Agent Attention: On the Integration of Softmax and Linear Attention

TL;DR

This paper introduces Agent Attention, a novel attention mechanism that balances efficiency and expressiveness by integrating softmax and linear attention, demonstrating superior performance across vision tasks and high-resolution scenarios.

Contribution

The paper proposes Agent Attention, a new attention paradigm that combines the strengths of softmax and linear attention, improving efficiency while maintaining global context modeling.

Findings

Agent Attention outperforms traditional softmax attention in efficiency.

It achieves comparable or better accuracy in vision tasks.

Significantly accelerates image generation in high-resolution scenarios.

Abstract

The attention module is the key component in Transformers. While the global attention mechanism offers high expressiveness, its excessive computational cost restricts its applicability in various scenarios. In this paper, we propose a novel attention paradigm, Agent Attention, to strike a favorable balance between computational efficiency and representation power. Specifically, the Agent Attention, denoted as a quadruple $(Q, A, K, V)$, introduces an additional set of agent tokens $A$ into the conventional attention module. The agent tokens first act as the agent for the query tokens $Q$ to aggregate information from $K$ and $V$, and then broadcast the information back to $Q$. Given the number of agent tokens can be designed to be much smaller than the number of query tokens, the agent attention is significantly more efficient than the widely adopted Softmax attention, while preserving global context modelling capability. Interestingly, we show that the proposed agent attention is equivalent to a generalized form of linear attention. Therefore, agent attention seamlessly integrates the powerful Softmax attention and the highly efficient linear attention. Extensive experiments demonstrate the effectiveness of agent attention with various vision Transformers and across diverse vision tasks, including image classification, object detection, semantic segmentation and image generation. Notably, agent attention has shown remarkable performance in high-resolution scenarios, owning to its linear attention nature. For instance, when applied to Stable Diffusion, our agent attention accelerates generation and substantially enhances image generation quality without any additional training. Code is available at https://github.com/LeapLabTHU/Agent-Attention.