mirror of https://github.com/hpcaitech/ColossalAI
aibig-modeldata-parallelismdeep-learningdistributed-computingfoundation-modelsheterogeneous-traininghpcinferencelarge-scalemodel-parallelismpipeline-parallelism
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205 lines
6.1 KiB
205 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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