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# torch.nn.utils.clip_grad_norm_

torch.nn.utils.clip_grad_norm_(parameters, max_norm, norm_type=2.0, error_if_nonfinite=False, foreach=None)

  Clips gradient norm of an iterable of parameters.

  The norm is computed over all gradients together, as if they were concatenated into a single vector. Gradients are modified in-place.

  Parameters:

    - parameters (Iterable[Tensor] or Tensor) – an iterable of Tensors or a single Tensor that will have gradients normalized

    - max_norm (float) – max norm of the gradients

    - norm_type (float) – type of the used p-norm. Can be `'inf'` for infinity norm.

    - error_if_nonfinite (bool) – if True, an error is thrown if the total norm of the gradients from `parameters` is `nan`, `inf`, or `-inf`. Default: False (will switch to True in the future)

    - foreach (bool) – use the faster foreach-based implementation. If `None`, use the foreach implementation for CUDA and CPU native tensors and silently fall back to the slow implementation for other device types. Default: `None`

  Returns:

    Total norm of the parameter gradients (viewed as a single vector).

  Return type:

    Tensor

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