| ''' |
| 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' |
| |
| |
| 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}') |
