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: 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/*", ] }, )