44 lines
1.4 KiB
Python
44 lines
1.4 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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import torch
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class NativeAMP:
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def __init__(self, trainer):
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self.trainer = trainer
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def connect(self, model, optimizers):
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return model, optimizers
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def backward(self, closure_loss, optimizer, opt_idx, *args, **kwargs):
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closure_loss = self.trainer.scaler.scale(closure_loss)
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# do backward pass
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if self.trainer.train_loop.automatic_optimization:
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model = self.trainer.get_model()
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model.backward(closure_loss, optimizer, opt_idx)
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else:
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closure_loss.backward(*args, **kwargs)
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# once backward has been applied, release graph
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closure_loss = closure_loss.detach()
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return closure_loss
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def training_step(self, fx, args):
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with torch.cuda.amp.autocast():
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output = fx(*args)
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return output
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