| |
| import torch |
| from torch._six import inf |
|
|
|
|
| class NativeScalerWithGradNormCount: |
| state_dict_key = "amp_scaler" |
|
|
| def __init__(self): |
| self._scaler = torch.cuda.amp.GradScaler() |
|
|
| def __call__( |
| self, |
| cfg, |
| loss, |
| optimizer, |
| clip_grad=None, |
| parameters=None, |
| create_graph=False, |
| update_grad=True, |
| step=0, |
| ): |
| self._scaler.scale(loss).backward(create_graph=create_graph) |
|
|
| if update_grad: |
| if clip_grad is not None: |
| assert parameters is not None |
| self._scaler.unscale_( |
| optimizer |
| ) |
| norm = torch.nn.utils.clip_grad_norm_(parameters, clip_grad) |
| else: |
| self._scaler.unscale_(optimizer) |
| norm = get_grad_norm_(parameters) |
|
|
| if cfg.TRAIN.optimizer != "SAM": |
| self._scaler.step(optimizer) |
| else: |
| if step == 0: |
| optimizer.first_step(zero_grad=True) |
| else: |
| self._scaler = optimizer.second_step( |
| zero_grad=False, scaler=self._scaler |
| ) |
| self._scaler.update() |
| else: |
| norm = None |
| return norm |
|
|
| def state_dict(self): |
| return self._scaler.state_dict() |
|
|
| def load_state_dict(self, state_dict): |
| self._scaler.load_state_dict(state_dict) |
|
|
|
|
| def get_grad_norm_(parameters, norm_type: float = 2.0) -> torch.Tensor: |
| if isinstance(parameters, torch.Tensor): |
| parameters = [parameters] |
| parameters = [p for p in parameters if p.grad is not None] |
| norm_type = float(norm_type) |
| if len(parameters) == 0: |
| return torch.tensor(0.0) |
| device = parameters[0].grad.device |
| if norm_type == inf: |
| total_norm = max(p.grad.detach().abs().max().to(device) for p in parameters) |
| else: |
| total_norm = torch.norm( |
| torch.stack( |
| [torch.norm(p.grad.detach(), norm_type).to(device) for p in parameters] |
| ), |
| norm_type, |
| ) |
| return total_norm |
|
|
