Linformer: Self-Attention with Linear Complexity
Sinong Wang, Belinda Z. Li, Madian Khabsa, Han Fang, Hao Ma
June 8, 2020

Large transformer models have shown extraordinary success in achieving state-ofthe-art results in many natural language processing applications. However, training and deploying these models can be prohibitively costly for long sequences, as the standard self-attention mechanism of the Transformer uses O(n) time and space with respect to sequence length. In this paper, we demonstrate that the self-attention mechanism can be approximated by a low-rank matrix. We further exploit this finding to propose a new self-attention mechanism, which reduces the overall self-attention complexity from O(n) to O(n) in both time and space. The resulting linear transformer, the Linformer, performs on par with standard Transformer models, while being much more memoryand time-efficient.

Optimizers and activations

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