Add check for MacOS26 to use different code path in SDPA

This reverts commit f2e6f94081c02704adf027fe0c81bf25726828f5.

Reverted https://github.com/pytorch/pytorch/pull/167198 on behalf of https://github.com/yangw-dev due to synced with author, this breaks internal builds ([comment](https://github.com/pytorch/pytorch/pull/167198#issuecomment-3544065659))

.ci/aarch64_linux/README.md

@@ -1,19 +0,0 @@
-# Aarch64 (ARM/Graviton) Support Scripts
-Scripts for building aarch64 PyTorch PIP Wheels. These scripts build the following wheels:
-* torch
-* torchvision
-* torchaudio
-* torchtext
-* torchdata
-## Aarch64_ci_build.sh
-This script is design to support CD operations within PyPi manylinux aarch64 container, and be executed in the container. It prepares the container and then executes __aarch64_wheel_ci_build.py__ to build the wheels. The script "assumes" the PyTorch repo is located at: ```/pytorch``` and will put the wheels into ```/artifacts```.
-### Usage
-```DESIRED_PYTHON=<PythonVersion> aarch64_ci_build.sh```
-
-__NOTE:__ CI build is currently __EXPERMINTAL__
-
-## Build_aarch64_wheel.py
-This app allows a person to build using AWS EC3 resources and requires AWS-CLI and Boto3 with AWS credentials to support building EC2 instances for the wheel builds. Can be used in a codebuild CD or from a local system.
-
-### Usage
-```build_aarch64_wheel.py --key-name <YourPemKey> --use-docker --python 3.8 --branch <RCtag>```

.ci/aarch64_linux/aarch64_ci_build.sh

@@ -1,53 +0,0 @@
-#!/bin/bash
-set -eux -o pipefail
-
-GPU_ARCH_VERSION=${GPU_ARCH_VERSION:-}
-
-# Set CUDA architecture lists to match x86 build_cuda.sh
-if [[ "$GPU_ARCH_VERSION" == *"12.6"* ]]; then
-    export TORCH_CUDA_ARCH_LIST="8.0;9.0"
-elif [[ "$GPU_ARCH_VERSION" == *"12.8"* ]]; then
-    export TORCH_CUDA_ARCH_LIST="8.0;9.0;10.0;12.0"
-elif [[ "$GPU_ARCH_VERSION" == *"12.9"* ]]; then
-    export TORCH_CUDA_ARCH_LIST="8.0;9.0;10.0;12.0"
-elif [[ "$GPU_ARCH_VERSION" == *"13.0"* ]]; then
-    export TORCH_CUDA_ARCH_LIST="8.0;9.0;10.0;11.0;12.0+PTX"
-fi
-
-# Compress the fatbin with -compress-mode=size for CUDA 13
-if [[ "$DESIRED_CUDA" == *"13"* ]]; then
-    export TORCH_NVCC_FLAGS="-compress-mode=size"
-    # Bundle ptxas into the cu13 wheel, see https://github.com/pytorch/pytorch/issues/163801
-    export BUILD_BUNDLE_PTXAS=1
-fi
-
-SCRIPTPATH="$( cd -- "$(dirname "$0")" >/dev/null 2>&1 ; pwd -P )"
-source $SCRIPTPATH/aarch64_ci_setup.sh
-
-###############################################################################
-# Run aarch64 builder python
-###############################################################################
-cd /
-# adding safe directory for git as the permissions will be
-# on the mounted pytorch repo
-git config --global --add safe.directory /pytorch
-pip install -r /pytorch/requirements.txt
-pip install auditwheel==6.2.0 wheel
-if [ "$DESIRED_CUDA" = "cpu" ]; then
-    echo "BASE_CUDA_VERSION is not set. Building cpu wheel."
-    python /pytorch/.ci/aarch64_linux/aarch64_wheel_ci_build.py --enable-mkldnn
-else
-    echo "BASE_CUDA_VERSION is set to: $DESIRED_CUDA"
-    export USE_SYSTEM_NCCL=1
-
-    # Check if we should use NVIDIA libs from PyPI (similar to x86 build_cuda.sh logic)
-    if [[ -z "$PYTORCH_EXTRA_INSTALL_REQUIREMENTS" ]]; then
-        echo "Bundling CUDA libraries with wheel for aarch64."
-    else
-        echo "Using nvidia libs from pypi for aarch64."
-        echo "Updated PYTORCH_EXTRA_INSTALL_REQUIREMENTS for aarch64: $PYTORCH_EXTRA_INSTALL_REQUIREMENTS"
-        export USE_NVIDIA_PYPI_LIBS=1
-    fi
-
-    python /pytorch/.ci/aarch64_linux/aarch64_wheel_ci_build.py --enable-mkldnn --enable-cuda
-fi

.ci/aarch64_linux/aarch64_ci_setup.sh

@@ -1,21 +0,0 @@
-#!/bin/bash
-set -eux -o pipefail
-
-# This script is used to prepare the Docker container for aarch64_ci_wheel_build.py python script
-# By creating symlinks from desired /opt/python to /usr/local/bin/
-
-NUMPY_VERSION=2.0.2
-if [[ "$DESIRED_PYTHON"  == "3.13" || "$DESIRED_PYTHON" == "3.13t" ]]; then
-    NUMPY_VERSION=2.1.2
-fi
-
-SCRIPTPATH="$( cd "$(dirname "$0")" ; pwd -P )"
-source $SCRIPTPATH/../manywheel/set_desired_python.sh
-
-pip install -q numpy==${NUMPY_VERSION} pyyaml==6.0.2 scons==4.7.0 ninja==1.11.1 patchelf==0.17.2
-
-for tool in python python3 pip pip3 ninja scons patchelf; do
-    ln -sf ${DESIRED_PYTHON_BIN_DIR}/${tool} /usr/local/bin;
-done
-
-python --version

.ci/aarch64_linux/aarch64_wheel_ci_build.py

@@ -1,333 +0,0 @@
-#!/usr/bin/env python3
-# encoding: UTF-8
-
-import os
-import shutil
-from subprocess import check_call, check_output
-
-
-def list_dir(path: str) -> list[str]:
-    """'
-    Helper for getting paths for Python
-    """
-    return check_output(["ls", "-1", path]).decode().split("\n")
-
-
-def replace_tag(filename) -> None:
-    with open(filename) as f:
-        lines = f.readlines()
-    for i, line in enumerate(lines):
-        if line.startswith("Tag:"):
-            lines[i] = line.replace("-linux_", "-manylinux_2_28_")
-            print(f"Updated tag from {line} to {lines[i]}")
-            break
-
-    with open(filename, "w") as f:
-        f.writelines(lines)
-
-
-def patch_library_rpath(
-    folder: str,
-    lib_name: str,
-    use_nvidia_pypi_libs: bool = False,
-    desired_cuda: str = "",
-) -> None:
-    """Apply patchelf to set RPATH for a library in torch/lib"""
-    lib_path = f"{folder}/tmp/torch/lib/{lib_name}"
-
-    if use_nvidia_pypi_libs:
-        # For PyPI NVIDIA libraries, construct CUDA RPATH
-        cuda_rpaths = [
-            "$ORIGIN/../../nvidia/cudnn/lib",
-            "$ORIGIN/../../nvidia/nvshmem/lib",
-            "$ORIGIN/../../nvidia/nccl/lib",
-            "$ORIGIN/../../nvidia/cusparselt/lib",
-        ]
-
-        if "130" in desired_cuda:
-            cuda_rpaths.append("$ORIGIN/../../nvidia/cu13/lib")
-        else:
-            cuda_rpaths.extend(
-                [
-                    "$ORIGIN/../../nvidia/cublas/lib",
-                    "$ORIGIN/../../nvidia/cuda_cupti/lib",
-                    "$ORIGIN/../../nvidia/cuda_nvrtc/lib",
-                    "$ORIGIN/../../nvidia/cuda_runtime/lib",
-                    "$ORIGIN/../../nvidia/cufft/lib",
-                    "$ORIGIN/../../nvidia/curand/lib",
-                    "$ORIGIN/../../nvidia/cusolver/lib",
-                    "$ORIGIN/../../nvidia/cusparse/lib",
-                    "$ORIGIN/../../nvidia/nvtx/lib",
-                    "$ORIGIN/../../nvidia/cufile/lib",
-                ]
-            )
-
-        # Add $ORIGIN for local torch libs
-        rpath = ":".join(cuda_rpaths) + ":$ORIGIN"
-    else:
-        # For bundled libraries, just use $ORIGIN
-        rpath = "$ORIGIN"
-
-    if os.path.exists(lib_path):
-        os.system(
-            f"cd {folder}/tmp/torch/lib/; "
-            f"patchelf --set-rpath '{rpath}' --force-rpath {lib_name}"
-        )
-
-
-def copy_and_patch_library(
-    src_path: str,
-    folder: str,
-    use_nvidia_pypi_libs: bool = False,
-    desired_cuda: str = "",
-) -> None:
-    """Copy a library to torch/lib and patch its RPATH"""
-    if os.path.exists(src_path):
-        lib_name = os.path.basename(src_path)
-        shutil.copy2(src_path, f"{folder}/tmp/torch/lib/{lib_name}")
-        patch_library_rpath(folder, lib_name, use_nvidia_pypi_libs, desired_cuda)
-
-
-def package_cuda_wheel(wheel_path, desired_cuda) -> None:
-    """
-    Package the cuda wheel libraries
-    """
-    folder = os.path.dirname(wheel_path)
-    os.mkdir(f"{folder}/tmp")
-    os.system(f"unzip {wheel_path} -d {folder}/tmp")
-    # Delete original wheel since it will be repackaged
-    os.system(f"rm {wheel_path}")
-
-    # Check if we should use PyPI NVIDIA libraries or bundle system libraries
-    use_nvidia_pypi_libs = os.getenv("USE_NVIDIA_PYPI_LIBS", "0") == "1"
-
-    if use_nvidia_pypi_libs:
-        print("Using nvidia libs from pypi - skipping CUDA library bundling")
-        # For PyPI approach, we don't bundle CUDA libraries - they come from PyPI packages
-        # We only need to bundle non-NVIDIA libraries
-        minimal_libs_to_copy = [
-            "/lib64/libgomp.so.1",
-            "/usr/lib64/libgfortran.so.5",
-            "/acl/build/libarm_compute.so",
-            "/acl/build/libarm_compute_graph.so",
-            "/usr/local/lib/libnvpl_lapack_lp64_gomp.so.0",
-            "/usr/local/lib/libnvpl_blas_lp64_gomp.so.0",
-            "/usr/local/lib/libnvpl_lapack_core.so.0",
-            "/usr/local/lib/libnvpl_blas_core.so.0",
-        ]
-
-        # Copy minimal libraries to unzipped_folder/torch/lib
-        for lib_path in minimal_libs_to_copy:
-            copy_and_patch_library(lib_path, folder, use_nvidia_pypi_libs, desired_cuda)
-
-        # Patch torch libraries used for searching libraries
-        torch_libs_to_patch = [
-            "libtorch.so",
-            "libtorch_cpu.so",
-            "libtorch_cuda.so",
-            "libtorch_cuda_linalg.so",
-            "libtorch_global_deps.so",
-            "libtorch_python.so",
-            "libtorch_nvshmem.so",
-            "libc10.so",
-            "libc10_cuda.so",
-            "libcaffe2_nvrtc.so",
-            "libshm.so",
-        ]
-        for lib_name in torch_libs_to_patch:
-            patch_library_rpath(folder, lib_name, use_nvidia_pypi_libs, desired_cuda)
-    else:
-        print("Bundling CUDA libraries with wheel")
-        # Original logic for bundling system CUDA libraries
-        # Common libraries for all CUDA versions
-        common_libs = [
-            # Non-NVIDIA system libraries
-            "/lib64/libgomp.so.1",
-            "/usr/lib64/libgfortran.so.5",
-            "/acl/build/libarm_compute.so",
-            "/acl/build/libarm_compute_graph.so",
-            # Common CUDA libraries (same for all versions)
-            "/usr/local/lib/libnvpl_lapack_lp64_gomp.so.0",
-            "/usr/local/lib/libnvpl_blas_lp64_gomp.so.0",
-            "/usr/local/lib/libnvpl_lapack_core.so.0",
-            "/usr/local/lib/libnvpl_blas_core.so.0",
-            "/usr/local/cuda/extras/CUPTI/lib64/libnvperf_host.so",
-            "/usr/local/cuda/lib64/libcudnn.so.9",
-            "/usr/local/cuda/lib64/libcusparseLt.so.0",
-            "/usr/local/cuda/lib64/libcurand.so.10",
-            "/usr/local/cuda/lib64/libnccl.so.2",
-            "/usr/local/cuda/lib64/libnvshmem_host.so.3",
-            "/usr/local/cuda/lib64/libcudnn_adv.so.9",
-            "/usr/local/cuda/lib64/libcudnn_cnn.so.9",
-            "/usr/local/cuda/lib64/libcudnn_graph.so.9",
-            "/usr/local/cuda/lib64/libcudnn_ops.so.9",
-            "/usr/local/cuda/lib64/libcudnn_engines_runtime_compiled.so.9",
-            "/usr/local/cuda/lib64/libcudnn_engines_precompiled.so.9",
-            "/usr/local/cuda/lib64/libcudnn_heuristic.so.9",
-            "/usr/local/cuda/lib64/libcufile.so.0",
-            "/usr/local/cuda/lib64/libcufile_rdma.so.1",
-            "/usr/local/cuda/lib64/libcusparse.so.12",
-        ]
-
-        # CUDA version-specific libraries
-        if "13" in desired_cuda:
-            minor_version = desired_cuda[-1]
-            version_specific_libs = [
-                "/usr/local/cuda/extras/CUPTI/lib64/libcupti.so.13",
-                "/usr/local/cuda/lib64/libcublas.so.13",
-                "/usr/local/cuda/lib64/libcublasLt.so.13",
-                "/usr/local/cuda/lib64/libcudart.so.13",
-                "/usr/local/cuda/lib64/libcufft.so.12",
-                "/usr/local/cuda/lib64/libcusolver.so.12",
-                "/usr/local/cuda/lib64/libnvJitLink.so.13",
-                "/usr/local/cuda/lib64/libnvrtc.so.13",
-                f"/usr/local/cuda/lib64/libnvrtc-builtins.so.13.{minor_version}",
-            ]
-        elif "12" in desired_cuda:
-            # Get the last character for libnvrtc-builtins version (e.g., "129" -> "9")
-            minor_version = desired_cuda[-1]
-            version_specific_libs = [
-                "/usr/local/cuda/extras/CUPTI/lib64/libcupti.so.12",
-                "/usr/local/cuda/lib64/libcublas.so.12",
-                "/usr/local/cuda/lib64/libcublasLt.so.12",
-                "/usr/local/cuda/lib64/libcudart.so.12",
-                "/usr/local/cuda/lib64/libcufft.so.11",
-                "/usr/local/cuda/lib64/libcusolver.so.11",
-                "/usr/local/cuda/lib64/libnvJitLink.so.12",
-                "/usr/local/cuda/lib64/libnvrtc.so.12",
-                f"/usr/local/cuda/lib64/libnvrtc-builtins.so.12.{minor_version}",
-            ]
-        else:
-            raise ValueError(f"Unsupported CUDA version: {desired_cuda}.")
-
-        # Combine all libraries
-        libs_to_copy = common_libs + version_specific_libs
-
-        # Copy libraries to unzipped_folder/torch/lib
-        for lib_path in libs_to_copy:
-            copy_and_patch_library(lib_path, folder, use_nvidia_pypi_libs, desired_cuda)
-
-    # Make sure the wheel is tagged with manylinux_2_28
-    for f in os.scandir(f"{folder}/tmp/"):
-        if f.is_dir() and f.name.endswith(".dist-info"):
-            replace_tag(f"{f.path}/WHEEL")
-            break
-
-    os.system(f"wheel pack {folder}/tmp/ -d {folder}")
-    os.system(f"rm -rf {folder}/tmp/")
-
-
-def complete_wheel(folder: str) -> str:
-    """
-    Complete wheel build and put in artifact location
-    """
-    wheel_name = list_dir(f"/{folder}/dist")[0]
-
-    # Please note for cuda we don't run auditwheel since we use custom script to package
-    # the cuda dependencies to the wheel file using update_wheel() method.
-    # However we need to make sure filename reflects the correct Manylinux platform.
-    if "pytorch" in folder and not enable_cuda:
-        print("Repairing Wheel with AuditWheel")
-        check_call(["auditwheel", "repair", f"dist/{wheel_name}"], cwd=folder)
-        repaired_wheel_name = list_dir(f"/{folder}/wheelhouse")[0]
-
-        print(f"Moving {repaired_wheel_name} wheel to /{folder}/dist")
-        os.rename(
-            f"/{folder}/wheelhouse/{repaired_wheel_name}",
-            f"/{folder}/dist/{repaired_wheel_name}",
-        )
-    else:
-        repaired_wheel_name = list_dir(f"/{folder}/dist")[0]
-
-    print(f"Copying {repaired_wheel_name} to artifacts")
-    shutil.copy2(
-        f"/{folder}/dist/{repaired_wheel_name}", f"/artifacts/{repaired_wheel_name}"
-    )
-
-    return repaired_wheel_name
-
-
-def parse_arguments():
-    """
-    Parse inline arguments
-    """
-    from argparse import ArgumentParser
-
-    parser = ArgumentParser("AARCH64 wheels python CD")
-    parser.add_argument("--debug", action="store_true")
-    parser.add_argument("--build-only", action="store_true")
-    parser.add_argument("--test-only", type=str)
-    parser.add_argument("--enable-mkldnn", action="store_true")
-    parser.add_argument("--enable-cuda", action="store_true")
-    return parser.parse_args()
-
-
-if __name__ == "__main__":
-    """
-    Entry Point
-    """
-    args = parse_arguments()
-    enable_mkldnn = args.enable_mkldnn
-    enable_cuda = args.enable_cuda
-    branch = check_output(
-        ["git", "rev-parse", "--abbrev-ref", "HEAD"], cwd="/pytorch"
-    ).decode()
-
-    print("Building PyTorch wheel")
-    build_vars = ""
-    # MAX_JOB=5 is not required for CPU backend (see commit 465d98b)
-    if enable_cuda:
-        build_vars += "MAX_JOBS=5 "
-
-        # Handle PyPI NVIDIA libraries vs bundled libraries
-        use_nvidia_pypi_libs = os.getenv("USE_NVIDIA_PYPI_LIBS", "0") == "1"
-        if use_nvidia_pypi_libs:
-            print("Configuring build for PyPI NVIDIA libraries")
-            # Configure for dynamic linking (matching x86 logic)
-            build_vars += "ATEN_STATIC_CUDA=0 USE_CUDA_STATIC_LINK=0 USE_CUPTI_SO=1 "
-        else:
-            print("Configuring build for bundled NVIDIA libraries")
-            # Keep existing static linking approach - already configured above
-
-    override_package_version = os.getenv("OVERRIDE_PACKAGE_VERSION")
-    desired_cuda = os.getenv("DESIRED_CUDA")
-    if override_package_version is not None:
-        version = override_package_version
-        build_vars += (
-            f"BUILD_TEST=0 PYTORCH_BUILD_VERSION={version} PYTORCH_BUILD_NUMBER=1 "
-        )
-    elif branch in ["nightly", "main"]:
-        build_date = (
-            check_output(["git", "log", "--pretty=format:%cs", "-1"], cwd="/pytorch")
-            .decode()
-            .replace("-", "")
-        )
-        version = (
-            check_output(["cat", "version.txt"], cwd="/pytorch").decode().strip()[:-2]
-        )
-        if enable_cuda:
-            build_vars += f"BUILD_TEST=0 PYTORCH_BUILD_VERSION={version}.dev{build_date}+{desired_cuda} PYTORCH_BUILD_NUMBER=1 "
-        else:
-            build_vars += f"BUILD_TEST=0 PYTORCH_BUILD_VERSION={version}.dev{build_date} PYTORCH_BUILD_NUMBER=1 "
-    elif branch.startswith(("v1.", "v2.")):
-        build_vars += f"BUILD_TEST=0 PYTORCH_BUILD_VERSION={branch[1 : branch.find('-')]} PYTORCH_BUILD_NUMBER=1 "
-
-    if enable_mkldnn:
-        print("build pytorch with mkldnn+acl backend")
-        build_vars += "USE_MKLDNN=ON USE_MKLDNN_ACL=ON "
-        build_vars += "ACL_ROOT_DIR=/acl "
-        if enable_cuda:
-            build_vars += "BLAS=NVPL "
-        else:
-            build_vars += "BLAS=OpenBLAS OpenBLAS_HOME=/opt/OpenBLAS "
-    else:
-        print("build pytorch without mkldnn backend")
-
-    os.system(f"cd /pytorch; {build_vars} python3 -m build --wheel --no-isolation")
-    if enable_cuda:
-        print("Updating Cuda Dependency")
-        filename = os.listdir("/pytorch/dist/")
-        wheel_path = f"/pytorch/dist/{filename[0]}"
-        package_cuda_wheel(wheel_path, desired_cuda)
-    pytorch_wheel_name = complete_wheel("/pytorch/")
-    print(f"Build Complete. Created {pytorch_wheel_name}..")

.ci/aarch64_linux/build_aarch64_wheel.py

@@ -1,999 +0,0 @@
-#!/usr/bin/env python3
-
-# This script is for building  AARCH64 wheels using AWS EC2 instances.
-# To generate binaries for the release follow these steps:
-# 1. Update mappings for each of the Domain Libraries by adding new row to a table like this:
-#         "v1.11.0": ("0.11.0", "rc1"),
-# 2. Run script with following arguments for each of the supported python versions and required tag, for example:
-# build_aarch64_wheel.py --key-name <YourPemKey> --use-docker --python 3.8 --branch v1.11.0-rc3
-
-
-import os
-import subprocess
-import sys
-import time
-from typing import Optional, Union
-
-import boto3
-
-
-# AMI images for us-east-1, change the following based on your ~/.aws/config
-os_amis = {
-    "ubuntu20_04": "ami-052eac90edaa9d08f",  # login_name: ubuntu
-    "ubuntu22_04": "ami-0c6c29c5125214c77",  # login_name: ubuntu
-    "redhat8": "ami-0698b90665a2ddcf1",  # login_name: ec2-user
-}
-
-ubuntu20_04_ami = os_amis["ubuntu20_04"]
-
-
-def compute_keyfile_path(key_name: Optional[str] = None) -> tuple[str, str]:
-    if key_name is None:
-        key_name = os.getenv("AWS_KEY_NAME")
-        if key_name is None:
-            return os.getenv("SSH_KEY_PATH", ""), ""
-
-    homedir_path = os.path.expanduser("~")
-    default_path = os.path.join(homedir_path, ".ssh", f"{key_name}.pem")
-    return os.getenv("SSH_KEY_PATH", default_path), key_name
-
-
-ec2 = boto3.resource("ec2")
-
-
-def ec2_get_instances(filter_name, filter_value):
-    return ec2.instances.filter(
-        Filters=[{"Name": filter_name, "Values": [filter_value]}]
-    )
-
-
-def ec2_instances_of_type(instance_type="t4g.2xlarge"):
-    return ec2_get_instances("instance-type", instance_type)
-
-
-def ec2_instances_by_id(instance_id):
-    rc = list(ec2_get_instances("instance-id", instance_id))
-    return rc[0] if len(rc) > 0 else None
-
-
-def start_instance(
-    key_name, ami=ubuntu20_04_ami, instance_type="t4g.2xlarge", ebs_size: int = 50
-):
-    inst = ec2.create_instances(
-        ImageId=ami,
-        InstanceType=instance_type,
-        SecurityGroups=["ssh-allworld"],
-        KeyName=key_name,
-        MinCount=1,
-        MaxCount=1,
-        BlockDeviceMappings=[
-            {
-                "DeviceName": "/dev/sda1",
-                "Ebs": {
-                    "DeleteOnTermination": True,
-                    "VolumeSize": ebs_size,
-                    "VolumeType": "standard",
-                },
-            }
-        ],
-    )[0]
-    print(f"Create instance {inst.id}")
-    inst.wait_until_running()
-    running_inst = ec2_instances_by_id(inst.id)
-    print(f"Instance started at {running_inst.public_dns_name}")
-    return running_inst
-
-
-class RemoteHost:
-    addr: str
-    keyfile_path: str
-    login_name: str
-    container_id: Optional[str] = None
-    ami: Optional[str] = None
-
-    def __init__(self, addr: str, keyfile_path: str, login_name: str = "ubuntu"):
-        self.addr = addr
-        self.keyfile_path = keyfile_path
-        self.login_name = login_name
-
-    def _gen_ssh_prefix(self) -> list[str]:
-        return [
-            "ssh",
-            "-o",
-            "StrictHostKeyChecking=no",
-            "-i",
-            self.keyfile_path,
-            f"{self.login_name}@{self.addr}",
-            "--",
-        ]
-
-    @staticmethod
-    def _split_cmd(args: Union[str, list[str]]) -> list[str]:
-        return args.split() if isinstance(args, str) else args
-
-    def run_ssh_cmd(self, args: Union[str, list[str]]) -> None:
-        subprocess.check_call(self._gen_ssh_prefix() + self._split_cmd(args))
-
-    def check_ssh_output(self, args: Union[str, list[str]]) -> str:
-        return subprocess.check_output(
-            self._gen_ssh_prefix() + self._split_cmd(args)
-        ).decode("utf-8")
-
-    def scp_upload_file(self, local_file: str, remote_file: str) -> None:
-        subprocess.check_call(
-            [
-                "scp",
-                "-i",
-                self.keyfile_path,
-                local_file,
-                f"{self.login_name}@{self.addr}:{remote_file}",
-            ]
-        )
-
-    def scp_download_file(
-        self, remote_file: str, local_file: Optional[str] = None
-    ) -> None:
-        if local_file is None:
-            local_file = "."
-        subprocess.check_call(
-            [
-                "scp",
-                "-i",
-                self.keyfile_path,
-                f"{self.login_name}@{self.addr}:{remote_file}",
-                local_file,
-            ]
-        )
-
-    def start_docker(self, image="quay.io/pypa/manylinux2014_aarch64:latest") -> None:
-        self.run_ssh_cmd("sudo apt-get install -y docker.io")
-        self.run_ssh_cmd(f"sudo usermod -a -G docker {self.login_name}")
-        self.run_ssh_cmd("sudo service docker start")
-        self.run_ssh_cmd(f"docker pull {image}")
-        self.container_id = self.check_ssh_output(
-            f"docker run -t -d -w /root {image}"
-        ).strip()
-
-    def using_docker(self) -> bool:
-        return self.container_id is not None
-
-    def run_cmd(self, args: Union[str, list[str]]) -> None:
-        if not self.using_docker():
-            return self.run_ssh_cmd(args)
-        assert self.container_id is not None
-        docker_cmd = self._gen_ssh_prefix() + [
-            "docker",
-            "exec",
-            "-i",
-            self.container_id,
-            "bash",
-        ]
-        p = subprocess.Popen(docker_cmd, stdin=subprocess.PIPE)
-        p.communicate(
-            input=" ".join(["source .bashrc && "] + self._split_cmd(args)).encode(
-                "utf-8"
-            )
-        )
-        rc = p.wait()
-        if rc != 0:
-            raise subprocess.CalledProcessError(rc, docker_cmd)
-
-    def check_output(self, args: Union[str, list[str]]) -> str:
-        if not self.using_docker():
-            return self.check_ssh_output(args)
-        assert self.container_id is not None
-        docker_cmd = self._gen_ssh_prefix() + [
-            "docker",
-            "exec",
-            "-i",
-            self.container_id,
-            "bash",
-        ]
-        p = subprocess.Popen(docker_cmd, stdin=subprocess.PIPE, stdout=subprocess.PIPE)
-        (out, err) = p.communicate(
-            input=" ".join(["source .bashrc && "] + self._split_cmd(args)).encode(
-                "utf-8"
-            )
-        )
-        rc = p.wait()
-        if rc != 0:
-            raise subprocess.CalledProcessError(rc, docker_cmd, output=out, stderr=err)
-        return out.decode("utf-8")
-
-    def upload_file(self, local_file: str, remote_file: str) -> None:
-        if not self.using_docker():
-            return self.scp_upload_file(local_file, remote_file)
-        tmp_file = os.path.join("/tmp", os.path.basename(local_file))
-        self.scp_upload_file(local_file, tmp_file)
-        self.run_ssh_cmd(
-            ["docker", "cp", tmp_file, f"{self.container_id}:/root/{remote_file}"]
-        )
-        self.run_ssh_cmd(["rm", tmp_file])
-
-    def download_file(self, remote_file: str, local_file: Optional[str] = None) -> None:
-        if not self.using_docker():
-            return self.scp_download_file(remote_file, local_file)
-        tmp_file = os.path.join("/tmp", os.path.basename(remote_file))
-        self.run_ssh_cmd(
-            ["docker", "cp", f"{self.container_id}:/root/{remote_file}", tmp_file]
-        )
-        self.scp_download_file(tmp_file, local_file)
-        self.run_ssh_cmd(["rm", tmp_file])
-
-    def download_wheel(
-        self, remote_file: str, local_file: Optional[str] = None
-    ) -> None:
-        if self.using_docker() and local_file is None:
-            basename = os.path.basename(remote_file)
-            local_file = basename.replace(
-                "-linux_aarch64.whl", "-manylinux2014_aarch64.whl"
-            )
-        self.download_file(remote_file, local_file)
-
-    def list_dir(self, path: str) -> list[str]:
-        return self.check_output(["ls", "-1", path]).split("\n")
-
-
-def wait_for_connection(addr, port, timeout=15, attempt_cnt=5):
-    import socket
-
-    for i in range(attempt_cnt):
-        try:
-            with socket.create_connection((addr, port), timeout=timeout):
-                return
-        except (ConnectionRefusedError, TimeoutError):  # noqa: PERF203
-            if i == attempt_cnt - 1:
-                raise
-            time.sleep(timeout)
-
-
-def update_apt_repo(host: RemoteHost) -> None:
-    time.sleep(5)
-    host.run_cmd("sudo systemctl stop apt-daily.service || true")
-    host.run_cmd("sudo systemctl stop unattended-upgrades.service || true")
-    host.run_cmd(
-        "while systemctl is-active --quiet apt-daily.service; do sleep 1; done"
-    )
-    host.run_cmd(
-        "while systemctl is-active --quiet unattended-upgrades.service; do sleep 1; done"
-    )
-    host.run_cmd("sudo apt-get update")
-    time.sleep(3)
-    host.run_cmd("sudo apt-get update")
-
-
-def install_condaforge(
-    host: RemoteHost, suffix: str = "latest/download/Miniforge3-Linux-aarch64.sh"
-) -> None:
-    print("Install conda-forge")
-    host.run_cmd(f"curl -OL https://github.com/conda-forge/miniforge/releases/{suffix}")
-    host.run_cmd(f"sh -f {os.path.basename(suffix)} -b")
-    host.run_cmd(f"rm -f {os.path.basename(suffix)}")
-    if host.using_docker():
-        host.run_cmd("echo 'PATH=$HOME/miniforge3/bin:$PATH'>>.bashrc")
-    else:
-        host.run_cmd(
-            [
-                "sed",
-                "-i",
-                "'/^# If not running interactively.*/i PATH=$HOME/miniforge3/bin:$PATH'",
-                ".bashrc",
-            ]
-        )
-
-
-def install_condaforge_python(host: RemoteHost, python_version="3.8") -> None:
-    if python_version == "3.6":
-        # Python-3.6 EOLed and not compatible with conda-4.11
-        install_condaforge(
-            host, suffix="download/4.10.3-10/Miniforge3-4.10.3-10-Linux-aarch64.sh"
-        )
-        host.run_cmd(f"conda install -y python={python_version} numpy pyyaml")
-    else:
-        install_condaforge(
-            host, suffix="download/4.11.0-4/Miniforge3-4.11.0-4-Linux-aarch64.sh"
-        )
-        # Pytorch-1.10 or older are not compatible with setuptools=59.6 or newer
-        host.run_cmd(
-            f"conda install -y python={python_version} numpy pyyaml setuptools>=59.5.0"
-        )
-
-
-def embed_libgomp(host: RemoteHost, use_conda, wheel_name) -> None:
-    host.run_cmd("pip3 install auditwheel")
-    host.run_cmd(
-        "conda install -y patchelf" if use_conda else "sudo apt-get install -y patchelf"
-    )
-    from tempfile import NamedTemporaryFile
-
-    with NamedTemporaryFile() as tmp:
-        tmp.write(embed_library_script.encode("utf-8"))
-        tmp.flush()
-        host.upload_file(tmp.name, "embed_library.py")
-
-    print("Embedding libgomp into wheel")
-    if host.using_docker():
-        host.run_cmd(f"python3 embed_library.py {wheel_name} --update-tag")
-    else:
-        host.run_cmd(f"python3 embed_library.py {wheel_name}")
-
-
-def checkout_repo(
-    host: RemoteHost,
-    *,
-    branch: str = "main",
-    url: str,
-    git_clone_flags: str,
-    mapping: dict[str, tuple[str, str]],
-) -> Optional[str]:
-    for prefix in mapping:
-        if not branch.startswith(prefix):
-            continue
-        tag = f"v{mapping[prefix][0]}-{mapping[prefix][1]}"
-        host.run_cmd(f"git clone {url} -b {tag} {git_clone_flags}")
-        return mapping[prefix][0]
-
-    host.run_cmd(f"git clone {url} -b {branch} {git_clone_flags}")
-    return None
-
-
-def build_torchvision(
-    host: RemoteHost,
-    *,
-    branch: str = "main",
-    use_conda: bool = True,
-    git_clone_flags: str,
-    run_smoke_tests: bool = True,
-) -> str:
-    print("Checking out TorchVision repo")
-    build_version = checkout_repo(
-        host,
-        branch=branch,
-        url="https://github.com/pytorch/vision",
-        git_clone_flags=git_clone_flags,
-        mapping={
-            "v1.7.1": ("0.8.2", "rc2"),
-            "v1.8.0": ("0.9.0", "rc3"),
-            "v1.8.1": ("0.9.1", "rc1"),
-            "v1.9.0": ("0.10.0", "rc1"),
-            "v1.10.0": ("0.11.1", "rc1"),
-            "v1.10.1": ("0.11.2", "rc1"),
-            "v1.10.2": ("0.11.3", "rc1"),
-            "v1.11.0": ("0.12.0", "rc1"),
-            "v1.12.0": ("0.13.0", "rc4"),
-            "v1.12.1": ("0.13.1", "rc6"),
-            "v1.13.0": ("0.14.0", "rc4"),
-            "v1.13.1": ("0.14.1", "rc2"),
-            "v2.0.0": ("0.15.1", "rc2"),
-            "v2.0.1": ("0.15.2", "rc2"),
-        },
-    )
-    print("Building TorchVision wheel")
-
-    # Please note libnpg and jpeg are required to build image.so extension
-    if use_conda:
-        host.run_cmd("conda install -y libpng jpeg")
-        # Remove .so files to force static linking
-        host.run_cmd(
-            "rm miniforge3/lib/libpng.so miniforge3/lib/libpng16.so miniforge3/lib/libjpeg.so"
-        )
-        # And patch setup.py to include libz dependency for libpng
-        host.run_cmd(
-            [
-                'sed -i -e \'s/image_link_flags\\.append("png")/image_link_flags += ["png", "z"]/\' vision/setup.py'
-            ]
-        )
-
-    build_vars = ""
-    if branch == "nightly":
-        version = host.check_output(
-            ["if [ -f vision/version.txt ]; then cat vision/version.txt; fi"]
-        ).strip()
-        if len(version) == 0:
-            # In older revisions, version was embedded in setup.py
-            version = (
-                host.check_output(["grep", '"version = \'"', "vision/setup.py"])
-                .strip()
-                .split("'")[1][:-2]
-            )
-        build_date = (
-            host.check_output("cd vision && git log --pretty=format:%s -1")
-            .strip()
-            .split()[0]
-            .replace("-", "")
-        )
-        build_vars += f"BUILD_VERSION={version}.dev{build_date}"
-    elif build_version is not None:
-        build_vars += f"BUILD_VERSION={build_version} PYTORCH_VERSION={branch[1:].split('-', maxsplit=1)[0]}"
-    if host.using_docker():
-        build_vars += " CMAKE_SHARED_LINKER_FLAGS=-Wl,-z,max-page-size=0x10000"
-
-    host.run_cmd(f"cd vision && {build_vars} python3 -m build --wheel --no-isolation")
-    vision_wheel_name = host.list_dir("vision/dist")[0]
-    embed_libgomp(host, use_conda, os.path.join("vision", "dist", vision_wheel_name))
-
-    print("Copying TorchVision wheel")
-    host.download_wheel(os.path.join("vision", "dist", vision_wheel_name))
-    if run_smoke_tests:
-        host.run_cmd(
-            f"pip3 install {os.path.join('vision', 'dist', vision_wheel_name)}"
-        )
-        host.run_cmd("python3 vision/test/smoke_test.py")
-    print("Delete vision checkout")
-    host.run_cmd("rm -rf vision")
-
-    return vision_wheel_name
-
-
-def build_torchdata(
-    host: RemoteHost,
-    *,
-    branch: str = "main",
-    use_conda: bool = True,
-    git_clone_flags: str = "",
-) -> str:
-    print("Checking out TorchData repo")
-    git_clone_flags += " --recurse-submodules"
-    build_version = checkout_repo(
-        host,
-        branch=branch,
-        url="https://github.com/pytorch/data",
-        git_clone_flags=git_clone_flags,
-        mapping={
-            "v1.13.1": ("0.5.1", ""),
-            "v2.0.0": ("0.6.0", "rc5"),
-            "v2.0.1": ("0.6.1", "rc1"),
-        },
-    )
-    print("Building TorchData wheel")
-    build_vars = ""
-    if branch == "nightly":
-        version = host.check_output(
-            ["if [ -f data/version.txt ]; then cat data/version.txt; fi"]
-        ).strip()
-        build_date = (
-            host.check_output("cd data && git log --pretty=format:%s -1")
-            .strip()
-            .split()[0]
-            .replace("-", "")
-        )
-        build_vars += f"BUILD_VERSION={version}.dev{build_date}"
-    elif build_version is not None:
-        build_vars += f"BUILD_VERSION={build_version} PYTORCH_VERSION={branch[1:].split('-', maxsplit=1)[0]}"
-    if host.using_docker():
-        build_vars += " CMAKE_SHARED_LINKER_FLAGS=-Wl,-z,max-page-size=0x10000"
-
-    host.run_cmd(f"cd data && {build_vars} python3 -m build --wheel --no-isolation")
-    wheel_name = host.list_dir("data/dist")[0]
-    embed_libgomp(host, use_conda, os.path.join("data", "dist", wheel_name))
-
-    print("Copying TorchData wheel")
-    host.download_wheel(os.path.join("data", "dist", wheel_name))
-
-    return wheel_name
-
-
-def build_torchtext(
-    host: RemoteHost,
-    *,
-    branch: str = "main",
-    use_conda: bool = True,
-    git_clone_flags: str = "",
-) -> str:
-    print("Checking out TorchText repo")
-    git_clone_flags += " --recurse-submodules"
-    build_version = checkout_repo(
-        host,
-        branch=branch,
-        url="https://github.com/pytorch/text",
-        git_clone_flags=git_clone_flags,
-        mapping={
-            "v1.9.0": ("0.10.0", "rc1"),
-            "v1.10.0": ("0.11.0", "rc2"),
-            "v1.10.1": ("0.11.1", "rc1"),
-            "v1.10.2": ("0.11.2", "rc1"),
-            "v1.11.0": ("0.12.0", "rc1"),
-            "v1.12.0": ("0.13.0", "rc2"),
-            "v1.12.1": ("0.13.1", "rc5"),
-            "v1.13.0": ("0.14.0", "rc3"),
-            "v1.13.1": ("0.14.1", "rc1"),
-            "v2.0.0": ("0.15.1", "rc2"),
-            "v2.0.1": ("0.15.2", "rc2"),
-        },
-    )
-    print("Building TorchText wheel")
-    build_vars = ""
-    if branch == "nightly":
-        version = host.check_output(
-            ["if [ -f text/version.txt ]; then cat text/version.txt; fi"]
-        ).strip()
-        build_date = (
-            host.check_output("cd text && git log --pretty=format:%s -1")
-            .strip()
-            .split()[0]
-            .replace("-", "")
-        )
-        build_vars += f"BUILD_VERSION={version}.dev{build_date}"
-    elif build_version is not None:
-        build_vars += f"BUILD_VERSION={build_version} PYTORCH_VERSION={branch[1:].split('-', maxsplit=1)[0]}"
-    if host.using_docker():
-        build_vars += " CMAKE_SHARED_LINKER_FLAGS=-Wl,-z,max-page-size=0x10000"
-
-    host.run_cmd(f"cd text && {build_vars} python3 -m build --wheel --no-isolation")
-    wheel_name = host.list_dir("text/dist")[0]
-    embed_libgomp(host, use_conda, os.path.join("text", "dist", wheel_name))
-
-    print("Copying TorchText wheel")
-    host.download_wheel(os.path.join("text", "dist", wheel_name))
-
-    return wheel_name
-
-
-def build_torchaudio(
-    host: RemoteHost,
-    *,
-    branch: str = "main",
-    use_conda: bool = True,
-    git_clone_flags: str = "",
-) -> str:
-    print("Checking out TorchAudio repo")
-    git_clone_flags += " --recurse-submodules"
-    build_version = checkout_repo(
-        host,
-        branch=branch,
-        url="https://github.com/pytorch/audio",
-        git_clone_flags=git_clone_flags,
-        mapping={
-            "v1.9.0": ("0.9.0", "rc2"),
-            "v1.10.0": ("0.10.0", "rc5"),
-            "v1.10.1": ("0.10.1", "rc1"),
-            "v1.10.2": ("0.10.2", "rc1"),
-            "v1.11.0": ("0.11.0", "rc1"),
-            "v1.12.0": ("0.12.0", "rc3"),
-            "v1.12.1": ("0.12.1", "rc5"),
-            "v1.13.0": ("0.13.0", "rc4"),
-            "v1.13.1": ("0.13.1", "rc2"),
-            "v2.0.0": ("2.0.1", "rc3"),
-            "v2.0.1": ("2.0.2", "rc2"),
-        },
-    )
-    print("Building TorchAudio wheel")
-    build_vars = ""
-    if branch == "nightly":
-        version = (
-            host.check_output(["grep", '"version = \'"', "audio/setup.py"])
-            .strip()
-            .split("'")[1][:-2]
-        )
-        build_date = (
-            host.check_output("cd audio && git log --pretty=format:%s -1")
-            .strip()
-            .split()[0]
-            .replace("-", "")
-        )
-        build_vars += f"BUILD_VERSION={version}.dev{build_date}"
-    elif build_version is not None:
-        build_vars += f"BUILD_VERSION={build_version} PYTORCH_VERSION={branch[1:].split('-', maxsplit=1)[0]}"
-    if host.using_docker():
-        build_vars += " CMAKE_SHARED_LINKER_FLAGS=-Wl,-z,max-page-size=0x10000"
-
-    host.run_cmd(
-        f"cd audio && export FFMPEG_ROOT=$(pwd)/third_party/ffmpeg && export USE_FFMPEG=1 \
-        && ./packaging/ffmpeg/build.sh \
-        && {build_vars} python3 -m build --wheel --no-isolation"
-    )
-
-    wheel_name = host.list_dir("audio/dist")[0]
-    embed_libgomp(host, use_conda, os.path.join("audio", "dist", wheel_name))
-
-    print("Copying TorchAudio wheel")
-    host.download_wheel(os.path.join("audio", "dist", wheel_name))
-
-    return wheel_name
-
-
-def configure_system(
-    host: RemoteHost,
-    *,
-    compiler: str = "gcc-8",
-    use_conda: bool = True,
-    python_version: str = "3.8",
-) -> None:
-    if use_conda:
-        install_condaforge_python(host, python_version)
-
-    print("Configuring the system")
-    if not host.using_docker():
-        update_apt_repo(host)
-        host.run_cmd("sudo apt-get install -y ninja-build g++ git cmake gfortran unzip")
-    else:
-        host.run_cmd("yum install -y sudo")
-        host.run_cmd("conda install -y ninja scons")
-
-    if not use_conda:
-        host.run_cmd(
-            "sudo apt-get install -y python3-dev python3-yaml python3-setuptools python3-wheel python3-pip"
-        )
-    host.run_cmd("pip3 install dataclasses typing-extensions")
-    if not use_conda:
-        print("Installing Cython + numpy from PyPy")
-        host.run_cmd("sudo pip3 install Cython")
-        host.run_cmd("sudo pip3 install numpy")
-
-
-def build_domains(
-    host: RemoteHost,
-    *,
-    branch: str = "main",
-    use_conda: bool = True,
-    git_clone_flags: str = "",
-) -> tuple[str, str, str, str]:
-    vision_wheel_name = build_torchvision(
-        host, branch=branch, use_conda=use_conda, git_clone_flags=git_clone_flags
-    )
-    audio_wheel_name = build_torchaudio(
-        host, branch=branch, use_conda=use_conda, git_clone_flags=git_clone_flags
-    )
-    data_wheel_name = build_torchdata(
-        host, branch=branch, use_conda=use_conda, git_clone_flags=git_clone_flags
-    )
-    text_wheel_name = build_torchtext(
-        host, branch=branch, use_conda=use_conda, git_clone_flags=git_clone_flags
-    )
-    return (vision_wheel_name, audio_wheel_name, data_wheel_name, text_wheel_name)
-
-
-def start_build(
-    host: RemoteHost,
-    *,
-    branch: str = "main",
-    compiler: str = "gcc-8",
-    use_conda: bool = True,
-    python_version: str = "3.8",
-    pytorch_only: bool = False,
-    pytorch_build_number: Optional[str] = None,
-    shallow_clone: bool = True,
-    enable_mkldnn: bool = False,
-) -> tuple[str, str, str, str, str]:
-    git_clone_flags = " --depth 1 --shallow-submodules" if shallow_clone else ""
-    if host.using_docker() and not use_conda:
-        print("Auto-selecting conda option for docker images")
-        use_conda = True
-    if not host.using_docker():
-        print("Disable mkldnn for host builds")
-        enable_mkldnn = False
-
-    configure_system(
-        host, compiler=compiler, use_conda=use_conda, python_version=python_version
-    )
-
-    if host.using_docker():
-        print("Move libgfortant.a into a standard location")
-        # HACK: pypa gforntran.a is compiled without PIC, which leads to the following error
-        # libgfortran.a(error.o)(.text._gfortrani_st_printf+0x34): unresolvable R_AARCH64_ADR_PREL_PG_HI21 relocation against symbol `__stack_chk_guard@@GLIBC_2.17'  # noqa: E501, B950
-        # Workaround by copying gfortran library from the host
-        host.run_ssh_cmd("sudo apt-get install -y gfortran-8")
-        host.run_cmd("mkdir -p /usr/lib/gcc/aarch64-linux-gnu/8")
-        host.run_ssh_cmd(
-            [
-                "docker",
-                "cp",
-                "/usr/lib/gcc/aarch64-linux-gnu/8/libgfortran.a",
-                f"{host.container_id}:/opt/rh/devtoolset-10/root/usr/lib/gcc/aarch64-redhat-linux/10/",
-            ]
-        )
-
-    print("Checking out PyTorch repo")
-    host.run_cmd(
-        f"git clone --recurse-submodules -b {branch} https://github.com/pytorch/pytorch {git_clone_flags}"
-    )
-
-    host.run_cmd("pytorch/.ci/docker/common/install_openblas.sh")
-
-    print("Building PyTorch wheel")
-    build_opts = ""
-    if pytorch_build_number is not None:
-        build_opts += f" -C--build-option=--build-number={pytorch_build_number}"
-    # Breakpad build fails on aarch64
-    build_vars = "USE_BREAKPAD=0 "
-    if branch == "nightly":
-        build_date = (
-            host.check_output("cd pytorch && git log --pretty=format:%s -1")
-            .strip()
-            .split()[0]
-            .replace("-", "")
-        )
-        version = host.check_output("cat pytorch/version.txt").strip()[:-2]
-        build_vars += f"BUILD_TEST=0 PYTORCH_BUILD_VERSION={version}.dev{build_date} PYTORCH_BUILD_NUMBER=1"
-    if branch.startswith(("v1.", "v2.")):
-        build_vars += f"BUILD_TEST=0 PYTORCH_BUILD_VERSION={branch[1 : branch.find('-')]} PYTORCH_BUILD_NUMBER=1"
-    if host.using_docker():
-        build_vars += " CMAKE_SHARED_LINKER_FLAGS=-Wl,-z,max-page-size=0x10000"
-    if enable_mkldnn:
-        host.run_cmd("pytorch/.ci/docker/common/install_acl.sh")
-        print("build pytorch with mkldnn+acl backend")
-        build_vars += " USE_MKLDNN=ON USE_MKLDNN_ACL=ON"
-        build_vars += " BLAS=OpenBLAS"
-        build_vars += " OpenBLAS_HOME=/opt/OpenBLAS"
-        build_vars += " ACL_ROOT_DIR=/acl"
-        host.run_cmd(
-            f"cd $HOME/pytorch && {build_vars} python3 -m build --wheel --no-isolation{build_opts}"
-        )
-        print("Repair the wheel")
-        pytorch_wheel_name = host.list_dir("pytorch/dist")[0]
-        ld_library_path = "/acl/build:$HOME/pytorch/build/lib"
-        host.run_cmd(
-            f"export LD_LIBRARY_PATH={ld_library_path} && auditwheel repair $HOME/pytorch/dist/{pytorch_wheel_name}"
-        )
-        print("replace the original wheel with the repaired one")
-        pytorch_repaired_wheel_name = host.list_dir("wheelhouse")[0]
-        host.run_cmd(
-            f"cp $HOME/wheelhouse/{pytorch_repaired_wheel_name} $HOME/pytorch/dist/{pytorch_wheel_name}"
-        )
-    else:
-        print("build pytorch without mkldnn backend")
-        host.run_cmd(
-            f"cd pytorch && {build_vars} python3 -m build --wheel --no-isolation{build_opts}"
-        )
-
-    print("Deleting build folder")
-    host.run_cmd("cd pytorch && rm -rf build")
-    pytorch_wheel_name = host.list_dir("pytorch/dist")[0]
-    embed_libgomp(host, use_conda, os.path.join("pytorch", "dist", pytorch_wheel_name))
-    print("Copying the wheel")
-    host.download_wheel(os.path.join("pytorch", "dist", pytorch_wheel_name))
-
-    print("Installing PyTorch wheel")
-    host.run_cmd(f"pip3 install pytorch/dist/{pytorch_wheel_name}")
-
-    if pytorch_only:
-        return (pytorch_wheel_name, None, None, None, None)
-    domain_wheels = build_domains(
-        host, branch=branch, use_conda=use_conda, git_clone_flags=git_clone_flags
-    )
-
-    return (pytorch_wheel_name, *domain_wheels)
-
-
-embed_library_script = """
-#!/usr/bin/env python3
-
-from auditwheel.patcher import Patchelf
-from auditwheel.wheeltools import InWheelCtx
-from auditwheel.elfutils import elf_file_filter
-from auditwheel.repair import copylib
-from auditwheel.lddtree import lddtree
-from subprocess import check_call
-import os
-import shutil
-import sys
-from tempfile import TemporaryDirectory
-
-
-def replace_tag(filename):
-   with open(filename, 'r') as f:
-     lines = f.read().split("\\n")
-   for i,line in enumerate(lines):
-       if not line.startswith("Tag: "):
-           continue
-       lines[i] = line.replace("-linux_", "-manylinux2014_")
-       print(f'Updated tag from {line} to {lines[i]}')
-
-   with open(filename, 'w') as f:
-       f.write("\\n".join(lines))
-
-
-class AlignedPatchelf(Patchelf):
-    def set_soname(self, file_name: str, new_soname: str) -> None:
-        check_call(['patchelf', '--page-size', '65536', '--set-soname', new_soname, file_name])
-
-    def replace_needed(self, file_name: str, soname: str, new_soname: str) -> None:
-        check_call(['patchelf', '--page-size', '65536', '--replace-needed', soname, new_soname, file_name])
-
-
-def embed_library(whl_path, lib_soname, update_tag=False):
-    patcher = AlignedPatchelf()
-    out_dir = TemporaryDirectory()
-    whl_name = os.path.basename(whl_path)
-    tmp_whl_name = os.path.join(out_dir.name, whl_name)
-    with InWheelCtx(whl_path) as ctx:
-        torchlib_path = os.path.join(ctx._tmpdir.name, 'torch', 'lib')
-        ctx.out_wheel=tmp_whl_name
-        new_lib_path, new_lib_soname = None, None
-        for filename, elf in elf_file_filter(ctx.iter_files()):
-            if not filename.startswith('torch/lib'):
-                continue
-            libtree = lddtree(filename)
-            if lib_soname not in libtree['needed']:
-                continue
-            lib_path = libtree['libs'][lib_soname]['path']
-            if lib_path is None:
-                print(f"Can't embed {lib_soname} as it could not be found")
-                break
-            if lib_path.startswith(torchlib_path):
-                continue
-
-            if new_lib_path is None:
-                new_lib_soname, new_lib_path = copylib(lib_path, torchlib_path, patcher)
-            patcher.replace_needed(filename, lib_soname, new_lib_soname)
-            print(f'Replacing {lib_soname} with {new_lib_soname} for {filename}')
-        if update_tag:
-            # Add manylinux2014 tag
-            for filename in ctx.iter_files():
-                if os.path.basename(filename) != 'WHEEL':
-                    continue
-                replace_tag(filename)
-    shutil.move(tmp_whl_name, whl_path)
-
-
-if __name__ == '__main__':
-    embed_library(sys.argv[1], 'libgomp.so.1', len(sys.argv) > 2 and sys.argv[2] == '--update-tag')
-"""
-
-
-def run_tests(host: RemoteHost, whl: str, branch="main") -> None:
-    print("Configuring the system")
-    update_apt_repo(host)
-    host.run_cmd("sudo apt-get install -y python3-pip git")
-    host.run_cmd("sudo pip3 install Cython")
-    host.run_cmd("sudo pip3 install numpy")
-    host.upload_file(whl, ".")
-    host.run_cmd(f"sudo pip3 install {whl}")
-    host.run_cmd("python3 -c 'import torch;print(torch.rand((3,3))'")
-    host.run_cmd(f"git clone -b {branch} https://github.com/pytorch/pytorch")
-    host.run_cmd("cd pytorch/test; python3 test_torch.py -v")
-
-
-def get_instance_name(instance) -> Optional[str]:
-    if instance.tags is None:
-        return None
-    for tag in instance.tags:
-        if tag["Key"] == "Name":
-            return tag["Value"]
-    return None
-
-
-def list_instances(instance_type: str) -> None:
-    print(f"All instances of type {instance_type}")
-    for instance in ec2_instances_of_type(instance_type):
-        ifaces = instance.network_interfaces
-        az = ifaces[0].subnet.availability_zone if len(ifaces) > 0 else None
-        print(
-            f"{instance.id} {get_instance_name(instance)} {instance.public_dns_name} {instance.state['Name']} {az}"
-        )
-
-
-def terminate_instances(instance_type: str) -> None:
-    print(f"Terminating all instances of type {instance_type}")
-    instances = list(ec2_instances_of_type(instance_type))
-    for instance in instances:
-        print(f"Terminating {instance.id}")
-        instance.terminate()
-    print("Waiting for termination to complete")
-    for instance in instances:
-        instance.wait_until_terminated()
-
-
-def parse_arguments():
-    from argparse import ArgumentParser
-
-    parser = ArgumentParser("Build and test AARCH64 wheels using EC2")
-    parser.add_argument("--key-name", type=str)
-    parser.add_argument("--debug", action="store_true")
-    parser.add_argument("--build-only", action="store_true")
-    parser.add_argument("--test-only", type=str)
-    group = parser.add_mutually_exclusive_group()
-    group.add_argument("--os", type=str, choices=list(os_amis.keys()))
-    group.add_argument("--ami", type=str)
-    parser.add_argument(
-        "--python-version",
-        type=str,
-        choices=[f"3.{d}" for d in range(6, 12)],
-        default=None,
-    )
-    parser.add_argument("--alloc-instance", action="store_true")
-    parser.add_argument("--list-instances", action="store_true")
-    parser.add_argument("--pytorch-only", action="store_true")
-    parser.add_argument("--keep-running", action="store_true")
-    parser.add_argument("--terminate-instances", action="store_true")
-    parser.add_argument("--instance-type", type=str, default="t4g.2xlarge")
-    parser.add_argument("--ebs-size", type=int, default=50)
-    parser.add_argument("--branch", type=str, default="main")
-    parser.add_argument("--use-docker", action="store_true")
-    parser.add_argument(
-        "--compiler",
-        type=str,
-        choices=["gcc-7", "gcc-8", "gcc-9", "clang"],
-        default="gcc-8",
-    )
-    parser.add_argument("--use-torch-from-pypi", action="store_true")
-    parser.add_argument("--pytorch-build-number", type=str, default=None)
-    parser.add_argument("--disable-mkldnn", action="store_true")
-    return parser.parse_args()
-
-
-if __name__ == "__main__":
-    args = parse_arguments()
-    ami = (
-        args.ami
-        if args.ami is not None
-        else os_amis[args.os]
-        if args.os is not None
-        else ubuntu20_04_ami
-    )
-    keyfile_path, key_name = compute_keyfile_path(args.key_name)
-
-    if args.list_instances:
-        list_instances(args.instance_type)
-        sys.exit(0)
-
-    if args.terminate_instances:
-        terminate_instances(args.instance_type)
-        sys.exit(0)
-
-    if len(key_name) == 0:
-        raise RuntimeError("""
-            Cannot start build without key_name, please specify
-            --key-name argument or AWS_KEY_NAME environment variable.""")
-    if len(keyfile_path) == 0 or not os.path.exists(keyfile_path):
-        raise RuntimeError(f"""
-            Cannot find keyfile with name: [{key_name}] in path: [{keyfile_path}], please
-            check `~/.ssh/` folder or manually set SSH_KEY_PATH environment variable.""")
-
-    # Starting the instance
-    inst = start_instance(
-        key_name, ami=ami, instance_type=args.instance_type, ebs_size=args.ebs_size
-    )
-    instance_name = f"{args.key_name}-{args.os}"
-    if args.python_version is not None:
-        instance_name += f"-py{args.python_version}"
-    inst.create_tags(
-        DryRun=False,
-        Tags=[
-            {
-                "Key": "Name",
-                "Value": instance_name,
-            }
-        ],
-    )
-    addr = inst.public_dns_name
-    wait_for_connection(addr, 22)
-    host = RemoteHost(addr, keyfile_path)
-    host.ami = ami
-    if args.use_docker:
-        update_apt_repo(host)
-        host.start_docker()
-
-    if args.test_only:
-        run_tests(host, args.test_only)
-        sys.exit(0)
-
-    if args.alloc_instance:
-        if args.python_version is None:
-            sys.exit(0)
-        install_condaforge_python(host, args.python_version)
-        sys.exit(0)
-
-    python_version = args.python_version if args.python_version is not None else "3.10"
-
-    if args.use_torch_from_pypi:
-        configure_system(host, compiler=args.compiler, python_version=python_version)
-        print("Installing PyTorch wheel")
-        host.run_cmd("pip3 install torch")
-        build_domains(
-            host, branch=args.branch, git_clone_flags=" --depth 1 --shallow-submodules"
-        )
-    else:
-        start_build(
-            host,
-            branch=args.branch,
-            compiler=args.compiler,
-            python_version=python_version,
-            pytorch_only=args.pytorch_only,
-            pytorch_build_number=args.pytorch_build_number,
-            enable_mkldnn=not args.disable_mkldnn,
-        )
-    if not args.keep_running:
-        print(f"Waiting for instance {inst.id} to terminate")
-        inst.terminate()
-        inst.wait_until_terminated()

