How can you integrate PyTorch s torch utils checkpoint for memory-efficient training of generative models

Question

With the help of code snippets, can you explain to me how I can integrate PyTorch’s torch.utils.checkpoint for memory-efficient training of generative models?

Answer 1

You can integrate PyTorch's torch.utils.checkpoint for memory-efficient training by recomputing intermediate activations during the backward pass instead of storing them in memory. 

Here is the code example you can refer to:

In the above code, we are using the following approaches:

• torch.utils.checkpoint: Wrap memory-intensive layers for recomputation during backpropagation.
• Trade-off: Saves memory at the cost of additional computation during training.
• Usage: Ideal for deep generative models with limited GPU memory.

Hence, by referring to the above, you can integrate PyTorch's torch utils checkpoint for memory-efficient training of generative models.