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Principal type-schemes for functional programs
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Phil Freeman
June 28, 2017
Programming
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Principal type-schemes for functional programs
Phil Freeman
June 28, 2017
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Transcript
Principal type-schemes for functional programs Luis Damas and Robin Milner
(POPL `82)
Agenda • Slides • Code
ML • Meta Language for LCF • Type inference •
Influence on Haskell, Rust, F#, OCaml, ... • “Sweet spot” in type system design
ML letrec f xs = if null xs then nil
else snoc (f (tl xs)) (hd xs) What type does this function have? null : ∀ ( list → bool) snoc : ∀ ( list → → list) hd, tl : ∀ ( list → ) nil : ∀ ( list)
ML What about: let s x y z = x
z (y z) ?
Type Inference f : ∀ ( list → list) •
Given f, how can we infer this type? • What does it even mean for a value to have a type? • How can we be sure we have the most general type?
Lambda Calculus Expressions e: • Identifiers: , , … •
Applications: e e’ • Abstractions: . e • Let bindings: let = e in e’
Lambda Calculus For example: . . . . let =
. . in
Types Monotypes : • Variables: • Primitives: • Functions: →
Type Schemes Type schemes : • Monomorphic: • Polymorphic: ∀
. Type schemes are types with identifiers bound by ∀ at the front.
Substitutions Mappings from variables to types • Can instantiate type
schemes using substitutions • Gives a simple subtyping relation on type schemes
Semantics Construct a semantic domain (CPO) V containing • Primitives
• Functions • An error element and a semantic function : e → (Id → V) → V
Semantics Identify types with subsets of V Define the judgment
A ╞ e : when (∀ ( : ’) ∈ A. ∈ ’) ⇒ e ∈
Declarative System Variable rule:
Declarative System Application rule:
Declarative System Abstraction rule:
Declarative System Let rule:
Declarative System Instantiation rule:
Declarative System Generalization rule:
Soundness If A e : then A ╞ e :
“Static behavior determines dynamic behavior”
Example Prove: . : ∀ . ( → → )
→ →
Algorithm W • The inference rules do not translate easily
into an algorithm (why not?) • Introduce w : Exp → Env → (Env, )
Algorithm W • W attempts to build a substitution, bottom-up
• W can fail with an error if there is no valid typing • Intuition: ◦ Collect constraints ◦ Then solve constraints • Reality: W is the fusion of these two steps • See the code!
Unification • Unification gives local information about types • We
assemble a global solution from local information
Unification Example: ( → ) ~ (( → ) →
) ~ ( → ) ~ ~ ( → )
Occurs Check Prevents inference of infinite types w( . ,
nil) = error! Can’t unify ~ if occurs in the body of . E.g. ~ → ~ ((… → ) → ) →
Soundness If w(A, e) = (S, ) then A e
: “Algorithm W constructs typing judgments”
Completeness If A e : then w(A, e) constructs a
typing judgment for e which generalises the above. “Algorithm W constructs principal types”
Further Reading More type systems • System F, F⍵ •
Rank-N types • Type Classes • Dependent Types • Refinement Types Other approaches • Constraints • Bidirectional typechecking • SMT See TAPL & ATAPL!
Acknowledgments DHM axioms reproduced from Wikipedia under the CC-3.0 Attribution/ShareAlike
license