Initial type coverage

.ci/docker/almalinux/build.sh

@@ -36,7 +36,11 @@ case ${DOCKER_TAG_PREFIX} in
     ;;
   rocm*)
     BASE_TARGET=rocm
-    PYTORCH_ROCM_ARCH="gfx900;gfx906;gfx908;gfx90a;gfx942;gfx1030;gfx1100;gfx1101;gfx1102;gfx1200;gfx1201;gfx950;gfx1150;gfx1151"
+    PYTORCH_ROCM_ARCH="gfx900;gfx906;gfx908;gfx90a;gfx942;gfx1030;gfx1100;gfx1101;gfx1102;gfx1200;gfx1201"
+    # add gfx950, gfx115x conditionally starting in ROCm 7.0
+    if [[ "$ROCM_VERSION" == *"7.0"* ]]; then
+        PYTORCH_ROCM_ARCH="${PYTORCH_ROCM_ARCH};gfx950;gfx1150;gfx1151"
+    fi
     EXTRA_BUILD_ARGS="${EXTRA_BUILD_ARGS} --build-arg PYTORCH_ROCM_ARCH=${PYTORCH_ROCM_ARCH}"
     ;;
   *)

.ci/docker/build.sh

@@ -168,18 +168,6 @@ case "$tag" in
     VISION=yes
     TRITON=yes
     ;;
-  pytorch-linux-jammy-py3.11-clang12)
-    ANACONDA_PYTHON_VERSION=3.11
-    CLANG_VERSION=12
-    VISION=no
-    TRITON=no
-    ;;
-  pytorch-linux-jammy-py3.12-clang12)
-    ANACONDA_PYTHON_VERSION=3.12
-    CLANG_VERSION=12
-    VISION=no
-    TRITON=no
-    ;;
   pytorch-linux-jammy-rocm-n-py3 | pytorch-linux-jammy-rocm-n-py3-benchmarks | pytorch-linux-noble-rocm-n-py3)
     if [[ $tag =~ "jammy" ]]; then
       ANACONDA_PYTHON_VERSION=3.10
@@ -207,9 +195,9 @@ case "$tag" in
     NINJA_VERSION=1.9.0
     TRITON=yes
     ;;
-  pytorch-linux-noble-xpu-n-py3 | pytorch-linux-noble-xpu-n-py3-inductor-benchmarks)
+  pytorch-linux-jammy-xpu-n-py3 | pytorch-linux-jammy-xpu-n-py3-inductor-benchmarks)
     ANACONDA_PYTHON_VERSION=3.10
-    GCC_VERSION=13
+    GCC_VERSION=11
     VISION=yes
     XPU_VERSION=2025.2
     NINJA_VERSION=1.9.0
@@ -260,12 +248,6 @@ case "$tag" in
     HALIDE=yes
     TRITON=yes
     ;;
-  pytorch-linux-jammy-cuda12.8-py3.12-pallas)
-    CUDA_VERSION=12.8.1
-    ANACONDA_PYTHON_VERSION=3.12
-    GCC_VERSION=11
-    PALLAS=yes
-    ;;
   pytorch-linux-jammy-py3.12-triton-cpu)
     CUDA_VERSION=12.6
     ANACONDA_PYTHON_VERSION=3.12
@@ -387,7 +369,6 @@ docker build \
        --build-arg "INDUCTOR_BENCHMARKS=${INDUCTOR_BENCHMARKS}" \
        --build-arg "EXECUTORCH=${EXECUTORCH}" \
        --build-arg "HALIDE=${HALIDE}" \
-       --build-arg "PALLAS=${PALLAS}" \
        --build-arg "XPU_VERSION=${XPU_VERSION}" \
        --build-arg "UNINSTALL_DILL=${UNINSTALL_DILL}" \
        --build-arg "ACL=${ACL:-}" \

.ci/docker/ci_commit_pins/jax.txt

@@ -1 +0,0 @@
-0.8.0

.ci/docker/common/install_jax.sh

@@ -1,40 +0,0 @@
-#!/bin/bash
-
-set -ex
-
-source "$(dirname "${BASH_SOURCE[0]}")/common_utils.sh"
-
-# Get the pinned JAX version (same for all CUDA versions)
-JAX_VERSION=$(get_pinned_commit /ci_commit_pins/jax)
-
-function install_jax_12() {
-  echo "Installing JAX ${JAX_VERSION} with CUDA 12 support"
-  pip_install "jax[cuda12]==${JAX_VERSION}" -f https://storage.googleapis.com/jax-releases/jax_cuda_releases.html
-
-  # Verify installation
-  python -c "import jax"  # check for errors
-  echo "JAX ${JAX_VERSION} installation completed successfully for CUDA 12"
-}
-
-function install_jax_13() {
-  echo "Installing JAX ${JAX_VERSION} with CUDA 13 support"
-  pip_install "jax[cuda13]==${JAX_VERSION}" -f https://storage.googleapis.com/jax-releases/jax_cuda_releases.html
-
-  # Verify installation
-  python -c "import jax"  # check for errors
-  echo "JAX ${JAX_VERSION} installation completed successfully for CUDA 13"
-}
-
-# idiomatic parameter and option handling in sh
-while test $# -gt 0
-do
-    case "$1" in
-    12.4|12.6|12.6.*|12.8|12.8.*|12.9|12.9.*) install_jax_12;
-        ;;
-    13.0|13.0.*) install_jax_13;
-        ;;
-    *) echo "bad argument $1"; exit 1
-        ;;
-    esac
-    shift
-done

.ci/docker/common/install_xpu.sh

@@ -9,7 +9,7 @@ set -xe
 
 function install_ubuntu() {
     . /etc/os-release
-    if [[ ! " jammy noble " =~ " ${VERSION_CODENAME} " ]]; then
+    if [[ ! " jammy " =~ " ${VERSION_CODENAME} " ]]; then
         echo "Ubuntu version ${VERSION_CODENAME} not supported"
         exit
     fi
@@ -35,24 +35,25 @@ function install_ubuntu() {
     # The xpu-smi packages
     apt-get install -y flex bison xpu-smi
 
-    # Compute and Media Runtimes
-    if [[ " ${VERSION_CODENAME} " =~ " noble " ]]; then
+    if [[ "${XPU_DRIVER_TYPE,,}" == "lts" ]]; then
+        # Compute and Media Runtimes
         apt-get install -y \
-            intel-opencl-icd libze-intel-gpu1 libze1 \
-            intel-media-va-driver-non-free libmfx-gen1 libvpl2 \
-            libegl-mesa0 libegl1-mesa-dev libgbm1 libgl1-mesa-dev libgl1-mesa-dri \
+            intel-opencl-icd intel-level-zero-gpu level-zero \
+            intel-media-va-driver-non-free libmfx1 libmfxgen1 libvpl2 \
+            libegl-mesa0 libegl1-mesa libegl1-mesa-dev libgbm1 libgl1-mesa-dev libgl1-mesa-dri \
             libglapi-mesa libgles2-mesa-dev libglx-mesa0 libigdgmm12 libxatracker2 mesa-va-drivers \
-            mesa-vdpau-drivers mesa-vulkan-drivers va-driver-all vainfo hwinfo clinfo intel-ocloc
-    else # jammy
+            mesa-vdpau-drivers mesa-vulkan-drivers va-driver-all vainfo hwinfo clinfo
+        # Development Packages
+        apt-get install -y libigc-dev intel-igc-cm libigdfcl-dev libigfxcmrt-dev level-zero-dev
+    else # rolling driver
         apt-get install -y \
             intel-opencl-icd libze-intel-gpu1 libze1 \
             intel-media-va-driver-non-free libmfx-gen1 libvpl2 \
             libegl-mesa0 libegl1-mesa libegl1-mesa-dev libgbm1 libgl1-mesa-dev libgl1-mesa-dri \
             libglapi-mesa libglx-mesa0 libigdgmm12 libxatracker2 mesa-va-drivers \
             mesa-vdpau-drivers mesa-vulkan-drivers va-driver-all vainfo hwinfo clinfo intel-ocloc
+        apt-get install -y libigc-dev intel-igc-cm libigdfcl-dev libigfxcmrt-dev libze-dev
     fi
-    # Development Packages
-    apt-get install -y libigc-dev intel-igc-cm libigdfcl-dev libigfxcmrt-dev libze-dev
 
     # Install Intel Support Packages
     apt-get install -y ${XPU_PACKAGES}
@@ -65,7 +66,7 @@ function install_ubuntu() {
 function install_rhel() {
     . /etc/os-release
     if [[ "${ID}" == "rhel" ]]; then
-        if [[ ! " 8.8 8.10 9.0 9.2 9.3 " =~ " ${VERSION_ID} " ]]; then
+        if [[ ! " 8.8 8.9 9.0 9.2 9.3 " =~ " ${VERSION_ID} " ]]; then
             echo "RHEL version ${VERSION_ID} not supported"
             exit
         fi
@@ -146,7 +147,7 @@ function install_sles() {
 XPU_DRIVER_VERSION=""
 if [[ "${XPU_DRIVER_TYPE,,}" == "lts" ]]; then
     # Use GPU driver LTS releases
-    XPU_DRIVER_VERSION="/lts/2523"
+    XPU_DRIVER_VERSION="/lts/2350"
 fi
 
 # Default use Intel® oneAPI Deep Learning Essentials 2025.1

.ci/docker/libtorch/build.sh

@@ -49,7 +49,11 @@ case ${DOCKER_TAG_PREFIX} in
         fi
         BASE_TARGET=rocm
         GPU_IMAGE=rocm/dev-ubuntu-22.04:${GPU_ARCH_VERSION}-complete
-        PYTORCH_ROCM_ARCH="gfx900;gfx906;gfx908;gfx90a;gfx942;gfx1030;gfx1100;gfx1101;gfx1102;gfx1200;gfx1201;gfx950;gfx1150;gfx1151"
+        PYTORCH_ROCM_ARCH="gfx900;gfx906;gfx908;gfx90a;gfx942;gfx1030;gfx1100;gfx1101;gfx1102;gfx1200;gfx1201"
+        # add gfx950, gfx115x conditionally starting in ROCm 7.0
+        if [[ "$GPU_ARCH_VERSION" == *"7.0"* ]]; then
+            PYTORCH_ROCM_ARCH="${PYTORCH_ROCM_ARCH};gfx950;gfx1150;gfx1151"
+        fi
         DOCKER_GPU_BUILD_ARG="--build-arg PYTORCH_ROCM_ARCH=${PYTORCH_ROCM_ARCH} --build-arg ROCM_VERSION=${GPU_ARCH_VERSION}"
         ;;
     *)

.ci/docker/manywheel/build.sh

@@ -87,7 +87,11 @@ case ${image} in
         MANY_LINUX_VERSION="2_28"
         DEVTOOLSET_VERSION="11"
         GPU_IMAGE=rocm/dev-almalinux-8:${GPU_ARCH_VERSION}-complete
-        PYTORCH_ROCM_ARCH="gfx900;gfx906;gfx908;gfx90a;gfx942;gfx1030;gfx1100;gfx1101;gfx1102;gfx1200;gfx1201;gfx950;gfx1150;gfx1151"
+        PYTORCH_ROCM_ARCH="gfx900;gfx906;gfx908;gfx90a;gfx942;gfx1030;gfx1100;gfx1101;gfx1102;gfx1200;gfx1201"
+        # add gfx950, gfx115x conditionally starting in ROCm 7.0
+        if [[ "$GPU_ARCH_VERSION" == *"7.0"* ]]; then
+            PYTORCH_ROCM_ARCH="${PYTORCH_ROCM_ARCH};gfx950;gfx1150;gfx1151"
+        fi
         DOCKER_GPU_BUILD_ARG="--build-arg ROCM_VERSION=${GPU_ARCH_VERSION} --build-arg PYTORCH_ROCM_ARCH=${PYTORCH_ROCM_ARCH} --build-arg DEVTOOLSET_VERSION=${DEVTOOLSET_VERSION}"
         ;;
     manylinux2_28-builder:xpu)

.ci/docker/ubuntu/Dockerfile

@@ -143,15 +143,6 @@ COPY ci_commit_pins/halide.txt halide.txt
 RUN if [ -n "${HALIDE}" ]; then bash ./install_halide.sh; fi
 RUN rm install_halide.sh common_utils.sh halide.txt
 
-ARG PALLAS
-ARG CUDA_VERSION
-# Install JAX with CUDA support (for Pallas)
-COPY ./common/install_jax.sh install_jax.sh
-COPY ./common/common_utils.sh common_utils.sh
-COPY ./ci_commit_pins/jax.txt /ci_commit_pins/jax.txt
-RUN if [ -n "${PALLAS}" ]; then bash ./install_jax.sh ${CUDA_VERSION}; fi
-RUN rm -f install_jax.sh common_utils.sh /ci_commit_pins/jax.txt
-
 ARG ONNX
 # Install ONNX dependencies
 COPY ./common/install_onnx.sh ./common/common_utils.sh ./

.ci/lumen_cli/cli/lib/common/cli_helper.py

@@ -8,11 +8,9 @@ from abc import ABC, abstractmethod
 
 
 try:
-    from collections.abc import Callable  # Python 3.11+
-    from typing import Any, Required, TypedDict
+    from typing import Any, Callable, Required, TypedDict  # Python 3.11+
 except ImportError:
-    from collections.abc import Callable
-    from typing import Any, TypedDict
+    from typing import Any, Callable, TypedDict
 
     from typing_extensions import Required  # Fallback for Python <3.11
 

.ci/magma-rocm/README.md

@@ -30,6 +30,7 @@ into a tarball, with the following structure:
 More specifically, `build_magma.sh` copies over the relevant files from the `package_files` directory depending on the ROCm version.
 Outputted binaries should be in the `output` folder.
 
+
 ## Pushing
 
 Packages can be uploaded to an S3 bucket using:

.ci/pytorch/build.sh

@@ -168,16 +168,14 @@ if [[ "$BUILD_ENVIRONMENT" == *xpu* ]]; then
   # shellcheck disable=SC1091
   source /opt/intel/oneapi/compiler/latest/env/vars.sh
   # shellcheck disable=SC1091
-  source /opt/intel/oneapi/umf/latest/env/vars.sh
-  # shellcheck disable=SC1091
   source /opt/intel/oneapi/ccl/latest/env/vars.sh
   # shellcheck disable=SC1091
   source /opt/intel/oneapi/mpi/latest/env/vars.sh
-  # shellcheck disable=SC1091
-  source /opt/intel/oneapi/pti/latest/env/vars.sh
   # Enable XCCL build
   export USE_XCCL=1
   export USE_MPI=0
+  # XPU kineto feature dependencies are not fully ready, disable kineto build as temp WA
+  export USE_KINETO=0
   export TORCH_XPU_ARCH_LIST=pvc
 fi
 

.ci/pytorch/common_utils.sh

@@ -96,6 +96,7 @@ function pip_build_and_install() {
     python3 -m pip wheel \
       --no-build-isolation \
       --no-deps \
+      --no-use-pep517 \
       -w "${wheel_dir}" \
       "${build_target}"
   fi

.ci/pytorch/test.sh

@@ -208,8 +208,6 @@ if [[ "$BUILD_ENVIRONMENT" == *xpu* ]]; then
   source /opt/intel/oneapi/ccl/latest/env/vars.sh
   # shellcheck disable=SC1091
   source /opt/intel/oneapi/mpi/latest/env/vars.sh
-  # shellcheck disable=SC1091
-  source /opt/intel/oneapi/pti/latest/env/vars.sh
   # Check XPU status before testing
   timeout 30 xpu-smi discovery || true
 fi
@@ -826,11 +824,6 @@ test_inductor_halide() {
   assert_git_not_dirty
 }
 
-test_inductor_pallas() {
-  python test/run_test.py --include inductor/test_pallas.py --verbose
-  assert_git_not_dirty
-}
-
 test_inductor_triton_cpu() {
   python test/run_test.py --include inductor/test_triton_cpu_backend.py inductor/test_torchinductor_strided_blocks.py --verbose
   assert_git_not_dirty
@@ -1731,8 +1724,6 @@ elif [[ "${TEST_CONFIG}" == *inductor_distributed* ]]; then
   test_inductor_distributed
 elif [[ "${TEST_CONFIG}" == *inductor-halide* ]]; then
   test_inductor_halide
-elif [[ "${TEST_CONFIG}" == *inductor-pallas* ]]; then
-  test_inductor_pallas
 elif [[ "${TEST_CONFIG}" == *inductor-triton-cpu* ]]; then
   test_inductor_triton_cpu
 elif [[ "${TEST_CONFIG}" == *inductor-micro-benchmark* ]]; then

.github/actionlint.yaml

@@ -63,7 +63,7 @@ self-hosted-runner:
     - linux.rocm.gpu.gfx942.1
     - linux.rocm.gpu.gfx942.2
     - linux.rocm.gpu.gfx942.4
-    - linux.rocm.gfx942.docker-cache
+    - rocm-docker
     # Org wise AWS `mac2.metal` runners (2020 Mac mini hardware powered by Apple silicon M1 processors)
     - macos-m1-stable
     - macos-m1-14

.github/ci_commit_pins/vision.txt

@@ -1 +1 @@
-ccb801b88af136454798b945175c4c87e636ac33
+cfbc5c2f1c798991715a6b06bb3ce46478c4487c

.github/ci_commit_pins/xla.txt

@@ -1 +1 @@
-e4d25697f9dc5eedaf8f0a5bf085c62c5455a53a
+c8b09f5f77d6bf6fb7ed7a9aa83e5d8156b3a5e9

.github/labeler.yml

@@ -138,8 +138,7 @@
 - test/test_matmul_cuda.py
 - test/test_scaled_matmul_cuda.py
 - test/inductor/test_fp8.py
-- aten/src/ATen/native/cuda/*Blas.cpp
-- aten/src/ATen/cuda/CUDA*Blas.*
+- aten/src/ATen/native/cuda/Blas.cpp
 - torch/**/*cublas*
 - torch/_inductor/kernel/mm.py
 - test/inductor/test_max_autotune.py
@@ -149,8 +148,7 @@
 - test/test_matmul_cuda.py
 - test/test_scaled_matmul_cuda.py
 - test/inductor/test_fp8.py
-- aten/src/ATen/native/cuda/*Blas.cpp
-- aten/src/ATen/cuda/CUDA*Blas.*
+- aten/src/ATen/native/cuda/Blas.cpp
 - torch/**/*cublas*
 - torch/_inductor/kernel/mm.py
 - test/inductor/test_max_autotune.py
@@ -160,21 +158,7 @@
 - test/test_matmul_cuda.py
 - test/test_scaled_matmul_cuda.py
 - test/inductor/test_fp8.py
-- aten/src/ATen/native/cuda/*Blas.cpp
-- aten/src/ATen/cuda/CUDA*Blas.*
+- aten/src/ATen/native/cuda/Blas.cpp
 - torch/_inductor/kernel/mm.py
 - test/inductor/test_max_autotune.py
 - third_party/fbgemm
-
-"ciflow/mps":
-- aten/src/ATen/mps/**
-- aten/src/ATen/native/mps/**
-- torch/_inductor/codegen/mps.py
-- test/test_mps.py
-- test/inductor/test_mps_basic.py
-
-"ciflow/h100-symm-mem":
-- torch/csrc/distributed/c10d/symm_mem/**
-- torch/distributed/_symmetric_memory/**
-- test/distributed/**/*mem*
-- test/distributed/**/*mem*/**

.github/nitpicks.yml

@@ -10,4 +10,3 @@
   pathFilter:
     - 'torch/csrc/inductor/aoti_torch/c/*'
     - 'torch/csrc/inductor/aoti_torch/generated/*'
-    - 'torch/csrc/stable/c/*'

.github/pytorch-probot.yml

@@ -2,8 +2,8 @@ tracking_issue: 24422
 ciflow_tracking_issue: 64124
 ciflow_push_tags:
 - ciflow/b200
-- ciflow/b200-distributed
 - ciflow/b200-symm-mem
+- ciflow/b200-distributed
 - ciflow/binaries
 - ciflow/binaries_libtorch
 - ciflow/binaries_wheel
@@ -22,8 +22,6 @@ ciflow_push_tags:
 - ciflow/inductor-perf-test-nightly-xpu
 - ciflow/inductor-periodic
 - ciflow/inductor-rocm
-- ciflow/inductor-rocm-mi200
-- ciflow/inductor-rocm-mi300
 - ciflow/linux-aarch64
 - ciflow/mps
 - ciflow/nightly
@@ -35,13 +33,11 @@ ciflow_push_tags:
 - ciflow/quantization-periodic
 - ciflow/riscv64
 - ciflow/rocm
-- ciflow/rocm-mi200
 - ciflow/rocm-mi300
 - ciflow/rocm-mi355
 - ciflow/rocm-navi31
 - ciflow/s390
 - ciflow/slow
-- ciflow/slow-rocm-mi200
 - ciflow/torchbench
 - ciflow/triton_binaries
 - ciflow/trunk

.github/scripts/delete_old_branches.py

@@ -1,11 +1,10 @@
 # Delete old branches
 import os
 import re
-from collections.abc import Callable
 from datetime import datetime
 from functools import lru_cache
 from pathlib import Path
-from typing import Any
+from typing import Any, Callable
 
 from github_utils import gh_fetch_json_dict, gh_graphql
 from gitutils import GitRepo

