Existential quantification

Published on November 11, 2018

In this post I’m going to give an idea of how existentials can be useful in Haskell. For some time I wanted to write such a post to explain the topic in a way that an older version of me could find helpful, because I still remember the confusion that I felt when I first ran into rank-N types and the terms universal and existential quantification.

I’d like to start from the beginning to make the post useful to as many readers as possible, but I’ll be presenting the basics in a concise form lest make more experienced readers bored.

Type variables and the forall keyword

Type variables in Haskell are always introduced with the keyword forall:

id :: forall a. a -> a

forall a means exactly what it suggests: id works for all a. a will unify with (or will be fixed to) any type that consumer of id may choose.

Fixing or unifying a variable doesn’t necessarily mean that we’re putting a concrete type in its place. It might as well unify with another variable:

idInt :: Int -> Int
idInt = id -- 'a' unifies with 'Int'

id' :: forall b. b -> b
id' = id -- 'a' unifies with 'b'

What matters is that choice of type for such a variable is made.

By default Haskell type signatures allow us to omit forall, but it’s still useful to remember that those foralls are there. In the post I’ll be writing foralls for clarity even when they could have been omitted.

Rank-N types

Only variables introduced with foralls at the beginning of type signature will be fixed when we use the corresponding function. Other foralls deal with independent type variables:

  :: forall a. Show a -- 'a' will be fixed when we use 'myPrettyPrinter'
  => (forall b. Show b => b -> String) -- but not 'b'
  -> Int
  -> Bool
  -> a
  -> String

When we have two levels of foralls it’s called rank-2 type. And in general such constructions are called rank-N types.

Universal and existential quantification

Here are the key things:

  • A variable is universally quantified when the consumer of the expression it appears in can choose what it will be.
  • A variable is existentially quantified when the consumer of the expression it appears in have to deal with the fact that the choice was made for him.

Both universally and existentially quantified variables are introduced with forall. There is no exists in Haskell. In fact, it’s not necessary.

A few examples should help:

  • In the function myPrettyPrinter above, for consumers of myPrettyPrinter a is universally quantified (we can choose what the type will be)
  • …while b is existentially quantified (we have to be prepared to deal with any b that will be given to the callback).
  • Inside the body of myPrettyPrinter a is existentially quantified because the caller of myPrettyPrinter already has chosen the type for us.
  • When we apply the function with b in its type signature (the first argument of myPrettyPrinter), we will be able to choose its concrete type. In the body of myPrettyPrinter b is universally quantified.

Existential wrappers

Apart from the cases I have mentioned above there is a way to have existentials by putting values in wrappers that “hide” type variables from signatures.

data Something where
  Something :: forall a. a -> Something

The constructor accepts any a we like, but after construction we lose the type information and pattern matching afterwards only reveals that there is some a, but nothing regarding what it is. Compare this to passing a value to id: we can pass anything to it but we lack any information about the argument inside of the body of id.

One thing you can do with existential wrappers that is impossible without them is returning existentially quantified data from a function. The wrapper allows us to avoid unification of existentials with outer context and “escaping” of type variables.

Why existentials?

Existentials are always about throwing type information away. Why would we want to do that? The answer is: sometimes we want to work with types that we don’t know at compile time. The types typically depend on the state of external world: they could depend on user’s input, on contents of a file we’re trying to parse, etc. Fortunately Haskell’s type system is powerful enough to allow us to do interesting things even in that case.

How to make use of existentials

We want to work with values of types that we don’t know at compile time, but at run time there are no types at all: they have been erased!

We have to preserve some information about existentially quantified type to make use of it, otherwise we’ll be in the same position as implementers of id having a value and only being able to pass it around never doing anything meaningful with it.

There are various degrees of how much we might want to preserve:

  • We could have a in the type [a] existentially quantified. There are still some things we could do with a value of this type. For example, we could compute length of the list. So knowing nothing about a type is also an option sometimes when it parametrizes another type and we have parametrically-polymorphic functions that work on that type. In this case the set of possible types for a is open i.e. it can grow.

  • We could assume that the existentially quantified type has certain properties (instances):

    data Showable where
      Showable :: forall a. Show a => Showable

    Pattern-matching on Showable will give us the corresponding dictionary back. This allows us to do as much as the knowledge from the attached constraint permits. Again, the set of possible types for a is open (new instances of Show can be defined).

  • We could use GADTs to restore exact types of existentially quantified variables later:

    data EType a where
      ETypeWord8  :: EType Word8
      ETypeInt    :: EType Int
      ETypeFloat  :: EType Float
      ETypeDouble :: EType Double
      ETypeString :: EType String
    data Something where
      Something :: EType a -> a -> Something

    Matching on one of the data constructors of EType reveals a and after that we are free to do anything with the value of corresponding type because we know it.

