## Scheduling Effects

After the last post, Noah Easterly pointed me to their tree-traversals library, and in particular the `Phases`

applicative transformer. It allows you to batch applicative effects to be run together: for the breadth-first traversal, we can batch the effects from each level together, giving us a lovely short solution to the problem.

```
breadthFirst c = runPhasesForwards . go
where
go (x:<xs) = liftA2 (:<) (now (c x)) (delay (traverse go xs))
```

In my efforts to speed this implementation up, I came across a wide and interesting literature on scheduling effects, which I’ll go through a little here.

# Coroutines

The first thing that jumps to mind, for me, when I think of “scheduling” is coroutines. These are constructs that let you finely control the order of execution of effects. They’re well explored in Haskell by now, and most libraries will let you do something like the following:

We first print `1`

, then, after a delay, we print `2`

. The `delay`

doesn’t make a difference if we just run the whole thing:

But you can see its effect when we use the `interleave`

combinator:

Hopefully you can see how useful this might be, and the similarity to the `Phases`

construction.

The genealogy of most coroutine libraries in Haskell seems to trace back to Blažević (2011) or Kiselyov (2012): the implementation I have been using in these past few examples (`IterT`

) comes from a slightly different place. Let’s take a quick detour to explore it a little.

# Partiality

In functional programming, there are several constructions for modeling error-like states: `Maybe`

for your nulls, `Either`

for your exceptions. What separates these approaches from the “unsafe” variants (null pointers, unchecked exceptions) is that we can *prove*, in the type system, that the error case is handled correctly.

Conspicuously absent from the usual toolbox for modeling partiality is a way to model *nontermination*. At first glance, it may seem strange to attempt to do so in Haskell. After all, if I have a function of type:

I can prove that I won’t throw any errors (with `Either`

, that is), because the type `Int`

doesn’t contain `Left _`

. I’ve also proved, miraculously, that I won’t make any null dereferences, because `Int`

also doesn’t contain `Nothing`

. I *haven’t* proved, however, that I won’t loop infinitely, because (in Haskell), `Int`

absolutely *does* contain $\bot$.

So we’re somewhat scuppered. On the other hand, While we can’t *prove* termination in Haskell, we can:

- Model it.
- Prove it in something else.

Which is exactly what Venanzio Capretta did in the fascinating (and quite accessible) talk “Partiality is an effect” (Capretta, Altenkirch, and Uustalu 2004)^{1}.

The monad in question looks like this:

We’re writing in Idris for the time being, so that we can prove termination and so on. The “recursive call” to `Iter`

is guarded by the `Inf`

type: this turns on a different kind of totality checking in the compiler. Usually, Idris will prevent you from constructing infinite values. But that’s exactly what we want to do here. Take the little-known function `until`

:

It’s clearly not necessarily total, and the totality checker will complain as such when we try and implement it directly:

But we can use `Iter`

to model that possible totality:

```
until : (a -> Bool) -> (a -> a) -> a -> Iter a
until p f x = if p x then Now x else Later (until p f (f x))
```

Of course, nothing’s for free: when we get the ability to construct infinite values, we lose the ability to consume them.

We get an error on the `run`

function. However, as you would expect, we can run *guarded* iteration: iteration up until some finite point.

```
runUntil : Nat -> Iter a -> Maybe a
runUntil Z _ = Nothing
runUntil (S n) (Now x) = Just x
runUntil (S n) (Later x) = runUntil n x
```

Making our way back to Haskell, we must first—as is the law—add a type parameter, and upgrade our humble monad to a monad transformer:

The semantic meaning of the extra `m`

here is interesting: each layer adds not just a recursive step, or a single iteration, but a single effect. Interpreting things in this way gets us back to the original goal:

# Scheduling

The `Later`

constructor above can be translated to a `delay`

function on the transformer:

And using this again, we can write the following incredibly short definition for `unfoldTreeM_BF`

:

```
unfoldTreeM_BF :: Monad m => (b -> m (a, [b])) -> b -> m (Tree a)
unfoldTreeM_BF f = retract . go
where
go b = do
(x,xs) <- lift (f b)
fmap (Node x) (interleave (map (delay . go) xs))
```

# Applicative

It would be nice to bring this back to traversals, but alas, `IterT`

is pretty monad-centric. What’s more, if it’s analogous to `Phases`

it certainly doesn’t look like it:

However, in the documentation for `IterT`

, there’s the following little note:

