"Interpreters a la Carte" in Advent of Code 2017 Duet

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This post is just a fun one exploring a wide range of techniques that I applied to solve the Day 18 puzzles of this past year’s great Advent of Code. The puzzles involved interpreting an assembly language on an abstract machine. The twist is that Part A gave you a description of one abstract machine, and Part B gave you a different abstract machine to interpret the same language in.

This twist (one language, but different interpreters/abstract machines) is basically one of the textbook applications of the interpreter pattern in Haskell and functional programming, so it was fun to implement my solution in that pattern — the assembly language source was “compiled” to an abstract monad once, and the difference between Part A and Part B was just a different choice of interpreter.

Even more interesting is that the two machines are only “half different” – there’s one aspect of the virtual machines that are the same between the two parts, and aspect that is different. This means that we can apply the “data types a la carte” technique in order to mix and match isolated components of virtual machine interpreters, and re-use code whenever possible in assembling our interpreters for our different machines! This can be considered an extension of the traditional interpreter pattern: the modular interpeter pattern.

This blog post will not necessarily be a focused tutorial on this trick/pattern, but rather an explanation on my solution centered around this pattern, where I will also add in insight on how I approach and solve non-trivial Haskell problems. We’ll be using the operational package to implement our interpreter pattern program and the type-combinators package to implement the modularity aspect, and along the way we’ll also use mtl typeclasses and classy lenses.

The source code is available online and is executable as a stack script. This post is written to be accessible for early-intermediate Haskell programmers.

The Puzzle

The puzzle is Advent of Code 2017 Day 18, and Part A is:

You discover a tablet containing some strange assembly code labeled simply “Duet”. Rather than bother the sound card with it, you decide to run the code yourself. Unfortunately, you don’t see any documentation, so you’re left to figure out what the instructions mean on your own.

It seems like the assembly is meant to operate on a set of registers that are each named with a single letter and that can each hold a single integer. You suppose each register should start with a value of 0.

There aren’t that many instructions, so it shouldn’t be hard to figure out what they do. Here’s what you determine:

  • snd X plays a sound with a frequency equal to the value of X.
  • set X Y sets register X to the value of Y.
  • add X Y increases register X by the value of Y.
  • mul X Y sets register X to the result of multiplying the value contained in register X by the value of Y.
  • mod X Y sets register X to the remainder of dividing the value contained in register X by the value of Y (that is, it sets X to the result of X modulo Y).
  • rcv X recovers the frequency of the last sound played, but only when the value of X is not zero. (If it is zero, the command does nothing.)
  • jgz X Y jumps with an offset of the value of Y, but only if the value of X is greater than zero. (An offset of 2 skips the next instruction, an offset of -1 jumps to the previous instruction, and so on.)

Many of the instructions can take either a register (a single letter) or a number. The value of a register is the integer it contains; the value of a number is that number.

After each jump instruction, the program continues with the instruction to which the jump jumped. After any other instruction, the program continues with the next instruction. Continuing (or jumping) off either end of the program terminates it.

What is the value of the recovered frequency (the value of the most recently played sound) the first time a rcv instruction is executed with a non-zero value?

Part B, however, says:

As you congratulate yourself for a job well done, you notice that the documentation has been on the back of the tablet this entire time. While you actually got most of the instructions correct, there are a few key differences. This assembly code isn’t about sound at all - it’s meant to be run twice at the same time.

Each running copy of the program has its own set of registers and follows the code independently - in fact, the programs don't even necessarily run at the same speed. To coordinate, they use the send (snd) and receive (rcv) instructions:

  • snd X sends the value of X to the other program. These values wait in a queue until that program is ready to receive them. Each program has its own message queue, so a program can never receive a message it sent.
  • rcv X receives the next value and stores it in register X. If no values are in the queue, the program waits for a value to be sent to it. Programs do not continue to the next instruction until they have received a value. Values are received in the order they are sent.

Each program also has its own program ID (one 0 and the other 1); the register p should begin with this value.

Once both of your programs have terminated (regardless of what caused them to do so), how many times did program 1 send a value?

(In each of these, “the program” is a program (written in the Duet assembly language), which is different for each user and given to us by the site. If you sign up and view the page, you will see a link to your own unique program to run.)

What’s going on here is that both parts execute the same program in two different virtual machines — one has “sound” and “recover”, and the other has “send” and “receive”. We are supposed to run the same program in both of these machines.

