SageAttention: Accurate 8-Bit Attention for Plug-and-play Inference Acceleration
Jintao Zhang, Jia wei, Pengle Zhang, Jun Zhu, Jianfei Chen
2024-10-04
Summary
This paper presents SageAttention, a new method for optimizing how attention works in large language models by using 8-bit quantization to speed up processing without losing accuracy.
What's the problem?
In transformer models, which are widely used in AI, the attention mechanism is crucial for understanding relationships in data. However, it can be very slow and resource-intensive, especially when dealing with long sequences of information. Current methods to speed up processing mainly focus on linear layers, leaving attention as a bottleneck that slows everything down.
What's the solution?
To solve this problem, the authors developed SageAttention, a technique that allows the attention mechanism to use 8-bit quantization. This means that the model can process information using less memory and faster speeds while still maintaining accuracy. SageAttention improves performance by about 2.1 times compared to FlashAttention2 and 2.7 times compared to xformers. The method also ensures that there is almost no loss in the quality of the outputs across various tasks like language processing and image generation.
Why it matters?
This research is important because it makes it possible to run complex AI models more efficiently on devices with limited resources, such as smartphones or laptops. By enhancing the speed and efficiency of attention mechanisms, SageAttention can help improve applications in natural language processing, computer vision, and other fields where quick and accurate data processing is essential.
Abstract
The transformer architecture predominates across various models. As the heart of the transformer, attention has a computational complexity of O(N^2), compared to O(N) for linear transformations. When handling large sequence lengths, attention becomes the primary time-consuming component. Although quantization has proven to be an effective method for accelerating model inference, existing quantization methods primarily focus on optimizing the linear layer. In response, we first analyze the feasibility of quantization in attention detailedly. Following that, we propose SageAttention, a highly efficient and accurate quantization method for attention. The OPS (operations per second) of our approach outperforms FlashAttention2 and xformers by about 2.1 times and 2.7 times, respectively. SageAttention also achieves superior accuracy performance over FlashAttention3. Comprehensive experiments confirm that our approach incurs almost no end-to-end metrics loss across diverse models, including those for large language processing, image generation, and video generation.