Prune-then-Quantize or Quantize-then-Prune? Understanding the Impact of Compression Order in Joint Model Compression
arXiv:2603.18426v1 Announce Type: new Abstract: What happens when multiple compression methods are combined-does the order in which they are applied matter? Joint model compression has emerged as a powerful strategy to achieve higher efficiency by combining multiple methods such as pruning and quantization. A central but underexplored factor in joint model compression is the compression order, or the sequence of different methods within the compression pipeline. Most prior studies have either sidestepped the issue by assuming orthogonality between techniques, while a few have examined them only in highly constrained cases. Consequently, the broader role of compression order in shaping model performance remains poorly understood. In this paper, we address the overlooked problem of compression order and provide both theoretical and empirical analysis. We formulate the problem of optimizing the compression order and introduce the Progressive Intensity Hypothesis, which states that weaker
arXiv:2603.18426v1 Announce Type: new Abstract: What happens when multiple compression methods are combined-does the order in which they are applied matter? Joint model compression has emerged as a powerful strategy to achieve higher efficiency by combining multiple methods such as pruning and quantization. A central but underexplored factor in joint model compression is the compression order, or the sequence of different methods within the compression pipeline. Most prior studies have either sidestepped the issue by assuming orthogonality between techniques, while a few have examined them only in highly constrained cases. Consequently, the broader role of compression order in shaping model performance remains poorly understood. In this paper, we address the overlooked problem of compression order and provide both theoretical and empirical analysis. We formulate the problem of optimizing the compression order and introduce the Progressive Intensity Hypothesis, which states that weaker perturbations should precede stronger ones. We provide theoretical guarantees showing that the relative benefit of one order increases with the underlying performance gap. Extensive experiments on both language and vision models validate the hypothesis, and further show its generality to broader setups such as multi-stage compression and mixed-precision quantization.
Executive Summary
This article explores the impact of compression order in joint model compression, specifically the effects of pruning and quantization. The authors introduce the Progressive Intensity Hypothesis, which posits that weaker perturbations should precede stronger ones. Through theoretical analysis and extensive experiments, the authors demonstrate the importance of compression order in shaping model performance. The findings have significant implications for the development of more efficient and effective model compression strategies. The study highlights the need for a more nuanced understanding of the interactions between different compression methods and provides a foundation for future research in this area. The results are generalizable to various setups, including multi-stage compression and mixed-precision quantization.
Key Points
- ▸ The authors investigate the impact of compression order in joint model compression.
- ▸ The Progressive Intensity Hypothesis is introduced, stating that weaker perturbations should precede stronger ones.
- ▸ Theoretical guarantees and empirical analysis demonstrate the importance of compression order in shaping model performance.
Merits
Strength
The study provides a comprehensive theoretical and empirical analysis of the compression order problem, offering a nuanced understanding of the interactions between different compression methods.
Strength
The Progressive Intensity Hypothesis is a novel and insightful contribution to the field of model compression, providing a framework for optimizing compression order.
Demerits
Limitation
The study assumes a specific pruning and quantization framework, which may limit its generalizability to other compression methods.
Limitation
The experiments are primarily conducted on language and vision models, which may not fully capture the complexity of real-world applications.
Expert Commentary
This article is a significant contribution to the field of model compression, offering a comprehensive analysis of the compression order problem. The Progressive Intensity Hypothesis is a novel and insightful concept that provides a framework for optimizing compression order. The study's findings have far-reaching implications for the development of more efficient and effective model compression strategies. However, the study's assumptions and limitations should be carefully considered when generalizing the results to other compression methods and real-world applications.
Recommendations
- ✓ Future research should focus on extending the Progressive Intensity Hypothesis to other compression methods and exploring its applicability to more complex models and applications.
- ✓ The study's findings should be further validated through experiments on a wider range of models and compression methods to ensure the generalizability of the results.
