Let there be let

We’ve shown how to infer the type of an expression. But how does it apply to definitions? Should the following be legal?

id = \x.x
one = id succ(id 0)

We infer id has type X → X where X is a type variable. Should we be allowed to substitute Nat → Nat for X in one occurence of id and Nat for the other?

If we can only pick one type constant for X, then it seems we must duplicate code:

id = \x.x
id2 = \x.x
one = id succ(id2 0)

On the other hand, before type inference, we could expand one to:

one = ((\x.x) succ)((\x.x) 0)

and only then introduce type variables. Afterwards, we can substitute different types for different uses of a definition.

The same discussion applies to local let expressions. That is, suppose we allow let _ = _ in _ anywhere we expect a term. Then it might be reasonable to permit the following:

one = let f = \x.x in f succ (f 0)

Otherwise we might be forced to duplicate code:

one = let f = \x.x in let g = \x.x in f succ (g 0)

Allowing different uses of a definition to have different types is called let-polymorphism. We demonstrate it with an interpreter based on PCF (Programming Computable Functions), a simply typed lambda calculus with the base type Nat with the constant 0 and extended with:

  • pred, succ: these functions have type Nat → Nat and return the predecessor and successor of their input; evaluating pred 0 anywhere in a term returns the Err term which represents this exception.

  • ifz-then-else: when given 0, an ifz expression evaluates to its then branch, otherwise it evaluates to its else branch.

  • fix: the fixpoint operator, allowing recursion (but breaking normalization).

For convenience, we parse all natural numbers as constants of type Nat. We also provide an undefined keyword that throws an error.

Avoiding the fixpoint operator guarantees normalization, that is, programs must terminate. Even with this restriction, the language is surprisingly expressive: we can sort lists without fix!

Some presentations of PCF also add the base type Bool along with constants True, False and replace ifz with if and iszero, which is similar to our last interpreter.

The original PCF lacks type inference and let-polymorphism, unlike later definitions of PCF.

Memoized type inference

We should mention how to evaluate local let definitions. Suppose we write:

\x y.let z = \a b.a in z x y

Evaluating this is trivial:

eval env (Let x y z) = eval env $ beta (x, y) z

That is, we add a new binding to the environment before evaluating the let body. An easy exercise is to add this to our previous interpreter.

As for type inference, we could treat let as a macro: we could fully expand all let definitions before type checking if we accept that work may be repeated. For example:

let f = \x.x in f succ (f 0)

could be expanded to:

(\x.x) succ ((\x.x) 0)

We would determine the first (\x.x) has type _0 → _0 where _0 is a generated type variable, before deducing further that _0 must be Nat → Nat. Afterwards, we would repeat computations to determine that the second (\x.x) has type _1 → _1, before deducing _1 must be Nat.

However, this approach has drawbacks. Functions can be more complicated than \x.x and let expansions can be deeply nested, leading to prohibitively many repeated computations. Also, we may one day wish to support a recursive variant of let, where full expansion is impossible.

Better to memoize: we cache the results of a computation for later reuse. We introduce generalized type variables for this purpose. A generalized type variable is a placeholder that generates a fresh type variable on demand.

In our example above, we first infer that id has type X → X where X is a type variable. Next, we mark X as a generalized type variable. Then each time id is used in an expression, we replace X with a newly generated ordinary type variable before proceeding with type inference.

Universally quantified types

Memoization is also useful for understanding the theory. Rather than vaguely say id is a sort of macro, we say that id = \x.x has type ∀X.X → X. The symbol indicates a given type variable is generalized. Lambda calculus with generalized type variables from let-polymorphism is known as the Hindley-Milner type system, or HM for short. Like simply typed lambda calculus, HM is strongly normalizing.

We might then wonder if this notation is redundant. Since let definitions are like macros, shouldn’t we generalize all type variables returned by the type inference algorithm? Why would we ever need to distinguish between generalized type variables and plain type variables if they’re always going to be generalized?

The reason becomes clear when we consider lower-level let expressions. Our code must mix generalized and ordinary type variables, and carefully keep track of them to correctly infer types. Consider the following example from Benjamin C. Pierce, Types and Programming Languages, where the language has base types Nat and Bool:

(\f:X->X x:X. let g=f in g 0)
  (\x:Bool. if x then True else False)

This program is invalid. But if we blithely assume all type variables in let expressions should be generalized, then we would mistakenly conclude otherwise. We would infer g has type ∀X.X→X. In g 0, this would generate a new type variable (that we then infer should be Nat).

