GenCoG: A DSL-Based Approach to Generating Computation Graphs for TVM Testing
Zihan Wang, Pengbo Nie, Xinyuan Miao, Yuting Chen, Chengcheng Wan, Lei Bu, Jianjun Zhao
Abstract
TVM is a popular deep learning (DL) compiler. It is designed for compiling DL models, which are naturally computation graphs, and as well promoting the efficiency of DL computation. State-of-the-art methods, such as Muffin and NNSmith, allow developers to generate computation graphs for testing DL compilers. However, these techniques are inefficient — their generated computation graphs are either type-invalid or inexpressive, and hence not able to test the core functionalities of a DL compiler.
Topics & Concepts
CompilerComputer scienceComputationDigital subscriber lineProgramming languageOptimizing compilerParallel computingTheoretical computer scienceTelecommunicationsSoftware Testing and Debugging TechniquesSoftware Engineering ResearchAdversarial Robustness in Machine Learning