FlashAttention-2: Faster Attention with Better Parallelism

This reviews the paper that introduces FlashAttention-2, an optimized attention algorithm designed to significantly improve the speed and efficiency of Transformer models, particularly for longer sequence lengths. Building upon its predecessor, FlashAttention, which made attention calculations more memory-efficient by leveraging GPU memory hierarchies, FlashAttention-2 further refines performance. The key innovations involve tweaking the algorithm to reduce non-matrix multiplication operations, enhancing parallelism across different thread blocks for better GPU occupancy, and optimizing work partitioning within thread blocks to minimize shared memory communication. These advancements lead to approximately 2x speedup compared to FlashAttention and up to 10x faster performance than standard implementations, enabling more efficient training of large-scale language models and supporting new applications in areas like long document understanding and high-resolution media generation.

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