.github/scripts/filter_test_configs.py

@@ -8,11 +8,10 @@ import re
 import subprocess
 import sys
 import warnings
-from collections.abc import Callable
 from enum import Enum
 from functools import cache
 from logging import info
-from typing import Any, Optional
+from typing import Any, Callable, Optional
 from urllib.request import Request, urlopen
 
 import yaml

.github/scripts/get_workflow_job_id.py

@@ -11,8 +11,7 @@ import sys
 import time
 import urllib
 import urllib.parse
-from collections.abc import Callable
-from typing import Any, Optional
+from typing import Any, Callable, Optional
 from urllib.request import Request, urlopen
 
 

.github/scripts/github_utils.py

@@ -3,9 +3,8 @@
 import json
 import os
 import warnings
-from collections.abc import Callable
 from dataclasses import dataclass
-from typing import Any, cast, Optional, Union
+from typing import Any, Callable, cast, Optional, Union
 from urllib.error import HTTPError
 from urllib.parse import quote
 from urllib.request import Request, urlopen

.github/scripts/gitutils.py

@@ -4,10 +4,10 @@ import os
 import re
 import tempfile
 from collections import defaultdict
-from collections.abc import Callable, Iterator
+from collections.abc import Iterator
 from datetime import datetime
 from functools import wraps
-from typing import Any, cast, Optional, TypeVar, Union
+from typing import Any, Callable, cast, Optional, TypeVar, Union
 
 
 T = TypeVar("T")

.github/scripts/lintrunner.sh

@@ -34,9 +34,6 @@ python3 torch/utils/data/datapipes/gen_pyi.py
 # Also check generated pyi files
 find torch -name '*.pyi' -exec git add --force -- "{}" +
 
-# Print current environment
-python3 -m pip freeze
-
 RC=0
 # Run lintrunner on all files
 if ! lintrunner --force-color --tee-json=lint.json ${ADDITIONAL_LINTRUNNER_ARGS} 2> /dev/null; then

.github/scripts/trymerge.py

@@ -17,12 +17,12 @@ import re
 import time
 import urllib.parse
 from collections import defaultdict
-from collections.abc import Callable, Iterable
+from collections.abc import Iterable
 from dataclasses import dataclass
 from functools import cache
 from pathlib import Path
 from re import Pattern
-from typing import Any, cast, NamedTuple, Optional
+from typing import Any, Callable, cast, NamedTuple, Optional
 from warnings import warn
 
 import yaml

.github/workflows/b200-distributed.yml

@@ -37,6 +37,7 @@ jobs:
     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'

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

@@ -37,6 +37,7 @@ jobs:
     needs: get-label-type
     with:
       runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
+      runner: linux.12xlarge.memory
       build-environment: linux-jammy-cuda12.8-py3.10-gcc11-sm100-symm
       docker-image-name: ci-image:pytorch-linux-jammy-cuda12.8-cudnn9-py3-gcc11
       cuda-arch-list: '10.0'

.github/workflows/docker-builds.yml

@@ -56,8 +56,6 @@ jobs:
           pytorch-linux-jammy-cuda12.8-cudnn9-py3-gcc9,
           pytorch-linux-jammy-cuda12.4-cudnn9-py3-gcc11,
           pytorch-linux-jammy-py3.10-clang12,
-          pytorch-linux-jammy-py3.11-clang12,
-          pytorch-linux-jammy-py3.12-clang12,
           pytorch-linux-jammy-py3.13-clang12,
           pytorch-linux-jammy-py3.14-clang12,
           pytorch-linux-jammy-rocm-n-py3,
@@ -67,10 +65,9 @@ jobs:
           pytorch-linux-jammy-py3.10-gcc11,
           pytorch-linux-jammy-py3-gcc11-inductor-benchmarks,
           pytorch-linux-jammy-py3.12-halide,
-          pytorch-linux-jammy-cuda12.8-py3.12-pallas,
           pytorch-linux-jammy-xpu-n-1-py3,
-          pytorch-linux-noble-xpu-n-py3,
-          pytorch-linux-noble-xpu-n-py3-inductor-benchmarks,
+          pytorch-linux-jammy-xpu-n-py3,
+          pytorch-linux-jammy-xpu-n-py3-inductor-benchmarks,
           pytorch-linux-jammy-py3-clang18-asan,
           pytorch-linux-jammy-py3-clang12-onnx,
           pytorch-linux-jammy-linter,
@@ -119,22 +116,6 @@ jobs:
         with:
           docker-image: ${{ steps.build-docker-image.outputs.docker-image }}
 
-      - name: Generate output
-        if: contains(matrix.docker-image-name, 'rocm')
-        id: generate_output
-        run: |
-          docker_image_name="${{ matrix.docker-image-name }}"
-          docker_image_tag="${{ steps.build-docker-image.outputs.docker-image }}"
-          echo "${docker_image_name}=${docker_image_tag}" >> docker-builds-output-${docker_image_name}.txt
-
-      - name: Upload artifacts
-        uses: actions/upload-artifact@v4.4.0
-        if: contains(matrix.docker-image-name, 'rocm')
-        with:
-          name: docker-builds-artifacts-${{ matrix.docker-image-name }}
-          retention-days: 14
-          path: ./docker-builds-output-${{ matrix.docker-image-name }}.txt
-
       - uses: nick-fields/retry@7152eba30c6575329ac0576536151aca5a72780e # v3.0.0
         name: Push to https://ghcr.io/
         id: push-to-ghcr-io

.github/workflows/docker-cache-mi300.yml

@@ -0,0 +1,55 @@
+name: docker-cache-mi300
+
+on:
+  # run every 6 hours
+  schedule:
+    - cron: 0 0,6,12,18 * * *
+  workflow_dispatch:
+
+concurrency:
+  group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.sha }}-${{ github.event_name }}
+  cancel-in-progress: true
+
+permissions:
+  id-token: write
+  contents: read
+
+jobs:
+  docker-cache:
+    if: github.repository_owner == 'pytorch'
+    runs-on: rocm-docker
+    steps:
+      - name: Checkout PyTorch
+        uses: pytorch/pytorch/.github/actions/checkout-pytorch@main
+        with:
+          no-sudo: true
+
+      - name: configure aws credentials
+        id: aws_creds
+        uses: aws-actions/configure-aws-credentials@ececac1a45f3b08a01d2dd070d28d111c5fe6722 # v4.1.0
+        with:
+          role-to-assume: arn:aws:iam::308535385114:role/gha_workflow_s3_and_ecr_read_only
+          aws-region: us-east-1
+          role-duration-seconds: 18000
+
+      - name: Login to Amazon ECR
+        id: login-ecr
+        continue-on-error: false
+        uses: aws-actions/amazon-ecr-login@062b18b96a7aff071d4dc91bc00c4c1a7945b076 # v2.0.1
+
+      - name: Calculate docker image
+        id: calculate-docker-image
+        uses: pytorch/test-infra/.github/actions/calculate-docker-image@main
+        with:
+          docker-image-name: ci-image:pytorch-linux-jammy-rocm-n-py3
+          push: false
+
+      - name: Pull docker image
+        uses: pytorch/test-infra/.github/actions/pull-docker-image@main
+        with:
+          docker-image: ${{ steps.calculate-docker-image.outputs.docker-image }}
+
+      - name: Tar and upload to S3 bucket
+        run: |
+          sudo docker save -o ~/docker-data/pytorch/pytorch_docker_image.tar ${{ steps.calculate-docker-image.outputs.docker-image }}
+          sudo rclone copy -P --s3-upload-concurrency 64 --s3-chunk-size 200M --s3-upload-cutoff 300M ~/docker-data/pytorch/pytorch_docker_image.tar oci:pytorchbucket0002/pytorch_docker_image --progress

.github/workflows/docker-cache-rocm.yml

@@ -1,105 +0,0 @@
-name: docker-cache-rocm
-
-on:
-  workflow_run:
-    workflows: [docker-builds]
-    branches: [main, release]
-    types:
-      - completed
-  workflow_dispatch:
-
-concurrency:
-  group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.sha }}-${{ github.event_name }}
-  cancel-in-progress: true
-
-permissions:
-  id-token: write
-  contents: read
-  actions: read
-
-jobs:
-  download-docker-builds-artifacts:
-    if: github.repository_owner == 'pytorch'
-    name: download-docker-builds-artifacts
-    runs-on: ubuntu-latest
-    outputs:
-      pytorch-linux-jammy-rocm-n-py3: ${{ steps.process-artifacts.outputs.pytorch-linux-jammy-rocm-n-py3 }}
-      pytorch-linux-noble-rocm-n-py3: ${{ steps.process-artifacts.outputs.pytorch-linux-noble-rocm-n-py3 }}
-      pytorch-linux-jammy-rocm-n-py3-benchmarks: ${{ steps.process-artifacts.outputs.pytorch-linux-jammy-rocm-n-py3-benchmarks }}
-    steps:
-      - name: Download artifacts
-        uses: actions/download-artifact@v4.1.7
-        with:
-          run-id: ${{ github.event.workflow_run.id }}
-          path: ./docker-builds-artifacts
-          merge-multiple: true
-          github-token: ${{ secrets.GITHUB_TOKEN }}
-
-      - name: Process artifacts
-        id: process-artifacts
-        run: |
-          ls -R ./docker-builds-artifacts
-          cat ./docker-builds-artifacts/*txt >> "${GITHUB_OUTPUT}"
-          cat "${GITHUB_OUTPUT}"
-
-  docker-cache:
-    if: github.repository_owner == 'pytorch'
-    needs: download-docker-builds-artifacts
-    strategy:
-      fail-fast: false
-      matrix:
-        runner: [linux.rocm.gfx942.docker-cache]
-        docker-image: [
-          "${{ needs.download-docker-builds-artifacts.outputs.pytorch-linux-jammy-rocm-n-py3 }}",
-          "${{ needs.download-docker-builds-artifacts.outputs.pytorch-linux-noble-rocm-n-py3 }}",
-          "${{ needs.download-docker-builds-artifacts.outputs.pytorch-linux-jammy-rocm-n-py3-benchmarks }}"
-        ]
-    runs-on: "${{ matrix.runner }}"
-    steps:
-      - name: debug
-        run: |
-          JSON_STRINGIFIED="${{ toJSON(needs.download-docker-builds-artifacts.outputs) }}"
-          echo "Outputs of download-docker-builds-artifacts job: ${JSON_STRINGIFIED}"
-
-      - name: configure aws credentials
-        id: aws_creds
-        uses: aws-actions/configure-aws-credentials@ececac1a45f3b08a01d2dd070d28d111c5fe6722 # v4.1.0
-        with:
-          role-to-assume: arn:aws:iam::308535385114:role/gha_workflow_s3_and_ecr_read_only
-          aws-region: us-east-1
-          role-duration-seconds: 18000
-
-      - name: Login to Amazon ECR
-        id: login-ecr
-        continue-on-error: false
-        uses: aws-actions/amazon-ecr-login@062b18b96a7aff071d4dc91bc00c4c1a7945b076 # v2.0.1
-
-      - name: Generate ghrc.io tag
-        id: ghcr-io-tag
-        run: |
-            ecr_image="${{ matrix.docker-image }}"
-            ghcr_image="ghcr.io/pytorch/ci-image:${ecr_image##*:}"
-            echo "ghcr_image=${ghcr_image}" >> "$GITHUB_OUTPUT"
-
-      - name: Pull docker image
-        uses: pytorch/test-infra/.github/actions/pull-docker-image@main
-        with:
-          docker-image: ${{ steps.ghcr-io-tag.outputs.ghcr_image }}
-
-      - name: Save as tarball
-        run: |
-          docker_image_tag=${{ matrix.docker-image }}
-          docker_image_tag="${docker_image_tag#*:}" # Remove everything before and including first ":"
-          docker_image_tag="${docker_image_tag%-*}" # Remove everything after and including last "-"
-          ref_name=${{ github.event.workflow_run.head_branch }}
-          if [[ $ref_name =~ "release/" ]]; then
-            ref_suffix="release"
-          elif [[ $ref_name == "main" ]]; then
-            ref_suffix="main"
-          else
-            echo "Unexpected branch in ref_name: ${ref_name}" && exit 1
-          fi
-          docker tag ${{ steps.ghcr-io-tag.outputs.ghcr_image }} ${{ matrix.docker-image }}
-          # mv is atomic operation, so we use intermediate tar.tmp file to prevent read-write contention
-          docker save -o ~/pytorch-data/docker/${docker_image_tag}.tar.tmp ${{ matrix.docker-image }}
-          mv ~/pytorch-data/docker/${docker_image_tag}.tar.tmp ~/pytorch-data/docker/${docker_image_tag}_${ref_suffix}.tar

.github/workflows/h100-distributed.yml

@@ -37,6 +37,7 @@ jobs:
     needs: get-label-type
     with:
       runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
+      runner: "linux.c7i.12xlarge"
       build-environment: linux-jammy-cuda12.8-py3.10-gcc11-sm90-dist
       docker-image-name: ci-image:pytorch-linux-jammy-cuda12.8-cudnn9-py3-gcc11
       cuda-arch-list: '9.0'

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

@@ -83,8 +83,8 @@ jobs:
     needs: get-label-type
     with:
       runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
-      build-environment: linux-noble-xpu-n-py3.10
-      docker-image-name: ci-image:pytorch-linux-noble-xpu-n-py3-inductor-benchmarks
+      build-environment: linux-jammy-xpu-n-py3.10
+      docker-image-name: ci-image:pytorch-linux-jammy-xpu-n-py3-inductor-benchmarks
       runner: linux.c7i.12xlarge
       test-matrix: |
         { include: [
@@ -117,7 +117,7 @@ jobs:
     uses: ./.github/workflows/_xpu-test.yml
     needs: xpu-n-py3_10-inductor-benchmark-build
     with:
-      build-environment: linux-noble-xpu-n-py3.10
+      build-environment: linux-jammy-xpu-n-py3.10
       dashboard-tag: training-true-inference-true-default-true-dynamic-true-cudagraphs-false-cppwrapper-true-aotinductor-true-freezing_cudagraphs-false-cudagraphs_low_precision-false
       docker-image: ${{ needs.xpu-n-py3_10-inductor-benchmark-build.outputs.docker-image }}
       test-matrix: ${{ needs.xpu-n-py3_10-inductor-benchmark-build.outputs.test-matrix }}
@@ -137,7 +137,7 @@ jobs:
     uses: ./.github/workflows/_xpu-test.yml
     needs: xpu-n-py3_10-inductor-benchmark-build
     with:
-      build-environment: linux-noble-xpu-n-py3.10
+      build-environment: linux-jammy-xpu-n-py3.10
       dashboard-tag: training-${{ inputs.training }}-inference-${{ inputs.inference }}-default-${{ inputs.default }}-dynamic-${{ inputs.dynamic }}-cudagraphs-${{ inputs.cudagraphs }}-cppwrapper-${{ inputs.cppwrapper }}-aotinductor-${{ inputs.aotinductor }}-maxautotune-${{ inputs.maxautotune }}-freezing_cudagraphs-${{ inputs.freezing_cudagraphs }}-cudagraphs_low_precision-${{ inputs.cudagraphs }}
       docker-image: ${{ needs.xpu-n-py3_10-inductor-benchmark-build.outputs.docker-image }}
       test-matrix: ${{ needs.xpu-n-py3_10-inductor-benchmark-build.outputs.test-matrix }}

.github/workflows/inductor-rocm-mi300.yml

@@ -7,7 +7,6 @@ on:
       - release/*
     tags:
       - ciflow/inductor-rocm/*
-      - ciflow/inductor-rocm-mi300/*
   workflow_dispatch:
 
 concurrency:

.github/workflows/inductor-rocm.yml

@@ -1,4 +1,4 @@
-name: inductor-rocm-mi200
+name: inductor-rocm
 
 on:
   schedule:
@@ -7,7 +7,7 @@ on:
     branches:
       - release/*
     tags:
-      - ciflow/inductor-rocm-mi200/*
+      - ciflow/inductor-rocm/*
   workflow_dispatch:
 
 concurrency:

.github/workflows/inductor-unittest.yml

@@ -81,32 +81,6 @@ jobs:
       test-matrix: ${{ needs.inductor-halide-build.outputs.test-matrix }}
     secrets: inherit
 
-  inductor-pallas-build:
-    name: inductor-pallas-build
-    uses: ./.github/workflows/_linux-build.yml
-    needs: get-label-type
-    with:
-      build-environment: linux-jammy-cuda12.8-py3.12-gcc11
-      docker-image-name: ci-image:pytorch-linux-jammy-cuda12.8-py3.12-pallas
-      cuda-arch-list: '8.9'
-      runner: linux.8xlarge.memory
-      runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
-      test-matrix: |
-        { include: [
-          { config: "inductor-pallas", shard: 1, num_shards: 1, runner: "${{ needs.get-label-type.outputs.label-type }}linux.g5.12xlarge.nvidia.gpu" },
-        ]}
-    secrets: inherit
-
-  inductor-pallas-test:
-    name: inductor-pallas-test
-    uses: ./.github/workflows/_linux-test.yml
-    needs: inductor-pallas-build
-    with:
-      build-environment: linux-jammy-py3.12-gcc11
-      docker-image: ${{ needs.inductor-pallas-build.outputs.docker-image }}
-      test-matrix: ${{ needs.inductor-pallas-build.outputs.test-matrix }}
-    secrets: inherit
-
   inductor-triton-cpu-build:
     name: inductor-triton-cpu-build
     uses: ./.github/workflows/_linux-build.yml

.github/workflows/nightly.yml

@@ -5,11 +5,9 @@ on:
     - cron: 0 0 * * *
   push:
     tags:
-      # NOTE: Doc build pipelines should only get triggered on:
-      # Major or minor release candidates builds
-      - v[0-9]+.[0-9]+.0+-rc[0-9]+
-      # Final RC for major, minor and patch releases
-      - v[0-9]+.[0-9]+.[0-9]+
+      # NOTE: Doc build pipelines should only get triggered on release candidate builds
+      # Release candidate tags look like: v1.11.0-rc1
+      - v[0-9]+.[0-9]+.[0-9]+-rc[0-9]+
       - ciflow/nightly/*
   workflow_dispatch:
 

.github/workflows/periodic-rocm-mi200.yml

@@ -11,6 +11,7 @@ on:
     - cron: 29 8 * * *  # about 1:29am PDT, for mem leak check and rerun disabled tests
   push:
     tags:
+      - ciflow/periodic/*
       - ciflow/periodic-rocm-mi200/*
     branches:
       - release/*

.github/workflows/periodic-rocm-mi300.yml

@@ -11,7 +11,6 @@ on:
     - cron: 29 8 * * *  # about 1:29am PDT, for mem leak check and rerun disabled tests
   push:
     tags:
-      - ciflow/periodic/*
       - ciflow/periodic-rocm-mi300/*
     branches:
       - release/*

.github/workflows/pull.yml

@@ -342,16 +342,16 @@ jobs:
       test-matrix: ${{ needs.linux-jammy-cuda12_8-py3_10-gcc9-inductor-build.outputs.test-matrix }}
     secrets: inherit
 
-  linux-noble-xpu-n-py3_10-build:
-    name: linux-noble-xpu-n-py3.10
+  linux-jammy-xpu-n-py3_10-build:
+    name: linux-jammy-xpu-n-py3.10
     uses: ./.github/workflows/_linux-build.yml
     needs: get-label-type
     with:
       # This should sync with the build in xpu.yml but xpu uses a larger runner
       # sync-tag: linux-xpu-n-build
       runner_prefix: ${{ needs.get-label-type.outputs.label-type }}
-      build-environment: linux-noble-xpu-n-py3.10
-      docker-image-name: ci-image:pytorch-linux-noble-xpu-n-py3
+      build-environment: linux-jammy-xpu-n-py3.10
+      docker-image-name: ci-image:pytorch-linux-jammy-xpu-n-py3
       test-matrix: |
         { include: [
           { config: "default", shard: 1, num_shards: 4, runner: "linux.idc.xpu" },

.github/workflows/rocm-mi300.yml

@@ -6,7 +6,6 @@ on:
       - main
       - release/*
     tags:
-      - ciflow/rocm/*
       - ciflow/rocm-mi300/*
   workflow_dispatch:
   schedule:

.github/workflows/rocm.yml

@@ -1,11 +1,11 @@
-name: rocm-mi200
+name: rocm
 
 on:
   push:
     branches:
       - release/*
     tags:
-      - ciflow/rocm-mi200/*
+      - ciflow/rocm/*
   workflow_dispatch:
   schedule:
     - cron: 29 8 * * *  # about 1:29am PDT

