Unlocking Data-free Low-bit Quantization with Matrix Decomposition for KV Cache Compression
Peiyu Liu, Ze-Feng Gao, Xin Zhao, Yipeng Ma, Tao Wang, Ji-Rong Wen
Abstract
Key-value (KV) caching is an important technique to accelerate the inference of large language models (LLMs), but incurs significant memory overhead. To compress the size of KV cache, existing methods often compromise precision or require extra data for calibration, limiting their practicality in LLM deployment. In this paper, we introduce DecoQuant, a novel data-free low-bit quantization technique based on tensor decomposition methods, to effectively compress KV cache. Our core idea is to adjust the outlier distribution of the original matrix by performing tensor decomposition, so that the quantization difficulties are migrated from the matrix to decomposed local tensors. Specially, we find that outliers mainly concentrate on small local tensors, while large tensors tend to have a narrower value range. Based on this finding, we propose to apply low-bit quantization to the large tensor, while maintaining high-precision representation for the small tensor. Furthermore, we utilize the proposed quantization method to compress the KV cache of LLMs to accelerate the inference, and develop an efficient dequantization kernel tailored specifically for DecoQuant. Through extensive experiments, DecoQuant demonstrates remarkable efficiency gains, showcasing up to a 75% reduction in memory footprint while maintaining comparable generation quality.- Anthology ID:
- 2024.acl-long.133
- Volume:
- Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Month:
- August
- Year:
- 2024
- Address:
- Bangkok, Thailand
- Editors:
- Lun-Wei Ku, Andre Martins, Vivek Srikumar
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 2430–2440
- Language:
- URL:
- https://aclanthology.org/2024.acl-long.133
- DOI:
- Cite (ACL):
- Peiyu Liu, Ze-Feng Gao, Xin Zhao, Yipeng Ma, Tao Wang, and Ji-Rong Wen. 2024. Unlocking Data-free Low-bit Quantization with Matrix Decomposition for KV Cache Compression. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 2430–2440, Bangkok, Thailand. Association for Computational Linguistics.
- Cite (Informal):
- Unlocking Data-free Low-bit Quantization with Matrix Decomposition for KV Cache Compression (Liu et al., ACL 2024)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2024.acl-long.133.pdf