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1560 changed files with 19494 additions and 53617 deletions

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@ -13,4 +13,3 @@ exclude:
- "**/benchmarks/**"
- "**/test_*.py"
- "**/*_test.py"
- "tools/**"

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@ -7,13 +7,13 @@ ENV LC_ALL en_US.UTF-8
ENV LANG en_US.UTF-8
ENV LANGUAGE en_US.UTF-8
ARG DEVTOOLSET_VERSION=13
ARG DEVTOOLSET_VERSION=11
RUN yum -y update
RUN yum -y install epel-release
# install glibc-langpack-en make sure en_US.UTF-8 locale is available
RUN yum -y install glibc-langpack-en
RUN yum install -y sudo wget curl perl util-linux xz bzip2 git patch which perl zlib-devel openssl-devel yum-utils autoconf automake make gcc-toolset-${DEVTOOLSET_VERSION}-gcc gcc-toolset-${DEVTOOLSET_VERSION}-gcc-c++ gcc-toolset-${DEVTOOLSET_VERSION}-gcc-gfortran gcc-toolset-${DEVTOOLSET_VERSION}-gdb
RUN yum install -y sudo wget curl perl util-linux xz bzip2 git patch which perl zlib-devel openssl-devel yum-utils autoconf automake make gcc-toolset-${DEVTOOLSET_VERSION}-toolchain
# Just add everything as a safe.directory for git since these will be used in multiple places with git
RUN git config --global --add safe.directory '*'
ENV PATH=/opt/rh/gcc-toolset-${DEVTOOLSET_VERSION}/root/usr/bin:$PATH
@ -41,7 +41,6 @@ RUN bash ./install_conda.sh && rm install_conda.sh
# Install CUDA
FROM base as cuda
ARG CUDA_VERSION=12.6
ARG DEVTOOLSET_VERSION=13
RUN rm -rf /usr/local/cuda-*
ADD ./common/install_cuda.sh install_cuda.sh
COPY ./common/install_nccl.sh install_nccl.sh
@ -51,8 +50,7 @@ ENV CUDA_HOME=/usr/local/cuda-${CUDA_VERSION}
# Preserve CUDA_VERSION for the builds
ENV CUDA_VERSION=${CUDA_VERSION}
# Make things in our path by default
ENV PATH=/usr/local/cuda-${CUDA_VERSION}/bin:/opt/rh/gcc-toolset-${DEVTOOLSET_VERSION}/root/usr/bin:$PATH
ENV PATH=/usr/local/cuda-${CUDA_VERSION}/bin:$PATH
FROM cuda as cuda12.6
RUN bash ./install_cuda.sh 12.6
@ -70,22 +68,8 @@ FROM cuda as cuda13.0
RUN bash ./install_cuda.sh 13.0
ENV DESIRED_CUDA=13.0
FROM ${ROCM_IMAGE} as rocm_base
ARG DEVTOOLSET_VERSION=13
ENV LC_ALL en_US.UTF-8
ENV LANG en_US.UTF-8
ENV LANGUAGE en_US.UTF-8
# Install devtoolset on ROCm base image
RUN yum -y update && \
yum -y install epel-release && \
yum -y install glibc-langpack-en && \
yum install -y sudo wget curl perl util-linux xz bzip2 git patch which perl zlib-devel openssl-devel yum-utils autoconf automake make gcc-toolset-${DEVTOOLSET_VERSION}-gcc gcc-toolset-${DEVTOOLSET_VERSION}-gcc-c++ gcc-toolset-${DEVTOOLSET_VERSION}-gcc-gfortran gcc-toolset-${DEVTOOLSET_VERSION}-gdb
RUN git config --global --add safe.directory '*'
ENV PATH=/opt/rh/gcc-toolset-${DEVTOOLSET_VERSION}/root/usr/bin:$PATH
FROM rocm_base as rocm
FROM ${ROCM_IMAGE} as rocm
ARG PYTORCH_ROCM_ARCH
ARG DEVTOOLSET_VERSION=13
ENV PYTORCH_ROCM_ARCH ${PYTORCH_ROCM_ARCH}
ADD ./common/install_mkl.sh install_mkl.sh
RUN bash ./install_mkl.sh && rm install_mkl.sh
@ -104,7 +88,6 @@ COPY --from=cuda13.0 /usr/local/cuda-13.0 /usr/local/cuda-13.0
# Final step
FROM ${BASE_TARGET} as final
ARG DEVTOOLSET_VERSION=13
COPY --from=openssl /opt/openssl /opt/openssl
COPY --from=patchelf /patchelf /usr/local/bin/patchelf
COPY --from=conda /opt/conda /opt/conda

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@ -36,7 +36,11 @@ case ${DOCKER_TAG_PREFIX} in
;;
rocm*)
BASE_TARGET=rocm
PYTORCH_ROCM_ARCH="gfx900;gfx906;gfx908;gfx90a;gfx942;gfx1030;gfx1100;gfx1101;gfx1102;gfx1200;gfx1201;gfx950;gfx1150;gfx1151"
PYTORCH_ROCM_ARCH="gfx900;gfx906;gfx908;gfx90a;gfx942;gfx1030;gfx1100;gfx1101;gfx1102;gfx1200;gfx1201"
# add gfx950, gfx115x conditionally starting in ROCm 7.0
if [[ "$ROCM_VERSION" == *"7.0"* ]]; then
PYTORCH_ROCM_ARCH="${PYTORCH_ROCM_ARCH};gfx950;gfx1150;gfx1151"
fi
EXTRA_BUILD_ARGS="${EXTRA_BUILD_ARGS} --build-arg PYTORCH_ROCM_ARCH=${PYTORCH_ROCM_ARCH}"
;;
*)
@ -59,7 +63,7 @@ docker build \
--target final \
--progress plain \
--build-arg "BASE_TARGET=${BASE_TARGET}" \
--build-arg "DEVTOOLSET_VERSION=13" \
--build-arg "DEVTOOLSET_VERSION=11" \
${EXTRA_BUILD_ARGS} \
-t ${tmp_tag} \
$@ \

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@ -168,18 +168,6 @@ case "$tag" in
VISION=yes
TRITON=yes
;;
pytorch-linux-jammy-py3.11-clang12)
ANACONDA_PYTHON_VERSION=3.11
CLANG_VERSION=12
VISION=no
TRITON=no
;;
pytorch-linux-jammy-py3.12-clang12)
ANACONDA_PYTHON_VERSION=3.12
CLANG_VERSION=12
VISION=no
TRITON=no
;;
pytorch-linux-jammy-rocm-n-py3 | pytorch-linux-jammy-rocm-n-py3-benchmarks | pytorch-linux-noble-rocm-n-py3)
if [[ $tag =~ "jammy" ]]; then
ANACONDA_PYTHON_VERSION=3.10
@ -207,16 +195,13 @@ case "$tag" in
NINJA_VERSION=1.9.0
TRITON=yes
;;
pytorch-linux-noble-xpu-n-py3 | pytorch-linux-noble-xpu-n-py3-inductor-benchmarks)
pytorch-linux-jammy-xpu-n-py3)
ANACONDA_PYTHON_VERSION=3.10
GCC_VERSION=13
GCC_VERSION=11
VISION=yes
XPU_VERSION=2025.2
NINJA_VERSION=1.9.0
TRITON=yes
if [[ $tag =~ "benchmarks" ]]; then
INDUCTOR_BENCHMARKS=yes
fi
;;
pytorch-linux-jammy-py3-gcc11-inductor-benchmarks)
ANACONDA_PYTHON_VERSION=3.10
@ -260,12 +245,6 @@ case "$tag" in
HALIDE=yes
TRITON=yes
;;
pytorch-linux-jammy-cuda12.8-py3.12-pallas)
CUDA_VERSION=12.8.1
ANACONDA_PYTHON_VERSION=3.12
GCC_VERSION=11
PALLAS=yes
;;
pytorch-linux-jammy-py3.12-triton-cpu)
CUDA_VERSION=12.6
ANACONDA_PYTHON_VERSION=3.12
@ -279,9 +258,9 @@ case "$tag" in
PYTHON_VERSION=3.10
CUDA_VERSION=12.8.1
;;
pytorch-linux-jammy-aarch64-py3.10-gcc13)
pytorch-linux-jammy-aarch64-py3.10-gcc11)
ANACONDA_PYTHON_VERSION=3.10
GCC_VERSION=13
GCC_VERSION=11
ACL=yes
VISION=yes
OPENBLAS=yes
@ -289,19 +268,9 @@ case "$tag" in
# from pytorch/llvm:9.0.1 is x86 specific
SKIP_LLVM_SRC_BUILD_INSTALL=yes
;;
pytorch-linux-jammy-aarch64-py3.10-clang21)
pytorch-linux-jammy-aarch64-py3.10-gcc11-inductor-benchmarks)
ANACONDA_PYTHON_VERSION=3.10
CLANG_VERSION=21
ACL=yes
VISION=yes
OPENBLAS=yes
# snadampal: skipping llvm src build install because the current version
# from pytorch/llvm:9.0.1 is x86 specific
SKIP_LLVM_SRC_BUILD_INSTALL=yes
;;
pytorch-linux-jammy-aarch64-py3.10-gcc13-inductor-benchmarks)
ANACONDA_PYTHON_VERSION=3.10
GCC_VERSION=13
GCC_VERSION=11
ACL=yes
VISION=yes
OPENBLAS=yes
@ -387,7 +356,6 @@ docker build \
--build-arg "INDUCTOR_BENCHMARKS=${INDUCTOR_BENCHMARKS}" \
--build-arg "EXECUTORCH=${EXECUTORCH}" \
--build-arg "HALIDE=${HALIDE}" \
--build-arg "PALLAS=${PALLAS}" \
--build-arg "XPU_VERSION=${XPU_VERSION}" \
--build-arg "UNINSTALL_DILL=${UNINSTALL_DILL}" \
--build-arg "ACL=${ACL:-}" \

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@ -1 +0,0 @@
0.8.0

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@ -1 +1 @@
bfeb066872bc1e8b2d2bc0a3b295b99dd77206e7
7416ffcb92cdbe98d9f97e4e6f95247e46dfc9fd

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@ -3,7 +3,7 @@
set -eux
ACL_VERSION=${ACL_VERSION:-"v52.6.0"}
ACL_VERSION=${ACL_VERSION:-"v25.02"}
ACL_INSTALL_DIR="/acl"
# Clone ACL

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@ -8,8 +8,8 @@ if [ -n "$CLANG_VERSION" ]; then
# work around ubuntu apt-get conflicts
sudo apt-get -y -f install
wget --no-check-certificate -O - https://apt.llvm.org/llvm-snapshot.gpg.key | sudo apt-key add -
if [[ $CLANG_VERSION -ge 18 ]]; then
apt-add-repository "deb http://apt.llvm.org/jammy/ llvm-toolchain-jammy-${CLANG_VERSION} main"
if [[ $CLANG_VERSION == 18 ]]; then
apt-add-repository "deb http://apt.llvm.org/jammy/ llvm-toolchain-jammy-18 main"
fi
fi

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@ -49,20 +49,12 @@ if [ -n "$ANACONDA_PYTHON_VERSION" ]; then
export SYSROOT_DEP="sysroot_linux-64=2.17"
fi
# Install correct Python version
# Also ensure sysroot is using a modern GLIBC to match system compilers
if [ "$ANACONDA_PYTHON_VERSION" = "3.14" ]; then
as_jenkins conda create -n py_$ANACONDA_PYTHON_VERSION -y\
python="3.14.0" \
${SYSROOT_DEP} \
-c conda-forge
else
# Install correct Python version
# Also ensure sysroot is using a modern GLIBC to match system compilers
as_jenkins conda create -n py_$ANACONDA_PYTHON_VERSION -y\
python="$ANACONDA_PYTHON_VERSION" \
${SYSROOT_DEP}
fi
# libstdcxx from conda default channels are too old, we need GLIBCXX_3.4.30
# which is provided in libstdcxx 12 and up.
conda_install libstdcxx-ng=12.3.0 --update-deps -c conda-forge

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@ -10,7 +10,7 @@ else
arch_path='sbsa'
fi
NVSHMEM_VERSION=3.4.5
NVSHMEM_VERSION=3.3.24
function install_cuda {
version=$1

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@ -7,11 +7,11 @@ if [ -n "$GCC_VERSION" ]; then
# Need the official toolchain repo to get alternate packages
add-apt-repository ppa:ubuntu-toolchain-r/test
apt-get update
apt-get install -y g++-$GCC_VERSION gfortran-$GCC_VERSION
apt-get install -y g++-$GCC_VERSION
update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-"$GCC_VERSION" 50
update-alternatives --install /usr/bin/g++ g++ /usr/bin/g++-"$GCC_VERSION" 50
update-alternatives --install /usr/bin/gcov gcov /usr/bin/gcov-"$GCC_VERSION" 50
update-alternatives --install /usr/bin/gfortran gfortran /usr/bin/gfortran-"$GCC_VERSION" 50
# Cleanup package manager
apt-get autoclean && apt-get clean

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

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@ -1,56 +0,0 @@
#!/bin/bash
# Script used only in CD pipeline
set -ex
# install dependencies
dnf -y install gmp-devel libmpc-devel texinfo flex bison
cd /usr/local/src
# fetch source for gcc 13
git clone --depth 1 --single-branch -b releases/gcc-13.3.0 https://github.com/gcc-mirror/gcc.git gcc-13.3.0
mkdir -p gcc-13.3.0/build-gomp
cd gcc-13.3.0/build-gomp
# configure gcc build
# I got these flags by:
# 1. downloading the source rpm for gcc-11 on AlmaLinux 8 container
# dnf install -y dnf-plugins-core rpmdevtools
# dnf download --source libgomp
# 2. extracting the gcc.spec from the source.
# rpmdev-extract gcc-xx.src.rpm
# 3. extracting optflags and ld_flags from gcc.spec:
# rpm --eval '%{optflags}'
# rpm --eval '%{build_ldflags}'
#
# I had to remove the following flags because they didn't compile for this version of libgomp:
# -Werror=format-security
# -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1
# -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1
#
# I added -march=armv8-a -mtune=generic to make them explicit. I don't think they're strictly needed.
OPT_FLAGS='-O2 -march=armv8-a -mtune=generic'\
' -fexceptions -g -grecord-gcc-switches -pipe -Wall'\
' -Wp,-D_FORTIFY_SOURCE=2 -Wp,-D_GLIBCXX_ASSERTIONS'\
' -fstack-protector-strong -fasynchronous-unwind-tables'\
' -fstack-clash-protection'
LDFLAGS='-Wl,-z,relro -Wl,--as-needed -Wl,-z,now'
CFLAGS="$OPT_FLAGS" \
CXXFLAGS="$OPT_FLAGS" \
LDFLAGS="$LDFLAGS" \
../configure \
--prefix=/usr \
--libdir=/usr/lib64 \
--enable-languages=c,c++ \
--disable-multilib \
--disable-bootstrap \
--enable-libgomp
# only build libgomp
make -j$(nproc) all-target-libgomp
make install-target-libgomp

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@ -10,7 +10,6 @@ git clone https://github.com/OpenMathLib/OpenBLAS.git -b "${OPENBLAS_VERSION}" -
OPENBLAS_CHECKOUT_DIR="OpenBLAS"
OPENBLAS_BUILD_FLAGS="
CC=gcc
NUM_THREADS=128
USE_OPENMP=1
NO_SHARED=0

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@ -40,7 +40,11 @@ EOF
# Default url values
rocm_baseurl="http://repo.radeon.com/rocm/apt/${ROCM_VERSION}"
amdgpu_baseurl="https://repo.radeon.com/amdgpu/${ROCM_VERSION}/ubuntu"
# Add amdgpu repository
UBUNTU_VERSION_NAME=`cat /etc/os-release | grep UBUNTU_CODENAME | awk -F= '{print $2}'`
echo "deb [arch=amd64] ${amdgpu_baseurl} ${UBUNTU_VERSION_NAME} main" > /etc/apt/sources.list.d/amdgpu.list
# Add rocm repository
wget -qO - http://repo.radeon.com/rocm/rocm.gpg.key | apt-key add -

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

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

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

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@ -149,7 +149,7 @@ FROM cpu_final as rocm_final
ARG ROCM_VERSION=6.0
ARG PYTORCH_ROCM_ARCH
ENV PYTORCH_ROCM_ARCH ${PYTORCH_ROCM_ARCH}
ARG DEVTOOLSET_VERSION=13
ARG DEVTOOLSET_VERSION=11
ENV LDFLAGS="-Wl,-rpath=/opt/rh/gcc-toolset-${DEVTOOLSET_VERSION}/root/usr/lib64 -Wl,-rpath=/opt/rh/gcc-toolset-${DEVTOOLSET_VERSION}/root/usr/lib"
# Somewhere in ROCm stack, we still use non-existing /opt/rocm/hip path,
# below workaround helps avoid error

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@ -50,10 +50,6 @@ RUN rm install_ninja.sh
ENV PATH=/opt/rh/gcc-toolset-${GCCTOOLSET_VERSION}/root/usr/bin:$PATH
ENV LD_LIBRARY_PATH=/opt/rh/gcc-toolset-${GCCTOOLSET_VERSION}/root/usr/lib64:/opt/rh/gcc-toolset-${GCCTOOLSET_VERSION}/root/usr/lib:$LD_LIBRARY_PATH
# Build a newer version of libgomp than that supported in in Almalinux 8.
COPY ./common/install_libgomp.sh install_libgomp.sh
RUN bash ./install_libgomp.sh && rm install_libgomp.sh
# git236+ would refuse to run git commands in repos owned by other users
# Which causes version check to fail, as pytorch repo is bind-mounted into the image
# Override this behaviour by treating every folder as safe

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@ -87,13 +87,17 @@ case ${image} in
MANY_LINUX_VERSION="2_28"
DEVTOOLSET_VERSION="11"
GPU_IMAGE=rocm/dev-almalinux-8:${GPU_ARCH_VERSION}-complete
PYTORCH_ROCM_ARCH="gfx900;gfx906;gfx908;gfx90a;gfx942;gfx1030;gfx1100;gfx1101;gfx1102;gfx1200;gfx1201;gfx950;gfx1150;gfx1151"
PYTORCH_ROCM_ARCH="gfx900;gfx906;gfx908;gfx90a;gfx942;gfx1030;gfx1100;gfx1101;gfx1102;gfx1200;gfx1201"
# add gfx950, gfx115x conditionally starting in ROCm 7.0
if [[ "$GPU_ARCH_VERSION" == *"7.0"* ]]; then
PYTORCH_ROCM_ARCH="${PYTORCH_ROCM_ARCH};gfx950;gfx1150;gfx1151"
fi
DOCKER_GPU_BUILD_ARG="--build-arg ROCM_VERSION=${GPU_ARCH_VERSION} --build-arg PYTORCH_ROCM_ARCH=${PYTORCH_ROCM_ARCH} --build-arg DEVTOOLSET_VERSION=${DEVTOOLSET_VERSION}"
;;
manylinux2_28-builder:xpu)
TARGET=xpu_final
GPU_IMAGE=amd64/almalinux:8
DOCKER_GPU_BUILD_ARG=" --build-arg DEVTOOLSET_VERSION=13"
DOCKER_GPU_BUILD_ARG=" --build-arg DEVTOOLSET_VERSION=11"
MANY_LINUX_VERSION="2_28"
;;
*)

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@ -138,12 +138,10 @@ numba==0.60.0 ; python_version == "3.12" and platform_machine != "s390x"
#test_binary_ufuncs.py
numpy==1.22.4; python_version == "3.10"
numpy==1.26.2; python_version == "3.11" or python_version == "3.12"
numpy==2.1.2; python_version >= "3.13" and python_version < "3.14"
numpy==2.3.4; python_version >= "3.14"
numpy==2.1.2; python_version >= "3.13"
pandas==2.0.3; python_version < "3.13"
pandas==2.2.3; python_version >= "3.13" and python_version < "3.14"
pandas==2.3.3; python_version >= "3.14"
pandas==2.2.3; python_version >= "3.13"
#onnxruntime
#Description: scoring engine for Open Neural Network Exchange (ONNX) models
@ -155,8 +153,7 @@ opt-einsum==3.3
#Pinned versions: 3.3
#test that import: test_linalg.py
optree==0.13.0 ; python_version < "3.14"
optree==0.17.0 ; python_version >= "3.14"
optree==0.13.0
#Description: A library for tree manipulation
#Pinned versions: 0.13.0
#test that import: test_vmap.py, test_aotdispatch.py, test_dynamic_shapes.py,
@ -255,8 +252,7 @@ scikit-image==0.22.0
#test that import:
scipy==1.10.1 ; python_version <= "3.11"
scipy==1.14.1 ; python_version > "3.11" and python_version < "3.14"
scipy==1.16.2 ; python_version >= "3.14"
scipy==1.14.1 ; python_version >= "3.12"
# Pin SciPy because of failing distribution tests (see #60347)
#Description: scientific python
#Pinned versions: 1.10.1
@ -328,8 +324,7 @@ pywavelets==1.7.0 ; python_version >= "3.12"
#Pinned versions: 1.4.1
#test that import:
lxml==5.3.0 ; python_version < "3.14"
lxml==6.0.2 ; python_version >= "3.14"
lxml==5.3.0
#Description: This is a requirement of unittest-xml-reporting
PyGithub==2.3.0
@ -339,9 +334,7 @@ sympy==1.13.3
#Pinned versions:
#test that import:
onnx==1.19.1 ; python_version < "3.14"
# Unpin once Python 3.14 is supported. See onnxruntime issue 26309.
onnx==1.18.0 ; python_version == "3.14"
onnx==1.19.1
#Description: Required by onnx tests, and mypy and test_public_bindings.py when checking torch.onnx._internal
#Pinned versions:
#test that import:
@ -366,7 +359,7 @@ pwlf==2.2.1
#test that import: test_sac_estimator.py
# To build PyTorch itself
pyyaml==6.0.3
pyyaml==6.0.2
pyzstd
setuptools==78.1.1
packaging==23.1

View File

@ -1,11 +1,15 @@
sphinx==7.2.6
sphinx==5.3.0
#Description: This is used to generate PyTorch docs
#Pinned versions: 7.2.6
#Pinned versions: 5.3.0
pytorch_sphinx_theme2==0.2.0
#Description: This is needed to generate PyTorch docs
#Pinned versions: 0.2.0
standard-imghdr==3.13.0; python_version >= "3.13"
#Description: This is needed by Sphinx, so it needs to be added here.
# The reasons are as follows:
# 1) This module has been removed from the Python standard library since Python 3.13(https://peps.python.org/pep-0594/#imghdr);
# 2) The current version of Sphinx (5.3.0) is not compatible with Python 3.13.
# Once Sphinx is upgraded to a version compatible with Python 3.13 or later, we can remove this dependency.
-e git+https://github.com/pytorch/pytorch_sphinx_theme.git@71e55749be14ceb56e7f8211a9fb649866b87ad4#egg=pytorch_sphinx_theme2
# TODO: sphinxcontrib.katex 0.9.0 adds a local KaTeX server to speed up pre-rendering
# but it doesn't seem to work and hangs around idly. The initial thought that it is probably
# something related to Docker setup. We can investigate this later.
@ -32,17 +36,17 @@ tensorboard==2.18.0 ; python_version >= "3.13"
#Description: This is used to generate PyTorch docs
#Pinned versions: 2.13.0
breathe==4.36.0
breathe==4.34.0
#Description: This is used to generate PyTorch C++ docs
#Pinned versions: 4.36.0
#Pinned versions: 4.34.0
exhale==0.3.7
exhale==0.2.3
#Description: This is used to generate PyTorch C++ docs
#Pinned versions: 0.3.7
#Pinned versions: 0.2.3
docutils==0.20
docutils==0.16
#Description: This is used to generate PyTorch C++ docs
#Pinned versions: 0.20
#Pinned versions: 0.16
bs4==0.0.1
#Description: This is used to generate PyTorch C++ docs
@ -52,13 +56,13 @@ IPython==8.12.0
#Description: This is used to generate PyTorch functorch docs
#Pinned versions: 8.12.0
myst-nb==1.3.0
myst-nb==0.17.2
#Description: This is used to generate PyTorch functorch and torch.compile docs.
#Pinned versions: 1.3.0
#Pinned versions: 0.17.2
# The following are required to build torch.distributed.elastic.rendezvous.etcd* docs
python-etcd==0.4.5
sphinx-copybutton==0.5.0
sphinx-design==0.6.1
sphinx-design==0.4.0
sphinxcontrib-mermaid==1.0.0
myst-parser==4.0.1
myst-parser==0.18.1

View File

@ -1 +1 @@
3.5.1
3.5.0

View File

@ -54,15 +54,12 @@ ENV OPENSSL_DIR /opt/openssl
RUN rm install_openssl.sh
ARG INDUCTOR_BENCHMARKS
ARG ANACONDA_PYTHON_VERSION
ENV ANACONDA_PYTHON_VERSION=$ANACONDA_PYTHON_VERSION
COPY ./common/install_inductor_benchmark_deps.sh install_inductor_benchmark_deps.sh
COPY ./common/common_utils.sh common_utils.sh
COPY ci_commit_pins/huggingface-requirements.txt huggingface-requirements.txt
COPY ci_commit_pins/timm.txt timm.txt
COPY ci_commit_pins/torchbench.txt torchbench.txt
RUN if [ -n "${INDUCTOR_BENCHMARKS}" ]; then bash ./install_inductor_benchmark_deps.sh; fi
RUN rm install_inductor_benchmark_deps.sh common_utils.sh timm.txt huggingface-requirements.txt torchbench.txt
RUN rm install_inductor_benchmark_deps.sh common_utils.sh timm.txt huggingface-requirements.txt
# Install XPU Dependencies
ARG XPU_VERSION

View File

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

View File

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

View File

@ -6,7 +6,7 @@ dependencies = [
"GitPython==3.1.45",
"docker==7.1.0",
"pytest==7.3.2",
"uv==0.9.6"
"uv==0.9.5"
]
[tool.setuptools]

View File

@ -1,7 +1,7 @@
SHELL=/usr/bin/env bash
DOCKER_CMD ?= docker
DESIRED_ROCM ?= 7.1
DESIRED_ROCM ?= 7.0
DESIRED_ROCM_SHORT = $(subst .,,$(DESIRED_ROCM))
PACKAGE_NAME = magma-rocm
# inherit this from underlying docker image, do not pass this env var to docker
@ -16,7 +16,6 @@ DOCKER_RUN = set -eou pipefail; ${DOCKER_CMD} run --rm -i \
magma-rocm/build_magma.sh
.PHONY: all
all: magma-rocm71
all: magma-rocm70
all: magma-rocm64
@ -25,11 +24,6 @@ clean:
$(RM) -r magma-*
$(RM) -r output
.PHONY: magma-rocm71
magma-rocm71: DESIRED_ROCM := 7.1
magma-rocm71:
$(DOCKER_RUN)
.PHONY: magma-rocm70
magma-rocm70: DESIRED_ROCM := 7.0
magma-rocm70:

