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setup.py
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setup.py
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# Environment flags to control different options
#
# - USE_MKL_BLAS=1:
# Enables use of MKL BLAS (requires PyTorch to be built with MKL support)
import importlib
import os
import os.path as osp
import re
import subprocess
import warnings
from setuptools import Extension, find_packages, setup
from setuptools.command.build_ext import build_ext
__version__ = '0.4.0'
URL = 'https://github.com/pyg-team/pyg-lib'
class CMakeExtension(Extension):
def __init__(self, name, sourcedir=''):
Extension.__init__(self, name, sources=[])
self.sourcedir = osp.abspath(sourcedir)
class CMakeBuild(build_ext):
@staticmethod
def check_env_flag(name: str, default: str = "") -> bool:
value = os.getenv(name, default).upper()
return value in ["1", "ON", "YES", "TRUE", "Y"]
def get_ext_filename(self, ext_name):
# Remove Python ABI suffix:
ext_filename = super().get_ext_filename(ext_name)
ext_filename_parts = ext_filename.split('.')
ext_filename_parts = ext_filename_parts[:-2] + ext_filename_parts[-1:]
return '.'.join(ext_filename_parts)
def build_extension(self, ext):
import sysconfig
import torch
extdir = osp.abspath(osp.dirname(self.get_ext_fullpath(ext.name)))
self.build_type = "DEBUG" if self.debug else "RELEASE"
if self.debug is None:
if CMakeBuild.check_env_flag("DEBUG"):
self.build_type = "DEBUG"
elif CMakeBuild.check_env_flag("REL_WITH_DEB_INFO"):
self.build_type = "RELWITHDEBINFO"
if not osp.exists(self.build_temp):
os.makedirs(self.build_temp)
WITH_CUDA = torch.cuda.is_available()
WITH_CUDA = bool(int(os.getenv('FORCE_CUDA', WITH_CUDA)))
cmake_args = [
'-DBUILD_TEST=OFF',
'-DBUILD_BENCHMARK=OFF',
'-DUSE_PYTHON=ON',
f'-DWITH_CUDA={"ON" if WITH_CUDA else "OFF"}',
f'-DCMAKE_LIBRARY_OUTPUT_DIRECTORY={extdir}',
f'-DCMAKE_RUNTIME_OUTPUT_DIRECTORY={extdir}',
f'-DCMAKE_BUILD_TYPE={self.build_type}',
f'-DCMAKE_PREFIX_PATH={torch.utils.cmake_prefix_path}',
]
if CMakeBuild.check_env_flag('USE_MKL_BLAS'):
include_dir = f"{sysconfig.get_path('data')}{os.sep}include"
cmake_args.append(f'-DBLAS_INCLUDE_DIR={include_dir}')
cmake_args.append('-DUSE_MKL_BLAS=ON')
with_ninja = importlib.util.find_spec('ninja') is not None
with_ninja |= os.environ.get('FORCE_NINJA') is not None
if with_ninja:
cmake_args += ['-GNinja']
else:
warnings.warn("Building times of 'pyg-lib' can be heavily improved"
" by installing 'ninja': `pip install ninja`")
build_args = []
subprocess.check_call(['cmake', ext.sourcedir] + cmake_args,
cwd=self.build_temp)
subprocess.check_call(['cmake', '--build', '.'] + build_args,
cwd=self.build_temp)
def mkl_dependencies():
if not CMakeBuild.check_env_flag('USE_MKL_BLAS'):
return []
import torch
dependencies = []
torch_config = torch.__config__.show()
with_mkl_blas = 'BLAS_INFO=mkl' in torch_config
if torch.backends.mkl.is_available() and with_mkl_blas:
product_version = '2023.1.0'
pattern = r'oneAPI Math Kernel Library Version [0-9]{4}\.[0-9]+'
match = re.search(pattern, torch_config)
if match:
product_version = match.group(0).split(' ')[-1]
dependencies.append(f'mkl-include=={product_version}')
dependencies.append(f'mkl-static=={product_version}')
return dependencies
install_requires = [] + mkl_dependencies()
triton_requires = [
'triton',
]
test_requires = [
'pytest',
'pytest-cov',
]
dev_requires = [
'pre-commit',
]
if not bool(os.getenv('BUILD_DOCS', 0)):
ext_modules = [CMakeExtension('libpyg')]
cmdclass = {'build_ext': CMakeBuild}
else:
ext_modules = None
cmdclass = {}
setup(
name='pyg_lib',
version=__version__,
description='Low-Level Graph Neural Network Operators for PyG',
author='PyG Team',
author_email='team@pyg.org',
url=URL,
download_url=f'{URL}/archive/{__version__}.tar.gz',
keywords=[
'deep-learning',
'pytorch',
'geometric-deep-learning',
'graph-neural-networks',
'graph-convolutional-networks',
],
python_requires='>=3.9',
install_requires=install_requires,
extras_require={
'triton': triton_requires,
'test': test_requires,
'dev': dev_requires,
},
packages=find_packages(),
ext_modules=ext_modules,
cmdclass=cmdclass,
)