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Author SHA1 Message Date
4a45e48a3c [aoti] Add cpp loader
ghstack-source-id: ccb800e2667132afdd1ab6f2b974be635f581e24
Pull Request resolved: https://github.com/pytorch/pytorch/pull/134865
2024-08-30 14:26:09 -07:00
2477 changed files with 58839 additions and 106739 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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@ -236,7 +236,7 @@ case "$image" in
TRITON=yes
;;
pytorch-linux-focal-py3-clang10-onnx)
ANACONDA_PYTHON_VERSION=3.9
ANACONDA_PYTHON_VERSION=3.8
CLANG_VERSION=10
PROTOBUF=yes
DB=yes
@ -245,7 +245,7 @@ case "$image" in
ONNX=yes
;;
pytorch-linux-focal-py3-clang9-android-ndk-r21e)
ANACONDA_PYTHON_VERSION=3.9
ANACONDA_PYTHON_VERSION=3.8
CLANG_VERSION=9
LLVMDEV=yes
PROTOBUF=yes
@ -254,8 +254,8 @@ case "$image" in
GRADLE_VERSION=6.8.3
NINJA_VERSION=1.9.0
;;
pytorch-linux-focal-py3.9-clang10)
ANACONDA_PYTHON_VERSION=3.9
pytorch-linux-focal-py3.8-clang10)
ANACONDA_PYTHON_VERSION=3.8
CLANG_VERSION=10
PROTOBUF=yes
DB=yes
@ -276,8 +276,8 @@ case "$image" in
CONDA_CMAKE=yes
TRITON=yes
;;
pytorch-linux-focal-py3.9-gcc9)
ANACONDA_PYTHON_VERSION=3.9
pytorch-linux-focal-py3.8-gcc9)
ANACONDA_PYTHON_VERSION=3.8
GCC_VERSION=9
PROTOBUF=yes
DB=yes
@ -286,23 +286,23 @@ case "$image" in
TRITON=yes
;;
pytorch-linux-focal-rocm-n-1-py3)
ANACONDA_PYTHON_VERSION=3.10
ANACONDA_PYTHON_VERSION=3.8
GCC_VERSION=9
PROTOBUF=yes
DB=yes
VISION=yes
ROCM_VERSION=6.1
ROCM_VERSION=6.0
NINJA_VERSION=1.9.0
CONDA_CMAKE=yes
TRITON=yes
;;
pytorch-linux-focal-rocm-n-py3)
ANACONDA_PYTHON_VERSION=3.10
ANACONDA_PYTHON_VERSION=3.8
GCC_VERSION=9
PROTOBUF=yes
DB=yes
VISION=yes
ROCM_VERSION=6.2
ROCM_VERSION=6.1
NINJA_VERSION=1.9.0
CONDA_CMAKE=yes
TRITON=yes
@ -318,8 +318,8 @@ case "$image" in
CONDA_CMAKE=yes
TRITON=yes
;;
pytorch-linux-jammy-py3.9-gcc11-inductor-benchmarks)
ANACONDA_PYTHON_VERSION=3.9
pytorch-linux-jammy-py3.8-gcc11-inductor-benchmarks)
ANACONDA_PYTHON_VERSION=3.8
GCC_VERSION=11
PROTOBUF=yes
DB=yes
@ -330,8 +330,8 @@ case "$image" in
DOCS=yes
INDUCTOR_BENCHMARKS=yes
;;
pytorch-linux-jammy-cuda11.8-cudnn9-py3.9-clang12)
ANACONDA_PYTHON_VERSION=3.9
pytorch-linux-jammy-cuda11.8-cudnn9-py3.8-clang12)
ANACONDA_PYTHON_VERSION=3.8
CUDA_VERSION=11.8
CUDNN_VERSION=9
CLANG_VERSION=12
@ -355,14 +355,8 @@ case "$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
pytorch-linux-jammy-py3.8-gcc11)
ANACONDA_PYTHON_VERSION=3.8
GCC_VERSION=11
PROTOBUF=yes
DB=yes
@ -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
69472e5c43481324ad923ceb29392ab72830acee

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@ -1 +1 @@
461c12871f336fe6f57b55d6a297f13ef209161b
340136fec6d3ebc73e7a19eba1663e9b0ba8ab2d

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@ -1 +0,0 @@
c7711371cace304afe265c1ffa906415ab82fc66

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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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@ -13,18 +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
if [[ $CLANG_VERSION -ge 18 ]]; then
apt-get install -y libomp-${CLANG_VERSION}-dev libclang-rt-${CLANG_VERSION}-dev clang-"$CLANG_VERSION" llvm-"$CLANG_VERSION"
else
apt-get install -y --no-install-recommends clang-"$CLANG_VERSION" llvm-"$CLANG_VERSION"
fi
apt-get install -y --no-install-recommends clang-"$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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@ -5,22 +5,32 @@ set -ex
# Optionally install conda
if [ -n "$ANACONDA_PYTHON_VERSION" ]; then
BASE_URL="https://repo.anaconda.com/miniconda"
CONDA_FILE="Miniconda3-latest-Linux-x86_64.sh"
if [[ $(uname -m) == "aarch64" ]] || [[ "$BUILD_ENVIRONMENT" == *xpu* ]]; then
BASE_URL="https://github.com/conda-forge/miniforge/releases/latest/download"
CONDA_FILE="Miniforge3-Linux-$(uname -m).sh"
fi
MAJOR_PYTHON_VERSION=$(echo "$ANACONDA_PYTHON_VERSION" | cut -d . -f 1)
MINOR_PYTHON_VERSION=$(echo "$ANACONDA_PYTHON_VERSION" | cut -d . -f 2)
if [[ $(uname -m) == "aarch64" ]]; then
BASE_URL="https://github.com/conda-forge/miniforge/releases/latest/download"
case "$MAJOR_PYTHON_VERSION" in
3);;
3)
CONDA_FILE="Miniforge3-Linux-aarch64.sh"
;;
*)
echo "Unsupported ANACONDA_PYTHON_VERSION: $ANACONDA_PYTHON_VERSION"
exit 1
;;
esac
else
case "$MAJOR_PYTHON_VERSION" in
3)
CONDA_FILE="Miniconda3-latest-Linux-x86_64.sh"
;;
*)
echo "Unsupported ANACONDA_PYTHON_VERSION: $ANACONDA_PYTHON_VERSION"
exit 1
;;
esac
fi
mkdir -p /opt/conda
chown jenkins:jenkins /opt/conda
@ -65,10 +75,23 @@ if [ -n "$ANACONDA_PYTHON_VERSION" ]; then
# Install PyTorch conda deps, as per https://github.com/pytorch/pytorch README
if [[ $(uname -m) == "aarch64" ]]; then
conda_install "openblas==0.3.25=*openmp*"
CONDA_COMMON_DEPS="astunparse pyyaml setuptools openblas==0.3.25=*openmp* ninja==1.11.1 scons==4.5.2"
if [ "$ANACONDA_PYTHON_VERSION" = "3.8" ]; then
NUMPY_VERSION=1.24.4
else
NUMPY_VERSION=1.26.2
fi
else
conda_install "mkl=2021.4.0 mkl-include=2021.4.0"
CONDA_COMMON_DEPS="astunparse pyyaml mkl=2021.4.0 mkl-include=2021.4.0 setuptools"
if [ "$ANACONDA_PYTHON_VERSION" = "3.11" ] || [ "$ANACONDA_PYTHON_VERSION" = "3.12" ] || [ "$ANACONDA_PYTHON_VERSION" = "3.13" ]; then
NUMPY_VERSION=1.26.0
else
NUMPY_VERSION=1.21.2
fi
fi
conda_install ${CONDA_COMMON_DEPS}
# Install llvm-8 as it is required to compile llvmlite-0.30.0 from source
# and libpython-static for torch deploy
@ -90,6 +113,8 @@ if [ -n "$ANACONDA_PYTHON_VERSION" ]; then
# Install some other packages, including those needed for Python test reporting
pip_install -r /opt/conda/requirements-ci.txt
pip_install numpy=="$NUMPY_VERSION"
pip_install -U scikit-learn
if [ -n "$DOCS" ]; then
apt-get update

View File

@ -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

View File

@ -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

View File

@ -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

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@ -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

@ -15,7 +15,7 @@ pip_install \
flatbuffers==2.0 \
mock==5.0.1 \
ninja==1.10.2 \
networkx==2.5 \
networkx==2.0 \
numpy==1.24.2
# ONNXRuntime should be installed before installing
@ -30,9 +30,10 @@ pip_install \
pip_install coloredlogs packaging
pip_install onnxruntime==1.18.1
pip_install onnx==1.16.2
pip_install onnxscript==0.1.0.dev20241009 --no-deps
pip_install onnxruntime==1.18
pip_install onnx==1.16.0
# pip_install "onnxscript@git+https://github.com/microsoft/onnxscript@3e869ef8ccf19b5ebd21c10d3e9c267c9a9fa729" --no-deps
pip_install onnxscript==0.1.0.dev20240613 --no-deps
# required by onnxscript
pip_install ml_dtypes

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
@ -109,7 +104,7 @@ networkx==2.8.8
#test that import: run_test.py, test_cpp_extensions_aot.py,test_determination.py
numba==0.49.0 ; python_version < "3.9"
numba==0.55.2 ; python_version == "3.9"
numba==0.54.1 ; python_version == "3.9"
numba==0.55.2 ; python_version == "3.10"
#Description: Just-In-Time Compiler for Numerical Functions
#Pinned versions: 0.54.1, 0.49.0, <=0.49.1
@ -118,7 +113,7 @@ numba==0.55.2 ; python_version == "3.10"
#numpy
#Description: Provides N-dimensional arrays and linear algebra
#Pinned versions: 1.26.2
#Pinned versions: 1.20
#test that import: test_view_ops.py, test_unary_ufuncs.py, test_type_promotion.py,
#test_type_info.py, test_torch.py, test_tensorexpr_pybind.py, test_tensorexpr.py,
#test_tensorboard.py, test_tensor_creation_ops.py, test_static_runtime.py,
@ -128,10 +123,6 @@ numba==0.55.2 ; python_version == "3.10"
#test_nn.py, test_namedtensor.py, test_linalg.py, test_jit_cuda_fuser.py,
#test_jit.py, test_indexing.py, test_datapipe.py, test_dataloader.py,
#test_binary_ufuncs.py
numpy==1.21.2; python_version == "3.9"
numpy==1.22.4; python_version == "3.10"
numpy==1.26.2; python_version == "3.11" or python_version == "3.12"
numpy==2.1.2; python_version >= "3.13"
#onnxruntime
#Description: scoring engine for Open Neural Network Exchange (ONNX) models
@ -143,9 +134,9 @@ opt-einsum==3.3
#Pinned versions: 3.3
#test that import: test_linalg.py
optree==0.13.0
optree==0.12.1
#Description: A library for tree manipulation
#Pinned versions: 0.13.0
#Pinned versions: 0.12.1
#test that import: test_vmap.py, test_aotdispatch.py, test_dynamic_shapes.py,
#test_pytree.py, test_ops.py, test_control_flow.py, test_modules.py,
#common_utils.py, test_eager_transforms.py, test_python_dispatch.py,
@ -326,6 +317,7 @@ lxml==5.0.0
PyGithub==2.3.0
sympy==1.12.1 ; python_version == "3.8"
sympy==1.13.1 ; python_version >= "3.9"
#Description: Required by coremltools, also pinned in .github/requirements/pip-requirements-macOS.txt
#Pinned versions:
@ -340,31 +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:
#Description: required for testing torch/distributed/_tools/sac_estimator.py
#Pinned versions: 1.24.0
#test that import: test_sac_estimator.py
pwlf==2.2.1 ; python_version >= "3.8"
#Description: required for testing torch/distributed/_tools/sac_estimator.py
#Pinned versions: 2.2.1
#test that import: test_sac_estimator.py
# To build PyTorch itself
astunparse
PyYAML
setuptools
ninja==1.11.1 ; platform_machine == "aarch64"
scons==4.5.2 ; platform_machine == "aarch64"
pulp==2.9.0 ; python_version >= "3.8"
#Description: required for testing ilp formulaiton under torch/distributed/_tools
#Pinned versions: 2.9.0
#test that import: test_sac_ilp.py

View File

@ -1 +1 @@
3.1.0
3.0.0

View File

@ -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

@ -30,7 +30,6 @@ RUN bash ./install_docs_reqs.sh && rm install_docs_reqs.sh
ARG ANACONDA_PYTHON_VERSION
ARG CONDA_CMAKE
ARG DOCS
ARG BUILD_ENVIRONMENT
ENV ANACONDA_PYTHON_VERSION=$ANACONDA_PYTHON_VERSION
ENV PATH /opt/conda/envs/py_$ANACONDA_PYTHON_VERSION/bin:/opt/conda/bin:$PATH
ENV DOCS=$DOCS

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

@ -1,10 +0,0 @@
#!/usr/bin/env bash
# This is mostly just a shim to manywheel/build.sh
# TODO: Make this a dedicated script to build just libtorch
set -ex
SCRIPTPATH="$( cd "$( dirname "${BASH_SOURCE[0]}" )" >/dev/null 2>&1 && pwd )"
USE_CUSPARSELT=0 BUILD_PYTHONLESS=1 DESIRED_PYTHON="3.9" ${SCRIPTPATH}/../manywheel/build.sh

View File

@ -1,21 +0,0 @@
The MIT License (MIT)
Copyright (c) 2016 manylinux
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.

View File

@ -1,25 +0,0 @@
#!/usr/bin/env bash
set -ex
SCRIPTPATH="$( cd "$( dirname "${BASH_SOURCE[0]}" )" >/dev/null 2>&1 && pwd )"
case "${GPU_ARCH_TYPE:-BLANK}" in
BLANK)
# Legacy behavior for CircleCI
bash "${SCRIPTPATH}/build_cuda.sh"
;;
cuda)
bash "${SCRIPTPATH}/build_cuda.sh"
;;
rocm)
bash "${SCRIPTPATH}/build_rocm.sh"
;;
cpu | cpu-cxx11-abi | cpu-s390x | xpu)
bash "${SCRIPTPATH}/build_cpu.sh"
;;
*)
echo "Un-recognized GPU_ARCH_TYPE '${GPU_ARCH_TYPE}', exiting..."
exit 1
;;
esac