.ci/aarch64_linux/embed_library.py

@@ -1,87 +0,0 @@
-#!/usr/bin/env python3
-
-import os
-import shutil
-import sys
-from subprocess import check_call
-from tempfile import TemporaryDirectory
-
-from auditwheel.elfutils import elf_file_filter
-from auditwheel.lddtree import lddtree
-from auditwheel.patcher import Patchelf
-from auditwheel.repair import copylib
-from auditwheel.wheeltools import InWheelCtx
-
-
-def replace_tag(filename):
-    with open(filename) as f:
-        lines = f.read().split("\\n")
-    for i, line in enumerate(lines):
-        if not line.startswith("Tag: "):
-            continue
-        lines[i] = line.replace("-linux_", "-manylinux2014_")
-        print(f"Updated tag from {line} to {lines[i]}")
-
-    with open(filename, "w") as f:
-        f.write("\\n".join(lines))
-
-
-class AlignedPatchelf(Patchelf):
-    def set_soname(self, file_name: str, new_soname: str) -> None:
-        check_call(
-            ["patchelf", "--page-size", "65536", "--set-soname", new_soname, file_name]
-        )
-
-    def replace_needed(self, file_name: str, soname: str, new_soname: str) -> None:
-        check_call(
-            [
-                "patchelf",
-                "--page-size",
-                "65536",
-                "--replace-needed",
-                soname,
-                new_soname,
-                file_name,
-            ]
-        )
-
-
-def embed_library(whl_path, lib_soname, update_tag=False):
-    patcher = AlignedPatchelf()
-    out_dir = TemporaryDirectory()
-    whl_name = os.path.basename(whl_path)
-    tmp_whl_name = os.path.join(out_dir.name, whl_name)
-    with InWheelCtx(whl_path) as ctx:
-        torchlib_path = os.path.join(ctx._tmpdir.name, "torch", "lib")
-        ctx.out_wheel = tmp_whl_name
-        new_lib_path, new_lib_soname = None, None
-        for filename, _ in elf_file_filter(ctx.iter_files()):
-            if not filename.startswith("torch/lib"):
-                continue
-            libtree = lddtree(filename)
-            if lib_soname not in libtree["needed"]:
-                continue
-            lib_path = libtree["libs"][lib_soname]["path"]
-            if lib_path is None:
-                print(f"Can't embed {lib_soname} as it could not be found")
-                break
-            if lib_path.startswith(torchlib_path):
-                continue
-
-            if new_lib_path is None:
-                new_lib_soname, new_lib_path = copylib(lib_path, torchlib_path, patcher)
-            patcher.replace_needed(filename, lib_soname, new_lib_soname)
-            print(f"Replacing {lib_soname} with {new_lib_soname} for {filename}")
-        if update_tag:
-            # Add manylinux2014 tag
-            for filename in ctx.iter_files():
-                if os.path.basename(filename) != "WHEEL":
-                    continue
-                replace_tag(filename)
-    shutil.move(tmp_whl_name, whl_path)
-
-
-if __name__ == "__main__":
-    embed_library(
-        sys.argv[1], "libgomp.so.1", len(sys.argv) > 2 and sys.argv[2] == "--update-tag"
-    )

.ci/manywheel/build.sh

@@ -4,14 +4,17 @@ set -ex
 
 SCRIPTPATH="$( cd "$( dirname "${BASH_SOURCE[0]}" )" >/dev/null 2>&1 && pwd )"
 
+# Source the common build script for architecture-specific configurations (MKLDNN, ACL, etc.)
+source "${SCRIPTPATH}/../pytorch/build.sh" || true
+
 case "${GPU_ARCH_TYPE:-BLANK}" in
-    cuda)
+    cuda | cuda-aarch64)
         bash "${SCRIPTPATH}/build_cuda.sh"
         ;;
     rocm)
         bash "${SCRIPTPATH}/build_rocm.sh"
         ;;
-    cpu | cpu-cxx11-abi | cpu-s390x)
+    cpu | cpu-cxx11-abi | cpu-aarch64 | cpu-s390x)
         bash "${SCRIPTPATH}/build_cpu.sh"
         ;;
     xpu)

.ci/manywheel/build_common.sh

@@ -18,12 +18,31 @@ retry () {
     $*  || (sleep 1 && $*) || (sleep 2 && $*) || (sleep 4 && $*) || (sleep 8 && $*)
 }
 
+# Detect architecture first
+ARCH=$(uname -m)
+echo "Detected architecture: $ARCH"
+
 PLATFORM=""
 # TODO move this into the Docker images
 OS_NAME=$(awk -F= '/^NAME/{print $2}' /etc/os-release)
 if [[ "$OS_NAME" == *"AlmaLinux"* ]]; then
     retry yum install -q -y zip openssl
-    PLATFORM="manylinux_2_28_x86_64"
+    # Set platform based on architecture
+    case $ARCH in
+        x86_64)
+            PLATFORM="manylinux_2_28_x86_64"
+            ;;
+        aarch64)
+            PLATFORM="manylinux_2_28_aarch64"
+            ;;
+        s390x)
+            PLATFORM="manylinux_2_28_s390x"
+            ;;
+        *)
+            echo "Unsupported architecture: $ARCH"
+            exit 1
+            ;;
+    esac
 elif [[ "$OS_NAME" == *"Red Hat Enterprise Linux"* ]]; then
     retry dnf install -q -y zip openssl
 elif [[ "$OS_NAME" == *"Ubuntu"* ]]; then
@@ -38,6 +57,8 @@ else
     exit 1
 fi
 
+echo "Platform set to: $PLATFORM"
+
 # We use the package name to test the package by passing this to 'pip install'
 # This is the env variable that setup.py uses to name the package. Note that
 # pip 'normalizes' the name first by changing all - to _
@@ -299,8 +320,8 @@ for pkg in /$WHEELHOUSE_DIR/torch_no_python*.whl /$WHEELHOUSE_DIR/torch*linux*.w
             # ROCm workaround for roctracer dlopens
             if [[ "$DESIRED_CUDA" == *"rocm"* ]]; then
                 patchedpath=$(fname_without_so_number $destpath)
-            # Keep the so number for XPU dependencies and libgomp.so.1 to avoid twice load
-            elif [[ "$DESIRED_CUDA" == *"xpu"* || "$filename" == "libgomp.so.1" ]]; then
+            # Keep the so number for XPU dependencies, libgomp.so.1, ACL libraries, and NVPL libraries to avoid twice load
+            elif [[ "$DESIRED_CUDA" == *"xpu"* || "$filename" == "libgomp.so.1" || "$filename" == libarm_compute* || "$filename" == libnvpl* || "$filename" == "libgfortran.so.5" ]]; then
                 patchedpath=$destpath
             else
                 patchedpath=$(fname_with_sha256 $destpath)
@@ -346,9 +367,22 @@ for pkg in /$WHEELHOUSE_DIR/torch_no_python*.whl /$WHEELHOUSE_DIR/torch*linux*.w
     done
 
     # create Manylinux 2_28 tag this needs to happen before regenerate the RECORD
-    if [[ $PLATFORM == "manylinux_2_28_x86_64" && $GPU_ARCH_TYPE != "cpu-s390x" && $GPU_ARCH_TYPE != "xpu" ]]; then
+    # Support all architectures (x86_64, aarch64, s390x)
+    if [[ "$IS_MANYLINUX2_28" == "1" && $GPU_ARCH_TYPE != "xpu" ]]; then
         wheel_file=$(echo $(basename $pkg) | sed -e 's/-cp.*$/.dist-info\/WHEEL/g')
-        sed -i -e s#linux_x86_64#"${PLATFORM}"# $wheel_file;
+        echo "Updating wheel tag for $ARCH architecture"
+        # Replace linux_* with manylinux_2_28_* based on architecture
+        case $ARCH in
+            x86_64)
+                sed -i -e 's#linux_x86_64#manylinux_2_28_x86_64#g' $wheel_file
+                ;;
+            aarch64)
+                sed -i -e 's#linux_aarch64#manylinux_2_28_aarch64#g' $wheel_file
+                ;;
+            s390x)
+                sed -i -e 's#linux_s390x#manylinux_2_28_s390x#g' $wheel_file
+                ;;
+        esac
     fi
 
     # regenerate the RECORD file with new hashes

.ci/manywheel/build_cpu.sh

@@ -15,6 +15,10 @@ if [[ -z "$EXTRA_CAFFE2_CMAKE_FLAGS" ]]; then
     EXTRA_CAFFE2_CMAKE_FLAGS=()
 fi
 
+# Detect architecture
+ARCH=$(uname -m)
+echo "Building CPU wheel for architecture: $ARCH"
+
 WHEELHOUSE_DIR="wheelhousecpu"
 LIBTORCH_HOUSE_DIR="libtorch_housecpu"
 if [[ -z "$PYTORCH_FINAL_PACKAGE_DIR" ]]; then
@@ -34,8 +38,10 @@ elif [[ "$OS_NAME" == *"Red Hat Enterprise Linux"* ]]; then
 elif [[ "$OS_NAME" == *"AlmaLinux"* ]]; then
     LIBGOMP_PATH="/usr/lib64/libgomp.so.1"
 elif [[ "$OS_NAME" == *"Ubuntu"* ]]; then
-    if [[ "$(uname -m)" == "s390x" ]]; then
+    if [[ "$ARCH" == "s390x" ]]; then
         LIBGOMP_PATH="/usr/lib/s390x-linux-gnu/libgomp.so.1"
+    elif [[ "$ARCH" == "aarch64" ]]; then
+        LIBGOMP_PATH="/usr/lib/aarch64-linux-gnu/libgomp.so.1"
     else
         LIBGOMP_PATH="/usr/lib/x86_64-linux-gnu/libgomp.so.1"
     fi
@@ -49,6 +55,34 @@ DEPS_SONAME=(
     "libgomp.so.1"
 )
 
+# Add ARM-specific library dependencies for CPU builds
+if [[ "$ARCH" == "aarch64" ]]; then
+    echo "Adding ARM-specific CPU library dependencies"
+
+    # ARM Compute Library (if available)
+    if [[ -d "/acl/build" ]]; then
+        echo "Adding ARM Compute Library for CPU"
+        DEPS_LIST+=(
+            "/acl/build/libarm_compute.so"
+            "/acl/build/libarm_compute_graph.so"
+        )
+        DEPS_SONAME+=(
+            "libarm_compute.so"
+            "libarm_compute_graph.so"
+        )
+    fi
+
+    # ARM system libraries
+    DEPS_LIST+=(
+        "/usr/lib64/libgfortran.so.5"
+        "/opt/OpenBLAS/lib/libopenblas.so.0"
+    )
+    DEPS_SONAME+=(
+        "libgfortran.so.5"
+        "libopenblas.so.0"
+    )
+fi
+
 rm -rf /usr/local/cuda*
 
 SOURCE_DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" >/dev/null && pwd )"

.ci/manywheel/build_cuda.sh

@@ -29,6 +29,10 @@ if [[ -z "$EXTRA_CAFFE2_CMAKE_FLAGS" ]]; then
     EXTRA_CAFFE2_CMAKE_FLAGS=()
 fi
 
+# Detect architecture
+ARCH=$(uname -m)
+echo "Building for architecture: $ARCH"
+
 # Determine CUDA version and architectures to build for
 #
 # NOTE: We should first check `DESIRED_CUDA` when determining `CUDA_VERSION`,
@@ -53,34 +57,60 @@ fi
 cuda_version_nodot=$(echo $CUDA_VERSION | tr -d '.')
 EXTRA_CAFFE2_CMAKE_FLAGS+=("-DATEN_NO_TEST=ON")
 
+# Function to remove architectures from a list
+remove_archs() {
+    local result="$1"
+    shift
+    for arch in "$@"; do
+        result="${result//${arch};/}"
+    done
+    echo "$result"
+}
+
+# Function to filter CUDA architectures for aarch64
+# aarch64 ARM GPUs only support certain compute capabilities
+# Keep: 8.0 (A100), 9.0+ (Hopper, Grace Hopper, newer)
+# Remove: < 8.0 (no ARM GPUs), 8.6 (x86_64 RTX 3090/A6000 only)
+filter_aarch64_archs() {
+    local arch_list="$1"
+    # Explicitly remove architectures not needed on aarch64
+    arch_list=$(remove_archs "$arch_list" "5.0" "6.0" "7.0" "7.5" "8.6")
+    echo "$arch_list"
+}
+
+# Base: Common architectures across all modern CUDA versions
+TORCH_CUDA_ARCH_LIST="7.0;7.5;8.0;8.6;9.0"
+
 case ${CUDA_VERSION} in
-    #removing sm_50-sm_60 as these architectures are deprecated in CUDA 12.8/9 and will be removed in future releases
-    #however we would like to keep sm_70 architecture see: https://github.com/pytorch/pytorch/issues/157517
-    12.8)
-        TORCH_CUDA_ARCH_LIST="7.0;7.5;8.0;8.6;9.0;10.0;12.0"
-        ;;
-    12.9)
-        TORCH_CUDA_ARCH_LIST="7.0;7.5;8.0;8.6;9.0;10.0;12.0+PTX"
-        # WAR to resolve the ld error in libtorch build with CUDA 12.9
+    12.6) TORCH_CUDA_ARCH_LIST="5.0;6.0;${TORCH_CUDA_ARCH_LIST}" ;;  # Only 12.6 includes Legacy Maxwell/Pascal that will be removed in future releases
+    12.8) TORCH_CUDA_ARCH_LIST="${TORCH_CUDA_ARCH_LIST};10.0;12.0" ;;  # +Hopper/Blackwell support
+    12.9) TORCH_CUDA_ARCH_LIST="${TORCH_CUDA_ARCH_LIST};10.0;12.0+PTX" # +Hopper/Blackwell support + PTX for forward compatibility
         if [[ "$PACKAGE_TYPE" == "libtorch" ]]; then
-            TORCH_CUDA_ARCH_LIST="7.5;8.0;9.0;10.0;12.0+PTX"
+            TORCH_CUDA_ARCH_LIST="${TORCH_CUDA_ARCH_LIST//7.0;/}"  # Remove 7.0 to resolve the ld error
+            TORCH_CUDA_ARCH_LIST="${TORCH_CUDA_ARCH_LIST//8.6;/}"  # Remove 8.6 for libtorch
         fi
         ;;
     13.0)
-        TORCH_CUDA_ARCH_LIST="7.5;8.0;8.6;9.0;10.0;12.0+PTX"
-        ;;
-    12.6)
-        TORCH_CUDA_ARCH_LIST="5.0;6.0;7.0;7.5;8.0;8.6;9.0"
-        ;;
-    *)
-        echo "unknown cuda version $CUDA_VERSION"
-        exit 1
+        TORCH_CUDA_ARCH_LIST="7.5;8.0;8.6;9.0;10.0;$([[ "$ARCH" == "aarch64" ]] && echo "11.0;" || echo "")12.0+PTX"
+        export TORCH_NVCC_FLAGS="-compress-mode=size"
+        export BUILD_BUNDLE_PTXAS=1
         ;;
+    *) echo "unknown cuda version $CUDA_VERSION"; exit 1 ;;
 esac
 
+# Filter for aarch64: Remove < 8.0 and 8.6
+[[ "$ARCH" == "aarch64" ]] && TORCH_CUDA_ARCH_LIST=$(filter_aarch64_archs "$TORCH_CUDA_ARCH_LIST")
+
+echo "TORCH_CUDA_ARCH_LIST set to: $TORCH_CUDA_ARCH_LIST"
 export TORCH_CUDA_ARCH_LIST=${TORCH_CUDA_ARCH_LIST}
 echo "${TORCH_CUDA_ARCH_LIST}"
 
+# Disable MAGMA for aarch64 as pre-built libraries are x86-64 only
+if [[ "$ARCH" == "aarch64" ]]; then
+    echo "Disabling MAGMA for aarch64 architecture"
+    export USE_MAGMA=0
+fi
+
 # Package directories
 WHEELHOUSE_DIR="wheelhouse$cuda_version_nodot"
 LIBTORCH_HOUSE_DIR="libtorch_house$cuda_version_nodot"
@@ -244,6 +274,51 @@ else
     exit 1
 fi
 
+# Add ARM-specific library dependencies
+if [[ "$ARCH" == "aarch64" ]]; then
+    echo "Adding ARM-specific library dependencies"
+
+    # ARM Compute Library (if available)
+    if [[ -d "/acl/build" ]]; then
+        echo "Adding ARM Compute Library"
+        DEPS_LIST+=(
+            "/acl/build/libarm_compute.so"
+            "/acl/build/libarm_compute_graph.so"
+        )
+        DEPS_SONAME+=(
+            "libarm_compute.so"
+            "libarm_compute_graph.so"
+        )
+    fi
+
+    # ARM system libraries
+    DEPS_LIST+=(
+        "/lib64/libgomp.so.1"
+        "/usr/lib64/libgfortran.so.5"
+    )
+    DEPS_SONAME+=(
+        "libgomp.so.1"
+        "libgfortran.so.5"
+    )
+
+    # NVPL libraries (ARM optimized BLAS/LAPACK)
+    if [[ -d "/usr/local/lib" && -f "/usr/local/lib/libnvpl_blas_lp64_gomp.so.0" ]]; then
+        echo "Adding NVPL libraries for ARM"
+        DEPS_LIST+=(
+            "/usr/local/lib/libnvpl_lapack_lp64_gomp.so.0"
+            "/usr/local/lib/libnvpl_blas_lp64_gomp.so.0"
+            "/usr/local/lib/libnvpl_lapack_core.so.0"
+            "/usr/local/lib/libnvpl_blas_core.so.0"
+        )
+        DEPS_SONAME+=(
+            "libnvpl_lapack_lp64_gomp.so.0"
+            "libnvpl_blas_lp64_gomp.so.0"
+            "libnvpl_lapack_core.so.0"
+            "libnvpl_blas_core.so.0"
+        )
+    fi
+fi
+
 # run_tests.sh requires DESIRED_CUDA to know what tests to exclude
 export DESIRED_CUDA="$cuda_version_nodot"
 
@@ -251,9 +326,11 @@ export DESIRED_CUDA="$cuda_version_nodot"
 rm -rf /usr/local/cuda || true
 ln -s "/usr/local/cuda-${CUDA_VERSION}" /usr/local/cuda
 
-# Switch `/usr/local/magma` to the desired CUDA version
-rm -rf /usr/local/magma || true
-ln -s /usr/local/cuda-${CUDA_VERSION}/magma /usr/local/magma
+# Switch `/usr/local/magma` to the desired CUDA version (skip for aarch64)
+if [[ "$ARCH" != "aarch64" ]]; then
+    rm -rf /usr/local/magma || true
+    ln -s /usr/local/cuda-${CUDA_VERSION}/magma /usr/local/magma
+fi
 
 export CUDA_VERSION=$(ls /usr/local/cuda/lib64/libcudart.so.*|sort|tac | head -1 | rev | cut -d"." -f -3 | rev) # 10.0.130
 export CUDA_VERSION_SHORT=$(ls /usr/local/cuda/lib64/libcudart.so.*|sort|tac | head -1 | rev | cut -d"." -f -3 | rev | cut -f1,2 -d".") # 10.0

.ci/pytorch/build.sh

@@ -86,10 +86,20 @@ else
   fi
 fi
 
+# Enable MKLDNN with ARM Compute Library for ARM builds
 if [[ "$BUILD_ENVIRONMENT" == *aarch64* ]]; then
   export USE_MKLDNN=1
+
+  # ACL is required for aarch64 builds
+  if [[ ! -d "/acl" ]]; then
+    echo "ERROR: ARM Compute Library not found at /acl"
+    echo "ACL is required for aarch64 builds. Check Docker image setup."
+    exit 1
+  fi
+
   export USE_MKLDNN_ACL=1
   export ACL_ROOT_DIR=/acl
+  echo "ARM Compute Library enabled for MKLDNN: ACL_ROOT_DIR=/acl"
 fi
 
 if [[ "$BUILD_ENVIRONMENT" == *riscv64* ]]; then

.ci/pytorch/smoke_test/check_binary_symbols.py

@@ -100,337 +100,6 @@ def check_lib_statically_linked_libstdc_cxx_abi_symbols(lib: str) -> None:
         )
 
 
-def _compile_and_extract_symbols(
-    cpp_content: str, compile_flags: list[str], exclude_list: list[str] | None = None
-) -> list[str]:
-    """
-    Helper to compile a C++ file and extract all symbols.
-
-    Args:
-        cpp_content: C++ source code to compile
-        compile_flags: Compilation flags
-        exclude_list: List of symbol names to exclude. Defaults to ["main"].
-
-    Returns:
-        List of all symbols found in the object file (excluding those in exclude_list).
-    """
-    import subprocess
-    import tempfile
-
-    if exclude_list is None:
-        exclude_list = ["main"]
-
-    with tempfile.TemporaryDirectory() as tmpdir:
-        tmppath = Path(tmpdir)
-        cpp_file = tmppath / "test.cpp"
-        obj_file = tmppath / "test.o"
-
-        cpp_file.write_text(cpp_content)
-
-        result = subprocess.run(
-            compile_flags + [str(cpp_file), "-o", str(obj_file)],
-            capture_output=True,
-            text=True,
-            timeout=60,
-        )
-
-        if result.returncode != 0:
-            raise RuntimeError(f"Compilation failed: {result.stderr}")
-
-        symbols = get_symbols(str(obj_file))
-
-        # Return all symbol names, excluding those in the exclude list
-        return [name for _addr, _stype, name in symbols if name not in exclude_list]
-
-
-def check_stable_only_symbols(install_root: Path) -> None:
-    """
-    Test TORCH_STABLE_ONLY and TORCH_TARGET_VERSION by compiling test code and comparing symbol counts.
-
-    This approach tests:
-    1. WITHOUT macros -> many torch symbols exposed
-    2. WITH TORCH_STABLE_ONLY -> zero torch symbols (all hidden)
-    3. WITH TORCH_TARGET_VERSION -> zero torch symbols (all hidden)
-    4. WITH both macros -> zero torch symbols (all hidden)
-    """
-    include_dir = install_root / "include"
-    assert include_dir.exists(), f"Expected {include_dir} to be present"
-
-    test_cpp_content = """
-// Main torch C++ API headers
-#include <torch/torch.h>
-#include <torch/all.h>
-
-// ATen tensor library
-#include <ATen/ATen.h>
-
-// Core c10 headers (commonly used)
-#include <c10/core/Device.h>
-#include <c10/core/DeviceType.h>
-#include <c10/core/ScalarType.h>
-#include <c10/core/TensorOptions.h>
-#include <c10/util/Optional.h>
-
-int main() { return 0; }
-"""
-
-    base_compile_flags = [
-        "g++",
-        "-std=c++17",
-        f"-I{include_dir}",
-        f"-I{include_dir}/torch/csrc/api/include",
-        "-c",  # Compile only, don't link
-    ]
-
-    # Compile WITHOUT any macros
-    symbols_without = _compile_and_extract_symbols(
-        cpp_content=test_cpp_content,
-        compile_flags=base_compile_flags,
-    )
-
-    # We expect constexpr symbols, inline functions used by other headers etc.
-    # to produce symbols
-    num_symbols_without = len(symbols_without)
-    print(f"Found {num_symbols_without} symbols without any macros defined")
-    assert num_symbols_without != 0, (
-        "Expected a non-zero number of symbols without any macros"
-    )
-
-    # Compile WITH TORCH_STABLE_ONLY (expect 0 symbols)
-    compile_flags_with_stable_only = base_compile_flags + ["-DTORCH_STABLE_ONLY"]
-
-    symbols_with_stable_only = _compile_and_extract_symbols(
-        cpp_content=test_cpp_content,
-        compile_flags=compile_flags_with_stable_only,
-    )
-
-    num_symbols_with_stable_only = len(symbols_with_stable_only)
-    assert num_symbols_with_stable_only == 0, (
-        f"Expected no symbols with TORCH_STABLE_ONLY macro, but found {num_symbols_with_stable_only}"
-    )
-
-    # Compile WITH TORCH_TARGET_VERSION (expect 0 symbols)
-    compile_flags_with_target_version = base_compile_flags + [
-        "-DTORCH_TARGET_VERSION=1"
-    ]
-
-    symbols_with_target_version = _compile_and_extract_symbols(
-        cpp_content=test_cpp_content,
-        compile_flags=compile_flags_with_target_version,
-    )
-
-    num_symbols_with_target_version = len(symbols_with_target_version)
-    assert num_symbols_with_target_version == 0, (
-        f"Expected no symbols with TORCH_TARGET_VERSION macro, but found {num_symbols_with_target_version}"
-    )
-
-    # Compile WITH both macros (expect 0 symbols)
-    compile_flags_with_both = base_compile_flags + [
-        "-DTORCH_STABLE_ONLY",
-        "-DTORCH_TARGET_VERSION=1",
-    ]
-
-    symbols_with_both = _compile_and_extract_symbols(
-        cpp_content=test_cpp_content,
-        compile_flags=compile_flags_with_both,
-    )
-
-    num_symbols_with_both = len(symbols_with_both)
-    assert num_symbols_with_both == 0, (
-        f"Expected no symbols with both macros, but found {num_symbols_with_both}"
-    )
-
-
-def check_stable_api_symbols(install_root: Path) -> None:
-    """
-    Test that stable API headers still expose symbols with TORCH_STABLE_ONLY.
-    The torch/csrc/stable/c/shim.h header is tested in check_stable_c_shim_symbols
-    """
-    include_dir = install_root / "include"
-    assert include_dir.exists(), f"Expected {include_dir} to be present"
-
-    stable_dir = include_dir / "torch" / "csrc" / "stable"
-    assert stable_dir.exists(), f"Expected {stable_dir} to be present"
-
-    stable_headers = list(stable_dir.rglob("*.h"))
-    if not stable_headers:
-        raise RuntimeError("Could not find any stable headers")
-
-    includes = []
-    for header in stable_headers:
-        rel_path = header.relative_to(include_dir)
-        includes.append(f"#include <{rel_path.as_posix()}>")
-
-    includes_str = "\n".join(includes)
-    test_stable_content = f"""
-{includes_str}
-int main() {{ return 0; }}
-"""
-
-    compile_flags = [
-        "g++",
-        "-std=c++17",
-        f"-I{include_dir}",
-        f"-I{include_dir}/torch/csrc/api/include",
-        "-c",
-        "-DTORCH_STABLE_ONLY",
-    ]
-
-    symbols_stable = _compile_and_extract_symbols(
-        cpp_content=test_stable_content,
-        compile_flags=compile_flags,
-    )
-    num_symbols_stable = len(symbols_stable)
-    print(f"Found {num_symbols_stable} symbols in torch/csrc/stable")
-    assert num_symbols_stable > 0, (
-        f"Expected stable headers to expose symbols with TORCH_STABLE_ONLY, "
-        f"but found {num_symbols_stable} symbols"
-    )
-
-
-def check_headeronly_symbols(install_root: Path) -> None:
-    """
-    Test that header-only utility headers still expose symbols with TORCH_STABLE_ONLY.
-    """
-    include_dir = install_root / "include"
-    assert include_dir.exists(), f"Expected {include_dir} to be present"
-
-    # Find all headers in torch/headeronly
-    headeronly_dir = include_dir / "torch" / "headeronly"
-    assert headeronly_dir.exists(), f"Expected {headeronly_dir} to be present"
-    headeronly_headers = list(headeronly_dir.rglob("*.h"))
-    if not headeronly_headers:
-        raise RuntimeError("Could not find any headeronly headers")
-
-    # Filter out platform-specific headers that may not compile everywhere
-    platform_specific_keywords = [
-        "cpu/vec",
-    ]
-
-    filtered_headers = []
-    for header in headeronly_headers:
-        rel_path = header.relative_to(include_dir).as_posix()
-        if not any(
-            keyword in rel_path.lower() for keyword in platform_specific_keywords
-        ):
-            filtered_headers.append(header)
-
-    includes = []
-    for header in filtered_headers:
-        rel_path = header.relative_to(include_dir)
-        includes.append(f"#include <{rel_path.as_posix()}>")
-
-    includes_str = "\n".join(includes)
-    test_headeronly_content = f"""
-{includes_str}
-int main() {{ return 0; }}
-"""
-
-    compile_flags = [
-        "g++",
-        "-std=c++17",
-        f"-I{include_dir}",
-        f"-I{include_dir}/torch/csrc/api/include",
-        "-c",
-        "-DTORCH_STABLE_ONLY",
-    ]
-
-    symbols_headeronly = _compile_and_extract_symbols(
-        cpp_content=test_headeronly_content,
-        compile_flags=compile_flags,
-    )
-    num_symbols_headeronly = len(symbols_headeronly)
-    print(f"Found {num_symbols_headeronly} symbols in torch/headeronly")
-    assert num_symbols_headeronly > 0, (
-        f"Expected headeronly headers to expose symbols with TORCH_STABLE_ONLY, "
-        f"but found {num_symbols_headeronly} symbols"
-    )
-
-
-def check_aoti_shim_symbols(install_root: Path) -> None:
-    """
-    Test that AOTI shim headers still expose symbols with TORCH_STABLE_ONLY.
-    """
-    include_dir = install_root / "include"
-    assert include_dir.exists(), f"Expected {include_dir} to be present"
-
-    # There are no constexpr symbols etc., so we need to actually use functions
-    # so that some symbols are found.
-    test_shim_content = """
-#include <torch/csrc/inductor/aoti_torch/c/shim.h>
-int main() {
-    int32_t (*fp1)() = &aoti_torch_device_type_cpu;
-    int32_t (*fp2)() = &aoti_torch_dtype_float32;
-    (void)fp1; (void)fp2;
-    return 0;
-}
-"""
-
-    compile_flags = [
-        "g++",
-        "-std=c++17",
-        f"-I{include_dir}",
-        f"-I{include_dir}/torch/csrc/api/include",
-        "-c",
-        "-DTORCH_STABLE_ONLY",
-    ]
-
-    symbols_shim = _compile_and_extract_symbols(
-        cpp_content=test_shim_content,
-        compile_flags=compile_flags,
-    )
-    num_symbols_shim = len(symbols_shim)
-    assert num_symbols_shim > 0, (
-        f"Expected shim headers to expose symbols with TORCH_STABLE_ONLY, "
-        f"but found {num_symbols_shim} symbols"
-    )
-
-
-def check_stable_c_shim_symbols(install_root: Path) -> None:
-    """
-    Test that stable C shim headers still expose symbols with TORCH_STABLE_ONLY.
-    """
-    include_dir = install_root / "include"
-    assert include_dir.exists(), f"Expected {include_dir} to be present"
-
-    # Check if the stable C shim exists
-    stable_shim = include_dir / "torch" / "csrc" / "stable" / "c" / "shim.h"
-    if not stable_shim.exists():
-        raise RuntimeError("Could not find stable c shim")
-
-    # There are no constexpr symbols etc., so we need to actually use functions
-    # so that some symbols are found.
-    test_stable_shim_content = """
-#include <torch/csrc/stable/c/shim.h>
-int main() {
-    // Reference stable C API functions to create undefined symbols
-    AOTITorchError (*fp1)(const char*, uint32_t*, int32_t*) = &torch_parse_device_string;
-    AOTITorchError (*fp2)(uint32_t*) = &torch_get_num_threads;
-    (void)fp1; (void)fp2;
-    return 0;
-}
-"""
-
-    compile_flags = [
-        "g++",
-        "-std=c++17",
-        f"-I{include_dir}",
-        f"-I{include_dir}/torch/csrc/api/include",
-        "-c",
-        "-DTORCH_STABLE_ONLY",
-    ]
-
-    symbols_stable_shim = _compile_and_extract_symbols(
-        cpp_content=test_stable_shim_content,
-        compile_flags=compile_flags,
-    )
-    num_symbols_stable_shim = len(symbols_stable_shim)
-    assert num_symbols_stable_shim > 0, (
-        f"Expected stable C shim headers to expose symbols with TORCH_STABLE_ONLY, "
-        f"but found {num_symbols_stable_shim} symbols"
-    )
-
-
 def check_lib_symbols_for_abi_correctness(lib: str) -> None:
     print(f"lib: {lib}")
     cxx11_symbols = grep_symbols(lib, LIBTORCH_CXX11_PATTERNS)
@@ -460,13 +129,6 @@ def main() -> None:
     check_lib_symbols_for_abi_correctness(libtorch_cpu_path)
     check_lib_statically_linked_libstdc_cxx_abi_symbols(libtorch_cpu_path)
 
