mirror of
https://github.com/facebookresearch/pytorch3d.git
synced 2025-08-02 03:42:50 +08:00
5fbdb99aec
Summary: Add builds corresponding to the new pytorch 1.10.0. We omit CUDA 11.3 testing because it fails with current hardware, and omit the main build too for the moment. Also move to the newer GPU circle CI executors. Reviewed By: gkioxari Differential Revision: D32335934 fbshipit-source-id: 416d92a8eecd06ef7fc742664a5f2d46f93415f8
359 lines
14 KiB
Bash
359 lines
14 KiB
Bash
# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
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# shellcheck shell=bash
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# A set of useful bash functions for common functionality we need to do in
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# many build scripts
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# Setup CUDA environment variables, based on CU_VERSION
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#
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# Inputs:
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# CU_VERSION (cu92, cu100, cu101, cu102)
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# NO_CUDA_PACKAGE (bool)
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# BUILD_TYPE (conda, wheel)
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#
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# Outputs:
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# VERSION_SUFFIX (e.g., "")
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# PYTORCH_VERSION_SUFFIX (e.g., +cpu)
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# WHEEL_DIR (e.g., cu100/)
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# CUDA_HOME (e.g., /usr/local/cuda-9.2, respected by torch.utils.cpp_extension)
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# FORCE_CUDA (respected by pytorch3d setup.py)
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# NVCC_FLAGS (respected by pytorch3d setup.py)
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#
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# Precondition: CUDA versions are installed in their conventional locations in
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# /usr/local/cuda-*
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#
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# NOTE: Why VERSION_SUFFIX versus PYTORCH_VERSION_SUFFIX? If you're building
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# a package with CUDA on a platform we support CUDA on, VERSION_SUFFIX ==
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# PYTORCH_VERSION_SUFFIX and everyone is happy. However, if you are building a
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# package with only CPU bits (e.g., torchaudio), then VERSION_SUFFIX is always
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# empty, but PYTORCH_VERSION_SUFFIX is +cpu (because that's how you get a CPU
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# version of a Python package. But that doesn't apply if you're on OS X,
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# since the default CU_VERSION on OS X is cpu.
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setup_cuda() {
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# First, compute version suffixes. By default, assume no version suffixes
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export VERSION_SUFFIX=""
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export PYTORCH_VERSION_SUFFIX=""
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export WHEEL_DIR=""
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# Wheel builds need suffixes (but not if they're on OS X, which never has suffix)
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if [[ "$BUILD_TYPE" == "wheel" ]] && [[ "$(uname)" != Darwin ]]; then
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# The default CUDA has no suffix
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if [[ "$CU_VERSION" != "cu102" ]]; then
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export PYTORCH_VERSION_SUFFIX="+$CU_VERSION"
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fi
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# Match the suffix scheme of pytorch, unless this package does not have
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# CUDA builds (in which case, use default)
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if [[ -z "$NO_CUDA_PACKAGE" ]]; then
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export VERSION_SUFFIX="$PYTORCH_VERSION_SUFFIX"
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export WHEEL_DIR="$CU_VERSION/"
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fi
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fi
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# Now work out the CUDA settings
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case "$CU_VERSION" in
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cu113)
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if [[ "$OSTYPE" == "msys" ]]; then
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export CUDA_HOME="C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v11.3"
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else
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export CUDA_HOME=/usr/local/cuda-11.3/
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fi
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export FORCE_CUDA=1
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# Hard-coding gencode flags is temporary situation until
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# https://github.com/pytorch/pytorch/pull/23408 lands
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export NVCC_FLAGS="-gencode=arch=compute_35,code=sm_35 -gencode=arch=compute_50,code=sm_50 -gencode=arch=compute_60,code=sm_60 -gencode=arch=compute_70,code=sm_70 -gencode=arch=compute_75,code=sm_75 -gencode=arch=compute_80,code=sm_80 -gencode=arch=compute_86,code=sm_86 -gencode=arch=compute_50,code=compute_50"
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;;
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cu112)
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if [[ "$OSTYPE" == "msys" ]]; then
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export CUDA_HOME="C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v11.2"
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else
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export CUDA_HOME=/usr/local/cuda-11.2/
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fi
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export FORCE_CUDA=1
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# Hard-coding gencode flags is temporary situation until
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# https://github.com/pytorch/pytorch/pull/23408 lands
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export NVCC_FLAGS="-gencode=arch=compute_35,code=sm_35 -gencode=arch=compute_50,code=sm_50 -gencode=arch=compute_60,code=sm_60 -gencode=arch=compute_70,code=sm_70 -gencode=arch=compute_75,code=sm_75 -gencode=arch=compute_80,code=sm_80 -gencode=arch=compute_86,code=sm_86 -gencode=arch=compute_50,code=compute_50"
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;;
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cu111)
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if [[ "$OSTYPE" == "msys" ]]; then
