How would you monitor gradient sparsity in QLoRA training

Question

Can you tell me How would you monitor gradient sparsity in QLoRA training?

Answer 1

You can monitor gradient sparsity in QLoRA training by counting zero elements in gradients during on_after_backward hook in PyTorch Lightning.

Here is the code snippet you can refer to:

In the above code we are using the following key strategies:

• Uses on_after_backward to access gradients post-backward pass.

• Computes sparsity as ratio of zero-valued gradient elements.

• Logs grad_sparsity per training step for analysis.

Hence, gradient sparsity tracking in QLoRA provides insights into parameter efficiency and optimization dynamics during training.