We’re back to continue on our journey in using dependent types to write type-safe neural networks! In Part 1, we wrote things out in normal, untyped Haskell, and looked at red flags and general design principles that nudged us in the direction of adding dependent types to our program. We learned to appreciate what dependent types offered in terms of guiding us in writing our code, helping the compiler check our correctness, providing a better interface for users, and more.

We also learned how to use singletons to work around some of Haskell’s fundamental limitations to let us “pattern match” on the structure of types, and how to use typeclasses to generate singletons reflecting the structure of types we are dealing with.

(If you read Part 1 before the singletons section was re-written to use the singletons library, here’s a link to the section in specific. This tutorial will assume familiarity with what is discussed there!)

All of what we’ve dealt with so far has essentially been with types that are fixed at compile-time. All the networks we’ve made have had “static” types, with their sizes in their types indicated directly in the source code. In this post, we’re going to dive into the world of types that depend on factors unknown until runtime, and see how dependent types in a strongly typed language like Haskell helps us write safer, more correct, and more maintainable code.

This post was written for GHC 8 on stackage snapshot nightly-2016-06-28, but should work with GHC 7.10 for the most part. All of the set-up instructions and caveats (like the singletons-2.0.1 bug affecting GHC 7.10 users and the unreleased hmatrix version) are the same as for part 1’s setup.

All of the code in this post is downloadable as a standalone source file so you can follow along!

A fair disclosure: a lot of this post doesn’t actually directly deal with machine learning or neural networks. Most of it will be learning general principles for working with dependent types through implementing things you’d want to do with neural networks. More stuff with ML will come in the next posts!

Types at Runtime

Recall the type we had for our neural networks:

> : k Network ghci Network :: Nat -> [ Nat ] -> Nat -> *

They’re of the form Network i hs o , where i is the size of the input vector it expects, hs is the list of hidden layer sizes, and o is the size of the output vector it produces. Something of type Network 10 '[6, 4] 3 is a network with 10 input nodes, two input layers of size 6 and 4, and 3 output nodes.

This is great and all, but there’s an severe limitation to this: Haskell is a statically typed language, right? So doesn’t this mean that using a network requires that you know the entire structure of the network at compile-time?

It’s conceivable that you might be able to have the input and output sizes known at compile-time, but it’s possible that you don’t care or know your hidden layer structure. You might load it from a configuration file, or have it depend on user input. You might even want to receive one over a network channel without knowing what the internal structure is. But can a type really depend on things that you can’t know until runtime?

To illustrate more clearly:

main :: IO () () = do main putStrLn "What hidden layer structure do you want?" <- readLn :: IO [ Integer ] hs <- randomNet :: IO ( Network 10 ??? 3 ) -- what is ??? net -- ...?

We want to put hs there where ??? is, but… ??? has to be a type (of kind [Nat] ). hs is a value (of type [Integer] ). It’s clear here that the type of our network depends on something we can’t write down or decide until runtime.

An Existential Crisis

There are two main ways to go about solving this issue in Haskell. We’ll look at both, and then see that they are really actually just two styles of doing the same thing.

Types hiding behind constructors

Now, having the entire structure of your neural network in the type is nice and all for cool tricks like randomNet …but do you really want to work with this directly? After all, from the user’s perspective, the user really only ever needs to know i and o : What vectors the network expects and what vectors the network outputs. In the end, a (feed-forward) Neural Network is really just a fancy R i -> R o .

Remember, the main benefits of having the entire structure in the type was to help us implement our functions more safely, with the compiler’s help, and also for cute return type polymorphism tricks like randomNet and getNet and some stronger documentation. The user of the network really only benefits from the second two types of benefits.

One practical downside of having the structure in the type is that you can’t store them in the same list or data structure. A Network 10 '[5,3] 1 won’t share a list with a Network 10 '[5,2] 1 , despite having the same inputs/outputs (and API).

Imagine that we had written a Network type that didn’t have the internal structure in the type —

data OpaqueNet i o i o

Recall that our issue earlier was that we had to write Network i ??? o , but we had no idea what to put in for ??? . But, if we worked with an OpaqueNet i o , we wouldn’t even care! We wouldn’t have to tell GHC what the internal structure is.

