Dobi-SVD: Differentiable SVD for LLM Compression and Some New Perspectives

• URL: http://arxiv.org/abs/2502.02723v1
• Date: Tue, 04 Feb 2025 21:17:51 GMT
• Title: Dobi-SVD: Differentiable SVD for LLM Compression and Some New Perspectives
• Authors: Qinsi Wang, Jinghan Ke, Masayoshi Tomizuka, Yiran Chen, Kurt Keutzer, Chenfeng Xu,
• Abstract summary: We argue that the optimal use of SVD lies in truncating activations, rather than merely using activations as an optimization distance. We propose Dobi-SVD, which establishes a new, principled approach to SVD-based LLM compression.
• Score: 59.46211685419206
• License:
• Abstract: We provide a new LLM-compression solution via SVD, unlocking new possibilities for LLM compression beyond quantization and pruning. We point out that the optimal use of SVD lies in truncating activations, rather than merely using activations as an optimization distance. Building on this principle, we address three critical challenges in SVD-based LLM compression: including (1) How can we determine the optimal activation truncation position for each weight matrix in LLMs? (2) How can we efficiently reconstruct the weight matrices based on truncated activations? (3) How can we address the inherent "injection" nature that results in the information loss of the SVD? We propose Dobi-SVD, which establishes a new, principled approach to SVD-based LLM compression.

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