Progressive Binarization with Semi-Structured Pruning for LLMs

• URL: http://arxiv.org/abs/2502.01705v3
• Date: Mon, 30 Jun 2025 05:16:02 GMT
• Title: Progressive Binarization with Semi-Structured Pruning for LLMs
• Authors: Xianglong Yan, Tianao Zhang, Zhiteng Li, Yulun Zhang,
• Abstract summary: We propose Progressive Binarization with Semi-Structured Pruning (PBS$2$P), a novel post-training compression framework.<n>We show that PBS$2$P consistently outperforms state-of-the-art binary post-training quantization methods in both perplexity and downstream accuracy.
• Score: 36.32239429974179
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large language models (LLMs) have achieved remarkable progress in natural language processing, but their high computational and memory costs hinder deployment on resource-constrained devices. Binarization, which reduces model weights to 1 bit, is a promising solution for efficient inference. However, binarized LLMs still exhibit redundancy that can be further compressed. Semi-structured pruning offers a favorable trade-off between model performance and hardware efficiency, but naively combining it with binarization often leads to severe performance degradation. To address this, we propose Progressive Binarization with Semi-Structured Pruning (PBS$^2$P), a novel post-training compression framework. We propose Stepwise semi-structured Pruning with Binarization Optimization (SPBO) to jointly reduce pruning and binarization error. Additionally, we develop a Coarse-to-Fine Search (CFS) strategy to more effectively select pruning elements. Extensive experiments across multiple LLM families show that PBS$^2$P consistently outperforms state-of-the-art binary post-training quantization methods in both perplexity and downstream accuracy. The code and models will be available at: https://github.com/XIANGLONGYAN/PBS2P.

Related papers

• Highly Efficient and Effective LLMs with Multi-Boolean Architectures [1.4195677954898822]
  Weight binarization has emerged as a promising strategy to drastically reduce the complexity of large language models (LLMs)<n>We introduce a novel framework that effectively transforms LLMs into multi- kernel Boolean parameters, for the first time, finetunes them directly in the Boolean domain, eliminating the need for expensive latent weights.<n>Our method outperforms recent ultra low-bit quantization and binarization methods.
  arXiv  Detail & Related papers  (2025-05-28T19:40:34Z)
• SPAP: Structured Pruning via Alternating Optimization and Penalty Methods [2.1388885579612804]
  Large language models (LLMs) are often constrained by their substantial computational and memory demands.<n>We propose SPAP (Structured Pruning via Alternating Optimization and Penalty Methods), a novel and efficient structured pruning framework for LLMs grounded in optimization theory.<n>Our work offers a practical, optimization-driven solution for pruning LLMs while preserving model performance.
  arXiv  Detail & Related papers  (2025-05-06T09:47:53Z)
• Bilevel ZOFO: Bridging Parameter-Efficient and Zeroth-Order Techniques for Efficient LLM Fine-Tuning and Meta-Training [44.48966200270378]
  Fine-tuning pre-trained Large Language Models (LLMs) for downstream tasks using First-Order (FO)imats presents significant computational challenges. We propose a bilevel optimization framework that complements ZO methods with PEFT to mitigate sensitivity to hard prompts. Our Bilevel ZOFO method employs a double-loop optimization strategy, where only the gradient of the PEFT model and the forward pass of the base model are required.
  arXiv  Detail & Related papers  (2025-02-05T20:47:44Z)
• Pivoting Factorization: A Compact Meta Low-Rank Representation of Sparsity for Efficient Inference in Large Language Models [1.6385815610837167]
  Pivoting Factorization (PIFA) is a novel low-rank representation that unsupervisedly learns a compact form of any low-rank representation.<n>PIFA achieves 24.2% additional memory savings and 24.6% faster inference over low-rank layers at rank = 50% of dimension.<n>MPIFA, combining M and PIFA into an end-to-end framework, significantly outperforms existing low-rank pruning methods.
  arXiv  Detail & Related papers  (2025-01-31T12:36:31Z)
• Pruning Foundation Models for High Accuracy without Retraining [48.256389781305415]
  It is challenging to deploy foundation models or large language models (LLMs) due to their massive parameters and computations. Post-training pruning methods are proposed to prune LLMs in one-shot without retraining. Our experiments demonstrate the superior performance of the proposed methods in comparison to SOTA baselines.
  arXiv  Detail & Related papers  (2024-10-21T01:23:34Z)
• ARB-LLM: Alternating Refined Binarizations for Large Language Models [82.24826360906341]
  ARB-LLM is a novel 1-bit post-training quantization (PTQ) technique tailored for Large Language Models (LLMs) As a binary PTQ method, our ARB-LLM$_textRC$ is the first to surpass FP16 models of the same size.
  arXiv  Detail & Related papers  (2024-10-04T03:50:10Z)
• 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)
• Greedy Output Approximation: Towards Efficient Structured Pruning for LLMs Without Retraining [16.026565606764954]
  We simplify the pruning process for Transformer-based large language models (LLMs) We propose two inference-aware pruning criteria derived from the optimization perspective of output approximation. We also introduce a two-step reconstruction technique to mitigate pruning errors without model retraining.
  arXiv  Detail & Related papers  (2024-07-26T23:53:59Z)
• 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)
• BiLLM: Pushing the Limit of Post-Training Quantization for LLMs [53.31402059062365]
  BiLLM is a groundbreaking 1-bit post-training quantization scheme tailored for pretrained large language models. It achieves for the first time high-accuracy inference (e.g. 8.41 perplexity on LLaMA2-70B) with only 1.08-bit weights across various LLMs families.
  arXiv  Detail & Related papers  (2024-02-06T09:26:34Z)
• Just CHOP: Embarrassingly Simple LLM Compression [27.64461490974072]
  Large language models (LLMs) enable unparalleled few- and zero-shot reasoning capabilities but at a high computational footprint. We show that simple layer pruning coupled with an extended language model pretraining produces state-of-the-art results against structured and even semi-structured compression of models at a 7B scale. We also show how distillation, which has been super effective in task-agnostic compression of smaller BERT-style models, becomes inefficient against our simple pruning technique.
  arXiv  Detail & Related papers  (2023-05-24T08:18:35Z)
• 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)
• Efficient Micro-Structured Weight Unification and Pruning for Neural Network Compression [56.83861738731913]
  Deep Neural Network (DNN) models are essential for practical applications, especially for resource limited devices. Previous unstructured or structured weight pruning methods can hardly truly accelerate inference. We propose a generalized weight unification framework at a hardware compatible micro-structured level to achieve high amount of compression and acceleration.
  arXiv  Detail & Related papers  (2021-06-15T17:22:59Z)

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.