Mosaic: Composite Projection Pruning for Resource-efficient LLMs

• URL: http://arxiv.org/abs/2504.06323v1
• Date: Tue, 08 Apr 2025 11:51:35 GMT
• Title: Mosaic: Composite Projection Pruning for Resource-efficient LLMs
• Authors: Bailey J. Eccles, Leon Wong, Blesson Varghese,
• Abstract summary: This paper introduces projection pruning, a novel fine-grained method for pruning large language models (LLMs)<n>We develop Mosaic, a novel system to create and deploy pruned LLMs using composite projection pruning.<n>Mosaic models achieve up to 84.2% lower perplexity and 31.4% higher accuracy than models obtained from coarse-grained pruning.
• Score: 2.6831773062745863
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Extensive compute and memory requirements limit the deployment of large language models (LLMs) on any hardware. Compression methods, such as pruning, can reduce model size, which in turn reduces resource requirements. State-of-the-art pruning is based on coarse-grained methods. They are time-consuming and inherently remove critical model parameters, adversely impacting the quality of the pruned model. This paper introduces projection pruning, a novel fine-grained method for pruning LLMs. In addition, LLM projection pruning is enhanced by a new approach we refer to as composite projection pruning - the synergistic combination of unstructured pruning that retains accuracy and structured pruning that reduces model size. We develop Mosaic, a novel system to create and deploy pruned LLMs using composite projection pruning. Mosaic is evaluated using a range of performance and quality metrics on multiple hardware platforms, LLMs, and datasets. Mosaic is 7.19x faster in producing models than existing approaches. Mosaic models achieve up to 84.2% lower perplexity and 31.4% higher accuracy than models obtained from coarse-grained pruning. Up to 67% faster inference and 68% lower GPU memory use is noted for Mosaic models.

Related papers

• Can LLMs Revolutionize the Design of Explainable and Efficient TinyML Models? [8.953379216683736]
  This paper introduces a novel framework for designing efficient neural network architectures specifically tailored to tiny machine learning (TinyML) platforms. By leveraging large language models (LLMs) for neural architecture search (NAS), a vision transformer (ViT)-based knowledge distillation (KD) strategy, and an explainability module, the approach strikes an optimal balance between accuracy, computational efficiency, and memory usage.
  arXiv  Detail & Related papers  (2025-04-13T18:36:03Z)
• 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)<n>Specifically, AdaPruner effectively removes redundant parameters from LLMs by constructing a structured pruning solution space.<n>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)
• Lightweight and Post-Training Structured Pruning for On-Device Large Lanaguage Models [11.93284417365518]
  We introduce COMP, a lightweight post-training structured pruning method that employs a hybrid-granularity pruning strategy.<n> COMP improves performance by 6.13% on the LLaMA-2-7B model with a 20% pruning ratio compared to LLM-Pruner.
  arXiv  Detail & Related papers  (2025-01-25T16:03:58Z)
• FlexiGPT: Pruning and Extending Large Language Models with Low-Rank Weight Sharing [59.12511498024836]
  We present a method to prune large language models (LLMs) that selectively prunes model blocks based on an importance score.<n>We propose a principled metric to replace each pruned block using a weight-sharing mechanism.<n> Empirical evaluations demonstrate substantial performance gains over existing methods.
  arXiv  Detail & Related papers  (2025-01-24T18:46:37Z)
• 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)<n>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.<n>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)
• MoE-Pruner: Pruning Mixture-of-Experts Large Language Model using the Hints from Its Router [55.88046193872355]
  Mixture-of-Experts (MoE) architectures face challenges such as high memory consumption and redundancy in experts. We propose MoE-Pruner, a method that prunes weights with the smallest magnitudes multiplied by the corresponding input activations and router weights. Our pruning method is one-shot, requiring no retraining or weight updates.
  arXiv  Detail & Related papers  (2024-10-15T19:22:27Z)
• Pruning Large Language Models with Semi-Structural Adaptive Sparse Training [17.381160429641316]
  Adaptive Sparse Trainer (AST) is a novel and efficient retraining framework tailored for semi-structured sparse models.<n>AST reduces the perplexity and zero-shot accuracy gap between dense and 2:4 semi-structured sparse models to 0.6 and 1.16%, respectively.
  arXiv  Detail & Related papers  (2024-07-30T06:33:44Z)
• Compact Language Models via Pruning and Knowledge Distillation [61.56557874432008]
  Minitron models exhibit up to a 16% improvement in MMLU scores compared to training from scratch. Deriving 8B and 4B models from an already pretrained 15B model using our approach requires up to 40x fewer training tokens per model compared to training from scratch.
  arXiv  Detail & Related papers  (2024-07-19T21:47:57Z)
• 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)
• LaCo: Large Language Model Pruning via Layer Collapse [56.92068213969036]
  Large language models (LLMs) based on transformer are witnessing a notable trend of size expansion. Existing methods such as model quantization, knowledge distillation, and model pruning are constrained by various issues. We propose a concise layer-wise structured pruner called textitLayer Collapse (LaCo), in which rear model layers collapse into a prior layer.
  arXiv  Detail & Related papers  (2024-02-17T04:16:30Z)
• Everybody Prune Now: Structured Pruning of LLMs with only Forward Passes [72.09861461921663]
  We develop a gradient-free, perturbative pruning method capable of delivering small, fast, and accurate pruned models. We also leverage Bonsai to produce a new sub-2B model using a single A6000 that yields state-of-the-art performance on 4/6 tasks on the Huggingface Open LLM leaderboard.
  arXiv  Detail & Related papers  (2024-02-08T04:48:26Z)
• 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)

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.