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New Rondo testing method finds 117 bugs in deep learning compilers

By PulseAugur Editorial · [1 sources] ·

Researchers have developed Rondo, a novel stateful testing method designed to uncover bugs in deep learning compilers. By representing test cases as model-invocation sequences, Rondo effectively identifies issues arising from retained compiler state, which can cause incorrect outputs even in isolated invocations. An evaluation on four frameworks and 24 model architectures demonstrated Rondo's ability to detect 117 bugs, including 35 stateful ones, leading to 39 bug reports submitted to developers. The method achieved high behavior coverage and alarm precision, significantly shortening bug-exposing invocation sequences. AI

IMPACT Improves the reliability and correctness of deep learning model compilation, potentially leading to faster and more stable deployment of AI models.

RANK_REASON Academic paper detailing a new testing methodology for deep learning compilers. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New Rondo testing method finds 117 bugs in deep learning compilers

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Academic paper detailing a new testing methodology for deep learning compilers. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Jiatao Ge, Ruixiang Qian, Yibo Hu, Yanzhou Mu, Yinglong Zou, Jiawei Liu, Zhenyu Chen, Chunrong Fang ·

    Stateful Testing for Deep Learning Compilers

    arXiv:2610.22003v1 Announce Type: cross Abstract: 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 i…

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