That language is an instrument of human reason, and not merely a medium for the expression of thought, is a truth generally admitted.

â€” George Boole

# Lambda Calculus

Fundamental to all functional languages is the most atomic notion of composition, function abstraction of a single variable. The lambda calculus consists very simply of three terms and all valid recursive combinations thereof:

This compact notation looks slightly different from what you're used to in Haskell but it's actually not: $$\lambda x.xa$$ is equivalent to \x -> x a. This means what you see in the picture above would translate to (\x -> x) (\y -> y), which would be equivalent to writing id id (which of course evaluates to id).

The three terms are typically referred to in code by several contractions of their names:

• Var - A variable
• Lam - A lambda abstraction
• App - An application

A lambda term is said to bind its variable. For example the lambda here binds $$x$$. In mathematics we would typically write:

$f(x) = e$

Using the lambda calculus notation we write:

$f = \lambda x. e$

In other words, $$\lambda x.e$$ is a function that takes a variable $$x$$ and returns $$e$$.

\begin{aligned} e :=\ & x & \trule{Var} \\ & \lambda x. e & \trule{Lam} \\ & e\ e & \trule{App} \\ \end{aligned}

The lambda calculus is often called the "assembly language" of functional programming, and variations and extensions on it form the basis of many functional compiler intermediate forms for languages like Haskell, OCaml, Standard ML, etc. The variation we will discuss first is known as untyped lambda calculus, by contrast later we will discuss the typed lambda calculus which is an extension thereof.

There are several syntactical conventions that we will adopt when writing lambda expressions. Application of multiple expressions associates to the left.

$x_1\ x_2\ x_3\ ... x_n = (... ((x_1 x_2 )x_3 ) ... x_n )$

By convention application extends as far to the right as is syntactically meaningful. Parentheses are used to disambiguate.

In the lambda calculus, each lambda abstraction binds a single variable, and the lambda abstraction's body may be another lambda abstraction. Out of convenience we often write multiple lambda abstractions with their variables on one lambda symbol. This is merely a syntactical convention and does not change the underlying meaning.

$\lambda xy.z = \lambda x. \lambda y.z$

The actual implementation of the lambda calculus admits several degrees of freedom in how lambda abstractions are represented. The most notable is the choice of identifiers for the binding variables. A variable is said to be bound if it is contained in a lambda expression of the same variable binding. Conversely a variable is free if it is not bound.

A term with free variables is said to be an open term while one without free variables is said to be closed or a combinator.

\begin{aligned} e_0 &= \lambda x . x \\ e_1 &= \lambda x. (x (\lambda y. y a) x) y \\ \end{aligned}

$$e_0$$ is a combinator while $$e_1$$ is not. In $$e_1$$ both occurrences of $$x$$ are bound. The first $$y$$ is bound, while the second is free. $$a$$ is also free.

Multiple lambda abstractions may bind the same variable name. Each occurrence of a variable is then bound by the nearest enclosing binder. For example, the $$x$$ variable in the following expression is bound on the inner lambda, while $$y$$ is bound on the outer lambda. This phenomenon is referred to as name shadowing.

$\lambda x y. (\lambda x z. x + y)$

## SKI Combinators

There are three fundamental closed expressions called the SKI combinators.

\begin{aligned} \textbf{S} &= \lambda f .( \lambda g .( \lambda x. f x ( g x ) ) ) \\ \textbf{K} &= \lambda x . \lambda y. x \\ \textbf{I} &= \lambda x.x \\ \end{aligned}

In Haskell these are written simply:

s f g x = f x (g x)
k x y = x
i x = x

Rather remarkably Moses SchÃ¶nfinkel showed that all closed lambda expressions can be expressed in terms of only the S and K combinators - even the I combinator. For example one can easily show that SKK reduces to I.

\begin{aligned} &\textbf{S} \textbf{K} \textbf{K}\\ &= ((\lambda xyz.x z (y z)) (\lambda xy.x) (\lambda xy.x)) \\ &= ((\lambda yz.(\lambda xy.x) z (y z)) ( \lambda xy.x)) \\ &= \lambda z.(\lambda xy.x) z ((\lambda xy.x) z) \\ &= \lambda z.(\lambda y.z) ((\lambda xy.x) z) \\ &= \lambda z.z \\ &= \textbf{I}\\ \end{aligned}

This fact is a useful sanity check when testing an implementation of the lambda calculus.

