Implementing Hindley-Milner with the unification-fd library

For a current project, I needed to implement type inference for a Hindley-Milner-based type system. (More about that project in an upcoming post!) If you don’t know, Hindley-Milner is what you get when you add polymorphism to the simply typed lambda calculus, but only allow \forall to show up at the very outermost layer of a type. This is the fundamental basis for many real-world type systems (e.g. OCaml or Haskell without RankNTypes enabled).

One of the core operations in any Hindley-Milner type inference algorithm is unification, where we take two types that might contain unification variables (think “named holes”) and try to make them equal, which might fail, or might provide more information about the values that the unification variables should take. For example, if we try to unify a -> Int and Char -> b, we will learn that a = Char and b = Int; on the other hand, trying to unify a -> Int and (b, Char) will fail, because there is no way to make those two types equal (the first is a function type whereas the second is a pair).

I’ve implemented this from scratch before, and it was a great learning experience, but I wasn’t looking forward to implementing it again. But then I remembered the unification-fd library and wondered whether I could use it to simplify the implementation. Long story short, although the documentation for unification-fd claims it can be used to implement Hindley-Milner, I couldn’t find any examples online, and apparently neither could anyone else. So I set out to make my own example, and you’re reading it. It turns out that unification-fd is incredibly powerful, but using it can be a bit finicky, so I hope this example can be helpful to others who wish to use it. Along the way, resources I found especially helpful include this basic unification-fd tutorial by the author, Wren Romano, as well as a blog post by Roman Cheplyaka, and the unification-fd Haddock documentation itself. I also referred to the Wikipedia page on Hindley-Milner, which is extremely thorough.

This blog post is rendered automatically from a literate Haskell file; you can find the complete working source code and blog post on GitHub. I’m always happy to receive comments, fixes, or suggestions for improvement.

Prelude: A Bunch of Extensions and Imports

We will make GHC and other people’s libraries work very hard for us. You know the drill.

> {-# LANGUAGE DeriveAnyClass        #-}
> {-# LANGUAGE DeriveFoldable        #-}
> {-# LANGUAGE DeriveFunctor         #-}
> {-# LANGUAGE DeriveGeneric         #-}
> {-# LANGUAGE DeriveTraversable     #-}
> {-# LANGUAGE FlexibleContexts      #-}
> {-# LANGUAGE FlexibleInstances     #-}
> {-# LANGUAGE GADTs                 #-}
> {-# LANGUAGE LambdaCase            #-}
> {-# LANGUAGE MultiParamTypeClasses #-}
> {-# LANGUAGE PatternSynonyms       #-}
> {-# LANGUAGE StandaloneDeriving    #-}
> {-# LANGUAGE UndecidableInstances  #-}
> import           Control.Category ((>>>))
> import           Control.Monad.Except
> import           Control.Monad.Reader
> import           Data.Foldable              (fold)
> import           Data.Functor.Identity
> import           Data.List                  (intercalate)
> import           Data.Map                   (Map)
> import qualified Data.Map                   as M
> import           Data.Maybe
> import           Data.Set                   (Set, (\\))
> import qualified Data.Set                   as S
> import           Prelude                    hiding (lookup)
> import           Text.Printf
> import           Text.Parsec
> import           Text.Parsec.Expr
> import           Text.Parsec.Language       (emptyDef)
> import           Text.Parsec.String
> import qualified Text.Parsec.Token          as L
> import           Control.Unification        hiding ((=:=), applyBindings)
> import qualified Control.Unification        as U
> import           Control.Unification.IntVar
> import           Data.Functor.Fixedpoint
> import           GHC.Generics               (Generic1)
> import           System.Console.Repline

Representing our types

We’ll be implementing a language with lambas, application, and let-expressions—as well as natural numbers with an addition operation, just to give us a base type and something to do with it. So we will have a natural number type and function types, along with polymorphism, i.e. type variables and forall. (Adding more features like sum and product types, additional base types, recursion, etc. is left as an exercise for the reader!)

So notionally, we want something like this:

type Var = String
data Type = TyVar Var | TyNat | TyFun Type Type

However, when using unification-fd, we have to encode our Type data type (i.e. the thing we want to do unification on) using a “two-level type” (see Tim Sheard’s original paper).

