True
-+        with torch._dynamo.error_on_graph_break(False):
-+            class Context:
-+                def __enter__(self):
-+                    return self
-+                def __exit__ (self, exc_type, exc_value, exc_tb):
-+                    return True
-         obj = MyObj()
-         wr = weakref.ref(obj)
-         with Context():
-@@ -1027,11 +1089,12 @@ class ExceptionTests(unittest.TestCase):
-     def _check_generator_cleanup_exc_state(self, testfunc):
-         # Issue #12791: exception state is cleaned up as soon as a generator
-         # is closed (reference cycles are broken).
--        class MyException(Exception):
--            def __init__(self, obj):
--                self.obj = obj
--        class MyObj:
--            pass
-+        with torch._dynamo.error_on_graph_break(False):
-+            class MyException(Exception):
-+                def __init__(self, obj):
-+                    self.obj = obj
-+            class MyObj:
-+                pass
- 
-         def raising_gen():
-             try:
-@@ -1090,10 +1153,11 @@ class ExceptionTests(unittest.TestCase):
-     def test_3114(self):
-         # Bug #3114: in its destructor, MyObject retrieves a pointer to
-         # obsolete and/or deallocated objects.
--        class MyObject:
--            def __del__(self):
--                nonlocal e
--                e = sys.exception()
-+        with torch._dynamo.error_on_graph_break(False):
-+            class MyObject:
-+                def __del__(self):
-+                    nonlocal e
-+                    e = sys.exception()
-         e = ()
-         try:
-             raise Exception(MyObject())
-@@ -1103,12 +1167,13 @@ class ExceptionTests(unittest.TestCase):
-         self.assertIsNone(e)
- 
-     def test_raise_does_not_create_context_chain_cycle(self):
--        class A(Exception):
--            pass
--        class B(Exception):
--            pass
--        class C(Exception):
--            pass
-+        with torch._dynamo.error_on_graph_break(False):
-+            class A(Exception):
-+                pass
-+            class B(Exception):
-+                pass
-+            class C(Exception):
-+                pass
- 
-         # Create a context chain:
-         # C -> B -> A
-@@ -1164,12 +1229,13 @@ class ExceptionTests(unittest.TestCase):
-     def test_no_hang_on_context_chain_cycle2(self):
-         # See issue 25782. Cycle at head of context chain.
- 
--        class A(Exception):
--            pass
--        class B(Exception):
--            pass
--        class C(Exception):
--            pass
-+        with torch._dynamo.error_on_graph_break(False):
-+            class A(Exception):
-+                pass
-+            class B(Exception):
-+                pass
-+            class C(Exception):
-+                pass
- 
-         # Context cycle:
-         # +-----------+
-@@ -1200,16 +1266,17 @@ class ExceptionTests(unittest.TestCase):
-     def test_no_hang_on_context_chain_cycle3(self):
-         # See issue 25782. Longer context chain with cycle.
- 
--        class A(Exception):
--            pass
--        class B(Exception):
--            pass
--        class C(Exception):
--            pass
--        class D(Exception):
--            pass
--        class E(Exception):
--            pass
-+        with torch._dynamo.error_on_graph_break(False):
-+            class A(Exception):
-+                pass
-+            class B(Exception):
-+                pass
-+            class C(Exception):
-+                pass
-+            class D(Exception):
-+                pass
-+            class E(Exception):
-+                pass
- 
-         # Context cycle:
-         #             +-----------+
-@@ -1364,11 +1431,12 @@ class ExceptionTests(unittest.TestCase):
-     def test_badisinstance(self):
-         # Bug #2542: if issubclass(e, MyException) raises an exception,
-         # it should be ignored
--        class Meta(type):
--            def __subclasscheck__(cls, subclass):
--                raise ValueError()
--        class MyException(Exception, metaclass=Meta):
--            pass
-+        with torch._dynamo.error_on_graph_break(False):
-+            class Meta(type):
-+                def __subclasscheck__(cls, subclass):
-+                    raise ValueError()
-+            class MyException(Exception, metaclass=Meta):
-+                pass
- 
-         with captured_stderr() as stderr:
-             try:
-@@ -1602,8 +1670,9 @@ class ExceptionTests(unittest.TestCase):
-         self.assertTrue(issubclass(error3, error2))
- 
-         # test with explicit base tuple
--        class C(object):
--            pass
-+        with torch._dynamo.error_on_graph_break(False):
-+            class C(object):
-+                pass
-         error4 = _testcapi.make_exception_with_doc("_testcapi.error4", doc4,
-                                                    (error3, C))
-         self.assertTrue(issubclass(error4, error3))
-@@ -1623,8 +1692,9 @@ class ExceptionTests(unittest.TestCase):
-         # Issue #5437: preallocated MemoryError instances should not keep
-         # traceback objects alive.
-         from _testcapi import raise_memoryerror
--        class C:
--            pass
-+        with torch._dynamo.error_on_graph_break(False):
-+            class C:
-+                pass
-         wr = None
-         def inner():
-             nonlocal wr
-@@ -1644,8 +1714,9 @@ class ExceptionTests(unittest.TestCase):
-     @no_tracing
-     def test_recursion_error_cleanup(self):
-         # Same test as above, but with "recursion exceeded" errors
--        class C:
--            pass
-+        with torch._dynamo.error_on_graph_break(False):
-+            class C:
-+                pass
-         wr = None
-         def inner():
-             nonlocal wr
-@@ -1670,11 +1741,12 @@ class ExceptionTests(unittest.TestCase):
- 
-     def test_unraisable(self):
-         # Issue #22836: PyErr_WriteUnraisable() should give sensible reports
--        class BrokenDel:
--            def __del__(self):
--                exc = ValueError("del is broken")
--                # The following line is included in the traceback report:
--                raise exc
-+        with torch._dynamo.error_on_graph_break(False):
-+            class BrokenDel:
-+                def __del__(self):
-+                    exc = ValueError("del is broken")
-+                    # The following line is included in the traceback report:
-+                    raise exc
- 
-         obj = BrokenDel()
-         with support.catch_unraisable_exception() as cm:
-@@ -1728,11 +1800,12 @@ class ExceptionTests(unittest.TestCase):
- 
-     def test_yield_in_nested_try_excepts(self):
-         #Issue #25612
--        class MainError(Exception):
--            pass
-+        with torch._dynamo.error_on_graph_break(False):
-+            class MainError(Exception):
-+                pass
- 
--        class SubError(Exception):
--            pass
-+            class SubError(Exception):
-+                pass
- 
-         def main():
-             try:
-@@ -1807,8 +1880,9 @@ class ExceptionTests(unittest.TestCase):
-         # subclass object. Finally, it checks that creating a new MemoryError
-         # succeeds, proving that the freelist is not corrupted.
- 
--        class TestException(MemoryError):
--            pass
-+        with torch._dynamo.error_on_graph_break(False):
-+            class TestException(MemoryError):
-+                pass
- 
-         try:
-             raise MemoryError
-@@ -1844,7 +1918,7 @@ class ExceptionTests(unittest.TestCase):
+@@ -1844,7 +1900,7 @@ class ExceptionTests(unittest.TestCase):
          self.assertIn(b'MemoryError', err)
  
