Academic

Diagonal-Tiled Mixed-Precision Attention for Efficient Low-Bit MXFP Inference

arXiv:2604.03950v1 Announce Type: new Abstract: Transformer-based large language models (LLMs) have demonstrated remarkable performance across a wide range of real-world tasks, but their inference cost remains prohibitively high due to the quadratic complexity of attention and the memory bandwidth limitations of high-precision operations. In this work, we present a low-bit mixed-precision attention kernel using the microscaling floating-point (MXFP) data format, utilizing the computing capability on next-generation GPU architectures. Our Diagonal-Tiled Mixed-Precision Attention (DMA) incorporates two kinds of low-bit computation at the tiling-level, and is a delicate fused kernel implemented using Triton, exploiting hardware-level parallelism and memory efficiency to enable fast and efficient inference without compromising model performance. Extensive empirical evaluations on NVIDIA B200 GPUs show that our kernel maintains generation quality with negligible degradation, and meanwhile

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Yifu Ding, Xinhao Zhang, Jinyang Guo · · 1 min read · 25 views
#cs.LG #cs.AI

arXiv:2604.03950v1 Announce Type: new Abstract: Transformer-based large language models (LLMs) have demonstrated remarkable performance across a wide range of real-world tasks, but their inference cost remains prohibitively high due to the quadratic complexity of attention and the memory bandwidth limitations of high-precision operations. In this work, we present a low-bit mixed-precision attention kernel using the microscaling floating-point (MXFP) data format, utilizing the computing capability on next-generation GPU architectures. Our Diagonal-Tiled Mixed-Precision Attention (DMA) incorporates two kinds of low-bit computation at the tiling-level, and is a delicate fused kernel implemented using Triton, exploiting hardware-level parallelism and memory efficiency to enable fast and efficient inference without compromising model performance. Extensive empirical evaluations on NVIDIA B200 GPUs show that our kernel maintains generation quality with negligible degradation, and meanwhile achieves significant speedup by kernel fusion. We release our code at https://github.com/yifu-ding/MP-Sparse-Attn.

Executive Summary

This article presents a novel approach to efficient low-bit mixed-precision attention for transformer-based large language models. Diagonal-Tiled Mixed-Precision Attention (DMA) exploits next-generation GPU architectures to achieve significant speedup and maintain model performance. The proposed method utilizes microscaling floating-point (MXFP) data format and incorporates low-bit computation at the tiling-level, implemented using Triton for hardware-level parallelism and memory efficiency. Empirical evaluations on NVIDIA B200 GPUs demonstrate the effectiveness of DMA, with negligible degradation in generation quality and substantial speedup through kernel fusion. The article's findings have significant implications for the development of efficient and scalable machine learning models for real-world applications.

Key Points

  • ▸ Proposes Diagonal-Tiled Mixed-Precision Attention (DMA) for efficient low-bit MXFP inference
  • ▸ Utilizes microscaling floating-point (MXFP) data format and low-bit computation at the tiling-level
  • ▸ Exploits next-generation GPU architectures for hardware-level parallelism and memory efficiency

Merits

Strength in Design

The proposed DMA approach demonstrates a well-structured and efficient design, leveraging the capabilities of next-generation GPU architectures to achieve significant speedup and maintain model performance.

Empirical Evaluation

The article's empirical evaluations on NVIDIA B200 GPUs provide concrete evidence of the effectiveness of DMA, showcasing its ability to maintain generation quality with negligible degradation and achieve substantial speedup through kernel fusion.

Code Release

The authors' decision to release their code on GitHub facilitates reproducibility and enables the broader research community to build upon and extend their work.

Demerits

Limited Scalability

The proposed DMA approach may not be directly applicable to larger-scale models or more complex architectures, which could limit its scalability in real-world applications.

Dependency on GPU Architectures

The effectiveness of DMA relies on the specific capabilities of next-generation GPU architectures, which may not be universally available or accessible, potentially limiting its adoption in certain environments.

Expert Commentary

The article presents a well-crafted and effective approach to efficient low-bit mixed-precision attention for transformer-based large language models. The proposed DMA approach demonstrates a strong understanding of the complexities involved in machine learning inference and leverages next-generation GPU architectures to achieve significant speedup and maintain model performance. However, the article's limitations, such as its reliance on specific GPU architectures and potential scalability issues, should be carefully considered in future work. Overall, the article makes a valuable contribution to the field of machine learning and has significant implications for the development of efficient and scalable machine learning models for real-world applications.

Recommendations

  • ✓ Future research should focus on extending the proposed DMA approach to larger-scale models and more complex architectures to improve its scalability and applicability.
  • ✓ The use of next-generation GPU architectures should be further explored and optimized to improve the efficiency and effectiveness of machine learning models in real-world applications.

Sources

Original: arXiv - cs.LG

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