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// Copyright Yahoo. 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.DimensionRenamer;
import ai.vespa.rankingexpression.importer.OrderedTensorType;
import com.yahoo.searchlib.rankingexpression.Reference;
import com.yahoo.tensor.TensorType;
import com.yahoo.tensor.functions.TensorFunction;
import java.util.List;
import java.util.Optional;
public class OnnxConcat extends IntermediateOperation {
private final AttributeMap attributeMap;
private String concatDimensionName;
private int concatDimensionIndex;
public OnnxConcat(String modelName, String nodeName, List<IntermediateOperation> inputs, AttributeMap attributeMap) {
super(modelName, nodeName, inputs);
this.attributeMap = attributeMap;
if (attributeMap.get("axis").isEmpty())
throw new IllegalArgumentException("OnnxConcat in " + name + ": Required attribute 'axis' is missing.");
this.concatDimensionIndex = (int) attributeMap.get("axis").get().asDouble();
}
@Override
protected OrderedTensorType lazyGetType() {
if ( ! inputs.stream().map(IntermediateOperation::type).allMatch(Optional::isPresent)) return null;
OrderedTensorType aType = inputs.get(0).type().get();
if (concatDimensionIndex < 0) {
concatDimensionIndex = aType.dimensions().size() + concatDimensionIndex;
}
long concatDimSize = aType.dimensions().get(concatDimensionIndex).size().orElse(-1L);
for (int i = 1; i < inputs.size(); ++i) {
OrderedTensorType bType = inputs.get(i).type().get();
if (bType.rank() != aType.rank())
throw new IllegalArgumentException("OnnxConcat in " + name + ": Inputs must have the same rank.");
for (int j = 0; j < aType.rank(); ++j) {
long dimSizeA = aType.dimensions().get(j).size().orElse(-1L);
long dimSizeB = bType.dimensions().get(j).size().orElse(-1L);
if (j == concatDimensionIndex) {
concatDimSize += dimSizeB;
} else if (dimSizeA != dimSizeB) {
throw new IllegalArgumentException("OnnxConcat in " + name + ": " +
"input dimension " + j + " differs in input tensors.");
}
}
}
OrderedTensorType.Builder typeBuilder = new OrderedTensorType.Builder(resultValueType());
int dimensionIndex = 0;
for (TensorType.Dimension dimension : aType.dimensions()) {
if (dimensionIndex == concatDimensionIndex) {
concatDimensionName = dimension.name();
typeBuilder.add(TensorType.Dimension.indexed(concatDimensionName, concatDimSize));
} else {
typeBuilder.add(dimension);
}
dimensionIndex++;
}
return typeBuilder.build();
}
@Override
protected TensorFunction<Reference> lazyGetFunction() {
if (!inputs.stream().map(IntermediateOperation::function).allMatch(Optional::isPresent)) {
return null;
}
TensorFunction<Reference> result = inputs.get(0).function().get();
for (int i = 1; i < inputs.size(); ++i) {
TensorFunction<Reference> b = inputs.get(i).function().get();
result = new com.yahoo.tensor.functions.Concat<>(result, b, concatDimensionName);
}
return result;
}
@Override
public void addDimensionNameConstraints(DimensionRenamer renamer) {
if (!inputs.stream().map(IntermediateOperation::type).allMatch(Optional::isPresent)) {
return;
}
OrderedTensorType a = inputs.get(0).type().get();
for (int i = 1; i < inputs.size(); ++i) {
OrderedTensorType b = inputs.get(i).type().get();
String bDim = b.dimensions().get(concatDimensionIndex).name();
String aDim = a.dimensions().get(concatDimensionIndex).name();
renamer.addConstraint(aDim, bDim, DimensionRenamer.Constraint.equal(false), this);
}
}
@Override
public void renameDimensions(DimensionRenamer renamer) {
super.renameDimensions(renamer);
concatDimensionName = renamer.dimensionNameOf(concatDimensionName).orElse(concatDimensionName);
}
@Override
public OnnxConcat withInputs(List<IntermediateOperation> inputs) {
return new OnnxConcat(modelName(), name(), inputs, attributeMap);
}
@Override
public String operationName() { return "ConcatV2"; }
}
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