However, note that these two machines aren’t completely different — they both have the ability to manipulate memory and read/shift program data. So really, we want to be able to create a “modular” spec and implementation of these machines, so that we may re-use this memory manipulation aspect when constructing our machine, without duplicating any code.

Parsing Duet

First, let’s get the parsing of the actual input program out of the way. We’ll be parsing a program into a list of “ops” that we will read as our program.

Our program will be interpreted as a list of Op values, a data type representing opcodes. There are four categories: “snd”, “rcv”, “jgz”, and the binary mathematical operations:

It’s important to remember that “snd”, “jgz”, and the binary operations can all take either numbers or other registers.

Now, parsing a single Op is just a matter of pattern matching on words:

We’re going to store our program in a PointedList from the pointedlist package, which is a non-empty list with a “focus” at a given index, which we use to represent the program counter/program head/current instruction. Parsing our program is then just parsing each line in the program string, and collecting them into a PointedList. We’re ready to go!

Note that it is possible to skip this parsing step and instead operate directly on the original strings for the rest of the program, but this pre-processing step lets us isolate our partial code and acts as a verification step as well, to get rid of impossible states and commands right off the bat. Definitely more in line with the Haskell Way™.

Our Virtual Machine

Operational

We’re going to be using the great operational library1 to build our representation of our interpreted language. Another common choice is to use free, and a lot of other tutorials go down this route. However, free is a bit more power than you really need for the interpreter pattern, and I always felt like the implementation of interpreter pattern programs in free was a bit awkward, since it relies on manually (and carefully) constructing continuations.

operational lets us construct a language (and a monad) using GADTs to represent command primitives. For example, to implement something like State Int (which we’ll call IntState), you might use this GADT:

For those unfamiliar with GADT syntax, this is declaring a data type StateCommand a with two constructors — Put, which takes an Int and creates a StateCommand (), and Get, which takes no parameters and creates a StateCommand Int. We give StateCommand a kind signature, Type -> Type, meaning that it is a single-argument type constructor (Type is just a synonym for *).

Our GADT here says that the two “primitive” commands of IntState are “putting” (which requires an Int and produces a () result) and “getting” (which requires no inputs, and produces an Int result).

You can then write IntState as:

which automatically has the appropriate Functor, Applicative, and Monad instances.

Our primitives can be constructed using singleton:

With this, we can write an IntState action like we would write an action in any other monad.

Now, we interpret an IntState in a monadic context using the appropriately named interpretWithMonad:

If you’re unfamiliar with -XRankNTypes, forall x. StateCommand x -> m x is the type of a handler that can handle a StateCommand of any type, and return a value of m x (an action returning the same type as the StateCommand). So, you can’t give it something like StateCommand Int -> m Bool, or StateCommand x -> m ()…it has to be able to handle a StateCommand a of any type a and return an action in the interpreting context producing a result of the same type. If given a StateCommand Int, it has to return an m Int, and if given a StateCommand (), it has to return an m (), etc. etc.

Now, if we wanted to use IO and IORefs as the mechanism for interpreting our IntState:

interpretIO r is our interpreter, in IO. interpretWithMonad will interpret each primitive (Put and Get) using interpretIO and generate the result for us.

The GADT property of StateCommand ensures us that the result of our IO action matches with the result that the GADT constructor implies, due to the magic of dependent pattern matching. For the Put x :: StateCommand () branch, the result has to be IO (); for the Get :: StateCommand Int branch, the result has to be IO Int.

We can also be boring and interpret it using State Int:

Basically, an IntState a is an abstract representation of a program (as a Monad), and interpretIO and interpretState are different ways of interpreting that program, in different monadic contexts. To “run” or interpret our program in a context, we provide a function forall x. StateCommand x -> m x, which interprets each individual primitive command.

Duet Commands

Now let’s specify the “primitives” of our program. It’ll be useful to separate out the “memory-based” primitive commands from the “communication-based” primitive commands. This is so that we can write interpreters that operate on each one individually, and re-use our memory-based primitives and interpreters for both parts of the puzzle.

For memory, we can access and modify register values, as well as jump around in the program tape and read the Op at the current program head:

For communication, we must be able to “snd” and “rcv”.

Part A requires CRcv to take, as an argument, a number, since whether or not CRcv is a no-op depends on the value of a certain register for Part A’s virtual machine.

