Type Safety in Two Easy Lemmas
Wow, it's been seven years already since I blogged about Type Safety in Three Easy Lemmas. Time flies! In that blog post I showed how to prove type safety of a simple language whose semantics was specified by a definitional interpreter. I still like that approach, and it has proved useful to other researchers on much larger projects such as the verified CakeML compiler.
In the meantime, I've learned about the Agda proof assistant thanks to the book Programming Language Foundations in Agda (PLFA) and I've become excited by Agda's abstraction mechanisms that enable proof reuse. I'm working on an Agda library for reusable programming language metatheory, called abstract-binding-trees. As the name suggests, it represents abstract syntax trees using Robert Harper's notion of abstract binding trees (ABT), that is, trees that are enhanced to know about variable bindings and variable occurrences (See the book Practical Foundations for Programming Languages). My library provides a suite of useful functions on abstract binding trees, such as substitution, and theorems about those functions. The neat thing about these theorems is that they automatically apply to any language whose grammar is built using abstract binding trees!
In this blog post I'll prove type safety of the simply-typed lambda calculus (STLC) with respect to a semantic specified in the standard way using a reduction semantics (standard for PL theory). The proof includes just two easy lemmas: progress and preservation. Normally a proof via progress and preservation also requires quite a few technical lemmas about substitution, but in this case we get those lemmas for free thanks to the abstract-binding-trees library.
This blog post is a literate Agda file, so the text will be interspersed with the Agda code that defines the STLC and proves type safety.
module examples.BlogTypeSafetyTwoEasy where
We'll be making use of the following items from the Agda standard library.
open import Data.List using (List; []; _∷_; length) open import Data.Nat using (ℕ; zero; suc) open import Data.Product using (_×_; proj₁; proj₂) renaming (_,_ to ⟨_,_⟩ ) open import Data.Unit.Polymorphic using (⊤; tt) open import Data.Vec using (Vec) renaming ([] to []̌; _∷_ to _∷̌_) open import Relation.Binary.PropositionalEquality using (_≡_; refl; sym)
Syntax of the STLC
The abstract-binding-trees library provides a module named Syntax that provides facilities for creating abstract binding trees.
open import Syntax
An abstract binding tree ABT consists of two kinds of nodes:
Variables: A variable node is a leaf (no children) and stores the de Bruijn index for the variable.
Operators: An operator node is tagged with the kind of operator and it has zero or more children, depending on the kind of operator.
The ABT data type is parameterized by the kinds of operators and their signatures, which specifies things like the number of child nodes for each kind of operator. To specify the operators, you create a data type definition with one constructor for each kind of operator. For the STLC the operators are lambda abstraction and application.
data Op : Set where op-lam : Op op-app : Op
To specify the operator signatures, write a function that maps the operators to a list of the Sig data type. The length of the list says the number of children nodes and the Sig controls changes in variable scoping for the child. The Sig data type is defined by the abstract-binding-trees library as follows:
data Sig : Set where
■ : Sig
ν : Sig → Sig
∁ : Sig → SigThe ν brings a variable into scope. The ∁ clears the scope of the child, so that the child does not have access to the surrounding lexical scope. The ■ terminates the changes in scope.
For the STLC, the signature function is defined as follows.
sig : Op → List Sig sig op-lam = (ν ■) ∷ [] sig op-app = ■ ∷ ■ ∷ []
With Op and sig defined, we can import the abstract binding tree data type ABT from the Syntax library. We choose to rename it to Term.
open Syntax.OpSig Op sig renaming (ABT to Term)
The raw abstract binding trees are verbose to deal with, so we use Agda pattern synonyms to obtain syntax that is closer to the pen-and-paper STLC. We write ƛ N for a lambda abstraction with body N and we write L · M for the application of the function produces by L to the argument produced by M.
pattern ƛ N = op-lam ⦅ cons (bind (ast N)) nil ⦆ infixl 7 _·_ pattern _·_ L M = op-app ⦅ cons (ast L) (cons (ast M) nil) ⦆
Reduction Semantics
We define the reduction semantics for the STLC in the usual way, with several congruence rules (the ξ's) and the β rule for function application. In the β rule, we use the substitution function defined in the abstract-binding-trees library, writing N [ M ] for replacing all the occurrences of de Bruijn index 0 inside N with the term M.
infix 2 _—→_ data _—→_ : Term → Term → Set where ξ-·₁ : ∀ {L L′ M : Term} → L —→ L′ --------------- → L · M —→ L′ · M ξ-·₂ : ∀ {L M M′ : Term} → M —→ M′ --------------- → L · M —→ L · M′ ξ-ƛ : ∀ {N N′ : Term} → N —→ N′ --------------- → (ƛ N) —→ (ƛ N′) β-ƛ : ∀ {N M : Term} -------------------- → (ƛ N) · M —→ N [ M ]
Type System
To make use of the theorems in the abstract-binding-trees library, we need to use its approach to defining type systems. Instead of defining the whole type system ourselves using an Agda data type, we instead specify 1) the types and 2) the side-conditions for each typing rule.