Where `FreeT`

is the free monad transformer. This seems to strongly hint that we could get the same thing for applicatives with `ApT`

. Let’s try it:

The `Applicative`

instance is a little hairy, but it *seems* correct:

## Applicative Instance

```
instance Applicative f =>
Applicative (Phases f) where
pure = Phases . pure
liftA2 f' (Phases (ApT xs')) (Phases (ApT ys')) =
Phases (ApT (liftA2 (go f') xs' ys'))
where
go
:: ∀ a b c.
(a -> b -> c)
-> ApF Identity f a
-> ApF Identity f b
-> ApF Identity f c
go f (Pure x) ys = fmap (f x) ys
go f xs (Pure y) = fmap (`f` y) xs
go f (Ap x (ApT xs)) (Ap y (ApT ys)) =
Ap
(liftA2 (,) x y)
(ApT (liftA2 (go (\xx yy -> uncurry f . (xx *** yy))) xs ys))
```

(on a side note: thank *goodness* for `liftA2`

finally getting into `Applicative`

)

And we get all the normal combinators:

```
delay :: Applicative f => Phases f a -> Phases f a
delay = Phases . ApT . pure . Ap (pure ()) . fmap const . runPhases
lift :: Functor f => f a -> Phases f a
lift = Phases . liftApO
```

The issue comes with running the thing at the end: `Monad`

creeps back in.

```
retract :: Monad f => Phases f a -> f a
retract = fmap (runIdentity . retractAp) . joinApT . runPhases
```

Because the effects are all layered on top of each other, you need to flatten them out at the end, which requires `join`

. Mind you, it does work: it’s just not as general as it could be.

All’s not lost, though. Turns out, we never needed the transformer in the first place: we could just define the different applicative instance straight off.

```
newtype Phases f a = Phases
{ runPhases :: Ap f a
} deriving Functor
instance Applicative f =>
Applicative (Phases f) where
pure = Phases . Pure
liftA2 f' (Phases xs') (Phases ys') = Phases (go f' xs' ys')
where
go :: ∀ a b c.
(a -> b -> c)
-> Ap f a
-> Ap f b
-> Ap f c
go f (Pure x) ys = fmap (f x) ys
go f xs (Pure y) = fmap (`f` y) xs
go f (Ap x xs) (Ap y ys) =
Ap
(liftA2 (,) x y)
(go (\xx yy -> uncurry f . (xx *** yy)) xs ys)
delay :: Applicative f => Phases f a -> Phases f a
delay = Phases . Ap (pure ()) . fmap const . runPhases
retract :: Applicative f => Phases f a -> f a
retract = retractAp . runPhases
lift :: f a -> Phases f a
lift = Phases . liftAp
```

# More Coroutines

In the wonderful article Coroutine Pipelines (Blažević 2011), several different threads on coroutine-like constructions are unified. What I’ve demonstrated above isn’t yet as powerful as what you might see in a full coroutine library: ideally, you’d want generators and sinks. As it turns out, when we look back at the note from `IterT`

:

We can get both of those other constructs by swapping out `Identity`

^{2}:

(`Sink`

is usually called an `Iteratee`

)

This is the fundamental abstraction that underlies things like the pipes library (Gonzalez 2018).

# Interleaving

The only missing part from the first coroutine example by now is `interleave`

. In the free library, it has the following signature:

But we should be able to spot that, really, it’s a traversal. And, as a traversal, it should rely on some underlying `Applicative`

instance. Let’s try and come up with one:

```
newtype Parallel m f a = Parallel
{ runParallel :: FreeT m f a
}
instance (Functor f, Functor m) =>
Functor (Parallel m f) where
fmap f = Parallel . FreeT . fmap go . runFreeT . runParallel
where
go = bimap f (FreeT . fmap go . runFreeT)
instance (Applicative f, Applicative m) =>
Applicative (Parallel m f) where
pure = Parallel . FreeT . pure . Pure
Parallel fs' <*> Parallel xs' = Parallel (unw fs' xs')
where
unw (FreeT fs) (FreeT xs) = FreeT (liftA2 go fs xs)
go (Pure f) = bimap f (runParallel . fmap f . Parallel)
go (Free fs) = Free . \case
Pure x -> fmap (runParallel . fmap ($x) . Parallel) fs
Free xs -> liftA2 unw fs xs
```

Now, interleave is just `sequenceA`

!

