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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 com.yahoo.searchlib.rankingexpression.Reference;
import com.yahoo.searchlib.rankingexpression.evaluation.DoubleValue;
import com.yahoo.searchlib.rankingexpression.evaluation.Value;
import ai.vespa.rankingexpression.importer.DimensionRenamer;
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.Reduce;
import com.yahoo.tensor.functions.ScalarFunctions;
import com.yahoo.tensor.functions.TensorFunction;
import java.util.ArrayList;
import java.util.Iterator;
import java.util.List;
import java.util.Optional;
public class Mean extends IntermediateOperation {
private final AttributeMap attributeMap;
private List<String> reduceDimensions;
public Mean(String modelName, String nodeName, List<IntermediateOperation> inputs, AttributeMap attributeMap) {
super(modelName, nodeName, inputs);
this.attributeMap = attributeMap;
}
@Override
protected OrderedTensorType lazyGetType() {
if ( ! allInputTypesPresent(2)) return null;
IntermediateOperation reductionIndices = inputs.get(1);
if ( ! reductionIndices.getConstantValue().isPresent()) {
throw new IllegalArgumentException("Mean in " + name + ": Reduction indices must be a constant.");
}
Tensor indices = reductionIndices.getConstantValue().get().asTensor();
reduceDimensions = new ArrayList<>();
OrderedTensorType inputType = inputs.get(0).type().get();
for (Iterator<Tensor.Cell> cellIterator = indices.cellIterator(); cellIterator.hasNext();) {
Tensor.Cell cell = cellIterator.next();
int dimensionIndex = cell.getValue().intValue();
if (dimensionIndex < 0) {
dimensionIndex = inputType.dimensions().size() - dimensionIndex;
}
reduceDimensions.add(inputType.dimensions().get(dimensionIndex).name());
}
return reducedType(inputType, shouldKeepDimensions());
}
// optimization: if keepDims and one reduce dimension that has size 1: same as identity.
@Override
protected TensorFunction<Reference> lazyGetFunction() {
if ( ! allInputTypesPresent(2)) return null;
TensorFunction<Reference> inputFunction = inputs.get(0).function().get();
TensorFunction<Reference> output = new Reduce<>(inputFunction, Reduce.Aggregator.avg, reduceDimensions);
if (shouldKeepDimensions()) {
// multiply with a generated tensor created from the reduced dimensions
TensorType.Builder typeBuilder = new TensorType.Builder(resultValueType());
for (String name : reduceDimensions) {
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());
output = new com.yahoo.tensor.functions.Join<>(output, generatedFunction, ScalarFunctions.multiply());
}
return output;
}
@Override
public void renameDimensions(DimensionRenamer renamer) {
super.renameDimensions(renamer);
List<String> renamedDimensions = new ArrayList<>(reduceDimensions.size());
for (String name : reduceDimensions) {
Optional<String> newName = renamer.dimensionNameOf(name);
if (!newName.isPresent()) {
return; // presumably, already renamed
}
renamedDimensions.add(newName.get());
}
reduceDimensions = renamedDimensions;
}
@Override
public Mean withInputs(List<IntermediateOperation> inputs) {
return new Mean(modelName(), name(), inputs, attributeMap);
}
private boolean shouldKeepDimensions() {
Optional<Value> keepDims = attributeMap.get("keep_dims");
return keepDims.isPresent() && keepDims.get().asBoolean();
}
private OrderedTensorType reducedType(OrderedTensorType inputType, boolean keepDimensions) {
OrderedTensorType.Builder builder = new OrderedTensorType.Builder(resultValueType());
for (TensorType.Dimension dimension: inputType.type().dimensions()) {
if ( ! reduceDimensions.contains(dimension.name())) {
builder.add(dimension);
} else if (keepDimensions) {
builder.add(TensorType.Dimension.indexed(dimension.name(), 1L));
}
}
return builder.build();
}
@Override
public String operationName() { return "Mean"; }
}
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