Fix unintended updates to submodules

.ci/docker/almalinux/build.sh

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

.ci/docker/build.sh

@@ -188,7 +188,7 @@ case "$tag" in
     fi
     GCC_VERSION=11
     VISION=yes
-    ROCM_VERSION=7.1
+    ROCM_VERSION=7.0
     NINJA_VERSION=1.9.0
     TRITON=yes
     KATEX=yes
@@ -260,12 +260,6 @@ case "$tag" in
     HALIDE=yes
     TRITON=yes
     ;;
-  pytorch-linux-jammy-cuda12.8-py3.12-pallas)
-    CUDA_VERSION=12.8.1
-    ANACONDA_PYTHON_VERSION=3.12
-    GCC_VERSION=11
-    PALLAS=yes
-    ;;
   pytorch-linux-jammy-py3.12-triton-cpu)
     CUDA_VERSION=12.6
     ANACONDA_PYTHON_VERSION=3.12
@@ -387,7 +381,6 @@ docker build \
        --build-arg "INDUCTOR_BENCHMARKS=${INDUCTOR_BENCHMARKS}" \
        --build-arg "EXECUTORCH=${EXECUTORCH}" \
        --build-arg "HALIDE=${HALIDE}" \
-       --build-arg "PALLAS=${PALLAS}" \
        --build-arg "XPU_VERSION=${XPU_VERSION}" \
        --build-arg "UNINSTALL_DILL=${UNINSTALL_DILL}" \
        --build-arg "ACL=${ACL:-}" \

.ci/docker/ci_commit_pins/jax.txt

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

.ci/docker/common/install_jax.sh

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

.ci/docker/common/install_rocm.sh

@@ -60,16 +60,14 @@ EOF
         DEBIAN_FRONTEND=noninteractive apt-get install -y --allow-unauthenticated rocm-llvm-dev
     fi
 
-    if [[ $(ver $ROCM_VERSION) -lt $(ver 7.1) ]]; then
-      # precompiled miopen kernels added in ROCm 3.5, renamed in ROCm 5.5, removed in ROCm 7.1
-      # search for all unversioned packages
-      # if search fails it will abort this script; use true to avoid case where search fails
-      MIOPENHIPGFX=$(apt-cache search --names-only miopen-hip-gfx | awk '{print $1}' | grep -F -v . || true)
-      if [[ "x${MIOPENHIPGFX}" = x ]]; then
-        echo "miopen-hip-gfx package not available" && exit 1
-      else
-        DEBIAN_FRONTEND=noninteractive apt-get install -y --allow-unauthenticated ${MIOPENHIPGFX}
-      fi
+    # precompiled miopen kernels added in ROCm 3.5, renamed in ROCm 5.5
+    # search for all unversioned packages
+    # if search fails it will abort this script; use true to avoid case where search fails
+    MIOPENHIPGFX=$(apt-cache search --names-only miopen-hip-gfx | awk '{print $1}' | grep -F -v . || true)
+    if [[ "x${MIOPENHIPGFX}" = x ]]; then
+      echo "miopen-hip-gfx package not available" && exit 1
+    else
+      DEBIAN_FRONTEND=noninteractive apt-get install -y --allow-unauthenticated ${MIOPENHIPGFX}
     fi
 
     # ROCm 6.0 had a regression where journal_mode was enabled on the kdb files resulting in permission errors at runtime

.ci/docker/common/install_rocm_magma.sh

@@ -12,8 +12,8 @@ function do_install() {
 
     rocm_version_nodot=${rocm_version//./}
 
-    # https://github.com/icl-utk-edu/magma/pull/65
-    MAGMA_VERSION=d6e4117bc88e73f06d26c6c2e14f064e8fc3d1ec
+    # post merge of https://github.com/icl-utk-edu/magma/pull/65
+    MAGMA_VERSION=c0792ae825fb36872784892ea643dd6f3456bc5f
     magma_archive="magma-rocm${rocm_version_nodot}-${MAGMA_VERSION}-1.tar.bz2"
 
     rocm_dir="/opt/rocm"

.ci/docker/libtorch/build.sh

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

.ci/docker/manywheel/build.sh

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

.ci/docker/ubuntu/Dockerfile

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

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

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

.ci/magma-rocm/README.md

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

.ci/onnx/common.sh

@@ -21,87 +21,3 @@ if [[ "${BUILD_ENVIRONMENT}" == *rocm* ]]; then
 fi
 
 mkdir -p "$pytest_reports_dir" || true
-
-##########################################
-# copied from .ci/pytorch/common_utils.sh
-##########################################
-
-function get_pinned_commit() {
-  cat .github/ci_commit_pins/"${1}".txt
-}
-
-function pip_install_whl() {
-  # This is used to install PyTorch and other build artifacts wheel locally
-  # without using any network connection
-
-  # Convert the input arguments into an array
-  local args=("$@")
-
-  # Check if the first argument contains multiple paths separated by spaces
-  if [[ "${args[0]}" == *" "* ]]; then
-    # Split the string by spaces into an array
-    IFS=' ' read -r -a paths <<< "${args[0]}"
-    # Loop through each path and install individually
-    for path in "${paths[@]}"; do
-      echo "Installing $path"
-      python3 -mpip install --no-index --no-deps "$path"
-    done
-  else
-    # Loop through each argument and install individually
-    for path in "${args[@]}"; do
-      echo "Installing $path"
-      python3 -mpip install --no-index --no-deps "$path"
-    done
-  fi
-}
-
-function pip_build_and_install() {
-  local build_target=$1
-  local wheel_dir=$2
-
-  local found_whl=0
-  for file in "${wheel_dir}"/*.whl
-  do
-    if [[ -f "${file}" ]]; then
-      found_whl=1
-      break
-    fi
-  done
-
-  # Build the wheel if it doesn't exist
-  if [ "${found_whl}" == "0" ]; then
-    python3 -m pip wheel \
-      --no-build-isolation \
-      --no-deps \
-      -w "${wheel_dir}" \
-      "${build_target}"
-  fi
-
-  for file in "${wheel_dir}"/*.whl
-  do
-    pip_install_whl "${file}"
-  done
-}
-
-function install_torchvision() {
-  local orig_preload
-  local commit
-  commit=$(get_pinned_commit vision)
-  orig_preload=${LD_PRELOAD}
-  if [ -n "${LD_PRELOAD}" ]; then
-    # Silence dlerror to work-around glibc ASAN bug, see https://sourceware.org/bugzilla/show_bug.cgi?id=27653#c9
-    echo 'char* dlerror(void) { return "";}'|gcc -fpic -shared -o "${HOME}/dlerror.so" -x c -
-    LD_PRELOAD=${orig_preload}:${HOME}/dlerror.so
-  fi
-
-  if [[ "${BUILD_ENVIRONMENT}" == *cuda* ]]; then
-    # Not sure if both are needed, but why not
-    export FORCE_CUDA=1
-    export WITH_CUDA=1
-  fi
-  pip_build_and_install "git+https://github.com/pytorch/vision.git@${commit}" dist/vision
-
-  if [ -n "${LD_PRELOAD}" ]; then
-    LD_PRELOAD=${orig_preload}
-  fi
-}

.ci/onnx/test.sh

@@ -19,7 +19,7 @@ git config --global --add safe.directory /var/lib/jenkins/workspace
 
 if [[ "$BUILD_ENVIRONMENT" == *onnx* ]]; then
   # TODO: This can be removed later once vision is also part of the Docker image
-  install_torchvision
+  pip install -q --no-use-pep517 "git+https://github.com/pytorch/vision.git@$(cat .github/ci_commit_pins/vision.txt)"
   # JIT C++ extensions require ninja, so put it into PATH.
   export PATH="/var/lib/jenkins/.local/bin:$PATH"
   # NB: ONNX test is fast (~15m) so it's ok to retry it few more times to avoid any flaky issue, we

.ci/pytorch/build.sh

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

.ci/pytorch/common_utils.sh

@@ -96,6 +96,7 @@ function pip_build_and_install() {
     python3 -m pip wheel \
       --no-build-isolation \
       --no-deps \
+      --no-use-pep517 \
       -w "${wheel_dir}" \
       "${build_target}"
   fi
@@ -307,28 +308,6 @@ function install_torchao() {
   pip_build_and_install "git+https://github.com/pytorch/ao.git@${commit}" dist/ao
 }
 
-function install_flash_attn_cute() {
-  echo "Installing FlashAttention CuTe from GitHub..."
-  # Grab latest main til we have a pinned commit
-  local flash_attn_commit
-  flash_attn_commit=$(git ls-remote https://github.com/Dao-AILab/flash-attention.git HEAD | cut -f1)
-
-  # Clone the repo to a temporary directory
-  rm -rf flash-attention-build
-  git clone --depth 1 --recursive https://github.com/Dao-AILab/flash-attention.git flash-attention-build
-
-  pushd flash-attention-build
-  git checkout "${flash_attn_commit}"
-
-  # Install only the 'cute' sub-directory
-  pip_install -e flash_attn/cute/
-  popd
-
-  # remove the local repo
-  rm -rf flash-attention-build
-  echo "FlashAttention CuTe installation complete."
-}
-
 function print_sccache_stats() {
   echo 'PyTorch Build Statistics'
   sccache --show-stats

.ci/pytorch/smoke_test/check_binary_symbols.py

@@ -100,347 +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.
-
-    This approach tests:
-    1. WITHOUT macros -> many torch symbols exposed (compilation succeeds)
-    2. WITH TORCH_STABLE_ONLY -> compilation fails with #error directive
-    3. WITH TORCH_TARGET_VERSION -> compilation fails with #error directive
-    4. WITH both macros -> compilation fails with #error directive
-    """
-    import subprocess
-    import tempfile
-
-    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 - should succeed
-    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"
-    )
-
-    # Helper to verify compilation fails with expected error
-    def _expect_compilation_failure(compile_flags: list[str], macro_name: str) -> None:
-        with tempfile.TemporaryDirectory() as tmpdir:
-            tmppath = Path(tmpdir)
-            cpp_file = tmppath / "test.cpp"
-            obj_file = tmppath / "test.o"
-
-            cpp_file.write_text(test_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"Expected compilation to fail with {macro_name} defined, but it succeeded"
-                )
-
-            stderr = result.stderr
-            expected_error_msg = (
-                "This file should not be included when either TORCH_STABLE_ONLY "
-                "or TORCH_TARGET_VERSION is defined."
-            )
-
-            if expected_error_msg not in stderr:
-                raise RuntimeError(
-                    f"Expected error message to contain:\n  '{expected_error_msg}'\n"
-                    f"but got:\n{stderr[:1000]}"
-                )
-
-            print(f"Compilation correctly failed with {macro_name} defined")
-
-    compile_flags_with_stable_only = base_compile_flags + ["-DTORCH_STABLE_ONLY"]
-    _expect_compilation_failure(compile_flags_with_stable_only, "TORCH_STABLE_ONLY")
-
-    compile_flags_with_target_version = base_compile_flags + [
-        "-DTORCH_TARGET_VERSION=1"
-    ]
-    _expect_compilation_failure(
-        compile_flags_with_target_version, "TORCH_TARGET_VERSION"
-    )
-
-    compile_flags_with_both = base_compile_flags + [
-        "-DTORCH_STABLE_ONLY",
-        "-DTORCH_TARGET_VERSION=1",
-    ]
-    _expect_compilation_failure(compile_flags_with_both, "both macros")
-
-
-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)
@@ -470,13 +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/smoke_test/smoke_test.py

@@ -353,17 +353,6 @@ def test_linalg(device="cpu") -> None:
             torch.linalg.svd(A)
 
