Continuation-passing style

Last updated

In functional programming, continuation-passing style (CPS) is a style of programming in which control is passed explicitly in the form of a continuation. This is contrasted with direct style, which is the usual style of programming. Gerald Jay Sussman and Guy L. Steele, Jr. coined the phrase in AI Memo 349 (1975), which sets out the first version of the Scheme programming language. [1] [2] John C. Reynolds gives a detailed account of the numerous discoveries of continuations. [3]

Contents

A function written in continuation-passing style takes an extra argument: an explicit "continuation"; i.e., a function of one argument. When the CPS function has computed its result value, it "returns" it by calling the continuation function with this value as the argument. That means that when invoking a CPS function, the calling function is required to supply a procedure to be invoked with the subroutine's "return" value. Expressing code in this form makes a number of things explicit which are implicit in direct style. These include: procedure returns, which become apparent as calls to a continuation; intermediate values, which are all given names; order of argument evaluation, which is made explicit; and tail calls, which simply call a procedure with the same continuation, unmodified, that was passed to the caller.

Programs can be automatically transformed from direct style to CPS. Functional and logic compilers often use CPS as an intermediate representation where a compiler for an imperative or procedural programming language would use static single assignment form (SSA). [4] SSA is formally equivalent to a subset of CPS (excluding non-local control flow, which does not occur when CPS is used as intermediate representation). [5] Functional compilers can also use A-normal form (ANF) (but only for languages requiring eager evaluation), rather than with 'thunks' (described in the examples below) in CPS. CPS is used more frequently by compilers than by programmers as a local or global style.

Examples

In CPS, each procedure takes an extra argument representing what should be done with the result the function is calculating. This, along with a restrictive style prohibiting a variety of constructs usually available, is used to expose the semantics of programs, making them easier to analyze. This style also makes it easy to express unusual control structures, like catch/throw or other non-local transfers of control.

The key to CPS is to remember that (a) every function takes an extra argument known as its continuation, and (b) every argument in a function call must be either a variable or a lambda expression (not a more complex expression). This has the effect of turning expressions "inside-out" because the innermost parts of the expression must be evaluated first, thus CPS makes explicit the order of evaluation as well as the control flow. Some examples of code in direct style and the corresponding CPS appear below. These examples are written in the Scheme programming language; by convention the continuation function is represented as a parameter named "k":

Direct style
Continuation passing style
(define(pythxy)(sqrt(+(*xx)(*yy))))
(define(pyth&xyk)(*&xx(lambda(x2)(*&yy(lambda(y2)(+&x2y2(lambda(x2py2)(sqrt&x2py2k))))))))
(define(factorialn)(if(=n0)1; NOT tail-recursive(*n(factorial(-n1)))))
(define(factorial&nk)(=&n0(lambda(b)(ifb; growing continuation(k1); in the recursive call(-&n1(lambda(nm1)(factorial&nm1(lambda(f)(*&nfk)))))))))
(define(factorialn)(f-auxn1))(define(f-auxna)(if(=n0)a; tail-recursive(f-aux(-n1)(*na))))
(define(factorial&nk)(f-aux&n1k))(define(f-aux&nak)(=&n0(lambda(b)(ifb; unmodified continuation(ka); in the recursive call(-&n1(lambda(nm1)(*&na(lambda(nta)(f-aux&nm1ntak)))))))))

Note that in the CPS versions, the primitives used, like +& and *& are themselves CPS, not direct style, so to make the above examples work in a Scheme system we would need to write these CPS versions of primitives, with for instance *& defined by:

(define(*&xyk)(k(*xy)))

To do this in general, we might write a conversion routine:

(define(cps-primf)(lambdaargs(let((r(reverseargs)))((carr)(applyf(reverse(cdrr)))))))(define*&(cps-prim*))(define+&(cps-prim+))

In order to call a procedure written in CPS from a procedure written in direct style, it is necessary to provide a continuation that will receive the result computed by the CPS procedure. In the example above (assuming that CPS primitives have been provided), we might call (factorial& 10 (lambda (x) (display x) (newline))).

