61 lines
2.0 KiB
Python
61 lines
2.0 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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try:
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from apex import amp
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except ImportError:
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APEX_AVAILABLE = False
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else:
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APEX_AVAILABLE = True
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class GPUBackend(object):
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def __init__(self, trainer):
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self.trainer = trainer
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def setup(self, model):
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# call setup
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if not self.trainer.testing:
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self.trainer.setup('fit')
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model.setup('fit')
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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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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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# TODO: remove with dropping NVIDIA AMP support
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native_amp_available = hasattr(torch.cuda, "amp") and hasattr(torch.cuda.amp, "autocast")
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if self.trainer.use_amp and not native_amp_available:
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model = self._setup_nvidia_apex(model)
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return model
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def train(self, model):
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results = self.trainer.run_pretrain_routine(model)
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return results
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def _setup_nvidia_apex(self, model):
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model, optimizers = model.configure_apex(amp, model, self.trainer.optimizers, self.trainer.amp_level)
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self.trainer.optimizers = optimizers
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self.trainer.reinit_scheduler_properties(self.trainer.optimizers, self.trainer.lr_schedulers)
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return model
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