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An Explanation of Unification.hs

In order to make this code useful to others, I would like to take the time to explain exactly how it works. In this file, we will go through the unification algorithm being used and how it is implemented in the code.

The Problem

Before beginning, it's worth clarifying the problem that we're attempting to solve with this code, namely, what is higher order unification? The simple answer is that we want to take two terms with "holes" in them, called metavariables. We then want to figure out how to replace those metavariables with programs so that the two terms, once fully filled in, evaluate to the same term. Our language will contain the following constructs,

  1. Variables
  2. Functions (lambdas) and correspondingly application
  3. Metavariables
  4. Function types, in the typical style for a dependently typed language: pi types
  5. Universe types

This was originally designed to be part of a typechecker for a particular dependently typed language, hence the pi types and universes, but they can safely be ignored and treated as particular constants.

The main issue is, that it's actually undecidable to do this in the general case. It's therefore only possible to implement a semidecision procedure that performs relatively well in practice. By a semidecision procedure, I mean an algorithm that will terminate with a solution when possible and reject only some of the time. This procedure is called Huet's algorithm and it's essentially a refinement of the following algorithm

  1. Generate a solution
  2. Test it
  3. If the solution was correct, stop
  4. Else, go to 1

This is not exactly the most sophisticated algorithm but it does have the benefit of being obviously a correct semidecision procedure for our problem. The idea with Huet's algorithm is to gradually produce a solution and to only produce solutions that are at least not obviously wrong. By doing this, we drastically cut down the search space and produce answers reasonably quickly.

The Set Up

To begin with, we introduce the tools that we will need to even code up the unification algorithm. The first critical point is how we define the language we're unifying in the first place. I will represent terms using the so-called "locally nameless" approach. This means that we use de Bruijn to represent bound variables. However, for free variables we will generate globally unique identifiers to simplify the process of carrying around contexts or the like. This does mean that our AST has two different constructors for variables, free and bound.

    type Id = Int
    type Index = Int
    data Term = FreeVar Id
              | LocalVar Index
              | MetaVar Id
              | Uni
              | Ap Term Term
              | Lam Term
              | Pi Term Term
              deriving (Eq, Show, Ord)

Since we're using de Bruijn indices, we also need to define a crucial helper function called raise :: Int -> Term -> Term. This raises all the variables wrapped in a LocalVar constructor up by i. This is done by recursing over the inputted term.

    raise :: Int -> Term -> Term
    raise = go 0
      where go lower i t = case t of
              FreeVar i -> FreeVar i
              LocalVar j -> if i > lower then LocalVar (i + j) else LocalVar j
              MetaVar i -> MetaVar i
              Uni -> Uni
              Ap l r -> go lower i l `Ap` go lower i r
              Lam body -> Lam (go (lower + 1) i body)
              Pi tp body -> Pi (go lower i tp) (go (lower + 1) i body)

Using this, we can define substitution on terms. This will be useful later on directly. For this, we first define the notion of replacing a de Bruijn variable with a term.

    subst :: Term -> Int -> Term -> Term
    subst new i t = case t of
      FreeVar i -> FreeVar i
      LocalVar j -> case compare j i of
        LT -> LocalVar j
        EQ -> new
        GT -> LocalVar (j - 1)
      MetaVar i -> MetaVar i
      Uni -> Uni
      Ap l r -> subst new i l `Ap` subst new i r
      Lam body -> Lam (subst (raise 1 new) (i + 1) body)
      Pi tp body -> Pi (subst new i tp) (subst (raise 1 new) (i + 1) body)

Notice that we have used raise to escape new as we go under binders to avoid capturing variables. Similarly, since we're removing a binding level, if we have any de Bruijn variables that refer to a binding site outside of the one we're working with we have to lower it to compensate. That is the reason for the line GT -> LocalVar (j - 1). Apart from these two complications, substitution is just hunting for all occurrences of LocalVar i and replacing it with new. However, we also have this metavariables so it makes sense that we have a notion of substitution for these as well. It's simpler than the above substitution function because we don't have to worry about lowering variables that might be affected by deleting a metavariable binding since we're using globally unique identifiers for them.

    substMV :: Term -> Id -> Term -> Term
    substMV new i t = case t of
      FreeVar i -> FreeVar i
      LocalVar i -> LocalVar i
      MetaVar j -> if i == j then new else MetaVar j
      Uni -> Uni
      Ap l r -> substMV new i l `Ap` substMV new i r
      Lam body -> Lam (substMV (raise 1 new) i body)
      Pi tp body -> Pi (substMV new i tp) (substMV (raise 1 new) i body)