    With this approach the set of possible types for a is limited and closed. It can be expanded by changing the definition of EType though.

Let’s take a closer look at the last method.

Constraints vs GADTs

How did the approach with GADTs come into existence? Usually we start with the second approach—the one with constraints. Well, let’s see…

  • The approach with constraints is good, except as you develop an application you’ll want to know more and more about existential types, so the list of constraints will grow and grow.
  • It’s even worse though: at some point there will be parts of code that require incompatible instances, so there won’t be any type a that satisfies all of them. That’s a problem.

One solution is to add more constructors to the wrapper:

data Something where
  SomethingIntegral :: forall a. (Show a, Integral a) => a -> Something
  SomethingFloating :: forall a. (Show a, Floating a) => a -> Something
  SomethingStringy  :: forall a. (Show a, IsString a) => a -> Something

But we haven’t solved the problem properly because now we classify the existential value by a single pre-chosen criteria: it’s either Integral or Floating or IsString. After a short while we are likely to find ourselves in a situation when we want to use a different criteria that is completely unrelated to the previously chosen one:

  • We could want to do something with instances of Num. In that case we add Num a to constraints of both data constructors SomethingIntegral and SomethingFloating and then we have to pattern match on both and do the same thing in each branch.
  • We could be interested in an instance that only Word8 has, or only Word8 and String have… you see the point.

GADT-driven approach allows us to recover any dictionaries of interest if the actual value is an instance of right type classes. This is as fine-grained as it can be:

reifyIntegralDict :: EType a -> Maybe (Dict (Integral a))
reifyIntegralDict = \case
  ETypeWord8 -> Just Dict
  ETypeInt   -> Just Dict
  _          -> Nothing

reifyFloatingDict :: EType a -> Maybe (Dict (Floating a))
reifyFloatingDict = \case
  ETypeFloat  -> Just Dict
  ETypeDouble -> Just Dict
  _           -> Nothing

reifyFooDict :: EType a -> Maybe (Dict (Foo a))
reifyFooDict = \case
  ETypeWord8  -> Just Dict
  ETypeString -> Just Dict
  _           -> Nothing

Example: vector indexed by existential length

You might be thinking that this approach actually amounts to having a sum data type like this:

data EType
  = ETypeWord8 Word8
  | ETypeInt Int
  | ETypeFloat Float
  | ETypeDouble Double
  | ETypeString String

And that is correct in this case, but there also can be:

  • Several existentially quantified type variables in a wrapper which leads to combinatorial explosion if we try to enumerate all the combinations.
  • Types that are recursively defined and there is no way to enumerate all their variants at all. It could be a vector indexed by existential length or something indexed by a list of type-level.

Here is an example of that—a vector indexed by existential length:

data Vector (n :: Nat) a where
  Nil :: Vector 0 a
  Cons :: a -> Vector n a -> Vector (n + 1) a

data SomeVector where
  SomeVector :: KnownNat n => EType a -> Vector n a -> SomeVector

This is a sort of combination of all the three approaches I listed above:

  • We still can do some things with Vector without knowing type of its elements or its length.
  • We can recover exact type of the elements if we wish.
  • Luckily, there is enough type-level machinery around Nats (provided by the base library) to avoid using singletons, so that knowing that n is an instance of KnownNat is sufficient for everything we might want to do.

Type assertions: back to concrete types

It is not always necessary to write programs passing around existentials and reifying dictionaries as needed, or worse yet, proving things with Decision and (:~:). Some would be inclined to say that these are the techniques of perverts. In many cases we want to make an assumption about types and throw an exception when the assumption turns out to be wrong. This way we can indeed go back to concrete types!

Here is an example of this that uses the definition of Vector given above:

  :: SomeVector -- we could have parsed this from a file
  -> Vector 5 Int -- and we only want to continue if the type is this
assertVector (SomeVector etype (v :: Vector n a)) =
  case etype of
    ETypeInt -> -- reifies that a ~ Int
      case sameNat (Proxy :: Proxy n) (Proxy :: Proxy 5) of
        Nothing -> error "expected a vector of length 5"
        Just Refl -> -- reifies that n ~ 5
          v -- now we can return v because we know enough
    _ -> error "expected a vector of Ints"

The method allows us to write e.g. parsers that return existentials such as SomeVector while still being able to go to the more comfortable realm of concrete types later in the program.

A nice conclusion

When rich types and the real world collide, existentials often arise. Yet with due diligence one can tame them and still write beautiful and meaningful programs.