# Introduction to Dependent Types: Haskell on Steroids

I’d like to start another series of blog posts. This time on something that I’ve wanted to write about for a while, dependent types.

There’s a noticeable lack of accessible materials introducing dependent types at a high level aimed at functional programmers. That’s what this series sets out help fill. Therefore, if you’re a Haskell programmer and don’t understand something, it’s a bug! Please comment so I can help make this a more useful resource for you :)

There are four parts to this series, each answering one question

- What are dependent types?
- What does a dependently typed language look like?
- What does it feel like to write programs with dependent types?
- What does it mean to “prove” something?

So first things first, what are dependent types? Most people by now have heard the unhelpful quick answer

A dependent type is a type that depends on a value, not just other types.

But that’s not helpful! What does this actually look like? To try to understand this we’re going to write some Haskell code that pushes us as close as we can get to dependent types in Haskell.

## Kicking GHC in the Teeth

Let’s start with the flurry of extensions we need

```
{-# LANGUAGE DataKinds #-}
{-# LANGUAGE KindSignatures #-}
{-# LANGUAGE GADTs #-}
{-# LANGUAGE TypeFamilies #-}
{-# LANGUAGE UndecidableInstances #-}
```

Now our first definition is a standard formulation of natural numbers

` data Nat = Z | S Nat`

Here `Z`

represents 0 and `S`

means `+ 1`

. So you should read `S Z`

as 1, `S (S Z)`

as 2 and so on and so on.

If you’re having some trouble, this function to convert an `Int`

to a `Nat`

might help

```
-- Naively assume n >= 0
toNat :: Int -> Nat
toNat 0 = Z
toNat n = S (toNat $ n - 1)
```

We can use this definition to formulate addition

```
plus :: Nat -> Nat -> Nat
plus Z n = n
plus (S n) m = S (plus n m)
```

This definition proceeds by “structural induction”. That’s a scary word that pops up around dependent types. It’s not all that complicated, all that it means is that we use recursion only on *strictly smaller terms*.

There is a way to formally define smaller, if a term is a constructor applied to several (recursive) arguments. Any argument to the constructor is strictly smaller than the original terms. In a strict language if we restrict ourselves to only structural recursion we’re guaranteed that our function will terminate. This isn’t quite the case in Haskell since we have infinite structures.

```
toInt :: Nat -> Int
toInt (S n) = 1 + toInt n
toInt Z = 0
bigNumber = S bigNumber
main = print (toInt bigNumber) -- Uh oh!
```

Often people will cheerfully ignore this part of Haskell when talking about reasoning with Haskell and I’ll stick to that tradition (for now).

Now back to the matter at hand. Since our definition of `Nat`

is quite straightforward, it get’s promoted to the kind level by `DataKinds`

.

Now we can “reflect” values back up to this new kind with a second GADTed definition of natural numbers.

```
data RNat :: Nat -> * where
RZ :: RNat Z
RS :: RNat n -> RNat (S n)
```

Now, let’s precisely specify the somewhat handwavy term “reflection”. I’m using it in the imprecise sense meaning that we’ve lifted a value into something isomorphic at the type level. Later we’ll talk about reflection precisely mean lifting a value into the type level. That’s currently not possible since we can’t have values in our types!

What on earth could that be useful for? Well with this we can do something fancy with the definition of addition.

```
type family Plus n m :: Nat where
Plus Z n = n
Plus (S n) m = S (Plus n m)
```

Now we’ve reflected our definition of addition to the type family. More than that, what we’ve written above is fairly obviously correct. We can now force our value level definition of addition to respect this type family

```
plus' :: RNat n -> RNat m -> RNat (Plus n m)
plus' RZ n = n
plus' (RS n) m = RS (plus' n m)
```

Now if we messed up this definition we’d get a type error!

```
plus' :: RNat n -> RNat m -> RNat (Plus n m)
plus' RZ n = n
plus' (RS n) m = plus' n m -- Unification error! n ~ S n
```

Super! We know have types that express strict guarantees about our program. But how useable is this?

To put it to the test, let’s try to write some code that reads to integers for standard input and prints their sum.

We can easily do this with our normal `plus`

```
readNat :: IO Nat
readNat = toNat <$> readLn
main :: IO ()
main = plus <$> readNat <*> readNat
```

Easy as pie! But what about `RNat`

, how can we convert a `Nat`

to an `RNat`

? Well we could try something with type classes I guess

```
class Reify a where
type N
reify :: a -> RNat N
```

But wait, that doesn’t work since we can only have once instance for all `Nat`

s. What if we did the opposite

```
class Reify (n :: Nat) where
nat :: RNat n -> Nat
```

This let’s us go in the other direction.. but that doesn’t help us! In fact there’s no obvious way to propagate runtime values back into the types. We’re stuck.

## GHC with Iron Dentures

Now, if we could add some magical extension to GHC could we write something like above program? Yes of course! The key idea is to not reflect up our types with data kinds, but rather just allow the values to exist in the types on their own.

