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Computer Science > Mathematical Software

arXiv:2109.12449 (cs)
[Submitted on 25 Sep 2021 (v1), last revised 4 Feb 2022 (this version, v2)]

Title:AbstractDifferentiation.jl: Backend-Agnostic Differentiable Programming in Julia

Authors:Frank Schäfer, Mohamed Tarek, Lyndon White, Chris Rackauckas
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Abstract: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 this http URL 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.
Comments: 3 figures, 2 tables 15 pages
Subjects: Mathematical Software (cs.MS); Machine Learning (cs.LG); Software Engineering (cs.SE)
Cite as: arXiv:2109.12449 [cs.MS]
  (or arXiv:2109.12449v2 [cs.MS] for this version)
  https://doi.org/10.48550/arXiv.2109.12449

Submission history

From: Mohamed Tarek [view email]
[v1] Sat, 25 Sep 2021 22:19:12 UTC (231 KB)
[v2] Fri, 4 Feb 2022 22:28:20 UTC (1,192 KB)
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