Native Hybrid Attention for Efficient Sequence Modeling

Summary

This paper introduces a new way to build transformer models, called Native Hybrid Attention (NHA), that tries to get the best of both worlds: the power of traditional transformers and the speed of more efficient, but usually less accurate, attention mechanisms.

What's the problem?

Transformers are really good at understanding sequences of information, like sentences, but they become very slow and require a lot of computing power when dealing with long sequences. Simpler, faster attention methods exist, but they often forget important details from earlier in the sequence, leading to less accurate results, especially when needing to remember things over long distances.

What's the solution?

The researchers created NHA, which combines both full and linear attention in a clever way. It uses a linear attention method to quickly update a memory of the long-term context, and then adds in information from a small, recent window of tokens. Finally, it uses a standard attention calculation to weigh all this information together. Importantly, the amount of 'recent' information considered can be easily adjusted, allowing a smooth transition between fully linear and fully traditional attention, and all layers of the model can be built the same way.

Why it matters?

This work is important because it offers a way to build transformer models that are both accurate and efficient. The experiments show NHA performs better than existing methods on tasks requiring good memory and reasoning, and it can even be used to speed up existing large language models without sacrificing too much accuracy. This could lead to faster and more accessible AI applications.