UniPruning: Unifying Local Metric and Global Feedback for Scalable Sparse LLMs

• URL: http://arxiv.org/abs/2510.03291v1
• Date: Mon, 29 Sep 2025 13:38:28 GMT
• Title: UniPruning: Unifying Local Metric and Global Feedback for Scalable Sparse LLMs
• Authors: Yizhuo Ding, Wanying Qu, Jiawei Geng, Wenqi Shao, Yanwei Fu,
• Abstract summary: Large Language Models (LLMs) achieve strong performance across diverse tasks but face prohibitive computational and memory costs.<n>We present UniPruning, a unified post-training pruning framework that combines the speed of local saliency metrics with the stability of global coordination.<n>UniPruning consistently delivers competitive or superior perplexity and zero-shot accuracy.
• Score: 46.12497343562301
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large Language Models (LLMs) achieve strong performance across diverse tasks but face prohibitive computational and memory costs. Pruning offers a promising path by inducing sparsity while preserving architectural flexibility. However, existing methods struggle to balance efficiency and robustness: local metric approaches prune layer by layer but often collapse under high sparsity, whereas global feedback methods enforce consistency at the cost of expensive weight updates or restrictive semi-structured formats. We present UniPruning, a unified post-training pruning framework that combines the speed of local saliency metrics with the stability of global coordination, enabled by a mirror descent based optimization, all without updating model weights. UniPruning leverages fast layer-wise scoring and a lightweight global controller to allocate a single sparsity budget, supporting both unstructured and semi-structured N :M pruning within one framework. After a brief calibration, it can generate pruning masks for arbitrary sparsity levels in one shot, and adapts seamlessly to hardware-aware constraints. Extensive experiments on multiple pretrained LLM families and standard benchmarks show that UniPruning consistently delivers competitive or superior perplexity and zero-shot accuracy. Ablation studies further highlight the importance of mirror descent and local saliency anchoring. Overall, UniPruning provides an efficient, principled, and scalable solution for sparsifying large-scale LLMs. Our code is available at: https://github.com/RainbowQTT/UniPruning.

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