For these I propose two basic ideas

- A special reflective function type
- Lifting expressions into types

For our special function types, we allow the return *type* to use the supplied *value*. These are called pi types. We’ll give this the following syntax

`(x :: A) -> B x`

Where `A :: *`

and `B :: A -> *`

are some sort of type. Notice that that `A`

in `B`

’s kind isn’t the data kind promoted version, but just the goodness to honest normal value.

Now in order to allow `B`

to actually make use of it’s supplied value, our second idea let’s normal types be indexed on values! Just like how GADTs can be indexed on types. We’ll call these GGADTs.

So let’s define a new version of `RNat`

```
data RNat :: Nat -> * where
RZ :: RNat Z
RS :: RNat n -> RNat (S n)
```

This looks exactly like what we had before, but our semantics are different now. Those `Z`

’s and `S`

’s are meant to represent actual values, not members of some kind. There’s no promoting types to singleton kinds anymore, just plain old values being held in fancier types.

Because we can depend on normal values, we don’t even have to use our simple custom natural numbers.

```
data RInt :: Int -> * where
RZ :: RInt 0
RS :: RInt n -> RInt (1 + n)
```

Notice that we allowed our types to call functions, like `+`

. This can potentially be undecidable, something that we’ll address later.

Now we can write our function with a combination of these two ideas

```
toRInt :: (n :: Int) -> RInt n
toRInt 0 = RZ
toRInt n = RS (toRInt $ n - 1)
```

Notice how we used pi types to change the return type dependent on the input *value*. Now we can feed this any old value, including ones we read from standard input.

` main = print . toInt $ plus' <$> fmap toRInt readLn <*> fmap toRInt readLn`

Now, one might wonder how the typechecker could possibly know how to handle such things, after all how could it know what’ll be read from stdin!

The answer is that it doesn’t. When a value is reflected to the type level we can’t do anything with it. For example, if we had a type like

` (n :: Int) -> (if n == 0 then Bool else ())`

Then we would have to pattern match on `n`

at the value level to propagate information about `n`

back to the type level.

If we did something like

```
foo :: (n :: Int) -> (if n == 0 then Bool else ())
foo n = case n of
0 -> True
_ -> ()
```

Then the typechecker would see that we’re matching on `n`

, so if we get into the `0 -> ...`

branch then `n`

must be `0`

. It can then reduce the return type to `if 0 == 0 then Bool else ()`

and finally `Bool`

. A very important thing to note here is that the typechecker *doesn’t evaluate the program*. It’s examining the function in isolation of all other values. This means we sometimes have to hold its hand to ensure that it can figure out that all branches have the correct type.

This means that when we use pi types we often have to pattern match on our arguments in order to help the typechecker figure out what’s going on.

To make this clear, let’s play the typechecker for this function. I’m reverting to the `Nat`

type since it’s nicer for pattern matching.

```
toRNat :: (n :: Nat) -> RNat n
toRNat Z = RZ -- We know that n is `Z` in this branch
toRNat (S n) = RS (toRNat n {- This has the type RNat n' -})
p :: (n :: Nat) -> (m :: Int) -> RNat (plus n m)
p Z m = toRNat m
p (S n) m = RS (toRNat n m)
```

First the type checker goes through `toRNat`

.

In the first branch we have `n`

equals `Z`

, so `RZ`

trivially typechecks. Next we have the case `S n`

.

- We know that
`toRNat n`

has the type`RNat n'`

by induction - We also know that
`S n' = n`

. - Therefore
`RS`

builds us a term of type`RNat n`

.

Now for `p`

. We start in much the same manner.

if we enter the `p Z m`

case

- we know that
`n`

is`Z`

. - we can reduce
`plus n m`

since`plus Z m`

is by definition equal to`m`

Look at the definition of`plus`

to confirm this). - We know how to produce
`RNat m`

easily since we have a function`toRNat :: (n :: Nat) -> RNat n`

. - We can apply this to
`m`

and the resulting term has the type`RNat m`

.

In the `RS`

case we know that we’re trying to produce a term of type `RNat (plus (S n) m)`

.

- Now since we know that the constructor for the first argument of
`plus`

, we can reduce`plus (S n) m`

to`S (plus n m)`

by the definition of`plus`

. - We’re looking to build a term of type
`plus n m`

and that’s as simple as a recursive call. - From here we just need to apply
`RS`

to give us`S (plus n m)`

- As we previously noted
`S (plus n m)`

is equal to`plus (S n) m`

Notice how as we stepped through this as the typechecker we never needed to do any arbitrary reductions. We only ever reduce definitions when we have the outer constructor (WHNF) of one of the arguments.

While I’m not actually proposing adding `{-# LANGUAGE PiTypes #-}`

to GHC, it’s clear that with only a few orthogonal editions to system F we can get some seriously cool types.

## Wrap Up

Believe or not we’ve just gone through two of the most central concepts in dependent types

- Indexed type families (GGADTs)
- Dependent function types (Pi types)

Not so bad was it? :) From here we’ll look in the next post how to translate our faux Haskell into actual Agda code. From there we’ll go through a few more detailed examples of pi types and GGADTs by poking through some of the Agda standard library.

Thanks for reading, I must run since I’m late for class. It’s an FP class ironically enough.

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