# ExtractRunning Coq programs in ML

*Logical Foundations*gave a simple example of Coq's program extraction features. In this chapter, we'll take a deeper look.

Set Warnings "-extraction-inside-module". (* turn off a warning message *)

Require Import Perm.

Module Sort1.

Fixpoint insert (i:nat) (l: list nat) :=

match l with

| nil ⇒ i::nil

| h::t ⇒ if i <=? h then i::h::t else h :: insert i t

end.

Fixpoint sort (l: list nat) : list nat :=

match l with

| nil ⇒ nil

| h::t ⇒ insert h (sort t)

end.

Require Import Perm.

Module Sort1.

Fixpoint insert (i:nat) (l: list nat) :=

match l with

| nil ⇒ i::nil

| h::t ⇒ if i <=? h then i::h::t else h :: insert i t

end.

Fixpoint sort (l: list nat) : list nat :=

match l with

| nil ⇒ nil

| h::t ⇒ insert h (sort t)

end.

The Extraction command prints out a function as Ocaml code.

Require Coq.extraction.Extraction.

Extraction sort.

Extraction sort.

You can see the translation of "sort" from Coq to Ocaml,
in the "Messages" window of your IDE.
Examine it there, and notice the similarities and differences.
However, we really want the whole program, including the insert
function. We get that as follows:

Recursive Extraction sort.

The first thing you see there is a redefinition of the bool type.
But Ocaml already has a bool type whose inductive structure is
isomorphic. We want our extracted functions to be compatible with,
callable by, ordinary Ocaml code. So we want to use Ocaml's standard
notation for the inductive definition, bool. The following directive
accomplishes that:

Extract Inductive bool ⇒ "bool" [ "true" "false" ].

Extract Inductive list ⇒ "list" [ "[]" "(::)" ].

Recursive Extraction sort.

End Sort1.

Extract Inductive list ⇒ "list" [ "[]" "(::)" ].

Recursive Extraction sort.

End Sort1.

This is better. But the program still uses a unary representation of
natural numbers: the number 7 is really (S (S (S (S (S (S (S O))))))). which in
Ocaml will be a data structure that's seven pointers deep. The leb function
takes time proportional to the difference in value between n and m,
which is terrible. We'd like natural numbers to be represented as Ocaml int.
Unfortunately, there are only a finite number of int values in Ocaml
(2^31, or 2^63, depending on your implementation); so there are things
you could prove about some programs, in Coq, that wouldn't be true in Ocaml.
There are two solutions to this problem:
Z's log-time per operation is much better than linear time; but in
Ocaml we are used to having constant-time operations. Thus, here
we will explore the second alternative: program with abstract types,
then use an extraction directive to get efficiency.

- Instead of using nat, use a more efficient constructive type, such as Z.
- Instead of using nat, use an
*abstract type*, and instantiate it with Ocaml integers.

Require Import ZArith.

Open Scope Z_scope.

Open Scope Z_scope.

We will be using Parameter and Axiom in Coq. You already saw
these keywords, in a Module Type, in the ADT chapter.
There, they describe interface components that must be instantiated
by any Module that satisfies the type. Here, we will use this
feature in a different (and more dangerous) way: To axiomatize
a mathematical theory without actually constructing it. The reason
that's dangerous is that if your axioms are inconsistent, then you
can prove False, or in fact, you can prove
Here, we will axiomatize a

*anything*, so all your proofs are worthless. So we must take care!*very weak*mathematical theory: We claim that there exists some type int with a function ltb, so that int injects into Z, and ltb corresponds to the < relation on Z. That seems true enough (for example, take int=Z), but we're not*proving*it here.
Parameter int : Type. (* This is the Ocaml int type. *)

Extract Inlined Constant int ⇒ "int". (* so, extract it that way! *)

Parameter ltb: int → int → bool. (* This is the Ocaml (<) operator. *)

Extract Inlined Constant ltb ⇒ "(<)". (* so, extract it that way! *)

Extract Inlined Constant int ⇒ "int". (* so, extract it that way! *)

Parameter ltb: int → int → bool. (* This is the Ocaml (<) operator. *)

Extract Inlined Constant ltb ⇒ "(<)". (* so, extract it that way! *)

Now, we need to axiomatize ltb so that we can reason about
programs that use it. We need to take great care here: the
axioms had better be consistent with Ocaml's behavior, otherwise
our proofs will be meaningless.
One axiomatization of ltb is just that it's a total order, irreflexive
and transitive. This would work just fine. But instead, I choose to
claim that there's an injection from "int" into the mathematical integers,
Coq's Z type. The reason to do this is then we get to use the omega
tactic, and other Coq libraries about integer comparisons.

