| """ |
| Helpers for distributed training. |
| """ |
|
|
| import io |
| import os |
| import socket |
|
|
| import blobfile as bf |
| import numpy as np |
| import torch |
| import torch.distributed as dist |
| import torch.multiprocessing as mp |
|
|
|
|
| def set_device(): |
| if torch.cuda.is_available(): |
| device = torch.device("cuda:0") |
| else: |
| device = torch.device("cpu") |
| return device |
|
|
| def setup_dist(args): |
| def set_function(main_worker): |
| """ |
| Setup a distributed process group. |
| """ |
| torch.cuda.empty_cache() |
| torch.backends.cudnn.benchmark = False |
| torch.backends.cudnn.fastest = True |
| os.environ["CUDA_VISIBLE_DEVICES"] = args.cuda_devices |
| os.environ["MASTER_ADDR"] = "127.0.0.1" |
| os.environ["MASTER_PORT"] = "8888" |
| world_size = 1 |
| port_id = 10002 + np.random.randint(0, 1000) + int(args.cuda_devices[0]) |
| dist_url = "tcp://127.0.0.1:" + str(port_id) |
| ngpus_per_node = torch.cuda.device_count() |
| world_size = ngpus_per_node * world_size |
| print("multiprocessing_distributed") |
| torch.multiprocessing.set_start_method("spawn") |
| mp.spawn( |
| main_worker, nprocs=ngpus_per_node, args=(args,ngpus_per_node, world_size, dist_url) |
| ) |
| return set_function |
|
|
|
|
|
|
|
|
| def sync_params(params): |
| """ |
| Synchronize a sequence of Tensors across ranks from rank 0. |
| """ |
| for p in params: |
| with torch.no_grad(): |
| dist.broadcast(p, 0) |
|
|
|
|
| def _find_free_port(): |
| try: |
| s = socket.socket(socket.AF_INET, socket.SOCK_STREAM) |
| s.bind(("", 0)) |
| s.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1) |
| return s.getsockname()[1] |
| finally: |
| s.close() |
|
|
