mirror of https://github.com/hpcaitech/ColossalAI
aibig-modeldata-parallelismdeep-learningdistributed-computingfoundation-modelsheterogeneous-traininghpcinferencelarge-scalemodel-parallelismpipeline-parallelism
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105 lines
3.0 KiB
105 lines
3.0 KiB
2 years ago
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import os
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import re
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from pathlib import Path
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from typing import List
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import torch
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def get_cuda_cc_flag() -> List:
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"""get_cuda_cc_flag
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cc flag for your GPU arch
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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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return cc_flag
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class Builder(object):
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def colossalai_src_path(self, code_path):
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if os.path.isabs(code_path):
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return code_path
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else:
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return os.path.join(Path(__file__).parent.parent.absolute(), code_path)
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def get_cuda_home_include(self):
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"""
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return include path inside the cuda home.
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"""
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from torch.utils.cpp_extension import CUDA_HOME
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if CUDA_HOME is None:
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raise RuntimeError("CUDA_HOME is None, please set CUDA_HOME to compile C++/CUDA kernels in ColossalAI.")
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cuda_include = os.path.join(CUDA_HOME, "include")
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return cuda_include
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# functions must be overrided begin
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def sources_files(self):
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raise NotImplementedError
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def include_dirs(self):
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raise NotImplementedError
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def cxx_flags(self):
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raise NotImplementedError
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def nvcc_flags(self):
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raise NotImplementedError
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# functions must be overrided over
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def strip_empty_entries(self, args):
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'''
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Drop any empty strings from the list of compile and link flags
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'''
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return [x for x in args if len(x) > 0]
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def load(self, verbose=True):
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"""
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load and compile cpu_adam lib at runtime
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Args:
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verbose (bool, optional): show detailed info. Defaults to True.
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"""
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import time
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from torch.utils.cpp_extension import load
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start_build = time.time()
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op_module = load(name=self.name,
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sources=self.strip_empty_entries(self.sources_files()),
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extra_include_paths=self.strip_empty_entries(self.include_dirs()),
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extra_cflags=self.cxx_flags(),
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extra_cuda_cflags=self.nvcc_flags(),
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extra_ldflags=[],
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verbose=verbose)
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build_duration = time.time() - start_build
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if verbose:
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print(f"Time to load {self.name} op: {build_duration} seconds")
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return op_module
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def builder(self, name) -> 'CUDAExtension':
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"""
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get a CUDAExtension instance used for setup.py
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"""
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from torch.utils.cpp_extension import CUDAExtension
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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 self.sources_files()],
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include_dirs=self.include_dirs(),
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extra_compile_args={
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'cxx': self.cxx_flags(),
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'nvcc': self.nvcc_flags()
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})
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