MetaLoRA: Tensor-Enhanced Adaptive Low-Rank Fine-tuning

TL;DR

This work addresses the challenge of efficiently adapting large pretrained models across diverse tasks by extending LoRA with meta-learning and tensor-network based dynamic parameter generation. MetaLoRA introduces a three-component architecture that extracts features from inputs, maps them into a generated parameter seed, and integrates these seeds into weight updates via CP or Tensor Ring factorizations, enabling task-aware, low-rank adaptations for convolutional networks. Early results on ResNet and MLP-Mixer show that MetaLoRA, especially the Tensor Ring variant, can outperform fixed-LoRA and prior variants, achieving up to 73.87% accuracy in certain settings and demonstrating improved adaptation while preserving efficiency. The framework lays groundwork for broader applicability, including potential extensions to transformers and personalized applications, highlighting its significance for scalable, dynamic model fine-tuning in real-world deployments.

Abstract

There has been a significant increase in the deployment of neural network models, presenting substantial challenges in model adaptation and fine-tuning. Efficient adaptation is crucial in maintaining model performance across diverse tasks and domains. While Low-Rank Adaptation (LoRA) has emerged as a promising parameter-efficient fine-tuning method, its fixed parameter nature limits its ability to handle dynamic task requirements effectively. Adapting models to new tasks can be challenging due to the need for extensive fine-tuning. Current LoRA variants primarily focus on general parameter reduction while overlooking the importance of dynamic parameter adjustment and meta-learning capabilities. Moreover, existing approaches mainly address static adaptations, neglecting the potential benefits of task-aware parameter generation in handling diverse task distributions. To address these limitations, this Ph.D. research proposes a LoRA generation approach to model task relationships and introduces MetaLoRA, a novel parameter-efficient adaptation framework incorporating meta-learning principles. This work develops a comprehensive architecture that integrates meta-parameter generation with adaptive low-rank decomposition, enabling efficient handling of both task-specific and task-agnostic features. MetaLoRA accurately captures task patterns by incorporating meta-learning mechanisms and dynamic parameter adjustment strategies. To our knowledge, this research represents the first attempt to provide a meta-learning enhanced LoRA variant, offering improved adaptation capability while maintaining computational efficiency in model fine-tuning.

Figures (4)

• Figure 1: Illustration of baisc tensor diagrams of a vector (1st-order tensor), a matrix (2rd-order tensor), a 3rd-order tensor, convolution representation and Tensor Contraction
• Figure 2: A tensor network representation of a simple convolutional layer, which two dummy tensors can represent to illustrate the most common image convolution. The convolution itself is a multilinear tensor operation.
• Figure 3: A tensor network representation of LoRA and our proposed Conv-LoRA. It can be observed that Conv-LoRA can be equivalently represented as a small convolution followed by a channel reduction (known as a 1$\times$1 convolution). The intuitive nature of tensor networks makes it easy to extend LoRA, which is applicable to matrices, to tensor operations.
• Figure 4: An overview of MetaLoRA's design. The figure illustrates the generation of the parameter $c$ by the mapping net, which is subsequently integrated into the model's weight matrices through CP and Tensor Ring format strategies. $\mathbf{\Lambda}$ is the identity diagonal tensor, meaning that all the elements on its diagonal are 1. The top section shows the generation of $\mathbf{c}$ or $\mathbf{C}$, while the bottom section demonstrates the application of the TR and CP formats to both general weight matrices and convolutional tensors.