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DeltaBlue.dart
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// Copyright 2011 Google Inc. All Rights Reserved.
// Copyright 1996 John Maloney and Mario Wolczko
//
// This file is part of GNU Smalltalk.
//
// GNU Smalltalk is free software; you can redistribute it and/or modify it
// under the terms of the GNU General Public License as published by the Free
// Software Foundation; either version 2, or (at your option) any later version.
//
// GNU Smalltalk is distributed in the hope that it will be useful, but WITHOUT
// ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
// FOR A PARTICULAR PURPOSE. See the GNU General Public License for more
// details.
//
// You should have received a copy of the GNU General Public License along with
// GNU Smalltalk; see the file COPYING. If not, write to the Free Software
// Foundation, 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA.
//
// Translated first from Smalltalk to JavaScript, and finally to
// Dart by Google 2008-2010.
/**
* A Dart implementation of the DeltaBlue constraint-solving
* algorithm, as described in:
*
* "The DeltaBlue Algorithm: An Incremental Constraint Hierarchy Solver"
* Bjorn N. Freeman-Benson and John Maloney
* January 1990 Communications of the ACM,
* also available as University of Washington TR 89-08-06.
*
* Beware: this benchmark is written in a grotesque style where
* the constraint model is built by side-effects from constructors.
* I've kept it this way to avoid deviating too much from the original
* implementation.
*/
import 'dart:io';
//Greg: Not using the dart benchmark harness, so I can observe warm up behaviour.
main(List<String> args) {
int iterations = 1000;
var options = '';
if (args != null && !args.isEmpty) {
iterations = int.parse(args[0], onError: (s) => iterations);
if (args.length > 1)
options = args[1];
}
var start = new DateTime.now();
for (int i = 0; i < iterations; i++) {
chainTest(100);
projectionTest(100);
}
var end = new DateTime.now();
print('DeltaBlue\tDart\t$options\t${iterations}x\t${(end.millisecondsSinceEpoch - start.millisecondsSinceEpoch) / iterations}ms');
}
/**
* Strengths are used to measure the relative importance of constraints.
* New strengths may be inserted in the strength hierarchy without
* disrupting current constraints. Strengths cannot be created outside
* this class, so == can be used for value comparison.
*/
class Strength {
final int value;
final String name;
const Strength(this.value, this.name);
Strength nextWeaker() =>
const <Strength>[WEAKEST, WEAK_DEFAULT, NORMAL, STRONG_DEFAULT,
PREFERRED, STRONG_REFERRED][value];
static bool stronger(Strength s1, Strength s2) {
return s1.value < s2.value;
}
static bool weaker(Strength s1, Strength s2) {
return s1.value > s2.value;
}
static Strength weakest(Strength s1, Strength s2) {
return weaker(s1, s2) ? s1 : s2;
}
static Strength strongest(Strength s1, Strength s2) {
return stronger(s1, s2) ? s1 : s2;
}
}
// Compile time computed constants.
const REQUIRED = const Strength(0, "required");
const STRONG_REFERRED = const Strength(1, "strongPreferred");
const PREFERRED = const Strength(2, "preferred");
const STRONG_DEFAULT = const Strength(3, "strongDefault");
const NORMAL = const Strength(4, "normal");
const WEAK_DEFAULT = const Strength(5, "weakDefault");
const WEAKEST = const Strength(6, "weakest");
abstract class Constraint {
final Strength strength;
const Constraint(this.strength);
bool isSatisfied();
void markUnsatisfied();
void addToGraph();
void removeFromGraph();
void chooseMethod(int mark);
void markInputs(int mark);
bool inputsKnown(int mark);
Variable output();
void execute();
void recalculate();
/// Activate this constraint and attempt to satisfy it.
void addConstraint() {
addToGraph();
planner.incrementalAdd(this);
}
/**
* Attempt to find a way to enforce this constraint. If successful,
* record the solution, perhaps modifying the current dataflow
* graph. Answer the constraint that this constraint overrides, if
* there is one, or nil, if there isn't.
* Assume: I am not already satisfied.
