Efficient Long-Range Transformers: You Need to Attend More, but Not
  Necessarily at Every Layer
Qingru Zhang Dhananjay Ram Cole Hawkins Sheng Zha Tuo Zhao
Pretrained transformer models have demonstrated remarkable performance across
various natural language processing tasks. These models leverage the attention
mechanism to capture long- and short-range dependencies in the sequence.
However, the (full) attention mechanism incurs high computational cost -
quadratic in the sequence length, which is not affordable in tasks with long
sequences, e.g., inputs with 8k tokens. Although sparse attention can be used
to improve computational efficiency, as suggested in existing work, it has
limited modeling capacity and often fails to capture complicated dependencies
in long sequences. To tackle this challenge, we propose MASFormer, an
easy-to-implement transformer variant with Mixed Attention Spans. Specifically,
MASFormer is equipped with full attention to capture long-range dependencies,
but only at a small number of layers. For the remaining layers, MASformer only
employs sparse attention to capture short-range dependencies. Our experiments
on natural language modeling and generation tasks show that a decoder-only
MASFormer model of 1.3B parameters can achieve competitive performance to
vanilla transformers with full attention while significantly reducing
computational cost (up to 75%). Additionally, we investigate the effectiveness
of continual training with long sequence data and how sequence length impacts
downstream generation performance, which may be of independent interest.