Towards Extreme Pruning of LLMs with Plug-and-Play Mixed Sparsity

• URL: http://arxiv.org/abs/2503.11164v1
• Date: Fri, 14 Mar 2025 08:05:49 GMT
• Title: Towards Extreme Pruning of LLMs with Plug-and-Play Mixed Sparsity
• Authors: Chi Xu, Gefei Zhang, Yantong Zhu, Luca Benini, Guosheng Hu, Yawei Li, Zhihong Zhang,
• Abstract summary: Existing pruning methods mainly focus on designing metrics to measure the importance of network components to guide pruning.<n>We propose an efficient method based on the trace of Fisher Information Matrix (FIM) to quantitatively measure and verify the different sensitivities across layers.<n>Based on this, we propose Mixed Sparsity Pruning (MSP) which uses a pruning-oriented evolutionary algorithm (EA) to determine the optimal sparsity levels for different layers.
• Score: 32.668409666483626
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: N:M structured pruning is essential for large language models (LLMs) because it can remove less important network weights and reduce the memory and computation requirements. Existing pruning methods mainly focus on designing metrics to measure the importance of network components to guide pruning. Apart from the impact of these metrics, we observe that different layers have different sensitivities over the network performance. Thus, we propose an efficient method based on the trace of Fisher Information Matrix (FIM) to quantitatively measure and verify the different sensitivities across layers. Based on this, we propose Mixed Sparsity Pruning (MSP) which uses a pruning-oriented evolutionary algorithm (EA) to determine the optimal sparsity levels for different layers. To guarantee fast convergence and achieve promising performance, we utilize efficient FIM-inspired layer-wise sensitivity to initialize the population of EA. In addition, our MSP can work as a plug-and-play module, ready to be integrated into existing pruning methods. Extensive experiments on LLaMA and LLaMA-2 on language modeling and zero-shot tasks demonstrate our superior performance. In particular, in extreme pruning ratio (e.g. 75%), our method significantly outperforms existing methods in terms of perplexity (PPL) by orders of magnitude (Figure 1).

