FlashAttention-4

FlashAttention-4 is the fourth generation of the FlashAttention series of efficient attention computation algorithms and kernel implementations, designed to accelerate the attention mechanism in large-scale Transformer models. Its core approach uses tiling and IO-aware optimization to reduce the number of read/write operations to GPU high-bandwidth memory (HBM), thereby lowering memory consumption and improving computational throughput. This version includes deep adaptations and optimizations for next-generation GPU architectures and is widely used in large language model training and inference.