Instead, we must infer g has type X→X, that is, X is an plain type variable and not generalized. This enables type inference to find two contradictory constraints (X = Nat and X = Bool) and reject the term.

On the other hand, we should generalize type variables in let expressions absent from higher levels. For example, in the following expression:

\f:X->X x:X. let g=\y.f in g

type inference should determine the function g has type ∀Y.Y→(X→X)→(X→X), that is, Y is generalized while X is not.

These details only matter when implementing languages. Users can blissfully ignore the distinction, because in top-level let definitions, all type variables are generalized, and in evaluated terms, all generalized type variables are replaced by plain type variables. When else does a user ask for a term’s type?

Indeed, our demo will follow Haskell and omit the (∀) symbol. We’ll say, for example, the const function has type a → b → a even though a and b are generalized type variables; its type is really ∀a b.a → b →a.

Halfway to Haskell

Syntax aside, we’re surprisingly close to Haskell 98, which is based on HM extended with the fixpoint operator. We lack many base types and primitive functions, but these have little theoretical significance.

The juicy missing pieces are algebraic data types and type classes.

Later versions of Haskell go beyond Hindley-Milner to a variant of System F, and there are plans to go even further. As a result, type inference is no longer guaranteed to succeed, and often the programmer must supply annotations to help the type checker.

We would be close to ML if we had chosen eager evaluation instead of lazy evaluation.


Despite the advanced capabilities of HM, we can almost reuse the data structures of simply typed lambda calculus. In a way, we could do with less. HM is rich enough that we can get by with no base types whatsoever. However, we’re implementing PCF so we provide Nat.

To our data type representing types, we add type variables and generalized type variables: our TV and GV constructors. And to our data type representing terms, we add a Let constructor to represent let expressions.

To keep the code simple, we show generalized type variables in a nonstandard manner: we simply prepend an at sign to the variable name. It’s understood that (@x → y) → @z really means ∀@x @z.(@x → y) → @z.

Since we follow Haskell’s convention by showing non-generalized type variables for top-level let expressions, under normal operation we’ll never show a generalized type variable. Roughly speaking, we lazily generalize the type variables of let statements, that is, we store them as ordinary type variables, and generalize them on demand during evaluation. A generalized type variable would only be printed if we, say, added a logging statement for debugging.

#ifdef __HASTE__
import Haste.DOM
import Haste.Events
import System.Console.Haskeline
import Control.Arrow
import Control.Monad
import Data.Char
import Data.List
import Data.Maybe
import Text.Parsec

data Type = Nat | TV String | GV String | Type :-> Type deriving Eq
data Term = Var String | App Term Term | Lam (String, Type) Term
  | Ifz Term Term Term | Let String Term Term | Err

instance Show Type where
  show Nat = "Nat"
  show (TV s) = s
  show (GV s) = '@':s
  show (t :-> u) = showL t ++ " -> " ++ show u where
    showL (_ :-> _) = "(" ++ show t ++ ")"
    showL _         = show t

instance Show Term where
  show (Lam (x, t) y)    = "\0955" ++ x ++ ":" ++ show t ++ showB y where
    showB (Lam (x, t) y) = " " ++ x ++ ":" ++ show t ++ showB y
    showB expr           = "." ++ show expr
  show (Var s)    = s
  show (App x y)  = showL x ++ showR y where
    showL (Lam _ _) = "(" ++ show x ++ ")"
    showL _         = show x
    showR (Var s)   = ' ':s
    showR _         = "(" ++ show y ++ ")"
  show (Ifz x y z) =
    "ifz " ++ show x ++ " then " ++ show y ++ " else " ++ show z
  show (Let x y z) =
    "let " ++ x ++ " = " ++ show y ++ " in " ++ show z
  show Err         = "*exception*"


The biggest change is the parsing of types in lambda abstractions. If omitted, we supply the type variable _ which indicates we should automatically generate a unique variable name for it later. Any name but Nat is a user-supplied type variable name.

We also rename Let to TopLet (for top-level let expressions) to avoid clashing with our above Let constructor.