.github/workflows/slow-rocm-mi200.yml

@@ -1,81 +0,0 @@
-# This workflow is dedicated to host slow jobs that are run only periodically because
-# they are too slow to run in every commit.  The list of slow tests can be found in
-# https://github.com/pytorch/test-infra/blob/generated-stats/stats/slow-tests.json
-name: slow-rocm-mi200
-
-on:
-  push:
-    branches:
-      - release/*
-    tags:
-      - ciflow/slow/*
-      - ciflow/slow-rocm-mi200/*
-  schedule:
-    - cron: 0 */3 * * *
-  workflow_dispatch:
-
-concurrency:
-  group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.ref_name }}-${{ github.ref_type == 'branch' && github.sha }}-${{ github.event_name == 'workflow_dispatch' }}-${{ github.event_name == 'schedule' }}-${{ github.event.schedule }}
-  cancel-in-progress: true
-
-permissions:
-  id-token: write
-  contents: read
-
-jobs:
-  llm-td:
-    if: github.repository_owner == 'pytorch'
-    name: before-test
-    uses: ./.github/workflows/llm_td_retrieval.yml
-    permissions:
-      id-token: write
-      contents: read
-
-  target-determination:
-    name: before-test
-    uses: ./.github/workflows/target_determination.yml
-    needs: llm-td
-    permissions:
-      id-token: write
-      contents: read
-
-  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: ${{ github.ref_type }}
-
-  linux-jammy-rocm-py3_10-build:
-    name: linux-jammy-rocm-py3.10
-    uses: ./.github/workflows/_linux-build.yml
-    needs: get-label-type
-    with:
-      runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
-      build-environment: linux-jammy-rocm-py3.10
-      docker-image-name: ci-image:pytorch-linux-jammy-rocm-n-py3
-      sync-tag: rocm-build
-      test-matrix: |
-        { include: [
-          { config: "slow", shard: 1, num_shards: 2, runner: "linux.rocm.gpu.2", owners: ["module:rocm"] },
-          { config: "slow", shard: 2, num_shards: 2, runner: "linux.rocm.gpu.2", owners: ["module:rocm"] },
-        ]}
-    secrets: inherit
-
-  linux-jammy-rocm-py3_10-test:
-    permissions:
-      id-token: write
-      contents: read
-    name: linux-jammy-rocm-py3.10
-    uses: ./.github/workflows/_rocm-test.yml
-    needs:
-      - linux-jammy-rocm-py3_10-build
-      - target-determination
-    with:
-      build-environment: linux-jammy-rocm-py3.10
-      docker-image: ${{ needs.linux-jammy-rocm-py3_10-build.outputs.docker-image }}
-      test-matrix: ${{ needs.linux-jammy-rocm-py3_10-build.outputs.test-matrix }}
-    secrets: inherit

.github/workflows/slow.yml

@@ -105,6 +105,36 @@ jobs:
       test-matrix: ${{ needs.linux-jammy-py3_10-clang12-build.outputs.test-matrix }}
     secrets: inherit
 
+  linux-jammy-rocm-py3_10-build:
+    name: linux-jammy-rocm-py3.10
+    uses: ./.github/workflows/_linux-build.yml
+    needs: get-label-type
+    with:
+      runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
+      build-environment: linux-jammy-rocm-py3.10
+      docker-image-name: ci-image:pytorch-linux-jammy-rocm-n-py3
+      test-matrix: |
+        { include: [
+          { config: "slow", shard: 1, num_shards: 2, runner: "linux.rocm.gpu.2", owners: ["module:rocm"] },
+          { config: "slow", shard: 2, num_shards: 2, runner: "linux.rocm.gpu.2", owners: ["module:rocm"] },
+        ]}
+    secrets: inherit
+
+  linux-jammy-rocm-py3_10-test:
+    permissions:
+      id-token: write
+      contents: read
+    name: linux-jammy-rocm-py3.10
+    uses: ./.github/workflows/_rocm-test.yml
+    needs:
+      - linux-jammy-rocm-py3_10-build
+      - target-determination
+    with:
+      build-environment: linux-jammy-rocm-py3.10
+      docker-image: ${{ needs.linux-jammy-rocm-py3_10-build.outputs.docker-image }}
+      test-matrix: ${{ needs.linux-jammy-rocm-py3_10-build.outputs.test-matrix }}
+    secrets: inherit
+
   linux-jammy-py3_10-clang18-asan-build:
     name: linux-jammy-py3.10-clang18-asan
     uses: ./.github/workflows/_linux-build.yml

.github/workflows/test-b200.yml

@@ -52,6 +52,7 @@ jobs:
     needs: get-label-type
     with:
       runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
+      runner: linux.12xlarge.memory
       build-environment: linux-jammy-cuda12.8-py3.10-gcc11-sm100
       docker-image-name: ci-image:pytorch-linux-jammy-cuda12.8-cudnn9-py3-gcc11
       cuda-arch-list: '10.0'
@@ -72,4 +73,4 @@ jobs:
       docker-image: ${{ needs.linux-jammy-cuda12_8-py3_10-gcc11-sm100-build.outputs.docker-image }}
       test-matrix: ${{ needs.linux-jammy-cuda12_8-py3_10-gcc11-sm100-build.outputs.test-matrix }}
       aws-role-to-assume: arn:aws:iam::308535385114:role/gha_workflow_s3_and_ecr_read_only
-    secrets: inherit
+    secrets: inherit

.github/workflows/test-h100.yml

@@ -41,6 +41,7 @@ jobs:
     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-sm90
       docker-image-name: ci-image:pytorch-linux-jammy-cuda12.8-cudnn9-py3-gcc11
       cuda-arch-list: '9.0'

.github/workflows/upload-test-stats.yml

@@ -11,16 +11,15 @@ on:
       - inductor
       - unstable
       - slow
-      - slow-rocm-mi200
       - unstable-periodic
       - inductor-periodic
-      - rocm-mi200
+      - rocm
       - rocm-mi300
       - rocm-mi355
       - inductor-micro-benchmark
       - inductor-micro-benchmark-x86
       - inductor-cu124
-      - inductor-rocm-mi200
+      - inductor-rocm
       - inductor-rocm-mi300
       - mac-mps
       - linux-aarch64

.github/workflows/xpu.yml

@@ -47,15 +47,15 @@ jobs:
         ]}
     secrets: inherit
 
-  linux-noble-xpu-n-py3_10-build:
-    name: linux-noble-xpu-n-py3.10
+  linux-jammy-xpu-n-py3_10-build:
+    name: linux-jammy-xpu-n-py3.10
     uses: ./.github/workflows/_linux-build.yml
     needs: get-label-type
     with:
       sync-tag: linux-xpu-n-build
       runner_prefix: ${{ needs.get-label-type.outputs.label-type }}
-      build-environment: linux-noble-xpu-n-py3.10
-      docker-image-name: ci-image:pytorch-linux-noble-xpu-n-py3
+      build-environment: linux-jammy-xpu-n-py3.10
+      docker-image-name: ci-image:pytorch-linux-jammy-xpu-n-py3
       runner: linux.c7i.12xlarge
       test-matrix: |
         { include: [
@@ -74,17 +74,17 @@ jobs:
         ]}
     secrets: inherit
 
-  linux-noble-xpu-n-py3_10-test:
-    name: linux-noble-xpu-n-py3.10
+  linux-jammy-xpu-n-py3_10-test:
+    name: linux-jammy-xpu-n-py3.10
     uses: ./.github/workflows/_xpu-test.yml
-    needs: linux-noble-xpu-n-py3_10-build
+    needs: linux-jammy-xpu-n-py3_10-build
     permissions:
       id-token: write
       contents: read
     with:
-      build-environment: linux-noble-xpu-n-py3.10
-      docker-image: ${{ needs.linux-noble-xpu-n-py3_10-build.outputs.docker-image }}
-      test-matrix: ${{ needs.linux-noble-xpu-n-py3_10-build.outputs.test-matrix }}
+      build-environment: linux-jammy-xpu-n-py3.10
+      docker-image: ${{ needs.linux-jammy-xpu-n-py3_10-build.outputs.docker-image }}
+      test-matrix: ${{ needs.linux-jammy-xpu-n-py3_10-build.outputs.test-matrix }}
     secrets: inherit
 
   windows-xpu-n-1-build:

.lintrunner.toml

@@ -143,8 +143,7 @@ init_command = [
     'tools/linter/adapters/pip_init.py',
     '--dry-run={{DRYRUN}}',
     'numpy==1.26.4 ; python_version >= "3.10" and python_version <= "3.11"',
-    'numpy==2.1.0 ; python_version >= "3.12" and python_version <= "3.13"',
-    'numpy==2.3.4 ; python_version >= "3.14"',
+    'numpy==2.1.0 ; python_version >= "3.12"',
     'expecttest==0.3.0',
     'pyrefly==0.36.2',
     'sympy==1.13.3',
@@ -186,8 +185,6 @@ include_patterns = [
     'aten/src/ATen/native/nested/cuda/*.h',
     'aten/src/ATen/native/nested/*.cpp',
     'aten/src/ATen/native/nested/*.h',
-    'aten/src/ATen/xpu/**/*.h',
-    'aten/src/ATen/xpu/**/*.cpp',
     'c10/**/*.cpp',
     'c10/**/*.h',
     'torch/*.h',
@@ -1404,7 +1401,7 @@ init_command = [
     '--dry-run={{DRYRUN}}',
     'usort==1.0.8.post1',
     'isort==6.0.1',
-    'ruff==0.14.4',  # sync with RUFF
+    'ruff==0.13.1',  # sync with RUFF
 ]
 is_formatter = true
 
@@ -1539,7 +1536,7 @@ init_command = [
     'python3',
     'tools/linter/adapters/pip_init.py',
     '--dry-run={{DRYRUN}}',
-    'ruff==0.14.4',  # sync with PYFMT
+    'ruff==0.13.1',  # sync with PYFMT
 ]
 is_formatter = true
 

CMakeLists.txt

@@ -736,44 +736,6 @@ if(NOT DEFINED USE_BLAS)
   set(USE_BLAS ON)
 endif()
 
-# Prioritized Text Linker Optimization
-if(USE_PRIORITIZED_TEXT_FOR_LD)
-
-  set(LINKER_SCRIPT_FILE_IN "${CMAKE_SOURCE_DIR}/cmake/prioritized_text.txt")
-  set(LINKER_SCRIPT_FILE_OUT "${CMAKE_SOURCE_DIR}/cmake/linker_script.ld")
-
-  execute_process(
-    COMMAND ${Python_EXECUTABLE}
-            ${CMAKE_SOURCE_DIR}/tools/setup_helpers/generate_linker_script.py
-            --filein "${LINKER_SCRIPT_FILE_IN}"
-            --fout  "${LINKER_SCRIPT_FILE_OUT}"
-    RESULT_VARIABLE _gen_result
-    OUTPUT_VARIABLE _gen_output
-    ERROR_VARIABLE  _gen_error
-  )
-
-  if(NOT _gen_result EQUAL 0)
-    message(FATAL_ERROR
-      "Failed to generate linker script:\n${_gen_output}\n${_gen_error}")
-  endif()
-
-  append_cxx_flag_if_supported("-ffunction-sections" CMAKE_CXX_FLAGS)
-  append_cxx_flag_if_supported("-fdata-sections" CMAKE_CXX_FLAGS)
-  append_c_flag_if_supported("-ffunction-sections" CMAKE_C_FLAGS)
-  append_c_flag_if_supported("-fdata-sections" CMAKE_C_FLAGS)
-
-  set(CMAKE_SHARED_LINKER_FLAGS "${CMAKE_SHARED_LINKER_FLAGS} -T${LINKER_SCRIPT_FILE_OUT}")
-  set(CMAKE_MODULE_LINKER_FLAGS "${CMAKE_MODULE_LINKER_FLAGS} -T${LINKER_SCRIPT_FILE_OUT}")
-
-else()
-  if(LINUX AND CPU_AARCH64)
-    message(WARNING [[
-    It is strongly recommend to enable linker script optimization for all AArch64 Linux builds.
-    To do so please export USE_PRIORITIZED_TEXT_FOR_LD=1
-    ]])
-  endif()
-endif()
-
 # Build libtorch mobile library, which contains ATen/TH ops and native support
 # for TorchScript model, but doesn't contain not-yet-unified caffe2 ops;
 if(INTERN_BUILD_MOBILE)
@@ -1440,6 +1402,9 @@ if(BUILD_JNI)
   add_subdirectory(android/pytorch_android)
 endif()
 
+include(cmake/Summary.cmake)
+caffe2_print_configuration_summary()
+
 # Parse custom debug info
 if(DEFINED USE_CUSTOM_DEBINFO)
   string(REPLACE ";" " " SOURCE_FILES "${USE_CUSTOM_DEBINFO}")
@@ -1479,5 +1444,56 @@ if(BUILD_BUNDLE_PTXAS AND USE_CUDA)
           DESTINATION "${CMAKE_INSTALL_BINDIR}")
 endif()
 
-include(cmake/Summary.cmake)
-caffe2_print_configuration_summary()
+if(USE_PRIORITIZED_TEXT_FOR_LD)
+  add_compile_options(
+    $<$<COMPILE_LANGUAGE:C,CXX>:-ffunction-sections>
+    $<$<COMPILE_LANGUAGE:C,CXX>:-fdata-sections>
+  )
+  set(LINKER_SCRIPT_FILE_OUT "${CMAKE_SOURCE_DIR}/cmake/linker_script.ld")
+  set(LINKER_SCRIPT_FILE_IN "${CMAKE_SOURCE_DIR}/cmake/prioritized_text.txt")
+
+  add_custom_command(
+    OUTPUT "${LINKER_SCRIPT_FILE_OUT}"
+    COMMAND ${Python_EXECUTABLE} ${CMAKE_SOURCE_DIR}/tools/setup_helpers/generate_linker_script.py --filein "${LINKER_SCRIPT_FILE_IN}" --fout "${LINKER_SCRIPT_FILE_OUT}"
+    DEPENDS ${CMAKE_SOURCE_DIR}/tools/setup_helpers/generate_linker_script.py "${LINKER_SCRIPT_FILE_IN}"
+    COMMENT "Generating prioritized text linker files"
+    VERBATIM
+  )
+
+  add_custom_target(generate_linker_script DEPENDS "${LINKER_SCRIPT_FILE_OUT}")
+
+  if(BUILD_PYTHON)
+    set(LINKER_OPT_TARGETS torch_python)
+  endif()
+
+  if(NOT BUILD_LIBTORCHLESS)
+    list(APPEND LINKER_OPT_TARGETS torch_cpu c10)
+    if(USE_CUDA)
+      list(APPEND LINKER_OPT_TARGETS torch_cuda c10_cuda)
+    endif()
+    if(USE_XPU)
+      list(APPEND LINKER_OPT_TARGETS torch_xpu c10_xpu)
+    endif()
+    if(USE_ROCM)
+      list(APPEND LINKER_OPT_TARGETS torch_hip c10_hip)
+    endif()
+  endif()
+
+  foreach(tgt IN LISTS LINKER_OPT_TARGETS)
+    if(TARGET ${tgt})
+      add_dependencies("${tgt}" generate_linker_script)
+      target_link_options_if_supported(${tgt} "-T,${LINKER_SCRIPT_FILE_OUT}")
+      set_property(TARGET ${tgt} APPEND PROPERTY LINK_DEPENDS "${LINKER_SCRIPT_FILE_OUT}")
+    else()
+       message(WARNING "Requested target '${tgt}' for linker script optimization was not found.")
+    endif()
+  endforeach()
+
+else()
+  if(LINUX AND CPU_AARCH64)
+    message(WARNING [[
+    It is strongly recommend to enable linker script optimization for all AArch64 Linux builds.
+    To do so please export USE_PRIORITIZED_TEXT_FOR_LD=1
+    ]])
+  endif()
+endif()

CODEOWNERS

@@ -210,12 +210,8 @@ torch/backends/cudnn/ @eqy @syed-ahmed @Aidyn-A
 /test/inductor/test_flex_attention.py @drisspg
 /test/inductor/test_flex_decoding.py @drisspg
 
-# Low Precision & Grouped GEMMs
+# Low Precision GEMMs
 /aten/src/ATen/native/cuda/Blas.cpp @drisspg @slayton58
-/aten/src/ATen/native/cuda/GroupedBlas.cpp @drisspg @slayton58
-/aten/src/ATen/native/cuda/ScaledBlas.cpp @drisspg @slayton58
 /aten/src/ATen/cuda/CUDABlas.cpp @drisspg @slayton58
 /aten/src/ATen/cuda/CUDABlas.h @drisspg @slayton58
-/aten/src/ATen/cuda/CUDAScaledBlas.cpp @drisspg @slayton58
-/aten/src/ATen/cuda/CUDAScaledBlas.h @drisspg @slayton58
 /test/test_scaled_matmul_cuda.py @drisspg @slayton58

LICENSE

@@ -37,7 +37,7 @@ Copyright (c) 2024 Tri Dao.
 All rights reserved.
 
 All contributions by Arm:
-Copyright (c) 2021, 2023-2025 Arm Limited and/or its affiliates
+Copyright (c) 2021, 2023-2024 Arm Limited and/or its affiliates
 
 All contributions from Caffe:
 Copyright(c) 2013, 2014, 2015, the respective contributors

SECURITY.md

@@ -18,8 +18,6 @@ Please report security issues using https://github.com/pytorch/pytorch/security/
 
 All reports submitted through the security advisories mechanism would **either be made public or dismissed by the team within 90 days of the submission**. If advisory has been closed on the grounds that it is not a security issue, please do not hesitate to create an [new issue](https://github.com/pytorch/pytorch/issues/new?template=bug-report.yml) as it is still likely a valid issue within the framework.
 
-**Note on crashes and out of bounds access**: PyTorch is a computational framework that performs operations on behalf of the caller. Like many low-level libraries, PyTorch generally does not validate all inputs to every function—the responsibility for providing valid arguments lies with the calling code. While crashes and out of bounds memory access should be reported as bugs, they are generally not considered security vulnerabilities in PyTorch's threat model.
-
 Please refer to the following page for our responsible disclosure policy, reward guidelines, and those things that should not be reported:
 
 https://www.facebook.com/whitehat

aten/src/ATen/Context.h

@@ -174,12 +174,6 @@ class TORCH_API Context {
   static long versionCuDNN() {
     return detail::getCUDAHooks().versionCuDNN();
   }
-  static long versionRuntimeCuDNN() {
-    return detail::getCUDAHooks().versionRuntimeCuDNN();
-  }
-  static long versionCuDNNFrontend() {
-    return detail::getCUDAHooks().versionCuDNNFrontend();
-  }
   static bool hasCuSOLVER() {
     return detail::getCUDAHooks().hasCuSOLVER();
   }

aten/src/ATen/DeviceAccelerator.h

@@ -94,11 +94,6 @@ TORCH_API inline void resetPeakStats(c10::DeviceIndex device_index) {
   at::getDeviceAllocator(device_type)->resetPeakStats(device_index);
 }
 
-TORCH_API inline std::pair<size_t, size_t> getMemoryInfo(
-    c10::DeviceIndex device_index) {
-  const auto device_type = getAccelerator(true).value();
-  return at::getDeviceAllocator(device_type)->getMemoryInfo(device_index);
-}
 } // namespace at::accelerator
 
 namespace at {

aten/src/ATen/Dispatch.h

@@ -6,7 +6,6 @@
 #include <c10/util/Half.h>
 #include <c10/util/Metaprogramming.h>
 #include <c10/util/complex.h>
-#include <torch/headeronly/core/Dispatch.h>
 
 #ifdef __CUDACC__
 #include <cuda.h> // For CUDA_VERSION
@@ -62,9 +61,12 @@ TORCH_API void record_kernel_function_dtype(std::string name);
     }                                                 \
   } while (0)
 
-#define AT_PRIVATE_CASE_TYPE_USING_HINT(enum_type, HINT, ...) \
-  THO_PRIVATE_CASE_TYPE_USING_HINT_TMPL(                      \
-      AT_PRIVATE_CHECK_SELECTIVE_BUILD, enum_type, HINT, __VA_ARGS__)
+#define AT_PRIVATE_CASE_TYPE_USING_HINT(enum_type, HINT, ...)                 \
+  case enum_type: {                                                           \
+    AT_PRIVATE_CHECK_SELECTIVE_BUILD(enum_type);                              \
+    using HINT [[maybe_unused]] = c10::impl::ScalarTypeToCPPTypeT<enum_type>; \
+    return __VA_ARGS__();                                                     \
+  }
 
 #define AT_DISPATCH_CASE(enum_type, ...) \
   AT_PRIVATE_CASE_TYPE_USING_HINT(enum_type, scalar_t, __VA_ARGS__)
@@ -93,6 +95,14 @@ TORCH_API void record_kernel_function_dtype(std::string name);
     return __VA_ARGS__();                                                   \
   }
 
+namespace detail {
+
+inline at::ScalarType scalar_type(at::ScalarType s) {
+  return s;
+}
+
+} // namespace detail
+
 // The AT_DISPATCH_* family of macros provides the ability to
 // conveniently generate specializations of a kernel over all of the
 // dtypes we care about in PyTorch.  We call it "dispatch" because
@@ -180,13 +190,27 @@ TORCH_API void record_kernel_function_dtype(std::string name);
 // but we're just being safe (and it doesn't hurt.)  Note we must
 // use it to shut up warnings about unused store.
 