Parameter int2Z: int → Z.

Axiom ltb_lt : ∀ n m : int, ltb n m = true ↔ int2Z n < int2Z m.

Axiom ltb_lt : ∀ n m : int, ltb n m = true ↔ int2Z n < int2Z m.

Both of these axioms are sound. There does (abstractly) exist a
function from "int" to Z, and that function
Notice that we do not give extraction directives for int2Z or ltb_lt.
That's because they will not appear in
Now, here's a dangerous axiom:
Parameter ocaml_plus : int → int → int.
Extract Inlined Constant ocaml_plus ⇒ "(+)".
Axiom ocaml_plus_plus:
∀a b c: int,
ocaml_plus a b = c ↔ int2Z a + int2Z b = int2Z c.
The first two lines are OK: there really is a "+" function in Ocaml,
and its type really is int → int → int.
But ocaml_plus_plus is unsound! From it, you could prove,
(int2Z max_int + int2Z max_int) = int2Z (ocaml_plus max_int max_int),
which is not true in Ocaml, because overflow wraps around, modulo
2^(wordsize-1).
So we won't axiomatize Ocaml addition.
Just like in Perm.v, but for int and Z instead of nat.

*is*consistent with the ltb_lt axiom. But you should think about this until you are convinced.*programs*, only in proofs that are not meant to be extracted.# Utilities for OCaml Integer Comparisons

Lemma int_blt_reflect : ∀ x y, reflect (int2Z x < int2Z y) (ltb x y).

Proof.

intros x y.

apply iff_reflect. symmetry. apply ltb_lt.

Qed.

Lemma Z_eqb_reflect : ∀ x y, reflect (x=y) (Z.eqb x y).

Proof.

intros x y.

apply iff_reflect. symmetry. apply Z.eqb_eq.

Qed.

Lemma Z_ltb_reflect : ∀ x y, reflect (x<y) (Z.ltb x y).

Proof.

intros x y.

apply iff_reflect. symmetry. apply Z.ltb_lt.

Qed.

(* Add these three lemmas to the Hint database for bdestruct,

so the bdestruct tactic will work with them. *)

Hint Resolve int_blt_reflect Z_eqb_reflect Z_ltb_reflect : bdestruct.

Proof.

intros x y.

apply iff_reflect. symmetry. apply ltb_lt.

Qed.

Lemma Z_eqb_reflect : ∀ x y, reflect (x=y) (Z.eqb x y).

Proof.

intros x y.

apply iff_reflect. symmetry. apply Z.eqb_eq.

Qed.

Lemma Z_ltb_reflect : ∀ x y, reflect (x<y) (Z.ltb x y).

Proof.

intros x y.

apply iff_reflect. symmetry. apply Z.ltb_lt.

Qed.

(* Add these three lemmas to the Hint database for bdestruct,

so the bdestruct tactic will work with them. *)

Hint Resolve int_blt_reflect Z_eqb_reflect Z_ltb_reflect : bdestruct.

# SearchTrees, Extracted

## Maps, on Z Instead of nat

Our original proof with nats used Maps.total_map in its abstraction relation, but that won't work here because we need maps over Z rather than nat. So, we copy-paste-edit to make total_map over Z.
Require Import Coq.Logic.FunctionalExtensionality.

Module IntMaps.

Definition total_map (A:Type) := Z → A.

Definition t_empty {A:Type} (v : A) : total_map A := (fun _ ⇒ v).

Definition t_update {A:Type} (m : total_map A) (x : Z) (v : A) :=

fun x' ⇒ if Z.eqb x x' then v else m x'.

