I have been working with dependent types for a bit now, and it really changes how I view even the most fundamental of functional operations. Dependent types allow us to explore functions where the type of an intermediate or output value dynamically changes type depending on the input value. We will look at the standard fold functions, and how they take on a new meaning in a dependently typed context.

# The standard fold

In functional programming languages like Haskell, we like to use higher order functions as a way to simplify control flow. One of the most ubiquitous higher order functions is the fold.

```
-- A right fold. If we unroll the fold, it would look like
-- step x₀ (step x₁ (step x₂ … (step xₙ base)))
foldr :: (a -> b -> b) -> b -> [a] -> b
foldr step base [] = base
foldr step base (x:xs) = step x (foldr step base xs)
```

The fold allows us to consume some data structure (a list in this case) one element at a time, producing an intermediate result each time an element of the structure is passed to the `step`

function. For example, we can convert a list into the sum of its elements by using the `step`

function `(+)`

that adds the sum of the elements it has already seen to the next element of the list to be consumed.

```
sum :: (Num c) => [c] -> c
sum xs = foldr (\value intermediate -> value + intermediate) 0 xs
-- └──────────────────┘
-- The new intermediate value after
-- each step of the fold.
```

To get a better handle on what happens at each step, let’s look at the type that GHC will infer given our type signature. When GHC type checks our `sum`

function, it attempts to unify all type variables, meaning that the type checker looks for types that make sense every place that a type variable is used.

```
-- These must be the same type b
-- ↓ ↓ ↓ ↓
-- foldr :: (a -> b -> b) -> b -> [a] -> b
sum xs = foldr (\value intermediate -> value + intermediate) 0 xs
-- ↑ └────────↑─────────┘ ↑
-- These must be values of the same concrete type
```

If we want to figure out `a`

and `b`

when type checking the `sum`

function, we can determine what constraints GHC will use to find the type for each type variable. Focusing on the constraints for `b`

, using the notation `b ~ constraint`

to mean “`b`

must satisfy the `constraint`

”^{1}, we can derive the following.

`b ~ Num c => c`

(from the base case 0)`b ~ c`

(from`(+)`

)

From these constraints, the compiler will determine that `b`

must be a `c`

, as `c`

is the only type that satisfies both constraints above.

Folds have another trick up their sleeve; they can be used to *build up a new structure*. We can define another common higher order function, the `map`

function, in terms of `foldr`

to demonstrate this property.

```
map :: (c -> d) -> [c] -> [d]
map f xs = foldr (\value intermediate -> f value : intermediate) [] xs
-- └────────────────────┘
-- The new intermediate _list_ after
-- each step of the fold.
```

If we do the same analysis for `map`

as we did with `sum`

to find out what the type variables should be

```
-- These must be the same b
-- ↓ ↓ ↓ ↓
-- foldr :: (a -> b -> b) -> b -> [a] -> b
map (+ 1) xs = foldr (\value intermediate -> value + 1 : intermediate) [] xs
-- ↑ └──────────↑───────────┘ ↑
-- These must be values of the same concrete type
```

we get the following constraints, where `c`

and `d`

come from the map function.

`b ~ [d]`

(from the base case`[]`

)`d ~ c`

(by unifying`c`

from the input list and the`(+ 1)`

function)

From these constraints, the Haskell compiler can derive an extra constraint that is noteworthy.

`-> b -> b) ~ c -> [c] -> [c] -- (from the step function passed to `foldr`) (a `

This constraint means that at each step of the fold, a list is given as input and a list is returned as output. What is different about this fold from the `sum`

case is that the underlying *structure* of the intermediate value in each step of the fold is different. To elaborate a bit more, if we take the sum of a list of 64-bit `Int`

s, our intermediate values after each invocation of `step`

have the same structure.

```
-- 64 bit Int 64 bit Int
-- ↓ ↓
Num c => c -> c -> c
-- ↑
-- 64 bit Int
```

If we apply `map`

to a list of 64-bit `Int`

s, we have a structure that *grows* in length each time `step`

is called.

```
-- Int [Int]
-- ∣ of length n + 1!
-- ↓ ↓
step :: c -> [c] -> [c]
-- ↑
-- [Int]
-- of length n
```

For types that do not keep track of structure (the length of the list), there is no difference between how the `sum`

and `map`

folds operate. What if we did start keeping track of the structure in the type? Can the `sum`

function and the `map`

function still be defined in terms of the same fold function?

