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There exists a wide variety of efficiency methods for natural languageprocessing (NLP) tasks, such as pruning, distillation, dynamic inference,quantization, etc. We can consider an efficiency method as an operator appliedon a model. Naturally, we may construct a pipeline of multiple efficiencymethods, i.e., to apply multiple operators on the model sequentially. In thispaper, we study the plausibility of this idea, and more importantly, thecommutativity and cumulativeness of efficiency operators. We make twointeresting observations: (1) Efficiency operators are commutative -- the orderof efficiency methods within the pipeline has little impact on the finalresults; (2) Efficiency operators are also cumulative -- the final results ofcombining several efficiency methods can be estimated by combining the resultsof individual methods. These observations deepen our understanding ofefficiency operators and provide useful guidelines for their real-worldapplications.

Building an Efficiency Pipeline: Commutativity and Cumulativeness of Efficiency Operators for Transformers