Lemma t_update_eq : ∀ A (m: total_map A) x v, (t_update m x v) x = v.

Theorem t_update_neq : ∀ (X:Type) v x

x

Lemma t_update_shadow : ∀ A (m: total_map A) v

t_update (t_update m x v

End IntMaps.

Import IntMaps.

Module IntMaps.

Definition total_map (A:Type) := Z → A.

Definition t_empty {A:Type} (v : A) : total_map A := (fun _ ⇒ v).

Definition t_update {A:Type} (m : total_map A) (x : Z) (v : A) :=

fun x' ⇒ if Z.eqb x x' then v else m x'.

Lemma t_update_eq : ∀ A (m: total_map A) x v, (t_update m x v) x = v.

Proof.

intros. unfold t_update.

bdestruct (x=?x); auto.

omega.

Qed.

intros. unfold t_update.

bdestruct (x=?x); auto.

omega.

Qed.

Theorem t_update_neq : ∀ (X:Type) v x

_{1}x_{2}(m : total_map X),x

_{1}≠ x_{2}→ (t_update m x_{1}v) x_{2}= m x_{2}.
Proof.

intros. unfold t_update.

bdestruct (x

omega.

Qed.

intros. unfold t_update.

bdestruct (x

_{1}=?x_{2}); auto.omega.

Qed.

Lemma t_update_shadow : ∀ A (m: total_map A) v

_{1}v_{2}x,t_update (t_update m x v

_{1}) x v_{2}= t_update m x v_{2}.
Proof.

intros. unfold t_update.

extensionality x'.

bdestruct (x=?x'); auto.

Qed.

intros. unfold t_update.

extensionality x'.

bdestruct (x=?x'); auto.

Qed.

End IntMaps.

Import IntMaps.

Module SearchTree2.

Section TREES.

Variable V : Type.

Variable default: V.

Definition key := int.

Inductive tree : Type :=

| E : tree

| T: tree → key → V → tree → tree.

Definition empty_tree : tree := E.

Fixpoint lookup (x: key) (t : tree) : V :=

match t with

| E ⇒ default

| T tl k v tr ⇒ if ltb x k then lookup x tl

else if ltb k x then lookup x tr

else v

end.

Fixpoint insert (x: key) (v: V) (s: tree) : tree :=

match s with

| E ⇒ T E x v E

| T a y v' b ⇒ if ltb x y then T (insert x v a) y v' b

else if ltb y x then T a y v' (insert x v b)

else T a x v b

end.

Fixpoint elements' (s: tree) (base: list (key*V)) : list (key * V) :=

match s with

| E ⇒ base

| T a k v b ⇒ elements' a ((k,v) :: elements' b base)

end.

Definition elements (s: tree) : list (key * V) := elements' s nil.

Definition combine {A} (pivot: Z) (m

_{1}m

_{2}: total_map A) : total_map A :=

fun x ⇒ if Z.ltb x pivot then m

_{1}x else m

_{2}x.

Inductive Abs: tree → total_map V → Prop :=

| Abs_E: Abs E (t_empty default)

| Abs_T: ∀ a b l k v r,

Abs l a →

Abs r b →

Abs (T l k v r) (t_update (combine (int2Z k) a b) (int2Z k) v).

Theorem empty_tree_relate: Abs empty_tree (t_empty default).

Proof.

constructor.

Qed.

Theorem lookup_relate:

∀ k t cts , Abs t cts → lookup k t = cts (int2Z k).

Proof. (* Copy your proof from SearchTree.v, and adapt it. *)

(* FILL IN HERE *) Admitted.

☐
∀ k t cts , Abs t cts → lookup k t = cts (int2Z k).

Proof. (* Copy your proof from SearchTree.v, and adapt it. *)

(* FILL IN HERE *) Admitted.

Theorem insert_relate:

∀ k v t cts,

Abs t cts →

Abs (insert k v t) (t_update cts (int2Z k) v).

Proof. (* Copy your proof from SearchTree.v, and adapt it. *)

(* FILL IN HERE *) Admitted.