> type Var = String
> data TypeF a = TyVarF Var | TyNatF | TyFunF a a
>   deriving (Show, Eq, Functor, Foldable, Traversable, Generic1, Unifiable)
> type Type = Fix TypeF

TypeF is a “structure functor” that just defines a single level of structure; notice TypeF is not recursive at all, but uses the type parameter a to mark the places where a recursive instance would usually be. unification-fd provides a Fix type to “tie the knot” and make it recursive. (I’m not sure why unification-fd defines its own Fix type instead of using the one from Data.Fix, but perhaps the reason is that it was written before Data.Fix existed—unification-fd was first published in 2007!)

We have to derive a whole bunch of instances for TypeF which fortunately we get for free. Note in particular Generic1 and Unifiable: Unifiable is a type class from unification-fd which describes how to match up values of our type. Thanks to the work of Roman Cheplyaka, there is a default implementation for Unifiable based on a Generic1 instance—which GHC derives for us in turn—and the default implementation works great for our purposes.

unification-fd also provides a second type for tying the knot, called UTerm, defined like so:

data UTerm t v
  = UVar  !v               -- ^ A unification variable.
  | UTerm !(t (UTerm t v)) -- ^ Some structure containing subterms.

It’s similar to Fix, except it also adds unification variables of some type v. (If it means anything to you, note that UTerm is actually the free monad over t.) We also define a version of Type using UTerm, which we will use during type inference:

> type UType = UTerm TypeF IntVar

IntVar is a type provided by unification-fd representing variables as Int values, with a mapping from variables to bindings stored in an IntMap. unification-fd also provies an STVar type which implements variables via STRefs; I presume using STVars would be faster (since no intermediate lookups in an IntMap are required) but forces us to work in the ST monad. For now I will just stick with IntVar, which makes things simpler.

At this point you might wonder: why do we have type variables in our definition of TypeF, but also use UTerm to add unification variables? Can’t we just get rid of the TyVarF constructor, and let UTerm provide the variables? Well, type variables and unification variables are subtly different, though intimately related. A type variable is actually part of a type, whereas a unification variable is not itself a type, but only stands for some type which is (as yet) unknown. After we are completely done with type inference, we won’t have a UTerm any more, but we might have a type like forall a. a -> a which still contains type variables, so we need a way to represent them. We could only get rid of the TyVarF constructor if we were doing type inference for a language without polymorphism (and yes, unification could still be helpful in such a situation—for example, to do full type reconstruction for the simply typed lambda calculus, where lambdas do not have type annotations).

Polytype represents a polymorphic type, with a forall at the front and a list of bound type variables (note that regular monomorphic types can be represented as Forall [] ty). We don’t need to make an instance of Unifiable for Polytype, since we never unify polytypes, only (mono)types. However, we can have polytypes with unification variables in them, so we need two versions, one containing a Type and one containing a UType.

> data Poly t = Forall [Var] t
>   deriving (Eq, Show, Functor)
> type Polytype  = Poly Type
> type UPolytype = Poly UType

Finally, for convenience, we can make a bunch of pattern synonyms that let us work with Type and UType just as if they were directly recursive types. This isn’t required; I just like not having to write Fix and UTerm everywhere.

> pattern TyVar :: Var -> Type
> pattern TyVar v = Fix (TyVarF v)
> pattern TyNat :: Type
> pattern TyNat = Fix TyNatF
> pattern TyFun :: Type -> Type -> Type
> pattern TyFun t1 t2 = Fix (TyFunF t1 t2)
> pattern UTyNat :: UType
> pattern UTyNat = UTerm TyNatF
> pattern UTyFun :: UType -> UType -> UType
> pattern UTyFun t1 t2 = UTerm (TyFunF t1 t2)
> pattern UTyVar :: Var -> UType
> pattern UTyVar v = UTerm (TyVarF v)


Here’s a data type to represent expressions. There’s nothing much interesting to see here, since we don’t need to do anything fancy with expressions. Note that lambdas don’t have type annotations, but let-expressions can have an optional polytype annotation, which will let us talk about checking polymorphic types in addition to inferring them (a lot of presentations of Hindley-Milner don’t talk about this).

> data Expr where
>   EVar  :: Var -> Expr
>   EInt  :: Integer -> Expr
>   EPlus :: Expr -> Expr -> Expr
>   ELam  :: Var -> Expr -> Expr
>   EApp  :: Expr -> Expr -> Expr
>   ELet  :: Var -> Maybe Polytype -> Expr -> Expr -> Expr

Normally at this point we would write parsers and pretty-printers for types and expressions, but that’s boring and has very little to do with unification-fd, so I’ve left those to the end. Let’s get on with the interesting bits!