# Dependent types enable types that keep track of structure

Dependent types allows us to have the type of some value *depend* on another value. This allows us to define a type similar to a list called `Vec`

(short for vector) where the length of the list is encoded in the type. The common definition for `Vec`

is as follows^{2}.

```
data Vec (n :: Nat) (a :: Type) where
Nil :: Vec 0 a
Cons :: a -> Vec n a -> Vec (n + 1) a
```

What does the `n :: Nat`

notation mean? We are saying that the *type* of the input to `Vec`

has a *kind* named `Nat`

. A kind is essentially the type of a type. Types that have a value, such as `Int`

, often have the kind `Type`

, while the kind `Nat`

represents the *types* 0, 1, 2, etc.

The `Vec`

type^{3} we have defined can have values of type `a`

and must be lists of length `n`

. For example, if we have the type `Vec 4 Int`

, we know that any values of this type must have four elements and each element must be an `Int`

, for a total of 256 bytes of memory used (for 64 bit `Int`

s).

Given that we are working with objects that are the same as lists, it seems reasonable that we can define a fold for `Vec`

.

```
-- foldr for the Vec type
vfoldr :: (a -> b -> b) -> b -> Vec n a -> b
Nil = base
vfoldr step base `Cons` xs) = step x (vfoldr step base xs) vfoldr step base (x
```

This is precisely the same definition as before; the only component that has changed is that we are keeping track of the length of the input `Vec`

and the names of the data constructors (`Cons`

instead of `:`

, `Nil`

instead of `[]`

). We can define the sum of a vector in the same way as we did on lists.

```
vsum :: Num c => Vec n c -> c
= vfoldr (+) 0 xs vsum xs
```

So we should be able to recreate `map`

using `vfoldr`

, right?

```
vmap :: (c -> d) -> Vec n c -> Vec n d
= vfoldr (\value intermediate -> f value `Cons` intermediate) Nil xs
vmap f xs {-
• Couldn't match type ‘n’ with ‘0’
‘n’ is a rigid type variable bound by
the type signature for:
vmap :: forall c d (n :: Nat). (c -> d) -> Vec n c -> Vec n d
Expected type: Vec n d
Actual type: Vec 0 d
-}
```

Oh no, what happened?! Let’s look at the constraints that GHC is attempting to resolve in this case.

```
-- These must be the same b
-- ↓ ↓ ↓ ↓
-- vfoldr :: (a -> b -> b) -> b -> Vec n a -> b
= vfoldr (\value intermediate -> f value `Cons` intermediate) Nil xs
vmap f xs -- ↑ └────────────↑────────────┘ ↑
-- These must be values of the same concrete type
```

`b ~ Vec n d`

(from the result type of`vmap`

)`b ~ Vec 0 d`

(from the type of`Nil`

)

The constraints tell us that GHC wants to show that `Vec n d ~ Vec 0 d`

since both types of `Vec`

must are associated with the same type variable `b`

. GHC rightly tells us that this is not possible, for it would also have to show that `n ~ 0`

for all possible `n`

s. And unfortunately there are more natural numbers than just zero!

There is a second conundrum in the constraints GHC is trying to solve, but GHC only reports the first error. To find this error, we need to look at the `step`

function types.

`(a -> b -> b) ~ c -> Vec n d -> Vec (n + 1) d`

(from the definition of`vfoldr`

and`Cons`

)`b ~ Vec n d`

(from the second argument type in the first constraint)`b ~ Vec (n + 1) d`

(from the result type in the first constraint)

In a dependently typed context, the fold that preserves a structure (`vsum`

) is *fundamentally different* from a fold that can change the structure at each step (`vmap`

). This is to say that in the case of `vsum`

, each invocation takes and returns the same type for the intermediate value, but for the `vmap`

case, the *type is different for each intermediate value*. Luckily, it is possible to define a fold where we provide the compiler some information on how the type changes at each step using a *dependently typed fold*.

