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1857 changed files with 50704 additions and 63186 deletions

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@ -21,3 +21,6 @@
cxx = /usr/bin/clang++
cxxpp = /usr/bin/clang++
ld = /usr/bin/clang++
[project]
default_flavors_mode=all

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@ -1,5 +1,5 @@
0.7b
0.6b
manylinux_2_17
rocm6.2
9be04068c3c0857a4cfd17d7e39e71d0423ebac2
3e9e1959d23b93d78a08fcc5f868125dc3854dece32fd9458be9ef4467982291
7f07e8a1cb1f99627eb6d77f5c0e9295c775f3c7
e4ab195d2bd19e939c675a13280c29714c6ef9f2cf420690da150fa0cac043b1

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@ -286,23 +286,23 @@ case "$image" in
TRITON=yes
;;
pytorch-linux-focal-rocm-n-1-py3)
ANACONDA_PYTHON_VERSION=3.10
ANACONDA_PYTHON_VERSION=3.8
GCC_VERSION=9
PROTOBUF=yes
DB=yes
VISION=yes
ROCM_VERSION=6.1
ROCM_VERSION=6.0
NINJA_VERSION=1.9.0
CONDA_CMAKE=yes
TRITON=yes
;;
pytorch-linux-focal-rocm-n-py3)
ANACONDA_PYTHON_VERSION=3.10
ANACONDA_PYTHON_VERSION=3.8
GCC_VERSION=9
PROTOBUF=yes
DB=yes
VISION=yes
ROCM_VERSION=6.2
ROCM_VERSION=6.1
NINJA_VERSION=1.9.0
CONDA_CMAKE=yes
TRITON=yes
@ -355,12 +355,6 @@ case "$image" in
CONDA_CMAKE=yes
VISION=yes
;;
pytorch-linux-jammy-py3-clang18-asan)
ANACONDA_PYTHON_VERSION=3.10
CLANG_VERSION=18
CONDA_CMAKE=yes
VISION=yes
;;
pytorch-linux-jammy-py3.9-gcc11)
ANACONDA_PYTHON_VERSION=3.9
GCC_VERSION=11
@ -385,14 +379,6 @@ case "$image" in
GCC_VERSION=11
CONDA_CMAKE=yes
HALIDE=yes
TRITON=yes
;;
pytorch-linux-jammy-py3.12-triton-cpu)
CUDA_VERSION=12.4
ANACONDA_PYTHON_VERSION=3.12
GCC_VERSION=11
CONDA_CMAKE=yes
TRITON_CPU=yes
;;
pytorch-linux-focal-linter)
# TODO: Use 3.9 here because of this issue https://github.com/python/mypy/issues/13627.
@ -523,7 +509,6 @@ docker build \
--build-arg "UCC_COMMIT=${UCC_COMMIT}" \
--build-arg "CONDA_CMAKE=${CONDA_CMAKE}" \
--build-arg "TRITON=${TRITON}" \
--build-arg "TRITON_CPU=${TRITON_CPU}" \
--build-arg "ONNX=${ONNX}" \
--build-arg "DOCS=${DOCS}" \
--build-arg "INDUCTOR_BENCHMARKS=${INDUCTOR_BENCHMARKS}" \

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@ -108,10 +108,10 @@ ENV CMAKE_C_COMPILER cc
ENV CMAKE_CXX_COMPILER c++
COPY ./common/install_triton.sh install_triton.sh
COPY ./common/common_utils.sh common_utils.sh
COPY ci_commit_pins/triton.txt triton.txt
COPY ci_commit_pins/triton-rocm.txt triton-rocm.txt
COPY triton_version.txt triton_version.txt
RUN if [ -n "${TRITON}" ]; then bash ./install_triton.sh; fi
RUN rm install_triton.sh common_utils.sh triton.txt triton_version.txt
RUN rm install_triton.sh common_utils.sh triton-rocm.txt triton_version.txt
# Install AOTriton (Early fail)
COPY ./aotriton_version.txt aotriton_version.txt

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@ -1 +1 @@
cd1c833b079adb324871dcbbe75b43d42ffc0ade
d519b4d3a1ffdc81b45e2b1d4733423ce0577813

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@ -1 +0,0 @@
6a333f1b05671f6fada4ba7bbfae4a02a9d96f4f

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@ -0,0 +1 @@
21eae954efa5bf584da70324b640288c3ee7aede

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@ -1 +1 @@
91b14bf5593cf58a8541f3e6b9125600a867d4ef
1b2f15840e0d70eec50d84c7a0575cb835524def

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@ -1 +1 @@
cf34004b8a67d290a962da166f5aa2fc66751326
dedb7bdf339a3546896d4820366ca562c586bfa0

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@ -4,12 +4,12 @@ set -ex
source "$(dirname "${BASH_SOURCE[0]}")/common_utils.sh"
TARBALL='aotriton.tar.gz'
TARBALL='aotriton.tar.bz2'
# This read command alwasy returns with exit code 1
read -d "\n" VER MANYLINUX ROCMBASE PINNED_COMMIT SHA256 < aotriton_version.txt || true
ARCH=$(uname -m)
AOTRITON_INSTALL_PREFIX="$1"
AOTRITON_URL="https://github.com/ROCm/aotriton/releases/download/${VER}/aotriton-${VER}-${MANYLINUX}_${ARCH}-${ROCMBASE}-shared.tar.gz"
AOTRITON_URL="https://github.com/ROCm/aotriton/releases/download/${VER}/aotriton-${VER}-${MANYLINUX}_${ARCH}-${ROCMBASE}-shared.tar.bz2"
cd "${AOTRITON_INSTALL_PREFIX}"
# Must use -L to follow redirects

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@ -13,17 +13,11 @@ if [ -n "$CLANG_VERSION" ]; then
elif [[ $UBUNTU_VERSION == 22.04 ]]; 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 [[ $CLANG_VERSION == 18 ]]; then
apt-add-repository "deb http://apt.llvm.org/jammy/ llvm-toolchain-jammy-18 main"
fi
fi
sudo apt-get update
apt-get install -y --no-install-recommends clang-"$CLANG_VERSION" llvm-"$CLANG_VERSION"
if [[ $CLANG_VERSION == 18 ]]; then
apt-get install -y --no-install-recommends libomp-18-dev
fi
apt-get install -y --no-install-recommends clang-"$CLANG_VERSION"
apt-get install -y --no-install-recommends llvm-"$CLANG_VERSION"
# Install dev version of LLVM.
if [ -n "$LLVMDEV" ]; then

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@ -7,7 +7,7 @@ PYTHON_DOWNLOAD_GITHUB_BRANCH=https://github.com/python/cpython/archive/refs/hea
GET_PIP_URL=https://bootstrap.pypa.io/get-pip.py
# Python versions to be installed in /opt/$VERSION_NO
CPYTHON_VERSIONS=${CPYTHON_VERSIONS:-"3.8.1 3.9.0 3.10.1 3.11.0 3.12.0 3.13.0 3.13.0t"}
CPYTHON_VERSIONS=${CPYTHON_VERSIONS:-"3.8.1 3.9.0 3.10.1 3.11.0 3.12.0 3.13.0"}
function check_var {
if [ -z "$1" ]; then
@ -22,13 +22,6 @@ function do_cpython_build {
check_var $py_ver
check_var $py_folder
tar -xzf Python-$py_ver.tgz
local additional_flags=""
if [ "$py_ver" == "3.13.0t" ]; then
additional_flags=" --disable-gil"
mv cpython-3.13/ cpython-3.13t/
fi
pushd $py_folder
local prefix="/opt/_internal/cpython-${py_ver}"
@ -44,10 +37,8 @@ function do_cpython_build {
local openssl_flags="--with-openssl=${WITH_OPENSSL} --with-openssl-rpath=auto"
fi
# -Wformat added for https://bugs.python.org/issue17547 on Python 2.6
CFLAGS="-Wformat" ./configure --prefix=${prefix} ${openssl_flags} ${shared_flags} ${additional_flags} > /dev/null
CFLAGS="-Wformat" ./configure --prefix=${prefix} ${openssl_flags} ${shared_flags} > /dev/null
make -j40 > /dev/null
make install > /dev/null
@ -78,14 +69,7 @@ function build_cpython {
check_var $py_ver
check_var $PYTHON_DOWNLOAD_URL
local py_ver_folder=$py_ver
if [ "$py_ver" = "3.13.0t" ]; then
PY_VER_SHORT="3.13"
PYT_VER_SHORT="3.13t"
check_var $PYTHON_DOWNLOAD_GITHUB_BRANCH
wget $PYTHON_DOWNLOAD_GITHUB_BRANCH/$PY_VER_SHORT.tar.gz -O Python-$py_ver.tgz
do_cpython_build $py_ver cpython-$PYT_VER_SHORT
elif [ "$py_ver" = "3.13.0" ]; then
if [ "$py_ver" = "3.13.0" ]; then
PY_VER_SHORT="3.13"
check_var $PYTHON_DOWNLOAD_GITHUB_BRANCH
wget $PYTHON_DOWNLOAD_GITHUB_BRANCH/$PY_VER_SHORT.tar.gz -O Python-$py_ver.tgz

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@ -105,7 +105,7 @@ function install_121 {
}
function install_124 {
echo "Installing CUDA 12.4.1 and cuDNN ${CUDNN_VERSION} and NCCL ${NCCL_VERSION} and cuSparseLt-0.6.2"
echo "Installing CUDA 12.4.1 and cuDNN ${CUDNN_VERSION} and NCCL ${NCCL_VERSION} and cuSparseLt-0.5.2"
rm -rf /usr/local/cuda-12.4 /usr/local/cuda
# install CUDA 12.4.1 in the same container
wget -q https://developer.download.nvidia.com/compute/cuda/12.4.1/local_installers/cuda_12.4.1_550.54.15_linux.run

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@ -5,19 +5,19 @@ set -ex
NCCL_VERSION=v2.21.5-1
function install_cusparselt_062 {
function install_cusparselt_052 {
# cuSparseLt license: https://docs.nvidia.com/cuda/cusparselt/license.html
mkdir tmp_cusparselt && pushd tmp_cusparselt
wget -q https://developer.download.nvidia.com/compute/cusparselt/redist/libcusparse_lt/linux-sbsa/libcusparse_lt-linux-sbsa-0.6.2.3-archive.tar.xz
tar xf libcusparse_lt-linux-sbsa-0.6.2.3-archive.tar.xz
cp -a libcusparse_lt-linux-sbsa-0.6.2.3-archive/include/* /usr/local/cuda/include/
cp -a libcusparse_lt-linux-sbsa-0.6.2.3-archive/lib/* /usr/local/cuda/lib64/
wget -q https://developer.download.nvidia.com/compute/cusparselt/redist/libcusparse_lt/linux-sbsa/libcusparse_lt-linux-sbsa-0.5.2.1-archive.tar.xz
tar xf libcusparse_lt-linux-sbsa-0.5.2.1-archive.tar.xz
cp -a libcusparse_lt-linux-sbsa-0.5.2.1-archive/include/* /usr/local/cuda/include/
cp -a libcusparse_lt-linux-sbsa-0.5.2.1-archive/lib/* /usr/local/cuda/lib64/
popd
rm -rf tmp_cusparselt
}
function install_124 {
echo "Installing CUDA 12.4.1 and cuDNN 9.1 and NCCL ${NCCL_VERSION} and cuSparseLt-0.6.2"
echo "Installing CUDA 12.4.1 and cuDNN 9.1 and NCCL ${NCCL_VERSION} and cuSparseLt-0.5.2"
rm -rf /usr/local/cuda-12.4 /usr/local/cuda
# install CUDA 12.4.1 in the same container
wget -q https://developer.download.nvidia.com/compute/cuda/12.4.1/local_installers/cuda_12.4.1_550.54.15_linux_sbsa.run
@ -44,7 +44,7 @@ function install_124 {
cd ..
rm -rf nccl
install_cusparselt_062
install_cusparselt_052
ldconfig
}

View File

@ -5,7 +5,7 @@ set -ex
# cuSPARSELt license: https://docs.nvidia.com/cuda/cusparselt/license.html
mkdir tmp_cusparselt && cd tmp_cusparselt
if [[ ${CUDA_VERSION:0:4} =~ ^12\.[2-6]$ ]]; then
if [[ ${CUDA_VERSION:0:4} =~ ^12\.[2-4]$ ]]; then
arch_path='sbsa'
export TARGETARCH=${TARGETARCH:-$(uname -m)}
if [ ${TARGETARCH} = 'amd64' ] || [ "${TARGETARCH}" = 'x86_64' ]; then

View File

@ -10,21 +10,6 @@ if [[ -z $ROCM_VERSION ]]; then
exit 1;
fi
IS_UBUNTU=0
ID=$(grep -oP '(?<=^ID=).+' /etc/os-release | tr -d '"')
case "$ID" in
ubuntu)
IS_UBUNTU=1
;;
centos)
IS_UBUNTU=0
;;
*)
echo "Unable to determine OS..."
exit 1
;;
esac
# To make version comparison easier, create an integer representation.
save_IFS="$IFS"
IFS=. ROCM_VERSION_ARRAY=(${ROCM_VERSION})
@ -72,11 +57,9 @@ MIOPEN_CMAKE_COMMON_FLAGS="
-DMIOPEN_BUILD_DRIVER=OFF
"
# Pull MIOpen repo and set DMIOPEN_EMBED_DB based on ROCm version
if [[ $ROCM_INT -ge 60300 ]]; then
echo "ROCm 6.3+ MIOpen does not need any patches, do not build from source"
if [[ $ROCM_INT -ge 60200 ]] && [[ $ROCM_INT -lt 60300 ]]; then
echo "ROCm 6.2 MIOpen does not need any patches, do not build from source"
exit 0
elif [[ $ROCM_INT -ge 60200 ]] && [[ $ROCM_INT -lt 60300 ]]; then
MIOPEN_BRANCH="release/rocm-rel-6.2-staging"
elif [[ $ROCM_INT -ge 60100 ]] && [[ $ROCM_INT -lt 60200 ]]; then
echo "ROCm 6.1 MIOpen does not need any patches, do not build from source"
exit 0
@ -110,21 +93,12 @@ else
exit 1
fi
if [[ ${IS_UBUNTU} == 1 ]]; then
apt-get remove -y miopen-hip
else
yum remove -y miopen-hip
fi
yum remove -y miopen-hip
git clone https://github.com/ROCm/MIOpen -b ${MIOPEN_BRANCH}
pushd MIOpen
# remove .git to save disk space since CI runner was running out
rm -rf .git
# Don't build CK to save docker build time
if [[ $ROCM_INT -ge 60200 ]]; then
sed -i '/composable_kernel/d' requirements.txt
fi
# Don't build MLIR to save docker build time
# since we are disabling MLIR backend for MIOpen anyway
if [[ $ROCM_INT -ge 50400 ]] && [[ $ROCM_INT -lt 50500 ]]; then
@ -137,15 +111,10 @@ cmake -P install_deps.cmake --minimum
# clean up since CI runner was running out of disk space
rm -rf /tmp/*
if [[ ${IS_UBUNTU} == 1 ]]; then
apt-get autoclean && apt-get clean
rm -rf /var/lib/apt/lists/* /tmp/* /var/tmp/*
else
yum clean all
rm -rf /var/cache/yum
rm -rf /var/lib/yum/yumdb
rm -rf /var/lib/yum/history
fi
yum clean all
rm -rf /var/cache/yum
rm -rf /var/lib/yum/yumdb
rm -rf /var/lib/yum/history
## Build MIOpen
mkdir -p build
@ -162,11 +131,7 @@ make -j $(nproc) package
# clean up since CI runner was running out of disk space
rm -rf /usr/local/cget
if [[ ${IS_UBUNTU} == 1 ]]; then
sudo dpkg -i miopen-hip*.deb
else
yum install -y miopen-*.rpm
fi
yum install -y miopen-*.rpm
popd
rm -rf MIOpen

View File

@ -12,14 +12,14 @@ conda_reinstall() {
as_jenkins conda install -q -n py_$ANACONDA_PYTHON_VERSION -y --force-reinstall $*
}
if [ -n "${XPU_VERSION}" ]; then
if [ -n "${ROCM_VERSION}" ]; then
TRITON_REPO="https://github.com/openai/triton"
TRITON_TEXT_FILE="triton-rocm"
elif [ -n "${XPU_VERSION}" ]; then
TRITON_REPO="https://github.com/intel/intel-xpu-backend-for-triton"
TRITON_TEXT_FILE="triton-xpu"
elif [ -n "${TRITON_CPU}" ]; then
TRITON_REPO="https://github.com/triton-lang/triton-cpu"
TRITON_TEXT_FILE="triton-cpu"
else
TRITON_REPO="https://github.com/triton-lang/triton"
TRITON_REPO="https://github.com/openai/triton"
TRITON_TEXT_FILE="triton"
fi
@ -47,10 +47,9 @@ chown -R jenkins /var/lib/jenkins/triton
chgrp -R jenkins /var/lib/jenkins/triton
pushd /var/lib/jenkins/
as_jenkins git clone --recursive ${TRITON_REPO} triton
as_jenkins git clone ${TRITON_REPO} triton
cd triton
as_jenkins git checkout ${TRITON_PINNED_COMMIT}
as_jenkins git submodule update --init --recursive
cd python
# TODO: remove patch setup.py once we have a proper fix for https://github.com/triton-lang/triton/issues/4527

View File

@ -37,12 +37,6 @@ esac
(
set -x
# TODO: Remove LimitNOFILE=1048576 patch once https://github.com/pytorch/test-infra/issues/5712
# is resolved. This patch is required in order to fix timing out of Docker build on Amazon Linux 2023.
sudo sed -i s/LimitNOFILE=infinity/LimitNOFILE=1048576/ /usr/lib/systemd/system/docker.service
sudo systemctl daemon-reload
sudo systemctl restart docker
docker build \
--target final \
--progress plain \

View File

@ -10,7 +10,6 @@ ENV LANG en_US.UTF-8
ENV LANGUAGE en_US.UTF-8
ARG DEVTOOLSET_VERSION=9
# Note: This is required patch since CentOS have reached EOL
# otherwise any yum install setp will fail
RUN sed -i s/mirror.centos.org/vault.centos.org/g /etc/yum.repos.d/*.repo

View File

@ -124,14 +124,7 @@ if [[ -n ${MANY_LINUX_VERSION} && -z ${DOCKERFILE_SUFFIX} ]]; then
fi
(
set -x
# TODO: Remove LimitNOFILE=1048576 patch once https://github.com/pytorch/test-infra/issues/5712
# is resolved. This patch is required in order to fix timing out of Docker build on Amazon Linux 2023.
sudo sed -i s/LimitNOFILE=infinity/LimitNOFILE=1048576/ /usr/lib/systemd/system/docker.service
sudo systemctl daemon-reload
sudo systemctl restart docker
DOCKER_BUILDKIT=1 docker build \
DOCKER_BUILDKIT=1 docker build \
${DOCKER_GPU_BUILD_ARG} \
--build-arg "GPU_IMAGE=${GPU_IMAGE}" \
--target "${TARGET}" \

View File

@ -30,14 +30,9 @@ dill==0.3.7
#Pinned versions: 0.3.7
#test that import: dynamo/test_replay_record.py test_dataloader.py test_datapipe.py test_serialization.py
expecttest==0.2.1
expecttest==0.1.6
#Description: method for writing tests where test framework auto populates
# the expected output based on previous runs
#Pinned versions: 0.2.1
#test that import:
fbscribelogger==0.1.6
#Description: write to scribe from authenticated jobs on CI
#Pinned versions: 0.1.6
#test that import:
@ -90,7 +85,7 @@ librosa>=0.6.2 ; python_version < "3.11"
#Pinned versions:
#test that import:
mypy==1.11.2
mypy==1.10.0
# Pin MyPy version because new errors are likely to appear with each release
#Description: linter
#Pinned versions: 1.10.0
@ -337,8 +332,3 @@ onnxscript==0.1.0.dev20240817
#Description: Required by mypy and test_public_bindings.py when checking torch.onnx._internal
#Pinned versions:
#test that import:
parameterized==0.8.1
#Description: Parameterizes unittests, both the tests themselves and the entire testing class
#Pinned versions:
#test that import:

View File

@ -1 +1 @@
3.1.0
3.0.0

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@ -68,8 +68,6 @@ RUN rm install_rocm.sh
COPY ./common/install_rocm_magma.sh install_rocm_magma.sh
RUN bash ./install_rocm_magma.sh
RUN rm install_rocm_magma.sh
ADD ./common/install_miopen.sh install_miopen.sh
RUN bash ./install_miopen.sh ${ROCM_VERSION} && rm install_miopen.sh
ENV ROCM_PATH /opt/rocm
ENV PATH /opt/rocm/bin:$PATH
ENV PATH /opt/rocm/hcc/bin:$PATH
@ -102,10 +100,10 @@ ARG TRITON
# try to reach out to S3, which docker build runners don't have access
COPY ./common/install_triton.sh install_triton.sh
COPY ./common/common_utils.sh common_utils.sh
COPY ci_commit_pins/triton.txt triton.txt
COPY ci_commit_pins/triton-rocm.txt triton-rocm.txt
COPY triton_version.txt triton_version.txt
RUN if [ -n "${TRITON}" ]; then bash ./install_triton.sh; fi
RUN rm install_triton.sh common_utils.sh triton.txt triton_version.txt
RUN rm install_triton.sh common_utils.sh triton-rocm.txt triton_version.txt
# Install AOTriton
COPY ./aotriton_version.txt aotriton_version.txt
@ -123,8 +121,5 @@ RUN bash ./install_cache.sh && rm install_cache.sh
ARG BUILD_ENVIRONMENT
ENV BUILD_ENVIRONMENT ${BUILD_ENVIRONMENT}
# Install LLVM dev version (Defined in the pytorch/builder github repository)
COPY --from=pytorch/llvm:9.0.1 /opt/llvm /opt/llvm
USER jenkins
CMD ["bash"]

View File

@ -147,13 +147,6 @@ COPY ci_commit_pins/triton.txt triton.txt
RUN if [ -n "${TRITON}" ]; then bash ./install_triton.sh; fi
RUN rm install_triton.sh common_utils.sh triton.txt
ARG TRITON_CPU
COPY ./common/install_triton.sh install_triton.sh
COPY ./common/common_utils.sh common_utils.sh
COPY ci_commit_pins/triton-cpu.txt triton-cpu.txt
RUN if [ -n "${TRITON_CPU}" ]; then bash ./install_triton.sh; fi
RUN rm install_triton.sh common_utils.sh triton-cpu.txt
ARG EXECUTORCH
# Build and install executorch
COPY ./common/install_executorch.sh install_executorch.sh

View File

@ -49,8 +49,13 @@ if [[ ${BUILD_ENVIRONMENT} == *"parallelnative"* ]]; then
fi
# Enable LLVM dependency for TensorExpr testing
export USE_LLVM=/opt/llvm
export LLVM_DIR=/opt/llvm/lib/cmake/llvm
if [[ "$BUILD_ENVIRONMENT" == *rocm* ]]; then
export USE_LLVM=/opt/rocm/llvm
export LLVM_DIR=/opt/rocm/llvm/lib/cmake/llvm
else
export USE_LLVM=/opt/llvm
export LLVM_DIR=/opt/llvm/lib/cmake/llvm
fi
if [[ "$BUILD_ENVIRONMENT" == *executorch* ]]; then
# To build test_edge_op_registration
@ -232,7 +237,7 @@ fi
# Do not change workspace permissions for ROCm CI jobs
# as it can leave workspace with bad permissions for cancelled jobs
if [[ "$BUILD_ENVIRONMENT" != *rocm* && "$BUILD_ENVIRONMENT" != *s390x* ]]; then
if [[ "$BUILD_ENVIRONMENT" != *rocm* ]]; then
# Workaround for dind-rootless userid mapping (https://github.com/pytorch/ci-infra/issues/96)
WORKSPACE_ORIGINAL_OWNER_ID=$(stat -c '%u' "/var/lib/jenkins/workspace")
cleanup_workspace() {
@ -278,7 +283,6 @@ else
# set only when building other architectures
# or building non-XLA tests.
if [[ "$BUILD_ENVIRONMENT" != *rocm* &&
"$BUILD_ENVIRONMENT" != *s390x* &&
"$BUILD_ENVIRONMENT" != *xla* ]]; then
if [[ "$BUILD_ENVIRONMENT" != *py3.8* ]]; then
# Install numpy-2.0.2 for builds which are backward compatible with 1.X
@ -341,11 +345,11 @@ else
CUSTOM_OP_BUILD="${CUSTOM_TEST_ARTIFACT_BUILD_DIR}/custom-op-build"
CUSTOM_OP_TEST="$PWD/test/custom_operator"
python --version
SITE_PACKAGES="$(python -c 'import site; print(";".join([x for x in site.getsitepackages()] + [x + "/torch" for x in site.getsitepackages()]))')"
SITE_PACKAGES="$(python -c 'from distutils.sysconfig import get_python_lib; print(get_python_lib())')"
mkdir -p "$CUSTOM_OP_BUILD"
pushd "$CUSTOM_OP_BUILD"
cmake "$CUSTOM_OP_TEST" -DCMAKE_PREFIX_PATH="$SITE_PACKAGES" -DPython_EXECUTABLE="$(which python)" \
cmake "$CUSTOM_OP_TEST" -DCMAKE_PREFIX_PATH="$SITE_PACKAGES/torch;$SITE_PACKAGES" -DPython_EXECUTABLE="$(which python)" \
-DCMAKE_MODULE_PATH="$CUSTOM_TEST_MODULE_PATH" -DUSE_ROCM="$CUSTOM_TEST_USE_ROCM"
make VERBOSE=1
popd
@ -355,10 +359,10 @@ else
JIT_HOOK_BUILD="${CUSTOM_TEST_ARTIFACT_BUILD_DIR}/jit-hook-build"
JIT_HOOK_TEST="$PWD/test/jit_hooks"
python --version
SITE_PACKAGES="$(python -c 'import site; print(";".join([x for x in site.getsitepackages()] + [x + "/torch" for x in site.getsitepackages()]))')"
SITE_PACKAGES="$(python -c 'from distutils.sysconfig import get_python_lib; print(get_python_lib())')"
mkdir -p "$JIT_HOOK_BUILD"
pushd "$JIT_HOOK_BUILD"
cmake "$JIT_HOOK_TEST" -DCMAKE_PREFIX_PATH="$SITE_PACKAGES" -DPython_EXECUTABLE="$(which python)" \
cmake "$JIT_HOOK_TEST" -DCMAKE_PREFIX_PATH="$SITE_PACKAGES/torch;$SITE_PACKAGES" -DPython_EXECUTABLE="$(which python)" \
-DCMAKE_MODULE_PATH="$CUSTOM_TEST_MODULE_PATH" -DUSE_ROCM="$CUSTOM_TEST_USE_ROCM"
make VERBOSE=1
popd
@ -370,7 +374,7 @@ else
python --version
mkdir -p "$CUSTOM_BACKEND_BUILD"
pushd "$CUSTOM_BACKEND_BUILD"
cmake "$CUSTOM_BACKEND_TEST" -DCMAKE_PREFIX_PATH="$SITE_PACKAGES" -DPython_EXECUTABLE="$(which python)" \
cmake "$CUSTOM_BACKEND_TEST" -DCMAKE_PREFIX_PATH="$SITE_PACKAGES/torch;$SITE_PACKAGES" -DPython_EXECUTABLE="$(which python)" \
-DCMAKE_MODULE_PATH="$CUSTOM_TEST_MODULE_PATH" -DUSE_ROCM="$CUSTOM_TEST_USE_ROCM"
make VERBOSE=1
popd
@ -403,6 +407,6 @@ fi
# snadampal: skipping it till sccache support added for aarch64
# https://github.com/pytorch/pytorch/issues/121559
if [[ "$BUILD_ENVIRONMENT" != *aarch64* && "$BUILD_ENVIRONMENT" != *s390x* ]]; then
if [[ "$BUILD_ENVIRONMENT" != *aarch64* ]]; then
print_sccache_stats
fi

View File

@ -191,22 +191,9 @@ function install_torchrec_and_fbgemm() {
pip_uninstall torchrec-nightly
pip_uninstall fbgemm-gpu-nightly
pip_install setuptools-git-versioning scikit-build pyre-extensions
# TODO (huydhn): I still have no clue on why sccache doesn't work with only fbgemm_gpu here, but it
# seems to be an sccache-related issue
if [[ "$IS_A100_RUNNER" == "1" ]]; then
unset CMAKE_CUDA_COMPILER_LAUNCHER
sudo mv /opt/cache/bin /opt/cache/bin-backup
fi
# See https://github.com/pytorch/pytorch/issues/106971
CUDA_PATH=/usr/local/cuda-12.1 pip_install --no-use-pep517 --user "git+https://github.com/pytorch/FBGEMM.git@${fbgemm_commit}#egg=fbgemm-gpu&subdirectory=fbgemm_gpu"
pip_install --no-use-pep517 --user "git+https://github.com/pytorch/torchrec.git@${torchrec_commit}"
if [[ "$IS_A100_RUNNER" == "1" ]]; then
export CMAKE_CUDA_COMPILER_LAUNCHER=/opt/cache/bin/sccache
sudo mv /opt/cache/bin-backup /opt/cache/bin
fi
}
function clone_pytorch_xla() {

View File

@ -1,4 +1,4 @@
from datetime import datetime, timedelta, timezone
from datetime import datetime, timedelta
from tempfile import mkdtemp
from cryptography import x509
@ -42,10 +42,10 @@ def create_cert(path, C, ST, L, O, key):
.issuer_name(issuer)
.public_key(key.public_key())
.serial_number(x509.random_serial_number())
.not_valid_before(datetime.now(timezone.utc))
.not_valid_before(datetime.utcnow())
.not_valid_after(
# Our certificate will be valid for 10 days
datetime.now(timezone.utc)
datetime.utcnow()
+ timedelta(days=10)
)
.add_extension(
@ -88,10 +88,10 @@ def sign_certificate_request(path, csr_cert, ca_cert, private_ca_key):
.issuer_name(ca_cert.subject)
.public_key(csr_cert.public_key())
.serial_number(x509.random_serial_number())
.not_valid_before(datetime.now(timezone.utc))
.not_valid_before(datetime.utcnow())
.not_valid_after(
# Our certificate will be valid for 10 days
datetime.now(timezone.utc)
datetime.utcnow()
+ timedelta(days=10)
# Sign our certificate with our private key
)

View File

@ -9,13 +9,15 @@ if [[ -n "$CONDA_ENV" ]]; then
export PATH="$CONDA_ENV/bin":$PATH
fi
# Test that OpenMP is enabled
pushd test
if [[ ! $(python -c "import torch; print(int(torch.backends.openmp.is_available()))") == "1" ]]; then
echo "Build should have OpenMP enabled, but torch.backends.openmp.is_available() is False"
exit 1
# Test that OpenMP is enabled for non-arm64 build
if [[ ${BUILD_ENVIRONMENT} != *arm64* ]]; then
pushd test
if [[ ! $(python -c "import torch; print(int(torch.backends.openmp.is_available()))") == "1" ]]; then
echo "Build should have OpenMP enabled, but torch.backends.openmp.is_available() is False"
exit 1
fi
popd
fi
popd
setup_test_python() {
# The CircleCI worker hostname doesn't resolve to an address.
@ -25,9 +27,8 @@ setup_test_python() {
echo "Ninja version: $(ninja --version)"
echo "Python version: $(which python) ($(python --version))"
# Set the limit on open file handles to 16384
# might help with intermittent compiler test failures
ulimit -n 16384
# Increase default limit on open file handles from 256 to 1024
ulimit -n 1024
}
test_python_all() {