There is some variety between compilers in the way primitive functions are provided in CPS. Above we have used the simplest convention, however sometimes Boolean primitives are provided that take two thunks to be called in the two possible cases, so the (=& n 0 (lambda (b) (if b ...))) call inside f-aux& definition above would be written instead as (=& n 0 (lambda () (k a)) (lambda () (-& n 1 ...))). Similarly, sometimes the if primitive itself is not included in CPS, and instead a function if& is provided which takes three arguments: a Boolean condition and the two thunks corresponding to the two arms of the conditional.

The translations shown above show that CPS is a global transformation. The direct-style factorial takes, as might be expected, a single argument; the CPS factorial& takes two: the argument and a continuation. Any function calling a CPS-ed function must either provide a new continuation or pass its own; any calls from a CPS-ed function to a non-CPS function will use implicit continuations. Thus, to ensure the total absence of a function stack, the entire program must be in CPS.

CPS in Haskell

In this section we will write a function pyth that calculates the hypotenuse using the Pythagorean theorem. A traditional implementation of the pyth function looks like this:

pow2::Float->Floatpow2x=x**2add::Float->Float->Floataddxy=x+ypyth::Float->Float->Floatpythxy=sqrt(add(pow2x)(pow2y))

To transform the traditional function to CPS, we need to change its signature. The function will get another argument of function type, and its return type depends on that function:

pow2'::Float->(Float->a)->apow2'xcont=cont(x**2)add'::Float->Float->(Float->a)->aadd'xycont=cont(x+y)-- Types a -> (b -> c) and a -> b -> c are equivalent, so CPS function-- may be viewed as a higher order functionsqrt'::Float->((Float->a)->a)sqrt'x=\cont->cont(sqrtx)pyth'::Float->Float->(Float->a)->apyth'xycont=pow2'x(\x2->pow2'y(\y2->add'x2y2(\anb->sqrt'anbcont)))

First we calculate the square of a in pyth' function and pass a lambda function as a continuation which will accept a square of a as a first argument. And so on until we reach the result of our calculations. To get the result of this function we can pass id function as a final argument which returns the value that was passed to it unchanged: pyth' 3 4 id == 5.0.

The mtl library, which is shipped with GHC, has the module Control.Monad.Cont. This module provides the Cont type, which implements Monad and some other useful functions. The following snippet shows the pyth' function using Cont:

pow2_m::Float->ContaFloatpow2_ma=return(a**2)pyth_m::Float->Float->ContaFloatpyth_mab=doa2<-pow2_mab2<-pow2_mbanb<-cont(add'a2b2)r<-cont(sqrt'anb)returnr

Not only has the syntax become cleaner, but this type allows us to use a function callCC with type MonadCont m => ((a -> m b) -> m a) -> m a. This function has one argument of a function type; that function argument accepts the function too, which discards all computations going after its call. For example, let's break the execution of the pyth function if at least one of its arguments is negative returning zero:

pyth_m::Float->Float->ContaFloatpyth_mab=callCC$\exitF->do-- $ sign helps to avoid parentheses: a $ b + c == a (b + c)when(b<0||a<0)(exitF0.0)-- when :: Applicative f => Bool -> f () -> f ()a2<-pow2_mab2<-pow2_mbanb<-cont(add'a2b2)r<-cont(sqrt'anb)returnr

Continuations as objects

Programming with continuations can also be useful when a caller does not want to wait until the callee completes. For example, in user-interface (UI) programming, a routine can set up dialog box fields and pass these, along with a continuation function, to the UI framework. This call returns right away, allowing the application code to continue while the user interacts with the dialog box. Once the user presses the "OK" button, the framework calls the continuation function with the updated fields. Although this style of coding uses continuations, it is not full CPS.[ clarification needed ]

functionconfirmName(){fields.name=name;framework.Show_dialog_box(fields,confirmNameContinuation);}functionconfirmNameContinuation(fields){name=fields.name;}