  
@@ -378,18 +80,7 @@ index c91f6662948..3a62dec411c 100644
      def test_name_error_has_name(self):
          try:
              bluch
-@@ -1886,15 +1960,16 @@ class NameErrorTests(unittest.TestCase):
- 
-     def test_gh_111654(self):
-         def f():
--            class TestClass:
--                TestClass
-+            with torch._dynamo.error_on_graph_break(False):
-+                class TestClass:
-+                    TestClass
- 
-         self.assertRaises(NameError, f)
- 
+@@ -1894,7 +1950,7 @@ class NameErrorTests(unittest.TestCase):
      # Note: name suggestion tests live in `test_traceback`.
  
  
@@ -398,33 +89,7 @@ index c91f6662948..3a62dec411c 100644
      def test_attributes(self):
          # Setting 'attr' should not be a problem.
          exc = AttributeError('Ouch!')
-@@ -1907,8 +1982,9 @@ class AttributeErrorTests(unittest.TestCase):
-         self.assertIs(exc.obj, sentinel)
- 
-     def test_getattr_has_name_and_obj(self):
--        class A:
--            blech = None
-+        with torch._dynamo.error_on_graph_break(False):
-+            class A:
-+                blech = None
- 
-         obj = A()
-         try:
-@@ -1923,9 +1999,10 @@ class AttributeErrorTests(unittest.TestCase):
-             self.assertEqual(obj, exc.obj)
- 
-     def test_getattr_has_name_and_obj_for_method(self):
--        class A:
--            def blech(self):
--                return
-+        with torch._dynamo.error_on_graph_break(False):
-+            class A:
-+                def blech(self):
-+                    return
- 
-         obj = A()
-         try:
-@@ -1937,7 +2014,7 @@ class AttributeErrorTests(unittest.TestCase):
+@@ -1937,7 +1993,7 @@ class AttributeErrorTests(unittest.TestCase):
      # Note: name suggestion tests live in `test_traceback`.
  
