The article further discusses the usefulness of theoretical bounds, emphasizing their role in validating the quality of implementations and predicting the impact of architectural changes. It also explores the concept of group query attention and its impact on the ALU:bandwidth ratio and KV-cache memory size. The author concludes by stressing the importance of evaluating group query attention for every transformer-based language model due to its significant benefits.
Key takeaways:
- The speed of light for the inference process in a language model is a critical consideration, and this post discusses the theoretical limit and its implications.
- The language model does two types of operations when processing a token: a matrix-vector multiplication and attention computation. Both have one important characteristic in common: for each element read from the matrix or KV-cache, a very small number of floating-point operations need to be done.
- Theoretical speed of light modeling is important as it helps validate the quality of implementations and predict the impact of architectural changes. It's crucial to calculate the achieved effective bandwidth carefully during profiling as it's the main source of guidance.
- Group-query attention (GQA) is a technique that allows to reduce the size and the required bandwidth for the KV-cache, which is critical for KV-cache memory size. The benefits of GQA are too significant to ignore for every transformer-based language model.