Type inference infrastructure

Before we get to the type inference algorithm proper, we’ll need to develop a bunch of infrastructure. First, here’s the concrete monad we will be using for type inference. The ReaderT Ctx will keep track of variables and their types; ExceptT TypeError of course allows us to fail with type errors; and IntBindingT is a monad transformer provided by unification-fd which supports various operations such as generating fresh variables and unifying things. Note, for reasons that will become clear later, it’s very important that the IntBindingT is on the bottom of the stack, and the ExceptT comes right above it. Beyond that we can add whatever we like on top.

> type Infer = ReaderT Ctx (ExceptT TypeError (IntBindingT TypeF Identity))

Normally, I would prefer to write everything in a “capability style” where the code is polymorphic in the monad, and just specifies what capabilites/effects it needs (either just using mtl classes directly, or using an effects library like polysemy or fused-effects), but the way the unification-fd API is designed seems to make that a bit tricky.

A type context is a mapping from variable names to polytypes; we also have a function for looking up the type of a variable in the context, and a function for running a local subcomputation in an extended context.

> type Ctx = Map Var UPolytype
> lookup :: Var -> Infer UType
> lookup x = do
>   ctx <- ask
>   maybe (throwError $ UnboundVar x) instantiate (M.lookup x ctx)
> withBinding :: MonadReader Ctx m => Var -> UPolytype -> m a -> m a
> withBinding x ty = local (M.insert x ty)

The lookup function throws an error if the variable is not in the context, and otherwise returns a UType. Conversion from the UPolytype stored in the context to a UType happens via a function called instantiate, which opens up the UPolytype and replaces each of the variables bound by the forall with a fresh unification variable. We will see the implementation of instantiate later.

We will often need to recurse over UTypes. We could just write directly recursive functions ourselves, but there is a better way. Although the unification-fd library provides a function cata for doing a fold over a term built with Fix, it doesn’t provide a counterpart for UTerm; but no matter, we can write one ourselves, like so:

> ucata :: Functor t => (v -> a) -> (t a -> a) -> UTerm t v -> a
> ucata f _ (UVar v) = f v
> ucata f g (UTerm t) = g (fmap (ucata f g) t)

Now, we can write some utilities for finding free variables. Inexplicably, IntVar does not have an Ord instance (even though it is literally just a newtype over Int), so we have to derive one if we want to store them in a Set. Notice that our freeVars function finds free unification variables and free type variables; I will talk about why we need this later (this is something I got wrong at first!).

> deriving instance Ord IntVar
> class FreeVars a where
>   freeVars :: a -> Infer (Set (Either Var IntVar))

Finding the free variables in a UType is our first application of ucata. First, to find the free unification variables, we just use the getFreeVars function provided by unification-fd and massage the output into the right form. To find free type variables, we fold using ucata: we ignore unification variables, capture a singleton set in the TyVarF case, and in the recursive case we call fold, which will turn a TypeF (Set ...) into a Set ... using the Monoid instance for Set, i.e. union.

> instance FreeVars UType where
>   freeVars ut = do
>     fuvs <- fmap (S.fromList . map Right) . lift . lift $ getFreeVars ut
>     let ftvs = ucata (const S.empty)
>                      (\case {TyVarF x -> S.singleton (Left x); f -> fold f})
>                      ut
>     return $ fuvs `S.union` ftvs

Why don’t we just find free unification variables with ucata at the same time as the free type variables, and forget about using getFreeVars? Well, I looked at the source, and getFreeVars is actually a complicated beast. I’m really not sure what it’s doing, and I don’t trust that just manually getting the unification variables ourselves would be doing the right thing!

Now we can leverage the above instance to find free varaibles in UPolytypes and type contexts. For a UPolytype, we of course have to subtract off any variables bound by the forall.

> instance FreeVars UPolytype where
>   freeVars (Forall xs ut) = (\\ (S.fromList (map Left xs))) <$> freeVars ut
> instance FreeVars Ctx where
>   freeVars = fmap S.unions . mapM freeVars . M.elems

And here’s a simple utility function to generate fresh unification variables, built on top of the freeVar function provided by unification-fd:

> fresh :: Infer UType
> fresh = UVar <$> lift (lift freeVar)

One thing to note is the annoying calls to lift we have to do in the definition of FreeVars for UType, and in the definition of fresh. The getFreeVars and freeVar functions provided by unification-fv have to run in a monad which is an instance of BindingMonad, and the BindingMonad class does not provide instances for mtl transformers. We could write our own instances so that these functions would work in our Infer monad automatically, but honestly that sounds like a lot of work. Sprinkling a few lifts here and there isn’t so bad.

