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Imperative programming allows users to implement their deep neural networks(DNNs) easily and has become an essential part of recent deep learning (DL)frameworks. Recently, several systems have been proposed to combine theusability of imperative programming with the optimized performance of symbolicgraph execution. Such systems convert imperative Python DL programs tooptimized symbolic graphs and execute them. However, they cannot fully supportthe usability of imperative programming. For example, if an imperative DLprogram contains a Python feature with no corresponding symbolic representation(e.g., third-party library calls or unsupported dynamic control flows) theyfail to execute the program. To overcome this limitation, we propose Terra, animperative-symbolic co-execution system that can handle any imperative DLprograms while achieving the optimized performance of symbolic graph execution.To achieve this, Terra builds a symbolic graph by decoupling DL operations fromPython features. Then, Terra conducts the imperative execution to support allPython features, while delegating the decoupled operations to the symbolicexecution. We evaluated the performance improvement and coverage of Terra withten imperative DL programs for several DNN architectures. The results show thatTerra can speed up the execution of all ten imperative DL programs, whereasAutoGraph, one of the state-of-the-art systems, fails to execute five of them.

Terra: Imperative-Symbolic Co-Execution of Imperative Deep Learning Programs