View File

@ -375,8 +375,9 @@ test_inductor_cpp_wrapper_abi_compatible() {
mkdir -p "$TEST_REPORTS_DIR"
echo "Testing Inductor cpp wrapper mode with TORCHINDUCTOR_ABI_COMPATIBLE=1"
# cpu stack allocation causes segfault and needs more investigation
PYTORCH_TESTING_DEVICE_ONLY_FOR="" python test/run_test.py --include inductor/test_cpu_cpp_wrapper
python test/run_test.py --include inductor/test_cuda_cpp_wrapper inductor/test_cpu_repro inductor/test_extension_backend
python test/run_test.py --include inductor/test_cuda_cpp_wrapper
TORCHINDUCTOR_CPP_WRAPPER=1 python benchmarks/dynamo/timm_models.py --device cuda --accuracy --amp \
--training --inductor --disable-cudagraphs --only vit_base_patch16_224 \
@ -400,10 +401,10 @@ pr_time_benchmarks() {
TEST_REPORTS_DIR=$(pwd)/test/test-reports
mkdir -p "$TEST_REPORTS_DIR"
PYTHONPATH=$(pwd)/benchmarks/dynamo/pr_time_benchmarks source benchmarks/dynamo/pr_time_benchmarks/benchmark_runner.sh "$TEST_REPORTS_DIR/pr_time_benchmarks_results.csv" "benchmarks/dynamo/pr_time_benchmarks/benchmarks"
PYTHONPATH=$(pwd)/benchmarks/dynamo/pr_time_benchmarks source benchmarks/dynamo/pr_time_benchmarks/benchmark_runner.sh "$TEST_REPORTS_DIR/pr_time_benchmarks_after.txt" "benchmarks/dynamo/pr_time_benchmarks/benchmarks"
echo "benchmark results on current PR: "
cat "$TEST_REPORTS_DIR/pr_time_benchmarks_results.csv"
PYTHONPATH=$(pwd)/benchmarks/dynamo/pr_time_benchmarks python benchmarks/dynamo/pr_time_benchmarks/check_results.py "benchmarks/dynamo/pr_time_benchmarks/expected_results.csv" "$TEST_REPORTS_DIR/pr_time_benchmarks_results.csv"
cat "$TEST_REPORTS_DIR/pr_time_benchmarks_after.txt"
}
if [[ "${TEST_CONFIG}" == *pr_time_benchmarks* ]]; then
@ -595,9 +596,6 @@ test_single_dynamo_benchmark() {
test_inductor_micro_benchmark() {
TEST_REPORTS_DIR=$(pwd)/test/test-reports
if [[ "${TEST_CONFIG}" == *cpu* ]]; then
test_inductor_set_cpu_affinity
fi
python benchmarks/gpt_fast/benchmark.py --output "${TEST_REPORTS_DIR}/gpt_fast_benchmark.csv"
}
@ -606,11 +604,6 @@ test_inductor_halide() {
assert_git_not_dirty
}
test_inductor_triton_cpu() {
python test/run_test.py --include inductor/test_triton_cpu_backend.py --verbose
assert_git_not_dirty
}
test_dynamo_benchmark() {
# Usage: test_dynamo_benchmark huggingface 0
TEST_REPORTS_DIR=$(pwd)/test/test-reports
@ -665,6 +658,15 @@ test_inductor_torchbench_smoketest_perf() {
# The threshold value needs to be actively maintained to make this check useful
python benchmarks/dynamo/check_perf_csv.py -f "$TEST_REPORTS_DIR/inductor_training_smoketest.csv" -t 1.4
TORCHINDUCTOR_ABI_COMPATIBLE=1 python benchmarks/dynamo/torchbench.py --device cuda --performance --bfloat16 --inference \
--export-aot-inductor --only nanogpt --output "$TEST_REPORTS_DIR/inductor_inference_smoketest.csv"
# The threshold value needs to be actively maintained to make this check useful
# The perf number of nanogpt seems not very stable, e.g.
# https://github.com/pytorch/pytorch/actions/runs/7158691360/job/19491437314,
# and thus we lower its threshold to reduce flakiness. If this continues to be a problem,
# we switch to use some other model.
python benchmarks/dynamo/check_perf_csv.py -f "$TEST_REPORTS_DIR/inductor_inference_smoketest.csv" -t 4.9
# Check memory compression ratio for a few models
for test in hf_Albert timm_vision_transformer; do
python benchmarks/dynamo/torchbench.py --device cuda --performance --backend inductor --amp --training \
@ -708,10 +710,6 @@ test_inductor_set_cpu_affinity(){
export KMP_BLOCKTIME=1
fi
cores=$(test_inductor_get_core_number)
# Set number of cores to 16 on Aarch64 for performance runs.
if [[ "${TEST_CONFIG}" == *aarch64* && $cores -gt 16 ]]; then
cores=16
fi
export OMP_NUM_THREADS=$cores
end_core=$((cores-1))
export TASKSET="taskset -c 0-$end_core"
@ -1382,16 +1380,14 @@ test_executorch() {
assert_git_not_dirty
}
test_linux_aarch64() {
test_linux_aarch64(){
python test/run_test.py --include test_modules test_mkldnn test_mkldnn_fusion test_openmp test_torch test_dynamic_shapes \
test_transformers test_multiprocessing test_numpy_interop \
--shard "$SHARD_NUMBER" "$NUM_TEST_SHARDS" --verbose
test_transformers test_multiprocessing test_numpy_interop --verbose
# Dynamo tests
python test/run_test.py --include dynamo/test_compile dynamo/test_backends dynamo/test_comptime dynamo/test_config \
dynamo/test_functions dynamo/test_fx_passes_pre_grad dynamo/test_interop dynamo/test_model_output dynamo/test_modules \
dynamo/test_optimizers dynamo/test_recompile_ux dynamo/test_recompiles \
--shard "$SHARD_NUMBER" "$NUM_TEST_SHARDS" --verbose
dynamo/test_optimizers dynamo/test_recompile_ux dynamo/test_recompiles --verbose
# Inductor tests
python test/run_test.py --include inductor/test_torchinductor inductor/test_benchmark_fusion inductor/test_codecache \
@ -1401,8 +1397,7 @@ test_linux_aarch64() {
inductor/test_max_autotune inductor/test_memory_planning inductor/test_metrics inductor/test_multi_kernel inductor/test_pad_mm \
inductor/test_pattern_matcher inductor/test_perf inductor/test_profiler inductor/test_select_algorithm inductor/test_smoke \
inductor/test_split_cat_fx_passes inductor/test_standalone_compile inductor/test_torchinductor \
inductor/test_torchinductor_codegen_dynamic_shapes inductor/test_torchinductor_dynamic_shapes inductor/test_memory \
--shard "$SHARD_NUMBER" "$NUM_TEST_SHARDS" --verbose
inductor/test_torchinductor_codegen_dynamic_shapes inductor/test_torchinductor_dynamic_shapes --verbose
}
if ! [[ "${BUILD_ENVIRONMENT}" == *libtorch* || "${BUILD_ENVIRONMENT}" == *-bazel-* ]]; then
@ -1435,8 +1430,6 @@ elif [[ "${TEST_CONFIG}" == *inductor_distributed* ]]; then
test_inductor_distributed
elif [[ "${TEST_CONFIG}" == *inductor-halide* ]]; then
test_inductor_halide
elif [[ "${TEST_CONFIG}" == *inductor-triton-cpu* ]]; then
test_inductor_triton_cpu
elif [[ "${TEST_CONFIG}" == *inductor-micro-benchmark* ]]; then
test_inductor_micro_benchmark
elif [[ "${TEST_CONFIG}" == *huggingface* ]]; then
@ -1460,7 +1453,7 @@ elif [[ "${TEST_CONFIG}" == *torchbench* ]]; then
# https://github.com/opencv/opencv-python/issues/885
pip_install opencv-python==4.8.0.74
if [[ "${TEST_CONFIG}" == *inductor_torchbench_smoketest_perf* ]]; then
checkout_install_torchbench hf_Bert hf_Albert timm_vision_transformer
checkout_install_torchbench hf_Bert hf_Albert nanogpt timm_vision_transformer
PYTHONPATH=$(pwd)/torchbench test_inductor_torchbench_smoketest_perf
elif [[ "${TEST_CONFIG}" == *inductor_torchbench_cpu_smoketest_perf* ]]; then
checkout_install_torchbench timm_vision_transformer phlippe_densenet basic_gnn_edgecnn \

View File

@ -26,7 +26,7 @@ fi
export SCRIPT_HELPERS_DIR=$SCRIPT_PARENT_DIR/win-test-helpers
set +ex
grep -E -R 'PyLong_(From|As)(Unsigned|)Long\(' --exclude=python_numbers.h --exclude=pythoncapi_compat.h --exclude=eval_frame.c torch/
grep -E -R 'PyLong_(From|As)(Unsigned|)Long\(' --exclude=python_numbers.h --exclude=eval_frame.c torch/
PYLONG_API_CHECK=$?
if [[ $PYLONG_API_CHECK == 0 ]]; then
echo "Usage of PyLong_{From,As}{Unsigned}Long API may lead to overflow errors on Windows"

View File

@ -24,12 +24,6 @@ call %INSTALLER_DIR%\install_sccache.bat
if errorlevel 1 goto fail
if not errorlevel 0 goto fail
if "%USE_XPU%"=="1" (
:: Install xpu support packages
call %INSTALLER_DIR%\install_xpu.bat
if errorlevel 1 exit /b 1
)
:: Miniconda has been installed as part of the Windows AMI with all the dependencies.
:: We just need to activate it here
call %INSTALLER_DIR%\activate_miniconda3.bat
@ -49,16 +43,6 @@ if "%VC_VERSION%" == "" (
)
if errorlevel 1 goto fail
if not errorlevel 0 goto fail
if "%USE_XPU%"=="1" (
:: Activate xpu environment - VS env is required for xpu
call "C:\Program Files (x86)\Intel\oneAPI\setvars.bat"
if errorlevel 1 exit /b 1
:: Reduce build time. Only have MTL self-hosted runner now
SET TORCH_XPU_ARCH_LIST=xe-lpg
SET USE_KINETO=0
)
@echo on
popd

View File

@ -1,91 +0,0 @@
@echo on
REM Description: Install Intel Support Packages on Windows
REM BKM reference: https://www.intel.com/content/www/us/en/developer/articles/tool/pytorch-prerequisites-for-intel-gpu/2-5.html
set XPU_INSTALL_MODE=%~1
if "%XPU_INSTALL_MODE%"=="" goto xpu_bundle_install_start
if "%XPU_INSTALL_MODE%"=="bundle" goto xpu_bundle_install_start
if "%XPU_INSTALL_MODE%"=="driver" goto xpu_driver_install_start
if "%XPU_INSTALL_MODE%"=="all" goto xpu_driver_install_start
:arg_error
echo Illegal XPU installation mode. The value can be "bundle"/"driver"/"all"
echo If keep the value as space, will use default "bundle" mode
exit /b 1
:xpu_driver_install_start
:: TODO Need more testing for driver installation
set XPU_DRIVER_LINK=https://downloadmirror.intel.com/830975/gfx_win_101.5972.exe
curl -o xpu_driver.exe --retry 3 --retry-all-errors -k %XPU_DRIVER_LINK%
echo "XPU Driver installing..."
start /wait "Intel XPU Driver Installer" "xpu_driver.exe"
if errorlevel 1 exit /b 1
del xpu_driver.exe
if "%XPU_INSTALL_MODE%"=="driver" goto xpu_install_end
:xpu_bundle_install_start
set XPU_BUNDLE_PARENT_DIR=C:\Program Files (x86)\Intel\oneAPI
set XPU_BUNDLE_URL=https://registrationcenter-download.intel.com/akdlm/IRC_NAS/9d1a91e2-e8b8-40a5-8c7f-5db768a6a60c/w_intel-for-pytorch-gpu-dev_p_0.5.3.37_offline.exe
set XPU_PTI_URL=https://registrationcenter-download.intel.com/akdlm/IRC_NAS/9d1a91e2-e8b8-40a5-8c7f-5db768a6a60c/w_intel-pti-dev_p_0.9.0.37_offline.exe
set XPU_BUNDLE_VERSION=0.5.3+31
set XPU_PTI_VERSION=0.9.0+36
set XPU_BUNDLE_PRODUCT_NAME=intel.oneapi.win.intel-for-pytorch-gpu-dev.product
set XPU_PTI_PRODUCT_NAME=intel.oneapi.win.intel-pti-dev.product
set XPU_BUNDLE_INSTALLED=0
set XPU_PTI_INSTALLED=0
set XPU_BUNDLE_UNINSTALL=0
set XPU_PTI_UNINSTALL=0
:: Check if XPU bundle is target version or already installed
if exist "%XPU_BUNDLE_PARENT_DIR%\Installer\installer.exe" goto xpu_bundle_ver_check
goto xpu_bundle_install
:xpu_bundle_ver_check
"%XPU_BUNDLE_PARENT_DIR%\Installer\installer.exe" --list-products > xpu_bundle_installed_ver.log
for /f "tokens=1,2" %%a in (xpu_bundle_installed_ver.log) do (
if "%%a"=="%XPU_BUNDLE_PRODUCT_NAME%" (
echo %%a Installed Version: %%b
set XPU_BUNDLE_INSTALLED=1
if not "%XPU_BUNDLE_VERSION%"=="%%b" (
start /wait "Installer Title" "%XPU_BUNDLE_PARENT_DIR%\Installer\installer.exe" --action=remove --eula=accept --silent --product-id %XPU_BUNDLE_PRODUCT_NAME% --product-ver %%b --log-dir uninstall_bundle
set XPU_BUNDLE_UNINSTALL=1
)
)
if "%%a"=="%XPU_PTI_PRODUCT_NAME%" (
echo %%a Installed Version: %%b
set XPU_PTI_INSTALLED=1
if not "%XPU_PTI_VERSION%"=="%%b" (
start /wait "Installer Title" "%XPU_BUNDLE_PARENT_DIR%\Installer\installer.exe" --action=remove --eula=accept --silent --product-id %XPU_PTI_PRODUCT_NAME% --product-ver %%b --log-dir uninstall_bundle
set XPU_PTI_UNINSTALL=1
)
)
)
if errorlevel 1 exit /b 1
if exist xpu_bundle_installed_ver.log del xpu_bundle_installed_ver.log
if "%XPU_BUNDLE_INSTALLED%"=="0" goto xpu_bundle_install
if "%XPU_BUNDLE_UNINSTALL%"=="1" goto xpu_bundle_install
if "%XPU_PTI_INSTALLED%"=="0" goto xpu_pti_install
if "%XPU_PTI_UNINSTALL%"=="1" goto xpu_pti_install
goto xpu_install_end
:xpu_bundle_install
curl -o xpu_bundle.exe --retry 3 --retry-all-errors -k %XPU_BUNDLE_URL%
echo "XPU Bundle installing..."
start /wait "Intel Pytorch Bundle Installer" "xpu_bundle.exe" --action=install --eula=accept --silent --log-dir install_bundle
if errorlevel 1 exit /b 1
del xpu_bundle.exe
:xpu_pti_install
curl -o xpu_pti.exe --retry 3 --retry-all-errors -k %XPU_PTI_URL%
echo "XPU PTI installing..."
start /wait "Intel PTI Installer" "xpu_pti.exe" --action=install --eula=accept --silent --log-dir install_bundle
if errorlevel 1 exit /b 1
del xpu_pti.exe
:xpu_install_end

View File

@ -43,9 +43,6 @@ python -m pip install z3-solver==4.12.2.0
# Install tlparse for test\dynamo\test_structured_trace.py UTs.
python -m pip install tlparse==0.3.25
# Install parameterized
python -m pip install parameterized==0.8.1
run_tests() {
# Run nvidia-smi if available
for path in '/c/Program Files/NVIDIA Corporation/NVSMI/nvidia-smi.exe' /c/Windows/System32/nvidia-smi.exe; do

View File

@ -27,11 +27,12 @@ if [[ "$PACKAGE_TYPE" == conda ]]; then
source activate testenv >/dev/null
elif [[ "$PACKAGE_TYPE" != libtorch ]]; then
python_path="/opt/python/cp\$python_nodot-cp\${python_nodot}"
if [[ "\$python_nodot" = *t ]]; then
python_digits="\$(echo $DESIRED_PYTHON | tr -cd [:digit:])"
python_path="/opt/python/cp\$python_digits-cp\${python_digits}t"
# Prior to Python 3.8 paths were suffixed with an 'm'
if [[ -d "\${python_path}/bin" ]]; then
export PATH="\${python_path}/bin:\$PATH"
elif [[ -d "\${python_path}m/bin" ]]; then
export PATH="\${python_path}m/bin:\$PATH"
fi
export PATH="\${python_path}/bin:\$PATH"
fi
EXTRA_CONDA_FLAGS=""
@ -118,11 +119,6 @@ fi
# Test the package
/builder/check_binary.sh
if [[ "\$GPU_ARCH_TYPE" != *s390x* && "\$GPU_ARCH_TYPE" != *xpu* && "\$GPU_ARCH_TYPE" != *rocm* && "$PACKAGE_TYPE" != libtorch ]]; then
# Exclude s390, xpu, rocm and libtorch builds from smoke testing
python /builder/test/smoke_test/smoke_test.py --package=torchonly --torch-compile-check disabled
fi
# Clean temp files
cd /builder && git clean -ffdx

View File

@ -90,7 +90,7 @@ fi
if [[ "$PACKAGE_TYPE" =~ .*wheel.* && -n "$PYTORCH_BUILD_VERSION" && "$PYTORCH_BUILD_VERSION" =~ .*rocm.* && $(uname) == "Linux" ]]; then
TRITON_REQUIREMENT="pytorch-triton-rocm==${TRITON_VERSION}; ${TRITON_CONSTRAINT}"
if [[ -n "$PYTORCH_BUILD_VERSION" && "$PYTORCH_BUILD_VERSION" =~ .*dev.* ]]; then
TRITON_SHORTHASH=$(cut -c1-10 $PYTORCH_ROOT/.ci/docker/ci_commit_pins/triton.txt)
TRITON_SHORTHASH=$(cut -c1-10 $PYTORCH_ROOT/.ci/docker/ci_commit_pins/triton-rocm.txt)
TRITON_REQUIREMENT="pytorch-triton-rocm==${TRITON_VERSION}+${TRITON_SHORTHASH}; ${TRITON_CONSTRAINT}"
fi
if [[ -z "${PYTORCH_EXTRA_INSTALL_REQUIREMENTS:-}" ]]; then

View File

@ -44,9 +44,7 @@ ContinuationIndentWidth: 4
Cpp11BracedListStyle: true
DerivePointerAlignment: false
DisableFormat: false
ForEachMacros:
- FOR_EACH_RANGE
- FOR_EACH
ForEachMacros: [ FOR_EACH_RANGE, FOR_EACH, ]
IncludeCategories:
- Regex: '^<.*\.h(pp)?>'
Priority: 1
@ -60,24 +58,6 @@ IndentWrappedFunctionNames: false
KeepEmptyLinesAtTheStartOfBlocks: false
MacroBlockBegin: ''
MacroBlockEnd: ''
Macros:
- >-
PyObject_HEAD_INIT(type)={
/* this is not exactly match with PyObject_HEAD_INIT in Python source code
* but it is enough for clang-format */
{ 0xFFFFFFFF },
(type)
},
- >-
PyVarObject_HEAD_INIT(type, size)={
{
/* manually expand PyObject_HEAD_INIT(type) above
* because clang-format do not support recursive expansion */
{ 0xFFFFFFFF },
(type)
},
(size)
},
MaxEmptyLinesToKeep: 1
NamespaceIndentation: None
PenaltyBreakBeforeFirstCallParameter: 1
@ -99,11 +79,7 @@ SpacesInContainerLiterals: true
SpacesInCStyleCastParentheses: false
SpacesInParentheses: false
SpacesInSquareBrackets: false
Standard: c++17
StatementMacros:
- PyObject_HEAD
- PyObject_VAR_HEAD
- PyException_HEAD
Standard: Cpp11
TabWidth: 8
UseTab: Never
---

38
.github/ISSUE_TEMPLATE.md vendored Normal file
View File

@ -0,0 +1,38 @@
If you have a question or would like help and support, please ask at our
[forums](https://discuss.pytorch.org/).
If you are submitting a feature request, please preface the title with [feature request].
If you are submitting a bug report, please fill in the following details.
## Issue description
Provide a short description.
## Code example
Please try to provide a minimal example to repro the bug.
Error messages and stack traces are also helpful.
## System Info
Please copy and paste the output from our
[environment collection script](https://raw.githubusercontent.com/pytorch/pytorch/main/torch/utils/collect_env.py)
(or fill out the checklist below manually).
You can get the script and run it with:
```
wget https://raw.githubusercontent.com/pytorch/pytorch/main/torch/utils/collect_env.py
# For security purposes, please check the contents of collect_env.py before running it.
python collect_env.py
```
- PyTorch or Caffe2:
- How you installed PyTorch (conda, pip, source):
- Build command you used (if compiling from source):
- OS:
- PyTorch version:
- Python version:
- CUDA/cuDNN version:
- GPU models and configuration:
- GCC version (if compiling from source):
- CMake version:
- Versions of any other relevant libraries:

View File

@ -5,8 +5,7 @@ about: Tracking incidents for PyTorch's CI infra.
> NOTE: Remember to label this issue with "`ci: sev`"
<!-- uncomment the below line if you don't want this SEV to block merges -->
<!-- **MERGE BLOCKING** -->
**MERGE BLOCKING** <!-- remove this line if you don't want this SEV to block merges -->
## Current Status
*Status could be: preemptive, ongoing, mitigated, closed. Also tell people if they need to take action to fix it (i.e. rebase)*.

View File

@ -32,6 +32,30 @@ self-hosted-runner:
- lf.linux.8xlarge.nvidia.gpu
- lf.linux.16xlarge.nvidia.gpu
- lf.linux.g5.4xlarge.nvidia.gpu
# Organization-wide AWS Linux Runners with new Amazon 2023 AMI
- amz2023.linux.large
- amz2023.linux.2xlarge
- amz2023.linux.4xlarge
- amz2023.linux.12xlarge
- amz2023.linux.24xlarge
- amz2023.linux.arm64.2xlarge
- amz2023.linux.arm64.m7g.4xlarge
- amz2023.linux.arm64.m7g.4xlarge.ephemeral
- amz2023.linux.4xlarge.nvidia.gpu
- amz2023.linux.8xlarge.nvidia.gpu
- amz2023.linux.16xlarge.nvidia.gpu
- amz2023.linux.g5.4xlarge.nvidia.gpu
# Pytorch/pytorch AWS Linux Runners with the new Amazon 2023 AMI on Linux Foundation account
- amz2023.lf.linux.large
- amz2023.lf.linux.2xlarge
- amz2023.lf.linux.4xlarge
- amz2023.lf.linux.12xlarge
- amz2023.lf.linux.24xlarge
- amz2023.lf.linux.arm64.2xlarge
- amz2023.lf.linux.4xlarge.nvidia.gpu
- amz2023.lf.linux.8xlarge.nvidia.gpu
- amz2023.lf.linux.16xlarge.nvidia.gpu
- amz2023.lf.linux.g5.4xlarge.nvidia.gpu
# Repo-specific IBM hosted S390x runner
- linux.s390x
# Organization wide AWS Windows runners

View File

@ -18,14 +18,8 @@ inputs:
runs:
using: composite
steps:
- name: Check if in a container runner
shell: bash
id: check_container_runner
run: echo "IN_CONTAINER_RUNNER=$(if [ -f /.inarc ] || [ -f /.incontainer ]; then echo true ; else echo false; fi)" >> "$GITHUB_OUTPUT"
- name: Clean workspace
shell: bash
if: ${{ steps.check_container_runner.outputs.IN_CONTAINER_RUNNER == 'false' }}
env:
NO_SUDO: ${{ inputs.no-sudo }}
run: |

View File

@ -85,25 +85,15 @@ runs:
with:
docker-image: ${{ steps.calculate-docker-image.outputs.docker-image }}
- name: Check if in a container runner
- name: Check if in a ARC runner
shell: bash
id: check_container_runner
run: echo "IN_CONTAINER_RUNNER=$(if [ -f /.inarc ] || [ -f /.incontainer ]; then echo true ; else echo false; fi)" >> "$GITHUB_OUTPUT"
id: check_arc_runner
run: echo "IN_ARC_RUNNER=$([ -f /.inarc ] && echo true || echo false)" >> "$GITHUB_OUTPUT"
- name: Install nvidia driver, nvidia-docker runtime, set GPU_FLAG
id: install-nvidia-driver
uses: pytorch/test-infra/.github/actions/setup-nvidia@main
if: ${{ contains(inputs.build-environment, 'cuda') && !contains(matrix.config, 'nogpu') && steps.check_container_runner.outputs.IN_CONTAINER_RUNNER == 'false' }}
- name: Setup GPU_FLAG for docker run
id: setup-gpu-flag
run: echo "GPU_FLAG=--gpus all -e NVIDIA_DRIVER_CAPABILITIES=all" >> "${GITHUB_ENV}"
if: ${{ contains(inputs.build-environment, 'cuda') && !contains(matrix.config, 'nogpu') && steps.check_container_runner.outputs.IN_CONTAINER_RUNNER == 'true' }}
- name: Setup SCCACHE_SERVER_PORT environment for docker run when on container
id: setup-sscache-port-flag
run: echo "SCCACHE_SERVER_PORT_DOCKER_FLAG=-e SCCACHE_SERVER_PORT=$((RUNNER_UID + 4226))" >> "${GITHUB_ENV}"
if: ${{ steps.check_container_runner.outputs.IN_CONTAINER_RUNNER == 'true' }}
if: ${{ contains(inputs.build-environment, 'cuda') && !contains(matrix.config, 'nogpu') && steps.check_arc_runner.outputs.IN_ARC_RUNNER == 'false' }}
- name: Lock NVIDIA A100 40GB Frequency
shell: bash
@ -111,7 +101,7 @@ runs:
sudo nvidia-smi -pm 1
sudo nvidia-smi -ac 1215,1410
nvidia-smi
if: ${{ contains(matrix.runner, 'a100') && steps.check_container_runner.outputs.IN_CONTAINER_RUNNER == 'false' }}
if: contains(matrix.runner, 'a100')
- name: Start monitoring script
id: monitor-script
@ -182,7 +172,6 @@ runs:
NO_TD: ${{ steps.keep-going.outputs.ci-no-td }}
TD_DISTRIBUTED: ${{ steps.keep-going.outputs.ci-td-distributed }}
SCCACHE_BUCKET: ossci-compiler-cache-circleci-v2
SCCACHE_REGION: us-east-1
SCCACHE_S3_KEY_PREFIX: ${{ github.workflow }}
SHM_SIZE: ${{ contains(inputs.build-environment, 'cuda') && '2g' || '1g' }}
DOCKER_IMAGE: ${{ inputs.docker-image }}
@ -192,9 +181,6 @@ runs:
PYTORCH_TEST_RERUN_DISABLED_TESTS: ${{ matrix.rerun_disabled_tests && '1' || '0' }}
DASHBOARD_TAG: ${{ inputs.dashboard-tag }}
HUGGING_FACE_HUB_TOKEN: ${{ inputs.HUGGING_FACE_HUB_TOKEN }}
SCRIBE_GRAPHQL_ACCESS_TOKEN: ${{ secrets.SCRIBE_GRAPHQL_ACCESS_TOKEN }}
IS_A100_RUNNER: ${{ contains(matrix.runner, 'a100') && '1' || '0' }}
shell: bash
run: |
set -x
@ -213,7 +199,6 @@ runs:
# shellcheck disable=SC2086,SC2090
container_name=$(docker run \
${GPU_FLAG:-} \
${SCCACHE_SERVER_PORT_DOCKER_FLAG:-} \
-e BUILD_ENVIRONMENT \
-e PR_NUMBER \
-e GITHUB_ACTIONS \
@ -242,7 +227,6 @@ runs:
-e PR_LABELS \
-e MAX_JOBS="$(nproc --ignore=2)" \
-e SCCACHE_BUCKET \
-e SCCACHE_REGION \
-e SCCACHE_S3_KEY_PREFIX \
-e XLA_CUDA \
-e XLA_CLANG_CACHE_S3_BUCKET_NAME \
@ -250,9 +234,7 @@ runs:
-e PYTORCH_TEST_RERUN_DISABLED_TESTS \
-e SKIP_SCCACHE_INITIALIZATION=1 \
-e HUGGING_FACE_HUB_TOKEN \
-e SCRIBE_GRAPHQL_ACCESS_TOKEN \
-e DASHBOARD_TAG \
-e IS_A100_RUNNER \
--env-file="/tmp/github_env_${GITHUB_RUN_ID}" \
--security-opt seccomp=unconfined \
--cap-add=SYS_PTRACE \
@ -323,7 +305,7 @@ runs:
- name: Teardown Linux
uses: pytorch/test-infra/.github/actions/teardown-linux@main
if: always() && steps.check_container_runner.outputs.IN_CONTAINER_RUNNER == 'false'
if: always()
# NB: We are currently having an intermittent GPU-related issue on G5 runners with
# A10G GPU. Once this happens, trying to reset the GPU as done in setup-nvidia does

View File

@ -28,14 +28,14 @@ runs:
echo "instance-type: $(get_ec2_metadata instance-type)"
echo "system info $(uname -a)"
- name: Check if in a container runner
- name: Check if in a ARC runner
shell: bash
id: check_container_runner
run: echo "IN_CONTAINER_RUNNER=$(if [ -f /.inarc ] || [ -f /.incontainer ]; then echo true ; else echo false; fi)" >> "$GITHUB_OUTPUT"
id: check_arc_runner
run: echo "IN_ARC_RUNNER=$([ -f /.inarc ] && echo true || echo false)" >> $GITHUB_OUTPUT
- name: Start docker if docker deamon is not running
shell: bash
if: ${{ steps.check_container_runner.outputs.IN_CONTAINER_RUNNER == 'false' }}
if: ${{ steps.check_arc_runner.outputs.IN_ARC_RUNNER == 'false' }}
run: |
if systemctl is-active --quiet docker; then
echo "Docker daemon is running...";
@ -73,7 +73,7 @@ runs:
env | grep '^CI' >> "/tmp/github_env_${GITHUB_RUN_ID}"
- name: Kill any existing containers, clean up images
if: ${{ steps.check_container_runner.outputs.IN_CONTAINER_RUNNER == 'false' }}
if: ${{ steps.check_arc_runner.outputs.IN_ARC_RUNNER == 'false' }}
shell: bash
run: |
# ignore expansion of "docker ps -q" since it could be empty
@ -116,7 +116,7 @@ runs:
- name: Check that the docker daemon is running
shell: bash
continue-on-error: true
if: ${{ steps.check_container_runner.outputs.IN_CONTAINER_RUNNER == 'true' }}
if: ${{ steps.check_arc_runner.outputs.IN_ARC_RUNNER == 'true' }}
run: |
set +x

View File

@ -1 +1 @@
3f0569939c4369bec943fc27d1c9d8dfbc828c26
97ed7b36b7a741253d4e41e4da3c901d83294503