Next, a data type to represent type errors, and an instance of the Fallible class, needed so that unification-fd can inject errors into our error type when it encounters unification errors. Basically we just need to provide two specific constructors to represent an “occurs check” failure (i.e. an infinite type), or a unification mismatch failure.

> data TypeError where
>   UnboundVar   :: String -> TypeError
>   Infinite     :: IntVar -> UType -> TypeError
>   Mismatch     :: TypeF UType -> TypeF UType -> TypeError
> instance Fallible TypeF IntVar TypeError where
>   occursFailure   = Infinite
>   mismatchFailure = Mismatch

The =:= operator provided by unification-fd is how we unify two types. It has a kind of bizarre type:

(=:=) :: ( BindingMonad t v m, Fallible t v e
         , MonadTrans em, Functor (em m), MonadError e (em m))
      => UTerm t v -> UTerm t v -> em m (UTerm t v)

(Apparently I am not the only one who thinks this type is bizarre; the unification-fd source code contains the comment -- TODO: what was the reason for the MonadTrans madness?)

I had to stare at this for a while to understand it. It says that the output will be in some BindingMonad (such as IntBindingT), and there must be a single error monad transformer on top of it, with an error type that implements Fallible. So =:= can return ExceptT TypeError (IntBindingT Identity) UType, but it cannot be used directly in our Infer monad, because that has a ReaderT on top of the ExceptT. So I just made my own version with an extra lift to get it to work directly in the Infer monad. While we’re at it, we’ll make a lifted version of applyBindings, which has the same issue.

> (=:=) :: UType -> UType -> Infer UType
> s =:= t = lift $ s U.=:= t
> applyBindings :: UType -> Infer UType
> applyBindings = lift . U.applyBindings

Converting between mono- and polytypes

Central to the way Hindley-Milner works is the way we move back and forth between polytypes and monotypes. First, let’s see how to turn UPolytypes into UTypes, hinted at earlier in the definition of the lookup function. To instantiate a UPolytype, we generate a fresh unification variable for each variable bound by the Forall, and then substitute them throughout the type.

> instantiate :: UPolytype -> Infer UType
> instantiate (Forall xs uty) = do
>   xs' <- mapM (const fresh) xs
>   return $ substU (M.fromList (zip (map Left xs) xs')) uty

The substU function can substitute for either kind of variable in a UType (right now we only need it to substitute for type variables, but we will need it to substitute for unification variables later). Of course, it is implemented via ucata. In the variable cases we make sure to leave the variable alone if it is not a key in the given substitution mapping. In the recursive non-variable case, we just roll up the TypeF UType into a UType by applying UTerm. This is the power of ucata: we can deal with all the boring recursive cases in one fell swoop. This function won’t have to change if we add new types to the language in the future.

> substU :: Map (Either Var IntVar) UType -> UType -> UType
> substU m = ucata
>   (\v -> fromMaybe (UVar v) (M.lookup (Right v) m))
>   (\case
>       TyVarF v -> fromMaybe (UTyVar v) (M.lookup (Left v) m)
>       f -> UTerm f
>   )

There is one other way to convert a UPolytype to a UType, which happens when we want to check that an expression has a polymorphic type specified by the user. For example, let foo : forall a. a -> a = \x.3 in ... should be a type error, because the user specified that foo should have type forall a. a -> a, but then gave the implementation \x.3 which is too specific. In this situation we can’t just instantiate the polytype—that would create a unification variable for a, and while typechecking \x.3 it would unify a with nat. But in this case we don’t want a to unify with nat—it has to be held entirely abstract, because the user’s claim is that this function will work for any type a.

