How do attention bottlenecks affect the scalability of Generative AI

Question

With the help of Python programming, can you tell me how attention bottlenecks affect the scalability of Generative AI?

Answer 1

To mitigate these issues, techniques like sparse attention (e.g., Longformer, BigBird) or efficient transformers (e.g., Performer, Linformer) are employed. These methods optimize attention computations, enabling Generative AI models to scale effectively for long-context tasks. 

Here is the code snippet you can refer to:

In the above code, we are using the following :

• Resource Limitation: High memory and computation costs restrict scalability.
• Efficient Mechanisms: Employ sparse or linear attention for long-sequence processing.
• Scalability: Optimized attention mechanisms improve model efficiency on large datasets.

Hence, this is how attention bottlenecks affect the scalability of Generative AI.