data PCFLine = Blank | TopLet String Term | Run Term

line :: Parsec String () PCFLine
line = between ws eof $ option Blank $
    (try $ TopLet <$> v <*> (str "=" >> term)) <|> (Run <$> term) where
  term = ifz <|> letx <|> lam <|> app
  letx = Let <$> (str "let" >> v) <*> (str "=" >> term)
    <*> (str "in" >> term)
  ifz = Ifz <$> (str "ifz" >> term) <*> (str "then" >> term)
    <*> (str "else" >> term)
  lam = flip (foldr Lam) <$> between lam0 lam1 (many1 vt) <*> term
  lam0 = str "\\" <|> str "\0955"
  lam1 = str "."
  vt   = (,) <$> v <*> option (TV "_") (str ":" >> typ)
  typ = ((str "Nat" >> pure Nat) <|> (TV <$> v)
    <|> between (str "(") (str ")") typ)
      `chainr1` (str "->" >> pure (:->))
  app = foldl1' App <$> many1 ((Var <$> v) <|> between (str "(") (str ")") term)
  v   = try $ do
    s <- many1 alphaNum
    when (s `elem` words "ifz then else let in") $ fail "unexpected keyword"
    pure s
  str = try . (>> ws) . string
  ws = spaces >> optional (try $ string "--" >> many anyChar)

Type Inference

We add generalized variables to our implementation of Algorithm W. The instantiate function generates fresh type variables for any generalized type variables. This time, we write it without the state monad so we can compare styles. On balance, I prefer the state monad version.

If a variable name is absent from gamma, then the term is invalid. We abuse the GV constructor to represent this error.

We’re careful with let expressions: we only generalize those type variables that are absent from gamma before recursively calling gather.

We always generate a fresh variable for undefined so it can fit anywhere.

readInteger s = listToMaybe $ fst <$> (reads s :: [(Integer, String)])

gather gamma i term = case term of
  Var "undefined" -> (TV $ '_':show i, [], i + 1)
  Var "fix" -> ((a :-> a) :-> a, [], i + 1) where a = TV $ '_':show i
  Var "pred" -> (Nat :-> Nat, [], i)
  Var "succ" -> (Nat :-> Nat, [], i)
  Var s
    | Just _ <- readInteger s -> (Nat, [], i)
    | Just t <- lookup s gamma ->
      let (t', _, j) = instantiate t i in (t', [], j)
    | otherwise -> (TV "_", [(GV $ "undefined: " ++ s, GV "?")], i)
  Lam (s, TV "_") u -> (x :-> tu, cs, j) where
    (tu, cs, j) = gather ((s, x):gamma) (i + 1) u
    x = TV $ '_':show i
  Lam (s, t) u -> (t :-> tu, cs, j) where
    (tu, cs, j) = gather ((s, t):gamma) i u
  App t u -> (x, [(tt, uu :-> x)] `union` cs1 `union` cs2, k + 1) where
    (tt, cs1, j) = gather gamma i t
    (uu, cs2, k) = gather gamma j u
    x = TV $ '_':show k
  Ifz s t u -> (tt, foldl1' union [[(ts, Nat), (tt, tu)], cs1, cs2, cs3], l)
    where (ts, cs1, j) = gather gamma i s
          (tt, cs2, k) = gather gamma j t
          (tu, cs3, l) = gather gamma k u
  Let s t u -> (tu, cs1 `union` cs2, k) where
    gen = generalize (concatMap (freeTV . snd) gamma) tt
    (tt, cs1, j) = gather gamma i t
    (tu, cs2, k) = gather ((s, gen):gamma) j u

instantiate = f [] where
  f m ty i = case ty of
    GV s | Just t <- lookup s m -> (t, m, i)
         | otherwise            -> (x, (s, x):m, i + 1) where
           x = TV ('_':show i)
    t :-> u -> (t' :-> u', m'', i'') where
      (t', m' , i')  = f m  t i
      (u', m'', i'') = f m' u i'
    _       -> (ty, m, i)

generalize fvs ty = case ty of
  TV s | s `notElem` fvs -> GV s
  s :-> t                -> generalize fvs s :-> generalize fvs t
  _                      -> ty

freeTV (a :-> b) = freeTV a ++ freeTV b
freeTV (TV tv)   = [tv]
freeTV _         = []

unify ((GV s, GV "?"):_)   = Left s
unify []                   = Right []
unify ((s, t):cs) | s == t = unify cs
unify ((TV x, t):cs)
  | x `elem` freeTV t = Left $ "infinite: " ++ x ++ " = " ++ show t
  | otherwise         = ((x, t):) <$> unify (join (***) (subst (x, t)) <$> cs)
unify ((s, TV y):cs)  = unify ((TV y, s):cs)
unify ((s1 :-> s2, t1 :-> t2):cs) = unify $ (s1, t1):(s2, t2):cs
unify ((s, t):_)      = Left $ "mismatch: " ++ show s ++ " /= " ++ show t

subst (x, t) ty = case ty of
  a :-> b       -> subst (x, t) a :-> subst (x, t) b
  TV y | x == y -> t
  _             -> ty

The function typeOf is little more than a wrapper around gather and unify. It applies all the substitutions found during unify to the type expression returned by gather to compute the principal type of a given closed term in a given context.

typeOf gamma term = foldl' (flip subst) ty <$> unify cs where
  (ty, cs, _) = gather gamma 0 term


Once we’re certain a closed term is well-typed, we can ignore the types and evaluate as we would in untyped lambda calculus.

