FlashAttention-3: Fast and Accurate Attention with Asynchrony and Low-precision
<|reference_start|>FlashAttention-3: Fast and Accurate Attention with Asynchrony and Low-precision: Attention, as a core layer of the ubiquitous Transformer architecture, is the bottleneck for large language models and long-context applications. FlashAttention elaborated an approach to speed up attention on GPUs through minimizing memory reads/writes. However, it has yet to take advantage of new capabilities present in recent hardware, with FlashAttention-2 achieving only 35% utilization on the H100 GPU. We develop three main techniques to speed up attention on Hopper GPUs: exploiting asynchrony of the Tensor Cores and TMA to (1) overlap overall computation and data movement via warp-specialization and (2) interleave block-wise matmul and softmax operations, and (3) block quantization and incoherent processing that leverages hardware support for FP8 low-precision. We demonstrate that our method, FlashAttention-3, achieves speedup on H100 GPUs by 1.5-2.0$\times$ with FP16 reaching up to 740 TFLOPs/s (75% utilization), and with FP8 reaching close to 1.2 PFLOPs/s. We validate that FP8 FlashAttention-3 achieves 2.6$\times$ lower numerical error than a baseline FP8 attention.<|reference_end|>
@article{shah2024flashattention-3:,
    title={FlashAttention-3: Fast and Accurate Attention with Asynchrony and
  Low-precision},
    author={Jay Shah, Ganesh Bikshandi, Ying Zhang, Vijay Thakkar, Pradeep Ramani,
  Tri Dao},
    journal={arXiv preprint arXiv:2407.08608},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.08608},
    primaryClass={cs.LG cs.AI}
}