Now, we can leverage the :|: type from type-combinators:

:|: is a “functor disjunction” — a value of type (f :|: g) a is either f a or g a. :|: is in base twice, as :+: in GHC.Generics and as Sum in Data.Functor.Sum. However, the version in type-combinators has some nice utility combinators we will be using and is more fully-featured.

We can use :|: to create the type Mem :|: Com. If Mem and Com represent “primitives” in our Duet language, then Mem :|: Com represents primitives from either Mem or Com. It’s a type that contains all of the primitives of Mem and the primitives of Com. It contains:

etc.

Our final data monad, then — a monad that encompasses all possible Duet primitive commands — is:

We can write some convenient utility primitives to make things easier for us in the long run:

Constructing Duet Programs

Armed with our Duet monad, we can now write a real-life Duet action to represent one step of our duet programs:

This is basically a straightforward interpretation of the “rules” of our language, and what to do when encountering each op code.

The only non-trivial thing is the ORcv branch, where we include the contents of the register in question, so that our interpreter will know whether or not to treat it as a no-op.

The Interpreters

Now for the fun part!

Interpreting Memory Primitives

To interpret our Mem primitives, we need to be in some sort of stateful monad that contains the program state. First, let’s make a type describing our relevant program state, along with classy lenses for operating on it polymorphically:

We store the current program and program head with the PointedList, and also represent the register contents with a Map Char Int.

Brief Aside on Lenses with State

We’re going to be implementing our interpreters using lens machinery. Keep in mind that this isn’t necessary — to me, this just makes things a lot simpler. Using lens with classy lenses is one of the things that make programming against State and MonadState with non-trivial state bearable for me, personally! However, keep in mind that the lens aspect is more or less unrelated to the interpreter pattern and is not necessary for it. We’re just using it here to make State and MonadState a little nicer to work with!

makeClassy gives us a typeclass HasProgState, which is for things that “have” a ProgState, as well as lenses into the psOps and psRegs field for that type:

We can use these lenses with lens library functions for working with State:

So, for example, we have:

The nice thing about lenses is that they compose. For example, we have:

We can use at 'c' to give us a lens from our registers (Map Char Int) into the specific register 'c' as a Maybe Int — it’s Nothing if the item is not in the Map, and Just if it is (with the value).

However, we want to treat all registers as 0 by default, not as Nothing, so we can use non 0:

non 0 is a Lens (actually an Iso, but who’s counting?) into a Maybe Int to treat Nothing as if it was 0, and to treat Just x as if it was x.

We can chain at r with non 0 to get a lens into a Map Char Int, which we can use to edit a specific item, treating non-present-items as 0.

Interpreting Mem

With these tools to make life easier, we can write an interpreter for our Mem commands:

Nothing too surprising here — we just interpret every primitive in our monadic context.

We use MonadFail to explicitly state that we rely on a failed pattern match for control flow.2 P.moveN :: Int -> P.PointedList a -> Maybe (P.PointedList a) will “shift” a PointedList by a given amount, but will return Nothing if it goes out of bounds. Our program is meant to terminate if we ever go out of bounds, so we can implement this by using a do block pattern match with MonadFail. For instances like MaybeT/Maybe, this means empty/Nothing/short-circuit. So when we P.move, we do-block pattern match on Just t'.

We also use P.focus :: Lens' (P.PointedList a) a, a lens that the pointedlist library provides to the current “focus” of the PointedList.

Again, this usage of lens with State is not exactly necessary (we can manually use modify, gets, etc. instead of lenses and their combinators, which gets ugly pretty quickly), but it does make things a bit more convenient to write.

We’re programming against abstract interfaces (like MonadState, MonadFail) instead of actual instances (like StateT, etc.) because, as we will see later, this lets us combine interpreters together much more smoothly.

GADT Property

Again, the GADT-ness of Mem (and Com) works to enforce that the “results” that each primitive expects is the result that we give.

For example, MGet 'c' :: Mem Int requires us to return m Int. This is what use gives us. MSet 'c' 3 :: Mem () requires us to return m (), which is what (.=) returns.

We have MPeek :: Mem Op, which requires us to return m Op. That’s exactly what use (psOps . P.focus) :: (MonadState s m, HasProgState s) => m Op gives.

The fact that we can use GADTs to specify the “result type” of each of our primitives is a key part about how Program from operational works, and how it implements the interpreter pattern.

This is enforced in Haskell’s type system (through the “dependent pattern match”), so GHC will complain to us if we ever return something of the wrong type while handling a given constructor/primitive.