-    # Check symbols when TORCH_STABLE_ONLY is defined
-    check_stable_only_symbols(install_root)
-    check_stable_api_symbols(install_root)
-    check_headeronly_symbols(install_root)
-    check_aoti_shim_symbols(install_root)
-    check_stable_c_shim_symbols(install_root)
-
 
 if __name__ == "__main__":
     main()

.ci/pytorch/test.sh

@@ -389,6 +389,13 @@ test_lazy_tensor_meta_reference_disabled() {
   export -n TORCH_DISABLE_FUNCTIONALIZATION_META_REFERENCE
 }
 
+test_dynamo_core() {
+  time python test/run_test.py \
+    --include-dynamo-core-tests \
+    --verbose \
+    --upload-artifacts-while-running
+  assert_git_not_dirty
+}
 
 test_dynamo_wrapped_shard() {
   if [[ -z "$NUM_TEST_SHARDS" ]]; then
@@ -1814,6 +1821,8 @@ elif [[ "${TEST_CONFIG}" == *inductor* ]]; then
   test_inductor_shard "${SHARD_NUMBER}"
 elif [[ "${TEST_CONFIG}" == *einops* ]]; then
   test_einops
+elif [[ "${TEST_CONFIG}" == *dynamo_core* ]]; then
+  test_dynamo_core
 elif [[ "${TEST_CONFIG}" == *dynamo_wrapped* ]]; then
   install_torchvision
   test_dynamo_wrapped_shard "${SHARD_NUMBER}"

.github/ci_commit_pins/audio.txt

@@ -1 +1 @@
-07b6cbde121417a70e4dc871adb6d27030e0ce3f
+ee1a1350eb37804b94334768f328144f058f14e9

.github/ci_commit_pins/vision.txt

@@ -1 +1 @@
-acccf86477759b2d3500f1ae1be065f7b1e409ec
+2d82dc5caa336d179d9b46ac4a0fb8c43d84c5cc

.github/ci_commit_pins/xla.txt

@@ -1 +1 @@
-e4d25697f9dc5eedaf8f0a5bf085c62c5455a53a
+94631807d22c09723dd006f7be5beb649d5f88d0

.github/pytorch-probot.yml

@@ -7,6 +7,7 @@ ciflow_push_tags:
 - ciflow/binaries
 - ciflow/binaries_libtorch
 - ciflow/binaries_wheel
+- ciflow/dynamo
 - ciflow/h100
 - ciflow/h100-cutlass-backend
 - ciflow/h100-distributed

.github/scripts/generate_pytorch_version.py

@@ -50,7 +50,7 @@ def get_tag() -> str:
 
 def get_base_version() -> str:
     root = get_pytorch_root()
-    dirty_version = open(root / "version.txt").read().strip()
+    dirty_version = Path(root / "version.txt").read_text().strip()
     # Strips trailing a0 from version.txt, not too sure why it's there in the
     # first place
     return re.sub(LEGACY_BASE_VERSION_SUFFIX_PATTERN, "", dirty_version)

.github/workflows/_binary-build-linux.yml

@@ -260,11 +260,8 @@ jobs:
             "${DOCKER_IMAGE}"
           )
           docker exec -t -w "${PYTORCH_ROOT}" "${container_name}" bash -c "bash .circleci/scripts/binary_populate_env.sh"
-          if [[ ${BUILD_ENVIRONMENT} == *"aarch64"* ]]; then
-            docker exec -t "${container_name}" bash -c "source ${BINARY_ENV_FILE} && bash /pytorch/.ci/aarch64_linux/aarch64_ci_build.sh"
-          else
-            docker exec -t "${container_name}" bash -c "source ${BINARY_ENV_FILE} && bash /pytorch/.ci/${{ inputs.PACKAGE_TYPE }}/build.sh"
-          fi
+          # Unified build script for all architectures (x86_64, aarch64, s390x)
+          docker exec -t "${container_name}" bash -c "source ${BINARY_ENV_FILE} && bash /pytorch/.ci/${{ inputs.PACKAGE_TYPE }}/build.sh"
 
       - name: Chown artifacts
         if: ${{ steps.filter.outputs.is-test-matrix-empty == 'False' && inputs.build_environment != 'linux-s390x-binary-manywheel' }}

.github/workflows/_linux-test.yml

@@ -326,7 +326,7 @@ jobs:
           SCCACHE_BUCKET: ${{ !contains(matrix.runner, 'b200') && 'ossci-compiler-cache-circleci-v2' || '' }}
           SCCACHE_REGION: ${{ !contains(matrix.runner, 'b200') && 'us-east-1' || '' }}
           SHM_SIZE: ${{ contains(inputs.build-environment, 'cuda') && '2g' || '1g' }}
-          DOCKER_IMAGE: ${{ inputs.docker-image }}
+          DOCKER_IMAGE: ${{ steps.calculate-docker-image.outputs.docker-image }}
           XLA_CUDA: ${{ contains(inputs.build-environment, 'xla') && '0' || '' }}
           XLA_CLANG_CACHE_S3_BUCKET_NAME: ossci-compiler-clang-cache-circleci-xla
           PYTORCH_TEST_CUDA_MEM_LEAK_CHECK: ${{ matrix.mem_leak_check && '1' || '0' }}

.github/workflows/dynamo-unittest.yml

@@ -0,0 +1,70 @@
+# Workflow: Dynamo Unit Test
+# runs unit tests for dynamo.
+name: dynamo-unittest
+
+on:
+  push:
+    tags:
+      - ciflow/dynamo/*
+  workflow_call:
+  schedule:
+    - cron: 29 8 * * * # about 1:29am PDT
+
+concurrency:
+  group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.sha }}-${{ github.event_name == 'workflow_dispatch' }}
+  cancel-in-progress: true
+
+permissions:
+  id-token: write
+  contents: read
+
+jobs:
+  get-label-type:
+    name: get-label-type
+    uses: pytorch/pytorch/.github/workflows/_runner-determinator.yml@main
+    if: ${{ (github.event_name != 'schedule' || github.repository == 'pytorch/pytorch') && github.repository_owner == 'pytorch' }}
+    with:
+      triggering_actor: ${{ github.triggering_actor }}
+      issue_owner: ${{ github.event.pull_request.user.login || github.event.issue.user.login }}
+      curr_branch: ${{ github.head_ref || github.ref_name }}
+      curr_ref_type: ${{ github.ref_type }}
+      opt_out_experiments: lf
+
+  dynamo-build:
+    name: dynamo-build
+    uses: ./.github/workflows/_linux-build.yml
+    needs: get-label-type
+    strategy:
+      matrix:
+        python-version: ['3.11', '3.12']
+    with:
+      runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
+      build-environment: linux-jammy-py${{ matrix.python-version }}-clang12
+      docker-image-name: ci-image:pytorch-linux-jammy-py${{ matrix.python-version }}-clang12
+      test-matrix: |
+        { include: [
+          { config: "dynamo_core", shard: 1, num_shards: 1, runner: "${{ needs.get-label-type.outputs.label-type }}linux.c7i.2xlarge" },
+          { config: "dynamo_wrapped", shard: 1, num_shards: 3, runner: "${{ needs.get-label-type.outputs.label-type }}linux.c7i.2xlarge" },
+          { config: "dynamo_wrapped", shard: 2, num_shards: 3, runner: "${{ needs.get-label-type.outputs.label-type }}linux.c7i.2xlarge" },
+          { config: "dynamo_wrapped", shard: 3, num_shards: 3, runner: "${{ needs.get-label-type.outputs.label-type }}linux.c7i.2xlarge" },
+        ]}
+    secrets: inherit
+
+  dynamo-test:
+    name: dynamo-test
+    uses: ./.github/workflows/_linux-test.yml
+    needs: [get-label-type, dynamo-build]
+    strategy:
+      matrix:
+        python-version: ['3.11', '3.12']
+    with:
+      build-environment: linux-jammy-py${{ matrix.python-version }}-clang12
+      docker-image: ci-image:pytorch-linux-jammy-py${{ matrix.python-version }}-clang12
+      test-matrix: |
+        { include: [
+          { config: "dynamo_core", shard: 1, num_shards: 1, runner: "${{ needs.get-label-type.outputs.label-type }}linux.c7i.2xlarge" },
+          { config: "dynamo_wrapped", shard: 1, num_shards: 3, runner: "${{ needs.get-label-type.outputs.label-type }}linux.c7i.2xlarge" },
+          { config: "dynamo_wrapped", shard: 2, num_shards: 3, runner: "${{ needs.get-label-type.outputs.label-type }}linux.c7i.2xlarge" },
+          { config: "dynamo_wrapped", shard: 3, num_shards: 3, runner: "${{ needs.get-label-type.outputs.label-type }}linux.c7i.2xlarge" },
+        ]}
+    secrets: inherit

.spin/cmds.py

@@ -0,0 +1,330 @@
+import hashlib
+import subprocess
+import sys
+from pathlib import Path
+
+import click
+import spin
+
+
+def file_digest(file, algorithm: str):
+    try:
+        return hashlib.file_digest(file, algorithm)
+    except AttributeError:
+        pass  # Fallback to manual implementation below
+    hash = hashlib.new(algorithm)
+    while chunk := file.read(8192):
+        hash.update(chunk)
+    return hash
+
+
+def _hash_file(file):
+    with open(file, "rb") as f:
+        hash = file_digest(f, "sha256")
+    return hash.hexdigest()
+
+
+def _hash_files(files):
+    hashes = {file: _hash_file(file) for file in files}
+    return hashes
+
+
+def _read_hashes(hash_file: Path):
+    if not hash_file.exists():
+        return {}
+    with hash_file.open("r") as f:
+        lines = f.readlines()
+    hashes = {}
+    for line in lines:
+        hash = line[:64]
+        file = line[66:].strip()
+        hashes[file] = hash
+    return hashes
+
+
+def _updated_hashes(hash_file, files_to_hash):
+    old_hashes = _read_hashes(hash_file)
+    new_hashes = _hash_files(files_to_hash)
+    if new_hashes != old_hashes:
+        return new_hashes
+    return None
+
+
+@click.command()
+def regenerate_version():
+    """Regenerate version.py."""
+    cmd = [
+        sys.executable,
+        "-m",
+        "tools.generate_torch_version",
+        "--is-debug=false",
+    ]
+    spin.util.run(cmd)
+
+
+TYPE_STUBS = [
+    (
+        "Pytorch type stubs",
+        Path(".lintbin/.pytorch-type-stubs.sha256"),
+        [
+            "aten/src/ATen/native/native_functions.yaml",
+            "aten/src/ATen/native/tags.yaml",
+            "tools/autograd/deprecated.yaml",
+        ],
+        [
+            sys.executable,
+            "-m",
+            "tools.pyi.gen_pyi",
+            "--native-functions-path",
+            "aten/src/ATen/native/native_functions.yaml",
+            "--tags-path",
+            "aten/src/ATen/native/tags.yaml",
+            "--deprecated-functions-path",
+            "tools/autograd/deprecated.yaml",
+        ],
+    ),
+    (
+        "Datapipes type stubs",
+        None,
+        [],
+        [
+            sys.executable,
+            "torch/utils/data/datapipes/gen_pyi.py",
+        ],
+    ),
+]
+
+
+@click.command()
+def regenerate_type_stubs():
+    """Regenerate type stubs."""
+    for name, hash_file, files_to_hash, cmd in TYPE_STUBS:
+        if hash_file:
+            if hashes := _updated_hashes(hash_file, files_to_hash):
+                click.echo(
+                    f"Changes detected in type stub files for {name}. Regenerating..."
+                )
+                spin.util.run(cmd)
+                hash_file.parent.mkdir(parents=True, exist_ok=True)
+                with hash_file.open("w") as f:
+                    for file, hash in hashes.items():
+                        f.write(f"{hash}  {file}\n")
+                click.echo("Type stubs and hashes updated.")
+            else:
+                click.echo(f"No changes detected in type stub files for {name}.")
+        else:
+            click.echo(f"No hash file for {name}. Regenerating...")
+            spin.util.run(cmd)
+            click.echo("Type stubs regenerated.")
+
+
+@click.command()
+def regenerate_clangtidy_files():
+    """Regenerate clang-tidy files."""
+    cmd = [
+        sys.executable,
+        "-m",
+        "tools.linter.clang_tidy.generate_build_files",
+    ]
+    spin.util.run(cmd)
+
+
+#: These linters are expected to need less than 3s cpu time total
+VERY_FAST_LINTERS = {
+    "ATEN_CPU_GPU_AGNOSTIC",
+    "BAZEL_LINTER",
+    "C10_NODISCARD",
+    "C10_UNUSED",
+    "CALL_ONCE",
+    "CMAKE_MINIMUM_REQUIRED",
+    "CONTEXT_DECORATOR",
+    "COPYRIGHT",
+    "CUBINCLUDE",
+    "DEPLOY_DETECTION",
+    "ERROR_PRONE_ISINSTANCE",
+    "EXEC",
+    "HEADER_ONLY_LINTER",
+    "IMPORT_LINTER",
+    "INCLUDE",
+    "LINTRUNNER_VERSION",
+    "MERGE_CONFLICTLESS_CSV",
+    "META_NO_CREATE_UNBACKED",
+    "NEWLINE",
+    "NOQA",
+    "NO_WORKFLOWS_ON_FORK",
+    "ONCE_FLAG",
+    "PYBIND11_INCLUDE",
+    "PYBIND11_SPECIALIZATION",
+    "PYPIDEP",
+    "PYPROJECT",
+    "RAWCUDA",
+    "RAWCUDADEVICE",
+    "ROOT_LOGGING",
+    "TABS",
+    "TESTOWNERS",
+    "TYPEIGNORE",
+    "TYPENOSKIP",
+    "WORKFLOWSYNC",
+}
+
+
+#: These linters are expected to take a few seconds, but less than 10s cpu time total
+FAST_LINTERS = {
+    "CMAKE",
+    "DOCSTRING_LINTER",
+    "GHA",
+    "NATIVEFUNCTIONS",
+    "RUFF",
+    "SET_LINTER",
+    "SHELLCHECK",
+    "SPACES",
+}
+
+
+#: These linters are expected to take more than 10s cpu time total;
+#: some need more than 1 hour.
+SLOW_LINTERS = {
+    "ACTIONLINT",
+    "CLANGFORMAT",
+    "CLANGTIDY",
+    "CODESPELL",
+    "FLAKE8",
+    "GB_REGISTRY",
+    "PYFMT",
+    "PYREFLY",
+    "TEST_DEVICE_BIAS",
+    "TEST_HAS_MAIN",
+}
+
+
+ALL_LINTERS = VERY_FAST_LINTERS | FAST_LINTERS | SLOW_LINTERS
+
+
+LINTRUNNER_CACHE_INFO = (
+    Path(".lintbin/.lintrunner.sha256"),
+    [
+        "requirements.txt",
+        "pyproject.toml",
+        ".lintrunner.toml",
+    ],
+)
+
+
+LINTRUNNER_BASE_CMD = [
+    "uvx",
+    "--python",
+    "3.10",
+    "lintrunner@0.12.7",
+]
+
+
+@click.command()
+def setup_lint():
+    """Set up lintrunner with current CI version."""
+    cmd = LINTRUNNER_BASE_CMD + ["init"]
+    subprocess.run(cmd, check=True, capture_output=True, text=True)
+
+
+def _check_linters():
+    cmd = LINTRUNNER_BASE_CMD + ["list"]
+    ret = spin.util.run(cmd, output=False, stderr=subprocess.PIPE)
+    linters = {l.strip() for l in ret.stdout.decode().strip().split("\n")[1:]}
+    unknown_linters = linters - ALL_LINTERS
+    missing_linters = ALL_LINTERS - linters
+    if unknown_linters:
+        click.secho(
+            f"Unknown linters found; please add them to the correct category "
+            f"in .spin/cmds.py: {', '.join(unknown_linters)}",
+            fg="yellow",
+        )
+    if missing_linters:
+        click.secho(
+            f"Missing linters found; please update the corresponding category "
+            f"in .spin/cmds.py: {', '.join(missing_linters)}",
+            fg="yellow",
+        )
+    return unknown_linters, missing_linters
+
+
+@spin.util.extend_command(
+    setup_lint,
+    doc=f"""
+        If configuration has changed, update lintrunner.
+
+        Compares the stored old hashes of configuration files with new ones and
+        performs setup via setup-lint if the hashes have changed.
+        Hashes are stored in {LINTRUNNER_CACHE_INFO[0]}; the following files are
+        considered: {", ".join(LINTRUNNER_CACHE_INFO[1])}.
+        """,
+)
+@click.pass_context
+def lazy_setup_lint(ctx, parent_callback, **kwargs):
+    if hashes := _updated_hashes(*LINTRUNNER_CACHE_INFO):
+        click.echo(
+            "Changes detected in lint configuration files. Setting up linting tools..."
+        )
+        parent_callback(**kwargs)
+        hash_file = LINTRUNNER_CACHE_INFO[0]
+        hash_file.parent.mkdir(parents=True, exist_ok=True)
+        with hash_file.open("w") as f:
+            for file, hash in hashes.items():
+                f.write(f"{hash}  {file}\n")
+        click.echo("Linting tools set up and hashes updated.")
+    else:
+        click.echo("No changes detected in lint configuration files. Skipping setup.")
+    click.echo("Regenerating version...")
+    ctx.invoke(regenerate_version)
+    click.echo("Regenerating type stubs...")
+    ctx.invoke(regenerate_type_stubs)
+    click.echo("Done.")
+    _check_linters()
+
+
+@click.command()
+@click.option("-a", "--apply-patches", is_flag=True)
+@click.pass_context
+def lint(ctx, apply_patches, **kwargs):
+    """Lint all files."""
+    ctx.invoke(lazy_setup_lint)
+    all_files_linters = VERY_FAST_LINTERS | FAST_LINTERS
+    changed_files_linters = SLOW_LINTERS
+    cmd = LINTRUNNER_BASE_CMD
+    if apply_patches:
+        cmd += ["--apply-patches"]
+    all_files_cmd = cmd + [
+        "--take",
+        ",".join(all_files_linters),
+        "--all-files",
+    ]
+    spin.util.run(all_files_cmd)
+    changed_files_cmd = cmd + [
+        "--take",
+        ",".join(changed_files_linters),
+    ]
+    spin.util.run(changed_files_cmd)
+
+
+@click.command()
+@click.pass_context
+def fixlint(ctx, **kwargs):
+    """Autofix all files."""
+    ctx.invoke(lint, apply_patches=True)
+
+
+@click.command()
+@click.option("-a", "--apply-patches", is_flag=True)
+@click.pass_context
+def quicklint(ctx, apply_patches, **kwargs):
+    """Lint changed files."""
+    ctx.invoke(lazy_setup_lint)
+    cmd = LINTRUNNER_BASE_CMD
+    if apply_patches:
+        cmd += ["--apply-patches"]
+    spin.util.run(cmd)
+
+
+@click.command()
+@click.pass_context
+def quickfix(ctx, **kwargs):
+    """Autofix changed files."""
+    ctx.invoke(quicklint, apply_patches=True)

aten/src/ATen/DeviceAccelerator.h

@@ -94,6 +94,11 @@ 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/core/TensorAccessor.h

@@ -1,5 +1,6 @@
 #pragma once
 
+#include <torch/headeronly/core/TensorAccessor.h>
 #include <c10/macros/Macros.h>
 #include <c10/util/ArrayRef.h>
 #include <c10/util/Deprecated.h>
@@ -11,252 +12,37 @@
 
 namespace at {
 
-// The PtrTraits argument to the TensorAccessor/GenericPackedTensorAccessor
-// is used to enable the __restrict__ keyword/modifier for the data
-// passed to cuda.
-template <typename T>
-struct DefaultPtrTraits {
-  typedef T* PtrType;
-};
-
+using torch::headeronly::DefaultPtrTraits;
 #if defined(__CUDACC__) || defined(__HIPCC__)
-template <typename T>
-struct RestrictPtrTraits {
-  typedef T* __restrict__ PtrType;
-};
+  using torch::headeronly::RestrictPtrTraits;
 #endif
 
-// TensorAccessorBase and TensorAccessor are used for both CPU and CUDA tensors.
-// For CUDA tensors it is used in device code (only). This means that we restrict ourselves
-// to functions and types available there (e.g. IntArrayRef isn't).
-
-// The PtrTraits argument is only relevant to cuda to support `__restrict__` pointers.
 template<typename T, size_t N, template <typename U> class PtrTraits = DefaultPtrTraits, typename index_t = int64_t>
-class TensorAccessorBase {
-public:
-  typedef typename PtrTraits<T>::PtrType PtrType;
+using TensorAccessorBase = torch::headeronly::detail::TensorAccessorBase<c10::IntArrayRef, T, N, PtrTraits, index_t>;
 
-  C10_HOST_DEVICE TensorAccessorBase(
-      PtrType data_,
-      const index_t* sizes_,
-      const index_t* strides_)
-      : data_(data_), sizes_(sizes_), strides_(strides_) {}
-  C10_HOST IntArrayRef sizes() const {
-    return IntArrayRef(sizes_,N);
-  }
-  C10_HOST IntArrayRef strides() const {
-    return IntArrayRef(strides_,N);
-  }
-  C10_HOST_DEVICE index_t stride(index_t i) const {
-    return strides_[i];
-  }
-  C10_HOST_DEVICE index_t size(index_t i) const {
-    return sizes_[i];
-  }
-  C10_HOST_DEVICE PtrType data() {
-    return data_;
-  }
-  C10_HOST_DEVICE const PtrType data() const {
-    return data_;
-  }
-protected:
-  PtrType data_;
-  const index_t* sizes_;
-  const index_t* strides_;
-};
-
-// The `TensorAccessor` is typically instantiated for CPU `Tensor`s using
-// `Tensor.accessor<T, N>()`.
-// For CUDA `Tensor`s, `GenericPackedTensorAccessor` is used on the host and only
-// indexing on the device uses `TensorAccessor`s.
 template<typename T, size_t N, template <typename U> class PtrTraits = DefaultPtrTraits, typename index_t = int64_t>
-class TensorAccessor : public TensorAccessorBase<T,N,PtrTraits,index_t> {
-public:
-  typedef typename PtrTraits<T>::PtrType PtrType;
+using TensorAccessor = torch::headeronly::detail::TensorAccessor<c10::IntArrayRef, T, N, PtrTraits, index_t>;
 
-  C10_HOST_DEVICE TensorAccessor(
-      PtrType data_,
-      const index_t* sizes_,
-      const index_t* strides_)
-      : TensorAccessorBase<T, N, PtrTraits, index_t>(data_,sizes_,strides_) {}
+namespace detail {
 
-  C10_HOST_DEVICE TensorAccessor<T, N - 1, PtrTraits, index_t> operator[](index_t i) {
-    return TensorAccessor<T,N-1,PtrTraits,index_t>(this->data_ + this->strides_[0]*i,this->sizes_+1,this->strides_+1);
-  }
-
-  C10_HOST_DEVICE const TensorAccessor<T, N-1, PtrTraits, index_t> operator[](index_t i) const {
-    return TensorAccessor<T,N-1,PtrTraits,index_t>(this->data_ + this->strides_[0]*i,this->sizes_+1,this->strides_+1);
-  }
-};
-
-template<typename T, template <typename U> class PtrTraits, typename index_t>
-class TensorAccessor<T,1,PtrTraits,index_t> : public TensorAccessorBase<T,1,PtrTraits,index_t> {
-public:
-  typedef typename PtrTraits<T>::PtrType PtrType;
-
-  C10_HOST_DEVICE TensorAccessor(
-      PtrType data_,
-      const index_t* sizes_,
-      const index_t* strides_)
-      : TensorAccessorBase<T, 1, PtrTraits, index_t>(data_,sizes_,strides_) {}
-  C10_HOST_DEVICE T & operator[](index_t i) {
-    // NOLINTNEXTLINE(clang-analyzer-core.NullDereference)
-    return this->data_[this->strides_[0]*i];
-  }
-  C10_HOST_DEVICE const T & operator[](index_t i) const {
-    return this->data_[this->strides_[0]*i];
-  }
-};
-
-
-// GenericPackedTensorAccessorBase and GenericPackedTensorAccessor are used on for CUDA `Tensor`s on the host
-// and as
-// In contrast to `TensorAccessor`s, they copy the strides and sizes on instantiation (on the host)
-// in order to transfer them on the device when calling kernels.
-// On the device, indexing of multidimensional tensors gives to `TensorAccessor`s.
-// Use RestrictPtrTraits as PtrTraits if you want the tensor's data pointer to be marked as __restrict__.
-// Instantiation from data, sizes, strides is only needed on the host and std::copy isn't available
-// on the device, so those functions are host only.
-template<typename T, size_t N, template <typename U> class PtrTraits = DefaultPtrTraits, typename index_t = int64_t>
-class GenericPackedTensorAccessorBase {
-public:
-  typedef typename PtrTraits<T>::PtrType PtrType;
-  C10_HOST GenericPackedTensorAccessorBase(
-      PtrType data_,
-      const index_t* sizes_,
-      const index_t* strides_)
-      : data_(data_) {
-    std::copy(sizes_, sizes_ + N, std::begin(this->sizes_));
-    std::copy(strides_, strides_ + N, std::begin(this->strides_));
-  }
-
-  // if index_t is not int64_t, we want to have an int64_t constructor
-  template <typename source_index_t, class = std::enable_if_t<std::is_same_v<source_index_t, int64_t>>>
-  C10_HOST GenericPackedTensorAccessorBase(
-      PtrType data_,
-      const source_index_t* sizes_,
-      const source_index_t* strides_)
-      : data_(data_) {
-    for (const auto i : c10::irange(N)) {
-      this->sizes_[i] = sizes_[i];
-      this->strides_[i] = strides_[i];
-    }
-  }
-
-  C10_HOST_DEVICE index_t stride(index_t i) const {
-    return strides_[i];
-  }
-  C10_HOST_DEVICE index_t size(index_t i) const {
-    return sizes_[i];
-  }
-  C10_HOST_DEVICE PtrType data() {
-    return data_;
-  }
-  C10_HOST_DEVICE const PtrType data() const {
-    return data_;
-  }
-protected:
-  PtrType data_;
-  // NOLINTNEXTLINE(*c-arrays*)
-  index_t sizes_[N];
-  // NOLINTNEXTLINE(*c-arrays*)
-  index_t strides_[N];
-  C10_HOST void bounds_check_(index_t i) const {
-    TORCH_CHECK_INDEX(
+template <size_t N, typename index_t>
+struct IndexBoundsCheck {
+    IndexBoundsCheck(index_t i) {
+      TORCH_CHECK_INDEX(
         0 <= i && i < index_t{N},
         "Index ",
         i,
         " is not within bounds of a tensor of dimension ",
         N);
-  }
+    }
 };
+}  // namespace detail
 
 template<typename T, size_t N, template <typename U> class PtrTraits = DefaultPtrTraits, typename index_t = int64_t>
-class GenericPackedTensorAccessor : public GenericPackedTensorAccessorBase<T,N,PtrTraits,index_t> {
-public:
-  typedef typename PtrTraits<T>::PtrType PtrType;
-
-  C10_HOST GenericPackedTensorAccessor(
-      PtrType data_,
-      const index_t* sizes_,
-      const index_t* strides_)
-      : GenericPackedTensorAccessorBase<T, N, PtrTraits, index_t>(data_, sizes_, strides_) {}
-
-  // if index_t is not int64_t, we want to have an int64_t constructor
-  template <typename source_index_t, class = std::enable_if_t<std::is_same_v<source_index_t, int64_t>>>
-  C10_HOST GenericPackedTensorAccessor(
-      PtrType data_,
-      const source_index_t* sizes_,
-      const source_index_t* strides_)
-      : GenericPackedTensorAccessorBase<T, N, PtrTraits, index_t>(data_, sizes_, strides_) {}
-
-  C10_DEVICE TensorAccessor<T, N - 1, PtrTraits, index_t> operator[](index_t i) {
-    index_t* new_sizes = this->sizes_ + 1;
-    index_t* new_strides = this->strides_ + 1;
-    return TensorAccessor<T,N-1,PtrTraits,index_t>(this->data_ + this->strides_[0]*i, new_sizes, new_strides);
-  }
-
-  C10_DEVICE const TensorAccessor<T, N - 1, PtrTraits, index_t> operator[](index_t i) const {
-    const index_t* new_sizes = this->sizes_ + 1;
-    const index_t* new_strides = this->strides_ + 1;
-    return TensorAccessor<T,N-1,PtrTraits,index_t>(this->data_ + this->strides_[0]*i, new_sizes, new_strides);
-  }
-
-  /// Returns a PackedTensorAccessor of the same dimension after transposing the
-  /// two dimensions given. Does not actually move elements; transposition is
-  /// made by permuting the size/stride arrays. If the dimensions are not valid,
-  /// asserts.
-  C10_HOST GenericPackedTensorAccessor<T, N, PtrTraits, index_t> transpose(
-      index_t dim1,
-      index_t dim2) const {
-    this->bounds_check_(dim1);
-    this->bounds_check_(dim2);
-    GenericPackedTensorAccessor<T, N, PtrTraits, index_t> result(
-        this->data_, this->sizes_, this->strides_);
-    std::swap(result.strides_[dim1], result.strides_[dim2]);
-    std::swap(result.sizes_[dim1], result.sizes_[dim2]);
-    return result;
-  }
-};
-
-template<typename T, template <typename U> class PtrTraits, typename index_t>
-class GenericPackedTensorAccessor<T,1,PtrTraits,index_t> : public GenericPackedTensorAccessorBase<T,1,PtrTraits,index_t> {
-public:
-  typedef typename PtrTraits<T>::PtrType PtrType;
-  C10_HOST GenericPackedTensorAccessor(
-      PtrType data_,
-      const index_t* sizes_,
-      const index_t* strides_)
-      : GenericPackedTensorAccessorBase<T, 1, PtrTraits, index_t>(data_, sizes_, strides_) {}
-
-  // if index_t is not int64_t, we want to have an int64_t constructor
-  template <typename source_index_t, class = std::enable_if_t<std::is_same_v<source_index_t, int64_t>>>
-  C10_HOST GenericPackedTensorAccessor(
-      PtrType data_,
-      const source_index_t* sizes_,
-      const source_index_t* strides_)
-      : GenericPackedTensorAccessorBase<T, 1, PtrTraits, index_t>(data_, sizes_, strides_) {}
-
-  C10_DEVICE T & operator[](index_t i) {
-    return this->data_[this->strides_[0] * i];
-  }
-  C10_DEVICE const T& operator[](index_t i) const {
-    return this->data_[this->strides_[0]*i];
-  }
-
-  // Same as in the general N-dimensional case, but note that in the
-  // 1-dimensional case the returned PackedTensorAccessor will always be an
-  // identical copy of the original
-  C10_HOST GenericPackedTensorAccessor<T, 1, PtrTraits, index_t> transpose(
-      index_t dim1,
-      index_t dim2) const {
-    this->bounds_check_(dim1);
-    this->bounds_check_(dim2);
-    return GenericPackedTensorAccessor<T, 1, PtrTraits, index_t>(
-        this->data_, this->sizes_, this->strides_);
-  }
-};
+using GenericPackedTensorAccessorBase = torch::headeronly::detail::GenericPackedTensorAccessorBase<detail::IndexBoundsCheck<N, index_t>, T, N, PtrTraits, index_t>;
 
+template<typename T, size_t N, template <typename U> class PtrTraits = DefaultPtrTraits, typename index_t = int64_t>
+using GenericPackedTensorAccessor = torch::headeronly::detail::GenericPackedTensorAccessor<TensorAccessor<T, N-1, PtrTraits, index_t>, detail::IndexBoundsCheck<N, index_t>, T, N, PtrTraits, index_t>;
 
 // Can't put this directly into the macro function args because of commas
 #define AT_X GenericPackedTensorAccessor<T, N, PtrTraits, index_t>

aten/src/ATen/core/TensorBase.h

@@ -245,6 +245,9 @@ class TORCH_API TensorBase {
   size_t weak_use_count() const noexcept {
     return impl_.weak_use_count();
   }
+  bool is_uniquely_owned() const noexcept {
+    return impl_.is_uniquely_owned();
+  }
 
   std::string toString() const;
 

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

@@ -223,6 +223,62 @@ CONVERT_FROM_BF16_TEMPLATE(double)
 CONVERT_FROM_BF16_TEMPLATE(float16_t)
 #endif
 
+#ifdef __ARM_FEATURE_BF16
+
+// clang-[17, 20] crashes when autovectorizing static cast to bf16
+// Below is a workaround to have some vectorization
+// Works decently well for smaller int types
+template <typename from_type>
+inline void convertToBf16Impl(
+    const from_type* __restrict src,
+    c10::BFloat16* __restrict dst,
+    uint64_t n) {
+  bfloat16_t* dstPtr = reinterpret_cast<bfloat16_t*>(dst);
+  uint64_t loopBound = n - (n % 16);
+  uint64_t i = 0;
+  for (; i < loopBound; i += 16) {
+    float32x4_t a, b, c, d;
+    a[0] = static_cast<float>(src[i]);
+    a[1] = static_cast<float>(src[i + 1]);
+    a[2] = static_cast<float>(src[i + 2]);
+    a[3] = static_cast<float>(src[i + 3]);
+    b[0] = static_cast<float>(src[i + 4]);
+    b[1] = static_cast<float>(src[i + 5]);
+    b[2] = static_cast<float>(src[i + 6]);
+    b[3] = static_cast<float>(src[i + 7]);
+    c[0] = static_cast<float>(src[i + 8]);
+    c[1] = static_cast<float>(src[i + 9]);
+    c[2] = static_cast<float>(src[i + 10]);
+    c[3] = static_cast<float>(src[i + 11]);
+    d[0] = static_cast<float>(src[i + 12]);
+    d[1] = static_cast<float>(src[i + 13]);
+    d[2] = static_cast<float>(src[i + 14]);
+    d[3] = static_cast<float>(src[i + 15]);
+
+    vst1q_bf16(dstPtr + i, vcvtq_high_bf16_f32(vcvtq_low_bf16_f32(a), b));
+    vst1q_bf16(dstPtr + i + 8, vcvtq_high_bf16_f32(vcvtq_low_bf16_f32(c), d));
+  }
+
+#pragma clang loop vectorize(disable) interleave(disable) unroll(disable)
+  for (; i < n; i++) {
+    float a = static_cast<float>(src[i]);
+    dstPtr[i] = vcvth_bf16_f32(a);
+  }
+}
+
+#define CONVERT_TO_BF16_TEMPLATE(from_type)                                  \
+  template <>                                                                \
+  inline void convert(const from_type* src, c10::BFloat16* dst, int64_t n) { \
+    return convertToBf16Impl<from_type>(src, dst, n);                        \
+  }
+
+CONVERT_TO_BF16_TEMPLATE(uint8_t)
+CONVERT_TO_BF16_TEMPLATE(int8_t)
+CONVERT_TO_BF16_TEMPLATE(int16_t)
+CONVERT_TO_BF16_TEMPLATE(int32_t)
+
+#endif
+
 inline void convertBoolToBfloat16Impl(
     const bool* __restrict src,
     c10::BFloat16* __restrict dst,

aten/src/ATen/cuda/CUDAContextLight.h

@@ -3,6 +3,7 @@
 
 #include <cstdint>
 #include <map>
+#include <shared_mutex>
 
 #include <cuda_runtime_api.h>
 #include <cusparse.h>
@@ -88,8 +89,13 @@ TORCH_CUDA_CPP_API cublasHandle_t getCurrentCUDABlasHandle();
 TORCH_CUDA_CPP_API cublasLtHandle_t getCurrentCUDABlasLtHandle();
 
 TORCH_CUDA_CPP_API void clearCublasWorkspaces();
-TORCH_CUDA_CPP_API std::map<std::tuple<void *, void *>, at::DataPtr>& cublas_handle_stream_to_workspace();
-TORCH_CUDA_CPP_API std::map<std::tuple<void *, void *>, at::DataPtr>& cublaslt_handle_stream_to_workspace();
+struct WorkspaceMapWithMutex {
+  std::map<std::tuple<void*, void*>, at::DataPtr> map;
+  std::shared_mutex mutex;
+};
+
+TORCH_CUDA_CPP_API WorkspaceMapWithMutex& cublas_handle_stream_to_workspace();
+TORCH_CUDA_CPP_API WorkspaceMapWithMutex& cublaslt_handle_stream_to_workspace();
 TORCH_CUDA_CPP_API size_t getChosenWorkspaceSize();
 TORCH_CUDA_CPP_API size_t getCUDABlasLtWorkspaceSize();
 TORCH_CUDA_CPP_API void* getCUDABlasLtWorkspace();

aten/src/ATen/cuda/CUDAGraph.cpp

@@ -175,17 +175,24 @@ void CUDAGraph::instantiate() {
     // Trailing NULL, NULL, 0 arguments were recommended by Cuda driver people,
     // who prefer not to report error message through these arguments moving forward
     // (they prefer return value, or errors on api calls internal to the capture)
-#if (defined(CUDA_VERSION) && CUDA_VERSION >= 12000)
-    AT_CUDA_CHECK(cudaGraphInstantiate(&graph_exec_, graph_, 0));
+    // ROCM appears to fail with HIP error: invalid argument
+#if (defined(CUDA_VERSION) && CUDA_VERSION >= 12000) && !defined(USE_ROCM)
+    AT_CUDA_CHECK(cudaGraphInstantiate(&graph_exec_, graph_, cudaGraphInstantiateFlagUseNodePriority));
 #else
     AT_CUDA_CHECK(cudaGraphInstantiate(&graph_exec_, graph_, NULL, NULL, 0));
 #endif
 //Since ROCm 6.2, we want to go down this path as hipGraphExecDestroy in the destructor will not immediately free the memory.
 //It will wait for the next sync operation. cudaGraphInstantiateFlagAutoFreeOnLaunch will add async frees after graph launch.
   } else {
+#if !defined(USE_ROCM)
+    AT_CUDA_CHECK(cudaGraphInstantiateWithFlags(&graph_exec_,
+                                                graph_,
+                                                cudaGraphInstantiateFlagAutoFreeOnLaunch | cudaGraphInstantiateFlagUseNodePriority));
+#else
     AT_CUDA_CHECK(cudaGraphInstantiateWithFlags(&graph_exec_,
                                                 graph_,
                                                 cudaGraphInstantiateFlagAutoFreeOnLaunch));
+#endif
   }
   has_graph_exec_ = true;
 }

aten/src/ATen/cuda/CublasHandlePool.cpp

@@ -99,7 +99,7 @@ void destroyCublasHandle(cublasHandle_t handle) {
 //   - Comments of @soumith copied from cuDNN handle pool implementation
 #ifdef NO_CUDNN_DESTROY_HANDLE
 #else
-    cublasDestroy(handle);
+  cublasDestroy(handle);
 #endif
 }
 
