import torch import torch.nn as nn import subprocess import sys import os from lib.utils.tools.logger import Logger as Log def is_distributed(): return torch.distributed.is_initialized() def get_world_size(): if not torch.distributed.is_initialized(): return 1 return torch.distributed.get_world_size() def get_rank(): if not torch.distributed.is_initialized(): return 0 return torch.distributed.get_rank() def all_reduce_numpy(array): tensor = torch.from_numpy(array).cuda() torch.distributed.all_reduce(tensor) return tensor.cpu().numpy() def handle_distributed(args, main_file): if not args.distributed: os.environ['CUDA_VISIBLE_DEVICES'] = ','.join(map(str, args.gpu)) return if args.local_rank >= 0: _setup_process_group(args) return current_env = os.environ.copy() if current_env.get('CUDA_VISIBLE_DEVICES') is None: current_env['CUDA_VISIBLE_DEVICES'] = ','.join(map(str, args.gpu)) world_size = len(args.gpu) else: world_size = len(current_env['CUDA_VISIBLE_DEVICES'].split(',')) current_env['WORLD_SIZE'] = str(world_size) print('World size:', world_size) # Logic for spawner python_exec = sys.executable command_args = sys.argv Log.info('{}'.format(command_args)) try: main_index = command_args.index('main_contrastive.py') except: main_index = command_args.index('main.py') command_args = command_args[main_index+1:] print(command_args) command_args = [ python_exec, '-u', '-m', 'torch.distributed.launch', '--nproc_per_node', str(world_size), '--master_port', str(29961), main_file, ] + command_args process = subprocess.Popen(command_args, env=current_env) process.wait() if process.returncode != 0: raise subprocess.CalledProcessError(returncode=process.returncode, cmd=command_args) sys.exit(process.returncode) def _setup_process_group(args): local_rank = args.local_rank torch.cuda.set_device(local_rank) torch.distributed.init_process_group( 'nccl', init_method='env://', # rank=local_rank )