Sparsity Induction for Accurate Post-Training Pruning of Large Language Models
arXiv:2602.21652v1 Announce Type: new Abstract: Large language models have demonstrated capabilities in text generation, while their increasing parameter scales present challenges in computational and memory efficiency. Post-training sparsity (PTS), which reduces model cost by removing weights from dense networks, is an effective approach. However, native dense matrices lack high sparsity, making existing approaches that directly remove weights disrupt model states, resulting in unsatisfactory performance recovery even with post-tuning. We propose Sparsity Induction, which promotes models toward higher sparsity at both distribution and feature levels before pruning, to push the limits of PTS. At the distribution level, we enhance distributional sparsity through mathematically equivalent scaling transformations, which are fully absorbable and incur no extra parameters or inference-time overhead. At the feature level, we introduce Spectral Norm Loss to promote feature sparsity from a lo
arXiv:2602.21652v1 Announce Type: new Abstract: Large language models have demonstrated capabilities in text generation, while their increasing parameter scales present challenges in computational and memory efficiency. Post-training sparsity (PTS), which reduces model cost by removing weights from dense networks, is an effective approach. However, native dense matrices lack high sparsity, making existing approaches that directly remove weights disrupt model states, resulting in unsatisfactory performance recovery even with post-tuning. We propose Sparsity Induction, which promotes models toward higher sparsity at both distribution and feature levels before pruning, to push the limits of PTS. At the distribution level, we enhance distributional sparsity through mathematically equivalent scaling transformations, which are fully absorbable and incur no extra parameters or inference-time overhead. At the feature level, we introduce Spectral Norm Loss to promote feature sparsity from a low-rank perspective. Experiments across diverse model architectures and tasks demonstrate that our method further enhances sparsity-friendliness, achieving superior pruning performance over existing approaches.
Executive Summary
The article titled 'Sparsity Induction for Accurate Post-Training Pruning of Large Language Models' addresses the challenge of computational and memory efficiency in large language models (LLMs) by proposing a method called Sparsity Induction. This method aims to enhance the sparsity of LLMs at both the distribution and feature levels before pruning, thereby improving the effectiveness of post-training sparsity (PTS). The authors introduce mathematically equivalent scaling transformations to increase distributional sparsity without additional parameters or inference-time overhead, and a Spectral Norm Loss to promote feature sparsity from a low-rank perspective. Experiments across various model architectures and tasks demonstrate superior pruning performance compared to existing approaches.
Key Points
- ▸ Large language models face challenges in computational and memory efficiency due to their increasing parameter scales.
- ▸ Post-training sparsity (PTS) is an effective approach to reduce model cost by removing weights from dense networks.
- ▸ Sparsity Induction promotes higher sparsity at both distribution and feature levels before pruning.
- ▸ Mathematically equivalent scaling transformations enhance distributional sparsity without extra parameters or overhead.
- ▸ Spectral Norm Loss promotes feature sparsity from a low-rank perspective.
- ▸ Experiments show superior pruning performance over existing approaches.
Merits
Innovative Approach
The article introduces a novel method, Sparsity Induction, which addresses the limitations of existing PTS approaches by promoting sparsity at both distribution and feature levels. This innovative approach enhances the sparsity-friendliness of LLMs, leading to better pruning performance.
Mathematical Rigor
The use of mathematically equivalent scaling transformations ensures that the proposed method does not incur additional parameters or inference-time overhead, making it a practical and efficient solution.
Comprehensive Experiments
The article presents experiments across diverse model architectures and tasks, demonstrating the effectiveness of the proposed method in achieving superior pruning performance compared to existing approaches.
Demerits
Implementation Complexity
The implementation of Spectral Norm Loss and scaling transformations may require significant computational resources and expertise, potentially limiting its accessibility to researchers and practitioners with limited resources.
Generalizability
While the experiments cover diverse model architectures and tasks, the generalizability of the proposed method to other types of models or applications remains to be thoroughly explored.
Expert Commentary
The article presents a significant advancement in the field of model compression, particularly for large language models. The proposed Sparsity Induction method addresses a critical challenge in the deployment of LLMs by enhancing their sparsity-friendliness. The use of mathematically equivalent scaling transformations and Spectral Norm Loss demonstrates a rigorous and innovative approach to promoting sparsity at both distribution and feature levels. The comprehensive experiments across diverse model architectures and tasks provide strong evidence of the method's effectiveness. However, the implementation complexity and potential limitations in generalizability should be carefully considered. Future research could explore the application of this method to other types of models and investigate its long-term impact on model performance and efficiency. Overall, this article makes a valuable contribution to the ongoing efforts to develop efficient and scalable machine learning models.
Recommendations
- ✓ Further research should be conducted to explore the generalizability of the proposed method to other types of models and applications.
- ✓ Practical guidelines and tools should be developed to facilitate the implementation of Sparsity Induction, making it more accessible to researchers and practitioners with limited resources.