 
-def test_sdpa(device="cpu", dtype=torch.float16) -> None:
-    """Regression test for https://github.com/pytorch/pytorch/issues/167602
-    Without nvrtc_builtins on CuDNN-9.13 on CUDA-13 fails with ` No valid execution plans built.`
-    """
-    print(f"Testing SDPA on {device} using type {dtype}")
-    k, q, v = torch.rand(3, 1, 16, 77, 64, dtype=dtype, device=device).unbind(0)
-    attn = torch.rand(1, 1, 77, 77, dtype=dtype, device=device)
-    rc = torch.nn.functional.scaled_dot_product_attention(q, k, v, attn)
-    assert rc.isnan().any().item() is False
-
-
 def smoke_test_compile(device: str = "cpu") -> None:
     supported_dtypes = [torch.float16, torch.float32, torch.float64]
 
@@ -500,12 +489,10 @@ def main() -> None:
     smoke_test_conv2d()
     test_linalg()
     test_numpy()
-    test_sdpa()
 
     if is_cuda_system:
         test_linalg("cuda")
         test_cuda_gds_errors_captured()
-        test_sdpa("cuda")
 
     if options.package == "all":
         smoke_test_modules()

.ci/pytorch/test.sh

@@ -208,8 +208,6 @@ if [[ "$BUILD_ENVIRONMENT" == *xpu* ]]; then
   source /opt/intel/oneapi/ccl/latest/env/vars.sh
   # shellcheck disable=SC1091
   source /opt/intel/oneapi/mpi/latest/env/vars.sh
-  # shellcheck disable=SC1091
-  source /opt/intel/oneapi/pti/latest/env/vars.sh
   # Check XPU status before testing
   timeout 30 xpu-smi discovery || true
 fi
@@ -344,18 +342,8 @@ test_python_smoke() {
 }
 
 test_python_smoke_b200() {
-  # Targeted smoke tests for B200 including FlashAttention CuTe coverage
-  install_flash_attn_cute
-  time python test/run_test.py \
-    --include \
-      test_matmul_cuda \
-      test_scaled_matmul_cuda \
-      inductor/test_fp8 \
-      nn/attention/test_fa4 \
-      nn/attention/test_open_registry \
-      inductor/test_flex_flash \
-    $PYTHON_TEST_EXTRA_OPTION \
-    --upload-artifacts-while-running
+  # Targeted smoke tests for B200 - staged approach to avoid too many failures
+  time python test/run_test.py --include test_matmul_cuda test_scaled_matmul_cuda inductor/test_fp8 $PYTHON_TEST_EXTRA_OPTION --upload-artifacts-while-running
   assert_git_not_dirty
 }
 
@@ -389,13 +377,6 @@ 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
@@ -843,11 +824,6 @@ test_inductor_halide() {
   assert_git_not_dirty
 }
 
-test_inductor_pallas() {
-  python test/run_test.py --include inductor/test_pallas.py --verbose
-  assert_git_not_dirty
-}
-
 test_inductor_triton_cpu() {
   python test/run_test.py --include inductor/test_triton_cpu_backend.py inductor/test_torchinductor_strided_blocks.py --verbose
   assert_git_not_dirty
@@ -1250,97 +1226,6 @@ test_custom_script_ops() {
   assert_git_not_dirty
 }
 
-test_libtorch_agnostic_targetting() {
-    echo "Testing libtorch_agnostic runs correctly on TORCH_TARGET_VERSION"
-
-    REPO_DIR=$(pwd)
-    WHEEL_DIR="${REPO_DIR}/test/cpp_extensions/.wheels"
-
-    # Build wheel with current PyTorch (this has TORCH_TARGET_VERSION 2_9_0)
-    echo "Building 2.9 extension wheel with current PyTorch..."
-    pushd test/cpp_extensions/libtorch_agnostic_2_9_extension
-    time python setup.py bdist_wheel
-
-    # Save the wheel
-    mkdir -p "$WHEEL_DIR"
-    cp dist/*.whl "$WHEEL_DIR/"
-    WHEEL_FILE=$(find "$WHEEL_DIR" -maxdepth 1 -name "*.whl" -type f | head -1)
-    echo "Built wheel: $(basename "$WHEEL_FILE")"
-    popd
-
-    # Create venv and install PyTorch 2.9
-    python -m venv venv_pytorch_2_9
-    # shellcheck disable=SC1091
-    . venv_pytorch_2_9/bin/activate
-
-    # Clear PYTHONPATH to avoid using the development PyTorch
-    echo "Clearing PYTHONPATH to use only venv packages..."
-    unset PYTHONPATH
-
-    # Upgrade pip to latest version
-    echo "Upgrading pip to latest version..."
-    pip install --upgrade pip
-    pip --version
-
-    echo "Installing PyTorch 2.9..."
-
-    # Install from release channel only
-    PYTORCH_VERSION="2.9.0"
-
-    # Extract CUDA version from BUILD_ENVIRONMENT (e.g., "cuda12.1" -> "cu121")
-    if [[ "$BUILD_ENVIRONMENT" =~ cuda([0-9]+)\.([0-9]+) ]]; then
-        CUDA_MAJOR="${BASH_REMATCH[1]}"
-        CUDA_MINOR="${BASH_REMATCH[2]}"
-        CUDA_VERSION="cu${CUDA_MAJOR}${CUDA_MINOR}"
-        echo "  Detected CUDA ${CUDA_MAJOR}.${CUDA_MINOR} from BUILD_ENVIRONMENT, using ${CUDA_VERSION}"
-    else
-        # Default to CPU build
-        CUDA_VERSION="cpu"
-        echo "  No CUDA detected in BUILD_ENVIRONMENT, using CPU build"
-    fi
-
-    if pip install torch=="${PYTORCH_VERSION}" --index-url https://download.pytorch.org/whl/${CUDA_VERSION}/; then
-        echo "Installed PyTorch ${PYTORCH_VERSION} from release channel (${CUDA_VERSION})"
-    else
-        echo "  FAILED to install PyTorch 2.9.0 from release channel"
-        echo "  URL: https://download.pytorch.org/whl/${CUDA_VERSION}/"
-        deactivate
-        rm -rf venv_pytorch_2_9
-        return 1
-    fi
-
-    INSTALLED_VERSION=$(python -c "import torch; print(torch.__version__)" 2>/dev/null || echo "unknown")
-    echo "  Installed version: $INSTALLED_VERSION"
-
-    # Install test dependencies
-    echo "Installing test dependencies..."
-    pip install expecttest numpy unittest-xml-reporting
-
-    # Install the pre-built wheel
-    echo ""
-    echo "Installing pre-built 2.9 extension wheel (built with PyTorch 2.10)..."
-    pip install "$WHEEL_FILE"
-    echo "Installed $(basename "$WHEEL_FILE") into PyTorch 2.9 environment"
-
-    # Run tests with PyTorch 2.9 runtime (2.10 tests will be skipped automatically)
-    echo ""
-    echo "Running tests with PyTorch 2.9 runtime (using wheel built on PyTorch 2.10)..."
-    if time python test/cpp_extensions/test_libtorch_agnostic.py -v; then
-        echo ""
-        echo "  Wheel built with current torch and TORCH_TARGET_VERSION 2_9_0 works with PyTorch 2.9 runtime!"
-    else
-        echo "targeting test failed"
-        deactivate
-        rm -rf venv_pytorch_2_9 "$WHEEL_DIR"
-        return 1
-    fi
-
-    deactivate
-    rm -rf venv_pytorch_2_9 "$WHEEL_DIR"
-
-    assert_git_not_dirty
-}
-
 test_jit_hooks() {
   echo "Testing jit hooks in cpp"
   HOOK_BUILD="${CUSTOM_TEST_ARTIFACT_BUILD_DIR}/jit-hook-build"
@@ -1778,22 +1663,6 @@ test_operator_microbenchmark() {
   done
 }
 
-test_attention_microbenchmark() {
-  TEST_REPORTS_DIR=$(pwd)/test/test-reports
-  mkdir -p "$TEST_REPORTS_DIR"
-  TEST_DIR=$(pwd)
-
-  # Install attention-gym dependency
-  echo "Installing attention-gym..."
-  python -m pip install git+https://github.com/meta-pytorch/attention-gym.git@main
-  pip show triton
-
-  cd "${TEST_DIR}"/benchmarks/transformer
-
-  $TASKSET python score_mod.py --config configs/config_basic.yaml \
-    --output-json-for-dashboard "${TEST_REPORTS_DIR}/attention_microbenchmark.json"
-}
-
 if ! [[ "${BUILD_ENVIRONMENT}" == *libtorch* || "${BUILD_ENVIRONMENT}" == *-bazel-* ]]; then
   (cd test && python -c "import torch; print(torch.__config__.show())")
   (cd test && python -c "import torch; print(torch.__config__.parallel_info())")
@@ -1813,8 +1682,6 @@ elif [[ "${BUILD_ENVIRONMENT}" == *aarch64* && "${TEST_CONFIG}" == 'default' ]];
 elif [[ "${TEST_CONFIG}" == *backward* ]]; then
   test_forward_backward_compatibility
   # Do NOT add tests after bc check tests, see its comment.
-elif [[ "${TEST_CONFIG}" == *libtorch_agnostic_targetting* ]]; then
-  test_libtorch_agnostic_targetting
 elif [[ "${TEST_CONFIG}" == *xla* ]]; then
   install_torchvision
   build_xla
@@ -1853,14 +1720,10 @@ elif [[ "${TEST_CONFIG}" == *operator_benchmark* ]]; then
   fi
 elif [[ "${TEST_CONFIG}" == *operator_microbenchmark* ]]; then
   test_operator_microbenchmark
-elif [[ "${TEST_CONFIG}" == *attention_microbenchmark* ]]; then
-  test_attention_microbenchmark
 elif [[ "${TEST_CONFIG}" == *inductor_distributed* ]]; then
   test_inductor_distributed
 elif [[ "${TEST_CONFIG}" == *inductor-halide* ]]; then
   test_inductor_halide
-elif [[ "${TEST_CONFIG}" == *inductor-pallas* ]]; then
-  test_inductor_pallas
 elif [[ "${TEST_CONFIG}" == *inductor-triton-cpu* ]]; then
   test_inductor_triton_cpu
 elif [[ "${TEST_CONFIG}" == *inductor-micro-benchmark* ]]; then
@@ -1914,8 +1777,6 @@ elif [[ "${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/actionlint.yaml

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

.github/ci_commit_pins/audio.txt

@@ -1 +1 @@
-ee1a1350eb37804b94334768f328144f058f14e9
+ad5816f0eee1c873df1b7d371c69f1f811a89387

.github/ci_commit_pins/vision.txt

@@ -1 +1 @@
-2d82dc5caa336d179d9b46ac4a0fb8c43d84c5cc
+ca2212438fdd8ce29b66999ed70ed54b0f9372d1

.github/ci_commit_pins/xla.txt

@@ -1 +1 @@
-94631807d22c09723dd006f7be5beb649d5f88d0
+c8b09f5f77d6bf6fb7ed7a9aa83e5d8156b3a5e9

