  Transformer has become ubiquitous in natural language processing (e.g.,
machine translation, question answering); however, it requires enormous amount
of computations to achieve high performance, which makes it not suitable for
mobile applications that are tightly constrained by the hardware resources and
battery. In this paper, we present an efficient mobile NLP architecture, Lite
Transformer to facilitate deploying mobile NLP applications on edge devices.
The key primitive is the Long-Short Range Attention (LSRA), where one group of
heads specializes in the local context modeling (by convolution) while another
group specializes in the long-distance relationship modeling (by attention).
Such specialization brings consistent improvement over the vanilla transformer
on three well-established language tasks: machine translation, abstractive
summarization, and language modeling. Under constrained resources (500M/100M
MACs), Lite Transformer outperforms transformer on WMT'14 English-French by
1.2/1.7 BLEU, respectively. Lite Transformer reduces the computation of
transformer base model by 2.5x with 0.3 BLEU score degradation. Combining with
pruning and quantization, we further compressed the model size of Lite
Transformer by 18.2x. For language modeling, Lite Transformer achieves 1.8
lower perplexity than the transformer at around 500M MACs. Notably, Lite
Transformer outperforms the AutoML-based Evolved Transformer by 0.5 higher BLEU
for the mobile NLP setting without the costly architecture search that requires
more than 250 GPU years. Code has been made available at
https://github.com/mit-han-lab/lite-transformer.