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export CUDA_HOME="C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v11.1"
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else
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export CUDA_HOME=/usr/local/cuda-11.1/
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fi
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export FORCE_CUDA=1
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# Hard-coding gencode flags is temporary situation until
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# https://github.com/pytorch/pytorch/pull/23408 lands
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export NVCC_FLAGS="-gencode=arch=compute_35,code=sm_35 -gencode=arch=compute_50,code=sm_50 -gencode=arch=compute_60,code=sm_60 -gencode=arch=compute_70,code=sm_70 -gencode=arch=compute_75,code=sm_75 -gencode=arch=compute_80,code=sm_80 -gencode=arch=compute_86,code=sm_86 -gencode=arch=compute_50,code=compute_50"
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;;
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cu110)
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if [[ "$OSTYPE" == "msys" ]]; then
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export CUDA_HOME="C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v11.0"
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else
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export CUDA_HOME=/usr/local/cuda-11.0/
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fi
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export FORCE_CUDA=1
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# Hard-coding gencode flags is temporary situation until
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# https://github.com/pytorch/pytorch/pull/23408 lands
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export NVCC_FLAGS="-gencode=arch=compute_35,code=sm_35 -gencode=arch=compute_50,code=sm_50 -gencode=arch=compute_60,code=sm_60 -gencode=arch=compute_70,code=sm_70 -gencode=arch=compute_75,code=sm_75 -gencode=arch=compute_80,code=sm_80 -gencode=arch=compute_50,code=compute_50"
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;;
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cu102)
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if [[ "$OSTYPE" == "msys" ]]; then
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export CUDA_HOME="C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v10.2"
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else
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export CUDA_HOME=/usr/local/cuda-10.2/
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fi
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export FORCE_CUDA=1
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# Hard-coding gencode flags is temporary situation until
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# https://github.com/pytorch/pytorch/pull/23408 lands
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export NVCC_FLAGS="-gencode=arch=compute_35,code=sm_35 -gencode=arch=compute_50,code=sm_50 -gencode=arch=compute_60,code=sm_60 -gencode=arch=compute_70,code=sm_70 -gencode=arch=compute_75,code=sm_75 -gencode=arch=compute_50,code=compute_50"
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;;
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cu101)
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if [[ "$OSTYPE" == "msys" ]]; then
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export CUDA_HOME="C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v10.1"
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else
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export CUDA_HOME=/usr/local/cuda-10.1/
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fi
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export FORCE_CUDA=1
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# Hard-coding gencode flags is temporary situation until
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# https://github.com/pytorch/pytorch/pull/23408 lands
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export NVCC_FLAGS="-gencode=arch=compute_35,code=sm_35 -gencode=arch=compute_50,code=sm_50 -gencode=arch=compute_60,code=sm_60 -gencode=arch=compute_70,code=sm_70 -gencode=arch=compute_75,code=sm_75 -gencode=arch=compute_50,code=compute_50"
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;;
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cu100)
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if [[ "$OSTYPE" == "msys" ]]; then
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export CUDA_HOME="C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v10.0"
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else
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export CUDA_HOME=/usr/local/cuda-10.0/
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fi
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export FORCE_CUDA=1
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# Hard-coding gencode flags is temporary situation until
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# https://github.com/pytorch/pytorch/pull/23408 lands
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export NVCC_FLAGS="-gencode=arch=compute_35,code=sm_35 -gencode=arch=compute_50,code=sm_50 -gencode=arch=compute_60,code=sm_60 -gencode=arch=compute_70,code=sm_70 -gencode=arch=compute_75,code=sm_75 -gencode=arch=compute_50,code=compute_50"
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;;
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cu92)
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if [[ "$OSTYPE" == "msys" ]]; then
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export CUDA_HOME="C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v9.2"
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else
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export CUDA_HOME=/usr/local/cuda-9.2/
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fi
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export FORCE_CUDA=1
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export NVCC_FLAGS="-gencode=arch=compute_35,code=sm_35 -gencode=arch=compute_50,code=sm_50 -gencode=arch=compute_60,code=sm_60 -gencode=arch=compute_70,code=sm_70 -gencode=arch=compute_50,code=compute_50"
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;;
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cpu)
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;;
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*)
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echo "Unrecognized CU_VERSION=$CU_VERSION"
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exit 1
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;;
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esac
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}
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# Populate build version if necessary, and add version suffix
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#
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# Inputs:
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# BUILD_VERSION (e.g., 0.2.0 or empty)
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# VERSION_SUFFIX (e.g., +cpu)
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#
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# Outputs:
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# BUILD_VERSION (e.g., 0.2.0.dev20190807+cpu)
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#