For STLC, we have function types, written A ⇒ B, and the bottom type Bot.
data Type : Set where Bot : Type _⇒_ : Type → Type → Type
The library asks that we specify a side condition for the variable rule that mediates the variable's type A in the environment with the expected type B, for which we define the following predicate 𝑉. For the STLC we simply require that A ≡ B.
𝑉 : List Type → Var → Type → Type → Set 𝑉 Γ x A B = A ≡ B
Next we define the predicate 𝑃 that specifies the side conditions for all the other syntax nodes. The definition of 𝑃 includes one line for each operator. The Vec parameter contains the types of the child nodes. The BTypes parameter contains the types of the bound variables. The last Type parameter is the type assigned to the current node. So for lambda abstractions (op-lam), the body has type B, the lambda's bound variable has type A, and we require that the type C of the lambda is a function type from A to B, that is, C ≡ A ⇒ B. For application (op-app), the function has type C, the argument has type A, and the result type is B provided that C is a function type from A to B, that is, C ≡ A ⇒ B.
𝑃 : (op : Op) → Vec Type (length (sig op)) → BTypes Type (sig op) → Type → Set 𝑃 op-lam (B ∷̌ []̌) ⟨ ⟨ A , tt ⟩ , tt ⟩ C = C ≡ A ⇒ B 𝑃 op-app (C ∷̌ A ∷̌ []̌) ⟨ tt , ⟨ tt , tt ⟩ ⟩ B = C ≡ A ⇒ B
We import the ABTPredicate module, using our definitions of 𝑉 and 𝑃, to obtain the type system for the STLC.
open import ABTPredicate Op sig 𝑉 𝑃
The raw typing rules are verbose, so we again use Agda's pattern synonyms to create abbreviations to match the rule names in PLFA.
pattern ⊢` ∋x = var-p ∋x refl pattern ⊢ƛ ⊢N eq = op-p {op = op-lam} (cons-p (bind-p (ast-p ⊢N)) nil-p) eq pattern ⊢· ⊢L ⊢M eq = op-p {op = op-app} (cons-p (ast-p ⊢L) (cons-p (ast-p ⊢M) nil-p)) eq
Proof of Type Safety
We prove type safety with two lemmas: progress and preservation.
Proof of Progress
The progress lemma states that every closed, well-typed term is either a value (so it's finished computing) or it can reduce.
In the STLC, lambda abstractions are values.
data Value : Term → Set where V-ƛ : ∀ {N : Term} -------------- → Value (ƛ N)
Following PLFA, we define an auxiliary data type to express the conclusion of the progress lemma.
data Progress (M : Term) : Set where done : Value M ---------- → Progress M step : ∀ {N} → M —→ N ---------- → Progress M
The proof of progress is by induction on the typing derivation. The variable case is vacuous because M is closed (well typed in an empty environment). In the lambda case, we're done. Regarding an application L · M, the induction hypothesis tells us that term L either takes a step or is already a lambda abstraction. In the former case, the whole application reduces using the congruence rule ξ-·₁. In the later case, the whole application reduces using β reduction.
progress : ∀ {M A} → [] ⊢ M ⦂ A ---------- → Progress M progress (⊢` ()) progress (⊢ƛ ⊢N _) = done V-ƛ progress (⊢· ⊢L ⊢M _) with progress ⊢L ... | step L—→L′ = step (ξ-·₁ L—→L′) ... | done V-ƛ = step β-ƛ
As you can see, to prove progress we didn't need help from the abstract-binding-trees library.
Proof of Preservation
The preservation lemma says that if a well-typed term reduces to another term, then that term is also well typed. The proof is by induction on the derivation of the reduction. The only interesting case is the one for β reduction:
(ƛ N) · M —→ N [ M ]We know that
(A ∷ Γ) ⊢ N ⦂ B
Γ ⊢ M ⦂ Aand we need prove that
Γ ⊢ N [ M ] ⦂ BThis requires the lemma that substitution preserves typing, which is provided in the SubstPreserve module of the abstract-binding-trees library. This module places four restrictions on 𝑉, for which we provide the proofs (λ x → refl), etc.
open import SubstPreserve Op sig Type 𝑉 𝑃 (λ x → refl) (λ { refl refl → refl }) (λ x → x) (λ { refl ⊢M → ⊢M }) using (preserve-substitution)
So here is the proof of preservation.
preserve : ∀ {Γ M N A} → Γ ⊢ M ⦂ A → M —→ N ---------- → Γ ⊢ N ⦂ A preserve (⊢· ⊢L ⊢M refl) (ξ-·₁ L—→L′) = ⊢· (preserve ⊢L L—→L′) ⊢M refl preserve (⊢· ⊢L ⊢M refl) (ξ-·₂ M—→M′) = ⊢· ⊢L (preserve ⊢M M—→M′) refl preserve (⊢ƛ ⊢M refl) (ξ-ƛ M—→N) = ⊢ƛ (preserve ⊢M M—→N) refl preserve {M = (ƛ N) · M} (⊢· (⊢ƛ ⊢N refl) ⊢M refl) β-ƛ = preserve-substitution N M ⊢N ⊢M
Thus we conclude the proof of type safety, having only needed to prove two lemmas, progress and preservation. Thanks to the abstract-binding-trees library, we did not need to prove that substitution preserves types nor any of the many technical lemmas that it depends on.