View File

@ -1,505 +0,0 @@
#!/usr/bin/env bash
# meant to be called only from the neighboring build.sh and build_cpu.sh scripts
set -ex
SOURCE_DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" >/dev/null && pwd )"
# Require only one python installation
if [[ -z "$DESIRED_PYTHON" ]]; then
echo "Need to set DESIRED_PYTHON env variable"
exit 1
fi
if [[ -n "$BUILD_PYTHONLESS" && -z "$LIBTORCH_VARIANT" ]]; then
echo "BUILD_PYTHONLESS is set, so need LIBTORCH_VARIANT to also be set"
echo "LIBTORCH_VARIANT should be one of shared-with-deps shared-without-deps static-with-deps static-without-deps"
exit 1
fi
# Function to retry functions that sometimes timeout or have flaky failures
retry () {
$* || (sleep 1 && $*) || (sleep 2 && $*) || (sleep 4 && $*) || (sleep 8 && $*)
}
# TODO move this into the Docker images
OS_NAME=$(awk -F= '/^NAME/{print $2}' /etc/os-release)
if [[ "$OS_NAME" == *"CentOS Linux"* ]]; then
retry yum install -q -y zip openssl
elif [[ "$OS_NAME" == *"AlmaLinux"* ]]; then
retry yum install -q -y zip openssl
elif [[ "$OS_NAME" == *"Red Hat Enterprise Linux"* ]]; then
retry dnf install -q -y zip openssl
elif [[ "$OS_NAME" == *"Ubuntu"* ]]; then
# TODO: Remove this once nvidia package repos are back online
# Comment out nvidia repositories to prevent them from getting apt-get updated, see https://github.com/pytorch/pytorch/issues/74968
# shellcheck disable=SC2046
sed -i 's/.*nvidia.*/# &/' $(find /etc/apt/ -type f -name "*.list")
retry apt-get update
retry apt-get -y install zip openssl
fi
# We use the package name to test the package by passing this to 'pip install'
# This is the env variable that setup.py uses to name the package. Note that
# pip 'normalizes' the name first by changing all - to _
if [[ -z "$TORCH_PACKAGE_NAME" ]]; then
TORCH_PACKAGE_NAME='torch'
fi
if [[ -z "$TORCH_NO_PYTHON_PACKAGE_NAME" ]]; then
TORCH_NO_PYTHON_PACKAGE_NAME='torch_no_python'
fi
TORCH_PACKAGE_NAME="$(echo $TORCH_PACKAGE_NAME | tr '-' '_')"
TORCH_NO_PYTHON_PACKAGE_NAME="$(echo $TORCH_NO_PYTHON_PACKAGE_NAME | tr '-' '_')"
echo "Expecting the built wheels to all be called '$TORCH_PACKAGE_NAME' or '$TORCH_NO_PYTHON_PACKAGE_NAME'"
# Version: setup.py uses $PYTORCH_BUILD_VERSION.post$PYTORCH_BUILD_NUMBER if
# PYTORCH_BUILD_NUMBER > 1
build_version="$PYTORCH_BUILD_VERSION"
build_number="$PYTORCH_BUILD_NUMBER"
if [[ -n "$OVERRIDE_PACKAGE_VERSION" ]]; then
# This will be the *exact* version, since build_number<1
build_version="$OVERRIDE_PACKAGE_VERSION"
build_number=0
fi
if [[ -z "$build_version" ]]; then
build_version=1.0.0
fi
if [[ -z "$build_number" ]]; then
build_number=1
fi
export PYTORCH_BUILD_VERSION=$build_version
export PYTORCH_BUILD_NUMBER=$build_number
export CMAKE_LIBRARY_PATH="/opt/intel/lib:/lib:$CMAKE_LIBRARY_PATH"
export CMAKE_INCLUDE_PATH="/opt/intel/include:$CMAKE_INCLUDE_PATH"
if [[ -e /opt/openssl ]]; then
export OPENSSL_ROOT_DIR=/opt/openssl
export CMAKE_INCLUDE_PATH="/opt/openssl/include":$CMAKE_INCLUDE_PATH
fi
# If given a python version like 3.6m or 2.7mu, convert this to the format we
# expect. The binary CI jobs pass in python versions like this; they also only
# ever pass one python version, so we assume that DESIRED_PYTHON is not a list
# in this case
if [[ -n "$DESIRED_PYTHON" && $DESIRED_PYTHON =~ ([0-9].[0-9]+)t ]]; then
python_digits="$(echo $DESIRED_PYTHON | tr -cd [:digit:])"
py_majmin="${DESIRED_PYTHON}"
DESIRED_PYTHON="cp${python_digits}-cp${python_digits}t"
elif [[ -n "$DESIRED_PYTHON" && "$DESIRED_PYTHON" != cp* ]]; then
python_nodot="$(echo $DESIRED_PYTHON | tr -d m.u)"
DESIRED_PYTHON="cp${python_nodot}-cp${python_nodot}"
if [[ ${python_nodot} -ge 310 ]]; then
py_majmin="${DESIRED_PYTHON:2:1}.${DESIRED_PYTHON:3:2}"
else
py_majmin="${DESIRED_PYTHON:2:1}.${DESIRED_PYTHON:3:1}"
fi
fi
pydir="/opt/python/$DESIRED_PYTHON"
export PATH="$pydir/bin:$PATH"
echo "Will build for Python version: ${DESIRED_PYTHON} with ${python_installation}"
mkdir -p /tmp/$WHEELHOUSE_DIR
export PATCHELF_BIN=/usr/local/bin/patchelf
patchelf_version=$($PATCHELF_BIN --version)
echo "patchelf version: " $patchelf_version
if [[ "$patchelf_version" == "patchelf 0.9" ]]; then
echo "Your patchelf version is too old. Please use version >= 0.10."
exit 1
fi
########################################################
# Compile wheels as well as libtorch
#######################################################
if [[ -z "$PYTORCH_ROOT" ]]; then
echo "Need to set PYTORCH_ROOT env variable"
exit 1
fi
pushd "$PYTORCH_ROOT"
python setup.py clean
retry pip install -qr requirements.txt
case ${DESIRED_PYTHON} in
cp31*)
retry pip install -q --pre numpy==2.1.0
;;
# Should catch 3.9+
*)
retry pip install -q --pre numpy==2.0.2
;;
esac
if [[ "$DESIRED_DEVTOOLSET" == *"cxx11-abi"* ]]; then
export _GLIBCXX_USE_CXX11_ABI=1
else
export _GLIBCXX_USE_CXX11_ABI=0
fi
if [[ "$DESIRED_CUDA" == *"rocm"* ]]; then
echo "Calling build_amd.py at $(date)"
python tools/amd_build/build_amd.py
fi
# This value comes from binary_linux_build.sh (and should only be set to true
# for master / release branches)
BUILD_DEBUG_INFO=${BUILD_DEBUG_INFO:=0}
if [[ $BUILD_DEBUG_INFO == "1" ]]; then
echo "Building wheel and debug info"
else
echo "BUILD_DEBUG_INFO was not set, skipping debug info"
fi
if [[ "$DISABLE_RCCL" = 1 ]]; then
echo "Disabling NCCL/RCCL in pyTorch"
USE_RCCL=0
USE_NCCL=0
USE_KINETO=0
else
USE_RCCL=1
USE_NCCL=1
USE_KINETO=1
fi
echo "Calling setup.py bdist at $(date)"
if [[ "$USE_SPLIT_BUILD" == "true" ]]; then
echo "Calling setup.py bdist_wheel for split build (BUILD_LIBTORCH_WHL)"
time EXTRA_CAFFE2_CMAKE_FLAGS=${EXTRA_CAFFE2_CMAKE_FLAGS[@]} \
BUILD_LIBTORCH_WHL=1 BUILD_PYTHON_ONLY=0 \
BUILD_LIBTORCH_CPU_WITH_DEBUG=$BUILD_DEBUG_INFO \
USE_NCCL=${USE_NCCL} USE_RCCL=${USE_RCCL} USE_KINETO=${USE_KINETO} \
python setup.py bdist_wheel -d /tmp/$WHEELHOUSE_DIR
echo "Finished setup.py bdist_wheel for split build (BUILD_LIBTORCH_WHL)"
echo "Calling setup.py bdist_wheel for split build (BUILD_PYTHON_ONLY)"
time EXTRA_CAFFE2_CMAKE_FLAGS=${EXTRA_CAFFE2_CMAKE_FLAGS[@]} \
BUILD_LIBTORCH_WHL=0 BUILD_PYTHON_ONLY=1 \
BUILD_LIBTORCH_CPU_WITH_DEBUG=$BUILD_DEBUG_INFO \
USE_NCCL=${USE_NCCL} USE_RCCL=${USE_RCCL} USE_KINETO=${USE_KINETO} \
python setup.py bdist_wheel -d /tmp/$WHEELHOUSE_DIR --cmake
echo "Finished setup.py bdist_wheel for split build (BUILD_PYTHON_ONLY)"
else
time CMAKE_ARGS=${CMAKE_ARGS[@]} \
EXTRA_CAFFE2_CMAKE_FLAGS=${EXTRA_CAFFE2_CMAKE_FLAGS[@]} \
BUILD_LIBTORCH_CPU_WITH_DEBUG=$BUILD_DEBUG_INFO \
USE_NCCL=${USE_NCCL} USE_RCCL=${USE_RCCL} USE_KINETO=${USE_KINETO} \
python setup.py bdist_wheel -d /tmp/$WHEELHOUSE_DIR
fi
echo "Finished setup.py bdist at $(date)"
# Build libtorch packages
if [[ -n "$BUILD_PYTHONLESS" ]]; then
# Now build pythonless libtorch
# Note - just use whichever python we happen to be on
python setup.py clean
if [[ $LIBTORCH_VARIANT = *"static"* ]]; then
STATIC_CMAKE_FLAG="-DTORCH_STATIC=1"
fi
mkdir -p build
pushd build
echo "Calling tools/build_libtorch.py at $(date)"
time CMAKE_ARGS=${CMAKE_ARGS[@]} \
EXTRA_CAFFE2_CMAKE_FLAGS="${EXTRA_CAFFE2_CMAKE_FLAGS[@]} $STATIC_CMAKE_FLAG" \
python ../tools/build_libtorch.py
echo "Finished tools/build_libtorch.py at $(date)"
popd
mkdir -p libtorch/{lib,bin,include,share}
cp -r build/build/lib libtorch/
# for now, the headers for the libtorch package will just be copied in
# from one of the wheels (this is from when this script built multiple
# wheels at once)
ANY_WHEEL=$(ls /tmp/$WHEELHOUSE_DIR/torch*.whl | head -n1)
unzip -d any_wheel $ANY_WHEEL
if [[ -d any_wheel/torch/include ]]; then
cp -r any_wheel/torch/include libtorch/
else
cp -r any_wheel/torch/lib/include libtorch/
fi
cp -r any_wheel/torch/share/cmake libtorch/share/
rm -rf any_wheel
echo $PYTORCH_BUILD_VERSION > libtorch/build-version
echo "$(pushd $PYTORCH_ROOT && git rev-parse HEAD)" > libtorch/build-hash
mkdir -p /tmp/$LIBTORCH_HOUSE_DIR
if [[ "$DESIRED_DEVTOOLSET" == *"cxx11-abi"* ]]; then
LIBTORCH_ABI="cxx11-abi-"
else
LIBTORCH_ABI=
fi
zip -rq /tmp/$LIBTORCH_HOUSE_DIR/libtorch-$LIBTORCH_ABI$LIBTORCH_VARIANT-$PYTORCH_BUILD_VERSION.zip libtorch
cp /tmp/$LIBTORCH_HOUSE_DIR/libtorch-$LIBTORCH_ABI$LIBTORCH_VARIANT-$PYTORCH_BUILD_VERSION.zip \
/tmp/$LIBTORCH_HOUSE_DIR/libtorch-$LIBTORCH_ABI$LIBTORCH_VARIANT-latest.zip
fi
popd
#######################################################################
# ADD DEPENDENCIES INTO THE WHEEL
#
# auditwheel repair doesn't work correctly and is buggy
# so manually do the work of copying dependency libs and patchelfing
# and fixing RECORDS entries correctly
######################################################################
fname_with_sha256() {
HASH=$(sha256sum $1 | cut -c1-8)
DIRNAME=$(dirname $1)
BASENAME=$(basename $1)
# Do not rename nvrtc-builtins.so as they are dynamically loaded
# by libnvrtc.so
# Similarly don't mangle libcudnn and libcublas library names
if [[ $BASENAME == "libnvrtc-builtins.s"* || $BASENAME == "libcudnn"* || $BASENAME == "libcublas"* ]]; then
echo $1
else
INITNAME=$(echo $BASENAME | cut -f1 -d".")
ENDNAME=$(echo $BASENAME | cut -f 2- -d".")
echo "$DIRNAME/$INITNAME-$HASH.$ENDNAME"
fi
}
fname_without_so_number() {
LINKNAME=$(echo $1 | sed -e 's/\.so.*/.so/g')
echo "$LINKNAME"
}
make_wheel_record() {
FPATH=$1
if echo $FPATH | grep RECORD >/dev/null 2>&1; then
# if the RECORD file, then
echo "$FPATH,,"
else
HASH=$(openssl dgst -sha256 -binary $FPATH | openssl base64 | sed -e 's/+/-/g' | sed -e 's/\//_/g' | sed -e 's/=//g')
FSIZE=$(ls -nl $FPATH | awk '{print $5}')
echo "$FPATH,sha256=$HASH,$FSIZE"
fi
}
replace_needed_sofiles() {
find $1 -name '*.so*' | while read sofile; do
origname=$2
patchedname=$3
if [[ "$origname" != "$patchedname" ]] || [[ "$DESIRED_CUDA" == *"rocm"* ]]; then
set +e
origname=$($PATCHELF_BIN --print-needed $sofile | grep "$origname.*")
ERRCODE=$?
set -e
if [ "$ERRCODE" -eq "0" ]; then
echo "patching $sofile entry $origname to $patchedname"
$PATCHELF_BIN --replace-needed $origname $patchedname $sofile
fi
fi
done
}
echo 'Built this wheel:'
ls /tmp/$WHEELHOUSE_DIR
mkdir -p "/$WHEELHOUSE_DIR"
mv /tmp/$WHEELHOUSE_DIR/torch*linux*.whl /$WHEELHOUSE_DIR/
if [[ "$USE_SPLIT_BUILD" == "true" ]]; then
mv /tmp/$WHEELHOUSE_DIR/torch_no_python*.whl /$WHEELHOUSE_DIR/ || true
fi
if [[ -n "$BUILD_PYTHONLESS" ]]; then
mkdir -p /$LIBTORCH_HOUSE_DIR
mv /tmp/$LIBTORCH_HOUSE_DIR/*.zip /$LIBTORCH_HOUSE_DIR
rm -rf /tmp/$LIBTORCH_HOUSE_DIR
fi
rm -rf /tmp/$WHEELHOUSE_DIR
rm -rf /tmp_dir
mkdir /tmp_dir
pushd /tmp_dir
for pkg in /$WHEELHOUSE_DIR/torch_no_python*.whl /$WHEELHOUSE_DIR/torch*linux*.whl /$LIBTORCH_HOUSE_DIR/libtorch*.zip; do
# if the glob didn't match anything
if [[ ! -e $pkg ]]; then
continue
fi
rm -rf tmp
mkdir -p tmp
cd tmp
cp $pkg .
unzip -q $(basename $pkg)
rm -f $(basename $pkg)
if [[ -d torch ]]; then
PREFIX=torch
else
PREFIX=libtorch
fi
if [[ $pkg != *"without-deps"* ]]; then
# copy over needed dependent .so files over and tag them with their hash
patched=()
for filepath in "${DEPS_LIST[@]}"; do
filename=$(basename $filepath)
destpath=$PREFIX/lib/$filename
if [[ "$filepath" != "$destpath" ]]; then
cp $filepath $destpath
fi
# ROCm workaround for roctracer dlopens
if [[ "$DESIRED_CUDA" == *"rocm"* ]]; then
patchedpath=$(fname_without_so_number $destpath)
# Keep the so number for XPU dependencies
elif [[ "$DESIRED_CUDA" == *"xpu"* ]]; then
patchedpath=$destpath
else
patchedpath=$(fname_with_sha256 $destpath)
fi
patchedname=$(basename $patchedpath)
if [[ "$destpath" != "$patchedpath" ]]; then
mv $destpath $patchedpath
fi
patched+=("$patchedname")
echo "Copied $filepath to $patchedpath"
done
echo "patching to fix the so names to the hashed names"
for ((i=0;i<${#DEPS_LIST[@]};++i)); do
replace_needed_sofiles $PREFIX ${DEPS_SONAME[i]} ${patched[i]}
# do the same for caffe2, if it exists
if [[ -d caffe2 ]]; then
replace_needed_sofiles caffe2 ${DEPS_SONAME[i]} ${patched[i]}
fi
done
# copy over needed auxiliary files
for ((i=0;i<${#DEPS_AUX_SRCLIST[@]};++i)); do
srcpath=${DEPS_AUX_SRCLIST[i]}
dstpath=$PREFIX/${DEPS_AUX_DSTLIST[i]}
mkdir -p $(dirname $dstpath)
cp $srcpath $dstpath
done
fi
# set RPATH of _C.so and similar to $ORIGIN, $ORIGIN/lib
find $PREFIX -maxdepth 1 -type f -name "*.so*" | while read sofile; do
echo "Setting rpath of $sofile to ${C_SO_RPATH:-'$ORIGIN:$ORIGIN/lib'}"
$PATCHELF_BIN --set-rpath ${C_SO_RPATH:-'$ORIGIN:$ORIGIN/lib'} ${FORCE_RPATH:-} $sofile
$PATCHELF_BIN --print-rpath $sofile
done
# set RPATH of lib/ files to $ORIGIN
find $PREFIX/lib -maxdepth 1 -type f -name "*.so*" | while read sofile; do
echo "Setting rpath of $sofile to ${LIB_SO_RPATH:-'$ORIGIN'}"
$PATCHELF_BIN --set-rpath ${LIB_SO_RPATH:-'$ORIGIN'} ${FORCE_RPATH:-} $sofile
$PATCHELF_BIN --print-rpath $sofile
done
# regenerate the RECORD file with new hashes
record_file=$(echo $(basename $pkg) | sed -e 's/-cp.*$/.dist-info\/RECORD/g')
if [[ -e $record_file ]]; then
echo "Generating new record file $record_file"
: > "$record_file"
# generate records for folders in wheel
find * -type f | while read fname; do
make_wheel_record "$fname" >>"$record_file"
done
fi
if [[ $BUILD_DEBUG_INFO == "1" ]]; then
pushd "$PREFIX/lib"
# Duplicate library into debug lib
cp libtorch_cpu.so libtorch_cpu.so.dbg
# Keep debug symbols on debug lib
strip --only-keep-debug libtorch_cpu.so.dbg
# Remove debug info from release lib
strip --strip-debug libtorch_cpu.so
objcopy libtorch_cpu.so --add-gnu-debuglink=libtorch_cpu.so.dbg
# Zip up debug info
mkdir -p /tmp/debug
mv libtorch_cpu.so.dbg /tmp/debug/libtorch_cpu.so.dbg
CRC32=$(objcopy --dump-section .gnu_debuglink=>(tail -c4 | od -t x4 -An | xargs echo) libtorch_cpu.so)
pushd /tmp
PKG_NAME=$(basename "$pkg" | sed 's/\.whl$//g')
zip /tmp/debug-whl-libtorch-"$PKG_NAME"-"$CRC32".zip /tmp/debug/libtorch_cpu.so.dbg
cp /tmp/debug-whl-libtorch-"$PKG_NAME"-"$CRC32".zip "$PYTORCH_FINAL_PACKAGE_DIR"
popd
popd
fi
# zip up the wheel back
zip -rq $(basename $pkg) $PREIX*
# replace original wheel
rm -f $pkg
mv $(basename $pkg) $pkg
cd ..
rm -rf tmp
done
# Copy wheels to host machine for persistence before testing
if [[ -n "$PYTORCH_FINAL_PACKAGE_DIR" ]]; then
mkdir -p "$PYTORCH_FINAL_PACKAGE_DIR" || true
if [[ -n "$BUILD_PYTHONLESS" ]]; then
cp /$LIBTORCH_HOUSE_DIR/libtorch*.zip "$PYTORCH_FINAL_PACKAGE_DIR"
else
cp /$WHEELHOUSE_DIR/torch*.whl "$PYTORCH_FINAL_PACKAGE_DIR"
fi
fi
# remove stuff before testing
rm -rf /opt/rh
if ls /usr/local/cuda* >/dev/null 2>&1; then
rm -rf /usr/local/cuda*
fi
# Test that all the wheels work
if [[ -z "$BUILD_PYTHONLESS" ]]; then
export OMP_NUM_THREADS=4 # on NUMA machines this takes too long
pushd $PYTORCH_ROOT/test
# Install the wheel for this Python version
if [[ "$USE_SPLIT_BUILD" == "true" ]]; then
pip uninstall -y "$TORCH_NO_PYTHON_PACKAGE_NAME" || true
fi
pip uninstall -y "$TORCH_PACKAGE_NAME"
if [[ "$USE_SPLIT_BUILD" == "true" ]]; then
pip install "$TORCH_NO_PYTHON_PACKAGE_NAME" --no-index -f /$WHEELHOUSE_DIR --no-dependencies -v
fi
pip install "$TORCH_PACKAGE_NAME" --no-index -f /$WHEELHOUSE_DIR --no-dependencies -v
# Print info on the libraries installed in this wheel
# Rather than adjust find command to skip non-library files with an embedded *.so* in their name,
# since this is only for reporting purposes, we add the || true to the ldd command.
installed_libraries=($(find "$pydir/lib/python${py_majmin}/site-packages/torch/" -name '*.so*'))
echo "The wheel installed all of the libraries: ${installed_libraries[@]}"
for installed_lib in "${installed_libraries[@]}"; do
ldd "$installed_lib" || true
done
# Run the tests
echo "$(date) :: Running tests"
pushd "$PYTORCH_ROOT"
#TODO: run_tests.sh and check_binary.sh should be moved to pytorch/pytorch project
LD_LIBRARY_PATH=/usr/local/nvidia/lib64 \
"/builder/run_tests.sh" manywheel "${py_majmin}" "$DESIRED_CUDA"
popd
echo "$(date) :: Finished tests"
fi

