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arXiv Machine Learning By Jiatao Ge, Ruixiang Qian, Yibo Hu, Yanzhou Mu, Yinglong Zou, Jiawei Liu, Zhenyu Chen, Chunrong Fang

Stateful Testing for Deep Learning Compilers

Read the original on arXiv Machine Learning →

The paper introduces Rondo, a stateful testing framework for deep‑learning compilers that models each test as a model plus an invocation sequence. Rondo mutates sequences using a Markov chain Monte Carlo sampler guided by compilation behavior, and employs a three‑way differential oracle to detect bugs that arise only when compiler state is retained. Evaluated on four major frameworks and 24 model architectures, Rondo uncovered 117 bugs—35 of which were stateful—leading to 39 bug reports, 35 confirmed by developers, and 18 fixes, while achieving high coverage and precision.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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