.github/labeler.yml

@@ -91,6 +91,13 @@
 "ciflow/trunk":
 - .ci/docker/ci_commit_pins/triton.txt
 
+"oncall: distributed":
+- torch/csrc/distributed/**
+- torch/distributed/**
+- torch/nn/parallel/**
+- test/distributed/**
+- torch/testing/_internal/distributed/**
+
 "release notes: distributed (checkpoint)":
 - torch/distributed/checkpoint/**
 - test/distributed/checkpoint/**
@@ -131,8 +138,7 @@
 - test/test_matmul_cuda.py
 - test/test_scaled_matmul_cuda.py
 - test/inductor/test_fp8.py
-- aten/src/ATen/native/cuda/*Blas.cpp
-- aten/src/ATen/cuda/CUDA*Blas.*
+- aten/src/ATen/native/cuda/Blas.cpp
 - torch/**/*cublas*
 - torch/_inductor/kernel/mm.py
 - test/inductor/test_max_autotune.py
@@ -142,8 +148,7 @@
 - test/test_matmul_cuda.py
 - test/test_scaled_matmul_cuda.py
 - test/inductor/test_fp8.py
-- aten/src/ATen/native/cuda/*Blas.cpp
-- aten/src/ATen/cuda/CUDA*Blas.*
+- aten/src/ATen/native/cuda/Blas.cpp
 - torch/**/*cublas*
 - torch/_inductor/kernel/mm.py
 - test/inductor/test_max_autotune.py
@@ -153,21 +158,7 @@
 - test/test_matmul_cuda.py
 - test/test_scaled_matmul_cuda.py
 - test/inductor/test_fp8.py
-- aten/src/ATen/native/cuda/*Blas.cpp
-- aten/src/ATen/cuda/CUDA*Blas.*
+- aten/src/ATen/native/cuda/Blas.cpp
 - torch/_inductor/kernel/mm.py
 - test/inductor/test_max_autotune.py
 - third_party/fbgemm
-
-"ciflow/mps":
-- aten/src/ATen/mps/**
-- aten/src/ATen/native/mps/**
-- torch/_inductor/codegen/mps.py
-- test/test_mps.py
-- test/inductor/test_mps_basic.py
-
-"ciflow/h100-symm-mem":
-- torch/csrc/distributed/c10d/symm_mem/**
-- torch/distributed/_symmetric_memory/**
-- test/distributed/**/*mem*
-- test/distributed/**/*mem*/**

.github/nitpicks.yml

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

.github/pytorch-probot.yml

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

.github/scripts/delete_old_branches.py

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

.github/scripts/filter_test_configs.py

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

.github/scripts/generate_pytorch_version.py

@@ -50,7 +50,7 @@ def get_tag() -> str:
 
 def get_base_version() -> str:
     root = get_pytorch_root()
-    dirty_version = Path(root / "version.txt").read_text().strip()
+    dirty_version = open(root / "version.txt").read().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/scripts/get_workflow_job_id.py

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

.github/scripts/github_utils.py

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

.github/scripts/gitutils.py

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

.github/scripts/lintrunner.sh

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

.github/scripts/trymerge.py

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

.github/workflows/_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: ${{ steps.calculate-docker-image.outputs.docker-image }}
+          DOCKER_IMAGE: ${{ inputs.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/attention_op_microbenchmark.yml

@@ -1,73 +0,0 @@
-name: attention_op_microbenchmark
-
-on:
-  push:
-    tags:
-      - ciflow/op-benchmark/*
-  workflow_dispatch:
-  schedule:
-    # Run at 06:00 UTC everyday
-    - cron: 0 7 * * *
-
-concurrency:
-  group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.ref_name }}-${{ github.ref_type == 'branch' && github.sha }}-${{ github.event_name == 'workflow_dispatch' }}-${{ github.event_name == 'schedule' }}
-  cancel-in-progress: true
-
-permissions:
-  id-token: write
-  contents: read
-
-jobs:
-  attn-microbenchmark-build:
-    if: github.repository_owner == 'pytorch'
-    uses: ./.github/workflows/_linux-build.yml
-    with:
-      runner: linux.12xlarge.memory
-      build-environment: linux-jammy-cuda12.8-py3.10-gcc9-sm80
-      docker-image-name: ci-image:pytorch-linux-jammy-cuda12.8-cudnn9-py3-gcc11
-      cuda-arch-list: '8.0 9.0'
-      test-matrix: |
-        { include: [
-          { config: "attention_microbenchmark_test", shard: 1, num_shards: 1, runner: "linux.aws.a100" },
-          { config: "attention_microbenchmark_test", shard: 1, num_shards: 1, runner: "linux.aws.h100" },
-        ]}
-    secrets: inherit
-
-  attn-microbenchmark-test:
-    name: attn-microbenchmark-test
-    uses: ./.github/workflows/_linux-test.yml
-    needs: attn-microbenchmark-build
-    with:
-      timeout-minutes: 500
-      build-environment: linux-jammy-cuda12.8-py3.10-gcc9-sm80
-      docker-image: ${{ needs.attn-microbenchmark-build.outputs.docker-image }}
-      test-matrix: ${{ needs.attn-microbenchmark-build.outputs.test-matrix }}
-    secrets: inherit
-
-  # B200 runner
-  opmicrobenchmark-build-b200:
-    if: github.repository_owner == 'pytorch'
-    name: opmicrobenchmark-build-b200
-    uses: ./.github/workflows/_linux-build.yml
-    with:
-      runner: linux.12xlarge.memory
-      build-environment: linux-jammy-cuda12.8-py3.10-gcc9-sm100
-      docker-image-name: ci-image:pytorch-linux-jammy-cuda12.8-cudnn9-py3-gcc11
-      cuda-arch-list: '10.0'
-      test-matrix: |
-        { include: [
-          { config: "operator_microbenchmark_test", shard: 1, num_shards: 1, runner: "linux.dgx.b200" },
-        ]}
-    secrets: inherit
-
-  opmicrobenchmark-test-b200:
-    name: opmicrobenchmark-test-b200
-    uses: ./.github/workflows/_linux-test.yml
-    needs: opmicrobenchmark-build-b200
-    with:
-      timeout-minutes: 500
-      build-environment: linux-jammy-cuda12.8-py3.10-gcc9-sm100
-      docker-image: ${{ needs.opmicrobenchmark-build-b200.outputs.docker-image }}
-      test-matrix: ${{ needs.opmicrobenchmark-build-b200.outputs.test-matrix }}
-      aws-role-to-assume: arn:aws:iam::308535385114:role/gha_workflow_s3_and_ecr_read_only
-    secrets: inherit

.github/workflows/docker-builds.yml

@@ -67,7 +67,6 @@ jobs:
           pytorch-linux-jammy-py3.10-gcc11,
           pytorch-linux-jammy-py3-gcc11-inductor-benchmarks,
           pytorch-linux-jammy-py3.12-halide,
-          pytorch-linux-jammy-cuda12.8-py3.12-pallas,
           pytorch-linux-jammy-xpu-n-1-py3,
           pytorch-linux-noble-xpu-n-py3,
           pytorch-linux-noble-xpu-n-py3-inductor-benchmarks,
@@ -119,22 +118,6 @@ jobs:
         with:
           docker-image: ${{ steps.build-docker-image.outputs.docker-image }}
 
-      - name: Generate output
-        if: contains(matrix.docker-image-name, 'rocm')
-        id: generate_output
-        run: |
-          docker_image_name="${{ matrix.docker-image-name }}"
-          docker_image_tag="${{ steps.build-docker-image.outputs.docker-image }}"
-          echo "${docker_image_name}=${docker_image_tag}" >> docker-builds-output-${docker_image_name}.txt
-
-      - name: Upload artifacts
-        uses: actions/upload-artifact@v4.4.0
-        if: contains(matrix.docker-image-name, 'rocm')
-        with:
-          name: docker-builds-artifacts-${{ matrix.docker-image-name }}
-          retention-days: 14
-          path: ./docker-builds-output-${{ matrix.docker-image-name }}.txt
-
       - uses: nick-fields/retry@7152eba30c6575329ac0576536151aca5a72780e # v3.0.0
         name: Push to https://ghcr.io/
         id: push-to-ghcr-io

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

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

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

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

.github/workflows/dynamo-unittest.yml

@@ -1,70 +0,0 @@
-# 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

.github/workflows/h100-distributed.yml

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

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

@@ -1,4 +1,4 @@
-name: inductor-rocm-mi200
+name: inductor-rocm
 
 on:
   schedule:

.github/workflows/inductor-unittest.yml

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

.github/workflows/nightly.yml

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

.github/workflows/pull.yml

@@ -70,7 +70,6 @@ jobs:
           { config: "distributed", shard: 1, num_shards: 2, runner: "${{ needs.get-label-type.outputs.label-type }}linux.2xlarge" },
           { config: "distributed", shard: 2, num_shards: 2, runner: "${{ needs.get-label-type.outputs.label-type }}linux.2xlarge" },
           { config: "numpy_2_x", shard: 1, num_shards: 1, runner: "${{ needs.get-label-type.outputs.label-type }}linux.c7i.2xlarge" },
-          { config: "libtorch_agnostic_targetting", shard: 1, num_shards: 1, runner: "${{ needs.get-label-type.outputs.label-type }}linux.2xlarge" },
         ]}
     secrets: inherit
 

.github/workflows/rocm-mi200.yml

@@ -1,4 +1,4 @@
-name: rocm-mi200
+name: rocm
 
 on:
   push:

.github/workflows/test-b200.yml

@@ -5,9 +5,7 @@
 # Flow:
 # 1. Builds PyTorch with CUDA 12.8+ and sm100 architecture for B200
 # 2. Runs smoke tests on linux.dgx.b200 runner
-# 3. Tests executed are defined in .ci/pytorch/test.sh -> test_python_smoke_b200() function
-#    - Includes matmul, scaled_matmul, FP8, and FlashAttention CuTe tests
-#    - FlashAttention CuTe DSL is installed as part of test execution
+# 3. Tests executed are defined in .ci/pytorch/test.sh -> test_python_smoke() function
 #
 # Triggered by:
 # - Pull requests modifying this workflow file

.github/workflows/test-h100.yml

@@ -41,6 +41,7 @@ jobs:
     needs: get-label-type
     with:
       runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
+      runner: linux.12xlarge.memory
       build-environment: linux-jammy-cuda12.8-py3.10-gcc11-sm90
       docker-image-name: ci-image:pytorch-linux-jammy-cuda12.8-cudnn9-py3-gcc11
       cuda-arch-list: '9.0'