Instead of generating unification variables, we instead want to generate what are known as Skolem variables. Skolem variables do not unify with anything other than themselves. So how can we get unification-fd to do that? It does not have any built-in notion of Skolem variables. What we can do instead is to just embed the variables within the UType as UTyVars instead of UVars! unification-fd does not even know those are variables; it just sees them as another rigid part of the structure that must be matched exactly, just as a TyFun has to match another TyFun. The one remaining issue is that we need to generate fresh Skolem variables; it certainly would not do to have them collide with Skolem variables from some other forall. We could carry around our own supply of unique names in the Infer monad for this purpose, which would probably be the “proper” way to do things; but for now I did something more expedient: just get unification-fd to generate fresh unification variables, then rip the (unique! fresh!) Ints out of them and use those to make our Skolem variables.

> skolemize :: UPolytype -> Infer UType
> skolemize (Forall xs uty) = do
>   xs' <- mapM (const fresh) xs
>   return $ substU (M.fromList (zip (map Left xs) (map toSkolem xs'))) uty
>   where
>     toSkolem (UVar v) = UTyVar (mkVarName "s" v)

When unification-fd generates fresh IntVars it seems that it starts at minBound :: Int and increments, so we can add maxBound + 1 to get numbers that look reasonable. Again, this is not very principled, but for this toy example, who cares?

> mkVarName :: String -> IntVar -> Var
> mkVarName nm (IntVar v) = nm ++ show (v + (maxBound :: Int) + 1)

Next, how do we convert from a UType back to a UPolytype? This happens when we have inferred the type of a let-bound variable and go to put it in the context; typically, Hindley-Milner systems generalize the inferred type to a polytype. If a unification variable still occurs free in a type, it means it was not constrained at all, so we can universally quantify over it. However, we have to be careful: unification variables that occur in some type that is already in the context do not count as free, because we might later discover that they need to be constrained.

Also, just before we do the generalization, it’s very important that we use applyBindings. unification-fd has been collecting a substitution from unification variables to types, but for efficiency’s sake it does not actually apply the substitution until we ask it to, by calling applyBindings. Any unification variables which still remain after applyBindings really are unconstrained so far. So after applyBindings, we get the free unification variables from the type, subtract off any unification variables which are free in the context, and close over the remaining variables with a forall, substituting normal type variables for them. It does not particularly matter if these type variables are fresh (so long as they are distinct). But we can’t look only at unification variables! We have to look at free type variables too (this is the reason that our freeVars function needs to find both free type and unification variables). Why is that? Well, we might have some free type variables floating around if we previously generated some Skolem variables while checking a polymorphic type. (A term which illustrates this behavior is \y. let x : forall a. a -> a = y in x 3.) Free Skolem variables should also be generalized over.

> generalize :: UType -> Infer UPolytype
> generalize uty = do
>   uty' <- applyBindings uty
>   ctx <- ask
>   tmfvs  <- freeVars uty'
>   ctxfvs <- freeVars ctx
>   let fvs = S.toList $ tmfvs \\ ctxfvs
>       xs  = map (either id (mkVarName "a")) fvs
>   return $ Forall xs (substU (M.fromList (zip fvs (map UTyVar xs))) uty')

Finally, we need a way to convert Polytypes entered by the user into UPolytypes, and a way to convert the final UPolytype back into a Polytype. unification-fd provides functions unfreeze : Fix t -> UTerm t v and freeze : UTerm t v -> Maybe (Fix t) to convert between terms built with UTerm (with unification variables) and Fix (without unification variables). Converting to UPolytype is easy: we just use unfreeze to convert the underlying Type to a UType.

> toUPolytype :: Polytype -> UPolytype
> toUPolytype = fmap unfreeze

When converting back, notice that freeze returns a Maybe; it fails if there are any unification variables remaining. So we must be careful to only use fromUPolytype when we know there are no unification variables remaining in a polytype. In fact, we will use this only at the very top level, after generalizing the type that results from inference over a top-level term. Since at the top level we only perform inference on closed terms, in an empty type context, the final generalize step will generalize over all the remaining free unification variables, since there will be no free variables in the context.

> fromUPolytype :: UPolytype -> Polytype
> fromUPolytype = fmap (fromJust . freeze)

Type inference

Finally, the type inference algorithm proper! First, to check that an expression has a given type, we infer the type of the expression and then demand (via =:=) that the inferred type must be equal to the given one. Note that =:= actually returns a UType, and it can apparently be more efficient to use the result of =:= in preference to either of the arguments to it (although they will all give equivalent results). However, in our case this doesn’t seem to make much difference.

> check :: Expr -> UType -> Infer ()
> check e ty = do
>   ty' <- infer e
>   _ <- ty =:= ty'
>   return ()

And now for the infer function. The EVar, EInt, and EPlus cases are straightforward.

