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