Interpreting Com for Part A

Now, Part A requires an environment where:

  1. CSnd “emits” items (as sounds), keeping track only of the last emitted item
  2. CRcv “catches” the last thing seen by CSnd, keeping track of only the first caught item

We can keep track of this using MonadWriter (First Int) to interpret CRcv (if there are two rcv’s, we only care about the first rcv’d thing), and MonadAccum (Last Int) to interpret CSnd. A MonadAccum is just like MonadWriter (where you can “tell” things and accumulate things), but you also have the ability to read the accumulated log at any time. We use Last Int because, if there are two snd’s, we only care about the last snd’d thing.

Note add :: MonadAccum w m => w -> m () and look :: MonadAccum w w, the functions to “tell” to a MonadAccum and the function to “get”/“ask” from a MonadAccum.

MonadAccum

Small relevant note — MonadAccum does not yet exist in mtl, though it probably will in the next version. It’s the classy version of AccumT, which is already in transformers-0.5.5.0.

For now, I’ve added MonadAccum and appropriate instances in the sample source code, but when the new version of mtl comes out, I’ll be sure to update this post to take this into account!

Interpreting Com for Part B

Part B requires an environment where:

  1. CSnd “emits” items into into some accumulating log of items, and we need to keep track of all of them.
  2. CRcv “consumes” items from some external environment, and fails when there are no more items to consume.

We can interpret CSnd’s effects using MonadWriter [Int], to collect all emitted Ints. We can interpret CRcv’s effects using MonadState s, where s contains an [Int] acting as a source of Ints to consume.

We’re going to use a Thread type to keep track of all thread state. We do this so we can merge the contexts of interpMem and interpComB, and really treat them (using type inference) as both working in the same interpretation context.

(We write an instance for HasProgState Thread, so we can use interpMem in a MonadState Thread m, since psRegs :: Lens' Thread (M.Map Char Int), for example, will refer to the psRegs inside the ProgState in the Thread)

And now, to interpret:

Note again the usage of do block pattern matches and MonadFail.

Combining Interpreters

To combine interpreters, we’re going to be using, from type-combinators:

Basically, >|< lets us write a “handler” for a :|: by providing a handler for each side. For example, with more concrete types:

We can use this to build an interpreter for Duet, which goes into interpretWithMonad, by using >|< to generate our compound interpreters.

This is how we can create interpreters on Duet by “combining”, in a modular way, interpreters for Mem and Com. This is the essence of the “data types a la carte” technique and the modular interpreter pattern.

Getting the Results

We now just have to pick concrete monads now for us to interpret into.

Part A

Our interpreter for Part A is interpMem >|< interpComA — we interpret the Mem primitives the usual way, and interpret the Com primitives the Part A way.

Let’s check what capabilities our interpreter must have:

So it looks like we need to be MonadWriter (First Int), MonadAccum (Last Int), MonadFail m, and MonadState s m, where HasProgState s.

Now, we can write such a Monad from scratch, or we can use the transformers library to generate a transformer with all of those instances for us. For the sake of brevity and reducing duplicated code, let’s take the latter route. We can use:

And so we can write our final “step” function in that context:

stepA will make a single step of the tape, according to the interpreters interpMem and interpComA.

Our final answer is then just the result of repeating this over and over again until there’s a failure (we jump out-of-bounds). We take advantage of the fact that MaybeT’s Alternative instance uses empty for fail, so we can use many :: MaybeT m a -> MaybeT m [a], which repeats a MaybeT action several times until a failure is encountered. In our case, this means we repeat until we jump out of bounds.

As a nice benefit of laziness, note that if we only want the value of the First Int in the WriterT, this will actually only repeat stepA until the first valid CRcv uses tell. If we only ask for the First Int, it’ll stop running the rest of the computation, bypassing many!

Here is the entirety of running Part A — as you can see, it consists mostly of unwrapping transformers newtype wrappers.

A Nothing result means that the Writer log never received any outputs before many ends looping, which means that the tape goes out of bounds before a successful rcv.

Part B

Our interpreter’s type for Part B is a little simpler:

We can really just use:

Writing our concrete stepB is a little more involved, since we have to juggle the state of each thread separately. We can do this using:

To “lift” our actions on one thread to be actions on a “tuple” of threads. We have, in the end:

Our final stepB really doesn’t need the WriterT [Int] — we just need that internally to collect snd outputs. So we use execWriter after “interpreting” our actions (along with many, to repeat our thread steps until they block) to just get the resulting logs immediately.

