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arXiv · 2607.25651

Demystifying Deep Learning Compiler Frontend Bugs: An LLM-Aided Empirical Study

Xinyi Yuan·Wei Chen·Jinyi Liu·Pengyu Chen·Jun Wei·Guoquan Wu·Jiaxin Zhu·Tao Huang
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Abstract

Deep learning compilers (DLCs) are designed to translate deep learning programs into optimized, hardware-specific code. Typically, DLC frontends translate programs into graph-based intermediate representations (IRs) to enable optimizations. Defects introduced during this stage (termed \emph{fBug}s) are severe yet understudied, as prior work predominantly focuses on low-level APIs and operators or treats DLCs as monolithic entities. To bridge this gap, we conduct the first systematic empirical study of \emph{fBug}s in TorchDynamo, the default DLC frontend for PyTorch 2, the most popular DL framework. Leveraging a domain-knowledge-enhanced LLM-aided methodology, we analyze 123 \emph{fBug}s and construct a taxonomy comprising 7 root cause categories and 15 subcategories. Our findings provide actionable insights for DLC development and testing. Furthermore, we leverage the LLM to generate targeted, root cause-aware test cases to detect new bugs. We uncovered 23 previously unknown \emph{fBug}s in recent releases (15 confirmed) across eight (sub)categories, demonstrating the efficacy of our methodology in testing and hardening DLC frontends.

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Xinyi Yuan, Wei Chen, Jinyi Liu, Pengyu Chen, Jun Wei, Guoquan Wu, Jiaxin Zhu, Tao Huang. 2026-07-28. Demystifying Deep Learning Compiler Frontend Bugs: An LLM-Aided Empirical Study. https://arxiv.org/abs/2607.25651

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