Towards Migrating Neural Network Implementations

TL;DR

This paper presents an automated approach for migrating neural network code between deep learning frameworks like PyTorch and TensorFlow, using a pivot model to ensure functional equivalence.

Contribution

It introduces a novel method leveraging a pivot neural network model to facilitate accurate and automated migration across different deep learning libraries.

Findings

Successfully migrated five neural networks with functional equivalence.

The approach reduces manual effort and time in updating neural network implementations.

Validated on popular frameworks, demonstrating practical applicability.

Abstract

The development of smart systems (i.e., systems enhanced with AI components) has thrived thanks to the rapid advancements in neural networks (NNs). A wide range of libraries and frameworks have consequently emerged to support NN design and implementation. The choice depends on factors such as available functionalities, ease of use, documentation and community support. After adopting a given NN framework, organizations might later choose to switch to another if performance declines, requirements evolve, or new features are introduced. Unfortunately, migrating NN implementations across libraries is challenging due to the lack of migration approaches specifically tailored for NNs. This leads to increased time and effort to modernize NNs, as manual updates are necessary to avoid relying on outdated implementations and ensure compatibility with new features. In this paper, we propose an approach to automatically migrate neural network code across deep learning frameworks. Our method makes use of a pivot NN model to create an abstraction of the NN prior to migration. We validate our approach using two popular NN frameworks, namely PyTorch and TensorFlow. We also discuss the challenges of migrating code between the two frameworks and how they were approached in our method. Experimental evaluation on five NNs shows that our approach successfully migrates their code and produces NNs that are functionally equivalent to the originals. Artefacts from our work are available online.