# Copyright The PyTorch Lightning team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import torch class NativeAMP: def __init__(self, trainer): self.trainer = trainer def connect(self, model, optimizers): return model, optimizers def backward(self, closure_loss, optimizer, opt_idx, *args, **kwargs): closure_loss = self.trainer.scaler.scale(closure_loss) # do backward pass if self.trainer.train_loop.automatic_optimization: model = self.trainer.get_model() model.backward(closure_loss, optimizer, opt_idx) else: closure_loss.backward(*args, **kwargs) # once backward has been applied, release graph closure_loss = closure_loss.detach() return closure_loss def training_step(self, fx, args): with torch.cuda.amp.autocast(): output = fx(*args) return output