After finishing my master’s degree, I applied to several companies I was interested in. During one of the selection processes, the interviewer asked me to do the following exercise:

“Write a stack-based interpreted language that includes: literals, arithmetic operations, variables, and control flow primitives. As a bonus, add asynchronous primitives such as fork and await.”

Fortunately, I was already familiar with the assignment because I implemented a statically typed programming language a year ago. Consequently, I decided to take this as a chance and do something different than the rest of the candidates. Spoiler: haskell + free monads!

The source code from this blog can be found here. My recommendation is to have both opened side-by-side.

The concept of a free monad comes from the field of Category Theory (CT). The formal definition is hard to grasp without a background in CT. In terms of programming, a more pragmatic definition is

Def. For any functor $f$, there exists a uniquely determined monad called the free monad of the functor $f$ 1

In plain words, we can transform any functor $f$ into a monad…And you may be wondering “how is that useful to write an interpreter in Haskell? You’ll see in a bit, stick with me.

Let’s show how free monads is translated into Haskell. We are going to base our work on the encoding of free monads of the haskell package free by Edward Kmett. The definition is as follows:

 1 data Free f a = Pure a | Free (f (Free f a)) 

You can think of Free f as a stack of layers f on top of a value a.

We will encode our interpreted language as a set of free monadic actions (instructions) represented as a datatype with a functorial shape. And our programs will be just a stack of thes instructions.

The interesting part about Free f is that Free f is a monad as long as f is a functor:

 1234 instance Functor f => Monad (Free f) where return = pure Pure a >>= f = f a Free m >>= f = Free ((>>= f) <$> m)  This will allow us to compose programs for free2. On top of that, do-notation gives us a nice syntax to compose ‘em all! In the next section, we will finally see how to put this into practise. # Solving the assignment When modeling a language3 using free monads, you usually divide the problem in two parts: 1. Define your language. 2. Define the interpreter for your language. ## Defining our eDSL First, we need to define our language. We are free to model any kind of language, but the type that represents our language must be a functor. Otherwise, our language will not be able to be composed using bind (=<<). Here is the sum type representing our byte code language:  1234567891011121314151617181920212223242526 type ByteCode = Free ByteCodeF data ByteCodeF next = Lit Value next | Load Var next | Write Var next | BinaryOp OpCode next | Loop (ByteCode Bool) (ByteCode ()) next | Ret (Value -> next) | NewChan (Channel -> next) | Send Channel next | Recv Channel next | Fork (ByteCode ()) (Future () -> next) | Await (Future ()) next deriving (Functor) data Value = B Bool | I Integer deriving stock (Eq, Show, Ord) newtype Chan a = Chan { getChan :: MVar a} deriving newtype (Eq) data Future a where Future :: Exception e => Async (Either e a) -> Future a  • Lit instruction to instantiate literals (integer and boolean values). • Load/Write instructions to load and write variables, respectively. • BinaryOp instruction to apply binary operations (+, *, <) on the top values of the stack. • Loop instruction to execute a set of instructions iteratively based on a certain condition. • Ret instruction to stop the program and return the value on top of the stack. • Fork/Await instructions to start and await an asynchronous program, respectively. • NewChan/Send/Recv instructions to create a new asynchronous channel, send and receive a value through the channel, respectively. This channel allow to independent asynchronous programs to communicate. The careful reader may have notice that our type ByteCodeF has a type parameter next :: Type. This type parameter is required for ByteCodeF to be a functor. We can think of next as the next instruction in our program. In fact, when we interpret the ByteCode free monad, next will be replaced by the (evaluated) next instruction of our program. Next, for each constructor of our ByteCodeF, we need a function that lifts ByteCodeF into ByteCode. For example, here is the definition of that function for Lit:  12 lit :: Value -> ByteCode () lit v = Free (Lit v (Pure ()))  Implementing this for each constructor is tedious and error prone. This problem can be solved with metaprogramming. In particular, with template haskell (for an introduction to template haskell see my blog post). Fortunately, this is already implemented in Control.Monad.Free.TH. Therefore, we are going to use makeFree to automatically derive all these free monadic actions  1 $(makeFree ''ByteCodeF) 

which generates the following code4

 123456789 lit :: Value -> ByteCode () load :: Var -> ByteCode () write :: Var -> ByteCode () loop :: ByteCode Value -> ByteCode () -> ByteCode () newChan :: ByteCode Channel send :: Channel -> ByteCode () recv :: Channel -> ByteCode () fork :: ByteCode () -> ByteCode (Async ()) await :: Async () -> ByteCode () 

Once we have the basic building blocks of our language, we can start building programs by composing smaller programs using monadic composition!