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

@@ -1,83 +0,0 @@
-name: trunk-rocm-mi300
-
-on:
-  push:
-    branches:
-      - main
-      - release/*
-  workflow_dispatch:
-  schedule:
-    - cron: 29 8 * * *  # about 1:29am PDT
-
-concurrency:
-  group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.ref_name }}-${{ github.ref_type == 'branch' && github.sha }}-${{ github.event_name == 'workflow_dispatch' }}-${{ github.event_name == 'schedule' }}
-  cancel-in-progress: true
-
-permissions:
-  id-token: write
-  contents: read
-
-jobs:
-  llm-td:
-    if: github.repository_owner == 'pytorch'
-    name: before-test
-    uses: ./.github/workflows/llm_td_retrieval.yml
-    permissions:
-      id-token: write
-      contents: read
-
-  target-determination:
-    name: before-test
-    uses: ./.github/workflows/target_determination.yml
-    needs: llm-td
-    permissions:
-      id-token: write
-      contents: read
-
-  get-label-type:
-    name: get-label-type
-    uses: pytorch/pytorch/.github/workflows/_runner-determinator.yml@main
-    if: ${{ (github.event_name != 'schedule' || github.repository == 'pytorch/pytorch') && github.repository_owner == 'pytorch' }}
-    with:
-      triggering_actor: ${{ github.triggering_actor }}
-      issue_owner: ${{ github.event.pull_request.user.login || github.event.issue.user.login }}
-      curr_branch: ${{ github.head_ref || github.ref_name }}
-      curr_ref_type: ${{ github.ref_type }}
-
-  linux-jammy-rocm-py3_10-build:
-    name: linux-jammy-rocm-py3.10
-    uses: ./.github/workflows/_linux-build.yml
-    needs: get-label-type
-    with:
-      runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
-      build-environment: linux-jammy-rocm-py3.10
-      docker-image-name: ci-image:pytorch-linux-jammy-rocm-n-py3
-      sync-tag: rocm-build
-      test-matrix: |
-        { include: [
-          { config: "default", shard: 1, num_shards: 6, runner: "linux.rocm.gpu.gfx942.1.b" },
-          { config: "default", shard: 2, num_shards: 6, runner: "linux.rocm.gpu.gfx942.1.b" },
-          { config: "default", shard: 3, num_shards: 6, runner: "linux.rocm.gpu.gfx942.1.b" },
-          { config: "default", shard: 4, num_shards: 6, runner: "linux.rocm.gpu.gfx942.1.b" },
-          { config: "default", shard: 5, num_shards: 6, runner: "linux.rocm.gpu.gfx942.1.b" },
-          { config: "default", shard: 6, num_shards: 6, runner: "linux.rocm.gpu.gfx942.1.b" },
-          { config: "distributed", shard: 1, num_shards: 3, runner: "linux.rocm.gpu.gfx942.4.b" },
-          { config: "distributed", shard: 2, num_shards: 3, runner: "linux.rocm.gpu.gfx942.4.b" },
-          { config: "distributed", shard: 3, num_shards: 3, runner: "linux.rocm.gpu.gfx942.4.b" },
-        ]}
-    secrets: inherit
-
-  linux-jammy-rocm-py3_10-test:
-    permissions:
-      id-token: write
-      contents: read
-    name: linux-jammy-rocm-py3.10
-    uses: ./.github/workflows/_rocm-test.yml
-    needs:
-      - linux-jammy-rocm-py3_10-build
-      - target-determination
-    with:
-      build-environment: linux-jammy-rocm-py3.10
-      docker-image: ${{ needs.linux-jammy-rocm-py3_10-build.outputs.docker-image }}
-      test-matrix: ${{ needs.linux-jammy-rocm-py3_10-build.outputs.test-matrix }}
-    secrets: inherit

.github/workflows/trunk.yml

@@ -83,7 +83,6 @@ jobs:
           { config: "distributed", shard: 2, num_shards: 3, runner: "${{ needs.get-label-type.outputs.label-type }}linux.g4dn.12xlarge.nvidia.gpu" },
           { config: "distributed", shard: 3, num_shards: 3, runner: "${{ needs.get-label-type.outputs.label-type }}linux.g4dn.12xlarge.nvidia.gpu" },
           { config: "pr_time_benchmarks", shard: 1, num_shards: 1, runner: "linux.g4dn.metal.nvidia.gpu" },
-          { config: "libtorch_agnostic_targetting", shard: 1, num_shards: 1, runner: "linux.g4dn.metal.nvidia.gpu" },
         ]}
     secrets: inherit
 

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

@@ -5,7 +5,6 @@ on:
     workflows:
       - pull
       - trunk
-      - trunk-rocm-mi300
       - periodic
       - periodic-rocm-mi200
       - periodic-rocm-mi300

.lintrunner.toml

@@ -186,8 +186,6 @@ include_patterns = [
     'aten/src/ATen/native/nested/cuda/*.h',
     'aten/src/ATen/native/nested/*.cpp',
     'aten/src/ATen/native/nested/*.h',
-    'aten/src/ATen/xpu/**/*.h',
-    'aten/src/ATen/xpu/**/*.cpp',
     'c10/**/*.cpp',
     'c10/**/*.h',
     'torch/*.h',
@@ -1404,7 +1402,7 @@ init_command = [
     '--dry-run={{DRYRUN}}',
     'usort==1.0.8.post1',
     'isort==6.0.1',
-    'ruff==0.14.4',  # sync with RUFF
+    'ruff==0.13.1',  # sync with RUFF
 ]
 is_formatter = true
 
@@ -1539,7 +1537,7 @@ init_command = [
     'python3',
     'tools/linter/adapters/pip_init.py',
     '--dry-run={{DRYRUN}}',
-    'ruff==0.14.4',  # sync with PYFMT
+    'ruff==0.13.1',  # sync with PYFMT
 ]
 is_formatter = true
 

.spin/cmds.py

@@ -1,330 +0,0 @@
-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)

CMakeLists.txt

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

CODEOWNERS

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

LICENSE

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

SECURITY.md

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

aten/src/ATen/DeviceAccelerator.h

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

aten/src/ATen/LegacyBatchedTensorImpl.h

@@ -144,7 +144,7 @@ inline std::bitset<kVmapNumLevels> createVmapLevelsBitset(BatchDimsRef bdims) {
 }
 
 inline std::ostream& operator<<(std::ostream& out, const BatchDim& bdim) {
-  out << "(lvl=" << bdim.level() << ", dim=" << bdim.dim() << ')';
+  out << "(lvl=" << bdim.level() << ", dim=" << bdim.dim() << ")";
   return out;
 }
 

aten/src/ATen/TensorIndexing.cpp

@@ -9,7 +9,7 @@ namespace indexing {
 const EllipsisIndexType Ellipsis = EllipsisIndexType();
 
 std::ostream& operator<<(std::ostream& stream, const Slice& slice) {
-  stream << slice.start() << ':' << slice.stop() << ':' << slice.step();
+  stream << slice.start() << ":" << slice.stop() << ":" << slice.step();
   return stream;
 }
 
@@ -31,12 +31,12 @@ std::ostream& operator<<(std::ostream& stream, const TensorIndex& tensor_index)
 }
 
 std::ostream& operator<<(std::ostream& stream, const std::vector<TensorIndex>& tensor_indices) {
-  stream << '(';
+  stream << "(";
   for (const auto i : c10::irange(tensor_indices.size())) {
     stream << tensor_indices[i];
     if (i < tensor_indices.size() - 1) stream << ", ";
   }
-  stream << ')';
+  stream << ")";
   return stream;
 }
 

aten/src/ATen/TensorNames.cpp

@@ -113,7 +113,7 @@ void TensorNames::checkUnique(const char* op_name) const {
 std::ostream& operator<<(std::ostream& out, const TensorName& tensorname) {
   out << tensorname.name_ << " (index ";
   out << tensorname.origin_idx_ << " of ";
-  out << tensorname.origin_ << ')';
+  out << tensorname.origin_ << ")";
   return out;
 }
 

aten/src/ATen/TensorUtils.cpp

@@ -13,9 +13,9 @@ std::ostream& operator<<(std::ostream & out, const TensorGeometryArg& t) {
   if (t.pos == 0) {
     // 0 is distinguished; it usually indicates 'self' or the return
     // tensor
-    out << '\'' << t.name << '\'';
+    out << "'" << t.name << "'";
   } else {
-    out << "argument #" << t.pos << " '" << t.name << '\'';
+    out << "argument #" << t.pos << " '" << t.name << "'";
   }
   return out;
 }
@@ -154,7 +154,7 @@ void checkSameGPU(CheckedFrom c, const TensorArg& t1, const TensorArg& t2) {
       oss << "Tensor for " << t2 << " is on CPU, ";
     }
     oss << "but expected " << ((!t1->is_cpu() && !t2->is_cpu()) ? "them" : "it")
-        << " to be on GPU (while checking arguments for " << c << ')';
+        << " to be on GPU (while checking arguments for " << c << ")";
     TORCH_CHECK(false, oss.str());
   }
   TORCH_CHECK(
@@ -199,7 +199,7 @@ void checkScalarTypes(CheckedFrom c, const TensorArg& t,
         i++;
       }
       oss << "; but got " << t->toString()
-          << " instead (while checking arguments for " << c << ')';
+          << " instead (while checking arguments for " << c << ")";
       TORCH_CHECK(false, oss.str());
     }
 }

aten/src/ATen/Version.cpp

@@ -43,8 +43,8 @@ std::string get_mkldnn_version() {
     // https://github.com/intel/ideep/issues/29
     {
       const dnnl_version_t* ver = dnnl_version();
-      ss << "Intel(R) MKL-DNN v" << ver->major << '.' << ver->minor << '.' << ver->patch
-         << " (Git Hash " << ver->hash << ')';
+      ss << "Intel(R) MKL-DNN v" << ver->major << "." << ver->minor << "." << ver->patch
+         << " (Git Hash " << ver->hash << ")";
     }
   #else
     ss << "MKLDNN not found";
@@ -81,7 +81,7 @@ std::string get_openmp_version() {
           break;
       }
       if (ver_str) {
-        ss << " (a.k.a. OpenMP " << ver_str << ')';
+        ss << " (a.k.a. OpenMP " << ver_str << ")";
       }
     }
   #else
@@ -135,38 +135,38 @@ std::string show_config() {
 
 #if defined(__GNUC__)
   {
-    ss << "  - GCC " << __GNUC__ << '.' << __GNUC_MINOR__ << '\n';
+    ss << "  - GCC " << __GNUC__ << "." << __GNUC_MINOR__ << "\n";
   }
 #endif
 
 #if defined(__cplusplus)
   {
-    ss << "  - C++ Version: " << __cplusplus << '\n';
+    ss << "  - C++ Version: " << __cplusplus << "\n";
   }
 #endif
 
 #if defined(__clang_major__)
   {
-    ss << "  - clang " << __clang_major__ << '.' << __clang_minor__ << '.' << __clang_patchlevel__ << '\n';
+    ss << "  - clang " << __clang_major__ << "." << __clang_minor__ << "." << __clang_patchlevel__ << "\n";
   }
 #endif
 
 #if defined(_MSC_VER)
   {
-    ss << "  - MSVC " << _MSC_FULL_VER << '\n';
+    ss << "  - MSVC " << _MSC_FULL_VER << "\n";
   }
 #endif
 
 #if AT_MKL_ENABLED()
-  ss << "  - " << get_mkl_version() << '\n';
+  ss << "  - " << get_mkl_version() << "\n";
 #endif
 
 #if AT_MKLDNN_ENABLED()
-  ss << "  - " << get_mkldnn_version() << '\n';
+  ss << "  - " << get_mkldnn_version() << "\n";
 #endif
 
 #ifdef _OPENMP
-  ss << "  - " << get_openmp_version() << '\n';
+  ss << "  - " << get_openmp_version() << "\n";
 #endif
 
 #if AT_BUILD_WITH_LAPACK()
@@ -183,7 +183,7 @@ std::string show_config() {
   ss << "  - Cross compiling on MacOSX\n";
 #endif
 
-  ss << "  - "<< used_cpu_capability() << '\n';
+  ss << "  - "<< used_cpu_capability() << "\n";
 
   if (hasCUDA()) {
     ss << detail::getCUDAHooks().showConfig();
@@ -200,10 +200,10 @@ std::string show_config() {
   ss << "  - Build settings: ";
   for (const auto& pair : caffe2::GetBuildOptions()) {
     if (!pair.second.empty()) {
-      ss << pair.first << '=' << pair.second << ", ";
+      ss << pair.first << "=" << pair.second << ", ";
     }
   }
-  ss << '\n';
+  ss << "\n";
 
