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/*' ] })