*/
Constraint satisfy(mark) {
chooseMethod(mark);
if (!isSatisfied()) {
if (strength == REQUIRED) {
print("Could not satisfy a required constraint!");
}
return null;
}
markInputs(mark);
Variable out = output();
Constraint overridden = out.determinedBy;
if (overridden != null) overridden.markUnsatisfied();
out.determinedBy = this;
if (!planner.addPropagate(this, mark)) print("Cycle encountered");
out.mark = mark;
return overridden;
}
void destroyConstraint() {
if (isSatisfied()) planner.incrementalRemove(this);
removeFromGraph();
}
/**
* Normal constraints are not input constraints. An input constraint
* is one that depends on external state, such as the mouse, the
* keybord, a clock, or some arbitraty piece of imperative code.
*/
bool isInput() => false;
}
/**
* Abstract superclass for constraints having a single possible output variable.
*/
abstract class UnaryConstraint extends Constraint {
final Variable myOutput;
bool satisfied = false;
UnaryConstraint(this.myOutput, Strength strength) : super(strength) {
addConstraint();
}
/// Adds this constraint to the constraint graph
void addToGraph() {
myOutput.addConstraint(this);
satisfied = false;
}
/// Decides if this constraint can be satisfied and records that decision.
void chooseMethod(int mark) {
satisfied = (myOutput.mark != mark)
&& Strength.stronger(strength, myOutput.walkStrength);
}
/// Returns true if this constraint is satisfied in the current solution.
bool isSatisfied() => satisfied;
void markInputs(int mark) {
// has no inputs.
}
/// Returns the current output variable.
Variable output() => myOutput;
/**
* Calculate the walkabout strength, the stay flag, and, if it is
* 'stay', the value for the current output of this constraint. Assume
* this constraint is satisfied.
*/
void recalculate() {
myOutput.walkStrength = strength;
myOutput.stay = !isInput();
if (myOutput.stay) execute(); // Stay optimization.
}
/// Records that this constraint is unsatisfied.
void markUnsatisfied() {
satisfied = false;
}
bool inputsKnown(int mark) => true;
void removeFromGraph() {
if (myOutput != null) myOutput.removeConstraint(this);
satisfied = false;
}
}
/**
* Variables that should, with some level of preference, stay the same.
* Planners may exploit the fact that instances, if satisfied, will not
* change their output during plan execution. This is called "stay
* optimization".
*/
class StayConstraint extends UnaryConstraint {
StayConstraint(Variable v, Strength str) : super(v, str);
void execute() {
// Stay constraints do nothing.
}
}
/**
* A unary input constraint used to mark a variable that the client
* wishes to change.
*/
class EditConstraint extends UnaryConstraint {
EditConstraint(Variable v, Strength str) : super(v, str);
/// Edits indicate that a variable is to be changed by imperative code.
bool isInput() => true;
void execute() {
// Edit constraints do nothing.
}
}
// Directions.
const int NONE = 1;
const int FORWARD = 2;
const int BACKWARD = 0;
/**
* Abstract superclass for constraints having two possible output
* variables.
*/
abstract class BinaryConstraint extends Constraint {
Variable v1;
Variable v2;
int direction = NONE;
BinaryConstraint(this.v1, this.v2, Strength strength) : super(strength) {
addConstraint();
}
/**
* Decides if this constraint can be satisfied and which way it
* should flow based on the relative strength of the variables related,
* and record that decision.
*/
void chooseMethod(int mark) {
if (v1.mark == mark) {
direction = (v2.mark != mark &&
Strength.stronger(strength, v2.walkStrength))
? FORWARD : NONE;
}
if (v2.mark == mark) {
direction = (v1.mark != mark &&
Strength.stronger(strength, v1.walkStrength))
? BACKWARD : NONE;
}
if (Strength.weaker(v1.walkStrength, v2.walkStrength)) {
direction = Strength.stronger(strength, v1.walkStrength)
? BACKWARD : NONE;
} else {
direction = Strength.stronger(strength, v2.walkStrength)
? FORWARD : BACKWARD;
}
}
/// Add this constraint to the constraint graph.
void addToGraph() {
v1.addConstraint(this);
v2.addConstraint(this);
direction = NONE;
}
/// Answer true if this constraint is satisfied in the current solution.
bool isSatisfied() => direction != NONE;
/// Mark the input variable with the given mark.
void markInputs(int mark) {
input().mark = mark;
}
/// Returns the current input variable
Variable input() => direction == FORWARD ? v1 : v2;
/// Returns the current output variable.