View File

@ -7,14 +7,10 @@
# runners. Runners listed here will be available as self hosted
# runners, configuration is directly pulled from the main branch.
#
# NOTE (Apr, 5, 2021): Linux runners are currently all an amazonlinux2
#
# NOTES:
# - Linux runners are by default non-ephemeral to reduce the amount of CreateInstaces calls
# to avoid RequestLimitExceeded issues
# - When updating this file, run the following command to validate the YAML and to generate
# corresponding versions of scale-config for the pytorch/pytorch repo and merge the
# pytorch/pytorch changes before merging these changes.
# `python .github/scripts/validate_scale_config.py --test-infra-repo-root [path_to_test-infra_root] --pytorch-repo-root [path_to_pytorch_root]``
# NOTE (Jan 5, 2021): Linux runners are all non-ephemeral to reduce the amount of CreateInstaces calls
# to avoid RequestLimitExceeded issues
#
# TODO: Add some documentation on how the auto-scaling works
#
@ -35,36 +31,58 @@ runner_types:
is_ephemeral: false
max_available: 1000
os: linux
ami: al2023-ami-2023.5.202*-kernel-6.1-x86_64
ami: al2023-ami-2023.5.20240701.0-kernel-6.1-x86_64
variants:
amz2023:
ami: al2023-ami-2023.5.20240701.0-kernel-6.1-x86_64
am2:
ami: amzn2-ami-hvm-2.0.20240306.2-x86_64-ebs
lf.c.linux.10xlarge.avx2:
disk_size: 200
instance_type: m4.10xlarge
is_ephemeral: false
max_available: 450
os: linux
ami: al2023-ami-2023.5.202*-kernel-6.1-x86_64
ami: al2023-ami-2023.5.20240701.0-kernel-6.1-x86_64
variants:
amz2023:
ami: al2023-ami-2023.5.20240701.0-kernel-6.1-x86_64
am2:
ami: amzn2-ami-hvm-2.0.20240306.2-x86_64-ebs
lf.c.linux.24xl.spr-metal:
disk_size: 200
instance_type: c7i.metal-24xl
is_ephemeral: false
max_available: 150
os: linux
ami: al2023-ami-2023.5.202*-kernel-6.1-x86_64
ami: al2023-ami-2023.5.20240701.0-kernel-6.1-x86_64
variants:
amz2023:
ami: al2023-ami-2023.5.20240701.0-kernel-6.1-x86_64
am2:
ami: amzn2-ami-hvm-2.0.20240306.2-x86_64-ebs
lf.c.linux.16xlarge.spr:
disk_size: 200
instance_type: c7i.16xlarge
is_ephemeral: false
max_available: 150
os: linux
ami: al2023-ami-2023.5.202*-kernel-6.1-x86_64
ami: al2023-ami-2023.5.20240701.0-kernel-6.1-x86_64
variants:
amz2023:
ami: al2023-ami-2023.5.20240701.0-kernel-6.1-x86_64
am2:
ami: amzn2-ami-hvm-2.0.20240306.2-x86_64-ebs
lf.c.linux.9xlarge.ephemeral:
disk_size: 200
instance_type: c5.9xlarge
is_ephemeral: true
max_available: 50
os: linux
ami: al2023-ami-2023.5.202*-kernel-6.1-x86_64
ami: al2023-ami-2023.5.20240701.0-kernel-6.1-x86_64
variants:
amz2023:
ami: al2023-ami-2023.5.20240701.0-kernel-6.1-x86_64
am2:
ami: amzn2-ami-hvm-2.0.20240306.2-x86_64-ebs
lf.c.linux.12xlarge.ephemeral:
@ -73,140 +91,240 @@ runner_types:
is_ephemeral: true
max_available: 300
os: linux
ami: al2023-ami-2023.5.202*-kernel-6.1-x86_64
ami: al2023-ami-2023.5.20240701.0-kernel-6.1-x86_64
variants:
amz2023:
ami: al2023-ami-2023.5.20240701.0-kernel-6.1-x86_64
am2:
ami: amzn2-ami-hvm-2.0.20240306.2-x86_64-ebs
lf.c.linux.16xlarge.nvidia.gpu:
disk_size: 150
instance_type: g3.16xlarge
is_ephemeral: false
max_available: 150
os: linux
ami: al2023-ami-2023.5.202*-kernel-6.1-x86_64
ami: al2023-ami-2023.5.20240701.0-kernel-6.1-x86_64
variants:
amz2023:
ami: al2023-ami-2023.5.20240701.0-kernel-6.1-x86_64
am2:
ami: amzn2-ami-hvm-2.0.20240306.2-x86_64-ebs
lf.c.linux.24xlarge:
disk_size: 150
instance_type: c5.24xlarge
is_ephemeral: false
max_available: 500
os: linux
ami: al2023-ami-2023.5.202*-kernel-6.1-x86_64
ami: al2023-ami-2023.5.20240701.0-kernel-6.1-x86_64
variants:
amz2023:
ami: al2023-ami-2023.5.20240701.0-kernel-6.1-x86_64
am2:
ami: amzn2-ami-hvm-2.0.20240306.2-x86_64-ebs
lf.c.linux.24xlarge.ephemeral:
disk_size: 150
instance_type: c5.24xlarge
is_ephemeral: true
max_available: 200
os: linux
ami: al2023-ami-2023.5.202*-kernel-6.1-x86_64
ami: al2023-ami-2023.5.20240701.0-kernel-6.1-x86_64
variants:
amz2023:
ami: al2023-ami-2023.5.20240701.0-kernel-6.1-x86_64
am2:
ami: amzn2-ami-hvm-2.0.20240306.2-x86_64-ebs
lf.c.linux.2xlarge:
disk_size: 150
instance_type: c5.2xlarge
is_ephemeral: false
max_available: 3120
os: linux
ami: al2023-ami-2023.5.202*-kernel-6.1-x86_64
ami: al2023-ami-2023.5.20240701.0-kernel-6.1-x86_64
variants:
amz2023:
ami: al2023-ami-2023.5.20240701.0-kernel-6.1-x86_64
am2:
ami: amzn2-ami-hvm-2.0.20240306.2-x86_64-ebs
lf.c.linux.4xlarge:
disk_size: 150
instance_type: c5.4xlarge
is_ephemeral: false
max_available: 1000
os: linux
ami: al2023-ami-2023.5.202*-kernel-6.1-x86_64
ami: al2023-ami-2023.5.20240701.0-kernel-6.1-x86_64
variants:
amz2023:
ami: al2023-ami-2023.5.20240701.0-kernel-6.1-x86_64
am2:
ami: amzn2-ami-hvm-2.0.20240306.2-x86_64-ebs
lf.c.linux.4xlarge.nvidia.gpu:
disk_size: 150
instance_type: g3.4xlarge
is_ephemeral: false
max_available: 1000
os: linux
ami: al2023-ami-2023.5.202*-kernel-6.1-x86_64
ami: al2023-ami-2023.5.20240701.0-kernel-6.1-x86_64
variants:
amz2023:
ami: al2023-ami-2023.5.20240701.0-kernel-6.1-x86_64
am2:
ami: amzn2-ami-hvm-2.0.20240306.2-x86_64-ebs
lf.c.linux.8xlarge.nvidia.gpu:
disk_size: 150
instance_type: g3.8xlarge
is_ephemeral: false
max_available: 400
os: linux
ami: al2023-ami-2023.5.202*-kernel-6.1-x86_64
ami: al2023-ami-2023.5.20240701.0-kernel-6.1-x86_64
variants:
amz2023:
ami: al2023-ami-2023.5.20240701.0-kernel-6.1-x86_64
am2:
ami: amzn2-ami-hvm-2.0.20240306.2-x86_64-ebs
lf.c.linux.g4dn.12xlarge.nvidia.gpu:
disk_size: 150
instance_type: g4dn.12xlarge
is_ephemeral: false
max_available: 250
os: linux
ami: al2023-ami-2023.5.202*-kernel-6.1-x86_64
ami: al2023-ami-2023.5.20240701.0-kernel-6.1-x86_64
variants:
amz2023:
ami: al2023-ami-2023.5.20240701.0-kernel-6.1-x86_64
am2:
ami: amzn2-ami-hvm-2.0.20240306.2-x86_64-ebs
lf.c.linux.g4dn.metal.nvidia.gpu:
disk_size: 150
instance_type: g4dn.metal
is_ephemeral: false
max_available: 300
os: linux
ami: al2023-ami-2023.5.202*-kernel-6.1-x86_64
ami: al2023-ami-2023.5.20240701.0-kernel-6.1-x86_64
variants:
amz2023:
ami: al2023-ami-2023.5.20240701.0-kernel-6.1-x86_64
am2:
ami: amzn2-ami-hvm-2.0.20240306.2-x86_64-ebs
lf.c.linux.g5.48xlarge.nvidia.gpu:
disk_size: 150
instance_type: g5.48xlarge
is_ephemeral: false
max_available: 200
os: linux
ami: al2023-ami-2023.5.202*-kernel-6.1-x86_64
ami: al2023-ami-2023.5.20240701.0-kernel-6.1-x86_64
variants:
amz2023:
ami: al2023-ami-2023.5.20240701.0-kernel-6.1-x86_64
am2:
ami: amzn2-ami-hvm-2.0.20240306.2-x86_64-ebs
lf.c.linux.g5.12xlarge.nvidia.gpu:
disk_size: 150
instance_type: g5.12xlarge
is_ephemeral: false
max_available: 150
os: linux
ami: al2023-ami-2023.5.202*-kernel-6.1-x86_64
ami: al2023-ami-2023.5.20240701.0-kernel-6.1-x86_64
variants:
amz2023:
ami: al2023-ami-2023.5.20240701.0-kernel-6.1-x86_64
am2:
ami: amzn2-ami-hvm-2.0.20240306.2-x86_64-ebs
lf.c.linux.g5.4xlarge.nvidia.gpu:
disk_size: 150
instance_type: g5.4xlarge
is_ephemeral: false
max_available: 2400
os: linux
ami: al2023-ami-2023.5.202*-kernel-6.1-x86_64
ami: al2023-ami-2023.5.20240701.0-kernel-6.1-x86_64
variants:
amz2023:
ami: al2023-ami-2023.5.20240701.0-kernel-6.1-x86_64
am2:
ami: amzn2-ami-hvm-2.0.20240306.2-x86_64-ebs
lf.c.linux.g6.4xlarge.experimental.nvidia.gpu:
disk_size: 150
instance_type: g6.4xlarge
is_ephemeral: false
max_available: 50
os: linux
ami: al2023-ami-2023.5.202*-kernel-6.1-x86_64
ami: al2023-ami-2023.5.20240701.0-kernel-6.1-x86_64
variants:
amz2023:
ami: al2023-ami-2023.5.20240701.0-kernel-6.1-x86_64
am2:
ami: amzn2-ami-hvm-2.0.20240306.2-x86_64-ebs
lf.c.linux.large:
max_available: 1200
disk_size: 15
instance_type: c5.large
is_ephemeral: false
os: linux
ami: al2023-ami-2023.5.202*-kernel-6.1-x86_64
ami: al2023-ami-2023.5.20240701.0-kernel-6.1-x86_64
variants:
amz2023:
ami: al2023-ami-2023.5.20240701.0-kernel-6.1-x86_64
am2:
ami: amzn2-ami-hvm-2.0.20240306.2-x86_64-ebs
lf.c.linux.arm64.2xlarge:
disk_size: 256
instance_type: t4g.2xlarge
is_ephemeral: false
max_available: 200
os: linux
ami: al2023-ami-2023.5.202*-kernel-6.1-arm64
ami: al2023-ami-2023.5.20240701.0-kernel-6.1-arm64
variants:
amz2023:
ami: al2023-ami-2023.5.20240701.0-kernel-6.1-arm64
am2:
ami: amzn2-ami-hvm-2.0.20240306.2-arm64-gp2
lf.c.linux.arm64.m7g.4xlarge:
disk_size: 256
instance_type: m7g.4xlarge
is_ephemeral: false
max_available: 200
os: linux
ami: al2023-ami-2023.5.202*-kernel-6.1-arm64
ami: al2023-ami-2023.5.20240701.0-kernel-6.1-arm64
variants:
amz2023:
ami: al2023-ami-2023.5.20240701.0-kernel-6.1-arm64
am2:
ami: amzn2-ami-hvm-2.0.20240306.2-arm64-gp2
lf.c.linux.arm64.2xlarge.ephemeral:
disk_size: 256
instance_type: t4g.2xlarge
is_ephemeral: true
max_available: 200
os: linux
ami: al2023-ami-2023.5.202*-kernel-6.1-arm64
ami: al2023-ami-2023.5.20240701.0-kernel-6.1-arm64
variants:
amz2023:
ami: al2023-ami-2023.5.20240701.0-kernel-6.1-arm64
am2:
ami: amzn2-ami-hvm-2.0.20240306.2-arm64-gp2
lf.c.linux.arm64.m7g.4xlarge.ephemeral:
disk_size: 256
instance_type: m7g.4xlarge
is_ephemeral: true
max_available: 200
os: linux
ami: al2023-ami-2023.5.202*-kernel-6.1-arm64
ami: al2023-ami-2023.5.20240701.0-kernel-6.1-arm64
variants:
amz2023:
ami: al2023-ami-2023.5.20240701.0-kernel-6.1-arm64
am2:
ami: amzn2-ami-hvm-2.0.20240306.2-arm64-gp2
lf.c.linux.arm64.m7g.metal:
disk_size: 256
instance_type: m7g.metal
is_ephemeral: false
max_available: 100
os: linux
ami: al2023-ami-2023.5.202*-kernel-6.1-arm64
ami: al2023-ami-2023.5.20240701.0-kernel-6.1-arm64
variants:
amz2023:
ami: al2023-ami-2023.5.20240701.0-kernel-6.1-arm64
am2:
ami: amzn2-ami-hvm-2.0.20240306.2-arm64-gp2
lf.c.windows.g4dn.xlarge:
disk_size: 256
instance_type: g4dn.xlarge

View File

@ -7,14 +7,10 @@
# runners. Runners listed here will be available as self hosted
# runners, configuration is directly pulled from the main branch.
#
# NOTE (Apr, 5, 2021): Linux runners are currently all an amazonlinux2
#
# NOTES:
# - Linux runners are by default non-ephemeral to reduce the amount of CreateInstaces calls
# to avoid RequestLimitExceeded issues
# - When updating this file, run the following command to validate the YAML and to generate
# corresponding versions of scale-config for the pytorch/pytorch repo and merge the
# pytorch/pytorch changes before merging these changes.
# `python .github/scripts/validate_scale_config.py --test-infra-repo-root [path_to_test-infra_root] --pytorch-repo-root [path_to_pytorch_root]``
# NOTE (Jan 5, 2021): Linux runners are all non-ephemeral to reduce the amount of CreateInstaces calls
# to avoid RequestLimitExceeded issues
#
# TODO: Add some documentation on how the auto-scaling works
#
@ -35,36 +31,58 @@ runner_types:
is_ephemeral: false
max_available: 1000
os: linux
ami: al2023-ami-2023.5.202*-kernel-6.1-x86_64
ami: al2023-ami-2023.5.20240701.0-kernel-6.1-x86_64
variants:
amz2023:
ami: al2023-ami-2023.5.20240701.0-kernel-6.1-x86_64
am2:
ami: amzn2-ami-hvm-2.0.20240306.2-x86_64-ebs
lf.linux.10xlarge.avx2:
disk_size: 200
instance_type: m4.10xlarge
is_ephemeral: false
max_available: 450
os: linux
ami: al2023-ami-2023.5.202*-kernel-6.1-x86_64
ami: al2023-ami-2023.5.20240701.0-kernel-6.1-x86_64
variants:
amz2023:
ami: al2023-ami-2023.5.20240701.0-kernel-6.1-x86_64
am2:
ami: amzn2-ami-hvm-2.0.20240306.2-x86_64-ebs
lf.linux.24xl.spr-metal:
disk_size: 200
instance_type: c7i.metal-24xl
is_ephemeral: false
max_available: 150
os: linux
ami: al2023-ami-2023.5.202*-kernel-6.1-x86_64
ami: al2023-ami-2023.5.20240701.0-kernel-6.1-x86_64
variants:
amz2023:
ami: al2023-ami-2023.5.20240701.0-kernel-6.1-x86_64
am2:
ami: amzn2-ami-hvm-2.0.20240306.2-x86_64-ebs
lf.linux.16xlarge.spr:
disk_size: 200
instance_type: c7i.16xlarge
is_ephemeral: false
max_available: 150
os: linux
ami: al2023-ami-2023.5.202*-kernel-6.1-x86_64
ami: al2023-ami-2023.5.20240701.0-kernel-6.1-x86_64
variants:
amz2023:
ami: al2023-ami-2023.5.20240701.0-kernel-6.1-x86_64
am2:
ami: amzn2-ami-hvm-2.0.20240306.2-x86_64-ebs
lf.linux.9xlarge.ephemeral:
disk_size: 200
instance_type: c5.9xlarge
is_ephemeral: true
max_available: 50
os: linux
ami: al2023-ami-2023.5.202*-kernel-6.1-x86_64
ami: al2023-ami-2023.5.20240701.0-kernel-6.1-x86_64
variants:
amz2023:
ami: al2023-ami-2023.5.20240701.0-kernel-6.1-x86_64
am2:
ami: amzn2-ami-hvm-2.0.20240306.2-x86_64-ebs
lf.linux.12xlarge.ephemeral:
@ -73,140 +91,240 @@ runner_types:
is_ephemeral: true
max_available: 300
os: linux
ami: al2023-ami-2023.5.202*-kernel-6.1-x86_64
ami: al2023-ami-2023.5.20240701.0-kernel-6.1-x86_64
variants:
amz2023:
ami: al2023-ami-2023.5.20240701.0-kernel-6.1-x86_64
am2:
ami: amzn2-ami-hvm-2.0.20240306.2-x86_64-ebs
lf.linux.16xlarge.nvidia.gpu:
disk_size: 150
instance_type: g3.16xlarge
is_ephemeral: false
max_available: 150
os: linux
ami: al2023-ami-2023.5.202*-kernel-6.1-x86_64
ami: al2023-ami-2023.5.20240701.0-kernel-6.1-x86_64
variants:
amz2023:
ami: al2023-ami-2023.5.20240701.0-kernel-6.1-x86_64
am2:
ami: amzn2-ami-hvm-2.0.20240306.2-x86_64-ebs
lf.linux.24xlarge:
disk_size: 150
instance_type: c5.24xlarge
is_ephemeral: false
max_available: 500
os: linux
ami: al2023-ami-2023.5.202*-kernel-6.1-x86_64
ami: al2023-ami-2023.5.20240701.0-kernel-6.1-x86_64
variants:
amz2023:
ami: al2023-ami-2023.5.20240701.0-kernel-6.1-x86_64
am2:
ami: amzn2-ami-hvm-2.0.20240306.2-x86_64-ebs
lf.linux.24xlarge.ephemeral:
disk_size: 150
instance_type: c5.24xlarge
is_ephemeral: true
max_available: 200
os: linux
ami: al2023-ami-2023.5.202*-kernel-6.1-x86_64
ami: al2023-ami-2023.5.20240701.0-kernel-6.1-x86_64
variants:
amz2023:
ami: al2023-ami-2023.5.20240701.0-kernel-6.1-x86_64
am2:
ami: amzn2-ami-hvm-2.0.20240306.2-x86_64-ebs
lf.linux.2xlarge:
disk_size: 150
instance_type: c5.2xlarge
is_ephemeral: false
max_available: 3120
os: linux
ami: al2023-ami-2023.5.202*-kernel-6.1-x86_64
ami: al2023-ami-2023.5.20240701.0-kernel-6.1-x86_64
variants:
amz2023:
ami: al2023-ami-2023.5.20240701.0-kernel-6.1-x86_64
am2:
ami: amzn2-ami-hvm-2.0.20240306.2-x86_64-ebs
lf.linux.4xlarge:
disk_size: 150
instance_type: c5.4xlarge
is_ephemeral: false
max_available: 1000
os: linux
ami: al2023-ami-2023.5.202*-kernel-6.1-x86_64
ami: al2023-ami-2023.5.20240701.0-kernel-6.1-x86_64
variants:
amz2023:
ami: al2023-ami-2023.5.20240701.0-kernel-6.1-x86_64
am2:
ami: amzn2-ami-hvm-2.0.20240306.2-x86_64-ebs
lf.linux.4xlarge.nvidia.gpu:
disk_size: 150
instance_type: g3.4xlarge
is_ephemeral: false
max_available: 1000
os: linux
ami: al2023-ami-2023.5.202*-kernel-6.1-x86_64
ami: al2023-ami-2023.5.20240701.0-kernel-6.1-x86_64
variants:
amz2023:
ami: al2023-ami-2023.5.20240701.0-kernel-6.1-x86_64
am2:
ami: amzn2-ami-hvm-2.0.20240306.2-x86_64-ebs
lf.linux.8xlarge.nvidia.gpu:
disk_size: 150
instance_type: g3.8xlarge
is_ephemeral: false
max_available: 400
os: linux
ami: al2023-ami-2023.5.202*-kernel-6.1-x86_64
ami: al2023-ami-2023.5.20240701.0-kernel-6.1-x86_64
variants:
amz2023:
ami: al2023-ami-2023.5.20240701.0-kernel-6.1-x86_64
am2:
ami: amzn2-ami-hvm-2.0.20240306.2-x86_64-ebs
lf.linux.g4dn.12xlarge.nvidia.gpu:
disk_size: 150
instance_type: g4dn.12xlarge
is_ephemeral: false
max_available: 250
os: linux
ami: al2023-ami-2023.5.202*-kernel-6.1-x86_64
ami: al2023-ami-2023.5.20240701.0-kernel-6.1-x86_64
variants:
amz2023:
ami: al2023-ami-2023.5.20240701.0-kernel-6.1-x86_64
am2:
ami: amzn2-ami-hvm-2.0.20240306.2-x86_64-ebs
lf.linux.g4dn.metal.nvidia.gpu:
disk_size: 150
instance_type: g4dn.metal
is_ephemeral: false
max_available: 300
os: linux
ami: al2023-ami-2023.5.202*-kernel-6.1-x86_64
ami: al2023-ami-2023.5.20240701.0-kernel-6.1-x86_64
variants:
amz2023:
ami: al2023-ami-2023.5.20240701.0-kernel-6.1-x86_64
am2:
ami: amzn2-ami-hvm-2.0.20240306.2-x86_64-ebs
lf.linux.g5.48xlarge.nvidia.gpu:
disk_size: 150
instance_type: g5.48xlarge
is_ephemeral: false
max_available: 200
os: linux
ami: al2023-ami-2023.5.202*-kernel-6.1-x86_64
ami: al2023-ami-2023.5.20240701.0-kernel-6.1-x86_64
variants:
amz2023:
ami: al2023-ami-2023.5.20240701.0-kernel-6.1-x86_64
am2:
ami: amzn2-ami-hvm-2.0.20240306.2-x86_64-ebs
lf.linux.g5.12xlarge.nvidia.gpu:
disk_size: 150
instance_type: g5.12xlarge
is_ephemeral: false
max_available: 150
os: linux
ami: al2023-ami-2023.5.202*-kernel-6.1-x86_64
ami: al2023-ami-2023.5.20240701.0-kernel-6.1-x86_64
variants:
amz2023:
ami: al2023-ami-2023.5.20240701.0-kernel-6.1-x86_64
am2:
ami: amzn2-ami-hvm-2.0.20240306.2-x86_64-ebs
lf.linux.g5.4xlarge.nvidia.gpu:
disk_size: 150
instance_type: g5.4xlarge
is_ephemeral: false
max_available: 2400
os: linux
ami: al2023-ami-2023.5.202*-kernel-6.1-x86_64
ami: al2023-ami-2023.5.20240701.0-kernel-6.1-x86_64
variants:
amz2023:
ami: al2023-ami-2023.5.20240701.0-kernel-6.1-x86_64
am2:
ami: amzn2-ami-hvm-2.0.20240306.2-x86_64-ebs
lf.linux.g6.4xlarge.experimental.nvidia.gpu:
disk_size: 150
instance_type: g6.4xlarge
is_ephemeral: false
max_available: 50
os: linux
ami: al2023-ami-2023.5.202*-kernel-6.1-x86_64
ami: al2023-ami-2023.5.20240701.0-kernel-6.1-x86_64
variants:
amz2023:
ami: al2023-ami-2023.5.20240701.0-kernel-6.1-x86_64
am2:
ami: amzn2-ami-hvm-2.0.20240306.2-x86_64-ebs
lf.linux.large:
max_available: 1200
disk_size: 15
instance_type: c5.large
is_ephemeral: false
os: linux
ami: al2023-ami-2023.5.202*-kernel-6.1-x86_64
ami: al2023-ami-2023.5.20240701.0-kernel-6.1-x86_64
variants:
amz2023:
ami: al2023-ami-2023.5.20240701.0-kernel-6.1-x86_64
am2:
ami: amzn2-ami-hvm-2.0.20240306.2-x86_64-ebs
lf.linux.arm64.2xlarge:
disk_size: 256
instance_type: t4g.2xlarge
is_ephemeral: false
max_available: 200
os: linux
ami: al2023-ami-2023.5.202*-kernel-6.1-arm64
ami: al2023-ami-2023.5.20240701.0-kernel-6.1-arm64
variants:
amz2023:
ami: al2023-ami-2023.5.20240701.0-kernel-6.1-arm64
am2:
ami: amzn2-ami-hvm-2.0.20240306.2-arm64-gp2
lf.linux.arm64.m7g.4xlarge:
disk_size: 256
instance_type: m7g.4xlarge
is_ephemeral: false
max_available: 200
os: linux
ami: al2023-ami-2023.5.202*-kernel-6.1-arm64
ami: al2023-ami-2023.5.20240701.0-kernel-6.1-arm64
variants:
amz2023:
ami: al2023-ami-2023.5.20240701.0-kernel-6.1-arm64
am2:
ami: amzn2-ami-hvm-2.0.20240306.2-arm64-gp2
lf.linux.arm64.2xlarge.ephemeral:
disk_size: 256
instance_type: t4g.2xlarge
is_ephemeral: true
max_available: 200
os: linux
ami: al2023-ami-2023.5.202*-kernel-6.1-arm64
ami: al2023-ami-2023.5.20240701.0-kernel-6.1-arm64
variants:
amz2023:
ami: al2023-ami-2023.5.20240701.0-kernel-6.1-arm64
am2:
ami: amzn2-ami-hvm-2.0.20240306.2-arm64-gp2
lf.linux.arm64.m7g.4xlarge.ephemeral:
disk_size: 256
instance_type: m7g.4xlarge
is_ephemeral: true
max_available: 200
os: linux
ami: al2023-ami-2023.5.202*-kernel-6.1-arm64
ami: al2023-ami-2023.5.20240701.0-kernel-6.1-arm64
variants:
amz2023:
ami: al2023-ami-2023.5.20240701.0-kernel-6.1-arm64
am2:
ami: amzn2-ami-hvm-2.0.20240306.2-arm64-gp2
lf.linux.arm64.m7g.metal:
disk_size: 256
instance_type: m7g.metal
is_ephemeral: false
max_available: 100
os: linux
ami: al2023-ami-2023.5.202*-kernel-6.1-arm64
ami: al2023-ami-2023.5.20240701.0-kernel-6.1-arm64
variants:
amz2023:
ami: al2023-ami-2023.5.20240701.0-kernel-6.1-arm64
am2:
ami: amzn2-ami-hvm-2.0.20240306.2-arm64-gp2
lf.windows.g4dn.xlarge:
disk_size: 256
instance_type: g4dn.xlarge

View File

@ -86,18 +86,6 @@
- pull
- inductor
- name: OSS CI / pytorchbot / slow tests
patterns:
- test/slow_tests.json
approved_by:
- pytorchbot
ignore_flaky_failures: false
mandatory_checks_name:
- EasyCLA
- Lint
- pull
- slow
- name: OSS CI /pytorchbot / Executorch
patterns:
- .ci/docker/ci_commit_pins/executorch.txt
@ -544,7 +532,6 @@
- anijain2305
- bdhirsh
- zou3519
- isuruf
mandatory_checks_name:
- EasyCLA
- Lint

View File

@ -9,14 +9,12 @@ ciflow_push_tags:
- ciflow/inductor-rocm
- ciflow/inductor-perf-compare
- ciflow/inductor-micro-benchmark
- ciflow/inductor-micro-benchmark-cpu-x86
- ciflow/inductor-cu124
- ciflow/linux-aarch64
- ciflow/mps
- ciflow/nightly
- ciflow/periodic
- ciflow/rocm
- ciflow/s390
- ciflow/slow
- ciflow/trunk
- ciflow/unstable

View File

@ -1,7 +1,6 @@
boto3==1.19.12
hypothesis==6.56.4
expecttest==0.2.1
fbscribelogger==0.1.6
expecttest==0.1.6
librosa>=0.6.2
mpmath==1.3.0
networkx==2.8.7
@ -31,4 +30,3 @@ optree==0.12.1
# NB: test_hparams_* from test_tensorboard is failing with protobuf 5.26.0 in
# which the stringify metadata is wrong when escaping double quote
protobuf==3.20.2
parameterized==0.8.1

View File

@ -15,7 +15,9 @@ REPO_DIR = SCRIPT_DIR.parent.parent
def read_triton_pin(device: str = "cuda") -> str:
triton_file = "triton.txt"
if device == "xpu":
if device == "rocm":
triton_file = "triton-rocm.txt"
elif device == "xpu":
triton_file = "triton-xpu.txt"
with open(REPO_DIR / ".ci" / "docker" / "ci_commit_pins" / triton_file) as f:
return f.read().strip()

View File

@ -325,7 +325,6 @@ def generate_wheels_matrix(
os: str,
arches: Optional[List[str]] = None,
python_versions: Optional[List[str]] = None,
use_split_build: bool = False,
) -> List[Dict[str, str]]:
package_type = "wheel"
if os == "linux" or os == "linux-aarch64" or os == "linux-s390x":
@ -333,7 +332,7 @@ def generate_wheels_matrix(
package_type = "manywheel"
if python_versions is None:
python_versions = FULL_PYTHON_VERSIONS + ["3.13", "3.13t"]
python_versions = FULL_PYTHON_VERSIONS + ["3.13"]
if arches is None:
# Define default compute archivectures
@ -369,26 +368,10 @@ def generate_wheels_matrix(
# TODO: Enable python 3.13 on rocm, aarch64, windows
if (
gpu_arch_type == "rocm" or (os != "linux" and os != "linux-s390x")
) and (python_version == "3.13" or python_version == "3.13t"):
) and python_version == "3.13":
continue
# TODO: Enable python 3.13t on xpu and cpu-s390x
if (
gpu_arch_type == "xpu" or gpu_arch_type == "cpu-s390x"
) and python_version == "3.13t":
continue
if use_split_build and (
arch_version not in ["12.4", "12.1", "11.8", "cpu"] or os != "linux"
):
raise RuntimeError(
"Split build is only supported on linux with cuda 12.4, 12.1, 11.8, and cpu.\n"
f"Currently attempting to build on arch version {arch_version} and os {os}.\n"
"Please modify the matrix generation to exclude this combination."
)
# 12.1 linux wheels require PYTORCH_EXTRA_INSTALL_REQUIREMENTS to install
if (
arch_version in ["12.4", "12.1", "11.8"]
and os == "linux"
@ -402,7 +385,6 @@ def generate_wheels_matrix(
"desired_cuda": translate_desired_cuda(
gpu_arch_type, gpu_arch_version
),
"use_split_build": "True" if use_split_build else "False",
"devtoolset": (
"cxx11-abi" if arch_version == "cuda-aarch64" else ""
),
@ -418,8 +400,7 @@ def generate_wheels_matrix(
),
}
)
# Special build building to use on Colab. Python 3.11 for 12.1 CUDA
if python_version == "3.11" and arch_version == "12.1":
if arch_version != "cuda-aarch64":
ret.append(
{
"python_version": python_version,
@ -428,16 +409,40 @@ def generate_wheels_matrix(
"desired_cuda": translate_desired_cuda(
gpu_arch_type, gpu_arch_version
),
"use_split_build": "True" if use_split_build else "False",
"use_split_build": "True",
"devtoolset": "",
"container_image": WHEEL_CONTAINER_IMAGES[arch_version],
"package_type": package_type,
"pytorch_extra_install_requirements": "",
"build_name": f"{package_type}-py{python_version}-{gpu_arch_type}{gpu_arch_version}-full".replace( # noqa: B950
"pytorch_extra_install_requirements": (
PYTORCH_EXTRA_INSTALL_REQUIREMENTS[arch_version] # fmt: skip
if os != "linux-aarch64"
else ""
),
"build_name": f"{package_type}-py{python_version}-{gpu_arch_type}{gpu_arch_version}-split".replace( # noqa: B950
".", "_"
),
}
)
# Special build building to use on Colab. PyThon 3.10 for 12.1 CUDA
if python_version == "3.10" and arch_version == "12.1":
ret.append(
{
"python_version": python_version,
"gpu_arch_type": gpu_arch_type,
"gpu_arch_version": gpu_arch_version,
"desired_cuda": translate_desired_cuda(
gpu_arch_type, gpu_arch_version
),
"use_split_build": "False",
"devtoolset": "",
"container_image": WHEEL_CONTAINER_IMAGES[arch_version],
"package_type": package_type,
"pytorch_extra_install_requirements": "",
"build_name": f"{package_type}-py{python_version}-{gpu_arch_type}{gpu_arch_version}-full".replace( # noqa: B950
".", "_"
),
}
)
else:
ret.append(
{
@ -447,7 +452,6 @@ def generate_wheels_matrix(
"desired_cuda": translate_desired_cuda(
gpu_arch_type, gpu_arch_version
),
"use_split_build": "True" if use_split_build else "False",
"devtoolset": (
"cxx11-abi" if arch_version == "cpu-cxx11-abi" else ""
),
@ -463,7 +467,6 @@ def generate_wheels_matrix(
),
}
)
return ret