> infer :: Expr -> Infer UType
> infer (EVar x)      = lookup x
> infer (EInt _)      = return UTyNat
> infer (EPlus e1 e2) = do
>   check e1 UTyNat
>   check e2 UTyNat
>   return UTyNat

For an application EApp e1 e2, we infer the types funTy and argTy of e1 and e2 respectively, then demand that funTy =:= UTyFun argTy resTy for a fresh unification variable resTy. Again, =:= returns a more efficient UType which is equivalent to funTy, but we don’t need to use that type directly (we return resTy instead), so we just discard the result.

> infer (EApp e1 e2) = do
>   funTy <- infer e1
>   argTy <- infer e2
>   resTy <- fresh
>   _ <- funTy =:= UTyFun argTy resTy
>   return resTy

For a lambda, we make up a fresh unification variable for the type of the argument, then infer the type of the body under an extended context. Notice how we promote the freshly generated unification variable to a UPolytype by wrapping it in Forall []; we do not generalize it, since that would turn it into forall a. a! We just want the lambda argument to have the bare unification variable as its type.

> infer (ELam x body) = do
>   argTy <- fresh
>   withBinding x (Forall [] argTy) $ do
>     resTy <- infer body
>     return $ UTyFun argTy resTy

For a let expression without a type annotation, we infer the type of the definition, then generalize it and add it to the context to infer the type of the body. It is traditional for Hindley-Milner systems to generalize let-bound things this way (although note that GHC does not generalize let-bound things with -XMonoLocalBinds enabled, which is automatically implied by -XGADTs or -XTypeFamilies).

> infer (ELet x Nothing xdef body) = do
>   ty <- infer xdef
>   pty <- generalize ty
>   withBinding x pty $ infer body

For a let expression with a type annotation, we skolemize it and check the definition with the skolemized type; the rest is the same as the previous case.

> infer (ELet x (Just pty) xdef body) = do
>   let upty = toUPolytype pty
>   upty' <- skolemize upty
>   check xdef upty'
>   withBinding x upty $ infer body

Running the Infer monad

We need a way to run computations in the Infer monad. This is a bit fiddly, and it took me a long time to put all the pieces together. (But typed holes are sooooo great! It would have taken me way longer without them…) I’ve written the definition of runInfer using the backwards function composition operator, (>>>), so that the pipeline flows from top to bottom and I can intersperse it with explanation.

> runInfer :: Infer UType -> Either TypeError Polytype
> runInfer

The first thing we do is use applyBindings to make sure that we substitute for any unification variables that we know about. This results in another Infer UType.

>   =   (>>= applyBindings)

We can now generalize over any unification variables that are left, and then convert from UPolytype to Polytype. Again, this conversion is safe because at this top level we know we will be in an empty context, so the generalization step will definitely get rid of all the remaining unification variables.

>   >>> (>>= (generalize >>> fmap fromUPolytype))

Now all that’s left is to interpret the layers of our Infer monad one by one. As promised, we start with an empty type context.

>   >>> flip runReaderT M.empty
>   >>> runExceptT
>   >>> evalIntBindingT
>   >>> runIdentity

Finally, we can make a top-level function to infer a polytype for an expression, just by composing infer and runInfer.

> inferPolytype :: Expr -> Either TypeError Polytype
> inferPolytype = runInfer . infer


To be able to test things out, we can make a very simple REPL that takes input from the user and tries to parse, typecheck, and interpret it, printing either the results or an appropriate error message.

> eval :: String -> IO ()
> eval s = case parse expr "" s of
>   Left err -> print err
>   Right e -> case inferPolytype e of
>     Left tyerr -> putStrLn $ pretty tyerr
>     Right ty   -> do
>       putStrLn $ pretty e ++ " : " ++ pretty ty
>       when (ty == Forall [] TyNat) $ putStrLn $ pretty (interp e)
> main :: IO ()
> main = evalRepl (const (pure "HM> ")) (liftIO . eval) [] Nothing Nothing (Word (const (return []))) (return ()) (return Exit)

Here are a few examples to try out:

