Router-Tuning: A Simple and Effective Approach for Enabling
  Dynamic-Depth in Transformers
Authors: name: Shwai He, name: Tao Ge, name: Guoheng Sun, name: Bowei Tian, name: Xiaoyang Wang, name: Ang Li, name: Dong Yu

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) high training costs due to the need to train the
entire model along with the routers that determine which layers to skip, and
(2) 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 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
https://github.com/CASE-Lab-UMD/Router-Tuning.