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# Modified from https://github.com/open-mmlab/mmcv/blob/master/mmcv/runner/dist_utils.py # noqa: E501
import functools
import os
import subprocess
import torch
import torch.distributed as dist
import torch.multiprocessing as mp
from torch.nn.parallel import DataParallel, DistributedDataParallel
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 == 'slurm':
_init_dist_slurm(backend, **kwargs)
else:
raise ValueError(f'Invalid launcher type: {launcher}')
def _init_dist_pytorch(backend, **kwargs):
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_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'
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 master_only(func):
@functools.wraps(func)
def wrapper(*args, **kwargs):
rank, _ = get_dist_info()
if rank == 0:
return func(*args, **kwargs)
return wrapper
def get_bare_model(net):
"""Get bare model, especially under wrapping with
DistributedDataParallel or DataParallel.
"""
if isinstance(net, (DataParallel, DistributedDataParallel)):
net = net.module
return net
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