## Implementation

The simplest implementation of the lambda calculus syntax with named binders is the following definition.

type Name = String

data Expr
= Var Name
| App Expr Expr
| Lam Name Expr

There are several lexical syntax choices for lambda expressions, we will simply choose the Haskell convention which denotes lambda by the backslash (\) to the body with (->), and application by spaces. Named variables are simply alphanumeric sequences of characters.

• Logical notation: $$\mathtt{const} = \lambda x y . x$$
• Haskell notation: const = \x y -> x

In addition other terms like literal numbers or booleans can be added, and these make writing expository examples a little easier. For these we will add a Lit constructor.

data Expr
= ...
| Lit Lit

data Lit
= LInt Int
| LBool Bool

## Substitution

Evaluation of a lambda term ($$(\lambda x.e) a$$) proceeds by substitution of all free occurrences of the variable $$x$$ in $$e$$ with the argument $$a$$. A single substitution step is called a reduction. We write the substitution application in brackets before the expression it is to be applied over, $$[x / a]e$$ maps the variable $$x$$ to the new replacement $$a$$ over the expression $$e$$.

$(\lambda x. e) a \to [x / a] e$

A substitution metavariable will be written as $$[s]$$.

In detail, substitution is defined like this:

\begin{aligned} & [x/a] x &=& \ a\\ & [x/a] y &=& \ y & \text{if}\ x\neq y\\ & [x/a]ee' &=& \ ([x/a]e)([x/a]e')\\ & [x/a]\lambda x.e &=& \ \lambda x.e\\ & [x/a]\lambda y.e &=& \ \lambda y.[x/a]e & \text{if}\ x \neq y\ \text{and}\ y \notin\FV{a} \end{aligned}

where $$\FV{e}$$ is the set of free variables in $$e$$.

The fundamental issue with using locally named binders is the problem of name capture, or how to handle the case where a substitution conflicts with the names of free variables. We need the condition in the last case to avoid the naive substitution that would fundamentally alter the meaning of the following expression when $$y$$ is rewritten to $$x$$.

$[y / x] (\lambda x.xy) \to \lambda x.xx$

By convention we will always use a capture-avoiding substitution. Substitution will only proceed if the variable is not in the set of free variables of the expression, and if it does then a fresh variable will be created in its place.

$(\lambda x. e) a \to [x / a] e \quad \text{if}\ x \notin \FV{a}$

There are several binding libraries and alternative implementations of the lambda calculus syntax that avoid these problems. It is a very common problem and it is very easy to implement incorrectly even for experts.

## Conversion and Equivalences

Alpha equivalence

$(\lambda x.e) \overset{\alpha} = (\lambda y. [x / y] e)$

Alpha equivalence is the property ( when using named binders ) that changing the variable on the binder and throughout the body of the expression should not change the fundamental meaning of the whole expression. So for example the following are alpha-equivalent.

$\lambda x y. x y \quad \overset{\alpha} = \quad \lambda a b . a b$

Beta-reduction

Beta reduction is simply a single substitution step, replacing a variable bound by a lambda expression with the argument to the lambda throughout the body of the expression.

$(\lambda x.a) y \overset{\beta}{\rightarrow} [x / y] a$

Eta-reduction

$\lambda x.ex \overset{\eta}{\rightarrow} e \quad \text{if} \quad x \notin \FV{e}$

This is justified by the fact that if we apply both sides to a term, one step of beta reduction turns the left side to the right side:

$(\lambda x.ex)e' \overset{\beta}{\rightarrow} ee' \quad \text{if} \quad x \notin \FV{e}$

Eta-expansion

The opposite of eta reduction is eta-expansion, which takes a function that is not saturated and makes all variables explicitly bound in a lambda. Eta-expansion will be important when we discuss translation into STG.