  
@@ -433,7 +98,7 @@ index c91f6662948..3a62dec411c 100644
  
      def test_attributes(self):
          # Setting 'name' and 'path' should not be a problem.
-@@ -2024,7 +2101,7 @@ def run_script(source):
+@@ -2024,7 +2080,7 @@ def run_script(source):
      _rc, _out, err = script_helper.assert_python_failure('-Wd', '-X', 'utf8', TESTFN)
      return err.decode('utf-8').splitlines()
  
@@ -442,7 +107,7 @@ index c91f6662948..3a62dec411c 100644
      def tearDown(self):
          unlink(TESTFN)
  
-@@ -2159,7 +2236,7 @@ class AssertionErrorTests(unittest.TestCase):
+@@ -2159,7 +2215,7 @@ class AssertionErrorTests(unittest.TestCase):
  
  
  @support.force_not_colorized_test_class
@@ -451,19 +116,7 @@ index c91f6662948..3a62dec411c 100644
      maxDiff = None
  
      @force_not_colorized
-@@ -2254,8 +2331,9 @@ class SyntaxErrorTests(unittest.TestCase):
-                     the_exception = exc
- 
-     def test_subclass(self):
--        class MySyntaxError(SyntaxError):
--            pass
-+        with torch._dynamo.error_on_graph_break(False):
-+            class MySyntaxError(SyntaxError):
-+                pass
- 
-         try:
-             raise MySyntaxError("bad bad", ("bad.py", 1, 2, "abcdefg", 1, 7))
-@@ -2290,6 +2368,7 @@ class SyntaxErrorTests(unittest.TestCase):
+@@ -2290,6 +2346,7 @@ class SyntaxErrorTests(unittest.TestCase):
          err = run_script(b"\x89")
          self.assertIn("SyntaxError: Non-UTF-8 code starting with '\\x89' in file", err[-1])
  
@@ -471,7 +124,7 @@ index c91f6662948..3a62dec411c 100644
      def test_string_source(self):
          def try_compile(source):
              with self.assertRaises(SyntaxError) as cm:
-@@ -2405,7 +2484,7 @@ class SyntaxErrorTests(unittest.TestCase):
+@@ -2405,7 +2462,7 @@ class SyntaxErrorTests(unittest.TestCase):
          self.assertRaises(TypeError, SyntaxError, "bad bad", args)
  
  
@@ -480,7 +133,7 @@ index c91f6662948..3a62dec411c 100644
      def test_except_star_invalid_exception_type(self):
          with self.assertRaises(TypeError):
              try:
-@@ -2420,7 +2499,7 @@ class TestInvalidExceptionMatcher(unittest.TestCase):
+@@ -2420,7 +2477,7 @@ class TestInvalidExceptionMatcher(unittest.TestCase):
                  pass
  
  
@@ -489,42 +142,7 @@ index c91f6662948..3a62dec411c 100644
  
      def lineno_after_raise(self, f, *expected):
          try:
-@@ -2499,11 +2578,12 @@ class PEP626Tests(unittest.TestCase):
-         self.lineno_after_raise(in_finally_except, 4)
- 
-     def test_lineno_after_with(self):
--        class Noop:
--            def __enter__(self):
--                return self
--            def __exit__(self, *args):
--                pass
-+        with torch._dynamo.error_on_graph_break(False):
-+            class Noop:
-+                def __enter__(self):
-+                    return self
-+                def __exit__(self, *args):
-+                    pass
-         def after_with():
-             with Noop():
-                 1/0
-@@ -2518,16 +2598,17 @@ class PEP626Tests(unittest.TestCase):
-         self.lineno_after_raise(f, None)
- 
-     def test_lineno_after_raise_in_with_exit(self):
--        class ExitFails:
--            def __enter__(self):
--                return self
--            def __exit__(self, *args):
--                raise ValueError
-+        with torch._dynamo.error_on_graph_break(False):
-+            class ExitFails:
-+                def __enter__(self):
-+                    return self
-+                def __exit__(self, *args):
-+                    raise ValueError
- 
-         def after_with():
-             with ExitFails():
+@@ -2529,5 +2586,5 @@ class PEP626Tests(unittest.TestCase):
                  1/0
          self.lineno_after_raise(after_with, 1, 1)
  

test/dynamo/cpython/3_13/test_exceptions.py

@@ -399,13 +399,12 @@ class ExceptionTests(__TestCase):
         # test that setting an exception at the C level works even if the
         # exception object can't be constructed.
 
-        with torch._dynamo.error_on_graph_break(False):
-            class BadException(Exception):
-                def __init__(self_):
-                    raise RuntimeError("can't instantiate BadException")
+        class BadException(Exception):
+            def __init__(self_):
+                raise RuntimeError("can't instantiate BadException")
 
-            class InvalidException:
-                pass
+        class InvalidException:
+            pass
 
         @unittest.skipIf(_testcapi is None, "requires _testcapi")
         def test_capi1():
@@ -693,9 +692,8 @@ class ExceptionTests(__TestCase):
         self.assertIsInstance(e, IndexError)
         self.assertEqual(e.__traceback__, tb)
 
-        with torch._dynamo.error_on_graph_break(False):
-            class MyException(Exception):
-                pass
+        class MyException(Exception):
+            pass
 
         e = MyException().with_traceback(tb)
         self.assertIsInstance(e, MyException)
@@ -754,9 +752,8 @@ class ExceptionTests(__TestCase):
         self.assertIsNone(e.__context__)
         self.assertIsNone(e.__cause__)
 
