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SageAttention2: Efficient Attention with Thorough Outlier Smoothing and Per-thread INT4 Quantization

17 Nov 2024arXiv:2411.10958archive 2025-07-28

Jintao Zhang, Haofeng Huang, Pengle Zhang, Jia Wei, Jun Zhu, Jianfei Chen

Although quantization for linear layers has been widely used, its application to accelerate the attention process remains limited. To further enhance the efficiency of attention computation compared to SageAttention while maintaining precision, we propose SageAttention2, which utilizes significantly faster 4-bit matrix multiplication (Matmul) alongside additional precision-enhancing techniques. First, we propose to quantize matrices (Q, K) to INT4 in a hardware-friendly thread-level granularity and quantize matrices (P, V) to FP8. Second, we propose a method to smooth Q, enhancing the accuracy of INT4 QK^⊤. Third, we propose a two-level accumulation strategy for PV to enhance the accuracy of FP8 PV. The operations per second (OPS) of SageAttention2 surpass FlashAttention2 and xformers by about 3x and 4.5x on RTX4090, respectively. Moreover, SageAttention2 matches the speed of FlashAttention3(fp8) on the Hopper GPUs, while delivering much higher accuracy. Comprehensive experiments confirm that our approach incurs negligible end-to-end metrics loss across diverse models, including those for language, image, and video generation. The code is available at https://github.com/thu-ml/SageAttention.

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