We introduce SHARCS for adaptive inference that takes into account the hardness of input samples. SHARCS can train a router on any transformer network, enabling the model to direct different samples to sub-networks with varying widths. Our experiments demonstrate that: (1) SHARCS outperforms or complements existing per-sample adaptive inference methods across various classification tasks in terms of accuracy vs. FLOPs; (2) SHARCS generalizes across different architectures and can be even applied to compressed and efficient transformer encoders to further improve their efficiency; (3) SHARCS can provide a 2 times inference speed up at an insignificant drop in accuracy.
@article{arxiv.2310.12126,
title = {SHARCS: Efficient Transformers through Routing with Dynamic Width Sub-networks},
author = {Mohammadreza Salehi and Sachin Mehta and Aditya Kusupati and Ali Farhadi and Hannaneh Hajishirzi},
journal= {arXiv preprint arXiv:2310.12126},
year = {2023}
}