| """ 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 |
|
|
| from .clip_grad import dispatch_clip_grad |
|
|
|
|
| class ApexScaler: |
| state_dict_key = "amp" |
|
|
| def __call__( |
| self, |
| loss, |
| optimizer, |
| clip_grad=None, |
| clip_mode='norm', |
| parameters=None, |
| create_graph=False, |
| need_update=True, |
| ): |
| with amp.scale_loss(loss, optimizer) as scaled_loss: |
| scaled_loss.backward(create_graph=create_graph) |
| if need_update: |
| if clip_grad is not None: |
| dispatch_clip_grad(amp.master_params(optimizer), clip_grad, mode=clip_mode) |
| 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, device='cuda'): |
| try: |
| self._scaler = torch.amp.GradScaler(device=device) |
| except (AttributeError, TypeError) as e: |
| self._scaler = torch.cuda.amp.GradScaler() |
|
|
| def __call__( |
| self, |
| loss, |
| optimizer, |
| clip_grad=None, |
| clip_mode='norm', |
| parameters=None, |
| create_graph=False, |
| need_update=True, |
| ): |
| self._scaler.scale(loss).backward(create_graph=create_graph) |
| if need_update: |
| if clip_grad is not None: |
| assert parameters is not None |
| self._scaler.unscale_(optimizer) |
| dispatch_clip_grad(parameters, clip_grad, mode=clip_mode) |
| 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) |
|
|