# Applicatives, Again

So we can see that there’s a “parallel” applicative for both the free monad and the free applicative. To try and understand this type a little better, we can leverage our intuition about a much simpler, more familiar setting: lists. There’s an interesting similarity between lists and the free monad: `FreeT ((,) a)`

) looks a lot like “`ListT`

done right” (so much so, in fact, that most coroutine libraries provide their own version of it). More concretely, list also has a famous “parallel” applicative: `ZipList`

!

```
newtype ZipList a
= ZipList
{ getZipList :: [a]
} deriving Functor
instance Applicative ZipList where
pure = ZipList . repeat
liftA2 f (ZipList xs) (ZipList ys) = ZipList (zipWith f xs ys)
```

We’ll use some of our knowledge about `ZipList`

to help us in the next section.

# Timekeeping

We’ve seen that efforts to model both coroutines and partiality end up in the same neighborhood: there’s yet another way to get there, which seems (at first) almost the opposite of the second. It starts with a blog post from Conor McBride (2009) called “Time flies like an applicative functor”. Curiously, here too breadth-first labeling is the focus. Remember first the lovely circular solution from (**???**):

```
data Tree a = Leaf | Node a (Tree a) (Tree a)
relabel :: Tree x -> [[a]] -> (Tree a, [[a]])
relabel Leaf xss = (Leaf,xss)
relabel (Node _ l r) ((x:xs):xss0) =
let (l',xss1) = relabel l xss0
(r',xss2) = relabel r xss1
in (Node x l' r',xs:xss2)
bflabel :: Tree x -> [a] -> Tree a
bflabel tr xs = u
where
(u,xss) = relabel tr (xs:xss)
```

As lovely as it is, spare a thought for the poor totality checker: it’s hard to imagine how it would even *start* to show that something so lazy and circular would terminate. `IterT`

won’t help us here, either: it can help us express programs that *might* diverge, not weird-looking ones that definitely won’t.

The solution presented is a type (`De`

) which has a limited set of combinators: a fixpoint (`fix :: (De x -> x) -> x`

), and an applicative instance. As long as all problematic recursive calls are instead expressed using those combinators, the termination checker should be satisfied.

`De`

can be thought of as a “delay” wrapper. Values of type `De a`

are one step in the future, `De (De a)`

are two, and so on. This idea was later expanded upon in Atkey (2011) and Atkey and McBride (2013) to *clock variables*. Instead of types with a delay, types are tagged with how much more time they have (something like “fuel” in the Idris sense, maybe). So a value of type $a^\mathsf{K}$ is tagged with time $\mathsf{K}$, effectively meaning “I have $\mathsf{K}$ productive steps left before I diverge”. “Productive steps” will mean something different for every data type: for lists, it could mean that it can produce up until the $\mathsf{K}$th cons-cell. In the paper (Atkey and McBride 2013) this is fleshed out a little more, with fixpoint combinators and so on. As a concrete example, take the type of the cons operator on streams:

It increments the clock on the type, saying that it has one more productive step than it did before. This is kind of the opposite of a “delay”: previously, the scheduling types have meant “this is available $\mathsf{K}$ number of steps in the future” rather than “this is available for another $\mathsf{K}$ steps”. We can still describe delays in this system, though, using the $\rhd^\mathsf{K}$ notation:

$\begin{equation} \text{Cons} : \text{a} \rightarrow \rhd^\mathsf{K}\text{Stream a} \rightarrow \text{Stream a} \end{equation}$Let’s first try express some of this in the free monad:

```
data K = Z | S K
data Delay :: K -> (Type -> Type) -> (Type -> Type) -> Type -> Type where
Now :: a -> Delay n f m a
Later :: f (DelayT n f m a) -> Delay (S n) f m a
instance (Functor f, Functor m) => Functor (Delay n f m) where
fmap f (Now x) = Now (f x)
fmap f (Later xs) = Later (fmap (fmap f) xs)
newtype DelayT n f m a = DelayT { runDelayT :: m (Delay n f m a) }
instance (Functor f, Functor m) =>
Functor (DelayT n f m) where
fmap f = DelayT . fmap (fmap f) . runDelayT
```

We can straight away express one of the combinators from the paper, `force`

:

```
force :: Functor m => (∀ k. DelayT k f m a) -> m a
force (DelayT xs) = fmap f xs
where
f :: Delay Z f m a -> a
f (Now x) = x
```

Similar trick to `runST`

here: if the type is delayed however long we want it to be, then it mustn’t really be delayed at all.

