File size: 1,923 Bytes
ce9b7f3 | 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 69 70 71 72 73 74 75 76 77 | '''
Distributed training related functions.
From DeiT.
'''
import io
import os
import time
from collections import defaultdict, deque
import datetime
import torch
import torch.distributed as dist
def is_dist_avail_and_initialized():
if not dist.is_available():
return False
if not dist.is_initialized():
return False
return True
def get_world_size():
if not is_dist_avail_and_initialized():
return 1
return dist.get_world_size()
def get_rank():
if not is_dist_avail_and_initialized():
return 0
return dist.get_rank()
def is_main_process():
return get_rank() == 0
def save_on_master(*args, **kwargs):
if is_main_process():
torch.save(*args, **kwargs)
def init_distributed_mode(args):
if 'RANK' in os.environ and 'WORLD_SIZE' in os.environ:
args.rank = int(os.environ["RANK"])
args.world_size = int(os.environ['WORLD_SIZE'])
args.local_rank = int(os.environ['LOCAL_RANK'])
elif 'SLURM_PROCID' in os.environ:
args.rank = int(os.environ['SLURM_PROCID'])
args.local_rank = args.rank % torch.cuda.device_count()
else:
print('Not using distributed mode')
args.distributed = False
args.rank = 0
args.local_rank = 0
return
args.distributed = True
torch.cuda.set_device(args.local_rank)
args.dist_backend = 'nccl'
#print('| distributed init (rank {}): {}'.format(
# args.rank, args.dist_url), flush=True)
torch.distributed.init_process_group(backend=args.dist_backend, init_method=args.dist_url,
world_size=args.world_size, rank=args.rank)
torch.distributed.barrier()
def count_parameters(model):
num_params = 0
for param in model.parameters():
if param.requires_grad:
num_params += param.numel()
print(f'num_params is: {num_params}') |