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# Fill BUILD_VERSION if it doesn't exist already with a nightly string
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# Usage: setup_build_version 0.2.0
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setup_build_version() {
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if [[ -z "$BUILD_VERSION" ]]; then
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export BUILD_VERSION="$1.dev$(date "+%Y%m%d")$VERSION_SUFFIX"
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else
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export BUILD_VERSION="$BUILD_VERSION$VERSION_SUFFIX"
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fi
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}
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# Set some useful variables for OS X, if applicable
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setup_macos() {
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if [[ "$(uname)" == Darwin ]]; then
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export MACOSX_DEPLOYMENT_TARGET=10.9 CC=clang CXX=clang++
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fi
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}
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# Top-level entry point for things every package will need to do
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#
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# Usage: setup_env 0.2.0
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setup_env() {
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setup_cuda
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setup_build_version "$1"
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setup_macos
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}
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# Function to retry functions that sometimes timeout or have flaky failures
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retry () {
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$* || (sleep 1 && $*) || (sleep 2 && $*) || (sleep 4 && $*) || (sleep 8 && $*)
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}
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# Inputs:
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# PYTHON_VERSION (2.7, 3.5, 3.6, 3.7)
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# UNICODE_ABI (bool)
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#
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# Outputs:
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# PATH modified to put correct Python version in PATH
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#
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# Precondition: If Linux, you are in a soumith/manylinux-cuda* Docker image
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setup_wheel_python() {
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if [[ "$(uname)" == Darwin ]]; then
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eval "$(conda shell.bash hook)"
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conda env remove -n "env$PYTHON_VERSION" || true
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conda create -yn "env$PYTHON_VERSION" python="$PYTHON_VERSION"
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conda activate "env$PYTHON_VERSION"
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else
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case "$PYTHON_VERSION" in
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2.7)
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if [[ -n "$UNICODE_ABI" ]]; then
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python_abi=cp27-cp27mu
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else
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python_abi=cp27-cp27m
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fi
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;;
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3.5) python_abi=cp35-cp35m ;;
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3.6) python_abi=cp36-cp36m ;;
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3.7) python_abi=cp37-cp37m ;;
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3.8) python_abi=cp38-cp38 ;;
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*)
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echo "Unrecognized PYTHON_VERSION=$PYTHON_VERSION"
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exit 1
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;;
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esac
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export PATH="/opt/python/$python_abi/bin:$PATH"
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fi
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}
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# Install with pip a bit more robustly than the default
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pip_install() {
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retry pip install --progress-bar off "$@"
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}
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# Install torch with pip, respecting PYTORCH_VERSION, and record the installed
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# version into PYTORCH_VERSION, if applicable
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setup_pip_pytorch_version() {
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if [[ -z "$PYTORCH_VERSION" ]]; then
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# Install latest prerelease version of torch, per our nightlies, consistent
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# with the requested cuda version
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pip_install --pre torch -f "https://download.pytorch.org/whl/nightly/${WHEEL_DIR}torch_nightly.html"
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if [[ "$CUDA_VERSION" == "cpu" ]]; then
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# CUDA and CPU are ABI compatible on the CPU-only parts, so strip
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# in this case
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export PYTORCH_VERSION="$(pip show torch | grep ^Version: | sed 's/Version: *//' | sed 's/+.\+//')"
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else
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export PYTORCH_VERSION="$(pip show torch | grep ^Version: | sed 's/Version: *//')"
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fi
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else
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pip_install "torch==$PYTORCH_VERSION$CUDA_SUFFIX" \
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-f https://download.pytorch.org/whl/torch_stable.html \
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-f https://download.pytorch.org/whl/nightly/torch_nightly.html
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fi
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}
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# Fill PYTORCH_VERSION with the latest conda nightly version, and
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# CONDA_CHANNEL_FLAGS with appropriate flags to retrieve these versions
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#
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# You MUST have populated CUDA_SUFFIX before hand.
