SVD-LLM V2: Optimizing Singular Value Truncation for Large Language Model Compression

• URL: http://arxiv.org/abs/2503.12340v1
• Date: Sun, 16 Mar 2025 03:27:12 GMT
• Title: SVD-LLM V2: Optimizing Singular Value Truncation for Large Language Model Compression
• Authors: Xin Wang, Samiul Alam, Zhongwei Wan, Hui Shen, Mi Zhang,
• Abstract summary: Singular Value Decomposition (SVD) is a promising compression technique for Large Language Models (LLMs)<n>Existing SVD-based compression methods fall short in reducing truncation losses, leading to less competitive performance in compressed models.<n>We introduce SVD-LLM V2, a SVD-based LLM compression method that optimize singular value truncation in SVD compression with two techniques.
• Score: 10.991519727445231
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Despite significant advancements, the practical deployment of Large Language Models (LLMs) is often hampered by their immense sizes, highlighting the need for effective compression techniques. Singular Value Decomposition (SVD) is a promising LLM compression technique. However, existing SVD-based compression methods fall short in reducing truncation losses, leading to less competitive performance in compressed models. In this work, we introduce SVD-LLM V2, a SVD-based LLM compression method that optimizes singular value truncation in SVD compression with two techniques. First, SVD-LLM V2 proposes to use theoretical truncation loss of weight matrices to assign a unique compression ratio to each weight matrix at different layers to accommodate weight redundancy heterogeneity. Second, SVD-LLM V2 proposes loss-optimized weight truncation to ensure that the truncated singular values result in a lower and more stable truncation loss in practice. We evaluate SVD-LLM V2 on ten datasets and five LLMs at various scales. Our results show SVD-LLM V2 outperforms state-of-the-art SVD-based LLM compression methods. Our code is available at https://github.com/AIoT-MLSys-Lab/SVD-LLM

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