AdaRank: Disagreement Based Module Rank Prediction for Low-rank Adaptation (2408.09015v1)

Abstract: With the rise of language and multimodal models of ever-increasing size, pretraining a general-purpose foundational model and adapting it to downstream tasks has become common practice. To this end, adaptation efficiency can be a critical bottleneck given the large model sizes, hence efficient finetuning methods such as LoRA have become prevalent. However, LoRA is typically applied with the same rank across all model layers, despite mounting evidence from transfer learning literature that during finetuning, later layers diverge more from pretrained weights. Inspired by the theory and observations around feature learning and module criticality, we develop a simple model disagreement based technique to predict the rank of a given module relative to the other modules. Empirically, AdaRank generalizes notably better on unseen data than using uniform ranks with the same number of parameters. Compared to prior work, AdaRank has the unique advantage of leaving the pretraining and adaptation stages completely intact: no need for any additional objectives or regularizers, which can hinder adaptation accuracy and performance. Our code is publicly available at https://github.com/google-research/google-research/tree/master/adaptive_low_rank.

• The paper proposes a disagreement-based approach to predict module ranks, enhancing layer-specific finetuning.
• It dynamically allocates higher ranks to modules exhibiting greater sensitivity, resulting in improved model generalization.
• Empirical results demonstrate that AdaRank outperforms uniform rank methods, achieving practical gains in parameter efficiency.

Essay on "AdaRank: Disagreement Based Module Rank Prediction for Low-rank Adaptation"

The paper "AdaRank: Disagreement Based Module Rank Prediction for Low-rank Adaptation" by Yihe Dong addresses the critical challenge of efficient finetuning of large language and multimodal models. It introduces AdaRank, a novel method for determining layer-wise ranks for low-rank adaptations, deviating from the conventional uniform application of low ranks across all layers.

Overview

AdaRank stems from observations that later layers in large models diverge more from their pretrained weights during adaptation. The core motivation is rooted in the theoretical and empirical understanding of feature learning and module criticality, which suggest the necessity for differing ranks across layers to enhance expressiveness while reducing overfitting.

The method employs a two-step strategy to predict layer-wise ranks based on model output disagreements induced by random perturbations. This disagreement-based approach allows AdaRank to effectively distribute parameters by allocating higher ranks to more critical layers, as determined by their greater sensitivity to perturbations.

Methodology

AdaRank is grounded in the principle that modules should receive ranks proportional to their criticality; this is determined by assessing their impact on model output disagreement when disturbed. The prediction mechanism involves:

1. Module Importance Prediction: Perturb each module individually while keeping others fixed. The $\ell_1$ difference between logits of two perturbed model instances serves as the importance score.
2. Rank Generation: Normalize the importance scores to derive corresponding ranks, ensuring the overall parameter count aligns with predefined constraints.

This approach bypasses the need for additional objectives or regularizers, maintaining fidelity to the pretraining model's integrity and ensuring a seamless adaptation process.

Empirical Evaluation

Empirical results demonstrate that AdaRank outperforms uniform rank assignments across several datasets, with notable gains in tasks involving smaller amounts of data. By adapting individually across query, key, value, and dense modules, AdaRank consistently shows improved model generalization.

When applied to all modules concurrently, the gains are retained or enhanced—highlighting AdaRank's ability to efficiently manage parameter allocation across a model's architecture. The results indicate that AdaRank can produce ranks that enhance adaptability and model accuracy more effectively than conventional methods.

Implications and Future Directions

AdaRank presents compelling implications for both theoretical and practical applications:

• Theoretical Implications: The methodology provides a novel lens through which to understand and exploit model layer variances. It challenges existing paradigms, suggesting a non-uniform approach better mirrors the dynamic nature of model layer roles during adaptation.
• Practical Implications: By optimizing parameter allocation, AdaRank significantly increases efficiency, making it highly relevant for resource-conscious applications. It offers a method by which large models can be finetuned with precision, catering specifically to the criticality of individual layers.

Future research directions may focus on enhancing the granularity of AdaRank's predictions, exploring task-specific texts to fine-tune rank calculations further, and translating parameter-efficiency gains into computational savings. Additionally, examining the theoretical underpinnings of perturbation-based disagreement as a proxy for finetuning could provide richer insights into model adaptability.

Conclusion

AdaRank represents a novel advancement in the landscape of efficient model tuning, moving beyond traditional constraints of uniform rank application. By rigorously assigning layer-specific ranks, researchers and practitioners can achieve finer control over model adaptation processes, promising future developments in AI's growing capabilities and demands.