@@ -107,19 +107,27 @@ using CuBlasPoolType = DeviceThreadHandlePool<cublasHandle_t, createCublasHandle
 
 } // namespace
 
-std::map<std::tuple<void *, void *>, at::DataPtr>& cublas_handle_stream_to_workspace() {
-  static auto& instance = *new std::map<std::tuple<void *, void *>, at::DataPtr>;
+WorkspaceMapWithMutex& cublas_handle_stream_to_workspace() {
+  static auto& instance = *new WorkspaceMapWithMutex;
   return instance;
 }
 
-std::map<std::tuple<void *, void *>, at::DataPtr>& cublaslt_handle_stream_to_workspace() {
-  static auto& instance = *new std::map<std::tuple<void *, void *>, at::DataPtr>;
+WorkspaceMapWithMutex& cublaslt_handle_stream_to_workspace() {
+  static auto& instance = *new WorkspaceMapWithMutex;
   return instance;
 }
 
 void clearCublasWorkspaces() {
-  cublas_handle_stream_to_workspace().clear();
-  cublaslt_handle_stream_to_workspace().clear();
+  {
+    auto& workspace = cublas_handle_stream_to_workspace();
+    std::unique_lock<std::shared_mutex> lock(workspace.mutex);
+    workspace.map.clear();
+  }
+  {
+    auto& workspace = cublaslt_handle_stream_to_workspace();
+    std::unique_lock<std::shared_mutex> lock(workspace.mutex);
+    workspace.map.clear();
+  }
 }
 
 size_t parseChosenWorkspaceSize() {
@@ -233,6 +241,38 @@ at::DataPtr getNewCUDABlasLtWorkspace() {
   return c10::cuda::CUDACachingAllocator::get()->allocate(getCUDABlasLtWorkspaceSize());
 }
 
+void setWorkspaceForHandle(cublasHandle_t handle, c10::cuda::CUDAStream stream) {
+  cudaStream_t _stream = stream;
+  auto key = std::make_tuple(static_cast<void *>(handle), static_cast<void *>(_stream));
+
+  auto& workspace = cublas_handle_stream_to_workspace();
+
+  size_t workspace_size = getChosenWorkspaceSize();
+
+  // Fast path: check if workspace already exists
+  {
+    std::shared_lock<std::shared_mutex> lock(workspace.mutex);
+    auto workspace_it = workspace.map.find(key);
+    if (workspace_it != workspace.map.end()) {
+      TORCH_CUDABLAS_CHECK(cublasSetWorkspace(
+          handle, workspace_it->second.get(), workspace_size));
+      return;
+    }
+  }
+
+  // Slow path: allocate workspace outside the lock
+  auto new_workspace = getNewWorkspace();
+
+  // Insert with lock (double-check in case another thread inserted while we
+  // were allocating)
+  {
+    std::unique_lock<std::shared_mutex> lock(workspace.mutex);
+    auto workspace_it = workspace.map.try_emplace(key, std::move(new_workspace)).first;
+    TORCH_CUDABLAS_CHECK(
+        cublasSetWorkspace(handle, workspace_it->second.get(), workspace_size));
+  }
+}
+
 void* getCUDABlasLtWorkspace() {
 #ifndef USE_ROCM
   static bool unified = c10::utils::check_env(TORCH_CUBLASLT_UNIFIED_WORKSPACE) == true;
@@ -241,8 +281,10 @@ void* getCUDABlasLtWorkspace() {
     auto stream = c10::cuda::getCurrentCUDAStream();
     cudaStream_t _stream = stream;
     auto key = std::make_tuple(static_cast<void *>(handle), static_cast<void *>(_stream));
-    auto workspace_it = at::cuda::cublas_handle_stream_to_workspace().find(key);
-    TORCH_INTERNAL_ASSERT(workspace_it != at::cuda::cublas_handle_stream_to_workspace().end());
+    auto& workspace = at::cuda::cublas_handle_stream_to_workspace();
+    std::shared_lock<std::shared_mutex> lock(workspace.mutex);
+    auto workspace_it = workspace.map.find(key);
+    TORCH_INTERNAL_ASSERT(workspace_it != workspace.map.end());
     return workspace_it->second.mutable_get();
   }
 #endif
@@ -250,11 +292,29 @@ void* getCUDABlasLtWorkspace() {
   auto stream = c10::cuda::getCurrentCUDAStream();
   cudaStream_t _stream = stream;
   auto key = std::make_tuple(static_cast<void *>(handle), static_cast<void *>(_stream));
-  auto workspace_it = cublaslt_handle_stream_to_workspace().find(key);
-  if (workspace_it == cublaslt_handle_stream_to_workspace().end()) {
-    workspace_it = cublaslt_handle_stream_to_workspace().insert(workspace_it, {key, getNewCUDABlasLtWorkspace()});
+
+  auto& workspace = cublaslt_handle_stream_to_workspace();
+
+  // Fast path: check if workspace already exists
+  {
+    std::shared_lock<std::shared_mutex> lock(workspace.mutex);
+    auto workspace_it = workspace.map.find(key);
+    if (workspace_it != workspace.map.end()) {
+      return workspace_it->second.mutable_get();
+    }
+  }
+
+  // Slow path: allocate workspace outside the lock
+  auto new_workspace = getNewCUDABlasLtWorkspace();
+
+  // Insert with lock (double-check in case another thread inserted while we
+  // were allocating)
+  {
+    std::unique_lock<std::shared_mutex> lock(workspace.mutex);
+    auto workspace_it =
+          workspace.map.try_emplace(key, std::move(new_workspace)).first;
+    return workspace_it->second.mutable_get();
   }
-  return workspace_it->second.mutable_get();
 }
 
 cublasHandle_t getCurrentCUDABlasHandle() {
@@ -298,13 +358,8 @@ cublasHandle_t getCurrentCUDABlasHandle() {
   // will allocate memory dynamically (even if they're cheap) outside
   // PyTorch's CUDA caching allocator. It's possible that CCA used up
   // all the memory and cublas's cudaMallocAsync will return OOM
-  cudaStream_t _stream = stream;
-  auto key = std::make_tuple(static_cast<void *>(handle), static_cast<void *>(_stream));
-  auto workspace_it = cublas_handle_stream_to_workspace().find(key);
-  if (workspace_it == cublas_handle_stream_to_workspace().end()) {
-    workspace_it = cublas_handle_stream_to_workspace().insert(workspace_it, {key, getNewWorkspace()});
-  }
-  TORCH_CUDABLAS_CHECK(cublasSetWorkspace(handle, workspace_it->second.get(), getChosenWorkspaceSize()));
+  setWorkspaceForHandle(handle, stream);
+
 #if !defined(USE_ROCM)
   // On CUDA >= 11, and architecture >= Ampere, cuBLAS can use TF32 to speedup
   // FP32 data type calculations based on the value of the allow_tf32 flag.

aten/src/ATen/mps/MPSDevice.h

@@ -22,6 +22,7 @@ enum class MacOSVersion : uint32_t {
   MACOS_VER_15_0_PLUS,
   MACOS_VER_15_1_PLUS,
   MACOS_VER_15_2_PLUS,
+  MACOS_VER_26_0_PLUS,
 };
 
 //-----------------------------------------------------------------

aten/src/ATen/mps/MPSDevice.mm

@@ -65,6 +65,7 @@ bool MPSDevice::isMacOS13Plus(MacOSVersion version) const {
   static bool _macos_15_0_plus = is_os_version_at_least(15, 0);
   static bool _macos_15_1_plus = is_os_version_at_least(15, 1);
   static bool _macos_15_2_plus = is_os_version_at_least(15, 2);
+  static bool _macos_26_0_plus = is_os_version_at_least(26, 0);
 
   switch (version) {
     case MacOSVersion::MACOS_VER_14_4_PLUS:
@@ -75,6 +76,8 @@ bool MPSDevice::isMacOS13Plus(MacOSVersion version) const {
       return _macos_15_1_plus;
     case MacOSVersion::MACOS_VER_15_2_PLUS:
       return _macos_15_2_plus;
+    case MacOSVersion::MACOS_VER_26_0_PLUS:
+      return _macos_26_0_plus;
     default:
       return false;
   }

aten/src/ATen/native/LinearAlgebra.cpp

@@ -1936,7 +1936,7 @@ static bool should_fold(const Tensor& tensor1, const Tensor& tensor2, bool has_o
 
   // We order the tensors. t1 will be the larger tensor
   // We can always transpose tensor2 as the dimensions are always >= 1 (precondition from matmul)
-  // and tensor1_larger iff tensor2.dim() > tensor1.dim()
+  // and tensor1_larger iff tensor2.dim() > tensor1.dim(9
   const auto t1 = tensor1_larger ? MaybeOwned<Tensor>::borrowed(tensor1)
                                  : MaybeOwned<Tensor>::owned(tensor2.mT());
   const int64_t dim_t1 = t1->dim();
@@ -1948,11 +1948,20 @@ static bool should_fold(const Tensor& tensor1, const Tensor& tensor2, bool has_o
     return false;
   }
 
-  // If we require a gradient, we should fold to minimize backward memory usage - even if this
-  // leads to a copy in forward because is needed in backward,
-  // only time we avoid this strict pre-allocated memory usage (has_out = True)
-  bool requires_grad = tensor1.requires_grad() || tensor2.requires_grad();
-  if (requires_grad && !has_out) {
+  // In this case we *do* incur in an extra copy to avoid creating an unnecessary large tensor in the backward
+  // Suppose we don't fold here. Let t1.shape = [b, m, n] t2.shape = [n, k] like in a transformer
+  // t2 will be expanded to a tensor of shape [b, n, k] and then we do t1.bmm(t2_expanded)
+  // The issue appears in the backward.
+  // The output gradient g of this operation would have shape [b, m, k]
+  // The backward wrt. t2 of bmm would be given by t1.mH @ g, which has shape [b, n, k]
+  // Then, the backward of expand is simply `sum(0)`. As such, we are instantiating a tensor
+  // of shape [b, n, k] unnecessarily, which may cause a large memory footprint, and in the
+  // worst case, an OOM
+  bool t2_requires_grad = tensor1_larger ? tensor2.requires_grad() : tensor1.requires_grad();
+  if (t2_requires_grad && !has_out) {
+    // We should be checking !at::GradMode::is_enabled(), but apparently
+    // this regresses performance in some cases:
+    // https://github.com/pytorch/pytorch/issues/118548#issuecomment-1916022394
     return true;
   }
 

aten/src/ATen/native/TensorAdvancedIndexing.cpp

@@ -1087,7 +1087,8 @@ TORCH_IMPL_FUNC(index_copy_out)
     result.copy_(self);
 
   // See Note [Enabling Deterministic Operations]
-  if (result.is_cuda() && globalContext().deterministicAlgorithms()) {
+  if ((result.is_cuda() || result.is_xpu()) &&
+      globalContext().deterministicAlgorithms()) {
     torch::List<std::optional<Tensor>> indices;
     indices.resize(dim + 1);
     indices.set(dim, index);

aten/src/ATen/native/UnaryOps.cpp

@@ -904,19 +904,11 @@ Tensor mvlgamma(const Tensor& self, int64_t p) {
   return args.lgamma_().sum(-1).add_(p2_sub_p * std::log(c10::pi<double>) * QUARTER);
 }
 
+// since mvlgamma_ has different signature from its
+// out and functional variant, we explicitly
+// define it (instead of using structured kernel).
 Tensor& mvlgamma_(Tensor& self, int64_t p) {
-  mvlgamma_check(self, p);
-  Tensor args = native::arange(
-      -p *HALF  + HALF,
-      HALF,
-      HALF,
-      optTypeMetaToScalarType(self.options().dtype_opt()),
-      self.options().layout_opt(),
-      self.options().device_opt(),
-      self.options().pinned_memory_opt());
-  args = args.add(self.unsqueeze(-1));
-  const auto p2_sub_p = static_cast<double>(p * (p - 1));
-  return self.copy_(args.lgamma_().sum(-1).add_(p2_sub_p * std::log(c10::pi<double>) * QUARTER));
+  return at::mvlgamma_out(self, self, p);
 }
 
 Tensor& mvlgamma_out(const Tensor& self, int64_t p, Tensor& result) {

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

@@ -296,7 +296,7 @@ template <typename scalar_t, typename res_scalar_t = scalar_t>
 bool launchGemmAndBiasCublasLt(
     // args contains result which is modified
     cublasCommonArgs& args,
-    const Tensor& self,
+    const std::optional<Tensor>& self,
     const Scalar& alpha,
     Activation activation = Activation::None
 ) {
@@ -304,12 +304,8 @@ bool launchGemmAndBiasCublasLt(
   // or when it can be squeezed to 1D.
   // self_ptr == nullptr implies ignore bias epilogue
   // and use standard gemm-like API.
-  const auto* self_ptr = [&]() -> auto {
-    if (self.dim() == 1 || self.squeeze().dim() == 1) {
-      return self.const_data_ptr<scalar_t>();
-    }
-    return static_cast<const scalar_t*>(nullptr);
-  }();
+  const auto* self_ptr = self.has_value() ? self.value().const_data_ptr<scalar_t>() : static_cast<const scalar_t*>(nullptr);
+
 
   const auto tuning_ctx = at::cuda::tunable::getTuningContext();
   if (tuning_ctx->IsTunableOpEnabled()) {
@@ -392,35 +388,30 @@ Tensor& addmm_out_cuda_impl(Tensor& result, const Tensor& self, const Tensor& ma
   bool disable_addmm_cuda_lt = persistent_disable_addmm_cuda_lt || disable_addmm_cuda_lt_override;
   #ifdef USE_ROCM
   // Conditioned on the device index, which is not persistent
-  disable_addmm_cuda_lt = isGloballyDisabledAddmmCudaLt(self.device()) || disable_addmm_cuda_lt;
+  disable_addmm_cuda_lt = disable_addmm_cuda_lt || isGloballyDisabledAddmmCudaLt(self.device());
   #endif
   // Condition on the input
-  disable_addmm_cuda_lt = !isInputCompliesAddmmCudaLt(result, self, mat1, mat2, beta, alpha, activation) || disable_addmm_cuda_lt;
-  // }
+  disable_addmm_cuda_lt = disable_addmm_cuda_lt || !isInputCompliesAddmmCudaLt(result, self, mat1, mat2, beta, alpha, activation);
 
   at::ScalarType scalar_type = mat1.scalar_type();
   bool is_float_output_with_half_input = (scalar_type == at::ScalarType::Half || scalar_type == at::ScalarType::BFloat16) && result.scalar_type() == at::ScalarType::Float;
 
+  #ifdef USE_ROCM
+  disable_addmm_cuda_lt = disable_addmm_cuda_lt || is_float_output_with_half_input;
+  #endif
+
+  bool use_bias_ptr_lt = (self.dim() == 1) && !disable_addmm_cuda_lt;
+  // for float output with half input cublasLT with bias produces wrong results
+  use_bias_ptr_lt &= !is_float_output_with_half_input;
+
   // Handle result/self shapes
   if (!result.is_same(self)) {
     at::native::resize_output(result, {mat1.sizes()[0], mat2.sizes()[1]});
 
-    // We use bias ptr in the Lt path only when bias is 1D
-    const auto use_bias_ptr_lt = (self.dim() == 1) && !disable_addmm_cuda_lt;
-    const auto self_maybe_expanded = [&]() -> c10::MaybeOwned<Tensor> {
-      if (!use_bias_ptr_lt) {
-        // We do expand self even before
-        // check for beta != 0.0 to make sure that
-        // test_sparse_csr.py::TestSparseCSRCUDA::test_addmm_errors_*
-        // runs green.
-        return expand_size(self, result.sizes(), "addmm");
-      }
-      return c10::MaybeOwned<Tensor>::borrowed(self);
-    }();
-    // We do not copy bias only when we need the bias ptr
+      // We do not copy bias only when we need the bias ptr
     if (beta.toComplexDouble() != 0.0 && !use_bias_ptr_lt) {
       // NOTE: self should broadcast over result
-      at::native::copy_(result, *self_maybe_expanded);
+      at::native::copy_(result, *expand_size(self, result.sizes(), "addmm"));
     }
   }
 
@@ -468,7 +459,7 @@ Tensor& addmm_out_cuda_impl(Tensor& result, const Tensor& self, const Tensor& ma
         scalar_type,
         "addmm_cuda_lt",
         [&] {
-          lt_success = launchGemmAndBiasCublasLt<scalar_t, float>(args, self, alpha, activation);
+          lt_success = launchGemmAndBiasCublasLt<scalar_t, float>(args, use_bias_ptr_lt ? std::make_optional(self) : std::nullopt, alpha, activation);
         }
       );
       #endif
@@ -480,7 +471,7 @@ Tensor& addmm_out_cuda_impl(Tensor& result, const Tensor& self, const Tensor& ma
         scalar_type,
         "addmm_cuda_lt",
         [&] {
-          lt_success = launchGemmAndBiasCublasLt<scalar_t>(args, self, alpha, activation);
+          lt_success = launchGemmAndBiasCublasLt<scalar_t>(args, use_bias_ptr_lt ? std::make_optional(self) : std::nullopt, alpha, activation);
         }
       );
     } // end is_float_output_with_half_input
@@ -936,7 +927,7 @@ Tensor _int_mm_cuda(const Tensor& self, const Tensor& mat2) {
   return _int_mm_out_cuda(self, mat2, result);
 }
 
-static void baddbmm_bmm_out_dtype_checks(const Tensor& batch1, const Tensor& batch2, const Scalar& beta, const Scalar& alpha, const at::ScalarType out_dtype, bool is_bmm, const std::optional<Tensor>& self_baddbmm = std::nullopt) {
+static void baddbmm_bmm_out_dtype_checks(const Tensor& batch1, const Tensor& batch2, const Scalar& beta, const Scalar& alpha, const at::ScalarType out_dtype, const std::optional<Tensor>& self_baddbmm = std::nullopt) {
   // ref ATen/native/LinearAlgebra.cpp common_checks_baddbmm_bmm
   TORCH_CHECK(batch1.dim() == 3, "batch1 must be a 3D tensor");
   TORCH_CHECK(batch2.dim() == 3, "batch2 must be a 3D tensor");
@@ -960,7 +951,7 @@ static void baddbmm_bmm_out_dtype_checks(const Tensor& batch1, const Tensor& bat
     (out_dtype == at::ScalarType::Float && (batch1.scalar_type() == at::ScalarType::Half || batch1.scalar_type() == at::ScalarType::BFloat16)),
     "out_dtype must be the same as input dtype or fp32 for fp16/bf16 inputs");
 
-  if (!is_bmm && self_baddbmm.has_value()) {
+  if (self_baddbmm.has_value()) {
     const auto& self = self_baddbmm.value();
     TORCH_CHECK(self.dim() == 3, "self must be a 3D tensor");
     TORCH_CHECK(self.sizes() == output_size, "self must have the same shape as the output");
@@ -968,15 +959,12 @@ static void baddbmm_bmm_out_dtype_checks(const Tensor& batch1, const Tensor& bat
 }
 
 Tensor _bmm_dtype_cuda(const Tensor& batch1, const Tensor& batch2, const at::ScalarType out_dtype) {
-  IntArrayRef batch1_sizes = batch1.sizes();
-  IntArrayRef batch2_sizes = batch2.sizes();
-
-  Tensor out = at::empty({batch1_sizes[0], batch1_sizes[1], batch2_sizes[2]}, batch1.options().dtype(out_dtype));
+  Tensor out = at::empty({batch1.size(0), batch1.size(1), batch2.size(2)}, batch1.options().dtype(out_dtype));
   return _bmm_out_dtype_cuda(batch1, batch2, out_dtype, out);
 }
 
 Tensor& _bmm_out_dtype_cuda(const Tensor& batch1, const Tensor& batch2, const at::ScalarType out_dtype, Tensor &out) {
-  baddbmm_bmm_out_dtype_checks(batch1, batch2, 0.0, 1.0, out_dtype, true);
+  baddbmm_bmm_out_dtype_checks(batch1, batch2, 0.0, 1.0, out_dtype);
   Scalar beta(0.0);
   Scalar alpha(1.0);
   {
@@ -988,14 +976,16 @@ Tensor& _bmm_out_dtype_cuda(const Tensor& batch1, const Tensor& batch2, const at
 }
 
 Tensor _baddbmm_dtype_cuda(const Tensor& self, const Tensor& batch1, const Tensor& batch2, const at::ScalarType out_dtype, const Scalar& beta, const Scalar& alpha) {
-  // We need to copy the tensor
-  Tensor out = self.clone().to(self.options().dtype(out_dtype));
-
-  return _baddbmm_out_dtype_cuda(out, batch1, batch2, out_dtype, beta, alpha, out);
+  TORCH_CHECK(self.scalar_type() == out_dtype || self.scalar_type() == batch1.dtype(),
+  "self dtype must match either out_dtype or batch1 dtype");
+  Tensor out = at::empty({batch1.size(0), batch1.size(1), batch2.size(2)}, batch1.options().dtype(out_dtype));
+  return _baddbmm_out_dtype_cuda(self, batch1, batch2, out_dtype, beta, alpha, out);
 }
 
 Tensor& _baddbmm_out_dtype_cuda(const Tensor& self, const Tensor& batch1, const Tensor& batch2, const at::ScalarType out_dtype, const Scalar& beta, const Scalar& alpha, Tensor &out) {
-  baddbmm_bmm_out_dtype_checks(batch1, batch2, beta, alpha, out_dtype, false, self);
+  baddbmm_bmm_out_dtype_checks(batch1, batch2, beta, alpha, out_dtype, out);
+  // We need to copy the tensor
+  out.copy_(self);
   {
     NoNamesGuard guard;
     baddbmm_out_cuda_impl(out, out, batch1, batch2, beta, alpha);
@@ -1030,24 +1020,27 @@ Tensor& _mm_dtype_out_cuda(const Tensor& self, const Tensor& mat2, const at::Sca
 }
 
 Tensor _addmm_dtype_cuda(const Tensor& self, const Tensor& mat1, const Tensor& mat2, const at::ScalarType out_dtype, const Scalar& beta, const Scalar& alpha) {
-  Tensor result = at::empty(self.sizes(), self.options().dtype(out_dtype));
+  TORCH_CHECK(mat1.dim() == 2, "mat1 must be a matrix, got ", mat1.dim(), "-D tensor");
+  TORCH_CHECK(mat2.dim() == 2, "mat2 must be a matrix, got ", mat2.dim(), "-D tensor");
+  Tensor result = at::empty({mat1.size(0), mat2.size(1)}, self.options().dtype(out_dtype));
   return _addmm_dtype_out_cuda(self, mat1, mat2, out_dtype, beta, alpha, result);
 }
 
 Tensor& _addmm_dtype_out_cuda(const Tensor& self, const Tensor& mat1, const Tensor& mat2, const at::ScalarType out_dtype, const Scalar& beta, const Scalar& alpha, Tensor &out) {
-  TORCH_CHECK(self.scalar_type() == mat2.scalar_type(), "self and mat2 must have the same dtype, but got ", self.scalar_type(), " and ", mat2.scalar_type());
-  TORCH_CHECK(mat1.scalar_type() == mat2.scalar_type(), "mat1 and mat2 must have the same dtype, but got ", mat1.scalar_type(), " and ", mat2.scalar_type());
+// repeat dimensionality checks for direct calls to `out` overload
   TORCH_CHECK(mat1.dim() == 2, "mat1 must be a matrix, got ", mat1.dim(), "-D tensor");
   TORCH_CHECK(mat2.dim() == 2, "mat2 must be a matrix, got ", mat2.dim(), "-D tensor");
   TORCH_CHECK(
       mat1.sizes()[1] == mat2.sizes()[0], "mat1 and mat2 shapes cannot be multiplied (",
       mat1.sizes()[0], "x", mat1.sizes()[1], " and ", mat2.sizes()[0], "x", mat2.sizes()[1], ")");
+  TORCH_CHECK(mat1.scalar_type() == mat2.scalar_type(), "mat1 and mat2 must have the same dtype, but got ", mat1.scalar_type(), " and ", mat2.scalar_type());
+  TORCH_CHECK(out_dtype == mat1.scalar_type() ||
+  (out_dtype == at::ScalarType::Float && (mat1.scalar_type() == at::ScalarType::Half || mat1.scalar_type() == at::ScalarType::BFloat16)),
+  "out_dtype must be the same as input dtype or fp32 for fp16/bf16 inputs");
 
   TORCH_CHECK(out_dtype == out.scalar_type(), "out_dtype must be the same as the dtype of the provided out tensor");
-  TORCH_CHECK(out_dtype == self.scalar_type() ||
-    (out_dtype == at::ScalarType::Float && (self.scalar_type() == at::ScalarType::Half || self.scalar_type() == at::ScalarType::BFloat16)),
-    "out_dtype must be the same as input dtype or fp32 for fp16/bf16 inputs");
-  TORCH_CHECK(out_dtype == out.scalar_type(), "out_dtype must be the same as the dtype of the provided out tensor");
+  TORCH_CHECK(out_dtype == self.scalar_type() || self.scalar_type() == mat1.scalar_type(),
+    "self dtype must match either out_dtype or mat1 dtype");
 
   addmm_out_cuda_impl(out, self, mat1, mat2, beta, alpha);
 

aten/src/ATen/native/cuda/CompositeRandomAccessor.h

@@ -1,6 +1,7 @@
 #pragma once
 
 #include <ATen/native/CompositeRandomAccessorCommon.h>
+#include <thrust/swap.h>
 #include <thrust/tuple.h>
 
 namespace at { namespace native {

aten/src/ATen/native/cuda/DilatedMaxPool2d.cu

@@ -75,30 +75,52 @@ static inline bool can_use_int32_nhwc(
   return true;
 }
 
+static inline bool can_use_int32_nchw(
+    int64_t nbatch, int64_t channels,
+    int64_t height, int64_t width,
+    int64_t pooled_height, int64_t pooled_width) {
+  int64_t hw = height * width;
+  return can_use_int32_nhwc(
+      nbatch, channels, height, width,
+      pooled_height, pooled_width,
+      channels * hw,  // in_stride_n
+      hw, // in_stride_c
+      width, // in_stride_h
+      1 // in_stride_w
+  );
+}
+
 // kernels borrowed from Caffe
-template <typename scalar_t>
-__global__ void max_pool_forward_nchw(const int nthreads, const scalar_t* bottom_data,
-    const int64_t channels, const int64_t height,
-    const int64_t width, const int pooled_height, const int pooled_width,
-    const int kernel_h, const int kernel_w, const int stride_h,
-    const int stride_w, const int pad_h, const int pad_w,
-    const int dilation_h, const int dilation_w, scalar_t* top_data,
+template <typename scalar_t, typename index_t>
+__global__ void max_pool_forward_nchw(
+    const index_t nthreads,
+    const scalar_t* bottom_data,
+    const int64_t channels,
+    const int64_t height,
+    const int64_t width,
+    const int pooled_height,
+    const int pooled_width,
+    const int kernel_h, const int kernel_w,
+    const int stride_h, const int stride_w,
+    const int pad_h, const int pad_w,
+    const int dilation_h, const int dilation_w,
+    scalar_t* top_data,
     int64_t* top_mask) {
-  CUDA_KERNEL_LOOP(index, nthreads) {
-    int pw = index % pooled_width;
-    int ph = (index / pooled_width) % pooled_height;
-    int c = (index / pooled_width / pooled_height) % channels;
-    int n = index / pooled_width / pooled_height / channels;
-    int hstart = ph * stride_h - pad_h;
-    int wstart = pw * stride_w - pad_w;
-    int hend = min(hstart + (kernel_h - 1) * dilation_h + 1, height);
-    int wend = min(wstart + (kernel_w - 1) * dilation_w + 1, width);
+  CUDA_KERNEL_LOOP_TYPE(index, nthreads, index_t) {
+    index_t pw = index % pooled_width;
+    index_t ph = (index / pooled_width) % pooled_height;
+    index_t c = (index / pooled_width / pooled_height) % channels;
+    index_t n = index / pooled_width / pooled_height / channels;
+    index_t hstart = ph * stride_h - pad_h;
+    index_t wstart = pw * stride_w - pad_w;
+    index_t hend = min(hstart + (kernel_h - 1) * dilation_h + 1, height);
+    index_t wend = min(wstart + (kernel_w - 1) * dilation_w + 1, width);
     while(hstart < 0)
       hstart += dilation_h;
     while(wstart < 0)
       wstart += dilation_w;
     scalar_t maxval = at::numeric_limits<scalar_t>::lower_bound(); // -Infinity
-    int maxidx = hstart * width + wstart;
+    index_t maxidx = hstart * width + wstart;
     const scalar_t* btm_data = bottom_data + (n * channels + c) * height * width;
     for (int h = hstart; h < hend; h += dilation_h) {
       for (int w = wstart; w < wend; w += dilation_w) {
@@ -251,32 +273,39 @@ __global__ void max_pool_forward_nhwc(
 
 static constexpr int BLOCK_THREADS = 256;
 
-template <typename scalar_t, typename accscalar_t>
+template <typename scalar_t, typename accscalar_t, typename index_t>
 #if defined (USE_ROCM)
 C10_LAUNCH_BOUNDS_2(BLOCK_THREADS, 4)
 #else
 C10_LAUNCH_BOUNDS_2(BLOCK_THREADS, 8)
 #endif
-__global__ void max_pool_backward_nchw(const scalar_t* top_diff,
-    const int64_t* top_mask, const int num, const int64_t channels,
-    const int64_t height, const int64_t width, const int pooled_height,
-    const int pooled_width, const int kernel_h, const int kernel_w,
-    const int stride_h, const int stride_w, const int pad_h, const int pad_w,
+__global__ void max_pool_backward_nchw(
+    const scalar_t* top_diff,
+    const int64_t* top_mask,
+    const index_t num,
+    const index_t channels,
+    const index_t height,
+    const index_t width,
+    const index_t pooled_height,
+    const index_t pooled_width,
+    const int kernel_h, const int kernel_w,
+    const int stride_h, const int stride_w,
+    const int pad_h, const int pad_w,
     const int dilation_h, const int dilation_w,
     scalar_t* bottom_diff) {
-  CUDA_KERNEL_LOOP(index, height*width) {
-    int h = index / width;
-    int w = index - h * width;
-    int phstart = p_start(h, pad_h, kernel_h, dilation_h, stride_h);
-    int phend = p_end(h, pad_h, pooled_height, stride_h);
-    int pwstart = p_start(w, pad_w, kernel_w, dilation_w, stride_w);
-    int pwend = p_end(w, pad_w, pooled_width, stride_w);
-    for (int n = blockIdx.y; n < num; n += gridDim.y) {
-      for (int c = blockIdx.z; c < channels; c+= gridDim.z) {
+  CUDA_KERNEL_LOOP_TYPE(index, height*width, index_t) {
+    index_t h = index / width;
+    index_t w = index - h * width;
+    index_t phstart = p_start(h, pad_h, kernel_h, dilation_h, stride_h);
+    index_t phend = p_end(h, pad_h, pooled_height, stride_h);
+    index_t pwstart = p_start(w, pad_w, kernel_w, dilation_w, stride_w);
+    index_t pwend = p_end(w, pad_w, pooled_width, stride_w);
+    for (index_t n = blockIdx.y; n < num; n += gridDim.y) {
+      for (index_t c = blockIdx.z; c < channels; c += gridDim.z) {
         accscalar_t gradient = accscalar_t(0);
-        int offset = (n * channels + c) * pooled_height * pooled_width;
-        for (int ph = phstart; ph < phend; ++ph) {
-          for (int pw = pwstart; pw < pwend; ++pw) {
+        index_t offset = (n * channels + c) * pooled_height * pooled_width;
+        for (index_t ph = phstart; ph < phend; ++ph) {
+          for (index_t pw = pwstart; pw < pwend; ++pw) {
             if (top_mask[ph * pooled_width + pw + offset] == h * width + w) {
               gradient += static_cast<accscalar_t>(top_diff[ph * pooled_width + pw + offset]);
             }
@@ -469,8 +498,6 @@ const Tensor& indices) {
   const int64_t in_stride_h = input.stride(-2);
   const int64_t in_stride_w = input.stride(-1);
 
-  const int count = safe_downcast<int, int64_t>(output.numel());
-
   AT_DISPATCH_FLOATING_TYPES_AND2(kHalf, kBFloat16, input.scalar_type(),
     "max_pool2d_with_indices_out_cuda_frame",
     [&] {
@@ -553,14 +580,42 @@ const Tensor& indices) {
           break;
         }
         case MemoryFormat::Contiguous: {
-          const int num_threads = std::min(at::cuda::getCurrentDeviceProperties()->maxThreadsPerBlock,
-                                            BLOCK_THREADS);
-          max_pool_forward_nchw<scalar_t>
-              <<<ceil_div(count, num_threads), num_threads, 0, at::cuda::getCurrentCUDAStream()>>>(
-              count, input_data,
-                  nInputPlane, inputHeight, inputWidth, outputHeight, outputWidth,
-                  kH, kW, dH, dW, padH, padW, dilationH, dilationW,
-                  output_data, indices_data);
+          const int threads = std::min(
+              at::cuda::getCurrentDeviceProperties()->maxThreadsPerBlock,
+              BLOCK_THREADS);
+          const int64_t nthreads = output.numel();
+          bool use_int32 = can_use_int32_nchw(
+              nbatch, nInputPlane, inputHeight, inputWidth, outputHeight, outputWidth);
+          const int maxGridX = at::cuda::getCurrentDeviceProperties()->maxGridSize[0];
+          const int blocks = static_cast<int>(std::min<int64_t>(
+              ceil_div(nthreads, static_cast<int64_t>(threads)),
+              static_cast<int64_t>(maxGridX)));
+          auto stream = at::cuda::getCurrentCUDAStream();
+          if (use_int32) {
+            max_pool_forward_nchw<scalar_t, int32_t>
+                <<<blocks, threads, 0, stream>>>(
+                    static_cast<int32_t>(nthreads),
+                    input_data,
+                    static_cast<int32_t>(nInputPlane),
+                    static_cast<int32_t>(inputHeight),
+                    static_cast<int32_t>(inputWidth),
+                    static_cast<int32_t>(outputHeight),
+                    static_cast<int32_t>(outputWidth),
+                    kH, kW, dH, dW, padH, padW, dilationH, dilationW,
+                    output_data, indices_data);
+          } else {
+            max_pool_forward_nchw<scalar_t, int64_t>
+                <<<blocks, threads, 0, stream>>>(
+                    nthreads,
+                    input_data,
+                    nInputPlane,
+                    inputHeight,
+                    inputWidth,
+                    outputHeight,
+                    outputWidth,
+                    kH, kW, dH, dW, padH, padW, dilationH, dilationW,
+                    output_data, indices_data);
+          }
           C10_CUDA_KERNEL_LAUNCH_CHECK();
           break;
         }
@@ -633,8 +688,6 @@ const Tensor& gradInput) {
 
   gradInput.zero_();
 
-  int64_t count = input.numel();
-
   AT_DISPATCH_FLOATING_TYPES_AND2(kHalf, kBFloat16, input.scalar_type(),
     "max_pool2d_with_indices_out_cuda_frame",
     [&] {
@@ -692,25 +745,45 @@ const Tensor& gradInput) {
           break;
         }
         case MemoryFormat::Contiguous: {
-          int imgcount = inputWidth * inputHeight;
-          dim3 grid;
-          const int blocks = (imgcount + BLOCK_THREADS - 1) / BLOCK_THREADS;
-          grid.x = blocks;
-          grid.y = nbatch;
-          uint64_t maxGridY = at::cuda::getCurrentDeviceProperties()->maxGridSize[1];
-          if (maxGridY < grid.y) grid.y = maxGridY;
-          grid.z = nInputPlane;
-          uint64_t maxGridZ = at::cuda::getCurrentDeviceProperties()->maxGridSize[2];
-          if (maxGridZ < grid.z) grid.z = maxGridZ;
-
-          max_pool_backward_nchw<scalar_t, accscalar_t>
-          <<<grid, BLOCK_THREADS, 0, at::cuda::getCurrentCUDAStream()>>>(
-                  gradOutput_data,
-                  indices_data,
-                  nbatch,
-                  nInputPlane, inputHeight, inputWidth, outputHeight, outputWidth,
-                  kH, kW, dH, dW, padH, padW, dilationH, dilationW,
-                  gradInput_data);
+          const int threads = std::min(
+              at::cuda::getCurrentDeviceProperties()->maxThreadsPerBlock,
+              BLOCK_THREADS);
+          const int imgcount = inputWidth * inputHeight;
+          const int maxGridX = at::cuda::getCurrentDeviceProperties()->maxGridSize[0];
+          const int maxGridY = at::cuda::getCurrentDeviceProperties()->maxGridSize[1];
+          const int maxGridZ = at::cuda::getCurrentDeviceProperties()->maxGridSize[2];
+          const int blocks_x = std::min(ceil_div(imgcount, threads), maxGridX);
+          dim3 grid(blocks_x, static_cast<unsigned>(std::min<int64_t>(nbatch, maxGridY)), static_cast<unsigned>(std::min<int64_t>(nInputPlane, maxGridZ)));
+          bool use_int32 = can_use_int32_nchw(
+              nbatch, nInputPlane, inputHeight, inputWidth, outputHeight, outputWidth);
+          auto stream = at::cuda::getCurrentCUDAStream();
+          if (use_int32) {
+            max_pool_backward_nchw<scalar_t, accscalar_t, int32_t>
+                <<<grid, threads, 0, stream>>>(
+                    gradOutput_data,
+                    indices_data,
+                    static_cast<int32_t>(nbatch),
+                    static_cast<int32_t>(nInputPlane),
+                    static_cast<int32_t>(inputHeight),
+                    static_cast<int32_t>(inputWidth),
+                    static_cast<int32_t>(outputHeight),
+                    static_cast<int32_t>(outputWidth),
+                    kH, kW, dH, dW, padH, padW, dilationH, dilationW,
+                    gradInput_data);
+          } else {
+            max_pool_backward_nchw<scalar_t, accscalar_t, int64_t>
+                <<<grid, threads, 0, stream>>>(
+                    gradOutput_data,
+                    indices_data,
+                    nbatch,
+                    nInputPlane,
+                    inputHeight,
+                    inputWidth,
+                    outputHeight,
+                    outputWidth,
+                    kH, kW, dH, dW, padH, padW, dilationH, dilationW,
+                    gradInput_data);
+          }
           C10_CUDA_KERNEL_LAUNCH_CHECK();
           break;
         }

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

@@ -78,9 +78,18 @@ __global__ void EmbeddingBag_updateOutputKernel_max(
       scalar_t weightFeatMax = 0;
       int64_t bag_size_ = 0;
       int64_t maxWord = -1;
+
+      // Separate validation loop reduces register pressure in the main loop below.
+      // No early exit (break) on invalid input as benchmarking shows it degrades performance.
+      bool has_invalid_index = false;
+      for (int64_t emb = begin; emb < end; emb++) {
+        index_t input_idx = input[emb];
+        has_invalid_index = has_invalid_index || (input_idx < 0 || input_idx >= numRows);
+      }
+      CUDA_KERNEL_ASSERT(!has_invalid_index && "Invalid input index in EmbeddingBag: index out of range [0, numRows)");
+
       for (int64_t emb = begin; emb < end; emb++) {
         bool pad = (input[emb] == padding_idx);
-        CUDA_KERNEL_ASSERT(input[emb] < numRows);
         const int64_t weightRow = input[emb] * weight_stride0;
         scalar_t weightValue = weightFeat[weightRow];
         if (bag_size_ == 0 || weightValue > weightFeatMax) {
@@ -129,10 +138,19 @@ __global__ void EmbeddingBag_updateOutputKernel_sum_mean(
       CUDA_KERNEL_ASSERT(end >= begin);
       accscalar_t weightFeatSum = 0;
       int64_t bag_size_ = 0;
+
+      // Separate validation loop reduces register pressure in the main loop below.
+      // No early exit (break) on invalid input as benchmarking shows it degrades performance.
+      bool has_invalid_index = false;
+      for (int64_t emb = begin; emb < end; emb++) {
+        index_t input_idx = input[emb];
+        has_invalid_index = has_invalid_index || (input_idx < 0 || input_idx >= numRows);
+      }
+      CUDA_KERNEL_ASSERT(!has_invalid_index && "Invalid input index in EmbeddingBag: index out of range [0, numRows)");
+
       for (int64_t emb = begin; emb < end; emb++) {
         index_t input_idx = input[emb];
         bool pad = (input_idx == padding_idx);
-        CUDA_KERNEL_ASSERT(0 <= input_idx && input_idx < numRows);
         const int64_t weightRow = input_idx * weight_stride0;
         scalar_t weightValue = weightFeat[weightRow];
         weightValue = pad ? static_cast<scalar_t>(0) : weightValue;

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

@@ -78,9 +78,9 @@ _mx8_mx8_bf16_grouped_mm_fbgemm(
         const Tensor& mat_a,
         const Tensor& mat_b,
         const Tensor& scale_a,
-        const SwizzleType& swizzle_a,
+        const SwizzleType swizzle_a,
         const Tensor& scale_b,
-        const SwizzleType& swizzle_b,
+        const SwizzleType swizzle_b,
         const std::optional<at::Tensor>& offs,
         Tensor& out) {
     const bool a_is_2d = mat_a.dim() == 2;

