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// Copyright Vespa.ai. Licensed under the terms of the Apache 2.0 license. See LICENSE in the project root.
package ai.vespa.rankingexpression.importer.operations;
import ai.vespa.rankingexpression.importer.OrderedTensorType;
import ai.vespa.rankingexpression.importer.DimensionRenamer;
import com.yahoo.searchlib.rankingexpression.Reference;
import com.yahoo.tensor.TensorType;
import com.yahoo.tensor.functions.Reduce;
import com.yahoo.tensor.functions.ScalarFunctions;
import com.yahoo.tensor.functions.TensorFunction;
import java.util.ArrayList;
import java.util.List;
import java.util.Optional;
import java.util.function.DoubleBinaryOperator;
import java.util.function.DoubleUnaryOperator;
public class Join extends IntermediateOperation {
private final DoubleBinaryOperator operator;
public Join(String modelName, String nodeName, List<IntermediateOperation> inputs, DoubleBinaryOperator operator) {
super(modelName, nodeName, inputs);
this.operator = operator;
}
@Override
protected OrderedTensorType lazyGetType() {
if ( ! allInputTypesPresent(2)) return null;
OrderedTensorType a = largestInput().type().get();
OrderedTensorType b = smallestInput().type().get();
OrderedTensorType.Builder builder = new OrderedTensorType.Builder(resultValueType());
int sizeDifference = a.rank() - b.rank();
for (int i = 0; i < a.rank(); ++i) {
TensorType.Dimension aDim = a.dimensions().get(i);
long size = aDim.size().orElse(-1L);
if (i - sizeDifference >= 0) {
TensorType.Dimension bDim = b.dimensions().get(i - sizeDifference);
size = Math.max(size, bDim.size().orElse(-1L));
}
if (aDim.type() == TensorType.Dimension.Type.indexedBound) {
builder.add(TensorType.Dimension.indexed(aDim.name(), size));
} else if (aDim.type() == TensorType.Dimension.Type.indexedUnbound) {
builder.add(TensorType.Dimension.indexed(aDim.name()));
} else if (aDim.type() == TensorType.Dimension.Type.mapped) {
builder.add(TensorType.Dimension.mapped(aDim.name()));
}
}
return builder.build();
}
@Override
protected TensorFunction<Reference> lazyGetFunction() {
if ( ! allInputTypesPresent(2)) return null;
if ( ! allInputFunctionsPresent(2)) return null;
// Optimization: if inputs are the same, replace with a map function.
if (inputs.get(0).equals(inputs.get(1))) {
Optional<DoubleUnaryOperator> mapOperator = operatorAsUnary(operator);
if (mapOperator.isPresent()) {
IntermediateOperation input = inputs.get(0);
input.removeDuplicateOutputsTo(this); // avoids unnecessary function export
return new com.yahoo.tensor.functions.Map<Reference>(input.function().get(), mapOperator.get());
}
}
IntermediateOperation a = largestInput();
IntermediateOperation b = smallestInput();
List<String> aDimensionsToReduce = new ArrayList<>();
List<String> bDimensionsToReduce = new ArrayList<>();
int sizeDifference = a.type().get().rank() - b.type().get().rank();
for (int i = 0; i < b.type().get().rank(); ++i) {
TensorType.Dimension bDim = b.type().get().dimensions().get(i);
TensorType.Dimension aDim = a.type().get().dimensions().get(i + sizeDifference);
long bSize = bDim.size().orElse(-1L);
long aSize = aDim.size().orElse(-1L);
if (bSize == 1L && aSize != 1L) {
bDimensionsToReduce.add(bDim.name());
}
if (aSize == 1L && bSize != 1L) {
aDimensionsToReduce.add(bDim.name());
}
}
TensorFunction<Reference> aReducedFunction = a.function().get();
if (aDimensionsToReduce.size() > 0) {
aReducedFunction = new Reduce<Reference>(a.function().get(), Reduce.Aggregator.sum, aDimensionsToReduce);
}
TensorFunction<Reference> bReducedFunction = b.function().get();
if (bDimensionsToReduce.size() > 0) {
bReducedFunction = new Reduce<Reference>(b.function().get(), Reduce.Aggregator.sum, bDimensionsToReduce);
}
// retain order of inputs
if (a == inputs.get(1)) {
TensorFunction<Reference> temp = bReducedFunction;
bReducedFunction = aReducedFunction;
aReducedFunction = temp;
}
return new com.yahoo.tensor.functions.Join<Reference>(aReducedFunction, bReducedFunction, operator);
}
@Override
public void addDimensionNameConstraints(DimensionRenamer renamer) {
if ( ! allInputTypesPresent(2)) return;
OrderedTensorType a = largestInput().type().get();
OrderedTensorType b = smallestInput().type().get();
int sizeDifference = a.rank() - b.rank();
for (int i = 0; i < b.rank(); ++i) {
String bDim = b.dimensions().get(i).name();
String aDim = a.dimensions().get(i + sizeDifference).name();
renamer.addConstraint(aDim, bDim, DimensionRenamer.Constraint.equal(false), this);
}
}
private IntermediateOperation largestInput() {
OrderedTensorType a = inputs.get(0).type().get();
OrderedTensorType b = inputs.get(1).type().get();
return a.rank() >= b.rank() ? inputs.get(0) : inputs.get(1);
}
private IntermediateOperation smallestInput() {
OrderedTensorType a = inputs.get(0).type().get();
OrderedTensorType b = inputs.get(1).type().get();
return a.rank() < b.rank() ? inputs.get(0) : inputs.get(1);
}
@Override
public Join withInputs(List<IntermediateOperation> inputs) {
return new Join(modelName(), name(), inputs, operator);
}
@Override
public String operationName() { return "Join"; }
private Optional<DoubleUnaryOperator> operatorAsUnary(DoubleBinaryOperator op) {
String unaryRep;
if (op instanceof ScalarFunctions.Add) unaryRep = "f(a)(a + a)";
else if (op instanceof ScalarFunctions.Multiply) unaryRep = "f(a)(a * a)";
else if (op instanceof ScalarFunctions.Subtract) unaryRep = "f(a)(0)";
else if (op instanceof ScalarFunctions.Divide) unaryRep = "f(a)(1)";
else if (op instanceof ScalarFunctions.Equal) unaryRep = "f(a)(1)";
else if (op instanceof ScalarFunctions.Greater) unaryRep = "f(a)(0)";
else if (op instanceof ScalarFunctions.Less) unaryRep = "f(a)(0)";
else if (op instanceof ScalarFunctions.Max) unaryRep = "f(a)(a)";
else if (op instanceof ScalarFunctions.Min) unaryRep = "f(a)(a)";
else if (op instanceof ScalarFunctions.Mean) unaryRep = "f(a)(a)";
else if (op instanceof ScalarFunctions.Pow) unaryRep = "f(a)(pow(a,a))";
else if (op instanceof ScalarFunctions.SquaredDifference) unaryRep = "f(a)(0)";
else return Optional.empty();
return Optional.of(new DoubleUnaryOperator() {
@Override
public double applyAsDouble(double operand) { return op.applyAsDouble(operand, operand); }
@Override
public String toString() { return unaryRep; }
});
}
}
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