Mixture of Attention Schemes (MoAS): Dynamically Routing Between MHA, GQA, and MQA for Improved Transformer Efficiency
Analysis
This paper introduces Mixture of Attention Schemes (MoAS), a novel approach to dynamically select the optimal attention mechanism (MHA, GQA, or MQA) for each token in Transformer models. This addresses the trade-off between model quality and inference efficiency, where MHA offers high quality but suffers from large KV cache requirements, while GQA and MQA are more efficient but potentially less performant. The key innovation is a learned router that dynamically chooses the best scheme, outperforming static averaging. The experimental results on WikiText-2 validate the effectiveness of dynamic routing. The availability of the code enhances reproducibility and further research in this area. This research is significant for optimizing Transformer models for resource-constrained environments and improving overall efficiency without sacrificing performance.
Key Takeaways
- •MoAS dynamically selects the best attention scheme (MHA, GQA, MQA) for each token.
- •Dynamic routing outperforms static averaging of attention schemes.
- •MoAS achieves performance comparable to MHA with potential for conditional compute efficiency.
“We demonstrate that dynamic routing performs better than static averaging of schemes and achieves performance competitive with the MHA baseline while offering potential for conditional compute efficiency.”