Investigating and Detecting Silent Bugs in PyTorch Programs
Shuo Hong, Hailong Sun, Xiang Yang Gao, Shin Hwei Tan · 2024
Deep Learning (DL) has been widely applied in various fields. Unlike traditional software, DL programs possess the “black box” characteristic that can make it challenging for developers to debug when anomalous behaviors arise. In particular, silent bugs, a type of bugs in DL programs, can lead to erroneous behaviors without causing system crashes or suspensions, and they do not display error messages to users. This makes silent bugs more difficult for developers to discover, locate, and fix. In this paper, we present the first detailed study of silent bugs in PyTorch programs. We collect 14,523 posts from the official PyTorch forum and use a LLM-based semi-automated approach to filter the silent bugs. By analyzing the symptoms, root causes, and patterns of silent bugs, we have derived several important findings and implications: (1) most silent bugs cause abnormal outputs, which requires the design of more flexible test oracles to detect them, (2) the wide range of symptoms and root causes do not necessarily have one-to-one correspondences, which makes detecting and debugging silent bugs more challenging, (3) silent bugs exhibit common bug patterns, such as redundant, missing, or misplaced operations. Building upon these findings, we design and implement an extensible rule-based tool PYSIASSIST to help developer debug and resolve silent bugs. Evaluation results show that Pysiassist achieves 92.4% precision and 85.3% recall, outperforming existing techniques.