modify comments

.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

.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/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/smoke_test/check_binary_symbols.py

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

.ci/pytorch/test.sh

@@ -389,13 +389,6 @@ 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
@@ -1250,97 +1243,6 @@ 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"
@@ -1768,7 +1670,7 @@ test_operator_microbenchmark() {
 
   cd "${TEST_DIR}"/benchmarks/operator_benchmark
 
-  for OP_BENCHMARK_TESTS in optimizer; do
+  for OP_BENCHMARK_TESTS in matmul mm addmm bmm conv; do
     $TASKSET python -m pt.${OP_BENCHMARK_TESTS}_test --tag-filter long \
       --output-json-for-dashboard "${TEST_REPORTS_DIR}/operator_microbenchmark_${OP_BENCHMARK_TESTS}_compile.json" \
       --benchmark-name "PyTorch operator microbenchmark" --use-compile
@@ -1778,22 +1680,6 @@ 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 +1699,6 @@ 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 [[ "${TEST_CONFIG}" == *xla* ]]; then
   install_torchvision
   build_xla
@@ -1853,8 +1737,6 @@ elif [[ "${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
@@ -1914,8 +1796,6 @@ 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/ci_commit_pins/audio.txt

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

.github/ci_commit_pins/vision.txt

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

.github/ci_commit_pins/xla.txt

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

.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/**

.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/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/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/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/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/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
 

.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)

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/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/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/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/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);

aten/src/ATen/cudnn/Descriptors.cpp

@@ -93,31 +93,31 @@ std::string cudnnTypeToString(cudnnDataType_t dtype) {
       return "CUDNN_DATA_UINT8x4";
     default:
       std::ostringstream oss;
-      oss << "(unknown data-type " << static_cast<int>(dtype) << ')';
+      oss << "(unknown data-type " << static_cast<int>(dtype) << ")";
       return oss.str();
   }
 }
 
 std::ostream& operator<<(std::ostream & out, const TensorDescriptor& d) {
-  out << "TensorDescriptor " << static_cast<void*>(d.desc()) << '\n';
+  out << "TensorDescriptor " << static_cast<void*>(d.desc()) << "\n";
   int nbDims = 0;
   int dimA[CUDNN_DIM_MAX];
   int strideA[CUDNN_DIM_MAX];
   cudnnDataType_t dtype{};
   cudnnGetTensorNdDescriptor(d.desc(), CUDNN_DIM_MAX, &dtype, &nbDims, dimA, strideA);
-  out << "    type = " << cudnnTypeToString(dtype) << '\n';
-  out << "    nbDims = " << nbDims << '\n';
+  out << "    type = " << cudnnTypeToString(dtype) << "\n";
+  out << "    nbDims = " << nbDims << "\n";
   // Read out only nbDims of the arrays!
   out << "    dimA = ";
   for (auto i : ArrayRef<int>{dimA, static_cast<size_t>(nbDims)}) {
     out << i << ", ";
   }
-  out << '\n';
+  out << "\n";
   out << "    strideA = ";
   for (auto i : ArrayRef<int>{strideA, static_cast<size_t>(nbDims)}) {
     out << i << ", ";
   }
-  out << '\n';
+  out << "\n";
   return out;
 }
 
@@ -168,27 +168,27 @@ std::string cudnnMemoryFormatToString(cudnnTensorFormat_t tformat) {
       return "CUDNN_TENSOR_NHWC";
     default:
       std::ostringstream oss;
-      oss << "(unknown cudnn tensor format " << static_cast<int>(tformat) << ')';
+      oss << "(unknown cudnn tensor format " << static_cast<int>(tformat) << ")";
       return oss.str();
   }
 }
 
 std::ostream& operator<<(std::ostream & out, const FilterDescriptor& d) {
-  out << "FilterDescriptor " << static_cast<void*>(d.desc()) << '\n';
+  out << "FilterDescriptor " << static_cast<void*>(d.desc()) << "\n";
   int nbDims = 0;
   int dimA[CUDNN_DIM_MAX];
   cudnnDataType_t dtype{};
   cudnnTensorFormat_t tformat{};
   cudnnGetFilterNdDescriptor(d.desc(), CUDNN_DIM_MAX, &dtype, &tformat, &nbDims, dimA);
-  out << "    type = " << cudnnTypeToString(dtype) << '\n';
-  out << "    tensor_format = " << cudnnMemoryFormatToString(tformat) << '\n';
-  out << "    nbDims = " << nbDims << '\n';
+  out << "    type = " << cudnnTypeToString(dtype) << "\n";
+  out << "    tensor_format = " << cudnnMemoryFormatToString(tformat) << "\n";
+  out << "    nbDims = " << nbDims << "\n";
   // Read out only nbDims of the arrays!
   out << "    dimA = ";
   for (auto i : ArrayRef<int>{dimA, static_cast<size_t>(nbDims)}) {
     out << i << ", ";
   }
-  out << '\n';
+  out << "\n";
   return out;
 }
 

aten/src/ATen/functorch/DynamicLayer.cpp

@@ -346,15 +346,15 @@ void foreachTensorInplaceWithFlag(std::vector<IValue>& args, int64_t begin, int6
 }
 
 std::ostream& operator<< (std::ostream& os, const DynamicLayer& layer) {
-  os << layer.layerId() << ':' << layer.key();
+  os << layer.layerId() << ":" << layer.key();
   return os;
 }
 std::ostream& operator<< (std::ostream& os, const std::vector<DynamicLayer>& dls) {
   os << "DynamicLayerStack[ ";
   for (const auto& layer : dls) {
-    os << layer << ' ';
+    os << layer << " ";
   }
-  os << ']';
+  os << "]";
   return os;
 }
 

aten/src/ATen/functorch/TensorWrapper.cpp

@@ -22,7 +22,7 @@ void dumpTensor(std::ostream& ss, const Tensor& tensor) {
     if (batched) {
       ss << "Batched[lvl=" << batched->level() << " dim=" << batched->bdim() << ", ";
       dumpTensor(ss, batched->value());
-      ss << ']';
+      ss << "]";
       return;
     }
     ss << "Tensor" << tensor.sizes();
@@ -36,7 +36,7 @@ void dumpTensor(std::ostream& ss, const Tensor& tensor) {
     ss << "dead, ";
   }
   dumpTensor(ss, wrapped->value());
-  ss << ']';
+  ss << "]";
 }
 
 void TensorWrapper::refreshMetadata() {

aten/src/ATen/miopen/Descriptors.cpp

@@ -73,32 +73,32 @@ std::string miopenTypeToString(miopenDataType_t dtype) {
       return "miopenBFloat16";
     default:
       std::ostringstream oss;
-      oss << "(unknown data-type " << static_cast<int>(dtype) << ')';
+      oss << "(unknown data-type " << static_cast<int>(dtype) << ")";
       return oss.str();
   }
 }
 
