pytorch nn.utils.clip_grad_norm_

`nn.utils.clip_grad_norm_` is a function in PyTorch used to clip the norm of the gradients of a model's parameters to a specified maximum value. The syntax is as follows:

nn.utils.clip_grad_norm_(parameters, max_norm, norm_type=2) -> torch.Tensor

Here, `parameters` is an iterable of the model's parameters. `max_norm` is the maximum allowable norm of the gradients. `norm_type` specifies the type of the norm to be calculated (default is L2 norm).

This function is often used in deep learning to prevent exploding gradients during training, which can lead to unstable model training and failure to converge. The function clips the gradients such that their norm is at most `max_norm`. If the norm of the gradients exceeds `max_norm`, then the gradients are rescaled to have a norm of `max_norm`.

For example, the following code clips the gradients of a model's parameters to have a maximum norm of 1:

import torch.nn.utils as utils

# Assume model is already defined and loss is computed
loss.backward()

# Clip gradients
max_norm = 1.0
grad_norm = utils.clip_grad_norm_(model.parameters(), max_norm)

# Update parameters
optimizer.step()

Here, `loss.backward()` computes the gradients of the loss with respect to the model's parameters. `utils.clip_grad_norm_(model.parameters(), max_norm)` clips the gradients of the model's parameters to have a maximum norm of 1.0. Finally, `optimizer.step()` updates the model's parameters using the clipped gradients.