arxiv:2505.05542

A Common Interface for Automatic Differentiation

Published on May 8

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Abstract

A Julia package provides a unified interface for multiple AD backends, facilitating efficient computation and advanced features like sparsity handling.

AI-generated summary

For scientific machine learning tasks with a lot of custom code, picking the right Automatic Differentiation (AD) system matters. Our Julia package DifferentiationInterface.jl provides a common frontend to a dozen AD backends, unlocking easy comparison and modular development. In particular, its built-in preparation mechanism leverages the strengths of each backend by amortizing one-time computations. This is key to enabling sophisticated features like sparsity handling without putting additional burdens on the user.

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