mirror of https://github.com/hpcaitech/ColossalAI
244 lines
10 KiB
Python
244 lines
10 KiB
Python
import os
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import re
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import subprocess
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from setuptools import Extension, find_packages, setup
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try:
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import torch
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from torch.utils.cpp_extension import CUDA_HOME, BuildExtension, CUDAExtension
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print("\n\ntorch.__version__ = {}\n\n".format(torch.__version__))
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TORCH_MAJOR = int(torch.__version__.split('.')[0])
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TORCH_MINOR = int(torch.__version__.split('.')[1])
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if TORCH_MAJOR < 1 or (TORCH_MAJOR == 1 and TORCH_MINOR < 10):
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raise RuntimeError("Colossal-AI requires Pytorch 1.10 or newer.\n"
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"The latest stable release can be obtained from https://pytorch.org/")
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except ImportError:
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raise ModuleNotFoundError('torch is not found. You need to install PyTorch before installing Colossal-AI.')
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# ninja build does not work unless include_dirs are abs path
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this_dir = os.path.dirname(os.path.abspath(__file__))
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build_cuda_ext = True
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ext_modules = []
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if int(os.environ.get('NO_CUDA_EXT', '0')) == 1:
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build_cuda_ext = False
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def get_cuda_bare_metal_version(cuda_dir):
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raw_output = subprocess.check_output([cuda_dir + "/bin/nvcc", "-V"], universal_newlines=True)
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output = raw_output.split()
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release_idx = output.index("release") + 1
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release = output[release_idx].split(".")
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bare_metal_major = release[0]
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bare_metal_minor = release[1][0]
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return raw_output, bare_metal_major, bare_metal_minor
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def check_cuda_torch_binary_vs_bare_metal(cuda_dir):
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raw_output, bare_metal_major, bare_metal_minor = get_cuda_bare_metal_version(cuda_dir)
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torch_binary_major = torch.version.cuda.split(".")[0]
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torch_binary_minor = torch.version.cuda.split(".")[1]
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print("\nCompiling cuda extensions with")
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print(raw_output + "from " + cuda_dir + "/bin\n")
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if bare_metal_major != torch_binary_major:
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print(f'The detected CUDA version ({raw_output}) mismatches the version that was used to compile PyTorch '
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f'({torch.version.cuda}). CUDA extension will not be installed.')
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return False
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if bare_metal_minor != torch_binary_minor:
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print("\nWarning: Cuda extensions are being compiled with a version of Cuda that does "
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"not match the version used to compile Pytorch binaries. "
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f"Pytorch binaries were compiled with Cuda {torch.version.cuda}.\n"
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"In some cases, a minor-version mismatch will not cause later errors: "
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"https://github.com/NVIDIA/apex/pull/323#discussion_r287021798. ")
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return True
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def check_cuda_availability(cuda_dir):
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if not torch.cuda.is_available():
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# https://github.com/NVIDIA/apex/issues/486
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# Extension builds after https://github.com/pytorch/pytorch/pull/23408 attempt to query
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# torch.cuda.get_device_capability(), which will fail if you are compiling in an environment
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# without visible GPUs (e.g. during an nvidia-docker build command).
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print(
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'\nWarning: Torch did not find available GPUs on this system.\n',
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'If your intention is to cross-compile, this is not an error.\n'
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'By default, Colossal-AI will cross-compile for Pascal (compute capabilities 6.0, 6.1, 6.2),\n'
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'Volta (compute capability 7.0), Turing (compute capability 7.5),\n'
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'and, if the CUDA version is >= 11.0, Ampere (compute capability 8.0).\n'
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'If you wish to cross-compile for a single specific architecture,\n'
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'export TORCH_CUDA_ARCH_LIST="compute capability" before running setup.py.\n')
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if os.environ.get("TORCH_CUDA_ARCH_LIST", None) is None:
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_, bare_metal_major, _ = get_cuda_bare_metal_version(cuda_dir)
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if int(bare_metal_major) == 11:
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os.environ["TORCH_CUDA_ARCH_LIST"] = "6.0;6.1;6.2;7.0;7.5;8.0"
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else:
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os.environ["TORCH_CUDA_ARCH_LIST"] = "6.0;6.1;6.2;7.0;7.5"
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return False
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if cuda_dir is None:
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print("nvcc was not found. CUDA extension will not be installed. If you're installing within a container from "
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"https://hub.docker.com/r/pytorch/pytorch, only images whose names contain 'devel' will provide nvcc.")
