Automatic differentiation for ML-family languages: correctness via logical relations (Preprint)

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Type: Preprint
National /International: International
Title: Automatic differentiation for ML-family languages: correctness via logical relations
Publication Date: 2022-10-21
Authors: - Fernando Lucatelli Nunes
- Matthijs Vákár
Abstract:

We give a simple, direct and reusable logical relations technique for languages with recursive features and partially defined differentiable functions. We do so by working out the case of Automatic Differentiation (AD) correctness: namely, we present a proof of the dual numbers style AD macro correctness for realistic functional languages in the ML-family. We also show how this macro provides us with correct forward- and reverse-mode AD. The starting point was to interpret a functional programming language in a suitable freely generated categorical structure. In this setting, by the universal property of the syntactic categorical structure, the dual numbers AD macro and the basic ωCPo-semantics arise as structure preserving functors. The proof follows, then, by a novel logical relations argument. The key to much of our contribution is a powerful monadic logical relations technique for term recursion and recursive types. It provides us with a semantic correctness proof based on a simple approach for denotational semantics, making use only of the very basic concrete model of ω-cpos.

Institution: DMUC 22-32
Online version: http://www.mat.uc.pt...prints/eng_2022.html
Download: Not available
 
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