View File

@ -168,16 +168,14 @@ if [[ "$BUILD_ENVIRONMENT" == *xpu* ]]; then
# shellcheck disable=SC1091
source /opt/intel/oneapi/compiler/latest/env/vars.sh
# shellcheck disable=SC1091
source /opt/intel/oneapi/umf/latest/env/vars.sh
# shellcheck disable=SC1091
source /opt/intel/oneapi/ccl/latest/env/vars.sh
# shellcheck disable=SC1091
source /opt/intel/oneapi/mpi/latest/env/vars.sh
# shellcheck disable=SC1091
source /opt/intel/oneapi/pti/latest/env/vars.sh
# Enable XCCL build
export USE_XCCL=1
export USE_MPI=0
# XPU kineto feature dependencies are not fully ready, disable kineto build as temp WA
export USE_KINETO=0
export TORCH_XPU_ARCH_LIST=pvc
fi
@ -428,7 +426,7 @@ fi
if [[ "$BUILD_ENVIRONMENT" != *libtorch* && "$BUILD_ENVIRONMENT" != *bazel* ]]; then
# export test times so that potential sharded tests that'll branch off this build will use consistent data
# don't do this for libtorch as libtorch is C++ only and thus won't have python tests run on its build
PYTHONPATH=. python tools/stats/export_test_times.py
python tools/stats/export_test_times.py
fi
# don't do this for bazel or s390x or riscv64 as they don't use sccache
if [[ "$BUILD_ENVIRONMENT" != *s390x* && "$BUILD_ENVIRONMENT" != *riscv64* && "$BUILD_ENVIRONMENT" != *-bazel-* ]]; then

View File

@ -89,41 +89,23 @@ if [ "$is_main_doc" = true ]; then
make coverage
# Now we have the coverage report, we need to make sure it is empty.
# Sphinx 7.2.6+ format: python.txt contains a statistics table with a TOTAL row
# showing the undocumented count in the third column.
# Example: | TOTAL | 99.83% | 2 |
# Count the number of lines in the file and turn that number into a variable
# $lines. The `cut -f1 ...` is to only parse the number, not the filename
# Skip the report header by subtracting 2: the header will be output even if
# there are no undocumented items.
#
# Also: see docs/source/conf.py for "coverage_ignore*" items, which should
# be documented then removed from there.
# Extract undocumented count from TOTAL row in Sphinx 7.2.6 statistics table
# The table format is: | Module | Coverage | Undocumented |
# Extract the third column (undocumented count) from the TOTAL row
undocumented=$(grep "| TOTAL" build/coverage/python.txt | awk -F'|' '{print $4}' | tr -d ' ')
if [ -z "$undocumented" ] || ! [[ "$undocumented" =~ ^[0-9]+$ ]]; then
lines=$(wc -l build/coverage/python.txt 2>/dev/null |cut -f1 -d' ')
undocumented=$((lines - 2))
if [ $undocumented -lt 0 ]; then
echo coverage output not found
exit 1
elif [ "$undocumented" -gt 0 ]; then
set +x # Disable command echoing for cleaner output
echo ""
echo "====================="
echo "UNDOCUMENTED OBJECTS:"
echo "====================="
echo ""
# Find the line number of the TOTAL row and print only what comes after it
total_line=$(grep -n "| TOTAL" build/coverage/python.txt | cut -d: -f1)
if [ -n "$total_line" ]; then
# Print only the detailed list (skip the statistics table)
tail -n +$((total_line + 2)) build/coverage/python.txt
else
# Fallback to showing entire file if TOTAL line not found
cat build/coverage/python.txt
fi
echo ""
elif [ $undocumented -gt 0 ]; then
echo undocumented objects found:
cat build/coverage/python.txt
echo "Make sure you've updated relevant .rsts in docs/source!"
echo "You can reproduce locally by running 'cd docs && make coverage && tail -n +\$((grep -n \"| TOTAL\" build/coverage/python.txt | cut -d: -f1) + 2)) build/coverage/python.txt'"
set -x # Re-enable command echoing
echo "You can reproduce locally by running 'cd docs && make coverage && cat build/coverage/python.txt'"
exit 1
fi
else

View File

@ -208,8 +208,6 @@ if [[ "$BUILD_ENVIRONMENT" == *xpu* ]]; then
source /opt/intel/oneapi/ccl/latest/env/vars.sh
# shellcheck disable=SC1091
source /opt/intel/oneapi/mpi/latest/env/vars.sh
# shellcheck disable=SC1091
source /opt/intel/oneapi/pti/latest/env/vars.sh
# Check XPU status before testing
timeout 30 xpu-smi discovery || true
fi
@ -339,7 +337,7 @@ test_python() {
test_python_smoke() {
# Smoke tests for H100/B200
time python test/run_test.py --include test_matmul_cuda test_scaled_matmul_cuda inductor/test_fp8 inductor/test_max_autotune inductor/test_cutedsl_grouped_mm $PYTHON_TEST_EXTRA_OPTION --upload-artifacts-while-running
time python test/run_test.py --include test_matmul_cuda test_scaled_matmul_cuda inductor/test_fp8 inductor/test_max_autotune $PYTHON_TEST_EXTRA_OPTION --upload-artifacts-while-running
assert_git_not_dirty
}
@ -574,8 +572,6 @@ fi
if [[ "${TEST_CONFIG}" == *cpu* ]]; then
DYNAMO_BENCHMARK_FLAGS+=(--device cpu)
elif [[ "${TEST_CONFIG}" == *xpu* ]]; then
DYNAMO_BENCHMARK_FLAGS+=(--device xpu)
else
DYNAMO_BENCHMARK_FLAGS+=(--device cuda)
fi
@ -669,8 +665,6 @@ test_perf_for_dashboard() {
device=cuda_b200
elif [[ "${TEST_CONFIG}" == *rocm* ]]; then
device=rocm
elif [[ "${TEST_CONFIG}" == *xpu* ]]; then
device=xpu
fi
for mode in "${modes[@]}"; do
@ -826,11 +820,6 @@ test_inductor_halide() {
assert_git_not_dirty
}
test_inductor_pallas() {
python test/run_test.py --include inductor/test_pallas.py --verbose
assert_git_not_dirty
}
test_inductor_triton_cpu() {
python test/run_test.py --include inductor/test_triton_cpu_backend.py inductor/test_torchinductor_strided_blocks.py --verbose
assert_git_not_dirty
@ -1660,7 +1649,7 @@ test_operator_microbenchmark() {
cd "${TEST_DIR}"/benchmarks/operator_benchmark
for OP_BENCHMARK_TESTS in matmul mm addmm bmm conv; do
for OP_BENCHMARK_TESTS in matmul mm addmm bmm; 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
@ -1731,8 +1720,6 @@ elif [[ "${TEST_CONFIG}" == *inductor_distributed* ]]; then
test_inductor_distributed
elif [[ "${TEST_CONFIG}" == *inductor-halide* ]]; then
test_inductor_halide
elif [[ "${TEST_CONFIG}" == *inductor-pallas* ]]; then
test_inductor_pallas
elif [[ "${TEST_CONFIG}" == *inductor-triton-cpu* ]]; then
test_inductor_triton_cpu
elif [[ "${TEST_CONFIG}" == *inductor-micro-benchmark* ]]; then
@ -1770,7 +1757,7 @@ elif [[ "${TEST_CONFIG}" == *torchbench* ]]; then
else
# Do this after checkout_install_torchbench to ensure we clobber any
# nightlies that torchbench may pull in
if [[ "${TEST_CONFIG}" != *cpu* && "${TEST_CONFIG}" != *xpu* ]]; then
if [[ "${TEST_CONFIG}" != *cpu* ]]; then
install_torchrec_and_fbgemm
fi
PYTHONPATH=/torchbench test_dynamo_benchmark torchbench "$id"

View File

@ -70,7 +70,7 @@ sccache --zero-stats
sccache --show-stats
# Build the wheel
python -m build --wheel --no-isolation
python -m build --wheel --no-build-isolation
if ($LASTEXITCODE -ne 0) { exit 1 }
# Install the wheel locally

View File

@ -60,11 +60,9 @@ performance-*,
readability-container-size-empty,
readability-delete-null-pointer,
readability-duplicate-include,
readability-named-parameter,
readability-misplaced-array-index,
readability-redundant*,
readability-simplify-subscript-expr,
readability-static-definition-in-anonymous-namespace
readability-string-compare,
-readability-redundant-access-specifiers,
-readability-redundant-control-flow,

View File

@ -1,319 +0,0 @@
---
name: add-uint-support
description: Add unsigned integer (uint) type support to PyTorch operators by updating AT_DISPATCH macros. Use when adding support for uint16, uint32, uint64 types to operators, kernels, or when user mentions enabling unsigned types, barebones unsigned types, or uint support.
---
# Add Unsigned Integer (uint) Support to Operators
This skill helps add support for unsigned integer types (uint16, uint32, uint64) to PyTorch operators by updating their AT_DISPATCH macros.
## When to use this skill
Use this skill when:
- Adding uint16, uint32, or uint64 support to an operator
- User mentions "unsigned types", "uint support", "barebones unsigned types"
- Enabling support for kUInt16, kUInt32, kUInt64 in kernels
- Working with operator implementations that need expanded type coverage
## Quick reference
**Add unsigned types to existing dispatch:**
```cpp
// Before
AT_DISPATCH_V2(dtype, "op", AT_WRAP([&]() {
kernel<scalar_t>();
}), AT_EXPAND(AT_ALL_TYPES));
// After (method 1: add unsigned types explicitly)
AT_DISPATCH_V2(dtype, "op", AT_WRAP([&]() {
kernel<scalar_t>();
}), AT_EXPAND(AT_ALL_TYPES), AT_EXPAND(AT_BAREBONES_UNSIGNED_TYPES));
// After (method 2: use V2 integral types if AT_INTEGRAL_TYPES present)
AT_DISPATCH_V2(dtype, "op", AT_WRAP([&]() {
kernel<scalar_t>();
}), AT_EXPAND(AT_INTEGRAL_TYPES_V2), AT_EXPAND(AT_FLOATING_TYPES));
```
## Type group reference
**Unsigned type groups:**
- `AT_BAREBONES_UNSIGNED_TYPES`: kUInt16, kUInt32, kUInt64
- `AT_INTEGRAL_TYPES_V2`: AT_INTEGRAL_TYPES + AT_BAREBONES_UNSIGNED_TYPES
**Relationship:**
```cpp
AT_INTEGRAL_TYPES // kByte, kChar, kInt, kLong, kShort
AT_BAREBONES_UNSIGNED_TYPES // kUInt16, kUInt32, kUInt64
AT_INTEGRAL_TYPES_V2 // INTEGRAL_TYPES + BAREBONES_UNSIGNED_TYPES
```
## Instructions
### Step 1: Determine if conversion to V2 is needed
Check if the file uses AT_DISPATCH_V2:
**If using old AT_DISPATCH:**
- First convert to AT_DISPATCH_V2 using the at-dispatch-v2 skill
- Then proceed with adding uint support
**If already using AT_DISPATCH_V2:**
- Proceed directly to Step 2
### Step 2: Analyze the current dispatch macro
Identify what type groups are currently in use:
```cpp
AT_DISPATCH_V2(dtype, "op", AT_WRAP([&]() {
// body
}), AT_EXPAND(AT_ALL_TYPES), kHalf, kBFloat16);
^^^^^^^^^^^^^^^^^^^^^^^^^
Current type coverage
```
Common patterns:
- `AT_EXPAND(AT_ALL_TYPES)` → includes AT_INTEGRAL_TYPES + AT_FLOATING_TYPES
- `AT_EXPAND(AT_INTEGRAL_TYPES)` → signed integers only
- `AT_EXPAND(AT_FLOATING_TYPES)` → floating point types
### Step 3: Choose the uint addition method
Two approaches:
**Method 1: Add AT_BAREBONES_UNSIGNED_TYPES explicitly**
- Use when: You want to be explicit about adding uint support
- Add `AT_EXPAND(AT_BAREBONES_UNSIGNED_TYPES)` to the type list
**Method 2: Substitute AT_INTEGRAL_TYPES with AT_INTEGRAL_TYPES_V2**
- Use when: The dispatch already uses `AT_EXPAND(AT_INTEGRAL_TYPES)`
- More concise: replaces one type group with its superset
- Only applicable if AT_INTEGRAL_TYPES is present
### Step 4: Apply the transformation
**Method 1 example:**
```cpp
// Before
AT_DISPATCH_V2(
dtype,
"min_values_cuda",
AT_WRAP([&]() {
kernel_impl<scalar_t>(iter);
}),
AT_EXPAND(AT_ALL_TYPES),
kBFloat16, kHalf, kBool
);
// After (add unsigned types)
AT_DISPATCH_V2(
dtype,
"min_values_cuda",
AT_WRAP([&]() {
kernel_impl<scalar_t>(iter);
}),
AT_EXPAND(AT_ALL_TYPES),
AT_EXPAND(AT_BAREBONES_UNSIGNED_TYPES),
kBFloat16, kHalf, kBool
);
```
**Method 2 example:**
```cpp
// Before
AT_DISPATCH_V2(
dtype,
"integral_op",
AT_WRAP([&]() {
kernel<scalar_t>();
}),
AT_EXPAND(AT_INTEGRAL_TYPES)
);
// After (substitute with V2)
AT_DISPATCH_V2(
dtype,
"integral_op",
AT_WRAP([&]() {
kernel<scalar_t>();
}),
AT_EXPAND(AT_INTEGRAL_TYPES_V2)
);
```
### Step 5: Handle AT_ALL_TYPES vs individual type groups
If the dispatch uses `AT_EXPAND(AT_ALL_TYPES)`:
- `AT_ALL_TYPES` = `AT_INTEGRAL_TYPES` + `AT_FLOATING_TYPES`
- To add uint: add `AT_EXPAND(AT_BAREBONES_UNSIGNED_TYPES)` to the list
If the dispatch separately lists INTEGRAL and FLOATING:
```cpp
// Before
AT_EXPAND(AT_INTEGRAL_TYPES), AT_EXPAND(AT_FLOATING_TYPES)
// After (Method 2 preferred)
AT_EXPAND(AT_INTEGRAL_TYPES_V2), AT_EXPAND(AT_FLOATING_TYPES)
```
### Step 6: Verify all dispatch sites
Check the file for ALL dispatch macros that need uint support:
- Some operators have multiple dispatch sites (CPU, CUDA, different functions)
- Apply the transformation consistently across all sites
- Ensure each gets the same type coverage updates
### Step 7: Validate the changes
Check that:
- [ ] AT_DISPATCH_V2 format is used (not old AT_DISPATCH)
- [ ] Unsigned types are added via one of the two methods
- [ ] All relevant dispatch sites in the file are updated
- [ ] Type groups use `AT_EXPAND()`
- [ ] Arguments are properly formatted and comma-separated
## Common patterns
### Pattern 1: AT_ALL_TYPES + extras
```cpp
// Before
AT_DISPATCH_V2(dtype, "op", AT_WRAP([&]() {
kernel<scalar_t>();
}), AT_EXPAND(AT_ALL_TYPES), kHalf, kBFloat16);
// After
AT_DISPATCH_V2(dtype, "op", AT_WRAP([&]() {
kernel<scalar_t>();
}), AT_EXPAND(AT_ALL_TYPES), AT_EXPAND(AT_BAREBONES_UNSIGNED_TYPES), kHalf, kBFloat16);
```
### Pattern 2: Separate INTEGRAL + FLOATING
```cpp
// Before
AT_DISPATCH_V2(dtype, "op", AT_WRAP([&]() {
kernel<scalar_t>();
}), AT_EXPAND(AT_INTEGRAL_TYPES), AT_EXPAND(AT_FLOATING_TYPES));
// After
AT_DISPATCH_V2(dtype, "op", AT_WRAP([&]() {
kernel<scalar_t>();
}), AT_EXPAND(AT_INTEGRAL_TYPES_V2), AT_EXPAND(AT_FLOATING_TYPES));
```
### Pattern 3: Old dispatch needs conversion first
```cpp
// Before (needs v2 conversion first)
AT_DISPATCH_ALL_TYPES_AND2(kHalf, kBFloat16, dtype, "op", [&]() {
kernel<scalar_t>();
});
// After v2 conversion
AT_DISPATCH_V2(dtype, "op", AT_WRAP([&]() {
kernel<scalar_t>();
}), AT_EXPAND(AT_ALL_TYPES), kHalf, kBFloat16);
// After adding uint support
AT_DISPATCH_V2(dtype, "op", AT_WRAP([&]() {
kernel<scalar_t>();
}), AT_EXPAND(AT_ALL_TYPES), AT_EXPAND(AT_BAREBONES_UNSIGNED_TYPES), kHalf, kBFloat16);
```
## Multiple dispatch sites example
For a file with multiple functions:
```cpp
void min_values_kernel_cuda(TensorIterator& iter) {
AT_DISPATCH_V2(iter.dtype(), "min_values_cuda", AT_WRAP([&]() {
impl<scalar_t>(iter);
}), AT_EXPAND(AT_ALL_TYPES), AT_EXPAND(AT_BAREBONES_UNSIGNED_TYPES), kBFloat16, kHalf);
// ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
// Added uint support
}
void min_launch_kernel(TensorIterator &iter) {
AT_DISPATCH_V2(iter.input_dtype(), "min_cuda", AT_WRAP([&]() {
gpu_reduce_kernel<scalar_t>(iter);
}), AT_EXPAND(AT_ALL_TYPES), AT_EXPAND(AT_BAREBONES_UNSIGNED_TYPES), kBFloat16, kHalf);
// ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
// Added uint support here too
}
```
## Decision tree
Use this decision tree to determine the approach:
```
Is the file using AT_DISPATCH_V2?
├─ No → Use at-dispatch-v2 skill first, then continue
└─ Yes
└─ Does it use AT_EXPAND(AT_INTEGRAL_TYPES)?
├─ Yes → Replace with AT_EXPAND(AT_INTEGRAL_TYPES_V2)
└─ No → Add AT_EXPAND(AT_BAREBONES_UNSIGNED_TYPES) to type list
```
## Edge cases
### Case 1: Dispatch with only floating types
If the operator only supports floating point types, don't add uint support:
```cpp
// Leave as-is - floating point only operator
AT_DISPATCH_V2(dtype, "float_op", AT_WRAP([&]() {
kernel<scalar_t>();
}), AT_EXPAND(AT_FLOATING_TYPES), kHalf);
```
### Case 2: Complex types present
Unsigned types work alongside complex types:
```cpp
AT_DISPATCH_V2(dtype, "op", AT_WRAP([&]() {
kernel<scalar_t>();
}), AT_EXPAND(AT_ALL_TYPES),
AT_EXPAND(AT_BAREBONES_UNSIGNED_TYPES),
AT_EXPAND(AT_COMPLEX_TYPES),
kHalf, kBFloat16);
```
### Case 3: Already has uint support
Check if uint types are already present:
- If `AT_INTEGRAL_TYPES_V2` is used → already has uint support
- If `AT_BAREBONES_UNSIGNED_TYPES` is already in list → already has uint support
- Skip the file if uint support is already present
## Workflow
When asked to add uint support:
1. Read the target file
2. Check if using AT_DISPATCH_V2:
- If not → use at-dispatch-v2 skill first
3. Identify all dispatch macro sites
4. For each dispatch:
- Analyze current type groups
- Choose method (add BAREBONES_UNSIGNED or upgrade to V2)
- Apply transformation with Edit tool
5. Show the user the changes
6. Explain what was modified
## Important notes
- Always check if v2 conversion is needed first
- Apply changes consistently across all dispatch sites in the file
- Method 2 (AT_INTEGRAL_TYPES_V2) is cleaner when applicable
- Method 1 (explicit AT_BAREBONES_UNSIGNED_TYPES) is more explicit
- Unsigned types are: kUInt16, kUInt32, kUInt64 (not kByte which is uint8)
- Some operators may not semantically support unsigned types - use judgment
## Testing
After adding uint support, the operator should accept uint16, uint32, and uint64 tensors. The user is responsible for functional testing.

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@ -1,305 +0,0 @@
---
name: at-dispatch-v2
description: Convert PyTorch AT_DISPATCH macros to AT_DISPATCH_V2 format in ATen C++ code. Use when porting AT_DISPATCH_ALL_TYPES_AND*, AT_DISPATCH_FLOATING_TYPES*, or other dispatch macros to the new v2 API. For ATen kernel files, CUDA kernels, and native operator implementations.
---
# AT_DISPATCH to AT_DISPATCH_V2 Converter
This skill helps convert PyTorch's legacy AT_DISPATCH macros to the new AT_DISPATCH_V2 format, as defined in `aten/src/ATen/Dispatch_v2.h`.
## When to use this skill
Use this skill when:
- Converting AT_DISPATCH_* macros to AT_DISPATCH_V2
- Porting ATen kernels to use the new dispatch API
- Working with files in `aten/src/ATen/native/` that use dispatch macros
- User mentions "AT_DISPATCH", "dispatch v2", "Dispatch_v2.h", or macro conversion
## Quick reference
**Old format:**
```cpp
AT_DISPATCH_ALL_TYPES_AND3(kBFloat16, kHalf, kBool, dtype, "kernel_name", [&]() {
// lambda body
});
```
**New format:**
```cpp
AT_DISPATCH_V2(dtype, "kernel_name", AT_WRAP([&]() {
// lambda body
}), AT_EXPAND(AT_ALL_TYPES), kBFloat16, kHalf, kBool);
```
## Key transformations
1. **Reorder arguments**: `scalar_type` and `name` come first, then lambda, then types
2. **Wrap the lambda**: Use `AT_WRAP(lambda)` to handle internal commas
3. **Expand type groups**: Use `AT_EXPAND(AT_ALL_TYPES)` instead of implicit expansion
4. **List individual types**: Add extra types (kHalf, kBFloat16, etc.) after expanded groups
5. **Add include**: `#include <ATen/Dispatch_v2.h>` near other Dispatch includes
## Instructions
### Step 1: Add the Dispatch_v2.h include
Add the v2 header near the existing `#include <ATen/Dispatch.h>`:
```cpp
#include <ATen/Dispatch.h>
#include <ATen/Dispatch_v2.h>
```
Keep the old Dispatch.h include for now (other code may still need it).
### Step 2: Identify the old dispatch pattern
Common patterns to convert:
- `AT_DISPATCH_ALL_TYPES_AND{2,3,4}(type1, type2, ..., scalar_type, name, lambda)`
- `AT_DISPATCH_FLOATING_TYPES_AND{2,3}(type1, type2, ..., scalar_type, name, lambda)`
- `AT_DISPATCH_ALL_TYPES_AND_COMPLEX_AND{2,3}(type1, ..., scalar_type, name, lambda)`
- `AT_DISPATCH_FLOATING_AND_COMPLEX_TYPES_AND{2,3}(type1, ..., scalar_type, name, lambda)`
### Step 3: Map the old macro to type groups
Identify which type group macro corresponds to the base types:
| Old macro base | AT_DISPATCH_V2 type group |
|----------------|---------------------------|
| `ALL_TYPES` | `AT_EXPAND(AT_ALL_TYPES)` |
| `FLOATING_TYPES` | `AT_EXPAND(AT_FLOATING_TYPES)` |
| `INTEGRAL_TYPES` | `AT_EXPAND(AT_INTEGRAL_TYPES)` |
| `COMPLEX_TYPES` | `AT_EXPAND(AT_COMPLEX_TYPES)` |
| `ALL_TYPES_AND_COMPLEX` | `AT_EXPAND(AT_ALL_TYPES_AND_COMPLEX)` |
For combined patterns, use multiple `AT_EXPAND()` entries:
```cpp
// Old: AT_DISPATCH_ALL_TYPES_AND_COMPLEX_AND2(...)
// New: AT_EXPAND(AT_ALL_TYPES), AT_EXPAND(AT_COMPLEX_TYPES), type1, type2
```
### Step 4: Extract the individual types
From `AT_DISPATCH_*_AND2(type1, type2, ...)` or `AT_DISPATCH_*_AND3(type1, type2, type3, ...)`, extract the individual types (type1, type2, etc.).
These become the trailing arguments after the type group:
```cpp
AT_DISPATCH_V2(..., AT_EXPAND(AT_ALL_TYPES), kBFloat16, kHalf, kBool)
^^^^^^^^^^^^^^^^^^^^^^^^
Individual types from AND3
```
### Step 5: Transform to AT_DISPATCH_V2
Apply the transformation:
**Pattern:**
```cpp
AT_DISPATCH_V2(
scalar_type, // 1st: The dtype expression
"name", // 2nd: The debug string
AT_WRAP(lambda), // 3rd: The lambda wrapped in AT_WRAP
type_groups, // 4th+: Type groups with AT_EXPAND()
individual_types // Last: Individual types
)
```
**Example transformation:**
```cpp
// BEFORE
AT_DISPATCH_ALL_TYPES_AND3(
kBFloat16, kHalf, kBool,
iter.dtype(),
"min_values_cuda",
[&]() {
min_values_kernel_cuda_impl<scalar_t>(iter);
}
);
// AFTER
AT_DISPATCH_V2(
iter.dtype(),
"min_values_cuda",
AT_WRAP([&]() {
min_values_kernel_cuda_impl<scalar_t>(iter);
}),
AT_EXPAND(AT_ALL_TYPES),
kBFloat16, kHalf, kBool
);
```
### Step 6: Handle multi-line lambdas
For lambdas with internal commas or complex expressions, AT_WRAP is essential:
```cpp
AT_DISPATCH_V2(
dtype,
"complex_kernel",
AT_WRAP([&]() {
gpu_reduce_kernel<scalar_t, scalar_t>(
iter,
MinOps<scalar_t>{},
thrust::pair<scalar_t, int64_t>(upper_bound(), 0) // Commas inside!
);
}),
AT_EXPAND(AT_ALL_TYPES)
);
```
### Step 7: Verify the conversion
Check that:
- [ ] `AT_WRAP()` wraps the entire lambda
- [ ] Type groups use `AT_EXPAND()`
- [ ] Individual types don't have `AT_EXPAND()` (just `kBFloat16`, not `AT_EXPAND(kBFloat16)`)
- [ ] Argument order is: scalar_type, name, lambda, types
- [ ] Include added: `#include <ATen/Dispatch_v2.h>`
## Type group reference
Available type group macros (use with `AT_EXPAND()`):
```cpp
AT_INTEGRAL_TYPES // kByte, kChar, kInt, kLong, kShort
AT_FLOATING_TYPES // kDouble, kFloat
AT_COMPLEX_TYPES // kComplexDouble, kComplexFloat
AT_QINT_TYPES // kQInt8, kQUInt8, kQInt32
AT_ALL_TYPES // INTEGRAL_TYPES + FLOATING_TYPES
AT_ALL_TYPES_AND_COMPLEX // ALL_TYPES + COMPLEX_TYPES
AT_INTEGRAL_TYPES_V2 // INTEGRAL_TYPES + unsigned types
AT_BAREBONES_UNSIGNED_TYPES // kUInt16, kUInt32, kUInt64
AT_FLOAT8_TYPES // Float8 variants
```
## Common patterns
### Pattern: AT_DISPATCH_ALL_TYPES_AND2
```cpp
// Before
AT_DISPATCH_ALL_TYPES_AND2(kHalf, kBFloat16, dtype, "op", [&]() {
kernel<scalar_t>(data);
});
// After
AT_DISPATCH_V2(dtype, "op", AT_WRAP([&]() {
kernel<scalar_t>(data);
}), AT_EXPAND(AT_ALL_TYPES), kHalf, kBFloat16);
```
### Pattern: AT_DISPATCH_FLOATING_TYPES_AND3
```cpp
// Before
AT_DISPATCH_FLOATING_TYPES_AND3(kHalf, kBFloat16, kFloat8_e4m3fn,
tensor.scalar_type(), "float_op", [&] {
process<scalar_t>(tensor);
});
// After
AT_DISPATCH_V2(tensor.scalar_type(), "float_op", AT_WRAP([&] {
process<scalar_t>(tensor);
}), AT_EXPAND(AT_FLOATING_TYPES), kHalf, kBFloat16, kFloat8_e4m3fn);
```
### Pattern: AT_DISPATCH_ALL_TYPES_AND_COMPLEX_AND2
```cpp
// Before
AT_DISPATCH_ALL_TYPES_AND_COMPLEX_AND2(
kComplexHalf, kHalf,
self.scalar_type(),
"complex_op",
[&] {
result = compute<scalar_t>(self);
}
);
// After
AT_DISPATCH_V2(
self.scalar_type(),
"complex_op",
AT_WRAP([&] {
result = compute<scalar_t>(self);
}),
AT_EXPAND(AT_ALL_TYPES),
AT_EXPAND(AT_COMPLEX_TYPES),
kComplexHalf,
kHalf
);
```
## Edge cases
### Case 1: No extra types (rare)
```cpp
// Before
AT_DISPATCH_ALL_TYPES(dtype, "op", [&]() { kernel<scalar_t>(); });
// After
AT_DISPATCH_V2(dtype, "op", AT_WRAP([&]() {
kernel<scalar_t>();
}), AT_EXPAND(AT_ALL_TYPES));
```
### Case 2: Many individual types (AND4, AND5, etc.)
```cpp
// Before
AT_DISPATCH_FLOATING_TYPES_AND4(kHalf, kBFloat16, kFloat8_e4m3fn, kFloat8_e5m2,
dtype, "float8_op", [&]() { kernel<scalar_t>(); });
// After
AT_DISPATCH_V2(dtype, "float8_op", AT_WRAP([&]() {
kernel<scalar_t>();
}), AT_EXPAND(AT_FLOATING_TYPES), kHalf, kBFloat16, kFloat8_e4m3fn, kFloat8_e5m2);
```
### Case 3: Lambda with no captures
```cpp
// Before
AT_DISPATCH_ALL_TYPES_AND2(kHalf, kBool, dtype, "op", []() {
static_kernel<scalar_t>();
});
// After
AT_DISPATCH_V2(dtype, "op", AT_WRAP([]() {
static_kernel<scalar_t>();
}), AT_EXPAND(AT_ALL_TYPES), kHalf, kBool);
```
## Benefits of AT_DISPATCH_V2
1. **No arity in macro name**: Don't need different macros for AND2, AND3, AND4
2. **Composable type sets**: Mix and match type groups with `AT_EXPAND()`
3. **Extensible**: Easy to add more types without hitting macro limits
4. **Clearer**: Type groups are explicit, not implicit in macro name
## Important notes
- Keep `#include <ATen/Dispatch.h>` - other code may need it
- The `AT_WRAP()` is mandatory - prevents comma parsing issues in the lambda
- Type groups need `AT_EXPAND()`, individual types don't
- The v2 API is in `aten/src/ATen/Dispatch_v2.h` - refer to it for full docs
- See the header file for the Python script to regenerate the macro implementation
## Workflow
When asked to convert AT_DISPATCH macros:
1. Read the file to identify all AT_DISPATCH uses
2. Add `#include <ATen/Dispatch_v2.h>` if not present
3. For each dispatch macro:
- Identify the pattern and extract components
- Map the base type group
- Extract individual types
- Construct the AT_DISPATCH_V2 call
- Apply with Edit tool
4. Show the user the complete converted file
5. Explain what was changed
Do NOT compile or test the code - focus on accurate conversion only.