I’d actually argue that OpaqueNet might often be the more useful type to offer to your users (or to use yourself), because it only exposes the types that are relevant to its usage/API. You can store them in a list or MVar — [OpaqueNet 10 3] and MVar (OpaqueNet 10 3) , serialize/deserialize them without knowing their internal structure in advance ( loadNet :: FilePath -> IO (OpaqueNet 10 3) ), etc. (if you wanted to load a Network , you would need to know exactly what internal structure was stored, in advance). Though Network is a much easier type to implement, OpaqueNet is a often a more ideal type to use.

We can implement our vision for OpaqueNet as an “existential” wrapper over Network , actually:

-- source: https://github.com/mstksg/inCode/tree/master/code-samples/dependent-haskell/NetworkTyped2.hs#L110-L111 data OpaqueNet :: Nat -> Nat -> * where ONet :: Network i hs o -> OpaqueNet i o i hs oi o

So, if you have net :: Network 6 '[10,6,3] 2 , you can create ONet net :: OpaqueNet 6 2 . When you use the ONet constructor, the structure of the hidden layers disappears from the type!

We can use the network inside by pattern matching on ONet :

-- source: https://github.com/mstksg/inCode/tree/master/code-samples/dependent-haskell/NetworkTyped2.hs#L113-L125 runOpaqueNet :: ( KnownNat i, KnownNat o) i,o) => OpaqueNet i o i o -> R i -> R o ONet n) x = runNet n x runOpaqueNet (n) xrunNet n x numHiddens :: OpaqueNet i o -> Int i o ONet n) = go n numHiddens (n)go n where go :: Network i hs o -> Int i hs o = \ case go O _ -> 0 _ :&~ n' -> 1 + go n' n'go n'

With the ScopedTypeVariables extension, we can even bring hs back into scope, as in:

case oN of oN ONet ( n :: Network i hs o) -> ... i hs o)

This pattern is sometimes called the dependent pair, because pattern matching on ONet yields the hidden existentially quantified type ( hs ) and also a value whose type is based on it ( Network i hs o ). It’s like hs “paired” with Network i hs o . Pattern match on the results to give both the type ( hs ) and the data structure. (To make this more explicit, we could have implemented it as ONet :: Sing hs -> Network i hs o -> OpaqueNet i o )

And here’s the key to making this all work: once you pattern match on ONet , you have to handle the hs in a completely polymorphic way. You’re not allowed to assume anything about hs …you have to provide a completely parametrically polymorphic way of dealing with it!

For example, this function is completely not ok:

bad :: OpaqueNet i o -> Network i hs o i oi hs o ONet n) = n -- nope, not ok at all. bad (n)

Why not? Well, a type signature like OpaqueNet i o -> Network i hs o means that the caller can decide what hs can be — just like read :: Read a => String -> a , where the caller decides what a is.

Of course, this isn’t the case in the way we’ve written the function…the function can only return a specific hs (namely, the hs of the network that ONet hides). The caller has to accommodate whatever is inside ONet .

The Universal and the Existential

We just brushed here on something at the heart of using existential types in Haskell: the issue of who has the power to decide what the types will be instantiated as. Most polymorphic functions you work with in Haskell are “universally qualified”. For example, for a function like

map :: (a -> b) -> [a] -> [b] (ab)[a][b]

a and b are universally quantified, which means that the person who uses map gets to decide what a and b are. To be more explicit, that type signature can be written as:

map :: forall a b . (a -> b) -> [a] -> [b] a b(ab)[a][b]

This means that map is defined in a way that will work for any a and b that the caller wants. As a caller, you can request:

map :: ( Int -> Bool ) -> [ Int ] -> [ Bool ] map :: ( Double -> Void ) -> [ Double ] -> [ Void ] map :: ( String -> ( Bool -> Char )) -> [ String ] -> [ Bool -> Char ] ))

Or anything else!

Consequentially, the function has to be implemented in a way that will work for any a and b . The function’s implementation has the burden of being flexible enough to handle whatever the caller asks for.

