Skip to content

[test][xpu] Add test_config.py, test_model_architecture.py and test_utils.py in Intel GPU CI #26424

[test][xpu] Add test_config.py, test_model_architecture.py and test_utils.py in Intel GPU CI

[test][xpu] Add test_config.py, test_model_architecture.py and test_utils.py in Intel GPU CI #26424

Workflow file for this run

name: Run Regression Tests
on:
push:
branches:
- main
- 'gh/**'
pull_request:
branches:
- main
- 'gh/**'
concurrency:
group: regression_test-${{ github.workflow }}-${{ github.ref == 'refs/heads/main' && github.run_number || github.ref }}
cancel-in-progress: true
env:
HF_TOKEN: ${{ secrets.HF_TOKEN }}
jobs:
test-nightly:
strategy:
fail-fast: false
matrix:
include:
- name: CUDA Nightly
runs-on: linux.g5.12xlarge.nvidia.gpu
torch-spec: '--pre torch torchvision --index-url https://download.pytorch.org/whl/nightly/cu126'
gpu-arch-type: "cuda"
gpu-arch-version: "12.6"
compiler: ""
- name: CPU Nightly
runs-on: linux.4xlarge
torch-spec: '--pre torch torchvision --index-url https://download.pytorch.org/whl/nightly/cpu'
gpu-arch-type: "cpu"
gpu-arch-version: ""
compiler: ""
- name: CPU Nightly (Clang)
runs-on: linux.4xlarge
torch-spec: '--pre torch torchvision --index-url https://download.pytorch.org/whl/nightly/cpu'
gpu-arch-type: "cpu"
gpu-arch-version: ""
compiler: "clang"
permissions:
id-token: write
contents: read
uses: pytorch/test-infra/.github/workflows/linux_job_v2.yml@main
with:
timeout: 180
runner: ${{ matrix.runs-on }}
gpu-arch-type: ${{ matrix.gpu-arch-type }}
gpu-arch-version: ${{ matrix.gpu-arch-version }}
submodules: recursive
script: |
if [ "${{ matrix.compiler }}" = "clang" ]; then
# Install clang from the system package manager. The docker image's
# bundled conda has a broken menuinst plugin that rejects -c conda-forge,
# so we cannot install clangxx via conda here.
dnf install -y clang
clang --version
conda create -n venv python=3.10 libgcc-ng=11.2.0 libstdcxx-ng=11.2.0 -y
conda activate venv
export CC=clang
export CXX=clang++
# distutils (used by setup.py via cpp_extension.BuildExtension) reads
# CFLAGS from the env and applies it to both C and C++ compiles.
# It does NOT honor CXXFLAGS — that is a Make convention, not distutils.
export CFLAGS="-Werror -Wno-vla-cxx-extension"
# Compile the x86 CPU aten_kernels — they are off by default
# in setup.py and not otherwise exercised by any OSS CI job.
export USE_CPU_KERNELS=1
else
conda create -n venv python=3.10 libgcc-ng=11.2.0 libstdcxx-ng=11.2.0 -y
conda activate venv
fi
python -m pip install --upgrade pip
pip install ${{ matrix.torch-spec }}
pip install -r dev-requirements.txt
pip install . --no-build-isolation
export CONDA=$(dirname $(dirname $(which conda)))
export LD_LIBRARY_PATH=$CONDA/lib/:$LD_LIBRARY_PATH
pytest test --verbose -s
test:
strategy:
fail-fast: false
matrix:
include:
- name: CUDA 2.12
runs-on: linux.g5.12xlarge.nvidia.gpu
torch-spec: 'torch==2.12.0 torchvision==0.27.0 --index-url https://download.pytorch.org/whl/cu126'
gpu-arch-type: "cuda"
gpu-arch-version: "12.6"
dev-requirements-overrides: ""
- name: CPU 2.12
runs-on: linux.4xlarge
torch-spec: 'torch==2.12.0 torchvision==0.27.0 --index-url https://download.pytorch.org/whl/cpu'
gpu-arch-type: "cpu"
gpu-arch-version: ""
dev-requirements-overrides: ""
uses: pytorch/test-infra/.github/workflows/linux_job_v2.yml@main
with:
timeout: 180
runner: ${{ matrix.runs-on }}
gpu-arch-type: ${{ matrix.gpu-arch-type }}
gpu-arch-version: ${{ matrix.gpu-arch-version }}
submodules: recursive
script: |
conda create -n venv python=3.10 libgcc-ng=11.2.0 libstdcxx-ng=11.2.0 -y
conda activate venv
python -m pip install --upgrade pip
pip install ${{ matrix.torch-spec }}
sed -i '${{ matrix.dev-requirements-overrides }}' dev-requirements.txt
pip install -r dev-requirements.txt
pip install . --no-build-isolation
export CONDA=$(dirname $(dirname $(which conda)))
export LD_LIBRARY_PATH=$CONDA/lib/:$LD_LIBRARY_PATH
pytest test --verbose -s