So in my first post on chalk, I mentioned that unification and normalization of associated types were interesting topics. I’m going to write a two-part blog post series covering that. This first part begins with an overview of how ordinary type unification works during compilation. The next post will add in associated types and we can see what kinds of mischief they bring with them.

What is unification?

Let’s start with a brief overview of what unification is. When you are doing type-checking or trait-checking, it often happens that you wind up with types that you don’t know yet. For example, the user might write None – you know that this has type Option<T> , but you don’t know what that type T is. To handle this, the compiler will create a type variable. This basically represents an unknown, to-be-determined type. To denote this, I’ll write Option<?T> , where the leading question mark indicates a variable.

The idea then is that as we go about type-checking we will later find out some constraints that tell us what ?T has to be. For example, imagine that we know that Option<?T> must implement Foo , and we have a trait Foo that is implemented only for Option<String> :

trait Foo { } impl Foo for Option < String > { }

In order for this impl to apply, it must be the case that the self types are equal, i.e., the same type. (Note that trait matching never considers subtyping.) We write this as a constraint:

Option<?T> = Option<String>

Now you can probably see where this is going. Eventually, we’re going to figure out that ?T must be String . But it’s not immediately obvious – all we see right now is that two Option types have to be equal. In particular, we don’t yet have a simple constraint like ?T = String . To arrive at that, we have to do unification.

Basic unification

So, to restate the previous section in mildly more formal terms, the idea with unification is that we have:

a bunch of type variables like ?T . We often call these existential type variables because, when you look at things in a logical setting, they arise from asking questions like exists ?T. (Option<String> = Option<?T>) – i.e., does there exist a type ?T that can make Option<String> equal to Option<?T> .

like . We often call these because, when you look at things in a logical setting, they arise from asking questions like – i.e., does there exist a type that can make equal to . a bunch of unification constraints U1..Un like T1 = T2 , where T1 and T2 are types. These are equalities that we know have to be true.

We would like to process these unification constraints and get to one of two outcomes:

the unification cannot be solved (e.g., u32 = i32 just can’t be true);

just can’t be true); we’ve got a substitution (mapping) from type variables to their values (e.g., ?T => String ) that makes all of the unification constraints hold.

Let’s start out with a really simple type system where we only have two kinds of types (in particular, we don’t yet have associated types):

T = ?X // type variables | N<T1, ..., Tn> // "applicative" types

The first kind of type is type variables, as we’ve seen. The second kind of type I am calling “applicative” types, which is really not a great name, but that’s what I called it in chalk for whatever reason. Anyway they correspond to types like Option<T> , Vec<T> , and even types like i32 . Here the name N is the name of the type (i.e., Option , Vec , i32 ) and the type parameters T1...Tn represent the type parameters of the type. Note that there may be zero of them (as is the case for i32 , which is kind of “shorthand” for i32<> ).

So the idea for unification then is that we start out with an empty substitution S and we have this list of unification constraints U1..Un . We want to pop off the first constraint ( U1 ) and figure out what to do based on what category it falls into. At each step, we may update our substitution S (i.e., we may figure out the value of a variable). In that case, we’ll replace the variable with its value for all the later steps. Other times, we’ll create new, simpler unification problems.

?X = ?Y – if U equates two variables together, we can replace one variable with the other, so we add ?X => ?Y to our substitution, and then we replace all remaining uses of ?X with ?Y .

– if equates two variables together, we can replace one variable with the other, so we add to our substitution, and then we replace all remaining uses of with . ?X = N<T1..Tn> – if we see a type variable equated with an applicative type, we can add ?X => N<T1..Tn> to our substitution (and replace all uses of it). But there is catch – we have to do one check first, called the occurs check , which I’ll describe later on.

– if we see a type variable equated with an applicative type, we can add to our substitution (and replace all uses of it). But there is catch – we have to do one check first, called the , which I’ll describe later on. N<X1..Xn> = N<Y1..Yn> – if we see two applicative types with the same name being equated, we can convert that into a bunch of smaller unification problems like X1 = Y1 , X2 = Y2 , …, Xn = Yn . The idea here is that Option<Foo> = Option<Bar> is true if Foo = Bar is true; so we can convert the bigger problem into the smaller one, and then forget about the bigger one.

– if we see two applicative types with the same name being equated, we can convert that into a bunch of smaller unification problems like , , …, . The idea here is that is true if is true; so we can convert the bigger problem into the smaller one, and then forget about the bigger one. N<...> = M<...> where N != M – if we see two application types being equated, but their names are different, that’s just an error. This would be something like Option<T> = Vec<T> .

