Download testbed/open-mmlab__mmengine/mmengine/device/utils.py from hc99/FEA-Bench: direct link, hf CLI and curl.
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2.14 kB
| # Copyright (c) OpenMMLab. All rights reserved. | |
| from typing import Optional | |
| import torch | |
| def get_max_cuda_memory(device: Optional[torch.device] = None) -> int: | |
| """Returns the maximum GPU memory occupied by tensors in megabytes (MB) for | |
| a given device. By default, this returns the peak allocated memory since | |
| the beginning of this program. | |
| Args: | |
| device (torch.device, optional): selected device. Returns | |
| statistic for the current device, given by | |
| :func:`~torch.cuda.current_device`, if ``device`` is None. | |
| Defaults to None. | |
| Returns: | |
| int: The maximum GPU memory occupied by tensors in megabytes | |
| for a given device. | |
| """ | |
| mem = torch.cuda.max_memory_allocated(device=device) | |
| mem_mb = torch.tensor([int(mem) // (1024 * 1024)], | |
| dtype=torch.int, | |
| device=device) | |
| torch.cuda.reset_peak_memory_stats() | |
| return int(mem_mb.item()) | |
| def is_cuda_available() -> bool: | |
| """Returns True if cuda devices exist.""" | |
| return torch.cuda.is_available() | |
| def is_npu_available() -> bool: | |
| """Returns True if Ascend PyTorch and npu devices exist.""" | |
| try: | |
| import torch_npu # noqa: F401 | |
| except Exception: | |
| return False | |
| return hasattr(torch, 'npu') and torch.npu.is_available() | |
| def is_mlu_available() -> bool: | |
| """Returns True if Cambricon PyTorch and mlu devices exist.""" | |
| return hasattr(torch, 'is_mlu_available') and torch.is_mlu_available() | |
| def is_mps_available() -> bool: | |
| """Return True if mps devices exist. | |
| It's specialized for mac m1 chips and require torch version 1.12 or higher. | |
| """ | |
| return hasattr(torch.backends, 'mps') and torch.backends.mps.is_available() | |
| def get_device() -> str: | |
| """Returns the currently existing device type. | |
| Returns: | |
| str: cuda | npu | mlu | mps | cpu. | |
| """ | |
| if is_npu_available(): | |
| return 'npu' | |
| elif is_cuda_available(): | |
| return 'cuda' | |
| elif is_mlu_available(): | |
| return 'mlu' | |
| elif is_mps_available(): | |
| return 'mps' | |
| else: | |
| return 'cpu' | |