If we only wanted the weak head normal form, then we could take shortcuts: we could assume the first argument to any ifz, pred, or succ is a natural number. However, we want the normal form, necessitating extra checks.

If we encounter an Err term, we propagate it up the tree to halt computation.

eval env (Var "undefined") = Err
eval env t@(Ifz x y z) = case eval env x of
  Err -> Err
  Var s -> case readInteger s of
    Just 0 -> eval env y
    Just _ -> eval env z
    _      -> t
  _     -> t
eval env (Let x y z) = eval env $ beta (x, y) z
eval env (App m a) = let m' = eval env m in case m' of
  Err -> Err
  Lam (v, _) f -> eval env $ beta (v, a) f
  Var "pred" -> case eval env a of
    Err -> Err
    Var s -> case readInteger s of
      Just 0 -> Err
      Just i -> Var (show $ read s - 1)
      _      -> App m' (Var s)
    t -> App m' t
  Var "succ" -> case eval env a of
    Err -> Err
    Var s -> case readInteger s of
      Just i -> Var (show $ read s + 1)
      _      -> App m' (Var s)
    t -> App m' t
  Var "fix" -> eval env (App a (App m' a))
  _ -> App m' a
eval env (Var v) | Just x  <- lookup v env = eval env x
eval _   term                              = term

beta (v, a) f = case f of
  Var s | s == v         -> a
        | otherwise      -> Var s
  Lam (s, t) m
        | s == v         -> Lam (s, t) m
        | s `elem` fvs   -> let s1 = newName s fvs in
          Lam (s1, t) $ rec $ rename s s1 m
        | otherwise      -> Lam (s, t) (rec m)
  App m n                -> App (rec m) (rec n)
  Ifz x y z              -> Ifz (rec x) (rec y) (rec z)
  Let x y z              -> Let x (rec y) (rec z)
    rec = beta (v, a)
    fvs = fv [] a

fv vs (Var s) | s `elem` vs = []
              | otherwise   = [s]
fv vs (Lam (s, _) f)        = fv (s:vs) f
fv vs (App x y)             = fv vs x `union` fv vs y
fv vs (Let _ x y)           = fv vs x `union` fv vs y
fv vs (Ifz x y z)           = fv vs x `union` fv vs y `union` fv vs z

newName x ys = head $ filter (`notElem` ys) $ (s ++) . show <$> [1..] where
  s = dropWhileEnd isDigit x

rename x x1 term = case term of
  Var s | s == x    -> Var x1
        | otherwise -> term
  Lam (s, t) b
        | s == x    -> term
        | otherwise -> Lam (s, t) (rec b)
  App a b           -> App (rec a) (rec b)
  Ifz a b c         -> Ifz (rec a) (rec b) (rec c)
  Let a b c         -> Let a (rec b) (rec c)
  where rec = rename x x1
norm env term = case eval env term of
  Err          -> Err
  Var v        -> Var v
  Lam (v, t) m -> Lam (v, t) (rec m)
  App m n      -> App (rec m) (rec n)
  Ifz x y z    -> Ifz (rec x) (rec y) (rec z)
  where rec = norm env

User Interface

On error, our typing algorithm now returns a more detailed mesage instead of Nothing.

