Examples with TensorValue com.yahoo.searchlib.rankingexpression.evaluation.TensorValue used on opensource projects

Example 1 with TensorValue

use of com.yahoo.searchlib.rankingexpression.evaluation.TensorValue in project vespa by vespa-engine.

the class ConstantTensorTransformer method transformConstantReference.

private ExpressionNode transformConstantReference(ReferenceNode node, RankProfileTransformContext context) {
    Value value = context.constants().get(node.getName());
    if (value == null || value.type().rank() == 0) {
        return node;
    }
    TensorValue tensorValue = (TensorValue) value;
    String featureName = CONSTANT + "(" + node.getName() + ")";
    String tensorType = tensorValue.asTensor().type().toString();
    context.rankPropertiesOutput().put(featureName + ".value", tensorValue.toString());
    context.rankPropertiesOutput().put(featureName + ".type", tensorType);
    // TODO: This allows us to reference constant "a" as "a" instead of "constant(a)", but we shouldn't allow that
    return new ReferenceNode(CONSTANT, Arrays.asList(new NameNode(node.getName())), null);
}

Example 2 with TensorValue

use of com.yahoo.searchlib.rankingexpression.evaluation.TensorValue in project vespa by vespa-engine.

the class TestableTensorFlowModel method contextFrom.

private Context contextFrom(TensorFlowModel result) {
    MapContext context = new MapContext();
    result.largeConstants().forEach((name, tensor) -> context.put("constant(" + name + ")", new TensorValue(tensor)));
    result.smallConstants().forEach((name, tensor) -> context.put("constant(" + name + ")", new TensorValue(tensor)));
    return context;
}

Example 3 with TensorValue

use of com.yahoo.searchlib.rankingexpression.evaluation.TensorValue in project vespa by vespa-engine.

the class TestableTensorFlowModel method evaluateMacro.

private void evaluateMacro(Context context, TensorFlowModel model, String macroName) {
    if (!context.names().contains(macroName)) {
        RankingExpression e = model.macros().get(macroName);
        evaluateMacroDependencies(context, model, e.getRoot());
        context.put(macroName, new TensorValue(e.evaluate(context).asTensor()));
    }
}

Example 4 with TensorValue

use of com.yahoo.searchlib.rankingexpression.evaluation.TensorValue in project vespa by vespa-engine.

the class Shape method createConstantValue.

private void createConstantValue() {
    if (!allInputTypesPresent(1)) {
        return;
    }
    OrderedTensorType inputType = inputs.get(0).type().get();
    IndexedTensor.BoundBuilder builder = (IndexedTensor.BoundBuilder) Tensor.Builder.of(type().get().type());
    List<TensorType.Dimension> inputDimensions = inputType.dimensions();
    for (int i = 0; i < inputDimensions.size(); i++) {
        builder.cellByDirectIndex(i, inputDimensions.get(i).size().orElse(-1L));
    }
    this.setConstantValue(new TensorValue(builder.build()));
}

Example 5 with TensorValue

use of com.yahoo.searchlib.rankingexpression.evaluation.TensorValue in project vespa by vespa-engine.

the class TensorFlowImporter method importConstant.

private static Optional<TensorFunction> importConstant(TensorFlowModel model, TensorFlowOperation operation, SavedModelBundle bundle) {
    String name = operation.vespaName();
    if (model.largeConstants().containsKey(name) || model.smallConstants().containsKey(name)) {
        return operation.function();
    }
    Tensor tensor;
    if (operation.getConstantValue().isPresent()) {
        Value value = operation.getConstantValue().get();
        if (!(value instanceof TensorValue)) {
            // scalar values are inserted directly into the expression
            return operation.function();
        }
        tensor = value.asTensor();
    } else {
        // Here we use the type from the operation, which will have correct dimension names after name resolving
        tensor = TensorConverter.toVespaTensor(readVariable(operation.node().getName(), bundle), operation.type().get());
        operation.setConstantValue(new TensorValue(tensor));
    }
    if (tensor.type().rank() == 0 || tensor.size() <= 1) {
        model.smallConstant(name, tensor);
    } else {
        model.largeConstant(name, tensor);
    }
    return operation.function();
}