Low-precision fine-tuning of language models has gained prominence as a cost-effective and energy-efficient approach to deploying large-scale models in various applications. However, this approach is susceptible to the existence of outlier values in activation. The outlier values in the activation can negatively affect the performance of fine-tuning language models in the low-precision regime since they affect the scaling factor and thus make representing smaller values harder. This paper investigates techniques for mitigating outlier activation in low-precision integer fine-tuning of the language models. Our proposed novel approach enables us to represent the outlier activation values in 8-bit integers instead of floating-point (FP16) values. The benefit of using integers for outlier values is that it enables us to use operator tiling to avoid performing 16-bit integer matrix multiplication to address this problem effectively. We provide theoretical analysis and supporting experiments to demonstrate the effectiveness of our approach in improving the robustness and performance of low-precision fine-tuned language models.

低精度微调模型在大规模模型部署中变得日益重要，然而，异常值激活对其性能有负面影响。本文研究了在语言模型的低精度整数微调中缓解异常值激活的技术，提出了一种新颖方法，使用8位整数而不是浮点数表示异常值激活。通过理论分析和实验支持，证明了该方法在提高低精度微调语言模型的鲁棒性和性能方面的有效性。

降低语言模型低精度微调中的异常激活