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import os
import sys
from datetime import datetime
from typing import List
from setuptools import find_packages, setup
from op_builder.utils import (
check_cuda_availability,
check_pytorch_version,
check_system_pytorch_cuda_match,
get_cuda_bare_metal_version,
get_pytorch_version,
set_cuda_arch_list,
)
try:
import torch
from torch.utils.cpp_extension import CUDA_HOME, BuildExtension
TORCH_AVAILABLE = True
except ImportError:
TORCH_AVAILABLE = False
CUDA_HOME = None
# Some constants for installation checks
MIN_PYTORCH_VERSION_MAJOR = 1
MIN_PYTORCH_VERSION_MINOR = 10
THIS_DIR = os.path.dirname(os.path.abspath(__file__))
BUILD_CUDA_EXT = int(os.environ.get('CUDA_EXT', '0')) == 1
IS_NIGHTLY = int(os.environ.get('NIGHTLY', '0')) == 1
# a variable to store the op builder
ext_modules = []
# we do not support windows currently
if sys.platform == 'win32':
raise RuntimeError("Windows is not supported yet. Please try again within the Windows Subsystem for Linux (WSL).")
# check for CUDA extension dependencies
def environment_check_for_cuda_extension_build():
if not TORCH_AVAILABLE:
raise ModuleNotFoundError(
"[extension] PyTorch is not found while CUDA_EXT=1. You need to install PyTorch first in order to build CUDA extensions"
)
if not CUDA_HOME:
raise RuntimeError(
"[extension] CUDA_HOME is not found while CUDA_EXT=1. You need to export CUDA_HOME environment variable or install CUDA Toolkit first in order to build CUDA extensions"
)
check_system_pytorch_cuda_match(CUDA_HOME)
check_pytorch_version(MIN_PYTORCH_VERSION_MAJOR, MIN_PYTORCH_VERSION_MINOR)
check_cuda_availability()
def fetch_requirements(path) -> List[str]:
"""
This function reads the requirements file.
Args:
path (str): the path to the requirements file.
Returns:
The lines in the requirements file.
"""
with open(path, 'r') as fd:
return [r.strip() for r in fd.readlines()]
def fetch_readme() -> str:
"""
This function reads the README.md file in the current directory.
Returns:
The lines in the README file.
"""
with open('README.md', encoding='utf-8') as f:
return f.read()
def get_version() -> str:
"""
This function reads the version.txt and generates the colossalai/version.py file.
Returns:
The library version stored in version.txt.
"""
setup_file_path = os.path.abspath(__file__)
project_path = os.path.dirname(setup_file_path)
version_txt_path = os.path.join(project_path, 'version.txt')
version_py_path = os.path.join(project_path, 'colossalai/version.py')
with open(version_txt_path) as f:
version = f.read().strip()
# write version into version.py
with open(version_py_path, 'w') as f:
f.write(f"__version__ = '{version}'\n")
# look for pytorch and cuda version
if BUILD_CUDA_EXT:
torch_major, torch_minor, _ = get_pytorch_version()
torch_version = f'{torch_major}.{torch_minor}'
cuda_version = '.'.join(get_cuda_bare_metal_version(CUDA_HOME))
else:
torch_version = None
cuda_version = None
# write the version into the python file
if torch_version:
f.write(f'torch = "{torch_version}"\n')
else:
f.write('torch = None\n')
if cuda_version:
f.write(f'cuda = "{cuda_version}"\n')
else:
f.write('cuda = None\n')
return version
if BUILD_CUDA_EXT:
environment_check_for_cuda_extension_build()
set_cuda_arch_list(CUDA_HOME)
from op_builder import ALL_OPS
op_names = []
# load all builders
for name, builder_cls in ALL_OPS.items():
op_names.append(name)
ext_modules.append(builder_cls().builder())
# show log
op_name_list = ', '.join(op_names)
print(f"[extension] loaded builders for {op_name_list}")
# always put not nightly branch as the if branch
# otherwise github will treat colossalai-nightly as the project name
# and it will mess up with the dependency graph insights
if not IS_NIGHTLY:
version = get_version()
package_name = 'colossalai'
else:
# use date as the nightly version
version = datetime.today().strftime('%Y.%m.%d')
package_name = 'colossalai-nightly'
setup(name=package_name,
version=version,
packages=find_packages(exclude=(
'op_builder',
'benchmark',
'docker',
'tests',
'docs',
'examples',
'tests',
'scripts',
'requirements',
'*.egg-info',
)),
description='An integrated large-scale model training system with efficient parallelization techniques',
long_description=fetch_readme(),
long_description_content_type='text/markdown',
license='Apache Software License 2.0',
url='https://www.colossalai.org',
project_urls={
'Forum': 'https://github.com/hpcaitech/ColossalAI/discussions',
'Bug Tracker': 'https://github.com/hpcaitech/ColossalAI/issues',
'Examples': 'https://github.com/hpcaitech/ColossalAI-Examples',
'Documentation': 'http://colossalai.readthedocs.io',
'Github': 'https://github.com/hpcaitech/ColossalAI',
},
ext_modules=ext_modules,
cmdclass={'build_ext': BuildExtension} if ext_modules else {},
install_requires=fetch_requirements('requirements/requirements.txt'),
entry_points='''
[console_scripts]
colossalai=colossalai.cli:cli
''',
python_requires='>=3.6',
classifiers=[
'Programming Language :: Python :: 3',
'License :: OSI Approved :: Apache Software License',
'Environment :: GPU :: NVIDIA CUDA',
'Topic :: Scientific/Engineering :: Artificial Intelligence',
'Topic :: System :: Distributed Computing',
],
package_data={
'colossalai': [
'_C/*.pyi', 'kernel/cuda_native/csrc/*', 'kernel/cuda_native/csrc/kernel/*',
'kernel/cuda_native/csrc/kernels/include/*'
]
})
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