File size: 2,202 Bytes
2f85de4 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 |
# python3.7
"""Contains utility functions used for distribution."""
import contextlib
import os
import subprocess
import torch
import torch.distributed as dist
import torch.multiprocessing as mp
__all__ = ['init_dist', 'exit_dist', 'ddp_sync', 'get_ddp_module']
def init_dist(launcher, backend='nccl', **kwargs):
"""Initializes distributed environment."""
if mp.get_start_method(allow_none=True) is None:
mp.set_start_method('spawn')
if launcher == 'pytorch':
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)
elif launcher == 'slurm':
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')
port = os.environ.get('PORT', 29500)
os.environ['MASTER_PORT'] = str(port)
os.environ['MASTER_ADDR'] = addr
os.environ['WORLD_SIZE'] = str(ntasks)
os.environ['RANK'] = str(proc_id)
dist.init_process_group(backend=backend)
else:
raise NotImplementedError(f'Not implemented launcher type: '
f'`{launcher}`!')
def exit_dist():
"""Exits the distributed environment."""
if dist.is_initialized():
dist.destroy_process_group()
@contextlib.contextmanager
def ddp_sync(model, sync):
"""Controls whether the `DistributedDataParallel` model should be synced."""
assert isinstance(model, torch.nn.Module)
is_ddp = isinstance(model, torch.nn.parallel.DistributedDataParallel)
if sync or not is_ddp:
yield
else:
with model.no_sync():
yield
def get_ddp_module(model):
"""Gets the module from `DistributedDataParallel`."""
assert isinstance(model, torch.nn.Module)
is_ddp = isinstance(model, torch.nn.parallel.DistributedDataParallel)
if is_ddp:
return model.module
return model
|