But, for a function like:

foo :: [ Int ] -> OpaqueNet i o i o

While the caller can choose what i and o are, the function gets to choose what hs (in the hidden Network i hs o ) is. If I want to use the thing that foo returns…then I have to be flexible. I have the burden of being flexible enough to handle whatever hs the function returns.

In summary:

For universally quantified types, the caller chooses the type being instanced, and the function’s implementation has to accommodate any choice.

For existentially quantified types, the function’s implementation chooses the type being instanced, and the caller has to accommodate any choice.

Indeed, we saw earlier that if we ever wanted to use the Network i hs o inside the OpaqueNet i o , we were forced to deal with it in a parametrically polymorphic way. We had to be able to handle any hs that the ONet could throw at us!

A familiar friend

I called OpaqueNet i o a “dependent pair” earlier, pairing hs with Network i hs o . But there’s another common term for it: a dependent sum.

People familiar with Haskell might recognize that “sum types” are Either -like types that can be one thing or another. Sum types are one of the first things you learn about in Haskell — heck, even Maybe a is the sum of a and () . Dependent pairs/existential types actually are very similar to Either /sum types, in spirit, and it might help to see the parallel so that you can see that they’re nothing scary, and that the fundamentals/intuition of working with existential types in Haskell is no different than working with Either !

If I had:

foo :: String -> Either Int Bool

I have to handle the result for both the case where I get an Int and the case where I get a Bool . The function gets to pick what type I have to handle ( Int or Bool ), and I have to adapt to whatever it returns. Sound familiar? In fact, you can even imagine that OpaqueNet i o as being just a infinite Either over '[] , '[1] , '[1,2] , etc.

Remember that the basic way of handling an Either and figuring out what the type of the value is inside is through pattern matching on it. You can’t know if an Either Int Bool contains an Int or Bool until you pattern match. But, once you do, all is revealed, and GHC lets you take advantage of knowing the type.

For OpaqueNet i o , it’s the same! You don’t know the actual type of the Network i hs o it contains until you pattern match on the network (This time, it’s a “dependent pattern match”). Once you pattern match on it, all is revealed…and GHC lets you take advantage of knowing the type!

Reification

For simplicity, let’s re-write randomNet the more sensible way — with the explicit singleton input style:

-- source: https://github.com/mstksg/inCode/tree/master/code-samples/dependent-haskell/NetworkTyped2.hs#L79-L87 randomNet' :: forall m i hs o . ( MonadRandom m, KnownNat i, KnownNat o) m i hs om,i,o) => Sing hs -> m ( Network i hs o) hsm (i hs o) = \ case randomNet' SNil -> O <$> randomWeights randomWeights SNat `SCons` ss -> ( :&~ ) <$> randomWeights <*> randomNet' ss ssrandomWeightsrandomNet' ss randomNet :: forall m i hs o . ( MonadRandom m, KnownNat i, SingI hs, KnownNat o) m i hs om,i,hs,o) => m ( Network i hs o) m (i hs o) = randomNet' sing randomNetrandomNet' sing

We use sing :: SingI hs => Sing hs to go call the Sing hs -> -style function from the SingI hs => one.

Now, we still need to somehow get our list of integers to the type level so that we can create a Network i hs o to stuff into our ONet . For that, the singletons library offers the necessary tooling. It gives us SomeSing , which is a lot like our OpaqueNet above, wrapping the Sing a inside an existential data constructor. toSing takes the term-level value (for us, an [Integer] ) and returns a SomeSing wrapping the type-level value (for us, a [Nat] ). When we pattern match on the SomeSing constructor, we get a in scope!