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@ -1,99 +0,0 @@
#!/usr/bin/env bash
set -ex
GPU_ARCH_TYPE=${GPU_ARCH_TYPE:-cpu}
export TH_BINARY_BUILD=1
export USE_CUDA=0
# Keep an array of cmake variables to add to
if [[ -z "$CMAKE_ARGS" ]]; then
# These are passed to tools/build_pytorch_libs.sh::build()
CMAKE_ARGS=()
fi
if [[ -z "$EXTRA_CAFFE2_CMAKE_FLAGS" ]]; then
# These are passed to tools/build_pytorch_libs.sh::build_caffe2()
EXTRA_CAFFE2_CMAKE_FLAGS=()
fi
DIR_SUFFIX=cpu
if [[ "$GPU_ARCH_TYPE" == "xpu" ]]; then
DIR_SUFFIX=xpu
# Refer https://www.intel.com/content/www/us/en/developer/articles/tool/pytorch-prerequisites-for-intel-gpu/2-5.html
source /opt/intel/oneapi/pytorch-gpu-dev-0.5/oneapi-vars.sh
source /opt/intel/oneapi/pti/latest/env/vars.sh
export USE_STATIC_MKL=1
fi
WHEELHOUSE_DIR="wheelhouse$DIR_SUFFIX"
LIBTORCH_HOUSE_DIR="libtorch_house$DIR_SUFFIX"
if [[ -z "$PYTORCH_FINAL_PACKAGE_DIR" ]]; then
if [[ -z "$BUILD_PYTHONLESS" ]]; then
PYTORCH_FINAL_PACKAGE_DIR="/remote/wheelhouse$DIR_SUFFIX"
else
PYTORCH_FINAL_PACKAGE_DIR="/remote/libtorch_house$DIR_SUFFIX"
fi
fi
mkdir -p "$PYTORCH_FINAL_PACKAGE_DIR" || true
OS_NAME=$(awk -F= '/^NAME/{print $2}' /etc/os-release)
if [[ "$OS_NAME" == *"CentOS Linux"* ]]; then
LIBGOMP_PATH="/usr/lib64/libgomp.so.1"
elif [[ "$OS_NAME" == *"Red Hat Enterprise Linux"* ]]; then
LIBGOMP_PATH="/usr/lib64/libgomp.so.1"
elif [[ "$OS_NAME" == *"AlmaLinux"* ]]; then
LIBGOMP_PATH="/usr/lib64/libgomp.so.1"
elif [[ "$OS_NAME" == *"Ubuntu"* ]]; then
if [[ "$(uname -m)" == "s390x" ]]; then
LIBGOMP_PATH="/usr/lib/s390x-linux-gnu/libgomp.so.1"
else
LIBGOMP_PATH="/usr/lib/x86_64-linux-gnu/libgomp.so.1"
fi
fi
DEPS_LIST=(
"$LIBGOMP_PATH"
)
DEPS_SONAME=(
"libgomp.so.1"
)
if [[ "$GPU_ARCH_TYPE" == "xpu" ]]; then
echo "Bundling with xpu support package libs."
DEPS_LIST+=(
"/opt/intel/oneapi/compiler/latest/lib/libsycl-preview.so.7"
"/opt/intel/oneapi/compiler/latest/lib/libOpenCL.so.1"
"/opt/intel/oneapi/compiler/latest/lib/libxptifw.so"
"/opt/intel/oneapi/compiler/latest/lib/libsvml.so"
"/opt/intel/oneapi/compiler/latest/lib/libirng.so"
"/opt/intel/oneapi/compiler/latest/lib/libimf.so"
"/opt/intel/oneapi/compiler/latest/lib/libintlc.so.5"
"/opt/intel/oneapi/compiler/latest/lib/libpi_level_zero.so"
"/opt/intel/oneapi/pti/latest/lib/libpti_view.so.0.9"
"/opt/intel/oneapi/pti/latest/lib/libpti.so.0.9"
)
DEPS_SONAME+=(
"libsycl-preview.so.7"
"libOpenCL.so.1"
"libxptifw.so"
"libsvml.so"
"libirng.so"
"libimf.so"
"libintlc.so.5"
"libpi_level_zero.so"
"libpti_view.so.0.9"
"libpti.so.0.9"
)
fi
rm -rf /usr/local/cuda*
SOURCE_DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" >/dev/null && pwd )"
if [[ -z "$BUILD_PYTHONLESS" ]]; then
BUILD_SCRIPT=build_common.sh
else
BUILD_SCRIPT=build_libtorch.sh
fi
source ${SOURCE_DIR}/${BUILD_SCRIPT}

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@ -1,290 +0,0 @@
#!/usr/bin/env bash
set -ex
SCRIPTPATH="$( cd "$(dirname "$0")" ; pwd -P ))"
export TORCH_NVCC_FLAGS="-Xfatbin -compress-all"
export NCCL_ROOT_DIR=/usr/local/cuda
export TH_BINARY_BUILD=1
export USE_STATIC_CUDNN=1
export USE_STATIC_NCCL=1
export ATEN_STATIC_CUDA=1
export USE_CUDA_STATIC_LINK=1
export INSTALL_TEST=0 # dont install test binaries into site-packages
export USE_CUPTI_SO=0
export USE_CUSPARSELT=${USE_CUSPARSELT:-1} # Enable if not disabled by libtorch build
# Keep an array of cmake variables to add to
if [[ -z "$CMAKE_ARGS" ]]; then
# These are passed to tools/build_pytorch_libs.sh::build()
CMAKE_ARGS=()
fi
if [[ -z "$EXTRA_CAFFE2_CMAKE_FLAGS" ]]; then
# These are passed to tools/build_pytorch_libs.sh::build_caffe2()
EXTRA_CAFFE2_CMAKE_FLAGS=()
fi
# Determine CUDA version and architectures to build for
#
# NOTE: We should first check `DESIRED_CUDA` when determining `CUDA_VERSION`,
# because in some cases a single Docker image can have multiple CUDA versions
# on it, and `nvcc --version` might not show the CUDA version we want.
if [[ -n "$DESIRED_CUDA" ]]; then
# If the DESIRED_CUDA already matches the format that we expect
if [[ ${DESIRED_CUDA} =~ ^[0-9]+\.[0-9]+$ ]]; then
CUDA_VERSION=${DESIRED_CUDA}
else
# cu90, cu92, cu100, cu101
if [[ ${#DESIRED_CUDA} -eq 4 ]]; then
CUDA_VERSION="${DESIRED_CUDA:2:1}.${DESIRED_CUDA:3:1}"
elif [[ ${#DESIRED_CUDA} -eq 5 ]]; then
CUDA_VERSION="${DESIRED_CUDA:2:2}.${DESIRED_CUDA:4:1}"
fi
fi
echo "Using CUDA $CUDA_VERSION as determined by DESIRED_CUDA"
# There really has to be a better way to do this - eli
# Possibly limiting builds to specific cuda versions be delimiting images would be a choice
if [[ "$OS_NAME" == *"Ubuntu"* ]]; then
echo "Switching to CUDA version ${DESIRED_CUDA}"
/builder/conda/switch_cuda_version.sh "${DESIRED_CUDA}"
fi
else
CUDA_VERSION=$(nvcc --version|grep release|cut -f5 -d" "|cut -f1 -d",")
echo "CUDA $CUDA_VERSION Detected"
fi
cuda_version_nodot=$(echo $CUDA_VERSION | tr -d '.')
TORCH_CUDA_ARCH_LIST="5.0;6.0;7.0;7.5;8.0;8.6"
case ${CUDA_VERSION} in
12.4)
if [[ "$GPU_ARCH_TYPE" = "cuda-aarch64" ]]; then
TORCH_CUDA_ARCH_LIST="9.0"
else
TORCH_CUDA_ARCH_LIST="${TORCH_CUDA_ARCH_LIST};9.0+PTX"
fi
EXTRA_CAFFE2_CMAKE_FLAGS+=("-DATEN_NO_TEST=ON")
;;
12.1)
TORCH_CUDA_ARCH_LIST="${TORCH_CUDA_ARCH_LIST};9.0"
EXTRA_CAFFE2_CMAKE_FLAGS+=("-DATEN_NO_TEST=ON")
;;
11.8)
TORCH_CUDA_ARCH_LIST="${TORCH_CUDA_ARCH_LIST};3.7;9.0"
EXTRA_CAFFE2_CMAKE_FLAGS+=("-DATEN_NO_TEST=ON")
;;
11.[67])
TORCH_CUDA_ARCH_LIST="${TORCH_CUDA_ARCH_LIST};3.7"
EXTRA_CAFFE2_CMAKE_FLAGS+=("-DATEN_NO_TEST=ON")
;;
*)
echo "unknown cuda version $CUDA_VERSION"
exit 1
;;
esac
export TORCH_CUDA_ARCH_LIST=${TORCH_CUDA_ARCH_LIST}
echo "${TORCH_CUDA_ARCH_LIST}"
# Package directories
WHEELHOUSE_DIR="wheelhouse$cuda_version_nodot"
LIBTORCH_HOUSE_DIR="libtorch_house$cuda_version_nodot"
if [[ -z "$PYTORCH_FINAL_PACKAGE_DIR" ]]; then
if [[ -z "$BUILD_PYTHONLESS" ]]; then
PYTORCH_FINAL_PACKAGE_DIR="/remote/wheelhouse$cuda_version_nodot"
else
PYTORCH_FINAL_PACKAGE_DIR="/remote/libtorch_house$cuda_version_nodot"
fi
fi
mkdir -p "$PYTORCH_FINAL_PACKAGE_DIR" || true
OS_NAME=$(awk -F= '/^NAME/{print $2}' /etc/os-release)
if [[ "$OS_NAME" == *"CentOS Linux"* ]]; then
LIBGOMP_PATH="/usr/lib64/libgomp.so.1"
elif [[ "$OS_NAME" == *"AlmaLinux"* ]]; then
LIBGOMP_PATH="/usr/lib64/libgomp.so.1"
elif [[ "$OS_NAME" == *"Red Hat Enterprise Linux"* ]]; then
LIBGOMP_PATH="/usr/lib64/libgomp.so.1"
elif [[ "$OS_NAME" == *"Ubuntu"* ]]; then
LIBGOMP_PATH="/usr/lib/x86_64-linux-gnu/libgomp.so.1"
fi
DEPS_LIST=(
"$LIBGOMP_PATH"
)
DEPS_SONAME=(
"libgomp.so.1"
)
if [[ $USE_CUSPARSELT == "1" ]]; then
DEPS_SONAME+=(
"libcusparseLt.so.0"
)
DEPS_LIST+=(
"/usr/local/cuda/lib64/libcusparseLt.so.0"
)
fi
if [[ $CUDA_VERSION == "12.1" || $CUDA_VERSION == "12.4" ]]; then
export USE_STATIC_CUDNN=0
# Try parallelizing nvcc as well
export TORCH_NVCC_FLAGS="-Xfatbin -compress-all --threads 2"
if [[ -z "$PYTORCH_EXTRA_INSTALL_REQUIREMENTS" ]]; then
echo "Bundling with cudnn and cublas."
DEPS_LIST+=(
"/usr/local/cuda/lib64/libcudnn_adv.so.9"
"/usr/local/cuda/lib64/libcudnn_cnn.so.9"
"/usr/local/cuda/lib64/libcudnn_graph.so.9"
"/usr/local/cuda/lib64/libcudnn_ops.so.9"
"/usr/local/cuda/lib64/libcudnn_engines_runtime_compiled.so.9"
"/usr/local/cuda/lib64/libcudnn_engines_precompiled.so.9"
"/usr/local/cuda/lib64/libcudnn_heuristic.so.9"
"/usr/local/cuda/lib64/libcudnn.so.9"
"/usr/local/cuda/lib64/libcublas.so.12"
"/usr/local/cuda/lib64/libcublasLt.so.12"
"/usr/local/cuda/lib64/libcudart.so.12"
"/usr/local/cuda/lib64/libnvToolsExt.so.1"
"/usr/local/cuda/lib64/libnvrtc.so.12"
"/usr/local/cuda/lib64/libnvrtc-builtins.so"
)
DEPS_SONAME+=(
"libcudnn_adv.so.9"
"libcudnn_cnn.so.9"
"libcudnn_graph.so.9"
"libcudnn_ops.so.9"
"libcudnn_engines_runtime_compiled.so.9"
"libcudnn_engines_precompiled.so.9"
"libcudnn_heuristic.so.9"
"libcudnn.so.9"
"libcublas.so.12"
"libcublasLt.so.12"
"libcudart.so.12"
"libnvToolsExt.so.1"
"libnvrtc.so.12"
"libnvrtc-builtins.so"
)
else
echo "Using nvidia libs from pypi."
CUDA_RPATHS=(
'$ORIGIN/../../nvidia/cublas/lib'
'$ORIGIN/../../nvidia/cuda_cupti/lib'
'$ORIGIN/../../nvidia/cuda_nvrtc/lib'
'$ORIGIN/../../nvidia/cuda_runtime/lib'
'$ORIGIN/../../nvidia/cudnn/lib'
'$ORIGIN/../../nvidia/cufft/lib'
'$ORIGIN/../../nvidia/curand/lib'
'$ORIGIN/../../nvidia/cusolver/lib'
'$ORIGIN/../../nvidia/cusparse/lib'
'$ORIGIN/../../nvidia/nccl/lib'
'$ORIGIN/../../nvidia/nvtx/lib'
)
CUDA_RPATHS=$(IFS=: ; echo "${CUDA_RPATHS[*]}")
export C_SO_RPATH=$CUDA_RPATHS':$ORIGIN:$ORIGIN/lib'
export LIB_SO_RPATH=$CUDA_RPATHS':$ORIGIN'
export FORCE_RPATH="--force-rpath"
export USE_STATIC_NCCL=0
export USE_SYSTEM_NCCL=1
export ATEN_STATIC_CUDA=0
export USE_CUDA_STATIC_LINK=0
export USE_CUPTI_SO=1
export NCCL_INCLUDE_DIR="/usr/local/cuda/include/"
export NCCL_LIB_DIR="/usr/local/cuda/lib64/"
fi
elif [[ $CUDA_VERSION == "11.8" ]]; then
export USE_STATIC_CUDNN=0
# Try parallelizing nvcc as well
export TORCH_NVCC_FLAGS="-Xfatbin -compress-all --threads 2"
# Bundle ptxas into the wheel, see https://github.com/pytorch/pytorch/pull/119750
export BUILD_BUNDLE_PTXAS=1
if [[ -z "$PYTORCH_EXTRA_INSTALL_REQUIREMENTS" ]]; then
echo "Bundling with cudnn and cublas."
DEPS_LIST+=(
"/usr/local/cuda/lib64/libcudnn_adv.so.9"
"/usr/local/cuda/lib64/libcudnn_cnn.so.9"
"/usr/local/cuda/lib64/libcudnn_graph.so.9"
"/usr/local/cuda/lib64/libcudnn_ops.so.9"
"/usr/local/cuda/lib64/libcudnn_engines_runtime_compiled.so.9"
"/usr/local/cuda/lib64/libcudnn_engines_precompiled.so.9"
"/usr/local/cuda/lib64/libcudnn_heuristic.so.9"
"/usr/local/cuda/lib64/libcudnn.so.9"
"/usr/local/cuda/lib64/libcublas.so.11"
"/usr/local/cuda/lib64/libcublasLt.so.11"
"/usr/local/cuda/lib64/libcudart.so.11.0"
"/usr/local/cuda/lib64/libnvToolsExt.so.1"
"/usr/local/cuda/lib64/libnvrtc.so.11.2" # this is not a mistake, it links to more specific cuda version
"/usr/local/cuda/lib64/libnvrtc-builtins.so.11.8"
)
DEPS_SONAME+=(
"libcudnn_adv.so.9"
"libcudnn_cnn.so.9"
"libcudnn_graph.so.9"
"libcudnn_ops.so.9"
"libcudnn_engines_runtime_compiled.so.9"
"libcudnn_engines_precompiled.so.9"
"libcudnn_heuristic.so.9"
"libcudnn.so.9"
"libcublas.so.11"
"libcublasLt.so.11"
"libcudart.so.11.0"
"libnvToolsExt.so.1"
"libnvrtc.so.11.2"
"libnvrtc-builtins.so.11.8"
)
else
echo "Using nvidia libs from pypi."
CUDA_RPATHS=(
'$ORIGIN/../../nvidia/cublas/lib'
'$ORIGIN/../../nvidia/cuda_cupti/lib'
'$ORIGIN/../../nvidia/cuda_nvrtc/lib'
'$ORIGIN/../../nvidia/cuda_runtime/lib'
'$ORIGIN/../../nvidia/cudnn/lib'
'$ORIGIN/../../nvidia/cufft/lib'
'$ORIGIN/../../nvidia/curand/lib'
'$ORIGIN/../../nvidia/cusolver/lib'
'$ORIGIN/../../nvidia/cusparse/lib'
'$ORIGIN/../../nvidia/nccl/lib'
'$ORIGIN/../../nvidia/nvtx/lib'
)
CUDA_RPATHS=$(IFS=: ; echo "${CUDA_RPATHS[*]}")
export C_SO_RPATH=$CUDA_RPATHS':$ORIGIN:$ORIGIN/lib'
export LIB_SO_RPATH=$CUDA_RPATHS':$ORIGIN'
export FORCE_RPATH="--force-rpath"
export USE_STATIC_NCCL=0
export USE_SYSTEM_NCCL=1
export ATEN_STATIC_CUDA=0
export USE_CUDA_STATIC_LINK=0
export USE_CUPTI_SO=1
export NCCL_INCLUDE_DIR="/usr/local/cuda/include/"
export NCCL_LIB_DIR="/usr/local/cuda/lib64/"
fi
else
echo "Unknown cuda version $CUDA_VERSION"
exit 1
fi
# builder/test.sh requires DESIRED_CUDA to know what tests to exclude
export DESIRED_CUDA="$cuda_version_nodot"
# Switch `/usr/local/cuda` to the desired CUDA version
rm -rf /usr/local/cuda || true
ln -s "/usr/local/cuda-${CUDA_VERSION}" /usr/local/cuda
# Switch `/usr/local/magma` to the desired CUDA version
rm -rf /usr/local/magma || true
ln -s /usr/local/cuda-${CUDA_VERSION}/magma /usr/local/magma
export CUDA_VERSION=$(ls /usr/local/cuda/lib64/libcudart.so.*|sort|tac | head -1 | rev | cut -d"." -f -3 | rev) # 10.0.130
export CUDA_VERSION_SHORT=$(ls /usr/local/cuda/lib64/libcudart.so.*|sort|tac | head -1 | rev | cut -d"." -f -3 | rev | cut -f1,2 -d".") # 10.0
export CUDNN_VERSION=$(ls /usr/local/cuda/lib64/libcudnn.so.*|sort|tac | head -1 | rev | cut -d"." -f -3 | rev)
SCRIPTPATH="$( cd "$(dirname "$0")" ; pwd -P )"
if [[ -z "$BUILD_PYTHONLESS" ]]; then
BUILD_SCRIPT=build_common.sh
else
BUILD_SCRIPT=build_libtorch.sh
fi
source $SCRIPTPATH/${BUILD_SCRIPT}