## Reduction

Evaluation of lambda calculus expressions proceeds by beta reduction. The variables bound in a lambda are substituted across the body of the lambda. There are several degrees of freedom in the design space about how to do this, and in which order an expression should be evaluated. For instance we could evaluate under the lambda and then substitute variables into it, or instead evaluate the arguments and then substitute and then reduce the lambda expressions. More on this will be discussed in the section on Evaluation models.

Untyped> (\x.x) 1
1

Untyped> (\x y . y) 1 2
2

Untyped> (\x y z. x z (y z)) (\x y . x) (\x y . x)
=> \x y z . (x z (y z))
=> \y z . ((\x y . x) z (y z))
=> \x y . x
=> \y . z
=> z
=> \z . z
\z . z

Note that the last evaluation was SKK which we encountered earlier.

In the untyped lambda calculus we can freely represent infinitely diverging expressions:

Untyped> \f . (f (\x . (f x x)) (\x . (f x x)))
\f . (f (\x . (f x x)) (\x . (f x x)))

Untyped> (\f . (\x. (f x x)) (\x. (f x x))) (\f x . f f)
...

Untyped> (\x. x x) (\x. x x)
...

## Let

In addition to application, a construct known as a let binding is often added to the lambda calculus syntax. In the untyped lambda calculus, let bindings are semantically equivalent to applied lambda expressions.

$\mathtt{let}\ a = e\ \mathtt{in}\ b \quad := \quad (\lambda a.b) e$

In our languages we will write let statements like they appear in Haskell.

let a = e in b

Toplevel expressions will be written as let statements without a body to indicate that they are added to the global scope. The Haskell language does not use this convention but OCaml, StandardML use this convention. In Haskell the preceding let is simply omitted for toplevel declarations.

let S f g x = f x (g x);
let K x y = x;
let I x = x;

let skk = S K K;

For now the evaluation rule for let is identical to that of an applied lambda.

$\begin{array}{clll} (\lambda x. e) v & \rightarrow & [x/v] e & \trule{E-Lam} \\ \mathtt{let} \ x = v \ \mathtt{in} \ e & \rightarrow & [x/v] e & \trule{E-Let} \\ \end{array}$

In later variations of the lambda calculus let expressions will have different semantics and will differ from applied lambda expressions. More on this will be discussed in the section on Hindley-Milner inference.

• 0
• 1
• 2

• succ
• pred

• not
• and
• or

• mul

## Recursion

Probably the most famous combinator is Curry's Y combinator. Within an untyped lambda calculus, Y can be used to allow an expression to contain a reference to itself and reduce on itself permitting recursion and looping logic.

The $$\textbf{Y}$$ combinator is one of many so called fixed point combinators.

$\textbf{Y} = \lambda R.(\lambda x.(R (x x)) \lambda x.(R (x x)))$

$$\textbf{Y}$$ is quite special in that given $$\textbf{R}$$ It returns the fixed point of $$\textbf{R}$$.

\begin{aligned} \textbf{YR} &= \lambda f.(\lambda x.(f (x x)) \lambda x.(f (x x))) R \\ & = (\lambda x.(\textbf{R} (x x)) \lambda x.(\textbf{R} (x x))) \\ & = \textbf{R} (\lambda x.(\textbf{R} (x x)) \lambda x.(\textbf{R} (x x))) \\ & = \textbf{R Y R} \end{aligned}

For example the factorial function can be defined recursively in terms of repeated applications of itself to fixpoint until the base case of $$0!$$.

$n! = n (n-1)!$

\begin{aligned} \textbf{fac}\ 0 &= 1 \\ \textbf{fac}\ n &= \textbf{R}(\textbf{fac}) = \textbf{R}(\textbf{R}(\textbf{fac})) ... \end{aligned}

For fun one can prove that the Y-combinator can be expressed in terms of the S and K combinators.