View File

@ -61,7 +61,6 @@ class BinaryBuildWorkflow:
# Mainly for macos
cross_compile_arm64: bool = False
macos_runner: str = "macos-14-xlarge"
use_split_build: bool = False
def __post_init__(self) -> None:
if self.abi_version:
@ -70,9 +69,6 @@ class BinaryBuildWorkflow:
)
else:
self.build_environment = f"{self.os}-binary-{self.package_type}"
if self.use_split_build:
# added to distinguish concurrency groups
self.build_environment += "-split"
def generate_workflow_file(self, workflow_template: jinja2.Template) -> None:
output_file_path = (
@ -114,20 +110,6 @@ LINUX_BINARY_BUILD_WORFKLOWS = [
isolated_workflow=True,
),
),
BinaryBuildWorkflow(
os=OperatingSystem.LINUX,
package_type="manywheel",
build_configs=generate_binary_build_matrix.generate_wheels_matrix(
OperatingSystem.LINUX,
use_split_build=True,
arches=["11.8", "12.1", "12.4", "cpu"],
),
ciflow_config=CIFlowConfig(
labels={LABEL_CIFLOW_BINARIES, LABEL_CIFLOW_BINARIES_WHEEL},
isolated_workflow=True,
),
use_split_build=True,
),
BinaryBuildWorkflow(
os=OperatingSystem.LINUX,
package_type="conda",
@ -180,21 +162,6 @@ LINUX_BINARY_SMOKE_WORKFLOWS = [
),
branches="main",
),
BinaryBuildWorkflow(
os=OperatingSystem.LINUX,
package_type="manywheel",
build_configs=generate_binary_build_matrix.generate_wheels_matrix(
OperatingSystem.LINUX,
arches=["11.8", "12.1", "12.4"],
python_versions=["3.9"],
use_split_build=True,
),
ciflow_config=CIFlowConfig(
labels={LABEL_CIFLOW_PERIODIC},
),
branches="main",
use_split_build=True,
),
BinaryBuildWorkflow(
os=OperatingSystem.LINUX,
package_type="libtorch",

View File

@ -168,14 +168,6 @@ def gh_post_commit_comment(
)
def gh_close_pr(org: str, repo: str, pr_num: int, dry_run: bool = False) -> None:
url = f"{GITHUB_API_URL}/repos/{org}/{repo}/pulls/{pr_num}"
if dry_run:
print(f"Dry run closing PR {pr_num}")
else:
gh_fetch_url(url, method="PATCH", data={"state": "closed"})
def gh_delete_comment(org: str, repo: str, comment_id: int) -> None:
url = f"{GITHUB_API_URL}/repos/{org}/{repo}/issues/comments/{comment_id}"
gh_fetch_url(url, method="DELETE")

View File

@ -17,11 +17,6 @@ if [[ -d "${CACHE_DIRECTORY}" ]]; then
cp -r "${CACHE_DIRECTORY}" . || true
fi
# if lintrunner is not installed, install it
if ! command -v lintrunner &> /dev/null; then
python3 -m pip install lintrunner==0.12.5
fi
# This has already been cached in the docker image
lintrunner init 2> /dev/null
@ -38,7 +33,7 @@ python3 torch/utils/data/datapipes/gen_pyi.py
RC=0
# Run lintrunner on all files
if ! lintrunner --force-color --tee-json=lint.json ${ADDITIONAL_LINTRUNNER_ARGS} 2> /dev/null; then
if ! lintrunner --force-color --all-files --tee-json=lint.json ${ADDITIONAL_LINTRUNNER_ARGS} 2> /dev/null; then
echo ""
echo -e "\e[1m\e[36mYou can reproduce these results locally by using \`lintrunner -m origin/main\`. (If you don't get the same results, run \'lintrunner init\' to update your local linter)\e[0m"
echo -e "\e[1m\e[36mSee https://github.com/pytorch/pytorch/wiki/lintrunner for setup instructions.\e[0m"

View File

@ -1,103 +1,51 @@
# flake8: noqa: G004
# Note: Copies of this script in runner_determinator.py and _runner-determinator.yml
# must be kept in sync. You can do it easily by running the following command:
# python .github/scripts/update_runner_determinator.py
"""
This runner determinator is used to determine which set of runners to run a
GitHub job on. It uses the first comment of a GitHub issue (by default
https://github.com/pytorch/test-infra/issues/5132) to define the configuration
of which runners should be used to run which job.
The configuration has two parts, the settings and a list of opted-in users,
separated by a line containing "---". If the line is not present, the
settings are considered to be empty with only the second part, the user
list, defined.
The first part is a YAML block that defines the rollout settings. This can be
used to define any settings that are needed to determine which runners to use.
It's fields are defined by the RolloutSettings class below.
The second part is a list of users who are explicitly opted in to the LF fleet.
The user list is also a comma separated list of additional features or
experiments which the user could be opted in to.
https://github.com/pytorch/test-infra/issues/5132) as a user list to determine
which users will get their jobs to run on experimental runners. This user list
is also a comma separated list of additional features or experiments which the
user could be opted in to.
The user list has the following rules:
- Users are GitHub usernames, which must start with the @ prefix
- Users are GitHub usernames with the @ prefix
- If the first line is a "*" then all users will use the new runners
- If the first line is a "!" then all users will use the old runners
- Each user is also a comma-separated list of features/experiments to enable
- A "#" prefix opts the user out of all experiments
- A "#" prefix indicates the user is opted out of the new runners but is opting
into features/experiments.
Example config:
# A list of experiments that can be opted into.
# This defines the behavior they'll induce when opted into.
# Expected syntax is:
# [experiment_name]: # Name of the experiment. Also used for the label prefix.
# rollout_perc: [int] # % of workflows to run with this experiment when users are not opted in.
Example user list:
experiments:
lf:
rollout_percent: 25
---
# Opt-ins:
# Users can opt into the LF fleet by adding their GitHub username to this list
# and specifying experiments to enable in a comma-separated list.
# Experiments should be from the above list.
@User1,lf,split_build
@User2,lf
@User3,split_build
@User1
@User2,amz2023
#@UserOptOutOfNewRunner,amz2023
"""
import logging
import os
import random
from argparse import ArgumentParser
from logging import LogRecord
from typing import Any, Dict, Iterable, List, NamedTuple, Tuple
from typing import Any, Iterable
import yaml
from github import Auth, Github
from github.Issue import Issue
DEFAULT_LABEL_PREFIX = "" # use meta runners
WORKFLOW_LABEL_META = "" # use meta runners
WORKFLOW_LABEL_LF = "lf." # use runners from the linux foundation
WORKFLOW_LABEL_LF_CANARY = "lf.c." # use canary runners from the linux foundation
RUNNER_AMI_LEGACY = ""
RUNNER_AMI_AMZ2023 = "amz2023"
GITHUB_OUTPUT = os.getenv("GITHUB_OUTPUT", "")
GH_OUTPUT_KEY_AMI = "runner-ami"
GH_OUTPUT_KEY_LABEL_TYPE = "label-type"
SETTING_EXPERIMENTS = "experiments"
LF_FLEET_EXPERIMENT = "lf"
CANARY_FLEET_SUFFIX = ".c"
class Experiment(NamedTuple):
rollout_perc: float = (
0 # Percentage of workflows to experiment on when user is not opted-in.
)
all_branches: bool = (
False # If True, the experiment is also enabled on the exception branches
)
# Add more fields as needed
class Settings(NamedTuple):
"""
Settings for the experiments that can be opted into.
"""
experiments: Dict[str, Experiment] = {}
class ColorFormatter(logging.Formatter):
"""Color codes the log messages based on the log level"""
@ -219,194 +167,90 @@ def get_potential_pr_author(
def is_exception_branch(branch: str) -> bool:
"""
Branches that get opted out of experiments by default, until they're explicitly enabled.
Branches that get opted out of all experiments and should always use Meta runners
"""
return branch.split("/")[0] in {"main", "nightly", "release", "landchecks"}
def load_yaml(yaml_text: str) -> Any:
def get_fleet(rollout_state: str, workflow_requestors: Iterable[str]) -> str:
"""
Determines if the job should run on the LF fleet or the Meta fleet
Returns:
The appropriate label prefix for the runner, corresponding to the fleet to use.
This gets prefixed to the very start of the runner label.
"""
try:
data = yaml.safe_load(yaml_text)
return data
except yaml.YAMLError as exc:
log.exception("Error loading YAML")
raise
def extract_settings_user_opt_in_from_text(rollout_state: str) -> Tuple[str, str]:
"""
Extracts the text with settings, if any, and the opted in users from the rollout state.
If the issue body contains "---" then the text above that is the settings
and the text below is the list of opted in users.
If it doesn't contain "---" then the settings are empty and the rest is the users.
"""
rollout_state_parts = rollout_state.split("---")
if len(rollout_state_parts) >= 2:
return rollout_state_parts[0], rollout_state_parts[1]
else:
return "", rollout_state
class UserOptins(Dict[str, List[str]]):
"""
Dictionary of users with a list of features they have opted into
"""
def parse_user_opt_in_from_text(user_optin_text: str) -> UserOptins:
"""
Parse the user opt-in text into a key value pair of username and the list of features they have opted into
Users are GitHub usernames with the @ prefix. Each user is also a comma-separated list of features/experiments to enable.
- Example line: "@User1,lf,split_build"
- A "#" prefix indicates the user is opted out of all experiments
"""
optins = UserOptins()
for user in user_optin_text.split("\n"):
user = user.strip("\r\n\t -")
if not user or not user.startswith("@"):
# Not a valid user. Skip
continue
if user:
usr_name = user.split(",")[0].strip("@")
optins[usr_name] = [exp.strip(" ") for exp in user.split(",")[1:]]
return optins
def parse_settings_from_text(settings_text: str) -> Settings:
"""
Parse the experiments from the issue body into a list of ExperimentSettings
"""
try:
if settings_text:
# Escape the backtick as well so that we can have the settings in a code block on the GH issue
# for easy reading
# Note: Using ascii for the backtick so that the cat step in _runner-determinator.yml doesn't choke on
# the backtick character in shell commands.
backtick = chr(96) # backtick character
settings_text = settings_text.strip(f"\r\n\t{backtick} ")
settings = load_yaml(settings_text)
# For now we just load experiments. We can expand this if/when we add more settings
experiments = {}
for exp_name, exp_settings in settings.get(SETTING_EXPERIMENTS).items():
valid_settings = {}
for setting in exp_settings:
if setting not in Experiment._fields:
log.warning(
f"Unexpected setting in experiment: {setting} = {exp_settings[setting]}"
)
else:
valid_settings[setting] = exp_settings[setting]
experiments[exp_name] = Experiment(**valid_settings)
return Settings(experiments)
except Exception:
log.exception("Failed to parse settings")
return Settings()
def parse_settings(rollout_state: str) -> Settings:
"""
Parse settings, if any, from the rollout state.
If the issue body contains "---" then the text above that is the settings
and the text below is the list of opted in users.
If it doesn't contain "---" then the settings are empty and the default values are used.
"""
settings_text, _ = extract_settings_user_opt_in_from_text(rollout_state)
return parse_settings_from_text(settings_text)
def parse_users(rollout_state: str) -> UserOptins:
"""
Parse users from the rollout state.
"""
_, users_text = extract_settings_user_opt_in_from_text(rollout_state)
return parse_user_opt_in_from_text(users_text)
def is_user_opted_in(user: str, user_optins: UserOptins, experiment_name: str) -> bool:
"""
Check if a user is opted into an experiment
"""
return experiment_name in user_optins.get(user, [])
def get_runner_prefix(
rollout_state: str,
workflow_requestors: Iterable[str],
branch: str,
is_canary: bool = False,
) -> str:
settings = parse_settings(rollout_state)
user_optins = parse_users(rollout_state)
fleet_prefix = ""
prefixes = []
for experiment_name, experiment_settings in settings.experiments.items():
enabled = False
if not experiment_settings.all_branches and is_exception_branch(branch):
log.info(
f"Branch {branch} is an exception branch. Not enabling experiment {experiment_name}."
)
continue
# Is any workflow_requestor opted in to this experiment?
opted_in_users = [
requestor
for requestor in workflow_requestors
if is_user_opted_in(requestor, user_optins, experiment_name)
]
if opted_in_users:
log.info(
f"{', '.join(opted_in_users)} have opted into experiment {experiment_name}."
)
enabled = True
elif experiment_settings.rollout_perc:
# If no user is opted in, then we randomly enable the experiment based on the rollout percentage
if random.uniform(0, 100) <= experiment_settings.rollout_perc:
if rollout_state[0] == "!":
log.info("LF Workflows are disabled for everyone. Using meta runners.")
return WORKFLOW_LABEL_META
elif rollout_state[0] == "*":
log.info("LF Workflows are enabled for everyone. Using LF runners.")
return WORKFLOW_LABEL_LF
else:
all_opted_in_users = {
usr_raw.strip("\n\t@ ").split(",")[0]
for usr_raw in rollout_state.split()
}
opted_in_requestors = {
usr for usr in workflow_requestors if usr in all_opted_in_users
}
if opted_in_requestors:
log.info(
f"Based on rollout percentage of {experiment_settings.rollout_perc}%, enabling experiment {experiment_name}."
f"LF Workflows are enabled for {', '.join(opted_in_requestors)}. Using LF runners."
)
enabled = True
if enabled:
label = experiment_name
if experiment_name == LF_FLEET_EXPERIMENT:
# We give some special treatment to the "lf" experiment since determines the fleet we use
# - If it's enabled, then we always list it's prefix first
# - If we're in the canary branch, then we append ".c" to the lf prefix
if is_canary:
label += CANARY_FLEET_SUFFIX
fleet_prefix = label
return WORKFLOW_LABEL_LF
else:
prefixes.append(label)
log.info(
f"LF Workflows are disabled for {', '.join(workflow_requestors)}. Using meta runners."
)
return WORKFLOW_LABEL_META
if len(prefixes) > 1:
except Exception as e:
log.error(
f"Only a fleet and one other experiment can be enabled for a job at any time. Enabling {prefixes[0]} and ignoring the rest, which are {', '.join(prefixes[1:])}"
f"Failed to get determine workflow type. Falling back to meta runners. Exception: {e}"
)
prefixes = prefixes[:1]
return WORKFLOW_LABEL_META
# Fleet always comes first
if fleet_prefix:
prefixes.insert(0, fleet_prefix)
return ".".join(prefixes) + "." if prefixes else ""
def get_optin_feature(
rollout_state: str, workflow_requestors: Iterable[str], feature: str, fallback: str
) -> str:
"""
Used to dynamically opt in jobs to specific runner-type variants.
Returns:
The runner-type's variant name if the user has opted in to the feature, otherwise returns an empty string.
This variant name is prefixed to the runner-type in the label.
"""
try:
userlist = {u.lstrip("#").strip("\n\t@ ") for u in rollout_state.split()}
all_opted_in_users = set()
for user in userlist:
for i in user.split(","):
if i == feature:
all_opted_in_users.add(user.split(",")[0])
opted_in_requestors = {
usr for usr in workflow_requestors if usr in all_opted_in_users
}
if opted_in_requestors:
log.info(
f"Feature {feature} is enabled for {', '.join(opted_in_requestors)}. Using feature {feature}."
)
return feature
else:
log.info(
f"Feature {feature} is disabled for {', '.join(workflow_requestors)}. Using fallback \"{fallback}\"."
)
return fallback
except Exception as e:
log.error(
f'Failed to determine if user has opted-in to feature {feature}. Using fallback "{fallback}". Exception: {e}'
)
return fallback
def get_rollout_state_from_issue(github_token: str, repo: str, issue_num: int) -> str:
@ -423,36 +267,53 @@ def get_rollout_state_from_issue(github_token: str, repo: str, issue_num: int) -
def main() -> None:
args = parse_args()
runner_label_prefix = DEFAULT_LABEL_PREFIX
if args.github_ref_type == "branch" and is_exception_branch(args.github_branch):
log.info(f"Exception branch: '{args.github_branch}', using meta runners")
label_type = WORKFLOW_LABEL_META
runner_ami = RUNNER_AMI_LEGACY
else:
try:
rollout_state = get_rollout_state_from_issue(
args.github_token, args.github_issue_repo, args.github_issue
)
try:
rollout_state = get_rollout_state_from_issue(
args.github_token, args.github_issue_repo, args.github_issue
)
username = get_potential_pr_author(
args.github_token,
args.github_repo,
args.github_actor,
args.github_ref_type,
args.github_branch,
)
username = get_potential_pr_author(
args.github_token,
args.github_repo,
args.github_actor,
args.github_ref_type,
args.github_branch,
)
label_type = get_fleet(
rollout_state,
(
args.github_issue_owner,
username,
),
)
runner_ami = get_optin_feature(
rollout_state=rollout_state,
workflow_requestors=(
args.github_issue_owner,
username,
),
feature=RUNNER_AMI_AMZ2023,
fallback=RUNNER_AMI_LEGACY,
)
except Exception as e:
log.error(
f"Failed to get issue. Falling back to meta runners. Exception: {e}"
)
label_type = WORKFLOW_LABEL_META
runner_ami = RUNNER_AMI_LEGACY
is_canary = args.github_repo == "pytorch/pytorch-canary"
# For Canary builds use canary runners
if args.github_repo == "pytorch/pytorch-canary" and label_type == WORKFLOW_LABEL_LF:
label_type = WORKFLOW_LABEL_LF_CANARY
runner_label_prefix = get_runner_prefix(
rollout_state,
(args.github_issue_owner, username),
args.github_branch,
is_canary,
)
except Exception as e:
log.error(
f"Failed to get issue. Defaulting to Meta runners and no experiments. Exception: {e}"
)
set_github_output(GH_OUTPUT_KEY_LABEL_TYPE, runner_label_prefix)
set_github_output(GH_OUTPUT_KEY_LABEL_TYPE, label_type)
set_github_output(GH_OUTPUT_KEY_AMI, runner_ami)
if __name__ == "__main__":