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setup_conda_pytorch_constraint() {
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if [[ -z "$PYTORCH_VERSION" ]]; then
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export CONDA_CHANNEL_FLAGS="-c pytorch-nightly"
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export PYTORCH_VERSION="$(conda search --json 'pytorch[channel=pytorch-nightly]' | \
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python -c "import os, sys, json, re; cuver = os.environ.get('CU_VERSION'); \
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cuver_1 = cuver.replace('cu', 'cuda') if cuver != 'cpu' else cuver; \
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cuver_2 = (cuver[:-1] + '.' + cuver[-1]).replace('cu', 'cuda') if cuver != 'cpu' else cuver; \
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print(re.sub(r'\\+.*$', '', \
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[x['version'] for x in json.load(sys.stdin)['pytorch'] \
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if (x['platform'] == 'darwin' or cuver_1 in x['fn'] or cuver_2 in x['fn']) \
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and 'py' + os.environ['PYTHON_VERSION'] in x['fn']][-1]))")"
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if [[ -z "$PYTORCH_VERSION" ]]; then
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echo "PyTorch version auto detection failed"
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echo "No package found for CU_VERSION=$CU_VERSION and PYTHON_VERSION=$PYTHON_VERSION"
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exit 1
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fi
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else
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export CONDA_CHANNEL_FLAGS="-c pytorch"
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fi
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if [[ "$CU_VERSION" == cpu ]]; then
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export CONDA_PYTORCH_BUILD_CONSTRAINT="- pytorch==$PYTORCH_VERSION${PYTORCH_VERSION_SUFFIX}"
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export CONDA_PYTORCH_CONSTRAINT="- pytorch==$PYTORCH_VERSION"
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else
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export CONDA_PYTORCH_BUILD_CONSTRAINT="- pytorch==${PYTORCH_VERSION}${PYTORCH_VERSION_SUFFIX}"
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export CONDA_PYTORCH_CONSTRAINT="- pytorch==${PYTORCH_VERSION}${PYTORCH_VERSION_SUFFIX}"
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fi
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export PYTORCH_VERSION_NODOT=${PYTORCH_VERSION//./}
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}
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# Translate CUDA_VERSION into CUDA_CUDATOOLKIT_CONSTRAINT
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setup_conda_cudatoolkit_constraint() {
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export CONDA_CPUONLY_FEATURE=""
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export CONDA_CUB_CONSTRAINT=""
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if [[ "$(uname)" == Darwin ]]; then
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export CONDA_CUDATOOLKIT_CONSTRAINT=""
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else
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case "$CU_VERSION" in
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cu113)
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export CONDA_CUDATOOLKIT_CONSTRAINT="- cudatoolkit >=11.3,<11.4 # [not osx]"
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;;
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cu112)
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export CONDA_CUDATOOLKIT_CONSTRAINT="- cudatoolkit >=11.2,<11.3 # [not osx]"
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;;
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cu111)
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export CONDA_CUDATOOLKIT_CONSTRAINT="- cudatoolkit >=11.1,<11.2 # [not osx]"
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;;
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cu110)
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export CONDA_CUDATOOLKIT_CONSTRAINT="- cudatoolkit >=11.0,<11.1 # [not osx]"
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# Even though cudatoolkit 11.0 provides CUB we need our own, to control the
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# version, because the built-in 1.9.9 in the cudatoolkit causes problems.
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export CONDA_CUB_CONSTRAINT="- nvidiacub"
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;;
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cu102)
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export CONDA_CUDATOOLKIT_CONSTRAINT="- cudatoolkit >=10.2,<10.3 # [not osx]"
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export CONDA_CUB_CONSTRAINT="- nvidiacub"
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;;
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cu101)
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export CONDA_CUDATOOLKIT_CONSTRAINT="- cudatoolkit >=10.1,<10.2 # [not osx]"
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export CONDA_CUB_CONSTRAINT="- nvidiacub"
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;;
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cu100)
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export CONDA_CUDATOOLKIT_CONSTRAINT="- cudatoolkit >=10.0,<10.1 # [not osx]"
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export CONDA_CUB_CONSTRAINT="- nvidiacub"
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;;
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cu92)
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export CONDA_CUDATOOLKIT_CONSTRAINT="- cudatoolkit >=9.2,<9.3 # [not osx]"
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export CONDA_CUB_CONSTRAINT="- nvidiacub"
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;;
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cpu)
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export CONDA_CUDATOOLKIT_CONSTRAINT=""
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export CONDA_CPUONLY_FEATURE="- cpuonly"
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;;
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*)
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echo "Unrecognized CU_VERSION=$CU_VERSION"
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exit 1
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;;
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esac
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fi
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}
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# Build the proper compiler package before building the final package
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setup_visual_studio_constraint() {
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if [[ "$OSTYPE" == "msys" ]]; then
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export VSTOOLCHAIN_PACKAGE=vs2019
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export VSDEVCMD_ARGS=''
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# shellcheck disable=SC2086
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conda build $CONDA_CHANNEL_FLAGS --no-anaconda-upload packaging/$VSTOOLCHAIN_PACKAGE
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cp packaging/$VSTOOLCHAIN_PACKAGE/conda_build_config.yaml packaging/pytorch3d/conda_build_config.yaml
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fi
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}
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download_nvidiacub_if_needed() {
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case "$CU_VERSION" in
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cu110|cu102|cu101|cu100|cu92)
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echo "Downloading cub"
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wget --no-verbose https://github.com/NVIDIA/cub/archive/1.10.0.tar.gz
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tar xzf 1.10.0.tar.gz
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CUB_HOME=$(realpath ./cub-1.10.0)
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export CUB_HOME
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echo "CUB_HOME is now $CUB_HOME"
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;;
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esac
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# We don't need CUB for a cpu build or if cuda is 11.1 or higher
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}
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