Papers › EdgeFormer: A Parameter-Efficient Transformer for On-Device Seq2seq Generation

EdgeFormer: A Parameter-Efficient Transformer for On-Device Seq2seq Generation

16 Feb 2022arXiv:2202.07959archive 2025-07-28

Tao Ge, Si-Qing Chen, Furu Wei

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.

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Grammatical Error Correction Knowledge Distillation Machine Translation Translation

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Methods

Absolute Position Encodings Adam Attention BPE Dense Connections Dropout Knowledge Distillation LSTM Label Smoothing Layer Normalization Linear Layer Multi-Head Attention Position-Wise Feed-Forward Layer Residual Connection Seq2Seq Sigmoid Activation Softmax Tanh Activation Transformer

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