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This pull request makes a few targeted improvements to the training loop and utility functions. The most notable changes are a fix to the gradient clipping method to use the in-place version, and a reordering of the learning rate scheduler step to occur after model checkpointing.
The call to
scheduler.step()has been moved to after the model is saved at the end of each epoch intrain.py, ensuring the scheduler updates after checkpointing.In
train_utils.py, replacedtorch.nn.utils.clip_grad_normwith the in-place versionclip_grad_norm_to be compliant with pytorch