Improving Transformer Models by Reordering their Sublayers
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Multilayer transformer networks consist of interleaved self-attention and feedforward sublayers. Could ordering the sublayers in a different pattern achieve better performance? We generate randomly ordered transformers and train them with the language modeling objective. We observe that some of these models are able to achieve better performance than the interleaved baseline, and that those successful variants tend to have more self-attention at the bottom and more feedforward sublayers at the top. We propose a new transformer design pattern that adheres to this property, the sandwich transformer, and show that it improves perplexity on the WikiText-103 language modeling benchmark, at no cost in parameters, memory, or training time.
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