-        with torch._dynamo.error_on_graph_break(False):
-            class MyException(OSError):
-                pass
+        class MyException(OSError):
+            pass
 
         e = MyException()
         self.assertIsNone(e.__context__)
@@ -785,11 +782,10 @@ class ExceptionTests(__TestCase):
         # but user-defined subclasses can if they want
         self.assertRaises(TypeError, BaseException, a=1)
 
-        with torch._dynamo.error_on_graph_break(False):
-            class DerivedException(BaseException):
-                def __init__(self, fancy_arg):
-                    BaseException.__init__(self)
-                    self.fancy_arg = fancy_arg
+        class DerivedException(BaseException):
+            def __init__(self, fancy_arg):
+                BaseException.__init__(self)
+                self.fancy_arg = fancy_arg
 
         x = DerivedException(fancy_arg=42)
         self.assertEqual(x.fancy_arg, 42)
@@ -839,12 +835,11 @@ class ExceptionTests(__TestCase):
         # Make sure exception state is cleaned up as soon as the except
         # block is left. See #2507
 
-        with torch._dynamo.error_on_graph_break(False):
-            class MyException(Exception):
-                def __init__(self, obj):
-                    self.obj = obj
-            class MyObj:
-                pass
+        class MyException(Exception):
+            def __init__(self, obj):
+                self.obj = obj
+        class MyObj:
+            pass
 
         def inner_raising_func():
             # Create some references in exception value and traceback
@@ -942,12 +937,11 @@ class ExceptionTests(__TestCase):
         self.assertIsNone(obj)
 
         # Inside an exception-silencing "with" block
-        with torch._dynamo.error_on_graph_break(False):
-            class Context:
-                def __enter__(self):
-                    return self
-                def __exit__ (self, exc_type, exc_value, exc_tb):
-                    return True
+        class Context:
+            def __enter__(self):
+                return self
+            def __exit__ (self, exc_type, exc_value, exc_tb):
+                return True
         obj = MyObj()
         wr = weakref.ref(obj)
         with Context():
@@ -1089,12 +1083,11 @@ class ExceptionTests(__TestCase):
     def _check_generator_cleanup_exc_state(self, testfunc):
         # Issue #12791: exception state is cleaned up as soon as a generator
         # is closed (reference cycles are broken).
-        with torch._dynamo.error_on_graph_break(False):
-            class MyException(Exception):
-                def __init__(self, obj):
-                    self.obj = obj
-            class MyObj:
-                pass
+        class MyException(Exception):
+            def __init__(self, obj):
+                self.obj = obj
+        class MyObj:
+            pass
 
         def raising_gen():
             try:
@@ -1153,11 +1146,10 @@ class ExceptionTests(__TestCase):
     def test_3114(self):
         # Bug #3114: in its destructor, MyObject retrieves a pointer to
         # obsolete and/or deallocated objects.
-        with torch._dynamo.error_on_graph_break(False):
-            class MyObject:
-                def __del__(self):
-                    nonlocal e
-                    e = sys.exception()
+        class MyObject:
+            def __del__(self):
+                nonlocal e
+                e = sys.exception()
         e = ()
         try:
             raise Exception(MyObject())
@@ -1167,13 +1159,12 @@ class ExceptionTests(__TestCase):
         self.assertIsNone(e)
 
     def test_raise_does_not_create_context_chain_cycle(self):
-        with torch._dynamo.error_on_graph_break(False):
-            class A(Exception):
-                pass
-            class B(Exception):
-                pass
-            class C(Exception):
-                pass
+        class A(Exception):
+            pass
+        class B(Exception):
+            pass
+        class C(Exception):
+            pass
 
         # Create a context chain:
         # C -> B -> A
@@ -1229,13 +1220,12 @@ class ExceptionTests(__TestCase):
     def test_no_hang_on_context_chain_cycle2(self):
         # See issue 25782. Cycle at head of context chain.
 
-        with torch._dynamo.error_on_graph_break(False):
-            class A(Exception):
-                pass
-            class B(Exception):
-                pass
-            class C(Exception):
-                pass
+        class A(Exception):
+            pass
+        class B(Exception):
+            pass
+        class C(Exception):
+            pass
 
         # Context cycle:
         # +-----------+
@@ -1266,17 +1256,16 @@ class ExceptionTests(__TestCase):
     def test_no_hang_on_context_chain_cycle3(self):
         # See issue 25782. Longer context chain with cycle.
 
