TransMLA: Multi-head Latent Attention Is All You Need
Fanxu Meng, Zengwei Yao, Muhan Zhang
2025-02-13
Summary
This paper talks about TransMLA, a new method to make large language models (LLMs) work faster and more efficiently by changing how they process information internally. It's like giving these AI models a brain upgrade that helps them think quicker without needing more computer memory.
What's the problem?
Current large language models face a bottleneck in how quickly they can communicate information internally. This is like having a super smart brain but a slow nervous system, which limits how fast the AI can think and respond. Many popular AI models use a method called Group Query Attention (GQA), which works well but isn't the most efficient.
What's the solution?
The researchers developed TransMLA, which converts existing AI models that use GQA into models that use a more efficient method called Multi-head Latent Attention (MLA). MLA is like giving the AI a better way to organize and access its thoughts. TransMLA can take popular AI models like LLaMA or Mixtral and upgrade them to use this more efficient system. After the upgrade, the AI can be trained further to become even smarter without needing more computer memory.
Why it matters?
This matters because it could make AI models faster and more efficient without requiring more powerful computers. Faster AI means quicker responses in chatbots, more efficient language translation, and better performance in various AI applications. It's like turbocharging existing AI without having to build entirely new, more expensive systems. This could lead to more advanced AI being available on a wider range of devices, from smartphones to servers.
Abstract
Modern large language models (LLMs) often encounter communication bottlenecks on current hardware, rather than purely computational constraints. Multi-head Latent Attention (MLA) tackles this challenge by using low-rank matrices in the key-value (KV) layers, thereby allowing compressed latent KV states to be cached. This approach significantly reduces the KV cache size relative to traditional multi-head attention, leading to faster inference. Moreover, MLA employs an up-projection matrix to increase expressiveness, trading additional computation for reduced communication overhead. Although MLA has demonstrated efficiency and effectiveness in Deepseek V2/V3/R1, many major model providers still rely on Group Query Attention (GQA) and have not announced any plans to adopt MLA. In this paper, we show that GQA can always be represented by MLA while maintaining the same KV cache overhead, but the converse does not hold. To encourage broader use of MLA, we introduce **TransMLA**, a post-training method that converts widely used GQA-based pre-trained models (e.g., LLaMA, Qwen, Mixtral) into MLA-based models. After conversion, the model can undergo additional training to boost expressiveness without increasing the KV cache size. Furthermore, we plan to develop MLA-specific inference acceleration techniques to preserve low latency in transformed models, thus enabling more efficient distillation of Deepseek R1.