 std::ostream& operator<<(std::ostream & out, const TensorDescriptor& d) {
-  out << "TensorDescriptor " << static_cast<void*>(d.desc()) << '\n';
+  out << "TensorDescriptor " << static_cast<void*>(d.desc()) << "\n";
   int nbDims = 0;
   int dimA[MIOPEN_DIM_MAX];
   int strideA[MIOPEN_DIM_MAX];
   miopenDataType_t dtype;
   miopenGetTensorDescriptorSize(d.desc(), &nbDims);
   miopenGetTensorDescriptor(d.desc(), &dtype, dimA, strideA);
-  out << "    type = " << miopenTypeToString(dtype) << '\n';
-  out << "    nbDims = " << nbDims << '\n';
+  out << "    type = " << miopenTypeToString(dtype) << "\n";
+  out << "    nbDims = " << nbDims << "\n";
   // Read out only nbDims of the arrays!
   out << "    dimA = ";
   for (auto i : ArrayRef<int>{dimA, static_cast<size_t>(nbDims)}) {
     out << i << ", ";
   }
-  out << '\n';
+  out << "\n";
   out << "    strideA = ";
   for (auto i : ArrayRef<int>{strideA, static_cast<size_t>(nbDims)}) {
     out << i << ", ";
   }
-  out << '\n';
+  out << "\n";
   return out;
 }
 

aten/src/ATen/mps/MPSProfiler.h

@@ -91,7 +91,7 @@ struct OperationInfo : BaseInfo {
     std::stringstream kernelStr;
     kernelStr << kernelName;
     for (const Tensor& tensor : tensors) {
-      kernelStr << ':' << BaseInfo::buildTensorString(tensor, includeBufferId);
+      kernelStr << ":" << BaseInfo::buildTensorString(tensor, includeBufferId);
     }
     return kernelStr.str();
   }

aten/src/ATen/mps/MPSProfiler.mm

@@ -39,9 +39,9 @@ std::string BaseInfo::buildTensorString(const Tensor& tensor, bool includeBuffer
     // see comments for INCLUDE_BUFFER_ID
     if (includeBufferId && deviceType == at::kMPS) {
       id<MTLBuffer> buffer = __builtin_bit_cast(id<MTLBuffer>, tensor.storage().data());
-      tensorStr << "(buf#" << (getIMPSAllocator()->getBufferId(buffer)) << ':' << buffer.retainCount << ')';
+      tensorStr << "(buf#" << (getIMPSAllocator()->getBufferId(buffer)) << ":" << buffer.retainCount << ")";
     }
-    tensorStr << ':' << tensor.scalar_type() << tensor.sizes();
+    tensorStr << ":" << tensor.scalar_type() << tensor.sizes();
     return tensorStr.str();
   } else {
     return "undefined";

aten/src/ATen/native/ConvUtils.h

@@ -167,7 +167,7 @@ static void check_args(CheckedFrom c, IntArrayRef args, size_t expected_size, co
     std::stringstream ss;
     ss << arg_name << " should be greater than zero but got (";
     std::copy(args.begin(), args.end() - 1, std::ostream_iterator<int>(ss,", "));
-    ss << args.back() <<  ")" << " (while checking arguments for " << c << ')';
+    ss << args.back() <<  ")" << " (while checking arguments for " << c << ")";
     TORCH_CHECK(false, ss.str());
   }
 }

aten/src/ATen/native/Convolution.cpp

@@ -639,7 +639,7 @@ static std::ostream& operator<<(std::ostream & out, const ConvParams<T>& params)
       << "  deterministic = " << params.deterministic
       << "  cudnn_enabled = " << params.cudnn_enabled
       << "  allow_tf32 = " << params.allow_tf32
-      << '}';
+      << "}";
   return out;
 }
 

aten/src/ATen/native/LinearAlgebra.cpp

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

aten/src/ATen/native/SpectralOps.cpp

@@ -847,7 +847,7 @@ Tensor stft(const Tensor& self, const int64_t n_fft, const std::optional<int64_t
        << ", hop_length=" << hop_length << ", win_length=" << win_length \
        << ", window="; \
     if (window.defined()) { \
-      SS << window.toString() << '{' << window.sizes() << '}'; \
+      SS << window.toString() << "{" << window.sizes() << "}"; \
     } else { \
       SS << "None"; \
     } \
@@ -1046,7 +1046,7 @@ Tensor istft(const Tensor& self, const int64_t n_fft, const std::optional<int64_
        << ", hop_length=" << hop_length << ", win_length=" << win_length \
        << ", window="; \
     if (window.defined()) { \
-      SS << window.toString() << '{' << window.sizes() << '}'; \
+      SS << window.toString() << "{" << window.sizes() << "}"; \
     } else { \
       SS << "None"; \
     } \

aten/src/ATen/native/TensorAdvancedIndexing.cpp

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

aten/src/ATen/native/TensorCompare.cpp

@@ -523,7 +523,7 @@ Tensor _functional_assert_async_msg_cpu(
 }
 
 void _print(std::string_view s) {
-  std::cout << s << '\n';
+  std::cout << s << "\n";
 }
 
 // Sorting-based algorithm for isin(); used when the number of test elements is

aten/src/ATen/native/UnaryOps.cpp

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

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

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

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

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

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

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

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

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

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

@@ -78,9 +78,9 @@ _mx8_mx8_bf16_grouped_mm_fbgemm(
         const Tensor& mat_a,
         const Tensor& mat_b,
         const Tensor& scale_a,
-        const SwizzleType swizzle_a,
+        const SwizzleType& swizzle_a,
         const Tensor& scale_b,
-        const SwizzleType swizzle_b,
+        const SwizzleType& swizzle_b,
         const std::optional<at::Tensor>& offs,
         Tensor& out) {
     const bool a_is_2d = mat_a.dim() == 2;
@@ -607,8 +607,6 @@ _scaled_grouped_mm_cuda_v2(
       // scale shape checks
       _check_scales_blocked(mat_a, scale_a[0], 0 /* dim */, 0 /* arg_idx */);
       _check_scales_blocked(mat_b, scale_b[0], 1 /* dim */, 1 /* arg_idx */);
-      // swizze checks
-      TORCH_CHECK_VALUE(swizzle_a_enum.size() == 1 && swizzle_b_enum.size() == 1, "Expected single swizzle argument");
       return _mx8_mx8_bf16_grouped_mm_fbgemm(
           mat_a,
           mat_b,

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

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

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

@@ -11,7 +11,7 @@ static inline std::ostream& operator<<(std::ostream& out, dim3 dim) {
   if (dim.y == 1 && dim.z == 1) {
     out << dim.x;
   } else {
-    out << '[' << dim.x << ',' << dim.y << ',' << dim.z << ']';
+    out << "[" << dim.x << "," << dim.y << "," << dim.z << "]";
   }
   return out;
 }
@@ -27,7 +27,7 @@ std::ostream& operator<<(std::ostream& out, const ReduceConfig& config) {
   out << "input_mult=[";
   for (int i = 0; i < 3; i++) {
     if (i != 0) {
-      out << ',';
+      out << ",";
     }
     out << config.input_mult[i];
   }
@@ -35,7 +35,7 @@ std::ostream& operator<<(std::ostream& out, const ReduceConfig& config) {
   out << "output_mult=[";
   for (int i = 0; i < 2; i++) {
     if (i != 0) {
-      out << ',';
+      out << ",";
     }
     out << config.output_mult[i];
   }
@@ -49,7 +49,7 @@ std::ostream& operator<<(std::ostream& out, const ReduceConfig& config) {
   out << "block=" << config.block() << ", ";
   out << "grid=" << config.grid() << ", ";
   out << "global_memory_size=" << config.global_memory_size();
-  out << ')';
+  out << ")";
   return out;
 }
 

