Sparse Matrix in Large Language Model Fine-tuning

• URL: http://arxiv.org/abs/2405.15525v2
• Date: Thu, 30 May 2024 00:08:51 GMT
• Title: Sparse Matrix in Large Language Model Fine-tuning
• Authors: Haoze He, Juncheng Billy Li, Xuan Jiang, Heather Miller,
• Abstract summary: We introduce a method for selecting sparse sub-matrices that aim to minimize the performance gap between PEFT vs. full fine-tuning. In experiments, we demonstrate that our method consistently surpasses other PEFT baselines. We also examine how the performance of LoRA and DoRA tends to plateau and decline as the number of trainable parameters increases.
• Score: 1.9874264019909988
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: LoRA and its variants have become popular parameter-efficient fine-tuning (PEFT) methods due to their ability to avoid excessive computational costs. However, an accuracy gap often exists between PEFT methods and full fine-tuning (FT), and this gap has yet to be systematically studied. In this work, we introduce a method for selecting sparse sub-matrices that aim to minimize the performance gap between PEFT vs. full fine-tuning (FT) while also reducing both fine-tuning computational cost and memory cost. Our Sparse Matrix Tuning (SMT) method begins by identifying the most significant sub-matrices in the gradient update, updating only these blocks during the fine-tuning process. In our experiments, we demonstrate that SMT consistently surpasses other PEFT baseline (e.g. LoRA and DoRA) in fine-tuning popular large language models such as LLaMA across a broad spectrum of tasks, while reducing the GPU memory footprint by 67% compared to FT. We also examine how the performance of LoRA and DoRA tends to plateau and decline as the number of trainable parameters increases, in contrast, our SMT method does not suffer from such issue.

Related papers

• SparseGrad: A Selective Method for Efficient Fine-tuning of MLP Layers [88.68985153780514]
  We propose a new selective PEFT method, namely SparseGrad, that performs well on parameter blocks. We apply SparseGrad to fine-tune BERT and RoBERTa for the NLU task and LLaMa-2 for the Question-Answering task.
  arXiv  Detail & Related papers  (2024-10-09T19:03:52Z)
• LoRTA: Low Rank Tensor Adaptation of Large Language Models [70.32218116940393]
  Low Rank Adaptation (LoRA) is a popular Efficient Fine Tuning (PEFT) method that effectively adapts large pre-trained models for downstream tasks. We propose a novel approach that employs a low rank tensor parametrization for model updates. Our method is both efficient and effective for fine-tuning large language models, achieving a substantial reduction in the number of parameters while maintaining comparable performance.
  arXiv  Detail & Related papers  (2024-10-05T06:59:50Z)
• SHERL: Synthesizing High Accuracy and Efficient Memory for Resource-Limited Transfer Learning [63.93193829913252]
  We propose an innovative METL strategy called SHERL for resource-limited scenarios. In the early route, intermediate outputs are consolidated via an anti-redundancy operation. In the late route, utilizing minimal late pre-trained layers could alleviate the peak demand on memory overhead.
  arXiv  Detail & Related papers  (2024-07-10T10:22:35Z)
• LaMDA: Large Model Fine-Tuning via Spectrally Decomposed Low-Dimensional Adaptation [7.788139145984213]
  Low-rank adaptation (LoRA) has become the default approach to fine-tune large language models (LLMs) We introduce large model fine-tuning via spectrally decomposed low-dimensional adaptation (LaMDA) LaMDA achieves significant reductions in trainable parameters and peak GPU memory footprint.
  arXiv  Detail & Related papers  (2024-06-18T17:52:59Z)
• VeLoRA: Memory Efficient Training using Rank-1 Sub-Token Projections [35.133698935322634]
  Large language models (LLMs) have recently emerged as powerful tools for tackling many language-processing tasks. We identify and characterise the important components needed for effective model convergence using gradient descent. This result leads us to a cheap and memory-efficient algorithm for both fine-tuning and pre-training LLMs.
  arXiv  Detail & Related papers  (2024-05-28T09:23:14Z)
• FeDeRA:Efficient Fine-tuning of Language Models in Federated Learning Leveraging Weight Decomposition [7.229494183462913]
  Despite exceptional performance after fine-tuning, pre-trained language models (PLMs) face significant challenges due to privacy concerns. We consider federated learning (FL) to fine-tune PLMs in this paper. One promising solution is to exploit parameter-efficient fine-tuning (PEFT) into FL, which trains a much smaller set of parameters than full parameter fine-tuning (FFT)
  arXiv  Detail & Related papers  (2024-04-29T16:42:26Z)
• DoRA: Weight-Decomposed Low-Rank Adaptation [57.68678247436207]
  We introduce a novel weight decomposition analysis to investigate the inherent differences between FT and LoRA. Aiming to resemble the learning capacity of FT from the findings, we propose Weight-Decomposed Low-Rank Adaptation (DoRA) DoRA decomposes the pre-trained weight into two components, magnitude and direction, for fine-tuning.
  arXiv  Detail & Related papers  (2024-02-14T17:59:34Z)
• From PEFT to DEFT: Parameter Efficient Finetuning for Reducing Activation Density in Transformers [52.199303258423306]
  We propose a novel density loss that encourages higher activation sparsity in pre-trained models. Our proposed method, textbfDEFT, can consistently reduce activation density by up to textbf44.94% on RoBERTa$_mathrmLarge$ and by textbf53.19% (encoder density) and textbf90.60% (decoder density) on Flan-T5$_mathrmXXL$.
  arXiv  Detail & Related papers  (2024-02-02T21:25:46Z)
• SLoRA: Federated Parameter Efficient Fine-Tuning of Language Models [28.764782216513037]
  Federated Learning (FL) can benefit from distributed and private data of the FL edge clients for fine-tuning. We propose a method called SLoRA, which overcomes the key limitations of LoRA in high heterogeneous data scenarios. Our experimental results demonstrate that SLoRA achieves performance comparable to full fine-tuning.
  arXiv  Detail & Related papers  (2023-08-12T10:33:57Z)
• Make Pre-trained Model Reversible: From Parameter to Memory Efficient Fine-Tuning [6.451743797015637]
  We propose memory-efficient fine-tuning (MEFT) for pre-trained language models. MEFT inserts adapters into a PLM, preserving the PLM's starting point and making it reversible without additional pre-training. MEFT significantly reduces the activation memory up to 84% of full fine-tuning with a negligible amount of trainable parameters.
  arXiv  Detail & Related papers  (2023-06-01T09:26:17Z)
• 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)

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.