LQER: Low-Rank Quantization Error Reconstruction for LLMs

• URL: http://arxiv.org/abs/2402.02446v3
• Date: Thu, 30 May 2024 09:49:47 GMT
• Title: LQER: Low-Rank Quantization Error Reconstruction for LLMs
• Authors: Cheng Zhang, Jianyi Cheng, George A. Constantinides, Yiren Zhao,
• Abstract summary: We introduce Low-rank Quantization Error Reduction (LQER), which combines quantization and low-rank approximation to recover the model capability. Unlike existing methods, the computation pattern of LQER eliminates the need for specialized Scatter and Gather processes. 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.
• Score: 13.205129808742862
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: 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 open-source our framework at https://github.com/ChengZhang-98/lqer

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