Variable output() => direction == FORWARD ? v2 : v1;
/**
* Calculate the walkabout strength, the stay flag, and, if it is
* 'stay', the value for the current output of this
* constraint. Assume this constraint is satisfied.
*/
void recalculate() {
Variable ihn = input(), out = output();
out.walkStrength = Strength.weakest(strength, ihn.walkStrength);
out.stay = ihn.stay;
if (out.stay) execute();
}
/// Record the fact that this constraint is unsatisfied.
void markUnsatisfied() {
direction = NONE;
}
bool inputsKnown(int mark) {
Variable i = input();
return i.mark == mark || i.stay || i.determinedBy == null;
}
void removeFromGraph() {
if (v1 != null) v1.removeConstraint(this);
if (v2 != null) v2.removeConstraint(this);
direction = NONE;
}
}
/**
* Relates two variables by the linear scaling relationship: "v2 =
* (v1 * scale) + offset". Either v1 or v2 may be changed to maintain
* this relationship but the scale factor and offset are considered
* read-only.
*/
class ScaleConstraint extends BinaryConstraint {
final Variable scale;
final Variable offset;
ScaleConstraint(Variable src, this.scale, this.offset,
Variable dest, Strength strength)
: super(src, dest, strength);
/// Adds this constraint to the constraint graph.
void addToGraph() {
super.addToGraph();
scale.addConstraint(this);
offset.addConstraint(this);
}
void removeFromGraph() {
super.removeFromGraph();
if (scale != null) scale.removeConstraint(this);
if (offset != null) offset.removeConstraint(this);
}
void markInputs(int mark) {
super.markInputs(mark);
scale.mark = offset.mark = mark;
}
/// Enforce this constraint. Assume that it is satisfied.
void execute() {
if (direction == FORWARD) {
v2.value = v1.value * scale.value + offset.value;
} else {
v1.value = (v2.value - offset.value) ~/ scale.value;
}
}
/**
* Calculate the walkabout strength, the stay flag, and, if it is
* 'stay', the value for the current output of this constraint. Assume
* this constraint is satisfied.
*/
void recalculate() {
Variable ihn = input(), out = output();
out.walkStrength = Strength.weakest(strength, ihn.walkStrength);
out.stay = ihn.stay && scale.stay && offset.stay;
if (out.stay) execute();
}
}
/**
* Constrains two variables to have the same value.
*/
class EqualityConstraint extends BinaryConstraint {
EqualityConstraint(Variable v1, Variable v2, Strength strength)
: super(v1, v2, strength);
/// Enforce this constraint. Assume that it is satisfied.
void execute() {
output().value = input().value;
}
}
/**
* A constrained variable. In addition to its value, it maintain the
* structure of the constraint graph, the current dataflow graph, and
* various parameters of interest to the DeltaBlue incremental
* constraint solver.
**/
class Variable {
List<Constraint> constraints = <Constraint>[];
Constraint determinedBy;
int mark = 0;
Strength walkStrength = WEAKEST;
bool stay = true;
int value;
final String name;
Variable(this.name, this.value);
/**
* Add the given constraint to the set of all constraints that refer
* this variable.
*/
void addConstraint(Constraint c) {
constraints.add(c);
}
/// Removes all traces of c from this variable.
void removeConstraint(Constraint c) {
constraints = constraints.where((e) => c != e).toList();
if (determinedBy == c) determinedBy = null;
}
}
class Planner {
int currentMark = 0;
/**
* Attempt to satisfy the given constraint and, if successful,
* incrementally update the dataflow graph. Details: If satifying
* the constraint is successful, it may override a weaker constraint
* on its output. The algorithm attempts to resatisfy that
* constraint using some other method. This process is repeated
* until either a) it reaches a variable that was not previously
* determined by any constraint or b) it reaches a constraint that
* is too weak to be satisfied using any of its methods. The
* variables of constraints that have been processed are marked with
* a unique mark value so that we know where we've been. This allows
* the algorithm to avoid getting into an infinite loop even if the
* constraint graph has an inadvertent cycle.
