EdgeFormer: A Parameter-Efficient Transformer for On-Device Seq2seq Generation
Tao Ge, Si-Qing Chen, Furu Wei
Abstract
We introduce EdgeFormer – a parameter-efficient Transformer for on-device seq2seq generation under the strict computation and memory constraints. Compared with the previous parameter-efficient Transformers, EdgeFormer applies two novel principles for cost-effective parameterization, allowing it to perform better given the same parameter budget; moreover, EdgeFormer is further enhanced by layer adaptation innovation that is proposed for improving the network with shared layers.Extensive experiments show EdgeFormer can effectively outperform previous parameter-efficient Transformer baselines and achieve competitive results under both the computation and memory constraints. Given the promising results, we release EdgeLM – the pretrained version of EdgeFormer, which is the first publicly available pretrained on-device seq2seq model that can be easily fine-tuned for seq2seq tasks with strong results, facilitating on-device seq2seq generation in practice.
BibTeX
@inproceedings{ge-etal-2022-edgeformer,
title = "{E}dge{F}ormer: A Parameter-Efficient Transformer for On-Device Seq2seq Generation",
author = "Ge, Tao and
Chen, Si-Qing and
Wei, Furu",
editor = "Goldberg, Yoav and
Kozareva, Zornitsa and
Zhang, Yue",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.emnlp-main.741/",
doi = "10.18653/v1/2022.emnlp-main.741",
pages = "10786--10798"
}