# Defining the dependently typed fold on vectors

To define a dependently typed fold, we need a few pieces first. First we need a *type level function* that tells us what our type should be before and after each call to `step`

. We define a convenient name for how many steps we have taken so far in the fold

`type StepOfFold = Nat -- StepOfFold is a kind`

and then define the *kind* of the type level function that tells us what type we should expect at any step in the fold.

`StepFunction :: StepOfFold -> Type`

For example, an instance of `StepFunction`

could be the following function (in pseudocode)

```
NatToVec :: Type -> StepFunction
NatToVec a = \n -> Vec n a
```

to which we could apply a specific type `a`

and length `n`

to make a complete type^{4}.

`NatToVec Int 3 = Vec 3 Int`

Unfortunately Haskell cannot represent type level functions quite this cleanly. The first restriction is that the kind `StepFunction`

has to be encoded using the `TyFun`

construct

`type StepFunction = TyFun Nat Type -> Type`

where `TyFun Nat Type`

represents the `StepFunction`

we had previously defined and the the `-> Type`

piece is required when returning a data type from the type level step function^{5}.

When we are defining a `StepFunction`

function, we often want to keep track of an auxiliary type that is constant, for example the `a`

we end up needing to define `Vec n a`

. We could use `NatToVec`

to hold onto the type of the `Vec`

if we partial apply the function as `NatToVec a`

. However, Haskell’s type level functions cannot be partially applied^{6}. Therefore, we must store the type we want to use in another type such as `HoldMyType`

^{7}, and use the `Apply`

type family to define the `StepFunction`

function itself.

```
-- StepFunction
-- ┌───────────────────────────┐
data HoldMyType (a :: Type) (f :: TyFun Nat Type) :: Type
type instance Apply (HoldMyType a) nth_step = Vec nth_step a
```

`Apply`

is a type family instance which allows us to hold onto some types before we are passed the current step number, enabling us to bypass the problem of no partial type level functions. `Apply`

can be written infix as `@@`

; instead of writing

`NatToVec Int) 3 = Vec 3 Int (`

as we would with a normal value level function, we will write

`HoldMyType Int) @@ 3 = Vec 3 Int (`

to determine the type at a particular step.

With type level functions, we can now define the dependently typed fold. The function signature is as follows.

```
-- This function is copied from the Clash library, where it is
-- defined as Clash.Sized.Vector.dfold
dfoldr :: forall p n a . KnownNat n
=> Proxy (p :: StepFunction)
-> (forall l . SNat l -> a -> (p @@ l) -> (p @@ (l + 1)))
-> p @@ 0
-> Vec n a
-> p @@ n
```

There is a bunch going on in this type signature, so let’s break it down by each argument to `dfoldr`

. The first part of the signature is

`dfoldr :: forall p n a . KnownNat n =>`

which specifies the `KnownNat`

constraint for the length of our input vector `n`

. This constraint allows us to pass in the type level natural number of the input vector into other type level functions through the use of `snatProxy`

in the definition.

The next line

`Proxy (p :: StepFunction)`

says that our `dfoldr`

function will take in a type level function for how to generate the type at each step of the fold. This function is sometimes called the *motive*.

The next line

`forall l . SNat l -> a -> (p @@ l) -> (p @@ (l + 1))) (`

is the value level function that takes some input value (of type `a`

), the intermediate value we have thus far (of type `p @@ l`

for step `l`

), and generates the next intermediate value (of type `p @@ (l + 1)`

for step `l + 1`

). For example, we could have in a function that adds one to the incoming element and then combine the result with a vector that is being built up as the intermediate value.

```
step :: Num a
=> SNat l -- The current step number
-> a
-> (HoldMyType a) @@ l -- Vec l a
-> (HoldMyType a) @@ (l + 1) -- Vec (l + 1) a
= (x + 1) `Cons` xs step l x xs
```

For this particular step function, we do not need to use `l`

when defining the function, as the increase in the step is taken into account in the function’s type.