   // TODO: do HIP
   // TODO: do XLA

aten/src/ATen/code_template.h

@@ -209,7 +209,7 @@ struct CodeTemplate {
   // to indent correctly in the context.
   void emitIndent(std::ostream& out, size_t indent) const {
     for ([[maybe_unused]] const auto i : c10::irange(indent)) {
-      out << ' ';
+      out << " ";
     }
   }
   void emitStringWithIndents(

aten/src/ATen/core/CachingHostAllocator.h

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

aten/src/ATen/core/Dimname.cpp

@@ -10,7 +10,7 @@ std::ostream& operator<<(std::ostream& out, const Dimname& dimname) {
   if (dimname.type() == NameType::WILDCARD) {
     out << "None";
   } else {
-    out << '\'' << dimname.symbol().toUnqualString() << '\'';
+    out << "'" << dimname.symbol().toUnqualString() << "'";
   }
   return out;
 }

aten/src/ATen/core/Range.cpp

@@ -5,7 +5,7 @@
 namespace at {
 
 std::ostream& operator<<(std::ostream& out, const Range& range) {
-  out << "Range[" << range.begin << ", " << range.end << ']';
+  out << "Range[" << range.begin << ", " << range.end << "]";
   return out;
 }
 

aten/src/ATen/core/Tensor.cpp

@@ -71,7 +71,7 @@ void TensorBase::enforce_invariants() {
 
 void TensorBase::print() const {
   if (defined()) {
-    std::cerr << '[' << toString() << ' ' << sizes() << ']' << '\n';
+    std::cerr << "[" << toString() << " " << sizes() << "]" << '\n';
   } else {
     std::cerr << "[UndefinedTensor]" << '\n';
   }

aten/src/ATen/core/TensorAccessor.h

@@ -1,6 +1,5 @@
 #pragma once
 
-#include <torch/headeronly/core/TensorAccessor.h>
 #include <c10/macros/Macros.h>
 #include <c10/util/ArrayRef.h>
 #include <c10/util/Deprecated.h>
@@ -12,37 +11,252 @@
 
 namespace at {
 
-using torch::headeronly::DefaultPtrTraits;
+// 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;
+};
+
 #if defined(__CUDACC__) || defined(__HIPCC__)
-  using torch::headeronly::RestrictPtrTraits;
+template <typename T>
+struct RestrictPtrTraits {
+  typedef T* __restrict__ PtrType;
+};
 #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>
-using TensorAccessorBase = torch::headeronly::detail::TensorAccessorBase<c10::IntArrayRef, T, N, PtrTraits, index_t>;
+class TensorAccessorBase {
+public:
+  typedef typename PtrTraits<T>::PtrType PtrType;
 
+  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>
-using TensorAccessor = torch::headeronly::detail::TensorAccessor<c10::IntArrayRef, T, N, PtrTraits, index_t>;
+class TensorAccessor : public TensorAccessorBase<T,N,PtrTraits,index_t> {
+public:
+  typedef typename PtrTraits<T>::PtrType PtrType;
 
-namespace detail {
+  C10_HOST_DEVICE TensorAccessor(
+      PtrType data_,
+      const index_t* sizes_,
+      const index_t* strides_)
+      : TensorAccessorBase<T, N, PtrTraits, index_t>(data_,sizes_,strides_) {}
 
-template <size_t N, typename index_t>
-struct IndexBoundsCheck {
-    IndexBoundsCheck(index_t i) {
-      TORCH_CHECK_INDEX(
+  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(
         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>
-using GenericPackedTensorAccessorBase = torch::headeronly::detail::GenericPackedTensorAccessorBase<detail::IndexBoundsCheck<N, index_t>, T, N, PtrTraits, index_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_);
+  }
+};
 
-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

@@ -245,9 +245,6 @@ 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/core/Vitals.cpp

@@ -9,8 +9,8 @@ APIVitals VitalsAPI;
 
 std::ostream& operator<<(std::ostream& os, TorchVital const& tv) {
   for (const auto& m : tv.attrs) {
-    os << "[TORCH_VITAL] " << tv.name << '.' << m.first << "\t\t "
-       << m.second.value << '\n';
+    os << "[TORCH_VITAL] " << tv.name << "." << m.first << "\t\t "
+       << m.second.value << "\n";
   }
   return os;
 }

aten/src/ATen/core/alias_info.h

@@ -100,18 +100,18 @@ inline bool operator==(const AliasInfo& lhs, const AliasInfo& rhs) {
 
 // this does match the way things are represented in the schema
 inline std::ostream& operator<<(std::ostream& out, const AliasInfo& aliasInfo) {
-  out << '(';
+  out << "(";
   bool first = true;
   for (const auto& set : aliasInfo.beforeSets()) {
     if (first) {
       first = false;
     } else {
-      out << '|';
+      out << "|";
     }
     out << set.toUnqualString();
   }
   if (aliasInfo.isWrite()) {
-    out << '!';
+    out << "!";
   }
   if (aliasInfo.beforeSets() != aliasInfo.afterSets()) {
     out << " -> ";
@@ -120,12 +120,12 @@ inline std::ostream& operator<<(std::ostream& out, const AliasInfo& aliasInfo) {
       if (first) {
         first = false;
       } else {
-        out << '|';
+        out << "|";
       }
       out << set.toUnqualString();
     }
   }
-  out << ')';
+  out << ")";
   return out;
 }
 } // namespace c10

aten/src/ATen/core/blob.h

@@ -198,7 +198,7 @@ inline void swap(Blob& lhs, Blob& rhs)  noexcept {
 }
 
 inline std::ostream& operator<<(std::ostream& out, const Blob& v) {
-  return out << "Blob[" << v.TypeName() << ']';
+  return out << "Blob[" << v.TypeName() << "]";
 }
 
 } // namespace caffe2

aten/src/ATen/core/class_type.cpp

@@ -456,8 +456,8 @@ bool ClassType::isSubtypeOfExt(const Type& rhs, std::ostream* why_not) const {
           *why_not << "Method on class '" << repr_str()
                    << "' (1) is not compatible with interface '"
                    << rhs.repr_str() << "' (2)\n"
-                   << "  (1) " << self_method->getSchema() << '\n'
-                   << "  (2) " << schema << '\n';
+                   << "  (1) " << self_method->getSchema() << "\n"
+                   << "  (2) " << schema << "\n";
         }
         return false;
       }

aten/src/ATen/core/class_type.h

@@ -100,7 +100,7 @@ struct TORCH_API ClassType : public NamedType {
   std::string repr_str() const override {
     std::stringstream ss;
     ss << str()
-       << " (of Python compilation unit at: " << compilation_unit().get() << ')';
+       << " (of Python compilation unit at: " << compilation_unit().get() << ")";
     return ss.str();
   }
 

aten/src/ATen/core/dispatch/DispatchKeyExtractor.cpp

@@ -58,12 +58,12 @@ std::string DispatchKeyExtractor::dumpState() const {
   std::ostringstream oss;
   for (const auto i : c10::irange(c10::utils::bitset::NUM_BITS())) {
     if (dispatch_arg_indices_reverse_.get(i)) {
-      oss << '1';
+      oss << "1";
     } else {
-      oss << '0';
+      oss << "0";
     }
   }
-  oss << ' ' << nonFallthroughKeys_ << '\n';
+  oss << " " << nonFallthroughKeys_ << "\n";
   return oss.str();
 }
 

aten/src/ATen/core/dispatch/Dispatcher.cpp

@@ -69,8 +69,8 @@ private:
 
 void _print_dispatch_trace(const std::string& label, const std::string& op_name, const DispatchKeySet& dispatchKeySet) {
   auto nesting_value = dispatch_trace_nesting_value();
-  for (int64_t i = 0; i < nesting_value; ++i) std::cerr << ' ';
-  std::cerr << label << " op=[" << op_name << "], key=[" << toString(dispatchKeySet.highestPriorityTypeId()) << ']' << std::endl;
+  for (int64_t i = 0; i < nesting_value; ++i) std::cerr << " ";
+  std::cerr << label << " op=[" << op_name << "], key=[" << toString(dispatchKeySet.highestPriorityTypeId()) << "]" << std::endl;
 }
 } // namespace detail
 

aten/src/ATen/core/dispatch/OperatorEntry.cpp

@@ -570,7 +570,7 @@ void OperatorEntry::checkInvariants() const {
 
 std::string OperatorEntry::listAllDispatchKeys() const {
   std::ostringstream str;
-  str << '[';
+  str << "[";
 
   bool has_kernels = false;
   for (auto k : allDispatchKeysInFullSet()) {
@@ -584,7 +584,7 @@ std::string OperatorEntry::listAllDispatchKeys() const {
     str << k;
     has_kernels = true;
   }
-  str << ']';
+  str << "]";
   return str.str();
 }
 
@@ -683,12 +683,12 @@ void OperatorEntry::setReportErrorCallback_(std::unique_ptr<c10::SafePyObject> c
 // This WON'T report backend fallbacks.
 std::string OperatorEntry::dumpState() const {
   std::ostringstream oss;
-  oss << "name: " << name_ << '\n';
+  oss << "name: " << name_ << "\n";
   if (schema_) {
-    oss << "schema: " << schema_->schema << '\n';
-    oss << "debug: " << schema_->debug << '\n';
+    oss << "schema: " << schema_->schema << "\n";
+    oss << "debug: " << schema_->debug << "\n";
     oss << "alias analysis kind: " << toString(schema_->schema.aliasAnalysis())
-        << (schema_->schema.isDefaultAliasAnalysisKind() ? " (default)" : "") << '\n';
+        << (schema_->schema.isDefaultAliasAnalysisKind() ? " (default)" : "") << "\n";
   } else {
     oss << "schema: (none)\n";
   }

aten/src/ATen/core/function_schema.cpp

@@ -7,7 +7,7 @@
 namespace c10 {
 
 void FunctionSchema::dump() const {
-  std::cout << *this << '\n';
+  std::cout << *this << "\n";
 }
 
 const std::vector<Argument>& FunctionSchema::getCorrectList(SchemaArgType type) const {
@@ -210,9 +210,9 @@ std::ostream& operator<<(std::ostream& out, const FunctionSchema& schema) {
 
   out << schema.name();
   if (!schema.overload_name().empty()) {
-    out << '.' << schema.overload_name();
+    out << "." << schema.overload_name();
   }
-  out << '(';
+  out << "(";
 
   bool seen_kwarg_only = false;
   for (const auto i : c10::irange(schema.arguments().size())) {
@@ -273,7 +273,7 @@ std::ostream& operator<<(std::ostream& out, const FunctionSchema& schema) {
   }
 
   if (need_paren) {
-    out << '(';
+    out << "(";
   }
   for (const auto i : c10::irange(returns.size())) {
     if (i > 0) {
@@ -288,7 +288,7 @@ std::ostream& operator<<(std::ostream& out, const FunctionSchema& schema) {
     out << "...";
   }
   if (need_paren) {
-    out << ')';
+    out << ")";
   }
   return out;
 }
@@ -471,7 +471,7 @@ bool FunctionSchema::isForwardCompatibleWith(
     if (!arguments().at(i).isForwardCompatibleWith(old.arguments().at(i))) {
       if (why_not) {
         why_not
-            << '\'' << arguments().at(i).name() << '\''
+            << "'" << arguments().at(i).name() << "'"
             << " is not forward compatible with the older version of the schema";
       }
       return false;
@@ -511,7 +511,7 @@ bool FunctionSchema::isForwardCompatibleWith(
              .isForwardCompatibleWith(old.arguments().at(i))) {
       if (why_not) {
         why_not << "Out argument '"
-                << '\'' << arguments().at(i).name()
+                << "'" << arguments().at(i).name()
                 << " is not FC with the older version of the schema";
       }
       return false;

aten/src/ATen/core/function_schema.h

@@ -571,7 +571,7 @@ inline std::ostream& operator<<(std::ostream& out, const Argument& arg) {
     if (arg.N()) {
         N = std::to_string(*arg.N());
     }
-    out << '[' << N << ']';
+    out << "[" << N << "]";
   } else {
     out << unopt_type->str();
   }
@@ -582,15 +582,15 @@ inline std::ostream& operator<<(std::ostream& out, const Argument& arg) {
   }
 
   if (is_opt) {
-    out << '?';
+    out << "?";
   }
 
   if (!arg.name().empty()) {
-    out << ' ' << arg.name();
+    out << " " << arg.name();
   }
 
   if (arg.default_value()) {
-    out << '=';
+    out << "=";
     if ((type->kind() == c10::TypeKind::StringType ||
         unopt_type->kind() == c10::TypeKind::StringType) &&
         arg.default_value().value().isString()) {

aten/src/ATen/core/ivalue.cpp

@@ -66,7 +66,7 @@ bool operator==(const ivalue::Tuple& lhs, const ivalue::Tuple& rhs) {
 }
 
 std::ostream& operator<<(std::ostream& out, const ivalue::EnumHolder& v) {
-  out << v.qualifiedClassName() << '.' << v.name();
+  out << v.qualifiedClassName() << "." << v.name();
   return out;
 }
 
