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