Pruning Large Language Models with Semi-Structural Adaptive Sparse Training

• URL: http://arxiv.org/abs/2407.20584v2
• Date: Mon, 26 Aug 2024 13:19:48 GMT
• Title: Pruning Large Language Models with Semi-Structural Adaptive Sparse Training
• Authors: Weiyu Huang, Yuezhou Hu, Guohao Jian, Jun Zhu, Jianfei Chen,
• Abstract summary: We propose a pruning pipeline for semi-structured sparse models via retraining, termed Adaptive Sparse Trainer (AST) AST transforms dense models into sparse ones by applying decay to masked weights while allowing the model to adaptively select masks throughout the training process. Our work demonstrates the feasibility of deploying semi-structured sparse large language models and introduces a novel method for achieving highly compressed models.
• Score: 17.381160429641316
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The tremendous success of Large Language Models (LLMs) across various complex tasks relies heavily on their substantial scale, which raises challenges during model deployment due to their large memory consumption. Recently, numerous studies have attempted to compress LLMs using one-shot pruning methods. However, these methods often experience considerable performance degradation on complex language understanding tasks, calling into question the feasibility of pruning in LLMs. To address this issue, we propose a pruning pipeline for semi-structured sparse models via retraining, termed Adaptive Sparse Trainer (AST). Unlike previous one-shot pruning methods, AST incrementally transforms dense models into sparse ones by applying decay to masked weights while allowing the model to adaptively select masks throughout the training process. Furthermore, we observe that using distillation with a dense model as the teacher can prevent the sparse model from falling into local optima and accelerate convergence. In addition, we incorporate extra well-initialized parameters to further enhance model performance with minimal increase in memory footprint. AST can significantly enhance model performance, approaching the level of dense models. When applied to the LLaMA2-7B model, AST reduces the zero-shot accuracy gap between dense and semi-structured sparse models to 1.12% across multiple zero-shot tasks, utilizing less than 0.4% of the pretraining tokens. Our work demonstrates the feasibility of deploying semi-structured sparse large language models and introduces a novel method for achieving highly compressed models when combined with existing quantization techniques.

Related papers

• SLiM: One-shot Quantized Sparse Plus Low-rank Approximation of LLMs [2.7624021966289605]
  Large Language Models (LLMs) have revolutionized natural language understanding and generation tasks. LLMs suffer from high memory consumption and slow inference times due to their large parameter sizes. This paper introduces SLiM, a novel approach for compressing LLMs using a one-shot Quantized Sparse Plus Low-rank Approximation.
  arXiv  Detail & Related papers  (2024-10-12T18:36:07Z)
• Revisiting SMoE Language Models by Evaluating Inefficiencies with Task Specific Expert Pruning [78.72226641279863]
  Sparse Mixture of Expert (SMoE) models have emerged as a scalable alternative to dense models in language modeling. Our research explores task-specific model pruning to inform decisions about designing SMoE architectures. We introduce an adaptive task-aware pruning technique UNCURL to reduce the number of experts per MoE layer in an offline manner post-training.
  arXiv  Detail & Related papers  (2024-09-02T22:35:03Z)
• SMILE: Zero-Shot Sparse Mixture of Low-Rank Experts Construction From Pre-Trained Foundation Models [85.67096251281191]
  We present an innovative approach to model fusion called zero-shot Sparse MIxture of Low-rank Experts (SMILE) construction. SMILE allows for the upscaling of source models into an MoE model without extra data or further training. We conduct extensive experiments across diverse scenarios, such as image classification and text generation tasks, using full fine-tuning and LoRA fine-tuning.
  arXiv  Detail & Related papers  (2024-08-19T17:32:15Z)
• TRAWL: Tensor Reduced and Approximated Weights for Large Language Models [11.064868044313855]
  We introduce TRAWL (Tensor Reduced and Approximated Weights for Large Language Models), a technique that applies tensor decomposition across multiple weight matrices to effectively denoise LLMs by capturing global structural patterns. Our experiments show that TRAWL improves model performance by up to 16% over baseline models on benchmark datasets, without requiring additional data, training, or fine-tuning.
  arXiv  Detail & Related papers  (2024-06-25T04:01:32Z)
• Parameter-Efficient Sparsity Crafting from Dense to Mixture-of-Experts for Instruction Tuning on General Tasks [5.536630285985836]
  We introduce parameter-efficient sparsity crafting (PESC) PESC crafts dense models into sparse models using the mixture-of-experts (MoE) architecture. Our best sparse model outperforms other sparse and dense models and exhibits superior general capabilities compared to GP3.5.
  arXiv  Detail & Related papers  (2024-01-05T09:58:09Z)
• Retrieval-based Knowledge Transfer: An Effective Approach for Extreme Large Language Model Compression [64.07696663255155]
  Large-scale pre-trained language models (LLMs) have demonstrated exceptional performance in various natural language processing (NLP) tasks. However, the massive size of these models poses huge challenges for their deployment in real-world applications. We introduce a novel compression paradigm called Retrieval-based Knowledge Transfer (RetriKT) which effectively transfers the knowledge of LLMs to extremely small-scale models.
  arXiv  Detail & Related papers  (2023-10-24T07:58:20Z)
• Sheared LLaMA: Accelerating Language Model Pre-training via Structured Pruning [52.29522018586365]
  We study structured pruning as an effective means to develop smaller LLMs from pre-trained, larger models. Our approach employs two key techniques: (1) targeted structured pruning, which prunes a larger model to a specified target shape by removing layers, heads, and intermediate and hidden dimensions in an end-to-end manner, and (2) dynamic batch loading, which dynamically updates the composition of sampled data in each training batch based on varying losses across different domains.
  arXiv  Detail & Related papers  (2023-10-10T15:13:30Z)
• Efficient Large Scale Language Modeling with Mixtures of Experts [61.45159383372181]
  Mixture of Experts layers (MoEs) enable efficient scaling of language models through conditional computation. This paper presents a detailed empirical study of how autoregressive MoE language models scale in comparison with dense models in a wide range of settings.
  arXiv  Detail & Related papers  (2021-12-20T17:05:11Z)
• Dynamic Model Pruning with Feedback [64.019079257231]
  We propose a novel model compression method that generates a sparse trained model without additional overhead. We evaluate our method on CIFAR-10 and ImageNet, and show that the obtained sparse models can reach the state-of-the-art performance of dense models.
  arXiv  Detail & Related papers  (2020-06-12T15:07:08Z)

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.