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fbgemm_gpu_ci_cuda.yml
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# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree.
# This workflow is used for FBGEMM_GPU-CUDA CI as well as nightly builds of
# FBGEMM_GPU-CUDA against PyTorch-CUDA Nightly.
name: FBGEMM_GPU-CUDA CI
on:
# PR Trigger (enabled for regression checks and debugging)
#
pull_request:
branches:
- main
# Push Trigger (enable to catch errors coming out of multiple merges)
#
push:
branches:
- main
# Cron Trigger (UTC)
#
# Based on the Conda page for PyTorch-nightly, the GPU nightly releases appear
# around 02:30 PST every day (roughly 2 hours after the CPU releases)
#
schedule:
- cron: '45 12 * * *'
# Manual Trigger
#
workflow_dispatch:
inputs:
pytorch_channel_version:
description: Package Channel + Version to Use for PyTorch Installation, in `<channel>[/<version>]` Format
type: string
required: false
default: ""
publish_to_pypi:
description: Publish Artifact to PyPI
type: boolean
required: false
default: false
concurrency:
# Cancel previous runs in the PR if a new commit is pushed
group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.ref }}
cancel-in-progress: true
jobs:
# Build on CPU hosts and upload to GHA
build_artifact:
if: ${{ github.repository_owner == 'pytorch' }}
runs-on: ${{ matrix.host-machine.instance }}
container:
image: amazonlinux:2023
options: --user root
defaults:
run:
shell: bash
env:
PRELUDE: .github/scripts/setup_env.bash
BUILD_ENV: build_binary
BUILD_VARIANT: cuda
BUILD_CUDA_VERSION: ${{ matrix.cuda-version }}
continue-on-error: true
strategy:
# Don't fast-fail all the other builds if one of the them fails
fail-fast: false
matrix:
host-machine: [
{ arch: x86, instance: "linux.24xlarge" },
]
python-version: [ "3.9", "3.10", "3.11", "3.12", "3.13" ]
cuda-version: [ "11.8.0", "12.4.1", "12.6.3" ]
compiler: [ "gcc", "clang" ]
steps:
- name: Setup Build Container
run: yum update -y; yum install -y binutils findutils git pciutils sudo tar wget which
- name: Checkout the Repository
uses: actions/checkout@v4
with:
submodules: true
- name: Display System Info
run: . $PRELUDE; print_system_info
- name: Display GPU Info
run: . $PRELUDE; print_gpu_info
- name: Setup Miniconda
run: . $PRELUDE; setup_miniconda $HOME/miniconda
- name: Create Conda Environment
run: . $PRELUDE; create_conda_environment $BUILD_ENV ${{ matrix.python-version }}
- name: Install C/C++ Compilers
run: . $PRELUDE; install_cxx_compiler $BUILD_ENV ${{ matrix.compiler }}
- name: Install Build Tools
run: . $PRELUDE; install_build_tools $BUILD_ENV
- name: Install CUDA
run: . $PRELUDE; install_cuda $BUILD_ENV ${{ matrix.cuda-version }}
# Install via PIP to avoid defaulting to the CPU variant if the GPU variant of the day is not ready
- name: Install PyTorch Nightly
run: . $PRELUDE; install_pytorch_pip $BUILD_ENV ${{ (github.event_name == 'workflow_dispatch' && github.event.inputs.pytorch_channel_version) || 'nightly' }} cuda/${{ matrix.cuda-version }}
- name: Collect PyTorch Environment Info
if: ${{ success() || failure() }}
run: if . $PRELUDE && which conda; then collect_pytorch_env_info $BUILD_ENV; fi
- name: Install cuDNN
run: . $PRELUDE; install_cudnn $BUILD_ENV "$(pwd)/build_only/cudnn" ${{ matrix.cuda-version }}
- name: Prepare FBGEMM_GPU Build
run: . $PRELUDE; cd fbgemm_gpu; prepare_fbgemm_gpu_build $BUILD_ENV
- name: Build FBGEMM_GPU Wheel
run: . $PRELUDE; cd fbgemm_gpu; build_fbgemm_gpu_package $BUILD_ENV nightly cuda
- name: Upload Built Wheel as GHA Artifact
# Cannot upgrade to actions/upload-artifact@v4 yet because GLIBC on the instance is too old
uses: actions/upload-artifact@v4
with:
name: fbgemm_gpu_nightly_cuda_${{ matrix.host-machine.arch }}_${{ matrix.compiler }}_py${{ matrix.python-version }}_cu${{ matrix.cuda-version }}.whl
