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from typing import Iterable, Union |
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import torch |
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def get_grad_norm( |
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parameters: Union[torch.Tensor, Iterable[torch.Tensor]], norm_type: float = 2.0 |
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) -> torch.Tensor: |
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""" |
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Adapted from: https://pytorch.org/docs/stable/_modules/torch/nn/utils/clip_grad.html#clip_grad_norm_ |
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""" |
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if isinstance(parameters, torch.Tensor): |
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parameters = [parameters] |
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parameters = [p for p in parameters if p.grad is not None] |
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norm_type = float(norm_type) |
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if len(parameters) == 0: |
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return torch.tensor(0.0) |
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device = parameters[0].grad.device |
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total_norm = torch.norm( |
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torch.stack([torch.norm(p.grad.detach(), norm_type).to(device) for p in parameters]), |
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norm_type, |
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) |
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return total_norm |
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