Now there are only a few more utility functions left before we can get to the actual unification. We need a function to gather all of the metavariables in a term. For this we use a Set from containers and just fold over the structure of the term.

    metavars :: Term -> S.Set Id
    metavars t = case t of
      FreeVar i -> S.empty
      LocalVar i -> S.empty
      MetaVar j -> S.singleton j
      Uni -> S.empty
      Ap l r -> metavars l <> metavars r
      Lam body -> metavars body
      Pi tp body -> metavars tp <> metavars body

Another useful function will be necessary for enforcing the condition that we only unify metavariables with closed terms (no capturing). In order to handle this, we will need to check that a given term is closed. This is as simple as looking to see if it mentions the FreeVar constructor since LocalVar is used for only bound variables by invariant.

    isClosed :: Term -> Bool
    isClosed t = case t of
      FreeVar i -> False
      LocalVar i -> True
      MetaVar j -> True
      Uni -> True
      Ap l r -> isClosed l && isClosed r
      Lam body -> isClosed body
      Pi tp body -> isClosed tp && isClosed body

The last complicated utility function is reduce. This is actually just a simple interpreter for the language we defined earlier. It essentially repeatedly searches for Ap (Lam ...) ... and when it finds such an occurrence substitutes the argument into the body of the function as one might expect. I have made this function reduce everywhere because it seems to provide a significant performance improvement in many cases.

    reduce :: Term -> Term
    reduce t = case t of
      FreeVar i -> FreeVar i
      LocalVar j -> LocalVar j
      MetaVar i -> MetaVar i
      Uni -> Uni
      Ap l r -> case reduce l of
        Lam body -> reduce (subst r 0 body)
        l' -> Ap l' (reduce r)
      Lam body -> Lam (reduce body)
      Pi tp body -> Pi (reduce tp) (reduce body)

The remaining utility funcitons are simply checks and manipulations that we will frequently need on terms. We have a function which checks whether a term is of the form M e1 e2 e3 ... for some metavariable M; such terms are said to be stuck.

    isStuck :: Term -> Bool
    isStuck MetaVar {} = True
    isStuck (Ap f _) = isStuck f
    isStuck _ = False

The remaining utility functions simply convert telescopes of applications, f a1 a2 a3 ..., into an function and a list of arguments, (f, [a1 ... an]) and then we have a function to put things back again.

    peelApTelescope :: Term -> (Term, [Term])
    peelApTelescope t = go t []
      where go (Ap f r) rest = go f (r : rest)
            go t rest = (t, rest)

    applyApTelescope :: Term -> [Term] -> Term
    applyApTelescope = foldl' Ap

We are now in a position to turn to implementing the actual unification algorithm with all of our utilities in hand.

The Unification Algorithm

There are really only two key functions in implementing the unification algorithm. We can either take an existing constraint and simplify it, or take a constraint and produce a list of partial solutions, at least one of which is correct if the constraint is solvable. The first function is remarkably similar to the first-order case of unification, we essentially take a constraint and produce a set of constraints which are equivalent to the original one. For instance, if our constraint that we're trying to solve is

    FreeVar 0 `Ap` E === FreeVar 0 `Ap` E'

It's easy to see that we might as well solve constraint E === E' which is strictly simpler. This is what the function simplify does. It has the type

    simplify :: Constraint -> UnifyM (S.Set Constraint)

In order to work with generating fresh metavariables and (later) backtracking, we use the monad UnifyM. This is defined, as is Constraint, as a type synonym

    type UnifyM = LogicT (Gen Id)
    type Constraint = (Term, Term)

Here we are using the package logict to provide backtracking. My tutorial of this package can be found here. We are also using a package a threw together a few years ago called monad-gen, it just provides a simple monad for generating fresh values. The sort of thing that I always end up needing in compilers. Without further-ado, let's start going through the cases for simplify. Each one of which corresponds to a simplifying move we are allowed to make on a constraint, ordered in terms of priority.

    simplify (t1, t2)
      | t1 == t2 = return S.empty

We start out with a nice and simple case, if the two terms of the constraint are literally identical, we have no further goals. Next we have two cases integrating reduction. If either term is reducible at all we reduce it and try to simplify the remaining goals.

      | reduce t1 /= t1 = simplify (reduce t1, t2)
      | reduce t2 /= t2 = simplify (t1, reduce t2)

This is how we integrate the fact that our unification is modulo reduction (we allow two terms to unify if they reduce to the same thing). Next comes the cases that are a little more sophisticated and correspond more closely to our original motivating example. If our two terms are a several things applied to free variables, we know the following

  1. The free variables have to be the same
  2. All of the arguments must unify

This is captured by the following branch of simplify.

