Softmax in front of CrossEntropyLoss: 16 other bugs PyTorch won't catch (gaox.substack.com)

In a recent announcement, a design-time linter for PyTorch was introduced, specifically aimed at detecting structural bugs before they lead to runtime errors. The tool, developed by Neurarch, identifies 17 common architectural mistakes, such as the inappropriate placement of layers, including the use of Softmax in front of CrossEntropyLoss—a mistake that can lead to poor training outcomes due to double application of Softmax. This linter allows developers to catch such errors early in the design process, reducing wasted GPU hours and minimizing the troubleshooting burden. The significance of this tool lies in its ability to enhance neural network reliability through static analysis, a critical need in the fast-evolving AI/ML landscape where debugging can be costly and time-consuming. While traditional PyTorch tools only flag errors at runtime, this linter addresses the root of the problem by examining the architecture graph for compliance with best practices. By categorizing the types of errors—ranging from structural malformations to ordering issues—the linter serves as a proactive measure, helping developers maintain robust model integrity and accelerate the training process.