-#define AT_DISPATCH_SWITCH(TYPE, NAME, ...) \
-  THO_DISPATCH_SWITCH_TMPL(                 \
-      RECORD_KERNEL_FUNCTION_DTYPE,         \
-      TORCH_CHECK_NOT_IMPLEMENTED,          \
-      TYPE,                                 \
-      NAME,                                 \
-      __VA_ARGS__)
+#define AT_DISPATCH_SWITCH(TYPE, NAME, ...)                                 \
+  [&] {                                                                     \
+    const auto& the_type = TYPE;                                            \
+    constexpr const char* at_dispatch_name = NAME;                          \
+    /* don't use TYPE again in case it is an expensive or side-effect op */ \
+    at::ScalarType _st = ::detail::scalar_type(the_type);                   \
+    RECORD_KERNEL_FUNCTION_DTYPE(at_dispatch_name, _st);                    \
+    C10_DIAGNOSTIC_PUSH_AND_IGNORED_IF_DEFINED("-Wswitch-enum")             \
+    switch (_st) {                                                          \
+      __VA_ARGS__                                                           \
+      default:                                                              \
+        TORCH_CHECK_NOT_IMPLEMENTED(                                        \
+            false,                                                          \
+            '"',                                                            \
+            at_dispatch_name,                                               \
+            "\" not implemented for '",                                     \
+            toString(_st),                                                  \
+            "'");                                                           \
+    }                                                                       \
+    C10_DIAGNOSTIC_POP()                                                    \
+  }()
 
 #define AT_DISPATCH_CASE_FLOATING_TYPES(...)            \
   AT_DISPATCH_CASE(at::ScalarType::Double, __VA_ARGS__) \

aten/src/ATen/Dispatch_v2.h

@@ -1,8 +1,3 @@
-#pragma once
-
-#include <torch/headeronly/core/Dispatch_v2.h>
-
-// Get AT_DISPATCH_SWITCH and AT_DISPATCH_CASE:
 #include <ATen/Dispatch.h>
 
 // This is a new implementation of the AT_DISPATCH macro family from
@@ -79,19 +74,41 @@
 // macro expansion occurs, mediated with AT_EXPAND and AT_GUARD.  I mostly
 // relied on GPT4 to help me get it right.
 
+// Public API macros
+
 // See documentation above
 #define AT_DISPATCH_V2(TYPE, NAME, BODY, ...) \
-  THO_DISPATCH_V2_TMPL(                       \
-      AT_DISPATCH_SWITCH,                     \
-      AT_DISPATCH_CASE,                       \
-      TYPE,                                   \
-      NAME,                                   \
-      AT_WRAP(BODY),                          \
-      __VA_ARGS__)
+  AT_DISPATCH_SWITCH(TYPE, NAME, AT_AP_VAR(AT_WRAP(BODY), TYPE, __VA_ARGS__))
+
+// This macro lets you pass an arbitrary expression that may contain internal
+// commas to another macro without having the commas causing the expression
+// to be interpreted as being multiple arguments
+#define AT_WRAP(...) __VA_ARGS__
+
+#define AT_FLOAT8_TYPES                                          \
+  c10::kFloat8_e5m2, c10::kFloat8_e5m2fnuz, c10::kFloat8_e4m3fn, \
+      c10::kFloat8_e4m3fnuz, c10::kFloat8_e8m0fnu
+
+#define AT_INTEGRAL_TYPES \
+  c10::kByte, c10::kChar, c10::kInt, c10::kLong, c10::kShort
+#define AT_FLOATING_TYPES c10::kDouble, c10::kFloat
+#define AT_BAREBONES_UNSIGNED_TYPES c10::kUInt16, c10::kUInt32, c10::kUInt64
+#define AT_INTEGRAL_TYPES_V2 \
+  AT_EXPAND(AT_INTEGRAL_TYPES), AT_EXPAND(AT_BAREBONES_UNSIGNED_TYPES)
+#define AT_COMPLEX_TYPES c10::kComplexDouble, c10::kComplexFloat
+#define AT_QINT_TYPES c10::kQInt8, c10::kQUInt8, c10::kQInt32
+// NB: not *actually* all types
+#define AT_ALL_TYPES AT_EXPAND(AT_INTEGRAL_TYPES), AT_EXPAND(AT_FLOATING_TYPES)
+#define AT_ALL_TYPES_AND_COMPLEX \
+  AT_EXPAND(AT_ALL_TYPES), AT_EXPAND(AT_COMPLEX_TYPES)
+
+// Helper macros
 
-// Unused helper macros, kept for BC:
 #define AT_AP_VAR(N, T, ...) \
   AT_EXPAND(AT_CONCAT(AT_AP, AT_NUM_ARGS(__VA_ARGS__))(AT_WRAP(N), __VA_ARGS__))
+#define AT_CONCAT(a, b) AT_CONCAT_AUX(a, b)
+#define AT_CONCAT_AUX(a, b) a##b
+#define AT_EXPAND(X) X
 
 // Ensure we never have too many scalar types for the expansion here to
 // support.  To bump this, you must regenerate the macros below.
@@ -102,6 +119,12 @@ static_assert(static_cast<int>(c10::ScalarType::NumOptions) < 60);
 
 num_args = 60
 
+nums = ', '.join(str(i) for i in reversed(range(num_args+1)))
+args = ', '.join(f'_{i}' for i in range(1, num_args+1))
+
+print(f'#define AT_NUM_ARGS(...) AT_EXPAND(AT_NUM_ARGS_AUX(__VA_ARGS__, {nums}))')
+print(f'#define AT_NUM_ARGS_AUX({args}, N, ...) N')
+
 for i in range(1, num_args+1):
     args = ', '.join(f'_{i}' for i in range(1, i+1))
     cases = ' '.join([f'AT_DISPATCH_CASE(_{j}, N)' for j in range(1, i+1)])
@@ -112,6 +135,8 @@ for i in range(1, num_args+1):
 // Begin generated code
 // clang-format off
 
+#define AT_NUM_ARGS(...) AT_EXPAND(AT_NUM_ARGS_AUX(__VA_ARGS__, 60, 59, 58, 57, 56, 55, 54, 53, 52, 51, 50, 49, 48, 47, 46, 45, 44, 43, 42, 41, 40, 39, 38, 37, 36, 35, 34, 33, 32, 31, 30, 29, 28, 27, 26, 25, 24, 23, 22, 21, 20, 19, 18, 17, 16, 15, 14, 13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0))
+#define AT_NUM_ARGS_AUX(_1, _2, _3, _4, _5, _6, _7, _8, _9, _10, _11, _12, _13, _14, _15, _16, _17, _18, _19, _20, _21, _22, _23, _24, _25, _26, _27, _28, _29, _30, _31, _32, _33, _34, _35, _36, _37, _38, _39, _40, _41, _42, _43, _44, _45, _46, _47, _48, _49, _50, _51, _52, _53, _54, _55, _56, _57, _58, _59, _60, N, ...) N
 #define AT_AP1(N, _1) AT_DISPATCH_CASE(_1, N)
 #define AT_AP2(N, _1, _2) AT_DISPATCH_CASE(_1, N) AT_DISPATCH_CASE(_2, N)
 #define AT_AP3(N, _1, _2, _3) AT_DISPATCH_CASE(_1, N) AT_DISPATCH_CASE(_2, N) AT_DISPATCH_CASE(_3, N)

aten/src/ATen/core/CachingHostAllocator.h

@@ -226,8 +226,8 @@ template <
     typename B = HostBlock<S>>
 struct CachingHostAllocatorImpl {
   virtual ~CachingHostAllocatorImpl() {
-    if (active_) {
-      active_ = false;
+    active_ = false;
+    if (pinned_use_background_threads()) {
       getBackgroundThreadPool()->waitWorkComplete();
     }
   }
@@ -260,7 +260,6 @@ struct CachingHostAllocatorImpl {
     if (pinned_use_background_threads()) {
       // Launch the background thread and process events in a loop.
       static bool background_thread_flag [[maybe_unused]] = [this] {
-        active_ = true;
         getBackgroundThreadPool()->run([&]() {
           while (active_) {
             process_events();
@@ -684,9 +683,9 @@ struct CachingHostAllocatorImpl {
   alignas(hardware_destructive_interference_size) std::mutex events_mutex_;
   std::deque<std::pair<E, B*>> events_; // event queue paired with block
 
-  // Indicates whether the event-processing thread pool is active.
+  // Indicates whether the object is active.
   // Set to false in the destructor to signal background threads to stop.
-  std::atomic<bool> active_{false};
+  std::atomic<bool> active_{true};
 protected:
   alignas(hardware_destructive_interference_size) HostStatsStaged stats_;
 };

aten/src/ATen/cuda/NumericLimits.cuh

@@ -55,6 +55,14 @@ struct numeric_limits<int8_t> {
   static inline __host__ __device__ int8_t upper_bound() { return INT8_MAX; }
 };
 
+template <>
+struct numeric_limits<uint16_t> {
+  static inline __host__ __device__ uint16_t lowest() { return 0; }
+  static inline __host__ __device__ uint16_t max() { return UINT16_MAX; }
+  static inline __host__ __device__ uint16_t lower_bound() { return 0; }
+  static inline __host__ __device__ uint16_t upper_bound() { return UINT16_MAX; }
+};
+
 template <>
 struct numeric_limits<int16_t> {
   static inline __host__ __device__ int16_t lowest() { return INT16_MIN; }
@@ -63,6 +71,14 @@ struct numeric_limits<int16_t> {
   static inline __host__ __device__ int16_t upper_bound() { return INT16_MAX; }
 };
 
+template <>
+struct numeric_limits<uint32_t> {
+  static inline __host__ __device__ uint32_t lowest() { return 0; }
+  static inline __host__ __device__ uint32_t max() { return UINT32_MAX; }
+  static inline __host__ __device__ uint32_t lower_bound() { return 0; }
+  static inline __host__ __device__ uint32_t upper_bound() { return UINT32_MAX; }
+};
+
 template <>
 struct numeric_limits<int32_t> {
   static inline __host__ __device__ int32_t lowest() { return INT32_MIN; }
@@ -71,6 +87,21 @@ struct numeric_limits<int32_t> {
   static inline __host__ __device__ int32_t upper_bound() { return INT32_MAX; }
 };
 
+template <>
+struct numeric_limits<uint64_t> {
+#ifdef _MSC_VER
+  static inline __host__ __device__ uint64_t lowest() { return 0; }
+  static inline __host__ __device__ uint64_t max() { return _UI64_MAX; }
+  static inline __host__ __device__ uint64_t lower_bound() { return 0; }
+  static inline __host__ __device__ uint64_t upper_bound() { return _UI64_MAX; }
+#else
+  static inline __host__ __device__ uint64_t lowest() { return 0; }
+  static inline __host__ __device__ uint64_t max() { return UINT64_MAX; }
+  static inline __host__ __device__ uint64_t lower_bound() { return 0; }
+  static inline __host__ __device__ uint64_t upper_bound() { return UINT64_MAX; }
+#endif
+};
+
 template <>
 struct numeric_limits<int64_t> {
 #ifdef _MSC_VER

aten/src/ATen/cuda/detail/CUDAHooks.cpp

@@ -21,7 +21,6 @@
 
 #if AT_CUDNN_ENABLED()
 #include <ATen/cudnn/cudnn-wrapper.h>
-#include <cudnn_frontend.h>
 #endif
 
 #if AT_MAGMA_ENABLED()
@@ -352,26 +351,6 @@ long CUDAHooks::versionCuDNN() const {
 #endif
 }
 
-long CUDAHooks::versionRuntimeCuDNN() const {
-#if AT_CUDNN_ENABLED()
-#ifndef USE_STATIC_CUDNN
-  return cudnnGetVersion();
-#else
-  return CUDNN_VERSION;
-#endif
-#else
-  TORCH_CHECK(false, "Cannot query CuDNN version if ATen_cuda is not built with CuDNN");
-#endif
-}
-
-long CUDAHooks::versionCuDNNFrontend() const {
-#if AT_CUDNN_ENABLED()
-  return CUDNN_FRONTEND_VERSION;
-#else
-  TORCH_CHECK(false, "Cannot query CuDNN Frontend version if ATen_cuda is not built with CuDNN");
-#endif
-}
-
 long CUDAHooks::versionMIOpen() const {
 #if AT_ROCM_ENABLED()
   return MIOPEN_VERSION_MAJOR * 10000 +

aten/src/ATen/cuda/detail/CUDAHooks.h

@@ -49,8 +49,6 @@ struct CUDAHooks : public at::CUDAHooksInterface {
   bool hasCUDART() const override;
   long versionCUDART() const override;
   long versionCuDNN() const override;
-  long versionRuntimeCuDNN() const override;
-  long versionCuDNNFrontend() const override;
   long versionMIOpen() const override;
   std::string showConfig() const override;
   double batchnormMinEpsilonCuDNN() const override;

aten/src/ATen/detail/CUDAHooksInterface.h

@@ -174,14 +174,6 @@ struct TORCH_API CUDAHooksInterface : AcceleratorHooksInterface {
     TORCH_CHECK(false, "Cannot query cuDNN version without ATen_cuda library. ", CUDA_HELP);
   }
 
-  virtual long versionRuntimeCuDNN() const {
-    TORCH_CHECK(false, "Cannot query cuDNN version without ATen_cuda library. ", CUDA_HELP);
-  }
-
-  virtual long versionCuDNNFrontend() const {
-    TORCH_CHECK(false, "Cannot query cuDNN Frontend version without ATen_cuda library. ", CUDA_HELP);
-  }
-
   virtual long versionMIOpen() const {
     TORCH_CHECK(false, "Cannot query MIOpen version without ATen_cuda library. ", CUDA_HELP);
   }

aten/src/ATen/functorch/BatchedTensorImpl.h

@@ -157,8 +157,6 @@ constexpr DispatchKeySet kKeysToPropagateToWrapper({
   DispatchKey::Negative,
   DispatchKey::Conjugate,
   DispatchKey::XLA,
-  DispatchKey::XPU,
-  DispatchKey::HPU,
   DispatchKey::CUDA,
   DispatchKey::CPU,
   DispatchKey::PrivateUse1,

aten/src/ATen/mps/MPSAllocator.mm

@@ -440,7 +440,7 @@ bool MPSHeapAllocatorImpl::release_cached_buffers() {
   // we need to release the lock temporarily as synchronizing may cause deadlock with completion handlers.
   m_mutex.unlock();
   auto stream = getDefaultMPSStream();
-  dispatch_sync_with_rethrow(stream->queue(), ^() {
+  dispatch_sync(stream->queue(), ^() {
     stream->synchronize(SyncType::COMMIT_AND_WAIT);
   });
   m_mutex.lock();

aten/src/ATen/mps/MPSStream.h

@@ -110,9 +110,6 @@ class TORCH_API MPSStream {
     return _stream;
   }
 
-  MTLBuffer_t getErrorBuffer();
-  void checkLastError();
-
  private:
   Stream _stream;
   MTLCommandQueue_t _commandQueue = nil;
@@ -124,8 +121,6 @@ class TORCH_API MPSStream {
   dispatch_queue_t _serialQueue = nullptr;
   // CommitAndContinue is enabled by default
   bool _enableCommitAndContinue = true;
-  // Buffer that contains last raised error
-  MTLBuffer_t _errorBuffer = nil;
 
   // use synchronize() to access any of these commit functions outside MPSStream
   void commit();
@@ -160,7 +155,4 @@ class TORCH_API MPSStreamImpl {
   MPSStreamImpl();
 };
 
-#ifdef __OBJC__
-void dispatch_sync_with_rethrow(dispatch_queue_t queue, void (^block)());
-#endif
 } // namespace at::mps

aten/src/ATen/mps/MPSStream.mm

@@ -3,13 +3,13 @@
 #include <ATen/mps/MPSAllocatorInterface.h>
 #include <ATen/mps/MPSProfiler.h>
 #include <ATen/mps/MPSStream.h>
-#include <c10/metal/error.h>
 
 @interface MPSGraphExecutionDescriptor ()
 @property(readwrite, atomic) BOOL enableCommitAndContinue;
 @end
 
 namespace at::mps {
+
 //-----------------------------------------------------------------
 //  MPSStream
 //-----------------------------------------------------------------
@@ -30,10 +30,6 @@ MPSStream::MPSStream(Stream stream) : _stream(stream) {
   // Choose level which optimizes for GPU
   _compilationDescriptor.optimizationLevel = MPSGraphOptimizationLevel0;
   _executionDescriptor.compilationDescriptor = _compilationDescriptor;
-
-  _errorBuffer = [MPSDevice::getInstance()->device() newBufferWithLength:sizeof(c10::metal::ErrorMessages)
-                                                                 options:MTLResourceStorageModeShared];
-  std::memset([_errorBuffer contents], 0, 1024);
 }
 
 MPSStream::~MPSStream() {
@@ -42,8 +38,6 @@ MPSStream::~MPSStream() {
   [_executionDescriptor release];
   [_compilationDescriptor release];
   _executionDescriptor = nil;
-  [_errorBuffer release];
-  _errorBuffer = nil;
   _compilationDescriptor = nil;
 
   assert(_commandBuffer == nil);
@@ -110,7 +104,6 @@ void MPSStream::commitAndWait() {
     [_prevCommandBuffer waitUntilCompleted];
     [_prevCommandBuffer release];
     _prevCommandBuffer = nil;
-    checkLastError();
   }
 
   if (_commandBuffer) {
@@ -118,7 +111,6 @@ void MPSStream::commitAndWait() {
     [_commandBuffer waitUntilCompleted];
     [_commandBuffer release];
     _commandBuffer = nil;
-    checkLastError();
   }
 }
 
@@ -161,7 +153,7 @@ void MPSStream::fill(id<MTLBuffer> buffer, uint8_t value, size_t length, size_t
   if (length == 0) {
     return;
   }
-  dispatch_sync_with_rethrow(_serialQueue, ^() {
+  dispatch_sync(_serialQueue, ^() {
     @autoreleasepool {
       endKernelCoalescing();
       id<MTLBlitCommandEncoder> blitEncoder = [commandBuffer() blitCommandEncoder];
@@ -191,7 +183,7 @@ void MPSStream::copy(id<MTLBuffer> srcBuffer,
                      size_t dstOffset,
                      uint64_t profileId,
                      SyncType syncType) {
-  dispatch_sync_with_rethrow(_serialQueue, ^() {
+  dispatch_sync(_serialQueue, ^() {
     @autoreleasepool {
       endKernelCoalescing();
       id<MTLBlitCommandEncoder> blitEncoder = [commandBuffer() blitCommandEncoder];
@@ -244,7 +236,7 @@ void MPSStream::executeMPSGraph(MPSGraph* mpsGraph, NSDictionary* feeds, NSDicti
   auto& profiler = getMPSProfiler();
   const bool isGraphProfilingEnabled = profiler.isOperationProfilingEnabled();
 
-  dispatch_sync_with_rethrow(_serialQueue, ^() {
+  dispatch_sync(_serialQueue, ^() {
     endKernelCoalescing();
     if (isGraphProfilingEnabled) {
       // this function call is only relevant for interval-based Signposts
@@ -274,24 +266,6 @@ void MPSStream::executeMPSGraph(MPSGraph* mpsGraph, NSDictionary* feeds, NSDicti
   });
 }
 
-id<MTLBuffer> MPSStream::getErrorBuffer() {
-  return _errorBuffer;
-}
-
-void MPSStream::checkLastError() {
-  auto msgs = reinterpret_cast<c10::metal::ErrorMessages*>([_errorBuffer contents]);
-  const auto& msg = msgs->msg[0];
-  if (!msgs) {
-    return;
-  }
-  unsigned int count = 0;
-  std::swap(count, msgs->count);
-  if (!count) {
-    return;
-  }
-  throw c10::AcceleratorError({msg.func, msg.file, msg.line}, 1, msg.message);
-}
-
 //-----------------------------------------------------------------
 //  MPSStreamImpl
 //-----------------------------------------------------------------
@@ -315,19 +289,4 @@ MPSStream* getDefaultMPSStream() {
   return MPSStreamImpl::getInstance();
 }
 