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@ -1,353 +0,0 @@
#!/usr/bin/env bash
# meant to be called only from the neighboring build.sh and build_cpu.sh scripts
set -e pipefail
SOURCE_DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" >/dev/null && pwd )"
# Require only one python installation
if [[ -z "$DESIRED_PYTHON" ]]; then
echo "Need to set DESIRED_PYTHON env variable"
exit 1
fi
if [[ -n "$BUILD_PYTHONLESS" && -z "$LIBTORCH_VARIANT" ]]; then
echo "BUILD_PYTHONLESS is set, so need LIBTORCH_VARIANT to also be set"
echo "LIBTORCH_VARIANT should be one of shared-with-deps shared-without-deps static-with-deps static-without-deps"
exit 1
fi
# Function to retry functions that sometimes timeout or have flaky failures
retry () {
$* || (sleep 1 && $*) || (sleep 2 && $*) || (sleep 4 && $*) || (sleep 8 && $*)
}
# TODO move this into the Docker images
OS_NAME=`awk -F= '/^NAME/{print $2}' /etc/os-release`
if [[ "$OS_NAME" == *"CentOS Linux"* ]]; then
retry yum install -q -y zip openssl
elif [[ "$OS_NAME" == *"AlmaLinux"* ]]; then
retry yum install -q -y zip openssl
elif [[ "$OS_NAME" == *"Red Hat Enterprise Linux"* ]]; then
retry dnf install -q -y zip openssl
elif [[ "$OS_NAME" == *"Ubuntu"* ]]; then
# TODO: Remove this once nvidia package repos are back online
# Comment out nvidia repositories to prevent them from getting apt-get updated, see https://github.com/pytorch/pytorch/issues/74968
# shellcheck disable=SC2046
sed -i 's/.*nvidia.*/# &/' $(find /etc/apt/ -type f -name "*.list")
retry apt-get update
retry apt-get -y install zip openssl
fi
# Version: setup.py uses $PYTORCH_BUILD_VERSION.post$PYTORCH_BUILD_NUMBER if
# PYTORCH_BUILD_NUMBER > 1
build_version="$PYTORCH_BUILD_VERSION"
build_number="$PYTORCH_BUILD_NUMBER"
if [[ -n "$OVERRIDE_PACKAGE_VERSION" ]]; then
# This will be the *exact* version, since build_number<1
build_version="$OVERRIDE_PACKAGE_VERSION"
build_number=0
fi
if [[ -z "$build_version" ]]; then
build_version=1.0.0
fi
if [[ -z "$build_number" ]]; then
build_number=1
fi
export PYTORCH_BUILD_VERSION=$build_version
export PYTORCH_BUILD_NUMBER=$build_number
export CMAKE_LIBRARY_PATH="/opt/intel/lib:/lib:$CMAKE_LIBRARY_PATH"
export CMAKE_INCLUDE_PATH="/opt/intel/include:$CMAKE_INCLUDE_PATH"
# set OPENSSL_ROOT_DIR=/opt/openssl if it exists
if [[ -e /opt/openssl ]]; then
export OPENSSL_ROOT_DIR=/opt/openssl
export CMAKE_INCLUDE_PATH="/opt/openssl/include":$CMAKE_INCLUDE_PATH
fi
# If given a python version like 3.6m or 2.7mu, convert this to the format we
# expect. The binary CI jobs pass in python versions like this; they also only
# ever pass one python version, so we assume that DESIRED_PYTHON is not a list
# in this case
if [[ -n "$DESIRED_PYTHON" && "$DESIRED_PYTHON" != cp* ]]; then
python_nodot="$(echo $DESIRED_PYTHON | tr -d m.u)"
DESIRED_PYTHON="cp${python_nodot}-cp${python_nodot}"
fi
pydir="/opt/python/$DESIRED_PYTHON"
export PATH="$pydir/bin:$PATH"
export PATCHELF_BIN=/usr/local/bin/patchelf
patchelf_version=`$PATCHELF_BIN --version`
echo "patchelf version: " $patchelf_version
if [[ "$patchelf_version" == "patchelf 0.9" ]]; then
echo "Your patchelf version is too old. Please use version >= 0.10."
exit 1
fi
########################################################
# Compile wheels as well as libtorch
#######################################################
if [[ -z "$PYTORCH_ROOT" ]]; then
echo "Need to set PYTORCH_ROOT env variable"
exit 1
fi
pushd "$PYTORCH_ROOT"
python setup.py clean
retry pip install -qr requirements.txt
retry pip install -q numpy==2.0.1
if [[ "$DESIRED_DEVTOOLSET" == *"cxx11-abi"* ]]; then
export _GLIBCXX_USE_CXX11_ABI=1
else
export _GLIBCXX_USE_CXX11_ABI=0
fi
if [[ "$DESIRED_CUDA" == *"rocm"* ]]; then
echo "Calling build_amd.py at $(date)"
python tools/amd_build/build_amd.py
# TODO remove this work-around once pytorch sources are updated
export ROCclr_DIR=/opt/rocm/rocclr/lib/cmake/rocclr
fi
echo "Calling setup.py install at $(date)"
if [[ $LIBTORCH_VARIANT = *"static"* ]]; then
STATIC_CMAKE_FLAG="-DTORCH_STATIC=1"
fi
(
set -x
mkdir -p build
time CMAKE_ARGS=${CMAKE_ARGS[@]} \
EXTRA_CAFFE2_CMAKE_FLAGS="${EXTRA_CAFFE2_CMAKE_FLAGS[@]} $STATIC_CMAKE_FLAG" \
# TODO: Remove this flag once https://github.com/pytorch/pytorch/issues/55952 is closed
CFLAGS='-Wno-deprecated-declarations' \
BUILD_LIBTORCH_CPU_WITH_DEBUG=1 \
python setup.py install
mkdir -p libtorch/{lib,bin,include,share}
# Make debug folder separate so it doesn't get zipped up with the rest of
# libtorch
mkdir debug
# Copy over all lib files
cp -rv build/lib/* libtorch/lib/
cp -rv build/lib*/torch/lib/* libtorch/lib/
# Copy over all include files
cp -rv build/include/* libtorch/include/
cp -rv build/lib*/torch/include/* libtorch/include/
# Copy over all of the cmake files
cp -rv build/lib*/torch/share/* libtorch/share/
# Split libtorch into debug / release version
cp libtorch/lib/libtorch_cpu.so libtorch/lib/libtorch_cpu.so.dbg
# Keep debug symbols on debug lib
strip --only-keep-debug libtorch/lib/libtorch_cpu.so.dbg
# Remove debug info from release lib
strip --strip-debug libtorch/lib/libtorch_cpu.so
# Add a debug link to the release lib to the debug lib (debuggers will then
# search for symbols in a file called libtorch_cpu.so.dbg in some
# predetermined locations) and embed a CRC32 of the debug library into the .so
cd libtorch/lib
objcopy libtorch_cpu.so --add-gnu-debuglink=libtorch_cpu.so.dbg
cd ../..
# Move the debug symbols to its own directory so it doesn't get processed /
# zipped with all the other libraries
mv libtorch/lib/libtorch_cpu.so.dbg debug/libtorch_cpu.so.dbg
echo "${PYTORCH_BUILD_VERSION}" > libtorch/build-version
echo "$(pushd $PYTORCH_ROOT && git rev-parse HEAD)" > libtorch/build-hash
)
if [[ "$DESIRED_DEVTOOLSET" == *"cxx11-abi"* ]]; then
LIBTORCH_ABI="cxx11-abi-"
else
LIBTORCH_ABI=
fi
(
set -x
mkdir -p /tmp/$LIBTORCH_HOUSE_DIR
# objcopy installs a CRC32 into libtorch_cpu above so, so add that to the name here
CRC32=$(objcopy --dump-section .gnu_debuglink=>(tail -c4 | od -t x4 -An | xargs echo) libtorch/lib/libtorch_cpu.so)
# Zip debug symbols
zip /tmp/$LIBTORCH_HOUSE_DIR/debug-libtorch-$LIBTORCH_ABI$LIBTORCH_VARIANT-$PYTORCH_BUILD_VERSION-$CRC32.zip debug/libtorch_cpu.so.dbg
# Zip and copy libtorch
zip -rq /tmp/$LIBTORCH_HOUSE_DIR/libtorch-$LIBTORCH_ABI$LIBTORCH_VARIANT-$PYTORCH_BUILD_VERSION.zip libtorch
cp /tmp/$LIBTORCH_HOUSE_DIR/libtorch-$LIBTORCH_ABI$LIBTORCH_VARIANT-$PYTORCH_BUILD_VERSION.zip \
/tmp/$LIBTORCH_HOUSE_DIR/libtorch-$LIBTORCH_ABI$LIBTORCH_VARIANT-latest.zip
)
popd
#######################################################################
# ADD DEPENDENCIES INTO THE WHEEL
#
# auditwheel repair doesn't work correctly and is buggy
# so manually do the work of copying dependency libs and patchelfing
# and fixing RECORDS entries correctly
######################################################################
fname_with_sha256() {
HASH=$(sha256sum $1 | cut -c1-8)
DIRNAME=$(dirname $1)
BASENAME=$(basename $1)
if [[ $BASENAME == "libnvrtc-builtins.so" || $BASENAME == "libcudnn"* ]]; then
echo $1
else
INITNAME=$(echo $BASENAME | cut -f1 -d".")
ENDNAME=$(echo $BASENAME | cut -f 2- -d".")
echo "$DIRNAME/$INITNAME-$HASH.$ENDNAME"
fi
}
fname_without_so_number() {
LINKNAME=$(echo $1 | sed -e 's/\.so.*/.so/g')
echo "$LINKNAME"
}
make_wheel_record() {
FPATH=$1
if echo $FPATH | grep RECORD >/dev/null 2>&1; then
# if the RECORD file, then
echo "$FPATH,,"
else
HASH=$(openssl dgst -sha256 -binary $FPATH | openssl base64 | sed -e 's/+/-/g' | sed -e 's/\//_/g' | sed -e 's/=//g')
FSIZE=$(ls -nl $FPATH | awk '{print $5}')
echo "$FPATH,sha256=$HASH,$FSIZE"
fi
}
echo 'Built this package:'
(
set -x
mkdir -p /$LIBTORCH_HOUSE_DIR
mv /tmp/$LIBTORCH_HOUSE_DIR/*.zip /$LIBTORCH_HOUSE_DIR
rm -rf /tmp/$LIBTORCH_HOUSE_DIR
)
TMP_DIR=$(mktemp -d)
trap "rm -rf ${TMP_DIR}" EXIT
pushd "${TMP_DIR}"
for pkg in /$LIBTORCH_HOUSE_DIR/libtorch*.zip; do
# if the glob didn't match anything
if [[ ! -e $pkg ]]; then
continue
fi
rm -rf tmp
mkdir -p tmp
cd tmp
cp $pkg .
unzip -q $(basename $pkg)
rm -f $(basename $pkg)
PREFIX=libtorch
if [[ $pkg != *"without-deps"* ]]; then
# copy over needed dependent .so files over and tag them with their hash
patched=()
for filepath in "${DEPS_LIST[@]}"; do
filename=$(basename $filepath)
destpath=$PREFIX/lib/$filename
if [[ "$filepath" != "$destpath" ]]; then
cp $filepath $destpath
fi
if [[ "$DESIRED_CUDA" == *"rocm"* ]]; then
patchedpath=$(fname_without_so_number $destpath)
else
patchedpath=$(fname_with_sha256 $destpath)
fi
patchedname=$(basename $patchedpath)
if [[ "$destpath" != "$patchedpath" ]]; then
mv $destpath $patchedpath
fi
patched+=("$patchedname")
echo "Copied $filepath to $patchedpath"
done
echo "patching to fix the so names to the hashed names"
for ((i=0;i<${#DEPS_LIST[@]};++i)); do
find $PREFIX -name '*.so*' | while read sofile; do
origname=${DEPS_SONAME[i]}
patchedname=${patched[i]}
if [[ "$origname" != "$patchedname" ]] || [[ "$DESIRED_CUDA" == *"rocm"* ]]; then
set +e
origname=$($PATCHELF_BIN --print-needed $sofile | grep "$origname.*")
ERRCODE=$?
set -e
if [ "$ERRCODE" -eq "0" ]; then
echo "patching $sofile entry $origname to $patchedname"
$PATCHELF_BIN --replace-needed $origname $patchedname $sofile
fi
fi
done
done
# copy over needed auxiliary files
for ((i=0;i<${#DEPS_AUX_SRCLIST[@]};++i)); do
srcpath=${DEPS_AUX_SRCLIST[i]}
dstpath=$PREFIX/${DEPS_AUX_DSTLIST[i]}
mkdir -p $(dirname $dstpath)
cp $srcpath $dstpath
done
fi
# set RPATH of _C.so and similar to $ORIGIN, $ORIGIN/lib
find $PREFIX -maxdepth 1 -type f -name "*.so*" | while read sofile; do
echo "Setting rpath of $sofile to " '$ORIGIN:$ORIGIN/lib'
$PATCHELF_BIN --set-rpath '$ORIGIN:$ORIGIN/lib' $sofile
$PATCHELF_BIN --print-rpath $sofile
done
# set RPATH of lib/ files to $ORIGIN
find $PREFIX/lib -maxdepth 1 -type f -name "*.so*" | while read sofile; do
echo "Setting rpath of $sofile to " '$ORIGIN'
$PATCHELF_BIN --set-rpath '$ORIGIN' $sofile
$PATCHELF_BIN --print-rpath $sofile
done
# regenerate the RECORD file with new hashes
record_file=`echo $(basename $pkg) | sed -e 's/-cp.*$/.dist-info\/RECORD/g'`
if [[ -e $record_file ]]; then
echo "Generating new record file $record_file"
rm -f $record_file
# generate records for folders in wheel
find * -type f | while read fname; do
echo $(make_wheel_record $fname) >>$record_file
done
fi
# zip up the wheel back
zip -rq $(basename $pkg) $PREFIX*
# replace original wheel
rm -f $pkg
mv $(basename $pkg) $pkg
cd ..
rm -rf tmp
done
# Copy wheels to host machine for persistence before testing
if [[ -n "$PYTORCH_FINAL_PACKAGE_DIR" ]]; then
cp /$LIBTORCH_HOUSE_DIR/libtorch*.zip "$PYTORCH_FINAL_PACKAGE_DIR"
cp /$LIBTORCH_HOUSE_DIR/debug-libtorch*.zip "$PYTORCH_FINAL_PACKAGE_DIR"
fi

