Optimizing DNN Computation Graph using Graph Substitutions

Deep learning has achieved great success in various real-world applications. As deep neural networks (DNNs) are getting larger, the inference and training cost of DNNs increases significantly. Since one round of inference or one iteration in the training phase of a DNN is typically modeled as a computation graph, existing works propose to optimize computation graphs by performing a sequence of functionally equivalent graph substitutions, leading to higher inference and training efficiency. In this work, we formally define the Optimizing Computation Graph using Graph Substitutions (OCGGS) problem, and prove it to be NP-hard and Poly-APX-complete. We develop two exact and efficient methods to the OCGGS problem. The pruning-based algorithm eliminates the examination of redundant graph substitution sequences, and the dynamic programming with pruning algorithm makes use of the explored graph substitutions. To further speed up the search process, we propose a sampling heuristic which is effective to optimize complex computation graphs with polynomial time and space complexity. Extensive experiments on various DNN architectures and sizes are conducted to verify the effectiveness and efficiency of our proposed solutions compared with existing techniques.