path: fbgemm_gpu/dist/*.whl
if-no-files-found: error
# Download the built artifact from GHA, test on GPU, and push to PyPI
test_and_publish_artifact:
if: ${{ github.repository_owner == 'pytorch' }}
# runs-on: linux.4xlarge.nvidia.gpu
# Use available instance types - https://github.com/pytorch/test-infra/blob/main/.github/scale-config.yml
runs-on: ${{ matrix.host-machine.instance }}
defaults:
run:
shell: bash
env:
PRELUDE: .github/scripts/setup_env.bash
BUILD_ENV: build_binary
BUILD_VARIANT: cuda
BUILD_CUDA_VERSION: ${{ matrix.cuda-version }}
ENFORCE_CUDA_DEVICE: 1
strategy:
fail-fast: false
matrix:
host-machine: [
{ arch: x86, instance: "linux.g5.4xlarge.nvidia.gpu" },
# TODO: Enable when A100 machine queues are reasonably small enough for doing per-PR CI
# https://hud.pytorch.org/metrics
# { arch: x86, instance: "linux.gcp.a100" },
]
python-version: [ "3.9", "3.10", "3.11", "3.12", "3.13" ]
cuda-version: [ "11.8.0", "12.4.1", "12.6.3" ]
# Specify exactly ONE CUDA version for artifact publish
cuda-version-publish: [ "12.4.1" ]
compiler: [ "gcc", "clang" ]
needs: build_artifact
steps:
# Cannot upgrade to actions/checkout@v4 yet because GLIBC on the instance is too old
- name: Checkout the Repository
uses: actions/checkout@v4
with:
submodules: true
- name: Download Wheel Artifact from GHA
# Cannot upgrade to actions/download-artifact@v4 yet because GLIBC on the instance is too old
uses: actions/download-artifact@v4
with:
name: fbgemm_gpu_nightly_cuda_${{ matrix.host-machine.arch }}_${{ matrix.compiler }}_py${{ matrix.python-version }}_cu${{ matrix.cuda-version }}.whl
# Use PyTorch test infrastructure action - https://github.com/pytorch/test-infra/blob/main/.github/actions/setup-nvidia/action.yml
- name: Install NVIDIA Drivers and NVIDIA-Docker Runtime
uses: pytorch/test-infra/.github/actions/setup-nvidia@main
- name: Display System Info
run: . $PRELUDE; print_system_info; print_ec2_info
- name: Display GPU Info
run: . $PRELUDE; print_gpu_info
- name: Setup Miniconda
run: . $PRELUDE; setup_miniconda $HOME/miniconda
- name: Create Conda Environment
run: . $PRELUDE; create_conda_environment $BUILD_ENV ${{ matrix.python-version }}
- name: Install C/C++ Compilers for Updated LIBGCC
# NOTE: gcc is required for torch dynamo to work properly, as some of
# the compilation flags used by torch dynamo are gcc-specific:
#
# clang-16: error: unknown argument: '-fno-tree-loop-vectorize'
run: . $PRELUDE; install_cxx_compiler $BUILD_ENV gcc
- name: Install CUDA
run: . $PRELUDE; install_cuda $BUILD_ENV ${{ matrix.cuda-version }}
# Install via PIP to avoid defaulting to the CPU variant if the GPU variant of the day is not ready
- name: Install PyTorch Nightly
run: . $PRELUDE; install_pytorch_pip $BUILD_ENV ${{ (github.event_name == 'workflow_dispatch' && github.event.inputs.pytorch_channel_version) || 'nightly' }} cuda/${{ matrix.cuda-version }}
- name: Collect PyTorch Environment Info
if: ${{ success() || failure() }}
run: if . $PRELUDE && which conda; then collect_pytorch_env_info $BUILD_ENV; fi
- name: Prepare FBGEMM_GPU Build
run: . $PRELUDE; cd fbgemm_gpu; prepare_fbgemm_gpu_build $BUILD_ENV
- name: Install FBGEMM_GPU Wheel
run: . $PRELUDE; install_fbgemm_gpu_wheel $BUILD_ENV *.whl
- name: Test with PyTest
timeout-minutes: 40
run: . $PRELUDE; test_all_fbgemm_gpu_modules $BUILD_ENV
- name: Push Wheel to PyPI
if: ${{ (github.event_name == 'schedule' && matrix.cuda-version == matrix.cuda-version-publish) || (github.event_name == 'workflow_dispatch' && github.event.inputs.publish_to_pypi == 'true' && matrix.cuda-version == matrix.cuda-version-publish) }}
env:
PYPI_TOKEN: ${{ secrets.PYPI_TOKEN }}
run: . $PRELUDE; publish_to_pypi $BUILD_ENV "$PYPI_TOKEN" *.whl