View File

@ -1,263 +0,0 @@
#!/usr/bin/env bash
set -ex
export ROCM_HOME=/opt/rocm
export MAGMA_HOME=$ROCM_HOME/magma
# TODO: libtorch_cpu.so is broken when building with Debug info
export BUILD_DEBUG_INFO=0
# TODO Are these all used/needed?
export TH_BINARY_BUILD=1
export USE_STATIC_CUDNN=1
export USE_STATIC_NCCL=1
export ATEN_STATIC_CUDA=1
export USE_CUDA_STATIC_LINK=1
export INSTALL_TEST=0 # dont install test binaries into site-packages
# Set RPATH instead of RUNPATH when using patchelf to avoid LD_LIBRARY_PATH override
export FORCE_RPATH="--force-rpath"
# Keep an array of cmake variables to add to
if [[ -z "$CMAKE_ARGS" ]]; then
# These are passed to tools/build_pytorch_libs.sh::build()
CMAKE_ARGS=()
fi
if [[ -z "$EXTRA_CAFFE2_CMAKE_FLAGS" ]]; then
# These are passed to tools/build_pytorch_libs.sh::build_caffe2()
EXTRA_CAFFE2_CMAKE_FLAGS=()
fi
# Determine ROCm version and architectures to build for
#
# NOTE: We should first check `DESIRED_CUDA` when determining `ROCM_VERSION`
if [[ -n "$DESIRED_CUDA" ]]; then
if ! echo "${DESIRED_CUDA}"| grep "^rocm" >/dev/null 2>/dev/null; then
export DESIRED_CUDA="rocm${DESIRED_CUDA}"
fi
# rocm3.7, rocm3.5.1
ROCM_VERSION="$DESIRED_CUDA"
echo "Using $ROCM_VERSION as determined by DESIRED_CUDA"
else
echo "Must set DESIRED_CUDA"
exit 1
fi
# Package directories
WHEELHOUSE_DIR="wheelhouse$ROCM_VERSION"
LIBTORCH_HOUSE_DIR="libtorch_house$ROCM_VERSION"
if [[ -z "$PYTORCH_FINAL_PACKAGE_DIR" ]]; then
if [[ -z "$BUILD_PYTHONLESS" ]]; then
PYTORCH_FINAL_PACKAGE_DIR="/remote/wheelhouse$ROCM_VERSION"
else
PYTORCH_FINAL_PACKAGE_DIR="/remote/libtorch_house$ROCM_VERSION"
fi
fi
mkdir -p "$PYTORCH_FINAL_PACKAGE_DIR" || true
# To make version comparison easier, create an integer representation.
ROCM_VERSION_CLEAN=$(echo ${ROCM_VERSION} | sed s/rocm//)
save_IFS="$IFS"
IFS=. ROCM_VERSION_ARRAY=(${ROCM_VERSION_CLEAN})
IFS="$save_IFS"
if [[ ${#ROCM_VERSION_ARRAY[@]} == 2 ]]; then
ROCM_VERSION_MAJOR=${ROCM_VERSION_ARRAY[0]}
ROCM_VERSION_MINOR=${ROCM_VERSION_ARRAY[1]}
ROCM_VERSION_PATCH=0
elif [[ ${#ROCM_VERSION_ARRAY[@]} == 3 ]]; then
ROCM_VERSION_MAJOR=${ROCM_VERSION_ARRAY[0]}
ROCM_VERSION_MINOR=${ROCM_VERSION_ARRAY[1]}
ROCM_VERSION_PATCH=${ROCM_VERSION_ARRAY[2]}
else
echo "Unhandled ROCM_VERSION ${ROCM_VERSION}"
exit 1
fi
ROCM_INT=$(($ROCM_VERSION_MAJOR * 10000 + $ROCM_VERSION_MINOR * 100 + $ROCM_VERSION_PATCH))
# Required ROCm libraries
ROCM_SO_FILES=(
"libMIOpen.so"
"libamdhip64.so"
"libhipblas.so"
"libhipfft.so"
"libhiprand.so"
"libhipsolver.so"
"libhipsparse.so"
"libhsa-runtime64.so"
"libamd_comgr.so"
"libmagma.so"
"librccl.so"
"librocblas.so"
"librocfft.so"
"librocm_smi64.so"
"librocrand.so"
"librocsolver.so"
"librocsparse.so"
"libroctracer64.so"
"libroctx64.so"
"libhipblaslt.so"
"libhiprtc.so"
)
if [[ $ROCM_INT -ge 60100 ]]; then
ROCM_SO_FILES+=("librocprofiler-register.so")
fi
if [[ $ROCM_INT -ge 60200 ]]; then
ROCM_SO_FILES+=("librocm-core.so")
fi
OS_NAME=`awk -F= '/^NAME/{print $2}' /etc/os-release`
if [[ "$OS_NAME" == *"CentOS Linux"* ]]; then
LIBGOMP_PATH="/usr/lib64/libgomp.so.1"
LIBNUMA_PATH="/usr/lib64/libnuma.so.1"
LIBELF_PATH="/usr/lib64/libelf.so.1"
LIBTINFO_PATH="/usr/lib64/libtinfo.so.5"
LIBDRM_PATH="/opt/amdgpu/lib64/libdrm.so.2"
LIBDRM_AMDGPU_PATH="/opt/amdgpu/lib64/libdrm_amdgpu.so.1"
if [[ $ROCM_INT -ge 60100 ]]; then
# Below libs are direct dependencies of libhipsolver
LIBSUITESPARSE_CONFIG_PATH="/lib64/libsuitesparseconfig.so.4"
LIBCHOLMOD_PATH="/lib64/libcholmod.so.2"
# Below libs are direct dependencies of libcholmod
LIBAMD_PATH="/lib64/libamd.so.2"
LIBCAMD_PATH="/lib64/libcamd.so.2"
LIBCCOLAMD_PATH="/lib64/libccolamd.so.2"
LIBCOLAMD_PATH="/lib64/libcolamd.so.2"
LIBSATLAS_PATH="/lib64/atlas/libsatlas.so.3"
# Below libs are direct dependencies of libsatlas
LIBGFORTRAN_PATH="/lib64/libgfortran.so.3"
LIBQUADMATH_PATH="/lib64/libquadmath.so.0"
fi
MAYBE_LIB64=lib64
elif [[ "$OS_NAME" == *"Ubuntu"* ]]; then
LIBGOMP_PATH="/usr/lib/x86_64-linux-gnu/libgomp.so.1"
LIBNUMA_PATH="/usr/lib/x86_64-linux-gnu/libnuma.so.1"
LIBELF_PATH="/usr/lib/x86_64-linux-gnu/libelf.so.1"
if [[ $ROCM_INT -ge 50300 ]]; then
LIBTINFO_PATH="/lib/x86_64-linux-gnu/libtinfo.so.6"
else
LIBTINFO_PATH="/lib/x86_64-linux-gnu/libtinfo.so.5"
fi
LIBDRM_PATH="/usr/lib/x86_64-linux-gnu/libdrm.so.2"
LIBDRM_AMDGPU_PATH="/usr/lib/x86_64-linux-gnu/libdrm_amdgpu.so.1"
if [[ $ROCM_INT -ge 60100 ]]; then
# Below libs are direct dependencies of libhipsolver
LIBCHOLMOD_PATH="/lib/x86_64-linux-gnu/libcholmod.so.3"
# Below libs are direct dependencies of libcholmod
LIBSUITESPARSE_CONFIG_PATH="/lib/x86_64-linux-gnu/libsuitesparseconfig.so.5"
LIBAMD_PATH="/lib/x86_64-linux-gnu/libamd.so.2"
LIBCAMD_PATH="/lib/x86_64-linux-gnu/libcamd.so.2"
LIBCCOLAMD_PATH="/lib/x86_64-linux-gnu/libccolamd.so.2"
LIBCOLAMD_PATH="/lib/x86_64-linux-gnu/libcolamd.so.2"
LIBMETIS_PATH="/lib/x86_64-linux-gnu/libmetis.so.5"
LIBLAPACK_PATH="/lib/x86_64-linux-gnu/liblapack.so.3"
LIBBLAS_PATH="/lib/x86_64-linux-gnu/libblas.so.3"
# Below libs are direct dependencies of libblas
LIBGFORTRAN_PATH="/lib/x86_64-linux-gnu/libgfortran.so.5"
LIBQUADMATH_PATH="/lib/x86_64-linux-gnu/libquadmath.so.0"
fi
MAYBE_LIB64=lib
fi
OS_SO_PATHS=($LIBGOMP_PATH $LIBNUMA_PATH\
$LIBELF_PATH $LIBTINFO_PATH\
$LIBDRM_PATH $LIBDRM_AMDGPU_PATH\
$LIBSUITESPARSE_CONFIG_PATH\
$LIBCHOLMOD_PATH $LIBAMD_PATH\
$LIBCAMD_PATH $LIBCCOLAMD_PATH\
$LIBCOLAMD_PATH $LIBSATLAS_PATH\
$LIBGFORTRAN_PATH $LIBQUADMATH_PATH\
$LIBMETIS_PATH $LIBLAPACK_PATH\
$LIBBLAS_PATH)
OS_SO_FILES=()
for lib in "${OS_SO_PATHS[@]}"
do
file_name="${lib##*/}" # Substring removal of path to get filename
OS_SO_FILES[${#OS_SO_FILES[@]}]=$file_name # Append lib to array
done
# PyTorch-version specific
# AOTriton dependency only for PyTorch >= 2.4
if (( $(echo "${PYTORCH_VERSION} 2.4" | awk '{print ($1 >= $2)}') )); then
ROCM_SO_FILES+=("libaotriton_v2.so")
fi
# rocBLAS library files
ROCBLAS_LIB_SRC=$ROCM_HOME/lib/rocblas/library
ROCBLAS_LIB_DST=lib/rocblas/library
ARCH=$(echo $PYTORCH_ROCM_ARCH | sed 's/;/|/g') # Replace ; seperated arch list to bar for grep
ARCH_SPECIFIC_FILES=$(ls $ROCBLAS_LIB_SRC | grep -E $ARCH)
OTHER_FILES=$(ls $ROCBLAS_LIB_SRC | grep -v gfx)
ROCBLAS_LIB_FILES=($ARCH_SPECIFIC_FILES $OTHER_FILES)
# hipblaslt library files
HIPBLASLT_LIB_SRC=$ROCM_HOME/lib/hipblaslt/library
HIPBLASLT_LIB_DST=lib/hipblaslt/library
ARCH_SPECIFIC_FILES=$(ls $HIPBLASLT_LIB_SRC | grep -E $ARCH)
OTHER_FILES=$(ls $HIPBLASLT_LIB_SRC | grep -v gfx)
HIPBLASLT_LIB_FILES=($ARCH_SPECIFIC_FILES $OTHER_FILES)
# ROCm library files
ROCM_SO_PATHS=()
for lib in "${ROCM_SO_FILES[@]}"
do
file_path=($(find $ROCM_HOME/lib/ -name "$lib")) # First search in lib
if [[ -z $file_path ]]; then
if [ -d "$ROCM_HOME/lib64/" ]; then
file_path=($(find $ROCM_HOME/lib64/ -name "$lib")) # Then search in lib64
fi
fi
if [[ -z $file_path ]]; then
file_path=($(find $ROCM_HOME/ -name "$lib")) # Then search in ROCM_HOME
fi
if [[ -z $file_path ]]; then
echo "Error: Library file $lib is not found." >&2
exit 1
fi
ROCM_SO_PATHS[${#ROCM_SO_PATHS[@]}]="$file_path" # Append lib to array
done
DEPS_LIST=(
${ROCM_SO_PATHS[*]}
${OS_SO_PATHS[*]}
)
DEPS_SONAME=(
${ROCM_SO_FILES[*]}
${OS_SO_FILES[*]}
)
DEPS_AUX_SRCLIST=(
"${ROCBLAS_LIB_FILES[@]/#/$ROCBLAS_LIB_SRC/}"
"${HIPBLASLT_LIB_FILES[@]/#/$HIPBLASLT_LIB_SRC/}"
"/opt/amdgpu/share/libdrm/amdgpu.ids"
)
DEPS_AUX_DSTLIST=(
"${ROCBLAS_LIB_FILES[@]/#/$ROCBLAS_LIB_DST/}"
"${HIPBLASLT_LIB_FILES[@]/#/$HIPBLASLT_LIB_DST/}"
"share/libdrm/amdgpu.ids"
)
# MIOpen library files
MIOPEN_SHARE_SRC=$ROCM_HOME/share/miopen/db
MIOPEN_SHARE_DST=share/miopen/db
MIOPEN_SHARE_FILES=($(ls $MIOPEN_SHARE_SRC | grep -E $ARCH))
DEPS_AUX_SRCLIST+=(${MIOPEN_SHARE_FILES[@]/#/$MIOPEN_SHARE_SRC/})
DEPS_AUX_DSTLIST+=(${MIOPEN_SHARE_FILES[@]/#/$MIOPEN_SHARE_DST/})
# RCCL library files
RCCL_SHARE_SRC=$ROCM_HOME/share/rccl/msccl-algorithms
RCCL_SHARE_DST=share/rccl/msccl-algorithms
RCCL_SHARE_FILES=($(ls $RCCL_SHARE_SRC))
DEPS_AUX_SRCLIST+=(${RCCL_SHARE_FILES[@]/#/$RCCL_SHARE_SRC/})
DEPS_AUX_DSTLIST+=(${RCCL_SHARE_FILES[@]/#/$RCCL_SHARE_DST/})
echo "PYTORCH_ROCM_ARCH: ${PYTORCH_ROCM_ARCH}"
SCRIPTPATH="$( cd "$(dirname "$0")" ; pwd -P )"
if [[ -z "$BUILD_PYTHONLESS" ]]; then
BUILD_SCRIPT=build_common.sh
else
BUILD_SCRIPT=build_libtorch.sh
fi
source $SCRIPTPATH/${BUILD_SCRIPT}

