Multilingual Fault Localization for Deep Learning Compilers
Michael Aziz · 2024
Deep learning compilers play an increasingly important role in implementing learned algorithms efficiently.These compilers are commonly implemented using a set of different programming languages: languages suitable for manipulating high-level tensor graph representations differ from those used to implement efficient low-level operations on accelerator devices.Finding faults in these compilers remains a challenging problem, and previously proposed fault localization techniques have limitations when working with a multilingual codebase.To overcome the aforementioned limitations, this thesis proposes a multilingual fault localization technique based on a language-independent approach to mutant generation.We evaluated this technique using eleven real faults in a deep learning compiler codebase.The results of the empirical evaluation show that the proposed approach can precisely locate four of the eleven faults and correctly ranks the faulty elements as the most suspicious.i B MFL Mutation Operators 88 C DLA Fault Localization Results 90 vi List of Tables 3.1 Notation for spectra metrics collected during program execution. . . .3.2 Notation for mutant metrics computed from mutant test results. . . .5.1 Suspiciousness scores of highest-ranking mutants for defective mid programs in different languages. . . . . . . . . . . . . . . . . . . . . .5.2 EXAM scores for MFL technique on L0 example faults. . . . . . . . .6.1 Summary of the DLA codebase showing line counts grouped by programming language. . . . . . . . .