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English(EN) ZYFA: Speed and Accuracy of FP8 FlashAttention on AMD CDNA4

ZYFA 内核提升 AMD Instinct MI355X 上 FP8 FlashAttention 的速度与准确性

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了 ZYFA，这是一组用于 AMD Instinct MI355X 加速器的新预填充内核，旨在提高 FlashAttention 的速度和准确性。ZYFA 采用了三种关键技术：FP8 概率的直接计算、用于重叠 softmax 和矩阵乘法的乒乓调度，以及用于归一化的缩放方法。这些优化使得吞吐量比现有的 AITER 内核提高了 1.16-1.76 倍，并显著减少了数值误差，使其更接近 Composable Kernel 和 Triton 的水平。 AI

影响 提高了核心注意力机制的效率，有可能在特定硬件上加速大型语言模型的训练和推理。

排序理由 学术论文，详细介绍了用于核心人工智能模型组件硬件加速的新颖优化技术。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

ZYFA 内核提升 AMD Instinct MI355X 上 FP8 FlashAttention 的速度与准确性

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学术论文，详细介绍了用于核心人工智能模型组件硬件加速的新颖优化技术。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Quentin Anthony, Ganesh Bikshandi, Praneeth Medepalli, Stephen Ebert ·

    ZYFA：AMD CDNA4 上 FP8 FlashAttention 的速度与准确性

    arXiv:2611.07496v1 Announce Type: cross Abstract: Attention is a core building block of frontier models and often dominates compute-bound prefill at long context. FP8 matrix instructions offer twice the peak throughput of BF16, but they accelerate only matrix multiplication, and …

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