SlimGPT: Layer-wise Structured Pruning for Large Language Models

• URL: http://arxiv.org/abs/2412.18110v1
• Date: Tue, 24 Dec 2024 02:49:50 GMT
• Title: SlimGPT: Layer-wise Structured Pruning for Large Language Models
• Authors: Gui Ling, Ziyang Wang, Yuliang Yan, Qingwen Liu,
• Abstract summary: Batched Greedy Pruning for rapid and near-optimal pruning.<n>Incremental Pruning Ratio, a non-uniform pruning strategy to reduce performance degradation.<n> Experimental results on the LLaMA benchmark show that SlimGPT outperforms other methods and achieves state-of-the-art results.
• Score: 15.252798256418279
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large language models (LLMs) have garnered significant attention for their remarkable capabilities across various domains, whose vast parameter scales present challenges for practical deployment. Structured pruning is an effective method to balance model performance with efficiency, but performance restoration under computational resource constraints is a principal challenge in pruning LLMs. Therefore, we present a low-cost and fast structured pruning method for LLMs named SlimGPT based on the Optimal Brain Surgeon framework. We propose Batched Greedy Pruning for rapid and near-optimal pruning, which enhances the accuracy of head-wise pruning error estimation through grouped Cholesky decomposition and improves the pruning efficiency of FFN via Dynamic Group Size, thereby achieving approximate local optimal pruning results within one hour. Besides, we explore the limitations of layer-wise pruning from the perspective of error accumulation and propose Incremental Pruning Ratio, a non-uniform pruning strategy to reduce performance degradation. Experimental results on the LLaMA benchmark show that SlimGPT outperforms other methods and achieves state-of-the-art results.

