From PEFT to DEFT: Parameter Efficient Finetuning for Reducing Activation Density in Transformers

• URL: http://arxiv.org/abs/2402.01911v2
• Date: Sun, 14 Jul 2024 17:32:36 GMT
• Title: From PEFT to DEFT: Parameter Efficient Finetuning for Reducing Activation Density in Transformers
• Authors: Bharat Runwal, Tejaswini Pedapati, Pin-Yu Chen,
• Abstract summary: 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$_mathrmXXL$.
• Score: 52.199303258423306
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Pretrained Language Models (PLMs) have become the de facto starting point for fine-tuning on downstream tasks. However, as model sizes continue to increase, traditional fine-tuning of all the parameters becomes challenging. To address this, parameter-efficient fine-tuning (PEFT) methods have gained popularity as a means to adapt PLMs effectively. In parallel, recent studies have revealed the presence of activation sparsity within the intermediate outputs of the multilayer perceptron (MLP) blocks in transformers. Low activation density enables efficient model inference on sparsity-aware hardware. Building upon this insight, in this work, we propose a novel density loss that encourages higher activation sparsity (equivalently, lower activation density) in the pre-trained models. We demonstrate the effectiveness of our approach by utilizing mainstream PEFT techniques, including QLoRA, LoRA, Adapter, and Prompt/Prefix Tuning, to facilitate efficient model adaptation across diverse downstream tasks. Experiments show that our proposed method, \textbf{DEFT} (Density-Efficient Fine-Tuning), can consistently reduce activation density by up to \textbf{44.94\%} on RoBERTa$_\mathrm{Large}$ and by \textbf{53.19\%} (encoder density) and \textbf{90.60\%} (decoder density) on Flan-T5$_\mathrm{XXL}$ (\textbf{11B}) compared to PEFT, using GLUE and QA (SQuAD) benchmarks respectively. We also introduce \textbf{ADA-DEFT}, an adaptive variant of our DEFT approach, which achieves significant memory and runtime savings during inference. For instance, ADA-DEFT reduces runtime by \textbf{8.79\%}and memory usage by \textbf{17.46\%} in Flan-T5$_\mathrm{XL}$, and by \textbf{2.79\%} and \textbf{2.54\%} respectively in Flan-T5$_\mathrm{XXL}$. Additionally, we showcase that DEFT works complementarily with quantized and pruned models.

Related papers

• IntLoRA: Integral Low-rank Adaptation of Quantized Diffusion Models [68.55148272295916]
  We propose IntLoRA, to push the efficiency limits by using integer type (INT) low-rank parameters to adapt the quantized diffusion models. IntLoRA offers three key advantages: (i) for fine-tuning, the pre-trained weights are quantized, reducing memory usage; (ii) for storage, both pre-trained and low-rank weights are in INT which consumes less disk space; (iii) for inference, IntLoRA weights can be naturally merged into quantized pre-trained weights through efficient integer multiplication or bit-shifting.
  arXiv  Detail & Related papers  (2024-10-29T05:50:17Z)
• 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)
• Parameter-Efficient Fine-Tuning via Selective Discrete Cosine Transform [10.565509997395504]
  We propose a novel Selective Discrete Cosine Transformation (sDCTFT) fine-tuning scheme to push this frontier. Its general idea is to exploit the superior energy compaction and decorrelation properties of DCT. Experiments on four benchmark datasets demonstrate the superior accuracy, reduced computational cost, and lower storage requirements.
  arXiv  Detail & Related papers  (2024-10-09T16:07:42Z)
• Sparse Matrix in Large Language Model Fine-tuning [1.9874264019909988]
  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.
  arXiv  Detail & Related papers  (2024-05-24T13:12:14Z)
• Sparse-Tuning: Adapting Vision Transformers with Efficient Fine-tuning and Inference [14.030836300221756]
  textbfSparse-Tuning is a novel PEFT method that accounts for the information redundancy in images and videos. Sparse-Tuning minimizes the quantity of tokens processed at each layer, leading to a quadratic reduction in computational and memory overhead. Our results show that our Sparse-Tuning reduces GFLOPs to textbf62%-70% of the original ViT-B while achieving state-of-the-art performance.
  arXiv  Detail & Related papers  (2024-05-23T15:34:53Z)
• Dynamic Tuning Towards Parameter and Inference Efficiency for ViT Adaptation [67.13876021157887]
  Dynamic Tuning (DyT) is a novel approach to improve both parameter and inference efficiency for ViT adaptation. DyT achieves superior performance compared to existing PEFT methods while evoking only 71% of their FLOPs on the VTAB-1K benchmark.
  arXiv  Detail & Related papers  (2024-03-18T14:05:52Z)
• FFSplit: Split Feed-Forward Network For Optimizing Accuracy-Efficiency Trade-off in Language Model Inference [57.119047493787185]
  This paper shows how to reduce model size by 43.1% and bring $1.25sim1.56times$ wall clock time speedup on different hardware with negligible accuracy drop. In practice, our method can reduce model size by 43.1% and bring $1.25sim1.56times$ wall clock time speedup on different hardware with negligible accuracy drop.
  arXiv  Detail & Related papers  (2024-01-08T17:29:16Z)
• Sparse is Enough in Fine-tuning Pre-trained Large Language Models [98.46493578509039]
  We propose a gradient-based sparse fine-tuning algorithm, named Sparse Increment Fine-Tuning (SIFT) We validate its effectiveness on a range of tasks including the GLUE Benchmark and Instruction-tuning.
  arXiv  Detail & Related papers  (2023-12-19T06:06:30Z)
• 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)
• Adaptive Sparsity Level during Training for Efficient Time Series Forecasting with Transformers [20.23085795744602]
  We propose textbfAdaptive textbfSparsity textbfLevel (textbfPALS) to automatically seek a decent balance between loss and sparsity. PALS draws inspiration from sparse training and during-training methods. It introduces the novel "expand" mechanism in training sparse neural networks, allowing the model to dynamically shrink, expand, or remain stable to find a proper sparsity level.
  arXiv  Detail & Related papers  (2023-05-28T06:57:27Z)

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.