☐
∀ k v t cts,

Abs t cts →

Abs (insert k v t) (t_update cts (int2Z k) v).

Proof. (* Copy your proof from SearchTree.v, and adapt it. *)

(* FILL IN HERE *) Admitted.

Lemma unrealistically_strong_can_relate:

∀ t, ∃ cts, Abs t cts.

Proof. (* Copy-paste your proof from SearchTree.v; it should work as is. *)

(* FILL IN HERE *) Admitted.

☐
∀ t, ∃ cts, Abs t cts.

Proof. (* Copy-paste your proof from SearchTree.v; it should work as is. *)

(* FILL IN HERE *) Admitted.

End TREES.

Now, run this command and examine the results in the "results"
window of your IDE:

Recursive Extraction empty_tree insert lookup elements.

Next, we will extract it into an Ocaml source file, and measure its
performance.

Extraction "searchtree.ml" empty_tree insert lookup elements.

Note: we've done the extraction

*inside*the module, even though Coq warns against it, for a specific reason: We want to extract only the program, not the proofs.
End SearchTree2.

# Performance Tests

let test (f: int -> int) (n: int) = let rec build (j, t) = if j=0 then t else build(j-1, insert (f j) 1 t) in let t_{1}= build(n,empty_tree) in let rec g (j,count) = if j=0 then count else if lookup 0 (f j) t_{1}= 1 then g(j-1,count+1) else g(j-1,count) in let start = Sys.time() in let answer = g(n,0) in (answer, Sys.time() -. start) let print_test name (f: int -> int) n = let (answer, time) = test f n in (print_string "Insert and lookup "; print_int n; print_string " "; print_string name; print_string " integers in "; print_float time; print_endline " seconds.") let test_random n = print_test "random" (fun _ -> Random.int n) n let test_consec n = print_test "consecutive" (fun i -> n-i) n let run_tests() = (test_random 1000000; test_random 20000; test_consec 20000) let _ = run_tests ()

#use "searchtree.ml";; #use "test_searchtree.ml";; run_tests();;

Insert and lookup 1000000 random integers in 1.076 seconds. Insert and lookup 20000 random integers in 0.015 seconds. Insert and lookup 20000 consecutive integers in 5.054 seconds.

ocamlopt searchtree.mli searchtree.ml -open Searchtree test_searchtree.ml -o test_searchtree ./test_searchtree

Insert and lookup 1000000 random integers in 0.468 seconds. Insert and lookup 20000 random integers in 0. seconds. Insert and lookup 20000 consecutive integers in 0.374 seconds.

# Unbalanced Binary Search Trees

Why is the performance of the algorithm so much worse when the keys are all inserted consecutively? To examine this, let's compute with some searchtrees inside Coq. We cannot do this with the search trees defined thus far in this file, because they use a key-comparison function ltb that is abstract and uninstantiated (only during Extraction to Ocaml does ltb get instantiated).
Require SearchTree.

Module Experiments.

Open Scope nat_scope.

Definition empty_tree := SearchTree.empty_tree nat.

Definition insert := SearchTree.insert nat.

Definition lookup := SearchTree.lookup nat 0.

Definition E := SearchTree.E nat.

Definition T := SearchTree.T nat.

Goal insert 5 1 (insert 4 1 (insert 3 1 (insert 2 1 (insert 1 1 (insert 0 1 empty_tree))))) ≠ E.

simpl. fold E; repeat fold T.

Module Experiments.

Open Scope nat_scope.

Definition empty_tree := SearchTree.empty_tree nat.

Definition insert := SearchTree.insert nat.

Definition lookup := SearchTree.lookup nat 0.

Definition E := SearchTree.E nat.

Definition T := SearchTree.T nat.

Goal insert 5 1 (insert 4 1 (insert 3 1 (insert 2 1 (insert 1 1 (insert 0 1 empty_tree))))) ≠ E.

simpl. fold E; repeat fold T.

Look here! The tree is completely unbalanced. Looking up 5 will take linear time.
That's why the runtime on consecutive integers is so bad.

Abort.

# Balanced Binary Search Trees

End Experiments.