We then reset the input buffers appropriately (by putting in the collected outputs of the previous threads).

If both threads are blocking (they both have to external outputs to pass on), then we’re done (using guard, which acts as a “immediately fail here” action for MaybeT).

We return the number of items that “Program 1” (the second thread) outputs, because that’s what we need for our answer.

This is one “single pass” of both of our threads. As you probably guessed, we’ll use many again to run these multiple times until both threads block.

many :: MaybeT s Int -> MaybeT s [Int], so runMaybeT gives us a Maybe [Int], where each item in the resulting list is the number of items emitted by Program 1 at every iteration of stepB. Note that many produces an action that is guaranteed to succeed, so its result must be Just. To get our final answer, we only need to sum.

Examples

In the sample source code, I’ve included my own puzzle input provided to me from the advent of code website. We can now get actual answers given some sample puzzle input:

And, as a stack script, we can run this and see my own puzzle input’s answers:

Wrap Up

That’s it! Hope you enjoyed some of the techniques used in this post, including –

  1. Leveraging the interpreter pattern (with operational) to create a monad that can be interpreted in multiple contexts with multiple different interpreters
  2. Using functor disjunctions like :|: (or :+:, Sum, etc.) to combine interpretable primitives
  3. Writing modular interpreters for each set of primitives, then using deconstructors like >|< to easily combine and swap out interpreters.
  4. Lenses with State and classy lenses.
  5. Programming against polymorphic monadic contexts like MonadState, MonadWriter, MonadAccum, etc., which helps us combine interpreters in a very smooth way.

Pushing the Boundaries

That’s the main part of the post! However, just for fun, we can take things a little further and expand on this technique. The type-combinators library opens up a lot of doors to combining modular interpreters in more complex ways!

Functor conjunctions

We see that :|: (functor disjunction) can be used to merge sets of primitives. We can also use :&: (functor conjunction), also known as :*: from Generics.GHC and Product from Data.Functor.Product!

Used with GADTs representing primitives, :&: lets us “tag” our primitives with extra things.

For example, we were pretty sneaky earlier by using zoom to manually lift our Mem interpreter to work on specific threads. Instead, we can actually use Const Int to attach a thread ID to Mems:

The advantage we gain by using these tags is that we now have an approach that can be generalized to multiple threads, as well.

If we had a version of interpMem that takes a thread:

we can use the analogy of >|<, uncurryFan:

We can build interpreters of combinations of :|: and :&: by using combinations of >|< and uncurryFan.

Manipulating Disjunctions and Conjunctions

So, we have a Mem :|: Com. How could we “tag” our Mem after-the-fact, to add C Int? Well, we can manipulate the structure of conjunctions and disjunctions using the Bifunctor1 from Type.Class.Higher, in type-combinators.

bimap1 can be used to modify either half of a :|: or :&::

So we can “tag” the Mem in Mem :|: Cmd using:

Which we can use to re-tag a Program (Mem :|: Com), with the help of interpretWithMonad:

Combining many different sets of primitives

If we had three sets of primitives we wanted to combine, we might be tempted to use f :|: g :|: h and handleF >|< handleG >|< handleH. However, there’s a better way! Instead of f :|: g :|: h, you can use FSum '[f, g, h] to combine multiple sets of primitives in a clean way, using a type-level list.

If there are no duplicates in your type-level list, you can even use finj to create your FSums automatically:

There isn’t really a nice built-in way to build handlers for these (like we did earlier using >|<), but you can whip up a utility function with Prod (from type-combinators) and ifoldMapFSum.

Prod lets you bunch up a bunch of handlers together, so you can build handlers like:

Endless Possibilities

Hopefully this post inspires you a bit about this fun design pattern! And, if anything, I hope after reading this, you learn to recognize situations where this modular interpreter pattern might be useful in your everyday programming.


  1. You could also use the more-or-less identical MonadPrompt library. However, this is not to be confused with the prompt library, which is unrelated! The library is actually my own that I wrote a few years back before I knew about MonadPrompt, and this unfortunate naming collision is one of my greatest Haskell regrets.

  2. Using MonadFail in situations were we would normally use Alternative/MonadPlus, to take advantage of pattern match syntax in do block and have it work with Alternative combinators like many, is coming! For good hygiene, remember to turn on the -XMonadFailDesugaring extension so that pattern match failures explicitly use fail from MonadFail, thus requiring the typeclass constraint.

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