View File

@ -3,7 +3,7 @@
## Install prerequisites.
```
$ sudo dnf install podman podman-docker jq
$ sudo dnf install docker
```
## Add services.
@ -27,48 +27,23 @@ $ sudo systemctl enable --now qemu-user-static
## Rebuild the image
First build s390x builder image `docker.io/pytorch/manylinuxs390x-builder`,
using following commands:
```
$ cd ~
$ git clone https://github.com/pytorch/pytorch
$ cd pytorch
$ git submodule update --init --recursive
$ GPU_ARCH_TYPE=cpu-s390x "$(pwd)/.ci/docker/manywheel/build.sh" manylinuxs390x-builder
$ docker image tag localhost/pytorch/manylinuxs390x-builder docker.io/pytorch/manylinuxs390x-builder:cpu-s390x
$ docker image save -o ~/manywheel-s390x.tar docker.io/pytorch/manylinuxs390x-builder:cpu-s390x
```
Next step is to build `actions-runner` image using:
In order to build or update the `iiilinuxibmcom/actions-runner` image, e.g. to get the
latest OS security fixes, use the following commands:
```
$ cd self-hosted-builder
$ sudo docker build \
--build-arg repo=<owner>/<name> \
--build-arg token=<***> \
--pull \
-f actions-runner.Dockerfile \
-t iiilinuxibmcom/actions-runner.<name> \
-t iiilinuxibmcom/actions-runner \
.
```
If there are failures, ensure that selinux doesn't prevent it from working.
If it fails, ensure that selinux doesn't prevent it from working.
In worst case, selinux can be disabled with `setenforce 0`.
Now prepare all necessary files for runner registration:
```
$ sudo mkdir -p /etc/actions-runner/<name>
$ sudo chmod 700 /etc/actions-runner/<name>
$ sudo /bin/cp <github_app_private_key_file> /etc/actions-runner/<name>/key_private.pem
$ sudo echo <github_app_id> | sudo tee /etc/actions-runner/<name>/appid.env
$ sudo echo <github_app_install_id> | sudo tee /etc/actions-runner/<name>/installid.env
$ sudo echo NAME=<worker_name> | sudo tee /etc/actions-runner/<name>/env
$ sudo echo ORG=<github_org> | sudo tee -a /etc/actions-runner/<name>/env
$ cd self-hosted-builder
$ sudo /bin/cp helpers/*.sh /usr/local/bin/
$ sudo chmod 755 /usr/local/bin/app_token.sh /usr/local/bin/gh_token_generator.sh
```
## Autostart the runner.
```

View File

@ -1,12 +1,12 @@
# Self-Hosted IBM Z Github Actions Runner.
# Temporary image: amd64 dependencies.
FROM docker.io/amd64/ubuntu:23.10 as ld-prefix
FROM docker.io/amd64/ubuntu:22.04 as ld-prefix
ENV DEBIAN_FRONTEND=noninteractive
RUN apt-get update && apt-get -y install ca-certificates libicu72 libssl3
RUN apt-get update && apt-get -y install ca-certificates libicu70 libssl3
# Main image.
FROM docker.io/s390x/ubuntu:23.10
FROM docker.io/s390x/ubuntu:22.04
# Packages for pytorch building and testing.
ENV DEBIAN_FRONTEND=noninteractive
@ -16,7 +16,6 @@ RUN apt-get update && apt-get -y install \
gcc \
git \
jq \
zip \
libxml2-dev \
libxslt-dev \
ninja-build \
@ -44,28 +43,24 @@ COPY fs/ /
RUN chmod +x /usr/bin/actions-runner /usr/bin/entrypoint
# install podman
RUN apt -y install podman podman-docker
# amd64 Github Actions Runner.
RUN useradd -m actions-runner
USER actions-runner
WORKDIR /home/actions-runner
RUN curl -L https://github.com/actions/runner/releases/download/v2.309.0/actions-runner-linux-x64-2.309.0.tar.gz | tar -xz
# set up python virtual environment which is later used by runner.
# build workflows use "python -m pip install ...",
# and it doesn't work for non-root user
RUN virtualenv --system-site-packages venv
# repository
ARG repo
# copy prebuilt manywheel docker image for builds and tests
# build command is:
# GPU_ARCH_TYPE=cpu-s390x "$(pwd)/manywheel/build_docker.sh"
# and save command is:
# docker image save -o manywheel-s390x.tar pytorch/manylinuxs390x-builder:cpu-s390x
#
COPY --chown=actions-runner:actions-runner manywheel-s390x.tar /home/actions-runner/manywheel-s390x.tar
# repository token
ARG token
RUN curl -L https://github.com/actions/runner/releases/download/v2.317.0/actions-runner-linux-x64-2.317.0.tar.gz | tar -xz
RUN ./config.sh \
--unattended \
--url "https://github.com/${repo}" \
--token "${token}" \
--no-default-labels \
--labels self-hosted,linux.s390x
ENTRYPOINT ["/usr/bin/entrypoint"]
CMD ["/usr/bin/actions-runner"]

View File

@ -8,16 +8,12 @@ StartLimitIntervalSec=0
Type=simple
Restart=always
ExecStartPre=-/usr/bin/docker rm --force actions-runner.%i
ExecStartPre=-/usr/local/bin/gh_token_generator.sh /etc/actions-runner/%i/appid.env /etc/actions-runner/%i/installid.env /etc/actions-runner/%i/key_private.pem /etc/actions-runner/%i/ghtoken.env
ExecStart=/usr/bin/docker run \
--env-file=/etc/actions-runner/%i/env \
--env-file=/etc/actions-runner/%i/ghtoken.env \
--init \
--interactive \
--name=actions-runner.%i \
--rm \
--privileged \
iiilinuxibmcom/actions-runner.%i
iiilinuxibmcom/actions-runner
ExecStop=/bin/sh -c "docker exec actions-runner.%i kill -INT -- -1"
ExecStop=/bin/sh -c "docker wait actions-runner.%i"
ExecStop=/bin/sh -c "docker rm actions-runner.%i"

View File

@ -2,45 +2,5 @@
set -e -u
# first import docker image
if [ -f ./manywheel-s390x.tar ] ; then
docker image load --input manywheel-s390x.tar
docker image tag docker.io/pytorch/manylinuxs390x-builder:cpu-s390x docker.io/pytorch/manylinuxs390x-builder:cpu-s390x-main
rm -f manywheel-s390x.tar
fi
token_file=registration-token.json
# Generate registration token
curl \
-X POST \
-H "Accept: application/vnd.github.v3+json" \
-H "Authorization: Bearer ${ACCESS_TOKEN}" \
"https://api.github.com/orgs/${ORG}/actions/runners/registration-token" \
-o "$token_file"
unset ACCESS_TOKEN
# register runner as ephemeral runner
# it does one job, stops and unregisters
registration_token=$(jq --raw-output .token "$token_file")
./config.sh \
--unattended \
--ephemeral \
--url "https://github.com/${ORG}" \
--token "${registration_token}" \
--name "${NAME}" \
--no-default-labels \
--labels self-hosted,linux.s390x
unset registration_token
rm -f "$token_file"
# enter into python virtual environment.
# build workflows use "python -m pip install ...",
# and it doesn't work for non-root user
source venv/bin/activate
# Run one job.
./run.sh
./run.sh --once

View File

@ -1,84 +0,0 @@
#!/usr/bin/env bash
#
# Request an ACCESS_TOKEN to be used by a GitHub APP
# Environment variable that need to be set up:
# * APP_ID, the GitHub's app ID
# * INSTALL_ID, the Github's app's installation ID
# * APP_PRIVATE_KEY, the content of GitHub app's private key in PEM format.
#
# https://github.com/orgs/community/discussions/24743#discussioncomment-3245300
#
set -o pipefail
_GITHUB_HOST=${GITHUB_HOST:="github.com"}
# If URL is not github.com then use the enterprise api endpoint
if [[ ${GITHUB_HOST} = "github.com" ]]; then
URI="https://api.${_GITHUB_HOST}"
else
URI="https://${_GITHUB_HOST}/api/v3"
fi
API_VERSION=v3
API_HEADER="Accept: application/vnd.github.${API_VERSION}+json"
CONTENT_LENGTH_HEADER="Content-Length: 0"
APP_INSTALLATIONS_URI="${URI}/app/installations"
# JWT parameters based off
# https://docs.github.com/en/developers/apps/building-github-apps/authenticating-with-github-apps#authenticating-as-a-github-app
#
# JWT token issuance and expiration parameters
JWT_IAT_DRIFT=60
JWT_EXP_DELTA=600
JWT_JOSE_HEADER='{
"alg": "RS256",
"typ": "JWT"
}'
build_jwt_payload() {
now=$(date +%s)
iat=$((now - JWT_IAT_DRIFT))
jq -c \
--arg iat_str "${iat}" \
--arg exp_delta_str "${JWT_EXP_DELTA}" \
--arg app_id_str "${APP_ID}" \
'
($iat_str | tonumber) as $iat
| ($exp_delta_str | tonumber) as $exp_delta
| ($app_id_str | tonumber) as $app_id
| .iat = $iat
| .exp = ($iat + $exp_delta)
| .iss = $app_id
' <<< "{}" | tr -d '\n'
}
base64url() {
base64 | tr '+/' '-_' | tr -d '=\n'
}
rs256_sign() {
openssl dgst -binary -sha256 -sign <(echo "$1")
}
request_access_token() {
jwt_payload=$(build_jwt_payload)
encoded_jwt_parts=$(base64url <<<"${JWT_JOSE_HEADER}").$(base64url <<<"${jwt_payload}")
encoded_mac=$(echo -n "$encoded_jwt_parts" | rs256_sign "${APP_PRIVATE_KEY}" | base64url)
generated_jwt="${encoded_jwt_parts}.${encoded_mac}"
auth_header="Authorization: Bearer ${generated_jwt}"
app_installations_response=$(curl -sX POST \
-H "${auth_header}" \
-H "${API_HEADER}" \
--header "X-GitHub-Api-Version: 2022-11-28" \
--url "https://api.github.com/app/installations/${INSTALL_ID}/access_tokens" \
)
echo "$app_installations_response" | jq --raw-output '.token'
}
request_access_token

View File

@ -1,10 +0,0 @@
#!/usr/bin/env bash
SCRIPT_DIR=$(dirname "$0")
APP_ID=$1
INSTALL_ID=$2
APP_PRIVATE_KEY=$3
DST_FILE="$4"
ACCESS_TOKEN="$(APP_ID="$(<"${APP_ID}")" INSTALL_ID="$(<"${INSTALL_ID}")" APP_PRIVATE_KEY="$(<"${APP_PRIVATE_KEY}")" "${SCRIPT_DIR}/app_token.sh")"
echo "ACCESS_TOKEN=${ACCESS_TOKEN}" > "${DST_FILE}"

View File

@ -0,0 +1,35 @@
#!/bin/bash
set -eoux pipefail
SYNC_BRANCH=pytorch-stable-prototype
git config user.email "fake@example.com"
git config user.name "PyTorch Stable Bot"
git fetch origin main
git fetch origin "$SYNC_BRANCH"
git checkout "$SYNC_BRANCH"
# Using a hardcoded SHA here is a massive speedup as we can skip the entire history of the pytorch GitHub repo.
# This specific SHA was chosen as it was before the "branch point" of the stable branch
for SHA in $(git log ba3b05fdf37ddbc3c301294d6a560a816335e717..origin/main --pretty="%h" -- torch/distributed torch/csrc/distributed test/distributed test/cpp/c10d benchmarks/distributed)
do
# `git merge-base --is-ancestor` exits with code 0 if the given SHA is an ancestor, and non-0 otherwise
if git merge-base --is-ancestor $SHA HEAD || [[ $(git log --grep="(cherry picked from commit $SHA") ]]
then
echo "Skipping $SHA"
continue
fi
echo "Copying $SHA"
git cherry-pick -x "$SHA" -X theirs
git reset --soft HEAD~1
git add torch/distributed torch/csrc/distributed test/distributed test/cpp/c10d benchmarks/distributed
git checkout .
git commit --reuse-message=HEAD@{1}
git clean -f
done
if [[ "${WITH_PUSH}" == true ]]; then
git push
fi

View File

@ -51,8 +51,6 @@ def main() -> None:
for platform_image in platform_images: # type: ignore[attr-defined]
for arch in platform_image.keys(): # type: ignore[attr-defined]
if arch == "cpu-s390x":
continue
tag_image(
platform_image[arch], # type: ignore[index]
default_tag,

View File

@ -1,310 +0,0 @@
from unittest import main, TestCase
from unittest.mock import Mock, patch
import runner_determinator as rd
USER_BRANCH = "somebranch"
EXCEPTION_BRANCH = "main"
class TestRunnerDeterminatorIssueParser(TestCase):
def test_parse_settings(self) -> None:
settings_text = """
experiments:
lf:
rollout_perc: 25
otherExp:
rollout_perc: 0
---
Users:
@User1,lf
@User2,lf,otherExp
"""
settings = rd.parse_settings(settings_text)
self.assertTupleEqual(
rd.Experiment(rollout_perc=25),
settings.experiments["lf"],
"lf settings not parsed correctly",
)
self.assertTupleEqual(
rd.Experiment(rollout_perc=0),
settings.experiments["otherExp"],
"otherExp settings not parsed correctly",
)
def test_parse_settings_in_code_block(self) -> None:
settings_text = """
```
experiments:
lf:
rollout_perc: 25
otherExp:
rollout_perc: 0
```
---
Users:
@User1,lf
@User2,lf,otherExp
"""
settings = rd.parse_settings(settings_text)
self.assertTupleEqual(
rd.Experiment(rollout_perc=25),
settings.experiments["lf"],
"lf settings not parsed correctly",
)
self.assertTupleEqual(
rd.Experiment(rollout_perc=0),
settings.experiments["otherExp"],
"otherExp settings not parsed correctly",
)
def test_parse_all_branches_setting(self) -> None:
settings_text = """
```
experiments:
lf:
rollout_perc: 25
all_branches: true
otherExp:
all_branches: True
rollout_perc: 0
```
---
Users:
@User1,lf
@User2,lf,otherExp
"""
settings = rd.parse_settings(settings_text)
self.assertTupleEqual(
rd.Experiment(rollout_perc=25, all_branches=True),
settings.experiments["lf"],
"lf settings not parsed correctly",
)
self.assertTrue(settings.experiments["otherExp"].all_branches)
self.assertTupleEqual(
rd.Experiment(rollout_perc=0, all_branches=True),
settings.experiments["otherExp"],
"otherExp settings not parsed correctly",
)
def test_parse_users(self) -> None:
settings_text = """
experiments:
lf:
rollout_perc: 0
otherExp:
rollout_perc: 0
---
Users:
@User1,lf
@User2,lf,otherExp
"""
users = rd.parse_users(settings_text)
self.assertDictEqual(
{"User1": ["lf"], "User2": ["lf", "otherExp"]},
users,
"Users not parsed correctly",
)
def test_parse_users_without_settings(self) -> None:
settings_text = """
@User1,lf
@User2,lf,otherExp
"""
users = rd.parse_users(settings_text)
self.assertDictEqual(
{"User1": ["lf"], "User2": ["lf", "otherExp"]},
users,
"Users not parsed correctly",
)
class TestRunnerDeterminatorGetRunnerPrefix(TestCase):
def test_opted_in_user(self) -> None:
settings_text = """
experiments:
lf:
rollout_perc: 0
otherExp:
rollout_perc: 0
---
Users:
@User1,lf
@User2,lf,otherExp
"""
prefix = rd.get_runner_prefix(settings_text, ["User1"], USER_BRANCH)
self.assertEqual("lf.", prefix, "Runner prefix not correct for User1")
def test_opted_in_user_two_experiments(self) -> None:
settings_text = """
experiments:
lf:
rollout_perc: 0
otherExp:
rollout_perc: 0
---
Users:
@User1,lf
@User2,lf,otherExp
"""
prefix = rd.get_runner_prefix(settings_text, ["User2"], USER_BRANCH)
self.assertEqual("lf.otherExp.", prefix, "Runner prefix not correct for User2")
@patch("random.uniform", return_value=50)
def test_opted_out_user(self, mock_uniform: Mock) -> None:
settings_text = """
experiments:
lf:
rollout_perc: 25
otherExp:
rollout_perc: 25
---
Users:
@User1,lf
@User2,lf,otherExp
"""
prefix = rd.get_runner_prefix(settings_text, ["User3"], USER_BRANCH)
self.assertEqual("", prefix, "Runner prefix not correct for user")
@patch("random.uniform", return_value=10)
def test_opted_out_user_was_pulled_in_by_rollout(self, mock_uniform: Mock) -> None:
settings_text = """
experiments:
lf:
rollout_perc: 25
otherExp:
rollout_perc: 25
---
Users:
@User1,lf
@User2,lf,otherExp
"""
# User3 is opted out, but is pulled into both experiments by the 10% rollout
prefix = rd.get_runner_prefix(settings_text, ["User3"], USER_BRANCH)
self.assertEqual("lf.otherExp.", prefix, "Runner prefix not correct for user")
def test_lf_prefix_always_comes_first(self) -> None:
settings_text = """
experiments:
otherExp:
rollout_perc: 0
lf:
rollout_perc: 0
---
Users:
@User1,lf
@User2,otherExp,lf
"""
prefix = rd.get_runner_prefix(settings_text, ["User2"], USER_BRANCH)
self.assertEqual("lf.otherExp.", prefix, "Runner prefix not correct for user")
def test_ignores_commented_users(self) -> None:
settings_text = """
experiments:
lf:
rollout_perc: 0
otherExp:
rollout_perc: 0
---
Users:
#@User1,lf
@User2,lf,otherExp
"""
prefix = rd.get_runner_prefix(settings_text, ["User1"], USER_BRANCH)
self.assertEqual("", prefix, "Runner prefix not correct for user")
def test_ignores_extra_experiments(self) -> None:
settings_text = """
experiments:
lf:
rollout_perc: 0
otherExp:
rollout_perc: 0
foo:
rollout_perc: 0
---
Users:
@User1,lf,otherExp,foo
"""
prefix = rd.get_runner_prefix(settings_text, ["User1"], USER_BRANCH)
self.assertEqual("lf.otherExp.", prefix, "Runner prefix not correct for user")
def test_disables_experiment_on_exception_branches_when_not_explicitly_opted_in(
self,
) -> None:
settings_text = """
experiments:
lf:
rollout_perc: 100
---
Users:
@User,lf,otherExp
"""
prefix = rd.get_runner_prefix(settings_text, ["User1"], EXCEPTION_BRANCH)
self.assertEqual("", prefix, "Runner prefix not correct for user")
def test_allows_experiment_on_exception_branches_when_explicitly_opted_in(
self,
) -> None:
settings_text = """
experiments:
lf:
rollout_perc: 100
all_branches: true
---
Users:
@User,lf,otherExp
"""
prefix = rd.get_runner_prefix(settings_text, ["User1"], EXCEPTION_BRANCH)
self.assertEqual("lf.", prefix, "Runner prefix not correct for user")
if __name__ == "__main__":
main()

View File

@ -12,7 +12,7 @@ import json
import os
import warnings
from hashlib import sha256
from typing import Any, List, Optional
from typing import Any, Dict, List, Optional
from unittest import main, mock, skip, TestCase
from urllib.error import HTTPError
@ -24,6 +24,7 @@ from trymerge import (
find_matching_merge_rule,
get_classifications,
get_drci_classifications,
get_rockset_results,
gh_get_team_members,
GitHubPR,
JobCheckState,
@ -41,6 +42,7 @@ if "GIT_REMOTE_URL" not in os.environ:
os.environ["GIT_REMOTE_URL"] = "https://github.com/pytorch/pytorch"
GQL_MOCKS = "gql_mocks.json.gz"
ROCKSET_MOCKS = "rockset_mocks.json.gz"
DRCI_MOCKS = "drci_mocks.json.gz"
@ -75,11 +77,16 @@ def mock_query(
if err.code == 401 or err.code == 403:
err_msg = f"If you are seeing this message during workflow run, please make sure to update {file_name}"
err_msg += f" locally, by deleting it and running {os.path.basename(__file__)} with"
err_msg += " GitHub Personal Access Token passed via GITHUB_TOKEN"
err_msg += " GitHub Personal Access Token passed via GITHUB_TOKEN,"
err_msg += " the rockset api key passed via ROCKSET_API_KEY,"
err_msg += " and drci api key passed via DRCI_BOT_KEY environment variables"
if os.getenv("GITHUB_TOKEN") is None or os.getenv("DRCI_BOT_KEY") is None:
if (
os.getenv("GITHUB_TOKEN") is None
or os.getenv("ROCKSET_API_KEY") is None
or os.getenv("DRCI_BOT_KEY") is None
):
err_msg = (
"Failed to update cached queries as GITHUB_TOKEN or DRCI_BOT_KEY "
"Failed to update cached queries as GITHUB_TOKEN or ROCKSET_API_KEY or DRCI_BOT_KEY "
+ "is not defined. "
+ err_msg
)
@ -103,6 +110,16 @@ def mocked_gh_graphql(query: str, **kwargs: Any) -> Any:
return mock_query(gh_graphql_wrapper, GQL_MOCKS, key_function, query, kwargs)
def mocked_rockset_results(head_sha: str, merge_base: str, num_retries: int = 3) -> Any:
return mock_query(
get_rockset_results,
ROCKSET_MOCKS,
lambda x, y: f"{x} {y}",
head_sha,
merge_base,
)
def mocked_drci_classifications(pr_num: int, project: str, num_retries: int = 3) -> Any:
return mock_query(
get_drci_classifications,
@ -256,6 +273,10 @@ def xla_merge_rules(repo: Any, org: str, project: str) -> List[MergeRule]:
]
def empty_rockset_results(head_sha: str, merge_base: str) -> List[Dict[str, Any]]:
return []
class DummyGitRepo(GitRepo):
def __init__(self) -> None:
super().__init__(get_git_repo_dir(), get_git_remote_name())
@ -267,6 +288,7 @@ class DummyGitRepo(GitRepo):
return "super awsome commit message"
@mock.patch("trymerge.get_rockset_results", side_effect=empty_rockset_results)
@mock.patch("trymerge.gh_graphql", side_effect=mocked_gh_graphql)
@mock.patch(
"trymerge.get_drci_classifications", side_effect=mocked_drci_classifications
@ -582,6 +604,7 @@ class TestTryMerge(TestCase):
mocked_gh_fetch_merge_base.assert_called_once()
@mock.patch("trymerge.get_rockset_results", side_effect=mocked_rockset_results)
@mock.patch("trymerge.gh_graphql", side_effect=mocked_gh_graphql)
@mock.patch("trymerge.gh_fetch_merge_base", return_value="")
@mock.patch(
@ -820,7 +843,7 @@ class TestBypassFailures(TestCase):
checks = pr.get_checkrun_conclusions()
# Known flaky failure takes precedence over ignore current (need to set the
# merge base here to get the results from Dr. CI, and that categorize the
# merge base here to get the results from Rockset, and that categorize the
# broken trunk failure too
checks = get_classifications(
pr.pr_num,
@ -906,6 +929,7 @@ class TestBypassFailures(TestCase):
)
@mock.patch("trymerge.get_rockset_results", side_effect=mocked_rockset_results)
@mock.patch("trymerge.gh_graphql", side_effect=mocked_gh_graphql)
@mock.patch("trymerge.gh_fetch_merge_base", return_value="")
@mock.patch("trymerge.get_drci_classifications", return_value={})
@ -984,6 +1008,7 @@ class TestBypassFailuresOnSandCastle(TestCase):
self.assertTrue(len(failed) == 2)
@mock.patch("trymerge.get_rockset_results", side_effect=mocked_rockset_results)
@mock.patch("trymerge.gh_graphql", side_effect=mocked_gh_graphql)
@mock.patch("trymerge.gh_fetch_merge_base", return_value="")
@mock.patch(

View File

@ -36,7 +36,6 @@ from warnings import warn
import yaml
from github_utils import (
gh_close_pr,
gh_fetch_json_list,
gh_fetch_merge_base,
gh_fetch_url,
@ -452,6 +451,8 @@ RE_DIFF_REV = re.compile(r"^Differential Revision:.+?(D[0-9]+)", re.MULTILINE)
CIFLOW_LABEL = re.compile(r"^ciflow/.+")
CIFLOW_TRUNK_LABEL = re.compile(r"^ciflow/trunk")
MERGE_RULE_PATH = Path(".github") / "merge_rules.yaml"
ROCKSET_MERGES_COLLECTION = "merges"
ROCKSET_MERGES_WORKSPACE = "commons"
REMOTE_MAIN_BRANCH = "origin/main"
DRCI_CHECKRUN_NAME = "Dr.CI"
INTERNAL_CHANGES_CHECKRUN_NAME = "Meta Internal-Only Changes Check"
@ -1173,12 +1174,12 @@ class GitHubPR:
for pr in additional_merged_prs:
pr.add_numbered_label(MERGE_COMPLETE_LABEL, dry_run)
# When the merge process reaches this part, we can assume that the commit
# has been successfully pushed to trunk
merge_commit_sha = repo.rev_parse(name=self.default_branch())
if comment_id and self.pr_num:
# Finally, upload the record to s3. The list of pending and failed
# When the merge process reaches this part, we can assume that the commit
# has been successfully pushed to trunk
merge_commit_sha = repo.rev_parse(name=REMOTE_MAIN_BRANCH)
# Finally, upload the record to Rockset. The list of pending and failed
# checks are at the time of the merge
save_merge_record(
comment_id=comment_id,
@ -1200,18 +1201,7 @@ class GitHubPR:
ignore_current=bool(ignore_current_checks),
)
else:
print("Missing comment ID or PR number, couldn't upload to s3")
# Usually Github will see that the commit has "resolves <pr_num>" in the
# commit message and close the PR, but sometimes it doesn't, leading to
# confusion. When it doesn't, we close it manually.
time.sleep(60) # Give Github some time to close the PR
manually_close_merged_pr(
pr=self,
additional_merged_prs=additional_merged_prs,
merge_commit_sha=merge_commit_sha,
dry_run=dry_run,
)
print("Missing comment ID or PR number, couldn't upload to Rockset")
def merge_changes(
self,
@ -1479,7 +1469,7 @@ def find_matching_merge_rule(
# Categorize all checks when skip_mandatory_checks (force merge) is set. Do it here
# where the list of checks is readily available. These records will be saved into
# s3 merge records
# Rockset merge records
(
pending_mandatory_checks,
failed_mandatory_checks,
@ -1513,34 +1503,6 @@ def checks_to_markdown_bullets(
]
def manually_close_merged_pr(
pr: GitHubPR,
additional_merged_prs: List[GitHubPR],
merge_commit_sha: str,
dry_run: bool,
) -> None:
def _comment_and_close(pr: GitHubPR, comment: str) -> None:
pr = GitHubPR(pr.org, pr.project, pr.pr_num) # Refresh the PR
if not pr.is_closed():
gh_post_pr_comment(pr.org, pr.project, pr.pr_num, comment, dry_run)
gh_close_pr(pr.org, pr.project, pr.pr_num, dry_run)
message = (
f"This PR (#{pr.pr_num}) was merged in {merge_commit_sha} but it is still open, likely due to a Github bug, "
"so mergebot is closing it manually. If you think this is a mistake, please feel free to reopen and contact Dev Infra."
)
_comment_and_close(pr, message)
for additional_pr in additional_merged_prs:
message = (
f"This PR (#{additional_pr.pr_num}) was merged as part of PR #{pr.pr_num} in the stack under {merge_commit_sha} "
"but it is still open, likely due to a Github bug, so mergebot is closing it manually. "
"If you think this is a mistake, please feel free to reopen and contact Dev Infra."
)
_comment_and_close(additional_pr, message)
print(f"PR {pr.pr_num} and all additional PRs in the stack have been closed.")
@retries_decorator()
def save_merge_record(
comment_id: int,
@ -1566,7 +1528,7 @@ def save_merge_record(
This saves the merge records as a json, which can later be uploaded to s3
"""
# Prepare the record to be written into s3
# Prepare the record to be written into Rockset
data = [
{
"comment_id": comment_id,
@ -1588,8 +1550,7 @@ def save_merge_record(
"ignore_current": ignore_current,
"error": error,
# This is a unique identifier for the record for deduping purposes
# in Rockset. Any unique string would work. This will not be used
# after we migrate off Rockset
# in rockset. Any unique string would work
"_id": f"{project}-{pr_num}-{comment_id}-{os.environ.get('GITHUB_RUN_ID')}",
}
]
@ -1599,6 +1560,36 @@ def save_merge_record(
json.dump(data, f)
@retries_decorator(rc=[])
def get_rockset_results(head_sha: str, merge_base: str) -> List[Dict[str, Any]]:
query = f"""
SELECT
w.name as workflow_name,
j.id,
j.name,
j.conclusion,
j.completed_at,
j.html_url,
j.head_sha,
j.torchci_classification.captures as failure_captures,
LENGTH(j.steps) as steps,
FROM
commons.workflow_job j join commons.workflow_run w on w.id = j.run_id
where
j.head_sha in ('{head_sha}','{merge_base}')
"""
try:
import rockset # type: ignore[import]
res = rockset.RocksetClient(
host="api.usw2a1.rockset.com", api_key=os.environ["ROCKSET_API_KEY"]
).sql(query)
return cast(List[Dict[str, Any]], res.results)
except ModuleNotFoundError:
print("Could not use RockSet as rocket dependency is missing")
return []
@retries_decorator()
def get_drci_classifications(pr_num: int, project: str = "pytorch") -> Any:
"""
@ -2036,7 +2027,7 @@ def categorize_checks(
pending_checks: List[Tuple[str, Optional[str], Optional[int]]] = []
failed_checks: List[Tuple[str, Optional[str], Optional[int]]] = []
# failed_checks_categorization is used to keep track of all ignorable failures when saving the merge record on s3
# failed_checks_categorization is used to keep track of all ignorable failures when saving the merge record on Rockset
failed_checks_categorization: Dict[str, List[Any]] = defaultdict(list)
# If required_checks is not set or empty, consider all names are relevant
@ -2095,7 +2086,7 @@ def categorize_checks(
):
failed_checks = failed_checks + flaky_or_broken_trunk
# The list of failed_checks_categorization is returned so that it can be saved into the s3 merge record
# The list of failed_checks_categorization is returned so that it can be saved into the Rockset merge record
return (pending_checks, failed_checks, failed_checks_categorization)
@ -2379,7 +2370,7 @@ def main() -> None:
handle_exception(e)
if args.comment_id and args.pr_num:
# Finally, upload the record to s3, we don't have access to the
# Finally, upload the record to Rockset, we don't have access to the
# list of pending and failed checks here, but they are not really
# needed at the moment
save_merge_record(
@ -2402,7 +2393,7 @@ def main() -> None:
error=str(e),
)
else:
print("Missing comment ID or PR number, couldn't upload to s3")
print("Missing comment ID or PR number, couldn't upload to Rockset")
finally:
if not args.check_mergeability:
gh_remove_label(

View File

@ -1,31 +0,0 @@
#!/usr/bin/env python3
import re
# Read the contents of runner_determinator.py
with open(".github/scripts/runner_determinator.py") as script_file:
script_content = script_file.read()
# Indent the script content by 10 spaces to match destination indentation
indented_script_content = "\n".join(
[" " * 10 + line if line else line for line in script_content.splitlines()]
)
# Read the contents of _runner-determinator.yml
with open(".github/workflows/_runner-determinator.yml") as yml_file:
yml_content = yml_file.read()
# Replace the content between the markers
new_yml_content = re.sub(
r"(cat <<EOF > runner_determinator.py\n)(.*?)(\n\s+EOF)",
lambda match: match.group(1) + indented_script_content + match.group(3),
yml_content,
flags=re.DOTALL,
)
# Save the modified content back to _runner-determinator.yml
with open(".github/workflows/_runner-determinator.yml", "w") as yml_file:
yml_file.write(new_yml_content)
print("Updated _runner-determinator.yml with the contents of runner_determinator.py")

View File

@ -1,6 +1,6 @@
{%- set upload_artifact_s3_action = "seemethere/upload-artifact-s3@v5" -%}
{%- set download_artifact_s3_action = "seemethere/download-artifact-s3@v4" -%}
{%- set upload_artifact_action = "actions/upload-artifact@v4.4.0" -%}
{%- set upload_artifact_action = "actions/upload-artifact@v3" -%}
{%- set download_artifact_action = "actions/download-artifact@v4.1.7" -%}
{%- set timeout_minutes = 240 -%}

View File

@ -101,7 +101,7 @@ jobs:
# shellcheck disable=SC1091
source "${RUNNER_TEMP}/anaconda/bin/activate"
"${PYTORCH_ROOT}/.circleci/scripts/binary_macos_build.sh"
- uses: actions/upload-artifact@v4.4.0
- uses: actions/upload-artifact@v3
if: always()
with:
name: !{{ config["build_name"] }}

View File

@ -45,7 +45,7 @@
{%- if is_windows %}
# This is a dummy value for libtorch to work correctly with our batch scripts
# without this value pip does not get installed for some reason
DESIRED_PYTHON: "3.9"
DESIRED_PYTHON: "3.8"
{%- endif %}
{%- else %}

View File

@ -91,14 +91,14 @@ jobs:
with:
docker-image: ${{ steps.calculate-docker-image.outputs.docker-image }}
- name: Check if in a container runner
- name: Check if in a ARC runner
shell: bash
id: check_container_runner
run: echo "IN_CONTAINER_RUNNER=$(if [ -f /.inarc ] || [ -f /.incontainer ]; then echo true ; else echo false; fi)" >> "$GITHUB_OUTPUT"
id: check_arc_runner
run: echo "IN_ARC_RUNNER=$([ -f /.inarc ] && echo true || echo false)" >> "$GITHUB_OUTPUT"
- name: Install nvidia driver, nvidia-docker runtime, set GPU_FLAG
uses: pytorch/test-infra/.github/actions/setup-nvidia@main
if: ${{ inputs.cuda-version != 'cpu' && steps.check_container_runner.outputs.IN_CONTAINER_RUNNER == 'false' }}
if: ${{ inputs.cuda-version != 'cpu' && steps.check_arc_runner.outputs.IN_ARC_RUNNER == 'false' }}
- name: Output disk space left
run: |

View File

@ -283,7 +283,7 @@ jobs:
