Cluster-Driven Expert Pruning for Mixture-of-Experts Large Language Models

• URL: http://arxiv.org/abs/2504.07807v1
• Date: Thu, 10 Apr 2025 14:46:26 GMT
• Title: Cluster-Driven Expert Pruning for Mixture-of-Experts Large Language Models
• Authors: Hongcheng Guo, Juntao Yao, Boyang Wang, Junjia Du, Shaosheng Cao, Donglin Di, Shun Zhang, Zhoujun Li,
• Abstract summary: Cluster-driven Expert Pruning (C-Prune) is a novel two-stage framework for adaptive task-specific compression of large language models.<n>C-Prune operates through layer-wise expert clustering, which groups functionally similar experts within each MoE layer.<n>We validate C-Prune through extensive experiments on multiple MoE models and benchmarks.
• Score: 24.64757529640278
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Mixture-of-Experts (MoE) architectures have emerged as a promising paradigm for scaling large language models (LLMs) with sparse activation of task-specific experts. Despite their computational efficiency during inference, the massive overall parameter footprint of MoE models (e.g., GPT-4) introduces critical challenges for practical deployment. Current pruning approaches often fail to address two inherent characteristics of MoE systems: 1).intra-layer expert homogeneity where experts within the same MoE layer exhibit functional redundancy, and 2). inter-layer similarity patterns where deeper layers tend to contain progressively more homogeneous experts. To tackle these issues, we propose Cluster-driven Expert Pruning (C-Prune), a novel two-stage framework for adaptive task-specific compression of MoE LLMs. C-Prune operates through layer-wise expert clustering, which groups functionally similar experts within each MoE layer using parameter similarity metrics, followed by global cluster pruning, which eliminates redundant clusters across all layers through a unified importance scoring mechanism that accounts for cross-layer homogeneity. We validate C-Prune through extensive experiments on multiple MoE models and benchmarks. The results demonstrate that C-Prune effectively reduces model size while outperforming existing MoE pruning methods.

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