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| # Copyright (c) OpenMMLab. All rights reserved. | |
| import functools | |
| import os | |
| import subprocess | |
| from collections import OrderedDict | |
| import torch | |
| import torch.multiprocessing as mp | |
| from torch import distributed as dist | |
| from torch._utils import (_flatten_dense_tensors, _take_tensors, | |
| _unflatten_dense_tensors) | |
| 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): | |
| # TODO: use local_rank instead of rank % num_gpus | |
| 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() and dist.is_initialized(): | |
| rank = dist.get_rank() | |
| world_size = dist.get_world_size() | |
| else: | |
| rank = 0 | |
| world_size = 1 | |
| return rank, world_size | |
| def master_only(func): | |
| def wrapper(*args, **kwargs): | |
| rank, _ = get_dist_info() | |
| if rank == 0: | |
| return func(*args, **kwargs) | |
| return wrapper | |
| def allreduce_params(params, coalesce=True, bucket_size_mb=-1): | |
| """Allreduce parameters. | |
| Args: | |
| params (list[torch.Parameters]): List of parameters or buffers of a | |
| model. | |
| coalesce (bool, optional): Whether allreduce parameters as a whole. | |
| Defaults to True. | |
| bucket_size_mb (int, optional): Size of bucket, the unit is MB. | |
| Defaults to -1. | |
| """ | |
| _, world_size = get_dist_info() | |
| if world_size == 1: | |
| return | |
| params = [param.data for param in params] | |
| if coalesce: | |
| _allreduce_coalesced(params, world_size, bucket_size_mb) | |
| else: | |
| for tensor in params: | |
| dist.all_reduce(tensor.div_(world_size)) | |
| def allreduce_grads(params, coalesce=True, bucket_size_mb=-1): | |
| """Allreduce gradients. | |
| Args: | |
| params (list[torch.Parameters]): List of parameters of a model | |
| coalesce (bool, optional): Whether allreduce parameters as a whole. | |
| Defaults to True. | |
| bucket_size_mb (int, optional): Size of bucket, the unit is MB. | |
| Defaults to -1. | |
| """ | |
| grads = [ | |
| param.grad.data for param in params | |
| if param.requires_grad and param.grad is not None | |
| ] | |
| _, world_size = get_dist_info() | |
| if world_size == 1: | |
| return | |
| if coalesce: | |
| _allreduce_coalesced(grads, world_size, bucket_size_mb) | |
| else: | |
| for tensor in grads: | |
| dist.all_reduce(tensor.div_(world_size)) | |
| def _allreduce_coalesced(tensors, world_size, bucket_size_mb=-1): | |
| if bucket_size_mb > 0: | |
| bucket_size_bytes = bucket_size_mb * 1024 * 1024 | |
| buckets = _take_tensors(tensors, bucket_size_bytes) | |
| else: | |
| buckets = OrderedDict() | |
| for tensor in tensors: | |
| tp = tensor.type() | |
| if tp not in buckets: | |
| buckets[tp] = [] | |
| buckets[tp].append(tensor) | |
| buckets = buckets.values() | |
| for bucket in buckets: | |
| flat_tensors = _flatten_dense_tensors(bucket) | |
| dist.all_reduce(flat_tensors) | |
| flat_tensors.div_(world_size) | |
| for tensor, synced in zip( | |
| bucket, _unflatten_dense_tensors(flat_tensors, bucket)): | |
| tensor.copy_(synced) | |