View File

@ -1,26 +0,0 @@
#!/usr/bin/env bash
set -e
yum install -y wget git
rm -rf /usr/local/cuda*
# Install Anaconda
if ! ls /py
then
echo "Miniconda needs to be installed"
wget https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh -O ~/miniconda.sh
bash ~/miniconda.sh -b -p /py
else
echo "Miniconda is already installed"
fi
export PATH="/py/bin:$PATH"
# Anaconda token
if ls /remote/token
then
source /remote/token
fi
conda install -y conda-build anaconda-client

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 [[ "$BUILD_ENVIRONMENT" == *executorch* ]]; then
# To build test_edge_op_registration
@ -178,7 +183,7 @@ fi
# sccache will fail for CUDA builds if all cores are used for compiling
# gcc 7 with sccache seems to have intermittent OOM issue if all cores are used
if [ -z "$MAX_JOBS" ]; then
if { [[ "$BUILD_ENVIRONMENT" == *cuda* ]]; } && which sccache > /dev/null; then
if { [[ "$BUILD_ENVIRONMENT" == *cuda* ]] || [[ "$BUILD_ENVIRONMENT" == *gcc7* ]]; } && which sccache > /dev/null; then
export MAX_JOBS=$(($(nproc) - 1))
fi
fi
@ -203,6 +208,7 @@ if [[ "${BUILD_ENVIRONMENT}" == *clang* ]]; then
fi
if [[ "$BUILD_ENVIRONMENT" == *-clang*-asan* ]]; then
export LDSHARED="clang --shared"
export USE_CUDA=0
export USE_ASAN=1
export UBSAN_FLAGS="-fno-sanitize-recover=all;-fno-sanitize=float-divide-by-zero;-fno-sanitize=float-cast-overflow"
@ -217,6 +223,10 @@ if [[ "${BUILD_ENVIRONMENT}" == *-pch* ]]; then
export USE_PRECOMPILED_HEADERS=1
fi
if [[ "${BUILD_ENVIRONMENT}" == *linux-focal-py3.7-gcc7-build* ]]; then
export USE_GLOO_WITH_OPENSSL=ON
fi
if [[ "${BUILD_ENVIRONMENT}" != *android* && "${BUILD_ENVIRONMENT}" != *cuda* ]]; then
export BUILD_STATIC_RUNTIME_BENCHMARK=ON
fi
@ -227,7 +237,7 @@ fi
# Do not change workspace permissions for ROCm CI jobs
# as it can leave workspace with bad permissions for cancelled jobs
if [[ "$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=$(stat -c '%u' "/var/lib/jenkins/workspace")
cleanup_workspace() {
@ -273,11 +283,11 @@ else
# set only when building other architectures
# or building non-XLA tests.
if [[ "$BUILD_ENVIRONMENT" != *rocm* &&
"$BUILD_ENVIRONMENT" != *s390x* &&
"$BUILD_ENVIRONMENT" != *xla* ]]; then
if [[ "$BUILD_ENVIRONMENT" != *py3.8* ]]; then
# Install numpy-2.0.2 for builds which are backward compatible with 1.X
python -mpip install --pre numpy==2.0.2
# Install numpy-2.0 release candidate for builds
# Which should be backward compatible with Numpy-1.X
python -mpip install --pre numpy==2.0.0rc1
fi
WERROR=1 python setup.py clean
@ -336,11 +346,11 @@ else
CUSTOM_OP_BUILD="${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
@ -350,10 +360,10 @@ 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="$CUSTOM_TEST_USE_ROCM"
make VERBOSE=1
popd
@ -365,7 +375,7 @@ else
python --version
mkdir -p "$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
@ -398,6 +408,6 @@ 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 +42,11 @@ 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) + timedelta(days=10)
datetime.utcnow()
+ timedelta(days=10)
)
.add_extension(
x509.BasicConstraints(ca=True, path_length=None),
@ -87,10 +88,11 @@ 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) + timedelta(days=10)
datetime.utcnow()
+ timedelta(days=10)
# Sign our certificate with our private key
)
.sign(private_ca_key, hashes.SHA256())

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

@ -49,16 +49,16 @@ NUM_TEST_SHARDS="${NUM_TEST_SHARDS:=1}"
export VALGRIND=ON
# export TORCH_INDUCTOR_INSTALL_GXX=ON
if [[ "$BUILD_ENVIRONMENT" == *clang9* ]]; then
# clang9 appears to miscompile code involving std::optional<c10::SymInt>,
# clang9 appears to miscompile code involving c10::optional<c10::SymInt>,
# such that valgrind complains along these lines:
#
# Conditional jump or move depends on uninitialised value(s)
# at 0x40303A: ~optional_base (Optional.h:281)
# by 0x40303A: call (Dispatcher.h:448)
# by 0x40303A: call(at::Tensor const&, c10::ArrayRef<c10::SymInt>, c10::ArrayRef<c10::SymInt>, std::optional<c10::SymInt>) (basic.cpp:10)
# by 0x40303A: call(at::Tensor const&, c10::ArrayRef<c10::SymInt>, c10::ArrayRef<c10::SymInt>, c10::optional<c10::SymInt>) (basic.cpp:10)
# by 0x403700: main (basic.cpp:16)
# Uninitialised value was created by a stack allocation
# at 0x402AAA: call(at::Tensor const&, c10::ArrayRef<c10::SymInt>, c10::ArrayRef<c10::SymInt>, std::optional<c10::SymInt>) (basic.cpp:6)
# at 0x402AAA: call(at::Tensor const&, c10::ArrayRef<c10::SymInt>, c10::ArrayRef<c10::SymInt>, c10::optional<c10::SymInt>) (basic.cpp:6)
#
# The problem does not appear with gcc or newer versions of clang (we tested
# clang14). So we suppress valgrind testing for clang9 specifically.
@ -72,7 +72,7 @@ if [[ "$BUILD_ENVIRONMENT" == *clang9* ]]; then
#
# using namespace at;
#
# Tensor call(const at::Tensor & self, c10::SymIntArrayRef size, c10::SymIntArrayRef stride, std::optional<c10::SymInt> storage_offset) {
# Tensor call(const at::Tensor & self, c10::SymIntArrayRef size, c10::SymIntArrayRef stride, c10::optional<c10::SymInt> storage_offset) {
# auto op = c10::Dispatcher::singleton()
# .findSchemaOrThrow(at::_ops::as_strided::name, at::_ops::as_strided::overload_name)
# .typed<at::_ops::as_strided::schema>();
@ -233,8 +233,8 @@ if [[ "$BUILD_ENVIRONMENT" == *asan* ]]; then
# it depends on a ton of dynamic libraries that most programs aren't gonna
# have, and it applies to child processes.
LD_PRELOAD=$(clang --print-file-name=libclang_rt.asan-x86_64.so)
export LD_PRELOAD
# TODO: get rid of the hardcoded path
export LD_PRELOAD=/usr/lib/llvm-15/lib/clang/15.0.7/lib/linux/libclang_rt.asan-x86_64.so
# Disable valgrind for asan
export VALGRIND=OFF
@ -369,27 +369,22 @@ test_inductor_aoti() {
CPP_TESTS_DIR="${BUILD_BIN_DIR}" LD_LIBRARY_PATH="${TORCH_LIB_DIR}" python test/run_test.py --cpp --verbose -i cpp/test_aoti_abi_check cpp/test_aoti_inference
}
test_inductor_cpp_wrapper() {
export TORCHINDUCTOR_CPP_WRAPPER=1
test_inductor_cpp_wrapper_abi_compatible() {
export TORCHINDUCTOR_ABI_COMPATIBLE=1
TEST_REPORTS_DIR=$(pwd)/test/test-reports
mkdir -p "$TEST_REPORTS_DIR"
python benchmarks/dynamo/timm_models.py --device cuda --accuracy --amp \
echo "Testing Inductor cpp wrapper mode with TORCHINDUCTOR_ABI_COMPATIBLE=1"
# cpu stack allocation causes segfault and needs more investigation
PYTORCH_TESTING_DEVICE_ONLY_FOR="" python test/run_test.py --include inductor/test_cpu_cpp_wrapper
python test/run_test.py --include inductor/test_cuda_cpp_wrapper
TORCHINDUCTOR_CPP_WRAPPER=1 python benchmarks/dynamo/timm_models.py --device cuda --accuracy --amp \
--training --inductor --disable-cudagraphs --only vit_base_patch16_224 \
--output "$TEST_REPORTS_DIR/inductor_cpp_wrapper_training.csv"
python benchmarks/dynamo/check_accuracy.py \
--actual "$TEST_REPORTS_DIR/inductor_cpp_wrapper_training.csv" \
--expected "benchmarks/dynamo/ci_expected_accuracy/inductor_timm_training.csv"
python benchmarks/dynamo/torchbench.py --device cuda --accuracy \
--bfloat16 --inference --inductor --only hf_T5 --output "$TEST_REPORTS_DIR/inductor_cpp_wrapper_inference.csv"
python benchmarks/dynamo/torchbench.py --device cuda --accuracy \
--bfloat16 --inference --inductor --only llama --output "$TEST_REPORTS_DIR/inductor_cpp_wrapper_inference.csv"
python benchmarks/dynamo/torchbench.py --device cuda --accuracy \
--bfloat16 --inference --inductor --only moco --output "$TEST_REPORTS_DIR/inductor_cpp_wrapper_inference.csv"
python benchmarks/dynamo/check_accuracy.py \
--actual "$TEST_REPORTS_DIR/inductor_cpp_wrapper_inference.csv" \
--expected "benchmarks/dynamo/ci_expected_accuracy/inductor_torchbench_inference.csv"
}
# "Global" flags for inductor benchmarking controlled by TEST_CONFIG
@ -406,10 +401,10 @@ pr_time_benchmarks() {
TEST_REPORTS_DIR=$(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" "$TEST_REPORTS_DIR/new_expected_results.csv"
cat "$TEST_REPORTS_DIR/pr_time_benchmarks_after.txt"
}
if [[ "${TEST_CONFIG}" == *pr_time_benchmarks* ]]; then
@ -517,7 +512,7 @@ test_perf_for_dashboard() {
"${target_flag[@]}" --"$mode" --"$dtype" --export --disable-cudagraphs "$@" \
--output "$TEST_REPORTS_DIR/${backend}_export_${suite}_${dtype}_${mode}_${device}_${target}.csv"
fi
$TASKSET python "benchmarks/dynamo/$suite.py" \
TORCHINDUCTOR_ABI_COMPATIBLE=1 $TASKSET python "benchmarks/dynamo/$suite.py" \
"${target_flag[@]}" --"$mode" --"$dtype" --export-aot-inductor --disable-cudagraphs "$@" \
--output "$TEST_REPORTS_DIR/${backend}_aot_inductor_${suite}_${dtype}_${mode}_${device}_${target}.csv"
fi
@ -572,11 +567,18 @@ test_single_dynamo_benchmark() {
test_perf_for_dashboard "$suite" \
"${DYNAMO_BENCHMARK_FLAGS[@]}" "$@" "${partition_flags[@]}"
else
if [[ "${TEST_CONFIG}" == *aot_inductor* && "${TEST_CONFIG}" != *cpu_aot_inductor* ]]; then
# Test AOTInductor with the ABI-compatible mode on CI
# This can be removed once the ABI-compatible mode becomes default.
# For CPU device, we perfer non ABI-compatible mode on CI when testing AOTInductor.
export TORCHINDUCTOR_ABI_COMPATIBLE=1
fi
if [[ "${TEST_CONFIG}" == *_avx2* ]]; then
TEST_CONFIG=${TEST_CONFIG//_avx2/}
TEST_CONFIG=${TEST_CONFIG::-5}
fi
if [[ "${TEST_CONFIG}" == *_avx512* ]]; then
TEST_CONFIG=${TEST_CONFIG//_avx512/}
TEST_CONFIG=${TEST_CONFIG::-7}
fi
python "benchmarks/dynamo/$suite.py" \
--ci --accuracy --timing --explain \
@ -594,9 +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 "${TEST_REPORTS_DIR}/gpt_fast_benchmark.csv"
}
@ -605,11 +604,6 @@ test_inductor_halide() {
assert_git_not_dirty
}
test_inductor_triton_cpu() {
python test/run_test.py --include inductor/test_triton_cpu_backend.py --verbose
assert_git_not_dirty
}
test_dynamo_benchmark() {
# Usage: test_dynamo_benchmark huggingface 0
TEST_REPORTS_DIR=$(pwd)/test/test-reports
@ -647,12 +641,32 @@ test_inductor_torchbench_smoketest_perf() {
TEST_REPORTS_DIR=$(pwd)/test/test-reports
mkdir -p "$TEST_REPORTS_DIR"
# Test some models in the cpp wrapper mode
TORCHINDUCTOR_ABI_COMPATIBLE=1 TORCHINDUCTOR_CPP_WRAPPER=1 python benchmarks/dynamo/torchbench.py --device cuda --accuracy \
--bfloat16 --inference --inductor --only hf_T5 --output "$TEST_REPORTS_DIR/inductor_cpp_wrapper_inference.csv"
TORCHINDUCTOR_ABI_COMPATIBLE=1 TORCHINDUCTOR_CPP_WRAPPER=1 python benchmarks/dynamo/torchbench.py --device cuda --accuracy \
--bfloat16 --inference --inductor --only llama --output "$TEST_REPORTS_DIR/inductor_cpp_wrapper_inference.csv"
TORCHINDUCTOR_ABI_COMPATIBLE=1 TORCHINDUCTOR_CPP_WRAPPER=1 python benchmarks/dynamo/torchbench.py --device cuda --accuracy \
--bfloat16 --inference --inductor --only moco --output "$TEST_REPORTS_DIR/inductor_cpp_wrapper_inference.csv"
python benchmarks/dynamo/check_accuracy.py \
--actual "$TEST_REPORTS_DIR/inductor_cpp_wrapper_inference.csv" \
--expected "benchmarks/dynamo/ci_expected_accuracy/inductor_torchbench_inference.csv"
python benchmarks/dynamo/torchbench.py --device cuda --performance --backend inductor --float16 --training \
--batch-size-file "$(realpath benchmarks/dynamo/torchbench_models_list.txt)" --only hf_Bert \
--output "$TEST_REPORTS_DIR/inductor_training_smoketest.csv"
# 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 "$TEST_REPORTS_DIR/inductor_inference_smoketest.csv" -t 4.9
# Check memory compression ratio for a few models
for test in hf_Albert timm_vision_transformer; do
python benchmarks/dynamo/torchbench.py --device cuda --performance --backend inductor --amp --training \
@ -696,10 +710,6 @@ test_inductor_set_cpu_affinity(){
export KMP_BLOCKTIME=1
fi
cores=$(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"
@ -736,9 +746,19 @@ test_inductor_torchbench_cpu_smoketest_perf(){
fi
cat "$output_name"
# The threshold value needs to be actively maintained to make this check useful.
# Allow 1% variance for CPU perf to accommodate perf fluctuation
python benchmarks/dynamo/check_perf_csv.py -f "$output_name" -t "$speedup_target" -s 0.99
python benchmarks/dynamo/check_perf_csv.py -f "$output_name" -t "$speedup_target"
done
# Add a few ABI-compatible accuracy tests for CPU. These can be removed once we turn on ABI-compatible as default.
TORCHINDUCTOR_ABI_COMPATIBLE=1 python benchmarks/dynamo/timm_models.py --device cpu --accuracy \
--bfloat16 --inference --export-aot-inductor --disable-cudagraphs --only adv_inception_v3 \
--output "$TEST_REPORTS_DIR/aot_inductor_smoke_test.csv"
TORCHINDUCTOR_ABI_COMPATIBLE=1 python benchmarks/dynamo/timm_models.py --device cpu --accuracy \
--bfloat16 --inference --export-aot-inductor --disable-cudagraphs --only beit_base_patch16_224 \
--output "$TEST_REPORTS_DIR/aot_inductor_smoke_test.csv"
python benchmarks/dynamo/check_accuracy.py \
--actual "$TEST_REPORTS_DIR/aot_inductor_smoke_test.csv" \
--expected "benchmarks/dynamo/ci_expected_accuracy/aot_inductor_timm_inference.csv"
}
test_torchbench_gcp_smoketest(){
@ -1360,16 +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 \
@ -1379,8 +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
@ -1413,8 +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 [[ "${TEST_CONFIG}" == *huggingface* ]]; then
@ -1431,13 +1446,14 @@ elif [[ "${TEST_CONFIG}" == *torchbench* ]]; then
else
install_torchaudio cuda
fi
install_torchtext
install_torchvision
TORCH_CUDA_ARCH_LIST="8.0;8.6" pip_install git+https://github.com/pytorch/ao.git
id=$((SHARD_NUMBER-1))
# https://github.com/opencv/opencv-python/issues/885
pip_install opencv-python==4.8.0.74
if [[ "${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 \
@ -1456,16 +1472,16 @@ elif [[ "${TEST_CONFIG}" == *torchbench* ]]; then
fi
PYTHONPATH=$(pwd)/torchbench test_dynamo_benchmark torchbench "$id"
fi
elif [[ "${TEST_CONFIG}" == *inductor_cpp_wrapper* ]]; then
install_torchaudio cuda
elif [[ "${TEST_CONFIG}" == *inductor_cpp_wrapper_abi_compatible* ]]; then
install_torchvision
checkout_install_torchbench hf_T5 llama moco
PYTHONPATH=$(pwd)/torchbench test_inductor_cpp_wrapper
test_inductor_cpp_wrapper_abi_compatible
elif [[ "${TEST_CONFIG}" == *inductor* ]]; then
install_torchvision
test_inductor_shard "${SHARD_NUMBER}"
if [[ "${SHARD_NUMBER}" == 1 ]]; then
if [[ "${BUILD_ENVIRONMENT}" != linux-jammy-py3.9-gcc11-build ]]; then
if [[ "${BUILD_ENVIRONMENT}" != linux-jammy-py3.8-gcc11-build ]]; then
# Temporarily skip test_inductor_aoti due to https://github.com/pytorch/pytorch/issues/130311
test_inductor_aoti
test_inductor_distributed
fi
fi