*/
void incrementalAdd(Constraint c) {
int mark = newMark();
for(Constraint overridden = c.satisfy(mark);
overridden != null;
overridden = overridden.satisfy(mark));
}
/**
* Entry point for retracting a constraint. Remove the given
* constraint and incrementally update the dataflow graph.
* Details: Retracting the given constraint may allow some currently
* unsatisfiable downstream constraint to be satisfied. We therefore collect
* a list of unsatisfied downstream constraints and attempt to
* satisfy each one in turn. This list is traversed by constraint
* strength, strongest first, as a heuristic for avoiding
* unnecessarily adding and then overriding weak constraints.
* Assume: [c] is satisfied.
*/
void incrementalRemove(Constraint c) {
Variable out = c.output();
c.markUnsatisfied();
c.removeFromGraph();
List<Constraint> unsatisfied = removePropagateFrom(out);
Strength strength = REQUIRED;
do {
for (int i = 0; i < unsatisfied.length; i++) {
Constraint u = unsatisfied[i];
if (u.strength == strength) incrementalAdd(u);
}
strength = strength.nextWeaker();
} while (strength != WEAKEST);
}
/// Select a previously unused mark value.
int newMark() => ++currentMark;
/**
* Extract a plan for resatisfaction starting from the given source
* constraints, usually a set of input constraints. This method
* assumes that stay optimization is desired; the plan will contain
* only constraints whose output variables are not stay. Constraints
* that do no computation, such as stay and edit constraints, are
* not included in the plan.
* Details: The outputs of a constraint are marked when it is added
* to the plan under construction. A constraint may be appended to
* the plan when all its input variables are known. A variable is
* known if either a) the variable is marked (indicating that has
* been computed by a constraint appearing earlier in the plan), b)
* the variable is 'stay' (i.e. it is a constant at plan execution
* time), or c) the variable is not determined by any
* constraint. The last provision is for past states of history
* variables, which are not stay but which are also not computed by
* any constraint.
* Assume: [sources] are all satisfied.
*/
Plan makePlan(List<Constraint> sources) {
int mark = newMark();
Plan plan = new Plan();
List<Constraint> todo = sources;
while (todo.length > 0) {
Constraint c = todo.removeLast();
if (c.output().mark != mark && c.inputsKnown(mark)) {
plan.addConstraint(c);
c.output().mark = mark;
addConstraintsConsumingTo(c.output(), todo);
}
}
return plan;
}
/**
* Extract a plan for resatisfying starting from the output of the
* given [constraints], usually a set of input constraints.
*/
Plan extractPlanFromConstraints(List<Constraint> constraints) {
List<Constraint> sources = <Constraint>[];
for (int i = 0; i < constraints.length; i++) {
Constraint c = constraints[i];
// if not in plan already and eligible for inclusion.
if (c.isInput() && c.isSatisfied()) sources.add(c);
}
return makePlan(sources);
}
/**
* Recompute the walkabout strengths and stay flags of all variables
* downstream of the given constraint and recompute the actual
* values of all variables whose stay flag is true. If a cycle is
* detected, remove the given constraint and answer
* false. Otherwise, answer true.
* Details: Cycles are detected when a marked variable is
* encountered downstream of the given constraint. The sender is
* assumed to have marked the inputs of the given constraint with
* the given mark. Thus, encountering a marked node downstream of
* the output constraint means that there is a path from the
* constraint's output to one of its inputs.
*/
bool addPropagate(Constraint c, int mark) {
List<Constraint> todo = <Constraint>[c];
while (todo.length > 0) {
Constraint d = todo.removeLast();
if (d.output().mark == mark) {
incrementalRemove(c);
return false;
}
d.recalculate();
addConstraintsConsumingTo(d.output(), todo);
}
return true;
}
/**
* Update the walkabout strengths and stay flags of all variables
* downstream of the given constraint. Answer a collection of
* unsatisfied constraints sorted in order of decreasing strength.