#ifdef __HASTE__
main = withElems ["input", "output", "evalB", "resetB", "resetP",
    "sortB", "sortP"] $
  \[iEl, oEl, evalB, resetB, resetP, sortB, sortP] -> do
    reset = getProp resetP "value" >>= setProp iEl "value"
      >> setProp oEl "value" ""
    run (out, env) (Left err) =
      (out ++ "parse error: " ++ show err ++ "\n", env)
    run (out, env@(gamma, lets)) (Right m) = case m of
      Blank      -> (out, env)
      Run term   -> case typeOf gamma term of
        Left m  -> (concat
           [out, "type error: ", show term, ": ", m, "\n"], env)
        Right t -> (out ++ show (norm lets term) ++ "\n", env)
      TopLet s term -> case typeOf gamma term of
        Left m  -> (concat
           [out, "type error: ", show term, ": ", m, "\n"], env)
        Right t -> (out ++ "[" ++ s ++ ":" ++ show t ++ "]\n",
          ((s, generalize [] t):gamma, (s, term):lets))
  resetB `onEvent` Click $ const reset
  sortB `onEvent` Click $ const $
    getProp sortP "value" >>= setProp iEl "value" >> setProp oEl "value" ""
  evalB `onEvent` Click $ const $ do
    es <- map (parse line "") . lines <$> getProp iEl "value"
    setProp oEl "value" $ fst $ foldl' run ("", ([], [])) es
repl env@(gamma, lets) = do
  let redo = repl env
  ms <- getInputLine "> "
  case ms of
    Nothing -> outputStrLn ""
    Just s  -> do
      case parse line "" s of
        Left err  -> do
          outputStrLn $ "parse error: " ++ show err
        Right Blank -> redo
        Right (Run term) -> do
          case typeOf gamma term of
            Left msg -> outputStrLn $ "bad type: " ++ msg
            Right t  -> do
              outputStrLn $ "[" ++ show t ++ "]"
              outputStrLn $ show $ norm lets term
        Right (TopLet s term) -> case typeOf gamma term of
          Left msg -> outputStrLn ("bad type: " ++ msg) >> redo
          Right t  -> do
            outputStrLn $ "[" ++ s ++ " : " ++ show t ++ "]"
            repl ((s, generalize [] t):gamma, (s, term):lets)

main = runInputT defaultSettings $ repl ([], [])

The world’s simplest list API

What’s the desert island function from Haskell’s Data.List package?

It’s foldr. Fold has many superpowers. We describe but a few of them.

We can build the rest of the API from right-folding over a list:

map f = foldr (\x xs -> f x : xs) []
head  = foldr const undefined
null  = foldr (const . const False) True
foldl = foldr . flip

The tail function is less elegant. We apply the same trick used in computing the predecessor of a Church numeral:

tail = snd $ foldr (\x (as, _) -> (x:as, as)) ([], undefined)

Similarly, we can write a foldr-based function that inserts an element into a sorted list so it remains sorted:

ins y xs = case foldr f ([y], []) xs of ([],  t) -> t
                                        ([h], t) -> h:t

f x ([y], t) | x < y = ([], x:y:t)
f x (a  , t)         = (a , x:t)

Insertion sort immediately follows:

sort :: Ord a => [a] -> [a]
sort = foldr ins []

We can represent lists with right folds. The list is a function, and it acts just like foldr if we give it a folding function and an initial value:

nil = \c n->n
con = \h t c n->c h(t c n)
example = con 3(con 1(con 4 nil))
example (:) []            -- [3, 1, 4]
foldr   (:) [] [3, 1, 4]  -- [3, 1, 4]

In simply typed lambda calculus, we must fix the type of the fold result. For example, a list of integers might be represented as right fold that returns an integer, and we can compute the sum of a list of integers as follows:

nil=\c:I->I->I n:I.n
cons=\h:I t:(I->I->I)->I->I c:I->I->I n:I.c h(t c n)
sum=\xs:(I->I->I)->I->I.xs(\h:I t:I.add h t)0
sum (cons 1 (cons 125 (cons 27 nil)))

This is about as far as we can go. Without let-polymorphism, we’re stuck with a single fold return type, limiting what we can achieve.

Hindley-Milner frees us. Thanks to generalized type variables, a single fold can return any type we want. We can port our Haskell code to lambda calculus to obtain a sorting function free of fix. We do use fix in our less-than function, but in a practical language this would be a built-in primitive. Alternatively we can use Church numerals, which has a well-known fix-free less-than-or-equal-to function.

It almost seems we’re cheating to avoid explicit loops by piggybacking off the representation of the list, but this is merely a consequence of our strategy. When functions represent data, we can perform complex tasks with miraculously concise code.

One step back, ten steps forward

Hindley-Milner is considered a sweet spot in the language design space because type inference is simple and decidable, yet the type system is powerful. There is a blemish: type inference takes exponential time for certain pathological cases. Luckily, they never show up in real life.

Still, experience suggests we should weaken Hindley-Milner in practical programming languages. Let should not be generalised automatically for local bindings.

Removing implicit local let-polymorphism makes it easier to extend the type system. For example, we can add type classes and sidestep the monomorphism restriction controversy. At the same time, local let-polymorphism is rarely used, and in any case, we can trivially declare a type when needed. In other words, we can still support local let-polymorphism; it’s just no longer automatic.

Also, the pathological exponential cases disappear, which I think means type inference is guaranteed to be efficient, provided we use abbreviations for repeated parts of types.

But most exciting of all, this small tactical retreat may have a huge payoff in the hopefully near future: dependent types in Haskell!

Ben Lynn blynn@cs.stanford.edu 💡