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

@ -40,15 +40,6 @@ python -m pip install pytest-rerunfailures==10.3 pytest-cpp==2.3.0 tensorboard==
# Install Z3 optional dependency for Windows builds.
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
# Install pulp for testing ilps under torch\distributed\_tools
python -m pip install pulp==2.9.0
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

@ -10,11 +10,6 @@ export SCCACHE_BUCKET=ossci-compiler-cache
export SCCACHE_IGNORE_SERVER_IO_ERROR=1
export VC_YEAR=2019
if [[ "$DESIRED_CUDA" == 'xpu' ]]; then
export VC_YEAR=2022
export USE_SCCACHE=0
fi
echo "Free space on filesystem before build:"
df -h

View File

@ -6,10 +6,6 @@ source "${BINARY_ENV_FILE:-/c/w/env}"
export CUDA_VERSION="${DESIRED_CUDA/cu/}"
export VC_YEAR=2019
if [[ "$DESIRED_CUDA" == 'xpu' ]]; then
export VC_YEAR=2022
fi
pushd "$BUILDER_ROOT"
./windows/internal/smoke_test.bat

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

@ -3,6 +3,8 @@ self-hosted-runner:
# GitHub hosted x86 Linux runners
- linux.20_04.4x
- linux.20_04.16x
# Repo-specific LF hosted ARC runners
- linux.large.arc
# Organization-wide AWS Linux Runners
- linux.large
- linux.2xlarge
@ -32,6 +34,30 @@ 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

@ -31,10 +31,6 @@
- "module: flex attention"
then:
- "module: higher order operators"
- any:
- "module: aotinductor"
then:
- "oncall: export"
- any:
- "module: dynamo"
- "module: pt2-dispatcher"

6
.github/labeler.yml vendored
View File

@ -98,9 +98,3 @@
"module: distributed_checkpoint":
- torch/distributed/checkpoint/**
- test/distributed/checkpoint/**
"module: compiled autograd":
- torch/csrc/dynamo/python_compiled_autograd.cpp
- torch/csrc/dynamo/compiled_autograd.h
- torch/_dynamo/compiled_autograd.py
- torch/inductor/test_compiled_autograd.py

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
#
@ -33,38 +29,60 @@ runner_types:
disk_size: 200
instance_type: c5.12xlarge
is_ephemeral: false
max_available: 2000
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
@ -241,7 +359,7 @@ runner_types:
disk_size: 256
instance_type: p3.2xlarge
is_ephemeral: false
max_available: 300
max_available: 150
os: windows
lf.c.windows.g5.4xlarge.nvidia.gpu:
disk_size: 256

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
#
@ -33,38 +29,60 @@ runner_types:
disk_size: 200
instance_type: c5.12xlarge
is_ephemeral: false
max_available: 2000
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
@ -241,7 +359,7 @@ runner_types:
disk_size: 256
instance_type: p3.2xlarge
is_ephemeral: false
max_available: 300
max_available: 150
os: windows
lf.windows.g5.4xlarge.nvidia.gpu:
disk_size: 256

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
@ -119,8 +107,8 @@
mandatory_checks_name:
- EasyCLA
- Lint
- pull / linux-focal-py3_9-clang9-xla / build
- pull / linux-focal-py3_9-clang9-xla / test (xla, 1, 1, linux.12xlarge)
- pull / linux-focal-py3_8-clang9-xla / build
- pull / linux-focal-py3_8-clang9-xla / test (xla, 1, 1, linux.12xlarge)
- name: Documentation
patterns:
@ -544,7 +532,6 @@
- anijain2305
- bdhirsh
- zou3519
- isuruf
mandatory_checks_name:
- EasyCLA
- Lint

View File

@ -9,20 +9,17 @@ 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
- ciflow/xpu
- ciflow/torchbench
- ciflow/autoformat
retryable_workflows:
- pull
- trunk

View File

@ -1,4 +1,4 @@
# iOS simulator requirements
coremltools==5.0b5
protobuf==3.20.2
optree==0.13.0
optree==0.12.1

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
@ -27,8 +26,7 @@ pytest-cpp==2.3.0
rockset==1.0.3
z3-solver==4.12.2.0
tensorboard==2.13.0
optree==0.13.0
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

@ -27,12 +27,6 @@ def parse_args() -> Any:
parser = ArgumentParser("Check PR labels")
parser.add_argument("pr_num", type=int)
# add a flag to return a non-zero exit code if the PR does not have the required labels
parser.add_argument(
"--exit-non-zero",
action="store_true",
help="Return a non-zero exit code if the PR does not have the required labels",
)
return parser.parse_args()
@ -47,13 +41,10 @@ def main() -> None:
if not has_required_labels(pr):
print(LABEL_ERR_MSG)
add_label_err_comment(pr)
if args.exit_non_zero:
sys.exit(1)
else:
delete_all_label_err_comments(pr)
except Exception as e:
if args.exit_non_zero:
sys.exit(1)
pass
sys.exit(0)

View File

@ -77,7 +77,6 @@ PYTORCH_EXTRA_INSTALL_REQUIREMENTS = {
"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-cusparselt-cu12==0.6.2; 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'"
@ -326,7 +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":
@ -334,7 +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
@ -342,7 +340,7 @@ def generate_wheels_matrix(
if os == "linux":
arches += CPU_CXX11_ABI_ARCH + CUDA_ARCHES + ROCM_ARCHES + XPU_ARCHES
elif os == "windows":
arches += CUDA_ARCHES + XPU_ARCHES
arches += CUDA_ARCHES
elif os == "linux-aarch64":
# Only want the one arch as the CPU type is different and
# uses different build/test scripts
@ -369,28 +367,11 @@ def generate_wheels_matrix(
# TODO: Enable python 3.13 on rocm, aarch64, windows
if (
gpu_arch_type == "rocm"
or os not in ["linux", "linux-s390x", "macos-arm64"]
) and python_version in ["3.13", "3.13t"]:
gpu_arch_type == "rocm" or (os != "linux" and os != "linux-s390x")
) and python_version == "3.13":
continue
# TODO: Enable python 3.13t on xpu and cpu-s390x or MacOS
if (
gpu_arch_type in ["xpu", "cpu-s390x"] or os == "macos-arm64"
) 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"
@ -404,14 +385,13 @@ 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 ""
),
"container_image": WHEEL_CONTAINER_IMAGES[arch_version],
"package_type": package_type,
"pytorch_extra_install_requirements": (
PYTORCH_EXTRA_INSTALL_REQUIREMENTS[arch_version]
PYTORCH_EXTRA_INSTALL_REQUIREMENTS[arch_version] # fmt: skip
if os != "linux-aarch64"
else ""
),
@ -420,8 +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,
@ -430,16 +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(
{
@ -449,7 +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 ""
),
@ -459,13 +461,12 @@ def generate_wheels_matrix(
".", "_"
),
"pytorch_extra_install_requirements": (
PYTORCH_EXTRA_INSTALL_REQUIREMENTS["12.1"]
if os != "linux" and gpu_arch_type != "xpu"
PYTORCH_EXTRA_INSTALL_REQUIREMENTS["12.1"] # fmt: skip
if os != "linux"
else ""
),
}
)
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

@ -46,24 +46,16 @@ def gh_fetch_url_and_headers(
with urlopen(Request(url, headers=headers, data=data_, method=method)) as conn:
return conn.headers, reader(conn)
except HTTPError as err:
if (
err.code == 403
and all(
key in err.headers
for key in ["X-RateLimit-Limit", "X-RateLimit-Remaining"]
)
and int(err.headers["X-RateLimit-Remaining"]) == 0
if err.code == 403 and all(
key in err.headers for key in ["X-RateLimit-Limit", "X-RateLimit-Used"]
):
print(
f"""{url}
Rate limit exceeded:
f"""Rate limit exceeded:
Used: {err.headers['X-RateLimit-Used']}
Limit: {err.headers['X-RateLimit-Limit']}
Remaining: {err.headers['X-RateLimit-Remaining']}
Resets at: {err.headers['x-RateLimit-Reset']}"""
)
else:
print(f"Error fetching {url} {err}")
raise
@ -168,14 +160,6 @@ 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
@ -38,11 +33,10 @@ 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. To apply suggested patches automatically, use the -a flag. Before pushing another commit,\e[0m"
echo -e "\e[1m\e[36mplease verify locally and ensure everything passes.\e[0m"
echo -e "\e[1m\e[36mSee https://github.com/pytorch/pytorch/wiki/lintrunner for setup instructions.\e[0m"
RC=1
fi

View File

@ -1,107 +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
all_branches: false
default: true
---
# 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, FrozenSet, 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
)
default: bool = (
True # If True, the experiment is enabled by default for all queries
)
# 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"""
@ -144,12 +88,6 @@ def set_github_output(key: str, value: str) -> None:
f.write(f"{key}={value}\n")
def _str_comma_separated_to_set(value: str) -> FrozenSet[str]:
return frozenset(
filter(lambda itm: itm != "", map(str.strip, value.strip(" \n\t").split(",")))
)
def parse_args() -> Any:
parser = ArgumentParser("Get dynamic rollout settings")
parser.add_argument("--github-token", type=str, required=True, help="GitHub token")
@ -184,13 +122,6 @@ def parse_args() -> Any:
required=True,
help="Current GitHub ref type, branch or tag",
)
parser.add_argument(
"--eligible-experiments",
type=_str_comma_separated_to_set,
required=False,
default="",
help="comma separated list of experiments to check, if omitted all experiments marked with default=True are checked",
)
return parser.parse_args()
@ -206,14 +137,11 @@ def get_issue(gh: Github, repo: str, issue_num: int) -> Issue:
def get_potential_pr_author(
github_token: str, repo: str, username: str, ref_type: str, ref_name: str
gh: Github, repo: str, username: str, ref_type: str, ref_name: str
) -> str:
# If the trigger was a new tag added by a bot, this is a ciflow case
# Fetch the actual username from the original PR. The PR number is
# embedded in the tag name: ciflow/<name>/<pr-number>
gh = get_gh_client(github_token)
if username == "pytorch-bot[bot]" and ref_type == "tag":
split_tag = ref_name.split("/")
if (
@ -235,256 +163,126 @@ 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.
"""
return branch.split("/")[0] in {"main", "nightly", "release", "landchecks"}
def load_yaml(yaml_text: str) -> Any:
def get_workflow_type(issue: Issue, workflow_requestors: Iterable[str]) -> str:
try:
data = yaml.safe_load(yaml_text)
return data
except yaml.YAMLError as exc:
log.exception("Error loading YAML")
raise
first_comment = issue.get_comments()[0].body.strip("\n\t ")
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,
eligible_experiments: FrozenSet[str] = frozenset(),
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():
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
if eligible_experiments:
if experiment_name not in eligible_experiments:
exp_list = ", ".join(eligible_experiments)
if first_comment[0] == "!":
log.info("LF Workflows are disabled for everyone. Using meta runners.")
return WORKFLOW_LABEL_META
elif first_comment[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 first_comment.split()
}
opted_in_requestors = {
usr for usr in workflow_requestors if usr in all_opted_in_users
}
if opted_in_requestors:
log.info(
f"Skipping experiment '{experiment_name}', as it is not in the eligible_experiments list: {exp_list}"
f"LF Workflows are enabled for {', '.join(opted_in_requestors)}. Using LF runners."
)
continue
elif not experiment_settings.default:
log.info(
f"Skipping experiment '{experiment_name}', as it is not a default experiment"
)
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)
]
enabled = False
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:
log.info(
f"Based on rollout percentage of {experiment_settings.rollout_perc}%, enabling experiment {experiment_name}."
)
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]
# Fleet always comes first
if fleet_prefix:
prefixes.insert(0, fleet_prefix)
return ".".join(prefixes) + "." if prefixes else ""
return WORKFLOW_LABEL_META
def get_rollout_state_from_issue(github_token: str, repo: str, issue_num: int) -> str:
"""
Gets the first comment of the issue, which contains the desired rollout state.
def get_optin_feature(
issue: Issue, workflow_requestors: Iterable[str], feature: str, fallback: str
) -> str:
try:
first_comment = issue.get_comments()[0].body.strip("\n\t ")
userlist = {u.lstrip("#").strip("\n\t@ ") for u in first_comment.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
}
The default issue we use - https://github.com/pytorch/test-infra/issues/5132
"""
gh = get_gh_client(github_token)
issue = get_issue(gh, repo, issue_num)
return str(issue.get_comments()[0].body.strip("\n\t "))
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 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:
gh = get_gh_client(args.github_token)
# The default issue we use - https://github.com/pytorch/test-infra/issues/5132
issue = get_issue(gh, args.github_issue_repo, args.github_issue)
username = get_potential_pr_author(
gh,
args.github_repo,
args.github_actor,
args.github_ref_type,
args.github_branch,
)
label_type = get_workflow_type(
issue,
(
args.github_issue_owner,
username,
),
)
runner_ami = get_optin_feature(
issue=issue,
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
try:
rollout_state = get_rollout_state_from_issue(
args.github_token, args.github_issue_repo, args.github_issue
)
# 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
username = get_potential_pr_author(
args.github_token,
args.github_repo,
args.github_actor,
args.github_ref_type,
args.github_branch,
)
is_canary = args.github_repo == "pytorch/pytorch-canary"
runner_label_prefix = get_runner_prefix(
rollout_state,
(args.github_issue_owner, username),
args.github_branch,
args.eligible_experiments,
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

