Flash attention 2. It uses Nvidia's CUTLASS 3.

Flash attention 2 pdf. It improves the work partitioning and parallelism of FlashAttention, and reaches up to 73% of the theoretical maximum FLOPss on A100 GPU. Learn how to install, use, and cite FlashAttention, and explore its features and performance improvements. May 30, 2025 · Flash Attention 2 is an optimized attention algorithm that reduces the quadratic memory complexity of standard attention mechanisms. Compare the parallelization strategies, memory footprint, and speedup of these techniques. Sep 11, 2023 · Learn how Flash-Attention and Flash-Attention-2 optimize attention computation for large language models with long contexts. It uses Nvidia's CUTLASS 3. Instead of materializing the full attention matrix in GPU memory, it computes attention in blocks using tiling and recomputation strategies. FlashAttention-2 reduces the non-matmul FLOPs, parallelizes the attention across thread blocks, and distributes the work between warps to achieve up to 73% of the theoretical maximum FLOPs/s on A100 GPU. We've been very happy to see FlashAttention being widely adopted in such a short time after its release. Usage. . Paper: https://tridao. This page contains a partial list of places where FlashAttention is being used. me/publications/flash2/flash2. Jun 17, 2023 · FlashAttention-2 is a new algorithm to speed up attention and reduce its memory footprint in Transformers, without any approximation. FlashAttention-2 is a method to speed up the attention layer of Transformers, which is the main bottleneck in scaling to longer sequence lengths. Jul 17, 2023 · A paper by Tri Dao that proposes a new algorithm to improve the efficiency of attention computation in Transformers. x library, better parallelism, and work partitioning to achieve up to 230 TFLOPs/s on A100 GPUs. FlashAttention is a PyTorch package that implements FlashAttention and FlashAttention-2, two methods for fast and memory-efficient attention mechanisms. Jan 29, 2025 · FlashAttention-2: Faster Attention with Better Parallelism and Work Partitioning Tri Dao. fkwfft cvyu jgpj lsuqqv unjlwd vcfbd ifkzuy aaydk ovrg dznwa