Next, remember that we have types for streams (generators) from the `IterT`

monad:

And cons does indeed have the right type:

```
cons :: Applicative m => a -> Stream n a m b -> Stream (S n) a m b
cons x xs = DelayT (pure (Later (x,xs)))
```

We also get an applicative:

```
instance (Applicative f, Applicative m) =>
Applicative (DelayT n f m) where
pure = DelayT . pure . Now
DelayT fs' <*> DelayT xs' = DelayT (liftA2 go fs' xs')
where
go :: ∀ k a b. Delay k f m (a -> b) -> Delay k f m a -> Delay k f m b
go (Now f) = fmap f
go (Later fs) = Later . \case
Now x -> fmap (fmap ($x)) fs
Later xs -> liftA2 (<*>) fs xs
```

Now, I’m not sure how much this stuff actually corresponds to the paper, but what caught my eye is the statement that `De`

is a classic “applicative-not-monad”: just like `ZipList`

. However, under the analogy that the free monad is listy, and the parallel construction is ziplist-y, what we have in the `DelayT`

is the equivalent of a length-indexed list. These have an applicative instance similar to ziplists: but they also have a monad. Can we apply the same trick here?

# Future Posts

There’s a lot of fascinating stuff out there—about clock variables, especially—that I hope to get a chance to learn about once I get a chance. What I’m particularly interested to follow up on includes:

- Comonads and their relationship to these constructions. Streams are naturally expressed as comonads, could they be used as a basis on which to build a similar “delay” mechanism?
- I’d love to explore more efficient implementations like the ones in Spivey (2017).
- I’m interested to see the relationship between these types, power series, and algebras for combinatorial search (Spivey 2009).

# References

Atkey, Robert. 2011. “How to be a Productive Programmer - by putting things off until tomorrow.” Heriot-Watt University. https://bentnib.org/posts/2011-11-14-productive-programmer.html.

Atkey, Robert, and Conor McBride. 2013. “Productive coprogramming with guarded recursion.” In, 197. ACM Press. doi:10.1145/2500365.2500597. https://bentnib.org/productive.html.

Blažević, Mario. 2011. “Coroutine Pipelines.” *The Monad.Reader* 19 (19) (August): 29–50. https://themonadreader.files.wordpress.com/2011/10/issue19.pdf.

Capretta, Venanzio, Thorsten Altenkirch, and Tarmo Uustalu. 2004. “Partiality is an effect.” In *Dependently Typed Programming*, 04381:20. Dagstuhl seminar proceedings. Dagstuhl, Germany: Internationales Begegnungs- und Forschungszentrum für Informatik (IBFI), Schloss Dagstuhl, Germany. https://www.ioc.ee/~tarmo/tday-veskisilla/uustalu-slides.pdf.

Gonzalez, Gabriel. 2018. “Pipes: Compositional pipelines.” http://hackage.haskell.org/package/pipes.

Kiselyov, Oleg. 2012. “Iteratees.” In *Proceedings of the 11th International Conference on Functional and Logic Programming*, 166–181. Lecture notes in computer science. Berlin, Heidelberg: Springer, Berlin, Heidelberg. doi:10.1007/978-3-642-29822-6_15. http://okmij.org/ftp/Haskell/Iteratee/describe.pdf.

McBride, Conor. 2009. “Time flies like an applicative functor.” *Epilogue for Epigram*. https://mazzo.li/epilogue/index.html\%3Fp=186.html.

Spivey, J. Michael. 2009. “Algebras for combinatorial search.” *Journal of Functional Programming* 19 (3-4) (July): 469–487. doi:10.1017/S0956796809007321. https://pdfs.semanticscholar.org/db3e/373bb6e7e7837ebc524da0a25903958554ed.pdf.

Spivey, Michael. 2017. “Faster coroutine pipelines.” *Proceedings of the ACM on Programming Languages* 1 (ICFP) (August): 1–23. doi:10.1145/3110249. http://dl.acm.org/citation.cfm?doid=3136534.3110249.

There is a later, seemingly more formal version of the talk available (

**???**), but the one from 2004 was a little easier for me to understand, and had a lot more Haskell code.↩Small note:

`(,) a`

and`(->) a`

are adjunct. I wonder if there is any implication from this? Certainly, producers and consumers seem adjunct, but there’s no instance I can find for it in adjunctions.↩