GLU Attention Improve Transformer
This work addresses the need for more efficient and effective attention mechanisms in transformers, offering a lightweight solution that can integrate with existing technologies, though it appears incremental as it builds on established GLU concepts.
The paper tackles the problem of enhancing transformer performance by introducing GLU Attention, a novel attention mechanism that adds nonlinearity to attention values, resulting in improved model performance and convergence speed across text and vision modalities with zero additional parameters and negligible computational costs.
Gated Linear Units (GLU) have shown great potential in enhancing neural network performance. In this paper, I introduce a novel attention mechanism called GLU Attention, which introduces nonlinearity into the values of Attention. My experiments demonstrate that GLU Attention improves both model performance and convergence speed across text and vision modalities with zero additional parameters and negligible computational costs. GLU Attention is lightweight and can seamlessly integrate with other technologies, such as Flash Attention, Rotary Position Embedding (RoPE), and various Multi-Head Attention (MHA) variants such as Grouped-Query Attention (GQA). This project is open-sourced at github.