$\textbf{Y} = \textbf{SSK(S(K(SS(S(SSK))))K)}$

In an untyped lambda calculus language without explicit fixpoint or recursive let bindings, the Y combinator can be used to create both of these constructs out of nothing but lambda expressions. However it is more common to just add either an atomic fixpoint operator or a recursive let as a fundamental construct in the term syntax.

\begin{aligned} e :=\ & x \\ & e_1\ e_2 \\ & \lambda x . e \\ & \t{fix}\ e \\ \end{aligned}

Where $$\t{fix}$$ has the evaluation rule:

$\begin{array}{cll} \mathtt{fix} \ v & \rightarrow & v\ (\mathtt{fix}\ v) \\ \end{array}$

Together with the fixpoint (or the Y combinator) we can create let bindings which contain a reference to itself within the body of the bound expression. We'll call these recursive let bindings, they are written as let rec in ML dialects. For now we will implement recursive lets as simply syntactic sugar for wrapping a fixpoint around a lambda binding by the following equivalence.

let rec x = e1 in e2    =    let x = fix (\x. e1) in e2

So for example we can now write down every functional programmer's favorite two functions: factorial and fibonacci. To show both styles, one is written with let rec and the other with explicit fix.

let fact = fix (\fact -> \n ->
if (n == 0)
then 1
else (n * (fact (n-1))));
let rec fib n =
if (n == 0)
then 0
else if (n==1)
then 1
else ((fib (n-1)) + (fib (n-2)));

Omega Combinator

An important degenerate case that we'll test is the omega combinator which applies a single argument to itself.

$\omega = \lambda x. x x \\$

When we apply the $$\omega$$ combinator to itself we find that this results in an infinitely long repeating chain of reductions. A sequence of reductions that has no normal form ( i.e. it reduces indefinitely ) is said to diverge.

$(\lambda x. x x) (\lambda x. x x) \to \\ \quad (\lambda x. x x)(\lambda x. x x) \to \\ \quad \quad (\lambda x. x x)(\lambda x. x x) \ldots$

We'll call this expression the $$\Omega$$ combinator. It is the canonical looping term in the lambda calculus. Quite a few of our type systems which are statically typed will reject this term from being well-formed, so it is quite a useful tool for testing.

$\Omega = \omega \omega = (\lambda x. x x) (\lambda x. x x)\\$

## Pretty Printing

Hackage provides quite a few pretty printer libraries that ease the process of dumping out textual forms for our data types. Although there are some differences between the libraries most of them use the same set of combinators. We will use the Text.PrettyPrint module from the pretty package on Hackage. Most of our pretty printing will be unavoidable boilerplate but will make debugging internal state much easier.

Combinators
<> Concatenation
<+> Spaced concatenation
char Renders a character as a Doc
text Renders a string as a Doc
hsep Horizontally concatenates a list of Doc
vcat Vertically joins a list of Doc with newlines

The core type of the pretty printer is the Doc type which is the abstract type of documents. Combinators over this type will manipulate the internal structure of this document which is then finally reified to a physical string using the render function. Since we intend to pretty print across multiple types we will create a Pretty typeclass.

module Pretty where

import Text.PrettyPrint

class Pretty p where
ppr :: Int -> p -> Doc

pp :: p -> Doc
pp = ppr 0

First, we create two helper functions that collapse our lambda bindings so we can print them out as single lambda expressions.

viewVars :: Expr -> [Name]
viewVars (Lam n a) = n : viewVars a
viewVars _ = []

viewBody :: Expr -> Expr
viewBody (Lam _ a) = viewBody a
viewBody x = x

Then we create a helper function for parenthesizing subexpressions.

parensIf ::  Bool -> Doc -> Doc
parensIf True = parens
parensIf False = id

Finally, we define ppr. The p variable will indicate our depth within the current structure we're printing and allow us to print out differently to disambiguate it from its surroundings if necessary.

instance Pretty Expr where
ppr p e = case e of
Lit (LInt a)  -> text (show a)
Lit (LBool b) -> text (show b)
Var x   -> text x
App a b -> parensIf (p>0) $(ppr (p+1) a) <+> (ppr p b) Lam x a -> parensIf (p>0)$
char '\\'
<> hsep (fmap pp (viewVars e))
<+> "->"
<+> ppr (p+1) (viewBody e)

ppexpr :: Expr -> String
ppexpr = render . ppr 0