View File

@ -1,11 +1,11 @@
name: 🚀 New Feature for Release
name: 🚀 Release highlight for proposed Feature
description: Submit a Release highlight for proposed Feature
labels: ["release-feature-request"]
body:
- type: textarea
attributes:
label: New Feature for Release
label: Release highlight for proposed Feature
description: >
Example: “A torch.special module, analogous to SciPy's special module.”
- type: input

View File

@ -27,9 +27,7 @@ runs:
docker system prune -af
diskspace_new=$(df -H --output=pcent ${docker_root_dir} | sed -n 2p | sed 's/%//' | sed 's/ //')
if [[ "$diskspace_new" -gt "$diskspace_cutoff" ]] ; then
diskspace_cutoff_int=$((diskspace_cutoff + 0))
difference=$((100 - diskspace_cutoff_int))
echo "Error: Available diskspace is less than $difference percent. Not enough diskspace."
echo "Error: Available diskspace is less than $diskspace_cutoff percent. Not enough diskspace."
echo "$msg"
exit 1
else

View File

@ -38,9 +38,9 @@ runs:
run: |
python3 .github/scripts/pytest_cache.py \
--download \
--cache_dir "$GITHUB_WORKSPACE/$CACHE_DIR" \
--pr_identifier "$GITHUB_REF" \
--job_identifier "$JOB_IDENTIFIER" \
--temp_dir "$RUNNER_TEMP" \
--repo "$REPO" \
--bucket "$BUCKET" \
--cache_dir $GITHUB_WORKSPACE/$CACHE_DIR \
--pr_identifier $GITHUB_REF \
--job_identifier $JOB_IDENTIFIER \
--temp_dir $RUNNER_TEMP \
--repo $REPO \
--bucket $BUCKET \

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@ -47,11 +47,11 @@ runs:
run: |
python3 .github/scripts/pytest_cache.py \
--upload \
--cache_dir "$GITHUB_WORKSPACE/$CACHE_DIR" \
--pr_identifier "$GITHUB_REF" \
--job_identifier "$JOB_IDENTIFIER" \
--sha "$SHA" \
--test_config "$TEST_CONFIG" \
--shard "$SHARD" \
--repo "$REPO" \
--temp_dir "$RUNNER_TEMP" \
--cache_dir $GITHUB_WORKSPACE/$CACHE_DIR \
--pr_identifier $GITHUB_REF \
--job_identifier $JOB_IDENTIFIER \
--sha $SHA \
--test_config $TEST_CONFIG \
--shard $SHARD \
--repo $REPO \
--temp_dir $RUNNER_TEMP \

View File

@ -1 +1 @@
ad5816f0eee1c873df1b7d371c69f1f811a89387
69bbe7363897764f9e758d851cd0340147d27f94

View File

@ -1 +1 @@
ccb801b88af136454798b945175c4c87e636ac33
218d2ab791d437309f91e0486eb9fa7f00badc17

View File

@ -1 +1 @@
e4d25697f9dc5eedaf8f0a5bf085c62c5455a53a
df6798dfb931ce7c7fe5bed2447cd1092a5981af

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@ -1,125 +0,0 @@
# PyTorch Copilot Instructions
This is the PyTorch machine learning framework codebase. These instructions help AI agents navigate and contribute effectively.
## Architecture Overview
### Core Components
- **c10/** - Core library (C++-10 compatible) for essential, binary-size-conscious functionality
- **aten/** - ATen tensor library (C++), PyTorch's foundation without autograd
- `aten/src/ATen/native/` - Modern operator implementations (CPU/CUDA/MPS/sparse)
- `aten/src/ATen/native/native_functions.yaml` - **Critical**: Declarative operator registry
- **torch/** - Python bindings and public API
- `torch/csrc/` - C++ Python bindings (hand-written and generated)
- `torch/csrc/autograd/` - Reverse-mode automatic differentiation
- `torch/csrc/jit/` - TorchScript JIT compiler
- **torchgen/** - Code generation tooling that reads `native_functions.yaml`
- **tools/** - Build scripts, autograd derivatives, code generation
### The Code Generation Workflow
**Most operator changes require editing `native_functions.yaml`**, not direct C++ files. This YAML file:
1. Declares operator signatures, variants (function/method), and dispatch behavior
2. Gets processed by `torchgen/` to generate C++/Python bindings
3. Produces headers in `build/aten/src/ATen/` during compilation
Example entry structure:
```yaml
- func: my_op(Tensor self, Scalar alpha=1) -> Tensor
variants: function, method
dispatch:
CPU: my_op_cpu
CUDA: my_op_cuda
```
After editing `native_functions.yaml`, implement kernels in `aten/src/ATen/native/` (see `aten/src/ATen/native/README.md`).
## Development Workflows
### Building from Source
**Never run `setup.py` directly** - use pip with editable install:
```bash
python -m pip install --no-build-isolation -v -e .
```
Speed up builds:
- `DEBUG=1` - Debug symbols with `-g -O0`
- `USE_CUDA=0` - Skip CUDA compilation
- `BUILD_TEST=0` - Skip C++ test binaries
- Install `ninja` (`pip install ninja`) for faster builds
- Use `ccache` for incremental compilation caching
Rebuild specific targets: `(cd build && ninja <target>)`
### Testing
**Critical**: DO NOT run entire test suites. Run specific tests only:
```bash
python test/test_torch.py TestTorch.test_specific_case
```
**Test structure**: All tests use `torch.testing._internal.common_utils`:
```python
from torch.testing._internal.common_utils import run_tests, TestCase
class TestFeature(TestCase):
def test_something(self):
# Use self.assertEqual for tensor comparisons
pass
if __name__ == "__main__":
run_tests()
```
**For bug fixes**: Create a standalone reproduction script first, verify it fails, then fix and add to appropriate test file.
### Linting
Run linter (not pre-commit): `lintrunner -a` (auto-applies fixes)
## Project-Specific Conventions
### Memory and Storage
- **Storage is never nullptr** (but `StorageImpl.data` may be nullptr for unallocated outputs)
- CUDA device info lives in storage objects
### Python-C++ Integration (`torch/csrc/`)
- Always include `Python.h` **first** to avoid `_XOPEN_SOURCE` redefinition errors
- Use `pybind11::gil_scoped_acquire` before calling Python API or using `THPObjectPtr`
- Wrap entry points with `HANDLE_TH_ERRORS` / `END_HANDLE_TH_ERRORS` for exception conversion
### Dispatch System
- PyTorch uses operator dispatch to route calls to backend-specific kernels
- Prefer `CompositeExplicitAutograd` dispatch when writing device-agnostic compound ops
- See `aten/src/ATen/native/README.md` for dispatch keyword guidance
## Git Workflow (AI Agent Specific)
When preparing PRs from this environment:
```bash
git stash -u
git reset --hard $(cat /tmp/orig_work.txt) # Reset to LOCAL branch
git stash pop
# Resolve conflicts if necessary
```
## Common Gotchas
1. **Editing generated files** - If it's in `build/`, don't edit it. Edit the source template or `native_functions.yaml`
2. **NVCC template compilation** - NVCC is stricter about C++ than gcc/clang; code working on Linux may fail Windows CI
3. **Windows symbol visibility** - Use `TORCH_API` macros for exported symbols (required on Windows, optional on Linux)
4. **No internet access** - DO NOT attempt to install dependencies during development
## Key Files Reference
- `AGENTS.md` - Instructions specific to AI coding agents
- `CONTRIBUTING.md` - Comprehensive human contributor guide
- `GLOSSARY.md` - Terminology (ATen, kernels, operations, JIT, TorchScript)
- `aten/src/ATen/native/README.md` - Operator implementation guide
- `tools/autograd/derivatives.yaml` - Gradient definitions for autograd
## Performance Debugging
Use `TORCH_SHOW_CPP_STACKTRACES=1` for C++ traces in Python errors. For profiling, prefer `py-spy` over manual instrumentation.

22
.github/labeler.yml vendored
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@ -138,8 +138,7 @@
- test/test_matmul_cuda.py
- test/test_scaled_matmul_cuda.py
- test/inductor/test_fp8.py
- aten/src/ATen/native/cuda/*Blas.cpp
- aten/src/ATen/cuda/CUDA*Blas.*
- aten/src/ATen/native/cuda/Blas.cpp
- torch/**/*cublas*
- torch/_inductor/kernel/mm.py
- test/inductor/test_max_autotune.py
@ -149,8 +148,7 @@
- test/test_matmul_cuda.py
- test/test_scaled_matmul_cuda.py
- test/inductor/test_fp8.py
- aten/src/ATen/native/cuda/*Blas.cpp
- aten/src/ATen/cuda/CUDA*Blas.*
- aten/src/ATen/native/cuda/Blas.cpp
- torch/**/*cublas*
- torch/_inductor/kernel/mm.py
- test/inductor/test_max_autotune.py
@ -160,21 +158,7 @@
- test/test_matmul_cuda.py
- test/test_scaled_matmul_cuda.py
- test/inductor/test_fp8.py
- aten/src/ATen/native/cuda/*Blas.cpp
- aten/src/ATen/cuda/CUDA*Blas.*
- aten/src/ATen/native/cuda/Blas.cpp
- torch/_inductor/kernel/mm.py
- test/inductor/test_max_autotune.py
- third_party/fbgemm
"ciflow/mps":
- aten/src/ATen/mps/**
- aten/src/ATen/native/mps/**
- torch/_inductor/codegen/mps.py
- test/test_mps.py
- test/inductor/test_mps_basic.py
"ciflow/h100-symm-mem":
- torch/csrc/distributed/c10d/symm_mem/**
- torch/distributed/_symmetric_memory/**
- test/distributed/**/*mem*
- test/distributed/**/*mem*/**

View File

@ -540,26 +540,6 @@
- Lint
- pull
- name: PrivateUse1
patterns:
- torch/accelerator/**
- torch/utils/backend_registration.py
- torch/csrc/acc/**
- torch/csrc/DeviceAccelerator.*
- torch/csrc/profiler/standalone/privateuse1_observer.*
- aten/src/ATen/DeviceAccelerator.*
- aten/src/ATen/core/GeneratorForPrivateuseone.*
- aten/src/ATen/detail/PrivateUse1HooksInterface.*
- docs/source/accelerator/**
- test/cpp_extensions/open_registration_extension/torch_openreg/**
approved_by:
- albanD
- fffrog
mandatory_checks_name:
- EasyCLA
- Lint
- pull
- name: superuser
patterns:
- '*'

View File

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

View File

@ -2,8 +2,8 @@ tracking_issue: 24422
ciflow_tracking_issue: 64124
ciflow_push_tags:
- ciflow/b200
- ciflow/b200-distributed
- ciflow/b200-symm-mem
- ciflow/b200-distributed
- ciflow/binaries
- ciflow/binaries_libtorch
- ciflow/binaries_wheel
@ -19,29 +19,23 @@ ciflow_push_tags:
- ciflow/inductor-perf-test-nightly-rocm-mi300
- ciflow/inductor-perf-test-nightly-rocm-mi355
- ciflow/inductor-perf-test-nightly-x86-zen
- ciflow/inductor-perf-test-nightly-xpu
- ciflow/inductor-periodic
- ciflow/inductor-rocm
- ciflow/inductor-rocm-mi200
- ciflow/inductor-rocm-mi300
- ciflow/linux-aarch64
- ciflow/mps
- ciflow/nightly
- ciflow/op-benchmark
- ciflow/periodic
- ciflow/periodic-rocm-mi200
- ciflow/periodic-rocm-mi300
- ciflow/pull
- ciflow/quantization-periodic
- ciflow/riscv64
- ciflow/rocm
- ciflow/rocm-mi200
- ciflow/rocm-mi300
- ciflow/rocm-mi355
- ciflow/rocm-navi31
- ciflow/s390
- ciflow/slow
- ciflow/slow-rocm-mi200
- ciflow/torchbench
- ciflow/triton_binaries
- ciflow/trunk

View File

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

View File

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

View File

@ -11,24 +11,18 @@ architectures:
* Latest XPU
"""
import json
import os
import re
from pathlib import Path
from typing import Optional
SCRIPT_DIR = Path(__file__).absolute().parent
REPO_ROOT = SCRIPT_DIR.parent.parent
# NOTE: Please also update the CUDA sources in `PIP_SOURCES` in tools/nightly.py when changing this
CUDA_ARCHES = ["12.6", "12.8", "12.9", "13.0"]
CUDA_STABLE = "12.8"
CUDA_ARCHES_FULL_VERSION = {
"12.6": "12.6.3",
"12.8": "12.8.1",
"12.9": "12.9.1",
"13.0": "13.0.0",
"13.0": "13.0.2",
}
CUDA_ARCHES_CUDNN_VERSION = {
"12.6": "9",
@ -37,7 +31,8 @@ CUDA_ARCHES_CUDNN_VERSION = {
"13.0": "9",
}
ROCM_ARCHES = ["7.0", "7.1"]
# NOTE: Please also update the ROCm sources in `PIP_SOURCES` in tools/nightly.py when changing this
ROCM_ARCHES = ["6.4", "7.0"]
XPU_ARCHES = ["xpu"]
@ -61,7 +56,7 @@ PYTORCH_EXTRA_INSTALL_REQUIREMENTS = {
"nvidia-cusparse-cu12==12.5.4.2; platform_system == 'Linux' | "
"nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' | "
"nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' | "
"nvidia-nvshmem-cu12==3.4.5; platform_system == 'Linux' | "
"nvidia-nvshmem-cu12==3.3.24; platform_system == 'Linux' | "
"nvidia-nvtx-cu12==12.6.77; platform_system == 'Linux' | "
"nvidia-nvjitlink-cu12==12.6.85; platform_system == 'Linux' | "
"nvidia-cufile-cu12==1.11.1.6; platform_system == 'Linux'"
@ -78,7 +73,7 @@ PYTORCH_EXTRA_INSTALL_REQUIREMENTS = {
"nvidia-cusparse-cu12==12.5.8.93; platform_system == 'Linux' | "
"nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' | "
"nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' | "
"nvidia-nvshmem-cu12==3.4.5; platform_system == 'Linux' | "
"nvidia-nvshmem-cu12==3.3.24; platform_system == 'Linux' | "
"nvidia-nvtx-cu12==12.8.90; platform_system == 'Linux' | "
"nvidia-nvjitlink-cu12==12.8.93; platform_system == 'Linux' | "
"nvidia-cufile-cu12==1.13.1.3; platform_system == 'Linux'"
@ -95,7 +90,7 @@ PYTORCH_EXTRA_INSTALL_REQUIREMENTS = {
"nvidia-cusparse-cu12==12.5.10.65; platform_system == 'Linux' | "
"nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' | "
"nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' | "
"nvidia-nvshmem-cu12==3.4.5; platform_system == 'Linux' | "
"nvidia-nvshmem-cu12==3.3.20; platform_system == 'Linux' | "
"nvidia-nvtx-cu12==12.9.79; platform_system == 'Linux' | "
"nvidia-nvjitlink-cu12==12.9.86; platform_system == 'Linux' | "
"nvidia-cufile-cu12==1.14.1.1; platform_system == 'Linux'"
@ -112,7 +107,7 @@ PYTORCH_EXTRA_INSTALL_REQUIREMENTS = {
"nvidia-cusparse==12.6.3.3; platform_system == 'Linux' | "
"nvidia-cusparselt-cu13==0.8.0; platform_system == 'Linux' | "
"nvidia-nccl-cu13==2.27.7; platform_system == 'Linux' | "
"nvidia-nvshmem-cu13==3.4.5; platform_system == 'Linux' | "
"nvidia-nvshmem-cu13==3.3.24; platform_system == 'Linux' | "
"nvidia-nvtx==13.0.85; platform_system == 'Linux' | "
"nvidia-nvjitlink==13.0.88; platform_system == 'Linux' | "
"nvidia-cufile==1.15.1.6; platform_system == 'Linux'"
@ -142,48 +137,9 @@ PYTORCH_EXTRA_INSTALL_REQUIREMENTS = {
}
# Used by tools/nightly.py
PYTORCH_NIGHTLY_PIP_INDEX_URL = "https://download.pytorch.org/whl/nightly"
NIGHTLY_SOURCE_MATRIX = {
"cpu": dict(
name="cpu",
index_url=f"{PYTORCH_NIGHTLY_PIP_INDEX_URL}/cpu",
supported_platforms=["Linux", "macOS", "Windows"],
accelerator="cpu",
)
}
CUDA_NIGHTLY_SOURCE_MATRIX = {
f"cuda-{major}.{minor}": dict(
name=f"cuda-{major}.{minor}",
index_url=f"{PYTORCH_NIGHTLY_PIP_INDEX_URL}/cu{major}{minor}",
supported_platforms=["Linux", "Windows"],
accelerator="cuda",
)
for major, minor in (map(int, version.split(".")) for version in CUDA_ARCHES)
}
ROCM_NIGHTLY_SOURCE_MATRIX = {
f"rocm-{major}.{minor}": dict(
name=f"rocm-{major}.{minor}",
index_url=f"{PYTORCH_NIGHTLY_PIP_INDEX_URL}/rocm{major}.{minor}",
supported_platforms=["Linux"],
accelerator="rocm",
)
for major, minor in (map(int, version.split(".")) for version in ROCM_ARCHES)
}
XPU_NIGHTLY_SOURCE_MATRIX = {
"xpu": dict(
name="xpu",
index_url=f"{PYTORCH_NIGHTLY_PIP_INDEX_URL}/xpu",
supported_platforms=["Linux"],
accelerator="xpu",
)
}
NIGHTLY_SOURCE_MATRIX.update(CUDA_NIGHTLY_SOURCE_MATRIX)
NIGHTLY_SOURCE_MATRIX.update(ROCM_NIGHTLY_SOURCE_MATRIX)
NIGHTLY_SOURCE_MATRIX.update(XPU_NIGHTLY_SOURCE_MATRIX)
def get_nccl_wheel_version(arch_version: str) -> str:
import re
requirements = map(
str.strip, re.split("[;|]", PYTORCH_EXTRA_INSTALL_REQUIREMENTS[arch_version])
)
@ -191,14 +147,17 @@ def get_nccl_wheel_version(arch_version: str) -> str:
def read_nccl_pin(arch_version: str) -> str:
nccl_pin_path = (
REPO_ROOT
/ ".ci"
/ "docker"
/ "ci_commit_pins"
/ f"nccl-cu{arch_version[:2]}.txt"
from pathlib import Path
nccl_pin_path = os.path.join(
Path(__file__).absolute().parents[2],
".ci",
"docker",
"ci_commit_pins",
f"nccl-cu{arch_version[:2]}.txt",
)
return nccl_pin_path.read_text().strip()
with open(nccl_pin_path) as f:
return f.read().strip()
def validate_nccl_dep_consistency(arch_version: str) -> None:
@ -206,8 +165,7 @@ def validate_nccl_dep_consistency(arch_version: str) -> None:
wheel_ver = get_nccl_wheel_version(arch_version)
if not nccl_release_tag.startswith(f"v{wheel_ver}"):
raise RuntimeError(
f"{arch_version} NCCL release tag version {nccl_release_tag} "
f"does not correspond to wheel version {wheel_ver}"
f"{arch_version} NCCL release tag version {nccl_release_tag} does not correspond to wheel version {wheel_ver}"
)
@ -454,14 +412,7 @@ def generate_wheels_matrix(
return ret
arch_version = ""
for arch_version in CUDA_ARCHES:
validate_nccl_dep_consistency(arch_version)
del arch_version
if __name__ == "__main__":
# Used by tools/nightly.py
(SCRIPT_DIR / "nightly_source_matrix.json").write_text(
json.dumps(NIGHTLY_SOURCE_MATRIX, indent=4) + "\n"
)
validate_nccl_dep_consistency("13.0")
validate_nccl_dep_consistency("12.9")
validate_nccl_dep_consistency("12.8")
validate_nccl_dep_consistency("12.6")

View File

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

View File

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

View File

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

View File

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

View File

@ -97,8 +97,8 @@ jobs:
shell: bash
run: |
ngpu=$(rocminfo | grep -c -E 'Name:.*\sgfx')
if [[ $ngpu -lt 2 ]]; then #We are temporarily reducing this down to 2 from 4 so that we can run tests on nodes with less gpus.
echo "Error: only $ngpu GPU(s) detected, at least 2 GPUs are needed for distributed jobs"
if [[ $ngpu -lt 4 ]]; then
echo "Error: only $ngpu GPU(s) detected, at least 4 GPUs are needed for distributed jobs"
exit 1
fi

View File

@ -38,10 +38,6 @@ on:
default: ""
description: |
List of tests to include (empty string implies default list)
dashboard-tag:
required: false
type: string
default: ""
disable-monitor:
description: |
[Experimental] Disable utilization monitoring for tests.
@ -62,11 +58,6 @@ on:
required: false
type: number
default: 1
secrets:
HUGGING_FACE_HUB_TOKEN:
required: false
description: |
HF Auth token to avoid rate limits when downloading models or datasets from hub
permissions:
id-token: write
contents: read
@ -205,8 +196,6 @@ jobs:
PYTORCH_TEST_CUDA_MEM_LEAK_CHECK: ${{ matrix.mem_leak_check && '1' || '0' }}
PYTORCH_TEST_RERUN_DISABLED_TESTS: ${{ matrix.rerun_disabled_tests && '1' || '0' }}
TESTS_TO_INCLUDE: ${{ inputs.tests-to-include }}
DASHBOARD_TAG: ${{ inputs.dashboard-tag }}
HUGGING_FACE_HUB_TOKEN: ${{ secrets.HUGGING_FACE_HUB_TOKEN }}
timeout-minutes: ${{ fromJson(steps.test-timeout.outputs.timeout) }}
run: |
# Fetch aws credential from IMDs
@ -257,8 +246,6 @@ jobs:
-e PYTORCH_TEST_RERUN_DISABLED_TESTS \
-e TESTS_TO_INCLUDE \
-e ZE_AFFINITY_MASK \
-e HUGGING_FACE_HUB_TOKEN \
-e DASHBOARD_TAG \
--env-file="/tmp/github_env_${GITHUB_RUN_ID}" \
--ulimit stack=10485760:83886080 \
--ulimit core=0 \
@ -344,21 +331,5 @@ jobs:
if-no-files-found: ignore
path: ./**/core.[1-9]*
- name: Authenticate with AWS
uses: aws-actions/configure-aws-credentials@ececac1a45f3b08a01d2dd070d28d111c5fe6722 # v4.1.0
with:
role-to-assume: arn:aws:iam::308535385114:role/gha_workflow_upload-benchmark-results
# The max duration enforced by the server side
role-duration-seconds: 18000
aws-region: us-east-1
- name: Upload the benchmark results
uses: pytorch/test-infra/.github/actions/upload-benchmark-results@main
with:
benchmark-results-dir: test/test-reports
dry-run: false
schema-version: v3
github-token: ${{ secrets.GITHUB_TOKEN }}
- name: Teardown XPU
uses: ./.github/actions/teardown-xpu

