Post-training quantization of Large Language Models (LLMs) is challenging. In
this work, we introduce Low-rank Quantization Error Reduction (LQER), which
combines quantization and low-rank approximation to recover the model
capability. LQER leverages an activation-induced scale matrix to drive the
singular value distribution of quantization error towards a desirable
distribution, which enables nearly-lossless W4A8 quantization on various LLMs
and downstream tasks without the need for knowledge distillation, grid search,
or gradient-base iterative optimization. Unlike existing methods, the
computation pattern of LQER eliminates the need for specialized Scatter and
Gather processes to collect high-precision weights from irregular memory
locations. Our W4A8 LLMs achieve near-lossless performance on six popular
downstream tasks, while using 1.36$\times$ fewer hardware resources than the
leading state-of-the-art method. We will open-source our framework once the
paper is accepted.

本研究提出了一种通过低秩近似和量化相结合的方法，使用激活引起的比例矩阵将量化误差的奇异值分布调整到理想值，实现了在各种大型语言模型和相关任务上几乎无失真的 W4A8 量化，而无需知识蒸馏、网格搜索或基于梯度的迭代优化，并且与现有方法相比，计算模式中的 LQER 消除了从不规则内存位置收集高精度权重所需的专用 Scatter 和 Gather 过程，从而在六个热门任务上实现了接近无失真性能，同时使用的硬件资源比领先的最先进方法少 1.36 倍。