| # Copyright 2025 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. | |
| import os | |
| import torch | |
| from verl.utils.torch_functional import allgather_dict_into_dict | |
| if __name__ == "__main__": | |
| torch.distributed.init_process_group(backend="gloo") | |
| local_rank = int(os.environ["LOCAL_RANK"]) | |
| rank = int(os.environ["RANK"]) | |
| world_size = int(os.environ["WORLD_SIZE"]) | |
| metrics_dict = {"loss": [0 + rank, 1 + rank, 2 + rank], "grad_norm": rank} | |
| result = allgather_dict_into_dict(data=metrics_dict, group=None) | |
| assert result["loss"] == [[0, 1, 2], [1, 2, 3], [2, 3, 4], [3, 4, 5]] | |
| assert result["grad_norm"] == [0, 1, 2, 3] | |
| print(result) | |