Making large AI models cheaper, faster and more accessible
You can not select more than 25 topics Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
 
 
 
 
 

43 lines
1.3 KiB

import os
import torch.distributed.rpc as rpc
import torch.multiprocessing as mp
import pytest
from colossalai.pipeline.pipeline_process_group import ppg
from colossalai.initialize import launch
from colossalai.logging import disable_existing_loggers
from rpc_test_utils import pg_parse_args, rpc_is_initialized
def run_worker(rank, args):
os.environ['MASTER_ADDR'] = args.master_addr
os.environ['MASTER_PORT'] = args.master_port
device = args.device
world_size = args.world_size
dp_degree = args.dp_degree
tp_degree = args.tp_degree
num_worker_threads = args.num_worker_threads
host = args.master_addr
port = args.master_port
backend = 'nccl' if device == 'cuda' else 'gloo'
disable_existing_loggers()
launch(dict(), rank, world_size, host, int(port), backend, verbose=False)
ppg.set_global_info(rank=rank,
world_size=world_size,
dp_degree=dp_degree,
tp_degree=tp_degree,
num_worker_threads=num_worker_threads,
device=device)
if rpc_is_initialized():
rpc.shutdown()
if __name__ == "__main__":
args = pg_parse_args()
world_size = args.world_size
mp.spawn(run_worker, args=(args,), nprocs=world_size)