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

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

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

@@ -364,9 +364,9 @@ void f8f8bf16_grouped_gemm_impl_sm90(
   //       reinterpret_cast<ProblemShape::UnderlyingProblemShape*>(
   //           stride_output_h + group_count);
 
-  //   std::cout << "PTRS " << mat_a.data_ptr() << ' ' << mat_b.data_ptr() << "
+  //   std::cout << "PTRS " << mat_a.data_ptr() << " " << mat_b.data_ptr() << "
   //   "
-  //             << out.data_ptr() << ' ' << scale_a.data_ptr() << ' '
+  //             << out.data_ptr() << " " << scale_a.data_ptr() << " "
   //             << scale_b.data_ptr() << "\n";
   //   for (int i = 0; i < group_count; i++) {
   //     std::cout << "A " << (void*)inputA_ptrs_h[i] << "\n";

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

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

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

@@ -1057,14 +1057,14 @@ std::string generate_code(
     // TODO these arrays are potentially of the different types, use function
     // traits to determine the types
     declare_load_arrays << f_inputs_type << " arg" << std::to_string(i)
-                        << '[' << std::to_string(thread_work_size) << "];\n";
+                        << "[" << std::to_string(thread_work_size) << "];\n";
   }
   env.s("declare_load_arrays", declare_load_arrays.str());
 
   std::stringstream declare_store_arrays;
   for (int i = 0; i < nOutputs; i++) {
     declare_store_arrays << result_type << " out" << std::to_string(i)
-                        << '[' << std::to_string(thread_work_size) << "];\n";
+                        << "[" << std::to_string(thread_work_size) << "];\n";
   }
   env.s("declare_store_arrays", declare_store_arrays.str());
 
@@ -1217,7 +1217,7 @@ std::string generate_code(
   for (const auto i : c10::irange(nInputs)){
     auto i_string = std::to_string(i);
     vector_inputs << "auto * input" << i_string <<
-        " = reinterpret_cast<const scalar_t*>(data[" << i_string << '+' << nOutputs << "])" <<
+        " = reinterpret_cast<const scalar_t*>(data[" << i_string << "+" << nOutputs << "])" <<
         " + block_work_size * idx;\n";
   }
   env.s("vector_inputs", vector_inputs.str());
@@ -1543,17 +1543,17 @@ NvrtcFunction jit_pwise_function(
 
     // Constructs file path by appending constructed cubin name to cache path
     std::stringstream ss;
-    ss << *cache_dir << '/';
+    ss << *cache_dir << "/";
     ss << kernel_name;
 #ifdef USE_ROCM
     ss << "_arch" << prop->gcnArchName;
 #else
-    ss << "_arch" << cuda_major << '.' << cuda_minor;
+    ss << "_arch" << cuda_major << "." << cuda_minor;
 #endif
-    ss << "_nvrtc" << nvrtc_major << '.' << nvrtc_minor;
+    ss << "_nvrtc" << nvrtc_major << "." << nvrtc_minor;
     ss << (compile_to_sass ? "_sass" : "_ptx");
-    ss << '_' << code.length();
-    ss << '_' << hash_code;
+    ss << "_" << code.length();
+    ss << "_" << hash_code;
     file_path = ss.str();
 
     std::ifstream readin{file_path, std::ios::in | std::ifstream::binary};

aten/src/ATen/native/cudnn/ConvShared.cpp

@@ -82,15 +82,15 @@ namespace native {
 
 std::ostream& operator<<(std::ostream& out, const ConvolutionParams& params) {
   out << "ConvolutionParams \n"
-      << "    memory_format = " << params.memory_format << '\n'
-      << "    data_type = " << cudnnTypeToString(params.dataType) << '\n'
-      << "    padding = " << ArrayRef<int>{params.padding} << '\n'
-      << "    stride = " << ArrayRef<int>{params.stride} << '\n'
-      << "    dilation = " << ArrayRef<int>{params.dilation} << '\n'
-      << "    groups = " << params.groups << '\n'
+      << "    memory_format = " << params.memory_format << "\n"
+      << "    data_type = " << cudnnTypeToString(params.dataType) << "\n"
+      << "    padding = " << ArrayRef<int>{params.padding} << "\n"
+      << "    stride = " << ArrayRef<int>{params.stride} << "\n"
+      << "    dilation = " << ArrayRef<int>{params.dilation} << "\n"
+      << "    groups = " << params.groups << "\n"
       << "    deterministic = " << (params.deterministic ? "true" : "false")
-      << '\n'
-      << "    allow_tf32 = " << (params.allow_tf32 ? "true" : "false") << '\n';
+      << "\n"
+      << "    allow_tf32 = " << (params.allow_tf32 ? "true" : "false") << "\n";
 
   return out;
 }
@@ -173,16 +173,16 @@ std::string repro_from_args(const ConvolutionParams& params) {
             at::globalContext().float32Precision(
                 at::Float32Backend::CUDA, at::Float32Op::MATMUL) ==
             at::Float32Precision::TF32)
-     << '\n';
+     << "\n";
   ss << "torch.backends.cudnn.benchmark = "
-     << pybool(at::globalContext().benchmarkCuDNN()) << '\n';
+     << pybool(at::globalContext().benchmarkCuDNN()) << "\n";
   ss << "torch.backends.cudnn.deterministic = " << pybool(params.deterministic)
-     << '\n';
+     << "\n";
   ss << "torch.backends.cudnn.allow_tf32 = " << pybool(params.allow_tf32)
-     << '\n';
+     << "\n";
   ss << "data = torch.randn(" << ArrayRef<int>(params.input_size, dim)
      << ", dtype=" << full_dtype << ", ";
-  ss << "device='cuda', requires_grad=True)" << to_channels_last << '\n';
+  ss << "device='cuda', requires_grad=True)" << to_channels_last << "\n";
   ss << "net = torch.nn.Conv" << dim - 2 << "d(" << in_channels << ", "
      << out_channels << ", ";
   ss << "kernel_size=" << ArrayRef<int>(&params.weight_size[2], dim - 2)
@@ -192,7 +192,7 @@ std::string repro_from_args(const ConvolutionParams& params) {
   ss << "dilation=" << ArrayRef<int>(params.dilation, dim - 2) << ", ";
   ss << "groups=" << params.groups << ")\n";
   ss << "net = net.cuda()." << partial_dtype << "()" << to_channels_last
-     << '\n';
+     << "\n";
   ss << "out = net(data)\n";
   ss << "out.backward(torch.randn_like(out))\n";
   ss << "torch.cuda.synchronize()\n\n";

aten/src/ATen/native/cudnn/Conv_v7.cpp

@@ -93,10 +93,11 @@ std::ostream& operator<<(std::ostream& out, const ConvolutionArgs& args) {
       << "input: " << args.idesc // already has a trailing newline
       << "output: " << args.odesc // already has a trailing newline
       << "weight: " << args.wdesc // already has a trailing newline
-      << "Pointer addresses: " << '\n'
-      << "    input: " << args.input.const_data_ptr() << '\n'
-      << "    output: " << args.output.const_data_ptr() << '\n'
-      << "    weight: " << args.weight.const_data_ptr() << '\n';
+      << "Pointer addresses: "
+      << "\n"
+      << "    input: " << args.input.const_data_ptr() << "\n"
+      << "    output: " << args.output.const_data_ptr() << "\n"
+      << "    weight: " << args.weight.const_data_ptr() << "\n";
 
   return out;
 }

aten/src/ATen/native/metal/MetalTensorImplStorage.mm

@@ -115,7 +115,7 @@ std::ostream& operator<<(
   std::copy(
       strides.begin(), strides.end() - 1, std::ostream_iterator<int>(oss, ","));
   oss << sizes.back();
-  output << oss.str() << '}';
+  output << oss.str() << "}";
   return output;
 }
 