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

@@ -5,69 +5,11 @@
 #include <cuda_bf16.h>
 #endif
 
-// ROCm 6.3 is planned to have these functions, but until then here they are.
 #if defined(USE_ROCM)
 #include <device_functions.h>
 #include <hip/hip_fp16.h>
 #include <hip/hip_bf16.h>
-
-__device__ inline __hip_bfloat162 preview_unsafeAtomicAdd(__hip_bfloat162* address, __hip_bfloat162 value) {
-#if (defined(__gfx942__)) && \
-  __has_builtin(__builtin_amdgcn_flat_atomic_fadd_v2bf16)
-  typedef unsigned short __attribute__((ext_vector_type(2))) vec_short2;
-  static_assert(sizeof(vec_short2) == sizeof(__hip_bfloat162_raw));
-  union {
-    __hip_bfloat162_raw bf162_raw;
-    vec_short2 vs2;
-  } u{static_cast<__hip_bfloat162_raw>(value)};
-  u.vs2 = __builtin_amdgcn_flat_atomic_fadd_v2bf16((vec_short2*)address, u.vs2);
-  return static_cast<__hip_bfloat162>(u.bf162_raw);
-#else
-  static_assert(sizeof(unsigned int) == sizeof(__hip_bfloat162_raw));
-  union u_hold {
-    __hip_bfloat162_raw h2r;
-    unsigned int u32;
-  };
-  u_hold old_val, new_val;
-  old_val.u32 = __hip_atomic_load((unsigned int*)address, __ATOMIC_RELAXED, __HIP_MEMORY_SCOPE_AGENT);
-  do {
-    new_val.h2r = __hadd2(old_val.h2r, value);
-  } while (!__hip_atomic_compare_exchange_strong(
-        (unsigned int*)address, &old_val.u32, new_val.u32,
-        __ATOMIC_RELAXED, __ATOMIC_RELAXED, __HIP_MEMORY_SCOPE_AGENT));
-  return old_val.h2r;
-#endif
-}
-
-__device__ inline __half2 preview_unsafeAtomicAdd(__half2* address, __half2 value) {
-#if (defined(__gfx942__)) && \
-  __has_builtin(__builtin_amdgcn_flat_atomic_fadd_v2f16)
-  // The api expects an ext_vector_type of half
-  typedef _Float16 __attribute__((ext_vector_type(2))) vec_fp162;
-  static_assert(sizeof(vec_fp162) == sizeof(__half2_raw));
-  union {
-    __half2_raw h2r;
-    vec_fp162 fp16;
-  } u {static_cast<__half2_raw>(value)};
-  u.fp16 = __builtin_amdgcn_flat_atomic_fadd_v2f16((vec_fp162*)address, u.fp16);
-  return static_cast<__half2>(u.h2r);
-#else
-  static_assert(sizeof(__half2_raw) == sizeof(unsigned int));
-  union u_hold {
-    __half2_raw h2r;
-    unsigned int u32;
-  };
-  u_hold old_val, new_val;
-  old_val.u32 = __hip_atomic_load((unsigned int*)address, __ATOMIC_RELAXED, __HIP_MEMORY_SCOPE_AGENT);
-  do {
-    new_val.h2r = __hadd2(old_val.h2r, value);
-  } while (!__hip_atomic_compare_exchange_strong(
-        (unsigned int*)address, &old_val.u32, new_val.u32,
-        __ATOMIC_RELAXED, __ATOMIC_RELAXED, __HIP_MEMORY_SCOPE_AGENT));
-  return old_val.h2r;
-#endif
-}
-#define ATOMICADD preview_unsafeAtomicAdd
+#define ATOMICADD unsafeAtomicAdd
 #define NATIVE_ZERO_BF16 __float2bfloat16(0.0f)
 #else
 #define ATOMICADD atomicAdd

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

@@ -2,18 +2,250 @@
 #include <ATen/Dispatch.h>
 #include <ATen/native/DispatchStub.h>
 #include <ATen/native/cuda/Loops.cuh>
+#include <ATen/native/cuda/JitLoops.cuh>
+#include <ATen/native/cuda/jit_utils.h>
+#include <ATen/native/cuda/ScanUtils.cuh>
 #include <ATen/native/TensorIterator.h>
 #include <ATen/native/BinaryOps.h>
 #include <ATen/OpMathType.h>
 #include <c10/util/MathConstants.h>
+#include <c10/util/complex.h>
+
+#include <cmath>
+#include <limits>
 
 // NOTE: CUDA on Windows requires that the enclosing function
 // of a __device__ lambda not have internal linkage.
 
 namespace at::native {
 
+// custom min and max to be used in logaddexp for  complex arguments
+template <typename scalar_t, bool min>
+__host__ __device__ c10::complex<scalar_t> _logaddexp_minmax(const c10::complex<scalar_t>& x, const c10::complex<scalar_t>& y) {
+  scalar_t xr = std::real(x);
+  scalar_t yr = std::real(y);
+  if (::isnan(yr) || (::isnan(std::imag(y)))) {
+    return y;
+  } else if (::isnan(xr) || (::isnan(std::imag(x)))) {
+    return x;
+  } else if (min) { // min
+    return (xr < yr) ? x : y;
+  } else { // max
+    return (xr >= yr) ? x : y;
+  }
+}
+
+template <typename scalar_t>
+__host__ __device__ scalar_t _log_add_exp_helper(const scalar_t& x, const scalar_t& y) {
+  // Reference : https://www.tensorflow.org/api_docs/python/tf/math/cumulative_logsumexp
+  // Using the original expression: `at::_isnan(y) ? y : std::min(x, y)` causes an error in ROCM
+  const auto isnan_x = at::_isnan(x);
+  const auto isnan_y = at::_isnan(y);
+  scalar_t min = isnan_y ? y : (isnan_x ? x : std::min(x, y));
+  scalar_t max = isnan_y ? y : (isnan_x ? x : std::max(x, y));
+  if (min != max || ::isfinite(min)) {
+    // nan will be propagated here
+    return ::log1p(std::exp(min - max)) + max;
+  } else {
+    // special case to correctly handle infinite cases
+    return x;
+  }
+}
+
+template <typename scalar_t>
+__host__ __device__ c10::complex<scalar_t> _fast_build_exp(const c10::complex<scalar_t>& x) {
+  // complex exponential function, but implemented manually to get fast compilation time
+  // this function only handles the case where the x is finite (not inf nor nan)
+  const auto xreal = std::real(x);
+  const auto ximag = std::imag(x);
+  const auto exp_x_abs = std::exp(xreal);
+  auto exp_x_real = exp_x_abs * std::cos(ximag);
+  auto exp_x_imag = exp_x_abs * std::sin(ximag);
+  return {exp_x_real, exp_x_imag};
+}
+
+template <typename scalar_t>
+__host__ __device__ c10::complex<scalar_t> _fast_build_exp_inf(const c10::complex<scalar_t>& x) {
+  // complex exponential function, but implemented manually to get fast compilation time
+  // this function only handles the case where the real part of x is infinite
+  const auto ximag = std::imag(x);
+  constexpr auto exp_x_abs = std::numeric_limits<scalar_t>::infinity();
+  if (!::isfinite(ximag)) {  // add this to make consitent with std::exp(x+yi)
+    return {exp_x_abs, std::numeric_limits<scalar_t>::quiet_NaN()};
+  }
+  const auto sin = std::sin(ximag);
+  const auto cos = std::cos(ximag);
+  // special case if the angle is exactly the multiple of pi/2
+  auto exp_x_real = (cos == 0) ? (scalar_t)0.0 : exp_x_abs * cos;
+  auto exp_x_imag = (sin == 0) ? (scalar_t)0.0 : exp_x_abs * sin;
+  return {exp_x_real, exp_x_imag};
+}
+
+template <typename scalar_t>
+__host__ __device__ c10::complex<scalar_t> _log_add_exp_helper(const c10::complex<scalar_t>& x, const c10::complex<scalar_t>& y) {
+  c10::complex<scalar_t> min = _logaddexp_minmax<scalar_t, /*min=*/true>(x, y);
+  c10::complex<scalar_t> max = _logaddexp_minmax<scalar_t, /*min=*/false>(x, y);
+  scalar_t min_real = std::real(min);
+  scalar_t max_real = std::real(max);
+
+  if (::isnan(min_real) || ::isnan(std::imag(min))) {
+    // handling the "infectious" NaNs
+    return {std::numeric_limits<scalar_t>::quiet_NaN(), std::numeric_limits<scalar_t>::quiet_NaN()};
+  }
+  else if ((!::isfinite(min_real)) && (min_real == max_real)) {
+    if (min_real < 0) {
+      // handle the -inf case, the imaginary part here does not really matter as the exp(value)
+      // will be around 0.0 and the angle (i.e. the imaginary part) cannot be determined.
+      // It does not matter if we're taking the exp of this value
+      return min;
+    } else {
+      // handle the +inf case, we don't need the special precision for log1p for small values
+      // and to avoid producing nan in case of real(max) == real(min) == +inf
+      const auto exp_min = _fast_build_exp_inf(min);
+      const auto exp_max = _fast_build_exp_inf(max);
+      return ::log1p(exp_min + exp_max - 1);  // log1p(x - 1) builds faster than log
+    }
+  } else {
+    const auto minmax = min - max;
+    c10::complex<scalar_t> exp_minmax;
+    if (!::isfinite(minmax.real())) {
+        exp_minmax = minmax.real() < 0 ? c10::complex<scalar_t>{0.0, 0.0} : _fast_build_exp_inf(minmax);
+    } else {
+        exp_minmax = _fast_build_exp(minmax);
+    }
+    return ::log1p(exp_minmax) + max;
+  }
+}
+
+// Complex logaddexp jiterator string
+const auto logaddexp_complex_string = jiterator_stringify(
+    template<typename T>
+    std::complex<T> log1p(const std::complex<T>& z)
+    {
+      using complex_t = std::complex<T>;
+      T x = z.real();
+      T y = z.imag();
+      T zabs = abs(z);
+      T theta = atan2(y, x + T(1));
+      if (zabs < 0.5) {
+          T r = x * (T(2) + x) + y * y;
+          if (r == 0) { // handle underflow
+              return complex_t(x, theta);
+          }
+          return complex_t(T(0.5) * std::log1p(r), theta);
+      } else {
+          T z0 = std::hypot(x + 1, y);
+          return complex_t(log(z0), theta);
+      }
+    }
+
+    // separated _logaddexp_minmax into 2 different functions for jiterator_string
+    template <typename T>
+    std::complex<T> logaddexp_min(const std::complex<T>& x, const std::complex<T>& y) {
+        T xr = x.real();
+        T yr = y.real();
+        if (isnan(yr) || isnan(y.imag())) {
+            return y;
+        } else if (isnan(xr) || isnan(x.imag())) {
+            return x;
+        } else {
+            return (xr < yr) ? x : y;
+        }
+    }
+
+    template <typename T>
+    std::complex<T> logaddexp_max(const std::complex<T>& x, const std::complex<T>& y) {
+        T xr = x.real();
+        T yr = y.real();
+        if (isnan(yr) || isnan(y.imag())) {
+            return y;
+        } else if (isnan(xr) || isnan(x.imag())) {
+            return x;
+        } else {
+            return (xr >= yr) ? x : y;
+        }
+    }
+
+    template <typename T>
+    std::complex<T> fast_build_exp(const std::complex<T>& x) {
+        const auto xreal = x.real();
+        const auto ximag = x.imag();
+        const auto exp_x_abs = exp(xreal);
+        auto exp_x_real = exp_x_abs * cos(ximag);
+        auto exp_x_imag = exp_x_abs * sin(ximag);
+        return std::complex<T>(exp_x_real, exp_x_imag);
+    }
+
+    template <typename T>
+    std::complex<T> fast_build_exp_inf(const std::complex<T>& x) {
+        using complex_t = std::complex<T>;
+        const auto ximag = x.imag();
+        const T exp_x_abs = INFINITY;
+        if (!isfinite(ximag)) {
+            return complex_t(exp_x_abs, NAN);
+        }
+        const auto sin_val = sin(ximag);
+        const auto cos_val = cos(ximag);
+        auto exp_x_real = (cos_val == T(0)) ? T(0) : exp_x_abs * cos_val;
+        auto exp_x_imag = (sin_val == T(0)) ? T(0) : exp_x_abs * sin_val;
+        return complex_t(exp_x_real, exp_x_imag);
+    }
+
+    template <typename complex_t>
+    complex_t logaddexp_complex(complex_t x, complex_t y) {
+        using T = typename complex_t::value_type;
+        complex_t min_val = logaddexp_min(x, y);
+        complex_t max_val = logaddexp_max(x, y);
+        T min_real = min_val.real();
+        T max_real = max_val.real();
+
+        if (isnan(min_real) || isnan(min_val.imag())) {
+            return complex_t(NAN, NAN);
+        }
+        else if ((!isfinite(min_real)) && (min_real == max_real)) {
+            if (min_real < T(0)) {
+                return min_val;
+            } else {
+                const auto exp_min = fast_build_exp_inf<T>(min_val);
+                const auto exp_max = fast_build_exp_inf<T>(max_val);
+                return log1p(exp_min + exp_max - complex_t(1, 0));
+            }
+        } else {
+            const auto minmax = min_val - max_val;
+            complex_t exp_minmax;
+            if (!isfinite(minmax.real())) {
+                exp_minmax = (minmax.real() < T(0)) ? complex_t(0, 0) : fast_build_exp_inf<T>(minmax);
+            } else {
+                exp_minmax = fast_build_exp<T>(minmax);
+            }
+            return log1p(exp_minmax) + max_val;
+        }
+    }
+);
+
+constexpr char logaddexp_complex_name[] = "logaddexp_complex";
 void logaddexp_kernel_cuda(TensorIteratorBase& iter) {
-  AT_DISPATCH_FLOATING_TYPES_AND2(
+  if (at::isComplexType(iter.dtype())) {
+#if AT_USE_JITERATOR()
+    AT_DISPATCH_COMPLEX_TYPES_AND(at::ScalarType::ComplexHalf, iter.dtype(), "logaddexp_cuda", [&]() {
+      jitted_gpu_kernel<
+          /*name=*/logaddexp_complex_name,
+          /*return_dtype=*/scalar_t,
+          /*common_dtype=*/scalar_t,
+          /*arity=*/2>(iter, logaddexp_complex_string);
+    });
+#else
+    AT_DISPATCH_COMPLEX_TYPES_AND(at::ScalarType::ComplexHalf, iter.dtype(), "logaddexp_cuda", [&]() {
+      using opmath_t = at::opmath_type<scalar_t>;
+      gpu_kernel(iter, [] GPU_LAMBDA (scalar_t a_, scalar_t b_) -> scalar_t {
+        const auto a = static_cast<opmath_t>(a_);
+        const auto b = static_cast<opmath_t>(b_);
+        return static_cast<scalar_t>(_log_add_exp_helper(a, b));
+      });
+    });
+#endif
+  } else {
+    AT_DISPATCH_FLOATING_TYPES_AND2(
       ScalarType::BFloat16, ScalarType::Half,
       iter.dtype(), "logaddexp_cuda",
       [&]() {
@@ -29,6 +261,7 @@ void logaddexp_kernel_cuda(TensorIteratorBase& iter) {
           }
         });
       });
+  }
 }
 
 void logaddexp2_kernel_cuda(TensorIteratorBase& iter) {

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

@@ -740,7 +740,12 @@ _scaled_rowwise_rowwise(
   TORCH_CHECK_VALUE(scale_a.numel() == mat_a.size(0) && scale_a.scalar_type() == kFloat, "scale_a must have ", mat_a.size(0), " Float elements, got ", scale_a.numel())
   TORCH_CHECK_VALUE(scale_b.numel() == mat_b.size(1) && scale_b.scalar_type() == kFloat, "scale_b must have ", mat_b.size(1), " Float elements, got ", scale_b.numel())
 
-  TORCH_CHECK_VALUE(scale_a.stride(1) == 1, "expected scale_a.stride(1) to be 1, but got ", scale_a.stride(1));
+  // if we have a scale of shape [256, 1] (say), then stride can be [1, 0] - handle this case
+  TORCH_CHECK_VALUE(
+      scale_a.stride(1) == 1 ||
+      scale_a.size(1) == 1,
+      "expected scale_a.stride(1) to be 1, but got ", scale_a.stride(1)
+  );
   TORCH_CHECK_VALUE(scale_b.stride(1) == 1, "expected scale_b.stride(1) to be 1, but got ", scale_b.stride(1));
 
   auto scaling_choice_a = ScalingType::RowWise;
@@ -1096,6 +1101,19 @@ _scaled_mxfp8_mxfp8(
   return _scaled_gemm(mat_a, mat_b, scale_a, scale_b, scaling_choice_a, scaling_choice_b, bias, false /* use_fast_accum */, out);
 }
 
+void
+_check_mxfp4_support() {
+#ifndef USE_ROCM
+  auto dprops = at::cuda::getCurrentDeviceProperties();
+  // Only on B200 GPUs
+  TORCH_CHECK_NOT_IMPLEMENTED(
+    // B200 = 10.0, B300 = 10.3
+    dprops->major == 10,
+    "MXFP4 scaling only supported in CUDA for B200/B300"
+  );
+#endif
+}
+
 
 Tensor&
 _scaled_mxfp4_mxfp4(
@@ -1108,6 +1126,7 @@ _scaled_mxfp4_mxfp4(
 #if defined(_WIN32) || (!defined(USE_ROCM) && !defined(USE_FBGEMM_GENAI))
   TORCH_CHECK_NOT_IMPLEMENTED(false, "MXFP4 scaling supported on ROCM and CUDA+FBGEMM_GENAI only");
 #else
+  _check_mxfp4_support();
   // Restrictions:
   // A, B are FP4, scales are e8m0, A: shape K//32, B: K, N//32
   TORCH_CHECK_VALUE(mat_a.scalar_type() == at::kFloat4_e2m1fn_x2 && mat_b.scalar_type() == at::kFloat4_e2m1fn_x2, "mat_a and mat_b must be fp4 types, got: ",

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

@@ -267,15 +267,15 @@ void scan_dim_with_indices(const TensorBase& self, const TensorBase& values, con
  * outer dimensions, which contains several "inner rows").
  * Each thread processes a single inner row at a time.
  */
-template<typename scalar_t, class BinaryOp>
+template<typename scalar_t, typename index_t, class BinaryOp>
 __global__ void tensor_kernel_scan_outer_dim(scalar_t *tgt_, const scalar_t *src_,
                                               const uint32_t num_orows, const uint32_t num_irows, const uint32_t row_size,
                                               const scalar_t init, BinaryOp binary_op)
 {
   for (uint32_t orow = blockIdx.x; orow < num_orows; orow += gridDim.x) {
     for (uint32_t irow = blockIdx.y * blockDim.x + threadIdx.x; irow < num_irows; irow += gridDim.y * blockDim.x) {
-      const scalar_t *src = src_ + orow * row_size * num_irows + irow;
-      scalar_t *tgt = tgt_ + orow * row_size * num_irows + irow;
+      const scalar_t *src = src_ + static_cast<index_t>(orow) * row_size * num_irows + irow;
+      scalar_t *tgt = tgt_ + (index_t) orow * row_size * num_irows + irow;
       scalar_t acc = init;
 
       for (uint32_t col = 0; col < row_size; ++col) {
@@ -409,10 +409,15 @@ __host__ void scan_outer_dim(const TensorBase& self, const TensorBase& result,
   check_fits_in_unsigned(num_irows, "num_irows");
   check_fits_in_unsigned(num_orows, "num_orows");
   check_fits_in_unsigned(row_size, "row_size");
-
-  tensor_kernel_scan_outer_dim<scalar_t><<<grid, threads, 0, at::cuda::getCurrentCUDAStream()>>>(
+  if (static_cast<size_t>(num_irows) * num_orows * row_size <= UINT_MAX) {
+  tensor_kernel_scan_outer_dim<scalar_t, uint32_t><<<grid, threads, 0, at::cuda::getCurrentCUDAStream()>>>(
     result.mutable_data_ptr<scalar_t>(), self.const_data_ptr<scalar_t>(),
     num_orows, num_irows, row_size, init, binary_op);
+  } else  {
+  tensor_kernel_scan_outer_dim<scalar_t, size_t><<<grid, threads, 0, at::cuda::getCurrentCUDAStream()>>>(
+    result.mutable_data_ptr<scalar_t>(), self.const_data_ptr<scalar_t>(),
+    num_orows, num_irows, row_size, init, binary_op);
+  }
   C10_CUDA_KERNEL_LAUNCH_CHECK();
 }
 

aten/src/ATen/native/mkldnn/xpu/Conv.cpp

@@ -337,10 +337,6 @@ Tensor _convolution_out(
   TORCH_CHECK(
       3 == ndim || 4 == ndim || 5 == ndim,
       "convolution only supports 3D, 4D, 5D tensor");
-  // get computation format for Conv/TransposedConv
-  bool is_channels_last_suggested =
-      use_channels_last_for_conv(input_r, weight_r);
-
   Tensor input = input_r, weight = weight_r;
   // PyTorch does not support ChannelsLast1D case,
   // thus we need the transformation here
@@ -348,13 +344,8 @@ Tensor _convolution_out(
     input = view4d(input_r);
     weight = view4d(weight_r);
   }
-  // ensure the input/weight/bias/output are congituous in desired format
-  at::MemoryFormat mfmt = is_channels_last_suggested
-      ? get_cl_tag_by_ndim(input.ndimension())
-      : at::MemoryFormat::Contiguous;
-  auto bias = bias_r.defined() ? bias_r.contiguous() : bias_r;
-  input = input.contiguous(mfmt);
-  weight = weight.contiguous(mfmt);
+  // get computation format for Conv/TransposedConv
+  bool is_channels_last_suggested = use_channels_last_for_conv(input, weight);
 
   auto k = weight.ndimension();
   if (k == input.ndimension() + 1) {
@@ -388,6 +379,14 @@ Tensor _convolution_out(
         expand_param_if_needed(output_padding_, "output_padding", dim);
     params.groups = groups_;
   }
+
+  // ensure the input/weight/bias/output are congituous in desired format
+  at::MemoryFormat mfmt = is_channels_last_suggested
+      ? get_cl_tag_by_ndim(input.ndimension())
+      : at::MemoryFormat::Contiguous;
+  auto bias = bias_r.defined() ? bias_r.contiguous() : bias_r;
+  input = input.contiguous(mfmt);
+  weight = weight.contiguous(mfmt);
   check_shape_forward(input, weight, bias, params, true);
 
   Tensor output;
@@ -514,18 +513,9 @@ Tensor convolution_overrideable(
       at::borrow_from_optional_tensor(bias_r_opt);
   const Tensor& bias_r = *bias_r_maybe_owned;
 
-  auto k = weight_r.ndimension();
-  at::MemoryFormat backend_memory_format = at::MemoryFormat::Contiguous;
-  if (xpu_conv_use_channels_last(input_r, weight_r)) {
-    backend_memory_format = (k == 5) ? at::MemoryFormat::ChannelsLast3d
-                                     : at::MemoryFormat::ChannelsLast;
-  }
-  Tensor input_c = input_r.contiguous(backend_memory_format);
-  Tensor weight_c = weight_r.contiguous(backend_memory_format);
-
   return _convolution(
-      input_c,
-      weight_c,
+      input_r,
+      weight_r,
       bias_r,
       stride_,
       padding_,

aten/src/ATen/native/mkldnn/xpu/ScaledBlas.cpp

@@ -0,0 +1,342 @@
+#define TORCH_ASSERT_ONLY_METHOD_OPERATORS
+#include <ATen/BlasBackend.h>
+#include <ATen/WrapDimUtilsMulti.h>
+#include <ATen/ceil_div.h>
+#include <ATen/native/Resize.h>
+#include <ATen/native/mkldnn/xpu/detail/oneDNN.h>
+#include <ATen/native/xpu/Blas.h>
+#include <torch/library.h>
+
+#ifndef AT_PER_OPERATOR_HEADERS
+#include <ATen/Functions.h>
+#include <ATen/NativeFunctions.h>
+#else
+#include <ATen/ops/_addmm_activation_native.h>
+#include <ATen/ops/_efficientzerotensor.h>
+#include <ATen/ops/_scaled_mm_native.h>
+#include <ATen/ops/_unsafe_view_native.h>
+#include <ATen/ops/abs.h>
+#include <ATen/ops/addmm_native.h>
+#include <ATen/ops/addmv_native.h>
+#include <ATen/ops/baddbmm_native.h>
+#include <ATen/ops/bmm_native.h>
+#include <ATen/ops/copy_native.h>
+#include <ATen/ops/dot_native.h>
+#include <ATen/ops/empty.h>
+#include <ATen/ops/empty_strided.h>
+#include <ATen/ops/gelu.h>
+#include <ATen/ops/max.h>
+#include <ATen/ops/mm_native.h>
+#include <ATen/ops/mul.h>
+#include <ATen/ops/ones.h>
+#include <ATen/ops/relu.h>
+#include <ATen/ops/scalar_tensor_native.h>
+#include <ATen/ops/vdot_native.h>
+#endif
+
+namespace at::native {
+
+using at::blas::ScalingType;
+using at::blas::SwizzleType;
+
+namespace {
+/*
+ * Scaling Type Determination:
+ * ---------------------------
+ * Conditions and corresponding Scaling Types:
+ *
+ * - If scale tensor is `Float8_e8m0fnu` or `Float8_e4m3fn`:
+ *   - Returns BlockWise (with additional size checks).
+ *
+ * - Else if scale.numel() == 1:
+ *   - Returns TensorWise.
+ *
+ * - Else if scale.dim() == 2 && scale.size(0) == outer_dim && scale.size(1) ==
+ * 1:
+ *   - Returns RowWise.
+ *
+ * - Otherwise:
+ *   - Returns Error.
+ */
+
+bool is_tensorwise_scaling(const at::Tensor& t, const at::Tensor& scale) {
+  return at::isFloat8Type(t.scalar_type()) &&
+      scale.scalar_type() == at::kFloat && scale.numel() == 1;
+}
+
+bool is_rowwise_scaling(const at::Tensor& t, const at::Tensor& scale) {
+  return (
+      at::isFloat8Type(t.scalar_type()) && scale.scalar_type() == at::kFloat &&
+      scale.dim() == 2 && scale.size(0) == t.size(0) && scale.size(1) == 1 &&
+      scale.is_contiguous());
+}
+
+bool is_desired_scaling(
+    const at::Tensor& t,
+    const at::Tensor& scale,
+    ScalingType desired_scaling) {
+  auto result = desired_scaling == ScalingType::TensorWise
+      ? is_tensorwise_scaling(t, scale)
+      : is_rowwise_scaling(t, scale);
+  return result;
+}
+
+std::pair<ScalingType, ScalingType> get_joint_scaling(
+    std::initializer_list<std::pair<ScalingType, ScalingType>> options,
+    const at::Tensor& a,
+    const at::Tensor& b,
+    const at::Tensor& scale_a,
+    const at::Tensor& scale_b) {
+  for (auto [lhs, rhs] : options) {
+    if (is_desired_scaling(a, scale_a, lhs) &&
+        is_desired_scaling(b.t(), scale_b.t(), rhs)) {
+      return {lhs, rhs};
+    }
+  }
+  TORCH_CHECK(
+      false,
+      "Invalid scaling configuration.\n"
+      "- For TensorWise scaling, a and b should be float8, scales should be float and singletons.\n"
+      "- For RowWise scaling, a and b should be float8, scales should be float, scale_a should be (",
+      a.size(0),
+      ", 1) and scale_b should be (1, ",
+      b.size(1),
+      "), and both should be contiguous.\n"
+      "Got a.dtype()=",
+      a.scalar_type(),
+      ", scale_a.dtype()=",
+      scale_a.scalar_type(),
+      ", scale_a.size()=",
+      scale_a.sizes(),
+      ", scale_a.stride()=",
+      scale_a.strides(),
+      ", ",
+      "b.dtype()=",
+      b.scalar_type(),
+      ", scale_b.dtype()=",
+      scale_b.scalar_type(),
+      ", scale_b.size()=",
+      scale_b.sizes(),
+      " and scale_b.stride()=",
+      scale_b.strides());
+}
+
+Tensor& _scaled_gemm(
+    const Tensor& mat1,
+    const Tensor& mat2,
+    const Tensor& scale_a,
+    const Tensor& scale_b,
+    const ScalingType scaling_choice_a,
+    const ScalingType scaling_choice_b,
+    const std::optional<Tensor>& bias,
+    const bool use_fast_accum,
+    Tensor& out,
+    const std::optional<Tensor>& alpha = std::nullopt) {
+  // TODO: scale_result and alpha is not defined or used!
+  std::optional<Tensor> scaled_result = std::nullopt;
+  at::native::onednn::scaled_matmul(
+      mat1,
+      mat2,
+      out,
+      scale_a,
+      scale_b,
+      scaling_choice_a,
+      scaling_choice_b,
+      bias,
+      scaled_result,
+      use_fast_accum);
+
+  return out;
+}
+
+} // namespace
+
+// Computes matrix multiply + bias while applying scaling to input and output
+// matrices Scales are only applicable when matrices are of Float8 type and
+// assumed to be equal to 1.0 by default. If output matrix type is 16 or 32-bit
+// type, scale_result is not applied. Known limitations:
+//  - Only works if mat1 is row-major and mat2 is column-major
+//  - Only works if matrices sizes are divisible by 32
+//  - If 1-dimensional tensors are used then scale_a should be size =
+//  mat1.size(0)
+//    and scale_b should have size = to mat2.size(1)
+//  Arguments:
+//    - `mat1`: the first operand of the matrix multiply, can be type
+//    `torch.float8_e4m3fn` or `torch.float8_e5m2`
+//    - `mat2`: the second operand of the matrix multiply, can be type
+//    `torch.float8_e4m3fn` or `torch.float8_e5m2`
+//    - `bias`: the bias, can be type `torch.float16` or `torch.bfloat16`
+//    - `out_dtype`: the output dtype, can either be a float8 or a higher
+//    precision floating point type
+//    - `scale_a`: a tensor with the inverse scale of `mat1`, whose
+//    shape/strides/dtype depend on the scaling scheme
+//    - `scale_b`: a tensor with the inverse scale of `mat2`, whose
+//    shape/strides/dtype depend on the scaling scheme
+//    - `scale_result`: a scalar tensor with the scale of the output, only
+//    utilized if the output is a float8 type
+//    - `use_fast_accum`: Not applicable for XPU. For now, it should always be
+//    false.
+//    - `out`: a reference to the output tensor
+
+Tensor& _scaled_mm_out_xpu(
+    const Tensor& mat1,
+    const Tensor& mat2,
+    const Tensor& scale_a,
+    const Tensor& scale_b,
+    const std::optional<at::Tensor>& bias,
+    const std::optional<at::Tensor>& scale_result,
+    std::optional<c10::ScalarType> out_dtype,
+    bool use_fast_accum,
+    Tensor& out) {
+  // Note: fast_accum is not supported in XPU for now.
+  TORCH_CHECK(!use_fast_accum, "fast_accum is not supported in XPU for now.");
+
+  TORCH_CHECK(mat1.dim() == 2, "mat1 must be a matrix");
+  TORCH_CHECK(mat2.dim() == 2, "mat2 must be a matrix");
+
+  TORCH_CHECK(
+      mat1.sizes()[1] == mat2.sizes()[0],
+      "mat1 and mat2 shapes cannot be multiplied (",
+      mat1.sizes()[0],
+      "x",
+      mat1.sizes()[1],
+      " and ",
+      mat2.sizes()[0],
+      "x",
+      mat2.sizes()[1],
+      ")");
+
+  // Check what type of scaling we are doing based on inputs. This list is
+  // sorted by decreasing priority.
+
+  // List of supported datatypes for XPU with oneDNN:
+  // https://uxlfoundation.github.io/oneDNN/dev_guide_matmul.html#data-types
+  auto [scaling_choice_a, scaling_choice_b] = get_joint_scaling(
+      {
+          std::make_pair(ScalingType::TensorWise, ScalingType::TensorWise),
+          std::make_pair(ScalingType::RowWise, ScalingType::RowWise),
+      },
+      mat1,
+      mat2,
+      scale_a,
+      scale_b);
+  TORCH_CHECK(
+      !scale_result ||
+          (scale_result->numel() == 1 && scale_result->scalar_type() == kFloat),
+      "scale_result must be a float scalar");
+  TORCH_CHECK(
+      !bias || bias->numel() == mat2.sizes()[1],
+      "Bias must be size ",
+      mat2.sizes()[1],
+      " but got ",
+      bias->numel());
+  TORCH_CHECK(
+      mat1.sizes()[1] % 16 == 0,
+      "Expected trailing dimension of mat1 to be divisible by 16 ",
+      "but got mat1 shape: (",
+      mat1.sizes()[0],
+      "x",
+      mat1.sizes()[1],
+      ").");
+  TORCH_CHECK(
+      mat2.sizes()[0] % 16 == 0 && mat2.sizes()[1] % 16 == 0,
+      "mat2 shape (",
+      mat2.sizes()[0],
+      "x",
+      mat2.sizes()[1],
+      ") must be divisible by 16");
+  // Check types
+  TORCH_CHECK(
+      !out_dtype || *out_dtype == out.scalar_type(),
+      "out_dtype must match output matrix type");
+  TORCH_CHECK(
+      at::isFloat8Type(mat1.scalar_type()),
+      "Expected mat1 to be Float8 matrix got ",
+      mat1.scalar_type());
+  TORCH_CHECK(
+      at::isFloat8Type(mat2.scalar_type()),
+      "Expected mat2 to be Float8 matrix got ",
+      mat2.scalar_type());
+  // TODO: oneDNN Currently only supports e4m3 with group scales on BMG. Not
+  // support 2D scales, only 1D. Needs to add more checks there.
+
+  if (bias) {
+    TORCH_CHECK(
+        bias->scalar_type() == kFloat ||
+            bias->scalar_type() == c10::ScalarType::BFloat16 ||
+            bias->scalar_type() == c10::ScalarType::Half,
+        "Bias must be Float32 or BFloat16 or Half, but got ",
+        bias->scalar_type());
+  }
+
+  {
+    auto bias_ = bias.value_or(Tensor());
+    auto scale_result_ = scale_result.value_or(Tensor());
+
+    // NOLINTNEXTLINE(*c-array*)
+    TensorArg targs[]{
+        {out, "out", 0},
+        {mat1, "mat1", 1},
+        {mat2, "mat2", 2},
+        {bias_, "bias", 3},
+        {scale_a, "scale_a", 4},
+        {scale_b, "scale_b", 5},
+        {scale_result_, "scale_result", 6}};
+    checkAllSameGPU(__func__, targs);
+  }
+
+  // Validation checks have passed lets resize the output to actual size
+  IntArrayRef mat1_sizes = mat1.sizes();
+  IntArrayRef mat2_sizes = mat2.sizes();
+  at::native::resize_output(out, {mat1_sizes[0], mat2_sizes[1]});
+
+  // If any of M, K, N is 0 - return early (the tensorwise/rowwise float8 gemm
+  // kernels do not support this case).
+  if (mat1_sizes[0] == 0 || mat1_sizes[1] == 0 || mat2_sizes[1] == 0) {
+    // `out` was created with `at::empty`. In the case where we are multiplying
+    // MxK by KxN and K is the zero dim, we need to initialize here to properly
+    // return a tensor of zeros.
+    if (mat1_sizes[1] == 0) {
+      out.zero_();
+    }
+
+    return out;
+  }
+
+  // TODO: Scale_result is not supported by now!!
+  return _scaled_gemm(
+      mat1,
+      mat2,
+      scale_a,
+      scale_b,
+      scaling_choice_a,
+      scaling_choice_b,
+      bias,
+      use_fast_accum,
+      out);
+}
+
+Tensor _scaled_mm_xpu(
+    const Tensor& mat_a,
+    const Tensor& mat_b,
+    const Tensor& scale_a,
+    const Tensor& scale_b,
+    const std::optional<at::Tensor>& bias,
+    const std::optional<at::Tensor>& scale_result,
+    std::optional<c10::ScalarType> out_dtype,
+    bool use_fast_accum) {
+  const auto out_dtype_ = out_dtype.value_or(mat_a.scalar_type());
+  Tensor out = at::empty({0}, mat_a.options().dtype(out_dtype_));
+  return _scaled_mm_out_xpu(
+      mat_a,
+      mat_b,
+      scale_a,
+      scale_b,
+      bias,
+      scale_result,
+      out_dtype,
+      use_fast_accum,
+      out);
+}
+
+} // namespace at::native

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

@@ -1,3 +1,4 @@
+#include <ATen/BlasBackend.h>
 #include <ATen/Tensor.h>
 #include <ATen/core/Tensor.h>
 #include <c10/core/ScalarType.h>
@@ -8,7 +9,6 @@
 #include <oneapi/dnnl/dnnl.hpp>
 
 namespace at::native::onednn {
-
 at::Tensor broadcast_bias2D(
     at::Tensor& dst,
     at::Tensor& bias,
@@ -328,4 +328,236 @@ void quantized_matmul(
     result.copy_(dst);
 }
 
+// Describes how to configure oneDNN scales for a given role/ScalingType
+struct ScaleSpec {
+  // specifies the way scale values will be applied to an ARG tensor.
+  int mask;
+  // specifies how scales are grouped along dimensions where
+  // multiple scale factors are used.
+  dnnl::memory::dims groups;
+  // specifies data type for scale factors.
+  dnnl::memory::data_type dtype;
+
+  // Helper to compute expected number of elements for scale tensors
+  // arg_type: "src" for SRC (groups pattern {1, X}),
+  // "wei" for WEIGHTS (groups pattern {X, 1})
+  int64_t expected_numel(
+      int64_t outer_dim,
+      int64_t inner_dim,
+      const std::string& arg_type) const {
+    if (groups == dnnl::memory::dims{1, 1})
+      return 1; // tensorwise scaling
+
+    TORCH_CHECK(
+        arg_type == "src" || arg_type == "wei",
+        "Expected arg_type to be 'src' or 'wei', but got '",
+        arg_type,
+        "'");
+
+    // For rowwise: SRC groups={1, K}, WEI groups={K, 1}
+    TORCH_INTERNAL_ASSERT(
+        (groups == dnnl::memory::dims{1, inner_dim} ||
+         groups == dnnl::memory::dims{inner_dim, 1}),
+        "The groups must be either {1, inner_dim} or {inner_dim, 1}. But got ",
+        groups,
+        ".");
+    return outer_dim;
+  }
+
+  // Normalize an incoming scale tensor to contiguous storage and appropriate
+  // dtype/view
+  at::Tensor normalize(const at::Tensor& scale) const {
+    TORCH_INTERNAL_ASSERT(
+        dtype == dnnl::memory::data_type::f32,
+        "tensor scale currently must be f32, but got scale dtype: ",
+        scale.scalar_type());
+    return scale.to(at::kFloat).contiguous();
+  }
+};
+
+// This function defines how to set scales mask and groups according to:
+// https://github.com/uxlfoundation/oneDNN/blob/main/tests/benchdnn/doc/knobs_attr.md#--attr-scales
+// The returned value will be used in
+// `set_scales(arg, mask, groups, data_type)`.
+inline ScaleSpec make_scale_spec(
+    at::blas::ScalingType scaling_type,
+    int64_t M,
+    int64_t K,
+    int64_t N,
+    const std::string& arg_type) {
+  TORCH_CHECK(
+      arg_type == "src" || arg_type == "wei",
+      "Expected arg_type to be 'src' or 'wei', but got '",
+      arg_type,
+      "'");
+  TORCH_INTERNAL_ASSERT(
+      (scaling_type == at::blas::ScalingType::TensorWise ||
+       scaling_type == at::blas::ScalingType::RowWise),
+      "Currently only support scaling_type for TensorWise or RowWise");
+  int64_t dim = K; // Currently only K is used for grouping
+  bool is_src = (arg_type == "src");
+  if (scaling_type == at::blas::ScalingType::TensorWise) {
+    // Scale tensorwise. The same as `--attr-scales=common`.
+    // mask=0 : scale whole tensor
+    // groups={1, 1}: indicates that there is only one group for scaling
+    return {0, {1, 1}, dnnl::memory::data_type::f32};
+  } else {
+    // (scaling_type == at::blas::ScalingType::RowWise)
+    // Scale RowWise. The same as `--attr-scales=per_dim_01`.
+    // mask={(1 << 0) | (1 << 1)}: Scale on both dim0 and dim1
+    // SRC: groups={1, K}, WEIGHTS: groups={K, 1}
+    return {
+        (1 << 0) | (1 << 1),
+        is_src ? dnnl::memory::dims{1, dim} : dnnl::memory::dims{dim, 1},
+        dnnl::memory::data_type::f32};
+  }
+}
+
+sycl::event scaled_matmul(
+    const Tensor& mat1,
+    const Tensor& mat2,
+    Tensor& result,
+    const Tensor& scale_a,
+    const Tensor& scale_b,
+    at::blas::ScalingType scaling_choice_a,
+    at::blas::ScalingType scaling_choice_b,
+    const std::optional<at::Tensor>& bias,
+    const std::optional<at::Tensor>& scale_result,
+    bool use_fast_accum) {
+  auto& engine = GpuEngineManager::Instance().get_engine();
+  auto& stream = GpuStreamManager::Instance().get_stream();
+
+  // This function will do steps with following steps
+  // 1. create memory descriptor
+  // 2. call write_to_dnnl_memory() to actually write memory
+  // 3. execute
+
+  const int64_t M = mat1.size(0);
+  const int64_t K = mat1.size(1);
+  const int64_t N = mat2.size(1);
+
+  // 1.1 Create memory descriptor
+  dnnl::memory::desc src_md = get_onednn_md(mat1);
+  dnnl::memory::desc weights_md = get_onednn_md(mat2);
+  dnnl::memory::desc dst_md = get_onednn_md(result);
+
+  // scale_a and scale_b has already be checked in `is_desired_scaling()` call.
+  // So we could directly get their memory desc and set later.
+  dnnl::memory::desc scale_a_md = get_onednn_md(scale_a);
+  dnnl::memory::desc scale_b_md = get_onednn_md(scale_b);
+
+  dnnl::memory::desc bias_md;
+  bool with_bias = bias.has_value();
+  at::Tensor possible_reshaped_bias = bias.value_or(at::Tensor());
+  if (with_bias) {
+    if (possible_reshaped_bias.dim() == 1) {
+      possible_reshaped_bias =
+          possible_reshaped_bias.reshape({1, possible_reshaped_bias.size(0)});
+      bias_md = get_onednn_md(possible_reshaped_bias);
+    } else {
+      bias_md = get_onednn_md(possible_reshaped_bias);
+    }
+  }
+
+  // 1.2 Create primitive descriptor and set scales mask
+  const ScaleSpec src_spec = make_scale_spec(scaling_choice_a, M, K, N, "src");
+  const ScaleSpec wei_spec = make_scale_spec(scaling_choice_b, M, K, N, "wei");
+
+  dnnl::primitive_attr op_attr = dnnl::primitive_attr();
+
+#if ONEDNN_SUPPORT_DETERMINISTIC
+  if (at::globalContext().deterministicAlgorithms() ||
+      at::globalContext().deterministicMkldnn())
+    op_attr.set_deterministic(true);
+#endif
+
+  std::vector<int64_t> default_groups;
+  op_attr.set_scales(
+      DNNL_ARG_SRC, src_spec.mask, src_spec.groups, src_spec.dtype);
+  op_attr.set_scales(
+      DNNL_ARG_WEIGHTS, wei_spec.mask, wei_spec.groups, wei_spec.dtype);
+  // scale_result tensor currently only supports scalar(TensorWise Scaling).
+  bool with_dst_scale = scale_result && scale_result->defined();
+  if (with_dst_scale) {
+    op_attr.set_scales(DNNL_ARG_DST, 0, {1}, dnnl::memory::data_type::f32);
+  }
+
+  op_attr.set_scratchpad_mode(dnnl::scratchpad_mode::user);
+
+  // 1.3 Create the matmul primitive descriptor
+  dnnl::matmul::primitive_desc matmul_pd = with_bias
+      ? dnnl::matmul::primitive_desc(
+            engine, src_md, weights_md, bias_md, dst_md, op_attr)
+      : dnnl::matmul::primitive_desc(
+            engine, src_md, weights_md, dst_md, op_attr);
+
+  // 1.4 (Possible) Additional Checks
+  // TODO: In case there are memory desc does not align with the actual tensor,
+  // we might need to reorder weights similar to CPU's reorder_if_differ_in()
+  // call. For example, weights not the same as matmul_pd.weights_desc(),
+
+  // 2. Prepare memory
+
+  // Create memory
+  auto src_usr_m = make_onednn_memory(src_md, engine, mat1.data_ptr());
+  auto weights_usr_m = make_onednn_memory(weights_md, engine, mat2.data_ptr());
+  auto dst_usr_m = make_onednn_memory(dst_md, engine, result.data_ptr());
+  dnnl::memory b_usr_m;
+  if (with_bias) {
+    b_usr_m =
+        make_onednn_memory(bias_md, engine, possible_reshaped_bias.data_ptr());
+  }
+
+  // Prepare runtime scale memories (flat 1-D views) using the specs
+  auto make_scale_mem_from_spec = [&](const ScaleSpec& spec,
+                                      int64_t expected_numel,
+                                      const at::Tensor& scale_tensor) {
+    at::Tensor prepared = spec.normalize(scale_tensor);
+    TORCH_CHECK(
+        prepared.numel() == expected_numel,
+        "Scale buffer length mismatch. Expected ",
+        expected_numel,
+        ", got ",
+        prepared.numel());
+    dnnl::memory::desc scale_md(
+        {prepared.numel()}, spec.dtype, dnnl::memory::format_tag::x);
+    return make_onednn_memory(scale_md, engine, prepared.data_ptr());
+  };
+
+  auto scratchpad =
+      make_onednn_memory(matmul_pd.scratchpad_desc(), engine, nullptr);
+
+  // 3. Setup Args for exec
+  std::unordered_map<int, dnnl::memory> args;
+  args.insert({DNNL_ARG_SRC, src_usr_m});
+  args.insert({DNNL_ARG_WEIGHTS, weights_usr_m});
+  args.insert({DNNL_ARG_DST, dst_usr_m});
+  args.insert({DNNL_ARG_SCRATCHPAD, scratchpad});
+  if (with_bias) {
+    args.insert({DNNL_ARG_BIAS, b_usr_m});
+  }
+
+  // Attach runtime scales using specs
+  auto src_sc_mem = make_scale_mem_from_spec(
+      src_spec, src_spec.expected_numel(M, K, "src"), scale_a);
+  auto wei_sc_mem = make_scale_mem_from_spec(
+      wei_spec, wei_spec.expected_numel(N, K, "wei"), scale_b);
+  args.insert({DNNL_ARG_ATTR_SCALES | DNNL_ARG_SRC, src_sc_mem});
+  args.insert({DNNL_ARG_ATTR_SCALES | DNNL_ARG_WEIGHTS, wei_sc_mem});
+  if (with_dst_scale) {
+    // Bind single f32 scalar as DST scale
+    at::Tensor dst_scale_f32 = scale_result->to(at::kFloat).contiguous();
+    dnnl::memory::desc dst_sc_md(
+        {1}, dnnl::memory::data_type::f32, dnnl::memory::format_tag::x);
+    auto dst_sc_mem =
+        make_onednn_memory(dst_sc_md, engine, dst_scale_f32.data_ptr());
+    args.insert({DNNL_ARG_ATTR_SCALES | DNNL_ARG_DST, dst_sc_mem});
+  }
+
+  dnnl::matmul matmul_p = dnnl::matmul(matmul_pd);
+  sycl::event matmul_fwd_event =
+      dnnl::sycl_interop::execute(matmul_p, stream, args);
+  return matmul_fwd_event;
+}
+
 } // namespace at::native::onednn

