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AbstractDifferentiation.jl: Backend-Agnostic Differentiable Programming in Julia

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arxiv 2109.12449 v2 pith:JOJYXWOJ submitted 2021-09-25 cs.MS cs.LGcs.SE

Frank Schäfer , Mohamed Tarek , Lyndon White , Chris Rackauckas This is my paper · ORCID
classification cs.MScs.LGcs.SE
keywords packagedevelopersend-usersjuliaabstractdifferentiationboilerplatecodelanguage
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No single Automatic Differentiation (AD) system is the optimal choice for all problems. This means informed selection of an AD system and combinations can be a problem-specific variable that can greatly impact performance. In the Julia programming language, the major AD systems target the same input and thus in theory can compose. Hitherto, switching between AD packages in the Julia Language required end-users to familiarize themselves with the user-facing API of the respective packages. Furthermore, implementing a new, usable AD package required AD package developers to write boilerplate code to define convenience API functions for end-users. As a response to these issues, we present AbstractDifferentiation.jl for the automatized generation of an extensive, unified, user-facing API for any AD package. By splitting the complexity between AD users and AD developers, AD package developers only need to implement one or two primitive definitions to support various utilities for AD users like Jacobians, Hessians and lazy product operators from native primitives such as pullbacks or pushforwards, thus removing tedious -- but so far inevitable -- boilerplate code, and enabling the easy switching and composing between AD implementations for end-users.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. A Blueprint for Equilibrium-Based Differentiable Continuous-Variable Thermodynamic Computing

    cs.LG 2026-07 conditional novelty 5.0 of 10

    Tunable energy landscapes whose thermal averages equal sigmoid, softmax, and matrix-vector products can, in principle, form the basis of a low-energy analog computer, with a superconducting double-well device as a fir...

  2. A Common Interface for Automatic Differentiation

    cs.MS 2025-05 accept novelty 5.0 of 10

    A new Julia interface, DifferentiationInterface.jl, unifies a dozen AD backends with a preparation mechanism that amortizes one-time costs and adds sparsity handling.

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