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

@@ -53,7 +53,7 @@ std::ostream& operator<<(std::ostream& out, const ConvParams& params) {
       << "  transposed = " << params.transposed
       << "  output_padding = " << IntArrayRef{params.output_padding}
       << "  groups = " << params.groups << "  benchmark = " << params.benchmark
-      << "  deterministic = " << params.deterministic << '}';
+      << "  deterministic = " << params.deterministic << "}";
   return out;
 }
 
@@ -337,6 +337,10 @@ Tensor _convolution_out(
   TORCH_CHECK(
       3 == ndim || 4 == ndim || 5 == ndim,
       "convolution only supports 3D, 4D, 5D tensor");
+  // get computation format for Conv/TransposedConv
+  bool is_channels_last_suggested =
+      use_channels_last_for_conv(input_r, weight_r);
+
   Tensor input = input_r, weight = weight_r;
   // PyTorch does not support ChannelsLast1D case,
   // thus we need the transformation here
@@ -344,8 +348,13 @@ Tensor _convolution_out(
     input = view4d(input_r);
     weight = view4d(weight_r);
   }
-  // get computation format for Conv/TransposedConv
-  bool is_channels_last_suggested = use_channels_last_for_conv(input, weight);
+  // ensure the input/weight/bias/output are congituous in desired format
+  at::MemoryFormat mfmt = is_channels_last_suggested
+      ? get_cl_tag_by_ndim(input.ndimension())
+      : at::MemoryFormat::Contiguous;
+  auto bias = bias_r.defined() ? bias_r.contiguous() : bias_r;
+  input = input.contiguous(mfmt);
+  weight = weight.contiguous(mfmt);
 
   auto k = weight.ndimension();
   if (k == input.ndimension() + 1) {
@@ -379,14 +388,6 @@ Tensor _convolution_out(
         expand_param_if_needed(output_padding_, "output_padding", dim);
     params.groups = groups_;
   }
-
-  // ensure the input/weight/bias/output are congituous in desired format
-  at::MemoryFormat mfmt = is_channels_last_suggested
-      ? get_cl_tag_by_ndim(input.ndimension())
-      : at::MemoryFormat::Contiguous;
-  auto bias = bias_r.defined() ? bias_r.contiguous() : bias_r;
-  input = input.contiguous(mfmt);
-  weight = weight.contiguous(mfmt);
   check_shape_forward(input, weight, bias, params, true);
 
   Tensor output;
@@ -513,9 +514,18 @@ Tensor convolution_overrideable(
       at::borrow_from_optional_tensor(bias_r_opt);
   const Tensor& bias_r = *bias_r_maybe_owned;
 
+  auto k = weight_r.ndimension();
+  at::MemoryFormat backend_memory_format = at::MemoryFormat::Contiguous;
+  if (xpu_conv_use_channels_last(input_r, weight_r)) {
+    backend_memory_format = (k == 5) ? at::MemoryFormat::ChannelsLast3d
+                                     : at::MemoryFormat::ChannelsLast;
+  }
+  Tensor input_c = input_r.contiguous(backend_memory_format);
+  Tensor weight_c = weight_r.contiguous(backend_memory_format);
+
   return _convolution(
-      input_r,
-      weight_r,
+      input_c,
+      weight_c,
       bias_r,
       stride_,
       padding_,

