FlashAttention-2: Efficient Attention Algorithm for Transformer Acceleration and AIGC Applications
FlashAttention‑2 is an IO‑aware exact attention algorithm that cuts GPU HBM traffic through tiling and recomputation, optimizes non‑matmul FLOPs, expands sequence‑parallelism and warp‑level work distribution, delivering up to 2× speedup over FlashAttention, near‑GEMM efficiency, and enabling longer‑context Transformer training and inference for AIGC with fastunet and negligible accuracy loss.
FlashAttention-2 is an IO-aware exact attention algorithm that reduces GPU HBM accesses through tiling and recomputation, enabling faster attention computation with lower memory usage.
It optimizes non-matmul FLOPs, increases parallelism across the sequence length dimension, and improves warp-level work distribution, achieving up to 2× speedup over FlashAttention and approaching GEMM efficiency.
When applied to Transformer models and combined with fastunet for AIGC tasks, FlashAttention-2 supports longer context training and inference with negligible accuracy loss.
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