-// Helper methods
-void dispatch_sync_with_rethrow(dispatch_queue_t queue, void (^block)()) {
-  __block std::optional<std::exception_ptr> block_exception;
-  dispatch_sync(queue, ^() {
-    try {
-      block();
-    } catch (...) {
-      block_exception = std::current_exception();
-    }
-  });
-  if (block_exception) {
-    std::rethrow_exception(*block_exception);
-  }
-}
-
 } // namespace at::mps

aten/src/ATen/native/Convolution.cpp

@@ -409,7 +409,7 @@ struct ConvParams {
     if (!detail::getCUDAHooks().compiledWithCuDNN() || !input.is_cuda() || !cudnn_enabled) {
       return false;
     }
-    static long cudnn_version = detail::getCUDAHooks().versionRuntimeCuDNN();
+    static long cudnn_version = detail::getCUDAHooks().versionCuDNN();
     // broken on cuDNN 9.8 - 9.14
     if (cudnn_version >= 90800 && cudnn_version < 91500) {
       if (cudnn_conv_suggest_memory_format(input, weight) == at::MemoryFormat::Contiguous &&
@@ -453,7 +453,7 @@ struct ConvParams {
     }
     // native kernel doesn't support 64-bit non-splittable case
     if (!(canUse32BitIndexMath(input) && canUse32BitIndexMath(weight))) {
-      static long cudnn_version = detail::getCUDAHooks().compiledWithCuDNN() ? detail::getCUDAHooks().versionRuntimeCuDNN() : -1;
+      static long cudnn_version = detail::getCUDAHooks().compiledWithCuDNN() ? detail::getCUDAHooks().versionCuDNN() : -1;
       // TODO(eqy): remove this once cuDNN fixes 64-bit depthwise support, first broken in 9.11x
       if (cudnn_conv_suggest_memory_format(input, weight) != at::MemoryFormat::Contiguous) {
         if (cudnn_version < 0 || cudnn_version > 91000) {

aten/src/ATen/native/PackedSequence.cpp

@@ -142,7 +142,6 @@ Tensor _pack_padded_sequence_backward_symint(const Tensor& grad, c10::SymIntArra
 std::tuple<Tensor, Tensor> _pad_packed_sequence(const Tensor& data, const Tensor& _batch_sizes, bool batch_first, const Scalar& padding_value, int64_t total_length) {
   auto batch_sizes_t = _batch_sizes.contiguous();
   checkLongTensor(batch_sizes_t);
-  TORCH_CHECK(batch_sizes_t.numel() > 0, "batch_sizes can not be empty");
 
   int64_t * batch_sizes = batch_sizes_t.data_ptr<int64_t>();
   int64_t max_batch_size = batch_sizes[0];

aten/src/ATen/native/TensorCompare.cpp

@@ -23,7 +23,6 @@
 #include <ATen/ops/_aminmax_native.h>
 #include <ATen/ops/_assert_async_native.h>
 #include <ATen/ops/_assert_scalar_native.h>
-#include <ATen/ops/_async_error_native.h>
 #include <ATen/ops/_functional_assert_async_native.h>
 #include <ATen/ops/_functional_assert_scalar_native.h>
 #include <ATen/ops/_make_per_tensor_quantized_tensor.h>
@@ -480,14 +479,6 @@ Tensor isfinite(const Tensor& self) {
   });
 }
 
-void _async_error(std::string_view msg) {
-  TORCH_CHECK(0, msg);
-}
-
-void _async_error_meta(std::string_view msg) {
-  // Do NOT error, it's an async error!
-}
-
 void _assert_async_cpu(const Tensor& self) {
   TORCH_CHECK(
       native::is_nonzero(self),

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

@@ -5,6 +5,7 @@
 #include <ATen/native/ReduceOpsUtils.h>
 
 #include <ATen/Dispatch.h>
+#include <ATen/Dispatch_v2.h>
 #include <ATen/Parallel.h>
 #include <ATen/TensorIterator.h>
 #include <ATen/OpMathType.h>
@@ -78,12 +79,12 @@ void min_all_kernel_impl(Tensor& result, const Tensor& input) {
     reduce_all_impl<int64_t>(result, input, upper_bound<int64_t>(),
       [=](int64_t a, int64_t b) -> int64_t { return min_impl(a, b); });
   } else {
-    AT_DISPATCH_ALL_TYPES_AND2(kHalf, kBFloat16, input.scalar_type(), "min_all", [&] {
+    AT_DISPATCH_V2(input.scalar_type(), "min_all", AT_WRAP([&] {
       using Vec = Vectorized<opmath_type<scalar_t>>;
       reduce_all_impl_vec<scalar_t>(result, input, upper_bound<scalar_t>(),
         [=] (scalar_t a , scalar_t b) -> scalar_t { return min_impl(a, b); },
         [=](Vec a, Vec b) -> Vec { return minimum(a, b); });
-    });
+    }), AT_EXPAND(AT_ALL_TYPES), AT_EXPAND(AT_BAREBONES_UNSIGNED_TYPES), kHalf, kBFloat16);
   }
 }
 
@@ -103,12 +104,12 @@ void max_all_kernel_impl(Tensor& result, const Tensor& input) {
     reduce_all_impl<int64_t>(result, input, lower_bound<int64_t>(),
       [=](int64_t a, int64_t b) -> int64_t { return max_impl(a, b); });
   } else {
-    AT_DISPATCH_ALL_TYPES_AND2(kHalf, kBFloat16, input.scalar_type(), "max_all", [&] {
+    AT_DISPATCH_V2(input.scalar_type(), "max_all", AT_WRAP([&] {
       using Vec = Vectorized<opmath_type<scalar_t>>;
       reduce_all_impl_vec<scalar_t>(result, input, lower_bound<scalar_t>(),
         [=] (scalar_t a , scalar_t b) -> scalar_t { return max_impl(a, b); },
         [=](Vec a, Vec b) -> Vec { return maximum(a, b); });
-    });
+    }), AT_EXPAND(AT_ALL_TYPES), AT_EXPAND(AT_BAREBONES_UNSIGNED_TYPES), kHalf, kBFloat16);
   }
 }
 
@@ -199,7 +200,7 @@ void aminmax_allreduce_kernel(
       }
     );
   } else {
-    AT_DISPATCH_ALL_TYPES_AND2(kBFloat16, kHalf, input.scalar_type(), "aminmax_cpu", [&] {
+    AT_DISPATCH_V2(input.scalar_type(), "aminmax_cpu", AT_WRAP([&] {
       using Vec = Vectorized<opmath_type<scalar_t>>;
       using scalar_t_pair = std::pair<scalar_t, scalar_t>;
       reduce_all_impl_vec_two_outputs<scalar_t>(
@@ -214,7 +215,7 @@ void aminmax_allreduce_kernel(
         [=](Vec a, Vec b) -> Vec { return minimum(a, b); },
         [=](Vec a, Vec b) -> Vec { return maximum(a, b); }
       );
-    });
+    }), AT_EXPAND(AT_ALL_TYPES), AT_EXPAND(AT_BAREBONES_UNSIGNED_TYPES), kBFloat16, kHalf);
   }
 }
 

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

@@ -3,6 +3,7 @@
 
 #include <ATen/core/Tensor.h>
 #include <ATen/Dispatch.h>
+#include <ATen/Dispatch_v2.h>
 #include <ATen/OpMathType.h>
 #include <ATen/cpu/vec/vec.h>
 #include <ATen/cpu/vec/functional.h>
@@ -347,34 +348,35 @@ struct MinValuesOps: public at::native::MinOps<scalar_t> {
 };
 
 void min_values_kernel_impl(TensorIterator& iter) {
-  if (iter.dtype() == kLong) {
-    // This case is special because of Vectorized<int64_t> does not
-    // handle upper_bound<int64_t>().
-    // See: https://github.com/pytorch/pytorch/issues/43254
-    using scalar_t = int64_t;
-    binary_kernel_reduce(
-      iter,
-      MinValuesOps<scalar_t>{},
-      std::pair<scalar_t, int64_t>(upper_bound<scalar_t>(), -1));
+  // This case is special because of Vectorized<int64_t> does not
+  // handle upper_bound<int64_t>().
+  // See: https://github.com/pytorch/pytorch/issues/43254
+  if (iter.dtype() == kLong || iter.dtype() == kUInt64) {
+    AT_DISPATCH_V2(iter.dtype(), "min_values_cpu", AT_WRAP([&iter] {
+      binary_kernel_reduce(
+        iter,
+        MinValuesOps<scalar_t>{},
+        std::pair<scalar_t, int64_t>(upper_bound<scalar_t>(), -1));
+    }), kLong, kUInt64);
     return;
   }
-  AT_DISPATCH_ALL_TYPES_AND3(kBFloat16, kHalf, kBool, iter.dtype(), "min_values_cpu", [&iter] {
+  AT_DISPATCH_V2(iter.dtype(), "min_values_cpu", AT_WRAP([&iter] {
     binary_kernel_reduce_vec(
       iter,
       [](scalar_t a, scalar_t b) -> scalar_t { return min_impl(a, b); },
       [](Vectorized<scalar_t> a, Vectorized<scalar_t> b) { return minimum(a, b); },
       static_cast<double>(upper_bound<scalar_t>()));
-  });
+  }), AT_EXPAND(AT_ALL_TYPES), AT_EXPAND(AT_BAREBONES_UNSIGNED_TYPES), kBFloat16, kHalf, kBool);
 }
 
 void max_values_kernel_impl(TensorIterator& iter) {
-  AT_DISPATCH_ALL_TYPES_AND3(kBFloat16, kHalf, kBool, iter.dtype(), "max_values_cpu", [&iter] {
+  AT_DISPATCH_V2(iter.dtype(), "max_values_cpu", AT_WRAP([&iter] {
     binary_kernel_reduce_vec(
       iter,
       [](scalar_t a, scalar_t b) -> scalar_t { return max_impl(a, b); },
       [](Vectorized<scalar_t> a, Vectorized<scalar_t> b) { return maximum(a, b); },
       lower_bound<scalar_t>());
-  });
+  }), AT_EXPAND(AT_ALL_TYPES), AT_EXPAND(AT_BAREBONES_UNSIGNED_TYPES), kBFloat16, kHalf, kBool);
 }
 
 void argmax_kernel_impl(TensorIterator &iter) {

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

@@ -11,6 +11,7 @@
 #include <vector>
 
 #include <ATen/Dispatch.h>
+#include <ATen/Dispatch_v2.h>
 #include <ATen/Parallel.h>
 #include <ATen/NumericUtils.h>
 #include <ATen/TensorIterator.h>
@@ -106,7 +107,7 @@ void min_kernel_impl(
     bool keepdim) {
   int64_t self_dim_size = ensure_nonempty_size(self, dim);
 
-  AT_DISPATCH_ALL_TYPES_AND3(ScalarType::Half, ScalarType::BFloat16, ScalarType::Bool, self.scalar_type(), "min_cpu", [&] {
+  AT_DISPATCH_V2(self.scalar_type(), "min_cpu", AT_WRAP([&] {
     compare_base_kernel<scalar_t>(result, indice, self, dim, keepdim, [&] (
       scalar_t* result_data, int64_t* indice_data,
       const scalar_t* self_data, auto self_dim_stride) {
@@ -128,7 +129,7 @@ void min_kernel_impl(
         *indice_data = index;
       }
     );
-  });
+  }), AT_EXPAND(AT_ALL_TYPES), AT_EXPAND(AT_BAREBONES_UNSIGNED_TYPES), ScalarType::Half, ScalarType::BFloat16, ScalarType::Bool);
 }
 
 void max_kernel_impl(
@@ -139,7 +140,7 @@ void max_kernel_impl(
     bool keepdim) {
   int64_t self_dim_size = ensure_nonempty_size(self, dim);
 
-  AT_DISPATCH_ALL_TYPES_AND3(ScalarType::Half, ScalarType::BFloat16, ScalarType::Bool, self.scalar_type(), "max_cpu", [&] {
+  AT_DISPATCH_V2(self.scalar_type(), "max_cpu", AT_WRAP([&] {
     compare_base_kernel<scalar_t>(result, indice, self, dim, keepdim, [&] (
       scalar_t* result_data, int64_t* indice_data,
       const scalar_t* self_data, auto self_dim_stride) {
@@ -161,7 +162,7 @@ void max_kernel_impl(
         *indice_data = index;
       }
     );
-  });
+  }), AT_EXPAND(AT_ALL_TYPES), AT_EXPAND(AT_BAREBONES_UNSIGNED_TYPES), ScalarType::Half, ScalarType::BFloat16, ScalarType::Bool);
 }
 
 void aminmax_kernel(
@@ -186,7 +187,7 @@ void aminmax_kernel(
     return;
   }
 
-  AT_DISPATCH_ALL_TYPES_AND3(ScalarType::Bool, ScalarType::BFloat16, ScalarType::Half, self.scalar_type(), "aminmax_cpu", [&] {
+  AT_DISPATCH_V2(self.scalar_type(), "aminmax_cpu", AT_WRAP([&] {
     compare_base_kernel<scalar_t, scalar_t>(min_result, max_result, self, wrap_dim, keepdim, [&] (
       scalar_t* min_result_data, scalar_t* max_result_data,
       const scalar_t* self_data, auto self_dim_stride) {
@@ -209,7 +210,7 @@ void aminmax_kernel(
         *max_result_data = max_number;
       }
     );
-  });
+  }), AT_EXPAND(AT_ALL_TYPES), AT_EXPAND(AT_BAREBONES_UNSIGNED_TYPES), ScalarType::Bool, ScalarType::BFloat16, ScalarType::Half);
 }
 
 void where_kernel_impl(TensorIterator &iter) {

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

@@ -884,69 +884,6 @@ struct type_specialized_kernel_launcher {
   }
 };
 
-template <int arg_index>
-struct type_specialized_broadcast_kernel_launcher {
-  template <
-      typename func_t,
-      typename array_t,
-      typename dtypes_t,
-      typename calc_t>
-  static void apply(
-      int64_t numel,
-      func_t f,
-      array_t data,
-      dtypes_t dtypes,
-      calc_t offset_calc) {
-        using traits = function_traits<func_t>;
-        using ret_t = typename traits::result_type;
-        using arg0_t = typename traits::template arg<0>::type;
-        using arg1_t = typename traits::template arg<1>::type;
-        if (dtypes[0] == rt_binary_specializations[arg_index][0] &&
-          dtypes[1] == rt_binary_specializations[arg_index][1] &&
-          dtypes[2] == rt_binary_specializations[arg_index][2]) {
-            using ret_cpp_t = c10::impl::ScalarTypeToCPPTypeT<rt_binary_specializations[arg_index][0]>;
-            using arg0_cpp_t = c10::impl::ScalarTypeToCPPTypeT<rt_binary_specializations[arg_index][1]>;
-            using arg1_cpp_t = c10::impl::ScalarTypeToCPPTypeT<rt_binary_specializations[arg_index][2]>;
-            constexpr int grp_sz = 128;
-            launch_legacy_kernel_manual_unroll<grp_sz, 4>(numel, [=] GPU_LAMBDA(int idx, bool unrl) {
-              if (unrl) {
-                auto offsets0 = offset_calc.get(idx);
-                auto offsets1 = offset_calc.get(idx + grp_sz);
-                auto offsets2 = offset_calc.get(idx + grp_sz * 2);
-                auto offsets3 = offset_calc.get(idx + grp_sz * 3);
-                void* out0 = data[0] + offsets0[0];
-                void* out1 = data[0] + offsets1[0];
-                void* out2 = data[0] + offsets2[0];
-                void* out3 = data[0] + offsets3[0];
-                auto u = c10::load<arg0_cpp_t>(data[1] + offsets0[1]);
-                auto v = c10::load<arg1_cpp_t>(data[2] + offsets0[2]);
-                ret_t result0 = f(c10::convert<arg0_t>(u), c10::convert<arg1_t>(v));
-                auto u1 = c10::load<arg0_cpp_t>(data[1] + offsets1[1]);
-                auto v1 = c10::load<arg1_cpp_t>(data[2]+ offsets1[2]);
-                ret_t result1 = f(c10::convert<arg0_t>(u1), c10::convert<arg1_t>(v1));
-                auto u2 = c10::load<arg0_cpp_t>(data[1] + offsets2[1]);
-                auto v2 = c10::load<arg1_cpp_t>(data[2] + offsets2[2]);
-                ret_t result2 = f(c10::convert<arg0_t>(u2), c10::convert<arg1_t>(v2));
-                auto u3 = c10::load<arg0_cpp_t>(data[1] + offsets3[1]);
-                auto v3 = c10::load<arg1_cpp_t>(data[2] + offsets3[2]);
-                ret_t result3 = f(c10::convert<arg0_t>(u3), c10::convert<arg1_t>(v3));
-                *(ret_cpp_t*)out0 = c10::convert<ret_cpp_t>(result0);
-                *(ret_cpp_t*)out1 = c10::convert<ret_cpp_t>(result1);
-                *(ret_cpp_t*)out2 = c10::convert<ret_cpp_t>(result2);
-                *(ret_cpp_t*)out3 = c10::convert<ret_cpp_t>(result3);
-              } else {
-                auto offsets = offset_calc.get(idx);
-                void* out = data[0] + offsets[0];
-                auto u = c10::load<arg0_cpp_t>(data[1] + offsets[1]);
-                auto v = c10::load<arg1_cpp_t>(data[2] + offsets[2]);
-                ret_t result = f(c10::convert<arg0_t>(u), c10::convert<arg1_t>(v));
-                *(ret_cpp_t*)out = c10::convert<ret_cpp_t>(result);
-              }
-            });
-        }
-      }
-};
-
 } // namespace
 #endif
 
@@ -1065,32 +1002,6 @@ void gpu_kernel_impl(TensorIteratorBase& iter, const func_t& f) {
     }
     auto offset_calc = ::make_offset_calculator<traits::arity + 1>(iter);
 #ifdef USE_ROCM
-    if (check_binary_rt_types_for_specialization(iter)) {
-      // constexpr to reduce the amount of kernels generated for
-      // broadcast elementwise with mexed dtypes and limit which functors are actually
-      // applied to the load and store at compile time.
-      using func_tuple = typename traits::ArgsTuple;
-      if constexpr (
-        std::is_same_v<float, arg0_t> && traits::arity == 2 &&
-        check_binary_functor_types_for_specialization<
-          func_tuple,
-          float,
-          float,
-          traits::arity,
-          /*arg_num=*/0>::check()) {
-            memory::detail::static_unroll<
-              type_specialized_broadcast_kernel_launcher,
-              rt_binary_specializations.size()>::with_args(
-                numel,
-                f,
-                data,
-                dtypes,
-                offset_calc
-            );
-            return;
-      }
-    }
-
     constexpr int grp_sz = 128;
     launch_legacy_kernel_manual_unroll<grp_sz, 4>(numel, [=] GPU_LAMBDA(int idx, bool unrl) {
       if (unrl) {

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

@@ -669,12 +669,9 @@ std::optional<c10::ScalarType> out_dtype) {
   // _scaled_mm_allowed_device is used here within _grouped_mm_cuda which seems incorrect since scale is not used.
   // the _grouped_mm_fallback should be safe for any ROCm GPU since it's just calling typical mm/bmm
   bool use_fast_path = false;
-  // On non CK system(w/ ROCm), make sure use_fast_path is false
-#if defined(USE_ROCM_CK_GEMM)
   if (at::detail::getCUDAHooks().isGPUArch({"gfx942", "gfx950"})) {
     use_fast_path = true;
   }
-#endif //USE_ROCM_CK_GEMM
 #endif
   const auto out_dtype_ = _resolve_grouped_mm_out_dtype(mat_a, mat_b, out_dtype);
   Tensor out = create_grouped_gemm_output_tensor(mat_a, mat_b, offs, out_dtype_);
@@ -683,11 +680,7 @@ std::optional<c10::ScalarType> out_dtype) {
 #ifndef USE_ROCM
     at::cuda::detail::bf16bf16_grouped_mm(mat_a, mat_b, offs, bias, out);
 #else
-#if defined(USE_ROCM_CK_GEMM)
     at::hip::detail::group_gemm_ck(mat_a, mat_b, offs, bias, out);
-#else
-    TORCH_WARN("ROCm: Group Gemm through CK not selected.");
-#endif //USE_ROCM_CK_GEMM
 #endif
   } else {
     _grouped_mm_fallback(mat_a, mat_b, offs, bias, out_dtype, out);

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

@@ -1,5 +1,6 @@
 #define TORCH_ASSERT_NO_OPERATORS
 #include <ATen/Dispatch.h>
+#include <ATen/Dispatch_v2.h>
 #include <ATen/NumericUtils.h>
 #include <ATen/native/DispatchStub.h>
 #include <ATen/native/ReduceAllOps.h>
@@ -28,22 +29,22 @@ void _min_max_values_kernel_cuda_impl(TensorIterator& iter) {
 }
 
 void aminmax_allreduce_launch_kernel(TensorIterator& iter) {
-  AT_DISPATCH_ALL_TYPES_AND3(
-      kBFloat16, kHalf, kBool, iter.input_dtype(), "aminmax_all_cuda", [&] {
+  AT_DISPATCH_V2(
+      iter.input_dtype(), "aminmax_all_cuda", AT_WRAP([&] {
         _min_max_values_kernel_cuda_impl<scalar_t>(iter);
-      });
+      }), AT_EXPAND(AT_ALL_TYPES), AT_EXPAND(AT_BAREBONES_UNSIGNED_TYPES), kBFloat16, kHalf, kBool);
 }
 
 void aminmax_launch_kernel(TensorIterator& iter) {
-  AT_DISPATCH_ALL_TYPES_AND3(
-      kBFloat16, kHalf, kBool, iter.input_dtype(), "aminmax_cuda", [&]() {
+  AT_DISPATCH_V2(
+      iter.input_dtype(), "aminmax_cuda", AT_WRAP([&]() {
         gpu_reduce_kernel<scalar_t, scalar_t>(
             iter,
             MinMaxOps<scalar_t, scalar_t, int32_t>{},
             thrust::pair<scalar_t, scalar_t>(
                 at::numeric_limits<scalar_t>::upper_bound(),
                 at::numeric_limits<scalar_t>::lower_bound()));
-      });
+      }), AT_EXPAND(AT_ALL_TYPES), AT_EXPAND(AT_BAREBONES_UNSIGNED_TYPES), kBFloat16, kHalf, kBool);
 }
 