A similar idea can be used when the function must run in a different thread or on a different processor. The framework can execute the called function in a worker thread, then call the continuation function in the original thread with the worker's results. This is in Java 8 using the Swing UI framework:

voidbuttonHandler(){// This is executing in the Swing UI thread.// We can access UI widgets here to get query parameters.intparameter=getField();newThread(()->{// This code runs in a separate thread.// We can do things like access a database or a // blocking resource like the network to get data.intresult=lookup(parameter);javax.swing.SwingUtilities.invokeLater(()->{// This code runs in the UI thread and can use// the fetched data to fill in UI widgets.setField(result);});}).start();}

Tail calls

Every call in CPS is a tail call, and the continuation is explicitly passed. Using CPS without tail call optimization (TCO) will cause not only the constructed continuation to potentially grow during recursion, but also the call stack. This is usually undesirable, but has been used in interesting ways—see the Chicken Scheme compiler. As CPS and TCO eliminate the concept of an implicit function return, their combined use can eliminate the need for a run-time stack. Several compilers and interpreters for functional programming languages use this ability in novel ways. [6]

Use and implementation

Continuation passing style can be used to implement continuations and control flow operators in a functional language that does not feature first-class continuations but does have first-class functions and tail-call optimization. Without tail-call optimization, techniques such as trampolining, i.e. using a loop that iteratively invokes thunk-returning functions, can be used; without first-class functions, it is even possible to convert tail calls into just gotos in such a loop.

Writing code in CPS, while not impossible, is often error-prone. There are various translations, usually defined as one- or two-pass conversions of pure lambda calculus, which convert direct style expressions into CPS expressions. Writing in trampolined style, however, is extremely difficult; when used, it is usually the target of some sort of transformation, such as compilation.

Functions using more than one continuation can be defined to capture various control flow paradigms, for example (in Scheme):

(define(/&xyokerr)(=&y0.0(lambda(b)(ifb(err(list"div by zero!"xy))(ok(/xy))))))

It is of note that CPS transform is conceptually a Yoneda embedding. [7] It is also similar to the embedding of lambda calculus in π-calculus. [8] [9]

Use in other fields

Outside of computer science, CPS is of more general interest as an alternative to the conventional method of composing simple expressions into complex expressions. For example, within linguistic semantics, Chris Barker and his collaborators have suggested that specifying the denotations of sentences using CPS might explain certain phenomena in natural language. [10]

In mathematics, the Curry–Howard isomorphism between computer programs and mathematical proofs relates continuation-passing style translation to a variation of double-negation embeddings of classical logic into intuitionistic (constructive) logic. Unlike the regular double-negation translation, which maps atomic propositions p to ((p → ⊥) → ⊥), the continuation passing style replaces ⊥ by the type of the final expression. Accordingly, the result is obtained by passing the identity function as a continuation to the CPS expression, as in the above example.

Classical logic itself relates to manipulating the continuation of programs directly, as in Scheme's call-with-current-continuation control operator, an observation due to Tim Griffin (using the closely related C control operator). [11]

See also

Notes

  1. Sussman, Gerald Jay; Steele, Guy L. Jr. (December 1975). "Scheme: An interpreter for extended lambda calculus"  . AI Memo . 349: 19. That is, in this continuation-passing programming style, a function always "returns" its result by "sending" it to another function. This is the key idea.
  2. Sussman, Gerald Jay; Steele, Guy L. Jr. (December 1998). "Scheme: A Interpreter for Extended Lambda Calculus" (reprint). Higher-Order and Symbolic Computation. 11 (4): 405–439. doi:10.1023/A:1010035624696. S2CID   18040106. We believe that this was the first occurrence of the term "continuation-passing style" in the literature. It has turned out to be an important concept in source code analysis and transformation for compilers and other metaprogramming tools. It has also inspired a set of other "styles" of program expression.
  3. Reynolds, John C. (1993). "The Discoveries of Continuations". LISP and Symbolic Computation. 6 (3–4): 233–248. CiteSeerX   10.1.1.135.4705 . doi:10.1007/bf01019459. S2CID   192862.
  4. Appel, Andrew W. (1992). Compiling with Continuations. Cambridge University Press. ISBN   0-521-41695-7.
  5. Mike Stay, "The Continuation Passing Transform and the Yoneda Embedding"
  6. Mike Stay, "The Pi Calculus II"
  7. Boudol, Gérard (1997). "The π-Calculus in Direct Style". CiteSeerX   10.1.1.52.6034 .
  8. Barker, Chris (2002-09-01). "Continuations and the Nature of Quantification" (PDF). Natural Language Semantics. 10 (3): 211–242. doi:10.1023/A:1022183511876. ISSN   1572-865X. S2CID   118870676.
  9. Griffin, Timothy (January 1990). "A formulae-as-type notion of control". Proceedings of the 17th ACM SIGPLAN-SIGACT symposium on Principles of programming languages - POPL '90. Vol. 17. pp. 47–58. doi:10.1145/96709.96714. ISBN   978-0-89791-343-0. S2CID   3005134.

