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Basis Sharing: Cross-Layer Parameter Sharing for Large Language Model Compression

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arxiv 2410.03765 v1 pith:44YD2RP2 submitted 2024-10-02 cs.CL cs.LG

Jingcun Wang , Yu-Guang Chen , Ing-Chao Lin , Bing Li , Grace Li Zhang This is my paper
classification cs.CLcs.LG
keywords sharingllmsbasiscompressionlargematricesparameterweight
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Large Language Models (LLMs) have achieved remarkable breakthroughs. However, the huge number of parameters in LLMs require significant amount of memory storage in inference, which prevents their practical deployment in many applications. To reduce memory storage of LLMs, singular value decomposition (SVD) provides a promising solution to approximate weight matrices for compressing LLMs. In this paper, we take a step further to explore parameter sharing across different layers with SVD to achieve more effective compression for LLMs. Specifically, weight matrices in different layers are decomposed and represented as a linear combination of a set of shared basis vectors and unique coefficients. The types of weight matrices and the layer selection for basis sharing are examined when compressing LLMs to maintain the performance. Comprehensive experiments demonstrate that Basis Sharing outperforms state-of-the-art SVD-based compression approaches and parameter sharing techniques, especially under large compression ratios. Code is available at: https://github.com/TUDa-HWAI/Basis_Sharing

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Different Prompts, Different Ranks: Prompt-aware Dynamic Rank Selection for SVD-based LLM Compression

    cs.LG 2026-05 unverdicted novelty 6.0 of 10

    PARSE trains a prompt-aware linear router on dense-model outputs to select dynamic SVD ranks, improving accuracy up to 10% at 0.6 compression ratio on LLaMA-7B while delivering 2.5x prefill and 2.4x decode speedups.

  2. FlashSVD v1.5: Making Low-Rank Transformers Inference Actually Fast

    cs.LG 2026-05 unverdicted novelty 6.0 of 10

    FlashSVD v1.5 delivers up to 2.55x faster autoregressive decode and 2.39x end-to-end speedup for SVD-compressed transformers by reorganizing execution paths with dense-KV decode, packed MLP kernels, and per-layer CUDA graphs.

  3. KV Packet: Recomputation-Free Context-Independent KV Caching for LLMs

    cs.LG 2026-04 unverdicted novelty 6.0 of 10

    KV Packet enables recomputation-free context-independent KV caching for LLMs by using trainable soft-token adapters trained via self-supervised distillation to bridge attention shifts.

  4. CoSpaDi: Compressing LLMs via Calibration-Guided Sparse Dictionary Learning

    cs.CL 2025-09 conditional novelty 6.0 of 10

    CoSpaDi introduces a training-free sparse dictionary learning framework for post-training LLM compression that optimizes functional reconstruction error via activation-derived orthonormalization and achieves improved ...

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