Maximum Redundancy Pruning: A Principle-Driven Layerwise Sparsity Allocation for LLMs

Chang Gao, Kang Zhao, Jianfei Chen, Liping Jing·March 24, 2025

Summary

The paper tackles large language model size challenges by exploring layerwise sparsity allocation, introducing Maximum Redundancy Pruning (MRP). MRP iteratively prunes redundant layers, demonstrating superior effectiveness in experiments on public LLMs. It highlights varying layer responses, advocating for non-uniform layerwise sparsity and metric-dependent sparsity. The study outlines hyperparameters for different LLMs, emphasizing few-shot learning capabilities and sparse, trainable neural networks. It discusses optimization methods, focusing on layer-wise pruning, adaptive sparsity, and structural pruning, aiming to enhance efficiency and reduce resource consumption while maintaining performance.

Introduction
Background
Overview of large language models (LLMs)
Challenges associated with large model sizes
Importance of efficient resource utilization in AI models
Objective
To explore and introduce Maximum Redundancy Pruning (MRP) for layerwise sparsity allocation
To demonstrate MRP's effectiveness in pruning redundant layers within LLMs
To advocate for non-uniform layerwise sparsity and metric-dependent sparsity
Method
Data Collection
Gathering public LLM datasets for experimentation
Selection criteria for datasets and models
Data Preprocessing
Preparation of datasets for MRP application
Standardization of preprocessing steps across different models
Maximum Redundancy Pruning (MRP)
Detailed explanation of MRP algorithm
Iterative pruning process for identifying and removing redundant layers
Comparison with existing pruning techniques
Hyperparameters for Different LLMs
Identification and tuning of hyperparameters for various LLMs
Importance of hyperparameter optimization in MRP's effectiveness
Few-Shot Learning Capabilities
Exploration of MRP's impact on few-shot learning scenarios
Analysis of model performance with limited data
Sparse, Trainable Neural Networks
Discussion on the benefits of sparse networks
Integration of sparsity in model training and inference
Optimization Methods
Layer-wise Pruning
Explanation of layer-wise pruning techniques
Implementation details and considerations
Adaptive Sparsity
Introduction to adaptive sparsity mechanisms
Dynamic adjustment of sparsity levels based on model performance
Structural Pruning
Overview of structural pruning methods
Comparison with layer-wise and adaptive pruning
Results
Experiments on Public LLMs
Presentation of experimental setup and results
Analysis of MRP's performance across different models
Efficiency and Resource Consumption
Evaluation of MRP's impact on model efficiency
Comparison of resource consumption before and after pruning
Performance Maintenance
Discussion on maintaining model performance post-pruning
Case studies demonstrating performance stability
Conclusion
Summary of Findings
Recap of MRP's effectiveness in layerwise sparsity allocation
Highlighting the benefits of non-uniform and metric-dependent sparsity
Future Directions
Potential areas for further research
Recommendations for practical implementation in real-world applications
Implications for AI and Machine Learning
Broader impact on AI model development and deployment
Contribution to sustainable AI practices
Basic info
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machine learning
artificial intelligence
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