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https://github.com/open-mmlab/mmsegmentation.git
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0beaf69047
## Motivation We will support full precision training on the next generation Ascend NPU, so there is no need to enable mixed precision by default. ## Modification Determine whether the current chip supports full precision training, and automatically enable mixed precision. ## BC-breaking (Optional) Not affected. ## Use cases (Optional) We have verified the correctness on the Ascend NPU.
115 lines
4.0 KiB
Python
115 lines
4.0 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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import mmcv
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import torch
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from mmcv.parallel import MMDataParallel, MMDistributedDataParallel
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from mmseg import digit_version
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dp_factory = {'cuda': MMDataParallel, 'cpu': MMDataParallel}
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ddp_factory = {'cuda': MMDistributedDataParallel}
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def build_dp(model, device='cuda', dim=0, *args, **kwargs):
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"""build DataParallel module by device type.
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if device is cuda, return a MMDataParallel module; if device is mlu,
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return a MLUDataParallel module.
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Args:
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model (:class:`nn.Module`): module to be parallelized.
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device (str): device type, cuda, cpu or mlu. Defaults to cuda.
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dim (int): Dimension used to scatter the data. Defaults to 0.
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Returns:
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:class:`nn.Module`: parallelized module.
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"""
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if device == 'cuda':
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model = model.cuda()
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elif device == 'mlu':
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assert digit_version(mmcv.__version__) >= digit_version('1.5.0'), \
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'Please use MMCV >= 1.5.0 for MLU training!'
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from mmcv.device.mlu import MLUDataParallel
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dp_factory['mlu'] = MLUDataParallel
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model = model.mlu()
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elif device == 'npu':
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assert digit_version(mmcv.__version__) >= digit_version('1.7.0'), \
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'Please use MMCV >= 1.7.0 for NPU training!'
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from mmcv.device.npu import NPUDataParallel
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torch.npu.set_device(kwargs['device_ids'][0])
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torch.npu.set_compile_mode(jit_compile=False)
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dp_factory['npu'] = NPUDataParallel
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model = model.npu()
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return dp_factory[device](model, dim=dim, *args, **kwargs)
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def build_ddp(model, device='cuda', *args, **kwargs):
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"""Build DistributedDataParallel module by device type.
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If device is cuda, return a MMDistributedDataParallel module;
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if device is mlu, return a MLUDistributedDataParallel module.
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Args:
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model (:class:`nn.Module`): module to be parallelized.
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device (str): device type, mlu or cuda.
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Returns:
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:class:`nn.Module`: parallelized module.
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References:
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.. [1] https://pytorch.org/docs/stable/generated/torch.nn.parallel.
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DistributedDataParallel.html
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"""
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assert device in ['cuda', 'mlu', 'npu'], 'Only available for cuda, '\
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'npu or mlu devices.'
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if device == 'cuda':
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model = model.cuda()
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elif device == 'mlu':
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assert digit_version(mmcv.__version__) >= digit_version('1.5.0'), \
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'Please use MMCV >= 1.5.0 for MLU training!'
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from mmcv.device.mlu import MLUDistributedDataParallel
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ddp_factory['mlu'] = MLUDistributedDataParallel
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model = model.mlu()
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elif device == 'npu':
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assert digit_version(mmcv.__version__) >= digit_version('1.7.0'), \
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'Please use MMCV >= 1.7.0 for NPU training!'
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from mmcv.device.npu import NPUDistributedDataParallel
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torch.npu.set_compile_mode(jit_compile=False)
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ddp_factory['npu'] = NPUDistributedDataParallel
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model = model.npu()
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return ddp_factory[device](model, *args, **kwargs)
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def is_mlu_available():
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"""Returns a bool indicating if MLU is currently available."""
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return hasattr(torch, 'is_mlu_available') and torch.is_mlu_available()
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def is_npu_available():
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"""Returns a bool indicating if NPU is currently available."""
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return hasattr(torch, 'npu') and torch.npu.is_available()
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def is_npu_support_full_precision() -> bool:
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"""Returns True if npu devices support full precision training."""
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if not is_npu_available():
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return False
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import torch_npu.npu.utils as npu_utils
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version_of_support_full_precision = 220
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return npu_utils.get_soc_version() >= version_of_support_full_precision
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def get_device():
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"""Returns an available device, cpu, npu, cuda or mlu."""
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is_device_available = {
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'npu': is_npu_available(),
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'cuda': torch.cuda.is_available(),
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'mlu': is_mlu_available()
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}
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device_list = [k for k, v in is_device_available.items() if v]
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return device_list[0] if len(device_list) >= 1 else 'cpu'
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