  Transformers are slow and memory-hungry on long sequences, since the time and
memory complexity of self-attention are quadratic in sequence length.
Approximate attention methods have attempted to address this problem by trading
off model quality to reduce the compute complexity, but often do not achieve
wall-clock speedup. We argue that a missing principle is making attention
algorithms IO-aware -- accounting for reads and writes between levels of GPU
memory. We propose FlashAttention, an IO-aware exact attention algorithm that
uses tiling to reduce the number of memory reads/writes between GPU high
bandwidth memory (HBM) and GPU on-chip SRAM. We analyze the IO complexity of
FlashAttention, showing that it requires fewer HBM accesses than standard
attention, and is optimal for a range of SRAM sizes. We also extend
FlashAttention to block-sparse attention, yielding an approximate attention
algorithm that is faster than any existing approximate attention method.
FlashAttention trains Transformers faster than existing baselines: 15%
end-to-end wall-clock speedup on BERT-large (seq. length 512) compared to the
MLPerf 1.1 training speed record, 3$\times$ speedup on GPT-2 (seq. length 1K),
and 2.4$\times$ speedup on long-range arena (seq. length 1K-4K). FlashAttention
and block-sparse FlashAttention enable longer context in Transformers, yielding
higher quality models (0.7 better perplexity on GPT-2 and 6.4 points of lift on
long-document classification) and entirely new capabilities: the first
Transformers to achieve better-than-chance performance on the Path-X challenge
(seq. length 16K, 61.4% accuracy) and Path-256 (seq. length 64K, 63.1%
accuracy).