Related Research Articles

In computer science, functional programming is a programming paradigm where programs are constructed by applying and composing functions. It is a declarative programming paradigm in which function definitions are trees of expressions that map values to other values, rather than a sequence of imperative statements which update the running state of the program.

<span class="mw-page-title-main">Lisp (programming language)</span> Programming language family

Lisp is a family of programming languages with a long history and a distinctive, fully parenthesized prefix notation. Originally specified in the late 1950s, it is the second-oldest high-level programming language still in common use, after Fortran. Lisp has changed since its early days, and many dialects have existed over its history. Today, the best-known general-purpose Lisp dialects are Common Lisp, Scheme, Racket, and Clojure.

In programming language theory, lazy evaluation, or call-by-need, is an evaluation strategy which delays the evaluation of an expression until its value is needed and which also avoids repeated evaluations.

Lambda calculus is a formal system in mathematical logic for expressing computation based on function abstraction and application using variable binding and substitution. Untyped lambda calculus, the topic of this article, is a universal model of computation that can be used to simulate any Turing machine. It was introduced by the mathematician Alonzo Church in the 1930s as part of his research into the foundations of mathematics. In 1936, Church found a formulation which was logically consistent, and documented it in 1940.

<span class="mw-page-title-main">Scheme (programming language)</span> Dialect of Lisp

Scheme is a dialect of the Lisp family of programming languages. Scheme was created during the 1970s at the MIT Computer Science and Artificial Intelligence Laboratory and released by its developers, Guy L. Steele and Gerald Jay Sussman, via a series of memos now known as the Lambda Papers. It was the first dialect of Lisp to choose lexical scope and the first to require implementations to perform tail-call optimization, giving stronger support for functional programming and associated techniques such as recursive algorithms. It was also one of the first programming languages to support first-class continuations. It had a significant influence on the effort that led to the development of Common Lisp.

In programming languages, a closure, also lexical closure or function closure, is a technique for implementing lexically scoped name binding in a language with first-class functions. Operationally, a closure is a record storing a function together with an environment. The environment is a mapping associating each free variable of the function with the value or reference to which the name was bound when the closure was created. Unlike a plain function, a closure allows the function to access those captured variables through the closure's copies of their values or references, even when the function is invoked outside their scope.

In combinatory logic for computer science, a fixed-point combinator, is a higher-order function that returns some fixed point of its argument function, if one exists.

In compiler design, static single assignment form is a type of intermediate representation (IR) where each variable is assigned exactly once. SSA is used in most high-quality optimizing compilers for imperative languages, including LLVM, the GNU Compiler Collection, and many commercial compilers.

In computer science, a continuation is an abstract representation of the control state of a computer program. A continuation implements (reifies) the program control state, i.e. the continuation is a data structure that represents the computational process at a given point in the process's execution; the created data structure can be accessed by the programming language, instead of being hidden in the runtime environment. Continuations are useful for encoding other control mechanisms in programming languages such as exceptions, generators, coroutines, and so on.

In programming language theory, call-by-push-value (CBPV) is an intermediate language that embeds the call-by-value (CBV) and call-by-name (CBN) evaluation strategies. CBPV is structured as a polarized λ-calculus with two main types, "values" (+) and "computations" (-). Restrictions on interactions between the two types enforce a controlled order of evaluation, similar to monads or CPS. The calculus can embed computational effects, such as nontermination, mutable state, or nondeterminism. There are natural semantics-preserving translations from CBV and CBN into CBPV. This means that giving a CBPV semantics and proving its properties implicitly establishes CBV and CBN semantics and properties as well. Paul Blain Levy formulated and developed CBPV in several papers and his doctoral thesis.