*/
List<Constraint> removePropagateFrom(Variable out) {
out.determinedBy = null;
out.walkStrength = WEAKEST;
out.stay = true;
List<Constraint> unsatisfied = <Constraint>[];
List<Variable> todo = <Variable>[out];
while (todo.length > 0) {
Variable v = todo.removeLast();
for (int i = 0; i < v.constraints.length; i++) {
Constraint c = v.constraints[i];
if (!c.isSatisfied()) unsatisfied.add(c);
}
Constraint determining = v.determinedBy;
for (int i = 0; i < v.constraints.length; i++) {
Constraint next = v.constraints[i];
if (next != determining && next.isSatisfied()) {
next.recalculate();
todo.add(next.output());
}
}
}
return unsatisfied;
}
void addConstraintsConsumingTo(Variable v, List<Constraint> coll) {
Constraint determining = v.determinedBy;
for (int i = 0; i < v.constraints.length; i++) {
Constraint c = v.constraints[i];
if (c != determining && c.isSatisfied()) coll.add(c);
}
}
}
/**
* A Plan is an ordered list of constraints to be executed in sequence
* to resatisfy all currently satisfiable constraints in the face of
* one or more changing inputs.
*/
class Plan {
List<Constraint> list = <Constraint>[];
void addConstraint(Constraint c) {
list.add(c);
}
int size() => list.length;
void execute() {
for (int i = 0; i < list.length; i++) {
list[i].execute();
}
}
}
/**
* This is the standard DeltaBlue benchmark. A long chain of equality
* constraints is constructed with a stay constraint on one end. An
* edit constraint is then added to the opposite end and the time is
* measured for adding and removing this constraint, and extracting
* and executing a constraint satisfaction plan. There are two cases.
* In case 1, the added constraint is stronger than the stay
* constraint and values must propagate down the entire length of the
* chain. In case 2, the added constraint is weaker than the stay
* constraint so it cannot be accomodated. The cost in this case is,
* of course, very low. Typical situations lie somewhere between these
* two extremes.
*/
void chainTest(int n) {
planner = new Planner();
Variable prev = null, first = null, last = null;
// Build chain of n equality constraints.
for (int i = 0; i <= n; i++) {
Variable v = new Variable("v", 0);
if (prev != null) new EqualityConstraint(prev, v, REQUIRED);
if (i == 0) first = v;
if (i == n) last = v;
prev = v;
}
new StayConstraint(last, STRONG_DEFAULT);
EditConstraint edit = new EditConstraint(first, PREFERRED);
Plan plan = planner.extractPlanFromConstraints(<Constraint>[edit]);
for (int i = 0; i < 100; i++) {
first.value = i;
plan.execute();
if (last.value != i) {
print("Chain test failed.\n{last.value)\n{i}");
}
}
}
/**
* This test constructs a two sets of variables related to each
* other by a simple linear transformation (scale and offset). The
* time is measured to change a variable on either side of the
* mapping and to change the scale and offset factors.
*/
void projectionTest(int n) {
planner = new Planner();
Variable scale = new Variable("scale", 10);
Variable offset = new Variable("offset", 1000);
Variable src = null, dst = null;
List<Variable> dests = <Variable>[];
for (int i = 0; i < n; i++) {
src = new Variable("src", i);
dst = new Variable("dst", i);
dests.add(dst);
new StayConstraint(src, NORMAL);
new ScaleConstraint(src, scale, offset, dst, REQUIRED);
}
change(src, 17);
if (dst.value != 1170) print("Projection 1 failed");
change(dst, 1050);
if (src.value != 5) print("Projection 2 failed");
change(scale, 5);
for (int i = 0; i < n - 1; i++) {
if (dests[i].value != i * 5 + 1000) print("Projection 3 failed");
}
change(offset, 2000);
for (int i = 0; i < n - 1; i++) {
if (dests[i].value != i * 5 + 2000) print("Projection 4 failed");
}
}
void change(Variable v, int newValue) {
EditConstraint edit = new EditConstraint(v, PREFERRED);
Plan plan = planner.extractPlanFromConstraints(<EditConstraint>[edit]);
for (int i = 0; i < 10; i++) {
v.value = newValue;
plan.execute();
}
edit.destroyConstraint();
}
Planner planner;