  Differentiable architecture search (DARTS) is successfully applied in many
vision tasks. However, directly using DARTS for Transformers is
memory-intensive, which renders the search process infeasible. To this end, we
propose a multi-split reversible network and combine it with DARTS.
Specifically, we devise a backpropagation-with-reconstruction algorithm so that
we only need to store the last layer's outputs. By relieving the memory burden
for DARTS, it allows us to search with larger hidden size and more candidate
operations. We evaluate the searched architecture on three sequence-to-sequence
datasets, i.e., WMT'14 English-German, WMT'14 English-French, and WMT'14
English-Czech. Experimental results show that our network consistently
outperforms standard Transformers across the tasks. Moreover, our method
compares favorably with big-size Evolved Transformers, reducing search
computation by an order of magnitude.
