How can you fix memory consumption issues in a GPT-based model trained for long-text generation

Question

Can i know How can you fix memory consumption issues in a GPT-based model trained for long-text generation?

Answer 1

You can fix memory consumption issues in a GPT-based model by using techniques like gradient checkpointing, mixed precision training, and efficient batch sizing.

Here is the code snippet you can refer to:

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

• Uses gradient checkpointing to reduce memory usage during backpropagation.
• Enables mixed precision training (autocast()) for lower memory footprint and faster computation.
• Moves model and data to GPU when available for efficiency.

Hence, optimizing memory usage in a GPT-based model with techniques like gradient checkpointing and mixed precision training enables long-text generation without running into out-of-memory issues.