View File

@ -37,6 +37,7 @@ jobs:
needs: get-label-type
with:
runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
runner: linux.12xlarge.memory
build-environment: linux-jammy-cuda12.8-py3.10-gcc11-distributed-b200
docker-image-name: ci-image:pytorch-linux-jammy-cuda12.8-cudnn9-py3-gcc11
cuda-arch-list: '10.0'

View File

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

View File

@ -36,7 +36,7 @@ jobs:
runs-on: linux.9xlarge.ephemeral
strategy:
matrix:
tag: ["cuda12.6", "cuda12.8", "cuda12.9", "cuda13.0", "rocm7.0", "rocm7.1", "cpu"]
tag: ["cuda12.6", "cuda12.8", "cuda12.9", "cuda13.0", "rocm6.4", "rocm7.0", "cpu"]
steps:
- name: Build docker image
uses: pytorch/pytorch/.github/actions/binary-docker-build@main

View File

@ -52,8 +52,8 @@ jobs:
{ tag: "cuda12.9" },
{ tag: "cuda12.8" },
{ tag: "cuda12.6" },
{ tag: "rocm6.4" },
{ tag: "rocm7.0" },
{ tag: "rocm7.1" },
{ tag: "cpu" },
]
steps:

View File

@ -34,7 +34,7 @@ jobs:
id-token: write
strategy:
matrix:
rocm_version: ["71", "70"]
rocm_version: ["70", "64"]
steps:
- name: Checkout PyTorch
uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2

View File

@ -54,8 +54,8 @@ jobs:
{ name: "manylinuxaarch64-builder", tag: "cuda12.9", runner: "linux.arm64.2xlarge.ephemeral" },
{ name: "manylinuxaarch64-builder", tag: "cuda12.8", runner: "linux.arm64.2xlarge.ephemeral" },
{ name: "manylinuxaarch64-builder", tag: "cuda12.6", runner: "linux.arm64.2xlarge.ephemeral" },
{ name: "manylinux2_28-builder", tag: "rocm6.4", runner: "linux.9xlarge.ephemeral" },
{ name: "manylinux2_28-builder", tag: "rocm7.0", runner: "linux.9xlarge.ephemeral" },
{ name: "manylinux2_28-builder", tag: "rocm7.1", runner: "linux.9xlarge.ephemeral" },
{ name: "manylinux2_28-builder", tag: "cpu", runner: "linux.9xlarge.ephemeral" },
{ name: "manylinux2_28_aarch64-builder", tag: "cpu-aarch64", runner: "linux.arm64.2xlarge.ephemeral" },
{ name: "manylinux2_28-builder", tag: "xpu", runner: "linux.9xlarge.ephemeral" },

View File

@ -55,7 +55,7 @@ jobs:
docker-image: ["pytorch/manylinux2_28-builder:cpu"]
include:
- device: "rocm"
rocm_version: "7.1"
rocm_version: "7.0"
runs_on: "${{ needs.get-label-type.outputs.label-type }}linux.4xlarge"
- device: "cuda"
rocm_version: ""
@ -159,7 +159,12 @@ jobs:
WITH_CLANG_LDD="--with-clang-ldd"
fi
docker exec -t "${container_name}" bash -c "${PYTHON_EXECUTABLE} /pytorch/.github/scripts/build_triton_wheel.py --device=$BUILD_DEVICE $RELEASE $WITH_CLANG_LDD"
if [[ "${BUILD_DEVICE}" == xpu ]]; then
docker exec -t "${container_name}" bash -c "dnf install -y gcc-toolset-13-gcc-c++"
docker exec -t "${container_name}" bash -c "source /opt/rh/gcc-toolset-13/enable && ${PYTHON_EXECUTABLE} /pytorch/.github/scripts/build_triton_wheel.py --device=$BUILD_DEVICE $RELEASE"
else
docker exec -t "${container_name}" bash -c "${PYTHON_EXECUTABLE} /pytorch/.github/scripts/build_triton_wheel.py --device=$BUILD_DEVICE $RELEASE $WITH_CLANG_LDD"
fi
if [[ ("${{ matrix.device }}" == "cuda" || "${{ matrix.device }}" == "xpu") ]]; then
docker exec -t "${container_name}" bash -c "auditwheel repair --plat ${PLATFORM} //artifacts/*.whl"

View File

@ -56,10 +56,7 @@ jobs:
pytorch-linux-jammy-cuda12.8-cudnn9-py3-gcc9,
pytorch-linux-jammy-cuda12.4-cudnn9-py3-gcc11,
pytorch-linux-jammy-py3.10-clang12,
pytorch-linux-jammy-py3.11-clang12,
pytorch-linux-jammy-py3.12-clang12,
pytorch-linux-jammy-py3.13-clang12,
pytorch-linux-jammy-py3.14-clang12,
pytorch-linux-jammy-rocm-n-py3,
pytorch-linux-noble-rocm-n-py3,
pytorch-linux-jammy-rocm-n-py3-benchmarks,
@ -67,10 +64,8 @@ jobs:
pytorch-linux-jammy-py3.10-gcc11,
pytorch-linux-jammy-py3-gcc11-inductor-benchmarks,
pytorch-linux-jammy-py3.12-halide,
pytorch-linux-jammy-cuda12.8-py3.12-pallas,
pytorch-linux-jammy-xpu-n-1-py3,
pytorch-linux-noble-xpu-n-py3,
pytorch-linux-noble-xpu-n-py3-inductor-benchmarks,
pytorch-linux-jammy-xpu-n-py3,
pytorch-linux-jammy-py3-clang18-asan,
pytorch-linux-jammy-py3-clang12-onnx,
pytorch-linux-jammy-linter,
@ -80,11 +75,9 @@ jobs:
pytorch-linux-noble-riscv64-py3.12-gcc14
]
include:
- docker-image-name: pytorch-linux-jammy-aarch64-py3.10-gcc13
- docker-image-name: pytorch-linux-jammy-aarch64-py3.10-gcc11
runner: linux.arm64.m7g.4xlarge
- docker-image-name: pytorch-linux-jammy-aarch64-py3.10-clang21
runner: linux.arm64.m7g.4xlarge
- docker-image-name: pytorch-linux-jammy-aarch64-py3.10-gcc13-inductor-benchmarks
- docker-image-name: pytorch-linux-jammy-aarch64-py3.10-gcc11-inductor-benchmarks
runner: linux.arm64.m7g.4xlarge
timeout-minutes: 600
# Docker uploads fail from LF runners, see https://github.com/pytorch/pytorch/pull/137358

View File

@ -8,7 +8,6 @@ on:
- docker.Makefile
- .github/workflows/docker-release.yml
- .github/scripts/generate_docker_release_matrix.py
- .github/scripts/generate_binary_build_matrix.py
push:
branches:
- nightly