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

@@ -1,738 +0,0 @@
-#define TORCH_ASSERT_ONLY_METHOD_OPERATORS
-#include <ATen/BlasBackend.h>
-#include <ATen/WrapDimUtilsMulti.h>
-#include <ATen/ceil_div.h>
-#include <ATen/native/Resize.h>
-#include <ATen/native/mkldnn/xpu/detail/oneDNN.h>
-#include <ATen/native/xpu/Blas.h>
-#include <ATen/xpu/XPUScaledBlas.h>
-#include <torch/library.h>
-
-#ifndef AT_PER_OPERATOR_HEADERS
-#include <ATen/Functions.h>
-#include <ATen/NativeFunctions.h>
-#else
-#include <ATen/ops/_addmm_activation_native.h>
-#include <ATen/ops/_efficientzerotensor.h>
-#include <ATen/ops/_scaled_mm_native.h>
-#include <ATen/ops/_unsafe_view_native.h>
-#include <ATen/ops/abs.h>
-#include <ATen/ops/addmm_native.h>
-#include <ATen/ops/addmv_native.h>
-#include <ATen/ops/baddbmm_native.h>
-#include <ATen/ops/bmm_native.h>
-#include <ATen/ops/copy_native.h>
-#include <ATen/ops/dot_native.h>
-#include <ATen/ops/empty.h>
-#include <ATen/ops/empty_strided.h>
-#include <ATen/ops/gelu.h>
-#include <ATen/ops/max.h>
-#include <ATen/ops/mm_native.h>
-#include <ATen/ops/mul.h>
-#include <ATen/ops/ones.h>
-#include <ATen/ops/relu.h>
-#include <ATen/ops/scalar_tensor_native.h>
-#include <ATen/ops/vdot_native.h>
-#endif
-
-namespace at::native {
-
-using at::blas::ScalingType;
-using at::blas::SwizzleType;
-
-namespace {
-/*
- * Scaling Type Determination:
- * ---------------------------
- * Conditions and corresponding Scaling Types:
- *
- * - If scale tensor is `Float8_e8m0fnu` or `Float8_e4m3fn`:
- *   - Returns BlockWise (with additional size checks).
- *
- * - Else if scale.numel() == 1:
- *   - Returns TensorWise.
- *
- * - Else if scale.dim() == 2 && scale.size(0) == outer_dim && scale.size(1) ==
- * 1:
- *   - Returns RowWise.
- *
- * - Otherwise:
- *   - Returns Error.
- */
-
-bool is_tensorwise_scaling(const at::Tensor& t, const at::Tensor& scale) {
-  return at::isFloat8Type(t.scalar_type()) &&
-      scale.scalar_type() == at::kFloat && scale.numel() == 1;
-}
-
-bool is_rowwise_scaling(const at::Tensor& t, const at::Tensor& scale) {
-  return (
-      at::isFloat8Type(t.scalar_type()) && scale.scalar_type() == at::kFloat &&
-      scale.dim() == 2 && scale.size(0) == t.size(0) && scale.size(1) == 1 &&
-      scale.is_contiguous());
-}
-
-bool is_desired_scaling(
-    const at::Tensor& t,
-    const at::Tensor& scale,
-    ScalingType desired_scaling) {
-  auto result = desired_scaling == ScalingType::TensorWise
-      ? is_tensorwise_scaling(t, scale)
-      : is_rowwise_scaling(t, scale);
-  return result;
-}
-
-std::pair<ScalingType, ScalingType> get_joint_scaling(
-    std::initializer_list<std::pair<ScalingType, ScalingType>> options,
-    const at::Tensor& a,
-    const at::Tensor& b,
-    const at::Tensor& scale_a,
-    const at::Tensor& scale_b) {
-  for (auto [lhs, rhs] : options) {
-    if (is_desired_scaling(a, scale_a, lhs) &&
-        is_desired_scaling(b.t(), scale_b.t(), rhs)) {
-      return {lhs, rhs};
-    }
-  }
-  TORCH_CHECK(
-      false,
-      "Invalid scaling configuration.\n"
-      "- For TensorWise scaling, a and b should be float8, scales should be float and singletons.\n"
-      "- For RowWise scaling, a and b should be float8, scales should be float, scale_a should be (",
-      a.size(0),
-      ", 1) and scale_b should be (1, ",
-      b.size(1),
-      "), and both should be contiguous.\n"
-      "Got a.dtype()=",
-      a.scalar_type(),
-      ", scale_a.dtype()=",
-      scale_a.scalar_type(),
-      ", scale_a.size()=",
-      scale_a.sizes(),
-      ", scale_a.stride()=",
-      scale_a.strides(),
-      ", ",
-      "b.dtype()=",
-      b.scalar_type(),
-      ", scale_b.dtype()=",
-      scale_b.scalar_type(),
-      ", scale_b.size()=",
-      scale_b.sizes(),
-      " and scale_b.stride()=",
-      scale_b.strides());
-}
-
-Tensor& _scaled_gemm(
-    const Tensor& mat1,
-    const Tensor& mat2,
-    const Tensor& scale_a,
-    const Tensor& scale_b,
-    const ScalingType scaling_choice_a,
-    const ScalingType scaling_choice_b,
-    const std::optional<Tensor>& bias,
-    const bool use_fast_accum,
-    Tensor& out,
-    const std::optional<Tensor>& alpha = std::nullopt) {
-  // TODO: scale_result and alpha is not defined or used!
-  std::optional<Tensor> scaled_result = std::nullopt;
-  at::native::onednn::scaled_matmul(
-      mat1,
-      mat2,
-      out,
-      scale_a,
-      scale_b,
-      scaling_choice_a,
-      scaling_choice_b,
-      bias,
-      scaled_result,
-      use_fast_accum);
-
-  return out;
-}
-
-} // namespace
-
-// Computes matrix multiply + bias while applying scaling to input and output
-// matrices Scales are only applicable when matrices are of Float8 type and
-// assumed to be equal to 1.0 by default. If output matrix type is 16 or 32-bit
-// type, scale_result is not applied. Known limitations:
-//  - Only works if mat1 is row-major and mat2 is column-major
-//  - Only works if matrices sizes are divisible by 32
-//  - If 1-dimensional tensors are used then scale_a should be size =
-//  mat1.size(0)
-//    and scale_b should have size = to mat2.size(1)
-//  Arguments:
-//    - `mat1`: the first operand of the matrix multiply, can be type
-//    `torch.float8_e4m3fn` or `torch.float8_e5m2`
-//    - `mat2`: the second operand of the matrix multiply, can be type
-//    `torch.float8_e4m3fn` or `torch.float8_e5m2`
-//    - `bias`: the bias, can be type `torch.float16` or `torch.bfloat16`
-//    - `out_dtype`: the output dtype, can either be a float8 or a higher
-//    precision floating point type
-//    - `scale_a`: a tensor with the inverse scale of `mat1`, whose
-//    shape/strides/dtype depend on the scaling scheme
-//    - `scale_b`: a tensor with the inverse scale of `mat2`, whose
-//    shape/strides/dtype depend on the scaling scheme
-//    - `scale_result`: a scalar tensor with the scale of the output, only
-//    utilized if the output is a float8 type
-//    - `use_fast_accum`: Not applicable for XPU. For now, it should always be
-//    false.
-//    - `out`: a reference to the output tensor
-
-Tensor& _scaled_mm_out_xpu(
-    const Tensor& mat1,
-    const Tensor& mat2,
-    const Tensor& scale_a,
-    const Tensor& scale_b,
-    const std::optional<at::Tensor>& bias,
-    const std::optional<at::Tensor>& scale_result,
-    std::optional<c10::ScalarType> out_dtype,
-    bool use_fast_accum,
-    Tensor& out) {
-  // Note: fast_accum is not supported in XPU for now.
-  TORCH_CHECK(!use_fast_accum, "fast_accum is not supported in XPU for now.");
-
-  TORCH_CHECK(mat1.dim() == 2, "mat1 must be a matrix");
-  TORCH_CHECK(mat2.dim() == 2, "mat2 must be a matrix");
-
-  TORCH_CHECK(
-      mat1.sizes()[1] == mat2.sizes()[0],
-      "mat1 and mat2 shapes cannot be multiplied (",
-      mat1.sizes()[0],
-      "x",
-      mat1.sizes()[1],
-      " and ",
-      mat2.sizes()[0],
-      "x",
-      mat2.sizes()[1],
-      ")");
-
-  // Check what type of scaling we are doing based on inputs. This list is
-  // sorted by decreasing priority.
-
-  // List of supported datatypes for XPU with oneDNN:
-  // https://uxlfoundation.github.io/oneDNN/dev_guide_matmul.html#data-types
-  auto [scaling_choice_a, scaling_choice_b] = get_joint_scaling(
-      {
-          std::make_pair(ScalingType::TensorWise, ScalingType::TensorWise),
-          std::make_pair(ScalingType::RowWise, ScalingType::RowWise),
-      },
-      mat1,
-      mat2,
-      scale_a,
-      scale_b);
-  TORCH_CHECK(
-      !scale_result ||
-          (scale_result->numel() == 1 && scale_result->scalar_type() == kFloat),
-      "scale_result must be a float scalar");
-  TORCH_CHECK(
-      !bias || bias->numel() == mat2.sizes()[1],
-      "Bias must be size ",
-      mat2.sizes()[1],
-      " but got ",
-      bias->numel());
-  TORCH_CHECK(
-      mat1.sizes()[1] % 16 == 0,
-      "Expected trailing dimension of mat1 to be divisible by 16 ",
-      "but got mat1 shape: (",
-      mat1.sizes()[0],
-      "x",
-      mat1.sizes()[1],
-      ").");
-  TORCH_CHECK(
-      mat2.sizes()[0] % 16 == 0 && mat2.sizes()[1] % 16 == 0,
