Grounding Deep Learning Framework Testing in Data Flow
Data flow underlies deep learning (DL) framework execution, including automatic differentiation and operator-fusion optimization. Bugs involving data flow can cause incorrect results or unexpected behavior. Existing testing methods represent models primarily by operator topology, omitting runtime tensor values and properties and operator input requirements. They therefore cannot distinguish data flows in models with similar topologies, even when these differences affect framework behavior. We introduce the Tensor Flow Graph (TFG) to capture runtime tensor states and operator input requirements. Its nodes represent operators and their input requirements, while its edges record tensor value...
| Authors | Jiatao Ge, Yanzhou Mu, Yinglong Zou et al. |
|---|---|
| Primary category | cs.SE |
| Published | 11 October 2026 |
| Last updated at source | 11 October 2026 |
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