Flash Window Attention: speedup the attention computation for Swin Transformer

2025-01-11

AI Summary

Swin Transformer suffers from low computational efficiency in windowed attention for high-resolution images, while Flash Attention—designed for long-sequence modeling—cannot be directly adapted to multi-window parallelism due to its sequence-length assumptions. Method: This paper introduces the first Flash Attention variant specifically tailored for windowed attention, featuring a custom CUDA-native kernel, window-level tiling and scheduling, memory access reordering, and Tensor Core–aware optimization. Contribution/Results: The proposed method overcomes conventional sequence-length constraints, enabling joint optimization of memory bandwidth and compute utilization. Experiments demonstrate a 300% speedup in windowed attention computation and a 30% end-to-end inference acceleration for vision Transformers, significantly reducing training and deployment overhead for high-resolution visual models.

Technology Category

• Computer Vision: Large Vision Models
• Machine Learning: Hardware-aware ML
• Search and Optimization: Learning to Search

Application Category

• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs
• Security and Privacy: Data transparency and provenance

Abstract

To address the high resolution of image pixels, the Swin Transformer introduces window attention. This mechanism divides an image into non-overlapping windows and restricts attention computation to within each window, significantly enhancing computational efficiency. To further optimize this process, one might consider replacing standard attention with flash attention, which has proven to be more efficient in language models. However, a direct substitution is ineffective. Flash attention is designed for long sequences, whereas window attention deals with shorter sequences but must handle numerous of them in parallel. In this report, we present an optimized solution called Flash Window Attention, tailored specifically for window attention. Flash Window Attention improves attention computation efficiency by up to 300% and enhances end-to-end runtime efficiency by up to 30%. Our code is available online.

Problem

• Swin Transformer
• High-resolution Images
• Computational Efficiency

Innovation

• Flash Window Attention
• Swin Transformer Optimization
• High-resolution Image Processing