# Ensure the working directory gets chowned back to the current user
docker run --rm -v "${RUNNER_TEMP}/artifacts:/v" -w /v "${ALPINE_IMAGE}" chown -R "$(id -u):$(id -g)" .
- uses: actions/upload-artifact@v4.4.0
- uses: actions/upload-artifact@v3
if: ${{ steps.filter.outputs.is-test-matrix-empty == 'False' }}
with:
name: ${{ inputs.build_name }}

View File

@ -109,7 +109,6 @@ jobs:
steps:
- name: Setup SSH (Click me for login details)
uses: pytorch/test-infra/.github/actions/setup-ssh@main
if: inputs.build-environment != 'linux-s390x-binary-manywheel'
with:
github-secret: ${{ secrets.GITHUB_TOKEN }}
@ -119,16 +118,13 @@ jobs:
# checkout. In other cases you should prefer a local checkout.
- name: Checkout PyTorch
uses: pytorch/pytorch/.github/actions/checkout-pytorch@main
with:
no-sudo: ${{ inputs.build-environment == 'linux-s390x-binary-manywheel' }}
- name: Setup Linux
uses: ./.github/actions/setup-linux
if: inputs.build-environment != 'linux-s390x-binary-manywheel'
- name: configure aws credentials
uses: aws-actions/configure-aws-credentials@v3
if: ${{ inputs.aws-role-to-assume != '' && inputs.build-environment != 'linux-s390x-binary-manywheel' }}
if: ${{ inputs.aws-role-to-assume != '' }}
with:
role-to-assume: ${{ inputs.aws-role-to-assume }}
role-session-name: gha-linux-build
@ -137,13 +133,11 @@ jobs:
- name: Calculate docker image
id: calculate-docker-image
uses: pytorch/test-infra/.github/actions/calculate-docker-image@main
if: inputs.build-environment != 'linux-s390x-binary-manywheel'
with:
docker-image-name: ${{ inputs.docker-image-name }}
- name: Use following to pull public copy of the image
id: print-ghcr-mirror
if: inputs.build-environment != 'linux-s390x-binary-manywheel'
env:
ECR_DOCKER_IMAGE: ${{ steps.calculate-docker-image.outputs.docker-image }}
shell: bash
@ -153,7 +147,6 @@ jobs:
- name: Pull docker image
uses: pytorch/test-infra/.github/actions/pull-docker-image@main
if: inputs.build-environment != 'linux-s390x-binary-manywheel'
with:
docker-image: ${{ steps.calculate-docker-image.outputs.docker-image }}
@ -181,7 +174,6 @@ jobs:
- name: Download pytest cache
uses: ./.github/actions/pytest-cache-download
continue-on-error: true
if: inputs.build-environment != 'linux-s390x-binary-manywheel'
with:
cache_dir: .pytest_cache
job_identifier: ${{ github.workflow }}_${{ inputs.build-environment }}
@ -203,7 +195,6 @@ jobs:
PR_LABELS: ${{ toJson(github.event.pull_request.labels.*.name) }}
TORCH_CUDA_ARCH_LIST: ${{ inputs.cuda-arch-list }}
DOCKER_IMAGE: ${{ steps.calculate-docker-image.outputs.docker-image }}
DOCKER_IMAGE_S390X: ${{ inputs.docker-image-name }}
XLA_CUDA: ${{ contains(inputs.build-environment, 'xla') && '0' || '' }}
DEBUG: ${{ inputs.build-with-debug && '1' || '0' }}
OUR_GITHUB_JOB_ID: ${{ steps.get-job-id.outputs.job-id }}
@ -211,21 +202,7 @@ jobs:
SCRIBE_GRAPHQL_ACCESS_TOKEN: ${{ secrets.SCRIBE_GRAPHQL_ACCESS_TOKEN }}
USE_SPLIT_BUILD: ${{ inputs.use_split_build }}
run: |
if [[ ${BUILD_ENVIRONMENT} == *"s390x"* ]]; then
JENKINS_USER=
USED_IMAGE="${DOCKER_IMAGE_S390X}"
# since some steps are skipped on s390x, if they are necessary, run them here
env | grep '^GITHUB' >> "/tmp/github_env_${GITHUB_RUN_ID}"
env | grep '^CI' >> "/tmp/github_env_${GITHUB_RUN_ID}"
else
JENKINS_USER="--user jenkins"
USED_IMAGE="${DOCKER_IMAGE}"
fi
# detached container should get cleaned up by teardown_ec2_linux
# Used for JENKINS_USER, which can be empty
# shellcheck disable=SC2086
container_name=$(docker run \
-e BUILD_ENVIRONMENT \
-e MAX_JOBS="$(nproc --ignore=2)" \
@ -248,10 +225,10 @@ jobs:
--cap-add=SYS_PTRACE \
--tty \
--detach \
${JENKINS_USER} \
--user jenkins \
-v "${GITHUB_WORKSPACE}:/var/lib/jenkins/workspace" \
-w /var/lib/jenkins/workspace \
"${USED_IMAGE}"
"${DOCKER_IMAGE}"
)
docker exec -t "${container_name}" sh -c '.ci/pytorch/build.sh'
@ -262,7 +239,7 @@ jobs:
- name: Store PyTorch Build Artifacts on S3
uses: seemethere/upload-artifact-s3@v5
if: inputs.build-generates-artifacts && steps.build.outcome != 'skipped' && !inputs.use_split_build && inputs.build-environment != 'linux-s390x-binary-manywheel'
if: inputs.build-generates-artifacts && steps.build.outcome != 'skipped' && !inputs.use_split_build
with:
name: ${{ inputs.build-environment }}
retention-days: 14
@ -272,7 +249,7 @@ jobs:
- name: Store PyTorch Build Artifacts on S3 for split build
uses: seemethere/upload-artifact-s3@v5
if: inputs.build-generates-artifacts && steps.build.outcome != 'skipped' && inputs.use_split_build && inputs.build-environment != 'linux-s390x-binary-manywheel'
if: inputs.build-generates-artifacts && steps.build.outcome != 'skipped' && inputs.use_split_build
with:
name: ${{ inputs.build-environment }}-experimental-split-build
retention-days: 14
@ -280,26 +257,8 @@ jobs:
path: artifacts.zip
s3-bucket: ${{ inputs.s3-bucket }}
- name: Store PyTorch Build Artifacts for s390x
uses: actions/upload-artifact@v3
if: inputs.build-generates-artifacts && steps.build.outcome != 'skipped' && !inputs.use_split_build && inputs.build-environment == 'linux-s390x-binary-manywheel'
with:
name: ${{ inputs.build-environment }}
retention-days: 14
if-no-files-found: error
path: artifacts.zip
- name: Store PyTorch Build Artifacts for s390x for split build
uses: actions/upload-artifact@v3
if: inputs.build-generates-artifacts && steps.build.outcome != 'skipped' && inputs.use_split_build && inputs.build-environment == 'linux-s390x-binary-manywheel'
with:
name: ${{ inputs.build-environment }}-experimental-split-build
retention-days: 14
if-no-files-found: error
path: artifacts.zip
- name: Upload sccache stats
if: steps.build.outcome != 'skipped' && inputs.build-environment != 'linux-s390x-binary-manywheel'
if: steps.build.outcome != 'skipped'
uses: seemethere/upload-artifact-s3@v5
with:
s3-prefix: |
@ -311,13 +270,4 @@ jobs:
- name: Teardown Linux
uses: pytorch/test-infra/.github/actions/teardown-linux@main
if: always() && inputs.build-environment != 'linux-s390x-binary-manywheel'
- name: Cleanup docker
if: always() && inputs.build-environment == 'linux-s390x-binary-manywheel'
shell: bash
run: |
# on s390x stop the container for clean worker stop
# ignore expansion of "docker ps -q" since it could be empty
# shellcheck disable=SC2046
docker stop $(docker ps -q) || true
if: always()

View File

@ -114,32 +114,22 @@ jobs:
with:
docker-image: ${{ steps.calculate-docker-image.outputs.docker-image }}
- name: Check if in a container runner
- name: Check if in a ARC runner
shell: bash
id: check_container_runner
run: echo "IN_CONTAINER_RUNNER=$(if [ -f /.inarc ] || [ -f /.incontainer ]; then echo true ; else echo false; fi)" >> "$GITHUB_OUTPUT"
id: check_arc_runner
run: echo "IN_ARC_RUNNER=$([ -f /.inarc ] && echo true || echo false)" >> "$GITHUB_OUTPUT"
- name: Install nvidia driver, nvidia-docker runtime, set GPU_FLAG
id: install-nvidia-driver
uses: pytorch/test-infra/.github/actions/setup-nvidia@main
if: ${{ contains(inputs.build-environment, 'cuda') && !contains(matrix.config, 'nogpu') && steps.check_container_runner.outputs.IN_CONTAINER_RUNNER == 'false' }}
- name: Setup GPU_FLAG for docker run
id: setup-gpu-flag
run: echo "GPU_FLAG=--gpus all -e NVIDIA_DRIVER_CAPABILITIES=all" >> "${GITHUB_ENV}"
if: ${{ contains(inputs.build-environment, 'cuda') && !contains(matrix.config, 'nogpu') && steps.check_container_runner.outputs.IN_CONTAINER_RUNNER == 'true' }}
- name: Setup SCCACHE_SERVER_PORT environment for docker run when on container
id: setup-sscache-port-flag
run: echo "SCCACHE_SERVER_PORT_DOCKER_FLAG=-e SCCACHE_SERVER_PORT=$((RUNNER_UID + 4226))" >> "${GITHUB_ENV}"
if: ${{ steps.check_container_runner.outputs.IN_CONTAINER_RUNNER == 'true' }}
if: ${{ contains(inputs.build-environment, 'cuda') && !contains(matrix.config, 'nogpu') && steps.check_arc_runner.outputs.IN_ARC_RUNNER == 'false' }}
- name: Lock NVIDIA A100 40GB Frequency
run: |
sudo nvidia-smi -pm 1
sudo nvidia-smi -ac 1215,1410
nvidia-smi
if: ${{ contains(matrix.runner, 'a100') && steps.check_container_runner.outputs.IN_CONTAINER_RUNNER == 'false' }}
if: contains(matrix.runner, 'a100')
- name: Start monitoring script
id: monitor-script
@ -218,7 +208,6 @@ jobs:
NO_TD: ${{ steps.keep-going.outputs.ci-no-td }}
TD_DISTRIBUTED: ${{ steps.keep-going.outputs.ci-td-distributed }}
SCCACHE_BUCKET: ossci-compiler-cache-circleci-v2
SCCACHE_REGION: us-east-1
SCCACHE_S3_KEY_PREFIX: ${{ github.workflow }}
SHM_SIZE: ${{ contains(inputs.build-environment, 'cuda') && '2g' || '1g' }}
DOCKER_IMAGE: ${{ inputs.docker-image }}
@ -229,7 +218,6 @@ jobs:
DASHBOARD_TAG: ${{ inputs.dashboard-tag }}
HUGGING_FACE_HUB_TOKEN: ${{ secrets.HUGGING_FACE_HUB_TOKEN }}
SCRIBE_GRAPHQL_ACCESS_TOKEN: ${{ secrets.SCRIBE_GRAPHQL_ACCESS_TOKEN }}
IS_A100_RUNNER: ${{ contains(matrix.runner, 'a100') && '1' || '0' }}
run: |
set -x
@ -248,7 +236,6 @@ jobs:
# shellcheck disable=SC2086,SC2090
container_name=$(docker run \
${GPU_FLAG:-} \
${SCCACHE_SERVER_PORT_DOCKER_FLAG:-} \
-e BUILD_ENVIRONMENT \
-e PR_NUMBER \
-e GITHUB_ACTIONS \
@ -278,7 +265,6 @@ jobs:
-e PR_LABELS \
-e MAX_JOBS="$(nproc --ignore=2)" \
-e SCCACHE_BUCKET \
-e SCCACHE_REGION \
-e SCCACHE_S3_KEY_PREFIX \
-e XLA_CUDA \
-e XLA_CLANG_CACHE_S3_BUCKET_NAME \
@ -288,7 +274,6 @@ jobs:
-e HUGGING_FACE_HUB_TOKEN \
-e SCRIBE_GRAPHQL_ACCESS_TOKEN \
-e DASHBOARD_TAG \
-e IS_A100_RUNNER \
--env-file="/tmp/github_env_${GITHUB_RUN_ID}" \
--security-opt seccomp=unconfined \
--cap-add=SYS_PTRACE \
@ -358,7 +343,7 @@ jobs:
- name: Teardown Linux
uses: pytorch/test-infra/.github/actions/teardown-linux@main
if: always() && steps.check_container_runner.outputs.IN_CONTAINER_RUNNER == 'false'
if: always()
# NB: We are currently having an intermittent GPU-related issue on G5 runners with
# A10G GPU. Once this happens, trying to reset the GPU as done in setup-nvidia does

View File

@ -88,13 +88,6 @@ jobs:
environment-file: .github/requirements/conda-env-${{ runner.os }}-${{ runner.arch }}
pip-requirements-file: .github/requirements/pip-requirements-${{ runner.os }}.txt
- name: Get workflow job id
id: get-job-id
uses: ./.github/actions/get-workflow-job-id
if: always()
with:
github-token: ${{ secrets.GITHUB_TOKEN }}
- name: Install PyTorch and run MPS tests
id: test
env:
@ -110,14 +103,6 @@ jobs:
NO_TEST_TIMEOUT: ${{ needs.filter.outputs.ci-no-test-timeout }}
NO_TD: ${{ needs.filter.outputs.ci-no-td }}
PIP_REQUIREMENTS_FILE: .github/requirements/pip-requirements-${{ runner.os }}.txt
GITHUB_REPOSITORY: ${{ github.repository }}
GITHUB_WORKFLOW: ${{ github.workflow }}
GITHUB_JOB: ${{ github.job }}
GITHUB_RUN_ID: ${{ github.run_id }}
GITHUB_RUN_NUMBER: ${{ github.run_number }}
GITHUB_RUN_ATTEMPT: ${{ github.run_attempt }}
JOB_ID: ${{ steps.get-job-id.outputs.job-id }}
JOB_NAME: ${{ steps.get-job-id.outputs.job-name }}
REENABLED_ISSUES: ${{ needs.filter.outputs.reenabled-issues }}
run: |
# shellcheck disable=SC1090
@ -159,6 +144,13 @@ jobs:
run: |
cat test/**/*_toprint.log || true
- name: Get workflow job id
id: get-job-id
uses: ./.github/actions/get-workflow-job-id
if: always()
with:
github-token: ${{ secrets.GITHUB_TOKEN }}
- name: Upload test artifacts
uses: ./.github/actions/upload-test-artifacts
if: always() && steps.test.conclusion && steps.test.conclusion != 'skipped'

View File

@ -59,104 +59,52 @@ jobs:
cat <<EOF > runner_determinator.py
# flake8: noqa: G004
# Note: Copies of this script in runner_determinator.py and _runner-determinator.yml
# must be kept in sync. You can do it easily by running the following command:
# python .github/scripts/update_runner_determinator.py
"""
This runner determinator is used to determine which set of runners to run a
GitHub job on. It uses the first comment of a GitHub issue (by default
https://github.com/pytorch/test-infra/issues/5132) to define the configuration
of which runners should be used to run which job.
The configuration has two parts, the settings and a list of opted-in users,
separated by a line containing "---". If the line is not present, the
settings are considered to be empty with only the second part, the user
list, defined.
The first part is a YAML block that defines the rollout settings. This can be
used to define any settings that are needed to determine which runners to use.
It's fields are defined by the RolloutSettings class below.
The second part is a list of users who are explicitly opted in to the LF fleet.
The user list is also a comma separated list of additional features or
experiments which the user could be opted in to.
https://github.com/pytorch/test-infra/issues/5132) as a user list to determine
which users will get their jobs to run on experimental runners. This user list
is also a comma separated list of additional features or experiments which the
user could be opted in to.
The user list has the following rules:
- Users are GitHub usernames, which must start with the @ prefix
- Users are GitHub usernames with the @ prefix
- If the first line is a "*" then all users will use the new runners
- If the first line is a "!" then all users will use the old runners
- Each user is also a comma-separated list of features/experiments to enable
- A "#" prefix opts the user out of all experiments
- A "#" prefix indicates the user is opted out of the new runners but is opting
into features/experiments.
Example config:
# A list of experiments that can be opted into.
# This defines the behavior they'll induce when opted into.
# Expected syntax is:
# [experiment_name]: # Name of the experiment. Also used for the label prefix.
# rollout_perc: [int] # % of workflows to run with this experiment when users are not opted in.
Example user list:
experiments:
lf:
rollout_percent: 25
---
# Opt-ins:
# Users can opt into the LF fleet by adding their GitHub username to this list
# and specifying experiments to enable in a comma-separated list.
# Experiments should be from the above list.
@User1,lf,split_build
@User2,lf
@User3,split_build
@User1
@User2,amz2023
#@UserOptOutOfNewRunner,amz2023
"""
import logging
import os
import random
from argparse import ArgumentParser
from logging import LogRecord
from typing import Any, Dict, Iterable, List, NamedTuple, Tuple
from typing import Any, Iterable
import yaml
from github import Auth, Github
from github.Issue import Issue
DEFAULT_LABEL_PREFIX = "" # use meta runners
WORKFLOW_LABEL_META = "" # use meta runners
WORKFLOW_LABEL_LF = "lf." # use runners from the linux foundation
WORKFLOW_LABEL_LF_CANARY = "lf.c." # use canary runners from the linux foundation
RUNNER_AMI_LEGACY = ""
RUNNER_AMI_AMZ2023 = "amz2023"
GITHUB_OUTPUT = os.getenv("GITHUB_OUTPUT", "")
GH_OUTPUT_KEY_AMI = "runner-ami"
GH_OUTPUT_KEY_LABEL_TYPE = "label-type"
SETTING_EXPERIMENTS = "experiments"
LF_FLEET_EXPERIMENT = "lf"
CANARY_FLEET_SUFFIX = ".c"
class Experiment(NamedTuple):
rollout_perc: float = (
0 # Percentage of workflows to experiment on when user is not opted-in.
)
all_branches: bool = (
False # If True, the experiment is also enabled on the exception branches
)
# Add more fields as needed
class Settings(NamedTuple):
"""
Settings for the experiments that can be opted into.
"""
experiments: Dict[str, Experiment] = {}
class ColorFormatter(logging.Formatter):
"""Color codes the log messages based on the log level"""
@ -278,194 +226,90 @@ jobs:
def is_exception_branch(branch: str) -> bool:
"""
Branches that get opted out of experiments by default, until they're explicitly enabled.
Branches that get opted out of all experiments and should always use Meta runners
"""
return branch.split("/")[0] in {"main", "nightly", "release", "landchecks"}
def load_yaml(yaml_text: str) -> Any:
def get_fleet(rollout_state: str, workflow_requestors: Iterable[str]) -> str:
"""
Determines if the job should run on the LF fleet or the Meta fleet
Returns:
The appropriate label prefix for the runner, corresponding to the fleet to use.
This gets prefixed to the very start of the runner label.
"""
try:
data = yaml.safe_load(yaml_text)
return data
except yaml.YAMLError as exc:
log.exception("Error loading YAML")
raise
def extract_settings_user_opt_in_from_text(rollout_state: str) -> Tuple[str, str]:
"""
Extracts the text with settings, if any, and the opted in users from the rollout state.
If the issue body contains "---" then the text above that is the settings
and the text below is the list of opted in users.
If it doesn't contain "---" then the settings are empty and the rest is the users.
"""
rollout_state_parts = rollout_state.split("---")
if len(rollout_state_parts) >= 2:
return rollout_state_parts[0], rollout_state_parts[1]
else:
return "", rollout_state
class UserOptins(Dict[str, List[str]]):
"""
Dictionary of users with a list of features they have opted into
"""
def parse_user_opt_in_from_text(user_optin_text: str) -> UserOptins:
"""
Parse the user opt-in text into a key value pair of username and the list of features they have opted into
Users are GitHub usernames with the @ prefix. Each user is also a comma-separated list of features/experiments to enable.
- Example line: "@User1,lf,split_build"
- A "#" prefix indicates the user is opted out of all experiments
"""
optins = UserOptins()
for user in user_optin_text.split("\n"):
user = user.strip("\r\n\t -")
if not user or not user.startswith("@"):
# Not a valid user. Skip
continue
if user:
usr_name = user.split(",")[0].strip("@")
optins[usr_name] = [exp.strip(" ") for exp in user.split(",")[1:]]
return optins
def parse_settings_from_text(settings_text: str) -> Settings:
"""
Parse the experiments from the issue body into a list of ExperimentSettings
"""
try:
if settings_text:
# Escape the backtick as well so that we can have the settings in a code block on the GH issue
# for easy reading
# Note: Using ascii for the backtick so that the cat step in _runner-determinator.yml doesn't choke on
# the backtick character in shell commands.
backtick = chr(96) # backtick character
settings_text = settings_text.strip(f"\r\n\t{backtick} ")
settings = load_yaml(settings_text)
# For now we just load experiments. We can expand this if/when we add more settings
experiments = {}
for exp_name, exp_settings in settings.get(SETTING_EXPERIMENTS).items():
valid_settings = {}
for setting in exp_settings:
if setting not in Experiment._fields:
log.warning(
f"Unexpected setting in experiment: {setting} = {exp_settings[setting]}"
)
else:
valid_settings[setting] = exp_settings[setting]
experiments[exp_name] = Experiment(**valid_settings)
return Settings(experiments)
except Exception:
log.exception("Failed to parse settings")
return Settings()
def parse_settings(rollout_state: str) -> Settings:
"""
Parse settings, if any, from the rollout state.
If the issue body contains "---" then the text above that is the settings
and the text below is the list of opted in users.
If it doesn't contain "---" then the settings are empty and the default values are used.
"""
settings_text, _ = extract_settings_user_opt_in_from_text(rollout_state)
return parse_settings_from_text(settings_text)
def parse_users(rollout_state: str) -> UserOptins:
"""
Parse users from the rollout state.
"""
_, users_text = extract_settings_user_opt_in_from_text(rollout_state)
return parse_user_opt_in_from_text(users_text)
def is_user_opted_in(user: str, user_optins: UserOptins, experiment_name: str) -> bool:
"""
Check if a user is opted into an experiment
"""
return experiment_name in user_optins.get(user, [])
def get_runner_prefix(
rollout_state: str,
workflow_requestors: Iterable[str],
branch: str,
is_canary: bool = False,
) -> str:
settings = parse_settings(rollout_state)
user_optins = parse_users(rollout_state)
fleet_prefix = ""
prefixes = []
for experiment_name, experiment_settings in settings.experiments.items():
enabled = False
if not experiment_settings.all_branches and is_exception_branch(branch):
log.info(
f"Branch {branch} is an exception branch. Not enabling experiment {experiment_name}."
)
continue
# Is any workflow_requestor opted in to this experiment?
opted_in_users = [
requestor
for requestor in workflow_requestors
if is_user_opted_in(requestor, user_optins, experiment_name)
]
if opted_in_users:
log.info(
f"{', '.join(opted_in_users)} have opted into experiment {experiment_name}."
)
enabled = True
elif experiment_settings.rollout_perc:
# If no user is opted in, then we randomly enable the experiment based on the rollout percentage
if random.uniform(0, 100) <= experiment_settings.rollout_perc:
if rollout_state[0] == "!":
log.info("LF Workflows are disabled for everyone. Using meta runners.")
return WORKFLOW_LABEL_META
elif rollout_state[0] == "*":
log.info("LF Workflows are enabled for everyone. Using LF runners.")
return WORKFLOW_LABEL_LF
else:
all_opted_in_users = {
usr_raw.strip("\n\t@ ").split(",")[0]
for usr_raw in rollout_state.split()
}
opted_in_requestors = {
usr for usr in workflow_requestors if usr in all_opted_in_users
}
if opted_in_requestors:
log.info(
f"Based on rollout percentage of {experiment_settings.rollout_perc}%, enabling experiment {experiment_name}."
f"LF Workflows are enabled for {', '.join(opted_in_requestors)}. Using LF runners."
)
enabled = True
if enabled:
label = experiment_name
if experiment_name == LF_FLEET_EXPERIMENT:
# We give some special treatment to the "lf" experiment since determines the fleet we use
# - If it's enabled, then we always list it's prefix first
# - If we're in the canary branch, then we append ".c" to the lf prefix
if is_canary:
label += CANARY_FLEET_SUFFIX
fleet_prefix = label
return WORKFLOW_LABEL_LF
else:
prefixes.append(label)
log.info(
f"LF Workflows are disabled for {', '.join(workflow_requestors)}. Using meta runners."
)
return WORKFLOW_LABEL_META
if len(prefixes) > 1:
except Exception as e:
log.error(
f"Only a fleet and one other experiment can be enabled for a job at any time. Enabling {prefixes[0]} and ignoring the rest, which are {', '.join(prefixes[1:])}"
f"Failed to get determine workflow type. Falling back to meta runners. Exception: {e}"
)
prefixes = prefixes[:1]
return WORKFLOW_LABEL_META
# Fleet always comes first
if fleet_prefix:
prefixes.insert(0, fleet_prefix)
return ".".join(prefixes) + "." if prefixes else ""
def get_optin_feature(
rollout_state: str, workflow_requestors: Iterable[str], feature: str, fallback: str
) -> str:
"""
Used to dynamically opt in jobs to specific runner-type variants.
Returns:
The runner-type's variant name if the user has opted in to the feature, otherwise returns an empty string.
This variant name is prefixed to the runner-type in the label.
"""
try:
userlist = {u.lstrip("#").strip("\n\t@ ") for u in rollout_state.split()}
all_opted_in_users = set()
for user in userlist:
for i in user.split(","):
if i == feature:
all_opted_in_users.add(user.split(",")[0])
opted_in_requestors = {
usr for usr in workflow_requestors if usr in all_opted_in_users
}
if opted_in_requestors:
log.info(
f"Feature {feature} is enabled for {', '.join(opted_in_requestors)}. Using feature {feature}."
)
return feature
else:
log.info(
f"Feature {feature} is disabled for {', '.join(workflow_requestors)}. Using fallback \"{fallback}\"."
)
return fallback
except Exception as e:
log.error(
f'Failed to determine if user has opted-in to feature {feature}. Using fallback "{fallback}". Exception: {e}'
)
return fallback
def get_rollout_state_from_issue(github_token: str, repo: str, issue_num: int) -> str:
@ -482,36 +326,53 @@ jobs:
def main() -> None:
args = parse_args()
runner_label_prefix = DEFAULT_LABEL_PREFIX
if args.github_ref_type == "branch" and is_exception_branch(args.github_branch):
log.info(f"Exception branch: '{args.github_branch}', using meta runners")
label_type = WORKFLOW_LABEL_META
runner_ami = RUNNER_AMI_LEGACY
else:
try:
rollout_state = get_rollout_state_from_issue(
args.github_token, args.github_issue_repo, args.github_issue
)
try:
rollout_state = get_rollout_state_from_issue(
args.github_token, args.github_issue_repo, args.github_issue
)
username = get_potential_pr_author(
args.github_token,
args.github_repo,
args.github_actor,
args.github_ref_type,
args.github_branch,
)
username = get_potential_pr_author(
args.github_token,
args.github_repo,
args.github_actor,
args.github_ref_type,
args.github_branch,
)
label_type = get_fleet(
rollout_state,
(
args.github_issue_owner,
username,
),
)
runner_ami = get_optin_feature(
rollout_state=rollout_state,
workflow_requestors=(
args.github_issue_owner,
username,
),
feature=RUNNER_AMI_AMZ2023,
fallback=RUNNER_AMI_LEGACY,
)
except Exception as e:
log.error(
f"Failed to get issue. Falling back to meta runners. Exception: {e}"
)
label_type = WORKFLOW_LABEL_META
runner_ami = RUNNER_AMI_LEGACY
is_canary = args.github_repo == "pytorch/pytorch-canary"
# For Canary builds use canary runners
if args.github_repo == "pytorch/pytorch-canary" and label_type == WORKFLOW_LABEL_LF:
label_type = WORKFLOW_LABEL_LF_CANARY
runner_label_prefix = get_runner_prefix(
rollout_state,
(args.github_issue_owner, username),
args.github_branch,
is_canary,
)
except Exception as e:
log.error(
f"Failed to get issue. Defaulting to Meta runners and no experiments. Exception: {e}"
)
set_github_output(GH_OUTPUT_KEY_LABEL_TYPE, runner_label_prefix)
set_github_output(GH_OUTPUT_KEY_LABEL_TYPE, label_type)
set_github_output(GH_OUTPUT_KEY_AMI, runner_ami)
if __name__ == "__main__":

View File

@ -11,16 +11,6 @@ on:
required: true
type: string
description: What CUDA version to build with, "cpu" for none.
use-xpu:
required: false
type: boolean
default: false
description: If set, build with XPU support.
vc-year:
required: false
type: string
default: "2019"
description: The Visual Studio year to use for building.
build-with-debug:
required: false
type: boolean
@ -151,7 +141,7 @@ jobs:
SCCACHE_REGION: us-east-1
VC_PRODUCT: "BuildTools"
VC_VERSION: ""
VC_YEAR: "${{ inputs.vc-year }}"
VC_YEAR: "2019"
ALPINE_IMAGE: "308535385114.dkr.ecr.us-east-1.amazonaws.com/tool/alpine"
AWS_DEFAULT_REGION: us-east-1
PR_NUMBER: ${{ github.event.pull_request.number }}
@ -159,7 +149,6 @@ jobs:
DEBUG: ${{ inputs.build-with-debug && '1' || '0' }}
TORCH_CUDA_ARCH_LIST: "8.6"
USE_CUDA: ${{ inputs.cuda-version != 'cpu' && '1' || '0' }}
USE_XPU: ${{ inputs.use-xpu == true && '1' || '0' }}
OUR_GITHUB_JOB_ID: ${{ steps.get-job-id.outputs.job-id }}
run: |
.ci/pytorch/win-build.sh

View File

@ -32,7 +32,7 @@ concurrency:
jobs:
build-docker:
environment: ${{ (github.ref == 'refs/heads/main' || startsWith(github.event.ref, 'refs/tags/v')) && 'docker-build' || '' }}
runs-on: linux.9xlarge.ephemeral
runs-on: am2.linux.9xlarge.ephemeral
strategy:
matrix:
cuda_version: ["11.8", "12.1", "12.4", "cpu"]

View File

@ -29,19 +29,9 @@ concurrency:
cancel-in-progress: true
jobs:
get-label-type:
name: get-label-type
uses: ./.github/workflows/_runner-determinator.yml
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 }}
build-docker-cuda:
environment: ${{ (github.ref == 'refs/heads/main' || startsWith(github.event.ref, 'refs/tags/v')) && 'docker-build' || '' }}
needs: get-label-type
runs-on: "${{ needs.get-label-type.outputs.label-type }}linux.9xlarge.ephemeral"
runs-on: linux.9xlarge.ephemeral
strategy:
matrix:
cuda_version: ["12.4", "12.1", "11.8"]
@ -76,8 +66,7 @@ jobs:
.ci/docker/libtorch/build.sh libtorch-cxx11-builder:cuda${{matrix.cuda_version}}
build-docker-rocm:
environment: ${{ (github.ref == 'refs/heads/main' || startsWith(github.event.ref, 'refs/tags/v')) && 'docker-build' || '' }}
needs: get-label-type
runs-on: "${{ needs.get-label-type.outputs.label-type }}linux.9xlarge.ephemeral"
runs-on: linux.9xlarge.ephemeral
strategy:
matrix:
rocm_version: ["6.1", "6.2"]
@ -112,8 +101,7 @@ jobs:
.ci/docker/libtorch/build.sh libtorch-cxx11-builder:rocm${{matrix.rocm_version}}
build-docker-cpu:
environment: ${{ (github.ref == 'refs/heads/main' || startsWith(github.event.ref, 'refs/tags/v')) && 'docker-build' || '' }}
needs: get-label-type
runs-on: "${{ needs.get-label-type.outputs.label-type }}linux.9xlarge.ephemeral"
runs-on: linux.9xlarge.ephemeral
steps:
- name: Checkout PyTorch
uses: pytorch/pytorch/.github/actions/checkout-pytorch@main

View File

@ -33,19 +33,9 @@ concurrency:
cancel-in-progress: true
jobs:
get-label-type:
name: get-label-type
uses: ./.github/workflows/_runner-determinator.yml
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 }}
build-docker-cuda:
environment: ${{ (github.ref == 'refs/heads/main' || startsWith(github.event.ref, 'refs/tags/v')) && 'docker-build' || '' }}
needs: get-label-type
runs-on: "${{ needs.get-label-type.outputs.label-type }}linux.9xlarge.ephemeral"
runs-on: am2.linux.9xlarge.ephemeral
strategy:
matrix:
cuda_version: ["12.4", "12.1", "11.8"]
@ -83,8 +73,7 @@ jobs:
# NOTE: manylinux_2_28 are still experimental, see https://github.com/pytorch/pytorch/issues/123649
build-docker-cuda-manylinux_2_28:
environment: ${{ (github.ref == 'refs/heads/main' || startsWith(github.event.ref, 'refs/tags/v')) && 'docker-build' || '' }}
needs: get-label-type
runs-on: "${{ needs.get-label-type.outputs.label-type }}linux.9xlarge.ephemeral"
runs-on: linux.9xlarge.ephemeral
strategy:
matrix:
cuda_version: ["12.4", "12.1", "11.8"]
@ -121,8 +110,7 @@ jobs:
.ci/docker/manywheel/build.sh manylinux2_28-builder:cuda${{matrix.cuda_version}}
build-docker-cuda-aarch64:
environment: ${{ (github.ref == 'refs/heads/main' || startsWith(github.event.ref, 'refs/tags/v')) && 'docker-build' || '' }}
