74 lines
2.6 KiB
Python
74 lines
2.6 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 torch
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from pytorch_lightning.utilities.exceptions import MisconfigurationException
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from pytorch_lightning.accelerators.base_backend import Accelerator
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from pytorch_lightning.utilities import AMPType, rank_zero_warn
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class CPUBackend(Accelerator):
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def __init__(self, trainer):
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super().__init__(trainer)
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def setup(self, model):
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# run through amp wrapper
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if self.trainer.amp_backend:
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raise MisconfigurationException('amp + cpu is not supported. Please use a GPU option')
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# call setup after the ddp process has connected
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self.trainer.call_setup_hook(model)
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# CHOOSE OPTIMIZER
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# allow for lr schedulers as well
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optimizers, lr_schedulers, optimizer_frequencies = self.trainer.init_optimizers(model)
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self.trainer.optimizers = optimizers
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self.trainer.lr_schedulers = lr_schedulers
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self.trainer.optimizer_frequencies = optimizer_frequencies
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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 training_step(self, args):
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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 = self.trainer.model.training_step(*args)
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else:
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output = self.trainer.model.training_step(*args)
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return output
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def validation_step(self, args):
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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 = self.trainer.model.validation_step(*args)
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else:
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output = self.trainer.model.validation_step(*args)
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return output
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def test_step(self, args):
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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 = self.trainer.model.test_step(*args)
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else:
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output = self.trainer.model.test_step(*args)
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return output
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