In the Scheme computer programming language, the procedure call-with-current-continuation, abbreviated call/cc, is used as a control flow operator. It has been adopted by several other programming languages.

In a programming language, an evaluation strategy is a set of rules for evaluating expressions. The term is often used to refer to the more specific notion of a parameter-passing strategy that defines the kind of value that is passed to the function for each parameter and whether to evaluate the parameters of a function call, and if so in what order. The notion of reduction strategy is distinct, although some authors conflate the two terms and the definition of each term is not widely agreed upon.

In computing, a meta-circular evaluator (MCE) or meta-circular interpreter (MCI) is an interpreter which defines each feature of the interpreted language using a similar facility of the interpreter's host language. For example, interpreting a lambda application may be implemented using function application. Meta-circular evaluation is most prominent in the context of Lisp. A self-interpreter is a meta-circular interpreter where the interpreted language is nearly identical to the host language; the two terms are often used synonymously.

In computer science, A-normal form is an intermediate representation of programs in functional programming language compilers. In ANF, all arguments to a function must be trivial. That is, evaluation of each argument must halt immediately.

In computer programming, an anonymous function is a function definition that is not bound to an identifier. Anonymous functions are often arguments being passed to higher-order functions or used for constructing the result of a higher-order function that needs to return a function. If the function is only used once, or a limited number of times, an anonymous function may be syntactically lighter than using a named function. Anonymous functions are ubiquitous in functional programming languages and other languages with first-class functions, where they fulfil the same role for the function type as literals do for other data types.

In programming languages, a delimited continuation, composable continuation or partial continuation, is a "slice" of a continuation frame that has been reified into a function. Unlike regular continuations, delimited continuations return a value, and thus may be reused and composed. Control delimiters, the basis of delimited continuations, were introduced by Matthias Felleisen in 1988 though early allusions to composable and delimited continuations can be found in Carolyn Talcott's Stanford 1984 dissertation, Felleisen et al., Felleisen's 1987 dissertation, and algorithms for functional backtracking, e.g., for pattern matching, for parsing, in the Algebraic Logic Functional programming language, and in the functional implementations of Prolog where the failure continuation is often kept implicit and the reason of being for the success continuation is that it is composable.

In mathematics and computer science, apply is a function that applies a function to arguments. It is central to programming languages derived from lambda calculus, such as LISP and Scheme, and also in functional languages. It has a role in the study of the denotational semantics of computer programs, because it is a continuous function on complete partial orders. Apply is also a continuous function in homotopy theory, and, indeed underpins the entire theory: it allows a homotopy deformation to be viewed as a continuous path in the space of functions. Likewise, valid mutations (refactorings) of computer programs can be seen as those that are "continuous" in the Scott topology.

The history of the programming language Scheme begins with the development of earlier members of the Lisp family of languages during the second half of the twentieth century. During the design and development period of Scheme, language designers Guy L. Steele and Gerald Jay Sussman released an influential series of Massachusetts Institute of Technology (MIT) AI Memos known as the Lambda Papers (1975–1980). This resulted in the growth of popularity in the language and the era of standardization from 1990 onward. Much of the history of Scheme has been documented by the developers themselves.

Join-patterns provides a way to write concurrent, parallel and distributed computer programs by message passing. Compared to the use of threads and locks, this is a high level programming model using communication constructs model to abstract the complexity of concurrent environment and to allow scalability. Its focus is on the execution of a chord between messages atomically consumed from a group of channels.

A CEK Machine is an abstract machine invented by Matthias Felleisen and Daniel P. Friedman that implements left-to-right call by value. It is generally implemented as an interpreter for functional programming languages, but can also be used to implement simple imperative programming languages. A state in a CEK machine includes a control statement, environment and continuation. The control statement is the term being evaluated at that moment, the environment is (usually) a map from variable names to values, and the continuation stores another state, or a special halt case. It is a simplified form of another abstract machine called the SECD machine.

References