@ -18,7 +18,6 @@ def mock_parse_args() -> object:
class Object:
def __init__(self) -> None:
self.pr_num = 76123
self.exit_non_zero = False
return Object()

View File

@ -1,440 +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
default: false
---
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, default=False),
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
default: false
```
---
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, default=False),
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")
def test_opted_in_user_two_experiments_default(self) -> None:
settings_text = """
experiments:
lf:
rollout_perc: 0
otherExp:
rollout_perc: 0
default: false
---
Users:
@User1,lf
@User2,lf,otherExp
"""
prefix = rd.get_runner_prefix(settings_text, ["User2"], USER_BRANCH)
self.assertEqual("lf.", prefix, "Runner prefix not correct for User2")
def test_opted_in_user_two_experiments_default_exp(self) -> None:
settings_text = """
experiments:
lf:
rollout_perc: 0
otherExp:
rollout_perc: 0
default: false
---
Users:
@User1,lf
@User2,lf,otherExp
"""
prefix = rd.get_runner_prefix(
settings_text, ["User2"], USER_BRANCH, frozenset(["lf", "otherExp"])
)
self.assertEqual("lf.otherExp.", prefix, "Runner prefix not correct for User2")
def test_opted_in_user_two_experiments_default_exp_2(self) -> None:
settings_text = """
experiments:
lf:
rollout_perc: 0
otherExp:
rollout_perc: 0
default: false
---
Users:
@User1,lf
@User2,lf,otherExp
"""
prefix = rd.get_runner_prefix(
settings_text, ["User2"], USER_BRANCH, frozenset(["otherExp"])
)
self.assertEqual("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")
@patch("random.uniform", return_value=10)
def test_opted_out_user_was_pulled_in_by_rollout_excl_nondefault(
self, mock_uniform: Mock
) -> None:
settings_text = """
experiments:
lf:
rollout_perc: 25
otherExp:
rollout_perc: 25
default: false
---
Users:
@User1,lf
@User2,lf,otherExp
"""
# User3 is opted out, but is pulled into default experiments by the 10% rollout
prefix = rd.get_runner_prefix(settings_text, ["User3"], USER_BRANCH)
self.assertEqual("lf.", prefix, "Runner prefix not correct for user")
@patch("random.uniform", return_value=10)
def test_opted_out_user_was_pulled_in_by_rollout_filter_exp(
self, mock_uniform: Mock
) -> None:
settings_text = """
experiments:
lf:
rollout_perc: 25
otherExp:
rollout_perc: 25
default: false
---
Users:
@User1,lf
@User2,lf,otherExp
"""
# User3 is opted out, but is pulled into default experiments by the 10% rollout
prefix = rd.get_runner_prefix(
settings_text, ["User3"], USER_BRANCH, frozenset(["otherExp"])
)
self.assertEqual("otherExp.", prefix, "Runner prefix not correct for user")
@patch("random.uniform", return_value=25)
def test_opted_out_user_was_pulled_out_by_rollout_filter_exp(
self, mock_uniform: Mock
) -> None:
settings_text = """
experiments:
lf:
rollout_perc: 10
otherExp:
rollout_perc: 50
default: false
---
Users:
@User1,lf
@User2,lf,otherExp
"""
# User3 is opted out, but is pulled into default experiments by the 10% rollout
prefix = rd.get_runner_prefix(settings_text, ["User3"], USER_BRANCH)
self.assertEqual("", 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,
@ -1506,41 +1496,13 @@ def checks_to_str(checks: List[Tuple[str, Optional[str]]]) -> str:
def checks_to_markdown_bullets(
checks: List[Tuple[str, Optional[str], Optional[int]]],
checks: List[Tuple[str, Optional[str], Optional[int]]]
) -> List[str]:
return [
f"- [{c[0]}]({c[1]})" if c[1] is not None else f"- {c[0]}" for c in checks[:5]
]
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

@ -1,7 +1,7 @@
{%- 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 download_artifact_action = "actions/download-artifact@v4.1.7" -%}
{%- set upload_artifact_action = "actions/upload-artifact@v3" -%}
{%- set download_artifact_action = "actions/download-artifact@v3" -%}
{%- set timeout_minutes = 240 -%}

View File

@ -54,7 +54,7 @@ env:
jobs:
get-label-type:
name: get-label-type
uses: pytorch/pytorch/.github/workflows/_runner-determinator.yml@main
uses: ./.github/workflows/_runner-determinator.yml
with:
triggering_actor: ${{ github.triggering_actor }}
issue_owner: ${{ github.event.pull_request.user.login || github.event.issue.user.login }}
@ -68,7 +68,6 @@ jobs:
needs: get-label-type
with:!{{ upload.binary_env_as_input(config) }}
{%- if "aarch64" in build_environment %}
runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
runs_on: linux.arm64.m7g.4xlarge.ephemeral
ALPINE_IMAGE: "arm64v8/alpine"
{%- elif "s390x" in build_environment %}
@ -103,7 +102,6 @@ jobs:
build_name: !{{ config["build_name"] }}
build_environment: !{{ build_environment }}
{%- if "aarch64" in build_environment %}
runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
runs_on: linux.arm64.2xlarge
ALPINE_IMAGE: "arm64v8/alpine"
{%- elif "s390x" in build_environment %}

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

@ -55,7 +55,7 @@ env:
jobs:
get-label-type:
name: get-label-type
uses: pytorch/pytorch/.github/workflows/_runner-determinator.yml@main
uses: ./.github/workflows/_runner-determinator.yml
with:
triggering_actor: ${{ github.triggering_actor }}
issue_owner: ${{ github.event.pull_request.user.login || github.event.issue.user.login }}
@ -104,9 +104,9 @@ jobs:
- get-label-type
{%- if config["gpu_arch_type"] == "cuda" %}
{%- if branches == "nightly" %}
runs-on: "${{ needs.get-label-type.outputs.label-type }}windows.g4dn.xlarge"
runs-on: "${{ needs.get-label-type.outputs.label-type }}windows.8xlarge.nvidia.gpu"
{%- else %}
runs-on: "${{ needs.get-label-type.outputs.label-type }}windows.g4dn.xlarge.nonephemeral"
runs-on: "${{ needs.get-label-type.outputs.label-type }}windows.8xlarge.nvidia.gpu.nonephemeral"
{%- endif %}
{%- else %}
runs-on: "${{ needs.get-label-type.outputs.label-type }}windows.4xlarge.nonephemeral"

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

@ -272,8 +272,6 @@ jobs:
docker exec -t -w "${PYTORCH_ROOT}" "${container_name}" bash -c "bash .circleci/scripts/binary_populate_env.sh"
if [[ ${BUILD_ENVIRONMENT} == *"aarch64"* ]]; then
docker exec -t "${container_name}" bash -c "bash /builder/aarch64_linux/aarch64_ci_build.sh"
elif [[ ${{ inputs.PACKAGE_TYPE }} == "manywheel" || ${{ inputs.PACKAGE_TYPE }} == "libtorch" ]]; then
docker exec -t "${container_name}" bash -c "source ${BINARY_ENV_FILE} && bash /pytorch/.ci/${{ inputs.PACKAGE_TYPE }}/build.sh"
else
docker exec -t "${container_name}" bash -c "source ${BINARY_ENV_FILE} && bash /builder/${{ inputs.PACKAGE_TYPE }}/build.sh"
fi
@ -285,7 +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

@ -210,7 +210,7 @@ jobs:
- name: Download Build Artifacts
if: ${{ steps.filter.outputs.is-test-matrix-empty == 'False' }}
uses: actions/download-artifact@v4.1.7
uses: actions/download-artifact@v3
with:
name: ${{ inputs.build_name }}
path: "${{ runner.temp }}/artifacts/"

View File

@ -126,7 +126,7 @@ jobs:
# NB: When the previous build job is skipped, there won't be any artifacts and
# this step will fail. Binary build jobs can only be skipped on CI, not nightly
continue-on-error: true
uses: actions/download-artifact@v4.1.7
uses: actions/download-artifact@v3
with:
name: ${{ inputs.build_name }}
path: "${{ runner.temp }}/artifacts/"

View File

@ -292,7 +292,7 @@ jobs:
bundler-cache: true
- name: Download arm64 artifacts
uses: actions/download-artifact@v4.1.7
uses: actions/download-artifact@v3
with:
name: pytorch-ios-build-artifacts-arm64

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

@ -3,11 +3,6 @@ name: Check whether the workflow owner can use ARC runners
on:
workflow_call:
inputs:
check_experiments:
required: false
type: string
description: |
List of experiments for this workfow. If not defined, all default experiments are included.
triggering_actor:
required: true
type: string
@ -48,7 +43,6 @@ jobs:
ISSUE_NUMBER: ${{ inputs.issue_number }}
TRIGGERING_ACTOR: ${{ inputs.triggering_actor }}
ISSUE_OWNER: ${{ inputs.issue_owner }}
CHECK_EXPERIMENTS: ${{ inputs.check_experiments }}
steps:
# - name: Checkout PyTorch
# uses: pytorch/pytorch/.github/actions/checkout-pytorch@main
@ -65,108 +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
all_branches: false
default: true
---
# 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, FrozenSet, 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
)
default: bool = (
True # If True, the experiment is enabled by default for all queries
)
# 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"""
@ -209,12 +147,6 @@ jobs:
f.write(f"{key}={value}\n")
def _str_comma_separated_to_set(value: str) -> FrozenSet[str]:
return frozenset(
filter(lambda itm: itm != "", map(str.strip, value.strip(" \n\t").split(",")))
)
def parse_args() -> Any:
parser = ArgumentParser("Get dynamic rollout settings")
parser.add_argument("--github-token", type=str, required=True, help="GitHub token")
@ -249,13 +181,6 @@ jobs:
required=True,
help="Current GitHub ref type, branch or tag",
)
parser.add_argument(
"--eligible-experiments",
type=_str_comma_separated_to_set,
required=False,
default="",
help="comma separated list of experiments to check, if omitted all experiments marked with default=True are checked",
)
return parser.parse_args()
@ -271,14 +196,11 @@ jobs:
def get_potential_pr_author(
github_token: str, repo: str, username: str, ref_type: str, ref_name: str
gh: Github, repo: str, username: str, ref_type: str, ref_name: str
) -> str:
# If the trigger was a new tag added by a bot, this is a ciflow case
# Fetch the actual username from the original PR. The PR number is
# embedded in the tag name: ciflow/<name>/<pr-number>
gh = get_gh_client(github_token)
if username == "pytorch-bot[bot]" and ref_type == "tag":
split_tag = ref_name.split("/")
if (
@ -300,261 +222,130 @@ jobs:
def is_exception_branch(branch: str) -> bool:
"""
Branches that get opted out of experiments by default, until they're explicitly enabled.
"""
return branch.split("/")[0] in {"main", "nightly", "release", "landchecks"}
def load_yaml(yaml_text: str) -> Any:
def get_workflow_type(issue: Issue, workflow_requestors: Iterable[str]) -> str:
try:
data = yaml.safe_load(yaml_text)
return data
except yaml.YAMLError as exc:
log.exception("Error loading YAML")
raise
first_comment = issue.get_comments()[0].body.strip("\n\t ")
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,
eligible_experiments: FrozenSet[str] = frozenset(),
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():
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
if eligible_experiments:
if experiment_name not in eligible_experiments:
exp_list = ", ".join(eligible_experiments)
if first_comment[0] == "!":
log.info("LF Workflows are disabled for everyone. Using meta runners.")
return WORKFLOW_LABEL_META
elif first_comment[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 first_comment.split()
}
opted_in_requestors = {
usr for usr in workflow_requestors if usr in all_opted_in_users
}
if opted_in_requestors:
log.info(
f"Skipping experiment '{experiment_name}', as it is not in the eligible_experiments list: {exp_list}"
f"LF Workflows are enabled for {', '.join(opted_in_requestors)}. Using LF runners."
)
continue
elif not experiment_settings.default:
log.info(
f"Skipping experiment '{experiment_name}', as it is not a default experiment"
)
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)
]
enabled = False
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:
log.info(
f"Based on rollout percentage of {experiment_settings.rollout_perc}%, enabling experiment {experiment_name}."
)
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]
# Fleet always comes first
if fleet_prefix:
prefixes.insert(0, fleet_prefix)
return ".".join(prefixes) + "." if prefixes else ""
return WORKFLOW_LABEL_META
def get_rollout_state_from_issue(github_token: str, repo: str, issue_num: int) -> str:
"""
Gets the first comment of the issue, which contains the desired rollout state.
def get_optin_feature(
issue: Issue, workflow_requestors: Iterable[str], feature: str, fallback: str
) -> str:
try:
first_comment = issue.get_comments()[0].body.strip("\n\t ")
userlist = {u.lstrip("#").strip("\n\t@ ") for u in first_comment.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
}
The default issue we use - https://github.com/pytorch/test-infra/issues/5132
"""
gh = get_gh_client(github_token)
issue = get_issue(gh, repo, issue_num)
return str(issue.get_comments()[0].body.strip("\n\t "))
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 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:
gh = get_gh_client(args.github_token)
# The default issue we use - https://github.com/pytorch/test-infra/issues/5132
issue = get_issue(gh, args.github_issue_repo, args.github_issue)
username = get_potential_pr_author(
gh,
args.github_repo,
args.github_actor,
args.github_ref_type,
args.github_branch,
)
label_type = get_workflow_type(
issue,
(
args.github_issue_owner,
username,
),
)
runner_ami = get_optin_feature(
issue=issue,
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
try:
rollout_state = get_rollout_state_from_issue(
args.github_token, args.github_issue_repo, args.github_issue
)
# 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
username = get_potential_pr_author(
args.github_token,
args.github_repo,
args.github_actor,
args.github_ref_type,
args.github_branch,
)
is_canary = args.github_repo == "pytorch/pytorch-canary"
runner_label_prefix = get_runner_prefix(
rollout_state,
(args.github_issue_owner, username),
args.github_branch,
args.eligible_experiments,
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__":
main()
EOF
cat runner_determinator.py
@ -576,5 +367,4 @@ jobs:
--github-actor "$TRIGGERING_ACTOR" \
--github-issue-owner "$ISSUE_OWNER" \
--github-ref-type "$curr_ref_type" \
--github-repo "$GITHUB_REPOSITORY" \
--eligible-experiments "$CHECK_EXPERIMENTS" \
--github-repo "$GITHUB_REPOSITORY"

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