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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 com.yahoo.searchlib.rankingexpression.evaluation.DoubleValue;
import ai.vespa.rankingexpression.importer.DimensionRenamer;
import ai.vespa.rankingexpression.importer.OrderedTensorType;
import com.yahoo.searchlib.rankingexpression.Reference;
import com.yahoo.searchlib.rankingexpression.rule.ConstantNode;
import com.yahoo.searchlib.rankingexpression.rule.ExpressionNode;
import com.yahoo.searchlib.rankingexpression.rule.GeneratorLambdaFunctionNode;
import com.yahoo.tensor.Tensor;
import com.yahoo.tensor.TensorType;
import com.yahoo.tensor.functions.Generate;
import com.yahoo.tensor.functions.ScalarFunctions;
import com.yahoo.tensor.functions.TensorFunction;
import java.util.ArrayList;
import java.util.List;
import java.util.Optional;
public class ExpandDims extends IntermediateOperation {
private List<String> expandDimensions;
public ExpandDims(String modelName, String nodeName, List<IntermediateOperation> inputs) {
super(modelName, nodeName, inputs);
}
@Override
protected OrderedTensorType lazyGetType() {
if ( ! allInputTypesPresent(2)) return null;
IntermediateOperation axisOperation = inputs().get(1);
if ( ! axisOperation.getConstantValue().isPresent()) {
throw new IllegalArgumentException("ExpandDims in " + name + ": Axis must be a constant.");
}
Tensor axis = axisOperation.getConstantValue().get().asTensor();
if (axis.type().rank() != 0)
throw new IllegalArgumentException("ExpandDims in " + name + ": Axis argument must be a scalar.");
OrderedTensorType inputType = inputs.get(0).type().get();
int dimensionToInsert = (int)axis.asDouble();
if (dimensionToInsert < 0) {
dimensionToInsert = inputType.dimensions().size() + dimensionToInsert;
}
OrderedTensorType.Builder typeBuilder = new OrderedTensorType.Builder(resultValueType());
expandDimensions = new ArrayList<>();
int dimensionIndex = 0;
for (TensorType.Dimension dimension : inputType.dimensions()) {
if (dimensionIndex == dimensionToInsert)
addDimension(dimensionIndex, typeBuilder);
typeBuilder.add(dimension);
dimensionIndex++;
}
if (dimensionToInsert == inputType.dimensions().size()) { // Insert last dimension
addDimension(dimensionIndex, typeBuilder);
}
return typeBuilder.build();
}
private void addDimension(int dimensionIndex, OrderedTensorType.Builder typeBuilder) {
String name = String.format("%s_%d", vespaName(), dimensionIndex);
expandDimensions.add(name);
typeBuilder.add(TensorType.Dimension.indexed(name, 1L));
}
@Override
protected TensorFunction<Reference> lazyGetFunction() {
if ( ! allInputFunctionsPresent(2)) return null;
// multiply with a generated tensor created from the reduced dimensions
TensorType.Builder typeBuilder = new TensorType.Builder(resultValueType());
for (String name : expandDimensions) {
typeBuilder.indexed(name, 1);
}
TensorType generatedType = typeBuilder.build();
ExpressionNode generatedExpression = new ConstantNode(new DoubleValue(1));
Generate<Reference> generatedFunction = new Generate<>(generatedType,
new GeneratorLambdaFunctionNode(generatedType, generatedExpression).asLongListToDoubleOperator());
return new com.yahoo.tensor.functions.Join<>(inputs().get(0).function().get(), generatedFunction, ScalarFunctions.multiply());
}
@Override
public void addDimensionNameConstraints(DimensionRenamer renamer) {
addConstraintsFrom(type, renamer);
}
@Override
public void renameDimensions(DimensionRenamer renamer) {
super.renameDimensions(renamer);
List<String> renamedDimensions = new ArrayList<>(expandDimensions.size());
for (String name : expandDimensions) {
Optional<String> newName = renamer.dimensionNameOf(name);
if ( ! newName.isPresent()) {
return; // presumably, already renamed
}
renamedDimensions.add(newName.get());
}
expandDimensions = renamedDimensions;
}
@Override
public ExpandDims withInputs(List<IntermediateOperation> inputs) {
return new ExpandDims(modelName(), name(), inputs);
}
@Override
public String operationName() { return "ExpandDims"; }
}
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