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

@@ -78,6 +78,10 @@ dnnl::memory::data_type get_onednn_dtype(
       return dnnl::memory::data_type::f32;
     case at::ScalarType::BFloat16:
       return dnnl::memory::data_type::bf16;
+    case at::ScalarType::Float8_e4m3fn:
+      return dnnl::memory::data_type::f8_e4m3;
+    case at::ScalarType::Float8_e5m2:
+      return dnnl::memory::data_type::f8_e5m2;
     default:
       if (!allow_undef) {
         TORCH_CHECK(

aten/src/ATen/native/mkldnn/xpu/detail/oneDNN.h

@@ -1,6 +1,7 @@
 #pragma once
 
 #include <ATen/ATen.h>
+#include <ATen/BlasBackend.h>
 #include <ATen/native/mkldnn/xpu/detail/Attr.h>
 #include <ATen/native/mkldnn/xpu/detail/Utils.h>
 #include <ATen/native/mkldnn/xpu/detail/oneDNNContext.h>
@@ -202,4 +203,16 @@ void sdpa_backward(
     Tensor& grad_query,
     Tensor& grad_key,
     Tensor& grad_value);
+
+sycl::event scaled_matmul(
+    const Tensor& mat1,
+    const Tensor& mat2,
+    Tensor& result,
+    const Tensor& scale_a,
+    const Tensor& scale_b,
+    at::blas::ScalingType scaling_choice_a,
+    at::blas::ScalingType scaling_choice_b,
+    const std::optional<at::Tensor>& bias,
+    const std::optional<at::Tensor>& scale_result,
+    bool use_fast_accum);
 } // namespace at::native::onednn

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

@@ -82,6 +82,7 @@ NSArray<NSNumber*>* getTensorAxes(const TensorBase& t);
 NSArray<NSNumber*>* getTensorAxes(const IntArrayRef& sizes, at::OptionalIntArrayRef dim);
 std::string getMPSShapeString(MPSShape* shape);
 std::string getTensorsStringKey(const TensorList& tensors, bool short_dtype = true, bool exclude_shape = false);
+std::string to_hex_key(float);
 std::string getArrayRefString(const IntArrayRef s);
 // use has_storage() on the returned tensor to determine if src actually is a view
 Tensor gatherViewTensor(const Tensor& src, Tensor& dst);

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

@@ -301,6 +301,10 @@ std::string getArrayRefString(const IntArrayRef s) {
   return fmt::to_string(fmt::join(s, ","));
 }
 
+std::string to_hex_key(float f) {
+  return fmt::format("{:a}", f);
+}
+
 std::string getTensorsStringKey(const TensorList& tensors, bool short_dtype, bool exclude_shape) {
   fmt::basic_memory_buffer<char, 100> buffer;
   auto buf_iterator = std::back_inserter(buffer);

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

@@ -40,7 +40,7 @@ inline c10::metal::opmath_t<T> matmul_inner(
     threadgroup_barrier(mem_flags::mem_threadgroup);
 
     for (uint k = 0; k < TILE_DIM; k++) {
-      sum += A_tile[tid.y][k] * B_tile[k][tid.x];
+      sum += c10::metal::mul(A_tile[tid.y][k], B_tile[k][tid.x]);
     }
 
     threadgroup_barrier(mem_flags::mem_threadgroup);
@@ -96,7 +96,9 @@ kernel void addmm(
     auto bias =
         biasData[thread_id.y * strides[3].x + thread_id.x * strides[3].y];
     outputData[thread_id.y * strides[2].x + thread_id.x * strides[2].y] =
-        static_cast<T>(alpha_beta[0] * sum + alpha_beta[1] * bias);
+        static_cast<T>(
+            c10::metal::mul(alpha_beta[0], sum) +
+            c10::metal::mul(alpha_beta[1], bias));
   }
 }
 
@@ -832,6 +834,10 @@ INSTANTIATE_MM_OPS(float);
 INSTANTIATE_MM_OPS(half);
 INSTANTIATE_MM_OPS(bfloat);
 
+// Complex MM
+INSTANTIATE_MM_OPS(float2);
+INSTANTIATE_MM_OPS(half2);
+
 // Integral MM
 INSTANTIATE_MM_OPS(long);
 INSTANTIATE_MM_OPS(int);

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

@@ -69,75 +69,139 @@ static std::tuple<Tensor, Tensor> sdpa_general_mps(const Tensor& query,
   auto out = at::empty({batchSize, num_head, qSize, headSize}, query.options());
   auto attn = at::empty({batchSize, num_head, qSize, maxSeqLength}, query.options());
   auto scale_factor = sdp::calculate_scale(query, scale).expect_float();
+  static const bool is_macOS_26_0_or_newer = is_macos_13_or_newer(MacOSVersion::MACOS_VER_26_0_PLUS);
   @autoreleasepool {
     auto mkey = __func__ + getTensorsStringKey({query, key, value}) + ":" + std::to_string(is_causal) + ":" +
         std::to_string(attn_mask.has_value());
-    auto cachedGraph =
-        LookUpOrCreateCachedGraph<CachedGraph>(mkey, [&, q_ = query, k_ = key, v_ = value](auto mpsGraph, auto graph) {
-          auto qTensor = mpsGraphRankedPlaceHolder(mpsGraph, q_);
-          auto kTensor = mpsGraphRankedPlaceHolder(mpsGraph, k_);
-          auto vTensor = mpsGraphRankedPlaceHolder(mpsGraph, v_);
-          auto kT = [mpsGraph transposeTensor:kTensor dimension:2 withDimension:3 name:nil];
-          auto scaleTensor = [mpsGraph constantWithScalar:scale_factor
-                                                    shape:getMPSShape({1})
-                                                 dataType:MPSDataTypeFloat32];
 
-          auto maskedMM = [mpsGraph matrixMultiplicationWithPrimaryTensor:qTensor secondaryTensor:kT name:nil];
+    CachedGraph* cachedGraph;
+    //if(is_macOS_26_0_or_newer) {
+    if(true) {
+        cachedGraph =
+            LookUpOrCreateCachedGraph<CachedGraph>(mkey, [&, q_ = query, k_ = key, v_ = value](auto mpsGraph, auto graph) {
+              auto qTensor = mpsGraphRankedPlaceHolder(mpsGraph, q_);
+              auto kTensor = mpsGraphRankedPlaceHolder(mpsGraph, k_);
+              auto vTensor = mpsGraphRankedPlaceHolder(mpsGraph, v_);
 
-          if (macOS15_0_plus && [maskedMM dataType] == MPSDataTypeFloat32) {
-            // bug in MacOS15, without this trick SDPA leaks memory, adding 0.0f gets ignored(still takes SDPA sequence
-            // path which leaks)
-            auto oneTensor = [mpsGraph constantWithScalar:1e-20f shape:getMPSShape({1}) dataType:MPSDataTypeFloat32];
-            maskedMM = [mpsGraph additionWithPrimaryTensor:maskedMM secondaryTensor:oneTensor name:nil];
-          }
+              if (is_causal) {
+                MPSShape* maskShape = @[@(qSize), @(maxSeqLength)];
+                auto x = [mpsGraph coordinateAlongAxis:-1 withShape:@[@(qSize), @1] name:nil];
+                auto y = [mpsGraph coordinateAlongAxis:-2 withShape:@[@1, @(maxSeqLength)] name:nil];
+                auto isLess = [mpsGraph lessThanOrEqualToWithPrimaryTensor:x secondaryTensor:y name:nil];
+                auto causalMask = [mpsGraph selectWithPredicateTensor:isLess 
+                                            truePredicateTensor:[mpsGraph constantWithScalar:0 dataType:qTensor.dataType] 
+                                            falsePredicateTensor:[mpsGraph constantWithScalar:-INFINITY dataType:qTensor.dataType] 
+                                            name:nil];
+                graph->maskTensor = causalMask;
+              } else if (attn_mask) {
+                graph->maskTensor = mpsGraphRankedPlaceHolder(mpsGraph, *attn_mask);
+              }
 
-          // upcasting to float32 if needed to improve precision when multiplying by the scale factor
-          maskedMM = castMPSTensor(mpsGraph, maskedMM, MPSDataTypeFloat32);
-          maskedMM = [mpsGraph multiplicationWithPrimaryTensor:maskedMM secondaryTensor:scaleTensor name:nil];
+              // Account for case where all values were masked causing division by 0 in softmax (issue:#156707)
+              // Overwrites expected NANs in sm with zeros.
+//              auto negInfTensor = [mpsGraph constantWithScalar:-INFINITY shape:maskedMM.shape dataType:maskedMM.dataType];
+//              auto elem_neg_inf = [mpsGraph equalWithPrimaryTensor:maskedMM secondaryTensor:negInfTensor name:nil];
+//              auto all_neg_infs_along_axis = [mpsGraph reductionAndWithTensor:elem_neg_inf axis:3 name:nil];
+//              auto zero_mask = [mpsGraph broadcastTensor:all_neg_infs_along_axis toShape:maskedMM.shape name:nil];
+//              auto zeroTensor = [mpsGraph constantWithScalar:0.0 shape:maskedMM.shape dataType:maskedMM.dataType];
+//
+//              auto sm = [mpsGraph softMaxWithTensor:maskedMM axis:3 name:nil];
+//              MPSGraphTensor* correctedSM = [mpsGraph selectWithPredicateTensor:zero_mask
+//                                                            truePredicateTensor:zeroTensor
+//                                                           falsePredicateTensor:sm
+//                                                                           name:nil];
+//
+//              auto output = [mpsGraph matrixMultiplicationWithPrimaryTensor:correctedSM secondaryTensor:vTensor name:nil];
 
-          if (is_causal) {
-            auto causalMask = [mpsGraph constantWithScalar:1.0f
-                                                     shape:getMPSShape({qSize, maxSeqLength})
-                                                  dataType:MPSDataTypeBool];
-            causalMask = [mpsGraph bandPartWithTensor:causalMask numLower:-1 numUpper:0 name:nil];
-            auto minusInf = [mpsGraph constantWithScalar:-1e20 shape:maskedMM.shape dataType:maskedMM.dataType];
-            maskedMM = [mpsGraph selectWithPredicateTensor:causalMask
-                                       truePredicateTensor:maskedMM
-                                      falsePredicateTensor:minusInf
-                                                      name:nil];
-          } else if (attn_mask) {
-            graph->maskTensor = mpsGraphRankedPlaceHolder(mpsGraph, *attn_mask);
-            maskedMM = [mpsGraph additionWithPrimaryTensor:maskedMM
-                                           secondaryTensor:castMPSTensor(mpsGraph, graph->maskTensor, maskedMM.dataType)
-                                                      name:nil];
-          }
+              MPSGraphTensor* output;
+              if(graph->maskTensor != nil) {
+                output = [mpsGraph scaledDotProductAttentionWithQueryTensor:qTensor 
+                                                          keyTensor:kTensor 
+                                                        valueTensor:vTensor
+                                                         maskTensor:graph->maskTensor
+                                                              scale:scale_factor
+                                                               name:@"MPSGraph SDPA"];
+              } else {
+                output = [mpsGraph scaledDotProductAttentionWithQueryTensor:qTensor 
+                                                          keyTensor:kTensor 
+                                                        valueTensor:vTensor
+                                                              scale:scale_factor
+                                                               name:@"MPSGraph SDPA"];
+              }
+              graph->qTensor = qTensor;
+              graph->kTensor = kTensor;
+              graph->vTensor = vTensor;
+              graph->outputTensor = castMPSTensor(mpsGraph, output, qTensor.dataType);
+//              graph->attnTensor = castMPSTensor(mpsGraph, sm, qTensor.dataType);
+            });
+    } else {
+        cachedGraph =
+            LookUpOrCreateCachedGraph<CachedGraph>(mkey, [&, q_ = query, k_ = key, v_ = value](auto mpsGraph, auto graph) {
+              auto qTensor = mpsGraphRankedPlaceHolder(mpsGraph, q_);
+              auto kTensor = mpsGraphRankedPlaceHolder(mpsGraph, k_);
+              auto vTensor = mpsGraphRankedPlaceHolder(mpsGraph, v_);
+              auto kT = [mpsGraph transposeTensor:kTensor dimension:2 withDimension:3 name:nil];
+              auto scaleTensor = [mpsGraph constantWithScalar:scale_factor
+                                                        shape:getMPSShape({1})
+                                                     dataType:MPSDataTypeFloat32];
 
-          // Account for case where all values were masked causing division by 0 in softmax (issue:#156707)
-          // Overwrites expected NANs in sm with zeros.
-          auto negInfTensor = [mpsGraph constantWithScalar:-INFINITY shape:maskedMM.shape dataType:maskedMM.dataType];
-          auto elem_neg_inf = [mpsGraph equalWithPrimaryTensor:maskedMM secondaryTensor:negInfTensor name:nil];
-          auto all_neg_infs_along_axis = [mpsGraph reductionAndWithTensor:elem_neg_inf axis:3 name:nil];
-          auto zero_mask = [mpsGraph broadcastTensor:all_neg_infs_along_axis toShape:maskedMM.shape name:nil];
-          auto zeroTensor = [mpsGraph constantWithScalar:0.0 shape:maskedMM.shape dataType:maskedMM.dataType];
+              auto maskedMM = [mpsGraph matrixMultiplicationWithPrimaryTensor:qTensor secondaryTensor:kT name:nil];
 
-          auto sm = [mpsGraph softMaxWithTensor:maskedMM axis:3 name:nil];
-          MPSGraphTensor* correctedSM = [mpsGraph selectWithPredicateTensor:zero_mask
-                                                        truePredicateTensor:zeroTensor
-                                                       falsePredicateTensor:sm
-                                                                       name:nil];
+              if (macOS15_0_plus && [maskedMM dataType] == MPSDataTypeFloat32) {
+                // bug in MacOS15, without this trick SDPA leaks memory, adding 0.0f gets ignored(still takes SDPA sequence
+                // path which leaks)
+                auto oneTensor = [mpsGraph constantWithScalar:1e-20f shape:getMPSShape({1}) dataType:MPSDataTypeFloat32];
+                maskedMM = [mpsGraph additionWithPrimaryTensor:maskedMM secondaryTensor:oneTensor name:nil];
+              }
 
-          auto output = [mpsGraph matrixMultiplicationWithPrimaryTensor:correctedSM secondaryTensor:vTensor name:nil];
-          graph->qTensor = qTensor;
-          graph->kTensor = kTensor;
-          graph->vTensor = vTensor;
-          graph->outputTensor = castMPSTensor(mpsGraph, output, qTensor.dataType);
-          graph->attnTensor = castMPSTensor(mpsGraph, sm, qTensor.dataType);
-        });
+              // upcasting to float32 if needed to improve precision when multiplying by the scale factor
+              maskedMM = castMPSTensor(mpsGraph, maskedMM, MPSDataTypeFloat32);
+              maskedMM = [mpsGraph multiplicationWithPrimaryTensor:maskedMM secondaryTensor:scaleTensor name:nil];
+
+              if (is_causal) {
+                auto causalMask = [mpsGraph constantWithScalar:1.0f
+                                                         shape:getMPSShape({qSize, maxSeqLength})
+                                                      dataType:MPSDataTypeBool];
+                causalMask = [mpsGraph bandPartWithTensor:causalMask numLower:-1 numUpper:0 name:nil];
+                auto minusInf = [mpsGraph constantWithScalar:-1e20 shape:maskedMM.shape dataType:maskedMM.dataType];
+                maskedMM = [mpsGraph selectWithPredicateTensor:causalMask
+                                           truePredicateTensor:maskedMM
+                                          falsePredicateTensor:minusInf
+                                                          name:nil];
+              } else if (attn_mask) {
+                graph->maskTensor = mpsGraphRankedPlaceHolder(mpsGraph, *attn_mask);
+                maskedMM = [mpsGraph additionWithPrimaryTensor:maskedMM
+                                               secondaryTensor:castMPSTensor(mpsGraph, graph->maskTensor, maskedMM.dataType)
+                                                          name:nil];
+              }
+
+              // Account for case where all values were masked causing division by 0 in softmax (issue:#156707)
+              // Overwrites expected NANs in sm with zeros.
+              auto negInfTensor = [mpsGraph constantWithScalar:-INFINITY shape:maskedMM.shape dataType:maskedMM.dataType];
+              auto elem_neg_inf = [mpsGraph equalWithPrimaryTensor:maskedMM secondaryTensor:negInfTensor name:nil];
+              auto all_neg_infs_along_axis = [mpsGraph reductionAndWithTensor:elem_neg_inf axis:3 name:nil];
+              auto zero_mask = [mpsGraph broadcastTensor:all_neg_infs_along_axis toShape:maskedMM.shape name:nil];
+              auto zeroTensor = [mpsGraph constantWithScalar:0.0 shape:maskedMM.shape dataType:maskedMM.dataType];
+
+              auto sm = [mpsGraph softMaxWithTensor:maskedMM axis:3 name:nil];
+              MPSGraphTensor* correctedSM = [mpsGraph selectWithPredicateTensor:zero_mask
+                                                            truePredicateTensor:zeroTensor
+                                                           falsePredicateTensor:sm
+                                                                           name:nil];
+
+              auto output = [mpsGraph matrixMultiplicationWithPrimaryTensor:correctedSM secondaryTensor:vTensor name:nil];
+              graph->qTensor = qTensor;
+              graph->kTensor = kTensor;
+              graph->vTensor = vTensor;
+              graph->outputTensor = castMPSTensor(mpsGraph, output, qTensor.dataType);
+              graph->attnTensor = castMPSTensor(mpsGraph, sm, qTensor.dataType);
+            });
+    }
     auto qPlaceholder = Placeholder(cachedGraph->qTensor, query);
     auto kPlaceholder = Placeholder(cachedGraph->kTensor, key);
     auto vPlaceholder = Placeholder(cachedGraph->vTensor, value);
     auto outputPlaceholder = Placeholder(cachedGraph->outputTensor, out);
-    auto attnPlaceholder = Placeholder(cachedGraph->attnTensor, attn);
+//    auto attnPlaceholder = Placeholder(cachedGraph->attnTensor, attn);
     NSDictionary* feeds = nil;
     if (!attn_mask) {
       feeds = dictionaryFromPlaceholders(qPlaceholder, kPlaceholder, vPlaceholder);
@@ -145,7 +209,8 @@ static std::tuple<Tensor, Tensor> sdpa_general_mps(const Tensor& query,
       auto mPlaceholder = Placeholder(cachedGraph->maskTensor, *attn_mask);
       feeds = dictionaryFromPlaceholders(qPlaceholder, kPlaceholder, vPlaceholder, mPlaceholder);
     }
-    NSDictionary* outs = dictionaryFromPlaceholders(outputPlaceholder, attnPlaceholder);
+//    NSDictionary* outs = dictionaryFromPlaceholders(outputPlaceholder, attnPlaceholder);
+    NSDictionary* outs = dictionaryFromPlaceholders(outputPlaceholder);
     runMPSGraph(getCurrentMPSStream(), cachedGraph->graph(), feeds, outs);
   }
 

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

@@ -121,7 +121,7 @@ Tensor& do_metal_addmm(const Tensor& self,
                        const Scalar& alpha,
                        const Scalar& beta,
                        const Tensor& bias) {
-  if (beta.toDouble() == 0 && alpha.toDouble() == 1) {
+  if (beta.isFloatingPoint() && alpha.isFloatingPoint() && beta.toDouble() == 0 && alpha.toDouble() == 1) {
     return do_metal_mm(self, other, output);
   }
   auto stream = getCurrentMPSStream();
@@ -147,13 +147,15 @@ Tensor& do_metal_addmm(const Tensor& self,
         std::array<int64_t, 2> i64;
         std::array<int32_t, 2> i32;
         std::array<float, 2> f32;
-      } alpha_beta;
+        std::array<c10::complex<float>, 2> c64;
+      } alpha_beta{};
       if (output.scalar_type() == kLong) {
         alpha_beta.i64 = {alpha.toLong(), beta.toLong()};
       } else if (c10::isIntegralType(output.scalar_type(), true)) {
         alpha_beta.i32 = {alpha.toInt(), beta.toInt()};
+      } else if (c10::isComplexType(output.scalar_type())) {
+        alpha_beta.c64 = {alpha.toComplexFloat(), beta.toComplexFloat()};
       } else {
-        TORCH_INTERNAL_ASSERT(c10::isFloatingType(output.scalar_type()));
         alpha_beta.f32 = {alpha.toFloat(), beta.toFloat()};
       }
       constexpr uint32_t TILE_DIM = 16; // fastest performance from tests on multiple macs
@@ -190,10 +192,16 @@ std::tuple<MPSGraphTensor*, MPSGraphTensor*, MPSGraphTensor*> do_mm(MPSGraph* gr
 bool use_metal_mm(const Tensor& self, const Tensor& other, const Tensor& output) {
   static bool always_use_metal = c10::utils::has_env("PYTORCH_MPS_PREFER_METAL");
   constexpr auto max_stride_size = 32768;
+  constexpr auto max_complex_inner_size = 2048;
   static bool is_macos_14_4_or_newer = is_macos_13_or_newer(MacOSVersion::MACOS_VER_14_4_PLUS);
   if (always_use_metal || c10::isIntegralType(self.scalar_type(), true)) {
     return true;
   }
+  // multiplicationWithPrimaryTensor: returns incorrect results if inner size exceeds 2048
+  // See https://github.com/pytorch/pytorch/issues/167727#issuecomment-3529308548
+  if (c10::isComplexType(self.scalar_type()) && self.size(1) > max_complex_inner_size) {
+    return true;
+  }
   return !is_macos_14_4_or_newer &&
       (self.stride(0) > max_stride_size || self.stride(1) > max_stride_size || self.size(0) > max_stride_size ||
        self.size(1) > max_stride_size || other.stride(0) > max_stride_size || other.stride(1) > max_stride_size ||

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

@@ -91,25 +91,30 @@ static auto& lib = mps::MetalShaderLibrary::getBundledLibrary();
 #include <ATen/native/mps/Repeat_metallib.h>
 #endif
 
-template <typename index_t>
-void computeRepeatIndices(const index_t* repeat_ptr,
-                          const int64_t* cumsum_ptr,
-                          index_t* result_ptr,
-                          int64_t size,
-                          int64_t result_size) {
-  id<MTLBuffer> repeatBuffer = reinterpret_cast<id<MTLBuffer>>(repeat_ptr);
-  id<MTLBuffer> cumsumBuffer = reinterpret_cast<id<MTLBuffer>>(cumsum_ptr);
-  id<MTLBuffer> resultBuffer = reinterpret_cast<id<MTLBuffer>>(result_ptr);
-  TORCH_CHECK(repeatBuffer && cumsumBuffer && resultBuffer);
-
+Tensor repeat_interleave_mps(const Tensor& repeat, std::optional<int64_t> output_size) {
+  TORCH_CHECK(repeat.dim() == 1, "repeat_interleave only accept 1D vector as repeat");
   std::string scalar_type;
-  if constexpr (std::is_same_v<index_t, int32_t>) {
+  if (repeat.scalar_type() == kInt) {
     scalar_type = "int32_t";
-  } else if constexpr (std::is_same_v<index_t, int64_t>) {
+  } else if (repeat.scalar_type() == kLong) {
     scalar_type = "int64_t";
   } else {
-    TORCH_CHECK(false, "repeat_interleave: unsupported indexing data type");
+    TORCH_CHECK(false, "repeats has to be Long or Int tensor");
   }
+  if (repeat.size(0) == 0) {
+    return at::empty_like(repeat, LEGACY_CONTIGUOUS_MEMORY_FORMAT);
+  }
+  Tensor repeat_ = repeat.contiguous();
+  Tensor cumsum = repeat.cumsum(0);
+  int64_t total = 0;
+  if (output_size.has_value()) {
+    total = output_size.value();
+  } else {
+    total = cumsum[-1].item<int64_t>();
+    TORCH_CHECK((repeat >= 0).all().item<uint8_t>(), "repeats can not be negative");
+  }
+
+  auto result = at::empty({total}, repeat.options());
 
   MPSStream* mpsStream = getCurrentMPSStream();
   dispatch_sync(mpsStream->queue(), ^() {
@@ -121,20 +126,13 @@ void computeRepeatIndices(const index_t* repeat_ptr,
       getMPSProfiler().beginProfileKernel(pipelineState, "repeat_interleave:" + scalar_type, false);
 
       [computeEncoder setComputePipelineState:pipelineState];
-      mps::mtl_setArgs(computeEncoder, repeatBuffer, cumsumBuffer, resultBuffer, size);
-      mps::mtl_dispatch1DJob(computeEncoder, pipelineState, size);
+      mps::mtl_setArgs(computeEncoder, repeat_, cumsum, result, repeat.size(0));
+      mps::mtl_dispatch1DJob(computeEncoder, pipelineState, repeat.size(0));
 
       getMPSProfiler().endProfileKernel(pipelineState);
     }
   });
-}
-
-Tensor repeat_interleave_mps(const Tensor& repeat, std::optional<int64_t> output_size) {
-  Tensor output;
-  AT_DISPATCH_INDEX_TYPES(repeat.scalar_type(), "repeat_interleave_mps", [&]() {
-    output = repeat_interleave_common<index_t, computeRepeatIndices<index_t>>(repeat, output_size);
-  });
-  return output;
+  return result;
 }
 
 } // namespace at::native

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

@@ -5,6 +5,7 @@
 #include <ATen/native/Resize.h>
 #include <ATen/native/TensorCompare.h>
 #include <ATen/native/mps/OperationUtils.h>
+#include <algorithm>
 
 #ifndef AT_PER_OPERATOR_HEADERS
 #include <ATen/Functions.h>
@@ -89,13 +90,21 @@ static void check_min_max_dims(const OptionalTensorRef clamp_opt, const Tensor&
     auto clamp_shape = clamp_opt->sizes();
     auto input_shape = input_t.sizes();
 
-    TORCH_CHECK(num_clamp_dims <= num_input_dims,
-                op_name + ": clamp tensor number of dims must not be greater than that of input tensor")
+    if (num_clamp_dims > num_input_dims) {
+      auto leading_dims = num_clamp_dims - num_input_dims;
+      for (int64_t i = 0; i < leading_dims; ++i) {
+        TORCH_CHECK(clamp_shape[i] == 1,
+                    op_name + ": clamp tensor leading shape must be 1 to broadcast with input tensor");
+      }
+    }
 
-    for (int i = 0; i < num_clamp_dims; i++)
+    auto clamp_idx = num_clamp_dims - 1;
+    auto input_idx = num_input_dims - 1;
+    auto common_dims = std::min(num_clamp_dims, num_input_dims);
+    for (int64_t i = 0; i < common_dims; ++i)
       // One of the indices is allowed to be 1; will be handled by broadcast
-      TORCH_CHECK(clamp_shape[num_clamp_dims - 1 - i] == input_shape[num_input_dims - 1 - i] ||
-                      clamp_shape[num_clamp_dims - 1 - i] == 1 || input_shape[num_input_dims - 1 - i] == 1,
+      TORCH_CHECK(clamp_shape[clamp_idx - i] == input_shape[input_idx - i] || clamp_shape[clamp_idx - i] == 1 ||
+                      input_shape[input_idx - i] == 1,
                   op_name + ": clamp tensor trailing shape must match input tensor")
   }
 }
@@ -136,9 +145,6 @@ static void clamp_tensor_out_mps(const Tensor& input_t,
 
   auto result_type = output_t.scalar_type();
 
-  IntArrayRef new_min_shape;
-  IntArrayRef new_max_shape;
-
   auto num_min_dims = min_opt->dim();
   auto num_max_dims = max_opt->dim();
   auto num_input_dims = input_t.dim();
@@ -146,24 +152,32 @@ static void clamp_tensor_out_mps(const Tensor& input_t,
   std::vector<int64_t> new_min_arr(num_input_dims);
   std::vector<int64_t> new_max_arr(num_input_dims);
 
-  if (has_min && num_min_dims < num_input_dims) {
-    fill_new_shape(num_input_dims, num_min_dims, new_min_arr.data(), min_opt->sizes());
-    new_min_shape = IntArrayRef(new_min_arr);
-  }
-
-  if (has_max && num_max_dims < num_input_dims) {
-    fill_new_shape(num_input_dims, num_max_dims, new_max_arr.data(), max_opt->sizes());
-    new_max_shape = IntArrayRef(new_max_arr);
-  }
-
   Tensor min_opt_tensor;
   Tensor max_opt_tensor;
 
+  auto reshape_clamp_tensor = [&](const OptionalTensorRef clamp_tensor_ref,
+                                  int64_t num_clamp_dims,
+                                  std::vector<int64_t>& new_shape_storage) -> Tensor {
+    IntArrayRef clamp_shape = clamp_tensor_ref->sizes();
+    bool requires_view = false;
+
+    if (num_clamp_dims > num_input_dims) {
+      clamp_shape = clamp_shape.slice(num_clamp_dims - num_input_dims);
+      requires_view = true;
+    } else if (num_clamp_dims < num_input_dims) {
+      fill_new_shape(num_input_dims, num_clamp_dims, new_shape_storage.data(), clamp_shape);
+      clamp_shape = IntArrayRef(new_shape_storage);
+      requires_view = true;
+    }
+
+    return requires_view ? (*clamp_tensor_ref).view(clamp_shape) : *clamp_tensor_ref;
+  };
+
   if (has_min) {
-    min_opt_tensor = (num_min_dims < num_input_dims) ? (*min_opt).view(new_min_shape) : *min_opt;
+    min_opt_tensor = reshape_clamp_tensor(min_opt, num_min_dims, new_min_arr);
   }
   if (has_max) {
-    max_opt_tensor = (num_max_dims < num_input_dims) ? (*max_opt).view(new_max_shape) : *max_opt;
+    max_opt_tensor = reshape_clamp_tensor(max_opt, num_max_dims, new_max_arr);
   }
 
   @autoreleasepool {
@@ -244,8 +258,8 @@ static void clamp_scalar_out_mps(const Tensor& input_t,
 
   @autoreleasepool {
     // the optional min/max refs could affect how we build the cached graph
-    std::string key = op_name + (has_min ? ("_min:" + std::to_string(min_scalar)) : "") +
-        (has_max ? ("_max:" + std::to_string(max_scalar)) : "") + "_scalar:" + getTensorsStringKey({input_t});
+    std::string key = op_name + (has_min ? ("_min:" + to_hex_key(min_scalar)) : "") +
+        (has_max ? ("_max:" + to_hex_key(max_scalar)) : "") + "_scalar:" + getTensorsStringKey({input_t});
     auto cachedGraph = LookUpOrCreateCachedGraph<CachedGraph>(key, [&](auto mpsGraph, auto newCachedGraph) {
       if (has_min)
         newCachedGraph->minTensor = [mpsGraph constantWithScalar:min_scalar

aten/src/ATen/native/native_functions.yaml

@@ -4225,7 +4225,7 @@
     MTIA: mm_out_mtia
     MPS: mm_out_mps
     XPU: mm_out_xpu
-    SparseCPU, SparseCUDA: _sparse_mm_out
+    SparseCPU, SparseCUDA, SparseMPS: _sparse_mm_out
     SparseCsrCPU, SparseCsrCUDA, SparseCsrMeta: _sparse_csr_mm_out
 
 - func: mm.dtype(Tensor self, Tensor mat2, ScalarType out_dtype) -> Tensor
@@ -7518,7 +7518,7 @@
 - func: _sparse_mask_projection(Tensor self, Tensor mask, bool accumulate_matches=False) -> Tensor
   variants: method
   dispatch:
-    SparseCPU, SparseCUDA: sparse_mask_projection
+    SparseCPU, SparseCUDA, SparseMPS: sparse_mask_projection
   autogen: _sparse_mask_projection.out
 
 - func: _to_cpu(Tensor[] tensors) -> Tensor[]

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

@@ -30,10 +30,12 @@
 
 #include <thrust/binary_search.h>
 #include <thrust/device_ptr.h>
+#include <thrust/distance.h>
+#include <thrust/iterator/constant_iterator.h>
+#include <thrust/scan.h>
 #include <thrust/sequence.h>
 #include <thrust/sort.h>
 #include <thrust/system/cuda/execution_policy.h>
-#include <thrust/iterator/constant_iterator.h>
 
 #include <cuda_runtime_api.h>
 #include <cusparse.h>

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

@@ -445,6 +445,33 @@ static SparseTensor& mul_out_dense_sparse_mps(
   return out;
 }
 
+static std::tuple<Tensor, Tensor, int64_t> mps_intersect_binary_search(
+    const Tensor& A_keys,
+    const Tensor& B_keys,
+    int64_t lenA,
+    int64_t lenB,
+    bool boolean_flag) {
+
+  auto stream = getCurrentMPSStream();
+  auto outA_idx = at::empty({lenA}, A_keys.options().dtype(at::kLong));
+  auto outB_idx = at::empty({lenA}, A_keys.options().dtype(at::kLong));
+  auto counter = at::zeros({1}, A_keys.options().dtype(at::kInt));
+
+  dispatch_sync_with_rethrow(stream->queue(), ^() {
+    @autoreleasepool {
+      auto pso = lib.getPipelineStateForFunc("intersect_binary_search");
+      auto enc = stream->commandEncoder();
+      [enc setComputePipelineState:pso];
+      mtl_setArgs(enc, A_keys, B_keys, outA_idx, outB_idx, counter,
+                  static_cast<uint32_t>(lenB), boolean_flag);
+      mtl_dispatch1DJob(enc, pso, static_cast<uint32_t>(lenA));
+    }
+  });
+
+  const auto match_count = static_cast<int64_t>(counter.item<int32_t>());
+  return std::make_tuple(std::move(outA_idx), std::move(outB_idx), match_count);
+}
+
 
 SparseTensor& mul_out_sparse_mps(const Tensor& t_, const Tensor& src_, SparseTensor& r_) {
   TORCH_CHECK(r_.is_mps(), "mul: expected 'out' to be MPS, but got ", r_.device());
@@ -523,22 +550,10 @@ SparseTensor& mul_out_sparse_mps(const Tensor& t_, const Tensor& src_, SparseTen
   auto A_keys = A_is_lhs ? lhs_keys : rhs_keys;
   auto B_keys = A_is_lhs ? rhs_keys : lhs_keys;
 
-  auto outA_idx = at::empty({lenA}, at::device(device).dtype(kLong));
-  auto outB_idx = at::empty({lenA}, at::device(device).dtype(kLong));
-  auto counter = at::zeros({1}, at::device(device).dtype(kInt));
+  auto [outA_idx, outB_idx, M_int64] = mps_intersect_binary_search(
+      A_keys, B_keys, lenA, lenB, A_is_lhs);
 
-  dispatch_sync_with_rethrow(stream->queue(), ^() {
-    @autoreleasepool {
-      auto pso = lib.getPipelineStateForFunc("intersect_binary_search");
-      auto enc = stream->commandEncoder();
-      [enc setComputePipelineState:pso];
-      mtl_setArgs(enc, A_keys, B_keys, outA_idx, outB_idx, counter,
-                  static_cast<uint32_t>(lenB), A_is_lhs);
-      mtl_dispatch1DJob(enc, pso, static_cast<uint32_t>(lenA));
-    }
-  });
-
-  const uint32_t M = counter.item<int32_t>(); // number of structural matches
+  const auto M = static_cast<uint32_t>(M_int64); // number of structural matches
 
   r_.resize_as_(lhs);
 
@@ -762,6 +777,14 @@ SparseTensor& add_out_sparse_mps(const SparseTensor& self,
 
 using OptTensor = std::optional<Tensor>;
 
+static Tensor create_sparse_output_values(
+    const Tensor& template_values,
+    int64_t output_nnz,
+    ScalarType dtype) {
+  auto out_val_sizes = template_values.sizes().vec();
+  out_val_sizes[0] = output_nnz;
+  return at::zeros(out_val_sizes, template_values.options().dtype(dtype));
+}
 
 static void sparse_mask_apply_out_mps_kernel(
     Tensor& result,
@@ -783,9 +806,9 @@ static void sparse_mask_apply_out_mps_kernel(
   auto src  = src_in.coalesce();
   auto mask = coalesce_mask ? mask_in.coalesce() : mask_in;
 
-  const int64_t src_nnz = src._nnz();
-  const int64_t mask_nnz = mask._nnz();
-  const int64_t sd = src.sparse_dim();
+  const auto src_nnz = src._nnz();
+  const auto mask_nnz = mask._nnz();
+  const auto sd = src.sparse_dim();
   result.sparse_resize_(mask.sizes(), mask.sparse_dim(), mask.dense_dim());
 
   auto commonDtype = at::result_type(src, mask);
@@ -814,53 +837,27 @@ static void sparse_mask_apply_out_mps_kernel(
     return;
   }
 
+  auto mask_indices = mask._indices().contiguous();
+  auto src_values = src._values().to(commonDtype).contiguous();
+  auto out_values = create_sparse_output_values(src_values, mask_nnz, commonDtype);
+
   if (src_nnz == 0) {
-    auto out_indices = mask._indices().contiguous();
-    auto src_values  = src._values().to(commonDtype);
-    auto out_val_sizes = src_values.sizes().vec();
-    out_val_sizes[0] = mask_nnz;
-    auto out_values = at::zeros(out_val_sizes, src_values.options());
-    alias_into_sparse(result, out_indices, out_values);
+    alias_into_sparse(result, mask_indices, out_values);
     result._coalesced_(mask.is_coalesced());
     return;
   }
 
-  auto mask_indices = mask._indices().contiguous();
-  auto src_indices = src._indices().contiguous();
-  auto src_values = src._values().to(commonDtype).contiguous();
+  auto mask_keys = flatten_indices(mask._indices().contiguous(), mask.sizes().slice(0, sd)).contiguous();
+  auto src_keys  = flatten_indices(src._indices().contiguous(), src.sizes().slice(0, sd)).contiguous();
 
-  auto mask_keys = flatten_indices(mask_indices, mask.sizes().slice(0, sd)).contiguous();
-  auto src_keys  = flatten_indices(src_indices,  src.sizes().slice(0, sd)).contiguous();
-
-  const bool A_is_src = (src_nnz <= mask_nnz);
-  const int64_t lenA = A_is_src ? src_nnz  : mask_nnz;
-  const int64_t lenB = A_is_src ? mask_nnz : src_nnz;
+  const auto A_is_src = (src_nnz <= mask_nnz);
+  const auto lenA = A_is_src ? src_nnz  : mask_nnz;
+  const auto lenB = A_is_src ? mask_nnz : src_nnz;
   auto A_keys = A_is_src ? src_keys  : mask_keys;
   auto B_keys = A_is_src ? mask_keys : src_keys;
 
