How does attention head pruning optimize Generative AI for real-time applications

Question

Can I know how attention head pruning optimizes Generative AI for real-time applications?

Answer 1

Attention head pruning reduces the number of attention heads in transformer models, optimizing the model for faster inference and lower memory usage. 

Here is the code snippet showing how it is done:

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

• Attention Head Pruning: This involves removing or zeroing out certain attention heads to optimize the model for faster execution.
• Real-Time Efficiency: Pruned models require fewer computations, making them faster and more memory-efficient.
• Pruning During Fine-Tuning: Attention heads should ideally be pruned during model fine-tuning to maintain a balance between performance and efficiency.

Hence, pruning attention heads enhances computational efficiency in real-time applications by reducing redundant calculations and maintaining performance with less resource consumption.