As of singletons-2.2 and GHC 8 , SomeSing is implemented as:

data SomeSing :: * -> * where SomeSing :: Sing ( a :: k) -> SomeSing k k)

And you have:

foo :: SomeSing Bool = SomeSing STrue foo bar :: SomeSing Nat = SomeSing ( SNat :: Sing 10 ) bar

Pattern matching looks like:

main :: IO () () = do main putStrLn "How many cats do you own?" c <- readLn :: IO Integer case toSing c of toSing c SomeSing ( SNat :: Sing n) -> -- ... n)

Now, inside the case statement branch (the ... ), we have type n :: Nat in scope! And by pattern matching on the SNat constructor, we also have a KnownNat n instance (As discussed in previous part).

toSing works using a simple typeclass mechanism with an associated type whose job is to connect the types of values with the kinds of their singletons. It associates Bool (the type) with Bool (the kind), Integer (the type) with Nat (the kind), [Integer] (the type) with [Nat] (the kind), etc., and it does it with simple applications of type families (here’s a nice tutorial on type families courtesy of Oliver Charles, as a refresher). With it, we can convert any normal value x of type a to a singleton representing type x with kind a .

We now have enough to write our randomONet :

-- source: https://github.com/mstksg/inCode/tree/master/code-samples/dependent-haskell/NetworkTyped2.hs#L127-L131 randomONet :: ( MonadRandom m, KnownNat i, KnownNat o) m,i,o) => [ Integer ] -> m ( OpaqueNet i o) m (i o) = case toSing hs of randomONet hstoSing hs SomeSing ss -> ONet <$> randomNet' ss ssrandomNet' ss

This process of bringing a term-level value into the type level is known in Haskell as reification. With this, our original goal is (finally) within reach:

-- source: https://github.com/mstksg/inCode/tree/master/code-samples/dependent-haskell/NetworkTyped2.hs#L205-L213 main :: IO () () = do main putStrLn "What hidden layer structure do you want?" <- readLn hs n <- randomONet hs randomONet hs case n of ONet ( net :: Network 10 hs 3 ) -> do hs print net net -- blah blah stuff with our dynamically generated net

The Boundary

With the power of existentially quantified types (like in SomeSing ), we essentially gained the ability to work with types that depend on runtime results.

In a way, you can consider the toSing and the SomeSing as our “boundary” between the “untyped world” and the “typed world”. This layer (and the process of reification) cleanly separates the two.

This boundary can be thought of as a lot like the boundary we talk about between “pure” functions and values and “impure” (IO, etc.) ones. People say to always write as much of your program as possible in the “pure” world — to separate and pull out as much logic as you can to be pure logic. That’s one of the first things you learn about as a Haskell programmer: how to separate logic that can be pure from logic that is “impure” (IO, etc.), and then finally combine them at the very end, as late as possible.

It’s easy to think that just because the final program is going to “be in IO in the end anyway”, there isn’t any point in separating out pure and impure parts of your program logic. But we know that we gain separation of concerns, the increased ability to reason with your code and analyze what it does, the compiler’s ability to check what you write, the limitation of implementations, etc.

You can think of the general philosophy of working with typed/untyped worlds as being the same thing. You try to write as much of your program as possible in the “typed” world, like we did in Part 1. Take advantage of the increased ability to reason with your code, parametric polymorphism helping you write your code, limit your implementations, nab you compiler help, etc. All of those are benefits of working in the typed world.

Then, write what you absolutely must in your “untyped” world, such as dealing with values that pop up at runtime like the [Integer] above.

Finally, at the very end, unite them at the boundary. Pass the control football from the untyped world to the typed world!

The great part about this all is that GHC and the type system is there at every step holding your hand, guiding you as you implement your programs and making sure everything is type-safe and fits together! (This, after all, is why dependently typed programming with dynamically generated types is not the same thing as “dynamically typed programming”!)

Continuation-Based Existentials

There’s another way in Haskell that we work with existential types that can be more natural to use in a lot of cases. Remember that when we pattern match on an existential data type, we have to work with the values in the constructor in a parametrically polymorphic way. For example, if we had:

oNetToFoo :: OpaqueNet i o -> Foo i o ONet n) = f n oNetToFoo (n)f n

f has to take a Network i hs o but deal with it in a way that works for all hs . It can’t be written for only '[5] or only '[6,3] …it has to work for any hs . That’s the whole “existential vs. universal quantification” thing we just talked about.

Well, we could really also just skip the constructor altogether and represent an existential type as something taking the continuation f and giving it what it needs.