OK, let’s try to apply those rules to our example. Remember that we had one variable ( ?T ) and one unification problem ( Option<?T> = Option<String> ). We start an initial state like this:

S = [] // empty substitution U = [Option<?T> = Option<String>] // one constraint

The head constraint consists of two applicative types with the same name ( Option ), so we can convert that into a simpler equation, reaching this state:

S = [] // empty substitution U = [?T = String] // one constraint

Now the next constraint is of the kind ?T = String , so we can update our substitution. In this case, there are no more constraints, but if there were, we would replace any uses of ?T in those constraints with `String:

S = [?T => String] // empty substitution U = [] // zero constraints

Since there are no more constraints left, we’re done! We found a solution.

Let’s do another example. This one is a bit more interesting. Imagine that we had two variables ( ?T and ?U ) and this initial state:

S = [] U = [(?T, u32) = (i32, ?U), Option<?T> = Option<?U>]

The first constraint is unifying two tuples – you can think of a tuple as an applicative type, so (?T, u32) is kind of like Tuple2<?T, u32> . Hence, we will simplify the first equation into two smaller ones:

// After unifiying (?T, u32) = (i32, ?U) S = [] U = [?T = i32, ?U = u32, Option<?T> = Option<?U>]

To process the next equation ?T = i32 , we just update the substitution. We also replace ?T in the remaining problems with i32 , leaving us with this state:

// After unifiying ?T = i32 S = [?T => i32] U = [?U = u32, Option<i32> = Option<?U>]

We can do the same for ?U :

// After unifiying ?U = u32 S = [?T => i32, ?U = u32] U = [Option<i32> = Option<u32>]

Now we, as humans, see that this problem is going to wind up with an error, but the compiler isn’t that smart yet. It has to first break down the remaining unification problem by one more step:

// After unifiying Option<i32> = Option<u32> S = [?T => i32, ?U = u32] U = [i32 = u32] // --> Error!

And now we get an error, because we have two applicative types with different names ( i32 vs u32 ).

The occurs check: preventing infinite types

When describing the unification procedure, I left out one little bit, but it is kind of important. When we have a unification constraint like ?X = T for some type T , we can’t just immediately add ?X => T to our substitution. We have to first check and make sure that ?X does not appear in T ; if it does, that’s also an error. In other words, we would consider a unification constraint like this to be illegal:

?X = Option<?X>

The problem here is that this results in an infinitely big type. And I don’t mean a type that occupies an infinite amount of RAM on your computer (although that may be true). I mean a type that I can’t even write down. Like if I tried to write down a type that satisfies this inequality, it would look like:

Option < Option < Option < Option < /* ad infinitum */ >>>>

We don’t want types like that, they cause all manner of mischief (think non-terminating compilations). We already know that no such type arises from our input program (because it has finite size, and it contains all the types in textual form). But they can arise through inference if we’re not careful. So we prevent them by saying that whenever we unify a variable ?X with some value T , then ?X cannot occur in T (hence the name “occurs check”).

Here is an example Rust program where this could arise:

fn main () { let mut x ; // x has type ?X x = None ; // adds constraint: ?X = Option<?Y> x = Some ( x ); // adds constraint: ?X = Option<?X> }

And indeed if you try this example on the playpen, you will get “cyclic type of infinite size” as an error.

How this is implemented

In terms of how this algorithm is typically implemented, it’s quite a bit different than how I presented it here. For example, the “substitution” is usually implemented through a mutable unification table, which uses Tarjan’s Union-Find algorithm (there are a number of implementations available on crates.io); the set of unification constraints is not necessarily created as an explicit vector, but just through recursive calls to a unify procedure. The relevant code in chalk, if you are curious, can be found here.

The main procedure is unify_ty_ty , which unifies two types. It begins by normalizing them, which corresponds to applying the substitution that we have built up so far. It then analyzes the various cases in roughly the way we’ve described (ignoring the cases we haven’t talked about yet, like higher-ranked types or associated types):

(Note: these links are fixed to the head commit in chalk as of the time of this writing; that code may be quite out of date by the time you read this, of course.)

Conclusion

This post describes how basic unification works. The unification algorithm roughly as I presented it was first introduced by Robinson, I believe, and it forms the heart of Hindley-Milner type inference (used in ML, Haskell, and Rust as well) – as such, I’m sure there are tons of other blog posts covering the same material better, but oh well.

In the next post, I’ll talk about how I chose to extend this basic system to cover associated types. Other interesting topics I would like to cover include:

integrating subtyping and lifetimes;

how to handle generics (in particular, universal quantification like forall );

); why it is decidedly non-trivial to integrate add where-clauses like where T = i32 into Rust (it breaks some assumptions that we made in this post, in particular).

Post any comments or questions in this internals thread.