The Deepdive Llama3 From Scratch project demonstrates how KV-Cache technology can be used to optimize the multi-word generation process for Llama3 models. This technique is a key optimizer for the inference phase of large language models, and can dramatically improve generation efficiency.
The main process of the project to realize multi-word generation includes:
- Loop to predict the next token until the end token is encountered
- Use KV-Cache to store previously computed key values to avoid repeated computations
- Generation length is controlled by the max_seq_len parameter
The core advantage of the KV-Cache technique is that it avoids recomputing the key-value matrices of all previous tokens when generating new words, which reduces the computational complexity of the generation process from O(n²) to O(n), which is especially important for long text generation.
This answer comes from the articleDeepdive Llama3 From Scratch: Teaching You to Implement Llama3 Models From ScratchThe
