Examples with TensorType com.yahoo.tensor.TensorType used on opensource projects

Example 1 with TensorType

use of com.yahoo.tensor.TensorType in project vespa by vespa-engine.

the class RankProfile method typeContext.

/**
 * Creates a context containing the type information of all constants, attributes and query profiles
 * referable from this rank profile.
 */
public TypeContext<Reference> typeContext(QueryProfileRegistry queryProfiles) {
    MapEvaluationTypeContext context = new MapEvaluationTypeContext(getMacros().values().stream().map(Macro::asExpressionFunction).collect(Collectors.toList()));
    // Add small and large constants, respectively
    getConstants().forEach((k, v) -> context.setType(FeatureNames.asConstantFeature(k), v.type()));
    getSearch().getRankingConstants().forEach((k, v) -> context.setType(FeatureNames.asConstantFeature(k), v.getTensorType()));
    // Add attributes
    getSearch().allFields().forEach(field -> addAttributeFeatureTypes(field, context));
    getSearch().allImportedFields().forEach(field -> addAttributeFeatureTypes(field, context));
    // Add query features from rank profile types reached from the "default" profile
    for (QueryProfileType queryProfileType : queryProfiles.getTypeRegistry().allComponents()) {
        for (FieldDescription field : queryProfileType.declaredFields().values()) {
            TensorType type = field.getType().asTensorType();
            Optional<Reference> feature = Reference.simple(field.getName());
            if (!feature.isPresent() || !feature.get().name().equals("query"))
                continue;
            TensorType existingType = context.getType(feature.get());
            if (!Objects.equals(existingType, context.defaultTypeOf(feature.get())))
                type = existingType.dimensionwiseGeneralizationWith(type).orElseThrow(() -> new IllegalArgumentException(queryProfileType + " contains query feature " + feature.get() + " with type " + field.getType().asTensorType() + ", but this is already defined " + "in another query profile with type " + context.getType(feature.get())));
            context.setType(feature.get(), type);
        }
    }
    return context;
}

Example 2 with TensorType

use of com.yahoo.tensor.TensorType in project vespa by vespa-engine.

the class TensorFlowFeatureConverter method reduceBatchDimensions.

/**
 * Check if batch dimensions of inputs can be reduced out. If the input
 * macro specifies that a single exemplar should be evaluated, we can
 * reduce the batch dimension out.
 */
private void reduceBatchDimensions(RankingExpression expression, TensorFlowModel model, RankProfile profile, QueryProfileRegistry queryProfiles) {
    TypeContext<Reference> typeContext = profile.typeContext(queryProfiles);
    TensorType typeBeforeReducing = expression.getRoot().type(typeContext);
    // Check generated macros for inputs to reduce
    Set<String> macroNames = new HashSet<>();
    addMacroNamesIn(expression.getRoot(), macroNames, model);
    for (String macroName : macroNames) {
        if (!model.macros().containsKey(macroName)) {
            continue;
        }
        RankProfile.Macro macro = profile.getMacros().get(macroName);
        if (macro == null) {
            throw new IllegalArgumentException("Model refers to generated macro '" + macroName + "but this macro is not present in " + profile);
        }
        RankingExpression macroExpression = macro.getRankingExpression();
        macroExpression.setRoot(reduceBatchDimensionsAtInput(macroExpression.getRoot(), model, typeContext));
    }
    // Check expression for inputs to reduce
    ExpressionNode root = expression.getRoot();
    root = reduceBatchDimensionsAtInput(root, model, typeContext);
    TensorType typeAfterReducing = root.type(typeContext);
    root = expandBatchDimensionsAtOutput(root, typeBeforeReducing, typeAfterReducing);
    expression.setRoot(root);
}

Example 3 with TensorType

use of com.yahoo.tensor.TensorType in project vespa by vespa-engine.

the class TensorFlowFeatureConverter method transformLargeConstant.

private void transformLargeConstant(ModelStore store, RankProfile profile, QueryProfileRegistry queryProfiles, Set<String> constantsReplacedByMacros, String constantName, Tensor constantValue) {
    RankProfile.Macro macroOverridingConstant = profile.getMacros().get(constantName);
    if (macroOverridingConstant != null) {
        TensorType macroType = macroOverridingConstant.getRankingExpression().type(profile.typeContext(queryProfiles));
        if (!macroType.equals(constantValue.type()))
            throw new IllegalArgumentException("Macro '" + constantName + "' replaces the constant with this name. " + "The required type of this is " + constantValue.type() + ", but the macro returns " + macroType);
        // will replace constant(constantName) by constantName later
        constantsReplacedByMacros.add(constantName);
    } else {
        Path constantPath = store.writeLargeConstant(constantName, constantValue);
        if (!profile.getSearch().getRankingConstants().containsKey(constantName)) {
            profile.getSearch().addRankingConstant(new RankingConstant(constantName, constantValue.type(), constantPath.toString()));
        }
    }
}

Example 4 with TensorType

use of com.yahoo.tensor.TensorType in project vespa by vespa-engine.

the class TensorFlowFeatureConverter method expandBatchDimensionsAtOutput.

/**
 * If batch dimensions have been reduced away above, bring them back here
 * for any following computation of the tensor.
 * Todo: determine when this is not necessary!
 */
private ExpressionNode expandBatchDimensionsAtOutput(ExpressionNode node, TensorType before, TensorType after) {
    if (after.equals(before)) {
        return node;
    }
    TensorType.Builder typeBuilder = new TensorType.Builder();
    for (TensorType.Dimension dimension : before.dimensions()) {
        if (dimension.size().orElse(-1L) == 1 && !after.dimensionNames().contains(dimension.name())) {
            typeBuilder.indexed(dimension.name(), 1);
        }
    }
    TensorType expandDimensionsType = typeBuilder.build();
    if (expandDimensionsType.dimensions().size() > 0) {
        ExpressionNode generatedExpression = new ConstantNode(new DoubleValue(1.0));
        Generate generatedFunction = new Generate(expandDimensionsType, new GeneratorLambdaFunctionNode(expandDimensionsType, generatedExpression).asLongListToDoubleOperator());
        Join expand = new Join(TensorFunctionNode.wrapArgument(node), generatedFunction, ScalarFunctions.multiply());
        return new TensorFunctionNode(expand);
    }
    return node;
}

Example 5 with TensorType

use of com.yahoo.tensor.TensorType in project vespa by vespa-engine.

the class TensorFlowFeatureConverter method reduceBatchDimensionExpression.

private ExpressionNode reduceBatchDimensionExpression(TensorFunction function, TypeContext<Reference> context) {
    TensorFunction result = function;
    TensorType type = function.type(context);
    if (type.dimensions().size() > 1) {
        List<String> reduceDimensions = new ArrayList<>();
        for (TensorType.Dimension dimension : type.dimensions()) {
            if (dimension.size().orElse(-1L) == 1) {
                reduceDimensions.add(dimension.name());
            }
        }
        if (reduceDimensions.size() > 0) {
            result = new Reduce(function, Reduce.Aggregator.sum, reduceDimensions);
        }
    }
    return new TensorFunctionNode(result);
}