Related papers

• Týr-the-Pruner: Unlocking Accurate 50% Structural Pruning for LLMs via Global Sparsity Distribution Optimization [15.027017826182659]
  T'yr-the-Pruner is an efficient end-to-end search-based global structural pruning framework. We introduce an effective local pruning and an expectation error accumulation approach to improve supernet construction. Results show that T'yr-the-Pruner achieves state-of-the-art structural pruning, retaining 97% of the dense model's performance while removing a challenging 50% of Llama-3.1-70B's parameters.
  arXiv  Detail & Related papers  (2025-03-12T11:52:49Z)
• Sample-aware Adaptive Structured Pruning for Large Language Models [14.605017410864583]
  This study introduces AdaPruner, a sample-aware adaptive structured pruning framework for large language models (LLMs) Specifically, AdaPruner effectively removes redundant parameters from LLMs by constructing a structured pruning solution space. At a 20% pruning ratio, the model pruned with AdaPruner maintains 97% of the performance of the unpruned model.
  arXiv  Detail & Related papers  (2025-03-08T12:00:21Z)
• Determining Layer-wise Sparsity for Large Language Models Through a Theoretical Perspective [55.90119819642064]
  We address the challenge of determining the layer-wise sparsity rates of large language models (LLMs) through a theoretical perspective. This refers to the cumulative effect of reconstruction errors throughout the sparsification process. We derive a simple yet effective approach to layer-wise sparsity allocation that mitigates this issue.
  arXiv  Detail & Related papers  (2025-02-20T17:51:10Z)
• OPTISHEAR: Towards Efficient and Adaptive Pruning of Large Language Models via Evolutionary Optimization [18.57876883968734]
  We introduce textbftextscOptiShear, an efficient evolutionary optimization framework for adaptive LLM pruning. Our framework features two key innovations: an effective search space built on our Meta pruning metric, and a model-wise reconstruction error for rapid evaluation.
  arXiv  Detail & Related papers  (2025-02-15T09:17:38Z)
• FASP: Fast and Accurate Structured Pruning of Large Language Models [24.185245582500876]
  We introduce FASP (Fast and Accurate Structured Pruning), a novel structured pruning framework for large language models (LLMs) FASP employs a distinctive pruning structure that interlinks sequential layers, allowing for the removal of columns in one layer while simultaneously eliminating corresponding rows in the preceding layer without incurring additional performance loss. We evaluate FASP on the OPT and LLaMA model families, demonstrating superior performance in terms of perplexity and accuracy on downstream tasks compared to state-of-the-art methods.
  arXiv  Detail & Related papers  (2025-01-16T09:38:39Z)
• DRPruning: Efficient Large Language Model Pruning through Distributionally Robust Optimization [61.492590008258986]
  Large language models (LLMs) deliver impressive results but face challenges from increasing model sizes and computational costs. We propose DRPruning, which incorporates distributionally robust optimization to restore balanced performance across domains.
  arXiv  Detail & Related papers  (2024-11-21T12:02:39Z)
• A Convex-optimization-based Layer-wise Post-training Pruner for Large Language Models [24.185245582500876]
  We introduce FISTAPruner, the first post-training pruner based on convex optimization models and algorithms. FISTAPruner incorporates an intra-layer cumulative error correction mechanism and supports parallel pruning. We evaluate FISTAPruner on models such as OPT, LLaMA, LLaMA-2, and LLaMA-3 with 125M to 70B parameters under unstructured and 2:4 semi-structured sparsity.
  arXiv  Detail & Related papers  (2024-08-07T12:33:46Z)
• 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)
• LD-Pruner: Efficient Pruning of Latent Diffusion Models using Task-Agnostic Insights [2.8461446020965435]
  We introduce LD-Pruner, a novel performance-preserving structured pruning method for compressing Latent Diffusion Models. We demonstrate the effectiveness of our approach on three different tasks: text-to-image (T2I) generation, Unconditional Image Generation (UIG) and Unconditional Audio Generation (UAG)
  arXiv  Detail & Related papers  (2024-04-18T06:35:37Z)
• Toward Adaptive Large Language Models Structured Pruning via Hybrid-grained Weight Importance Assessment [58.030196381554745]
  We introduce the Hybrid-grained Weight Importance Assessment (HyWIA), a novel method that merges fine-grained and coarse-grained evaluations of weight importance for the pruning of large language models (LLMs)<n>Extensive experiments on LLaMA-V1/V2, Vicuna, Baichuan, and Bloom across various benchmarks demonstrate the effectiveness of HyWIA in pruning LLMs.
  arXiv  Detail & Related papers  (2024-03-16T04:12:50Z)
• LoRAPrune: Structured Pruning Meets Low-Rank Parameter-Efficient Fine-Tuning [56.88751562302793]
  Low-rank adaption (LoRA) has emerged to fine-tune large language models (LLMs) LoRAPrune is a new framework that delivers an accurate structured pruned model in a highly memory-efficient manner. LoRAPrune achieves a reduction in perplexity by 4.81 on WikiText2 and 3.46 on PTB, while also decreasing memory usage by 52.6%.
  arXiv  Detail & Related papers  (2023-05-28T15:15:48Z)
• Advancing Model Pruning via Bi-level Optimization [89.88761425199598]
  iterative magnitude pruning (IMP) is the predominant pruning method to successfully find 'winning tickets' One-shot pruning methods have been developed, but these schemes are usually unable to find winning tickets as good as IMP. We show that the proposed bi-level optimization-oriented pruning method (termed BiP) is a special class of BLO problems with a bi-linear problem structure.
  arXiv  Detail & Related papers  (2022-10-08T19:19:29Z)
• Attentive Fine-Grained Structured Sparsity for Image Restoration [63.35887911506264]
  N:M structured pruning has appeared as one of the effective and practical pruning approaches for making the model efficient with the accuracy constraint. We propose a novel pruning method that determines the pruning ratio for N:M structured sparsity at each layer.
  arXiv  Detail & Related papers  (2022-04-26T12:44:55Z)

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.