Operational Semantics and Polymorphic Type Inference

Three languages with polymorphic type disciplines are discussed, namely the λ-calculus with Milner's polymorphic type discipline; a language with imperative features (polymorphic references); and a skeletal module language with structures, signatures and functors. In each of the two first cases we show that the type inference system is consistent with an operational dynamic semantics. On the module level, polymorphic types correspond to signatures. There is a notion of principal signature. So-called signature checking is the module level equivalent of type checking. In particular, there exists an algorithm which either fails or produces a principal signature

A Simple Semantics for ML Polymorphism

We give a framework for denotational semantics for the polymorphic core of the programming language ML. This framework requires no more semantic material than what is needed for modeling the simple type discipline. In our view, the terms of ML are pairs consisting of a raw (untyped) lambda term and a type-scheme that ML\u27s type inference system can derive for the raw term. We interpret type-schemes as sets of simple types. Then, given any model M of the simply typed lambda calculus, the meaning of an ML term will be a set of pairs, each consisting of a simple type τ and an element of M of type τ. Hence, there is no need to interpret all raw terms, as was done in Milner\u27s original semantic framework. In comparison to Mitchell and Harper\u27s analysis, we avoid having to provide a very large type universe in which generic type-schemes are interpreted. Also, we show how to give meaning to ML terms rather than to derivations in the ML type inference system (which can be several for the same term). We give an axiomatization for the equational theory that corresponds to our semantic framework and prove the analogs of the compeleteness theorems that Friedman proved for the simply typed lambda calculus. The framework can be extended to languages with constants, type constructors and recursive types (via regular trees). For the extended language, we prove a theorem that allows the transfer of certain full abstraction results from languages based on the typed lambda calculus to ML-like languages

Formal Type Soundness for Cyclone's Region System

Cyclone is a polymorphic, type-safe programming language derived from C\@. The primary design goals of Cyclone are to let programmers control data representations and memory management without sacrificing type-safety. In this paper, we focus on the region-based memory management of Cyclone and its static typing discipline. The design incorporates several advancements, including support for region subtyping and a coherent integration with stack allocation and a garbage collector. To support separate compilation, Cyclone requires programmers to write some explicit region annotations, but uses a combination of default annotations, local type inference, and a novel treatment of region effects to reduce this burden. As a result, we integrate C idioms in a region-based framework. In our experience, porting legacy C to Cyclone has required altering about 8\% of the code; of the changes, only 6\% (of the 8\%) were region annotations. This technical report is really two documents in one: The first part is a paper submitted for publication in November, 2001. The second part is the full formal language and type-safety proof mentioned briefly in the first part. If you have already read a version of, ``Region-Based Memory Management in Cyclone'', then you should proceed directly to Section 9

Effect inference for deterministic parallelism

In this report we sketch a polymorphic type and effect inference system for ensuring deterministic execution of parallel programs containing shared mutable state. It differs from that of Gifford and Lucassen in being based on Hindley Milner polymorphism and in formalizing the operational semantics of parallel and sequential computation

Koka: Programming with Row Polymorphic Effect Types

We propose a programming model where effects are treated in a disciplined way, and where the potential side-effects of a function are apparent in its type signature. The type and effect of expressions can also be inferred automatically, and we describe a polymorphic type inference system based on Hindley-Milner style inference. A novel feature is that we support polymorphic effects through row-polymorphism using duplicate labels. Moreover, we show that our effects are not just syntactic labels but have a deep semantic connection to the program. For example, if an expression can be typed without an exn effect, then it will never throw an unhandled exception. Similar to Haskell's `runST` we show how we can safely encapsulate stateful operations. Through the state effect, we can also safely combine state with let-polymorphism without needing either imperative type variables or a syntactic value restriction. Finally, our system is implemented fully in a new language called Koka and has been used successfully on various small to medium-sized sample programs ranging from a Markdown processor to a tier-splitted chat application. You can try out Koka live at www.rise4fun.com/koka/tutorial.Comment: In Proceedings MSFP 2014, arXiv:1406.153

Lecture Notes on Formal Program Development

This document was originally produced as lecture notes for the MSc and PG course ``Formal Program Development'' early in 1997. After some initial general considerations on this subject the paper focusses on the way one can use Extended ML (EML) for formal program development, which features EML contains and why, and which pitfalls one has to avoid when formally developing ML programs. Usage, features, and pitfalls are all presented through examples

Constraint Handling Rules with Binders, Patterns and Generic Quantification

Constraint Handling Rules provide descriptions for constraint solvers. However, they fall short when those constraints specify some binding structure, like higher-rank types in a constraint-based type inference algorithm. In this paper, the term syntax of constraints is replaced by $\lambda$-tree syntax, in which binding is explicit; and a new $\nabla$ generic quantifier is introduced, which is used to create new fresh constants.Comment: Paper presented at the 33nd International Conference on Logic Programming (ICLP 2017), Melbourne, Australia, August 28 to September 1, 2017 16 pages, LaTeX, no PDF figure

Complete and easy type Inference for first-class polymorphism

The Hindley-Milner (HM) typing discipline is remarkable in that it allows statically typing programs without requiring the programmer to annotate programs with types themselves. This is due to the HM system offering complete type inference, meaning that if a program is well typed, the inference algorithm is able to determine all the necessary typing information. Let bindings implicitly perform generalisation, allowing a let-bound variable to receive the most general possible type, which in turn may be instantiated appropriately at each of the variable’s use sites. As a result, the HM type system has since become the foundation for type inference in programming languages such as Haskell as well as the ML family of languages and has been extended in a multitude of ways. The original HM system only supports prenex polymorphism, where type variables are universally quantified only at the outermost level. This precludes many useful programs, such as passing a data structure to a function in the form of a fold function, which would need to be polymorphic in the type of the accumulator. However, this would require a nested quantifier in the type of the overall function. As a result, one direction of extending the HM system is to add support for first-class polymorphism, allowing arbitrarily nested quantifiers and instantiating type variables with polymorphic types. In such systems, restrictions are necessary to retain decidability of type inference. This work presents FreezeML, a novel approach for integrating first-class polymorphism into the HM system, focused on simplicity. It eschews sophisticated yet hard to grasp heuristics in the type systems or extending the language of types, while still requiring only modest amounts of annotations. In particular, FreezeML leverages the mechanisms for generalisation and instantiation that are already at the heart of ML. Generalisation and instantiation are performed by let bindings and variables, respectively, but extended to types beyond prenex polymorphism. The defining feature of FreezeML is the ability to freeze variables, which prevents the usual instantiation of their types, allowing them instead to keep their original, fully polymorphic types. We demonstrate that FreezeML is as expressive as System F by providing a translation from the latter to the former; the reverse direction is also shown. Further, we prove that FreezeML is indeed a conservative extension of ML: When considering only ML programs, FreezeML accepts exactly the same programs as ML itself. # We show that type inference for FreezeML can easily be integrated into HM-like type systems by presenting a sound and complete inference algorithm for FreezeML that extends Algorithm W, the original inference algorithm for the HM system. Since the inception of Algorithm W in the 1970s, type inference for the HM system and its descendants has been modernised by approaches that involve constraint solving, which proved to be more modular and extensible. In such systems, a term is translated to a logical constraint, whose solutions correspond to the types of the original term. A solver for such constraints may then be defined independently. To this end, we demonstrate such a constraint-based inference approach for FreezeML. We also discuss the effects of integrating the value restriction into FreezeML and provide detailed comparisons with other approaches towards first-class polymorphism in ML alongside a collection of examples found in the literature