implementing type inference
Solution 1:
I found the following resources helpful for understanding type inference, in order of increasing difficulty:
- Chapter 30 (Type Inference) of the freely available book PLAI, Programming Languages: Application and Interpretation, sketches unification-based type inference.
- The summer course Interpreting types as abstract values presents elegant evaluators, type checkers, type reconstructors and inferencers using Haskell as a metalanguage.
- Chapter 7 (Types) of the book EOPL, Essentials of Programming Languages.
- Chapter 22 (Type Reconstruction) of the book TAPL, Types and Programming Languages, and the corresponding OCaml implementations recon and fullrecon.
- Chapter 13 (Type Reconstruction) of the new book DCPL, Design Concepts in Programming Languages.
- Selection of academic papers.
- Closure compiler's TypeInference is an example of the data-flow analysis approach to type inference, which is better suited to dynamic languages that the Hindler Milner approach.
However, since the best way to learn is to do, I strongly suggest implementing type inference for a toy functional language by working through a homework assignment of a programming languages course.
I recommend these two accessible homeworks in ML, which you can both complete in less than a day:
- PCF Interpreter (a solution) to warm up.
- PCF Type Inference (a solution) to implement algorithm W for Hindley-Milner type inference.
These assignments are from a more advanced course:
Implementing MiniML
Polymorphic, Existential, Recursive Types (PDF)
Bi-Directional Typechecking (PDF)
Subtyping and Objects (PDF)
Solution 2:
It's unfortunate that much of the literature on the subject is very dense. I too was in your shoes. I got my first introduction to the subject from Programming Languages: Applications and Interpretation
http://www.plai.org/
I'll try to summarize the abstract idea followed by details that I did not find immediately obvious. First, type inference can be thought of generating and then solving constraints. To generate constraints, you recurse through the syntax tree and generate one or more constraints on each node. For example, if the node is a +
operator, the operands and the results must all be numbers. A node that applies a function has the same type as the result of the function, and so on.
For a language without let
, you can blindly solve the above constraints by substitution. For example:
(if (= 1 2)
1
2)
here, we can say that the condition of the if statement must be Boolean, and that the type of the if statement is the same as the type of its then
and else
clauses. Since we know 1
and 2
are numbers, by substitution, we know the if
statement is a number.
Where things get nasty, and what I couldn't understand for a while, is dealing with let:
(let ((id (lambda (x) x)))
(id id))
Here, we've bound id
to a function that returns whatever you've passed in, otherwise known as the identity function. The problem is the type of the function's parameter x
is different on each usage of id
. The second id
is a function of type a -> a
, where a
can be anything. The first is of type (a -> a) -> (a -> a)
. This is known as let-polymorphism. The key is to solve constraints in a particular order: first solve constraints for the definition of id
. This will be a -> a
. Then fresh, separate copies of the type of id
can be substituted into the constraints for each place id
is used, for example a2 -> a2
and a3 -> a3
.
That wasn't readily explained in online resources. They'll mention algorithm W or M but not how they work in terms of solving constraints, or why it doesn't barf on let-polymorphism: each of those algorithms enforce an ordering on solving the constraints.
I found this resource extremely helpful to tie Algorithm W, M, and the general concept of constraint generation and solving all together. It's a little dense, but better than many:
http://www.cs.uu.nl/research/techreps/repo/CS-2002/2002-031.pdf
Many of the other papers there are nice too:
http://people.cs.uu.nl/bastiaan/papers.html
I hope that helps clarify a somewhat murky world.