@@ -526,7 +526,7 @@ std::ostream& printMaybeAnnotatedList(
       !elementTypeCanBeInferredFromMembers(list_elem_type)) {
     out << "annotate(" << the_list.type<c10::Type>()->annotation_str() << ", ";
     printList(out, the_list.toListRef(), "[", "]", formatter);
-    out << ')';
+    out << ")";
     return out;
   } else {
     return printList(out, the_list.toListRef(), "[", "]", formatter);
@@ -538,7 +538,7 @@ std::ostream& printDict(
     std::ostream& out,
     const Dict& v,
     const IValueFormatter& formatter) {
-  out << '{';
+  out << "{";
 
   bool first = true;
   for (const auto& pair : v) {
@@ -552,7 +552,7 @@ std::ostream& printDict(
     first = false;
   }
 
-  out << '}';
+  out << "}";
   return out;
 }
 }
@@ -565,8 +565,8 @@ static std::ostream& printMaybeAnnotatedDict(
   auto value_type = the_dict.type()->castRaw<DictType>()->getValueType();
   if (the_dict.toGenericDict().empty() ||
       !elementTypeCanBeInferredFromMembers(value_type)) {
-    out << "annotate(" << the_dict.type<c10::Type>()->annotation_str() << ',';
-    printDict(out, the_dict.toGenericDict(), formatter) << ')';
+    out << "annotate(" << the_dict.type<c10::Type>()->annotation_str() << ",";
+    printDict(out, the_dict.toGenericDict(), formatter) << ")";
   } else {
     return printDict(out, the_dict.toGenericDict(), formatter);
   }
@@ -577,7 +577,7 @@ static std::ostream& printComplex(std::ostream & out, const IValue & v) {
   c10::complex<double> d = v.toComplexDouble();
   IValue real(d.real()), imag(std::abs(d.imag()));
   auto sign = d.imag() >= 0 ? '+' : '-';
-  return out << real << sign << imag << 'j';
+  return out << real << sign << imag << "j";
 }
 
 std::ostream& IValue::repr(
@@ -605,9 +605,9 @@ std::ostream& IValue::repr(
         if (static_cast<double>(i) == d) {
           // -0.0 (signed zero) needs to be parsed as -0.
           if (i == 0 && std::signbit(d)) {
-            return out << '-' << i << '.';
+            return out << "-" << i << ".";
           }
-          return out << i << '.';
+          return out << i << ".";
         }
       }
       auto orig_prec = out.precision();
@@ -643,20 +643,20 @@ std::ostream& IValue::repr(
       device_stream << v.toDevice();
       out << "torch.device(";
       c10::printQuotedString(out, device_stream.str());
-      return out << ')';
+      return out << ")";
     }
     case IValue::Tag::Generator: {
       auto generator = v.toGenerator();
       out << "torch.Generator(device=";
       c10::printQuotedString(out, generator.device().str());
-      out << ", seed=" << generator.current_seed() << ')';
+      out << ", seed=" << generator.current_seed() << ")";
       return out;
     }
     case IValue::Tag::GenericDict:
       return printMaybeAnnotatedDict(out, v, formatter);
     case IValue::Tag::Enum: {
       auto enum_holder = v.toEnumHolder();
-      return out << enum_holder->qualifiedClassName() << '.' <<
+      return out << enum_holder->qualifiedClassName() << "." <<
           enum_holder->name();
     }
     case IValue::Tag::Object: {
@@ -801,7 +801,7 @@ std::ostream& operator<<(std::ostream & out, const IValue & v) {
       if (c == FP_NORMAL || c == FP_ZERO) {
         int64_t i = static_cast<int64_t>(d);
         if (static_cast<double>(i) == d) {
-          return out << i << '.';
+          return out << i << ".";
         }
       }
       auto orig_prec = out.precision();
@@ -852,7 +852,7 @@ std::ostream& operator<<(std::ostream & out, const IValue & v) {
       return printDict(out, v.toGenericDict(), formatter);
     case IValue::Tag::PyObject: {
       auto py_obj = v.toPyObject();
-      return out << "<PyObject at" << py_obj << '>';
+      return out << "<PyObject at" << py_obj << ">";
     }
     case IValue::Tag::Generator:
       return out << "Generator";
@@ -862,22 +862,22 @@ std::ostream& operator<<(std::ostream & out, const IValue & v) {
       // TODO we should attempt to call __str__ if the object defines it.
       auto obj = v.toObject();
       // print this out the way python would do it
-      return out << '<' << obj->name() << " object at " << obj.get() << '>';
+      return out << "<" << obj->name() << " object at " << obj.get() << ">";
     }
     case IValue::Tag::Enum: {
       auto enum_holder = v.toEnumHolder();
-      return out << "Enum<" << enum_holder->unqualifiedClassName() << '.' <<
-          enum_holder->name() << '>';
+      return out << "Enum<" << enum_holder->unqualifiedClassName() << "." <<
+          enum_holder->name() << ">";
     }
 
   }
-  return out << "<Invalid IValue tag=" << std::to_string(static_cast<uint32_t>(v.tag)) << '>';
+  return out << "<Invalid IValue tag=" << std::to_string(static_cast<uint32_t>(v.tag)) << ">";
 }
 
 #undef TORCH_FORALL_TAGS
 
 void IValue::dump() const {
-  std::cout << *this << '\n';
+  std::cout << *this << "\n";
 }
 
 std::shared_ptr<ClassType> ivalue::Object::type() const {
@@ -1050,7 +1050,7 @@ c10::intrusive_ptr<ivalue::Object> ivalue::Object::deepcopy(
       std::stringstream err;
       err << "Cannot serialize custom bound C++ class";
       if (auto qualname = type()->name()) {
-        err << ' ' << qualname->qualifiedName();
+        err << " " << qualname->qualifiedName();
       }
       err << ". Please define serialization methods via def_pickle() for "
             "this class.";

aten/src/ATen/core/ivalue.h

@@ -18,8 +18,6 @@
 #include <unordered_set>
 #include <utility>
 
-C10_DIAGNOSTIC_PUSH_AND_IGNORED_IF_DEFINED("-Wswitch-default")
-
 namespace torch {
 class TORCH_API CustomClassHolder : public c10::intrusive_ptr_target {};
 namespace jit {
@@ -1632,6 +1630,4 @@ struct TORCH_API WeakOrStrongTypePtr {
 
 } // namespace c10
 
-C10_DIAGNOSTIC_POP()
-
 #include <ATen/core/ivalue_inl.h> // IWYU pragma: keep

aten/src/ATen/core/ivalue_inl.h

@@ -29,8 +29,6 @@
 #include <c10/util/intrusive_ptr.h>
 #include <c10/util/irange.h>
 
-C10_DIAGNOSTIC_PUSH_AND_IGNORED_IF_DEFINED("-Wswitch-default")
-
 namespace torch {
 namespace jit {
 struct Function;
@@ -2569,5 +2567,3 @@ TypePtr IValue::type() const {
 }
 
 } // namespace c10
-
-C10_DIAGNOSTIC_POP()

aten/src/ATen/core/jit_type.h

@@ -211,7 +211,7 @@ struct TORCH_API OptionalType : public UnionType {
 
   std::string str() const override {
     std::stringstream ss;
-    ss << getElementType()->str() << '?';
+    ss << getElementType()->str() << "?";
     return ss.str();
   }
 
@@ -240,7 +240,7 @@ struct TORCH_API OptionalType : public UnionType {
 
   std::string annotation_str_impl(const TypePrinter& printer = nullptr) const override {
     std::stringstream ss;
-    ss << "Optional[" << getElementType()->annotation_str(printer) << ']';
+    ss << "Optional[" << getElementType()->annotation_str(printer) << "]";
     return ss.str();
   }
 };
@@ -906,7 +906,7 @@ struct TORCH_API ListType
 
   std::string annotation_str_impl(const TypePrinter& printer = nullptr) const override {
     std::stringstream ss;
-    ss << "List[" << getElementType()->annotation_str(printer) << ']';
+    ss << "List[" << getElementType()->annotation_str(printer) << "]";
     return ss.str();
   }
 };
@@ -946,7 +946,7 @@ struct TORCH_API DictType : public SharedType {
   std::string str() const override {
     std::stringstream ss;
     ss << "Dict(" << getKeyType()->str() << ", " << getValueType()->str()
-       << ')';
+       << ")";
     return ss.str();
   }
 
@@ -1018,7 +1018,7 @@ struct TORCH_API FutureType
 
   std::string str() const override {
     std::stringstream ss;
-    ss << "Future(" << getElementType()->str() << ')';
+    ss << "Future(" << getElementType()->str() << ")";
     return ss.str();
   }
   TypePtr createWithContained(
@@ -1041,7 +1041,7 @@ struct TORCH_API FutureType
 
   std::string annotation_str_impl(const TypePrinter& printer = nullptr) const override {
     std::stringstream ss;
-    ss << "Future[" << getElementType()->annotation_str(printer) << ']';
+    ss << "Future[" << getElementType()->annotation_str(printer) << "]";
     return ss.str();
   }
 };
@@ -1060,7 +1060,7 @@ struct TORCH_API AwaitType
 
   std::string str() const override {
     std::stringstream ss;
-    ss << "Await(" << getElementType()->str() << ')';
+    ss << "Await(" << getElementType()->str() << ")";
     return ss.str();
   }
   TypePtr createWithContained(
@@ -1083,7 +1083,7 @@ struct TORCH_API AwaitType
 
   std::string annotation_str_impl(const TypePrinter& printer = nullptr) const override {
     std::stringstream ss;
-    ss << "Await[" << getElementType()->annotation_str(printer) << ']';
+    ss << "Await[" << getElementType()->annotation_str(printer) << "]";
     return ss.str();
   }
 };
@@ -1102,7 +1102,7 @@ struct TORCH_API RRefType
 
   std::string str() const override {
     std::stringstream ss;
-    ss << "RRef(" << getElementType()->str() << ')';
+    ss << "RRef(" << getElementType()->str() << ")";
     return ss.str();
   }
   TypePtr createWithContained(
@@ -1115,7 +1115,7 @@ struct TORCH_API RRefType
 
   std::string annotation_str_impl(const TypePrinter& printer = nullptr) const override {
     std::stringstream ss;
-    ss << "RRef[" << getElementType()->annotation_str(printer) << ']';
+    ss << "RRef[" << getElementType()->annotation_str(printer) << "]";
     return ss.str();
   }
 };

aten/src/ATen/core/operator_name.cpp

@@ -11,7 +11,7 @@ std::string toString(const OperatorName& opName) {
 std::ostream& operator<<(std::ostream& os, const OperatorName& opName) {
   os << opName.name;
   if (!opName.overload_name.empty()) {
-    os << '.' << opName.overload_name;
+    os << "." << opName.overload_name;
   }
   return os;
 }

aten/src/ATen/core/tensor_type.cpp

@@ -65,7 +65,7 @@ VaryingShape<T> VaryingShape<T>::merge(const VaryingShape<T>& other) const {
 
 template <typename T>
 std::ostream& operator<<(std::ostream& out, const VaryingShape<T>& vs) {
-  out << '(';
+  out << "(";
   if (!vs.size()) {
     out << "*)";
     return out;
@@ -79,10 +79,10 @@ std::ostream& operator<<(std::ostream& out, const VaryingShape<T>& vs) {
     if (v.has_value()) {
       out << v.value();
     } else {
-      out << '*';
+      out << "*";
     }
   }
-  out << ')';
+  out << ")";
   return out;
 }
 
