Faster Neighborhood Attention: Reducing the O(n^2) Cost of Self Attention at the Threadblock Level

TL;DR

This paper introduces optimized GPU kernels for neighborhood attention, significantly reducing computational costs and memory usage, enabling faster and more scalable attention mechanisms for high-dimensional data.

Contribution

The authors develop new batched GEMM-based kernels for 1-D and 2-D neighborhood attention and propose fused attention implementations to improve efficiency and runtime performance.

Findings

895% and 272% runtime improvement over naive kernels

Fused neighborhood attention reduces memory footprint and enhances speed

Inherent inefficiencies in unfused implementations are mitigated by fusion techniques

Abstract

Neighborhood attention reduces the cost of self attention by restricting each token's attention span to its nearest neighbors. This restriction, parameterized by a window size and dilation factor, draws a spectrum of possible attention patterns between linear projection and self attention. Neighborhood attention, and more generally sliding window attention patterns, have long been bounded by infrastructure, particularly in higher-rank spaces (2-D and 3-D), calling for the development of custom kernels, which have been limited in either functionality, or performance, if not both. In this work, we aim to massively improve upon existing infrastructure by providing two new methods for implementing neighborhood attention. We first show that neighborhood attention can be represented as a batched GEMM problem, similar to standard attention, and implement it for 1-D and 2-D neighborhood attention. These kernels on average provide 895% and 272% improvement in full precision runtime compared to existing naive CUDA kernels for 1-D and 2-D neighborhood attention respectively. We find that aside from being heavily bound by memory bandwidth, certain inherent inefficiencies exist in all unfused implementations of neighborhood attention, which in most cases undo their theoretical efficiency gain. Motivated by the progress made into fused dot-product attention kernels, we developed fused neighborhood attention; an adaptation of fused dot-product attention kernels that allow fine-grained control over attention across different spatial axes. Known for reducing the quadratic time complexity of self attention to a linear complexity, neighborhood attention can now enjoy a reduced and constant memory footprint, and record-breaking half precision runtime. We observe that our fused implementation successfully circumvents some of the unavoidable inefficiencies in unfused implementations...