Stateful Testing for Deep Learning Compilers
Modern deep learning compilers compile models lazily and cache artifacts across invocations. Retained compiler state can make a later invocation incorrect even when it is correct in isolation. An analysis of 26 publicly reported issues in PyTorch, JAX, and TensorFlow shows that detecting such bugs requires sequences that first establish compiler state and then expose a failure. We present Rondo, a stateful testing method that represents each test case as a model paired with an invocation sequence. Rondo mutates invocation sequences and uses a Markov chain Monte Carlo sampler guided by compilation behavior to favor rarely observed behaviors. Its three-way differential oracle compares execu...
| Authors | Jiatao Ge, Ruixiang Qian, Yibo Hu et al. |
|---|---|
| Primary category | cs.SE |
| Published | 15 October 2026 |
| Last updated at source | 15 October 2026 |
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