@@ -105,7 +105,7 @@ std::ostream& operator<<(
   }
   auto sizes_opt = ss.sizes();
 
-  os << '(';
+  os << "(";
   for (size_t i = 0; i < rank_opt.value(); i++) {
     if (i > 0) {
       os << ", ";
@@ -113,10 +113,10 @@ std::ostream& operator<<(
     if(sizes_opt.has_value() && sizes_opt.value()[i].is_static()) {
       os << sizes_opt.value()[i];
     } else {
-      os << '*';
+      os << "*";
     }
   }
-  os << ')';
+  os << ")";
 
   return os;
 }
@@ -131,17 +131,17 @@ std::ostream& operator<<(std::ostream& os, const ShapeSymbol& s) {
 }
 
 std::ostream& operator<<(std::ostream& os, const Stride& s) {
-  os << '{';
+  os << "{";
   if (s.stride_index_.has_value()) {
     os << *s.stride_index_;
   } else {
-    os << '*';
+    os << "*";
   }
-  os << ':';
+  os << ":";
   if (s.stride_.has_value()) {
     os << *s.stride_;
   } else {
-    os << '*';
+    os << "*";
   }
   os << '}';
   return os;

aten/src/ATen/core/type.cpp

@@ -67,7 +67,7 @@ std::ostream& operator<<(std::ostream & out, const Type & t) {
       bool has_valid_strides_info = ndim > 0 &&
           value->strides().isComplete() && value->strides().size() == ndim;
 
-      out << '(';
+      out << "(";
       size_t i = 0;
       bool symbolic = type_verbosity() == TypeVerbosity::Symbolic;
       for (i = 0; i < *ndim; ++i) {
@@ -79,7 +79,7 @@ std::ostream& operator<<(std::ostream & out, const Type & t) {
         } else if (symbolic) {
           out << value->symbolic_sizes().at(i);
         } else {
-          out << '*';
+          out << "*";
         }
       }
       if (has_valid_strides_info &&
@@ -91,7 +91,7 @@ std::ostream& operator<<(std::ostream & out, const Type & t) {
           }
           out << value->strides()[i].value();
         }
-        out << ']';
+        out << "]";
       }
       if (type_verbosity() >= TypeVerbosity::Full) {
         if (value->requiresGrad()) {
@@ -107,12 +107,12 @@ std::ostream& operator<<(std::ostream & out, const Type & t) {
           out << "device=" << *value->device();
         }
       }
-      out << ')';
+      out << ")";
     } else {
       if (type_verbosity() >= TypeVerbosity::Full) {
         size_t i = 0;
         if (value->requiresGrad()) {
-          out << '('
+          out << "("
               << "requires_grad=" << *value->requiresGrad();
           i++;
         }
@@ -120,7 +120,7 @@ std::ostream& operator<<(std::ostream & out, const Type & t) {
           out << ((i++ > 0) ? ", " : "(") << "device=" << *value->device();
         }
         if (i > 0) {
-          out << ')';
+          out << ")";
         }
       }
     }
@@ -133,18 +133,18 @@ std::ostream& operator<<(std::ostream & out, const Type & t) {
     out << *prim << "[]";
   } else if (t.kind() == TypeKind::OptionalType) {
     auto prim = t.castRaw<OptionalType>()->getElementType();
-    out << *prim << '?';
+    out << *prim << "?";
   } else if(t.kind() == TypeKind::FutureType) {
     auto elem = t.castRaw<FutureType>()->getElementType();
-    out << "Future[" << *elem << ']';
+    out << "Future[" << *elem << "]";
   } else if(t.kind() == TypeKind::RRefType) {
     auto elem = t.castRaw<RRefType>()->getElementType();
-    out << "RRef[" << *elem << ']';
+    out << "RRef[" << *elem << "]";
   } else if(auto tup = t.cast<TupleType>()) {
     if (tup->schema()) {
       out << "NamedTuple";
     }
-    out << '(';
+    out << "(";
     for(size_t i = 0; i < tup->elements().size(); ++i) {
       if(i > 0)
         out << ", ";
@@ -160,7 +160,7 @@ std::ostream& operator<<(std::ostream & out, const Type & t) {
         out << *(tup->elements()[i]);
       }
     }
-    out << ')';
+    out << ")";
   } else if (t.kind() == TypeKind::FunctionType) {
     out << "Function";
   } else {
@@ -475,7 +475,7 @@ std::optional<TypePtr> unifyTypeList(
       why_not << "Could not unify type list since element " << i << " of type "
               << elements.at(i)->repr_str()
               << " did not match the types before it ("
-              << ret_type->repr_str() << ')';
+              << ret_type->repr_str() << ")";
       return std::nullopt;
     }
     ret_type = *maybe_unified;
@@ -907,13 +907,13 @@ std::string TupleType::str() const {
     // NOLINTNEXTLINE(bugprone-unchecked-optional-access)
     ss << name()->qualifiedName();
   } else {
-    ss << '(';
+    ss << "(";
     for(size_t i = 0; i < elements().size(); ++i) {
       if(i > 0)
         ss << ", ";
       ss << elements()[i]->str();
     }
-    ss << ')';
+    ss << ")";
   }
   return ss.str();
 }
@@ -1003,8 +1003,8 @@ bool InterfaceType::isSubTypeImpl(
           *why_not << "Method on interface '" << lhs.repr_str()
                    << "' (1) is not compatible with interface '"
                    << rhs.repr_str() << "' (2)\n"
-                   << "  (1) " << *self_schema << '\n'
-                   << "  (2) " << schema << '\n';
+                   << "  (1) " << *self_schema << "\n"
+                   << "  (2) " << schema << "\n";
           return false;
         }
         return false;
@@ -1078,7 +1078,7 @@ SymbolicShape SymbolicShape::merge(const SymbolicShape& other) const {
 }
 
 void SymbolicShape::dump() const {
-  std::cout << *this << '\n';
+  std::cout << *this << "\n";
 }
 
 bool EnumType::isSubtypeOfExt(const Type& rhs, std::ostream* why_not) const {

aten/src/ATen/core/union_type.cpp

@@ -205,9 +205,9 @@ UnionType::UnionType(std::vector<TypePtr> reference, TypeKind kind) : SharedType
     for (const auto i : c10::irange(reference.size())) {
       msg << reference[i]->repr_str();
       if (i > 0) {
-        msg << ',';
+        msg << ",";
       }
-      msg << ' ';
+      msg << " ";
     }
     msg << "} has the single type " << types_[0]->repr_str()
          << ". Use the common supertype instead of creating a Union"

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

@@ -223,62 +223,6 @@ 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/cpu/vec/vec128/vec128_float_neon.h

@@ -11,8 +11,6 @@
 #include <sleef.h>
 #endif
 
-C10_DIAGNOSTIC_PUSH_AND_IGNORED_IF_DEFINED("-Wswitch-default")
-
 // Sleef offers vectorized versions of some transcedentals
 // such as sin, cos, tan etc..
 // However for now opting for STL, since we are not building
@@ -652,5 +650,3 @@ inline Vectorized<float> Vectorized<float>::erf() const {
 
 } // namespace CPU_CAPABILITY
 } // namespace at::vec
-
-C10_DIAGNOSTIC_POP()

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

@@ -80,7 +80,7 @@ std::ostream& operator<<(std::ostream& stream, const Vectorized<T>& vec) {
     }
     stream << buf[i];
   }
-  stream << ']';
+  stream << "]";
   return stream;
 }
 

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

@@ -55,7 +55,7 @@ std::ostream& operator<<(std::ostream& stream, const Vectorized<T>& vec) {
     }
     stream << buf[i];
   }
-  stream << ']';
+  stream << "]";
   return stream;
 }
 

aten/src/ATen/cuda/CUDAContextLight.h

@@ -3,7 +3,6 @@
 
 #include <cstdint>
 #include <map>
-#include <shared_mutex>
 
 #include <cuda_runtime_api.h>
 #include <cusparse.h>
@@ -89,13 +88,8 @@ TORCH_CUDA_CPP_API cublasHandle_t getCurrentCUDABlasHandle();
 TORCH_CUDA_CPP_API cublasLtHandle_t getCurrentCUDABlasLtHandle();
 
 TORCH_CUDA_CPP_API void clearCublasWorkspaces();
-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 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();
 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

@@ -1,7 +1,6 @@
 #include <ATen/cuda/CUDAGeneratorImpl.h>
 #include <ATen/cuda/CUDAGraph.h>
 #include <ATen/cuda/Exceptions.h>
-#include <ATen/cuda/MemPool.h>
 #include <ATen/Functions.h>
 #include <c10/cuda/CUDAFunctions.h>
 
@@ -14,7 +13,7 @@ static bool _cuda_graphs_debug = false;
 MempoolId_t graph_pool_handle() {
   // Sets just the second value, to distinguish it from MempoolId_ts created from
   // cudaStreamGetCaptureInfo id_s in capture_begin.
-  return at::cuda::MemPool::graph_pool_handle();
+  return c10::cuda::MemPool::graph_pool_handle();
 }
 
 /**
@@ -91,7 +90,7 @@ void CUDAGraph::capture_begin(MempoolId_t pool/*=0*/, cudaStreamCaptureMode capt
   } else {
     // User did not ask us to share a mempool. Create graph pool handle using is_user_created=false.
     // Sets just the first value, to distinguish it from MempoolId_ts created by graph_pool_handle().
-    mempool_id_ = at::cuda::MemPool::graph_pool_handle(false);
+    mempool_id_ = c10::cuda::MemPool::graph_pool_handle(false);
     TORCH_INTERNAL_ASSERT(mempool_id_.first > 0);
   }
 
@@ -175,24 +174,17 @@ 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)
-    // 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));
+#if (defined(CUDA_VERSION) && CUDA_VERSION >= 12000)
+    AT_CUDA_CHECK(cudaGraphInstantiate(&graph_exec_, graph_, 0));
 #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
 }
 
@@ -107,27 +107,19 @@ using CuBlasPoolType = DeviceThreadHandlePool<cublasHandle_t, createCublasHandle
 
 } // namespace
 
-WorkspaceMapWithMutex& cublas_handle_stream_to_workspace() {
-  static auto& instance = *new WorkspaceMapWithMutex;
+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>;
   return instance;
 }
 
-WorkspaceMapWithMutex& cublaslt_handle_stream_to_workspace() {
-  static auto& instance = *new WorkspaceMapWithMutex;
+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>;
   return instance;
 }
 
 void clearCublasWorkspaces() {
-  {
-    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();
-  }
+  cublas_handle_stream_to_workspace().clear();
+  cublaslt_handle_stream_to_workspace().clear();
 }
 
 size_t parseChosenWorkspaceSize() {
@@ -241,38 +233,6 @@ 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;
@@ -281,10 +241,8 @@ 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 = 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());
+    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());
     return workspace_it->second.mutable_get();
   }
 #endif
@@ -292,29 +250,11 @@ 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 = 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();
+  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()});
   }
+  return workspace_it->second.mutable_get();
 }
 
 cublasHandle_t getCurrentCUDABlasHandle() {
@@ -358,8 +298,13 @@ 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
-  setWorkspaceForHandle(handle, stream);
-
+  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()));
 #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/cuda/MemPool.cpp

