"Atom:Low-bit Quantization for Efficient and Accurate LLM Serving"论文阅读 9iM 4 人赞同了该文章 论文信息 会议/期刊来源:MLSys 时间:2024 作者:Baris Kasikci(University of Washington) 引言 在提升LLM服务质量的工作中,通过batch技术将多个连续请求合并,提升了计算密度,分摊了加载权重矩阵的开销,能够有效提...
Low-bit quantization improves the efficiency of running large models on edge devices while also enabling model scaling by reducing the bits used to represent each parameter. This scaling enhances model capabilities, generality, and expressiveness, as shown by theBitNet model, which sta...
总之,我们提出的名为LSQ+的方法扩展了LSQ [7],通过为激活值量化添加一个简单却有效的可学习偏移参数,来恢复在采用类Swish激活函数的架构上损失的准确率。此外,我们的另一贡献在于揭示了适当初始化对于稳定训练的重要性,尤其是在低位量化的情况下。 2. 相关工作 文献[16]对量化基础知识进行了很好的概述,其中解释了...
论文阅读——LSQ+: Improving low-bit quantization through learnable offsets and better initialization,程序员大本营,技术文章内容聚合第一站。
LSQ+: Improving low-bit quantization through learnable offsets and better initialization,程序员大本营,技术文章内容聚合第一站。
In this paper, we formalize the linear quantization task as a Minimum Mean Squared Error (MMSE) problem for both weights and activations. This allows low-bit precision inference without the need for full network retraining. The main contributions of our approach is the optimization of the ...
Vector Post-Training Quantization (VPTQ) is a novel Post-Training Quantization method that leverages Vector Quantization to high accuracy on LLMs at an extremely low bit-width (<2-bit). VPTQ can compress 70B, even the 405B model, to 1-2 bits without retraining and maintain high accuracy....
We apply the SotA quantization methods on the baseline ASR model and examine the sensitive layers which make significant contribution to the performance drop. We've come up with the improvements to accelerate the convergence of quantization methods and to enhance the quantization representation quality....
To maximize LLMs' serving throughput, we introduce Atom, a low-bit quantization method that achieves high throughput improvements with negligible accuracy loss. Atom significantly boosts serving throughput by using low-bit operators and considerably reduces memory consumption via low-bit quantization. It...
Low-bit Quantization of Neural Networks for Efficient Inferencearxiv.org/abs/1902.06822 一、文章核心点 主要提供一种低bit量化方案。 使用均匀对称量化,channel wise量化weight(文中称之为kernel wise)。定义量化损失为:量化前后的权重或激活的最小均方误差(MSE)。 绕过硬件不友好的混合精度方式,使用多次量...