-      "mat2 shape (",
-      mat2.sizes()[0],
-      "x",
-      mat2.sizes()[1],
-      ") must be divisible by 16");
-  // Check types
-  TORCH_CHECK(
-      !out_dtype || *out_dtype == out.scalar_type(),
-      "out_dtype must match output matrix type");
-  TORCH_CHECK(
-      at::isFloat8Type(mat1.scalar_type()),
-      "Expected mat1 to be Float8 matrix got ",
-      mat1.scalar_type());
-  TORCH_CHECK(
-      at::isFloat8Type(mat2.scalar_type()),
-      "Expected mat2 to be Float8 matrix got ",
-      mat2.scalar_type());
-  // TODO: oneDNN Currently only supports e4m3 with group scales on BMG. Not
-  // support 2D scales, only 1D. Needs to add more checks there.
-
-  if (bias) {
-    TORCH_CHECK(
-        bias->scalar_type() == kFloat ||
-            bias->scalar_type() == c10::ScalarType::BFloat16 ||
-            bias->scalar_type() == c10::ScalarType::Half,
-        "Bias must be Float32 or BFloat16 or Half, but got ",
-        bias->scalar_type());
-  }
-
-  {
-    auto bias_ = bias.value_or(Tensor());
-    auto scale_result_ = scale_result.value_or(Tensor());
-
-    // NOLINTNEXTLINE(*c-array*)
-    TensorArg targs[]{
-        {out, "out", 0},
-        {mat1, "mat1", 1},
-        {mat2, "mat2", 2},
-        {bias_, "bias", 3},
-        {scale_a, "scale_a", 4},
-        {scale_b, "scale_b", 5},
-        {scale_result_, "scale_result", 6}};
-    checkAllSameGPU(__func__, targs);
-  }
-
-  // Validation checks have passed lets resize the output to actual size
-  IntArrayRef mat1_sizes = mat1.sizes();
-  IntArrayRef mat2_sizes = mat2.sizes();
-  at::native::resize_output(out, {mat1_sizes[0], mat2_sizes[1]});
-
-  // If any of M, K, N is 0 - return early (the tensorwise/rowwise float8 gemm
-  // kernels do not support this case).
-  if (mat1_sizes[0] == 0 || mat1_sizes[1] == 0 || mat2_sizes[1] == 0) {
-    // `out` was created with `at::empty`. In the case where we are multiplying
-    // MxK by KxN and K is the zero dim, we need to initialize here to properly
-    // return a tensor of zeros.
-    if (mat1_sizes[1] == 0) {
-      out.zero_();
-    }
-
-    return out;
-  }
-
-  // TODO: Scale_result is not supported by now!!
-  return _scaled_gemm(
-      mat1,
-      mat2,
-      scale_a,
-      scale_b,
-      scaling_choice_a,
-      scaling_choice_b,
-      bias,
-      use_fast_accum,
-      out);
-}
-
-Tensor _scaled_mm_xpu(
-    const Tensor& mat_a,
-    const Tensor& mat_b,
-    const Tensor& scale_a,
-    const Tensor& scale_b,
-    const std::optional<at::Tensor>& bias,
-    const std::optional<at::Tensor>& scale_result,
-    std::optional<c10::ScalarType> out_dtype,
-    bool use_fast_accum) {
-  const auto out_dtype_ = out_dtype.value_or(mat_a.scalar_type());
-  Tensor out = at::empty({0}, mat_a.options().dtype(out_dtype_));
-  return _scaled_mm_out_xpu(
-      mat_a,
-      mat_b,
-      scale_a,
-      scale_b,
-      bias,
-      scale_result,
-      out_dtype,
-      use_fast_accum,
-      out);
-}
-
-using acceptance_fn = std::function<bool(
-    c10::ScalarType,
-    std::vector<ScalingType>&,
-    ArrayRef<Tensor>&,
-    c10::ScalarType,
-    std::vector<ScalingType>&,
-    ArrayRef<Tensor>&)>;
-using namespace std::placeholders;
-
-namespace scaled_blas = at::native::onednn::scaled;
-using scaled_blas::convert_int_to_enum;
-using scaled_blas::ScaledGemmImplementation;
-
-std::array<std::tuple<std::string, acceptance_fn, ScaledGemmImplementation>, 2>
-    scale_kernel_dispatch = {{
-        {"tensorwise_tensorwise",
-         scaled_blas::check_tensorwise_recipe,
-         ScaledGemmImplementation::TENSORWISE_TENSORWISE},
-        {"rowwise_rowwise",
-         scaled_blas::check_rowwise_recipe,
-         ScaledGemmImplementation::ROWWISE_ROWWISE},
-
-    }};
-
-Tensor& _scaled_tensorwise_tensorwise(
-    const Tensor& mat_a,
-    const Tensor& mat_b,
-    const Tensor& scale_a,
-    const Tensor& scale_b,
-    const std::optional<Tensor>& bias,
-    const c10::ScalarType out_dtype,
-    bool use_fast_accum,
-    Tensor& out) {
-  // Restrictions:
-  // A, B are FP8, scales are fp32
-
-  TORCH_CHECK_VALUE(
-      isFloat8Type(mat_a.scalar_type()) && isFloat8Type(mat_b.scalar_type()),
-      "mat_a and mat_b must be fp8 types, got: ",
-      mat_a.scalar_type(),
-      mat_b.scalar_type());
-  TORCH_CHECK_VALUE(
-      scale_a.numel() == 1 && scale_a.scalar_type() == kFloat,
-      "scale_a must have 1 Float element")
-  TORCH_CHECK_VALUE(
-      scale_b.numel() == 1 && scale_b.scalar_type() == kFloat,
-      "scale_b must have 1 Float element")
-
-  auto scaling_choice_a = ScalingType::TensorWise;
-  auto scaling_choice_b = ScalingType::TensorWise;
-
-  _scaled_gemm(
-      mat_a,
-      mat_b,
-      scale_a,
-      scale_b,
-      scaling_choice_a,
-      scaling_choice_b,
-      bias,
-      use_fast_accum,
-      out);
-
-  return out;
-}
-
-Tensor& _scaled_rowwise_rowwise(
-    const Tensor& mat_a,
-    const Tensor& mat_b,
-    const Tensor& scale_a,
-    const Tensor& scale_b,
-    const std::optional<Tensor>& bias,
-    const c10::ScalarType out_dtype,
-    bool use_fast_accum,
-    Tensor& out) {
-  // Restrictions:
-  // A, B are FP8, scales are fp32, shape M/N for A/B
-  TORCH_CHECK_VALUE(
-      isFloat8Type(mat_a.scalar_type()) && isFloat8Type(mat_b.scalar_type()),
-      "mat_a and mat_b must be fp8 types, got: ",
-      mat_a.scalar_type(),
-      mat_b.scalar_type());
-  TORCH_CHECK_VALUE(
-      scale_a.size(0) == mat_a.size(0) && scale_a.size(1) == 1,
-      "scale_a must have shape [",
-      mat_a.size(0),
-      ", 1], got [",
-      scale_a.sizes(),
-      "]");
-  TORCH_CHECK_VALUE(
-      scale_a.numel() == mat_a.size(0) && scale_a.scalar_type() == kFloat,
-      "scale_a must have ",
-      mat_a.size(0),
-      " Float elements, got ",
-      scale_a.numel())
-  TORCH_CHECK_VALUE(
-      scale_b.numel() == mat_b.size(1) && scale_b.scalar_type() == kFloat,
-      "scale_b must have ",
-      mat_b.size(1),
-      " Float elements, got ",
-      scale_b.numel())
-
-  TORCH_CHECK_VALUE(
-      scale_a.stride(1) == 1,
-      "expected scale_a.stride(1) to be 1, but got ",
-      scale_a.stride(1));
-  TORCH_CHECK_VALUE(
-      scale_b.stride(1) == 1,
-      "expected scale_b.stride(1) to be 1, but got ",
-      scale_b.stride(1));
-
-  auto scaling_choice_a = ScalingType::RowWise;
-  auto scaling_choice_b = ScalingType::RowWise;
-
-  _scaled_gemm(
-      mat_a,
-      mat_b,
-      scale_a,
-      scale_b,
-      scaling_choice_a,
-      scaling_choice_b,
-      bias,
-      use_fast_accum,
-      out);
-
-  return out;
-}
-
-// V2: Computes matrix multiply + bias while applying scaling to input and
-// output matrices Scales are only applicable when matrices are of Float8 type
-// and assumed to be equal to 1.0 by default. If output matrix type is 16 or
-// 32-bit type, scale_result is not applied. Known limitations:
-//  - Only works if mat1 is row-major and mat2 is column-major
-//  - Only works if matrices sizes are divisible by 32
-//  - If 1-dimensional tensors are used then scale_a should be size =
-//  mat1.size(0)
-//    and scale_b should have size = to mat2.size(1)
-//  Arguments:
-//    - `mat_a`: the first operand of the matrix multiply, can be type
-//    `torch.float8_e4m3fn` or `torch.float8_e5m2`
-//    - `mat_b`: the second operand of the matrix multiply, can be type
-//    `torch.float8_e4m3fn` or `torch.float8_e5m2`
-//    - `scale_a`: a tensor with the inverse scale of `mat1`, whose
-//    shape/strides/dtype depend on the scaling scheme
-//    - `scale_recipe_a`: An integer corresponding to an enum describing the
-//    scaling scheme used for `scale_a`
-//    - `swizzle_a`: An integer corresponding to a `SwizzleType` enum describing
-//    the swizzling scheme for `scale_a`.
-//        Not supported for XPU for now.
-//    - `scale_b`: a tensor with the inverse scale of `mat2`, whose
-//    shape/strides/dtype depend on the scaling scheme
-//    - `scale_recipe_b`: An integer corresponding to an enum describing the
-//    scaling scheme used for `scale_b`
-//    - `swizzle_b`: An integer corresponding to a `SwizzleType` enum describing
