The utility of free monads can show up in surprising places. One of the projects I’m working on is an AI, and part of the strategy that it uses for responding to user input is quite simple: it generates many possible responses, and then evaluates them. Most of the computations it generates will be malformed, and so will fail; we just want to skip over these as quickly as possible and move onto the next possibility. In summary:
- A system generates many possible effectful computations only one of which will ultimately be used to form a response.
- The computations have to be executed in order to even be considered.
- Most of the computations will fail. Sometimes because of I/O, but mostly because the computation is malformed.
- We only want the effects of the chosen query to actually execute.
A lot of the I/O is very slow (involving expensive requests to other APIs), and a computation may make plenty of these requests only to fail later for a completely unrelated and trivial reason. The system will usually go through a large number of failing computation before hitting on one that succeeds, so we want failing computation to fail as quickly as possible. To do this we write different interpreters which mock certain APIs, providing realistic values for the rest of the computation. These interpreters will filter out dud computations in stages.
Free monads are a nice way to structure this problem because interpretations of free monads can be defined, composed and combined very flexibly, allowing us to build up a library of interpreters for solving our problem.
What is a free monad?
Interpreting means giving meaning to some piece of data, and the meaning is often provided by stuff that gets done, which in Haskell corresponds to monads. Free monads are very easy to interpret (in other monads) because they are free, and the definition of a free object (e.g. in category theory) says that they are easy to map from. So that’s the basic idea behind free monads: easy to interpret.
Specifically, a free object is generated by something less complex, and then to map to something we now only need to provide a definition over the generating object (which is easier, since it’s got less structure).
To give an example, in high-school you may have been asked to manipulate lots of
f :: ℝn -> ℝm. Instead of defining the function
over all the points of
ℝn, which would be tedious, we just define
it over the
(0,1,0,..,0), etc. This is enough because
ℝn happens to be a free object over any set of vectors that form a
n points get mapped to
n vectors in
ℝm, which we
stick together to form a grid of numbers: now you have a matrix. The matrices
are much more economical and much easier to manipulate.
This is the essence of the advantage of free monads: morphisms between free monads are economical and easy to manipulate. In fact the manipulations can be very similar to those on matrices.
Let’s dive in. We will generate monads with functors (because functors are
easier than monads). Given a functor
Free f is the monad it generates:
data Free f a = Pure a | Free (f (Free f a))
To try and understand this definition, let’s assume
f is a functor, and we let
Free f, then what are the types of
Pure :: a -> m a -- looks like pure Free :: f (m a) -> m a -- looks like join
So essentially all we are doing is “adjoining” these two operators, which we
well know are what makes up a monad. When you apply
Free to a functor, you get
another functor back:
instance Functor f => Functor (Free f) where fmap g (Free fx) = Free (fmap g <$> fx) fmap g (Pure x) = Pure (g x)
But more is true,
Free f is a monad:
instance Functor f => Monad (Free f) where return = Pure Pure x >>= g = g x Free fx >>= g = Free ((>>= g) <$> fx)
To really understand the properties of this construction, we need natural transformations, which is what we use when we want to talk about mapping one functor into another.
infixr 0 ~> type f ~> g = forall x. f x -> g x
An actual natural transformation
phi should obey this law:
fmap t . phi = phi . fmap t
One important piece of structure is that not only does
Free take functors to
functors, it also maps natural transformations to natural transformations:
freeM :: (Functor f, Functor g) => f ~> g -> Free f ~> Free g freeM phi (Pure x) = Pure x freeM phi (Free fx) = Free $ phi (freeM phi <$> fx)
Free a functor, not a Haskell-functor, but a functor of
categories: from the category of functors and natural transformations to itself.
m is already a monad, then there is a special interpretation of
into itself, which we’ll have more to say about later:
monad :: Monad m => Free m ~> m monad (Pure x) = pure x monad (Free mfx) = do fx <- mfx monad fx
If you have two monads,
n, then the proper notion of morphism between
them is a monad morphism. This is a natural transformation
phi :: m ~> n
with a bunch of extra properties that make sure you aren’t doing something
First of all:
phi . pure = pure. That is, it should take pure values to pure
values. Second, if we have something of type
m (m a) there are now several
ways we can get and
- Sequence in
m, and then translate:
phi . join.
- Or, translate the two parts independently, and then sequence in
join . (fmap phi) . phi.
If you want to translate between monads in a sensible way, these should produce the same thing!
Monad morphisms are a more precise term for what we’ve loosely been calling “interpretations” up till now.
The neat thing about free monads is that interpretations are cheap; interpreting them in other monads is easy. The idea is that all we need is a natural transformation of functors, and then we get a morphism of monads, for free.
-- | Buy one natural transformation, and get this monad morphism for free! interp :: (Functor f, Monad m) => f ~> m -> Free f ~> m interp phi = monad . freeM phi
Great! So let’s recap:
Free is actually a functor mapping Haskell-functors to
Haskell-monads and morphisms of monads
Free f ~> m are the same as natural
transformations of functors
f ~> m (via
interp). Furthermore, ALL
Free f can be obtained by using the
interp function. So
you don’t need to ever worry about some complicated interpretation not being
Free itself defines a monad (of the categorical sort) on the
category of haskell-functors. And the algebras of this monad are.. monads!
(the Haskell ones.)
So in fact
Free is so essential to the concept of monad that it contains the
definition of what a monad is within itself, and so, we could redefine Haskell’s
monad typeclass (we’ll call our new class
Monad') as just being algebras for
class Functor m => Monad' m where monad :: Free m ~> m
An amusing exercise is to write the
Monad' m => Monad m instance. Try on your
own but here’s the answer if you can’t be bothered:
pure' :: Monad' m => a -> m a pure' = monad . Pure join' :: Monad' m => m (m a) -> m a join' = monad . Free . fmap (Free . fmap Pure)
Free monads in the real world
Okay, so how do we use free monads in a codebase?
