# SwitchHead: Accelerating Transformers with Mixture-of-Experts Attention

**Authors:** Róbert Csordás, Piotr Piękos, Kazuki Irie, Jürgen Schmidhuber  
**arXiv:** [2312.07987](https://arxiv.org/abs/2312.07987)  
**Date:** 2023-12-13  
**Summary page:** https://www.summarizepaper.com/en/paper/2312.07987v2/

## Key points

- Limitations of self-attention layers in modern Transformers
- Significant memory and compute resources required, scaling quadratically with sequence length
- Existing approximation methods ineffective in achieving speedups
- Introduction of SwitchHead method to address challenges
- SwitchHead reduces compute and memory requirements while achieving wall-clock speedup
- Matches language modeling performance of baseline Transformers with same parameter budget
- Utilizes Mixture-of-Experts (MoE) layers for value and output projections
- Requires 4 to 8 times fewer attention matrices compared to standard Transformers
- Can be combined with MoE MLP layers for an efficient fully-MoE "SwitchAll" Transformer model
- Reduces resource requirements without compromising performance
- Stable method without requiring additional regularization to prevent degenerate solutions
- Visualizations of attention maps comparing standard Transformers with SwitchHead provided
- Code for implementing SwitchHead method is publicly available

## Summary

The existing summary discusses the limitations of self-attention layers in modern Transformers, which require significant memory and compute resources that scale quadratically with sequence length. Existing approximation methods have been ineffective in achieving significant speedups. However, a novel method called SwitchHead is introduced in this paper to address these challenges. SwitchHead reduces both compute and memory requirements while achieving wall-clock speedup. It matches the language modeling performance of baseline Transformers with the same parameter budget. The method utilizes Mixture-of-Experts (MoE) layers for value and output projections, requiring 4 to 8 times fewer attention matrices compared to standard Transformers. Furthermore, SwitchHead can be combined with MoE MLP layers, resulting in an efficient fully-MoE "SwitchAll" Transformer model. This approach significantly reduces resource requirements without compromising performance. The authors conducted experiments on various language modeling datasets with different model sizes. SwitchHead demonstrated comparable performance to dense counterparts while utilizing only a fraction of computational cost and memory usage. The method is stable and does not require additional regularization to prevent degenerate solutions, which is a common issue in existing MoE models. In addition, the paper provides visualizations of attention maps comparing standard Transformers with SwitchHead. These visualizations highlight the reduction in attention matrices achieved by SwitchHead without sacrificing the quality of attention. Overall, SwitchHead presents a promising solution for accelerating Transformers by reducing resource requirements while maintaining high-performance language modeling capabilities. The code for implementing this method is publicly available making it accessible for further research and development purposes.