Demystifying the Compression of Mixture-of-Experts Through a Unified Framework

The paper presents a unified framework for compressing Mixture of Experts (MoE) models, which are large language models that selectively activate subsets of experts to reduce computational costs. The framework introduces two main compression strategies: Expert Slimming, which focuses on compressing individual experts through techniques like pruning and quantization, and Expert Trimming, which involves removing entire experts or structured modules. The authors propose aggressive Expert Trimming methods, such as Layer Drop and Block Drop, to eliminate redundancy on a larger scale. The framework and the proposed compression recipe are validated through extensive experiments, demonstrating a significant speedup and memory reduction while preserving over 92% of the performance on a Mixtral-8×7B model. The research contributes to the field by systematizing the understanding of MoE compression, identifying new design spaces for optimization, and setting the stage for future advancements in the efficiency of MoE models.

Abstract

Scaling large language models has revolutionized the performance across diverse domains, yet the continual growth in model size poses significant challenges for real-world deployment. The Mixture of Experts (MoE) approach addresses this by dynamically selecting and activating only a subset of experts, significantly reducing computational costs while maintaining high performance. However, MoE introduces potential redundancy (e.g., parameters) and extra costs (e.g., communication overhead). Despite numerous compression techniques developed for mitigating the redundancy in dense models, the compression of MoE remains under-explored. We first bridge this gap with a cutting-edge unified framework that not only seamlessly integrates mainstream compression methods but also helps systematically understand MoE compression. This framework approaches compression from two perspectives: Expert Slimming which compresses individual experts and Expert Trimming which removes structured modules. Within this framework, we explore the optimization space unexplored by existing methods,and further introduce aggressive Expert Trimming techniques, i.e., Layer Drop and Block Drop, to eliminate redundancy at larger scales. Based on these insights,we present a comprehensive recipe to guide practitioners in compressing MoE effectively. Extensive experimental results demonstrate the effectiveness of the compression methods under our framework and the proposed recipe, achieving a 6.05x speedup and only 20.0GB memory usage while maintaining over 92% of performance on Mixtral-8x7B.

Method

The authors used a unified framework that integrates two main methodologies for compressing Mixture of Experts (MoE) models: Expert Slimming and Expert Trimming. Expert Slimming focuses on compressing individual experts through techniques such as pruning and quantization, while Expert Trimming involves removing structured modules or entire experts, including methods like Expert Drop, Layer Drop, and Block Drop. They also considered the impact of data selection on the compression process and conducted ablation studies to understand the effects of shared experts in residual MoE models. Their approach is systematic and aims to identify new design spaces for optimization, leading to a comprehensive recipe for effective MoE compression.

Main Finding

The authors of the paper have developed a unified framework for compressing Mixture of Experts (MoE) models, which are large language models that selectively activate a subset of experts to reduce computational costs. They identify two primary approaches within their framework: Expert Slimming, which involves compressing individual experts through techniques like pruning and quantization, and Expert Trimming, which focuses on removing entire experts or structured modules. The authors introduce aggressive Expert Trimming techniques such as Layer Drop and Block Drop to eliminate redundancy on a larger scale. Their comprehensive recipe for MoE compression, which integrates these methods, has been validated through extensive experiments, demonstrating a significant speedup and memory reduction while preserving over 92% of the performance on a Mixtral-8×7B model. This research contributes to the field by systematizing the understanding of MoE compression, identifying new design spaces for optimization, and setting the stage for future advancements in the efficiency of MoE models.

Conclusion

The conclusion of the paper is that the authors introduced a unified framework for the compression of Mixture-of-Experts (MoE) models, which allows for a systematic understanding of the efficiency issues of MoE models and the identification of new design spaces to further improve performance. They proposed a comprehensive recipe that integrates two main compression strategies: Expert Slimming, which focuses on compressing individual experts through techniques like pruning and quantization, and Expert Trimming, which involves removing entire experts or structured modules. The paper also discusses the potential for post-compression training to enhance the performance of compressed MoE architectures. The authors' experiments demonstrate that their framework can significantly speed up and reduce the memory footprint of MoE models while preserving a high percentage of their original performance.

Keywords

Mixture of Experts (MoE), Expert Slimming, Expert Trimming, Layer Drop, Block Drop, Model Compression, Large Language Models, Efficiency, Performance, Deployment Challenges, Unified Framework, Compression Techniques, Pruning, Quantization, Post-Compression Training, Granularity, Expert Drop, Multi-Head Attention, MoE Normalization, Open Book Question Answering, Physical Commonsense Reasoning, Natural Language Understanding, Benchmark, Analysis Platform, Model Serving, GPU Acceleration, Sparsity, Data-Aware Serving, Model Acceleration, Pruning Survey, Quantization Survey, Deep Compression, Sharpness-Aware Quantization, Binarization, Pruning Efficacy, Transfer Learning, Reasoning Challenges, Language Model Evaluation, Instruction-Following Models, Diverse Text Dataset.