needs: get-label-type
runs-on: "${{ needs.get-label-type.outputs.label-type }}linux.arm64.2xlarge.ephemeral"
runs-on: linux.arm64.2xlarge.ephemeral
strategy:
matrix:
cuda_version: ["12.4"]
@ -155,8 +143,7 @@ jobs:
.ci/docker/manywheel/build.sh manylinuxaarch64-builder:cuda${{matrix.cuda_version}}
build-docker-rocm:
environment: ${{ (github.ref == 'refs/heads/main' || startsWith(github.event.ref, 'refs/tags/v')) && 'docker-build' || '' }}
needs: get-label-type
runs-on: "${{ needs.get-label-type.outputs.label-type }}linux.9xlarge.ephemeral"
runs-on: am2.linux.9xlarge.ephemeral
strategy:
matrix:
rocm_version: ["6.1", "6.2"]
@ -191,8 +178,7 @@ jobs:
.ci/docker/manywheel/build.sh manylinux-builder:rocm${{matrix.rocm_version}}
build-docker-cpu:
environment: ${{ (github.ref == 'refs/heads/main' || startsWith(github.event.ref, 'refs/tags/v')) && 'docker-build' || '' }}
needs: get-label-type
runs-on: "${{ needs.get-label-type.outputs.label-type }}linux.9xlarge.ephemeral"
runs-on: am2.linux.9xlarge.ephemeral
steps:
- name: Checkout PyTorch
uses: pytorch/pytorch/.github/actions/checkout-pytorch@main
@ -221,8 +207,7 @@ jobs:
.ci/docker/manywheel/build.sh manylinux-builder:cpu
build-docker-cpu-manylinux_2_28:
environment: ${{ (github.ref == 'refs/heads/main' || startsWith(github.event.ref, 'refs/tags/v')) && 'docker-build' || '' }}
needs: get-label-type
runs-on: "${{ needs.get-label-type.outputs.label-type }}linux.9xlarge.ephemeral"
runs-on: linux.9xlarge.ephemeral
env:
GPU_ARCH_TYPE: cpu-manylinux_2_28
steps:
@ -253,8 +238,7 @@ jobs:
.ci/docker/manywheel/build.sh manylinux2_28-builder:cpu
build-docker-cpu-aarch64:
environment: ${{ (github.ref == 'refs/heads/main' || startsWith(github.event.ref, 'refs/tags/v')) && 'docker-build' || '' }}
needs: get-label-type
runs-on: "${{ needs.get-label-type.outputs.label-type }}linux.arm64.2xlarge.ephemeral"
runs-on: linux.arm64.2xlarge.ephemeral
env:
GPU_ARCH_TYPE: cpu-aarch64
steps:
@ -285,8 +269,7 @@ jobs:
.ci/docker/manywheel/build.sh manylinuxaarch64-builder:cpu-aarch64
build-docker-cpu-aarch64-2_28:
environment: ${{ (github.ref == 'refs/heads/main' || startsWith(github.event.ref, 'refs/tags/v')) && 'docker-build' || '' }}
needs: get-label-type
runs-on: "${{ needs.get-label-type.outputs.label-type }}linux.arm64.2xlarge.ephemeral"
runs-on: linux.arm64.2xlarge.ephemeral
env:
GPU_ARCH_TYPE: cpu-aarch64-2_28
steps:
@ -320,8 +303,7 @@ jobs:
.ci/docker/manywheel/build.sh manylinux2_28_aarch64-builder:cpu-aarch64
build-docker-cpu-cxx11-abi:
environment: ${{ (github.ref == 'refs/heads/main' || startsWith(github.event.ref, 'refs/tags/v')) && 'docker-build' || '' }}
needs: get-label-type
runs-on: "${{ needs.get-label-type.outputs.label-type }}linux.9xlarge.ephemeral"
runs-on: linux.9xlarge.ephemeral
env:
GPU_ARCH_TYPE: cpu-cxx11-abi
steps:
@ -352,8 +334,7 @@ jobs:
.ci/docker/manywheel/build.sh manylinuxcxx11-abi-builder:cpu-cxx11-abi
build-docker-xpu:
environment: ${{ (github.ref == 'refs/heads/main' || startsWith(github.event.ref, 'refs/tags/v')) && 'docker-build' || '' }}
needs: get-label-type
runs-on: "${{ needs.get-label-type.outputs.label-type }}linux.9xlarge.ephemeral"
runs-on: linux.9xlarge.ephemeral
env:
GPU_ARCH_TYPE: xpu
steps:

View File

@ -13,6 +13,7 @@ on:
- .github/scripts/build_triton_wheel.py
- .github/ci_commit_pins/triton.txt
- .ci/docker/ci_commit_pins/triton.txt
- .ci/docker/ci_commit_pins/triton-rocm.txt
- .ci/docker/ci_commit_pins/triton-xpu.txt
pull_request:
paths:
@ -20,6 +21,7 @@ on:
- .github/scripts/build_triton_wheel.py
- .github/ci_commit_pins/triton.txt
- .ci/docker/ci_commit_pins/triton.txt
- .ci/docker/ci_commit_pins/triton-rocm.txt
- .ci/docker/ci_commit_pins/triton-xpu.txt
concurrency:
@ -27,23 +29,13 @@ concurrency:
cancel-in-progress: true
jobs:
get-label-type:
name: get-label-type
uses: ./.github/workflows/_runner-determinator.yml
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: ${{ github.ref_type }}
build-wheel:
name: "Build Triton Wheel"
needs: get-label-type
runs-on: "${{ needs.get-label-type.outputs.label-type }}linux.4xlarge"
runs-on: [self-hosted, linux.2xlarge]
strategy:
fail-fast: false
matrix:
py_vers: [ "3.9", "3.10", "3.11", "3.12" ]
py_vers: [ "3.8", "3.9", "3.10", "3.11", "3.12" ]
device: ["cuda", "rocm", "xpu"]
include:
- device: "rocm"
@ -91,6 +83,9 @@ jobs:
# Determine python executable for given version
case $PY_VERS in
3.8)
PYTHON_EXECUTABLE=/opt/python/cp38-cp38/bin/python
;;
3.9)
PYTHON_EXECUTABLE=/opt/python/cp39-cp39/bin/python
;;
@ -125,7 +120,7 @@ jobs:
fi
docker exec -t "${container_name}" chown -R 1000.1000 /artifacts
- uses: actions/upload-artifact@v4.4.0
- uses: actions/upload-artifact@v3
with:
name: pytorch-triton-wheel-${{ matrix.py_vers }}-${{ matrix.device }}
if-no-files-found: error
@ -206,12 +201,11 @@ jobs:
build-conda:
name: "Build Triton Conda"
needs: get-label-type
runs-on: "${{ needs.get-label-type.outputs.label-type }}linux.2xlarge"
runs-on: [self-hosted, linux.2xlarge]
strategy:
fail-fast: false
matrix:
py_vers: [ "3.9", "3.10", "3.11", "3.12" ]
py_vers: [ "3.8", "3.9", "3.10", "3.11", "3.12" ]
timeout-minutes: 40
env:
DOCKER_IMAGE: pytorch/conda-builder:cpu
@ -259,7 +253,7 @@ jobs:
docker exec -t "${container_name}" python /pytorch/.github/scripts/build_triton_wheel.py --build-conda --py-version="${PY_VERS}" $RELEASE
docker exec -t "${container_name}" chown -R 1000.1000 /artifacts
- uses: actions/upload-artifact@v4.4.0
- uses: actions/upload-artifact@v3
with:
name: pytorch-triton-conda-${{ matrix.py_vers }}
if-no-files-found: error

View File

@ -30,9 +30,6 @@ concurrency:
jobs:
check-labels:
permissions:
contents: read
pull-requests: write
name: Check labels
if: github.repository_owner == 'pytorch'
runs-on: linux.20_04.4x

View File

@ -16,15 +16,6 @@ on:
paths: [.github/workflows/create_release.yml]
jobs:
get-label-type:
name: get-label-type
uses: ./.github/workflows/_runner-determinator.yml
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 }}
release:
if: ${{ github.repository == 'pytorch/pytorch' }}
name: Create Release
@ -72,7 +63,7 @@ jobs:
files: ${{env.PT_RELEASE_FILE}}
- name: Upload source distribution to GHA artifacts for release tags
if: ${{ github.event_name == 'push' && startsWith(github.ref, 'refs/tags/v') && contains(github.ref, 'rc') }}
uses: actions/upload-artifact@v4.4.0
uses: actions/upload-artifact@v2
with:
name: ${{ env.PT_RELEASE_FILE }}
path: ${{ env.PT_RELEASE_FILE }}
@ -82,14 +73,12 @@ jobs:
upload_source_code_to_s3:
if: ${{ github.repository == 'pytorch/pytorch' && github.event_name == 'push' && startsWith(github.ref, 'refs/tags/v') && contains(github.ref, 'rc') }}
runs-on: "${{ needs.get-label-type.outputs.label-type }}linux.2xlarge"
runs-on: linux.2xlarge
environment: sourcecode-upload
name: Upload source code to S3 for release tags
permissions:
id-token: write
needs:
- get-label-type
- release
needs: release
steps:
- uses: actions/download-artifact@v4.1.7
with:

View File

@ -30,18 +30,8 @@ env:
permissions: read-all
jobs:
get-label-type:
name: get-label-type
uses: ./.github/workflows/_runner-determinator.yml
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 }}
docker-build:
environment: ${{ (github.ref == 'refs/heads/main' || startsWith(github.event.ref, 'refs/tags/v')) && 'docker-build' || '' }}
needs: get-label-type
timeout-minutes: 240
strategy:
fail-fast: false
@ -67,7 +57,6 @@ jobs:
pytorch-linux-jammy-py3.12-halide,
pytorch-linux-jammy-xpu-2024.0-py3,
pytorch-linux-jammy-py3-clang15-asan,
pytorch-linux-jammy-py3-clang18-asan,
pytorch-linux-focal-py3-clang10-onnx,
pytorch-linux-focal-linter,
pytorch-linux-jammy-cuda11.8-cudnn9-py3.9-linter,
@ -79,9 +68,7 @@ jobs:
- docker-image-name: pytorch-linux-jammy-aarch64-py3.10-gcc11-inductor-benchmarks
runner: linux.arm64.m7g.4xlarge
timeout-minutes: 600
# Docker uploads fail from LF runners, see https://github.com/pytorch/pytorch/pull/137358
# runs-on: "${{ needs.get-label-type.outputs.label-type }}${{ matrix.runner }}"
runs-on: "${{ matrix.runner }}"
runs-on: [self-hosted, "${{ matrix.runner }}"]
env:
DOCKER_IMAGE_BASE: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/${{ matrix.docker-image-name }}
steps:

View File

@ -34,19 +34,9 @@ env:
permissions: read-all
jobs:
get-label-type:
name: get-label-type
uses: ./.github/workflows/_runner-determinator.yml
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: ${{ github.ref_type }}
generate-matrix:
if: github.repository_owner == 'pytorch'
needs: get-label-type
runs-on: "${{ needs.get-label-type.outputs.label-type }}linux.large"
runs-on: [self-hosted, linux.large]
outputs:
matrix: ${{ steps.generate-matrix.outputs.matrix }}
steps:
@ -64,12 +54,10 @@ jobs:
build:
if: ${{ github.repository == 'pytorch/pytorch' }}
runs-on: "${{ needs.get-label-type.outputs.label-type }}linux.2xlarge"
runs-on: [self-hosted, linux.2xlarge]
environment: ${{ (github.ref == 'refs/heads/nightly' || startsWith(github.event.ref, 'refs/tags/v')) && 'docker-build' || '' }}
timeout-minutes: 240
needs:
- generate-matrix
- get-label-type
needs: generate-matrix
strategy:
matrix: ${{ fromJson(needs.generate-matrix.outputs.matrix) }}
fail-fast: false

View File

@ -58,7 +58,6 @@ jobs:
DESIRED_CUDA: cpu
GPU_ARCH_TYPE: cpu-aarch64
DOCKER_IMAGE: pytorch/manylinuxaarch64-builder:cpu-aarch64-main
use_split_build: False
DESIRED_PYTHON: "3.9"
runs_on: linux.arm64.m7g.4xlarge.ephemeral
ALPINE_IMAGE: "arm64v8/alpine"
@ -82,7 +81,6 @@ jobs:
DESIRED_CUDA: cpu
GPU_ARCH_TYPE: cpu-aarch64
DOCKER_IMAGE: pytorch/manylinuxaarch64-builder:cpu-aarch64-main
use_split_build: False
DESIRED_PYTHON: "3.9"
build_name: manywheel-py3_9-cpu-aarch64
build_environment: linux-aarch64-binary-manywheel
@ -105,7 +103,6 @@ jobs:
DESIRED_CUDA: cpu
GPU_ARCH_TYPE: cpu-aarch64
DOCKER_IMAGE: pytorch/manylinuxaarch64-builder:cpu-aarch64-main
use_split_build: False
DESIRED_PYTHON: "3.9"
build_name: manywheel-py3_9-cpu-aarch64
secrets:
@ -128,7 +125,6 @@ jobs:
GPU_ARCH_TYPE: cuda-aarch64
DOCKER_IMAGE: pytorch/manylinuxaarch64-builder:cuda12.4-main
DESIRED_DEVTOOLSET: cxx11-abi
use_split_build: False
DESIRED_PYTHON: "3.9"
runs_on: linux.arm64.m7g.4xlarge.ephemeral
ALPINE_IMAGE: "arm64v8/alpine"
@ -153,7 +149,6 @@ jobs:
GPU_ARCH_TYPE: cuda-aarch64
DOCKER_IMAGE: pytorch/manylinuxaarch64-builder:cuda12.4-main
DESIRED_DEVTOOLSET: cxx11-abi
use_split_build: False
DESIRED_PYTHON: "3.9"
build_name: manywheel-py3_9-cuda-aarch64
secrets:
@ -175,7 +170,6 @@ jobs:
DESIRED_CUDA: cpu
GPU_ARCH_TYPE: cpu-aarch64
DOCKER_IMAGE: pytorch/manylinuxaarch64-builder:cpu-aarch64-main
use_split_build: False
DESIRED_PYTHON: "3.10"
runs_on: linux.arm64.m7g.4xlarge.ephemeral
ALPINE_IMAGE: "arm64v8/alpine"
@ -199,7 +193,6 @@ jobs:
DESIRED_CUDA: cpu
GPU_ARCH_TYPE: cpu-aarch64
DOCKER_IMAGE: pytorch/manylinuxaarch64-builder:cpu-aarch64-main
use_split_build: False
DESIRED_PYTHON: "3.10"
build_name: manywheel-py3_10-cpu-aarch64
build_environment: linux-aarch64-binary-manywheel
@ -222,7 +215,6 @@ jobs:
DESIRED_CUDA: cpu
GPU_ARCH_TYPE: cpu-aarch64
DOCKER_IMAGE: pytorch/manylinuxaarch64-builder:cpu-aarch64-main
use_split_build: False
DESIRED_PYTHON: "3.10"
build_name: manywheel-py3_10-cpu-aarch64
secrets:
@ -245,7 +237,6 @@ jobs:
GPU_ARCH_TYPE: cuda-aarch64
DOCKER_IMAGE: pytorch/manylinuxaarch64-builder:cuda12.4-main
DESIRED_DEVTOOLSET: cxx11-abi
use_split_build: False
DESIRED_PYTHON: "3.10"
runs_on: linux.arm64.m7g.4xlarge.ephemeral
ALPINE_IMAGE: "arm64v8/alpine"
@ -270,7 +261,6 @@ jobs:
GPU_ARCH_TYPE: cuda-aarch64
DOCKER_IMAGE: pytorch/manylinuxaarch64-builder:cuda12.4-main
DESIRED_DEVTOOLSET: cxx11-abi
use_split_build: False
DESIRED_PYTHON: "3.10"
build_name: manywheel-py3_10-cuda-aarch64
secrets:
@ -292,7 +282,6 @@ jobs:
DESIRED_CUDA: cpu
GPU_ARCH_TYPE: cpu-aarch64
DOCKER_IMAGE: pytorch/manylinuxaarch64-builder:cpu-aarch64-main
use_split_build: False
DESIRED_PYTHON: "3.11"
runs_on: linux.arm64.m7g.4xlarge.ephemeral
ALPINE_IMAGE: "arm64v8/alpine"
@ -316,7 +305,6 @@ jobs:
DESIRED_CUDA: cpu
GPU_ARCH_TYPE: cpu-aarch64
DOCKER_IMAGE: pytorch/manylinuxaarch64-builder:cpu-aarch64-main
use_split_build: False
DESIRED_PYTHON: "3.11"
build_name: manywheel-py3_11-cpu-aarch64
build_environment: linux-aarch64-binary-manywheel
@ -339,7 +327,6 @@ jobs:
DESIRED_CUDA: cpu
GPU_ARCH_TYPE: cpu-aarch64
DOCKER_IMAGE: pytorch/manylinuxaarch64-builder:cpu-aarch64-main
use_split_build: False
DESIRED_PYTHON: "3.11"
build_name: manywheel-py3_11-cpu-aarch64
secrets:
@ -362,7 +349,6 @@ jobs:
GPU_ARCH_TYPE: cuda-aarch64
DOCKER_IMAGE: pytorch/manylinuxaarch64-builder:cuda12.4-main
DESIRED_DEVTOOLSET: cxx11-abi
use_split_build: False
DESIRED_PYTHON: "3.11"
runs_on: linux.arm64.m7g.4xlarge.ephemeral
ALPINE_IMAGE: "arm64v8/alpine"
@ -387,7 +373,6 @@ jobs:
GPU_ARCH_TYPE: cuda-aarch64
DOCKER_IMAGE: pytorch/manylinuxaarch64-builder:cuda12.4-main
DESIRED_DEVTOOLSET: cxx11-abi
use_split_build: False
DESIRED_PYTHON: "3.11"
build_name: manywheel-py3_11-cuda-aarch64
secrets:
@ -409,7 +394,6 @@ jobs:
DESIRED_CUDA: cpu
GPU_ARCH_TYPE: cpu-aarch64
DOCKER_IMAGE: pytorch/manylinuxaarch64-builder:cpu-aarch64-main
use_split_build: False
DESIRED_PYTHON: "3.12"
runs_on: linux.arm64.m7g.4xlarge.ephemeral
ALPINE_IMAGE: "arm64v8/alpine"
@ -433,7 +417,6 @@ jobs:
DESIRED_CUDA: cpu
GPU_ARCH_TYPE: cpu-aarch64
DOCKER_IMAGE: pytorch/manylinuxaarch64-builder:cpu-aarch64-main
use_split_build: False
DESIRED_PYTHON: "3.12"
build_name: manywheel-py3_12-cpu-aarch64
build_environment: linux-aarch64-binary-manywheel
@ -456,7 +439,6 @@ jobs:
DESIRED_CUDA: cpu
GPU_ARCH_TYPE: cpu-aarch64
DOCKER_IMAGE: pytorch/manylinuxaarch64-builder:cpu-aarch64-main
use_split_build: False
DESIRED_PYTHON: "3.12"
build_name: manywheel-py3_12-cpu-aarch64
secrets:
@ -479,7 +461,6 @@ jobs:
GPU_ARCH_TYPE: cuda-aarch64
DOCKER_IMAGE: pytorch/manylinuxaarch64-builder:cuda12.4-main
DESIRED_DEVTOOLSET: cxx11-abi
use_split_build: False
DESIRED_PYTHON: "3.12"
runs_on: linux.arm64.m7g.4xlarge.ephemeral
ALPINE_IMAGE: "arm64v8/alpine"
@ -504,7 +485,6 @@ jobs:
GPU_ARCH_TYPE: cuda-aarch64
DOCKER_IMAGE: pytorch/manylinuxaarch64-builder:cuda12.4-main
DESIRED_DEVTOOLSET: cxx11-abi
use_split_build: False
DESIRED_PYTHON: "3.12"
build_name: manywheel-py3_12-cuda-aarch64
secrets:

View File

@ -54,7 +54,6 @@ jobs:
GPU_ARCH_VERSION: 11.8
GPU_ARCH_TYPE: cuda
DOCKER_IMAGE: pytorch/manylinux-builder:cuda11.8-main
use_split_build: False
DESIRED_PYTHON: "3.9"
runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
build_name: manywheel-py3_9-cuda11_8
@ -78,7 +77,6 @@ jobs:
GPU_ARCH_VERSION: 11.8
GPU_ARCH_TYPE: cuda
DOCKER_IMAGE: pytorch/manylinux-builder:cuda11.8-main
use_split_build: False
DESIRED_PYTHON: "3.9"
build_name: manywheel-py3_9-cuda11_8
build_environment: linux-binary-manywheel
@ -87,6 +85,53 @@ jobs:
secrets:
github-token: ${{ secrets.GITHUB_TOKEN }}
manywheel-py3_9-cuda11_8-split-build:
if: ${{ github.repository_owner == 'pytorch' }}
uses: ./.github/workflows/_binary-build-linux.yml
needs: get-label-type
with:
PYTORCH_ROOT: /pytorch
BUILDER_ROOT: /builder
PACKAGE_TYPE: manywheel
# TODO: This is a legacy variable that we eventually want to get rid of in
# favor of GPU_ARCH_VERSION
DESIRED_CUDA: cu118
GPU_ARCH_VERSION: 11.8
GPU_ARCH_TYPE: cuda
DOCKER_IMAGE: pytorch/manylinux-builder:cuda11.8-main
use_split_build: True
DESIRED_PYTHON: "3.9"
runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
build_name: manywheel-py3_9-cuda11_8-split
build_environment: linux-binary-manywheel
PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu11==11.8.89; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cuda-runtime-cu11==11.8.89; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cuda-cupti-cu11==11.8.87; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cudnn-cu11==9.1.0.70; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cublas-cu11==11.11.3.6; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cufft-cu11==10.9.0.58; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-curand-cu11==10.3.0.86; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cusolver-cu11==11.4.1.48; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cusparse-cu11==11.7.5.86; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-nccl-cu11==2.21.5; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-nvtx-cu11==11.8.86; platform_system == 'Linux' and platform_machine == 'x86_64'
secrets:
github-token: ${{ secrets.GITHUB_TOKEN }}
manywheel-py3_9-cuda11_8-split-test: # Testing
if: ${{ github.repository_owner == 'pytorch' }}
needs:
- manywheel-py3_9-cuda11_8-split-build
- get-label-type
uses: ./.github/workflows/_binary-test-linux.yml
with:
PYTORCH_ROOT: /pytorch
BUILDER_ROOT: /builder
PACKAGE_TYPE: manywheel
# TODO: This is a legacy variable that we eventually want to get rid of in
# favor of GPU_ARCH_VERSION
DESIRED_CUDA: cu118
GPU_ARCH_VERSION: 11.8
GPU_ARCH_TYPE: cuda
DOCKER_IMAGE: pytorch/manylinux-builder:cuda11.8-main
use_split_build: True
DESIRED_PYTHON: "3.9"
build_name: manywheel-py3_9-cuda11_8-split
build_environment: linux-binary-manywheel
runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
runs_on: linux.4xlarge.nvidia.gpu
secrets:
github-token: ${{ secrets.GITHUB_TOKEN }}
manywheel-py3_9-cuda12_1-build:
if: ${{ github.repository_owner == 'pytorch' }}
uses: ./.github/workflows/_binary-build-linux.yml
@ -101,7 +146,6 @@ jobs:
GPU_ARCH_VERSION: 12.1
GPU_ARCH_TYPE: cuda
DOCKER_IMAGE: pytorch/manylinux-builder:cuda12.1-main
use_split_build: False
DESIRED_PYTHON: "3.9"
runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
build_name: manywheel-py3_9-cuda12_1
@ -125,7 +169,6 @@ jobs:
GPU_ARCH_VERSION: 12.1
GPU_ARCH_TYPE: cuda
DOCKER_IMAGE: pytorch/manylinux-builder:cuda12.1-main
use_split_build: False
DESIRED_PYTHON: "3.9"
build_name: manywheel-py3_9-cuda12_1
build_environment: linux-binary-manywheel
@ -134,6 +177,53 @@ jobs:
secrets:
github-token: ${{ secrets.GITHUB_TOKEN }}
manywheel-py3_9-cuda12_1-split-build:
if: ${{ github.repository_owner == 'pytorch' }}
uses: ./.github/workflows/_binary-build-linux.yml
needs: get-label-type
with:
PYTORCH_ROOT: /pytorch
BUILDER_ROOT: /builder
PACKAGE_TYPE: manywheel
# TODO: This is a legacy variable that we eventually want to get rid of in
# favor of GPU_ARCH_VERSION
DESIRED_CUDA: cu121
GPU_ARCH_VERSION: 12.1
GPU_ARCH_TYPE: cuda
DOCKER_IMAGE: pytorch/manylinux-builder:cuda12.1-main
use_split_build: True
DESIRED_PYTHON: "3.9"
runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
build_name: manywheel-py3_9-cuda12_1-split
build_environment: linux-binary-manywheel
PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.1.105; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cuda-runtime-cu12==12.1.105; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cuda-cupti-cu12==12.1.105; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cudnn-cu12==9.1.0.70; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cublas-cu12==12.1.3.1; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cufft-cu12==11.0.2.54; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-curand-cu12==10.3.2.106; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cusolver-cu12==11.4.5.107; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cusparse-cu12==12.1.0.106; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-nccl-cu12==2.21.5; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-nvtx-cu12==12.1.105; platform_system == 'Linux' and platform_machine == 'x86_64'
secrets:
github-token: ${{ secrets.GITHUB_TOKEN }}
manywheel-py3_9-cuda12_1-split-test: # Testing
if: ${{ github.repository_owner == 'pytorch' }}
needs:
- manywheel-py3_9-cuda12_1-split-build
- get-label-type
uses: ./.github/workflows/_binary-test-linux.yml
with:
PYTORCH_ROOT: /pytorch
BUILDER_ROOT: /builder
PACKAGE_TYPE: manywheel
# TODO: This is a legacy variable that we eventually want to get rid of in
# favor of GPU_ARCH_VERSION
DESIRED_CUDA: cu121
GPU_ARCH_VERSION: 12.1
GPU_ARCH_TYPE: cuda
DOCKER_IMAGE: pytorch/manylinux-builder:cuda12.1-main
use_split_build: True
DESIRED_PYTHON: "3.9"
build_name: manywheel-py3_9-cuda12_1-split
build_environment: linux-binary-manywheel
runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
runs_on: linux.4xlarge.nvidia.gpu
secrets:
github-token: ${{ secrets.GITHUB_TOKEN }}
manywheel-py3_9-cuda12_4-build:
if: ${{ github.repository_owner == 'pytorch' }}
uses: ./.github/workflows/_binary-build-linux.yml
@ -148,7 +238,6 @@ jobs:
GPU_ARCH_VERSION: 12.4
GPU_ARCH_TYPE: cuda
DOCKER_IMAGE: pytorch/manylinux-builder:cuda12.4-main
use_split_build: False
DESIRED_PYTHON: "3.9"
runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
build_name: manywheel-py3_9-cuda12_4
@ -172,7 +261,6 @@ jobs:
GPU_ARCH_VERSION: 12.4
GPU_ARCH_TYPE: cuda
DOCKER_IMAGE: pytorch/manylinux-builder:cuda12.4-main
use_split_build: False
DESIRED_PYTHON: "3.9"
build_name: manywheel-py3_9-cuda12_4
build_environment: linux-binary-manywheel
@ -180,3 +268,50 @@ jobs:
runs_on: linux.4xlarge.nvidia.gpu
secrets:
github-token: ${{ secrets.GITHUB_TOKEN }}
manywheel-py3_9-cuda12_4-split-build:
if: ${{ github.repository_owner == 'pytorch' }}
uses: ./.github/workflows/_binary-build-linux.yml
needs: get-label-type
with:
PYTORCH_ROOT: /pytorch
BUILDER_ROOT: /builder
PACKAGE_TYPE: manywheel
# TODO: This is a legacy variable that we eventually want to get rid of in
# favor of GPU_ARCH_VERSION
DESIRED_CUDA: cu124
GPU_ARCH_VERSION: 12.4
GPU_ARCH_TYPE: cuda
DOCKER_IMAGE: pytorch/manylinux-builder:cuda12.4-main
use_split_build: True
DESIRED_PYTHON: "3.9"
runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
build_name: manywheel-py3_9-cuda12_4-split
build_environment: linux-binary-manywheel
PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.4.127; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cuda-runtime-cu12==12.4.127; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cuda-cupti-cu12==12.4.127; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cudnn-cu12==9.1.0.70; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cublas-cu12==12.4.5.8; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cufft-cu12==11.2.1.3; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-curand-cu12==10.3.5.147; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cusolver-cu12==11.6.1.9; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cusparse-cu12==12.3.1.170; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-nccl-cu12==2.21.5; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-nvtx-cu12==12.4.127; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-nvjitlink-cu12==12.4.127; platform_system == 'Linux' and platform_machine == 'x86_64'
secrets:
github-token: ${{ secrets.GITHUB_TOKEN }}
manywheel-py3_9-cuda12_4-split-test: # Testing
if: ${{ github.repository_owner == 'pytorch' }}
needs:
- manywheel-py3_9-cuda12_4-split-build
- get-label-type
uses: ./.github/workflows/_binary-test-linux.yml
with:
PYTORCH_ROOT: /pytorch
BUILDER_ROOT: /builder
PACKAGE_TYPE: manywheel
# TODO: This is a legacy variable that we eventually want to get rid of in
# favor of GPU_ARCH_VERSION
DESIRED_CUDA: cu124
GPU_ARCH_VERSION: 12.4
GPU_ARCH_TYPE: cuda
DOCKER_IMAGE: pytorch/manylinux-builder:cuda12.4-main
use_split_build: True
DESIRED_PYTHON: "3.9"
build_name: manywheel-py3_9-cuda12_4-split
build_environment: linux-binary-manywheel
runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
runs_on: linux.4xlarge.nvidia.gpu
secrets:
github-token: ${{ secrets.GITHUB_TOKEN }}

File diff suppressed because it is too large Load Diff

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@ -1,182 +0,0 @@
# @generated DO NOT EDIT MANUALLY
# Template is at: .github/templates/linux_binary_build_workflow.yml.j2
# Generation script: .github/scripts/generate_ci_workflows.py
name: linux-binary-manywheel-split
on:
push:
branches:
- main
tags:
- 'ciflow/periodic/*'
workflow_dispatch:
env:
# Needed for conda builds
ALPINE_IMAGE: "308535385114.dkr.ecr.us-east-1.amazonaws.com/tool/alpine"
ANACONDA_USER: pytorch
AWS_DEFAULT_REGION: us-east-1
BINARY_ENV_FILE: /tmp/env
BUILD_ENVIRONMENT: linux-binary-manywheel-split
BUILDER_ROOT: /builder
GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
PR_NUMBER: ${{ github.event.pull_request.number }}
PYTORCH_FINAL_PACKAGE_DIR: /artifacts
PYTORCH_ROOT: /pytorch
SHA1: ${{ github.event.pull_request.head.sha || github.sha }}
SKIP_ALL_TESTS: 0
concurrency:
group: linux-binary-manywheel-split-${{ github.event.pull_request.number || github.ref_name }}-${{ github.ref_type == 'branch' && github.sha }}-${{ github.event_name == 'workflow_dispatch' }}
cancel-in-progress: true
jobs:
get-label-type:
name: get-label-type
uses: ./.github/workflows/_runner-determinator.yml
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 }}
manywheel-py3_9-cuda11_8-build:
if: ${{ github.repository_owner == 'pytorch' }}
uses: ./.github/workflows/_binary-build-linux.yml
needs: get-label-type
with:
PYTORCH_ROOT: /pytorch
BUILDER_ROOT: /builder
PACKAGE_TYPE: manywheel
# TODO: This is a legacy variable that we eventually want to get rid of in
# favor of GPU_ARCH_VERSION
DESIRED_CUDA: cu118
GPU_ARCH_VERSION: 11.8
GPU_ARCH_TYPE: cuda
DOCKER_IMAGE: pytorch/manylinux-builder:cuda11.8-main
use_split_build: True
DESIRED_PYTHON: "3.9"
runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
build_name: manywheel-py3_9-cuda11_8
build_environment: linux-binary-manywheel-split
PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu11==11.8.89; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cuda-runtime-cu11==11.8.89; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cuda-cupti-cu11==11.8.87; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cudnn-cu11==9.1.0.70; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cublas-cu11==11.11.3.6; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cufft-cu11==10.9.0.58; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-curand-cu11==10.3.0.86; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cusolver-cu11==11.4.1.48; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cusparse-cu11==11.7.5.86; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-nccl-cu11==2.21.5; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-nvtx-cu11==11.8.86; platform_system == 'Linux' and platform_machine == 'x86_64'
secrets:
github-token: ${{ secrets.GITHUB_TOKEN }}
manywheel-py3_9-cuda11_8-test: # Testing
if: ${{ github.repository_owner == 'pytorch' }}
needs:
- manywheel-py3_9-cuda11_8-build
- get-label-type
uses: ./.github/workflows/_binary-test-linux.yml
with:
PYTORCH_ROOT: /pytorch
BUILDER_ROOT: /builder
PACKAGE_TYPE: manywheel
# TODO: This is a legacy variable that we eventually want to get rid of in
# favor of GPU_ARCH_VERSION
DESIRED_CUDA: cu118
GPU_ARCH_VERSION: 11.8
GPU_ARCH_TYPE: cuda
DOCKER_IMAGE: pytorch/manylinux-builder:cuda11.8-main
use_split_build: True
DESIRED_PYTHON: "3.9"
build_name: manywheel-py3_9-cuda11_8
build_environment: linux-binary-manywheel-split
runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
runs_on: linux.4xlarge.nvidia.gpu
secrets:
github-token: ${{ secrets.GITHUB_TOKEN }}
manywheel-py3_9-cuda12_1-build:
if: ${{ github.repository_owner == 'pytorch' }}
uses: ./.github/workflows/_binary-build-linux.yml
needs: get-label-type
with:
PYTORCH_ROOT: /pytorch
BUILDER_ROOT: /builder
PACKAGE_TYPE: manywheel
# TODO: This is a legacy variable that we eventually want to get rid of in
# favor of GPU_ARCH_VERSION
DESIRED_CUDA: cu121
GPU_ARCH_VERSION: 12.1
GPU_ARCH_TYPE: cuda
DOCKER_IMAGE: pytorch/manylinux-builder:cuda12.1-main
use_split_build: True
DESIRED_PYTHON: "3.9"
runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
build_name: manywheel-py3_9-cuda12_1
build_environment: linux-binary-manywheel-split
PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.1.105; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cuda-runtime-cu12==12.1.105; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cuda-cupti-cu12==12.1.105; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cudnn-cu12==9.1.0.70; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cublas-cu12==12.1.3.1; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cufft-cu12==11.0.2.54; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-curand-cu12==10.3.2.106; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cusolver-cu12==11.4.5.107; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cusparse-cu12==12.1.0.106; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-nccl-cu12==2.21.5; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-nvtx-cu12==12.1.105; platform_system == 'Linux' and platform_machine == 'x86_64'
secrets:
github-token: ${{ secrets.GITHUB_TOKEN }}
manywheel-py3_9-cuda12_1-test: # Testing
if: ${{ github.repository_owner == 'pytorch' }}
needs:
- manywheel-py3_9-cuda12_1-build
- get-label-type
uses: ./.github/workflows/_binary-test-linux.yml
with:
PYTORCH_ROOT: /pytorch
BUILDER_ROOT: /builder
PACKAGE_TYPE: manywheel
# TODO: This is a legacy variable that we eventually want to get rid of in
# favor of GPU_ARCH_VERSION
DESIRED_CUDA: cu121
GPU_ARCH_VERSION: 12.1
GPU_ARCH_TYPE: cuda
DOCKER_IMAGE: pytorch/manylinux-builder:cuda12.1-main
use_split_build: True
DESIRED_PYTHON: "3.9"
build_name: manywheel-py3_9-cuda12_1
build_environment: linux-binary-manywheel-split
runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