-//    the swizzling scheme for `scale_b`.
-//        Not supported for XPU for now.
-//    - `bias`: the bias, can be type `torch.float16` or `torch.bfloat16`
-//    - `out_dtype`: the output dtype, can either be a float8 or a higher
-//    precision floating point type
-//    - `contraction_dim`: describe which dimensions are `K` in the matmul.
-//       Not supported for XPU. Should always be empty.
-//    - `use_fast_accum`: Not supported for XPU, should always be false.
-//    - `out`: a reference to the output tensor
-Tensor& _scaled_mm_xpu_v2_out(
-    const Tensor& mat_a,
-    const Tensor& mat_b,
-    ArrayRef<Tensor> scale_a,
-    IntArrayRef scale_recipe_a,
-    IntArrayRef swizzle_a,
-    ArrayRef<Tensor> scale_b,
-    IntArrayRef scale_recipe_b,
-    IntArrayRef swizzle_b,
-    const std::optional<Tensor>& bias,
-    const std::optional<c10::ScalarType> out_dtype,
-    IntArrayRef contraction_dim,
-    bool use_fast_accum,
-    Tensor& out) {
-  TORCH_CHECK_VALUE(mat_a.dim() == 2, "mat_a must be a matrix");
-  TORCH_CHECK_VALUE(mat_b.dim() == 2, "mat_b must be a matrix");
-
-  // If any of M, K, N is 0 - return early (the tensorwise/rowwise float8 gemm
-  // kernels do not support this case).
-  if (mat_a.size(0) == 0 || mat_a.size(1) == 0 || mat_b.size(1) == 0) {
-    // `out` was created with `at::empty`. In the case where we are multiplying
-    // MxK by KxN and K is the zero dim, we need to initialize here to properly
-    // return a tensor of zeros.
-    at::native::resize_output(out, {mat_a.size(0), mat_b.size(1)});
-    if (mat_a.size(1) == 0) {
-      out.zero_();
-    }
-
-    return out;
-  }
-
-  // Note: The `contraction_dim` is not actually used for now. We will need to
-  // align this code when upstreamed CUDA code is done. Currently, only keeps
-  // the code here for check.
-
-  // Check if the input matrix sizes can be multiplied
-  // - if optional contraction dims are provided, use those
-  //   -- mostly for < 1B formats (i.e. nvfp4x2) where cheap .t() is not
-  //   available.
-  if (contraction_dim.size() > 0) {
-    TORCH_CHECK_VALUE(
-        contraction_dim.size() == 2,
-        "contraction_dim must have exactly 2 elements");
-    auto mat_a_dim = contraction_dim[0];
-    auto mat_b_dim = contraction_dim[1];
-    TORCH_CHECK_VALUE(
-        mat_a.size(mat_a_dim) == mat_b.size(mat_b_dim),
-        "mat_a and mat_b shapes cannot be multiplied (",
-        mat_a.size(0),
-        "x",
-        mat_a.size(1),
-        " and ",
-        mat_b.size(0),
-        "x",
-        mat_b.size(1),
-        ") ",
-        "with contraction dims mat_a: ",
-        mat_a_dim,
-        ", mat_b: ",
-        mat_b_dim);
-  } else {
-    TORCH_CHECK_VALUE(
-        mat_a.size(1) == mat_b.size(0),
-        "mat_a and mat_b shapes cannot be multiplied (",
-        mat_a.size(0),
-        "x",
-        mat_a.size(1),
-        " and ",
-        mat_b.size(0),
-        "x",
-        mat_b.size(1),
-        ")");
-  }
-
-  TORCH_CHECK_VALUE(
-      !bias || bias->numel() == mat_b.sizes()[1],
-      "Bias must be size ",
-      mat_b.sizes()[1],
-      " but got ",
-      bias->numel());
-
-  TORCH_CHECK_VALUE(
-      !out_dtype || *out_dtype == out.scalar_type(),
-      "out_dtype must match output matrix type");
-
-  if (bias) {
-    TORCH_CHECK_VALUE(
-        bias->scalar_type() == kFloat ||
-            bias->scalar_type() == c10::ScalarType::BFloat16 ||
-            bias->scalar_type() == c10::ScalarType::Half,
-        "Bias must be Float32 or BFloat16 or Half, but got ",
-        bias->scalar_type());
-  }
-  {
-    auto bias_ = bias.value_or(Tensor());
-    // NOLINTNEXTLINE(*c-array*)
-    TensorArg targs[]{
-        {out, "out", 0},
-        {mat_a, "mat_a", 1},
-        {mat_b, "mat_b", 2},
-        {bias_, "bias", 3},
-        {scale_a[0], "scale_a", 4},
-        {scale_b[0], "scale_b", 5}};
-    checkAllSameGPU(__func__, targs);
-  }
-  // Align with CUDA's default out to be bf16
-  auto out_dtype_ = out_dtype.value_or(c10::ScalarType::BFloat16);
-
-  // Conversion of implicitly-defined enums to explicit
-  auto scale_recipe_a_enum = convert_int_to_enum<ScalingType>(scale_recipe_a);
-  auto swizzle_a_enum = convert_int_to_enum<SwizzleType>(swizzle_a);
-  auto scale_recipe_b_enum = convert_int_to_enum<ScalingType>(scale_recipe_b);
-  auto swizzle_b_enum = convert_int_to_enum<SwizzleType>(swizzle_b);
-
-  // XPU does not support swizzle for now. So directly return false.
-  TORCH_CHECK_VALUE(
-      swizzle_a_enum[0] == at::blas::SwizzleType::NO_SWIZZLE &&
-          swizzle_b_enum[0] == at::blas::SwizzleType::NO_SWIZZLE,
-      "XPU does not support swizzle yet.");
-
-  // at this point we can start working out what we want to be doing
-  // Try to do as few steps as possible.
-  // NOTE: support is deliberately sparse, can explicitly enumerate all
-  // combinations allowed. Do this via a list of defined (name, acceptance,
-  // concrete_impl) tuples.
-  bool found_impl = false;
-  ScaledGemmImplementation gemm_impl = ScaledGemmImplementation::NONE;
-
-  for (const auto& fn_entry : scale_kernel_dispatch) {
-    const auto [name, accept_fn, scaled_gemm_impl] = fn_entry;
-    bool ok = accept_fn(
-        mat_a.scalar_type(),
-        scale_recipe_a_enum,
-        scale_a,
-        mat_b.scalar_type(),
-        scale_recipe_b_enum,
-        scale_b);
-    if (ok) {
-      gemm_impl = scaled_gemm_impl;
-      found_impl = true;
-      break;
-    }
-  }
-  TORCH_CHECK_VALUE(
-      found_impl,
-      "Invalid scaling configuration.\n"
-      "- For TensorWise scaling, a and b should be float8, scales should be float and singletons.\n"
-      "- For RowWise scaling, a and b should be float8, scales should be float, scale_a should be (",
-      mat_a.size(0),
-      ", 1) and scale_b should be (1, ",
-      mat_b.size(1),
-      "), and both should be contiguous.\n"
-      "Got mat_a.dtype()=",
-      mat_a.scalar_type(),
-      ", scale_a[0].dtype()=",
-      scale_a[0].scalar_type(),
-      ", scale_a[0].size()=",
-      scale_a[0].sizes(),
-      ", scale_a[0].stride()=",
-      scale_a[0].strides(),
-      ", ",
-      "mat_b.dtype()=",
-      mat_b.scalar_type(),
-      ", scale_b[0].dtype()=",
-      scale_b[0].scalar_type(),
-      ", scale_b[0].size()=",
-      scale_b[0].sizes(),
-      " and scale_b[0].stride()=",
-      scale_b[0].strides());
-
-  at::native::resize_output(out, {mat_a.size(0), mat_b.size(1)});
-
-  auto bias_ = bias.value_or(Tensor());
-
-  // dispatch to appropriate lower-level calls for error checking & execution
-  if (gemm_impl == ScaledGemmImplementation::TENSORWISE_TENSORWISE) {
-    return _scaled_tensorwise_tensorwise(
-        mat_a,
-        mat_b,
-        scale_a[0],
-        scale_b[0],
-        bias,
-        out_dtype_,
-        use_fast_accum,
-        out);
-  } else if (gemm_impl == ScaledGemmImplementation::ROWWISE_ROWWISE) {
-    return _scaled_rowwise_rowwise(
-        mat_a,
-        mat_b,
-        scale_a[0],
-        scale_b[0],
-        bias,
-        out_dtype_,
-        use_fast_accum,
-        out);
-  } else {
-    TORCH_CHECK_VALUE(
-        false, "Invalid state - found an implementation, but not really");
-  }
-}
-
-Tensor _scaled_mm_xpu_v2(
-    const Tensor& mat_a,
-    const Tensor& mat_b,
-    ArrayRef<Tensor> scale_a,
-    IntArrayRef scale_recipe_a,
-    IntArrayRef swizzle_a,
-    ArrayRef<Tensor> scale_b,
-    IntArrayRef scale_recipe_b,
-    IntArrayRef swizzle_b,
-    const std::optional<Tensor>& bias,
-    const std::optional<c10::ScalarType> out_dtype,
-    IntArrayRef contraction_dim,
-    bool use_fast_accum) {
-  const auto out_dtype_ = out_dtype.value_or(mat_a.scalar_type());
-  Tensor out = at::empty({0}, mat_a.options().dtype(out_dtype_));
-
-  return _scaled_mm_xpu_v2_out(
-      mat_a,
-      mat_b,
-      scale_a,
-      scale_recipe_a,
-      swizzle_a,
-      scale_b,
-      scale_recipe_b,
-      swizzle_b,
-      bias,
-      out_dtype,
-      contraction_dim,
-      use_fast_accum,
-      out);
-}
-
-} // namespace at::native