 1234567891011121314151617181920 loopN :: Integer -> ByteCode () loopN until = do let {n = "n"; i = "i"} litI until write n litI 1 write i loop (i < n) (i ++) (<) i n = do load n load i lessThan ret (++) i = do litI 1 load i add write i 

## Interpreting our program

Now that we have the building blocks to construct our domain specific programs, it is only remaining to implement an interpreter to evaluate this embedded DSL in our host language Haskell.

The interpreter is usually implemented using iterM, which allows us to collapse our Free f into a monadic value m a by providing a function f (m a) -> m a (usually called an algebra) that operates on a single layer of our free monad. If you are familiar with recursion schemes, iterM is a specialization of cataA. This may sound confusing at first, but it is easier than it sounds.

First of all, we need to choose the monadic value m. For our interpreted language, we choose m ~ Interpreter

 1234567891011121314 newtype Interpreter a = Interpreter { runInterpreter :: StateT Ctx (ExceptT Err IO) a } data Ctx = Ctx { stack :: [Value], variables :: Map Var Value } data Err = VariableNotFound Var | StackIsEmpty | BinaryOpExpectedTwoOperands | AsyncException Text | WhoNeedsTypes 

where Ctx is the current context of our program i.e. the state of the stack and the memory registers. Then, we specialize iterM to our example

 12 -- <---------------- algebra -----------------> iterm :: (ByteCodeF (Interpreter a) -> Interpreter a) -> ByteCode a -> Interpreter a 

The last step and usually the most difficult one is to implement the algebra of our DSL

 1234567891011121314151617181920212223242526272829303132333435 algebra :: ByteCodeF (Interpreter a) -> Interpreter a algebra = \case Ret f -> popI >>= f Lit i k -> pushI i >> k Load var k -> loadI var >>= pushI >> k Write var k -> popI >>= storeI var >> k BinaryOp op k -> do catchError (liftA2 (applyOp op) popI popI >>= either throwError pushI) ( \case StackIsEmpty -> throwError BinaryOpExpectedTwoOperands e -> throwError e ) >> k Loop cond expr k -> fix $\rec -> do b <- interpret cond if b then interpret expr >> rec else k NewChan f -> liftIO getChan >>= f Send chan k -> popI >>= (liftIO . sendChan chan) >> k Recv chan k -> liftIO (recvChan chan) >>= pushI >> k Fork branch k -> future branch >>= k Await (Future async') k -> do ea <- liftIO$ waitCatch async' case ea of Left (SomeException ex) -> throwError (AsyncException (T.pack $show ex)) Right (Left ex) -> throwError (AsyncException (T.pack$ show ex)) Right _r -> k 

Finally, we combine iterM and algebra to obtain

 12 interpret :: ByteCode a -> Interpreter a interpret = iterM algebra 

which allow us to interpret our embedded language in our host language. The result of interpret can be composed with other effectful programs and it can also be evaluated using runByteCode:

 12 runByteCode :: ByteCode a -> Either Err a runByteCode = unsafePerformIO . runExceptT . flip evalStateT emptyCtx . runInterpreter . interpret 

## All together

So far, we have built the following components:

1. An (embedded) stack-based language in Haskell with primitives like: lit, load, write, loop
2. An interpreter for that language: interpret and runByteCode.

Now that we have all the ingredients to create embedded stack-based programs and interpret them, we can put this into practise.

 12345678910111213141516171819202122 program :: ByteCode Value program = do chan1 <- newChan chan2 <- newChan _ <- fork $do loopN 100000 litI 1 send chan1 _ <- fork$ do loopN 100000 litI 1 send chan2 loopN 10 recv chan1 recv chan2 add ret main :: IO () main = case runByteCode program of Left err -> throw err Right res -> putStrLn \$ "Result = " <> show res 

The following program creates two asynchronous tasks that after a period of time, return the integer 1 through an asynchronous channel. The main program waits for these two asynchronous tasks to finish and outputs the sum of the results of the asynchronous tasks.

# Conclusion

In this post, we have introduced free monads and how they can be used to implement embedded domain specific languages. In particular, we have seen how to embed a stack-based language in Haskell. To see other examples of domain specific languages, we refer the reader to the examples of the free package.

This post only covered a half of the free package. In the next post of this series, we’ll explore the other half: church encoding, applicative free, cofree…

1. The term “free” in the context of CT refers to the fact that we have not added structure to the original object. Don’t confuse the term with the adjective free: “costing nothing”.

2. Pun intended.

3. Technically you are defining an embedded domain specific language (eDSL).

4. The actual signatures use MonadFree which is an mtl-style class that allow us to compose FreeT with other monad transformers. We were able to monomorphize the return value MonadFree f m => m ~ Free ByteCodeF thanks to the instance Functor f => MonadFree f (Free f)