The last few lines of the `dfoldr`

type are the same base case, input vector, and result type as `foldr`

, but with the base and result type parameterized by which step of the fold they are related to (`0`

for base, `n`

for the end of the fold).

```
-> p @@ 0 -- Base case, i.e. the first intermediate value (such as `Nil`)
-> Vec n a -- The `Vec` to fold over
-> p @@ n -- The type after the final step in the fold.
```

Now that we have the `dfoldr`

function type, how is it defined?

```
-- If we unroll the dependent fold, it would look like
-- step n x₀ (step (n - 1) x₁ (step (n - 2) x₂ … (step 0 xₙ base)))
= go (snatProxy (asNatProxy xs)) xs
dfoldr _ step base xs where
go :: SNat l -> Vec l a -> (p @@ l)
Nil = base
go _ `Cons` (ys :: Vec z a)) =
go step_num (y let step_num_minus_one = step_num `subSNat` d1 -- (d1 is the same as 1)
in step step_num_minus_one y (go step_num_minus_one ys)
```

If we look at just the definition of `go`

, we see that it is the same as `foldr`

with the minor addition of passing around a natural number using `SNat`

. This natural number represents the step we are on; as we go recursively deeper into the fold, we decrement this number until we hit zero, in which case we return the base case `base`

of type `p @@ 0`

. The rest of the `snatProxy`

, `asNatProxy`

, and `SNat`

components are to shepherd around the changing step number through each call to `go`

.

Notice that the motive function `p`

is not used in the definition of `dfoldr`

. This is because the function is used only at the type level; a value level `Proxy`

object is used to pass around the motive function to other functions and has no tangible use as a value.

# Can we dependently map now?

We now have all the blocks to define a map function for vectors! We will start out by defining the type level step function, which will be the same as `HoldMyType`

with a more descriptive name.

```
data MapMotive (a :: Type) (f :: TyFun Nat Type) :: Type
type instance Apply (MapMotive a) l = Vec l a
```

We can now define the map function by passing in our type step function and the normal `map`

value level step function. The `forall`

s are required to allow the `step`

type signature to refer to the same `c`

and `d`

that appear in the signature for `vmap`

^{8}.

```
vmap :: forall c d n. (KnownNat n) => (c -> d) -> Vec n c -> Vec n d
= dfoldr (Proxy :: Proxy (MapMotive d)) step Nil xs
vmap f xs where
step :: forall step.
SNat step
-> c
-> MapMotive d @@ step
-> MapMotive d @@ (step + 1)
= f x `Cons` xs step l x xs
```

GHC can infer where to apply the `MapMotive`

function if the definition of the step is inlined, leading to a more compact definition.

```
vmap' :: forall c d n. (KnownNat n) => (c -> d) -> Vec n c -> Vec n d
= dfoldr
vmap' f xs Proxy :: Proxy (MapMotive d)) -- type level step
(-> f value `Cons` intermediate) -- value level step
(\l value intermediate Nil xs
```

So we now have a way to specify `vmap`

that GHC is happy with! Let’s break down the constraints GHC reconstructs to be able to verify why this works for ourselves. The first problem we had was that the base case passed in had a different type than the result (`Vec 0 a ~ Vec n a`

).

`p @@ 0 ~ (MapMotive d) @@ 0 ~ Vec 0 d`

`p @@ n ~ (MapMotive d) @@ n ~ Vec n d`

This problem does not occur for `vmap`

because we no longer need to unify the base and end case; they are distinct types in the dependent fold. What about the second problem, where the step function changes the type? For that conundrum,

`p @@ l ~ (MapMotive d) @@ l ~ Vec l d`

`p @@ (l + 1) ~ (MapMotive d) @@ (l + 1) ~ Vec (l + 1) d`

`SNat l -> a -> p @@ l -> p @@ (l + 1) ~ SNat l -> c -> Vec l d -> Vec (l + 1) d`

(from unifying the step function for`dfoldr`

and the step function defined in`vmap`

)

we can see that the last constraint is consistent between the definition of `dfoldr`

and the definition of `vmap`

.