      | (FreeVar i, cxt) <- peelApTelescope t1,
        (FreeVar j, cxt') <- peelApTelescope t2 = do
          guard (i == j && length cxt == length cxt')
          fold <$> mapM simplify (zip cxt cxt')

This code just codifies the procedure that we have informally sketched above. If we're trying to unify A a1 ... an and B b1 ... bm for two free variables A and B then we must have A = B and n = m since we have to find a solution that works for any A and any B. Finally, we then just need to unify ai with bi. The next two cases are congruence type rules. We basically just produce new constraints saying that Lam e === Lam e' if and only if e === e'. There is a small amount of bookkeeping done to make sure that free variables are correctly represented by a globally unique FreeVar i. The same thing is done for Pi except, since Pis are annotated with a type we also add a constraint for these types as well.

      | Lam body1 <- t1,
        Lam body2 <- t2 = do
          v <- FreeVar <$> lift gen
          return $ S.singleton (subst v 0 body1, subst v 0 body2)
      | Pi tp1 body1 <- t1,
        Pi tp2 body2 <- t2 = do
          v <- FreeVar <$> lift gen
          return $ S.fromList
            [(subst v 0 body1, subst v 0 body2),
             (tp1, tp2)]

The final case is to decide whether or not the constraint is "stuck" on a metavariable, in which case we'll need to guess a solution for a metavariable or whether the constraint is just impossible. If neither constraint is stuck, we fail using mzero and if we're stuck then we just return the inputted constraint since we can make it no simpler.

      | otherwise =
        if isStuck t1 || isStuck t2 then return $ S.singleton (t1, t2) else mzero

Now we turn to the most complicated part of the algorithm, where we actual try and produce possible and partial solutions for our unification constraints. The basic idea is to work with constraints of the form

    M a1 a2 ... an = A b1 b2 ... bm

where M is a metavariable and A is a some term, probably a free variable. These are called flex-rigid equations because one half is flexible, a metavariable, while one half is rigid. The first part of this code is to extract the relevant pieces of information from the constraint. Therefore, the code roughly looks like

    tryFlexRigid :: Constraint -> [UnifyM [Subst]]
    tryFlexRigid (t1, t2)
      | (MetaVar i, cxt1) <- peelApTelescope t1,
        (stuckTerm, cxt2) <- peelApTelescope t2,
        not (i `S.member` metavars t2) = error "TODO"
      | (MetaVar i, cxt1) <- peelApTelescope t2,
        (stuckTerm, cxt2) <- peelApTelescope t1,
        not (i `S.member` metavars t1) = error "TODO"
      | otherwise = []

This simply uses peelApTelescope to extract the 4 components M, (a1 ... an), A and (b1 ... bm). The resulting type is "morally" supposed to be [Subst] but for technical reasons we need to [UnifyM [Subst]] because we need to generate metavariables for the substitutions. There are exactly 2 forms that M may take

  • M = λ x1. ... λ xn. xi (M1 x1 ... xn) ... (Mr x1 ... xn)
  • M = λ x1. ... λ xn. A (M1 x1 ... xn) ... (Mr x1 ... xn) (if A is closed)

These are the only two forms that M can take because if M is any other constant or free variable than it would immediately contradictory, M couldn't possibly unify with A b1 ... bm as we need it to. Therefore, tryFlexRigid will produce a list of such substitutions (mod effects) replacing M with both of these. Since we don't know how many subterms we must apply to xi or A this will be an infinitely long list. More on this complication will follow. Therefore, we can replace error "TODO" with

    type Subst = M.Map Id Term

    tryFlexRigid :: Constraint -> [UnifyM [Subst]]
    tryFlexRigid (t1, t2)
      | (MetaVar i, cxt1) <- peelApTelescope t1,
        (stuckTerm, cxt2) <- peelApTelescope t2,
        not (i `S.member` metavars t2) = proj (length cxt1) i stuckTerm 0
      | (MetaVar i, cxt1) <- peelApTelescope t2,
        (stuckTerm, cxt2) <- peelApTelescope t1,
        not (i `S.member` metavars t1) = proj (length cxt1) i stuckTerm 0
      | otherwise = []

Here proj generates the list of substitutions. It's arguments are

  1. The number of bound variables
  2. The metavariable we're trying to find substitutions for
  3. The term A that we may use to construct a substitution for M
  4. The number of subterms to generate (this will be incremented in the recursive call)

It's defined just as

    proj bvars mv f nargs =
      generateSubst bvars mv f nargs : proj bvars mv f (nargs + 1)

Now the work is done in the actual function generateSubst :: Int -> Id -> Term -> Int -> UnifyM [Subst]. We have already explained the behavior of generateSubst, it's just going to create all possible substitutions of the form described above. There is little more to say than to just show the code.