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return False
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return True
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def append_nvcc_threads(nvcc_extra_args):
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_, bare_metal_major, bare_metal_minor = get_cuda_bare_metal_version(CUDA_HOME)
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if int(bare_metal_major) >= 11 and int(bare_metal_minor) >= 2:
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return nvcc_extra_args + ["--threads", "4"]
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return nvcc_extra_args
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def fetch_requirements(path):
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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():
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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():
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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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if build_cuda_ext:
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torch_version = '.'.join(torch.__version__.split('.')[:2])
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cuda_version = '.'.join(get_cuda_bare_metal_version(CUDA_HOME)[1:])
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version += f'+torch{torch_version}cu{cuda_version}'
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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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return version
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if build_cuda_ext:
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build_cuda_ext = check_cuda_availability(CUDA_HOME) and check_cuda_torch_binary_vs_bare_metal(CUDA_HOME)
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if build_cuda_ext:
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# Set up macros for forward/backward compatibility hack around
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# https://github.com/pytorch/pytorch/commit/4404762d7dd955383acee92e6f06b48144a0742e
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# and
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# https://github.com/NVIDIA/apex/issues/456
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# https://github.com/pytorch/pytorch/commit/eb7b39e02f7d75c26d8a795ea8c7fd911334da7e#diff-4632522f237f1e4e728cb824300403ac
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version_dependent_macros = ['-DVERSION_GE_1_1', '-DVERSION_GE_1_3', '-DVERSION_GE_1_5']
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def cuda_ext_helper(name, sources, extra_cuda_flags, extra_cxx_flags=[]):
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return CUDAExtension(
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name=name,
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sources=[os.path.join('colossalai/kernel/cuda_native/csrc', path) for path in sources],
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include_dirs=[os.path.join(this_dir, 'colossalai/kernel/cuda_native/csrc/kernels/include')],
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extra_compile_args={
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'cxx': ['-O3'] + version_dependent_macros + extra_cxx_flags,
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'nvcc': append_nvcc_threads(['-O3', '--use_fast_math'] + version_dependent_macros + extra_cuda_flags)
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})
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cc_flag = []
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for arch in torch.cuda.get_arch_list():
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res = re.search(r'sm_(\d+)', arch)
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if res:
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arch_cap = res[1]
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if int(arch_cap) >= 60:
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cc_flag.extend(['-gencode', f'arch=compute_{arch_cap},code={arch}'])
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extra_cuda_flags = ['-lineinfo']
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ext_modules.append(
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cuda_ext_helper('colossalai._C.fused_optim', [
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'colossal_C_frontend.cpp', 'multi_tensor_sgd_kernel.cu', 'multi_tensor_scale_kernel.cu',
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'multi_tensor_adam.cu', 'multi_tensor_l2norm_kernel.cu', 'multi_tensor_lamb.cu'
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], extra_cuda_flags + cc_flag))
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extra_cuda_flags = [
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'-U__CUDA_NO_HALF_OPERATORS__', '-U__CUDA_NO_HALF_CONVERSIONS__', '--expt-relaxed-constexpr',
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'--expt-extended-lambda'
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]
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ext_modules.append(
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cuda_ext_helper('colossalai._C.scaled_upper_triang_masked_softmax',
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['scaled_upper_triang_masked_softmax.cpp', 'scaled_upper_triang_masked_softmax_cuda.cu'],
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extra_cuda_flags + cc_flag))
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ext_modules.append(
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cuda_ext_helper('colossalai._C.scaled_masked_softmax',
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['scaled_masked_softmax.cpp', 'scaled_masked_softmax_cuda.cu'], extra_cuda_flags + cc_flag))
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ext_modules.append(
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cuda_ext_helper('colossalai._C.moe', ['moe_cuda.cpp', 'moe_cuda_kernel.cu'], extra_cuda_flags + cc_flag))
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extra_cuda_flags = ['-maxrregcount=50']
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ext_modules.append(
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cuda_ext_helper('colossalai._C.layer_norm', ['layer_norm_cuda.cpp', 'layer_norm_cuda_kernel.cu'],
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extra_cuda_flags + cc_flag))
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extra_cuda_flags = [
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'-std=c++14', '-U__CUDA_NO_HALF_OPERATORS__', '-U__CUDA_NO_HALF_CONVERSIONS__', '-U__CUDA_NO_HALF2_OPERATORS__',
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'-DTHRUST_IGNORE_CUB_VERSION_CHECK'
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]
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ext_modules.append(
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cuda_ext_helper('colossalai._C.multihead_attention', [
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'multihead_attention_1d.cpp', 'kernels/cublas_wrappers.cu', 'kernels/transform_kernels.cu',
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'kernels/dropout_kernels.cu', 'kernels/normalize_kernels.cu', 'kernels/softmax_kernels.cu',
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'kernels/general_kernels.cu', 'kernels/cuda_util.cu'
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], extra_cuda_flags + cc_flag))
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extra_cxx_flags = ['-std=c++14', '-lcudart', '-lcublas', '-g', '-Wno-reorder', '-fopenmp', '-march=native']
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ext_modules.append(cuda_ext_helper('colossalai._C.cpu_optim', ['cpu_adam.cpp'], extra_cuda_flags, extra_cxx_flags))
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setup(name='colossalai',
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version=get_version(),
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packages=find_packages(exclude=(
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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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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={'colossalai': ['_C/*.pyi']})
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