SoLA: Leveraging Soft Activation Sparsity and Low-Rank Decomposition for Large Language Model Compression
arXiv:2604.03258v1 Announce Type: new Abstract: Large language models (LLMs) have demonstrated impressive capabilities across various tasks, but the billion-scale parameters pose deployment challenges. Although existing methods attempt to reduce the scale of LLMs, they require either special hardware support or expensive post-training to maintain model quality. To facilitate efficient and affordable model slimming, we propose a novel training-free compression method for LLMs, named "SoLA", which leverages \textbf{So}ft activation sparsity and \textbf{L}ow-r\textbf{A}nk decomposition. SoLA can identify and retain a minority of components significantly contributing to inference, while compressing the majority through low-rank decomposition, based on our analysis of the activation pattern in the feed-forward network (FFN) of modern LLMs. To alleviate the decomposition loss, SoLA is equipped with an adaptive component-wise low-rank allocation strategy to assign appropriate truncation posi
arXiv:2604.03258v1 Announce Type: new Abstract: Large language models (LLMs) have demonstrated impressive capabilities across various tasks, but the billion-scale parameters pose deployment challenges. Although existing methods attempt to reduce the scale of LLMs, they require either special hardware support or expensive post-training to maintain model quality. To facilitate efficient and affordable model slimming, we propose a novel training-free compression method for LLMs, named "SoLA", which leverages \textbf{So}ft activation sparsity and \textbf{L}ow-r\textbf{A}nk decomposition. SoLA can identify and retain a minority of components significantly contributing to inference, while compressing the majority through low-rank decomposition, based on our analysis of the activation pattern in the feed-forward network (FFN) of modern LLMs. To alleviate the decomposition loss, SoLA is equipped with an adaptive component-wise low-rank allocation strategy to assign appropriate truncation positions for different weight matrices. We conduct extensive experiments on LLaMA-2-7B/13B/70B and Mistral-7B models across a variety of benchmarks. SoLA exhibits remarkable improvement in both language modeling and downstream task accuracy without post-training. For example, with a 30\% compression rate on the LLaMA-2-70B model, SoLA surpasses the state-of-the-art method by reducing perplexity from 6.95 to 4.44 and enhancing downstream task accuracy by 10\%.
Executive Summary
The article introduces SoLA, a novel training-free compression method for large language models (LLMs) that addresses deployment challenges posed by billion-scale parameters. By leveraging soft activation sparsity and low-rank decomposition, SoLA identifies and retains critical components while compressing the majority of the model. Unlike existing methods, SoLA does not require special hardware or expensive post-training, making it a cost-effective solution. Experimental results on LLaMA-2 and Mistral models demonstrate significant improvements in language modeling and downstream task accuracy, outperforming state-of-the-art techniques. For instance, with a 30% compression rate on LLaMA-2-70B, SoLA reduces perplexity from 6.95 to 4.44 and enhances accuracy by 10%. The approach offers a promising direction for efficient LLM deployment without sacrificing performance.
Key Points
- ▸ SoLA is a training-free compression method for LLMs that leverages soft activation sparsity and low-rank decomposition.
- ▸ The method identifies and retains critical components while compressing the majority of the model, eliminating the need for special hardware or post-training.
- ▸ Extensive experiments on LLaMA-2 and Mistral models show significant improvements in language modeling and downstream task accuracy compared to state-of-the-art methods.
Merits
Innovative Approach
SoLA introduces a novel combination of soft activation sparsity and low-rank decomposition, enabling training-free compression without sacrificing model performance.
Cost-Effectiveness
By avoiding special hardware requirements and post-training, SoLA reduces the computational and financial costs associated with LLM compression.
Performance Superiority
Empirical results demonstrate that SoLA outperforms state-of-the-art compression methods in both perplexity reduction and task accuracy, highlighting its effectiveness.
Demerits
Limited Generalizability
The study primarily evaluates SoLA on LLaMA-2 and Mistral models, leaving its applicability to other LLM architectures unexamined.
Dependency on Activation Patterns
SoLA's effectiveness relies on the analysis of activation patterns in the FFN, which may vary across different models or tasks, potentially limiting its robustness.
Lack of Theoretical Foundation
While the method demonstrates empirical success, a deeper theoretical analysis of why soft activation sparsity and low-rank decomposition work synergistically in this context is lacking.
Expert Commentary
The introduction of SoLA represents a significant advancement in the field of LLM compression, particularly due to its training-free nature and reliance on soft activation sparsity and low-rank decomposition. The method's empirical success, as demonstrated by its performance on LLaMA-2 and Mistral models, underscores its potential to democratize access to high-performing LLMs by reducing computational barriers. However, the study's focus on specific models raises questions about generalizability, and the absence of a theoretical framework for the observed synergy between sparsity and low-rank decomposition warrants further investigation. From a practical standpoint, SoLA's ability to maintain or even improve model accuracy while compressing the model is commendable, and it aligns with the growing demand for sustainable and efficient AI deployment. For the academic and industrial communities, this work signals a promising direction toward more accessible and cost-effective LLM deployment, though broader validation across diverse architectures and tasks is essential to solidify its impact.
Recommendations
- ✓ Future research should validate SoLA across a broader range of LLM architectures and tasks to assess its generalizability and robustness.
- ✓ A theoretical analysis should be conducted to explore the underlying mechanisms of soft activation sparsity and low-rank decomposition in the context of LLM compression, providing a stronger foundation for the method.
- ✓ Practitioners should consider integrating SoLA into existing compression pipelines to evaluate its compatibility and potential synergies with other techniques like quantization or pruning.
Sources
Original: arXiv - cs.CL