View File

@ -132,7 +132,7 @@ jobs:
ALPINE_IMAGE: "arm64v8/alpine"
build_name: manywheel-py3_10-cuda-aarch64-12_6
build_environment: linux-aarch64-binary-manywheel
PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.6.77; platform_system == 'Linux' | nvidia-cuda-runtime-cu12==12.6.77; platform_system == 'Linux' | nvidia-cuda-cupti-cu12==12.6.80; platform_system == 'Linux' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' | nvidia-cublas-cu12==12.6.4.1; platform_system == 'Linux' | nvidia-cufft-cu12==11.3.0.4; platform_system == 'Linux' | nvidia-curand-cu12==10.3.7.77; platform_system == 'Linux' | nvidia-cusolver-cu12==11.7.1.2; platform_system == 'Linux' | nvidia-cusparse-cu12==12.5.4.2; platform_system == 'Linux' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' | nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' | nvidia-nvshmem-cu12==3.4.5; platform_system == 'Linux' | nvidia-nvtx-cu12==12.6.77; platform_system == 'Linux' | nvidia-nvjitlink-cu12==12.6.85; platform_system == 'Linux' | nvidia-cufile-cu12==1.11.1.6; platform_system == 'Linux'
PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.6.77; platform_system == 'Linux' | nvidia-cuda-runtime-cu12==12.6.77; platform_system == 'Linux' | nvidia-cuda-cupti-cu12==12.6.80; platform_system == 'Linux' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' | nvidia-cublas-cu12==12.6.4.1; platform_system == 'Linux' | nvidia-cufft-cu12==11.3.0.4; platform_system == 'Linux' | nvidia-curand-cu12==10.3.7.77; platform_system == 'Linux' | nvidia-cusolver-cu12==11.7.1.2; platform_system == 'Linux' | nvidia-cusparse-cu12==12.5.4.2; platform_system == 'Linux' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' | nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' | nvidia-nvshmem-cu12==3.3.24; platform_system == 'Linux' | nvidia-nvtx-cu12==12.6.77; platform_system == 'Linux' | nvidia-nvjitlink-cu12==12.6.85; platform_system == 'Linux' | nvidia-cufile-cu12==1.11.1.6; platform_system == 'Linux'
timeout-minutes: 420
secrets:
github-token: ${{ secrets.GITHUB_TOKEN }}
@ -178,7 +178,7 @@ jobs:
ALPINE_IMAGE: "arm64v8/alpine"
build_name: manywheel-py3_10-cuda-aarch64-12_8
build_environment: linux-aarch64-binary-manywheel
PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.8.93; platform_system == 'Linux' | nvidia-cuda-runtime-cu12==12.8.90; platform_system == 'Linux' | nvidia-cuda-cupti-cu12==12.8.90; platform_system == 'Linux' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' | nvidia-cublas-cu12==12.8.4.1; platform_system == 'Linux' | nvidia-cufft-cu12==11.3.3.83; platform_system == 'Linux' | nvidia-curand-cu12==10.3.9.90; platform_system == 'Linux' | nvidia-cusolver-cu12==11.7.3.90; platform_system == 'Linux' | nvidia-cusparse-cu12==12.5.8.93; platform_system == 'Linux' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' | nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' | nvidia-nvshmem-cu12==3.4.5; platform_system == 'Linux' | nvidia-nvtx-cu12==12.8.90; platform_system == 'Linux' | nvidia-nvjitlink-cu12==12.8.93; platform_system == 'Linux' | nvidia-cufile-cu12==1.13.1.3; platform_system == 'Linux'
PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.8.93; platform_system == 'Linux' | nvidia-cuda-runtime-cu12==12.8.90; platform_system == 'Linux' | nvidia-cuda-cupti-cu12==12.8.90; platform_system == 'Linux' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' | nvidia-cublas-cu12==12.8.4.1; platform_system == 'Linux' | nvidia-cufft-cu12==11.3.3.83; platform_system == 'Linux' | nvidia-curand-cu12==10.3.9.90; platform_system == 'Linux' | nvidia-cusolver-cu12==11.7.3.90; platform_system == 'Linux' | nvidia-cusparse-cu12==12.5.8.93; platform_system == 'Linux' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' | nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' | nvidia-nvshmem-cu12==3.3.24; platform_system == 'Linux' | nvidia-nvtx-cu12==12.8.90; platform_system == 'Linux' | nvidia-nvjitlink-cu12==12.8.93; platform_system == 'Linux' | nvidia-cufile-cu12==1.13.1.3; platform_system == 'Linux'
timeout-minutes: 420
secrets:
github-token: ${{ secrets.GITHUB_TOKEN }}
@ -224,7 +224,7 @@ jobs:
ALPINE_IMAGE: "arm64v8/alpine"
build_name: manywheel-py3_10-cuda-aarch64-12_9
build_environment: linux-aarch64-binary-manywheel
PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.9.86; platform_system == 'Linux' | nvidia-cuda-runtime-cu12==12.9.79; platform_system == 'Linux' | nvidia-cuda-cupti-cu12==12.9.79; platform_system == 'Linux' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' | nvidia-cublas-cu12==12.9.1.4; platform_system == 'Linux' | nvidia-cufft-cu12==11.4.1.4; platform_system == 'Linux' | nvidia-curand-cu12==10.3.10.19; platform_system == 'Linux' | nvidia-cusolver-cu12==11.7.5.82; platform_system == 'Linux' | nvidia-cusparse-cu12==12.5.10.65; platform_system == 'Linux' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' | nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' | nvidia-nvshmem-cu12==3.4.5; platform_system == 'Linux' | nvidia-nvtx-cu12==12.9.79; platform_system == 'Linux' | nvidia-nvjitlink-cu12==12.9.86; platform_system == 'Linux' | nvidia-cufile-cu12==1.14.1.1; platform_system == 'Linux'
PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.9.86; platform_system == 'Linux' | nvidia-cuda-runtime-cu12==12.9.79; platform_system == 'Linux' | nvidia-cuda-cupti-cu12==12.9.79; platform_system == 'Linux' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' | nvidia-cublas-cu12==12.9.1.4; platform_system == 'Linux' | nvidia-cufft-cu12==11.4.1.4; platform_system == 'Linux' | nvidia-curand-cu12==10.3.10.19; platform_system == 'Linux' | nvidia-cusolver-cu12==11.7.5.82; platform_system == 'Linux' | nvidia-cusparse-cu12==12.5.10.65; platform_system == 'Linux' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' | nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' | nvidia-nvshmem-cu12==3.3.20; platform_system == 'Linux' | nvidia-nvtx-cu12==12.9.79; platform_system == 'Linux' | nvidia-nvjitlink-cu12==12.9.86; platform_system == 'Linux' | nvidia-cufile-cu12==1.14.1.1; platform_system == 'Linux'
timeout-minutes: 420
secrets:
github-token: ${{ secrets.GITHUB_TOKEN }}
@ -270,7 +270,7 @@ jobs:
ALPINE_IMAGE: "arm64v8/alpine"
build_name: manywheel-py3_10-cuda-aarch64-13_0
build_environment: linux-aarch64-binary-manywheel
PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc==13.0.88; platform_system == 'Linux' | nvidia-cuda-runtime==13.0.96; platform_system == 'Linux' | nvidia-cuda-cupti==13.0.85; platform_system == 'Linux' | nvidia-cudnn-cu13==9.13.0.50; platform_system == 'Linux' | nvidia-cublas==13.1.0.3; platform_system == 'Linux' | nvidia-cufft==12.0.0.61; platform_system == 'Linux' | nvidia-curand==10.4.0.35; platform_system == 'Linux' | nvidia-cusolver==12.0.4.66; platform_system == 'Linux' | nvidia-cusparse==12.6.3.3; platform_system == 'Linux' | nvidia-cusparselt-cu13==0.8.0; platform_system == 'Linux' | nvidia-nccl-cu13==2.27.7; platform_system == 'Linux' | nvidia-nvshmem-cu13==3.4.5; platform_system == 'Linux' | nvidia-nvtx==13.0.85; platform_system == 'Linux' | nvidia-nvjitlink==13.0.88; platform_system == 'Linux' | nvidia-cufile==1.15.1.6; platform_system == 'Linux'
PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc==13.0.88; platform_system == 'Linux' | nvidia-cuda-runtime==13.0.96; platform_system == 'Linux' | nvidia-cuda-cupti==13.0.85; platform_system == 'Linux' | nvidia-cudnn-cu13==9.13.0.50; platform_system == 'Linux' | nvidia-cublas==13.1.0.3; platform_system == 'Linux' | nvidia-cufft==12.0.0.61; platform_system == 'Linux' | nvidia-curand==10.4.0.35; platform_system == 'Linux' | nvidia-cusolver==12.0.4.66; platform_system == 'Linux' | nvidia-cusparse==12.6.3.3; platform_system == 'Linux' | nvidia-cusparselt-cu13==0.8.0; platform_system == 'Linux' | nvidia-nccl-cu13==2.27.7; platform_system == 'Linux' | nvidia-nvshmem-cu13==3.3.24; platform_system == 'Linux' | nvidia-nvtx==13.0.85; platform_system == 'Linux' | nvidia-nvjitlink==13.0.88; platform_system == 'Linux' | nvidia-cufile==1.15.1.6; platform_system == 'Linux'
timeout-minutes: 420
secrets:
github-token: ${{ secrets.GITHUB_TOKEN }}
@ -381,7 +381,7 @@ jobs:
ALPINE_IMAGE: "arm64v8/alpine"
build_name: manywheel-py3_11-cuda-aarch64-12_6
build_environment: linux-aarch64-binary-manywheel
PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.6.77; platform_system == 'Linux' | nvidia-cuda-runtime-cu12==12.6.77; platform_system == 'Linux' | nvidia-cuda-cupti-cu12==12.6.80; platform_system == 'Linux' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' | nvidia-cublas-cu12==12.6.4.1; platform_system == 'Linux' | nvidia-cufft-cu12==11.3.0.4; platform_system == 'Linux' | nvidia-curand-cu12==10.3.7.77; platform_system == 'Linux' | nvidia-cusolver-cu12==11.7.1.2; platform_system == 'Linux' | nvidia-cusparse-cu12==12.5.4.2; platform_system == 'Linux' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' | nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' | nvidia-nvshmem-cu12==3.4.5; platform_system == 'Linux' | nvidia-nvtx-cu12==12.6.77; platform_system == 'Linux' | nvidia-nvjitlink-cu12==12.6.85; platform_system == 'Linux' | nvidia-cufile-cu12==1.11.1.6; platform_system == 'Linux'
PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.6.77; platform_system == 'Linux' | nvidia-cuda-runtime-cu12==12.6.77; platform_system == 'Linux' | nvidia-cuda-cupti-cu12==12.6.80; platform_system == 'Linux' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' | nvidia-cublas-cu12==12.6.4.1; platform_system == 'Linux' | nvidia-cufft-cu12==11.3.0.4; platform_system == 'Linux' | nvidia-curand-cu12==10.3.7.77; platform_system == 'Linux' | nvidia-cusolver-cu12==11.7.1.2; platform_system == 'Linux' | nvidia-cusparse-cu12==12.5.4.2; platform_system == 'Linux' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' | nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' | nvidia-nvshmem-cu12==3.3.24; platform_system == 'Linux' | nvidia-nvtx-cu12==12.6.77; platform_system == 'Linux' | nvidia-nvjitlink-cu12==12.6.85; platform_system == 'Linux' | nvidia-cufile-cu12==1.11.1.6; platform_system == 'Linux'
timeout-minutes: 420
secrets:
github-token: ${{ secrets.GITHUB_TOKEN }}
@ -427,7 +427,7 @@ jobs:
ALPINE_IMAGE: "arm64v8/alpine"
build_name: manywheel-py3_11-cuda-aarch64-12_8
build_environment: linux-aarch64-binary-manywheel
PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.8.93; platform_system == 'Linux' | nvidia-cuda-runtime-cu12==12.8.90; platform_system == 'Linux' | nvidia-cuda-cupti-cu12==12.8.90; platform_system == 'Linux' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' | nvidia-cublas-cu12==12.8.4.1; platform_system == 'Linux' | nvidia-cufft-cu12==11.3.3.83; platform_system == 'Linux' | nvidia-curand-cu12==10.3.9.90; platform_system == 'Linux' | nvidia-cusolver-cu12==11.7.3.90; platform_system == 'Linux' | nvidia-cusparse-cu12==12.5.8.93; platform_system == 'Linux' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' | nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' | nvidia-nvshmem-cu12==3.4.5; platform_system == 'Linux' | nvidia-nvtx-cu12==12.8.90; platform_system == 'Linux' | nvidia-nvjitlink-cu12==12.8.93; platform_system == 'Linux' | nvidia-cufile-cu12==1.13.1.3; platform_system == 'Linux'
PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.8.93; platform_system == 'Linux' | nvidia-cuda-runtime-cu12==12.8.90; platform_system == 'Linux' | nvidia-cuda-cupti-cu12==12.8.90; platform_system == 'Linux' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' | nvidia-cublas-cu12==12.8.4.1; platform_system == 'Linux' | nvidia-cufft-cu12==11.3.3.83; platform_system == 'Linux' | nvidia-curand-cu12==10.3.9.90; platform_system == 'Linux' | nvidia-cusolver-cu12==11.7.3.90; platform_system == 'Linux' | nvidia-cusparse-cu12==12.5.8.93; platform_system == 'Linux' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' | nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' | nvidia-nvshmem-cu12==3.3.24; platform_system == 'Linux' | nvidia-nvtx-cu12==12.8.90; platform_system == 'Linux' | nvidia-nvjitlink-cu12==12.8.93; platform_system == 'Linux' | nvidia-cufile-cu12==1.13.1.3; platform_system == 'Linux'
timeout-minutes: 420
secrets:
github-token: ${{ secrets.GITHUB_TOKEN }}
@ -473,7 +473,7 @@ jobs:
ALPINE_IMAGE: "arm64v8/alpine"
build_name: manywheel-py3_11-cuda-aarch64-12_9
build_environment: linux-aarch64-binary-manywheel
PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.9.86; platform_system == 'Linux' | nvidia-cuda-runtime-cu12==12.9.79; platform_system == 'Linux' | nvidia-cuda-cupti-cu12==12.9.79; platform_system == 'Linux' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' | nvidia-cublas-cu12==12.9.1.4; platform_system == 'Linux' | nvidia-cufft-cu12==11.4.1.4; platform_system == 'Linux' | nvidia-curand-cu12==10.3.10.19; platform_system == 'Linux' | nvidia-cusolver-cu12==11.7.5.82; platform_system == 'Linux' | nvidia-cusparse-cu12==12.5.10.65; platform_system == 'Linux' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' | nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' | nvidia-nvshmem-cu12==3.4.5; platform_system == 'Linux' | nvidia-nvtx-cu12==12.9.79; platform_system == 'Linux' | nvidia-nvjitlink-cu12==12.9.86; platform_system == 'Linux' | nvidia-cufile-cu12==1.14.1.1; platform_system == 'Linux'
PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.9.86; platform_system == 'Linux' | nvidia-cuda-runtime-cu12==12.9.79; platform_system == 'Linux' | nvidia-cuda-cupti-cu12==12.9.79; platform_system == 'Linux' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' | nvidia-cublas-cu12==12.9.1.4; platform_system == 'Linux' | nvidia-cufft-cu12==11.4.1.4; platform_system == 'Linux' | nvidia-curand-cu12==10.3.10.19; platform_system == 'Linux' | nvidia-cusolver-cu12==11.7.5.82; platform_system == 'Linux' | nvidia-cusparse-cu12==12.5.10.65; platform_system == 'Linux' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' | nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' | nvidia-nvshmem-cu12==3.3.20; platform_system == 'Linux' | nvidia-nvtx-cu12==12.9.79; platform_system == 'Linux' | nvidia-nvjitlink-cu12==12.9.86; platform_system == 'Linux' | nvidia-cufile-cu12==1.14.1.1; platform_system == 'Linux'
timeout-minutes: 420
secrets:
github-token: ${{ secrets.GITHUB_TOKEN }}
@ -519,7 +519,7 @@ jobs:
ALPINE_IMAGE: "arm64v8/alpine"
build_name: manywheel-py3_11-cuda-aarch64-13_0
build_environment: linux-aarch64-binary-manywheel
PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc==13.0.88; platform_system == 'Linux' | nvidia-cuda-runtime==13.0.96; platform_system == 'Linux' | nvidia-cuda-cupti==13.0.85; platform_system == 'Linux' | nvidia-cudnn-cu13==9.13.0.50; platform_system == 'Linux' | nvidia-cublas==13.1.0.3; platform_system == 'Linux' | nvidia-cufft==12.0.0.61; platform_system == 'Linux' | nvidia-curand==10.4.0.35; platform_system == 'Linux' | nvidia-cusolver==12.0.4.66; platform_system == 'Linux' | nvidia-cusparse==12.6.3.3; platform_system == 'Linux' | nvidia-cusparselt-cu13==0.8.0; platform_system == 'Linux' | nvidia-nccl-cu13==2.27.7; platform_system == 'Linux' | nvidia-nvshmem-cu13==3.4.5; platform_system == 'Linux' | nvidia-nvtx==13.0.85; platform_system == 'Linux' | nvidia-nvjitlink==13.0.88; platform_system == 'Linux' | nvidia-cufile==1.15.1.6; platform_system == 'Linux'
PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc==13.0.88; platform_system == 'Linux' | nvidia-cuda-runtime==13.0.96; platform_system == 'Linux' | nvidia-cuda-cupti==13.0.85; platform_system == 'Linux' | nvidia-cudnn-cu13==9.13.0.50; platform_system == 'Linux' | nvidia-cublas==13.1.0.3; platform_system == 'Linux' | nvidia-cufft==12.0.0.61; platform_system == 'Linux' | nvidia-curand==10.4.0.35; platform_system == 'Linux' | nvidia-cusolver==12.0.4.66; platform_system == 'Linux' | nvidia-cusparse==12.6.3.3; platform_system == 'Linux' | nvidia-cusparselt-cu13==0.8.0; platform_system == 'Linux' | nvidia-nccl-cu13==2.27.7; platform_system == 'Linux' | nvidia-nvshmem-cu13==3.3.24; platform_system == 'Linux' | nvidia-nvtx==13.0.85; platform_system == 'Linux' | nvidia-nvjitlink==13.0.88; platform_system == 'Linux' | nvidia-cufile==1.15.1.6; platform_system == 'Linux'
timeout-minutes: 420
secrets:
github-token: ${{ secrets.GITHUB_TOKEN }}
@ -630,7 +630,7 @@ jobs:
ALPINE_IMAGE: "arm64v8/alpine"
build_name: manywheel-py3_12-cuda-aarch64-12_6
build_environment: linux-aarch64-binary-manywheel
PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.6.77; platform_system == 'Linux' | nvidia-cuda-runtime-cu12==12.6.77; platform_system == 'Linux' | nvidia-cuda-cupti-cu12==12.6.80; platform_system == 'Linux' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' | nvidia-cublas-cu12==12.6.4.1; platform_system == 'Linux' | nvidia-cufft-cu12==11.3.0.4; platform_system == 'Linux' | nvidia-curand-cu12==10.3.7.77; platform_system == 'Linux' | nvidia-cusolver-cu12==11.7.1.2; platform_system == 'Linux' | nvidia-cusparse-cu12==12.5.4.2; platform_system == 'Linux' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' | nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' | nvidia-nvshmem-cu12==3.4.5; platform_system == 'Linux' | nvidia-nvtx-cu12==12.6.77; platform_system == 'Linux' | nvidia-nvjitlink-cu12==12.6.85; platform_system == 'Linux' | nvidia-cufile-cu12==1.11.1.6; platform_system == 'Linux'
PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.6.77; platform_system == 'Linux' | nvidia-cuda-runtime-cu12==12.6.77; platform_system == 'Linux' | nvidia-cuda-cupti-cu12==12.6.80; platform_system == 'Linux' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' | nvidia-cublas-cu12==12.6.4.1; platform_system == 'Linux' | nvidia-cufft-cu12==11.3.0.4; platform_system == 'Linux' | nvidia-curand-cu12==10.3.7.77; platform_system == 'Linux' | nvidia-cusolver-cu12==11.7.1.2; platform_system == 'Linux' | nvidia-cusparse-cu12==12.5.4.2; platform_system == 'Linux' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' | nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' | nvidia-nvshmem-cu12==3.3.24; platform_system == 'Linux' | nvidia-nvtx-cu12==12.6.77; platform_system == 'Linux' | nvidia-nvjitlink-cu12==12.6.85; platform_system == 'Linux' | nvidia-cufile-cu12==1.11.1.6; platform_system == 'Linux'
timeout-minutes: 420
secrets:
github-token: ${{ secrets.GITHUB_TOKEN }}
@ -676,7 +676,7 @@ jobs:
ALPINE_IMAGE: "arm64v8/alpine"
build_name: manywheel-py3_12-cuda-aarch64-12_8
build_environment: linux-aarch64-binary-manywheel
PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.8.93; platform_system == 'Linux' | nvidia-cuda-runtime-cu12==12.8.90; platform_system == 'Linux' | nvidia-cuda-cupti-cu12==12.8.90; platform_system == 'Linux' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' | nvidia-cublas-cu12==12.8.4.1; platform_system == 'Linux' | nvidia-cufft-cu12==11.3.3.83; platform_system == 'Linux' | nvidia-curand-cu12==10.3.9.90; platform_system == 'Linux' | nvidia-cusolver-cu12==11.7.3.90; platform_system == 'Linux' | nvidia-cusparse-cu12==12.5.8.93; platform_system == 'Linux' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' | nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' | nvidia-nvshmem-cu12==3.4.5; platform_system == 'Linux' | nvidia-nvtx-cu12==12.8.90; platform_system == 'Linux' | nvidia-nvjitlink-cu12==12.8.93; platform_system == 'Linux' | nvidia-cufile-cu12==1.13.1.3; platform_system == 'Linux'
PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.8.93; platform_system == 'Linux' | nvidia-cuda-runtime-cu12==12.8.90; platform_system == 'Linux' | nvidia-cuda-cupti-cu12==12.8.90; platform_system == 'Linux' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' | nvidia-cublas-cu12==12.8.4.1; platform_system == 'Linux' | nvidia-cufft-cu12==11.3.3.83; platform_system == 'Linux' | nvidia-curand-cu12==10.3.9.90; platform_system == 'Linux' | nvidia-cusolver-cu12==11.7.3.90; platform_system == 'Linux' | nvidia-cusparse-cu12==12.5.8.93; platform_system == 'Linux' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' | nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' | nvidia-nvshmem-cu12==3.3.24; platform_system == 'Linux' | nvidia-nvtx-cu12==12.8.90; platform_system == 'Linux' | nvidia-nvjitlink-cu12==12.8.93; platform_system == 'Linux' | nvidia-cufile-cu12==1.13.1.3; platform_system == 'Linux'
timeout-minutes: 420
secrets:
github-token: ${{ secrets.GITHUB_TOKEN }}
@ -722,7 +722,7 @@ jobs:
ALPINE_IMAGE: "arm64v8/alpine"
build_name: manywheel-py3_12-cuda-aarch64-12_9
build_environment: linux-aarch64-binary-manywheel
PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.9.86; platform_system == 'Linux' | nvidia-cuda-runtime-cu12==12.9.79; platform_system == 'Linux' | nvidia-cuda-cupti-cu12==12.9.79; platform_system == 'Linux' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' | nvidia-cublas-cu12==12.9.1.4; platform_system == 'Linux' | nvidia-cufft-cu12==11.4.1.4; platform_system == 'Linux' | nvidia-curand-cu12==10.3.10.19; platform_system == 'Linux' | nvidia-cusolver-cu12==11.7.5.82; platform_system == 'Linux' | nvidia-cusparse-cu12==12.5.10.65; platform_system == 'Linux' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' | nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' | nvidia-nvshmem-cu12==3.4.5; platform_system == 'Linux' | nvidia-nvtx-cu12==12.9.79; platform_system == 'Linux' | nvidia-nvjitlink-cu12==12.9.86; platform_system == 'Linux' | nvidia-cufile-cu12==1.14.1.1; platform_system == 'Linux'
PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.9.86; platform_system == 'Linux' | nvidia-cuda-runtime-cu12==12.9.79; platform_system == 'Linux' | nvidia-cuda-cupti-cu12==12.9.79; platform_system == 'Linux' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' | nvidia-cublas-cu12==12.9.1.4; platform_system == 'Linux' | nvidia-cufft-cu12==11.4.1.4; platform_system == 'Linux' | nvidia-curand-cu12==10.3.10.19; platform_system == 'Linux' | nvidia-cusolver-cu12==11.7.5.82; platform_system == 'Linux' | nvidia-cusparse-cu12==12.5.10.65; platform_system == 'Linux' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' | nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' | nvidia-nvshmem-cu12==3.3.20; platform_system == 'Linux' | nvidia-nvtx-cu12==12.9.79; platform_system == 'Linux' | nvidia-nvjitlink-cu12==12.9.86; platform_system == 'Linux' | nvidia-cufile-cu12==1.14.1.1; platform_system == 'Linux'
timeout-minutes: 420
secrets:
github-token: ${{ secrets.GITHUB_TOKEN }}
@ -768,7 +768,7 @@ jobs:
ALPINE_IMAGE: "arm64v8/alpine"
build_name: manywheel-py3_12-cuda-aarch64-13_0
build_environment: linux-aarch64-binary-manywheel
PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc==13.0.88; platform_system == 'Linux' | nvidia-cuda-runtime==13.0.96; platform_system == 'Linux' | nvidia-cuda-cupti==13.0.85; platform_system == 'Linux' | nvidia-cudnn-cu13==9.13.0.50; platform_system == 'Linux' | nvidia-cublas==13.1.0.3; platform_system == 'Linux' | nvidia-cufft==12.0.0.61; platform_system == 'Linux' | nvidia-curand==10.4.0.35; platform_system == 'Linux' | nvidia-cusolver==12.0.4.66; platform_system == 'Linux' | nvidia-cusparse==12.6.3.3; platform_system == 'Linux' | nvidia-cusparselt-cu13==0.8.0; platform_system == 'Linux' | nvidia-nccl-cu13==2.27.7; platform_system == 'Linux' | nvidia-nvshmem-cu13==3.4.5; platform_system == 'Linux' | nvidia-nvtx==13.0.85; platform_system == 'Linux' | nvidia-nvjitlink==13.0.88; platform_system == 'Linux' | nvidia-cufile==1.15.1.6; platform_system == 'Linux'
PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc==13.0.88; platform_system == 'Linux' | nvidia-cuda-runtime==13.0.96; platform_system == 'Linux' | nvidia-cuda-cupti==13.0.85; platform_system == 'Linux' | nvidia-cudnn-cu13==9.13.0.50; platform_system == 'Linux' | nvidia-cublas==13.1.0.3; platform_system == 'Linux' | nvidia-cufft==12.0.0.61; platform_system == 'Linux' | nvidia-curand==10.4.0.35; platform_system == 'Linux' | nvidia-cusolver==12.0.4.66; platform_system == 'Linux' | nvidia-cusparse==12.6.3.3; platform_system == 'Linux' | nvidia-cusparselt-cu13==0.8.0; platform_system == 'Linux' | nvidia-nccl-cu13==2.27.7; platform_system == 'Linux' | nvidia-nvshmem-cu13==3.3.24; platform_system == 'Linux' | nvidia-nvtx==13.0.85; platform_system == 'Linux' | nvidia-nvjitlink==13.0.88; platform_system == 'Linux' | nvidia-cufile==1.15.1.6; platform_system == 'Linux'
timeout-minutes: 420
secrets:
github-token: ${{ secrets.GITHUB_TOKEN }}
@ -879,7 +879,7 @@ jobs:
ALPINE_IMAGE: "arm64v8/alpine"
build_name: manywheel-py3_13-cuda-aarch64-12_6
build_environment: linux-aarch64-binary-manywheel
PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.6.77; platform_system == 'Linux' | nvidia-cuda-runtime-cu12==12.6.77; platform_system == 'Linux' | nvidia-cuda-cupti-cu12==12.6.80; platform_system == 'Linux' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' | nvidia-cublas-cu12==12.6.4.1; platform_system == 'Linux' | nvidia-cufft-cu12==11.3.0.4; platform_system == 'Linux' | nvidia-curand-cu12==10.3.7.77; platform_system == 'Linux' | nvidia-cusolver-cu12==11.7.1.2; platform_system == 'Linux' | nvidia-cusparse-cu12==12.5.4.2; platform_system == 'Linux' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' | nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' | nvidia-nvshmem-cu12==3.4.5; platform_system == 'Linux' | nvidia-nvtx-cu12==12.6.77; platform_system == 'Linux' | nvidia-nvjitlink-cu12==12.6.85; platform_system == 'Linux' | nvidia-cufile-cu12==1.11.1.6; platform_system == 'Linux'
PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.6.77; platform_system == 'Linux' | nvidia-cuda-runtime-cu12==12.6.77; platform_system == 'Linux' | nvidia-cuda-cupti-cu12==12.6.80; platform_system == 'Linux' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' | nvidia-cublas-cu12==12.6.4.1; platform_system == 'Linux' | nvidia-cufft-cu12==11.3.0.4; platform_system == 'Linux' | nvidia-curand-cu12==10.3.7.77; platform_system == 'Linux' | nvidia-cusolver-cu12==11.7.1.2; platform_system == 'Linux' | nvidia-cusparse-cu12==12.5.4.2; platform_system == 'Linux' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' | nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' | nvidia-nvshmem-cu12==3.3.24; platform_system == 'Linux' | nvidia-nvtx-cu12==12.6.77; platform_system == 'Linux' | nvidia-nvjitlink-cu12==12.6.85; platform_system == 'Linux' | nvidia-cufile-cu12==1.11.1.6; platform_system == 'Linux'
timeout-minutes: 420
secrets:
github-token: ${{ secrets.GITHUB_TOKEN }}
@ -925,7 +925,7 @@ jobs:
ALPINE_IMAGE: "arm64v8/alpine"
build_name: manywheel-py3_13-cuda-aarch64-12_8
build_environment: linux-aarch64-binary-manywheel
PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.8.93; platform_system == 'Linux' | nvidia-cuda-runtime-cu12==12.8.90; platform_system == 'Linux' | nvidia-cuda-cupti-cu12==12.8.90; platform_system == 'Linux' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' | nvidia-cublas-cu12==12.8.4.1; platform_system == 'Linux' | nvidia-cufft-cu12==11.3.3.83; platform_system == 'Linux' | nvidia-curand-cu12==10.3.9.90; platform_system == 'Linux' | nvidia-cusolver-cu12==11.7.3.90; platform_system == 'Linux' | nvidia-cusparse-cu12==12.5.8.93; platform_system == 'Linux' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' | nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' | nvidia-nvshmem-cu12==3.4.5; platform_system == 'Linux' | nvidia-nvtx-cu12==12.8.90; platform_system == 'Linux' | nvidia-nvjitlink-cu12==12.8.93; platform_system == 'Linux' | nvidia-cufile-cu12==1.13.1.3; platform_system == 'Linux'
PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.8.93; platform_system == 'Linux' | nvidia-cuda-runtime-cu12==12.8.90; platform_system == 'Linux' | nvidia-cuda-cupti-cu12==12.8.90; platform_system == 'Linux' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' | nvidia-cublas-cu12==12.8.4.1; platform_system == 'Linux' | nvidia-cufft-cu12==11.3.3.83; platform_system == 'Linux' | nvidia-curand-cu12==10.3.9.90; platform_system == 'Linux' | nvidia-cusolver-cu12==11.7.3.90; platform_system == 'Linux' | nvidia-cusparse-cu12==12.5.8.93; platform_system == 'Linux' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' | nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' | nvidia-nvshmem-cu12==3.3.24; platform_system == 'Linux' | nvidia-nvtx-cu12==12.8.90; platform_system == 'Linux' | nvidia-nvjitlink-cu12==12.8.93; platform_system == 'Linux' | nvidia-cufile-cu12==1.13.1.3; platform_system == 'Linux'
timeout-minutes: 420
secrets:
github-token: ${{ secrets.GITHUB_TOKEN }}
@ -971,7 +971,7 @@ jobs:
ALPINE_IMAGE: "arm64v8/alpine"
build_name: manywheel-py3_13-cuda-aarch64-12_9
build_environment: linux-aarch64-binary-manywheel
PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.9.86; platform_system == 'Linux' | nvidia-cuda-runtime-cu12==12.9.79; platform_system == 'Linux' | nvidia-cuda-cupti-cu12==12.9.79; platform_system == 'Linux' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' | nvidia-cublas-cu12==12.9.1.4; platform_system == 'Linux' | nvidia-cufft-cu12==11.4.1.4; platform_system == 'Linux' | nvidia-curand-cu12==10.3.10.19; platform_system == 'Linux' | nvidia-cusolver-cu12==11.7.5.82; platform_system == 'Linux' | nvidia-cusparse-cu12==12.5.10.65; platform_system == 'Linux' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' | nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' | nvidia-nvshmem-cu12==3.4.5; platform_system == 'Linux' | nvidia-nvtx-cu12==12.9.79; platform_system == 'Linux' | nvidia-nvjitlink-cu12==12.9.86; platform_system == 'Linux' | nvidia-cufile-cu12==1.14.1.1; platform_system == 'Linux'
PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.9.86; platform_system == 'Linux' | nvidia-cuda-runtime-cu12==12.9.79; platform_system == 'Linux' | nvidia-cuda-cupti-cu12==12.9.79; platform_system == 'Linux' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' | nvidia-cublas-cu12==12.9.1.4; platform_system == 'Linux' | nvidia-cufft-cu12==11.4.1.4; platform_system == 'Linux' | nvidia-curand-cu12==10.3.10.19; platform_system == 'Linux' | nvidia-cusolver-cu12==11.7.5.82; platform_system == 'Linux' | nvidia-cusparse-cu12==12.5.10.65; platform_system == 'Linux' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' | nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' | nvidia-nvshmem-cu12==3.3.20; platform_system == 'Linux' | nvidia-nvtx-cu12==12.9.79; platform_system == 'Linux' | nvidia-nvjitlink-cu12==12.9.86; platform_system == 'Linux' | nvidia-cufile-cu12==1.14.1.1; platform_system == 'Linux'
timeout-minutes: 420
secrets:
github-token: ${{ secrets.GITHUB_TOKEN }}
@ -1017,7 +1017,7 @@ jobs:
ALPINE_IMAGE: "arm64v8/alpine"
build_name: manywheel-py3_13-cuda-aarch64-13_0
build_environment: linux-aarch64-binary-manywheel
PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc==13.0.88; platform_system == 'Linux' | nvidia-cuda-runtime==13.0.96; platform_system == 'Linux' | nvidia-cuda-cupti==13.0.85; platform_system == 'Linux' | nvidia-cudnn-cu13==9.13.0.50; platform_system == 'Linux' | nvidia-cublas==13.1.0.3; platform_system == 'Linux' | nvidia-cufft==12.0.0.61; platform_system == 'Linux' | nvidia-curand==10.4.0.35; platform_system == 'Linux' | nvidia-cusolver==12.0.4.66; platform_system == 'Linux' | nvidia-cusparse==12.6.3.3; platform_system == 'Linux' | nvidia-cusparselt-cu13==0.8.0; platform_system == 'Linux' | nvidia-nccl-cu13==2.27.7; platform_system == 'Linux' | nvidia-nvshmem-cu13==3.4.5; platform_system == 'Linux' | nvidia-nvtx==13.0.85; platform_system == 'Linux' | nvidia-nvjitlink==13.0.88; platform_system == 'Linux' | nvidia-cufile==1.15.1.6; platform_system == 'Linux'
PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc==13.0.88; platform_system == 'Linux' | nvidia-cuda-runtime==13.0.96; platform_system == 'Linux' | nvidia-cuda-cupti==13.0.85; platform_system == 'Linux' | nvidia-cudnn-cu13==9.13.0.50; platform_system == 'Linux' | nvidia-cublas==13.1.0.3; platform_system == 'Linux' | nvidia-cufft==12.0.0.61; platform_system == 'Linux' | nvidia-curand==10.4.0.35; platform_system == 'Linux' | nvidia-cusolver==12.0.4.66; platform_system == 'Linux' | nvidia-cusparse==12.6.3.3; platform_system == 'Linux' | nvidia-cusparselt-cu13==0.8.0; platform_system == 'Linux' | nvidia-nccl-cu13==2.27.7; platform_system == 'Linux' | nvidia-nvshmem-cu13==3.3.24; platform_system == 'Linux' | nvidia-nvtx==13.0.85; platform_system == 'Linux' | nvidia-nvjitlink==13.0.88; platform_system == 'Linux' | nvidia-cufile==1.15.1.6; platform_system == 'Linux'
timeout-minutes: 420
secrets:
github-token: ${{ secrets.GITHUB_TOKEN }}
@ -1128,7 +1128,7 @@ jobs:
ALPINE_IMAGE: "arm64v8/alpine"
build_name: manywheel-py3_13t-cuda-aarch64-12_6
build_environment: linux-aarch64-binary-manywheel
PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.6.77; platform_system == 'Linux' | nvidia-cuda-runtime-cu12==12.6.77; platform_system == 'Linux' | nvidia-cuda-cupti-cu12==12.6.80; platform_system == 'Linux' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' | nvidia-cublas-cu12==12.6.4.1; platform_system == 'Linux' | nvidia-cufft-cu12==11.3.0.4; platform_system == 'Linux' | nvidia-curand-cu12==10.3.7.77; platform_system == 'Linux' | nvidia-cusolver-cu12==11.7.1.2; platform_system == 'Linux' | nvidia-cusparse-cu12==12.5.4.2; platform_system == 'Linux' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' | nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' | nvidia-nvshmem-cu12==3.4.5; platform_system == 'Linux' | nvidia-nvtx-cu12==12.6.77; platform_system == 'Linux' | nvidia-nvjitlink-cu12==12.6.85; platform_system == 'Linux' | nvidia-cufile-cu12==1.11.1.6; platform_system == 'Linux'
PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.6.77; platform_system == 'Linux' | nvidia-cuda-runtime-cu12==12.6.77; platform_system == 'Linux' | nvidia-cuda-cupti-cu12==12.6.80; platform_system == 'Linux' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' | nvidia-cublas-cu12==12.6.4.1; platform_system == 'Linux' | nvidia-cufft-cu12==11.3.0.4; platform_system == 'Linux' | nvidia-curand-cu12==10.3.7.77; platform_system == 'Linux' | nvidia-cusolver-cu12==11.7.1.2; platform_system == 'Linux' | nvidia-cusparse-cu12==12.5.4.2; platform_system == 'Linux' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' | nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' | nvidia-nvshmem-cu12==3.3.24; platform_system == 'Linux' | nvidia-nvtx-cu12==12.6.77; platform_system == 'Linux' | nvidia-nvjitlink-cu12==12.6.85; platform_system == 'Linux' | nvidia-cufile-cu12==1.11.1.6; platform_system == 'Linux'
timeout-minutes: 420
secrets:
github-token: ${{ secrets.GITHUB_TOKEN }}
@ -1174,7 +1174,7 @@ jobs:
ALPINE_IMAGE: "arm64v8/alpine"
build_name: manywheel-py3_13t-cuda-aarch64-12_8
build_environment: linux-aarch64-binary-manywheel
PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.8.93; platform_system == 'Linux' | nvidia-cuda-runtime-cu12==12.8.90; platform_system == 'Linux' | nvidia-cuda-cupti-cu12==12.8.90; platform_system == 'Linux' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' | nvidia-cublas-cu12==12.8.4.1; platform_system == 'Linux' | nvidia-cufft-cu12==11.3.3.83; platform_system == 'Linux' | nvidia-curand-cu12==10.3.9.90; platform_system == 'Linux' | nvidia-cusolver-cu12==11.7.3.90; platform_system == 'Linux' | nvidia-cusparse-cu12==12.5.8.93; platform_system == 'Linux' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' | nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' | nvidia-nvshmem-cu12==3.4.5; platform_system == 'Linux' | nvidia-nvtx-cu12==12.8.90; platform_system == 'Linux' | nvidia-nvjitlink-cu12==12.8.93; platform_system == 'Linux' | nvidia-cufile-cu12==1.13.1.3; platform_system == 'Linux'
PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.8.93; platform_system == 'Linux' | nvidia-cuda-runtime-cu12==12.8.90; platform_system == 'Linux' | nvidia-cuda-cupti-cu12==12.8.90; platform_system == 'Linux' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' | nvidia-cublas-cu12==12.8.4.1; platform_system == 'Linux' | nvidia-cufft-cu12==11.3.3.83; platform_system == 'Linux' | nvidia-curand-cu12==10.3.9.90; platform_system == 'Linux' | nvidia-cusolver-cu12==11.7.3.90; platform_system == 'Linux' | nvidia-cusparse-cu12==12.5.8.93; platform_system == 'Linux' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' | nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' | nvidia-nvshmem-cu12==3.3.24; platform_system == 'Linux' | nvidia-nvtx-cu12==12.8.90; platform_system == 'Linux' | nvidia-nvjitlink-cu12==12.8.93; platform_system == 'Linux' | nvidia-cufile-cu12==1.13.1.3; platform_system == 'Linux'
timeout-minutes: 420
secrets:
github-token: ${{ secrets.GITHUB_TOKEN }}
@ -1220,7 +1220,7 @@ jobs:
ALPINE_IMAGE: "arm64v8/alpine"
build_name: manywheel-py3_13t-cuda-aarch64-12_9
build_environment: linux-aarch64-binary-manywheel
PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.9.86; platform_system == 'Linux' | nvidia-cuda-runtime-cu12==12.9.79; platform_system == 'Linux' | nvidia-cuda-cupti-cu12==12.9.79; platform_system == 'Linux' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' | nvidia-cublas-cu12==12.9.1.4; platform_system == 'Linux' | nvidia-cufft-cu12==11.4.1.4; platform_system == 'Linux' | nvidia-curand-cu12==10.3.10.19; platform_system == 'Linux' | nvidia-cusolver-cu12==11.7.5.82; platform_system == 'Linux' | nvidia-cusparse-cu12==12.5.10.65; platform_system == 'Linux' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' | nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' | nvidia-nvshmem-cu12==3.4.5; platform_system == 'Linux' | nvidia-nvtx-cu12==12.9.79; platform_system == 'Linux' | nvidia-nvjitlink-cu12==12.9.86; platform_system == 'Linux' | nvidia-cufile-cu12==1.14.1.1; platform_system == 'Linux'
PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.9.86; platform_system == 'Linux' | nvidia-cuda-runtime-cu12==12.9.79; platform_system == 'Linux' | nvidia-cuda-cupti-cu12==12.9.79; platform_system == 'Linux' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' | nvidia-cublas-cu12==12.9.1.4; platform_system == 'Linux' | nvidia-cufft-cu12==11.4.1.4; platform_system == 'Linux' | nvidia-curand-cu12==10.3.10.19; platform_system == 'Linux' | nvidia-cusolver-cu12==11.7.5.82; platform_system == 'Linux' | nvidia-cusparse-cu12==12.5.10.65; platform_system == 'Linux' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' | nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' | nvidia-nvshmem-cu12==3.3.20; platform_system == 'Linux' | nvidia-nvtx-cu12==12.9.79; platform_system == 'Linux' | nvidia-nvjitlink-cu12==12.9.86; platform_system == 'Linux' | nvidia-cufile-cu12==1.14.1.1; platform_system == 'Linux'
timeout-minutes: 420
secrets:
github-token: ${{ secrets.GITHUB_TOKEN }}
@ -1266,7 +1266,7 @@ jobs:
ALPINE_IMAGE: "arm64v8/alpine"
build_name: manywheel-py3_13t-cuda-aarch64-13_0
build_environment: linux-aarch64-binary-manywheel
PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc==13.0.88; platform_system == 'Linux' | nvidia-cuda-runtime==13.0.96; platform_system == 'Linux' | nvidia-cuda-cupti==13.0.85; platform_system == 'Linux' | nvidia-cudnn-cu13==9.13.0.50; platform_system == 'Linux' | nvidia-cublas==13.1.0.3; platform_system == 'Linux' | nvidia-cufft==12.0.0.61; platform_system == 'Linux' | nvidia-curand==10.4.0.35; platform_system == 'Linux' | nvidia-cusolver==12.0.4.66; platform_system == 'Linux' | nvidia-cusparse==12.6.3.3; platform_system == 'Linux' | nvidia-cusparselt-cu13==0.8.0; platform_system == 'Linux' | nvidia-nccl-cu13==2.27.7; platform_system == 'Linux' | nvidia-nvshmem-cu13==3.4.5; platform_system == 'Linux' | nvidia-nvtx==13.0.85; platform_system == 'Linux' | nvidia-nvjitlink==13.0.88; platform_system == 'Linux' | nvidia-cufile==1.15.1.6; platform_system == 'Linux'
PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc==13.0.88; platform_system == 'Linux' | nvidia-cuda-runtime==13.0.96; platform_system == 'Linux' | nvidia-cuda-cupti==13.0.85; platform_system == 'Linux' | nvidia-cudnn-cu13==9.13.0.50; platform_system == 'Linux' | nvidia-cublas==13.1.0.3; platform_system == 'Linux' | nvidia-cufft==12.0.0.61; platform_system == 'Linux' | nvidia-curand==10.4.0.35; platform_system == 'Linux' | nvidia-cusolver==12.0.4.66; platform_system == 'Linux' | nvidia-cusparse==12.6.3.3; platform_system == 'Linux' | nvidia-cusparselt-cu13==0.8.0; platform_system == 'Linux' | nvidia-nccl-cu13==2.27.7; platform_system == 'Linux' | nvidia-nvshmem-cu13==3.3.24; platform_system == 'Linux' | nvidia-nvtx==13.0.85; platform_system == 'Linux' | nvidia-nvjitlink==13.0.88; platform_system == 'Linux' | nvidia-cufile==1.15.1.6; platform_system == 'Linux'
timeout-minutes: 420
secrets:
github-token: ${{ secrets.GITHUB_TOKEN }}
@ -1377,7 +1377,7 @@ jobs:
ALPINE_IMAGE: "arm64v8/alpine"
build_name: manywheel-py3_14-cuda-aarch64-12_6
build_environment: linux-aarch64-binary-manywheel
PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.6.77; platform_system == 'Linux' | nvidia-cuda-runtime-cu12==12.6.77; platform_system == 'Linux' | nvidia-cuda-cupti-cu12==12.6.80; platform_system == 'Linux' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' | nvidia-cublas-cu12==12.6.4.1; platform_system == 'Linux' | nvidia-cufft-cu12==11.3.0.4; platform_system == 'Linux' | nvidia-curand-cu12==10.3.7.77; platform_system == 'Linux' | nvidia-cusolver-cu12==11.7.1.2; platform_system == 'Linux' | nvidia-cusparse-cu12==12.5.4.2; platform_system == 'Linux' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' | nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' | nvidia-nvshmem-cu12==3.4.5; platform_system == 'Linux' | nvidia-nvtx-cu12==12.6.77; platform_system == 'Linux' | nvidia-nvjitlink-cu12==12.6.85; platform_system == 'Linux' | nvidia-cufile-cu12==1.11.1.6; platform_system == 'Linux'
PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.6.77; platform_system == 'Linux' | nvidia-cuda-runtime-cu12==12.6.77; platform_system == 'Linux' | nvidia-cuda-cupti-cu12==12.6.80; platform_system == 'Linux' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' | nvidia-cublas-cu12==12.6.4.1; platform_system == 'Linux' | nvidia-cufft-cu12==11.3.0.4; platform_system == 'Linux' | nvidia-curand-cu12==10.3.7.77; platform_system == 'Linux' | nvidia-cusolver-cu12==11.7.1.2; platform_system == 'Linux' | nvidia-cusparse-cu12==12.5.4.2; platform_system == 'Linux' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' | nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' | nvidia-nvshmem-cu12==3.3.24; platform_system == 'Linux' | nvidia-nvtx-cu12==12.6.77; platform_system == 'Linux' | nvidia-nvjitlink-cu12==12.6.85; platform_system == 'Linux' | nvidia-cufile-cu12==1.11.1.6; platform_system == 'Linux'
timeout-minutes: 420
secrets:
github-token: ${{ secrets.GITHUB_TOKEN }}
@ -1423,7 +1423,7 @@ jobs:
ALPINE_IMAGE: "arm64v8/alpine"
build_name: manywheel-py3_14-cuda-aarch64-12_8
build_environment: linux-aarch64-binary-manywheel
PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.8.93; platform_system == 'Linux' | nvidia-cuda-runtime-cu12==12.8.90; platform_system == 'Linux' | nvidia-cuda-cupti-cu12==12.8.90; platform_system == 'Linux' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' | nvidia-cublas-cu12==12.8.4.1; platform_system == 'Linux' | nvidia-cufft-cu12==11.3.3.83; platform_system == 'Linux' | nvidia-curand-cu12==10.3.9.90; platform_system == 'Linux' | nvidia-cusolver-cu12==11.7.3.90; platform_system == 'Linux' | nvidia-cusparse-cu12==12.5.8.93; platform_system == 'Linux' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' | nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' | nvidia-nvshmem-cu12==3.4.5; platform_system == 'Linux' | nvidia-nvtx-cu12==12.8.90; platform_system == 'Linux' | nvidia-nvjitlink-cu12==12.8.93; platform_system == 'Linux' | nvidia-cufile-cu12==1.13.1.3; platform_system == 'Linux'
PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.8.93; platform_system == 'Linux' | nvidia-cuda-runtime-cu12==12.8.90; platform_system == 'Linux' | nvidia-cuda-cupti-cu12==12.8.90; platform_system == 'Linux' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' | nvidia-cublas-cu12==12.8.4.1; platform_system == 'Linux' | nvidia-cufft-cu12==11.3.3.83; platform_system == 'Linux' | nvidia-curand-cu12==10.3.9.90; platform_system == 'Linux' | nvidia-cusolver-cu12==11.7.3.90; platform_system == 'Linux' | nvidia-cusparse-cu12==12.5.8.93; platform_system == 'Linux' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' | nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' | nvidia-nvshmem-cu12==3.3.24; platform_system == 'Linux' | nvidia-nvtx-cu12==12.8.90; platform_system == 'Linux' | nvidia-nvjitlink-cu12==12.8.93; platform_system == 'Linux' | nvidia-cufile-cu12==1.13.1.3; platform_system == 'Linux'
timeout-minutes: 420
secrets:
github-token: ${{ secrets.GITHUB_TOKEN }}
@ -1469,7 +1469,7 @@ jobs:
ALPINE_IMAGE: "arm64v8/alpine"
build_name: manywheel-py3_14-cuda-aarch64-12_9
build_environment: linux-aarch64-binary-manywheel
PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.9.86; platform_system == 'Linux' | nvidia-cuda-runtime-cu12==12.9.79; platform_system == 'Linux' | nvidia-cuda-cupti-cu12==12.9.79; platform_system == 'Linux' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' | nvidia-cublas-cu12==12.9.1.4; platform_system == 'Linux' | nvidia-cufft-cu12==11.4.1.4; platform_system == 'Linux' | nvidia-curand-cu12==10.3.10.19; platform_system == 'Linux' | nvidia-cusolver-cu12==11.7.5.82; platform_system == 'Linux' | nvidia-cusparse-cu12==12.5.10.65; platform_system == 'Linux' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' | nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' | nvidia-nvshmem-cu12==3.4.5; platform_system == 'Linux' | nvidia-nvtx-cu12==12.9.79; platform_system == 'Linux' | nvidia-nvjitlink-cu12==12.9.86; platform_system == 'Linux' | nvidia-cufile-cu12==1.14.1.1; platform_system == 'Linux'
PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.9.86; platform_system == 'Linux' | nvidia-cuda-runtime-cu12==12.9.79; platform_system == 'Linux' | nvidia-cuda-cupti-cu12==12.9.79; platform_system == 'Linux' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' | nvidia-cublas-cu12==12.9.1.4; platform_system == 'Linux' | nvidia-cufft-cu12==11.4.1.4; platform_system == 'Linux' | nvidia-curand-cu12==10.3.10.19; platform_system == 'Linux' | nvidia-cusolver-cu12==11.7.5.82; platform_system == 'Linux' | nvidia-cusparse-cu12==12.5.10.65; platform_system == 'Linux' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' | nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' | nvidia-nvshmem-cu12==3.3.20; platform_system == 'Linux' | nvidia-nvtx-cu12==12.9.79; platform_system == 'Linux' | nvidia-nvjitlink-cu12==12.9.86; platform_system == 'Linux' | nvidia-cufile-cu12==1.14.1.1; platform_system == 'Linux'
timeout-minutes: 420
secrets:
github-token: ${{ secrets.GITHUB_TOKEN }}
@ -1515,7 +1515,7 @@ jobs:
ALPINE_IMAGE: "arm64v8/alpine"
build_name: manywheel-py3_14-cuda-aarch64-13_0
build_environment: linux-aarch64-binary-manywheel
PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc==13.0.88; platform_system == 'Linux' | nvidia-cuda-runtime==13.0.96; platform_system == 'Linux' | nvidia-cuda-cupti==13.0.85; platform_system == 'Linux' | nvidia-cudnn-cu13==9.13.0.50; platform_system == 'Linux' | nvidia-cublas==13.1.0.3; platform_system == 'Linux' | nvidia-cufft==12.0.0.61; platform_system == 'Linux' | nvidia-curand==10.4.0.35; platform_system == 'Linux' | nvidia-cusolver==12.0.4.66; platform_system == 'Linux' | nvidia-cusparse==12.6.3.3; platform_system == 'Linux' | nvidia-cusparselt-cu13==0.8.0; platform_system == 'Linux' | nvidia-nccl-cu13==2.27.7; platform_system == 'Linux' | nvidia-nvshmem-cu13==3.4.5; platform_system == 'Linux' | nvidia-nvtx==13.0.85; platform_system == 'Linux' | nvidia-nvjitlink==13.0.88; platform_system == 'Linux' | nvidia-cufile==1.15.1.6; platform_system == 'Linux'
PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc==13.0.88; platform_system == 'Linux' | nvidia-cuda-runtime==13.0.96; platform_system == 'Linux' | nvidia-cuda-cupti==13.0.85; platform_system == 'Linux' | nvidia-cudnn-cu13==9.13.0.50; platform_system == 'Linux' | nvidia-cublas==13.1.0.3; platform_system == 'Linux' | nvidia-cufft==12.0.0.61; platform_system == 'Linux' | nvidia-curand==10.4.0.35; platform_system == 'Linux' | nvidia-cusolver==12.0.4.66; platform_system == 'Linux' | nvidia-cusparse==12.6.3.3; platform_system == 'Linux' | nvidia-cusparselt-cu13==0.8.0; platform_system == 'Linux' | nvidia-nccl-cu13==2.27.7; platform_system == 'Linux' | nvidia-nvshmem-cu13==3.3.24; platform_system == 'Linux' | nvidia-nvtx==13.0.85; platform_system == 'Linux' | nvidia-nvjitlink==13.0.88; platform_system == 'Linux' | nvidia-cufile==1.15.1.6; platform_system == 'Linux'
timeout-minutes: 420
secrets:
github-token: ${{ secrets.GITHUB_TOKEN }}
@ -1626,7 +1626,7 @@ jobs:
ALPINE_IMAGE: "arm64v8/alpine"
build_name: manywheel-py3_14t-cuda-aarch64-12_6
build_environment: linux-aarch64-binary-manywheel
PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.6.77; platform_system == 'Linux' | nvidia-cuda-runtime-cu12==12.6.77; platform_system == 'Linux' | nvidia-cuda-cupti-cu12==12.6.80; platform_system == 'Linux' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' | nvidia-cublas-cu12==12.6.4.1; platform_system == 'Linux' | nvidia-cufft-cu12==11.3.0.4; platform_system == 'Linux' | nvidia-curand-cu12==10.3.7.77; platform_system == 'Linux' | nvidia-cusolver-cu12==11.7.1.2; platform_system == 'Linux' | nvidia-cusparse-cu12==12.5.4.2; platform_system == 'Linux' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' | nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' | nvidia-nvshmem-cu12==3.4.5; platform_system == 'Linux' | nvidia-nvtx-cu12==12.6.77; platform_system == 'Linux' | nvidia-nvjitlink-cu12==12.6.85; platform_system == 'Linux' | nvidia-cufile-cu12==1.11.1.6; platform_system == 'Linux'
PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.6.77; platform_system == 'Linux' | nvidia-cuda-runtime-cu12==12.6.77; platform_system == 'Linux' | nvidia-cuda-cupti-cu12==12.6.80; platform_system == 'Linux' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' | nvidia-cublas-cu12==12.6.4.1; platform_system == 'Linux' | nvidia-cufft-cu12==11.3.0.4; platform_system == 'Linux' | nvidia-curand-cu12==10.3.7.77; platform_system == 'Linux' | nvidia-cusolver-cu12==11.7.1.2; platform_system == 'Linux' | nvidia-cusparse-cu12==12.5.4.2; platform_system == 'Linux' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' | nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' | nvidia-nvshmem-cu12==3.3.24; platform_system == 'Linux' | nvidia-nvtx-cu12==12.6.77; platform_system == 'Linux' | nvidia-nvjitlink-cu12==12.6.85; platform_system == 'Linux' | nvidia-cufile-cu12==1.11.1.6; platform_system == 'Linux'
timeout-minutes: 420
secrets:
github-token: ${{ secrets.GITHUB_TOKEN }}
@ -1672,7 +1672,7 @@ jobs:
ALPINE_IMAGE: "arm64v8/alpine"
build_name: manywheel-py3_14t-cuda-aarch64-12_8
build_environment: linux-aarch64-binary-manywheel
PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.8.93; platform_system == 'Linux' | nvidia-cuda-runtime-cu12==12.8.90; platform_system == 'Linux' | nvidia-cuda-cupti-cu12==12.8.90; platform_system == 'Linux' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' | nvidia-cublas-cu12==12.8.4.1; platform_system == 'Linux' | nvidia-cufft-cu12==11.3.3.83; platform_system == 'Linux' | nvidia-curand-cu12==10.3.9.90; platform_system == 'Linux' | nvidia-cusolver-cu12==11.7.3.90; platform_system == 'Linux' | nvidia-cusparse-cu12==12.5.8.93; platform_system == 'Linux' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' | nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' | nvidia-nvshmem-cu12==3.4.5; platform_system == 'Linux' | nvidia-nvtx-cu12==12.8.90; platform_system == 'Linux' | nvidia-nvjitlink-cu12==12.8.93; platform_system == 'Linux' | nvidia-cufile-cu12==1.13.1.3; platform_system == 'Linux'
PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.8.93; platform_system == 'Linux' | nvidia-cuda-runtime-cu12==12.8.90; platform_system == 'Linux' | nvidia-cuda-cupti-cu12==12.8.90; platform_system == 'Linux' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' | nvidia-cublas-cu12==12.8.4.1; platform_system == 'Linux' | nvidia-cufft-cu12==11.3.3.83; platform_system == 'Linux' | nvidia-curand-cu12==10.3.9.90; platform_system == 'Linux' | nvidia-cusolver-cu12==11.7.3.90; platform_system == 'Linux' | nvidia-cusparse-cu12==12.5.8.93; platform_system == 'Linux' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' | nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' | nvidia-nvshmem-cu12==3.3.24; platform_system == 'Linux' | nvidia-nvtx-cu12==12.8.90; platform_system == 'Linux' | nvidia-nvjitlink-cu12==12.8.93; platform_system == 'Linux' | nvidia-cufile-cu12==1.13.1.3; platform_system == 'Linux'
timeout-minutes: 420
secrets:
github-token: ${{ secrets.GITHUB_TOKEN }}
@ -1718,7 +1718,7 @@ jobs:
ALPINE_IMAGE: "arm64v8/alpine"
build_name: manywheel-py3_14t-cuda-aarch64-12_9
build_environment: linux-aarch64-binary-manywheel
PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.9.86; platform_system == 'Linux' | nvidia-cuda-runtime-cu12==12.9.79; platform_system == 'Linux' | nvidia-cuda-cupti-cu12==12.9.79; platform_system == 'Linux' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' | nvidia-cublas-cu12==12.9.1.4; platform_system == 'Linux' | nvidia-cufft-cu12==11.4.1.4; platform_system == 'Linux' | nvidia-curand-cu12==10.3.10.19; platform_system == 'Linux' | nvidia-cusolver-cu12==11.7.5.82; platform_system == 'Linux' | nvidia-cusparse-cu12==12.5.10.65; platform_system == 'Linux' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' | nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' | nvidia-nvshmem-cu12==3.4.5; platform_system == 'Linux' | nvidia-nvtx-cu12==12.9.79; platform_system == 'Linux' | nvidia-nvjitlink-cu12==12.9.86; platform_system == 'Linux' | nvidia-cufile-cu12==1.14.1.1; platform_system == 'Linux'
PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.9.86; platform_system == 'Linux' | nvidia-cuda-runtime-cu12==12.9.79; platform_system == 'Linux' | nvidia-cuda-cupti-cu12==12.9.79; platform_system == 'Linux' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' | nvidia-cublas-cu12==12.9.1.4; platform_system == 'Linux' | nvidia-cufft-cu12==11.4.1.4; platform_system == 'Linux' | nvidia-curand-cu12==10.3.10.19; platform_system == 'Linux' | nvidia-cusolver-cu12==11.7.5.82; platform_system == 'Linux' | nvidia-cusparse-cu12==12.5.10.65; platform_system == 'Linux' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' | nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' | nvidia-nvshmem-cu12==3.3.20; platform_system == 'Linux' | nvidia-nvtx-cu12==12.9.79; platform_system == 'Linux' | nvidia-nvjitlink-cu12==12.9.86; platform_system == 'Linux' | nvidia-cufile-cu12==1.14.1.1; platform_system == 'Linux'
timeout-minutes: 420
secrets:
github-token: ${{ secrets.GITHUB_TOKEN }}
@ -1764,7 +1764,7 @@ jobs:
ALPINE_IMAGE: "arm64v8/alpine"
build_name: manywheel-py3_14t-cuda-aarch64-13_0
build_environment: linux-aarch64-binary-manywheel
PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc==13.0.88; platform_system == 'Linux' | nvidia-cuda-runtime==13.0.96; platform_system == 'Linux' | nvidia-cuda-cupti==13.0.85; platform_system == 'Linux' | nvidia-cudnn-cu13==9.13.0.50; platform_system == 'Linux' | nvidia-cublas==13.1.0.3; platform_system == 'Linux' | nvidia-cufft==12.0.0.61; platform_system == 'Linux' | nvidia-curand==10.4.0.35; platform_system == 'Linux' | nvidia-cusolver==12.0.4.66; platform_system == 'Linux' | nvidia-cusparse==12.6.3.3; platform_system == 'Linux' | nvidia-cusparselt-cu13==0.8.0; platform_system == 'Linux' | nvidia-nccl-cu13==2.27.7; platform_system == 'Linux' | nvidia-nvshmem-cu13==3.4.5; platform_system == 'Linux' | nvidia-nvtx==13.0.85; platform_system == 'Linux' | nvidia-nvjitlink==13.0.88; platform_system == 'Linux' | nvidia-cufile==1.15.1.6; platform_system == 'Linux'
PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc==13.0.88; platform_system == 'Linux' | nvidia-cuda-runtime==13.0.96; platform_system == 'Linux' | nvidia-cuda-cupti==13.0.85; platform_system == 'Linux' | nvidia-cudnn-cu13==9.13.0.50; platform_system == 'Linux' | nvidia-cublas==13.1.0.3; platform_system == 'Linux' | nvidia-cufft==12.0.0.61; platform_system == 'Linux' | nvidia-curand==10.4.0.35; platform_system == 'Linux' | nvidia-cusolver==12.0.4.66; platform_system == 'Linux' | nvidia-cusparse==12.6.3.3; platform_system == 'Linux' | nvidia-cusparselt-cu13==0.8.0; platform_system == 'Linux' | nvidia-nccl-cu13==2.27.7; platform_system == 'Linux' | nvidia-nvshmem-cu13==3.3.24; platform_system == 'Linux' | nvidia-nvtx==13.0.85; platform_system == 'Linux' | nvidia-nvjitlink==13.0.88; platform_system == 'Linux' | nvidia-cufile==1.15.1.6; platform_system == 'Linux'
timeout-minutes: 420
secrets:
github-token: ${{ secrets.GITHUB_TOKEN }}

