AI 中文总结
对PyTorch 2默认深度学习编译器前端TorchDynamo的fBug展开首次系统实证研究，借助领域知识增强的大语言模型辅助方法，分析fBug并构建分类法，生成测试用例，发现多个新fBug，为深度学习编译器开发和测试提供见解。
AI 中文摘要
深度学习编译器旨在将深度学习程序转换为优化的、特定硬件的代码。通常，其前端将程序转换为基于图的中间表示以实现优化。此阶段引入的缺陷（称为fBug）虽严重但研究不足。我们对PyTorch 2默认的深度学习编译器前端TorchDynamo中的fBug进行首次系统实证研究。利用领域知识增强的大语言模型辅助方法，分析123个fBug并构建分类法。研究结果为开发和测试提供见解，还利用大语言模型生成测试用例，发现了23个先前未知的fBug。
英文摘要
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.
