FlashAttention Implementation › FlashAttention Implementation

FlashAttention changes how we think about attention computation. Rather than viewing the attention formula as a mathematical specification to implement directly, FlashAttention treats it as a memory optimization problem. The algorithm produces identical outputs to standard attention but restructures the computation to minimize memory transfers between GPU memory hierarchies. Understanding how FlashAttention achieves this requires diving into GPU architecture, memory access patterns, and the algorithmic techniques that make it all work.

This chapter explores the implementation details of FlashAttention, from the fundamentals of GPU memory to practical usage in PyTorch. We'll examine CUDA kernel basics, understand why memory access patterns matter so much for performance, trace the improvements from FlashAttention to FlashAttention-2, and see how to use these techniques in your own models. By the end, you'll understand both the theoretical foundations and practical applications of what has become the de facto standard for attention computation in modern LLMs.