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| """ CUDA / AMP utils | |
| Hacked together by / Copyright 2020 Ross Wightman | |
| """ | |
| import torch | |
| try: | |
| from apex import amp | |
| has_apex = True | |
| except ImportError: | |
| amp = None | |
| has_apex = False | |
| class ApexScaler: | |
| state_dict_key = "amp" | |
| def __call__(self, loss, optimizer, clip_grad=None, parameters=None, create_graph=False): | |
| with amp.scale_loss(loss, optimizer) as scaled_loss: | |
| scaled_loss.backward(create_graph=create_graph) | |
| if clip_grad is not None: | |
| torch.nn.utils.clip_grad_norm_(amp.master_params(optimizer), clip_grad) | |
| optimizer.step() | |
| def state_dict(self): | |
| if 'state_dict' in amp.__dict__: | |
| return amp.state_dict() | |
| def load_state_dict(self, state_dict): | |
| if 'load_state_dict' in amp.__dict__: | |
| amp.load_state_dict(state_dict) | |
| class NativeScaler: | |
| state_dict_key = "amp_scaler" | |
| def __init__(self): | |
| self._scaler = torch.cuda.amp.GradScaler() | |
| def __call__(self, loss, optimizer, clip_grad=None, parameters=None, create_graph=False): | |
| self._scaler.scale(loss).backward(create_graph=create_graph) | |
| if clip_grad is not None: | |
| assert parameters is not None | |
| self._scaler.unscale_(optimizer) # unscale the gradients of optimizer's assigned params in-place | |
| torch.nn.utils.clip_grad_norm_(parameters, clip_grad) | |
| self._scaler.step(optimizer) | |
| self._scaler.update() | |
| def state_dict(self): | |
| return self._scaler.state_dict() | |
| def load_state_dict(self, state_dict): | |
| self._scaler.load_state_dict(state_dict) | |