-        with torch._dynamo.error_on_graph_break(False):
-            class A(Exception):
-                pass
-            class B(Exception):
-                pass
-            class C(Exception):
-                pass
-            class D(Exception):
-                pass
-            class E(Exception):
-                pass
+        class A(Exception):
+            pass
+        class B(Exception):
+            pass
+        class C(Exception):
+            pass
+        class D(Exception):
+            pass
+        class E(Exception):
+            pass
 
         # Context cycle:
         #             +-----------+
@@ -1431,12 +1420,11 @@ class ExceptionTests(__TestCase):
     def test_badisinstance(self):
         # Bug #2542: if issubclass(e, MyException) raises an exception,
         # it should be ignored
-        with torch._dynamo.error_on_graph_break(False):
-            class Meta(type):
-                def __subclasscheck__(cls, subclass):
-                    raise ValueError()
-            class MyException(Exception, metaclass=Meta):
-                pass
+        class Meta(type):
+            def __subclasscheck__(cls, subclass):
+                raise ValueError()
+        class MyException(Exception, metaclass=Meta):
+            pass
 
         with captured_stderr() as stderr:
             try:
@@ -1670,9 +1658,8 @@ class ExceptionTests(__TestCase):
         self.assertTrue(issubclass(error3, error2))
 
         # test with explicit base tuple
-        with torch._dynamo.error_on_graph_break(False):
-            class C(object):
-                pass
+        class C(object):
+            pass
         error4 = _testcapi.make_exception_with_doc("_testcapi.error4", doc4,
                                                    (error3, C))
         self.assertTrue(issubclass(error4, error3))
@@ -1692,9 +1679,8 @@ class ExceptionTests(__TestCase):
         # Issue #5437: preallocated MemoryError instances should not keep
         # traceback objects alive.
         from _testcapi import raise_memoryerror
-        with torch._dynamo.error_on_graph_break(False):
-            class C:
-                pass
+        class C:
+            pass
         wr = None
         def inner():
             nonlocal wr
@@ -1714,9 +1700,8 @@ class ExceptionTests(__TestCase):
     @no_tracing
     def test_recursion_error_cleanup(self):
         # Same test as above, but with "recursion exceeded" errors
-        with torch._dynamo.error_on_graph_break(False):
-            class C:
-                pass
+        class C:
+            pass
         wr = None
         def inner():
             nonlocal wr
@@ -1741,12 +1726,11 @@ class ExceptionTests(__TestCase):
 
     def test_unraisable(self):
         # Issue #22836: PyErr_WriteUnraisable() should give sensible reports
-        with torch._dynamo.error_on_graph_break(False):
-            class BrokenDel:
-                def __del__(self):
-                    exc = ValueError("del is broken")
-                    # The following line is included in the traceback report:
-                    raise exc
+        class BrokenDel:
+            def __del__(self):
+                exc = ValueError("del is broken")
+                # The following line is included in the traceback report:
+                raise exc
 
         obj = BrokenDel()
         with support.catch_unraisable_exception() as cm:
@@ -1800,12 +1784,11 @@ class ExceptionTests(__TestCase):
 
     def test_yield_in_nested_try_excepts(self):
         #Issue #25612
-        with torch._dynamo.error_on_graph_break(False):
-            class MainError(Exception):
-                pass
+        class MainError(Exception):
+            pass
 
-            class SubError(Exception):
-                pass
+        class SubError(Exception):
+            pass
 
         def main():
             try:
@@ -1880,9 +1863,8 @@ class ExceptionTests(__TestCase):
         # subclass object. Finally, it checks that creating a new MemoryError
         # succeeds, proving that the freelist is not corrupted.
 
-        with torch._dynamo.error_on_graph_break(False):
-            class TestException(MemoryError):
-                pass
+        class TestException(MemoryError):
+            pass
 
         try:
             raise MemoryError
@@ -1960,9 +1942,8 @@ class NameErrorTests(__TestCase):
 
     def test_gh_111654(self):
         def f():
-            with torch._dynamo.error_on_graph_break(False):
-                class TestClass:
-                    TestClass
+            class TestClass:
+                TestClass
 
         self.assertRaises(NameError, f)
 
@@ -1982,9 +1963,8 @@ class AttributeErrorTests(__TestCase):
         self.assertIs(exc.obj, sentinel)
 
     def test_getattr_has_name_and_obj(self):
-        with torch._dynamo.error_on_graph_break(False):
-            class A:
-                blech = None
+        class A:
+            blech = None
 
         obj = A()
         try:
@@ -1999,10 +1979,9 @@ class AttributeErrorTests(__TestCase):
             self.assertEqual(obj, exc.obj)
 
     def test_getattr_has_name_and_obj_for_method(self):
-        with torch._dynamo.error_on_graph_break(False):
-            class A:
-                def blech(self):
-                    return
+        class A:
+            def blech(self):
+                return
 
         obj = A()
         try:
@@ -2331,9 +2310,8 @@ class SyntaxErrorTests(__TestCase):
                     the_exception = exc
 
     def test_subclass(self):
-        with torch._dynamo.error_on_graph_break(False):
-            class MySyntaxError(SyntaxError):
-                pass
+        class MySyntaxError(SyntaxError):
+            pass
 
         try:
             raise MySyntaxError("bad bad", ("bad.py", 1, 2, "abcdefg", 1, 7))
@@ -2578,12 +2556,11 @@ class PEP626Tests(__TestCase):
         self.lineno_after_raise(in_finally_except, 4)
 
