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Transformer is a powerful model for text understanding. However, it isinefficient due to its quadratic complexity to input sequence length. Althoughthere are many methods on Transformer acceleration, they are still eitherinefficient on long sequences or not effective enough. In this paper, wepropose Fastformer, which is an efficient Transformer model based on additiveattention. In Fastformer, instead of modeling the pair-wise interactionsbetween tokens, we first use additive attention mechanism to model globalcontexts, and then further transform each token representation based on itsinteraction with global context representations. In this way, Fastformer canachieve effective context modeling with linear complexity. Extensiveexperiments on five datasets show that Fastformer is much more efficient thanmany existing Transformer models and can meanwhile achieve comparable or evenbetter long text modeling performance.

Fastformer: Additive Attention Can Be All You Need