Investigating and Detecting Silent Bugs in PyTorch Programs
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".