Interoperability in Deep Learning: A User Survey and Failure Analysis of ONNX Model Converters
This program is tentative and subject to change.
Software engineers develop, fine-tune, and deploy deep learning (DL) models using a variety of development frameworks and runtime en- vironments. DL model converters move models between frameworks and to runtime environments. Conversion errors compromise model quality and disrupt deployment. However, the failure characteristics of DL model converters are unknown, adding risk when using DL interoperability technologies.
This paper analyzes failures in DL model converters. We survey software engineers about DL interoperability tools, use cases, and pain points (N=92). Then, we characterize failures in model convert- ers associated with the main interoperability tool, ONNX (N=200 issues in PyTorch and TensorFlow). Finally, we formulate and test two hypotheses about structural causes for the failures we studied. We find that the node conversion stage of a model converter accounts for ~75% of the defects and 33% of reported failure are related to semantically incorrect models. The cause of semantically incor- rect models is elusive, but models with behaviour inconsistencies share operator sequences. Our results motivate future research on making DL interoperability software simpler to maintain, extend, and validate. Research into behavioural tolerances and architectural coverage metrics could be fruitful.
This program is tentative and subject to change.
Wed 18 SepDisplayed time zone: Amsterdam, Berlin, Bern, Rome, Stockholm, Vienna change
15:30 - 17:10 | |||
15:30 20mTalk | Interoperability in Deep Learning: A User Survey and Failure Analysis of ONNX Model Converters Technical Papers Purvish Jajal Purdue University, Wenxin Jiang Purdue University, Arav Tewari Purdue University, Erik Kocinare Purdue University, Joseph Woo Purdue University, Anusha Sarraf Purdue University, Yung-Hsiang Lu Purdue University, George K. Thiruvathukal Loyola University Chicago and Argonne National Laboratory, James C. Davis Purdue University Pre-print | ||
15:50 20mTalk | Interpretability based Neural Network Repair Technical Papers Zuohui Chen Zhejiang University of Technology, Jun Zhou Zhejiang University of Technology, Youcheng Sun The University of Manchester, Jingyi Wang Zhejiang University, Qi Xuan Zhejiang University of Technology, Xiaoniu Yang Zhejiang University of Technology | ||
16:10 20mTalk | See the Forest, not Trees: Unveiling and Escaping the Pitfalls of Error-Triggering Inputs in Neural Network Testing Technical Papers Yuanyuan Yuan The Hong Kong University of Science and Technology, Shuai Wang The Hong Kong University of Science and Technology, Zhendong Su ETH Zurich | ||
16:30 20mTalk | Isolation-Based Debugging for Neural Networks Technical Papers Jialuo Chen Zhejiang University, Jingyi Wang Zhejiang University, Youcheng Sun The University of Manchester, Peng Cheng Zhejiang University, Jiming Chen Zhejiang University DOI | ||
16:50 20mTalk | Certified Continual Learning for Neural Network Regression Technical Papers Long Pham Hong Singapore Management University, Jun Sun School of Information Systems, Singapore Management University, Singapore | ||