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Transformers have reached remarkable success in sequence modeling. However,these models have efficiency issues as they need to store all the historytoken-level representations as memory. We present Memformer, an efficientneural network for sequence modeling, that utilizes an external dynamic memoryto encode and retrieve past information. Our model achieves linear timecomplexity and constant memory space complexity when processing long sequences.We also propose a new optimization scheme, memory replay back-propagation(MRBP), which promotes long-range back-propagation through time with asignificantly reduced memory requirement. Experimental results show thatMemformer has achieved comparable performance compared to the baselines byusing 8.1x less memory space and 3.2x faster on inference. Analysis of theattention pattern shows that our external memory slots can encode and retainimportant information through timesteps.

Memformer: A Memory-Augmented Transformer for Sequence Modeling