How do you handle memory constraints when training large generative models like GPT on limited hardware

Question

Can you tell me how you handle memory constraints when training large generative models like GPT on limited hardware?

Answer 1

To handle memory constraints when training large generative models like GPT on limited hardware, you can use techniques like model parallelism, gradient checkpointing, and mixed-precision training. Here is the code reference you can refer below:

In the above code, we are using techniques like:

• Gradient Checkpointing: Saves memory by recalculating intermediate results during the backward pass instead of storing them in memory.
• Mixed-Precision Training: Use torch.cuda.amp for lower precision (FP16) operations.
• Model Parallelism: Split the model across multiple GPUs.
• Batch Size Reduction: Use smaller batches to fit into limited memory.

Hence, each of these techniques allows you to work with limited hardware while training large models effectively.