 } // namespace at::native

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

@@ -1,5 +1,6 @@
 #define TORCH_ASSERT_NO_OPERATORS
 #include <ATen/Dispatch.h>
+#include <ATen/Dispatch_v2.h>
 #include <ATen/NumericUtils.h>
 #include <ATen/native/DispatchStub.h>
 #include <ATen/native/ReduceAllOps.h>
@@ -33,27 +34,27 @@ void max_values_kernel_cuda_impl(TensorIterator& iter) {
 }
 
 void max_values_kernel_cuda(TensorIterator& iter) {
-  AT_DISPATCH_ALL_TYPES_AND3(
-      kBFloat16, kHalf, kBool, iter.dtype(), "max_values_cuda", [&]() {
+  AT_DISPATCH_V2(
+      iter.dtype(), "max_values_cuda", AT_WRAP([&]() {
         max_values_kernel_cuda_impl<scalar_t>(iter);
-      });
+      }), AT_EXPAND(AT_ALL_TYPES), AT_EXPAND(AT_BAREBONES_UNSIGNED_TYPES), kBFloat16, kHalf, kBool);
 }
 
 void max_launch_kernel(TensorIterator& iter) {
-  AT_DISPATCH_ALL_TYPES_AND3(
-      kBFloat16, kHalf, kBool, iter.input_dtype(), "max_cuda", [&]() {
+  AT_DISPATCH_V2(
+      iter.input_dtype(), "max_cuda", AT_WRAP([&]() {
         gpu_reduce_kernel<scalar_t, scalar_t>(
             iter,
             MaxOps<scalar_t>{},
             thrust::pair<scalar_t, int64_t>(
                 at::numeric_limits<scalar_t>::lower_bound(), 0));
-      });
+      }), AT_EXPAND(AT_ALL_TYPES), AT_EXPAND(AT_BAREBONES_UNSIGNED_TYPES), kBFloat16, kHalf, kBool);
 }
 
 void max_all_launch_kernel(TensorIterator &iter) {
-  AT_DISPATCH_ALL_TYPES_AND3(kBFloat16, kHalf, kBool, iter.input_dtype(), "max_all_cuda", [&] {
+  AT_DISPATCH_V2(iter.input_dtype(), "max_all_cuda", AT_WRAP([&] {
     max_values_kernel_cuda_impl<scalar_t>(iter);
-  });
+  }), AT_EXPAND(AT_ALL_TYPES), AT_EXPAND(AT_BAREBONES_UNSIGNED_TYPES), kBFloat16, kHalf, kBool);
 }
 
 REGISTER_DISPATCH(max_values_stub, &max_values_kernel_cuda)

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

@@ -12,6 +12,7 @@
 #include <ATen/NumericUtils.h>
 
 #include <ATen/Dispatch.h>
+#include <ATen/Dispatch_v2.h>
 #include <ATen/NumericUtils.h>
 #include <ATen/cuda/NumericLimits.cuh>
 
@@ -33,24 +34,24 @@ void min_values_kernel_cuda_impl(TensorIterator& iter) {
 }
 
 void min_values_kernel_cuda(TensorIterator& iter) {
-  AT_DISPATCH_ALL_TYPES_AND3(kBFloat16, kHalf, kBool, iter.dtype(), "min_values_cuda", [&]() {
+  AT_DISPATCH_V2(iter.dtype(), "min_values_cuda", AT_WRAP([&]() {
     min_values_kernel_cuda_impl<scalar_t>(iter);
-  });
+  }), AT_EXPAND(AT_ALL_TYPES), AT_EXPAND(AT_BAREBONES_UNSIGNED_TYPES), kBFloat16, kHalf, kBool);
 }
 
 void min_launch_kernel(TensorIterator &iter) {
-  AT_DISPATCH_ALL_TYPES_AND3(kBFloat16, kHalf, kBool, iter.input_dtype(), "min_cuda", [&]() {
+  AT_DISPATCH_V2(iter.input_dtype(), "min_cuda", AT_WRAP([&]() {
     gpu_reduce_kernel<scalar_t, scalar_t>(
       iter,
       MinOps<scalar_t>{},
       thrust::pair<scalar_t, int64_t>(at::numeric_limits<scalar_t>::upper_bound(), 0));
-  });
+  }), AT_EXPAND(AT_ALL_TYPES), AT_EXPAND(AT_BAREBONES_UNSIGNED_TYPES), kBFloat16, kHalf, kBool);
 }
 
 void min_all_launch_kernel(TensorIterator &iter) {
-  AT_DISPATCH_ALL_TYPES_AND3(kBFloat16, kHalf, kBool, iter.input_dtype(), "min_all_cuda", [&] {
+  AT_DISPATCH_V2(iter.input_dtype(), "min_all_cuda", AT_WRAP([&] {
     min_values_kernel_cuda_impl<scalar_t>(iter);
-  });
+  }), AT_EXPAND(AT_ALL_TYPES), AT_EXPAND(AT_BAREBONES_UNSIGNED_TYPES), kBFloat16, kHalf, kBool);
 }
 
 REGISTER_DISPATCH(min_values_stub, &min_values_kernel_cuda)

aten/src/ATen/native/mkldnn/xpu/detail/QConv.cpp

@@ -133,7 +133,7 @@ at::Tensor quantized_convolution(
   // supported in conv.
   mask_weight = weight_zero_points.numel() > 1 ? 1 : 0;
   if (groups > 1 && weight_zero_points.numel() > 1)
-    mask_weight = (1 << 0) | (1 << 1); // 2^0 (group) | 2^1 (output channel)
+    mask_weight = (2 ^ 0) | (2 ^ 1); // 2^0 (group) | 2^1 (output channel)
   dnnl::primitive_attr pattr;
 
   bool src_need_zp = (act_zero_point != 0);

aten/src/ATen/native/mps/OperationUtils.h

@@ -40,6 +40,8 @@ using namespace at::mps;
 
 namespace at::native::mps {
 
+void dispatch_sync_with_rethrow(dispatch_queue_t queue, void (^block)());
+
 struct MPSScalar {
   id<MTLBuffer> getMTLBuffer() const {
     return __builtin_bit_cast(id<MTLBuffer>, buffer.get());

aten/src/ATen/native/mps/OperationUtils.mm

@@ -53,6 +53,21 @@
 @end
 
 namespace at::native::mps {
+
+void dispatch_sync_with_rethrow(dispatch_queue_t queue, void (^block)()) {
+  __block std::optional<std::exception_ptr> block_exception;
+  dispatch_sync(queue, ^() {
+    try {
+      block();
+    } catch (...) {
+      block_exception = std::current_exception();
+    }
+  });
+  if (block_exception) {
+    std::rethrow_exception(*block_exception);
+  }
+}
+
 /**
  * Computes distance from lowest to highest element offset in given tensor.
  */

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

@@ -1,5 +1,4 @@
 #include <c10/metal/atomic.h>
-#include <c10/metal/error.h>
 #include <c10/metal/indexing.h>
 #include <metal_stdlib>
 
@@ -32,24 +31,10 @@ OffsetT index_apply_indices(
     constant IndexAB* indices,
     constant int64_t* sizes,
     constant int64_t* strides,
-    uint num_indices,
-    thread bool& error,
-    device ErrorMessages* error_buf) {
+    uint num_indices) {
   OffsetT rc = offs.x;
   for (uint i = 0; i < num_indices; i++) {
     auto idx = indices[i].indexArray[offs.y];
-    if (idx < -sizes[i] || idx >= sizes[i]) {
-      TORCH_REPORT_ERROR(
-          error_buf,
-          "index ",
-          idx,
-          " is out of bounds for dimension ",
-          i,
-          " with size ",
-          sizes[i]);
-      error = true;
-      break;
-    }
     if (idx < 0) {
       idx += sizes[i];
     }
@@ -70,7 +55,6 @@ kernel void index_select(
     constant int64_t* index_sizes,
     constant int64_t* index_strides,
     constant uint4& ndim_nindices_numel,
-    device ErrorMessages* error_buffer,
     uint thread_index [[thread_position_in_grid]]) {
   const auto ndim = ndim_nindices_numel.x;
   const auto num_indices = ndim_nindices_numel.y;
@@ -81,19 +65,8 @@ kernel void index_select(
       indices_strides,
       ndim,
       thread_index);
-  bool error = false;
   auto input_offs = index_apply_indices<OffsetT>(
-      offs.yz,
-      indices,
-      index_sizes,
-      index_strides,
-      num_indices,
-      error,
-      error_buffer);
-  if (error) {
-    output[offs.x / sizeof(T)] = 0;
-    return;
-  }
+      offs.yz, indices, index_sizes, index_strides, num_indices);
   output[offs.x / sizeof(T)] = input[input_offs / sizeof(T)];
 }
 
@@ -109,9 +82,7 @@ inline void index_put_impl(
     constant int64_t* index_sizes,
     constant int64_t* index_strides,
     constant uint4& ndim_nindices_numel,
-    device ErrorMessages* error_buffer,
     uint thread_index) {
-  bool error = false;
   const auto ndim = ndim_nindices_numel.x;
   const auto num_indices = ndim_nindices_numel.y;
   const auto offs = index_get_offsets(
@@ -122,16 +93,7 @@ inline void index_put_impl(
       ndim,
       thread_index);
   auto output_offs = index_apply_indices<OffsetT>(
-      offs.xz,
-      indices,
-      index_sizes,
-      index_strides,
-      num_indices,
-      error,
-      error_buffer);
-  if (error) {
-    return;
-  }
+      offs.xz, indices, index_sizes, index_strides, num_indices);
   output[output_offs / sizeof(T)] = input[offs.y / sizeof(T)];
 }
 
@@ -147,7 +109,6 @@ kernel void index_put(
     constant int64_t* index_sizes,
     constant int64_t* index_strides,
     constant uint4& ndim_nindices_numel,
-    device ErrorMessages* error_buffer,
     uint thread_index [[thread_position_in_grid]]) {
   index_put_impl(
       output,
@@ -160,7 +121,6 @@ kernel void index_put(
       index_sizes,
       index_strides,
       ndim_nindices_numel,
-      error_buffer,
       thread_index);
 }
 
@@ -176,7 +136,6 @@ kernel void index_put_serial(
     constant int64_t* index_sizes,
     constant int64_t* index_strides,
     constant uint4& ndim_nindices_numel,
-    device ErrorMessages* error_buffer,
     uint thread_index [[thread_position_in_grid]]) {
   (void)thread_index; // Suppress unused vairable varning
   for (uint idx = 0; idx < ndim_nindices_numel.z; ++idx) {
@@ -191,7 +150,6 @@ kernel void index_put_serial(
         index_sizes,
         index_strides,
         ndim_nindices_numel,
-        error_buffer,
         idx);
   }
 }
@@ -208,7 +166,6 @@ kernel void index_put_accumulate(
     constant int64_t* index_sizes,
     constant int64_t* index_strides,
     constant uint4& ndim_nindices_numel,
-    device ErrorMessages* error_buffer,
     uint thread_index [[thread_position_in_grid]]) {
   const auto ndim = ndim_nindices_numel.x;
   const auto num_indices = ndim_nindices_numel.y;
@@ -219,18 +176,8 @@ kernel void index_put_accumulate(
       indices_strides,
       ndim,
       thread_index);
-  bool error = false;
   auto output_offs = index_apply_indices<OffsetT>(
-      offs.xz,
-      indices,
-      index_sizes,
-      index_strides,
-      num_indices,
-      error,
-      error_buffer);
-  if (error) {
-    return;
-  }
+      offs.xz, indices, index_sizes, index_strides, num_indices);
   AtomicType<T>::atomic_add(
       reinterpret_cast<device AtomicType_t<T>*>(output),
       output_offs / sizeof(T),
@@ -250,7 +197,6 @@ kernel void index_put_accumulate(
           constant int64_t* index_sizes,                            \
           constant int64_t* index_strides,                          \
           constant uint4& ndim_nindices_numel,                      \
-          device ErrorMessages* error_buffer,                       \
           uint thread_index [[thread_position_in_grid]])
 
 #define REGISTER_INDEX_OP_ALL_DTYPES(OP_NAME) \

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

@@ -141,9 +141,6 @@ static Tensor& addmv_out_mps_impl(const Tensor& self,
   };
 
   MPSStream* stream = at::mps::getCurrentMPSStream();
-  if (result.numel() == 0) {
-    return result;
-  }
   Tensor matMulVec = at::mm(mat, vec.unsqueeze(1)).squeeze(1);
 
   @autoreleasepool {

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

@@ -220,7 +220,7 @@ Tensor _embedding_bag_dense_backward_mps(const Tensor& output_grad,
   auto num_threads = (params.mode == EmbeddingBagMode::MAX) ? output_grad.numel() : num_indices * params.feature_size;
   MPSStream* stream = getCurrentMPSStream();
 
-  dispatch_sync_with_rethrow(stream->queue(), ^() {
+  mps::dispatch_sync_with_rethrow(stream->queue(), ^() {
     @autoreleasepool {
       id<MTLComputeCommandEncoder> computeEncoder = stream->commandEncoder();
       auto pipeline_state = lib.getPipelineStateForFunc(fmt::format("embedding_bag_backward_{}_{}",
@@ -273,7 +273,7 @@ Tensor _embedding_bag_per_sample_weights_backward_mps(const Tensor& output_grad,
   auto num_threads = num_indices * feature_size;
   MPSStream* stream = getCurrentMPSStream();
 
-  dispatch_sync_with_rethrow(stream->queue(), ^() {
+  mps::dispatch_sync_with_rethrow(stream->queue(), ^() {
     @autoreleasepool {
       id<MTLComputeCommandEncoder> computeEncoder = stream->commandEncoder();
       auto pipeline_state = lib.getPipelineStateForFunc(fmt::format("embedding_bag_per_sample_weights_backward_{}_{}",

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

@@ -179,8 +179,7 @@ static void dispatch_index_kernel(TensorIteratorBase& iter,
                    iter.strides(2),
                    index_size,
                    index_stride,
-                   ndim_nindiees,
-                   mpsStream->getErrorBuffer());
+                   ndim_nindiees);
     mtl_dispatch1DJob(computeEncoder, indexSelectPSO, serial ? 1 : iter.numel());
   });
 }
@@ -300,7 +299,7 @@ static Tensor& nonzero_out_native_mps(const Tensor& self, Tensor& out_) {
   MPSStream* stream = getCurrentMPSStream();
   using CachedGraph = MPSUnaryCachedGraph;
 
-  dispatch_sync_with_rethrow(stream->queue(), ^() {
+  dispatch_sync(stream->queue(), ^() {
     stream->synchronize(SyncType::COMMIT_AND_WAIT);
   });
   int64_t total_nonzero = at::count_nonzero(self).item<int64_t>();
@@ -385,7 +384,7 @@ Tensor& nonzero_out_mps(const Tensor& self, Tensor& out_) {
   MPSStream* stream = getCurrentMPSStream();
   using CachedGraph = MPSUnaryCachedGraph;
 
-  dispatch_sync_with_rethrow(stream->queue(), ^() {
+  dispatch_sync(stream->queue(), ^() {
     stream->synchronize(SyncType::COMMIT_AND_WAIT);
   });
   int64_t total_nonzero = at::count_nonzero(self).item<int64_t>();

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

@@ -923,7 +923,7 @@ std::tuple<Tensor, Tensor, Tensor> layer_norm_mps(const Tensor& input,
   MPSStream* stream = getCurrentMPSStream();
   TORCH_CHECK_NOT_IMPLEMENTED(input.scalar_type() != kLong, "Not implemented for long on MPS");
   @autoreleasepool {
-    dispatch_sync_with_rethrow(stream->queue(), ^() {
+    mps::dispatch_sync_with_rethrow(stream->queue(), ^() {
       // which kernel variant to use based on the normalized axis N size
       const int N_READS = 4;
       auto metalType = mps::scalarToMetalTypeString(input);

aten/src/ATen/native/native_functions.yaml

@@ -192,11 +192,6 @@
     CompositeExplicitAutograd: _assert_tensor_metadata
     Meta: _assert_tensor_metadata_meta_symint
 
-- func: _async_error(str msg) -> ()
-  dispatch:
-    CompositeExplicitAutograd: _async_error
-    Meta: _async_error_meta
-
 - func: _print(str s) -> ()
   dispatch:
     CompositeExplicitAutograd: _print
@@ -2808,7 +2803,7 @@
 - func: floor_divide.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
   device_check: NoCheck   # TensorIterator
   dispatch:
-    CPU, CUDA, MPS, MTIA: floor_divide_out
+    CPU, CUDA, MPS: floor_divide_out
     SparseCPU, SparseCUDA, SparseMPS: floor_divide_out_sparse_zerodim
 
 - func: floor_divide.Scalar(Tensor self, Scalar other) -> Tensor
@@ -4297,7 +4292,6 @@
   dispatch:
     SparseCPU: sparse_sparse_matmul_cpu
     SparseCUDA: sparse_sparse_matmul_cuda
-    SparseMPS: sparse_sparse_matmul_mps
   autogen: _sparse_sparse_matmul.out
 
 - func: mode(Tensor self, int dim=-1, bool keepdim=False) -> (Tensor values, Tensor indices)
@@ -4389,7 +4383,7 @@
   variants: function, method
   dispatch:
     CompositeExplicitAutograd: mv
-    SparseCPU, SparseCUDA, SparseMPS: mv_sparse
+    SparseCPU, SparseCUDA: mv_sparse
 
 - func: mv.out(Tensor self, Tensor vec, *, Tensor(a!) out) -> Tensor(a!)
   dispatch:
@@ -9838,7 +9832,7 @@
   structured_delegate: erfinv.out
   variants: method, function
   dispatch:
-    SparseCPU, SparseCUDA, SparseMPS: erfinv_sparse
+    SparseCPU, SparseCUDA: erfinv_sparse
     SparseCsrCPU, SparseCsrCUDA, SparseCsrMeta: erfinv_sparse_csr
   tags: pointwise
 
@@ -9847,7 +9841,7 @@
   structured_delegate: erfinv.out
   variants: method
   dispatch:
-    SparseCPU, SparseCUDA, SparseMPS: erfinv_sparse_
+    SparseCPU, SparseCUDA: erfinv_sparse_
     SparseCsrCPU, SparseCsrCUDA, SparseCsrMeta: erfinv_sparse_csr_
   tags: pointwise
 
@@ -9857,7 +9851,7 @@
   structured_inherits: TensorIteratorBase
   dispatch:
     CPU, CUDA, MPS: erfinv_out
-    SparseCPU, SparseCUDA, SparseMPS: erfinv_sparse_out
+    SparseCPU, SparseCUDA: erfinv_sparse_out
     SparseCsrCPU, SparseCsrCUDA, SparseCsrMeta: erfinv_sparse_csr_out
   tags: pointwise
 

aten/src/ATen/native/sparse/cuda/SoftMax.cu

@@ -47,7 +47,6 @@
 #include <c10/macros/Macros.h>
 #include <thrust/copy.h>
 #include <thrust/device_ptr.h>
-#include <thrust/distance.h>
 #include <thrust/for_each.h>
 #include <thrust/functional.h>
 #include <thrust/gather.h>

aten/src/ATen/native/sparse/mps/SparseMPSTensorMath.mm

@@ -10,10 +10,6 @@
 #include <ATen/NativeFunctions.h>
 #else
 #include <ATen/ops/_coalesce_native.h>
-#include <ATen/ops/repeat_interleave_native.h>
-#include <ATen/ops/cumsum.h>
-#include <ATen/ops/_sparse_sparse_matmul_native.h>
-#include <ATen/ops/_sparse_coo_tensor_unsafe.h>
 #include <ATen/ops/_sparse_coo_tensor_unsafe_native.h>
 #include <ATen/ops/cat.h>
 #include <ATen/ops/add_native.h>
@@ -892,114 +888,5 @@ static void sparse_mask_intersection_out_mps_kernel(
       /*coalesce_mask=*/false);
 }
 