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

aten/src/ATen/native/native_functions.yaml

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

aten/src/ATen/native/quantized/cpu/qnnpack/test/avgpool-microkernel-tester.h

@@ -301,12 +301,12 @@ class AvgPoolMicrokernelTester {
           ASSERT_NEAR(
               float(int32_t(y[i * yStride() + k])), yFP[i * kc() + k], 0.5001f)
               << "at pixel " << i << ", channel " << k << ", n = " << n()
-              << ", ks = " << kh() << 'x' << kw() << " (" << ks()
+              << ", ks = " << kh() << "x" << kw() << " (" << ks()
               << "), kc = " << kc() << ", acc = " << yAcc[i * kc() + k];
           ASSERT_EQ(
               uint32_t(yRef[i * kc() + k]), uint32_t(y[i * yStride() + k]))
               << "at pixel " << i << ", channel " << k << ", n = " << n()
-              << ", ks = " << kh() << 'x' << kw() << " (" << ks()
+              << ", ks = " << kh() << "x" << kw() << " (" << ks()
               << "), kc = " << kc() << ", acc = " << yAcc[i * kc() + k];
         }
       }
@@ -396,12 +396,12 @@ class AvgPoolMicrokernelTester {
           ASSERT_NEAR(
               float(int32_t(y[i * yStride() + k])), yFP[i * kc() + k], 0.5001f)
               << "at pixel " << i << ", channel " << k << ", n = " << n()
-              << ", ks = " << kh() << 'x' << kw() << " (" << ks()
+              << ", ks = " << kh() << "x" << kw() << " (" << ks()
               << "), kc = " << kc() << ", acc = " << yAcc[i * kc() + k];
           ASSERT_EQ(
               uint32_t(yRef[i * kc() + k]), uint32_t(y[i * yStride() + k]))
               << "at pixel " << i << ", channel " << k << ", n = " << n()
-              << ", ks = " << kh() << 'x' << kw() << " (" << ks()
+              << ", ks = " << kh() << "x" << kw() << " (" << ks()
               << "), kc = " << kc() << ", acc = " << yAcc[i * kc() + k];
         }
       }

aten/src/ATen/native/quantized/cpu/qnnpack/test/maxpool-microkernel-tester.h

@@ -232,7 +232,7 @@ class MaxPoolMicrokernelTester {
           ASSERT_EQ(
               uint32_t(yRef[i * kc() + k]), uint32_t(y[i * yStride() + k]))
               << "at pixel " << i << ", channel " << k << ", n = " << n()
-              << ", ks = " << kh() << 'x' << kw() << " (" << ks()
+              << ", ks = " << kh() << "x" << kw() << " (" << ks()
               << "), kc = " << kc();
         }
       }

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

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