| # 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. | |
| import torch | |
| import torch.distributed as dist | |
| import logging | |
| def log_gpu_memory_usage(head: str, logger: logging.Logger = None, level=logging.DEBUG, rank: int = 0): | |
| if (not dist.is_initialized()) or (rank is None) or (dist.get_rank() == rank): | |
| memory_allocated = torch.cuda.memory_allocated() / 1024**3 | |
| memory_reserved = torch.cuda.memory_reserved() / 1024**3 | |
| message = f'{head}, memory allocated (GB): {memory_allocated}, memory reserved (GB): {memory_reserved}' | |
| if logger is None: | |
| print(message) | |
| else: | |
| logger.log(msg=message, level=level) | |