View File

@ -384,6 +384,124 @@ jobs:
github-token: ${{ secrets.GITHUB_TOKEN }}
uses: ./.github/workflows/_binary-upload.yml
libtorch-rocm6_4-shared-with-deps-release-build:
if: ${{ github.repository_owner == 'pytorch' }}
uses: ./.github/workflows/_binary-build-linux.yml
needs: get-label-type
with:
PYTORCH_ROOT: /pytorch
PACKAGE_TYPE: libtorch
# TODO: This is a legacy variable that we eventually want to get rid of in
# favor of GPU_ARCH_VERSION
DESIRED_CUDA: rocm6.4
GPU_ARCH_VERSION: "6.4"
GPU_ARCH_TYPE: rocm
DOCKER_IMAGE: libtorch-cxx11-builder
DOCKER_IMAGE_TAG_PREFIX: rocm6.4
LIBTORCH_CONFIG: release
LIBTORCH_VARIANT: shared-with-deps
runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
timeout-minutes: 300
build_name: libtorch-rocm6_4-shared-with-deps-release
build_environment: linux-binary-libtorch
secrets:
github-token: ${{ secrets.GITHUB_TOKEN }}
libtorch-rocm6_4-shared-with-deps-release-test: # Testing
if: ${{ github.repository_owner == 'pytorch' }}
needs:
- libtorch-rocm6_4-shared-with-deps-release-build
- get-label-type
runs-on: linux.rocm.gpu.mi250
timeout-minutes: 240
env:
PYTORCH_ROOT: /pytorch
PACKAGE_TYPE: libtorch
# TODO: This is a legacy variable that we eventually want to get rid of in
# favor of GPU_ARCH_VERSION
DESIRED_CUDA: rocm6.4
GPU_ARCH_VERSION: "6.4"
GPU_ARCH_TYPE: rocm
SKIP_ALL_TESTS: 1
DOCKER_IMAGE: libtorch-cxx11-builder
DOCKER_IMAGE_TAG_PREFIX: rocm6.4
LIBTORCH_CONFIG: release
LIBTORCH_VARIANT: shared-with-deps
permissions:
id-token: write
contents: read
steps:
- name: Setup ROCm
uses: ./.github/actions/setup-rocm
- uses: actions/download-artifact@v4.1.7
name: Download Build Artifacts
with:
name: libtorch-rocm6_4-shared-with-deps-release
path: "${{ runner.temp }}/artifacts/"
- name: Checkout PyTorch
uses: actions/checkout@v4
with:
ref: ${{ github.event_name == 'pull_request' && github.event.pull_request.head.sha || github.sha }}
submodules: recursive
path: pytorch
show-progress: false
- name: Clean PyTorch checkout
run: |
# Remove any artifacts from the previous checkouts
git clean -fxd
working-directory: pytorch
- name: ROCm set GPU_FLAG
run: |
echo "GPU_FLAG=--device=/dev/mem --device=/dev/kfd --device=/dev/dri --group-add video --group-add daemon" >> "${GITHUB_ENV}"
- name: configure aws credentials
id: aws_creds
if: ${{ startsWith(github.event.ref, 'refs/tags/ciflow/') }}
uses: aws-actions/configure-aws-credentials@v4
with:
role-to-assume: arn:aws:iam::308535385114:role/gha_workflow_s3_and_ecr_read_only
aws-region: us-east-1
role-duration-seconds: 18000
- name: Calculate docker image
id: calculate-docker-image
uses: pytorch/test-infra/.github/actions/calculate-docker-image@main
with:
docker-registry: ${{ startsWith(github.event.ref, 'refs/tags/ciflow/') && '308535385114.dkr.ecr.us-east-1.amazonaws.com' || 'docker.io' }}
docker-image-name: libtorch-cxx11-builder
custom-tag-prefix: rocm6.4
docker-build-dir: .ci/docker
working-directory: pytorch
- name: Pull Docker image
uses: pytorch/test-infra/.github/actions/pull-docker-image@main
with:
docker-image: ${{ steps.calculate-docker-image.outputs.docker-image }}
- name: Test Pytorch binary
uses: ./pytorch/.github/actions/test-pytorch-binary
env:
DOCKER_IMAGE: ${{ steps.calculate-docker-image.outputs.docker-image }}
- name: Teardown ROCm
uses: ./.github/actions/teardown-rocm
libtorch-rocm6_4-shared-with-deps-release-upload: # Uploading
if: ${{ github.repository_owner == 'pytorch' }}
permissions:
id-token: write
contents: read
needs: libtorch-rocm6_4-shared-with-deps-release-test
with:
PYTORCH_ROOT: /pytorch
PACKAGE_TYPE: libtorch
# TODO: This is a legacy variable that we eventually want to get rid of in
# favor of GPU_ARCH_VERSION
DESIRED_CUDA: rocm6.4
GPU_ARCH_VERSION: "6.4"
GPU_ARCH_TYPE: rocm
DOCKER_IMAGE: libtorch-cxx11-builder
DOCKER_IMAGE_TAG_PREFIX: rocm6.4
LIBTORCH_CONFIG: release
LIBTORCH_VARIANT: shared-with-deps
build_name: libtorch-rocm6_4-shared-with-deps-release
secrets:
github-token: ${{ secrets.GITHUB_TOKEN }}
uses: ./.github/workflows/_binary-upload.yml
libtorch-rocm7_0-shared-with-deps-release-build:
if: ${{ github.repository_owner == 'pytorch' }}
uses: ./.github/workflows/_binary-build-linux.yml
@ -501,121 +619,3 @@ jobs:
secrets:
github-token: ${{ secrets.GITHUB_TOKEN }}
uses: ./.github/workflows/_binary-upload.yml
libtorch-rocm7_1-shared-with-deps-release-build:
if: ${{ github.repository_owner == 'pytorch' }}
uses: ./.github/workflows/_binary-build-linux.yml
needs: get-label-type
with:
PYTORCH_ROOT: /pytorch
PACKAGE_TYPE: libtorch
# TODO: This is a legacy variable that we eventually want to get rid of in
# favor of GPU_ARCH_VERSION
DESIRED_CUDA: rocm7.1
GPU_ARCH_VERSION: "7.1"
GPU_ARCH_TYPE: rocm
DOCKER_IMAGE: libtorch-cxx11-builder
DOCKER_IMAGE_TAG_PREFIX: rocm7.1
LIBTORCH_CONFIG: release
LIBTORCH_VARIANT: shared-with-deps
runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
timeout-minutes: 300
build_name: libtorch-rocm7_1-shared-with-deps-release
build_environment: linux-binary-libtorch
secrets:
github-token: ${{ secrets.GITHUB_TOKEN }}
libtorch-rocm7_1-shared-with-deps-release-test: # Testing
if: ${{ github.repository_owner == 'pytorch' }}
needs:
- libtorch-rocm7_1-shared-with-deps-release-build
- get-label-type
runs-on: linux.rocm.gpu.mi250
timeout-minutes: 240
env:
PYTORCH_ROOT: /pytorch
PACKAGE_TYPE: libtorch
# TODO: This is a legacy variable that we eventually want to get rid of in
# favor of GPU_ARCH_VERSION
DESIRED_CUDA: rocm7.1
GPU_ARCH_VERSION: "7.1"
GPU_ARCH_TYPE: rocm
SKIP_ALL_TESTS: 1
DOCKER_IMAGE: libtorch-cxx11-builder
DOCKER_IMAGE_TAG_PREFIX: rocm7.1
LIBTORCH_CONFIG: release
LIBTORCH_VARIANT: shared-with-deps
permissions:
id-token: write
contents: read
steps:
- name: Setup ROCm
uses: ./.github/actions/setup-rocm
- uses: actions/download-artifact@v4.1.7
name: Download Build Artifacts
with:
name: libtorch-rocm7_1-shared-with-deps-release
path: "${{ runner.temp }}/artifacts/"
- name: Checkout PyTorch
uses: actions/checkout@v4
with:
ref: ${{ github.event_name == 'pull_request' && github.event.pull_request.head.sha || github.sha }}
submodules: recursive
path: pytorch
show-progress: false
- name: Clean PyTorch checkout
run: |
# Remove any artifacts from the previous checkouts
git clean -fxd
working-directory: pytorch
- name: ROCm set GPU_FLAG
run: |
echo "GPU_FLAG=--device=/dev/mem --device=/dev/kfd --device=/dev/dri --group-add video --group-add daemon" >> "${GITHUB_ENV}"
- name: configure aws credentials
id: aws_creds
if: ${{ startsWith(github.event.ref, 'refs/tags/ciflow/') }}
uses: aws-actions/configure-aws-credentials@v4
with:
role-to-assume: arn:aws:iam::308535385114:role/gha_workflow_s3_and_ecr_read_only
aws-region: us-east-1
role-duration-seconds: 18000
- name: Calculate docker image
id: calculate-docker-image
uses: pytorch/test-infra/.github/actions/calculate-docker-image@main
with:
docker-registry: ${{ startsWith(github.event.ref, 'refs/tags/ciflow/') && '308535385114.dkr.ecr.us-east-1.amazonaws.com' || 'docker.io' }}
docker-image-name: libtorch-cxx11-builder
custom-tag-prefix: rocm7.1
docker-build-dir: .ci/docker
working-directory: pytorch
- name: Pull Docker image
uses: pytorch/test-infra/.github/actions/pull-docker-image@main
with:
docker-image: ${{ steps.calculate-docker-image.outputs.docker-image }}
- name: Test Pytorch binary
uses: ./pytorch/.github/actions/test-pytorch-binary
env:
DOCKER_IMAGE: ${{ steps.calculate-docker-image.outputs.docker-image }}
- name: Teardown ROCm
uses: ./.github/actions/teardown-rocm
libtorch-rocm7_1-shared-with-deps-release-upload: # Uploading
if: ${{ github.repository_owner == 'pytorch' }}
permissions:
id-token: write
contents: read
needs: libtorch-rocm7_1-shared-with-deps-release-test
with:
PYTORCH_ROOT: /pytorch
PACKAGE_TYPE: libtorch
# TODO: This is a legacy variable that we eventually want to get rid of in
# favor of GPU_ARCH_VERSION
DESIRED_CUDA: rocm7.1
GPU_ARCH_VERSION: "7.1"
GPU_ARCH_TYPE: rocm
DOCKER_IMAGE: libtorch-cxx11-builder
DOCKER_IMAGE_TAG_PREFIX: rocm7.1
LIBTORCH_CONFIG: release
LIBTORCH_VARIANT: shared-with-deps
build_name: libtorch-rocm7_1-shared-with-deps-release
secrets:
github-token: ${{ secrets.GITHUB_TOKEN }}
uses: ./.github/workflows/_binary-upload.yml