-Tensor sparse_sparse_matmul_mps(const Tensor& mat1_, const Tensor& mat2_) {
-  TORCH_CHECK(mat1_.is_sparse() && mat2_.is_sparse(),
-              "sparse_sparse_matmul_mps: both inputs must be sparse COO tensors");
-  TORCH_CHECK(mat1_.is_mps() && mat2_.is_mps(),
-              "sparse_sparse_matmul_mps: both inputs must be on MPS device");
-  TORCH_CHECK(mat1_.dim() == 2 && mat2_.dim() == 2,
-              "sparse_sparse_matmul_mps: both inputs must be 2D matrices");
-  TORCH_CHECK(mat1_.dense_dim() == 0 && mat2_.dense_dim() == 0,
-              "sparse_sparse_matmul_mps: only scalar values supported (dense_dim == 0)");
-  TORCH_CHECK(mat1_.size(1) == mat2_.size(0),
-              "mat1 and mat2 shapes cannot be multiplied (", mat1_.size(0), "x", mat1_.size(1), " and ", mat2_.size(0), "x", mat2_.size(1), ")");
-  TORCH_CHECK(mat1_.scalar_type() == mat2_.scalar_type(),
-              "sparse_sparse_matmul_mps: mat1 dtype ", mat1_.scalar_type(),
-              " does not match mat2 dtype ", mat2_.scalar_type());
-
-  const auto device = mat1_.device();
-
-  auto A = mat1_.coalesce();
-  auto B = mat2_.coalesce();
-
-  const auto I = A.size(0);
-  const auto K = A.size(1);
-  const auto N = B.size(1);
-
-  const auto nnzA = A._nnz();
-  const auto nnzB = B._nnz();
-
-  // Early empty result, return an empty, coalesced tensor
-  if (I == 0 || N == 0 || K == 0 || nnzA == 0 || nnzB == 0) {
-    auto empty_idx = at::empty({2, 0}, at::device(device).dtype(at::kLong));
-    auto empty_val = at::empty({0}, at::device(device).dtype(mat1_.scalar_type()));
-    auto out = _sparse_coo_tensor_unsafe(empty_idx, empty_val, {I, N}, mat1_.options());
-    out._coalesced_(true);
-    return out;
-  }
-
-  const auto computeDtype = at::result_type(mat1_, mat2_);
-
-  auto A_idx = A._indices().contiguous();
-  auto A_val = A._values().to(computeDtype).contiguous();
-  auto A_i = A_idx.select(0, 0).contiguous();
-  auto A_k = A_idx.select(0, 1).contiguous();
-
-  auto B_idx = B._indices().contiguous();
-  auto B_val = B._values().to(computeDtype).contiguous();
-  auto B_k = B_idx.select(0, 0).contiguous();
-  auto B_j = B_idx.select(0, 1).contiguous();
-
-  // csr-style row pointers for B by k (the shared dimension)
-  Tensor row_ptr_B;
-  {
-    auto batch_ptr = at::tensor({0LL, nnzB}, at::device(device).dtype(at::kLong));
-    row_ptr_B = at::empty({K + 1}, at::device(device).dtype(at::kLong));
-    build_row_ptr_per_batch_mps(B_k, batch_ptr, /*B=*/1, /*I=*/K, row_ptr_B);
-  }
-
-  auto row_ptr_B_lo = row_ptr_B.narrow(0, 0, K);
-  auto row_ptr_B_hi = row_ptr_B.narrow(0, 1, K);
-  auto deg_B = row_ptr_B_hi.sub(row_ptr_B_lo);
-
-  auto counts = deg_B.index_select(0, A_k);
-
-  const int64_t P = counts.sum().item<int64_t>();
-  if (P == 0) {
-    auto empty_idx = at::empty({2, 0}, at::device(device).dtype(at::kLong));
-    auto empty_val = at::empty({0}, at::device(device).dtype(mat1_.scalar_type()));
-    auto out = _sparse_coo_tensor_unsafe(empty_idx, empty_val, {I, N}, mat1_.options());
-    out._coalesced_(true);
-    return out;
-  }
-
-  auto group_ids = repeat_interleave_mps(counts);
-
-  // exclusive cumsum of counts
-  auto offsets = cumsum(counts, /*dim=*/0).sub(counts);
-  auto offsets_gather = offsets.index_select(0, group_ids);
-  auto within = at::arange(P, at::device(device).dtype(at::kLong)).sub(offsets_gather);
-
-  // Map each output element to its source B row and position
-  auto k_per_out = A_k.index_select(0, group_ids);
-  auto start_in_B = row_ptr_B.index_select(0, k_per_out);
-  auto seg_index = start_in_B.add(within);
-
-  // Assemble candidate coo pairs and values
-  auto i_out = A_i.index_select(0, group_ids).contiguous();
-  auto j_out = B_j.index_select(0, seg_index).contiguous();
-  auto vA_out = A_val.index_select(0, group_ids).contiguous();
-  auto vB_out = B_val.index_select(0, seg_index).contiguous();
-  auto v_out = vA_out.mul(vB_out);
-
-  // build (2, P) indices
-  auto out_indices = at::empty({2, P}, at::device(device).dtype(at::kLong)).contiguous();
-  out_indices.select(0, 0).copy_(i_out);
-  out_indices.select(0, 1).copy_(j_out);
-
-  auto result = _sparse_coo_tensor_unsafe(
-      out_indices, v_out, {I, N}, mat1_.options().dtype(computeDtype));
-
-  result = result.coalesce();
-
-  if (result.scalar_type() != mat1_.scalar_type()) {
-    auto cast_vals = result._values().to(mat1_.scalar_type());
-    auto out = _sparse_coo_tensor_unsafe(result._indices(), cast_vals, {I, N}, mat1_.options());
-    out._coalesced_(true);
-    return out;
-  }
-  return result;
-}
-
 REGISTER_MPS_DISPATCH(sparse_mask_intersection_out_stub, &sparse_mask_intersection_out_mps_kernel);
 } // namespace at::native

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

@@ -478,7 +478,7 @@ bool check_cudnn_tensor_shapes(sdp_params const& params, bool debug) {
   const auto s_k = params.key.sym_size(2);
   const auto d_qk = params.query.sym_size(3);
   const auto d_v = params.value.sym_size(3);
-  long cudnn_version = at::detail::getCUDAHooks().versionRuntimeCuDNN();
+  long cudnn_version = at::detail::getCUDAHooks().versionCuDNN();
   if (cudnn_version < 8903) {
     if (debug) {
       TORCH_WARN("SDPA fprop requires cudnn 8.9.3 or higher");
@@ -709,7 +709,7 @@ bool can_use_cudnn_attention(const sdp_params& params, bool debug) {
   return false;
 #endif
 #if defined(CUDNN_VERSION)
-  static auto cudnn_version = at::detail::getCUDAHooks().versionRuntimeCuDNN();
+  static auto cudnn_version = cudnnGetVersion();
   if (params.dropout > 0.0 && cudnn_version > 91100 && cudnn_version < 91400) {
     if (debug) {
       TORCH_WARN(CUDNN_VERSION, " cuDNN version does not support droppout in SDPA (9.11 - 9.13).");

benchmarks/dynamo/ci_expected_accuracy/rocm/dynamic_inductor_timm_training.csv

@@ -50,7 +50,7 @@ nfnet_l0,pass,7
 
 
 
-repvgg_a2,pass,7
+repvgg_a2,fail_accuracy,7
 
 
 

benchmarks/dynamo/common.py

@@ -952,7 +952,7 @@ def latency_experiment_summary(suite_name, args, model, timings, **kwargs):
         first_fields.append(kwargs["tag"])
     headers = first_headers + ["speedup", "abs_latency"]
     row = first_fields + [float(speedup), median[1] * 1000]
-    msg = f"{median[0] * 1000} ms, {median[1] * 1000} ms, {speedup:.3f}x"
+    msg = f"{speedup:.3f}x"
     if args.baseline:
         headers.extend(
             [
@@ -1010,7 +1010,7 @@ def latency_experiment_summary(suite_name, args, model, timings, **kwargs):
     # Hypothetically you can use this from other places, but it's currently
     # inaccessible, and when this assert fails you need to update the
     # event_name here to account for the other cases you are using this
-    assert any([args.quantization, args.optimus])
+    assert args.quantization is not None
     output_signpost(
         dict(zip(headers, row)),
         args,
@@ -2587,9 +2587,6 @@ class BenchmarkRunner:
                 **experiment_kwargs,
             )
 
-            # reset dynamo
-            torch._dynamo.reset()
-
             if self.args.export_aot_inductor:
                 optimized_model_iter_fn = optimize_ctx
             else:
@@ -2953,7 +2950,7 @@ class BenchmarkRunner:
             status = self.check_tolerance(name, model, example_inputs, optimize_ctx)
             print(status)
         elif self.args.performance:
-            if self.args.backend in ["torchao", "optimus"]:
+            if self.args.backend == "torchao":
                 status = self.run_performance_test_non_alternate(
                     name, model, example_inputs, optimize_ctx, experiment, tag
                 )
@@ -3529,12 +3526,6 @@ def parse_args(args=None):
         action="store_true",
         help="Measure speedup with TorchInductor",
     )
-    group.add_argument(
-        "--optimus",
-        choices=["vertical_opt", "horizontal_opt", "all"],
-        default=None,
-        help="Measure speedup of Optimus with TorchInductor baseline",
-    )
     group.add_argument(
         "--quantization",
         choices=[
@@ -3792,9 +3783,6 @@ def run(runner, args, original_dir=None):
     if args.inductor:
         assert args.backend is None
         args.backend = "inductor"
-    if args.optimus:
-        assert args.backend is None
-        args.backend = "optimus"
     if args.quantization:
         assert args.backend is None
         args.backend = "torchao"
@@ -4079,22 +4067,10 @@ def run(runner, args, original_dir=None):
 
             runner.model_iter_fn = model_iter_fn_and_mark_step
             optimize_ctx = torchao_optimize_ctx(args.quantization)
-        elif args.backend == "optimus":
-            from .optimus import get_baseline_ctx, get_optimus_optimize_ctx
-
-            baseline_ctx = get_baseline_ctx(
-                nopython=args.nopython, inductor_compile_mode=args.inductor_compile_mode
-            )
-            runner.model_iter_fn = baseline_ctx(runner.model_iter_fn)
-            optimize_ctx = get_optimus_optimize_ctx(
-                args.optimus, args.nopython, args.inductor_compile_mode
-            )
         else:
             optimize_ctx = torch._dynamo.optimize(args.backend, nopython=args.nopython)
         experiment = (
-            speedup_experiment
-            if args.backend not in ["torchao", "optimus"]
-            else latency_experiment
+            speedup_experiment if args.backend != "torchao" else latency_experiment
         )
         if args.accuracy:
             output_filename = f"accuracy_{args.backend}.csv"
@@ -4115,12 +4091,7 @@ def run(runner, args, original_dir=None):
     if args.only in runner.disable_cudagraph_models:
         args.disable_cudagraphs = True
 
-    if (
-        args.inductor
-        or args.backend == "inductor"
-        or args.export_aot_inductor
-        or args.backend == "optimus"
-    ):
+    if args.inductor or args.backend == "inductor" or args.export_aot_inductor:
         inductor_config.triton.cudagraphs = not args.disable_cudagraphs
         inductor_config.triton.persistent_reductions = (
             not args.disable_persistent_reductions

benchmarks/dynamo/optimus.py

@@ -1,62 +0,0 @@
-import functools
-
-import torch
-
-
-def get_baseline_ctx(nopython, inductor_compile_mode):
-    return functools.partial(
-        torch.compile,
-        backend="inductor",
-        fullgraph=nopython,
-        mode=inductor_compile_mode,
-    )
-
-
-def get_optimus_optimize_ctx(config, nopython, inductor_compile_mode):
-    if config == "vertical_opt":
-        optimus_inductor_config = {
-            "pre_grad_fusion_options": {
-                "normalization_pass": {},
-                "merge_splits_pass": {},
-                "split_cat_pass": {},
-                "unbind_stack_pass": {},
-                "unbind_cat_to_view_pass": {},
-            }
-        }
-    elif config == "horizontal_opt":
-        optimus_inductor_config = {
-            "pre_grad_fusion_options": {
-                "normalization_pass": {},
-                "batch_linear": {},
-                "batch_layernorm": {},
-            },
-        }
-    elif config == "all":
-        optimus_inductor_config = {
-            "pre_grad_fusion_options": {
-                "normalization_pass": {},
-                "batch_linear": {},
-                "batch_layernorm": {},
-                "merge_splits_pass": {},
-                "split_cat_pass": {},
-                "unbind_stack_pass": {},
-                "unbind_cat_to_view_pass": {},
-            },
-        }
-    else:
-        raise RuntimeError(f"Unknown optimus config: {config}")
-
-    def _inner(fn):
-        if "pre_grad_fusion_options" in optimus_inductor_config:
-            torch._inductor.config.pre_grad_fusion_options = optimus_inductor_config[
-                "pre_grad_fusion_options"
-            ]
-        if "post_grad_fusion_options" in optimus_inductor_config:
-            torch._inductor.config.post_grad_fusion_options = optimus_inductor_config[
-                "post_grad_fusion_options"
-            ]
-        return torch.compile(
-            fn, backend="inductor", fullgraph=nopython, mode=inductor_compile_mode
-        )
-
-    return _inner

benchmarks/dynamo/parse_logs.py

@@ -2,7 +2,6 @@ import csv
 import os
 import re
 import sys
-from pathlib import Path
 
 
 # This script takes the logs produced by the benchmark scripts (e.g.,
@@ -16,7 +15,8 @@ from pathlib import Path
 # This script is not very well written, feel free to rewrite it as necessary
 
 assert len(sys.argv) == 2
-full_log = Path(sys.argv[1]).read_text()
+
+full_log = open(sys.argv[1]).read()
 
 # If the log contains a gist URL, extract it so we can include it in the CSV
 gist_url = ""

benchmarks/operator_benchmark/aarch64_expected_ci_operator_benchmark_eager_float32_cpu.csv

@@ -484,106 +484,24 @@ PyTorch,sum,sum_R256_V512_dim0_contiguousTrue_cpu,short,False,50.954394,0.000000
 PyTorch,sum,sum_R256_V512_dim0_contiguousFalse_cpu,short,False,57.957757,0.000000
 PyTorch,sum,sum_R256_V512_dim1_contiguousTrue_cpu,short,False,53.592068,0.000000
 PyTorch,sum,sum_R256_V512_dim1_contiguousFalse_cpu,short,False,51.339726,0.000000
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.bool_dtype_twotorch.bool,short,False,0.927,0.000000
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.bool_dtype_twotorch.uint8,short,False,6.261,0.000000
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.bool_dtype_twotorch.int8,short,False,6.351,0.000000
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.bool_dtype_twotorch.int16,short,False,6.177,0.000000
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.bool_dtype_twotorch.int32,short,False,6.333,0.000000
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.bool_dtype_twotorch.int64,short,False,6.588,0.000000
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.bool_dtype_twotorch.float16,short,False,8.117,0.000000
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.bool_dtype_twotorch.bfloat16,short,False,9.358,0.000000
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.bool_dtype_twotorch.float32,short,False,7.844,0.000000
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.bool_dtype_twotorch.float64,short,False,8.097,0.000000
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.uint8_dtype_twotorch.bool,short,False,6.159,0.000000
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.uint8_dtype_twotorch.uint8,short,False,0.926,0.000000
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.uint8_dtype_twotorch.int8,short,False,6.192,0.000000
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.uint8_dtype_twotorch.int16,short,False,6.276,0.000000
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.uint8_dtype_twotorch.int32,short,False,6.461,0.000000
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.uint8_dtype_twotorch.int64,short,False,6.524,0.000000
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.uint8_dtype_twotorch.float16,short,False,8.136,0.000000
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.uint8_dtype_twotorch.bfloat16,short,False,6.854,0.000000
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.uint8_dtype_twotorch.float32,short,False,6.446,0.000000
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.uint8_dtype_twotorch.float64,short,False,6.829,0.000000
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.int8_dtype_twotorch.bool,short,False,6.088,0.000000
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.int8_dtype_twotorch.uint8,short,False,6.059,0.000000
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.int8_dtype_twotorch.int8,short,False,0.922,0.000000
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.int8_dtype_twotorch.int16,short,False,6.263,0.000000
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.int8_dtype_twotorch.int32,short,False,6.330,0.000000
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.int8_dtype_twotorch.int64,short,False,6.688,0.000000
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.int8_dtype_twotorch.float16,short,False,8.176,0.000000
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.int8_dtype_twotorch.bfloat16,short,False,6.959,0.000000
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.int8_dtype_twotorch.float32,short,False,6.430,0.000000
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.int8_dtype_twotorch.float64,short,False,6.818,0.000000
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.int16_dtype_twotorch.bool,short,False,6.350,0.000000
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.int16_dtype_twotorch.uint8,short,False,6.221,0.000000
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.int16_dtype_twotorch.int8,short,False,6.193,0.000000
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.int16_dtype_twotorch.int16,short,False,0.922,0.000000
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.int16_dtype_twotorch.int32,short,False,6.263,0.000000
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.int16_dtype_twotorch.int64,short,False,6.525,0.000000
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.int16_dtype_twotorch.float16,short,False,7.960,0.000000
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.int16_dtype_twotorch.bfloat16,short,False,6.801,0.000000
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.int16_dtype_twotorch.float32,short,False,6.594,0.000000
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.int16_dtype_twotorch.float64,short,False,7.089,0.000000
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.int32_dtype_twotorch.bool,short,False,6.498,0.000000
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.int32_dtype_twotorch.uint8,short,False,6.358,0.000000
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.int32_dtype_twotorch.int8,short,False,6.390,0.000000
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.int32_dtype_twotorch.int16,short,False,6.415,0.000000
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.int32_dtype_twotorch.int32,short,False,0.925,0.000000
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.int32_dtype_twotorch.int64,short,False,6.657,0.000000
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.int32_dtype_twotorch.float16,short,False,7.954,0.000000
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.int32_dtype_twotorch.bfloat16,short,False,6.930,0.000000
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.int32_dtype_twotorch.float32,short,False,6.737,0.000000
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.int32_dtype_twotorch.float64,short,False,6.948,0.000000
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.int64_dtype_twotorch.bool,short,False,6.757,0.000000
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.int64_dtype_twotorch.uint8,short,False,6.402,0.000000
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.int64_dtype_twotorch.int8,short,False,6.550,0.000000
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.int64_dtype_twotorch.int16,short,False,6.518,0.000000
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.int64_dtype_twotorch.int32,short,False,6.766,0.000000
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.int64_dtype_twotorch.int64,short,False,0.929,0.000000
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.int64_dtype_twotorch.float16,short,False,8.557,0.000000
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.int64_dtype_twotorch.bfloat16,short,False,9.045,0.000000
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.int64_dtype_twotorch.float32,short,False,7.672,0.000000
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.int64_dtype_twotorch.float64,short,False,7.276,0.000000
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.float16_dtype_twotorch.bool,short,False,6.414,0.000000
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.float16_dtype_twotorch.uint8,short,False,7.736,0.000000
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.float16_dtype_twotorch.int8,short,False,7.889,0.000000
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.float16_dtype_twotorch.int16,short,False,8.170,0.000000
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.float16_dtype_twotorch.int32,short,False,7.783,0.000000
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.float16_dtype_twotorch.int64,short,False,7.743,0.000000
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.float16_dtype_twotorch.float16,short,False,0.927,0.000000
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.float16_dtype_twotorch.bfloat16,short,False,7.018,0.000000
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.float16_dtype_twotorch.float32,short,False,8.428,0.000000
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.float16_dtype_twotorch.float64,short,False,6.767,0.000000
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.bfloat16_dtype_twotorch.bool,short,False,6.479,0.000000
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.bfloat16_dtype_twotorch.uint8,short,False,7.827,0.000000
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.bfloat16_dtype_twotorch.int8,short,False,6.450,0.000000
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.bfloat16_dtype_twotorch.int16,short,False,6.320,0.000000
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.bfloat16_dtype_twotorch.int32,short,False,6.385,0.000000
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.bfloat16_dtype_twotorch.int64,short,False,8.119,0.000000
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.bfloat16_dtype_twotorch.float16,short,False,8.063,0.000000
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.bfloat16_dtype_twotorch.bfloat16,short,False,0.925,0.000000
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.bfloat16_dtype_twotorch.float32,short,False,8.629,0.000000
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.bfloat16_dtype_twotorch.float64,short,False,6.638,0.000000
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.float32_dtype_twotorch.bool,short,False,6.425,0.000000
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.float32_dtype_twotorch.uint8,short,False,7.803,0.000000
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.float32_dtype_twotorch.int8,short,False,6.502,0.000000
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.float32_dtype_twotorch.int16,short,False,6.429,0.000000
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.float32_dtype_twotorch.int32,short,False,6.549,0.000000
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.float32_dtype_twotorch.int64,short,False,7.749,0.000000
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.float32_dtype_twotorch.float16,short,False,7.301,0.000000
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.float32_dtype_twotorch.bfloat16,short,False,7.682,0.000000
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.float32_dtype_twotorch.float32,short,False,0.930,0.000000
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.float32_dtype_twotorch.float64,short,False,6.738,0.000000
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.float64_dtype_twotorch.bool,short,False,6.798,0.000000
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.float64_dtype_twotorch.uint8,short,False,6.506,0.000000
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.float64_dtype_twotorch.int8,short,False,6.494,0.000000
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.float64_dtype_twotorch.int16,short,False,6.668,0.000000
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.float64_dtype_twotorch.int32,short,False,6.696,0.000000
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.float64_dtype_twotorch.int64,short,False,7.115,0.000000
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.float64_dtype_twotorch.float16,short,False,7.910,0.000000
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.float64_dtype_twotorch.bfloat16,short,False,7.410,0.000000
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.float64_dtype_twotorch.float32,short,False,6.868,0.000000
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.float64_dtype_twotorch.float64,short,False,0.924,0.000000
+PyTorch,FloatToHalfTensorConversionBenchmark,FloatToHalfTensorConversionBenchmark_M8_N16_cpu,short,False,7.040985,0.000000
+PyTorch,FloatToHalfTensorConversionBenchmark,FloatToHalfTensorConversionBenchmark_M8_N64_cpu,short,False,7.168604,0.000000
+PyTorch,FloatToHalfTensorConversionBenchmark,FloatToHalfTensorConversionBenchmark_M8_N128_cpu,short,False,7.434442,0.000000
+PyTorch,FloatToHalfTensorConversionBenchmark,FloatToHalfTensorConversionBenchmark_M16_N16_cpu,short,False,7.078318,0.000000
+PyTorch,FloatToHalfTensorConversionBenchmark,FloatToHalfTensorConversionBenchmark_M16_N64_cpu,short,False,7.426670,0.000000
+PyTorch,FloatToHalfTensorConversionBenchmark,FloatToHalfTensorConversionBenchmark_M16_N128_cpu,short,False,7.679027,0.000000
+PyTorch,FloatToHalfTensorConversionBenchmark,FloatToHalfTensorConversionBenchmark_M32_N16_cpu,short,False,7.281365,0.000000
+PyTorch,FloatToHalfTensorConversionBenchmark,FloatToHalfTensorConversionBenchmark_M32_N64_cpu,short,False,7.682783,0.000000
+PyTorch,FloatToHalfTensorConversionBenchmark,FloatToHalfTensorConversionBenchmark_M32_N128_cpu,short,False,8.381938,0.000000
+PyTorch,HalfToFloatTensorConversionBenchmark,HalfToFloatTensorConversionBenchmark_M8_N16_cpu,short,False,7.039854,0.000000
+PyTorch,HalfToFloatTensorConversionBenchmark,HalfToFloatTensorConversionBenchmark_M8_N64_cpu,short,False,7.399855,0.000000
+PyTorch,HalfToFloatTensorConversionBenchmark,HalfToFloatTensorConversionBenchmark_M8_N128_cpu,short,False,7.715193,0.000000
+PyTorch,HalfToFloatTensorConversionBenchmark,HalfToFloatTensorConversionBenchmark_M16_N16_cpu,short,False,7.255140,0.000000
+PyTorch,HalfToFloatTensorConversionBenchmark,HalfToFloatTensorConversionBenchmark_M16_N64_cpu,short,False,7.753522,0.000000
+PyTorch,HalfToFloatTensorConversionBenchmark,HalfToFloatTensorConversionBenchmark_M16_N128_cpu,short,False,8.364281,0.000000
+PyTorch,HalfToFloatTensorConversionBenchmark,HalfToFloatTensorConversionBenchmark_M32_N16_cpu,short,False,7.476377,0.000000
+PyTorch,HalfToFloatTensorConversionBenchmark,HalfToFloatTensorConversionBenchmark_M32_N64_cpu,short,False,8.458564,0.000000
+PyTorch,HalfToFloatTensorConversionBenchmark,HalfToFloatTensorConversionBenchmark_M32_N128_cpu,short,False,9.391939,0.000000
 PyTorch,addcmul,addcmul_M1_N2_cpu_dtypetorch.float32,short,False,4.461410,0.000000
 PyTorch,addcmul,addcmul_M1_N2_cpu_dtypetorch.bfloat16,short,False,4.560082,0.000000
 PyTorch,addcmul,addcmul_M32_N64_cpu_dtypetorch.float32,short,False,5.141248,0.000000

