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| # Copyright (c) OpenMMLab. All rights reserved. | |
| # https://github.com/open-mmlab/mmcv/blob/7540cf73ac7e5d1e14d0ffbd9b6759e83929ecfc/mmcv/runner/dist_utils.py | |
| import os | |
| import subprocess | |
| import torch | |
| import torch.multiprocessing as mp | |
| from torch import distributed as dist | |
| def init_dist(launcher, backend='nccl', **kwargs): | |
| if mp.get_start_method(allow_none=True) is None: | |
| mp.set_start_method('spawn') | |
| if launcher == 'pytorch': | |
| _init_dist_pytorch(backend, **kwargs) | |
| elif launcher == 'mpi': | |
| _init_dist_mpi(backend, **kwargs) | |
| elif launcher == 'slurm': | |
| _init_dist_slurm(backend, **kwargs) | |
| else: | |
| raise ValueError(f'Invalid launcher type: {launcher}') | |
| def _init_dist_pytorch(backend, **kwargs): | |
| # TODO: use local_rank instead of rank % num_gpus | |
| rank = int(os.environ['RANK']) | |
| num_gpus = torch.cuda.device_count() | |
| torch.cuda.set_device(rank % num_gpus) | |
| dist.init_process_group(backend=backend, **kwargs) | |
| def _init_dist_mpi(backend, **kwargs): | |
| rank = int(os.environ['OMPI_COMM_WORLD_RANK']) | |
| num_gpus = torch.cuda.device_count() | |
| torch.cuda.set_device(rank % num_gpus) | |
| dist.init_process_group(backend=backend, **kwargs) | |
| def _init_dist_slurm(backend, port=None): | |
| """Initialize slurm distributed training environment. | |
| If argument ``port`` is not specified, then the master port will be system | |
| environment variable ``MASTER_PORT``. If ``MASTER_PORT`` is not in system | |
| environment variable, then a default port ``29500`` will be used. | |
| Args: | |
| backend (str): Backend of torch.distributed. | |
| port (int, optional): Master port. Defaults to None. | |
| """ | |
| proc_id = int(os.environ['SLURM_PROCID']) | |
| ntasks = int(os.environ['SLURM_NTASKS']) | |
| node_list = os.environ['SLURM_NODELIST'] | |
| num_gpus = torch.cuda.device_count() | |
| torch.cuda.set_device(proc_id % num_gpus) | |
| addr = subprocess.getoutput( | |
| f'scontrol show hostname {node_list} | head -n1') | |
| # specify master port | |
| if port is not None: | |
| os.environ['MASTER_PORT'] = str(port) | |
| elif 'MASTER_PORT' in os.environ: | |
| pass # use MASTER_PORT in the environment variable | |
| else: | |
| # 29500 is torch.distributed default port | |
| os.environ['MASTER_PORT'] = '29500' | |
| # use MASTER_ADDR in the environment variable if it already exists | |
| if 'MASTER_ADDR' not in os.environ: | |
| os.environ['MASTER_ADDR'] = addr | |
| os.environ['WORLD_SIZE'] = str(ntasks) | |
| os.environ['LOCAL_RANK'] = str(proc_id % num_gpus) | |
| os.environ['RANK'] = str(proc_id) | |
| dist.init_process_group(backend=backend) | |
| def get_dist_info(): | |
| if dist.is_available(): | |
| initialized = dist.is_initialized() | |
| else: | |
| initialized = False | |
| if initialized: | |
| rank = dist.get_rank() | |
| world_size = dist.get_world_size() | |
| else: | |
| rank = 0 | |
| world_size = 1 | |
| return rank, world_size | |
| def setup_for_distributed(is_master): | |
| """ | |
| This function disables printing when not in master process | |
| """ | |
| import builtins as __builtin__ | |
| builtin_print = __builtin__.print | |
| def print(*args, **kwargs): | |
| force = kwargs.pop('force', False) | |
| if is_master or force: | |
| builtin_print(*args, **kwargs) | |
| __builtin__.print = print | |