luau/rfcs/type-normalization.md

Type normalizaton

Summary

Normalize types, for example removing redundant unions and intersections, to minimize memory usage and make user-visible inferred types easier to read.

Motivation

Once local type inference lands, we will infer union types more often. This is good, in that we will be inferring more precise types, but does mean we have to consider the impact this has on memory, and the complexity of the user-visible types.

Consider the program:

  function f()
    local x:T = ...
    local y:U = ...
    if g() then return x else return y end
  end

Currently, this program produces a type error, but with local type inference it does not, and instead introduces two subtyping constraints on the free return type:

  T <: R    U <: R

which is solved as

  R = T | U

This can produce unexpected types, for example number|number or Animal|Cat. Conversely, a program like

 local x:T = ...
 local y:U = ...
 local z = ...
 x = z
 y = z

will introduce a new free type Z for z, with constraints

  Z <: T    Z <: U

which is solved as

  Z = T & U

This can also produce unexpected types, for example number&number or Animal&Cat.

Design

In this section, we outline a number of possible designs. These fall into two broad camps: syntactic subtyping uses rewrite rules on the syntax of types, whereas semantic subtyping uses semantic models of types.

Syntactic subtyping

Alternative: check for pointer equality before adding to a union/intersection

To normalize a union or intersection, iterate through the options, adding them to a vector. Don't add them if the type is already present (a cheap check for pointer equality).

Advantages: easy to implement; fast.

Disadvantage: pointer equality is a very brittle test; it is easy to still get types T|T, caused by having two clones of T.

Alternative: check for suptyping before adding to a union/intersection

Ditto, but don't add a type if there's already a supertype in the vector (subtype in the case of intersections).

Advantages: easy to implement (reuses the existing subtyping machinery). Does most cases we're interesred in (e.g. number|number or Cat|Animal).

Disadvantages: doesn't deal with mixed intersections and unions (e.g. Animal&Cat doesn't simplify); depends on the order of types (e.g. Cat&Animal normalizes to Cat).

Alternative: ditto but convert to disjunctive normal form first

Use the fact that intersection distributes through union

  (T | U) & V  ==  (T & V) | (U & V)

to move union out of intersection before normalizing.

Advantage: does a better job of some examples with tables:

   ({ p : T } | { q : U }) & { r : V }
     == ({ p : T } & { r : V }) | ({ q : U } & { r : V })
     == ({ p : T, r : V }) | ({ q : U, r : V })

Disadvantage: exponential blowup.

Alternative: normalize union and intersection of tables to tables

We can work up to the equivalence used in the last example, normalizing { p : T } & { q : U } as { p : T, q : U }.

For read-write properties { p : T } & { p : U } is inhabited only when T == U, otherwise it is an uninhabited type (so equivalent to { p : never }).

For read-only properties { get p : T } & { get p : U } is inhabited by the same values as { get p : T&U }. (Ditto union).

Advantage: a better presentation of intersections of tables.

Disadvantage: developers might be surprised that { p : number } & { p : number? } is { p : never }; recursive unions and intersections increase time complexity; there is less normalization of unions-of-tables.

Alternative: normalize union and intersection of functions to functions

Similarly, we can treat (T -> U) | (T -> V) as T -> (U | V) and (T -> V) | (U -> V) as (T & U) -> V.

There is no good normalization of (T -> U) | (V -> W) in general. The obvious candidate is (T & V) -> (U | W) but there are functions of that type that are not of type T -> U or V -> W, such as function(x) if random() then U.new() else W.new() end.

Advantage: normalizes some functions.

Disadvantage: doesn't do what you expect. More recursive normalization. Has nasty interactions with overloaded functions. Requires union and intersection of type packs.

Semantic subtyping

The idea here is to think of types as sets of values, where intersection and union have their usual interpretation on sets.

Values can be thought of as trees, for example a table has children given by the properties, so types are sets of trees such as

   table     table
    / \       / \
{  p   q  ,  p   q  , ... }
   ↓   ↓     ↓   ↓
 true  1  false  5

is the set of trees corresponding to { p : boolean , q : number }.

In semantic subtyping, the subtyping replation is interpreted as subset order.

To make subset inclusion tractable, we can make use of finite state automata.

In the same way that sets of strings are the languages of automata, sets of trees are the languages of tree automata. Many of the same techniques, such as minimization, construction of union and intersection, etc, apply to tree automata. The main difference is that nondeterministic top-down tree automata are strictly more powerful than derministic ones.

Tree automata have:

• a set of states Q,
• an initial state q0, and
• transitions of the form q0 -> f(p1 = q1, ..., pN = qN) (where f is a function symbol, pI are distinct property names, and qI are states).

(Luau has namned properties rather than positional arguments, but otherwise this is standard).

Draw automata where the transition q0 -> f(p1 = q1, ..., pN = qN) as

         ◇
         |
         f
         ↓
      .--□--.
      |     |
     p1 ... pN
      ↓     ↓
      ◇     ◇

For example, the tree automaton for { p: boolean, q: number } is:

         ◇
         |
       table
         ↓
      .--□--.
     /       \
    p         q
    ↓         ↓
    ◇         ◇
   / \        |
true false    n
  ↓   ↓       ↓
  □   □       □

A relation R on states is a simulation whenever

• if (q, r) ∈ R and q -> f(p1=q1, ..., pM=qM)' then r -> f(p1=r1,...,pN=rN)' and for every qI, (qI, rI) ∈ R.

A relation is a bisimulation if both it and its inverse are simulations.

Partition refinement computes bisimulation on NFAs, which coincides with language equivalence on DFAs. It can be adapted to compute simulation. We can use it to compute a partition of types in the type graph, up to semantic equivalence of types.

Once we have a partition, there is a question of which representative to pick. We can read off the minimal type from the partitioned type graph, but this has lost any names given to types by type aliases. We could use any named type from the partition if there is one, and otherwise synthesize a minimal type.

Related work:

• XDuce: original work on types-as-tree-automata
• CDuce: home of semantic subtyping
• XML Schema, XPath, JSON Schema...: schema and query languages based on tree automata.

Advantages: Computes minimal types; has a sound foundation.

Disadvantages: complex; the size benefits of minimal types may be overwhelmed by the time to run partitioning.

Drawbacks

The cost of computing minimal types may be too much.

Alternatives

This is a draft RFC outlining the alternatives.