HM> 2 + 3
2 + 3 : nat
HM> \x. x
\x. x : forall a0. a0 -> a0
HM> \x.3
\x. 3 : forall a0. a0 -> nat
HM> \x. x + 1
\x. x + 1 : nat -> nat
HM> (\x. 3) (\y.y)
(\x. 3) (\y. y) : nat
HM> \x. y
Unbound variable y
HM> \x. x x
Infinite type u0 = u0 -> u1
HM> 3 3
Can't unify nat and nat -> u0
HM> let foo : forall a. a -> a = \x.3 in foo 5
Can't unify s0 and nat
HM> \f.\g.\x. f (g x)
\f. \g. \x. f (g x) : forall a2 a3 a4. (a3 -> a4) -> (a2 -> a3) -> a2 -> a4
HM> let f : forall a. a -> a = \x.x in let y : forall b. b -> b -> b = \z.\q. f z in y 2 3
let f : forall a. a -> a = \x. x in let y : forall b. b -> b -> b = \z. \q. f z in y 2 3 : nat
HM> \y. let x : forall a. a -> a = y in x 3
\y. let x : forall a. a -> a = y in x 3 : forall s1. (s1 -> s1) -> nat
HM> (\x. let y = x in y) (\z. \q. z)
(\x. let y = x in y) (\z. \q. z) : forall a1 a2. a1 -> a2 -> a1

And that’s it! Feel free to play around with this yourself, and adapt the code for your own projects if it’s helpful. And please do report any typos or bugs that you find.

Below, for completeness, you will find a simple, recursive, environment-passing interpreter, along with code for parsing and pretty-printing. I don’t give any commentary on them because, for the most part, they are straightforward and have nothing to do with unification-fd. But you are certainly welcome to look at them if you want to see how they work. The one interesting thing to say about the parser for types is that it checks that types entered by the user do not contain any free variables, and fails if they do. The parser is not really the right place to do this check, but again, it was expedient for this toy example. Also, I tend to use megaparsec for serious projects, but I had some parsec code for parsing something similar to this toy language lying around, so I just reused that.


> data Value where
>   VInt :: Integer -> Value
>   VClo :: Var -> Expr -> Env -> Value
> type Env = Map Var Value
> interp :: Expr -> Value
> interp = interp' M.empty
> interp' :: Env -> Expr -> Value
> interp' env (EVar x) = fromJust $ M.lookup x env
> interp' _   (EInt n) = VInt n
> interp' env (EPlus ea eb)   =
>   case (interp' env ea, interp' env eb) of
>     (VInt va, VInt vb) -> VInt (va + vb)
>     _ -> error "Impossible! interp' EPlus on non-Ints"
> interp' env (ELam x body) = VClo x body env
> interp' env (EApp fun arg) =
>   case interp' env fun of
>     VClo x body env' ->
>       interp' (M.insert x (interp' env arg) env') body
>     _ -> error "Impossible! interp' EApp on non-closure"
> interp' env (ELet x _ xdef body) =
>   let xval = interp' env xdef
>   in  interp' (M.insert x xval env) body


> lexer :: L.TokenParser u
> lexer = L.makeTokenParser emptyDef
>   { L.reservedNames = ["let", "in", "forall", "nat"] }
> parens :: Parser a -> Parser a
> parens = L.parens lexer
> identifier :: Parser String
> identifier = L.identifier lexer
> reserved :: String -> Parser ()
> reserved = L.reserved lexer
> reservedOp :: String -> Parser ()
> reservedOp = L.reservedOp lexer
> symbol :: String -> Parser String
> symbol = L.symbol lexer
> integer :: Parser Integer
> integer = L.natural lexer
> parseAtom :: Parser Expr
> parseAtom
>   =   EVar  <$> identifier
>   <|> EInt  <$> integer
>   <|> ELam  <$> (symbol "\\" *> identifier)
>             <*> (symbol "." *> parseExpr)
>   <|> ELet  <$> (reserved "let" *> identifier)
>             <*> optionMaybe (symbol ":" *> parsePolytype)
>             <*> (symbol "=" *> parseExpr)
>             <*> (reserved "in" *> parseExpr)
>   <|> parens parseExpr
> parseApp :: Parser Expr
> parseApp = chainl1 parseAtom (return EApp)
> parseExpr :: Parser Expr
> parseExpr = buildExpressionParser table parseApp
>   where
>     table = [ [ Infix (EPlus <$ reservedOp "+") AssocLeft ]
>             ]
> parsePolytype :: Parser Polytype
> parsePolytype = do
>   pty@(Forall xs ty) <- parsePolytype'
>   let fvs :: Set Var
>       fvs = flip cata ty $ \case
>         TyVarF x       -> S.singleton x
>         TyNatF         -> S.empty
>         TyFunF xs1 xs2 -> xs1 `S.union` xs2
>       unbound = fvs \\ S.fromList xs
>   unless (S.null unbound) $ fail $ "Unbound type variables: " ++ unwords (S.toList unbound)
>   return pty
> parsePolytype' :: Parser Polytype
> parsePolytype' =
>   Forall <$> (fromMaybe [] <$> optionMaybe (reserved "forall" *> many identifier <* symbol "."))
>           <*> parseType
> parseTypeAtom :: Parser Type
> parseTypeAtom =
>   (TyNat <$ reserved "nat") <|> (TyVar <$> identifier) <|> parens parseType
> parseType :: Parser Type
> parseType = buildExpressionParser table parseTypeAtom
>   where
>     table = [ [ Infix (TyFun <$ symbol "->") AssocRight ] ]
> expr :: Parser Expr
> expr = spaces *> parseExpr <* eof

