Lite Transformer with Long-Short Range Attention

TL;DR

Lite Transformer introduces Long-Short Range Attention to create an efficient mobile NLP model that balances local and global context modeling, significantly reducing computation while maintaining high performance across multiple language tasks.

Contribution

The paper proposes a novel Long-Short Range Attention mechanism and a lightweight transformer architecture optimized for mobile devices, outperforming existing models without extensive architecture search.

Findings

Outperforms transformer on WMT'14 English-French translation with fewer MACs.

Reduces transformer computation by 2.5x with minimal BLEU score loss.

Further compresses model size by 18.2x using pruning and quantization.

Abstract

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.