109 lines
3.4 KiB
Python
109 lines
3.4 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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from typing import Any, Callable, Optional, Union
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import torch
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from pytorch_lightning.accelerators.accelerator import Accelerator, ReduceOp
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from pytorch_lightning.cluster_environments import ClusterEnvironment
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from pytorch_lightning.distributed.dist import LightningDistributed
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from pytorch_lightning.utilities import AMPType
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class GPUAccelerator(Accelerator):
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amp_backend: AMPType
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def __init__(self, trainer, cluster_environment: Optional[ClusterEnvironment] = None):
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"""
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Runs training using a single GPU
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Example::
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# default
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trainer = Trainer(accelerator=GPUAccelerator())
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"""
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super().__init__(trainer, cluster_environment)
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self.dist = LightningDistributed()
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self.nickname = None
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def setup(self, model):
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# call setup
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self.trainer.call_setup_hook(model)
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torch.cuda.set_device(self.trainer.root_gpu)
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model.cuda(self.trainer.root_gpu)
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# CHOOSE OPTIMIZER
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# allow for lr schedulers as well
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self.setup_optimizers(model)
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# 16-bit
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model = self.trainer.precision_connector.connect(model)
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self.trainer.convert_to_lightning_optimizers()
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self.trainer.model = model
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def train(self):
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model = self.trainer.model
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# set up training routine
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self.trainer.train_loop.setup_training(model)
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# train or test
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results = self.train_or_test()
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return results
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def _step(self, model_step: Callable, args):
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args[0] = self.to_device(args[0])
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if self.trainer.amp_backend == AMPType.NATIVE:
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with torch.cuda.amp.autocast():
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output = model_step(*args)
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else:
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output = model_step(*args)
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return output
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def training_step(self, args):
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return self._step(self.trainer.model.training_step, args)
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def validation_step(self, args):
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return self._step(self.trainer.model.validation_step, args)
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def test_step(self, args):
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return self._step(self.trainer.model.test_step, args)
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def to_device(self, batch):
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gpu_id = 0
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if isinstance(self.trainer.data_parallel_device_ids, list):
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gpu_id = self.trainer.data_parallel_device_ids[0]
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# Don't copy the batch since there is a single gpu that the batch could
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# be referenced from and if there are multiple optimizers the batch will
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# wind up copying it to the same device repeatedly.
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return self.batch_to_device(batch, gpu_id)
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def sync_tensor(self,
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tensor: Union[torch.Tensor],
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group: Optional[Any] = None,
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reduce_op: Optional[Union[ReduceOp, str]] = None) -> torch.Tensor:
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return tensor
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@property
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def require_distributed_sampler(self):
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return False
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