-- source: https://github.com/mstksg/inCode/tree/master/code-samples/dependent-haskell/NetworkTyped2.hs#L154-L154 type OpaqueNet' i o r = ( forall hs . Network i hs o -> r) -> r i o rhsi hs or)

“Tell me how you would make an r if you had a Network i hs o (that works for any hs ) and I’ll make it for you!”

(This takes advantage of Rank-N types. If you’re unfamiliar with them, Gregor Riegler has a nice tutorial on the subject.)

Using these types is very similar to using the constructor-style ones:

-- source: https://github.com/mstksg/inCode/tree/master/code-samples/dependent-haskell/NetworkTyped2.hs#L159-L176 runOpaqueNet' :: ( KnownNat i, KnownNat o) i,o) => OpaqueNet' i o ( R o) i o (o) -> R i -> R o = oN (

-> runNet n x) runOpaqueNet' oN xoN (

runNet n x) -- :: ((forall hs. Network i hs o -> R o) -> R o) -- -> R i -- -> R o numHiddens' :: OpaqueNet' i o Int -> Int i o = oN go numHiddens' oNoN go where go :: Network i hs o -> Int i hs o = \ case go O _ -> 0 _ :&~ n' -> 1 + go n' n'go n' -- :: ((forall hs. Network i hs o -> Int) -> Int) -- -> Int

This “continuation transformation” is formally known as skolemization.

We can “wrap” a Network i hs o into an OpaqueNet' i o r :

-- source: https://github.com/mstksg/inCode/tree/master/code-samples/dependent-haskell/NetworkTyped2.hs#L156-L157 oNet' :: Network i hs o -> OpaqueNet' i o r i hs oi o r = \f -> f n oNet' n\ff n

Let’s write a version of randomONet that returns a continuation-style existential:

withRandomONet' :: ( MonadRandom m, KnownNat i, KnownNat o) m,i,o) => [ Integer ] -> ( forall hs . Network i hs o -> m r) hsi hs om r) -> m r m r -- aka, => [Integer] -- -> OpaqueNet' i o (m r) = case toSing hs of withRandomONet' hs ftoSing hs SomeSing ss -> do ss <- randomNet' ss netrandomNet' ss f net

But, hey, because we’re skolemizing everything, let’s do it with the skolemized version of toSing / SomeSing , withSomeSing :

-- a version of `toSing` that returns a skolemized `SomeSing` withSomeSing :: [ Integer ] -> ( forall ( hs :: [ Nat ]) . Sing hs -> r) ])hsr) -> r

Because why not? Skolemize all the things!

-- source: https://github.com/mstksg/inCode/tree/master/code-samples/dependent-haskell/NetworkTyped2.hs#L178-L186 withRandomONet' :: ( MonadRandom m, KnownNat i, KnownNat o) m,i,o) => [ Integer ] -> ( forall hs . Network i hs o -> m r) hsi hs om r) -> m r m r -- aka, => [Integer] -- -> OpaqueNet' i o (m r) = withSomeSing hs $ \ss -> do withRandomONet' hs fwithSomeSing hs\ss <- randomNet' ss netrandomNet' ss f net

We can use it to do the same things we used the constructor-based existential for, as well…and, in a way, it actually seems (oddly) more natural.

-- source: https://github.com/mstksg/inCode/tree/master/code-samples/dependent-haskell/NetworkTyped2.hs#L215-L221 main' :: IO () () = do main' putStrLn "What hidden layer structure do you want?" <- readLn hs $ \( net :: Network 10 hs 3 ) -> do withRandomONet' hs\(hs print net net -- blah blah stuff with our dynamically generated net

Like the case statement pattern match represented the lexical “wall”/“boundary” between the untyped and typed world when using constructor-style existentials, the ... $

et -> ... can be thought of the “wall” for the continuation-style existentials.

A Tale of Two Styles

We’ve just discussed two ways of doing the same thing. Two styles of representing/working with existential types. The two are equivalent, in that you can always “convert” between one or the other, but the choice of which one you use/reach for/offer can make a difference in code clarity. After working with both styles a lot (sometimes, libraries only offer one style), you start to get a feel for which one you like more in which situations. In the end, I don’t think there are any hard or fast rules. Just use whichever one you feel is more readable!

