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Task-agnostic pre-training followed by task-specific fine-tuning is a defaultapproach to train NLU models. Such models need to be deployed on devices acrossthe cloud and the edge with varying resource and accuracy constraints. For agiven task, repeating pre-training and fine-tuning across tens of devices isprohibitively expensive. We propose SuperShaper, a task agnostic pre-trainingapproach which simultaneously pre-trains a large number of Transformer modelsby varying shapes, i.e., by varying the hidden dimensions across layers. Thisis enabled by a backbone network with linear bottleneck matrices around eachTransformer layer which are sliced to generate differently shaped sub-networks.In spite of its simple design space and efficient implementation, SuperShaperdiscovers networks that effectively trade-off accuracy and model size:Discovered networks are more accurate than a range of hand-crafted andautomatically searched networks on GLUE benchmarks. Further, we find twocritical advantages of shape as a design variable for Neural ArchitectureSearch (NAS): (a) heuristics of good shapes can be derived and networks foundwith these heuristics match and even improve on carefully searched networksacross a range of parameter counts, and (b) the latency of networks acrossmultiple CPUs and GPUs are insensitive to the shape and thus enabledevice-agnostic search. In summary, SuperShaper radically simplifies NAS forlanguage models and discovers networks that generalize across tasks, parameterconstraints, and devices.

SuperShaper: Task-Agnostic Super Pre-training of BERT Models with Variable Hidden Dimensions