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

View File

@ -72,7 +72,7 @@ jobs:
runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
runner: linux.arm64.m7g.4xlarge
build-environment: linux-jammy-aarch64-py3.10
docker-image-name: ci-image:pytorch-linux-jammy-aarch64-py3.10-gcc13-inductor-benchmarks
docker-image-name: ci-image:pytorch-linux-jammy-aarch64-py3.10-gcc11-inductor-benchmarks
test-matrix: |
{ include: [
{ config: "inductor_huggingface_perf_cpu_aarch64", shard: 1, num_shards: 9, runner: "linux.arm64.m7g.metal" },

View File

@ -1,148 +0,0 @@
name: inductor-perf-nightly-xpu
on:
push:
tags:
- ciflow/inductor-perf-test-nightly-xpu/*
schedule:
- cron: 30 17 * * *
workflow_dispatch:
inputs:
training:
description: Run training (on by default)?
required: false
type: boolean
default: true
inference:
description: Run inference (on by default)?
required: false
type: boolean
default: true
default:
description: Run inductor_default?
required: false
type: boolean
default: false
dynamic:
description: Run inductor_dynamic_shapes?
required: false
type: boolean
default: false
cppwrapper:
description: Run inductor_cpp_wrapper?
required: false
type: boolean
default: false
cudagraphs:
description: Run inductor_cudagraphs?
required: false
type: boolean
default: false
freezing_cudagraphs:
description: Run inductor_cudagraphs with freezing for inference?
required: false
type: boolean
default: false
aotinductor:
description: Run aot_inductor for inference?
required: false
type: boolean
default: false
maxautotune:
description: Run inductor_max_autotune?
required: false
type: boolean
default: false
benchmark_configs:
description: The list of configs used the benchmark
required: false
type: string
default: inductor_huggingface_perf,inductor_timm_perf,inductor_torchbench_perf,cachebench
concurrency:
group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.ref_name }}-${{ github.ref_type == 'branch' && github.sha }}-${{ github.event_name == 'workflow_dispatch' }}-${{ github.event_name == 'schedule' }}
cancel-in-progress: true
permissions: read-all
jobs:
get-label-type:
name: get-label-type
uses: pytorch/pytorch/.github/workflows/_runner-determinator.yml@main
if: ${{ (github.event_name != 'schedule' || github.repository == 'pytorch/pytorch') && github.repository_owner == 'pytorch' }}
with:
triggering_actor: ${{ github.triggering_actor }}
issue_owner: ${{ github.event.pull_request.user.login || github.event.issue.user.login }}
curr_branch: ${{ github.head_ref || github.ref_name }}
curr_ref_type: ${{ github.ref_type }}
opt_out_experiments: lf
xpu-n-py3_10-inductor-benchmark-build:
name: xpu-n-py3.10-inductor-benchmark
uses: ./.github/workflows/_linux-build.yml
needs: get-label-type
with:
runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
build-environment: linux-noble-xpu-n-py3.10
docker-image-name: ci-image:pytorch-linux-noble-xpu-n-py3-inductor-benchmarks
runner: linux.c7i.12xlarge
test-matrix: |
{ include: [
{ config: "inductor_huggingface_perf_xpu", shard: 1, num_shards: 5, runner: "linux.idc.xpu" },
{ config: "inductor_huggingface_perf_xpu", shard: 2, num_shards: 5, runner: "linux.idc.xpu" },
{ config: "inductor_huggingface_perf_xpu", shard: 3, num_shards: 5, runner: "linux.idc.xpu" },
{ config: "inductor_huggingface_perf_xpu", shard: 4, num_shards: 5, runner: "linux.idc.xpu" },
{ config: "inductor_huggingface_perf_xpu", shard: 5, num_shards: 5, runner: "linux.idc.xpu" },
{ config: "inductor_timm_perf_xpu", shard: 1, num_shards: 6, runner: "linux.idc.xpu" },
{ config: "inductor_timm_perf_xpu", shard: 2, num_shards: 6, runner: "linux.idc.xpu" },
{ config: "inductor_timm_perf_xpu", shard: 3, num_shards: 6, runner: "linux.idc.xpu" },
{ config: "inductor_timm_perf_xpu", shard: 4, num_shards: 6, runner: "linux.idc.xpu" },
{ config: "inductor_timm_perf_xpu", shard: 5, num_shards: 6, runner: "linux.idc.xpu" },
{ config: "inductor_timm_perf_xpu", shard: 6, num_shards: 6, runner: "linux.idc.xpu" },
{ config: "inductor_torchbench_perf_xpu", shard: 1, num_shards: 6, runner: "linux.idc.xpu" },
{ config: "inductor_torchbench_perf_xpu", shard: 2, num_shards: 6, runner: "linux.idc.xpu" },
{ config: "inductor_torchbench_perf_xpu", shard: 3, num_shards: 6, runner: "linux.idc.xpu" },
{ config: "inductor_torchbench_perf_xpu", shard: 4, num_shards: 6, runner: "linux.idc.xpu" },
{ config: "inductor_torchbench_perf_xpu", shard: 5, num_shards: 6, runner: "linux.idc.xpu" },
{ config: "inductor_torchbench_perf_xpu", shard: 6, num_shards: 6, runner: "linux.idc.xpu" },
]}
secrets: inherit
xpu-n-py3_10-inductor-benchmark-test-nightly:
permissions:
id-token: write
contents: read
if: github.event_name != 'workflow_dispatch'
name: xpu-n-py3.10-inductor-benchmark
uses: ./.github/workflows/_xpu-test.yml
needs: xpu-n-py3_10-inductor-benchmark-build
with:
build-environment: linux-noble-xpu-n-py3.10
dashboard-tag: training-true-inference-true-default-true-dynamic-true-cudagraphs-false-cppwrapper-true-aotinductor-true-freezing_cudagraphs-false-cudagraphs_low_precision-false
docker-image: ${{ needs.xpu-n-py3_10-inductor-benchmark-build.outputs.docker-image }}
test-matrix: ${{ needs.xpu-n-py3_10-inductor-benchmark-build.outputs.test-matrix }}
timeout-minutes: 720
# Disable monitor in perf tests for more investigation
disable-monitor: true
monitor-log-interval: 10
monitor-data-collect-interval: 2
secrets: inherit
xpu-n-py3_10-inductor-benchmark-test:
permissions:
id-token: write
contents: read
if: github.event_name == 'workflow_dispatch'
name: xpu-n-py3.10-inductor-test
uses: ./.github/workflows/_xpu-test.yml
needs: xpu-n-py3_10-inductor-benchmark-build
with:
build-environment: linux-noble-xpu-n-py3.10
dashboard-tag: training-${{ inputs.training }}-inference-${{ inputs.inference }}-default-${{ inputs.default }}-dynamic-${{ inputs.dynamic }}-cudagraphs-${{ inputs.cudagraphs }}-cppwrapper-${{ inputs.cppwrapper }}-aotinductor-${{ inputs.aotinductor }}-maxautotune-${{ inputs.maxautotune }}-freezing_cudagraphs-${{ inputs.freezing_cudagraphs }}-cudagraphs_low_precision-${{ inputs.cudagraphs }}
docker-image: ${{ needs.xpu-n-py3_10-inductor-benchmark-build.outputs.docker-image }}
test-matrix: ${{ needs.xpu-n-py3_10-inductor-benchmark-build.outputs.test-matrix }}
timeout-minutes: 720
disable-monitor: false
monitor-log-interval: 15
monitor-data-collect-interval: 4
secrets: inherit

View File

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

View File

@ -1,13 +1,12 @@
name: inductor-rocm
on:
schedule:
- cron: 0 */3 * * *
push:
branches:
- main
- release/*
tags:
- ciflow/inductor-rocm-mi200/*
- ciflow/inductor-rocm/*
workflow_dispatch:
concurrency:

View File

@ -81,32 +81,6 @@ jobs:
test-matrix: ${{ needs.inductor-halide-build.outputs.test-matrix }}
secrets: inherit
inductor-pallas-build:
name: inductor-pallas-build
uses: ./.github/workflows/_linux-build.yml
needs: get-label-type
with:
build-environment: linux-jammy-cuda12.8-py3.12-gcc11
docker-image-name: ci-image:pytorch-linux-jammy-cuda12.8-py3.12-pallas
cuda-arch-list: '8.9'
runner: linux.8xlarge.memory
runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
test-matrix: |
{ include: [
{ config: "inductor-pallas", shard: 1, num_shards: 1, runner: "${{ needs.get-label-type.outputs.label-type }}linux.g5.12xlarge.nvidia.gpu" },
]}
secrets: inherit
inductor-pallas-test:
name: inductor-pallas-test
uses: ./.github/workflows/_linux-test.yml
needs: inductor-pallas-build
with:
build-environment: linux-jammy-py3.12-gcc11
docker-image: ${{ needs.inductor-pallas-build.outputs.docker-image }}
test-matrix: ${{ needs.inductor-pallas-build.outputs.test-matrix }}
secrets: inherit
inductor-triton-cpu-build:
name: inductor-triton-cpu-build
uses: ./.github/workflows/_linux-build.yml
@ -141,10 +115,10 @@ jobs:
runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
test-matrix: |
{ include: [
{ config: "inductor_amx", shard: 1, num_shards: 2, runner: "${{ needs.get-label-type.outputs.label-type }}linux.2xlarge.amx" },
{ config: "inductor_amx", shard: 2, num_shards: 2, runner: "${{ needs.get-label-type.outputs.label-type }}linux.2xlarge.amx" },
{ config: "inductor_avx2", shard: 1, num_shards: 2, runner: "${{ needs.get-label-type.outputs.label-type }}linux.2xlarge.avx2" },
{ config: "inductor_avx2", shard: 2, num_shards: 2, runner: "${{ needs.get-label-type.outputs.label-type }}linux.2xlarge.avx2" },
{ config: "inductor_amx", shard: 1, num_shards: 2, runner: "${{ needs.get-label-type.outputs.label-type }}linux.8xlarge.amx" },
{ config: "inductor_amx", shard: 2, num_shards: 2, runner: "${{ needs.get-label-type.outputs.label-type }}linux.8xlarge.amx" },
{ config: "inductor_avx2", shard: 1, num_shards: 2, runner: "${{ needs.get-label-type.outputs.label-type }}linux.10xlarge.avx2" },
{ config: "inductor_avx2", shard: 2, num_shards: 2, runner: "${{ needs.get-label-type.outputs.label-type }}linux.10xlarge.avx2" },
]}
secrets: inherit

View File

@ -84,13 +84,13 @@ jobs:
runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
test-matrix: |
{ include: [
{ config: "cpu_inductor_torchbench", shard: 1, num_shards: 2, runner: "${{ needs.get-label-type.outputs.label-type }}linux.2xlarge.amx" },
{ config: "cpu_inductor_torchbench", shard: 2, num_shards: 2, runner: "${{ needs.get-label-type.outputs.label-type }}linux.2xlarge.amx" },
{ config: "dynamic_cpu_inductor_huggingface", shard: 1, num_shards: 1, runner: "${{ needs.get-label-type.outputs.label-type }}linux.2xlarge.amx" },
{ config: "dynamic_cpu_inductor_timm", shard: 1, num_shards: 2, runner: "${{ needs.get-label-type.outputs.label-type }}linux.2xlarge.amx" },
{ config: "dynamic_cpu_inductor_timm", shard: 2, num_shards: 2, runner: "${{ needs.get-label-type.outputs.label-type }}linux.2xlarge.amx" },
{ config: "dynamic_cpu_inductor_torchbench", shard: 1, num_shards: 2, runner: "${{ needs.get-label-type.outputs.label-type }}linux.2xlarge.amx" },
{ config: "dynamic_cpu_inductor_torchbench", shard: 2, num_shards: 2, runner: "${{ needs.get-label-type.outputs.label-type }}linux.2xlarge.amx" },
{ config: "cpu_inductor_torchbench", shard: 1, num_shards: 2, runner: "${{ needs.get-label-type.outputs.label-type }}linux.8xlarge.amx" },
{ config: "cpu_inductor_torchbench", shard: 2, num_shards: 2, runner: "${{ needs.get-label-type.outputs.label-type }}linux.8xlarge.amx" },
{ config: "dynamic_cpu_inductor_huggingface", shard: 1, num_shards: 1, runner: "${{ needs.get-label-type.outputs.label-type }}linux.8xlarge.amx" },
{ config: "dynamic_cpu_inductor_timm", shard: 1, num_shards: 2, runner: "${{ needs.get-label-type.outputs.label-type }}linux.8xlarge.amx" },
{ config: "dynamic_cpu_inductor_timm", shard: 2, num_shards: 2, runner: "${{ needs.get-label-type.outputs.label-type }}linux.8xlarge.amx" },
{ config: "dynamic_cpu_inductor_torchbench", shard: 1, num_shards: 2, runner: "${{ needs.get-label-type.outputs.label-type }}linux.8xlarge.amx" },
{ config: "dynamic_cpu_inductor_torchbench", shard: 2, num_shards: 2, runner: "${{ needs.get-label-type.outputs.label-type }}linux.8xlarge.amx" },
{ config: "inductor_torchbench_cpu_smoketest_perf", shard: 1, num_shards: 1, runner: "${{ needs.get-label-type.outputs.label-type }}linux.24xl.spr-metal" },
]}
build-additional-packages: "vision audio torchao"

View File

@ -76,12 +76,11 @@ jobs:
# NOTE: mypy needs its own job because it depends on --all-files, without assessing all files it sometimes
# fails to find types when it should
# NOTE: We should be able to disable this and consolidate with Pyrefly
lintrunner-pyrefly:
lintrunner-mypy:
uses: pytorch/test-infra/.github/workflows/linux_job_v2.yml@main
name: lintrunner-pyrefly-${{ needs.get-changed-files.outputs.changed-files == '*' && 'all' || 'partial' }}
name: lintrunner-mypy-${{ needs.get-changed-files.outputs.changed-files == '*' && 'all' || 'partial' }}
needs: [get-label-type, get-changed-files]
# Only run if there are changed files relevant to pyrefly
# Only run if there are changed files relevant to mypy
if: |
github.repository_owner == 'pytorch' && (
needs.get-changed-files.outputs.changed-files == '*' ||
@ -99,8 +98,8 @@ jobs:
ref: ${{ github.event_name == 'pull_request' && github.event.pull_request.head.sha || github.sha }}
script: |
CHANGED_FILES="${{ needs.get-changed-files.outputs.changed-files }}"
echo "Running pyrefly"
ADDITIONAL_LINTRUNNER_ARGS="--take PYREFLY --all-files" .github/scripts/lintrunner.sh
echo "Running mypy"
ADDITIONAL_LINTRUNNER_ARGS="--take MYPY,MYPYSTRICT --all-files" .github/scripts/lintrunner.sh
lintrunner-noclang:
uses: pytorch/test-infra/.github/workflows/linux_job_v2.yml@main
@ -119,9 +118,9 @@ jobs:
CHANGED_FILES="${{ needs.get-changed-files.outputs.changed-files }}"
echo "Running all other linters"
if [ "$CHANGED_FILES" = '*' ]; then
ADDITIONAL_LINTRUNNER_ARGS="--skip CLANGTIDY,CLANGFORMAT,PYREFLY --all-files" .github/scripts/lintrunner.sh
ADDITIONAL_LINTRUNNER_ARGS="--skip CLANGTIDY,CLANGFORMAT,MYPY,MYPYSTRICT,PYREFLY --all-files" .github/scripts/lintrunner.sh
else
ADDITIONAL_LINTRUNNER_ARGS="--skip CLANGTIDY,CLANGFORMAT,PYREFLY ${CHANGED_FILES}" .github/scripts/lintrunner.sh
ADDITIONAL_LINTRUNNER_ARGS="--skip CLANGTIDY,CLANGFORMAT,MYPY,MYPYSTRICT,PYREFLY ${CHANGED_FILES}" .github/scripts/lintrunner.sh
fi
quick-checks:

View File

@ -33,7 +33,7 @@ jobs:
with:
runner_prefix: ${{ needs.get-label-type.outputs.label-type }}
build-environment: linux-jammy-aarch64-py3.10
docker-image-name: ci-image:pytorch-linux-jammy-aarch64-py3.10-gcc13
docker-image-name: ci-image:pytorch-linux-jammy-aarch64-py3.10-gcc11
runner: linux.arm64.m7g.4xlarge
test-matrix: |
{ include: [

View File

@ -41,7 +41,7 @@ jobs:
uses: ./.github/workflows/_linux-build.yml
needs: get-label-type
with:
runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
runner: "${{ needs.get-label-type.outputs.label-type }}linux.2xlarge"
build-environment: linux-jammy-py3.10-gcc11
docker-image-name: ci-image:pytorch-linux-jammy-py3.10-gcc11
secrets: inherit

View File

@ -60,7 +60,7 @@ jobs:
with:
build-environment: linux-jammy-aarch64-py3.10
runner: linux.arm64.m7g.4xlarge
docker-image-name: ci-image:pytorch-linux-jammy-aarch64-py3.10-gcc13
docker-image-name: ci-image:pytorch-linux-jammy-aarch64-py3.10-gcc11
test-matrix: |
{ include: [
{ config: "cpu_operator_benchmark_short", shard: 1, num_shards: 1, runner: "linux.arm64.m8g.4xlarge" },

View File

@ -1,83 +0,0 @@
name: periodic-rocm-mi200
on:
schedule:
# We have several schedules so jobs can check github.event.schedule to activate only for a fraction of the runs.
# Also run less frequently on weekends.
- cron: 45 0,8,16 * * 1-5
- cron: 45 4 * * 0,6
- cron: 45 4,12,20 * * 1-5
- cron: 45 12 * * 0,6
- cron: 29 8 * * * # about 1:29am PDT, for mem leak check and rerun disabled tests
push:
tags:
- ciflow/periodic-rocm-mi200/*
branches:
- release/*
workflow_dispatch:
concurrency:
group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.ref_name }}-${{ github.ref_type == 'branch' && github.sha }}-${{ github.event_name == 'workflow_dispatch' }}-${{ github.event_name == 'schedule' }}-${{ github.event.schedule }}
cancel-in-progress: true
permissions:
id-token: write
contents: read
jobs:
llm-td:
if: github.repository_owner == 'pytorch'
name: before-test
uses: ./.github/workflows/llm_td_retrieval.yml
permissions:
id-token: write
contents: read
target-determination:
name: before-test
uses: ./.github/workflows/target_determination.yml
needs: llm-td
permissions:
id-token: write
contents: read
get-label-type:
name: get-label-type
uses: pytorch/pytorch/.github/workflows/_runner-determinator.yml@main
if: (github.event_name != 'schedule' || github.repository == 'pytorch/pytorch') && github.repository_owner == 'pytorch'
with:
triggering_actor: ${{ github.triggering_actor }}
issue_owner: ${{ github.event.pull_request.user.login || github.event.issue.user.login }}
curr_branch: ${{ github.head_ref || github.ref_name }}
curr_ref_type: ${{ github.ref_type }}
linux-jammy-rocm-py3_10-build:
name: linux-jammy-rocm-py3.10
uses: ./.github/workflows/_linux-build.yml
needs: get-label-type
with:
runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
build-environment: linux-jammy-rocm-py3.10
docker-image-name: ci-image:pytorch-linux-jammy-rocm-n-py3
test-matrix: |
{ include: [
{ config: "distributed", shard: 1, num_shards: 3, runner: "linux.rocm.gpu.4", owners: ["module:rocm", "oncall:distributed"] },
{ config: "distributed", shard: 2, num_shards: 3, runner: "linux.rocm.gpu.4", owners: ["module:rocm", "oncall:distributed"] },
{ config: "distributed", shard: 3, num_shards: 3, runner: "linux.rocm.gpu.4", owners: ["module:rocm", "oncall:distributed"] },
]}
secrets: inherit
linux-jammy-rocm-py3_10-test:
permissions:
id-token: write
contents: read
name: linux-jammy-rocm-py3.10
uses: ./.github/workflows/_rocm-test.yml
needs:
- linux-jammy-rocm-py3_10-build
- target-determination
with:
build-environment: linux-jammy-rocm-py3.10
docker-image: ${{ needs.linux-jammy-rocm-py3_10-build.outputs.docker-image }}
test-matrix: ${{ needs.linux-jammy-rocm-py3_10-build.outputs.test-matrix }}
secrets: inherit

View File

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

View File

@ -204,6 +204,37 @@ jobs:
test-matrix: ${{ needs.linux-jammy-cuda13_0-py3_10-gcc11-build.outputs.test-matrix }}
secrets: inherit
linux-jammy-rocm-py3_10-build:
name: linux-jammy-rocm-py3.10
uses: ./.github/workflows/_linux-build.yml
needs: get-label-type
with:
runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
build-environment: linux-jammy-rocm-py3.10
docker-image-name: ci-image:pytorch-linux-jammy-rocm-n-py3
test-matrix: |
{ include: [
{ config: "distributed", shard: 1, num_shards: 3, runner: "linux.rocm.gpu.mi250.4", owners: ["module:rocm", "oncall:distributed"] },
{ config: "distributed", shard: 2, num_shards: 3, runner: "linux.rocm.gpu.mi250.4", owners: ["module:rocm", "oncall:distributed"] },
{ config: "distributed", shard: 3, num_shards: 3, runner: "linux.rocm.gpu.mi250.4", owners: ["module:rocm", "oncall:distributed"] },
]}
secrets: inherit
linux-jammy-rocm-py3_10-test:
permissions:
id-token: write
contents: read
name: linux-jammy-rocm-py3.10
uses: ./.github/workflows/_rocm-test.yml
needs:
- linux-jammy-rocm-py3_10-build
- target-determination
with:
build-environment: linux-jammy-rocm-py3.10
docker-image: ${{ needs.linux-jammy-rocm-py3_10-build.outputs.docker-image }}
test-matrix: ${{ needs.linux-jammy-rocm-py3_10-build.outputs.test-matrix }}
secrets: inherit
linux-jammy-cuda12_8-py3-gcc11-slow-gradcheck-build:
name: linux-jammy-cuda12.8-py3-gcc11-slow-gradcheck
uses: ./.github/workflows/_linux-build.yml

View File

@ -66,10 +66,10 @@ jobs:
{ config: "default", shard: 5, num_shards: 5, runner: "${{ needs.get-label-type.outputs.label-type }}linux.2xlarge" },
{ config: "docs_test", shard: 1, num_shards: 1, runner: "${{ needs.get-label-type.outputs.label-type }}linux.2xlarge" },
{ config: "jit_legacy", shard: 1, num_shards: 1, runner: "${{ needs.get-label-type.outputs.label-type }}linux.2xlarge" },
{ config: "backwards_compat", shard: 1, num_shards: 1, runner: "${{ needs.get-label-type.outputs.label-type }}linux.c7i.2xlarge" },
{ config: "backwards_compat", shard: 1, num_shards: 1, runner: "${{ needs.get-label-type.outputs.label-type }}linux.2xlarge" },
{ config: "distributed", shard: 1, num_shards: 2, runner: "${{ 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: "numpy_2_x", shard: 1, num_shards: 1, runner: "${{ needs.get-label-type.outputs.label-type }}linux.2xlarge" },
]}
secrets: inherit
@ -167,8 +167,8 @@ jobs:
docker-image-name: ci-image:pytorch-linux-jammy-py3-clang12-onnx
test-matrix: |
{ include: [
{ config: "default", shard: 1, num_shards: 2, runner: "${{ needs.get-label-type.outputs.label-type }}linux.c7i.2xlarge" },
{ config: "default", shard: 2, num_shards: 2, runner: "${{ needs.get-label-type.outputs.label-type }}linux.c7i.2xlarge" },
{ config: "default", shard: 1, num_shards: 2, runner: "${{ needs.get-label-type.outputs.label-type }}linux.2xlarge" },
{ config: "default", shard: 2, num_shards: 2, runner: "${{ needs.get-label-type.outputs.label-type }}linux.2xlarge" },
]}
secrets: inherit
@ -342,16 +342,16 @@ jobs:
test-matrix: ${{ needs.linux-jammy-cuda12_8-py3_10-gcc9-inductor-build.outputs.test-matrix }}
secrets: inherit
linux-noble-xpu-n-py3_10-build:
name: linux-noble-xpu-n-py3.10
linux-jammy-xpu-n-py3_10-build:
name: linux-jammy-xpu-n-py3.10
uses: ./.github/workflows/_linux-build.yml
needs: get-label-type
with:
# This should sync with the build in xpu.yml but xpu uses a larger runner
# sync-tag: linux-xpu-n-build
runner_prefix: ${{ needs.get-label-type.outputs.label-type }}
build-environment: linux-noble-xpu-n-py3.10
docker-image-name: ci-image:pytorch-linux-noble-xpu-n-py3
build-environment: linux-jammy-xpu-n-py3.10
docker-image-name: ci-image:pytorch-linux-jammy-xpu-n-py3
test-matrix: |
{ include: [
{ config: "default", shard: 1, num_shards: 4, runner: "linux.idc.xpu" },

View File

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

View File

@ -3,14 +3,13 @@ name: rocm
on:
push:
branches:
- main
- release/*
tags:
- ciflow/rocm-mi200/*
- ciflow/rocm/*
workflow_dispatch:
schedule:
- cron: 29 8 * * * # about 1:29am PDT
- cron: 0 */3 * * *
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' }}

View File

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

View File

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

View File

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

View File

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

View File

@ -204,7 +204,6 @@ jobs:
{ include: [
{ config: "default", shard: 1, num_shards: 2, runner: "linux.rocm.gpu.gfx942.1" },
{ config: "default", shard: 2, num_shards: 2, runner: "linux.rocm.gpu.gfx942.1" },
{ config: "distributed", shard: 1, num_shards: 1, runner: "linux.rocm.gpu.gfx942.4" },
]}
secrets: inherit
@ -222,7 +221,7 @@ jobs:
build-environment: linux-jammy-rocm-py3.10
docker-image: ${{ needs.linux-jammy-rocm-py3_10-build.outputs.docker-image }}
test-matrix: ${{ needs.linux-jammy-rocm-py3_10-build.outputs.test-matrix }}
tests-to-include: "test_nn test_torch test_cuda test_ops test_unary_ufuncs test_binary_ufuncs test_autograd inductor/test_torchinductor distributed/test_c10d_common distributed/test_c10d_nccl"
tests-to-include: "test_nn test_torch test_cuda test_ops test_unary_ufuncs test_binary_ufuncs test_autograd inductor/test_torchinductor"
secrets: inherit
inductor-build:

