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arXiv · 2207.09914

Constraint-based type inference for FreezeML

Frank Emrich·Jan Stolarek·James Cheney·Sam Lindley
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Abstract

FreezeML is a new approach to first-class polymorphic type inference that employs term annotations to control when and how polymorphic types are instantiated and generalised. It conservatively extends Hindley-Milner type inference and was first presented as an extension to Algorithm W. More modern type inference techniques such as HM(X) and OutsideIn($X$) employ constraints to support features such as type classes, type families, rows, and other extensions. We take the first step towards modernising FreezeML by presenting a constraint-based type inference algorithm. We introduce a new constraint language, inspired by the Pottier/R\'emy presentation of HM(X), in order to allow FreezeML type inference problems to be expressed as constraints. We present a deterministic stack machine for solving FreezeML constraints and prove its termination and correctness.

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Frank Emrich, Jan Stolarek, James Cheney, Sam Lindley. 2022-07-20. Constraint-based type inference for FreezeML. https://arxiv.org/abs/2207.09914

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