Tiny ghost, big memory savings
Flash Attention is a faster, memory-efficient algorithm for computing attention in transformers that avoids storing large attention matrices.
Imagine you're cleaning a huge messy room, but instead of taking every single item out and sorting them all at once (which fills the whole floor), you clean one small area at a time, using only a tiny basket to move things around. Flash Attention works like that: it processes chunks of data in a smart way, so your computer's memory doesn't get overwhelmed.
Flash Attention makes training and running large language models (like GPT-4) much faster and cheaper, especially for long sequences. You encounter it in modern AI chatbots, text generators, and any model that needs to handle lots of context without running out of memory.
Many people think Flash Attention is a new type of attention mechanism that changes how models understand relationships. Actually, it computes the same attention as before, just in a more hardware-efficient way—the math is identical, only the order of operations and memory use are optimized.
Flash Attention is an exact attention algorithm that computes scaled dot-product attention without materializing the full N×N attention matrix in high-bandwidth memory. It uses tiling, recomputation, and kernel fusion to reduce memory reads/writes, achieving up to linear memory complexity while maintaining numerical precision.