Long-Short Transformer: Efficient Transformers for Language and Vision

TL;DR

The paper introduces Long-Short Transformer, an efficient self-attention mechanism that scales linearly for long sequences in language and vision tasks, outperforming state-of-the-art models in speed and accuracy.

Contribution

It proposes a novel dual attention mechanism with dynamic projection and normalization, enabling scalable, efficient transformers for long sequences without additional complexity.

Findings

Achieves 0.97 BPC on enwik8 with half the parameters of previous models.

Outperforms state-of-the-art on Long Range Arena benchmark.

Attains 84.1% Top-1 accuracy on ImageNet with a moderate-sized model.

Abstract

Transformers have achieved success in both language and vision domains. However, it is prohibitively expensive to scale them to long sequences such as long documents or high-resolution images, because self-attention mechanism has quadratic time and memory complexities with respect to the input sequence length. In this paper, we propose Long-Short Transformer (Transformer-LS), an efficient self-attention mechanism for modeling long sequences with linear complexity for both language and vision tasks. It aggregates a novel long-range attention with dynamic projection to model distant correlations and a short-term attention to capture fine-grained local correlations. We propose a dual normalization strategy to account for the scale mismatch between the two attention mechanisms. Transformer-LS can be applied to both autoregressive and bidirectional models without additional complexity. Our method outperforms the state-of-the-art models on multiple tasks in language and vision domains, including the Long Range Arena benchmark, autoregressive language modeling, and ImageNet classification. For instance, Transformer-LS achieves 0.97 test BPC on enwik8 using half the number of parameters than previous method, while being faster and is able to handle 3x as long sequences compared to its full-attention version on the same hardware. On ImageNet, it can obtain the state-of-the-art results (e.g., a moderate size of 55.8M model solely trained on 224x224 ImageNet-1K can obtain Top-1 accuracy 84.1%), while being more scalable on high-resolution images. The source code and models are released at https://github.com/NVIDIA/transformer-ls .