Pretty printing

> type Prec = Int
> class Pretty p where
>   pretty :: p -> String
>   pretty = prettyPrec 0
>   prettyPrec :: Prec -> p -> String
>   prettyPrec _ = pretty
> instance Pretty (t (Fix t)) => Pretty (Fix t) where
>   prettyPrec p = prettyPrec p . unFix
> instance Pretty t => Pretty (TypeF t) where
>   prettyPrec _ (TyVarF v) = v
>   prettyPrec _ TyNatF = "nat"
>   prettyPrec p (TyFunF ty1 ty2) =
>     mparens (p > 0) $ prettyPrec 1 ty1 ++ " -> " ++ prettyPrec 0 ty2
> instance (Pretty (t (UTerm t v)), Pretty v) => Pretty (UTerm t v) where
>   pretty (UTerm t) = pretty t
>   pretty (UVar v)  = pretty v
> instance Pretty Polytype where
>   pretty (Forall [] t) = pretty t
>   pretty (Forall xs t) = unwords ("forall" : xs) ++ ". " ++ pretty t
> mparens :: Bool -> String -> String
> mparens True  = ("("++) . (++")")
> mparens False = id
> instance Pretty Expr where
>   prettyPrec _ (EVar x) = x
>   prettyPrec _ (EInt i) = show i
>   prettyPrec p (EPlus e1 e2) =
>     mparens (p>1) $
>       prettyPrec 1 e1 ++ " + " ++ prettyPrec 2 e2
>   prettyPrec p (ELam x body) =
>     mparens (p>0) $
>       "\\" ++ x ++ ". " ++ prettyPrec 0 body
>   prettyPrec p (ELet x mty xdef body) =
>     mparens (p>0) $
>       "let " ++ x ++ maybe "" (\ty -> " : " ++ pretty ty) mty
>             ++ " = " ++ prettyPrec 0 xdef
>             ++ " in " ++ prettyPrec 0 body
>   prettyPrec p (EApp e1 e2) =
>     mparens (p>2) $
>       prettyPrec 2 e1 ++ " " ++ prettyPrec 3 e2
> instance Pretty IntVar where
>   pretty = mkVarName "u"
> instance Pretty TypeError where
>   pretty (UnboundVar x)     = printf "Unbound variable %s" x
>   pretty (Infinite x ty)    = printf "Infinite type %s = %s" (pretty x) (pretty ty)
>   pretty (Mismatch ty1 ty2) = printf "Can't unify %s and %s" (pretty ty1) (pretty ty2)
> instance Pretty Value where
>   pretty (VInt n) = show n
>   pretty (VClo x body env)
>     = printf "<%s: %s %s>"
>       x (pretty body) (pretty env)
> instance Pretty Env where
>   pretty env = "[" ++ intercalate ", " bindings ++ "]"
>     where
>       bindings = map prettyBinding (M.assocs env)
>       prettyBinding (x, v) = x ++ " -> " ++ pretty v

About Brent

Assistant Professor of Computer Science at Hendrix College. Functional programmer, mathematician, teacher, pianist, follower of Jesus.
This entry was posted in haskell, teaching and tagged , , , . Bookmark the permalink.

1 Response to Implementing Hindley-Milner with the unification-fd library

  1. Pingback: Automatically updated, cached views with lens | blog :: Brent -> [String]

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