A Common Interface for Automatic Differentiation
Guillaume Dalle, Adrian Hill.
Year: 2026, Volume: 27, Issue: 25, Pages: 1−13
Abstract
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.