| # Copyright 2024 Bytedance Ltd. and/or its affiliates | |
| # | |
| # Licensed under the Apache License, Version 2.0 (the "License"); | |
| # you may not use this file except in compliance with the License. | |
| # You may obtain a copy of the License at | |
| # | |
| # http://www.apache.org/licenses/LICENSE-2.0 | |
| # | |
| # Unless required by applicable law or agreed to in writing, software | |
| # distributed under the License is distributed on an "AS IS" BASIS, | |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
| # See the License for the specific language governing permissions and | |
| # limitations under the License. | |
| """Utilities for distributed training.""" | |
| import os | |
| def initialize_global_process_group(timeout_second=36000): | |
| from datetime import timedelta | |
| import torch.distributed | |
| torch.distributed.init_process_group("nccl", timeout=timedelta(seconds=timeout_second)) | |
| local_rank = int(os.environ["LOCAL_RANK"]) | |
| rank = int(os.environ["RANK"]) | |
| world_size = int(os.environ["WORLD_SIZE"]) | |
| if torch.distributed.is_initialized(): | |
| torch.cuda.set_device(local_rank) | |
| return local_rank, rank, world_size | |