import os import re import sys from pathlib import Path import torch def get_cuda_cc_flag(): """get_cuda_cc_flag cc flag for your GPU arch """ cc_flag = [] for arch in torch.cuda.get_arch_list(): res = re.search(r'sm_(\d+)', arch) if res: arch_cap = res[1] if int(arch_cap) >= 60: cc_flag.extend(['-gencode', f'arch=compute_{arch_cap},code={arch}']) return cc_flag class Builder(object): def colossalai_src_path(self, code_path): if os.path.isabs(code_path): return code_path else: return os.path.join(Path(__file__).parent.parent.absolute(), code_path) def get_cuda_include(self): from torch.utils.cpp_extension import CUDA_HOME if CUDA_HOME is None: raise RuntimeError("CUDA_HOME is None, please set CUDA_HOME to compile C++/CUDA kernels in ColossalAI.") cuda_include = os.path.join(CUDA_HOME, "include") return cuda_include def strip_empty_entries(self, args): ''' Drop any empty strings from the list of compile and link flags ''' return [x for x in args if len(x) > 0] def load(self, verbose=True): """ load and compile cpu_adam lib at runtime Args: verbose (bool, optional): show detailed info. Defaults to True. """ import time from torch.utils.cpp_extension import load start_build = time.time() op_module = load(name=self.name, sources=self.strip_empty_entries(self.sources), extra_include_paths=self.strip_empty_entries(self.extra_include_paths), extra_cflags=self.extra_cxx_flags, extra_cuda_cflags=self.extra_cuda_flags, extra_ldflags=[], verbose=verbose) build_duration = time.time() - start_build if verbose: print(f"Time to load {self.name} op: {build_duration} seconds") return op_module