View File

@ -6,21 +6,19 @@ on:
- pull
- trunk
- periodic
- periodic-rocm-mi200
- periodic-rocm-mi300
- inductor
- unstable
- slow
- slow-rocm-mi200
- unstable-periodic
- inductor-periodic
- rocm-mi200
- rocm
- rocm-mi300
- rocm-mi355
- inductor-micro-benchmark
- inductor-micro-benchmark-x86
- inductor-cu124
- inductor-rocm-mi200
- inductor-rocm
- inductor-rocm-mi300
- mac-mps
- linux-aarch64

View File

@ -47,44 +47,40 @@ jobs:
]}
secrets: inherit
linux-noble-xpu-n-py3_10-build:
name: linux-noble-xpu-n-py3.10
linux-jammy-xpu-n-py3_10-build:
name: linux-jammy-xpu-n-py3.10
uses: ./.github/workflows/_linux-build.yml
needs: get-label-type
with:
sync-tag: linux-xpu-n-build
runner_prefix: ${{ needs.get-label-type.outputs.label-type }}
build-environment: linux-noble-xpu-n-py3.10
docker-image-name: ci-image:pytorch-linux-noble-xpu-n-py3
build-environment: linux-jammy-xpu-n-py3.10
docker-image-name: ci-image:pytorch-linux-jammy-xpu-n-py3
runner: linux.c7i.12xlarge
test-matrix: |
{ include: [
{ config: "default", shard: 1, num_shards: 12, runner: "linux.idc.xpu" },
{ config: "default", shard: 2, num_shards: 12, runner: "linux.idc.xpu" },
{ config: "default", shard: 3, num_shards: 12, runner: "linux.idc.xpu" },
{ config: "default", shard: 4, num_shards: 12, runner: "linux.idc.xpu" },
{ config: "default", shard: 5, num_shards: 12, runner: "linux.idc.xpu" },
{ config: "default", shard: 6, num_shards: 12, runner: "linux.idc.xpu" },
{ config: "default", shard: 7, num_shards: 12, runner: "linux.idc.xpu" },
{ config: "default", shard: 8, num_shards: 12, runner: "linux.idc.xpu" },
{ config: "default", shard: 9, num_shards: 12, runner: "linux.idc.xpu" },
{ config: "default", shard: 10, num_shards: 12, runner: "linux.idc.xpu" },
{ config: "default", shard: 11, num_shards: 12, runner: "linux.idc.xpu" },
{ config: "default", shard: 12, num_shards: 12, runner: "linux.idc.xpu" },
{ config: "default", shard: 1, num_shards: 8, runner: "linux.idc.xpu" },
{ config: "default", shard: 2, num_shards: 8, runner: "linux.idc.xpu" },
{ config: "default", shard: 3, num_shards: 8, runner: "linux.idc.xpu" },
{ config: "default", shard: 4, num_shards: 8, runner: "linux.idc.xpu" },
{ config: "default", shard: 5, num_shards: 8, runner: "linux.idc.xpu" },
{ config: "default", shard: 6, num_shards: 8, runner: "linux.idc.xpu" },
{ config: "default", shard: 7, num_shards: 8, runner: "linux.idc.xpu" },
{ config: "default", shard: 8, num_shards: 8, runner: "linux.idc.xpu" },
]}
secrets: inherit
linux-noble-xpu-n-py3_10-test:
name: linux-noble-xpu-n-py3.10
linux-jammy-xpu-n-py3_10-test:
name: linux-jammy-xpu-n-py3.10
uses: ./.github/workflows/_xpu-test.yml
needs: linux-noble-xpu-n-py3_10-build
needs: linux-jammy-xpu-n-py3_10-build
permissions:
id-token: write
contents: read
with:
build-environment: linux-noble-xpu-n-py3.10
docker-image: ${{ needs.linux-noble-xpu-n-py3_10-build.outputs.docker-image }}
test-matrix: ${{ needs.linux-noble-xpu-n-py3_10-build.outputs.test-matrix }}
build-environment: linux-jammy-xpu-n-py3.10
docker-image: ${{ needs.linux-jammy-xpu-n-py3_10-build.outputs.docker-image }}
test-matrix: ${{ needs.linux-jammy-xpu-n-py3_10-build.outputs.test-matrix }}
secrets: inherit
windows-xpu-n-1-build:

3
.gitignore vendored
View File

@ -127,7 +127,6 @@ torch/test/
torch/utils/benchmark/utils/valgrind_wrapper/callgrind.h
torch/utils/benchmark/utils/valgrind_wrapper/valgrind.h
torch/version.py
torch/_inductor/kernel/vendored_templates/*
minifier_launcher.py
aten/src/ATen/native/transformers/hip/flash_attn/ck/fmha_fwd_d*
aten/src/ATen/native/transformers/hip/flash_attn/ck/fmha_bwd_d*
@ -144,7 +143,6 @@ scripts/release_notes/*.json
sccache-stats*.json
lint.json
merge_record.json
.github/scripts/nightly_source_matrix.json
# These files get copied over on invoking setup.py
torchgen/packaged/*
@ -399,4 +397,3 @@ CLAUDE.local.md
/test_*.py
/debug_*.py
CLAUDE_CONTEXT/
/.claude/settings.local.json

View File

@ -121,6 +121,94 @@ command = [
]
is_formatter = true
[[linter]]
code = 'MYPY'
include_patterns = [
'setup.py',
'functorch/dim/**/*.py',
'torch/**/*.py',
'torch/**/*.pyi',
'caffe2/**/*.py',
'caffe2/**/*.pyi',
'test/test_bundled_images.py',
'test/test_bundled_inputs.py',
'test/test_complex.py',
'test/test_datapipe.py',
'test/test_futures.py',
'test/test_numpy_interop.py',
'test/test_torch.py',
'test/test_type_hints.py',
'test/test_type_info.py',
'test/test_utils.py',
]
exclude_patterns = [
'**/fb/**',
]
command = [
'python3',
'tools/linter/adapters/mypy_linter.py',
'--config=mypy.ini',
'--',
'@{{PATHSFILE}}'
]
init_command = [
'python3',
'tools/linter/adapters/pip_init.py',
'--dry-run={{DRYRUN}}',
'numpy==1.26.4 ; python_version >= "3.10" and python_version <= "3.11"',
'numpy==2.1.0 ; python_version >= "3.12"',
'expecttest==0.3.0',
'mypy==1.16.0',
'sympy==1.13.3',
'types-requests==2.27.25',
'types-pyyaml==6.0.2',
'types-tabulate==0.8.8',
'types-protobuf==5.29.1.20250403',
'types-setuptools==79.0.0.20250422',
'types-jinja2==2.11.9',
'types-colorama==0.4.6',
'filelock==3.18.0',
'junitparser==2.1.1',
'rich==14.1.0',
'pyyaml==6.0.2',
'optree==0.13.0',
'dataclasses-json==0.6.7',
'pandas==2.2.3',
]
[[linter]]
code = 'MYPYSTRICT'
include_patterns = [
'.github/**/*.py',
'benchmarks/instruction_counts/**/*.py',
'tools/**/*.py',
'torchgen/**/*.py',
'torch/utils/_pytree.py',
'torch/utils/_cxx_pytree.py',
'torch/utils/benchmark/utils/common.py',
'torch/utils/benchmark/utils/timer.py',
'torch/utils/benchmark/utils/valgrind_wrapper/**/*.py',
]
exclude_patterns = [
# (linbinyu) copied from internal repo
'**/fb/**',
'tools/code_analyzer/gen_operators_yaml.py',
'tools/dynamo/verify_dynamo.py',
'tools/gen_vulkan_spv.py',
'tools/test/gen_operators_yaml_test.py',
'tools/test/gen_oplist_test.py',
'tools/test/test_selective_build.py',
'tools/experimental/torchfuzz/**',
]
command = [
'python3',
'tools/linter/adapters/mypy_linter.py',
'--config=mypy-strict.ini',
'--code=MYPYSTRICT',
'--',
'@{{PATHSFILE}}'
]
[[linter]]
code = 'PYREFLY'
@ -142,9 +230,7 @@ init_command = [
'python3',
'tools/linter/adapters/pip_init.py',
'--dry-run={{DRYRUN}}',
'numpy==1.26.4 ; python_version >= "3.10" and python_version <= "3.11"',
'numpy==2.1.0 ; python_version >= "3.12" and python_version <= "3.13"',
'numpy==2.3.4 ; python_version >= "3.14"',
'numpy==2.1.0 ; python_version >= "3.12"',
'expecttest==0.3.0',
'pyrefly==0.36.2',
'sympy==1.13.3',
@ -212,6 +298,7 @@ exclude_patterns = [
'**/*pb.h',
'**/*inl.h',
'aten/src/ATen/cpu/FlushDenormal.cpp',
'aten/src/ATen/cpu/Utils.cpp',
'aten/src/ATen/cpu/vml.h',
'aten/src/ATen/CPUFixedAllocator.h',
'aten/src/ATen/Parallel*.h',
@ -230,6 +317,8 @@ exclude_patterns = [
'c10/util/win32-headers.h',
'c10/test/**/*.h',
'third_party/**/*',
'torch/csrc/api/include/torch/nn/modules/common.h',
'torch/csrc/api/include/torch/linalg.h',
'torch/csrc/autograd/generated/**',
'torch/csrc/distributed/**/*.cu',
'torch/csrc/distributed/c10d/WinSockUtils.hpp',
@ -241,6 +330,7 @@ exclude_patterns = [
'torch/csrc/utils/generated_serialization_types.h',
'torch/csrc/utils/pythoncapi_compat.h',
'torch/csrc/inductor/aoti_runtime/sycl_runtime_wrappers.h',
'aten/src/ATen/ExpandBase.h',
]
init_command = [
'python3',
@ -1402,7 +1492,7 @@ init_command = [
'--dry-run={{DRYRUN}}',
'usort==1.0.8.post1',
'isort==6.0.1',
'ruff==0.14.4', # sync with RUFF
'ruff==0.13.1', # sync with RUFF
]
is_formatter = true
@ -1537,7 +1627,7 @@ init_command = [
'python3',
'tools/linter/adapters/pip_init.py',
'--dry-run={{DRYRUN}}',
'ruff==0.14.4', # sync with PYFMT
'ruff==0.13.1', # sync with PYFMT
]
is_formatter = true

View File

@ -234,17 +234,7 @@ option(USE_COLORIZE_OUTPUT "Colorize output during compilation" ON)
option(USE_ASAN "Use Address+Undefined Sanitizers" OFF)
option(USE_LSAN "Use Leak Sanitizer" OFF)
option(USE_TSAN "Use Thread Sanitizer" OFF)
# Track whether USE_CUDA was explicitly set by the user (before option() is called)
# If USE_CUDA is already defined in cache, it means user explicitly set it
if(DEFINED CACHE{USE_CUDA})
set(_USE_CUDA_EXPLICITLY_SET TRUE)
else()
set(_USE_CUDA_EXPLICITLY_SET FALSE)
endif()
option(USE_CUDA "Use CUDA" ON)
option(USE_XPU "Use XPU" ON)
cmake_dependent_option(
BUILD_LAZY_CUDA_LINALG "Build cuda linalg ops as separate library" ON
@ -384,7 +374,7 @@ cmake_dependent_option(
"Build the lazy Torchscript backend, not compatible with mobile builds" ON
"NOT INTERN_BUILD_MOBILE" OFF)
cmake_dependent_option(BUILD_FUNCTORCH "Build Functorch" ON "BUILD_PYTHON" OFF)
cmake_dependent_option(BUILD_BUNDLE_PTXAS "Bundle PTX into torch/bin folder"
cmake_dependent_option(BUILD_BUNDLE_PTXAS "Bundle PTX into torch/bin fodler"
OFF "USE_CUDA" OFF)
cmake_dependent_option(USE_KLEIDIAI "Use KleidiAI for the ARM CPU & AARCH64 architecture." ON
"CPU_AARCH64" OFF)
@ -736,44 +726,6 @@ if(NOT DEFINED USE_BLAS)
set(USE_BLAS ON)
endif()
# Prioritized Text Linker Optimization
if(USE_PRIORITIZED_TEXT_FOR_LD)
set(LINKER_SCRIPT_FILE_IN "${CMAKE_SOURCE_DIR}/cmake/prioritized_text.txt")
set(LINKER_SCRIPT_FILE_OUT "${CMAKE_SOURCE_DIR}/cmake/linker_script.ld")
execute_process(
COMMAND ${Python_EXECUTABLE}
${CMAKE_SOURCE_DIR}/tools/setup_helpers/generate_linker_script.py
--filein "${LINKER_SCRIPT_FILE_IN}"
--fout "${LINKER_SCRIPT_FILE_OUT}"
RESULT_VARIABLE _gen_result
OUTPUT_VARIABLE _gen_output
ERROR_VARIABLE _gen_error
)
if(NOT _gen_result EQUAL 0)
message(FATAL_ERROR
"Failed to generate linker script:\n${_gen_output}\n${_gen_error}")
endif()
append_cxx_flag_if_supported("-ffunction-sections" CMAKE_CXX_FLAGS)
append_cxx_flag_if_supported("-fdata-sections" CMAKE_CXX_FLAGS)
append_c_flag_if_supported("-ffunction-sections" CMAKE_C_FLAGS)
append_c_flag_if_supported("-fdata-sections" CMAKE_C_FLAGS)
set(CMAKE_SHARED_LINKER_FLAGS "${CMAKE_SHARED_LINKER_FLAGS} -T${LINKER_SCRIPT_FILE_OUT}")
set(CMAKE_MODULE_LINKER_FLAGS "${CMAKE_MODULE_LINKER_FLAGS} -T${LINKER_SCRIPT_FILE_OUT}")
else()
if(LINUX AND CPU_AARCH64)
message(WARNING [[
It is strongly recommend to enable linker script optimization for all AArch64 Linux builds.
To do so please export USE_PRIORITIZED_TEXT_FOR_LD=1
]])
endif()
endif()
# Build libtorch mobile library, which contains ATen/TH ops and native support
# for TorchScript model, but doesn't contain not-yet-unified caffe2 ops;
if(INTERN_BUILD_MOBILE)
@ -1440,6 +1392,9 @@ if(BUILD_JNI)
add_subdirectory(android/pytorch_android)
endif()
include(cmake/Summary.cmake)
caffe2_print_configuration_summary()
# Parse custom debug info
if(DEFINED USE_CUSTOM_DEBINFO)
string(REPLACE ";" " " SOURCE_FILES "${USE_CUSTOM_DEBINFO}")
@ -1479,5 +1434,56 @@ if(BUILD_BUNDLE_PTXAS AND USE_CUDA)
DESTINATION "${CMAKE_INSTALL_BINDIR}")
endif()
include(cmake/Summary.cmake)
caffe2_print_configuration_summary()
if(USE_PRIORITIZED_TEXT_FOR_LD)
add_compile_options(
$<$<COMPILE_LANGUAGE:C,CXX>:-ffunction-sections>
$<$<COMPILE_LANGUAGE:C,CXX>:-fdata-sections>
)
set(LINKER_SCRIPT_FILE_OUT "${CMAKE_SOURCE_DIR}/cmake/linker_script.ld")
set(LINKER_SCRIPT_FILE_IN "${CMAKE_SOURCE_DIR}/cmake/prioritized_text.txt")
add_custom_command(
OUTPUT "${LINKER_SCRIPT_FILE_OUT}"
COMMAND ${Python_EXECUTABLE} ${CMAKE_SOURCE_DIR}/tools/setup_helpers/generate_linker_script.py --filein "${LINKER_SCRIPT_FILE_IN}" --fout "${LINKER_SCRIPT_FILE_OUT}"
DEPENDS ${CMAKE_SOURCE_DIR}/tools/setup_helpers/generate_linker_script.py "${LINKER_SCRIPT_FILE_IN}"
COMMENT "Generating prioritized text linker files"
VERBATIM
)
add_custom_target(generate_linker_script DEPENDS "${LINKER_SCRIPT_FILE_OUT}")
if(BUILD_PYTHON)
set(LINKER_OPT_TARGETS torch_python)
endif()
if(NOT BUILD_LIBTORCHLESS)
list(APPEND LINKER_OPT_TARGETS torch_cpu c10)
if(USE_CUDA)
list(APPEND LINKER_OPT_TARGETS torch_cuda c10_cuda)
endif()
if(USE_XPU)
list(APPEND LINKER_OPT_TARGETS torch_xpu c10_xpu)
endif()
if(USE_ROCM)
list(APPEND LINKER_OPT_TARGETS torch_hip c10_hip)
endif()
endif()
foreach(tgt IN LISTS LINKER_OPT_TARGETS)
if(TARGET ${tgt})
add_dependencies("${tgt}" generate_linker_script)
target_link_options_if_supported(${tgt} "-T,${LINKER_SCRIPT_FILE_OUT}")
set_property(TARGET ${tgt} APPEND PROPERTY LINK_DEPENDS "${LINKER_SCRIPT_FILE_OUT}")
else()
message(WARNING "Requested target '${tgt}' for linker script optimization was not found.")
endif()
endforeach()
else()
if(LINUX AND CPU_AARCH64)
message(WARNING [[
It is strongly recommend to enable linker script optimization for all AArch64 Linux builds.
To do so please export USE_PRIORITIZED_TEXT_FOR_LD=1
]])
endif()
endif()

View File

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

View File

@ -11,6 +11,7 @@ aspects of contributing to PyTorch.
<!-- toc -->
- [Developing PyTorch](#developing-pytorch)
- [Setup the development environment](#setup-the-development-environment)
- [Tips and Debugging](#tips-and-debugging)
- [Nightly Checkout & Pull](#nightly-checkout--pull)
- [Codebase structure](#codebase-structure)
@ -18,7 +19,7 @@ aspects of contributing to PyTorch.
- [Python Unit Testing](#python-unit-testing)
- [Better local unit tests with `pytest`](#better-local-unit-tests-with-pytest)
- [Local linting](#local-linting)
- [Running `pyrefly`](#running-pyrefly)
- [Running `mypy`](#running-mypy)
- [C++ Unit Testing](#c-unit-testing)
- [Run Specific CI Jobs](#run-specific-ci-jobs)
- [Merging your Change](#merging-your-change)
@ -66,6 +67,23 @@ aspects of contributing to PyTorch.
Follow the instructions for [installing PyTorch from source](https://github.com/pytorch/pytorch#from-source). If you get stuck when developing PyTorch on your machine, check out the [tips and debugging](#tips-and-debugging) section below for common solutions.
### Setup the development environment
First, you need to [fork the PyTorch project on GitHub](https://github.com/pytorch/pytorch/fork) and follow the instructions at [Connecting to GitHub with SSH](https://docs.github.com/en/authentication/connecting-to-github-with-ssh) to setup your SSH authentication credentials.
Then clone the PyTorch project and setup the development environment:
```bash
git clone git@github.com:<USERNAME>/pytorch.git
cd pytorch
git remote add upstream git@github.com:pytorch/pytorch.git
make setup-env
# Or run `make setup-env-cuda` for pre-built CUDA binaries
# Or run `make setup-env-rocm` for pre-built ROCm binaries
source venv/bin/activate # or `. .\venv\Scripts\activate` on Windows
```
### Tips and Debugging
* If you want to have no-op incremental rebuilds (which are fast), see [Make no-op build fast](#make-no-op-build-fast) below.
@ -281,7 +299,7 @@ dependencies as well as the nightly binaries into the repo directory.
**Prerequisites**:
The following packages should be installed with `pip`:
- `expecttest` and `hypothesis` - required to run tests
- `pyrefly` - recommended for type checking. [Pyrefly](https://pyrefly.org/)
- `mypy` - recommended for linting
- `pytest` - recommended to run tests more selectively
Running
```
@ -350,32 +368,15 @@ make lint
Learn more about the linter on the [lintrunner wiki page](https://github.com/pytorch/pytorch/wiki/lintrunner)
#### Running `pyrefly`
#### Running `mypy`
[Pyrefly](https://pyrefly.org/) is a high-performance static type checker for Python. It provides fast type checking along with IDE features like autocomplete and instant error feedback.
PyTorch uses Pyrefly for type checking across the codebase. The configuration is managed in `pyrefly.toml` at the root of the repository.
**Getting Started with Pyrefly:**
To run type checking on the PyTorch codebase:
```bash
pyrefly check
```
For more detailed error information with summaries:
```bash
pyrefly check --summarize-errors
```
**Learn More:**
- [Pyrefly Configuration](https://pyrefly.org/en/docs/configuration/) - Detailed configuration options
- [Pyrefly IDE Features](https://pyrefly.org/en/docs/IDE-features/) - Set up Pyrefly in your editor for real-time type checking
- [Python Typing Tutorial](https://pyrefly.org/en/docs/typing-for-python-developers/) - Learn about Python type annotations
`mypy` is an optional static type checker for Python. We have multiple `mypy`
configs for the PyTorch codebase that are automatically validated against whenever the linter is run.
See [Guide for adding type annotations to
PyTorch](https://github.com/pytorch/pytorch/wiki/Guide-for-adding-type-annotations-to-PyTorch)
for PyTorch-specific guidance on how to set up `pyrefly` and tackle type annotation tasks in this codebase.
for more information on how to set up `mypy` and tackle type annotation
tasks.
### C++ Unit Testing

View File

@ -1,7 +1,7 @@
# Security Policy
- [**Reporting a Vulnerability**](#reporting-a-vulnerability)
- [**Using PyTorch Securely**](#using-pytorch-securely)
- [**Using Pytorch Securely**](#using-pytorch-securely)
- [Untrusted models](#untrusted-models)
- [TorchScript models](#torchscript-models)
- [Untrusted inputs](#untrusted-inputs)
@ -10,28 +10,28 @@
- [**CI/CD security principles**](#cicd-security-principles)
## Reporting Security Issues
Beware that none of the topics under [Using PyTorch Securely](#using-pytorch-securely) are considered vulnerabilities of PyTorch.
Beware that none of the topics under [Using Pytorch Securely](#using-pytorch-securely) are considered vulnerabilities of Pytorch.
However, if you believe you have found a security vulnerability in PyTorch, we encourage you to let us know right away. We will investigate all legitimate reports and do our best to quickly fix the problem.
Please report security issues using https://github.com/pytorch/pytorch/security/advisories/new
All reports submitted through the security advisories mechanism would **either be made public or dismissed by the team within 90 days of the submission**. If advisory has been closed on the grounds that it is not a security issue, please do not hesitate to create an [new issue](https://github.com/pytorch/pytorch/issues/new?template=bug-report.yml) as it is still likely a valid issue within the framework.
All reports submitted thru the security advisories mechanism would **either be made public or dismissed by the team within 90 days of the submission**. If advisory has been closed on the grounds that it is not a security issue, please do not hesitate to create an [new issue](https://github.com/pytorch/pytorch/issues/new?template=bug-report.yml) as it is still likely a valid issue within the framework.
Please refer to the following page for our responsible disclosure policy, reward guidelines, and those things that should not be reported:
https://www.facebook.com/whitehat
## Using PyTorch Securely
**PyTorch models are programs**, so treat its security seriously -- running untrusted models is equivalent to running untrusted code. In general we recommend that model weights and the python code for the model are distributed independently. That said, be careful about where you get the python code from and who wrote it (preferentially check for a provenance or checksums, do not run any pip installed package).
## Using Pytorch Securely
**Pytorch models are programs**, so treat its security seriously -- running untrusted models is equivalent to running untrusted code. In general we recommend that model weights and the python code for the model are distributed independently. That said, be careful about where you get the python code from and who wrote it (preferentially check for a provenance or checksums, do not run any pip installed package).
### Untrusted models
Be careful when running untrusted models. This classification includes models created by unknown developers or utilizing data obtained from unknown sources[^data-poisoning-sources].
**Prefer to execute untrusted models within a secure, isolated environment such as a sandbox** (e.g., containers, virtual machines). This helps protect your system from potentially malicious code. You can find further details and instructions in [this page](https://developers.google.com/code-sandboxing).
**Be mindful of risky model formats**. Give preference to share and load weights with the appropriate format for your use case. [Safetensors](https://huggingface.co/docs/safetensors/en/index) gives the most safety but is the most restricted in what it supports. [`torch.load`](https://pytorch.org/docs/stable/generated/torch.load.html#torch.load) has a significantly larger surface of attack but is more flexible in what it can serialize. See the documentation for more details.
**Be mindful of risky model formats**. Give preference to share and load weights with the appropriate format for your use case. [safetensors](https://huggingface.co/docs/safetensors/en/index) gives the most safety but is the most restricted in what it supports. [`torch.load`](https://pytorch.org/docs/stable/generated/torch.load.html#torch.load) has a significantly larger surface of attack but is more flexible in what it can serialize. See the documentation for more details.
Even for more secure serialization formats, unexpected inputs to the downstream system can cause diverse security threats (e.g. denial of service, out of bound reads/writes) and thus we recommend extensive validation of any untrusted inputs.
@ -43,7 +43,7 @@ Important Note: The trustworthiness of a model is not binary. You must always de
### TorchScript models
TorchScript models should be treated the same way as locally executable code from an unknown source. Only run TorchScript models if you trust the provider. Please note, that tools for introspecting TorchScript models (such as `torch.utils.model_dump`) may also execute partial or full code stored in those models, therefore they should be used only if you trust the provider of the binary you are about to load.
TorchScript models should treated the same way as locally executable code from an unknown source. Only run TorchScript models if you trust the provider. Please note, that tools for introspecting TorchScript models (such as `torch.utils.model_dump`) may also execute partial or full code stored in those models, therefore they should be used only if you trust the provider of the binary you are about to load.
### Untrusted inputs during training and prediction
@ -59,9 +59,9 @@ If applicable, prepare your model against bad inputs and prompt injections. Some
### Data privacy
**Take special security measures if you train your models with sensitive data**. Prioritize [sandboxing](https://developers.google.com/code-sandboxing) your models and:
- Do not feed sensitive data to an untrusted model (even if runs in a sandboxed environment)
- If you consider publishing a model that was partially trained with sensitive data, be aware that data can potentially be recovered from the trained weights (especially if the model overfits).
**Take special security measures if your model if you train models with sensitive data**. Prioritize [sandboxing](https://developers.google.com/code-sandboxing) your models and:
- Do not feed sensitive data to untrusted model (even if runs in a sandboxed environment)
- If you consider publishing a model that was partially trained with sensitive data, be aware that data can potentially be recovered from the trained weights (especially if model overfits).
### Using distributed features

View File

@ -260,7 +260,7 @@ IF(USE_FBGEMM_GENAI)
if(USE_CUDA)
# To avoid increasing the build time/binary size unnecessarily, use an allow-list of kernels to build.
# If you want to integrate a kernel from FBGEMM into torch, you have to add it here.
set(FBGEMM_CUTLASS_KERNELS_REGEX ".*(mx8mx8bf16_grouped|f4f4bf16_grouped|f4f4bf16).*")
set(FBGEMM_CUTLASS_KERNELS_REGEX ".*(mx8mx8bf16_grouped|f4f4bf16_grouped).*")
file(GLOB_RECURSE fbgemm_genai_native_cuda_cu
"${FBGEMM_GENAI_SRCS}/cutlass_extensions/*.cu"
"${FBGEMM_GENAI_SRCS}/cutlass_extensions/**/*.cu")

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