How do I optimize sampling efficiency in text generation models like GPT-2

Question

With the help of code and examples, can you explain How do I optimize sampling efficiency in text generation models like GPT-2?

Answer 1

To improve sampling efficiency in text generation models like GPT-2, you can refer to the following techniques:

• Top-k Sampling: Limit the sampling pool to the top k most probable tokens.
• Top-p (Nucleus) Sampling: Sample from the smallest set of tokens whose cumulative probability is greater than p.
• Temperature Scaling: Control the randomness of predictions by adjusting the temperature parameter.
• Beam Search: Use beam search for more focused and diverse sampling.

Here is the code snippet showing how it is done:

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

• Top-k Sampling: Reduces the search space by only considering the top k most likely tokens.
• Top-p Sampling: Focuses on a dynamic set of tokens that represents the majority of probability mass.
• Temperature Scaling: Controls the sharpness of the probability distribution, increasing randomness for more diverse outputs.
• Beam Search: For more structured and less random output generation, though it’s computationally more expensive.

Hence, these methods help balance efficiency and creativity in text-generation tasks.