runs_on: linux.4xlarge.nvidia.gpu
secrets:
github-token: ${{ secrets.GITHUB_TOKEN }}
manywheel-py3_9-cuda12_4-build:
if: ${{ github.repository_owner == 'pytorch' }}
uses: ./.github/workflows/_binary-build-linux.yml
needs: get-label-type
with:
PYTORCH_ROOT: /pytorch
BUILDER_ROOT: /builder
PACKAGE_TYPE: manywheel
# TODO: This is a legacy variable that we eventually want to get rid of in
# favor of GPU_ARCH_VERSION
DESIRED_CUDA: cu124
GPU_ARCH_VERSION: 12.4
GPU_ARCH_TYPE: cuda
DOCKER_IMAGE: pytorch/manylinux-builder:cuda12.4-main
use_split_build: True
DESIRED_PYTHON: "3.9"
runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
build_name: manywheel-py3_9-cuda12_4
build_environment: linux-binary-manywheel-split
PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.4.127; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cuda-runtime-cu12==12.4.127; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cuda-cupti-cu12==12.4.127; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cudnn-cu12==9.1.0.70; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cublas-cu12==12.4.5.8; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cufft-cu12==11.2.1.3; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-curand-cu12==10.3.5.147; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cusolver-cu12==11.6.1.9; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cusparse-cu12==12.3.1.170; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-nccl-cu12==2.21.5; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-nvtx-cu12==12.4.127; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-nvjitlink-cu12==12.4.127; platform_system == 'Linux' and platform_machine == 'x86_64'
secrets:
github-token: ${{ secrets.GITHUB_TOKEN }}
manywheel-py3_9-cuda12_4-test: # Testing
if: ${{ github.repository_owner == 'pytorch' }}
needs:
- manywheel-py3_9-cuda12_4-build
- get-label-type
uses: ./.github/workflows/_binary-test-linux.yml
with:
PYTORCH_ROOT: /pytorch
BUILDER_ROOT: /builder
PACKAGE_TYPE: manywheel
# TODO: This is a legacy variable that we eventually want to get rid of in
# favor of GPU_ARCH_VERSION
DESIRED_CUDA: cu124
GPU_ARCH_VERSION: 12.4
GPU_ARCH_TYPE: cuda
DOCKER_IMAGE: pytorch/manylinux-builder:cuda12.4-main
use_split_build: True
DESIRED_PYTHON: "3.9"
build_name: manywheel-py3_9-cuda12_4
build_environment: linux-binary-manywheel-split
runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
runs_on: linux.4xlarge.nvidia.gpu
secrets:
github-token: ${{ secrets.GITHUB_TOKEN }}

File diff suppressed because it is too large Load Diff

View File

@ -58,7 +58,6 @@ jobs:
DESIRED_CUDA: cpu
GPU_ARCH_TYPE: cpu-s390x
DOCKER_IMAGE: pytorch/manylinuxs390x-builder:cpu-s390x-main
use_split_build: False
DESIRED_PYTHON: "3.9"
runs_on: linux.s390x
ALPINE_IMAGE: "docker.io/s390x/alpine"
@ -82,7 +81,6 @@ jobs:
DESIRED_CUDA: cpu
GPU_ARCH_TYPE: cpu-s390x
DOCKER_IMAGE: pytorch/manylinuxs390x-builder:cpu-s390x-main
use_split_build: False
DESIRED_PYTHON: "3.9"
build_name: manywheel-py3_9-cpu-s390x
build_environment: linux-s390x-binary-manywheel
@ -105,7 +103,6 @@ jobs:
DESIRED_CUDA: cpu
GPU_ARCH_TYPE: cpu-s390x
DOCKER_IMAGE: pytorch/manylinuxs390x-builder:cpu-s390x-main
use_split_build: False
DESIRED_PYTHON: "3.9"
build_name: manywheel-py3_9-cpu-s390x
secrets:
@ -127,7 +124,6 @@ jobs:
DESIRED_CUDA: cpu
GPU_ARCH_TYPE: cpu-s390x
DOCKER_IMAGE: pytorch/manylinuxs390x-builder:cpu-s390x-main
use_split_build: False
DESIRED_PYTHON: "3.10"
runs_on: linux.s390x
ALPINE_IMAGE: "docker.io/s390x/alpine"
@ -151,7 +147,6 @@ jobs:
DESIRED_CUDA: cpu
GPU_ARCH_TYPE: cpu-s390x
DOCKER_IMAGE: pytorch/manylinuxs390x-builder:cpu-s390x-main
use_split_build: False
DESIRED_PYTHON: "3.10"
build_name: manywheel-py3_10-cpu-s390x
build_environment: linux-s390x-binary-manywheel
@ -174,7 +169,6 @@ jobs:
DESIRED_CUDA: cpu
GPU_ARCH_TYPE: cpu-s390x
DOCKER_IMAGE: pytorch/manylinuxs390x-builder:cpu-s390x-main
use_split_build: False
DESIRED_PYTHON: "3.10"
build_name: manywheel-py3_10-cpu-s390x
secrets:
@ -196,7 +190,6 @@ jobs:
DESIRED_CUDA: cpu
GPU_ARCH_TYPE: cpu-s390x
DOCKER_IMAGE: pytorch/manylinuxs390x-builder:cpu-s390x-main
use_split_build: False
DESIRED_PYTHON: "3.11"
runs_on: linux.s390x
ALPINE_IMAGE: "docker.io/s390x/alpine"
@ -220,7 +213,6 @@ jobs:
DESIRED_CUDA: cpu
GPU_ARCH_TYPE: cpu-s390x
DOCKER_IMAGE: pytorch/manylinuxs390x-builder:cpu-s390x-main
use_split_build: False
DESIRED_PYTHON: "3.11"
build_name: manywheel-py3_11-cpu-s390x
build_environment: linux-s390x-binary-manywheel
@ -243,7 +235,6 @@ jobs:
DESIRED_CUDA: cpu
GPU_ARCH_TYPE: cpu-s390x
DOCKER_IMAGE: pytorch/manylinuxs390x-builder:cpu-s390x-main
use_split_build: False
DESIRED_PYTHON: "3.11"
build_name: manywheel-py3_11-cpu-s390x
secrets:
@ -265,7 +256,6 @@ jobs:
DESIRED_CUDA: cpu
GPU_ARCH_TYPE: cpu-s390x
DOCKER_IMAGE: pytorch/manylinuxs390x-builder:cpu-s390x-main
use_split_build: False
DESIRED_PYTHON: "3.12"
runs_on: linux.s390x
ALPINE_IMAGE: "docker.io/s390x/alpine"
@ -289,7 +279,6 @@ jobs:
DESIRED_CUDA: cpu
GPU_ARCH_TYPE: cpu-s390x
DOCKER_IMAGE: pytorch/manylinuxs390x-builder:cpu-s390x-main
use_split_build: False
DESIRED_PYTHON: "3.12"
build_name: manywheel-py3_12-cpu-s390x
build_environment: linux-s390x-binary-manywheel
@ -312,7 +301,6 @@ jobs:
DESIRED_CUDA: cpu
GPU_ARCH_TYPE: cpu-s390x
DOCKER_IMAGE: pytorch/manylinuxs390x-builder:cpu-s390x-main
use_split_build: False
DESIRED_PYTHON: "3.12"
build_name: manywheel-py3_12-cpu-s390x
secrets:
@ -334,7 +322,6 @@ jobs:
DESIRED_CUDA: cpu
GPU_ARCH_TYPE: cpu-s390x
DOCKER_IMAGE: pytorch/manylinuxs390x-builder:cpu-s390x-main
use_split_build: False
DESIRED_PYTHON: "3.13"
runs_on: linux.s390x
ALPINE_IMAGE: "docker.io/s390x/alpine"
@ -358,7 +345,6 @@ jobs:
DESIRED_CUDA: cpu
GPU_ARCH_TYPE: cpu-s390x
DOCKER_IMAGE: pytorch/manylinuxs390x-builder:cpu-s390x-main
use_split_build: False
DESIRED_PYTHON: "3.13"
build_name: manywheel-py3_13-cpu-s390x
build_environment: linux-s390x-binary-manywheel
@ -381,7 +367,6 @@ jobs:
DESIRED_CUDA: cpu
GPU_ARCH_TYPE: cpu-s390x
DOCKER_IMAGE: pytorch/manylinuxs390x-builder:cpu-s390x-main
use_split_build: False
DESIRED_PYTHON: "3.13"
build_name: manywheel-py3_13-cpu-s390x
secrets:

View File

@ -117,7 +117,7 @@ jobs:
# shellcheck disable=SC1091
source "${RUNNER_TEMP}/anaconda/bin/activate"
"${PYTORCH_ROOT}/.circleci/scripts/binary_macos_build.sh"
- uses: actions/upload-artifact@v4.4.0
- uses: actions/upload-artifact@v3
if: always()
with:
name: conda-py3_9-cpu
@ -232,7 +232,7 @@ jobs:
# shellcheck disable=SC1091
source "${RUNNER_TEMP}/anaconda/bin/activate"
"${PYTORCH_ROOT}/.circleci/scripts/binary_macos_build.sh"
- uses: actions/upload-artifact@v4.4.0
- uses: actions/upload-artifact@v3
if: always()
with:
name: conda-py3_10-cpu
@ -347,7 +347,7 @@ jobs:
# shellcheck disable=SC1091
source "${RUNNER_TEMP}/anaconda/bin/activate"
"${PYTORCH_ROOT}/.circleci/scripts/binary_macos_build.sh"
- uses: actions/upload-artifact@v4.4.0
- uses: actions/upload-artifact@v3
if: always()
with:
name: conda-py3_11-cpu
@ -462,7 +462,7 @@ jobs:
# shellcheck disable=SC1091
source "${RUNNER_TEMP}/anaconda/bin/activate"
"${PYTORCH_ROOT}/.circleci/scripts/binary_macos_build.sh"
- uses: actions/upload-artifact@v4.4.0
- uses: actions/upload-artifact@v3
if: always()
with:
name: conda-py3_12-cpu

View File

@ -49,7 +49,7 @@ jobs:
DESIRED_DEVTOOLSET: cxx11-abi
# This is a dummy value for libtorch to work correctly with our batch scripts
# without this value pip does not get installed for some reason
DESIRED_PYTHON: "3.9"
DESIRED_PYTHON: "3.8"
steps:
# NOTE: These environment variables are put here so that they can be applied on every job equally
# They are also here because setting them at a workflow level doesn't give us access to the
@ -121,7 +121,7 @@ jobs:
# shellcheck disable=SC1091
source "${RUNNER_TEMP}/anaconda/bin/activate"
"${PYTORCH_ROOT}/.circleci/scripts/binary_macos_build.sh"
- uses: actions/upload-artifact@v4.4.0
- uses: actions/upload-artifact@v3
if: always()
with:
name: libtorch-cpu-shared-with-deps-cxx11-abi

View File

@ -118,7 +118,7 @@ jobs:
# shellcheck disable=SC1091
source "${RUNNER_TEMP}/anaconda/bin/activate"
"${PYTORCH_ROOT}/.circleci/scripts/binary_macos_build.sh"
- uses: actions/upload-artifact@v4.4.0
- uses: actions/upload-artifact@v3
if: always()
with:
name: wheel-py3_9-cpu
@ -234,7 +234,7 @@ jobs:
# shellcheck disable=SC1091
source "${RUNNER_TEMP}/anaconda/bin/activate"
"${PYTORCH_ROOT}/.circleci/scripts/binary_macos_build.sh"
- uses: actions/upload-artifact@v4.4.0
- uses: actions/upload-artifact@v3
if: always()
with:
name: wheel-py3_10-cpu
@ -350,7 +350,7 @@ jobs:
# shellcheck disable=SC1091
source "${RUNNER_TEMP}/anaconda/bin/activate"
"${PYTORCH_ROOT}/.circleci/scripts/binary_macos_build.sh"
- uses: actions/upload-artifact@v4.4.0
- uses: actions/upload-artifact@v3
if: always()
with:
name: wheel-py3_11-cpu
@ -466,7 +466,7 @@ jobs:
# shellcheck disable=SC1091
source "${RUNNER_TEMP}/anaconda/bin/activate"
"${PYTORCH_ROOT}/.circleci/scripts/binary_macos_build.sh"
- uses: actions/upload-artifact@v4.4.0
- uses: actions/upload-artifact@v3
if: always()
with:
name: wheel-py3_12-cpu

View File

@ -132,7 +132,7 @@ jobs:
shell: bash
run: |
"${PYTORCH_ROOT}/.circleci/scripts/binary_windows_build.sh"
- uses: actions/upload-artifact@v4.4.0
- uses: actions/upload-artifact@v3
if: always()
with:
name: conda-py3_9-cpu
@ -378,7 +378,7 @@ jobs:
shell: bash
run: |
"${PYTORCH_ROOT}/.circleci/scripts/binary_windows_build.sh"
- uses: actions/upload-artifact@v4.4.0
- uses: actions/upload-artifact@v3
if: always()
with:
name: conda-py3_9-cuda11_8
@ -626,7 +626,7 @@ jobs:
shell: bash
run: |
"${PYTORCH_ROOT}/.circleci/scripts/binary_windows_build.sh"
- uses: actions/upload-artifact@v4.4.0
- uses: actions/upload-artifact@v3
if: always()
with:
name: conda-py3_9-cuda12_1
@ -874,7 +874,7 @@ jobs:
shell: bash
run: |
"${PYTORCH_ROOT}/.circleci/scripts/binary_windows_build.sh"
- uses: actions/upload-artifact@v4.4.0
- uses: actions/upload-artifact@v3
if: always()
with:
name: conda-py3_9-cuda12_4
@ -1121,7 +1121,7 @@ jobs:
shell: bash
run: |
"${PYTORCH_ROOT}/.circleci/scripts/binary_windows_build.sh"
- uses: actions/upload-artifact@v4.4.0
- uses: actions/upload-artifact@v3
if: always()
with:
name: conda-py3_10-cpu
@ -1367,7 +1367,7 @@ jobs:
shell: bash
run: |
"${PYTORCH_ROOT}/.circleci/scripts/binary_windows_build.sh"
- uses: actions/upload-artifact@v4.4.0
- uses: actions/upload-artifact@v3
if: always()
with:
name: conda-py3_10-cuda11_8
@ -1615,7 +1615,7 @@ jobs:
shell: bash
run: |
"${PYTORCH_ROOT}/.circleci/scripts/binary_windows_build.sh"
- uses: actions/upload-artifact@v4.4.0
- uses: actions/upload-artifact@v3
if: always()
with:
name: conda-py3_10-cuda12_1
@ -1863,7 +1863,7 @@ jobs:
shell: bash
run: |
"${PYTORCH_ROOT}/.circleci/scripts/binary_windows_build.sh"
- uses: actions/upload-artifact@v4.4.0
- uses: actions/upload-artifact@v3
if: always()
with:
name: conda-py3_10-cuda12_4
@ -2110,7 +2110,7 @@ jobs:
shell: bash
run: |
"${PYTORCH_ROOT}/.circleci/scripts/binary_windows_build.sh"
- uses: actions/upload-artifact@v4.4.0
- uses: actions/upload-artifact@v3
if: always()
with:
name: conda-py3_11-cpu
@ -2356,7 +2356,7 @@ jobs:
shell: bash
run: |
"${PYTORCH_ROOT}/.circleci/scripts/binary_windows_build.sh"
- uses: actions/upload-artifact@v4.4.0
- uses: actions/upload-artifact@v3
if: always()
with:
name: conda-py3_11-cuda11_8
@ -2604,7 +2604,7 @@ jobs:
shell: bash
run: |
"${PYTORCH_ROOT}/.circleci/scripts/binary_windows_build.sh"
- uses: actions/upload-artifact@v4.4.0
- uses: actions/upload-artifact@v3
if: always()
with:
name: conda-py3_11-cuda12_1
@ -2852,7 +2852,7 @@ jobs:
shell: bash
run: |
"${PYTORCH_ROOT}/.circleci/scripts/binary_windows_build.sh"
- uses: actions/upload-artifact@v4.4.0
- uses: actions/upload-artifact@v3
if: always()
with:
name: conda-py3_11-cuda12_4
@ -3099,7 +3099,7 @@ jobs:
shell: bash
run: |
"${PYTORCH_ROOT}/.circleci/scripts/binary_windows_build.sh"
- uses: actions/upload-artifact@v4.4.0
- uses: actions/upload-artifact@v3
if: always()
with:
name: conda-py3_12-cpu
@ -3345,7 +3345,7 @@ jobs:
shell: bash
run: |
"${PYTORCH_ROOT}/.circleci/scripts/binary_windows_build.sh"
- uses: actions/upload-artifact@v4.4.0
- uses: actions/upload-artifact@v3
if: always()
with:
name: conda-py3_12-cuda11_8
@ -3593,7 +3593,7 @@ jobs:
shell: bash
run: |
"${PYTORCH_ROOT}/.circleci/scripts/binary_windows_build.sh"
- uses: actions/upload-artifact@v4.4.0
- uses: actions/upload-artifact@v3
if: always()
with:
name: conda-py3_12-cuda12_1
@ -3841,7 +3841,7 @@ jobs:
shell: bash
run: |
"${PYTORCH_ROOT}/.circleci/scripts/binary_windows_build.sh"
- uses: actions/upload-artifact@v4.4.0
- uses: actions/upload-artifact@v3
if: always()
with:
name: conda-py3_12-cuda12_4

View File

@ -51,7 +51,7 @@ jobs:
LIBTORCH_VARIANT: shared-with-deps
# This is a dummy value for libtorch to work correctly with our batch scripts
# without this value pip does not get installed for some reason
DESIRED_PYTHON: "3.9"
DESIRED_PYTHON: "3.8"
steps:
- name: Display EC2 information
shell: bash
@ -129,7 +129,7 @@ jobs:
shell: bash
run: |
"${PYTORCH_ROOT}/.circleci/scripts/binary_windows_build.sh"
- uses: actions/upload-artifact@v4.4.0
- uses: actions/upload-artifact@v3
if: always()
with:
name: libtorch-cpu-shared-with-deps-debug
@ -169,7 +169,7 @@ jobs:
LIBTORCH_VARIANT: shared-with-deps
# This is a dummy value for libtorch to work correctly with our batch scripts
# without this value pip does not get installed for some reason
DESIRED_PYTHON: "3.9"
DESIRED_PYTHON: "3.8"
steps:
- name: Display EC2 information
shell: bash

View File

@ -58,7 +58,7 @@ jobs:
LIBTORCH_VARIANT: shared-with-deps
# This is a dummy value for libtorch to work correctly with our batch scripts
# without this value pip does not get installed for some reason
DESIRED_PYTHON: "3.9"
DESIRED_PYTHON: "3.8"
steps:
- name: Display EC2 information
shell: bash
@ -136,7 +136,7 @@ jobs:
shell: bash
run: |
"${PYTORCH_ROOT}/.circleci/scripts/binary_windows_build.sh"
- uses: actions/upload-artifact@v4.4.0
- uses: actions/upload-artifact@v3
if: always()
with:
name: libtorch-cpu-shared-with-deps-debug
@ -176,7 +176,7 @@ jobs:
LIBTORCH_VARIANT: shared-with-deps
# This is a dummy value for libtorch to work correctly with our batch scripts
# without this value pip does not get installed for some reason
DESIRED_PYTHON: "3.9"
DESIRED_PYTHON: "3.8"
steps:
- name: Display EC2 information
shell: bash
@ -290,7 +290,7 @@ jobs:
LIBTORCH_VARIANT: shared-with-deps
# This is a dummy value for libtorch to work correctly with our batch scripts
# without this value pip does not get installed for some reason
DESIRED_PYTHON: "3.9"
DESIRED_PYTHON: "3.8"
build_name: libtorch-cpu-shared-with-deps-debug
secrets:
github-token: ${{ secrets.GITHUB_TOKEN }}
@ -316,7 +316,7 @@ jobs:
LIBTORCH_VARIANT: shared-with-deps
# This is a dummy value for libtorch to work correctly with our batch scripts
# without this value pip does not get installed for some reason
DESIRED_PYTHON: "3.9"
DESIRED_PYTHON: "3.8"
steps:
- name: Display EC2 information
shell: bash
@ -394,7 +394,7 @@ jobs:
shell: bash
run: |
"${PYTORCH_ROOT}/.circleci/scripts/binary_windows_build.sh"
- uses: actions/upload-artifact@v4.4.0
- uses: actions/upload-artifact@v3
if: always()
with:
name: libtorch-cuda11_8-shared-with-deps-debug
@ -435,7 +435,7 @@ jobs:
LIBTORCH_VARIANT: shared-with-deps
# This is a dummy value for libtorch to work correctly with our batch scripts
# without this value pip does not get installed for some reason
DESIRED_PYTHON: "3.9"
DESIRED_PYTHON: "3.8"
steps:
- name: Display EC2 information
shell: bash
@ -550,7 +550,7 @@ jobs:
LIBTORCH_VARIANT: shared-with-deps
# This is a dummy value for libtorch to work correctly with our batch scripts
# without this value pip does not get installed for some reason
DESIRED_PYTHON: "3.9"
DESIRED_PYTHON: "3.8"
build_name: libtorch-cuda11_8-shared-with-deps-debug
secrets:
github-token: ${{ secrets.GITHUB_TOKEN }}
@ -576,7 +576,7 @@ jobs:
LIBTORCH_VARIANT: shared-with-deps
# This is a dummy value for libtorch to work correctly with our batch scripts
# without this value pip does not get installed for some reason
DESIRED_PYTHON: "3.9"
DESIRED_PYTHON: "3.8"
steps:
- name: Display EC2 information
shell: bash
@ -654,7 +654,7 @@ jobs:
shell: bash
run: |
"${PYTORCH_ROOT}/.circleci/scripts/binary_windows_build.sh"
- uses: actions/upload-artifact@v4.4.0
- uses: actions/upload-artifact@v3
if: always()
with:
name: libtorch-cuda12_1-shared-with-deps-debug
@ -695,7 +695,7 @@ jobs:
LIBTORCH_VARIANT: shared-with-deps
# This is a dummy value for libtorch to work correctly with our batch scripts
# without this value pip does not get installed for some reason
DESIRED_PYTHON: "3.9"
DESIRED_PYTHON: "3.8"
steps:
- name: Display EC2 information
shell: bash
@ -810,7 +810,7 @@ jobs:
LIBTORCH_VARIANT: shared-with-deps
# This is a dummy value for libtorch to work correctly with our batch scripts
# without this value pip does not get installed for some reason
DESIRED_PYTHON: "3.9"
DESIRED_PYTHON: "3.8"
build_name: libtorch-cuda12_1-shared-with-deps-debug
secrets:
github-token: ${{ secrets.GITHUB_TOKEN }}
@ -836,7 +836,7 @@ jobs:
LIBTORCH_VARIANT: shared-with-deps
# This is a dummy value for libtorch to work correctly with our batch scripts
# without this value pip does not get installed for some reason
DESIRED_PYTHON: "3.9"
DESIRED_PYTHON: "3.8"
steps:
- name: Display EC2 information
shell: bash
@ -914,7 +914,7 @@ jobs:
shell: bash
run: |
"${PYTORCH_ROOT}/.circleci/scripts/binary_windows_build.sh"
- uses: actions/upload-artifact@v4.4.0
- uses: actions/upload-artifact@v3
if: always()
with:
name: libtorch-cuda12_4-shared-with-deps-debug
@ -955,7 +955,7 @@ jobs:
LIBTORCH_VARIANT: shared-with-deps
# This is a dummy value for libtorch to work correctly with our batch scripts
# without this value pip does not get installed for some reason
DESIRED_PYTHON: "3.9"
DESIRED_PYTHON: "3.8"
steps:
- name: Display EC2 information
shell: bash
@ -1070,7 +1070,7 @@ jobs:
LIBTORCH_VARIANT: shared-with-deps
# This is a dummy value for libtorch to work correctly with our batch scripts
# without this value pip does not get installed for some reason
DESIRED_PYTHON: "3.9"
DESIRED_PYTHON: "3.8"
build_name: libtorch-cuda12_4-shared-with-deps-debug
secrets:
github-token: ${{ secrets.GITHUB_TOKEN }}

View File

@ -51,7 +51,7 @@ jobs:
LIBTORCH_VARIANT: shared-with-deps
# This is a dummy value for libtorch to work correctly with our batch scripts
# without this value pip does not get installed for some reason
DESIRED_PYTHON: "3.9"
DESIRED_PYTHON: "3.8"
steps:
- name: Display EC2 information
shell: bash
@ -129,7 +129,7 @@ jobs:
shell: bash
run: |
"${PYTORCH_ROOT}/.circleci/scripts/binary_windows_build.sh"
- uses: actions/upload-artifact@v4.4.0
- uses: actions/upload-artifact@v3
if: always()
with:
name: libtorch-cpu-shared-with-deps-release
@ -169,7 +169,7 @@ jobs:
LIBTORCH_VARIANT: shared-with-deps
# This is a dummy value for libtorch to work correctly with our batch scripts
# without this value pip does not get installed for some reason
DESIRED_PYTHON: "3.9"
DESIRED_PYTHON: "3.8"
steps:
- name: Display EC2 information
shell: bash

View File

@ -58,7 +58,7 @@ jobs:
LIBTORCH_VARIANT: shared-with-deps
# This is a dummy value for libtorch to work correctly with our batch scripts
# without this value pip does not get installed for some reason
DESIRED_PYTHON: "3.9"
DESIRED_PYTHON: "3.8"
steps:
- name: Display EC2 information
shell: bash
@ -136,7 +136,7 @@ jobs:
shell: bash
run: |
"${PYTORCH_ROOT}/.circleci/scripts/binary_windows_build.sh"
- uses: actions/upload-artifact@v4.4.0
- uses: actions/upload-artifact@v3
if: always()
with:
name: libtorch-cpu-shared-with-deps-release
@ -176,7 +176,7 @@ jobs:
LIBTORCH_VARIANT: shared-with-deps
# This is a dummy value for libtorch to work correctly with our batch scripts
# without this value pip does not get installed for some reason
DESIRED_PYTHON: "3.9"
DESIRED_PYTHON: "3.8"
steps:
- name: Display EC2 information
shell: bash
@ -290,7 +290,7 @@ jobs:
LIBTORCH_VARIANT: shared-with-deps
# This is a dummy value for libtorch to work correctly with our batch scripts
# without this value pip does not get installed for some reason
DESIRED_PYTHON: "3.9"
DESIRED_PYTHON: "3.8"
build_name: libtorch-cpu-shared-with-deps-release
secrets:
github-token: ${{ secrets.GITHUB_TOKEN }}
@ -316,7 +316,7 @@ jobs:
LIBTORCH_VARIANT: shared-with-deps
# This is a dummy value for libtorch to work correctly with our batch scripts
# without this value pip does not get installed for some reason
DESIRED_PYTHON: "3.9"
DESIRED_PYTHON: "3.8"
steps:
- name: Display EC2 information
shell: bash
@ -394,7 +394,7 @@ jobs:
shell: bash
run: |
"${PYTORCH_ROOT}/.circleci/scripts/binary_windows_build.sh"
- uses: actions/upload-artifact@v4.4.0
- uses: actions/upload-artifact@v3
if: always()
with:
name: libtorch-cuda11_8-shared-with-deps-release
@ -435,7 +435,7 @@ jobs:
LIBTORCH_VARIANT: shared-with-deps
# This is a dummy value for libtorch to work correctly with our batch scripts
# without this value pip does not get installed for some reason
DESIRED_PYTHON: "3.9"
DESIRED_PYTHON: "3.8"
steps:
- name: Display EC2 information
shell: bash
@ -550,7 +550,7 @@ jobs:
LIBTORCH_VARIANT: shared-with-deps
# This is a dummy value for libtorch to work correctly with our batch scripts
# without this value pip does not get installed for some reason
DESIRED_PYTHON: "3.9"
DESIRED_PYTHON: "3.8"
build_name: libtorch-cuda11_8-shared-with-deps-release
secrets:
github-token: ${{ secrets.GITHUB_TOKEN }}
@ -576,7 +576,7 @@ jobs:
LIBTORCH_VARIANT: shared-with-deps
# This is a dummy value for libtorch to work correctly with our batch scripts
# without this value pip does not get installed for some reason
DESIRED_PYTHON: "3.9"
DESIRED_PYTHON: "3.8"
steps:
- name: Display EC2 information
shell: bash
@ -654,7 +654,7 @@ jobs:
shell: bash
run: |
"${PYTORCH_ROOT}/.circleci/scripts/binary_windows_build.sh"
- uses: actions/upload-artifact@v4.4.0
- uses: actions/upload-artifact@v3
if: always()
with:
name: libtorch-cuda12_1-shared-with-deps-release
@ -695,7 +695,7 @@ jobs:
LIBTORCH_VARIANT: shared-with-deps
# This is a dummy value for libtorch to work correctly with our batch scripts
# without this value pip does not get installed for some reason
DESIRED_PYTHON: "3.9"
DESIRED_PYTHON: "3.8"
steps:
- name: Display EC2 information
shell: bash
@ -810,7 +810,7 @@ jobs:
LIBTORCH_VARIANT: shared-with-deps
# This is a dummy value for libtorch to work correctly with our batch scripts
# without this value pip does not get installed for some reason
DESIRED_PYTHON: "3.9"
DESIRED_PYTHON: "3.8"
build_name: libtorch-cuda12_1-shared-with-deps-release
secrets:
github-token: ${{ secrets.GITHUB_TOKEN }}
@ -836,7 +836,7 @@ jobs:
LIBTORCH_VARIANT: shared-with-deps
# This is a dummy value for libtorch to work correctly with our batch scripts
# without this value pip does not get installed for some reason
DESIRED_PYTHON: "3.9"
DESIRED_PYTHON: "3.8"
steps:
- name: Display EC2 information
shell: bash
@ -914,7 +914,7 @@ jobs:
shell: bash
run: |
"${PYTORCH_ROOT}/.circleci/scripts/binary_windows_build.sh"
- uses: actions/upload-artifact@v4.4.0
- uses: actions/upload-artifact@v3
if: always()
with:
name: libtorch-cuda12_4-shared-with-deps-release
@ -955,7 +955,7 @@ jobs:
LIBTORCH_VARIANT: shared-with-deps
# This is a dummy value for libtorch to work correctly with our batch scripts
# without this value pip does not get installed for some reason
DESIRED_PYTHON: "3.9"
DESIRED_PYTHON: "3.8"
steps:
- name: Display EC2 information
shell: bash
@ -1070,7 +1070,7 @@ jobs:
LIBTORCH_VARIANT: shared-with-deps
# This is a dummy value for libtorch to work correctly with our batch scripts
# without this value pip does not get installed for some reason
DESIRED_PYTHON: "3.9"
DESIRED_PYTHON: "3.8"
build_name: libtorch-cuda12_4-shared-with-deps-release
secrets:
github-token: ${{ secrets.GITHUB_TOKEN }}

View File

@ -133,7 +133,7 @@ jobs:
shell: bash
run: |
"${PYTORCH_ROOT}/.circleci/scripts/binary_windows_build.sh"
- uses: actions/upload-artifact@v4.4.0
- uses: actions/upload-artifact@v3
if: always()
with:
name: wheel-py3_9-cpu
@ -380,7 +380,7 @@ jobs:
shell: bash
run: |
"${PYTORCH_ROOT}/.circleci/scripts/binary_windows_build.sh"
- uses: actions/upload-artifact@v4.4.0
- uses: actions/upload-artifact@v3
if: always()
with:
name: wheel-py3_9-cuda11_8
@ -629,7 +629,7 @@ jobs:
shell: bash
run: |
"${PYTORCH_ROOT}/.circleci/scripts/binary_windows_build.sh"
- uses: actions/upload-artifact@v4.4.0
- uses: actions/upload-artifact@v3
if: always()
with:
name: wheel-py3_9-cuda12_1
@ -878,7 +878,7 @@ jobs:
shell: bash
run: |
"${PYTORCH_ROOT}/.circleci/scripts/binary_windows_build.sh"
- uses: actions/upload-artifact@v4.4.0
- uses: actions/upload-artifact@v3
if: always()
with:
name: wheel-py3_9-cuda12_4
@ -1125,7 +1125,7 @@ jobs:
shell: bash
run: |
"${PYTORCH_ROOT}/.circleci/scripts/binary_windows_build.sh"
- uses: actions/upload-artifact@v4.4.0
- uses: actions/upload-artifact@v3
if: always()
with:
name: wheel-py3_9-xpu
@ -1371,7 +1371,7 @@ jobs:
shell: bash
run: |
"${PYTORCH_ROOT}/.circleci/scripts/binary_windows_build.sh"
- uses: actions/upload-artifact@v4.4.0
- uses: actions/upload-artifact@v3
if: always()
with:
name: wheel-py3_10-cpu
@ -1618,7 +1618,7 @@ jobs:
shell: bash
run: |
"${PYTORCH_ROOT}/.circleci/scripts/binary_windows_build.sh"
- uses: actions/upload-artifact@v4.4.0
- uses: actions/upload-artifact@v3
if: always()
with:
name: wheel-py3_10-cuda11_8
@ -1867,7 +1867,7 @@ jobs:
shell: bash
run: |
"${PYTORCH_ROOT}/.circleci/scripts/binary_windows_build.sh"
- uses: actions/upload-artifact@v4.4.0
- uses: actions/upload-artifact@v3
if: always()
with:
name: wheel-py3_10-cuda12_1
@ -2116,7 +2116,7 @@ jobs:
shell: bash
run: |
"${PYTORCH_ROOT}/.circleci/scripts/binary_windows_build.sh"
- uses: actions/upload-artifact@v4.4.0
- uses: actions/upload-artifact@v3
if: always()
with:
name: wheel-py3_10-cuda12_4
@ -2363,7 +2363,7 @@ jobs:
shell: bash
run: |
"${PYTORCH_ROOT}/.circleci/scripts/binary_windows_build.sh"
- uses: actions/upload-artifact@v4.4.0
- uses: actions/upload-artifact@v3
if: always()
with:
name: wheel-py3_10-xpu
@ -2609,7 +2609,7 @@ jobs:
shell: bash
run: |
"${PYTORCH_ROOT}/.circleci/scripts/binary_windows_build.sh"
- uses: actions/upload-artifact@v4.4.0
- uses: actions/upload-artifact@v3
if: always()
with:
name: wheel-py3_11-cpu
@ -2856,7 +2856,7 @@ jobs:
shell: bash
run: |
"${PYTORCH_ROOT}/.circleci/scripts/binary_windows_build.sh"
- uses: actions/upload-artifact@v4.4.0
- uses: actions/upload-artifact@v3
if: always()
with:
name: wheel-py3_11-cuda11_8
@ -3105,7 +3105,7 @@ jobs:
shell: bash
run: |
"${PYTORCH_ROOT}/.circleci/scripts/binary_windows_build.sh"
- uses: actions/upload-artifact@v4.4.0
- uses: actions/upload-artifact@v3
if: always()
with:
name: wheel-py3_11-cuda12_1
@ -3354,7 +3354,7 @@ jobs:
shell: bash
run: |
"${PYTORCH_ROOT}/.circleci/scripts/binary_windows_build.sh"
- uses: actions/upload-artifact@v4.4.0
- uses: actions/upload-artifact@v3
if: always()
with:
name: wheel-py3_11-cuda12_4
@ -3601,7 +3601,7 @@ jobs:
shell: bash
run: |
"${PYTORCH_ROOT}/.circleci/scripts/binary_windows_build.sh"
- uses: actions/upload-artifact@v4.4.0
- uses: actions/upload-artifact@v3
if: always()
with:
name: wheel-py3_11-xpu
@ -3847,7 +3847,7 @@ jobs:
shell: bash
run: |
"${PYTORCH_ROOT}/.circleci/scripts/binary_windows_build.sh"
- uses: actions/upload-artifact@v4.4.0
- uses: actions/upload-artifact@v3
if: always()
with:
name: wheel-py3_12-cpu
@ -4094,7 +4094,7 @@ jobs:
shell: bash
run: |
"${PYTORCH_ROOT}/.circleci/scripts/binary_windows_build.sh"
- uses: actions/upload-artifact@v4.4.0
- uses: actions/upload-artifact@v3
if: always()
with:
name: wheel-py3_12-cuda11_8
@ -4343,7 +4343,7 @@ jobs:
shell: bash
run: |
"${PYTORCH_ROOT}/.circleci/scripts/binary_windows_build.sh"
- uses: actions/upload-artifact@v4.4.0
- uses: actions/upload-artifact@v3
if: always()
with:
name: wheel-py3_12-cuda12_1
@ -4592,7 +4592,7 @@ jobs:
shell: bash
run: |
"${PYTORCH_ROOT}/.circleci/scripts/binary_windows_build.sh"
- uses: actions/upload-artifact@v4.4.0
- uses: actions/upload-artifact@v3
if: always()
with:
name: wheel-py3_12-cuda12_4
@ -4839,7 +4839,7 @@ jobs:
shell: bash
run: |
"${PYTORCH_ROOT}/.circleci/scripts/binary_windows_build.sh"
- uses: actions/upload-artifact@v4.4.0
- uses: actions/upload-artifact@v3
if: always()
with:
name: wheel-py3_12-xpu

View File

@ -18,22 +18,11 @@ concurrency:
permissions: read-all
jobs:
get-label-type:
name: get-label-type
uses: ./.github/workflows/_runner-determinator.yml
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: ${{ github.ref_type }}
linux-focal-cuda12_4-py3_10-gcc9-inductor-build:
# Should be synced with the one in inductor.yml, but this doesn't run inductor_timm
name: cuda12.4-py3.10-gcc9-sm86
uses: ./.github/workflows/_linux-build.yml
needs: get-label-type
with:
runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
sync-tag: linux-focal-cuda12_4-py3_10-gcc9-inductor-build
build-environment: linux-focal-cuda12.4-py3.10-gcc9-sm86
docker-image-name: pytorch-linux-focal-cuda12.4-cudnn9-py3-gcc9-inductor-benchmarks

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