73 lines
2.7 KiB
Python
73 lines
2.7 KiB
Python
# Copyright The PyTorch Lightning team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import logging
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import os
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from typing import Any, TYPE_CHECKING
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import torch
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from pytorch_lightning.accelerators.accelerator import Accelerator
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from pytorch_lightning.plugins import DataParallelPlugin
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from pytorch_lightning.utilities.exceptions import MisconfigurationException
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if TYPE_CHECKING:
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from pytorch_lightning.core.lightning import LightningModule
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from pytorch_lightning.trainer.trainer import Trainer
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_log = logging.getLogger(__name__)
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class GPUAccelerator(Accelerator):
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def setup(self, trainer: 'Trainer', model: 'LightningModule') -> None:
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"""
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Raises:
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MisconfigurationException:
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If the selected device is not GPU.
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"""
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if "cuda" not in str(self.root_device):
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raise MisconfigurationException(f"Device should be GPU, got {self.root_device} instead")
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self.set_nvidia_flags()
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torch.cuda.set_device(self.root_device)
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return super().setup(trainer, model)
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def on_train_start(self) -> None:
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# clear cache before training
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# use context because of:
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# https://discuss.pytorch.org/t/out-of-memory-when-i-use-torch-cuda-empty-cache/57898
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with torch.cuda.device(self.root_device):
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torch.cuda.empty_cache()
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def on_train_end(self) -> None:
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# clean up memory
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self.model.cpu()
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with torch.cuda.device(self.root_device):
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torch.cuda.empty_cache()
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@staticmethod
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def set_nvidia_flags() -> None:
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# set the correct cuda visible devices (using pci order)
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os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
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all_gpu_ids = ",".join([str(x) for x in range(torch.cuda.device_count())])
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devices = os.getenv("CUDA_VISIBLE_DEVICES", all_gpu_ids)
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_log.info(f"LOCAL_RANK: {os.getenv('LOCAL_RANK', 0)} - CUDA_VISIBLE_DEVICES: [{devices}]")
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def to_device(self, batch: Any) -> Any:
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# no need to transfer batch to device in DP mode
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# TODO: Add support to allow batch transfer to device in Lightning for DP mode.
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if not isinstance(self.training_type_plugin, DataParallelPlugin):
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batch = super().to_device(batch)
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return batch
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