     def test_lineno_after_with(self):
-        with torch._dynamo.error_on_graph_break(False):
-            class Noop:
-                def __enter__(self):
-                    return self
-                def __exit__(self, *args):
-                    pass
+        class Noop:
+            def __enter__(self):
+                return self
+            def __exit__(self, *args):
+                pass
         def after_with():
             with Noop():
                 1/0
@@ -2598,12 +2575,11 @@ class PEP626Tests(__TestCase):
         self.lineno_after_raise(f, None)
 
     def test_lineno_after_raise_in_with_exit(self):
-        with torch._dynamo.error_on_graph_break(False):
-            class ExitFails:
-                def __enter__(self):
-                    return self
-                def __exit__(self, *args):
-                    raise ValueError
+        class ExitFails:
+            def __enter__(self):
+                return self
+            def __exit__(self, *args):
+                raise ValueError
 
         def after_with():
             with ExitFails():

test/dynamo/cpython/3_13/test_raise.diff

@@ -1,5 +1,5 @@
 diff --git a/test/dynamo/cpython/3_13/test_raise.py b/test/dynamo/cpython/3_13/test_raise.py
-index 6d26a61bee4..ce748433d28 100644
+index 6d26a61bee4..042d1ae3d7c 100644
 --- a/test/dynamo/cpython/3_13/test_raise.py
 +++ b/test/dynamo/cpython/3_13/test_raise.py
 @@ -1,3 +1,58 @@
@@ -70,35 +70,7 @@ index 6d26a61bee4..ce748433d28 100644
      def test_invalid_reraise(self):
          try:
              raise
-@@ -120,9 +175,10 @@ class TestRaise(unittest.TestCase):
-         self.assertRaises(StopIteration, lambda: next(g))
- 
-     def test_erroneous_exception(self):
--        class MyException(Exception):
--            def __init__(self):
--                raise RuntimeError()
-+        with torch._dynamo.error_on_graph_break(False):
-+            class MyException(Exception):
-+                def __init__(self):
-+                    raise RuntimeError()
- 
-         try:
-             raise MyException
-@@ -133,9 +189,10 @@ class TestRaise(unittest.TestCase):
- 
-     def test_new_returns_invalid_instance(self):
-         # See issue #11627.
--        class MyException(Exception):
--            def __new__(cls, *args):
--                return object()
-+        with torch._dynamo.error_on_graph_break(False):
-+            class MyException(Exception):
-+                def __new__(cls, *args):
-+                    return object()
- 
-         with self.assertRaises(TypeError):
-             raise MyException
-@@ -148,7 +205,7 @@ class TestRaise(unittest.TestCase):
+@@ -148,7 +203,7 @@ class TestRaise(unittest.TestCase):
  
  
  
@@ -107,37 +79,7 @@ index 6d26a61bee4..ce748433d28 100644
  
      def testCauseSyntax(self):
          try:
-@@ -186,10 +243,11 @@ class TestCause(unittest.TestCase):
-             self.fail("No exception raised")
- 
-     def test_class_cause_nonexception_result(self):
--        class ConstructsNone(BaseException):
--            @classmethod
--            def __new__(*args, **kwargs):
--                return None
-+        with torch._dynamo.error_on_graph_break(False):
-+            class ConstructsNone(BaseException):
-+                @classmethod
-+                def __new__(*args, **kwargs):
-+                    return None
-         try:
-             raise IndexError from ConstructsNone
-         except TypeError as e:
-@@ -209,9 +267,10 @@ class TestCause(unittest.TestCase):
-             self.fail("No exception raised")
- 
-     def test_erroneous_cause(self):
--        class MyException(Exception):
--            def __init__(self):
--                raise RuntimeError()
-+        with torch._dynamo.error_on_graph_break(False):
-+            class MyException(Exception):
-+                def __init__(self):
-+                    raise RuntimeError()
- 
-         try:
-             raise IndexError from MyException
-@@ -221,7 +280,7 @@ class TestCause(unittest.TestCase):
+@@ -221,7 +276,7 @@ class TestCause(unittest.TestCase):
              self.fail("No exception raised")
  
  
@@ -146,7 +88,7 @@ index 6d26a61bee4..ce748433d28 100644
  
      def test_sets_traceback(self):
          try:
-@@ -242,7 +301,7 @@ class TestTraceback(unittest.TestCase):
+@@ -242,7 +297,7 @@ class TestTraceback(unittest.TestCase):
              self.fail("No exception raised")
  
  
@@ -155,7 +97,7 @@ index 6d26a61bee4..ce748433d28 100644
  
      def raiser(self):
          raise ValueError
-@@ -308,7 +367,7 @@ class TestTracebackType(unittest.TestCase):
+@@ -308,7 +363,7 @@ class TestTracebackType(unittest.TestCase):
              types.TracebackType(other_tb, frame, 1, "nuh-uh")
  