@@ -1,69 +0,0 @@
-#include <ATen/core/CachingHostAllocator.h>
-#include <ATen/cuda/MemPool.h>
-
-namespace at::cuda {
-
-// uid_ is incremented when a user creates a MemPool,
-// for example: using graph_pool_handle() or c10::cuda::MemPool().
-//
-// uuid_ is incremented when CUDAGraph creates a MemPool
-// as a result of a user not providing a pool.
-//
-// MempoolId_t of {0, 0} is used to denote when no MemPool has been
-// passed to a function, either by user or CUDAGraphs. For example,
-// default value of MempoolId_t for capture_begin function is {0, 0}.
-// That's why uid_ and uuid_ start at 1.
-std::atomic<CaptureId_t> MemPool::uid_{1};
-std::atomic<CaptureId_t> MemPool::uuid_{1};
-
-MemPool::MemPool(
-    CUDACachingAllocator::CUDAAllocator* allocator,
-    bool is_user_created,
-    bool use_on_oom)
-    : allocator_(allocator), is_user_created_(is_user_created) {
-  if (is_user_created_) {
-    id_ = {0, uid_++};
-  } else {
-    id_ = {uuid_++, 0};
-  }
-  device_ = c10::cuda::current_device();
-  CUDACachingAllocator::createOrIncrefPool(device_, id_, allocator);
-  if (use_on_oom) {
-    CUDACachingAllocator::setUseOnOOM(device_, id_);
-  }
-}
-
-MemPool::~MemPool() {
-  // TORCH_INTERNAL_ASSERT(use_count() == 1);
-  // We used to assert that TORCH_INTERNAL_ASSERT(use_count() == 1);
-  // However, this assertion is not true if a memory pool is shared
-  // with a cuda graph. That CUDAGraph will increase the use count
-  // until it is reset.
-  CUDACachingAllocator::releasePool(device_, id_);
-  c10::cuda::CUDACachingAllocator::emptyCache(id_);
-}
-
-MempoolId_t MemPool::id() {
-  return id_;
-}
-
-CUDACachingAllocator::CUDAAllocator* MemPool::allocator() {
-  return allocator_;
-}
-
-int MemPool::use_count() {
-  return CUDACachingAllocator::getPoolUseCount(device_, id_);
-}
-
-c10::DeviceIndex MemPool::device() {
-  return device_;
-}
-
-MempoolId_t MemPool::graph_pool_handle(bool is_user_created) {
-  if (is_user_created) {
-    return {0, uid_++};
-  }
-  return {uuid_++, 0};
-}
-
-} // namespace at::cuda

aten/src/ATen/cuda/MemPool.h

@@ -1,44 +0,0 @@
-#pragma once
-
-#include <c10/core/Allocator.h>
-#include <c10/cuda/CUDACachingAllocator.h>
-
-namespace at::cuda {
-
-// Keep BC only
-using c10::CaptureId_t;
-using c10::MempoolId_t;
-
-// MemPool represents a pool of memory in a caching allocator. Currently,
-// it's just the ID of the pool object maintained in the CUDACachingAllocator.
-//
-// An allocator pointer can be passed to the MemPool to define how the
-// allocations should be done in the pool. For example: using a different
-// system allocator such as ncclMemAlloc.
-struct TORCH_CUDA_CPP_API MemPool {
-  MemPool(
-      c10::cuda::CUDACachingAllocator::CUDAAllocator* allocator = nullptr,
-      bool is_user_created = true,
-      bool use_on_oom = false);
-  MemPool(const MemPool&) = delete;
-  MemPool(MemPool&&) = default;
-  MemPool& operator=(const MemPool&) = delete;
-  MemPool& operator=(MemPool&&) = default;
-  ~MemPool();
-
-  MempoolId_t id();
-  c10::cuda::CUDACachingAllocator::CUDAAllocator* allocator();
-  int use_count();
-  c10::DeviceIndex device();
-  static MempoolId_t graph_pool_handle(bool is_user_created = true);
-
- private:
-  static std::atomic<CaptureId_t> uid_;
-  static std::atomic<CaptureId_t> uuid_;
-  c10::cuda::CUDACachingAllocator::CUDAAllocator* allocator_;
-  bool is_user_created_;
-  MempoolId_t id_;
-  c10::DeviceIndex device_;
-};
-
-} // namespace at::cuda

aten/src/ATen/cuda/NumericLimits.cuh

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

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

@@ -411,16 +411,16 @@ std::string CUDAHooks::showConfig() const {
     // HIP_VERSION value format was changed after ROCm v4.2 to include the patch number
     if(v < 500) {
       // If major=xx, minor=yy then format -> xxyy
-      oss << (v / 100) << '.' << (v % 10);
+      oss << (v / 100) << "." << (v % 10);
     }
     else {
       // If major=xx, minor=yy & patch=zzzzz then format -> xxyyzzzzz
-      oss << (v / 10000000) << '.' << (v / 100000 % 100) << '.' << (v % 100000);
+      oss << (v / 10000000) << "." << (v / 100000 % 100) << "." << (v % 100000);
     }
 #else
-    oss << (v / 1000) << '.' << (v / 10 % 100);
+    oss << (v / 1000) << "." << (v / 10 % 100);
     if (v % 10 != 0) {
-      oss << '.' << (v % 10);
+      oss << "." << (v % 10);
     }
 #endif
   };
@@ -431,16 +431,16 @@ std::string CUDAHooks::showConfig() const {
   oss << "  - HIP Runtime ";
 #endif
   printCudaStyleVersion(runtimeVersion);
-  oss << '\n';
+  oss << "\n";
 
   // TODO: Make HIPIFY understand CUDART_VERSION macro
 #if !defined(USE_ROCM)
   if (runtimeVersion != CUDART_VERSION) {
     oss << "  - Built with CUDA Runtime ";
     printCudaStyleVersion(CUDART_VERSION);
-    oss << '\n';
+    oss << "\n";
   }
-  oss << "  - NVCC architecture flags: " << NVCC_FLAGS_EXTRA << '\n';
+  oss << "  - NVCC architecture flags: " << NVCC_FLAGS_EXTRA << "\n";
 #endif
 
 #if !defined(USE_ROCM)
@@ -448,9 +448,9 @@ std::string CUDAHooks::showConfig() const {
 
 
   auto printCudnnStyleVersion = [&](size_t v) {
-    oss << (v / 1000) << '.' << (v / 100 % 10);
+    oss << (v / 1000) << "." << (v / 100 % 10);
     if (v % 100 != 0) {
-      oss << '.' << (v % 100);
+      oss << "." << (v % 100);
     }
   };
 
@@ -461,22 +461,22 @@ std::string CUDAHooks::showConfig() const {
   if (cudnnCudartVersion != CUDART_VERSION) {
     oss << "  (built against CUDA ";
     printCudaStyleVersion(cudnnCudartVersion);
-    oss << ')';
+    oss << ")";
   }
-  oss << '\n';
+  oss << "\n";
   if (cudnnVersion != CUDNN_VERSION) {
     oss << "    - Built with CuDNN ";
     printCudnnStyleVersion(CUDNN_VERSION);
-    oss << '\n';
+    oss << "\n";
   }
 #endif
 #else
   // TODO: Check if miopen has the functions above and unify
-  oss << "  - MIOpen " << MIOPEN_VERSION_MAJOR << '.' << MIOPEN_VERSION_MINOR << '.' << MIOPEN_VERSION_PATCH << '\n';
+  oss << "  - MIOpen " << MIOPEN_VERSION_MAJOR << "." << MIOPEN_VERSION_MINOR << "." << MIOPEN_VERSION_PATCH << "\n";
 #endif
 
 #if AT_MAGMA_ENABLED()
-  oss << "  - Magma " << MAGMA_VERSION_MAJOR << '.' << MAGMA_VERSION_MINOR << '.' << MAGMA_VERSION_MICRO << '\n';
+  oss << "  - Magma " << MAGMA_VERSION_MAJOR << "." << MAGMA_VERSION_MINOR << "." << MAGMA_VERSION_MICRO << "\n";
 #endif
 
   return oss.str();

aten/src/ATen/cuda/jiterator.cu

@@ -42,7 +42,7 @@ static inline void launch_jitted_vectorized_kernel_dynamic(
 
   // The cache key includes all the parameters to generate_code + vec_size + dev_idx
   std::stringstream ss;
-  ss << nInputs << '_' << nOutputs << f;
+  ss << nInputs << "_" << nOutputs << f;
   ss << f_inputs_type_str << compute_type_str << result_type_str;
   ss << static_cast<int>(at::cuda::jit::BinaryFuncVariant::NoScalar);
   ss << extra_args_types;
@@ -144,7 +144,7 @@ static inline void launch_jitted_unrolled_kernel_dynamic(
 
   // The cache key includes all the parameters to generate_code + dev_idx
   std::stringstream ss;
-  ss << nInputs << '_' << nOutputs << f;
+  ss << nInputs << "_" << nOutputs << f;
   ss << f_inputs_type_str << compute_type_str << result_type_str;
   ss << contiguous << dynamic_casting;
   ss << static_cast<int>(at::cuda::jit::BinaryFuncVariant::NoScalar);

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

@@ -52,10 +52,10 @@ TuningContext* getTuningContext() {
 std::ostream& operator<<(std::ostream& stream, const ResultEntry& entry) {
   static const bool blaslog = c10::utils::get_env("PYTORCH_TUNABLEOP_BLAS_LOG") == "1";
   if (!blaslog) {
-    return stream << entry.key_ << ',' << entry.time_;
+    return stream << entry.key_ << "," << entry.time_;
   }
   else {
-    return stream << entry.key_ << ',' << entry.time_ << ",BLAS_PARAMS: " << entry.blas_sig_;
+    return stream << entry.key_ << "," << entry.time_ << ",BLAS_PARAMS: " << entry.blas_sig_;
   }
 }
 
@@ -156,10 +156,10 @@ void TuningResultsManager::RecordUntuned( std::ofstream& untuned_file, const std
     if (isNew) {
       static const bool blaslog = c10::utils::get_env("PYTORCH_TUNABLEOP_BLAS_LOG") == "1";
       if (!blaslog) {
-        untuned_file << op_signature << ',' << params_signature << std::endl;
+        untuned_file << op_signature << "," << params_signature << std::endl;
       }
       else {
-        untuned_file << op_signature << ',' << params_signature << ",BLAS_PARAMS: " << blas_signature << std::endl;
+        untuned_file << op_signature << "," << params_signature << ",BLAS_PARAMS: " << blas_signature << std::endl;
       }
       TUNABLE_LOG3("Untuned,", op_signature, ",", params_signature);
     }
@@ -201,7 +201,7 @@ void TuningResultsManager::InitRealtimeAppend(const std::string& filename, const
 
   if(!file_exists || file_empty) {
     for(const auto& [key, val] : validators) {
-      (*realtime_out_) << "Validator," << key << ',' << val << std::endl;
+      (*realtime_out_) << "Validator," << key << "," << val << std::endl;
       realtime_out_->flush();
     }
     validators_written_ = true;
@@ -219,7 +219,7 @@ void TuningResultsManager::AppendResultLine(const std::string& op_sig, const std
     return;
   }
 
-  (*realtime_out_) << op_sig << ',' << param_sig << ',' << result << std::endl;
+  (*realtime_out_) << op_sig << "," << param_sig << "," << result << std::endl;
   realtime_out_->flush(); //ensure immediate write to disk
 
   TUNABLE_LOG3("Realtime append: ", op_sig, "(", param_sig, ") -> ", result);