That being said, here are some general Pros and Cons that I’ve noticed over the years:

Most obviously, continuation-style doesn’t require you to define a throwaway data type/constructor. While new types are cheap in Haskell, they force your users to learn a new set of types and constructors for every single existential type you return. If you or the library you’re writing uses/returns a lot of different existentially qualified types, all those extra dumb wrappers are a huge hassle.

Continuation-style existentials are in general smoother to use than constructor-style ones when functions return existentials. Especially if you intend to immediately use them, continuation-style basically saves you an extraneous pattern match.

When you have to use several existentials at once, continuation-style is much better because each nested existential doesn’t force another level of indentation: = withSomeSing x $ \sx -> foowithSomeSing x\sx $ \sy -> withSomeSing y\sy $ \sz -> withSomeSing z\sz -- ... vs. = case toSing x of footoSing x SomeSing sx -> sx case toSing y of toSing y SomeSing sy -> sy case toSing z of toSing z SomeSing sz -> sz -- ... Every time you nest a case statement, you actually waste two levels of indentation, which can be annoying even at 2-space indentation. But you don’t need any to nest in the continuation style!

If you’re working monadically, though, you can take advantage of do notation and ScopedTypeVariables for a nicer style that doesn’t require any nesting at all: = do main ONet n1 <- randomONet [ 7 , 5 , 3 ] :: IO ( OpaqueNet 10 1 ) n1randomONet [ ONet n2 <- randomONet [ 5 , 5 , 5 ] :: IO ( OpaqueNet 10 1 ) n2randomONet [ ONet n3 <- randomONet [ 5 , 4 , 3 ] :: IO ( OpaqueNet 10 1 ) n3randomONet [ <- readLn hs ONet ( n4 :: Network 10 hs 1 ) <- randomONet hs hsrandomONet hs -- ... Which is arguably nicer than = withRandomONet' [ 7 , 5 , 3 ] $

1 -> mainwithRandomONet' [

1 5 , 5 , 5 ] $

2 -> withRandomONet' [

2 5 , 4 , 3 ] $

3 -> do withRandomONet' [

3 <- readLn hs $ \( n4 :: Network 10 hs 1 ) -> do withRandomONet' hs\(hs -- ... A lot of libraries return existentials in Maybe ’s (base is guilty), so this trick can be useful for those, too! This trick is less useful for functions like toSing where things are not returned in a monad. You could wrap it in Identity, but that’s kind of silly: = runIdentity $ do foorunIdentity SomeSing sx <- Identity $ toSing x sxtoSing x SomeSing sy <- Identity $ toSing y sytoSing y SomeSing sz <- Identity $ toSing z sztoSing z return $ -- ...

Constructor-style is necessary for writing typeclass instances. You can’t write a Show instance for (forall hs. Network i hs o -> r) -> r , but you can write one for OpaqueNet i o . We’ll also be writing Binary instances later for serialization/deserialization, and it all only works in constructor-style.

Haskell doesn’t allow you to use Rank-N types as arguments to type constructors, so you can have [OpaqueNet i o] , but not [OpaqueNet' i o r] or [(forall hs. Network i hs o -> r) -> r] . You can have MVar (OpaqueNet i o) , but not MVar ((forall hs. Network i hs o -> r) -> r) . The latter are known as impredicative types, which are a big no-no in GHC Haskell. Don’t even go there! The constructor style is necessary in these situations. If the type constructor is a Monad, you can get away with a ContT-style skolemization, like (forall hs. Network i hs o -> [r]) -> [r] and (forall hs. Network i hs o -> IO r) -> IO r . But this doesn’t work for MVar and other useful type constructors you might want to put OpaqueNet in.

When writing functions that take existentials as inputs, the constructor-style is arguably more natural. But barely. For example, we wrote a function to find the number of hidden layers in a network earlier: numHiddens :: OpaqueNet i o -> Int i o But the continuation-style version has a slightly messier type: numHiddens' :: (( forall hs . Network i hs o -> Int ) -> Int ) ((hsi hs o -> Int Even with with the type synonym, it’s still a little awkward: numHiddens' :: OpaqueNet' i o Int -> Int i o This is why you’ll encounter many more functions returning continuation-style existentials in libraries than taking them, for the most part.

