Demystifying Deep Learning Compiler Front End Bugs: An LLM-Aided Empirical Study
The paper presents an empirical study of frontend bugs in deep learning compilers, focusing on TorchDynamo for PyTorch 2. Using a domain‑knowledge‑enhanced LLM‑aided approach, the authors analyzed 123 bugs and built a taxonomy with seven root cause categories and fifteen subcategories. They also generated targeted test cases that uncovered 23 previously unknown bugs, confirming 15 of them, demonstrating the method’s effectiveness for testing compiler frontends.
- ▪The study examines defects introduced during the frontend translation of deep learning programs into graph‑based intermediate representations, termed fBugs.
- ▪A taxonomy of seven root cause categories and fifteen subcategories was constructed after analyzing 123 fBugs in TorchDynamo.
- ▪The authors employed a large language model to create root cause‑aware test cases, leading to the discovery of 23 new fBugs, with 15 confirmed across eight subcategories.
- ▪The findings provide actionable insights for improving development and testing practices of deep learning compiler frontends.
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| Original publisher | arXiv.org |
| Canonical URL | https://arxiv.org/abs/2607.25651 |
| Publication time | Mon, 03 Aug 2026 03:01:29 +0000 |
| Retrieval time | 2026-08-03T03:05:43.772Z |
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Computer Science > Programming Languages arXiv:2607.25651 (cs) [Submitted on 28 Jul 2026] Title:Demystifying Deep Learning Compiler Frontend Bugs: An LLM-Aided Empirical Study Authors:Xinyi Yuan, Wei Chen, Jinyi Liu, Pengyu Chen, Jun Wei, Guoquan Wu, Jiaxin Zhu, Tao Huang View a PDF of the paper titled Demystifying Deep Learning Compiler Frontend Bugs: An LLM-Aided Empirical Study, by Xinyi Yuan and 7 other authors View PDF HTML (experimental) 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.
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