What Is Flash Attention?

Flash Attention is an attention algorithm designed to enhance the efficiency of Transformer models, particularly large language models (LLMs). It addresses the challenges of training time and inference latency, which are common issues in LLMs. Flash Attention is not only faster but also more memory-efficient than traditional attention mechanisms.

How does Flash Attention work?

Flash Attention operates as an IO-aware exact attention algorithm. It employs a technique called tiling to minimize memory reads and writes between the GPU's high bandwidth memory (HBM) and on-chip SRAM. During both forward and backward passes, Flash Attention divides attention matrices into smaller blocks, optimizing memory usage and computational efficiency.

What are the applications of Flash Attention?

Flash Attention is particularly beneficial for complex models and large-scale tasks. Attention mechanisms, such as Flash Attention, aid models in focusing on pertinent parts of input data, similar to how humans selectively pay attention to certain aspects of their environment. These mechanisms have been instrumental in enhancing the performance of various AI models, especially in sequence-to-sequence tasks.