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import glob
import os
import os.path as osp
import platform
import sys
from itertools import product
import torch
from setuptools import find_packages, setup
from torch.__config__ import parallel_info
from torch.utils.cpp_extension import (
CUDA_HOME,
BuildExtension,
CppExtension,
CUDAExtension,
)
__version__ = '0.6.18'
URL = 'https://github.com/rusty1s/pytorch_sparse'
WITH_CUDA = False
if torch.cuda.is_available():
WITH_CUDA = CUDA_HOME is not None or torch.version.hip
suffices = ['cpu', 'cuda'] if WITH_CUDA else ['cpu']
if os.getenv('FORCE_CUDA', '0') == '1':
suffices = ['cuda', 'cpu']
if os.getenv('FORCE_ONLY_CUDA', '0') == '1':
suffices = ['cuda']
if os.getenv('FORCE_ONLY_CPU', '0') == '1':
suffices = ['cpu']
BUILD_DOCS = os.getenv('BUILD_DOCS', '0') == '1'
WITH_METIS = True if os.getenv('WITH_METIS', '0') == '1' else False
WITH_MTMETIS = True if os.getenv('WITH_MTMETIS', '0') == '1' else False
WITH_SYMBOLS = True if os.getenv('WITH_SYMBOLS', '0') == '1' else False
def get_extensions():
extensions = []
extensions_dir = osp.join('csrc')
main_files = glob.glob(osp.join(extensions_dir, '*.cpp'))
# remove generated 'hip' files, in case of rebuilds
main_files = [path for path in main_files if 'hip' not in path]
for main, suffix in product(main_files, suffices):
define_macros = [('WITH_PYTHON', None)]
undef_macros = []
if sys.platform == 'win32':
define_macros += [('torchsparse_EXPORTS', None)]
libraries = []
if WITH_METIS:
define_macros += [('WITH_METIS', None)]
libraries += ['metis']
if WITH_MTMETIS:
define_macros += [('WITH_MTMETIS', None)]
define_macros += [('MTMETIS_64BIT_VERTICES', None)]
define_macros += [('MTMETIS_64BIT_EDGES', None)]
define_macros += [('MTMETIS_64BIT_WEIGHTS', None)]
define_macros += [('MTMETIS_64BIT_PARTITIONS', None)]
libraries += ['mtmetis', 'wildriver']
extra_compile_args = {'cxx': ['-O3']}
if not os.name == 'nt': # Not on Windows:
extra_compile_args['cxx'] += ['-Wno-sign-compare']
if sys.platform == 'darwin': # On macOS:
extra_compile_args['cxx'] += ['-D_LIBCPP_DISABLE_AVAILABILITY']
extra_link_args = [] if WITH_SYMBOLS else ['-s']
info = parallel_info()
if ('backend: OpenMP' in info and 'OpenMP not found' not in info
and sys.platform != 'darwin'):
extra_compile_args['cxx'] += ['-DAT_PARALLEL_OPENMP']
if sys.platform == 'win32':
extra_compile_args['cxx'] += ['/openmp']
else:
extra_compile_args['cxx'] += ['-fopenmp']
else:
print('Compiling without OpenMP...')
# Compile for mac arm64
if (sys.platform == 'darwin' and platform.machine() == 'arm64'):
extra_compile_args['cxx'] += ['-arch', 'arm64']
extra_link_args += ['-arch', 'arm64']
if suffix == 'cuda':
define_macros += [('WITH_CUDA', None)]
nvcc_flags = os.getenv('NVCC_FLAGS', '')
nvcc_flags = [] if nvcc_flags == '' else nvcc_flags.split(' ')
nvcc_flags += ['-O3']
if torch.version.hip:
# USE_ROCM was added to later versions of PyTorch
# Define here to support older PyTorch versions as well:
define_macros += [('USE_ROCM', None)]
undef_macros += ['__HIP_NO_HALF_CONVERSIONS__']
else:
nvcc_flags += ['--expt-relaxed-constexpr']
extra_compile_args['nvcc'] = nvcc_flags
name = main.split(os.sep)[-1][:-4]
sources = [main]
path = osp.join(extensions_dir, 'cpu', f'{name}_cpu.cpp')
if osp.exists(path):
sources += [path]
path = osp.join(extensions_dir, 'cuda', f'{name}_cuda.cu')
if suffix == 'cuda' and osp.exists(path):
sources += [path]
phmap_dir = osp.abspath("third_party/parallel-hashmap")
Extension = CppExtension if suffix == 'cpu' else CUDAExtension
extension = Extension(
f'torch_sparse._{name}_{suffix}',
sources,
include_dirs=[extensions_dir, phmap_dir],
define_macros=define_macros,
undef_macros=undef_macros,
extra_compile_args=extra_compile_args,
extra_link_args=extra_link_args,
libraries=libraries,
)
extensions += [extension]
return extensions
install_requires = [
'scipy',
]
test_requires = [
'pytest',
'pytest-cov',
]
# work-around hipify abs paths
include_package_data = True
if torch.cuda.is_available() and torch.version.hip:
include_package_data = False
setup(
name='torch_sparse',
version=__version__,
description=('PyTorch Extension Library of Optimized Autograd Sparse '
'Matrix Operations'),
author='Matthias Fey',
author_email='matthias.fey@tu-dortmund.de',
url=URL,
download_url=f'{URL}/archive/{__version__}.tar.gz',
keywords=[
'pytorch',
'sparse',
'sparse-matrices',
'autograd',
],
python_requires='>=3.8',
install_requires=install_requires,
extras_require={
'test': test_requires,
},
ext_modules=get_extensions() if not BUILD_DOCS else [],
cmdclass={
'build_ext': BuildExtension.with_options(no_python_abi_suffix=True)
},
packages=find_packages(),
include_package_data=include_package_data,
)
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