benchmarks/operator_benchmark/pt/tensor_to_test.py

@@ -4,84 +4,74 @@ import torch
 
 
 tensor_conversion_short_configs = op_bench.cross_product_configs(
-    M=[32],
-    N=[128],
+    M=(
+        8,
+        16,
+        32,
+    ),
+    N=(
+        16,
+        64,
+        128,
+    ),
     device=["cpu", "cuda"],
-    dtype_one=[
-        torch.bool,
-        torch.uint8,
-        torch.int8,
-        torch.int16,
-        torch.int32,
-        torch.int64,
-        torch.half,
-        torch.bfloat16,
-        torch.float,
-        torch.double,
-    ],
-    dtype_two=[
-        torch.bool,
-        torch.uint8,
-        torch.int8,
-        torch.int16,
-        torch.int32,
-        torch.int64,
-        torch.half,
-        torch.bfloat16,
-        torch.float,
-        torch.double,
-    ],
     tags=["short"],
 )
 
 tensor_conversion_long_configs = op_bench.cross_product_configs(
-    M=[1024],
-    N=[1024],
+    M=(
+        64,
+        128,
+        256,
+        512,
+    ),
+    N=(
+        256,
+        512,
+        1024,
+        2048,
+    ),
     device=["cpu", "cuda"],
-    dtype_one=[
-        torch.bool,
-        torch.uint8,
-        torch.int8,
-        torch.int16,
-        torch.int32,
-        torch.int64,
-        torch.half,
-        torch.bfloat16,
-        torch.float,
-        torch.double,
-    ],
-    dtype_two=[
-        torch.bool,
-        torch.uint8,
-        torch.int8,
-        torch.int16,
-        torch.int32,
-        torch.int64,
-        torch.half,
-        torch.bfloat16,
-        torch.float,
-        torch.double,
-    ],
     tags=["long"],
 )
 
 
-class TensorConversionBenchmark(op_bench.TorchBenchmarkBase):
-    def init(self, M, N, dtype_one, dtype_two, device):
+class FloatToHalfTensorConversionBenchmark(op_bench.TorchBenchmarkBase):
+    def init(self, M, N, device):
         self.inputs = {
             "input": torch.rand(
                 M, N, device=device, requires_grad=False, dtype=torch.float
-            ).to(dtype=dtype_one)
+            )
         }
-        self.dtype_one = dtype_one
-        self.dtype_two = dtype_two
 
     def forward(self, input):
-        return input.to(dtype=self.dtype_two)
+        return input.to(torch.half)
 
 
-op_bench.generate_pt_test(tensor_conversion_short_configs, TensorConversionBenchmark)
-op_bench.generate_pt_test(tensor_conversion_long_configs, TensorConversionBenchmark)
+class HalfToFloatTensorConversionBenchmark(op_bench.TorchBenchmarkBase):
+    def init(self, M, N, device):
+        self.inputs = {
+            "input": torch.rand(
+                M, N, device=device, requires_grad=False, dtype=torch.half
+            )
+        }
+
+    def forward(self, input):
+        return input.to(torch.float)
+
+
+op_bench.generate_pt_test(
+    tensor_conversion_short_configs, FloatToHalfTensorConversionBenchmark
+)
+op_bench.generate_pt_test(
+    tensor_conversion_long_configs, FloatToHalfTensorConversionBenchmark
+)
+op_bench.generate_pt_test(
+    tensor_conversion_short_configs, HalfToFloatTensorConversionBenchmark
+)
+op_bench.generate_pt_test(
+    tensor_conversion_long_configs, HalfToFloatTensorConversionBenchmark
+)
 
 if __name__ == "__main__":
     op_bench.benchmark_runner.main()

benchmarks/operator_benchmark/x86_64_expected_ci_operator_benchmark_eager_float32_cpu.csv

@@ -349,106 +349,24 @@ PyTorch,sum,sum_R256_V512_dim0_contiguousTrue_cpu,short,FALSE,12.5841
 PyTorch,sum,sum_R256_V512_dim0_contiguousFALSE_cpu,short,FALSE,20.8765
 PyTorch,sum,sum_R256_V512_dim1_contiguousTrue_cpu,short,FALSE,15.4414
 PyTorch,sum,sum_R256_V512_dim1_contiguousFALSE_cpu,short,FALSE,15.3287
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.bool_dtype_twotorch.bool,short,False,0.797
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.bool_dtype_twotorch.uint8,short,False,6.071
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.bool_dtype_twotorch.int8,short,False,6.031
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.bool_dtype_twotorch.int16,short,False,6.243
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.bool_dtype_twotorch.int32,short,False,7.231
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.bool_dtype_twotorch.int64,short,False,7.791
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.bool_dtype_twotorch.float16,short,False,12.661
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.bool_dtype_twotorch.bfloat16,short,False,11.225
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.bool_dtype_twotorch.float32,short,False,9.772
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.bool_dtype_twotorch.float64,short,False,9.872
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.uint8_dtype_twotorch.bool,short,False,6.033
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.uint8_dtype_twotorch.uint8,short,False,0.781
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.uint8_dtype_twotorch.int8,short,False,6.060
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.uint8_dtype_twotorch.int16,short,False,6.180
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.uint8_dtype_twotorch.int32,short,False,7.258
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.uint8_dtype_twotorch.int64,short,False,7.758
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.uint8_dtype_twotorch.float16,short,False,10.504
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.uint8_dtype_twotorch.bfloat16,short,False,6.749
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.uint8_dtype_twotorch.float32,short,False,7.679
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.uint8_dtype_twotorch.float64,short,False,7.797
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.int8_dtype_twotorch.bool,short,False,6.019
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.int8_dtype_twotorch.uint8,short,False,6.079
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.int8_dtype_twotorch.int8,short,False,0.785
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.int8_dtype_twotorch.int16,short,False,6.188
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.int8_dtype_twotorch.int32,short,False,7.288
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.int8_dtype_twotorch.int64,short,False,7.770
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.int8_dtype_twotorch.float16,short,False,10.466
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.int8_dtype_twotorch.bfloat16,short,False,6.676
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.int8_dtype_twotorch.float32,short,False,7.736
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.int8_dtype_twotorch.float64,short,False,7.780
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.int16_dtype_twotorch.bool,short,False,6.130
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.int16_dtype_twotorch.uint8,short,False,6.221
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.int16_dtype_twotorch.int8,short,False,6.101
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.int16_dtype_twotorch.int16,short,False,0.791
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.int16_dtype_twotorch.int32,short,False,6.254
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.int16_dtype_twotorch.int64,short,False,7.733
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.int16_dtype_twotorch.float16,short,False,10.562
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.int16_dtype_twotorch.bfloat16,short,False,6.704
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.int16_dtype_twotorch.float32,short,False,7.819
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.int16_dtype_twotorch.float64,short,False,8.276
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.int32_dtype_twotorch.bool,short,False,6.361
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.int32_dtype_twotorch.uint8,short,False,6.364
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.int32_dtype_twotorch.int8,short,False,6.309
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.int32_dtype_twotorch.int16,short,False,6.362
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.int32_dtype_twotorch.int32,short,False,0.791
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.int32_dtype_twotorch.int64,short,False,7.746
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.int32_dtype_twotorch.float16,short,False,9.462
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.int32_dtype_twotorch.bfloat16,short,False,6.678
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.int32_dtype_twotorch.float32,short,False,7.827
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.int32_dtype_twotorch.float64,short,False,8.200
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.int64_dtype_twotorch.bool,short,False,6.925
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.int64_dtype_twotorch.uint8,short,False,6.947
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.int64_dtype_twotorch.int8,short,False,6.962
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.int64_dtype_twotorch.int16,short,False,6.906
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.int64_dtype_twotorch.int32,short,False,7.664
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.int64_dtype_twotorch.int64,short,False,0.782
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.int64_dtype_twotorch.float16,short,False,10.528
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.int64_dtype_twotorch.bfloat16,short,False,10.123
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.int64_dtype_twotorch.float32,short,False,9.234
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.int64_dtype_twotorch.float64,short,False,8.694
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.float16_dtype_twotorch.bool,short,False,12.653
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.float16_dtype_twotorch.uint8,short,False,9.348
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.float16_dtype_twotorch.int8,short,False,8.774
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.float16_dtype_twotorch.int16,short,False,9.063
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.float16_dtype_twotorch.int32,short,False,10.012
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.float16_dtype_twotorch.int64,short,False,13.641
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.float16_dtype_twotorch.float16,short,False,0.788
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.float16_dtype_twotorch.bfloat16,short,False,13.757
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.float16_dtype_twotorch.float32,short,False,7.170
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.float16_dtype_twotorch.float64,short,False,12.511
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.bfloat16_dtype_twotorch.bool,short,False,6.516
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.bfloat16_dtype_twotorch.uint8,short,False,8.539
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.bfloat16_dtype_twotorch.int8,short,False,6.483
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.bfloat16_dtype_twotorch.int16,short,False,6.468
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.bfloat16_dtype_twotorch.int32,short,False,7.752
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.bfloat16_dtype_twotorch.int64,short,False,9.868
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.bfloat16_dtype_twotorch.float16,short,False,10.556
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.bfloat16_dtype_twotorch.bfloat16,short,False,0.792
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.bfloat16_dtype_twotorch.float32,short,False,7.577
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.bfloat16_dtype_twotorch.float64,short,False,8.267
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.float32_dtype_twotorch.bool,short,False,6.819
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.float32_dtype_twotorch.uint8,short,False,7.715
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.float32_dtype_twotorch.int8,short,False,6.754
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.float32_dtype_twotorch.int16,short,False,6.825
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.float32_dtype_twotorch.int32,short,False,7.790
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.float32_dtype_twotorch.int64,short,False,9.219
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.float32_dtype_twotorch.float16,short,False,5.977
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.float32_dtype_twotorch.bfloat16,short,False,7.069
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.float32_dtype_twotorch.float32,short,False,0.794
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.float32_dtype_twotorch.float64,short,False,8.301
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.float64_dtype_twotorch.bool,short,False,7.401
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.float64_dtype_twotorch.uint8,short,False,7.843
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.float64_dtype_twotorch.int8,short,False,7.117
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.float64_dtype_twotorch.int16,short,False,7.170
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.float64_dtype_twotorch.int32,short,False,8.000
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.float64_dtype_twotorch.int64,short,False,9.284
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.float64_dtype_twotorch.float16,short,False,7.179
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.float64_dtype_twotorch.bfloat16,short,False,7.645
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.float64_dtype_twotorch.float32,short,False,7.988
-PyTorch,TensorConversionBenchmark,TensorConversionBenchmark_M32_N128_cpu_dtype_onetorch.float64_dtype_twotorch.float64,short,False,0.792
+PyTorch,FloatToHalfTensorConversionBenchmark,FloatToHalfTensorConversionBenchmark_M8_N16_cpu,short,FALSE,5.0499
+PyTorch,FloatToHalfTensorConversionBenchmark,FloatToHalfTensorConversionBenchmark_M8_N64_cpu,short,FALSE,5.3229
+PyTorch,FloatToHalfTensorConversionBenchmark,FloatToHalfTensorConversionBenchmark_M8_N128_cpu,short,FALSE,5.4418
+PyTorch,FloatToHalfTensorConversionBenchmark,FloatToHalfTensorConversionBenchmark_M16_N16_cpu,short,FALSE,5.0868
+PyTorch,FloatToHalfTensorConversionBenchmark,FloatToHalfTensorConversionBenchmark_M16_N64_cpu,short,FALSE,5.4495
+PyTorch,FloatToHalfTensorConversionBenchmark,FloatToHalfTensorConversionBenchmark_M16_N128_cpu,short,FALSE,5.5578
+PyTorch,FloatToHalfTensorConversionBenchmark,FloatToHalfTensorConversionBenchmark_M32_N16_cpu,short,FALSE,5.2631
+PyTorch,FloatToHalfTensorConversionBenchmark,FloatToHalfTensorConversionBenchmark_M32_N64_cpu,short,FALSE,5.5646
+PyTorch,FloatToHalfTensorConversionBenchmark,FloatToHalfTensorConversionBenchmark_M32_N128_cpu,short,FALSE,5.7898
+PyTorch,HalfToFloatTensorConversionBenchmark,HalfToFloatTensorConversionBenchmark_M8_N16_cpu,short,FALSE,5.0228
+PyTorch,HalfToFloatTensorConversionBenchmark,HalfToFloatTensorConversionBenchmark_M8_N64_cpu,short,FALSE,5.3692
+PyTorch,HalfToFloatTensorConversionBenchmark,HalfToFloatTensorConversionBenchmark_M8_N128_cpu,short,FALSE,5.4006
+PyTorch,HalfToFloatTensorConversionBenchmark,HalfToFloatTensorConversionBenchmark_M16_N16_cpu,short,FALSE,5.1107
+PyTorch,HalfToFloatTensorConversionBenchmark,HalfToFloatTensorConversionBenchmark_M16_N64_cpu,short,FALSE,5.4119
+PyTorch,HalfToFloatTensorConversionBenchmark,HalfToFloatTensorConversionBenchmark_M16_N128_cpu,short,FALSE,5.5583
+PyTorch,HalfToFloatTensorConversionBenchmark,HalfToFloatTensorConversionBenchmark_M32_N16_cpu,short,FALSE,5.3818
+PyTorch,HalfToFloatTensorConversionBenchmark,HalfToFloatTensorConversionBenchmark_M32_N64_cpu,short,FALSE,5.5742
+PyTorch,HalfToFloatTensorConversionBenchmark,HalfToFloatTensorConversionBenchmark_M32_N128_cpu,short,FALSE,6.8414
 PyTorch,relu,"relu_dims(3,4,5)_contigFALSE_inplaceFALSE_dtypetorch.quint8",short,FALSE,9.4657
 PyTorch,relu,"relu_dims(3,4,5)_contigFALSE_inplaceFALSE_dtypetorch.qint8",short,FALSE,9.4625
 PyTorch,relu,"relu_dims(3,4,5)_contigFALSE_inplaceFALSE_dtypetorch.qint32",short,FALSE,9.4165

benchmarks/sparse/spmm.py

@@ -52,18 +52,19 @@ def test_sparse_coo_and_csr(m, n, k, nnz, test_count):
         start.record()
         coo.matmul(mat)
         stop.record()
+
         times.append(start.elapsed_time(stop))
 
-    coo_mean_time = sum(times) / len(times)
+        coo_mean_time = sum(times) / len(times)
 
-    times = []
-    for _ in range(test_count):
-        start.record()
-        csr.matmul(mat)
-        stop.record()
-        times.append(start.elapsed_time(stop))
+        times = []
+        for _ in range(test_count):
+            start.record()
+            csr.matmul(mat)
+            stop.record()
+            times.append(start.elapsed_time(stop))
 
-    csr_mean_time = sum(times) / len(times)
+            csr_mean_time = sum(times) / len(times)
 
     return coo_mean_time, csr_mean_time
 
@@ -83,13 +84,10 @@ if __name__ == "__main__":
 
     if args.outfile == "stdout":
         outfile = sys.stdout
-        need_close = False
     elif args.outfile == "stderr":
         outfile = sys.stderr
-        need_close = False
     else:
         outfile = open(args.outfile, "a")
-        need_close = True
 
     test_count = args.test_count
     m = args.m
@@ -150,5 +148,3 @@ if __name__ == "__main__":
             time,
             file=outfile,
         )
-    if need_close:
-        outfile.close()

benchmarks/sparse/spmv.py

@@ -82,13 +82,10 @@ if __name__ == "__main__":
 
     if args.outfile == "stdout":
         outfile = sys.stdout
-        need_close = False
     elif args.outfile == "stderr":
         outfile = sys.stderr
-        need_close = False
     else:
         outfile = open(args.outfile, "a")
-        need_close = True
 
     test_count = args.test_count
     m = args.m
@@ -135,5 +132,3 @@ if __name__ == "__main__":
             time_csr,
             file=outfile,
         )
-    if need_close:
-        outfile.close()

benchmarks/sparse/triton_ops.py

@@ -179,13 +179,10 @@ if __name__ == "__main__":
 
     if args.outfile == "stdout":
         outfile = sys.stdout
-        need_close = False
     elif args.outfile == "stderr":
         outfile = sys.stderr
-        need_close = False
     else:
         outfile = open(args.outfile, "a")
-        need_close = True
 
     ops = args.ops.split(",")
 
@@ -437,5 +434,3 @@ if __name__ == "__main__":
                 if op not in {"bsr_scatter_mm6", "bsr_dense_mm_with_meta"}:
                     # Break on operations that do not consume parameters
                     break
-    if need_close:
-        outfile.close()

c10/core/AutogradState.h

@@ -1,8 +1,6 @@
 #pragma once
 
-#include <c10/core/SafePyObject.h>
 #include <c10/macros/Export.h>
-#include <optional>
 
 namespace c10 {
 
@@ -17,8 +15,7 @@ struct C10_API AutogradState {
       bool inference_mode,
       bool fw_grad_mode,
       bool multithreading_enabled)
-      : graph_exec_group_(std::nullopt),
-        grad_mode_(grad_mode),
+      : grad_mode_(grad_mode),
         inference_mode_(inference_mode),
         fw_grad_mode_(fw_grad_mode),
         multithreading_enabled_(multithreading_enabled),
@@ -44,10 +41,6 @@ struct C10_API AutogradState {
     view_replay_enabled_ = view_replay_enabled;
   }
 
-  void set_graph_exec_group(std::optional<SafePyObject> group) {
-    graph_exec_group_ = std::move(group);
-  }
-
   bool get_grad_mode() const {
     return grad_mode_;
   }
@@ -68,12 +61,7 @@ struct C10_API AutogradState {
     return view_replay_enabled_;
   }
 
-  const std::optional<SafePyObject>& get_graph_exec_group() const {
-    return graph_exec_group_;
-  }
-
  private:
-  std::optional<SafePyObject> graph_exec_group_;
   bool grad_mode_ : 1;
   bool inference_mode_ : 1;
   bool fw_grad_mode_ : 1;