Making large AI models cheaper, faster and more accessible
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import os
import torch.distributed.rpc as rpc
from rpc_test_utils import pg_parse_args, rpc_is_initialized
from colossalai.initialize import launch
from colossalai.logging import disable_existing_loggers
from colossalai.pipeline.pipeline_process_group import ppg
from colossalai.testing import spawn
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
spawn(run_worker, world_size, args=args)