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Static Analysis of Shape in TensorFlow Programs

Authors Sifis Lagouvardos, Julian Dolby, Neville Grech, Anastasios Antoniadis, Yannis Smaragdakis

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Subject Classification

ACM Subject Classification
  • Theory of computation → Program analysis
  • Software and its engineering → Compilers
  • Software and its engineering → General programming languages
Keywords
  • Python
  • TensorFlow
  • static analysis
  • Doop
  • Wala

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Abstract

Machine learning has been widely adopted in diverse science and engineering domains, aided by reusable libraries and quick development patterns. The TensorFlow library is probably the best-known representative of this trend and most users employ the Python API to its powerful back-end. TensorFlow programs are susceptible to several systematic errors, especially in the dynamic typing setting of Python. We present Pythia, a static analysis that tracks the shapes of tensors across Python library calls and warns of several possible mismatches. The key technical aspects are a close modeling of library semantics with respect to tensor shape, and an identification of violations and error-prone patterns. Pythia is powerful enough to statically detect (with 84.62% precision) 11 of the 14 shape-related TensorFlow bugs in the recent Zhang et al. empirical study - an independent slice of real-world bugs.

Cite As Get BibTex

Sifis Lagouvardos, Julian Dolby, Neville Grech, Anastasios Antoniadis, and Yannis Smaragdakis. Static Analysis of Shape in TensorFlow Programs. In 34th European Conference on Object-Oriented Programming (ECOOP 2020). Leibniz International Proceedings in Informatics (LIPIcs), Volume 166, pp. 15:1-15:29, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2020) https://doi.org/10.4230/LIPIcs.ECOOP.2020.15

Author Details

Sifis Lagouvardos
  • University of Athens, Greece
Julian Dolby
  • IBM Research, Yorktown Heights, NY, USA
Neville Grech
  • University of Athens, Greece
Anastasios Antoniadis
  • University of Athens, Greece
Yannis Smaragdakis
  • University of Athens, Greece

Funding

We gratefully acknowledge funding by the European Research Council, grant 790340 (PARSE), and by the Hellenic Foundation for Research and Innovation (project DEAN-BLOCK).

Supplementary Materials

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