# One fold to rule them all

The `dfoldr`

fold is a strict superset of `vfoldr`

. To demonstrate this, we can define `vfoldr_by_dfoldr`

by returning *the same type* at each step of the fold

```
data FoldMotive (a :: Type) (f :: TyFun Nat Type) :: Type
type instance Apply (FoldMotive a) l = a
```

and applying the `step`

function to the fold to both the incoming and intermediate value.

```
vfoldr_by_dfoldr :: KnownNat n => (a -> b -> b) -> b -> Vec n a -> b
=
vfoldr_by_dfoldr step base xs Proxy :: Proxy (FoldMotive b))
dfoldr (-> step value intermediate) base xs (\l value intermediate
```

Finally, we define the sum function using `dfoldr`

via `vfoldr_by_dfoldr`

.

```
vsum_by_dfoldr :: (KnownNat n, Num c) => Vec n c -> c
= vfoldr_by_dfoldr (+) 0
vsum_by_dfoldr -- Ex: vsum_by_dfoldr (1 :> 2 :> 3 :> Nil) == 6
```

Since dependently typed folds subsume the standard folds^{9}, it would be possible to replace all folds with their dependent counterpart. I have doubts that this would be a good direction for Haskell; while we can likely automatically derive the definition of the standard fold from the dependent fold, the developer implementing an instance of the `Foldable`

typeclass would now need to use dependent types, which may be a rough barrier especially if the developer has no intention for the fold to be used in a dependent context.

# Comparison to a fully dependently typed language

One challenge that came up earlier in the post was the obfuscation of type level functions through `Proxy`

, `TyFun`

, `Apply`

. In fully dependent type languages such as Agda, there is no distinction between values, types, or kinds. This means that when we define a function, we can use it at *any level* in the type hierarchy. We can demonstrate this by reimplementing `dfoldr`

in Agda. The type of `dfoldr`

in Agda is

```
-- Set is the same as Type in Haskell.
-- Agda needs the specific type `a` passed in; the curly
-- braces allow us to omit the types if the Agda compiler
-- can figure the types out automatically.
: {a : Set}
dfoldr → {n : Nat}
→ (p : Nat → Set) -- Motive
→ ((l : Nat) → a → p l → p (1 + l)) -- step function
→ p 0 -- base case
→ Vec a n
→ p n
```

where the motive function `p`

can be defined directly without the use of `Proxy`

, and applied without a special operator (`@@`

). The definition of `dfoldr`

really demonstrates just how similar the regular and dependently typed fold are.

```
{n = 0} p step base [] = base
dfoldr {n = suc n-1} p step base (x ∷ xs) = step n-1 x (dfold p step base xs)
dfoldr -- suc n == 1 + n
-- List version for comparison; ∷ and [] can be used for either
-- List or Vec, where the type is inferred by context.
: {a b : Set} -> (a -> b -> b) -> b -> List a -> b
foldr = base
foldr step base [] (x ∷ xs) = step x (foldr step base xs) foldr step base
```

The only difference between the dependently typed fold on vectors and the fold on lists is passing around the type level step number `n`

and the motive `p`

. The `subSNat`

function in the Haskell `dfoldr`

is replaced by induction on `n`

; a `n + 1`

is passed into `dfoldr`

and `n`

is passed down to the recursive call.

We can define the map function as well. The first step is to define the motive function

```
_motive : Set -> Nat -> Set
map_motive a l = Vec a l map
```

and then pass this function into `dfoldr`

by *partially applying* it with the type of the output vector.

```
: {c d : Set} → {n : ℕ} → (c → d) → Vec c n → Vec d n
vmap {c} {d} {n} f xs = dfoldr (map_motive d) (λ _ x xs → f x ∷ xs) [] xs
vmap -- ↑
-- map_motive is partially applied,
-- leading to a Nat → Set function
```

Fully dependent type languages like Agda allow for type level shenanigans to be defined much more clearly because there is no difference between defining a type level and value level function. In fact, a function can often be used at any level of the type hierarchy; the `+`

function can be used on `Nat`

values, on `Nat`

types, on `Nat`

kinds, and beyond^{10}!

