Examples with OrderedTensorType com.yahoo.searchlib.rankingexpression.integration.tensorflow.importer.OrderedTensorType used on opensource projects

Example 11 with OrderedTensorType

use of com.yahoo.searchlib.rankingexpression.integration.tensorflow.importer.OrderedTensorType in project vespa by vespa-engine.

the class OrderedTensorTypeTestCase method testToFromSpec.

@Test
public void testToFromSpec() {
    String spec = "tensor(b[],c{},a[3])";
    OrderedTensorType type = OrderedTensorType.fromSpec(spec);
    assertEquals(spec, type.toString());
    assertEquals("tensor(a[3],b[],c{})", type.type().toString());
}

Example 12 with OrderedTensorType

use of com.yahoo.searchlib.rankingexpression.integration.tensorflow.importer.OrderedTensorType in project vespa by vespa-engine.

the class TensorFlowImporter method importInputExpression.

private static void importInputExpression(TensorFlowModel model, TensorFlowOperation operation) {
    if (operation.isInput() && isSignatureInput(model, operation)) {
        // All inputs must have dimensions with standard naming convention: d0, d1, ...
        OrderedTensorType standardNamingConvention = OrderedTensorType.fromTensorFlowType(operation.node());
        model.argument(operation.node().getName(), standardNamingConvention.type());
        model.requiredMacro(operation.vespaName(), standardNamingConvention.type());
    }
}

Example 13 with OrderedTensorType

use of com.yahoo.searchlib.rankingexpression.integration.tensorflow.importer.OrderedTensorType in project vespa by vespa-engine.

the class ExpandDims method lazyGetType.

@Override
protected OrderedTensorType lazyGetType() {
    if (!allInputTypesPresent(2)) {
        return null;
    }
    TensorFlowOperation axisOperation = inputs().get(1);
    if (!axisOperation.getConstantValue().isPresent()) {
        throw new IllegalArgumentException("ExpandDims in " + node.getName() + ": " + "axis must be a constant.");
    }
    Tensor axis = axisOperation.getConstantValue().get().asTensor();
    if (axis.type().rank() != 0) {
        throw new IllegalArgumentException("ExpandDims in " + node.getName() + ": " + "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(node);
    expandDimensions = new ArrayList<>();
    int dimensionIndex = 0;
    for (TensorType.Dimension dimension : inputType.dimensions()) {
        if (dimensionIndex == dimensionToInsert) {
            String name = String.format("%s_%d", vespaName(), dimensionIndex);
            expandDimensions.add(name);
            typeBuilder.add(TensorType.Dimension.indexed(name, 1L));
        }
        typeBuilder.add(dimension);
        dimensionIndex++;
    }
    return typeBuilder.build();
}

Example 14 with OrderedTensorType

use of com.yahoo.searchlib.rankingexpression.integration.tensorflow.importer.OrderedTensorType in project vespa by vespa-engine.

the class Join method addDimensionNameConstraints.

@Override
public void addDimensionNameConstraints(DimensionRenamer renamer) {
    if (!allInputTypesPresent(2)) {
        return;
    }
    OrderedTensorType a = largestInput().type().get();
    OrderedTensorType b = smallestInput().type().get();
    int sizeDifference = a.rank() - b.rank();
    for (int i = 0; i < b.rank(); ++i) {
        String bDim = b.dimensions().get(i).name();
        String aDim = a.dimensions().get(i + sizeDifference).name();
        renamer.addConstraint(aDim, bDim, DimensionRenamer::equals, this);
    }
}

Example 15 with OrderedTensorType

use of com.yahoo.searchlib.rankingexpression.integration.tensorflow.importer.OrderedTensorType in project vespa by vespa-engine.

the class Join method lazyGetType.

@Override
protected OrderedTensorType lazyGetType() {
    if (!allInputTypesPresent(2)) {
        return null;
    }
    OrderedTensorType a = largestInput().type().get();
    OrderedTensorType b = smallestInput().type().get();
    // Well now we have potentially entered the wonderful world of "broadcasting"
    // https://docs.scipy.org/doc/numpy/user/basics.broadcasting.html
    // In broadcasting, the size of each dimension is compared element-wise,
    // starting with the trailing dimensions and working forward. A special
    // case occurs when the size of one dimension is 1, while the other is not.
    // Then the dimension with size 1 is "stretched" to be of compatible size.
    // 
    // An example:
    // 
    // Tensor A: d0[5], d1[1], d2[3], d3[1]
    // Tensor B:        d1[4], d2[1], d3[2]
    // 
    // In TensorFlow and using the above rules of broadcasting, the resulting
    // type is:
    // d0[5], d1[4], d2[3], d2[2]
    // 
    // However, in Vespa's tensor logic, the join of the two above tensors would
    // result in a tensor of type:
    // d0[5], d1[1], d2[1], d3[1]
    // 
    // By reducing the dimensions of size 1 in each tensor before joining,
    // we get equal results as in TensorFlow.
    OrderedTensorType.Builder builder = new OrderedTensorType.Builder(node);
    int sizeDifference = a.rank() - b.rank();
    for (int i = 0; i < a.rank(); ++i) {
        TensorType.Dimension aDim = a.dimensions().get(i);
        long size = aDim.size().orElse(-1L);
        if (i - sizeDifference >= 0) {
            TensorType.Dimension bDim = b.dimensions().get(i - sizeDifference);
            size = Math.max(size, bDim.size().orElse(-1L));
        }
        if (aDim.type() == TensorType.Dimension.Type.indexedBound) {
            builder.add(TensorType.Dimension.indexed(aDim.name(), size));
        } else if (aDim.type() == TensorType.Dimension.Type.indexedUnbound) {
            builder.add(TensorType.Dimension.indexed(aDim.name()));
        } else if (aDim.type() == TensorType.Dimension.Type.mapped) {
            builder.add(TensorType.Dimension.mapped(aDim.name()));
        }
    }
    return builder.build();
}