Related papers

• PUMA: Layer-Pruned Language Model for Efficient Unified Multimodal Retrieval with Modality-Adaptive Learning [54.73049408950049]
  We propose a Layer-Pruned Language Model for Efficient Unified Multimodal Retrieval with Modality-Adaptive Learning.<n>Our approach improves unified multimodal retrieval from both structural and learning perspectives.
  arXiv  Detail & Related papers  (2025-07-10T16:47:25Z)
• Dynamic Context-oriented Decomposition for Task-aware Low-rank Adaptation with Less Forgetting and Faster Convergence [131.41894248194995]
  We propose context-oriented decomposition adaptation (CorDA), a novel method that initializes adapters in a task-aware manner.<n>Thanks to the task awareness, our method enables two optional adaptation modes, knowledge-preserved mode (KPM) and instruction-previewed mode (IPM)
  arXiv  Detail & Related papers  (2025-06-16T07:55:14Z)
• LOP: Learning Optimal Pruning for Efficient On-Demand MLLMs Scaling [52.1366057696919]
  LOP is an efficient neural pruning framework that learns optimal pruning strategies from the target pruning constraint.<n>LOP approach trains autoregressive neural networks (NNs) to directly predict layer-wise pruning strategies adaptive to the target pruning constraint.<n> Experimental results show that LOP outperforms state-of-the-art pruning methods in various metrics while achieving up to three orders of magnitude speedup.
  arXiv  Detail & Related papers  (2025-06-15T12:14:16Z)
• SkipGPT: Dynamic Layer Pruning Reinvented with Token Awareness and Module Decoupling [16.742839354514512]
  We introduce SkipGPT, a dynamic layer pruning framework to optimize large language models.<n>We show that SkipGPT reduces over 40% of model parameters while matching or exceeding the performance of the original dense model.
  arXiv  Detail & Related papers  (2025-06-04T17:26:31Z)
• SlimLLM: Accurate Structured Pruning for Large Language Models [36.84275777364218]
  structured pruning is an effective solution to compress the parameters of large language models.<n>We propose an effective and fast structured pruning method named SlimLLM for large language models.
  arXiv  Detail & Related papers  (2025-05-28T03:01:28Z)
• Maximum Redundancy Pruning: A Principle-Driven Layerwise Sparsity Allocation for LLMs [24.23702494859769]
  Large language models (LLMs) have demonstrated impressive capabilities, but their enormous size poses significant challenges for deployment in real-world applications. Recent sparsity allocation methods is often based on iteration or search that can easily lead to suboptimal performance. We propose Redundancy Pruning (MRP), an iterative pruning algorithm that prunes in the most redundant layers.
  arXiv  Detail & Related papers  (2025-03-24T06:17:30Z)
• Adaptive Pruning for Large Language Models with Structural Importance Awareness [66.2690963378878]
  Large language models (LLMs) have significantly improved language understanding and generation capabilities.<n>LLMs are difficult to deploy on resource-constrained edge devices due to their high computational and storage resource demands.<n>We propose structurally-aware adaptive pruning (SAAP) to significantly reduce the computational and memory costs while maintaining model performance.
  arXiv  Detail & Related papers  (2024-12-19T18:08:04Z)
• Refining Salience-Aware Sparse Fine-Tuning Strategies for Language Models [14.68920095399595]
  sparsity-based PEFT (SPEFT) introduces trainable sparse adaptations to the weight matrices in the model.<n>We conduct the first systematic evaluation of salience metrics for SPEFT, inspired by zero-cost NAS proxies.<n>Our work challenges the notion that complexity is necessary for effective PEFT.
  arXiv  Detail & Related papers  (2024-12-18T04:14:35Z)
• Read-ME: Refactorizing LLMs as Router-Decoupled Mixture of Experts with System Co-Design [59.00758127310582]
  We propose a novel framework Read-ME that transforms pre-trained dense LLMs into smaller MoE models. Our approach employs activation sparsity to extract experts. Read-ME outperforms other popular open-source dense models of similar scales.
  arXiv  Detail & Related papers  (2024-10-24T19:48:51Z)
• Layer-wise Importance Matters: Less Memory for Better Performance in Parameter-efficient Fine-tuning of Large Language Models [19.163639128631534]
  Importance-aware Sparse Tuning (IST) is a plug-and-play technique compatible with various PEFT methods that operate on a per-layer basis. IST dynamically updates selected layers in PEFT modules, leading to reduced memory demands.
  arXiv  Detail & Related papers  (2024-10-15T16:53:26Z)
• FactorLLM: Factorizing Knowledge via Mixture of Experts for Large Language Models [50.331708897857574]
  We introduce FactorLLM, a novel approach that decomposes well-trained dense FFNs into sparse sub-networks without requiring any further modifications. FactorLLM achieves comparable performance to the source model securing up to 85% model performance while obtaining over a 30% increase in inference speed.
  arXiv  Detail & Related papers  (2024-08-15T16:45:16Z)
• A deeper look at depth pruning of LLMs [49.30061112976263]
  Large Language Models (LLMs) are resource-intensive to train but more costly to deploy in production. Recent work has attempted to prune blocks of LLMs based on cheap proxies for estimating block importance. We show that adaptive metrics exhibit a trade-off in performance between tasks.
  arXiv  Detail & Related papers  (2024-07-23T08:40:27Z)
• Bypass Back-propagation: Optimization-based Structural Pruning for Large Language Models via Policy Gradient [57.9629676017527]
  We propose an optimization-based structural pruning on Large-Language Models. We learn the pruning masks in a probabilistic space directly by optimizing the loss of the pruned model. Our method operates for 2.7 hours with around 35GB memory for the 13B models on a single A100 GPU.
  arXiv  Detail & Related papers  (2024-06-15T09:31:03Z)
• SPP: Sparsity-Preserved Parameter-Efficient Fine-Tuning for Large Language Models [53.638791265113625]
  Sparsity-Preserved efficient fine-tuning method for large language models. Code will be made available at https://github.com/Lucky-Lance/SPP.
  arXiv  Detail & Related papers  (2024-05-25T04:55:27Z)
• One-Shot Sensitivity-Aware Mixed Sparsity Pruning for Large Language Models [42.95555008229016]
  We propose a method based on Hessian sensitivity-aware mixed sparsity pruning to prune LLMs to at least 50% sparsity without the need of any retraining. The advantages of the proposed method exhibit even more when the sparsity is extremely high.
  arXiv  Detail & Related papers  (2023-10-14T05:43:09Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.