Router-Tuning: A Simple and Effective Approach for Enabling Dynamic-Depth in Transformers

2024-10-17

arXiv.org

AI Summary

To address computational inefficiency caused by fixed-depth computation in Transformers, this paper proposes a lightweight dynamic depth routing framework. First, Router-Tuning fine-tunes only a compact router module, avoiding full-model retraining. Second, MindSkip introduces an attention-score-guided dynamic layer-skipping mechanism to prevent skipping semantically critical layers. Evaluated on standard benchmarks, the method retains 99.8% of the original model’s accuracy while accelerating inference by 21% and substantially reducing both computational FLOPs and memory footprint. The core contribution lies in the decoupled design of router adaptation and attention-aware layer skipping—enabling fine-grained, robust, on-demand computation allocation with minimal training overhead. This approach achieves a favorable trade-off among inference efficiency, accuracy preservation, and deployment practicality.

Technology Category

• Planning, Routing, and Scheduling: Planning with Language Models
• Search and Optimization: Learning to Search
• Machine Learning: Mixture of Experts (MoE)

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Traditional transformer models often allocate a fixed amount of computational resources to every input token, leading to inefficient and unnecessary computation. To address this, the Mixture of Depths (MoD) was introduced to dynamically adjust the computational depth by skipping less important layers. Despite its promise, current MoD approaches remain under-explored and face two main challenges: (1) extit{high training costs due to the need to train the entire model along with the routers that determine which layers to skip}, and (2) extit{the risk of performance degradation when important layers are bypassed}. In response to the first issue, we propose Router-Tuning, a method that fine-tunes only the router on a small dataset, drastically reducing the computational overhead associated with full model training. For the second challenge, we propose MindSkip, which deploys extit{Attention with Dynamic Depths}. This method preserves the model's performance while significantly enhancing computational and memory efficiency. Extensive experiments demonstrate that our approach delivers competitive results while dramatically improving the computation efficiency, e.g., 21% speedup and only a 0.2% performance drop. The code is released at url{https://github.com/CASE-Lab-UMD/Router-Tuning}.

Problem

• Transformer Models
• Computational Efficiency
• Deep Mixture of Depth (MoD)

Innovation

• Router Tuning
• MindSkip Mechanism
• Adaptive Depth Attention