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// Copyright Yahoo. 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.DimensionRenamer;
import ai.vespa.rankingexpression.importer.OrderedTensorType;
import com.yahoo.searchlib.rankingexpression.Reference;
import com.yahoo.searchlib.rankingexpression.evaluation.DoubleValue;
import com.yahoo.searchlib.rankingexpression.evaluation.Value;
import com.yahoo.searchlib.rankingexpression.rule.ConstantNode;
import com.yahoo.searchlib.rankingexpression.rule.EmbracedNode;
import com.yahoo.searchlib.rankingexpression.rule.ExpressionNode;
import com.yahoo.searchlib.rankingexpression.rule.ReferenceNode;
import com.yahoo.searchlib.rankingexpression.rule.TensorFunctionNode;
import com.yahoo.tensor.Tensor;
import com.yahoo.tensor.TensorType;
import com.yahoo.tensor.functions.Generate;
import com.yahoo.tensor.functions.TensorFunction;
import java.util.ArrayList;
import java.util.Iterator;
import java.util.List;
import java.util.Optional;
import static com.yahoo.searchlib.rankingexpression.rule.TensorFunctionNode.wrapScalar;
public class Expand extends IntermediateOperation {
public Expand(String modelName, String nodeName, List<IntermediateOperation> inputs) {
super(modelName, nodeName, inputs);
}
@Override
protected OrderedTensorType lazyGetType() {
if (!allInputTypesPresent(2)) return null;
// required as we use tensor create
inputs.get(0).exportAsRankingFunction = true;
Optional<Value> shapeValue = inputs.get(1).getConstantValue();
if (shapeValue.isEmpty())
throw new IllegalArgumentException("Expand " + name + ": shape must be a constant.");
Tensor shape = shapeValue.get().asTensor();
if (shape.type().rank() != 1)
throw new IllegalArgumentException("Expand " + name + ": shape must be a 1-d tensor.");
OrderedTensorType inputType = inputs.get(0).type().get();
int inputRank = inputType.rank();
int shapeSize = shape.type().dimensions().get(0).size().get().intValue();
int sizeDiff = shapeSize - inputRank;
OrderedTensorType.Builder typeBuilder = new OrderedTensorType.Builder(inputType.type().valueType());
Iterator<Double> iter = shape.valueIterator();
// Add any extra dimensions
for (int i = 0; i < sizeDiff; ++i) {
typeBuilder.add(TensorType.Dimension.indexed(vespaName() + "_" + i, iter.next().intValue()));
}
// Dimensions are matched innermost
for (int i = sizeDiff; i < shapeSize; i++) {
int shapeDimSize = iter.next().intValue();
int inputDimSize = inputType.dimensions().get(i - sizeDiff).size().get().intValue();
if (shapeDimSize != inputDimSize && shapeDimSize != 1 && inputDimSize != 1) {
throw new IllegalArgumentException("Expand " + name + ": dimension sizes of input and shape " +
"are not compatible. Either they must be equal or one must be of size 1.");
}
int dimSize = Math.max(shapeDimSize, inputDimSize);
typeBuilder.add(TensorType.Dimension.indexed(vespaName() + "_" + i, dimSize));
}
return typeBuilder.build();
}
@Override
protected TensorFunction<Reference> lazyGetFunction() {
if (!allInputFunctionsPresent(2)) return null;
IntermediateOperation input = inputs.get(0);
OrderedTensorType inputType = input.type().get();
OrderedTensorType type = type().get();
String inputFunctionName = input.rankingExpressionFunctionName();
List<com.yahoo.tensor.functions.Slice.DimensionValue<Reference>> dimensionValues = new ArrayList<>();
int sizeDiff = type().get().rank() - inputType.rank();
for (int i = sizeDiff; i < type().get().rank(); ++i) {
String inputDimensionName = inputType.dimensions().get(i - sizeDiff).name();
String typeDimensionName = type.dimensionNames().get(i);
long inputDimensionSize = inputType.dimensions().get(i - sizeDiff).size().get();
ExpressionNode index;
if (inputDimensionSize == 1) {
index = new ConstantNode(new DoubleValue(0.0));
} else {
index = new EmbracedNode(new ReferenceNode(typeDimensionName));
}
dimensionValues.add(new com.yahoo.tensor.functions.Slice.DimensionValue<>(Optional.of(inputDimensionName), wrapScalar(index)));
}
TensorFunction<Reference> externalRef = new TensorFunctionNode.ExpressionTensorFunction(new ReferenceNode(inputFunctionName));
com.yahoo.tensor.functions.Slice<Reference> sliceIndices = new com.yahoo.tensor.functions.Slice<>(externalRef, dimensionValues);
ExpressionNode sliceExpression = new TensorFunctionNode(sliceIndices);
return Generate.bound(type.type(), wrapScalar(sliceExpression));
}
@Override
public void addDimensionNameConstraints(DimensionRenamer renamer) {
addConstraintsFrom(type, renamer);
}
@Override
public Expand withInputs(List<IntermediateOperation> inputs) {
return new Expand(modelName(), name(), inputs);
}
@Override
public String operationName() { return "Expand"; }
}
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