-  const auto device = result.device();
-  auto stream = getCurrentMPSStream();
-
-  auto outA_idx = at::empty({lenA}, at::device(device).dtype(at::kLong));
-  auto outB_idx = at::empty({lenA}, at::device(device).dtype(at::kLong));
-  auto counter = at::zeros({1}, at::device(device).dtype(at::kInt));
-
-  dispatch_sync_with_rethrow(stream->queue(), ^() {
-    @autoreleasepool {
-      auto pso = lib.getPipelineStateForFunc("intersect_binary_search");
-      auto enc = stream->commandEncoder();
-      [enc setComputePipelineState:pso];
-      mtl_setArgs(enc, A_keys, B_keys, outA_idx, outB_idx, counter,
-                  static_cast<uint32_t>(lenB), A_is_src);
-      mtl_dispatch1DJob(enc, pso, static_cast<uint32_t>(lenA));
-    }
-  });
-
-  const int64_t M = static_cast<int64_t>(counter.item<int32_t>());
-
-  auto out_val_sizes = src_values.sizes().vec();
-  out_val_sizes[0] = mask_nnz;
-  auto out_values = at::zeros(out_val_sizes, src_values.options());
+  auto [outA_idx, outB_idx, M] = mps_intersect_binary_search(
+      A_keys, B_keys, lenA, lenB, A_is_src);
 
   if (M > 0) {
     auto src_match = outA_idx.narrow(0, 0, M);
@@ -878,6 +875,70 @@ static void sparse_mask_apply_out_mps_kernel(
   result._coalesced_(mask.is_coalesced());
 }
 
+static void sparse_mask_projection_out_mps_kernel(
+    Tensor& result,
+    const Tensor& lhs,
+    const Tensor& rhs,
+    const OptTensor& /*x_hash_opt*/,
+    bool accumulate_matches) {
+
+  TORCH_CHECK(lhs.is_sparse() && rhs.is_sparse(), "sparse_mask_projection: expected sparse COO");
+  TORCH_CHECK(lhs.is_mps() && rhs.is_mps(), "sparse_mask_projection: expected MPS tensors");
+  TORCH_CHECK(lhs.sparse_dim() == rhs.sparse_dim(), "sparse_dim mismatch");
+
+  auto lhs_c = lhs.coalesce();
+  auto rhs_c = rhs.coalesce();
+
+  const auto sd = lhs_c.sparse_dim();
+  const auto lhs_nnz = lhs_c._nnz();
+  const auto rhs_nnz = rhs_c._nnz();
+
+  auto commonDtype = at::result_type(lhs_c, rhs_c);
+  TORCH_CHECK(canCast(commonDtype, result.scalar_type()),
+              "Can't convert ", commonDtype, " to output ", result.scalar_type());
+
+  result.sparse_resize_(lhs.sizes(), lhs.sparse_dim(), lhs.dense_dim());
+
+  auto lhs_indices = lhs_c._indices().contiguous();
+  auto rhs_values  = rhs_c._values().to(commonDtype).contiguous();
+  auto out_values = create_sparse_output_values(rhs_values, lhs_nnz, commonDtype);
+
+  if (lhs_nnz > 0 && rhs_nnz > 0) {
+    auto lhs_keys = flatten_indices(lhs_indices, lhs_c.sizes().slice(0, sd)).contiguous();
+    auto rhs_keys = flatten_indices(rhs_c._indices().contiguous(), rhs_c.sizes().slice(0, sd)).contiguous();
+
+    const auto A_is_lhs = (lhs_nnz <= rhs_nnz);
+    const auto lenA = A_is_lhs ? lhs_nnz : rhs_nnz;
+    const auto lenB = A_is_lhs ? rhs_nnz : lhs_nnz;
+    auto A_keys = A_is_lhs ? lhs_keys : rhs_keys;
+    auto B_keys = A_is_lhs ? rhs_keys : lhs_keys;
+
+    auto [outA_idx, outB_idx, M] = mps_intersect_binary_search(
+        A_keys, B_keys, lenA, lenB, A_is_lhs);
+
+    if (M > 0) {
+      auto idx_in_A = outA_idx.narrow(0, 0, M);
+      auto idx_in_B = outB_idx.narrow(0, 0, M);
+      auto idx_in_lhs = A_is_lhs ? idx_in_A : idx_in_B;
+      auto idx_in_rhs = A_is_lhs ? idx_in_B : idx_in_A;
+
+      const auto view_cols = rhs_values.numel() / std::max<int64_t>(rhs_nnz, 1);
+      auto rhs_rows = rhs_values.index_select(0, idx_in_rhs).contiguous();
+      auto rhs_rows_2d = rhs_rows.view({M, view_cols});
+      auto out_2d = out_values.view({lhs_nnz, view_cols});
+
+      if (accumulate_matches) {
+        out_2d.index_add_(0, idx_in_lhs, rhs_rows_2d);
+      } else {
+        out_2d.index_copy_(0, idx_in_lhs, rhs_rows_2d);
+      }
+    }
+  }
+
+  alias_into_sparse(result, lhs._indices(), out_values);
+  result._coalesced_(lhs.is_coalesced());
+}
+
 static void sparse_mask_intersection_out_mps_kernel(
     Tensor& result,
     const Tensor& lhs,
@@ -1002,4 +1063,5 @@ Tensor sparse_sparse_matmul_mps(const Tensor& mat1_, const Tensor& mat2_) {
 }
 
 REGISTER_MPS_DISPATCH(sparse_mask_intersection_out_stub, &sparse_mask_intersection_out_mps_kernel);
+REGISTER_MPS_DISPATCH(sparse_mask_projection_out_stub, &sparse_mask_projection_out_mps_kernel);
 } // namespace at::native

aten/src/ATen/test/CMakeLists.txt

@@ -61,6 +61,7 @@ list(APPEND ATen_CUDA_TEST_SRCS
   ${CMAKE_CURRENT_SOURCE_DIR}/cuda_complex_math_test.cu
   ${CMAKE_CURRENT_SOURCE_DIR}/cuda_complex_test.cu
   ${CMAKE_CURRENT_SOURCE_DIR}/cuda_cub_test.cu
+  ${CMAKE_CURRENT_SOURCE_DIR}/cuda_cublas_handle_pool_test.cpp
   ${CMAKE_CURRENT_SOURCE_DIR}/cuda_device_test.cpp
   ${CMAKE_CURRENT_SOURCE_DIR}/cuda_distributions_test.cu
   ${CMAKE_CURRENT_SOURCE_DIR}/cuda_dlconvertor_test.cpp

aten/src/ATen/test/cuda_cublas_handle_pool_test.cpp

@@ -0,0 +1,77 @@
+#include <gtest/gtest.h>
+
+#include <ATen/cuda/CUDAContext.h>
+#include <c10/cuda/CUDACachingAllocator.h>
+#include <c10/cuda/CUDAGuard.h>
+
+#include <atomic>
+#include <thread>
+#include <vector>
+
+// Test concurrent access to getCurrentCUDABlasHandle and getCUDABlasLtWorkspace
+// to verify that the data race fix is working correctly
+
+TEST(CUDABlasHandlePoolTest, ConcurrentGetAndClearWorkspaces) {
+  if (!at::cuda::is_available()) {
+    return;
+  }
+
+  constexpr int num_accessor_threads = 15;
+  constexpr int num_clear_threads = 5;
+  constexpr int iterations_per_thread = 50;
+
+  std::atomic<bool> stop{false};
+  std::atomic<int> error_count{0};
+  std::vector<std::thread> threads;
+  threads.reserve(num_accessor_threads + num_clear_threads);
+
+  // Launch accessor threads
+  for (int i = 0; i < num_accessor_threads; ++i) {
+    threads.emplace_back([&stop, &error_count]() {
+      try {
+        at::cuda::CUDAGuard device_guard(0);
+
+        while (!stop.load(std::memory_order_relaxed)) {
+          const auto handle = at::cuda::getCurrentCUDABlasHandle();
+          const auto workspace = at::cuda::getCUDABlasLtWorkspace();
+
+          if (handle == nullptr || workspace == nullptr) {
+            error_count++;
+          }
+        }
+      } catch (const std::exception& e) {
+        error_count++;
+      }
+    });
+  }
+
+  // Launch threads that clear workspaces
+  for (int i = 0; i < num_clear_threads; ++i) {
+    threads.emplace_back([&error_count]() {
+      try {
+        for (int j = 0; j < iterations_per_thread; ++j) {
+          at::cuda::clearCublasWorkspaces();
+          std::this_thread::yield();
+        }
+      } catch (const std::exception& e) {
+        error_count++;
+      }
+    });
+  }
+
+  // Let them run for a bit
+  std::this_thread::sleep_for(std::chrono::milliseconds(100));
+  stop.store(true, std::memory_order_relaxed);
+
+  for (auto& thread : threads) {
+    thread.join();
+  }
+
+  EXPECT_EQ(error_count.load(), 0);
+}
+
+int main(int argc, char* argv[]) {
+  ::testing::InitGoogleTest(&argc, argv);
+  c10::cuda::CUDACachingAllocator::init(1);
+  return RUN_ALL_TESTS();
+}

aten/tools/valgrind.sup

@@ -10,6 +10,13 @@
    ...
 }
 
+{
+   ignore_empty_generic_uninitialised_conditional_jump
+   Memcheck:Cond
+   fun:_ZN2at6detail13empty_genericEN3c108ArrayRefIlEEPNS1_9AllocatorENS1_14DispatchKeySetENS1_10ScalarTypeESt8optionalINS1_12MemoryFormatEE
+   ...
+}
+
 {
    Cond_cuda
    Memcheck:Cond

benchmarks/dynamo/check_perf_csv.py

@@ -9,28 +9,61 @@ def check_perf_csv(filename, threshold, threshold_scale):
     """
     Basic performance checking.
     """
+    try:
+        df = pd.read_csv(filename)
+    except FileNotFoundError:
+        print(f"Error: File {filename} not found")
+        sys.exit(1)
 
-    df = pd.read_csv(filename)
+    effective_threshold = threshold * threshold_scale
+    print(f"Checking {filename} (speedup threshold >= {effective_threshold:.2f}x)\n")
 
     failed = []
     for _, row in df.iterrows():
         model_name = row["name"]
-        speedup = row["speedup"]
-        if speedup < threshold * threshold_scale:
-            failed.append(model_name)
+        speedup = float(row["speedup"])
+        abs_latency = float(row["abs_latency"])
+        compilation_latency = float(row["compilation_latency"])
+        compression_ratio = float(row["compression_ratio"])
+        eager_peak_mem = float(row["eager_peak_mem"])
+        dynamo_peak_mem = float(row["dynamo_peak_mem"])
 
-        print(f"{model_name:34} {speedup}")
+        perf_summary = f"{model_name:34} speedup={speedup:.3f}x"
+        if pd.notna(abs_latency):
+            perf_summary += f", latency={abs_latency:.1f} ms/iter"
+        if pd.notna(compilation_latency):
+            perf_summary += f", compile={compilation_latency:.3f}s"
+        if pd.notna(compression_ratio):
+            perf_summary += f", mem_ratio={1 / compression_ratio:.2f}x"
+            if pd.notna(eager_peak_mem) and pd.notna(dynamo_peak_mem):
+                perf_summary += (
+                    f" (eager={eager_peak_mem:.1f} GB, dynamo={dynamo_peak_mem:.1f} GB)"
+                )
+
+        if speedup < effective_threshold:
+            failed.append((model_name, speedup))
+
+        print(perf_summary)
 
     if failed:
         print(
             textwrap.dedent(
                 f"""
-                Error {len(failed)} models performance regressed
-                    {" ".join(failed)}
+                Error {len(failed)} model(s) performance regressed
+                    {" ".join([name for name, _ in failed])}
                 """
             )
         )
+        for name, sp in sorted(failed, key=lambda x: x[1]):
+            pct_from_target = (sp / effective_threshold - 1.0) * 100.0
+            print(
+                f"  - {name}: {sp:.3f}x (< {effective_threshold:.2f}x; {pct_from_target:.1f}% from target)"
+            )
         sys.exit(1)
+    else:
+        print(
+            f"\nAll {len(df)} model(s) passed threshold check (>= {effective_threshold:.2f}x)"
+        )
 
 
 if __name__ == "__main__":
@@ -44,7 +77,7 @@ if __name__ == "__main__":
         "-s",
         type=float,
         default=1.0,
-        help="multiple threshold by this value to relax the check",
+        help="multiply threshold by this value to relax the check",
     )
     args = parser.parse_args()
     check_perf_csv(args.file, args.threshold, args.threshold_scale)

benchmarks/dynamo/common.py

@@ -2379,7 +2379,9 @@ class BenchmarkRunner:
                     print(
                         f"Load model outputs from {self.args.compare_model_outputs_with} to compare"
                     )
-                    saved_result = torch.load(self.args.compare_model_outputs_with)
+                    saved_result = torch.load(
+                        self.args.compare_model_outputs_with, weights_only=False
+                    )
                     is_bitwise_same = bitwise_same(saved_result, new_result)
                     if not is_bitwise_same:
                         print(

benchmarks/dynamo/torchbench.yaml

@@ -189,6 +189,10 @@ skip:
     - hf_Whisper
     - hf_distil_whisper
     - timm_vision_transformer_large
+    # https://github.com/pytorch/pytorch/issues/167895
+    - stable_diffusion
+    - stable_diffusion_text_encoder
+    - stable_diffusion_unet
 
   device:
     cpu:

buckbuild.bzl

@@ -2,6 +2,7 @@
 # These load paths point to different files in internal and OSS environment
 
 load("@bazel_skylib//lib:paths.bzl", "paths")
+load("//tools/build_defs:cell_defs.bzl", "get_fbsource_cell")
 load("//tools/build_defs:fb_native_wrapper.bzl", "fb_native")
 load("//tools/build_defs:fb_xplat_cxx_library.bzl", "fb_xplat_cxx_library")
 load("//tools/build_defs:fb_xplat_genrule.bzl", "fb_xplat_genrule")
@@ -590,6 +591,9 @@ def pt_operator_query_codegen(
         pt_allow_forced_schema_registration = True,
         compatible_with = [],
         apple_sdks = None):
+    if get_fbsource_cell() == "fbcode":
+        return
+
     oplist_dir_name = name + "_pt_oplist"
 
     # @lint-ignore BUCKLINT
@@ -865,6 +869,9 @@ def define_buck_targets(
         pt_xplat_cxx_library = fb_xplat_cxx_library,
         c2_fbandroid_xplat_compiler_flags = [],
         labels = []):
+    if get_fbsource_cell() == "fbcode":
+        return
+
     # @lint-ignore BUCKLINT
     fb_native.filegroup(
         name = "metal_build_srcs",

c10/core/CachingDeviceAllocator.h

@@ -96,6 +96,13 @@ struct C10_API DeviceAllocator : public c10::Allocator {
 
   // Resets peak memory usage statistics for the specified device
   virtual void resetPeakStats(c10::DeviceIndex device) = 0;
+
+  // Return the free memory size and total memory size in bytes for the
+  // specified device.
+  virtual std::pair<size_t, size_t> getMemoryInfo(c10::DeviceIndex device) {
+    TORCH_CHECK_NOT_IMPLEMENTED(
+        false, "getMemoryInfo is not implemented for this allocator yet.");
+  }
 };
 
 // This function is used to get the DeviceAllocator for a specific device type

c10/core/SafePyObject.h

@@ -44,7 +44,7 @@ struct C10_API SafePyObject {
       (*other.pyinterpreter_)->incref(other.data_);
     }
     if (data_ != nullptr) {
-      (*pyinterpreter_)->decref(data_, /*has_pyobj_slot*/ false);
+      (*pyinterpreter_)->decref(data_);
     }
     data_ = other.data_;
     pyinterpreter_ = other.pyinterpreter_;
@@ -53,7 +53,7 @@ struct C10_API SafePyObject {
 
   ~SafePyObject() {
     if (data_ != nullptr) {
-      (*pyinterpreter_)->decref(data_, /*has_pyobj_slot*/ false);
+      (*pyinterpreter_)->decref(data_);
     }
   }
 

c10/core/ScalarType.h

@@ -34,20 +34,6 @@ namespace c10 {
 // See [dtype Macros note] in torch/headeronly/core/ScalarType.h
 // regarding macros.
 
-template <typename T>
-struct CppTypeToScalarType;
-
-#define SPECIALIZE_CppTypeToScalarType(cpp_type, scalar_type)                  \
-  template <>                                                                  \
-  struct CppTypeToScalarType<cpp_type>                                         \
-      : std::                                                                  \
-            integral_constant<c10::ScalarType, c10::ScalarType::scalar_type> { \
-  };
-
-AT_FORALL_SCALAR_TYPES_WITH_COMPLEX_AND_QINTS(SPECIALIZE_CppTypeToScalarType)
-
-#undef SPECIALIZE_CppTypeToScalarType
-
 #define DEFINE_CONSTANT(_, name) \
   constexpr ScalarType k##name = ScalarType::name;
 
@@ -106,13 +92,6 @@ inline bool isComplexType(ScalarType t) {
       t == ScalarType::ComplexDouble);
 }
 
-inline bool isQIntType(ScalarType t) {
-  // Don't forget to extend this when adding new QInt types
-  return t == ScalarType::QInt8 || t == ScalarType::QUInt8 ||
-      t == ScalarType::QInt32 || t == ScalarType::QUInt4x2 ||
-      t == ScalarType::QUInt2x4;
-}
-
 inline bool isBitsType(ScalarType t) {
   return t == ScalarType::Bits1x8 || t == ScalarType::Bits2x4 ||
       t == ScalarType::Bits4x2 || t == ScalarType::Bits8 ||

c10/core/StorageImpl.cpp

@@ -48,6 +48,30 @@ void warnDeprecatedDataPtr() {
   TORCH_CHECK(false, "Cannot access data pointer of Storage that is invalid.");
 }
 
+void StorageImpl::incref_pyobject() const {
+  // Because intrusive_ptr incref uses relaxed memory order, we need to
+  // do an acquire fence to ensure that the kHasPyObject bit was
+  // observed before the load of the PyObject* below.
+  // NB: This is a no-op on x86/x86-64
+  std::atomic_thread_fence(std::memory_order_acquire);
+
+  PyObject* obj = pyobj_slot_.load_pyobj();
+  (*pyobj_slot_.pyobj_interpreter())->incref(obj);
+}
+
+void StorageImpl::decref_pyobject() const {
+  PyObject* obj = pyobj_slot_.load_pyobj();
+  (*pyobj_slot_.pyobj_interpreter())->decref(obj);
+}
+
+bool StorageImpl::try_incref_pyobject() const {
+  c10::impl::PyInterpreter* interp = pyobj_slot_.pyobj_interpreter();
+  if (C10_UNLIKELY(!interp)) {
+    return false;
+  }
+  return (*interp)->try_incref(pyobj_slot_);
+}
+
 void SetStorageImplCreate(DeviceType t, StorageImplCreateHelper fptr) {
   // Allowlist verification.
   // Only if the devicetype is in the allowlist,

c10/core/StorageImpl.h

@@ -105,6 +105,12 @@ struct C10_API StorageImpl : public c10::intrusive_ptr_target {
     data_ptr_.clear();
   }
 
+  void incref_pyobject() const override final;
+
+  void decref_pyobject() const override final;
+
+  bool try_incref_pyobject() const override final;
+
   size_t nbytes() const {
     // OK to do this instead of maybe_as_int as nbytes is guaranteed positive
     TORCH_CHECK(!size_bytes_is_heap_allocated_);
@@ -370,4 +376,18 @@ C10_API c10::intrusive_ptr<c10::StorageImpl> make_storage_impl(
     bool resizable,
     std::optional<at::Device> device_opt);
 
+namespace detail {
+
+#ifndef C10_MOBILE
+template <class T>
+struct TargetTraits<
+    T,
+    std::enable_if_t<
+        std::is_base_of_v<c10::StorageImpl, std::remove_cv_t<T>>>> {
+  static constexpr bool can_have_pyobject = true;
+};
+#endif
+
+} // namespace detail
+
 } // namespace c10

c10/core/TensorImpl.cpp

@@ -277,7 +277,6 @@ void TensorImpl::release_resources() {
   if (storage_) {
     storage_ = {};
   }
-  pyobj_slot_.maybe_destroy_pyobj();
 }
 
 #ifndef C10_DISABLE_TENSORIMPL_EXTENSIBILITY
@@ -989,6 +988,30 @@ void TensorImpl::empty_tensor_restride_symint(MemoryFormat memory_format) {
   }
 }
 
+void TensorImpl::incref_pyobject() const {
+  // Because intrusive_ptr incref uses relaxed memory order, we need to
+  // do an acquire fence to ensure that the kHasPyObject bit was
+  // observed before the load of the PyObject* below.
+  // NB: This is a no-op on x86/x86-64
+  std::atomic_thread_fence(std::memory_order_acquire);
+
+  PyObject* obj = pyobj_slot_.load_pyobj();
+  (*pyobj_slot_.pyobj_interpreter())->incref(obj);
+}
+
+void TensorImpl::decref_pyobject() const {
+  PyObject* obj = pyobj_slot_.load_pyobj();
+  (*pyobj_slot_.pyobj_interpreter())->decref(obj);
+}
+
+bool TensorImpl::try_incref_pyobject() const {
+  c10::impl::PyInterpreter* interp = pyobj_slot_.pyobj_interpreter();
+  if (C10_UNLIKELY(!interp)) {
+    return false;
+  }
+  return (*interp)->try_incref(pyobj_slot_);
+}
+
 namespace impl {
 
 namespace {

c10/core/TensorImpl.h

@@ -2178,6 +2178,12 @@ struct C10_API TensorImpl : public c10::intrusive_ptr_target {
     return &pyobj_slot_;
   }
 
+  void incref_pyobject() const override final;
+
+  void decref_pyobject() const override final;
+
+  bool try_incref_pyobject() const override final;
+
  private:
   // See NOTE [std::optional operator usage in CUDA]
   // We probably don't want to expose this publicly until
@@ -3079,6 +3085,19 @@ struct C10_API TensorImpl : public c10::intrusive_ptr_target {
   friend class C10_TensorImpl_Size_Check_Dummy_Class;
 };
 
+namespace detail {
+
+#ifndef C10_MOBILE
+template <class T>
+struct TargetTraits<
+    T,
+    std::enable_if_t<std::is_base_of_v<c10::TensorImpl, std::remove_cv_t<T>>>> {
+  static constexpr bool can_have_pyobject = true;
+};
+#endif
+
+} // namespace detail
+
 // Note [TensorImpl size constraints]
 // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 // Changed the size of TensorImpl?  If the size went down, good for

c10/core/impl/PyInterpreter.cpp

@@ -11,8 +11,11 @@ struct NoopPyInterpreterVTable final : public PyInterpreterVTable {
 
   void incref(PyObject* pyobj) const override {} // do nothing
 
-  void decref(PyObject* pyobj, bool has_pyobj_slot) const override {
-  } // do nothing
+  void decref(PyObject* pyobj) const override {} // do nothing
+
+  bool try_incref(const c10::impl::PyObjectSlot& pyobj_slot) const override {
+    return false;
+  }
 
 #define PANIC(m)              \
   TORCH_INTERNAL_ASSERT(      \
@@ -20,6 +23,10 @@ struct NoopPyInterpreterVTable final : public PyInterpreterVTable {
       "attempted to call " #m \
       " on a Tensor with nontrivial PyObject after corresponding interpreter died")
 
+  size_t refcnt(PyObject* pyobj) const override {
+    PANIC(refcnt);
+  }
+
   c10::intrusive_ptr<TensorImpl> detach(const TensorImpl* self) const override {
     PANIC(detach);
   }

c10/core/impl/PyInterpreter.h

@@ -18,6 +18,9 @@ namespace c10 {
 struct IValue;
 class OperatorHandle;
 struct TensorImpl;
+namespace impl {
+struct PyObjectSlot;
+} // namespace impl
 } // namespace c10
 
 namespace torch::jit {
@@ -126,9 +129,12 @@ struct C10_API PyInterpreterVTable {
 
   // Run Py_INCREF on a PyObject.
   virtual void incref(PyObject* pyobj) const = 0;
-  // Run Py_DECREF on a PyObject.  We DO NOT assume the GIL is held on call
-  // See NOTE [PyInterpreter::decref takes a `has_pyobj_slot` arg]
-  virtual void decref(PyObject* pyobj, bool has_pyobj_slot) const = 0;
+  // Run Py_DECREF on a PyObject.  We DO NOT assume the GIL is held on call.
+  virtual void decref(PyObject* pyobj) const = 0;
+  // Run PyUnstable_TryIncRef on a PyObject if it's not NULL.
+  virtual bool try_incref(const c10::impl::PyObjectSlot& pyobj_slot) const = 0;
+  // Run Py_REFCNT on a PyObject.
+  virtual size_t refcnt(PyObject* pyobj) const = 0;
 
   // Perform a detach by deferring to the __torch_dispatch__ implementation of
   // detach, which will also arrange for the PyObject to get copied in this

c10/core/impl/PyObjectSlot.cpp

@@ -1,56 +0,0 @@
-#include <c10/core/impl/PyObjectSlot.h>
-
-namespace c10::impl {
-
-PyObjectSlot::PyObjectSlot() : pyobj_interpreter_(nullptr), pyobj_(nullptr) {}
-
-PyObjectSlot::~PyObjectSlot() {
-  maybe_destroy_pyobj();
-}
-
-void PyObjectSlot::maybe_destroy_pyobj() {
-  if (owns_pyobj()) {
-    TORCH_INTERNAL_ASSERT(pyobj_interpreter_ != nullptr);
-    TORCH_INTERNAL_ASSERT(pyobj_ != nullptr);
-    (*pyobj_interpreter_.load(std::memory_order_acquire))
-        ->decref(_unchecked_untagged_pyobj(), /*has_pyobj_slot*/ true);
-    // NB: this destructor can only be entered when there are no
-    // references to this C++ object (obviously), NOR any references
-    // to the PyObject (if there are references to the PyObject,
-    // then the PyObject holds an owning reference to the tensor).
-    // So it is OK to clear pyobj_ here as it is impossible for it to
-    // be used again (modulo weak reference races)
-    pyobj_ = nullptr; // for safety
-  }
-}
-
-PyInterpreter* PyObjectSlot::pyobj_interpreter() {
-  return pyobj_interpreter_.load(std::memory_order_acquire);
-}
-
-PyObject* PyObjectSlot::_unchecked_untagged_pyobj() const {
-  // NOLINTNEXTLINE(performance-no-int-to-ptr)
-  return reinterpret_cast<PyObject*>(
-      reinterpret_cast<uintptr_t>(pyobj_) & ~0x1ULL);
-}
-
-PyInterpreter& PyObjectSlot::load_pyobj_interpreter() const {
-  auto interpreter = pyobj_interpreter_.load(std::memory_order_acquire);
-  if (interpreter) {
-    return *interpreter;
-  }
-  TORCH_CHECK(false, "cannot access PyObject for Tensor - no interpreter set");
-}
-
-bool PyObjectSlot::owns_pyobj() {
-  // NOLINTNEXTLINE(performance-no-int-to-ptr)
-  return reinterpret_cast<uintptr_t>(pyobj_) & 1;
-}
-
-void PyObjectSlot::set_owns_pyobj(bool b) {
-  // NOLINTNEXTLINE(performance-no-int-to-ptr)
-  pyobj_ = reinterpret_cast<PyObject*>(
-      reinterpret_cast<uintptr_t>(_unchecked_untagged_pyobj()) | b);
-}
-
-} // namespace c10::impl

c10/core/impl/PyObjectSlot.h

@@ -8,117 +8,58 @@
 
 #include <atomic>
 
+namespace torch::utils {
+class PyObjectPreservation;
+}
+
 namespace c10::impl {
 
 struct C10_API PyObjectSlot {
  public:
-  PyObjectSlot();
-
-  ~PyObjectSlot();
-
-  void maybe_destroy_pyobj();
-
-  // Associate the TensorImpl with the specified PyObject, and, if necessary,
-  // also tag the interpreter.
-  //
-  // NB: This lives in a header so that we can inline away the switch on status
-  //
-  // NB: THIS FUNCTION CAN RAISE AN EXCEPTION.  Make sure to clean up after
-  // PyObject if necessary!
-  void init_pyobj(PyObject* pyobj) {
-    pyobj_interpreter_.store(
-        getGlobalPyInterpreter(), std::memory_order_relaxed);
-    pyobj_ = pyobj;
-  }
+  PyObjectSlot() : pyobj_interpreter_(nullptr), pyobj_(nullptr) {}
 
   // Query the PyObject interpreter.  This may return null if there is no
-  // interpreter.  This is racy!
-  PyInterpreter* pyobj_interpreter();
-
-  PyObject* _unchecked_untagged_pyobj() const;
-
-  // Test the interpreter tag.  If tagged for the current interpreter, return
-  // a non-nullopt (but possibly null) PyObject.  If (possibly) untagged,
-  // returns a nullopt.  If it is definitely invalid, raises an error.
-  //
-  // If `ignore_hermetic_tls` is false and this function is called from a
-  // hermetic context (ie, `HermeticPyObjectTLS::get_state()` is true), then
-  // nullopt is returned. If `ignore_hermetic_tls` is true, then the hermetic
-  // context is ignored, allowing you to check the interpreter tag of a
-  // nonhermetic PyObject from within a hermetic context. This is necessary
-  // because there are some cases where the deallocator function of a
-  // nonhermetic PyObject is called from within a hermetic context, so it must
-  // be properly treated as a nonhermetic PyObject.
-  //
-  // NB: this lives in header so that we can avoid actually creating the
-  // std::optional
-
-  // @todo alban: I'm not too sure what's going on here, we can probably delete
-  // it but it's worthwhile making sure
-  std::optional<PyObject*> check_pyobj(bool ignore_hermetic_tls = false) const {
-    impl::PyInterpreter* interpreter =
-        pyobj_interpreter_.load(std::memory_order_acquire);
-    if (interpreter == nullptr) {
-      return std::nullopt;
-    }
-
-    if (!ignore_hermetic_tls && c10::impl::HermeticPyObjectTLS::get_state()) {
-      return std::nullopt;
-    } else {
-      return _unchecked_untagged_pyobj();
-    }
+  // interpreter.
+  PyInterpreter* pyobj_interpreter() const {
+    return pyobj_interpreter_.load(std::memory_order_acquire);
   }
 
-  PyInterpreter& load_pyobj_interpreter() const;
+  PyInterpreter& load_pyobj_interpreter() const {
+    auto interpreter = pyobj_interpreter_.load(std::memory_order_acquire);
+    TORCH_INTERNAL_ASSERT(
+        interpreter, "cannot access PyObject for Tensor - no interpreter set");
+    return *interpreter;
+  }
 
-  bool owns_pyobj();
+  PyObject* load_pyobj() const {
+    return pyobj_.load(std::memory_order_acquire);
+  }
 
-  void set_owns_pyobj(bool b);
+  void store_pyobj(PyObject* obj) {
+    pyobj_.store(obj, std::memory_order_release);
+  }
+
+  bool has_unique_reference() const {
+    PyObject* pyobj = load_pyobj();
+    return pyobj != nullptr && load_pyobj_interpreter()->refcnt(pyobj) == 1;
+  }
+
+  void clear() {
+    pyobj_.store(nullptr, std::memory_order_relaxed);
+    pyobj_interpreter_.store(nullptr, std::memory_order_relaxed);
+  }
 
  private:
-  // This field contains the interpreter tag for this object.  See
-  // Note [Python interpreter tag] for general context
-  //
-  // Note [Memory ordering on Python interpreter tag]
-  // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-  // What memory_order do we need when accessing this atomic?  We don't
-  // need a single total modification order (as provided by
-  // memory_order_seq_cst) as pyobj_interpreter_ is monotonic: it can only
-  // transition from -1 to some positive integer and never changes afterwards.
-  // Because there is only one modification, it trivially already has a total
-  // modification order (e.g., we don't need fences or locked instructions on
-  // x86)
-  //
-  // In fact, one could make a reasonable argument that relaxed reads are OK,
-  // due to the presence of external locking (GIL) to ensure that interactions
-  // with other data structures are still correctly synchronized, so that
-  // we fall in the "Single-Location Data Structures" case as described in
-  // http://www.open-std.org/jtc1/sc22/wg21/docs/papers/2020/p2055r0.pdf
-  // However, on x86, it doesn't matter if I use acquire or relaxed on the load
-  // as I get the same assembly in both cases.  So I just use the more
-  // conservative acquire (which will impede compiler optimizations but I don't
-  // care)
+  // This is now always the global interpreter if the PyObject is set.
+  // Maybe we can remove this field some day...
   std::atomic<PyInterpreter*> pyobj_interpreter_;
 
-  // This field contains a reference to a PyObject representing this Tensor.
-  // If pyobj is nullptr, when we transfer Tensor to Python, we allocate a new
-  // PyObject for it and set this field.  This field does not have to be
-  // protected by an atomic as it is only allowed to be accessed when you hold
-  // the GIL, or during destruction of the tensor.
-  //
-  // When a PyObject dies, you are obligated to clear this field
-  // (otherwise, you will try to use-after-free the pyobj); this currently
-  // occurs in THPVariable_clear in torch/csrc/autograd/python_variable.cpp
-  //
-  // NB: Ordinarily, this should not be a strong reference, as if the
-  // PyObject owns the Tensor, this would create a reference cycle.
-  // However, sometimes this ownership flips.  To track who owns
-  // who, this has a single pointer tag indicating whether or not the
-  // C++ object owns the PyObject (the common case, zero, means PyObject
-  // owns the C++ object); see _unchecked_untagged_pyobj for raw access
-  // or check_pyobj for checked access.  See references to PyObject
-  // resurrection in torch/csrc/autograd/python_variable.cpp
-  PyObject* pyobj_;
+  // The PyObject representing this Tensor or nullptr. Ownership is managed
+  // by intrusive_ptr. By the time the PyObjectSlot is destroyed, this
+  // reference is already dead.
+  std::atomic<PyObject*> pyobj_;
+
+  friend class torch::utils::PyObjectPreservation;
 };
 
 } // namespace c10::impl

c10/cuda/CUDACachingAllocator.h

@@ -345,6 +345,13 @@ class CUDAAllocator : public DeviceAllocator {
       c10::DeviceIndex device,
       std::shared_ptr<AllocatorState> pps) = 0;
   virtual std::string name() = 0;
+  std::pair<size_t, size_t> getMemoryInfo(c10::DeviceIndex device) override {
+    c10::DeviceGuard device_guard({at::kCUDA, device});
+    size_t free = 0;
+    size_t total = 0;
+    C10_CUDA_CHECK(cudaMemGetInfo(&free, &total));
+    return {free, total};
+  }
 };
 
 // Allocator object, statically initialized

c10/util/ArrayRef.h

@@ -50,7 +50,13 @@ namespace c10 {
 /// However, you should prefer to use ArrayRef when possible, because its use
 /// of TORCH_CHECK will lead to better user-facing error messages.
 template <typename T>
-class ArrayRef final : public HeaderOnlyArrayRef<T> {
+// ArrayRef cannot be derived from. Normally, we would use `final`
+// specifier to force this constraint at compile time.  However, Intel
+// compiler does not recognize ArrayRef as a class template (which is
+// required in the definition of at::TensorAccessor, for instance)
+// when `final` specifier is used. So, we cannot define ArrayRef as
+// final because of the Intel compiler issue.
+class ArrayRef : public HeaderOnlyArrayRef<T> {
  public:
   /// @name Constructors, all inherited from HeaderOnlyArrayRef except for
   /// SmallVector. As inherited constructors won't work with class template

c10/util/Exception.h

@@ -379,7 +379,11 @@ C10_API std::string GetExceptionString(const std::exception& e);
 // ----------------------------------------------------------------------------
 
 #ifdef STRIP_ERROR_MESSAGES
-#define TORCH_RETHROW(e, ...) throw
+#define TORCH_RETHROW(e, ...)                       \
+  do {                                              \
+    (void)e; /* Suppress unused variable warning */ \
+    throw;                                          \
+  } while (false)
 #else
 #define TORCH_RETHROW(e, ...)               \
   do {                                      \

c10/util/intrusive_ptr.h

@@ -12,6 +12,10 @@ template <typename, typename...>
 class class_;
 }
 
+namespace torch::utils {
+class PyObjectPreservation;
+}
+
 namespace c10 {
 class intrusive_ptr_target;
 namespace raw {
@@ -33,6 +37,8 @@ constexpr uint64_t kImpracticallyHugeWeakReferenceCount =
 constexpr uint64_t kReferenceCountOne = 1;
 constexpr uint64_t kWeakReferenceCountOne = (kReferenceCountOne << 32);
 constexpr uint64_t kUniqueRef = (kReferenceCountOne | kWeakReferenceCountOne);
+// Indicates whether the object has a PyObject wrapper.
+constexpr uint64_t kHasPyObject = (uint64_t(1) << 63);
 
 template <class TTarget>
 struct intrusive_target_default_null_type final {
@@ -55,7 +61,11 @@ inline uint32_t refcount(uint64_t combined_refcount) {
 }
 
 inline uint32_t weakcount(uint64_t combined_refcount) {
-  return static_cast<uint32_t>(combined_refcount >> 32);
+  return static_cast<uint32_t>((combined_refcount & ~kHasPyObject) >> 32);
+}
+
+inline bool has_pyobject(uint64_t combined_refcount) {
+  return (combined_refcount & kHasPyObject) != 0;
 }
 
 // The only requirement for refcount increment is that it happens-before
@@ -66,12 +76,6 @@ inline uint64_t atomic_combined_refcount_increment(
   return combined_refcount.fetch_add(inc, std::memory_order_relaxed) + inc;
 }
 
-inline uint32_t atomic_refcount_increment(
-    std::atomic<uint64_t>& combined_refcount) {
-  return detail::refcount(atomic_combined_refcount_increment(
-      combined_refcount, kReferenceCountOne));
-}
-
 inline uint32_t atomic_weakcount_increment(
     std::atomic<uint64_t>& combined_refcount) {
   return detail::weakcount(atomic_combined_refcount_increment(
@@ -99,6 +103,11 @@ inline uint32_t atomic_weakcount_decrement(
       combined_refcount, kWeakReferenceCountOne));
 }
 
+template <class T, class = void>
+struct TargetTraits {
+  static constexpr bool can_have_pyobject = false;
+};
+
 } // namespace detail
 
 /**
@@ -155,6 +164,23 @@ class C10_API intrusive_ptr_target {
   // we can atomically operate on both at the same time for performance
   // and defined behaviors.
   //
+  // Note [PyObject preservation for Tensor and Storages]
+  // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+  // intrusive_ptr has special support for preserving PyObject wrappers
+  // for TensorImpl and StorageImpl. The most significant bit (kHasPyObject) of
+  // the combined_refcount_ is used to indicate whether the object has a
+  // PyObject wrapper.
+  //
+  //   - The PyObject, if it exists, holds a strong reference to the
+  //     intrusive_ptr_target.
+  //
+  //   - When the refcount goes from 1 to 2, we incref the PyObject.
+  //
+  //   - When the refcount goes from 2 to 1, we decref the PyObject.
+  //
+  // In other words, the intrusive_ptr keeps the PyObject alive as long as there
+  // are other C++ references to the intrusive_ptr_target.
+
   mutable std::atomic<uint64_t> combined_refcount_;
   static_assert(sizeof(std::atomic<uint64_t>) == 8);
   static_assert(alignof(std::atomic<uint64_t>) == 8);
@@ -172,6 +198,8 @@ class C10_API intrusive_ptr_target {
   template <typename T>
   friend struct ExclusivelyOwnedTensorTraits;
 
+  friend class torch::utils::PyObjectPreservation;
+
  protected:
   // protected destructor. We never want to destruct intrusive_ptr_target*
   // directly.
@@ -255,6 +283,16 @@ class C10_API intrusive_ptr_target {
    */
   virtual void release_resources() {}
 
+  /**
+   * These two methods are called when the refcount transitions between one
+   * and two and the object has a PyObject wrapper.
+   */
+  virtual void incref_pyobject() const {}
+  virtual void decref_pyobject() const {}
+  virtual bool try_incref_pyobject() const {
+    return false;
+  }
+
   uint32_t refcount(std::memory_order order = std::memory_order_relaxed) const {
     return detail::refcount(combined_refcount_.load(order));
   }
@@ -265,6 +303,19 @@ class C10_API intrusive_ptr_target {
   }
 };
 
+namespace detail {
+
+#ifndef C10_MOBILE
+template <>
+struct TargetTraits<c10::intrusive_ptr_target> {
+  // A generic intrusive_ptr<intrusive_ptr_target> may actually be a TensorImpl
+  // or StorageImpl, so we have to allow for PyObject support.
+  static constexpr bool can_have_pyobject = true;
+};
+#endif
+
+} // namespace detail
+
 template <class TTarget, class NullType>
 class weak_intrusive_ptr;
 
@@ -314,18 +365,34 @@ class intrusive_ptr final {
 
   void retain_() {
     if (target_ != NullType::singleton()) {
-      uint32_t new_refcount =
-          detail::atomic_refcount_increment(target_->combined_refcount_);
+      uint64_t combined = detail::atomic_combined_refcount_increment(
+          target_->combined_refcount_, detail::kReferenceCountOne);
+      uint32_t new_refcount = detail::refcount(combined);
       TORCH_INTERNAL_ASSERT_DEBUG_ONLY(
           new_refcount != 1,
           "intrusive_ptr: Cannot increase refcount after it reached zero.");
+
+      if constexpr (detail::TargetTraits<TTarget>::can_have_pyobject) {
+        // If the refcount transitioned from 1 to 2, we need to incref the
+        // PyObject. In other words, we need to ensure that the PyObject stays
+        // alive now that we have a C++ reference to this object in addition to
+        // the PyObject itself.
+        if (C10_UNLIKELY(
+                detail::has_pyobject(combined) &&
+                detail::refcount(combined) == 2)) {
+          target_->incref_pyobject();
+        }
+      } else {
+        TORCH_INTERNAL_ASSERT_DEBUG_ONLY(
+            !detail::has_pyobject(combined),
+            "TargetTraits indicates that type cannot have PyObject, but refcount has PyObject bit set.");
+      }
     }
   }
 
   void reset_() noexcept {
     if (target_ != NullType::singleton()) {
-      if (target_->combined_refcount_.load(std::memory_order_acquire) ==
-          detail::kUniqueRef) {
+      if (is_uniquely_owned()) {
         // Both counts are 1, so there are no weak references and
         // we are releasing the last strong reference. No other
         // threads can observe the effects of this target_ deletion
@@ -337,9 +404,10 @@ class intrusive_ptr final {
 
       auto combined_refcount = detail::atomic_combined_refcount_decrement(
           target_->combined_refcount_, detail::kReferenceCountOne);
-      if (detail::refcount(combined_refcount) == 0) {
-        bool should_delete =
-            (combined_refcount == detail::kWeakReferenceCountOne);
+      uint32_t new_refcount = detail::refcount(combined_refcount);
+      bool has_pyobject = detail::has_pyobject(combined_refcount);
+      if (new_refcount == 0) {
+        bool should_delete = detail::weakcount(combined_refcount) == 1;
         // See comment above about weakcount. As long as refcount>0,
         // weakcount is one larger than the actual number of weak references.
         // So we need to decrement it here.
@@ -356,6 +424,18 @@ class intrusive_ptr final {
         if (should_delete) {
           delete target_;
         }
+      } else if constexpr (detail::TargetTraits<TTarget>::can_have_pyobject) {
+        // If the refcount transitioned from 2 to 1, we need to decref the
+        // PyObject. In other words, we don't want to keep the PyObject alive if
+        // there are no C++ references to this object other than the PyObject
+        // itself.
+        if (C10_UNLIKELY(has_pyobject && new_refcount == 1)) {
+          target_->decref_pyobject();
+        }
+      } else {
+        TORCH_INTERNAL_ASSERT_DEBUG_ONLY(
+            !has_pyobject,
+            "TargetTraits indicates that type cannot have PyObject, but refcount has PyObject bit set.");
       }
     }
   }
@@ -522,6 +602,16 @@ class intrusive_ptr final {
     return use_count() == 1;
   }
 