    generateSubst bvars mv f nargs = do
      let mkLam tm = foldr ($) tm (replicate bvars Lam)
      let saturateMV tm = foldl' Ap tm (map LocalVar [0..bvars - 1])
      let mkSubst = M.singleton mv
      args <- map saturateMV . map MetaVar <$> replicateM nargs (lift gen)
      return [mkSubst . mkLam $ applyApTelescope t args
             | t <- map LocalVar [0..bvars - 1] ++
                    if isClosed f then [f] else []]

All that is left to do is to tie these two functions together in to try and produce a solution in general. One small caveat is that we need a few simple functions for working with substitutions. One to take a Subst and perform all the indicated replacements on a term and one to take two substitutions and perform a disjoint merge on them.

    manySubst :: Subst -> Term -> Term
    manySubst s t = M.foldrWithKey (\mv sol t -> substMV sol mv t) t s

    (<+>) :: Subst -> Subst -> Subst
    s1 <+> s2 | not (M.null (M.intersection s1 s2)) = error "Impossible"
    s1 <+> s2 = M.union (manySubst s1 <$> s2) s1

Now our main function, unify will take the current substitution and a set of constraints and produce a solution substitution and a set of flex-flex equations. These are equations of the form M a1 ... an = M' b1 ... bn. It is provable that so called flex-flex equations are always solvable (cf Huet's lemma) but solving them in a canonical way is impossible so we instead produce the solution "up to" flex-flex equations and let the user deal with the ambiguity however they choose. For example, such an equation in resulting from Agda's unification algorithm will produce the error "unresolved metavariables" because the metavariable is not canonically determined. Therefore, our main algorithm proceeds in the following steps

  1. Apply the given substitution to all our constraints.
  2. Simplify the set of constraints to remove any obvious ones.
  3. Separate flex-flex equations from flex-rigid ones.
  4. Pick a flex-rigid equation at random, if there are none, we're done.
  5. Use tryFlexRigid to get a list of possible solutions
  6. Try each solution and attempt to unify the remaining constraints, backtracking if we get stuck

In order to implement 2, we define a function which is simply the "closure" of simplify and applies it until there is no more simplification to be done.

    repeatedlySimplify :: S.Set Constraint -> UnifyM (S.Set Constraint)
    repeatedlySimplify cs = do
      cs' <- fold <$> traverse simplify (S.toList cs)
      if cs' == cs then return cs else repeatedlySimplify cs'

Apart from this, the main routine four unification is quite declarative

    unify :: Subst -> S.Set Constraint -> UnifyM (Subst, S.Set Constraint)
    unify s cs = do
      let cs' = applySubst s cs
      cs'' <- repeatedlySimplify cs'
      let (flexflexes, flexrigids) = S.partition flexflex cs''
      if S.null flexrigids
        then return (s, flexflexes)
        else do
          let psubsts = tryFlexRigid (S.findMax flexrigids)
          trySubsts psubsts (flexrigids <> flexflexes)

The first line implements step 1, using applySubst :: Subst -> S.Set Constraint -> S.Set Constraint to apply our substitution. The next line simplifies the constraints so we're left with flex-flex or flex-rigid constraints. After this, we can partition the constraints into these two classes. From here, we simply implement steps 4-6 making use of the helper function trySubst

    trySubsts :: [UnifyM [Subst]] -> S.Set Constraint -> UnifyM (Subst,S.Set Constraint)

This function takes care of peeling out each substitution and applying it to the constraints we have lying around. In order to cope with the fact that all of these are potentially infinite and we need to fairly search the resulting space, we make use of interleave :: m a -> m a -> m a from logict. It's essentially equivalent to mplus from the list monad but search fairly in the case of infinite lists. This takes care of handling backtracking in a seamless and mostly invisible way, Haskell is fun sometimes! The code for implementing this is essentially just interleave-ing all the recursive calls to unify that we need to make using mzero, failure, for when we've run out of substitutions to try.

    trySubsts [] cs = mzero
    trySubsts (mss : psubsts) cs = do
      ss <- mss
      let these = foldr interleave mzero [unify (newS <+> s) cs | newS <- ss]
      let those = trySubsts psubsts cs
      these `interleave` those

Putting all of this code together, we have completed a higher-order unificaiton algorithm! To make a top-level function to play with, we add a driver function which runs unify and strips out all of the monads of UnifyM

    driver :: Constraint -> Maybe (Subst, S.Set Constraint)
    driver = listToMaybe . runGenFrom 100 . observeAllT . unify M.empty . S.singleton