  
@@ -164,45 +106,7 @@ index 6d26a61bee4..ce748433d28 100644
      def test_instance_context_instance_raise(self):
          context = IndexError()
          try:
-@@ -392,11 +451,12 @@ class TestContext(unittest.TestCase):
-             self.fail("No exception raised")
- 
-     def test_context_manager(self):
--        class ContextManager:
--            def __enter__(self):
--                pass
--            def __exit__(self, t, v, tb):
--                xyzzy
-+        with torch._dynamo.error_on_graph_break(False):
-+            class ContextManager:
-+                def __enter__(self):
-+                    pass
-+                def __exit__(self, t, v, tb):
-+                    xyzzy
-         try:
-             with ContextManager():
-                 1/0
-@@ -471,12 +531,13 @@ class TestContext(unittest.TestCase):
-         import gc
-         # A re-raised exception in a __del__ caused the __context__
-         # to be cleared
--        class C:
--            def __del__(self):
--                try:
--                    1/0
--                except:
--                    raise
-+        with torch._dynamo.error_on_graph_break(False):
-+            class C:
-+                def __del__(self):
-+                    try:
-+                        1/0
-+                    except:
-+                        raise
- 
-         def f():
-             x = C()
-@@ -498,7 +559,7 @@ class TestContext(unittest.TestCase):
+@@ -498,7 +553,7 @@ class TestContext(unittest.TestCase):
              self.assertEqual(ZeroDivisionError, cm.unraisable.exc_type)
  
  
@@ -211,7 +115,7 @@ index 6d26a61bee4..ce748433d28 100644
      def test_tuples(self):
          try:
              raise (IndexError, KeyError) # This should be a tuple!
-@@ -517,4 +578,4 @@ class TestRemovedFunctionality(unittest.TestCase):
+@@ -517,4 +572,4 @@ class TestRemovedFunctionality(unittest.TestCase):
  
  
  if __name__ == "__main__":

test/dynamo/cpython/3_13/test_raise.py

@@ -175,10 +175,9 @@ class TestRaise(__TestCase):
         self.assertRaises(StopIteration, lambda: next(g))
 
     def test_erroneous_exception(self):
-        with torch._dynamo.error_on_graph_break(False):
-            class MyException(Exception):
-                def __init__(self):
-                    raise RuntimeError()
+        class MyException(Exception):
+            def __init__(self):
+                raise RuntimeError()
 
         try:
             raise MyException
@@ -189,10 +188,9 @@ class TestRaise(__TestCase):
 
     def test_new_returns_invalid_instance(self):
         # See issue #11627.
-        with torch._dynamo.error_on_graph_break(False):
-            class MyException(Exception):
-                def __new__(cls, *args):
-                    return object()
+        class MyException(Exception):
+            def __new__(cls, *args):
+                return object()
 
         with self.assertRaises(TypeError):
             raise MyException
@@ -243,11 +241,10 @@ class TestCause(__TestCase):
             self.fail("No exception raised")
 
     def test_class_cause_nonexception_result(self):
-        with torch._dynamo.error_on_graph_break(False):
-            class ConstructsNone(BaseException):
-                @classmethod
-                def __new__(*args, **kwargs):
-                    return None
+        class ConstructsNone(BaseException):
+            @classmethod
+            def __new__(*args, **kwargs):
+                return None
         try:
             raise IndexError from ConstructsNone
         except TypeError as e:
@@ -267,10 +264,9 @@ class TestCause(__TestCase):
             self.fail("No exception raised")
 
     def test_erroneous_cause(self):
-        with torch._dynamo.error_on_graph_break(False):
-            class MyException(Exception):
-                def __init__(self):
-                    raise RuntimeError()
+        class MyException(Exception):
+            def __init__(self):
+                raise RuntimeError()
 
         try:
             raise IndexError from MyException
@@ -451,12 +447,11 @@ class TestContext(__TestCase):
             self.fail("No exception raised")
 
     def test_context_manager(self):
-        with torch._dynamo.error_on_graph_break(False):
-            class ContextManager:
-                def __enter__(self):
-                    pass
-                def __exit__(self, t, v, tb):
-                    xyzzy
+        class ContextManager:
+            def __enter__(self):
+                pass
+            def __exit__(self, t, v, tb):
+                xyzzy
         try:
             with ContextManager():
                 1/0
@@ -531,13 +526,12 @@ class TestContext(__TestCase):
         import gc
         # A re-raised exception in a __del__ caused the __context__
         # to be cleared
-        with torch._dynamo.error_on_graph_break(False):
-            class C:
-                def __del__(self):
-                    try:
-                        1/0
-                    except:
-                        raise
+        class C:
+            def __del__(self):
+                try:
+                    1/0
+                except:
+                    raise
 
         def f():
             x = C()