To get the same functionality in Haskell, we had to go through quite a bit of extension gymnastics.

`GADTs`

to define`Vec`

.`TypeOperators`

to be able to use`+`

on the type level.`DataKinds`

to be able to define type level`Nat`

.`KindSignatures`

to be able to write out`n :: Nat`

as a kind.`TypeFamilies`

to be able to define an instance of`Apply`

`RankNTypes`

to require that the step function passed to`dfoldr`

works for all steps (the`forall l.`

part of the`dfoldr`

definition).`ScopedTypeVariables`

to be able to reference the same`c`

and`d`

in our`step`

function as in the top level`vmap`

function.

Haskell’s dependent type features are exciting; they represent a way to use dependent types in a mainstream language while keeping all of the current libraries that exist in Hackage. The current developer experience of using dependent types is a tad challenging, but as dependent types become more fleshed out in Haskell the experience should improve.

## Comments, questions?

If you have any comments or questions, feel free to do one of the following.

- Contact me at my email, which is this web address with the
`.`

after ryan replaced with an`@`

. - Open an issue on my github page.
- Say hi on LinkedIn

## Acknowledgements

I would like to thank Daniel Hensley and Jon Thacker for reviewing drafts of this article.

## Want to run the code in this blog post?

This blog post is a literate haskell file that you can run to play around with the topics discussed in the post. There is an included run.sh script that will run the code in the post without needing to install anything; the run script requires that Nix is installed.

GHC will probably find slightly different constraints to solve; these equations were chosen for pedagogical purposes.↩︎

More info on GADT syntax can be found here: https://typeclasses.com/ghc/gadt-syntax↩︎

More accurately we would say that

`Vec`

is an*indexed type family*that is*parameterized*by the type`a`

and*indexed*by the natural number`n`

.↩︎“Complete” here means that the type has kind

`Type`

. All types that hold a value during runtime must have the kind`Type`

.↩︎The reasoning here is the same as the prior note; types that eventually hold a value must end in

`-> Type`

. This is described in the paper “Promoting Functions to Type Families in Haskell” by Richard Eisenberg.↩︎All type level functions must be complete in the sense that all arguments have been given to the type level function when it is called.↩︎

Or beer :-D↩︎

Haskell is a bit strange with the type level function. If

`MapMotive`

is passed anything specified in the`forall`

that is not`d`

, it will correctly comment that argument passed to`MapMotive`

does not unify with the other types. However, it will happily accept any other unspecified type variable. My assumption here is that if a new variable is introduced (say`δ`

), it will implicitly add the`forall δ_new_identifier`

quantification with a new identifier during type inference (as to not alias with an existing δ) and then conclude`δ_new_identifier ~ d`

as the only way to make the types unify. Dependent languages similar to Haskell like Agda will not automatically universally quantify a stray type variable for you as generalized type inference is undecidable in a dependently typed context (see https://cs.stackexchange.com/a/12957).↩︎There is a version of dependent fold for lists as well, which can be demonstrated in Agda as follows.

`: {a : Set} {p : List a → Set} dfoldr → ((x : a) → {xs : List a} → p xs → p (x ∷ xs)) → p [] → (l : List a) → p l = base dfoldr step base [] (x ∷ xs) = step x (dfoldr step base xs) dfoldr step base`

The Agda variant uses a value level list reflected into the type (both for

`l`

and`xs`

), which is hard/potentially not possible to do in Haskell at the moment.↩︎In Haskell, the hierarchy of objects is

`values -> types -> kinds -> sorts`

. Languages like Agda take this further and define a*universe*type hierarchy, which is indexed by a natural number. Specifically, in Agda the hierarchy is`Set₀ -> Set₁ -> Set₂ -> …`

↩︎