+  /**
+   * Stronger than unique() in that it must not have any weakrefs as well.
+   */
+  bool is_uniquely_owned() const noexcept {
+    TORCH_INTERNAL_ASSERT_DEBUG_ONLY(target_ != NullType::singleton());
+    uint64_t combined =
+        target_->combined_refcount_.load(std::memory_order_acquire);
+    return (combined & ~detail::kHasPyObject) == detail::kUniqueRef;
+  }
+
   /**
    * Returns an owning (!) pointer to the underlying object and makes the
    * intrusive_ptr instance invalid. That means the refcount is not decreased.
@@ -932,6 +1022,7 @@ class weak_intrusive_ptr final {
     if (target_ == NullType::singleton()) {
       return intrusive_ptr<TTarget, NullType>();
     } else {
+      bool increfed = false;
       auto combined_refcount =
           target_->combined_refcount_.load(std::memory_order_relaxed);
       do {
@@ -940,12 +1031,31 @@ class weak_intrusive_ptr final {
           // Return nullptr.
           return intrusive_ptr<TTarget, NullType>();
         }
+        if constexpr (detail::TargetTraits<TTarget>::can_have_pyobject) {
+          if (detail::has_pyobject(combined_refcount) &&
+              detail::refcount(combined_refcount) == 1 && !increfed) {
+            // Object has a python wrapper with no other C++ references.
+            // We need to to incref the Python object before we acquire a
+            // strong reference to the C++ object to avoid a situation
+            // where the Python object is deallocated concurrently.
+            if (!target_->try_incref_pyobject()) {
+              return intrusive_ptr<TTarget, NullType>();
+            }
+            increfed = true;
+          }
+        }
       } while (!target_->combined_refcount_.compare_exchange_weak(
           combined_refcount,
           combined_refcount + detail::kReferenceCountOne,
           std::memory_order_acquire,
           std::memory_order_relaxed));
 
+      if constexpr (detail::TargetTraits<TTarget>::can_have_pyobject) {
+        if (increfed && detail::refcount(combined_refcount) != 1) {
+          target_->decref_pyobject();
+        }
+      }
+
       return intrusive_ptr<TTarget, NullType>(
           target_, raw::DontIncreaseRefcount{});
     }
@@ -1060,7 +1170,18 @@ namespace intrusive_ptr {
 // NullType::singleton to this function
 inline void incref(intrusive_ptr_target* self) {
   if (self) {
-    detail::atomic_refcount_increment(self->combined_refcount_);
+    uint64_t combined = detail::atomic_combined_refcount_increment(
+        self->combined_refcount_, detail::kReferenceCountOne);
+
+#ifndef C10_MOBILE
+    if (C10_UNLIKELY(
+            detail::has_pyobject(combined) &&
+            detail::refcount(combined) == 2)) {
+      self->incref_pyobject();
+    }
+#else
+    TORCH_INTERNAL_ASSERT_DEBUG_ONLY(!detail::has_pyobject(combined));
+#endif
   }
 }
 

c10/xpu/XPUCachingAllocator.cpp

@@ -15,6 +15,8 @@ using namespace c10::CachingDeviceAllocator;
 // newly allocated memory with 512-byte alignment.
 constexpr size_t kDeviceAlignment = 512;
 
+class XPUAllocator;
+
 namespace {
 using stream_set = ska::flat_hash_set<xpu::XPUStream>;
 
@@ -23,14 +25,19 @@ typedef bool (*Comparison)(const Block*, const Block*);
 bool BlockComparatorSize(const Block* a, const Block* b);
 bool BlockComparatorAddress(const Block* a, const Block* b);
 
+struct PrivatePool;
+
 struct BlockPool {
-  BlockPool(bool small)
+  BlockPool(bool small, PrivatePool* private_pool = nullptr)
       : blocks(BlockComparatorSize),
         unmapped(BlockComparatorAddress),
-        is_small(small) {}
+        is_small(small),
+        owner_PrivatePool(private_pool) {}
+
   std::set<Block*, Comparison> blocks;
   std::set<Block*, Comparison> unmapped;
   const bool is_small;
+  PrivatePool* owner_PrivatePool;
 };
 
 struct ExpandableSegment;
@@ -349,6 +356,43 @@ struct AllocParams {
   StatTypes stat_types = {};
 };
 
+// Internal implementation that manages actual memory blocks.
+// high level MemPool interface wraps PrivatePool via MempoolId.
+struct PrivatePool {
+  PrivatePool(MempoolId_t id, XPUAllocator* allocator = nullptr)
+      : id(std::move(id)),
+        allocator_(allocator),
+        large_blocks(/*small=*/false, this),
+        small_blocks(/*small=*/true, this) {}
+  PrivatePool(const PrivatePool&) = delete;
+  PrivatePool(PrivatePool&&) = delete;
+  PrivatePool& operator=(const PrivatePool&) = delete;
+  PrivatePool& operator=(PrivatePool&&) = delete;
+  ~PrivatePool() = default;
+
+  // default Mempool when no Mempool is specified
+  MempoolId_t id{0, 0};
+  // Number of live graphs using this pool
+  int use_count{1};
+  // Number of unfreed allocations made for this pool. When use_count and
+  // allocation_count drop to zero, we can delete this PrivatePool from
+  // graph_pools.
+  int allocation_count{0};
+  XPUAllocator* allocator_;
+  BlockPool large_blocks;
+  BlockPool small_blocks;
+
+ public:
+  XPUAllocator* allocator() {
+    return allocator_;
+  }
+};
+struct MempoolIdHash {
+  std::size_t operator()(const MempoolId_t& mempool_id) const noexcept {
+    return mempool_id.first != 0 ? mempool_id.first : mempool_id.second;
+  }
+};
+
 } // anonymous namespace
 
 class DeviceCachingAllocator {
@@ -365,6 +409,13 @@ class DeviceCachingAllocator {
   bool set_fraction = false;
   std::vector<ExpandableSegment*> expandable_segments;
   std::vector<c10::DeviceIndex> devices_with_peer_access; // reserved
+  std::vector<std::pair<MempoolId_t, std::function<bool(sycl::queue*)>>>
+      captures_underway;
+  ska::flat_hash_map<MempoolId_t, std::unique_ptr<PrivatePool>, MempoolIdHash>
+      graph_pools;
+  // Pools no longer referenced by any graph.
+  ska::flat_hash_map<MempoolId_t, PrivatePool*, MempoolIdHash>
+      graph_pools_freeable;
 
   size_t try_merge_blocks(Block* dst, Block* src, BlockPool& pool) {
     if (!src || src->allocated || src->event_count > 0 ||
@@ -463,7 +514,22 @@ class DeviceCachingAllocator {
     }
   }
 
-  BlockPool& get_pool(size_t size) {
+  BlockPool& get_pool(size_t size, sycl::queue* queue) {
+    if (C10_UNLIKELY(!captures_underway.empty())) {
+      for (auto& entry : captures_underway) {
+        // lookup for mempool id matching current capture graph
+        if (entry.second(queue)) {
+          auto it1 = graph_pools.find(entry.first);
+          // lookup mempool
+          TORCH_INTERNAL_ASSERT(it1 != graph_pools.end());
+          if (size <= kSmallSize) {
+            return it1->second->small_blocks;
+          } else {
+            return it1->second->large_blocks;
+          }
+        }
+      }
+    }
     if (size < kSmallSize) {
       return small_blocks;
     } else {
@@ -669,6 +735,10 @@ class DeviceCachingAllocator {
     if (!ptr) {
       return false;
     }
+
+    if (p.pool->owner_PrivatePool) {
+      p.pool->owner_PrivatePool->allocation_count++;
+    }
     p.block = new Block(device, p.queue(), size, p.pool, ptr);
     for_each_selected_stat_type(p.stat_types, [&](size_t stat_type) {
       stats.reserved_bytes[stat_type].increase(size);
@@ -677,11 +747,14 @@ class DeviceCachingAllocator {
     return true;
   }
 
-  void synchronize_and_free_events() {
+  void synchronize_and_free_events(PrivatePool* pool = nullptr) {
     for (auto& xe : xpu_events) {
       for (auto& e : xe.second) {
         auto event = e.first;
         auto* block = e.second;
+        if (pool && block->pool->owner_PrivatePool != pool) {
+          continue;
+        }
         event.wait();
         block->event_count--;
         if (block->event_count == 0) {
@@ -785,6 +858,13 @@ class DeviceCachingAllocator {
     for_each_selected_stat_type(stat_types, [&](size_t stat_type) {
       stats.reserved_bytes[stat_type].decrease(unmapped.size);
     });
+
+    if (block->pool->owner_PrivatePool) {
+      // The Freed block belonged to a XPU graph's PrivatePool.
+      TORCH_INTERNAL_ASSERT(
+          block->pool->owner_PrivatePool->allocation_count > 0);
+      block->pool->owner_PrivatePool->allocation_count--;
+    }
   }
 
   void release_blocks(BlockPool& pool) {
@@ -812,13 +892,41 @@ class DeviceCachingAllocator {
     }
   }
 
-  bool release_cached_blocks() {
-    synchronize_and_free_events();
-    // See Note [Safe to Free Blocks on BlockPool]
-    c10::xpu::syncStreamsOnDevice(device_index);
+  bool release_cached_blocks(MempoolId_t mempool_id) {
+    if (mempool_id.first == 0 && mempool_id.second == 0 &&
+        captures_underway.empty()) {
+      synchronize_and_free_events();
+      // See Note [Safe to Free Blocks on BlockPool]
+      c10::xpu::syncStreamsOnDevice(device_index);
 
-    release_blocks(large_blocks);
-    release_blocks(small_blocks);
+      release_blocks(large_blocks);
+      release_blocks(small_blocks);
+    }
+
+    for (auto it = graph_pools_freeable.begin();
+         it != graph_pools_freeable.end();) {
+      if (mempool_id.first != 0 || mempool_id.second != 0) {
+        if (it->first == mempool_id) {
+          // If there is an active mempool, we sync only the events
+          // associated with the pool
+          synchronize_and_free_events(it->second);
+        } else {
+          // otherwise we move on
+          ++it;
+          continue;
+        }
+      }
+      TORCH_INTERNAL_ASSERT(it->second->use_count == 0);
+      release_blocks(it->second->small_blocks);
+      release_blocks(it->second->large_blocks);
+      if (it->second->allocation_count == 0) {
+        auto erase_count = graph_pools.erase(it->first);
+        TORCH_INTERNAL_ASSERT(erase_count == 1);
+        it = graph_pools_freeable.erase(it);
+      } else {
+        ++it;
+      }
+    }
     return true;
   }
 
@@ -903,6 +1011,30 @@ class DeviceCachingAllocator {
     }
   }
 
+  void create_or_incref_pool(
+      MempoolId_t mempool_id,
+      XPUAllocator* allocator = nullptr) {
+    auto it = graph_pools.find(mempool_id);
+    if (it == graph_pools.end()) {
+      // mempool_id does not reference an existing pool.
+      // Make a new pool for XPU graph capture or memory pool usage.
+      graph_pools.emplace(
+          mempool_id, std::make_unique<PrivatePool>(mempool_id, allocator));
+    } else {
+      // mempool_id references an existing pool, which the current XPU graph
+      // capture will share.
+      TORCH_INTERNAL_ASSERT(it->second->use_count > 0);
+      TORCH_INTERNAL_ASSERT(allocator == nullptr);
+      it->second->use_count++;
+    }
+  }
+
+  PrivatePool* get_private_pool(MempoolId_t mempool_id) {
+    auto it = graph_pools.find(mempool_id);
+    TORCH_INTERNAL_ASSERT(it != graph_pools.end());
+    return it->second.get();
+  }
+
  public:
   DeviceCachingAllocator(DeviceIndex device_index)
       : large_blocks(/* small */ false),
@@ -911,9 +1043,11 @@ class DeviceCachingAllocator {
 
   Block* malloc(DeviceIndex device, size_t orig_size, sycl::queue& queue) {
     std::scoped_lock<std::recursive_mutex> lock(mutex);
-    process_events();
+    if (C10_LIKELY(captures_underway.empty())) {
+      process_events();
+    }
     size_t size = round_size(orig_size);
-    auto& pool = get_pool(size);
+    auto& pool = get_pool(size, &queue);
     const size_t alloc_size = get_allocation_size(size);
     AllocParams params(device, size, &queue, &pool, alloc_size);
     params.stat_types = get_stat_types_for_pool(pool);
@@ -923,18 +1057,17 @@ class DeviceCachingAllocator {
     // Can't reuse an existing block, try to get a new one.
     if (!block_found) {
       block_found = alloc_block(params, false) ||
-          (release_cached_blocks() && alloc_block(params, true));
+          (release_cached_blocks({0, 0}) && alloc_block(params, true));
     }
     if (!block_found) {
-      c10::xpu::DeviceProp device_prop;
-      c10::xpu::get_device_properties(&device_prop, device);
-      auto device_total = device_prop.global_mem_size;
+      const auto& raw_device = c10::xpu::get_raw_device(device);
+      const auto device_total =
+          raw_device.get_info<sycl::info::device::global_mem_size>();
       // Estimate the available device memory when the SYCL runtime does not
       // support the corresponding aspect (ext_intel_free_memory).
-      size_t device_free = device_prop.global_mem_size -
+      size_t device_free = device_total -
           stats.reserved_bytes[static_cast<size_t>(StatType::AGGREGATE)]
               .current;
-      auto& raw_device = c10::xpu::get_raw_device(device);
       // TODO: Remove the aspect check once the SYCL runtime bug is fixed on
       // affected devices.
       if (raw_device.has(sycl::aspect::ext_intel_free_memory)) {
@@ -1017,9 +1150,9 @@ class DeviceCachingAllocator {
     block->stream_uses.insert(stream);
   }
 
-  void emptyCache() {
+  void emptyCache(MempoolId_t mempool_id) {
     std::scoped_lock<std::recursive_mutex> lock(mutex);
-    release_cached_blocks();
+    release_cached_blocks(mempool_id);
   }
 
   DeviceStats getStats() {
@@ -1052,21 +1185,37 @@ class DeviceCachingAllocator {
     }
   }
 
+  std::pair<size_t, size_t> getMemoryInfo() {
+    const auto& device = c10::xpu::get_raw_device(device_index);
+    const size_t total = device.get_info<sycl::info::device::global_mem_size>();
+    TORCH_CHECK(
+        device.has(sycl::aspect::ext_intel_free_memory),
+        "The device (",
+        device.get_info<sycl::info::device::name>(),
+        ") doesn't support querying the available free memory. ",
+        "You can file an issue at https://github.com/pytorch/pytorch/issues ",
+        "to help us prioritize its implementation.");
+    const size_t free =
+        device.get_info<sycl::ext::intel::info::device::free_memory>();
+    return {free, total};
+  }
+
   double getMemoryFraction() {
     if (!set_fraction) {
       return 1.0;
     }
 
-    c10::xpu::DeviceProp device_prop;
-    c10::xpu::get_device_properties(&device_prop, device_index);
+    const auto device_total =
+        xpu::get_raw_device(device_index)
+            .get_info<sycl::info::device::global_mem_size>();
     return static_cast<double>(allowed_memory_maximum) /
-        static_cast<double>(device_prop.global_mem_size);
+        static_cast<double>(device_total);
   }
 
   void setMemoryFraction(double fraction) {
-    c10::xpu::DeviceProp device_prop;
-    c10::xpu::get_device_properties(&device_prop, device_index);
-    auto device_total = device_prop.global_mem_size;
+    const auto device_total =
+        xpu::get_raw_device(device_index)
+            .get_info<sycl::info::device::global_mem_size>();
     allowed_memory_maximum = static_cast<size_t>(fraction * device_total);
     set_fraction = true;
   }
@@ -1157,9 +1306,9 @@ class XPUAllocator : public DeviceAllocator {
     }
   }
 
-  void emptyCache(MempoolId_t mempool_id [[maybe_unused]] = {0, 0}) override {
+  void emptyCache(MempoolId_t mempool_id) override {
     for (auto& da : device_allocators) {
-      da->emptyCache();
+      da->emptyCache(mempool_id);
     }
   }
 
@@ -1240,6 +1389,11 @@ class XPUAllocator : public DeviceAllocator {
         c10::xpu::get_raw_device(dev_to_access));
   }
 
+  std::pair<size_t, size_t> getMemoryInfo(DeviceIndex device) override {
+    assertValidDevice(device);
+    return device_allocators[device]->getMemoryInfo();
+  }
+
   double getMemoryFraction(DeviceIndex device) {
     assertValidDevice(device);
     return device_allocators[device]->getMemoryFraction();
@@ -1270,8 +1424,8 @@ void init(DeviceIndex device_count) {
   return allocator.init(device_count);
 }
 
-void emptyCache() {
-  return allocator.emptyCache();
+void emptyCache(MempoolId_t mempool_id) {
+  return allocator.emptyCache(mempool_id);
 }
 
 void resetPeakStats(DeviceIndex device) {

c10/xpu/XPUCachingAllocator.h

@@ -10,7 +10,7 @@ C10_XPU_API Allocator* get();
 
 C10_XPU_API void init(DeviceIndex device_count);
 
-C10_XPU_API void emptyCache();
+C10_XPU_API void emptyCache(MempoolId_t mempool_id = {0, 0});
 
 C10_XPU_API void resetPeakStats(DeviceIndex device);
 

caffe2/serialize/inline_container.cc

@@ -734,7 +734,7 @@ void PyTorchStreamWriter::setup(const string& file_name) {
           file_name,
           std::ofstream::out | std::ofstream::trunc | std::ofstream::binary
         );
-    } catch (const std::ios_base::failure& e) {
+    } catch (const std::ios_base::failure&) {
 #ifdef _WIN32
       // Windows have verbose error code, we prefer to use it than std errno.
       uint32_t error_code = GetLastError();
@@ -773,8 +773,20 @@ void PyTorchStreamWriter::writeRecord(
     bool compress) {
   AT_ASSERT(!finalized_);
   AT_ASSERT(!archive_name_plus_slash_.empty());
-  TORCH_INTERNAL_ASSERT(
-      files_written_.count(name) == 0, "Tried to serialize file twice: ", name);
+  if (files_written_.count(name) > 0) {
+    // Allow multiple writes for triton binaries
+    bool is_triton_extension =
+        c10::ends_with(name, ".so") ||
+        c10::ends_with(name, ".cubin") ||
+        c10::ends_with(name, ".hsaco");
+
+    if (is_triton_extension) {
+      LOG(WARNING) << "File '" << name << "' is being serialized multiple times";
+      return;
+    }
+
+    TORCH_INTERNAL_ASSERT(false, "Tried to serialize file twice: ", name);
+  }
   if (name == kSerializationIdRecordName && serialization_id_.empty()) {
     // In case of copying records from another file, skip writing a different
     // serialization_id than the one computed in this writer.

cmake/Modules/FindSYCLToolkit.cmake

@@ -1,7 +1,7 @@
 # This will define the following variables:
 # SYCL_FOUND               : True if the system has the SYCL library.
 # SYCL_INCLUDE_DIR         : Include directories needed to use SYCL.
-# SYCL_LIBRARY_DIR         ：The path to the SYCL library.
+# SYCL_LIBRARY_DIR         : The path to the SYCL library.
 # SYCL_LIBRARY             : SYCL library fullname.
 # SYCL_COMPILER_VERSION    : SYCL compiler version.
 

docs/cpp/source/index.rst

@@ -10,7 +10,7 @@ API.  This API can roughly be divided into five parts:
 - **TorchScript**: An interface to the TorchScript JIT compiler and interpreter.
 - **C++ Extensions**: A means of extending the Python API with custom C++ and CUDA routines.
 
-Combining, these building blocks form a research and
+Combined, these building blocks form a research and
 production ready C++ library for tensor computation and dynamic neural
 networks with strong emphasis on GPU acceleration as well as fast CPU
 performance. It is currently in use at Facebook in research and
@@ -76,7 +76,7 @@ C++ Frontend
 ------------
 
 The PyTorch C++ frontend provides a high level, pure C++ modeling interface for
-neural network and general ML(Machine Learning) research and production use cases,
+neural networks and general ML (Machine Learning) research and production use cases,
 largely following the Python API in design and provided functionality. The C++
 frontend includes the following:
 

docs/source/accelerator.md

@@ -40,6 +40,7 @@
     :nosignatures:
 
      empty_cache
+     get_memory_info
      max_memory_allocated
      max_memory_reserved
      memory_allocated

docs/source/accelerator/device.md

@@ -0,0 +1,113 @@
+# Device Management
+
+## Background
+
+Device management handles basic operations like querying how many devices are available and switching between them. Accelerator backends need to wrap their device runtime's APIs and expose them to PyTorch.
+
+The OpenReg implementation ([`OpenRegFunctions.h/cpp`][OpenReg Device Management]) shows how to wrap a third-party runtime. These functions are used throughout the backend - by streams, events, generators, and Python bindings.
+
+## Design
+
+Accelerator vendors need to implement these core functions:
+
+| Function Name             | Description                                                      | Application Scenarios                                                                                          |
+| ------------------------- | ---------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------- |
+| `device_count()`          | Query the total number of available devices in the system        | - Application initialization<br>- Multi-device workload distribution<br>- Validating device indices before use |
+| `current_device()`        | Get the currently active device for the calling thread           | - Debugging and logging<br>- Determining tensor placement<br>- Guard implementations                           |
+| `set_device()`            | Change the active device for subsequent operations               | - Switching context between devices<br>- Initializing specific device resources<br>- Multi-GPU training loops  |
+| `exchange_device()`       | Atomically swap device and return the previous device            | - Implementing device guards<br>- Temporarily switching device context<br>- RAII-based device management       |
+| `maybe_exchange_device()` | Conditionally exchange device only if the index is valid (-1 OK) | - Safe device switching with optional indices<br>- Guard implementations with nullable device values           |
+
+These functions are building blocks for more complex features like streams, events, and memory management. Make sure to validate inputs and handle errors properly.
+
+## Implementation
+
+This section shows how to implement device management using `set_device` as an example. The implementation requires:
+1. C++ wrappers around the device runtime
+2. Python bindings to expose the C++ functions
+3. User-friendly Python APIs
+
+### C++ Side
+
+Wrap the device runtime's API and add error handling. The `SetDevice` function shows this pattern:
+
+```{eval-rst}
+.. literalinclude:: ../../../test/cpp_extensions/open_registration_extension/torch_openreg/csrc/runtime/OpenRegFunctions.cpp
+    :language: c++
+    :start-after: LITERALINCLUDE START: OPENREG SetDevice FUNCTION
+    :end-before: LITERALINCLUDE END: OPENREG SetDevice FUNCTION
+    :linenos:
+```
+```{eval-rst}
+.. literalinclude:: ../../../test/cpp_extensions/open_registration_extension/torch_openreg/csrc/runtime/OpenRegFunctions.cpp
+    :language: c++
+    :start-after: LITERALINCLUDE START: OPENREG set_device FUNCTION
+    :end-before: LITERALINCLUDE END: OPENREG set_device FUNCTION
+    :linenos:
+```
+
+### Binding
+
+Expose the C++ functions to Python using pybind11:
+
+```{eval-rst}
+.. literalinclude:: ../../../test/cpp_extensions/open_registration_extension/torch_openreg/torch_openreg/csrc/Module.cpp
+    :language: c++
+    :start-after: LITERALINCLUDE START: MODULE SET DEVICE HELPER
+    :end-before: LITERALINCLUDE END: MODULE SET DEVICE HELPER
+    :linenos:
+```
+```{eval-rst}
+.. literalinclude:: ../../../test/cpp_extensions/open_registration_extension/torch_openreg/torch_openreg/csrc/Module.cpp
+    :language: c++
+    :start-after: LITERALINCLUDE START: OPENREG MODULE METHODS
+    :end-before: LITERALINCLUDE END: OPENREG MODULE METHODS
+    :linenos:
+    :emphasize-lines: 5
+```
+
+### Python Side
+
+Wrap the C++ bindings with user-friendly Python functions:
+
+```{eval-rst}
+.. literalinclude:: ../../../test/cpp_extensions/open_registration_extension/torch_openreg/torch_openreg/openreg/__init__.py
+    :language: python
+    :start-after: LITERALINCLUDE START: PYTHON SET DEVICE FUNCTION
+    :end-before: LITERALINCLUDE END: PYTHON SET DEVICE FUNCTION
+    :linenos:
+```
+
+Here's the complete mapping from C++ to Python:
+
+| C++ Binding Function | C++ Binding API (pybind11)               | Python User API                  | Description                                  |
+| -------------------- | ---------------------------------------- | -------------------------------- | -------------------------------------------- |
+| `_getDeviceCount`    | `torch_openreg._C._get_device_count()`   | `torch.openreg.device_count()`   | Returns the total number of devices          |
+| `_getDevice`         | `torch_openreg._C._get_device()`         | `torch.openreg.current_device()` | Returns the current active device index      |
+| `_setDevice`         | `torch_openreg._C._set_device(idx)`      | `torch.openreg.set_device(idx)`  | Sets the active device                       |
+| `_exchangeDevice`    | `torch_openreg._C._exchange_device(idx)` | N/A (internal use only)          | Atomically swaps device and returns previous |
+
+## Guard
+
+Device guards provide automatic device switching with exception safety. They're similar to lock guards in C++ - they switch device on construction and restore it on destruction.
+
+Implement `DeviceGuardImplInterface` to integrate with PyTorch's guard system:
+
+```{eval-rst}
+.. literalinclude:: ../../../test/cpp_extensions/open_registration_extension/torch_openreg/csrc/runtime/OpenRegGuard.h
+    :language: c++
+    :start-after: LITERALINCLUDE START: OPENREG DEVICE MGMT GUARD IMPL EXAMPLE
+    :end-before: LITERALINCLUDE END: OPENREG DEVICE MGMT GUARD IMPL EXAMPLE
+    :linenos:
+```
+
+**What needs to be implemented:**
+
+1. **exchangeDevice()**: Switch to a new device and return the old one (used by guard constructors)
+2. **getDevice()**: Get the current device
+3. **setDevice()**: Set the active device
+4. **Type checking**: Validate that device type matches the backend
+
+This makes the guard available to PyTorch for the `PrivateUse1` device type. Users can then use standard PyTorch device guards with the custom backend.
+
+[OpenReg Device Management]: https://github.com/pytorch/pytorch/blob/main/test/cpp_extensions/open_registration_extension/torch_openreg/csrc/runtime/OpenRegFunctions.cpp "OpenReg Device Management"

docs/source/accelerator/index.md

@@ -42,6 +42,7 @@ Next, we will delve into each chapter of this guide. Each chapter focuses on a k
 :glob:
 :maxdepth: 1
 
+device
 hooks
 autoload
 operators

docs/source/notes/cuda.rst

@@ -254,7 +254,7 @@ To toggle the reduced precision reduction flags in C++, one can do
 
 .. _fp16accumulation:
 
-Full FP16 Accmumulation in FP16 GEMMs
+Full FP16 Accumulation in FP16 GEMMs
 -------------------------------------
 
 Certain GPUs have increased performance when doing _all_ FP16 GEMM accumulation

docs/source/torch.compiler_api.md

@@ -30,5 +30,6 @@ For a quick overview of `torch.compiler`, see {ref}`torch.compiler_overview`.
      skip_guard_on_all_nn_modules_unsafe
      keep_tensor_guards_unsafe
      skip_guard_on_globals_unsafe
+     skip_all_guards_unsafe
      nested_compile_region
 ```

functorch/op_analysis/gen_data.py

@@ -24,15 +24,11 @@ def gen_data(special_op_lists, analysis_name):
     all_ops = get_ops_for_key(None)
     composite_ops = get_ops_for_key("CompositeImplicitAutograd")
     noncomposite_ops = all_ops - composite_ops
+    with open("../../aten/src/ATen/native/native_functions.yaml") as f:
+        ops = yaml.load(f.read(), Loader=yaml.CLoader)
 
-    ops = yaml.load(
-        open("../../aten/src/ATen/native/native_functions.yaml").read(),
-        Loader=yaml.CLoader,
-    )
-
-    annotated_ops = {
-        a.strip(): b.strip() for a, b in list(csv.reader(open("annotated_ops")))
-    }
+    with open("annotated_ops") as f:
+        annotated_ops = {a.strip(): b.strip() for a, b in csv.reader(f)}
 
     uniq_ops = []
     uniq_names = set()

pyproject.toml

@@ -376,3 +376,19 @@ keep-runtime-typing = true
 
 [tool.codespell]
 ignore-words = "tools/linter/dictionary.txt"
+
+[tool.spin]
+package = 'torch'
+
+[tool.spin.commands]
+"Build" = [
+  ".spin/cmds.py:lint",
+  ".spin/cmds.py:fixlint",
+  ".spin/cmds.py:quicklint",
+  ".spin/cmds.py:quickfix",
+]
+"Regenerate" = [
+  ".spin/cmds.py:regenerate_version",
+  ".spin/cmds.py:regenerate_type_stubs",
+  ".spin/cmds.py:regenerate_clangtidy_files",
+]

pyrefly.toml

@@ -32,7 +32,7 @@ project-excludes = [
   "torch/utils/tensorboard/summary.py",
   # formatting issues, will turn on after adjusting where suppressions can be
   # in import statements
-  "tools/flight_recorder/components/types.py",
+  "torch/distributed/flight_recorder/components/types.py",
   "torch/linalg/__init__.py",
   "torch/package/importer.py",
   "torch/package/_package_pickler.py",

requirements.txt

@@ -14,6 +14,7 @@ lintrunner ; platform_machine != "s390x" and platform_machine != "riscv64"
 networkx>=2.5.1
 optree>=0.13.0
 psutil
+spin
 sympy>=1.13.3
 typing-extensions>=4.13.2
 wheel

setup.py

@@ -1358,45 +1358,6 @@ class concat_license_files:
 
 # Need to create the proper LICENSE.txt for the wheel
 class bdist_wheel(setuptools.command.bdist_wheel.bdist_wheel):
-    def _wrap_headers_with_macro(self, bdist_dir: Path) -> None:
-        """Wrap all header files with #if !defined(TORCH_STABLE_ONLY) && !defined(TORCH_TARGET_VERSION).
-
-        Excludes:
-        - torch/include/torch/headeronly/*
-        - torch/include/torch/csrc/stable/*
-        - torch/include/torch/csrc/inductor/aoti_torch/c/ (only shim headers)
-        - torch/include/torch/csrc/inductor/aoti_torch/generated/
-        """
-        header_extensions = (".h", ".hpp", ".cuh")
-        header_files = [
-            f for ext in header_extensions for f in bdist_dir.rglob(f"*{ext}")
-        ]
-
-        # Paths to exclude from wrapping
-        exclude_dir_patterns = [
-            "torch/include/torch/headeronly/",
-            "torch/include/torch/csrc/stable/",
-            "torch/include/torch/csrc/inductor/aoti_torch/c/",
-            "torch/include/torch/csrc/inductor/aoti_torch/generated/",
-        ]
-
-        for header_file in header_files:
-            rel_path = header_file.relative_to(bdist_dir).as_posix()
-
-            if any(rel_path.startswith(pattern) for pattern in exclude_dir_patterns):
-                report(f"Skipping header: {rel_path}")
-                continue
-
-            original_content = header_file.read_text(encoding="utf-8")
-            wrapped_content = (
-                "#if !defined(TORCH_STABLE_ONLY) && !defined(TORCH_TARGET_VERSION)\n"
-                f"{original_content}"
-                "\n#endif  // !defined(TORCH_STABLE_ONLY) && !defined(TORCH_TARGET_VERSION)\n"
-            )
-
-            header_file.write_text(wrapped_content, encoding="utf-8")
-            report(f"Wrapped header: {rel_path}")
-
     def run(self) -> None:
         with concat_license_files(include_files=True):
             super().run()
@@ -1419,14 +1380,6 @@ class bdist_wheel(setuptools.command.bdist_wheel.bdist_wheel):
             # need an __init__.py file otherwise we wouldn't have a package
             (bdist_dir / "torch" / "__init__.py").touch()
 
-        # Wrap all header files with TORCH_STABLE_ONLY macro
-        assert self.bdist_dir is not None, "bdist_dir should be set during wheel build"
-        bdist_dir = Path(self.bdist_dir)
-        report(
-            "-- Wrapping header files with if !defined(TORCH_STABLE_ONLY) && !defined(TORCH_TARGET_VERSION)"
-        )
-        self._wrap_headers_with_macro(bdist_dir)
-
 
 class clean(Command):
     user_options: ClassVar[list[tuple[str, str | None, str]]] = []
@@ -1632,7 +1585,7 @@ def configure_extension_build() -> tuple[
     if cmake_cache_vars["USE_DISTRIBUTED"]:
         # Only enable fr_trace command if distributed is enabled
         entry_points["console_scripts"].append(
-            "torchfrtrace = tools.flight_recorder.fr_trace:main",
+            "torchfrtrace = torch.distributed.flight_recorder.fr_trace:main",
         )
     return ext_modules, cmdclass, packages, entry_points, extra_install_requires
 

test/cpp/aoti_abi_check/CMakeLists.txt

@@ -8,6 +8,7 @@ set(AOTI_ABI_CHECK_TEST_ROOT ${TORCH_ROOT}/test/cpp/aoti_abi_check)
 # Build the cpp gtest binary containing the cpp-only tests.
 set(AOTI_ABI_CHECK_TEST_SRCS
   ${AOTI_ABI_CHECK_TEST_ROOT}/main.cpp
+  ${AOTI_ABI_CHECK_TEST_ROOT}/test_accessor.cpp
   ${AOTI_ABI_CHECK_TEST_ROOT}/test_cast.cpp
   ${AOTI_ABI_CHECK_TEST_ROOT}/test_devicetype.cpp
   ${AOTI_ABI_CHECK_TEST_ROOT}/test_dispatch.cpp

test/cpp/aoti_abi_check/test_accessor.cpp

@@ -0,0 +1,50 @@
+#include <gtest/gtest.h>
+#include <torch/headeronly/core/TensorAccessor.h>
+#include <string>
+
+TEST(TestAccessor, HeaderOnlyTensorAccessor) {
+  std::vector<int32_t> v = {11, 12, 13, 21, 22, 23};
+  std::vector<int64_t> sizes = {2, 3};
+  std::vector<int64_t> strides = {3, 1};
+
+  auto acc = torch::headeronly::HeaderOnlyTensorAccessor<int32_t, 2>(
+      v.data(), sizes.data(), strides.data());
+  EXPECT_EQ(acc[0][0], 11);
+  EXPECT_EQ(acc[0][1], 12);
+  EXPECT_EQ(acc[0][2], 13);
+  EXPECT_EQ(acc[1][0], 21);
+  EXPECT_EQ(acc[1][1], 22);
+  EXPECT_EQ(acc[1][2], 23);
+}
+
+TEST(TestAccessor, HeaderOnlyGenericPackedTensorAccessor) {
+  std::vector<int32_t> v = {11, 12, 13, 21, 22, 23};
+  std::vector<int64_t> sizes = {2, 3};
+  std::vector<int64_t> strides = {3, 1};
+
+  auto acc =
+      torch::headeronly::HeaderOnlyGenericPackedTensorAccessor<int32_t, 2>(
+          v.data(), sizes.data(), strides.data());
+  EXPECT_EQ(acc[0][0], 11);
+  EXPECT_EQ(acc[0][1], 12);
+  EXPECT_EQ(acc[0][2], 13);
+  EXPECT_EQ(acc[1][0], 21);
+  EXPECT_EQ(acc[1][1], 22);
+  EXPECT_EQ(acc[1][2], 23);
+
+  auto tacc = acc.transpose(0, 1);
+  EXPECT_EQ(tacc[0][0], 11);
+  EXPECT_EQ(tacc[0][1], 21);
+  EXPECT_EQ(tacc[1][0], 12);
+  EXPECT_EQ(tacc[1][1], 22);
+  EXPECT_EQ(tacc[2][0], 13);
+  EXPECT_EQ(tacc[2][1], 23);
+
+  try {
+    acc.transpose(0, 2);
+  } catch (const std::exception& e) {
+    EXPECT_TRUE(
+        std::string(e.what()).find("HeaderOnlyIndexBoundsCheck") !=
+        std::string::npos);
+  }
+}

test/cpp/aoti_abi_check/test_scalartype.cpp

@@ -13,6 +13,17 @@ TEST(TestScalarType, ScalarTypeToCPPTypeT) {
 #undef DEFINE_CHECK
 }
 
+TEST(TestScalarType, CppTypeToScalarType) {
+  using torch::headeronly::CppTypeToScalarType;
+  using torch::headeronly::ScalarType;
+
+#define DEFINE_CHECK(TYPE, SCALARTYPE) \
+  EXPECT_EQ(CppTypeToScalarType<TYPE>::value, ScalarType::SCALARTYPE);
+
+  AT_FORALL_SCALAR_TYPES_WITH_COMPLEX_AND_QINTS(DEFINE_CHECK);
+#undef DEFINE_CHECK
+}
+
 #define DEFINE_CHECK(TYPE, SCALARTYPE)                                       \
   {                                                                          \
     EXPECT_EQ(                                                               \
@@ -90,3 +101,14 @@ TEST(TestScalarType, toUnderlying) {
   AT_FORALL_FLOAT8_TYPES(DEFINE_CHECK);
 #undef DEFINE_CHECK
 }
+
+TEST(TestScalarType, isQIntType) {
+  using torch::headeronly::isQIntType;
+  using torch::headeronly::ScalarType;
+#define DEFINE_CHECK(_, name) EXPECT_TRUE(isQIntType(ScalarType::name));
+  AT_FORALL_QINT_TYPES(DEFINE_CHECK);
+#undef DEFINE_CHECK
+#define DEFINE_CHECK(_, name) EXPECT_FALSE(isQIntType(ScalarType::name));
+  AT_FORALL_SCALAR_TYPES_WITH_COMPLEX(DEFINE_CHECK);
+#undef DEFINE_CHECK
+}

test/cpp/jit/test_custom_operators.cpp

@@ -15,7 +15,7 @@ namespace jit {
 TEST(CustomOperatorTest, InferredSchema) {
   torch::RegisterOperators reg(
       "foo::bar", [](double a, at::Tensor b) { return a + b; });
-  auto& ops = getAllOperatorsFor(Symbol::fromQualString("foo::bar"));
+  auto ops = getAllOperatorsFor(Symbol::fromQualString("foo::bar"));
   ASSERT_EQ(ops.size(), 1);
 
   auto& op = ops.front();
@@ -43,8 +43,7 @@ TEST(CustomOperatorTest, ExplicitSchema) {
       "foo::bar_with_schema(float a, Tensor b) -> Tensor",
       [](double a, at::Tensor b) { return a + b; });
 
-  auto& ops =
-      getAllOperatorsFor(Symbol::fromQualString("foo::bar_with_schema"));
+  auto ops = getAllOperatorsFor(Symbol::fromQualString("foo::bar_with_schema"));
   ASSERT_EQ(ops.size(), 1);
 
   auto& op = ops.front();
@@ -77,7 +76,7 @@ TEST(CustomOperatorTest, ListParameters) {
          torch::List<c10::complex<double>> complexdoubles,
          torch::List<at::Tensor> tensors) { return floats; });
 
-  auto& ops = getAllOperatorsFor(Symbol::fromQualString("foo::lists"));
+  auto ops = getAllOperatorsFor(Symbol::fromQualString("foo::lists"));
   ASSERT_EQ(ops.size(), 1);
 
   auto& op = ops.front();
@@ -123,7 +122,7 @@ TEST(CustomOperatorTest, ListParameters2) {
       "foo::lists2(Tensor[] tensors) -> Tensor[]",
       [](torch::List<at::Tensor> tensors) { return tensors; });
 
-  auto& ops = getAllOperatorsFor(Symbol::fromQualString("foo::lists2"));
+  auto ops = getAllOperatorsFor(Symbol::fromQualString("foo::lists2"));
   ASSERT_EQ(ops.size(), 1);
 
   auto& op = ops.front();
@@ -213,7 +212,7 @@ TEST(TestCustomOperator, OperatorGeneratorUndeclared) {
       },
       aliasAnalysisFromSchema())});
 
-  auto& ops = getAllOperatorsFor(Symbol::fromQualString("foofoo::not_exist"));
+  auto ops = getAllOperatorsFor(Symbol::fromQualString("foofoo::not_exist"));
   ASSERT_EQ(ops.size(), 0);
 }
 
@@ -232,7 +231,7 @@ TEST(TestCustomOperator, OperatorGeneratorBasic) {
       },
       aliasAnalysisFromSchema())});
 
-  auto& ops = getAllOperatorsFor(Symbol::fromQualString("foofoo::bar"));
+  auto ops = getAllOperatorsFor(Symbol::fromQualString("foofoo::bar"));
   ASSERT_EQ(ops.size(), 1);
 
   auto& op = ops.front();

test/cpp_extensions/libtorch_agnostic_extension/libtorch_agnostic/csrc/cuda_kernel.cu

@@ -0,0 +1,30 @@
+#include "kernel.h"
+
+#include <torch/csrc/stable/library.h>
+#include <torch/csrc/stable/tensor.h>
+#include <torch/csrc/stable/ops.h>
+#include <cuda_runtime.h>
+
+using torch::stable::Tensor;
+
+Tensor mv_tensor_accessor_cuda(Tensor m, Tensor v) {
+  STD_TORCH_CHECK(m.dim() == 2, "m must be 2D");
+  STD_TORCH_CHECK(v.dim() == 1, "v must be 1D");
+  STD_TORCH_CHECK(m.size(1) == v.size(0), "m.shape[1] == v.shape[0] must hold");
+  STD_TORCH_CHECK(m.scalar_type() == v.scalar_type(), "m and v must have the same dtype");
+  STD_TORCH_CHECK(m.device() == v.device(), "m and v must be on the same device");
+  Tensor res = new_empty(m, {m.size(0)});
+  THO_DISPATCH_V2(m.scalar_type(), "mv_tensor_accessor_cuda",
+                  AT_WRAP(([&]() {
+                    auto resa = Accessor_cuda<scalar_t, 1>(reinterpret_cast<scalar_t*>(res.data_ptr()), res.sizes().data(), res.strides().data());
+                    auto ma = Accessor_cuda<scalar_t, 2>(reinterpret_cast<scalar_t*>(m.data_ptr()), m.sizes().data(), m.strides().data());
+                    auto va = Accessor_cuda<scalar_t, 1>(reinterpret_cast<scalar_t*>(v.data_ptr()), v.sizes().data(), v.strides().data());
+                    mv_tensor_accessor_kernel<Accessor_cuda, scalar_t><<<1, 1, 0, 0>>>(resa, ma, va);
+                  })),
+                  AT_FLOATING_TYPES);
+  return res;
+}
+
+STABLE_TORCH_LIBRARY_IMPL(libtorch_agnostic, CUDA, m) {
+  m.impl("mv_tensor_accessor", TORCH_BOX(&mv_tensor_accessor_cuda));
+}