The idea is to create languages defined by functors for each piece of functionality in our system. These can be thought of as APIs.
-- | Key value store functionality. data KeyValF a = GetKey String (Maybe String -> a) | PutKey String String a deriving (Functor) -- | Console functionality. data ConsoleF a = PutStrLn String a | GetLine (String -> a) deriving (Functor) type Console = Free ConsoleF type KeyVal = Free KeyValF
The following function helps when actually coding against these sorts of API:
liftF :: Functor f => f a -> Free f a liftF command = Free (fmap Pure command)
Since then we can create helper functions like so:
getKey :: String -> KeyVal (Maybe String) getKey k = liftF (GetKey k id) putStrLn :: String -> Console () putStrLn s = liftF (PutStrLn s ()) getLine :: Console String getLine = liftF (GetLine id)
At the top-most level, you want to create a functor representing your business logic. In this case, we can imagine making software for people who want to organise social clubs.
data ClubF a = GetClubMembers String (Maybe [String] -> a) | GetMemberClubs String (Maybe [String] -> a) | GetInput (String -> a) | Display String a deriving (Functor) type Club = Free ClubF -- plus helper functions
Now we can define our business logic in a clean, abstract way:
-- | Given a club id, shows the list of "sibling" clubs. showClubSiblings :: Club () showClubSiblings = do display "Enter club Id:" clubId <- getInput mmembers <- getClubMembers clubId case mmembers of Nothing -> display "Sorry, that club does not exist!" Just members -> do r <- sequence <$> traverse getMemberClubs members case r of Nothing -> display "Error getting club members." Just clubIdGroups -> do let siblings = nub $ concat clubIdGroups display $ "Here are the siblings of club " ++ clubId ++ ":" display (intercalate ", " siblings)
Remember when we talked about matrices? Matrices can easily be multiplied and
spliced together to make new matrices. The same is true of natural
transformations; they can be composed (this is just
.) and “co-paired”:
sumNat :: (f ~> t) -> (g ~> t) -> (Sum f g) ~> t sumNat phi _ (InL x) = phi x sumNat _ psi (InR x) = psi x
Sum is the coproduct in the category of functors.
Using some helper functions:
left :: (Functor f, Functor g) => Free f ~> Free (Sum f g) left = freeM InL right :: (Functor f, Functor g) => Free g ~> Free (Sum f g) right = freeM InR
We can (finally!) start writing some interpretations:
-- Console in IO: consoleIO :: ConsoleF ~> IO consoleIO (PutStrLn s v) = do Prelude.putStrLn s pure v consoleIO (GetLine cb) = do s <- Prelude.getLine pure (cb s) -- KeyValue in IO via Redis. keyValIO :: KeyValF ~> IO keyValIO (GetKey k cb) = do r <- Redis.lookupKey k pure (cb r) keyValIO (PutKey k v n) = do Redis.putKeyVal k v pure n
If we wanted to use a different key-value store one day, all we’d have to do is swap out this interpretation.
And for each component of our language we also write some mock interpreters:
-- Mocked reads and writes mockKeyValIO :: KeyValF ~> IO mockKeyValIO = ... -- Real reads but mock writes mockWritesKeyValValIO :: KeyValF ~> IO mockWritesKeyValValIO = ... mockConsoleIO :: ConsoleF ~> IO mockConsoleIO = ...
Finally, we interpret our business logic into a free monad representing all the
functionality we need:
KeyVal. This takes care of translating
our high-level API into the nitty-gritty of which keys are used in our Redis
system. Structuring the system in this way guarantees that such details are
banished from the rest of the code, and there is a single function where these
conventions may be changed.
clubI :: ClubF ~> (Free (Sum ConsoleF KeyValF)) clubI (GetClubMembers clubId next) = do r <- right $ getKey ("clubs." ++ clubId ++ ".members") pure $ next (words <$> r) clubI (GetMemberClubs memberId next) = do r <- right $ getKey ("users." ++ memberId ++ ".clubs") pure $ next (words <$> r) clubI (GetInput next) = do r <- left Free.getLine pure $ next r clubI (Display o next) = do left $ Free.putStrLn o pure next
Solving our initial problem
Now combining interpreters is easy, we can just use
more, we can mock certain aspects of our system selectively, and in varying
degrees, with great flexibility. It’s this flexibility which gives us the
ability to create a spectrum of mock interpreters which we use to filter the
large set of computations we need to test.
Our computations are expressed by the
CompF functor, and we could capture all
the data-requirements of our domain in a
DataF functor, which interprets into
various database models:
data :: CompF ~> DataF keyVal :: DataF ~> KevValF relational :: DataF ~> RelationalF graph :: DataF ~> GraphF
And each of
GraphF might point to several
specific implementations, each with their own mocking strategies.
We create several sorts of mocking interpreters:
DataF ~> IO with varying
accuracy and speed:
mockDataCached: uses the lower level mocks, reading data from files which have cached the responses.
mockDataGen: a cheaper direct interpretation into
IOwhich randomly generates plausible looking data of the right shape (à la
Using this sort of composability of interpreters, we can create our final set of interpreters:
fastButCrude :: CompF ~> IO mediumPlausible :: CompF ~> IO slowButAccurate :: CompF ~> IO
and use them in succession on the large list of possible computations we need to
check, filtering out the dud ones in stages. In this way we filter out the
easily detectable duds using
fastButCrude, so that
slowButAccurate is only
used for those remaining harder-to-detect duds.