These are just general principles, not hard fast rules. This list is nowhere near exhaustive and reflects my current progress in my journey towards a dependently typed lifestyle. If you come back in a month, you might see more things listed here!

All said, I do find myself very happy when I see that a library I’m using offers both styles for me to use. And I’ve been known to submit PR’s to a library to have it offer one style or another, if it’s lacking.

Be judicious. If you’re writing a library, don’t spam it with too many throwaway constructors. After a while, you’ll begin to intuitively see which style shines in which situations! (And, in some case, there might not even be a definitive “better” style to use.)

Serializing Networks

To drive things home, let’s apply what we learned about existential types and reification to another simple application: serialization.

Recap on the Binary Library

Serializing networks of known size — whose sizes are statically in their types — is pretty straightforward, and its ease is one of the often-quoted advantages of having sizes in your types. I’m going to be using the binary library, which offers a very standard typeclass-based approach for serializing and deserializing data. There are a lot of tutorials online (and I even wrote a small one myself a few years ago), but a very high-level view is that the library offers monads ( Get , Put ) for describing serialization schemes and also a typeclass used to provide serialization instructions for different types.

In practice, we usually don’t write our own instances from scratch. Instead, we use GHC’s generics features to give us instances for free:

-- source: https://github.com/mstksg/inCode/tree/master/code-samples/dependent-haskell/NetworkTyped2.hs#L25-L30 data Weights i o = W { wBiases :: ! ( R o) i oo) , wNodes :: ! ( L o i) o i) } deriving ( Show , Generic ) instance ( KnownNat i, KnownNat o) => Binary ( Weights i o) i,o)i o)

For simple types like Weights , which simply contain serializable things, the binary library is smart enough to write your instances automatically for you! This gives us get and put :

get :: ( KnownNat i, KnownNat o) i,o) => Get ( Weights i o) i o) put :: ( KnownNat i, KnownNat o) i,o) => Weights i o i o -> Put

However, for GADTs like Network , we have to things manually.

Serializing Network

Taking advantage of having the entire structure in the type, put is simple:

-- source: https://github.com/mstksg/inCode/tree/master/code-samples/dependent-haskell/NetworkTyped2.hs#L89-L94 putNet :: ( KnownNat i, KnownNat o) i,o) => Network i hs o i hs o -> Put = \ case putNet O w -> put w put w w :&~ n -> put w *> putNet n put wputNet n

If it’s an O w , just serialize the w . If it’s a w :&~ net , serialize the w then the rest of the net . Normally, we might have to put a “flag” to tell what constructor we serializing, so that the deserializer can know what constructor to deserialize at every step. But for Network , we don’t have to:

-- source: https://github.com/mstksg/inCode/tree/master/code-samples/dependent-haskell/NetworkTyped2.hs#L96-L101 getNet :: forall i hs o . ( KnownNat i, KnownNat o) i hs oi,o) => Sing hs hs -> Get ( Network i hs o) i hs o) = \ case getNet SNil -> O <$> get get SNat `SCons` ss -> ( :&~ ) <$> get <*> getNet ss ssgetgetNet ss

getNet doesn’t need flags because we already know how many :&~ layers to expect just from the type. If we want to deserialize a Network 5 '[10,6,3] 2 , we know we want three (:&~) ’s and one O .

getNet is written similarly to how we wrote randomNet' . We “pattern match” on hs (using singletons) to get the constructors we are expecting to deserialize and just follow what the singleton’s structure tells us.

To write a Binary instance for Network , we can’t have get take a Sing hs input — that’d change the arity/type of the function. We have to switch to SingI -style had have their Binary instances require a SingI hs constraint.

