ai.vespa.rankingexpression.importer.operations.Mean Maven / Gradle / Ivy

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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 reduceDimensions;

    public Mean(String modelName, String nodeName, List 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 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 lazyGetFunction() {
        if ( ! allInputTypesPresent(2)) return null;


        TensorFunction inputFunction = inputs.get(0).function().get();
        TensorFunction 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 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 renamedDimensions = new ArrayList<>(reduceDimensions.size());
        for (String name : reduceDimensions) {
            Optional newName = renamer.dimensionNameOf(name);
            if (!newName.isPresent()) {
                return;  // presumably, already renamed
            }
            renamedDimensions.add(newName.get());
        }
        reduceDimensions = renamedDimensions;
    }

    @Override
    public Mean withInputs(List inputs) {
        return new Mean(modelName(), name(), inputs, attributeMap);
    }

    private boolean shouldKeepDimensions() {
        Optional 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"; }

}