test/dynamo/test_aot_autograd.py

@@ -916,41 +916,43 @@ class AotAutogradFallbackTests(torch._inductor.test_case.TestCase):
             dedent(
                 """\
 SeqNr|OrigAten|SrcFn|FwdSrcFn
-0|aten.convolution.default|conv2d|
-0|aten.add.Tensor|add_|
-1|aten._native_batch_norm_legit_functional.default|batch_norm|
-2|aten.relu.default|relu|
-2|aten.detach.default|relu|
+0|aten.convolution.default|l__self___conv1|
+0|aten.add.Tensor|l__self___bn1|
+1|aten._native_batch_norm_legit_functional.default|l__self___bn1|
+2|aten.relu.default|l__self___relu1|
+2|aten.detach.default|l__self___relu1|
+2|aten.detach.default|l__self___relu1|
 3|aten.add.Tensor|add|
 4|aten.view.default|flatten|
-5|aten.view.default|linear|
-6|aten.t.default|linear|
-7|aten.addmm.default|linear|
-8|aten.view.default|linear|
-9|aten.sub.Tensor|l1_loss|
-10|aten.abs.default|l1_loss|
-11|aten.mean.default|l1_loss|
-11|aten.ones_like.default||l1_loss
-11|aten.expand.default||l1_loss
-11|aten.div.Scalar||l1_loss
-10|aten.sgn.default||l1_loss
-10|aten.mul.Tensor||l1_loss
-8|aten.view.default||linear
-7|aten.t.default||linear
-7|aten.mm.default||linear
-7|aten.t.default||linear
-7|aten.mm.default||linear
-7|aten.t.default||linear
-7|aten.sum.dim_IntList||linear
-7|aten.view.default||linear
-6|aten.t.default||linear
-5|aten.view.default||linear
+5|aten.view.default|l__self___fc1|
+6|aten.t.default|l__self___fc1|
+7|aten.addmm.default|l__self___fc1|
+8|aten.view.default|l__self___fc1|
+9|aten.sub.Tensor|l__self___loss_fn|
+10|aten.abs.default|l__self___loss_fn|
+11|aten.mean.default|l__self___loss_fn|
+11|aten.ones_like.default||l__self___loss_fn
+11|aten.expand.default||l__self___loss_fn
+11|aten.div.Scalar||l__self___loss_fn
+10|aten.sgn.default||l__self___loss_fn
+10|aten.mul.Tensor||l__self___loss_fn
+8|aten.view.default||l__self___fc1
+7|aten.t.default||l__self___fc1
+7|aten.mm.default||l__self___fc1
+7|aten.t.default||l__self___fc1
+7|aten.mm.default||l__self___fc1
+7|aten.t.default||l__self___fc1
+7|aten.sum.dim_IntList||l__self___fc1
+7|aten.view.default||l__self___fc1
+6|aten.t.default||l__self___fc1
+5|aten.view.default||l__self___fc1
 4|aten.view.default||flatten
-2|aten.detach.default||relu
-2|aten.threshold_backward.default||relu
-1|aten.native_batch_norm_backward.default||batch_norm
-0|aten.convolution_backward.default||conv2d
-11|aten.add.Tensor||l1_loss
+2|aten.detach.default||l__self___relu1
+2|aten.detach.default||l__self___relu1
+2|aten.threshold_backward.default||l__self___relu1
+1|aten.native_batch_norm_backward.default||l__self___bn1
+0|aten.convolution_backward.default||l__self___conv1
+11|aten.add.Tensor||l__self___loss_fn
 """
             ),
         )

test/dynamo/test_aot_compile.py

@@ -1,6 +1,5 @@
 # Owner(s): ["module: dynamo"]
 
-import functools
 import inspect
 import os
 import pickle
@@ -204,39 +203,6 @@ class TestAOTCompile(torch._inductor.test_case.TestCase):
             actual = compiled_fn(*example_inputs)
             self.assertEqual(expected, actual)
 
-    def test_decorated_function_with_functools_wrap_aot(self):
-        def check_inputs(fn):
-            @functools.wraps(fn)
-            def _fn(*args, **kwargs):
-                for arg in args:
-                    assert arg.shape[0] > 1
-
-                return fn(*args, **kwargs)
-
-            return _fn
-
-        @check_inputs
-        def foo(x, y):
-            a = x + x
-            b = y + y
-            c = a + b
-            return c
-
-        example_inputs = (torch.ones(3), torch.ones(3))
-        expected = foo(*example_inputs)
-
-        def backend(gm, example_inputs):
-            return CustomCompiledFunction(gm, example_inputs)
-
-        with torch.compiler.set_stance("fail_on_recompile"):
-            compiled_fn = torch.compile(
-                foo,
-                fullgraph=True,
-                backend=backend,
-            ).aot_compile((example_inputs, {}))
-            actual = compiled_fn(*example_inputs)
-            self.assertEqual(expected, actual)
-
     def test_aot_compile_disable_guard_check(self):
         def fn(x, y):
             return x + y