-- source: https://github.com/mstksg/inCode/tree/master/code-samples/dependent-haskell/NetworkTyped2.hs#L103-L105 instance ( KnownNat i, SingI hs, KnownNat o) => Binary ( Network i hs o) where i,hs,o)i hs o) = putNet putputNet = getNet sing getgetNet sing

Serializating OpaqueNet

It’s arguably much more useful to serialize/deserialize OpaqueNet s. Between different iterations of your program, you might have the same inputs/outputs, but want to try out different internal structures. You’d want to store them and access them uniformly, or send them over a network without requiring the receiver to know the internal structure beforehand. Remember, you can’t even load a Network i hs o without knowing its complete structure!

Because the complete structure of the network is not in the type, we need to encode it as a flag in the binary serialization so that the deserializer will know what constructors to expect and deserialize. We can write a simple function to get the [Integer] of a network’s structure:

-- source: https://github.com/mstksg/inCode/tree/master/code-samples/dependent-haskell/NetworkTyped2.hs#L72-L77 hiddenStruct :: Network i hs o -> [ Integer ] i hs o = \ case hiddenStruct O _ -> [] [] _ :&~ ( n' :: Network h hs' o) h hs' o) -> natVal ( Proxy @ h) natVal (h) : hiddenStruct n' hiddenStruct n'

Recall that natVal :: KnownNat n => Proxy n -> Integer returns the value-level Integer corresponding to the type-level n :: Nat . (I’m also using GHC 8’s fancy TypeApplications syntax, and Proxy @h is the same as Proxy :: Proxy h ).

natVal and hiddenStruct are kind of interesting — they take type-level information ( n , hs ) and turns them into term-level values ( Integer s, [Integer] s). They are the opposites of our reification functions (like toSing ). Going from the “type level” to the “value level” is known in Haskell as reflection, and is the dual concept of reification. (The singletons library offers reflectors for all of its singletons, as fromSing .)

That’s all we need!

-- source: https://github.com/mstksg/inCode/tree/master/code-samples/dependent-haskell/NetworkTyped2.hs#L133-L138 putONet :: ( KnownNat i, KnownNat o) i,o) => OpaqueNet i o i o -> Put ONet net) = do putONet (net) put (hiddenStruct net) putNet net

“Put the structure (as a binary flag), and then put the network itself.”

Now, to deserialize, we want to load the list of Integer s and reify it back to the type level to know what type of network we’re expecting to load:

-- source: https://github.com/mstksg/inCode/tree/master/code-samples/dependent-haskell/NetworkTyped2.hs#L140-L145 getONet :: ( KnownNat i, KnownNat o) i,o) => Get ( OpaqueNet i o) i o) = do getONet <- get hsget $ \ss -> withSomeSing hs\ss ONet <$> getNet ss getNet ss

We load our flag, reify it, and once we’re back in the typed land again, we can do our normal business. Isn’t it nice that GHC is also there at every step to make sure we make the transition safely?

Our final instance:

-- source: https://github.com/mstksg/inCode/tree/master/code-samples/dependent-haskell/NetworkTyped2.hs#L147-L149 instance ( KnownNat i, KnownNat o) => Binary ( OpaqueNet i o) where i,o)i o) = putONet putputONet = getONet getgetONet

And, of course, we used the constructor-style existential this whole time instead of the continuation-style one because we can’t directly write typeclass instances for the latter.

An Existence For All

We’ve learned about how to “cross” from the untyped world to the typed world and bring about contexts involving types that can depend on runtime factors. To me, this is really the point where dependently typed programming starts — when you start having to work with types that depend on run-time factors.

We’ve already been able to reap a lot of benefits. All of the type safety we discovered in the last part is now available to us in a fully dynamic world, as well. We also learned the advantages of separating the typed world from the untyped world and how the compiler helps us make the transition safely.

But really, this is all just the start of dependently typed programming. This is where things really start to get fun.

Stepping into this new world can be disorienting at first. There’s a lot of unexpected things that come up when we start working more with these fancy new types. We have to deal with types coming from different sources, convince the type system about their properties and relationships between them, and deal with a whole bunch of other concerns that just don’t happen when you program only at the value level. But don’t worry! Like all things, it will more naturally with practice.

Now that we have existential types and run-time types out of the way, come back for the next post in the series, where we start to have the real fun! :D

Exercises

Here are some fun exercises you can try, if you want to test your understanding! Links are to the solutions.