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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.OrderedTensorType;
import com.yahoo.searchlib.rankingexpression.Reference;
import com.yahoo.searchlib.rankingexpression.evaluation.DoubleValue;
import com.yahoo.searchlib.rankingexpression.evaluation.Value;
import com.yahoo.searchlib.rankingexpression.rule.ArithmeticNode;
import com.yahoo.searchlib.rankingexpression.rule.ArithmeticOperator;
import com.yahoo.searchlib.rankingexpression.rule.ConstantNode;
import com.yahoo.searchlib.rankingexpression.rule.EmbracedNode;
import com.yahoo.searchlib.rankingexpression.rule.ExpressionNode;
import com.yahoo.searchlib.rankingexpression.rule.ReferenceNode;
import com.yahoo.searchlib.rankingexpression.rule.TensorFunctionNode;
import com.yahoo.tensor.TensorType;
import com.yahoo.tensor.functions.Generate;
import com.yahoo.tensor.functions.TensorFunction;
import java.util.ArrayList;
import java.util.List;
import java.util.Optional;
import static com.yahoo.searchlib.rankingexpression.rule.TensorFunctionNode.wrapScalar;
public class Split extends IntermediateOperation {
private final AttributeMap attributes;
private final int output;
private final int axis;
private int start;
private int end;
public Split(String modelName, String nodeName, List<IntermediateOperation> inputs, AttributeMap attributes, int output) {
super(modelName, nodeName, inputs);
this.attributes = attributes;
this.output = output;
axis = (int) attributes.get("axis").orElse(DoubleValue.zero).asDouble();
}
@Override
protected OrderedTensorType lazyGetType() {
if (!allInputTypesPresent(1))
return null;
OrderedTensorType inputType = inputs.get(0).type().get();
// required as we use tensor create
inputs.get(0).exportAsRankingFunction = true;
int axisSize = inputType.dimensions().get(axis).size().get().intValue();
start = 0;
end = axisSize;
if (attributes.getList("split").isPresent()) {
List<Value> splitList = attributes.getList("split").get();
if (output > splitList.size()) {
throw new IllegalArgumentException("Split in " + name + ": output out of range of split list");
}
for (int i = 0; i < output; ++i) {
start += (int) splitList.get(i).asDouble();
}
if (output < splitList.size()) {
end = start + (int) splitList.get(output).asDouble();
}
} else {
start = axisSize / 2 * output;
end = start + axisSize / 2;
}
if (start >= axisSize || start < 0) {
throw new IllegalArgumentException("Split in " + name + ": split start index out of range (" + start + ")");
}
if (end > axisSize || end < 0) {
throw new IllegalArgumentException("Split in " + name + ": split end index out of range (" + end + ")");
}
OrderedTensorType.Builder typeBuilder = new OrderedTensorType.Builder(resultValueType());
for (int i = 0; i < inputType.rank(); ++i) {
TensorType.Dimension inputDimension = inputType.dimensions().get(i);
long dimSize = i == axis ? end - start : inputDimension.size().get();
typeBuilder.add(TensorType.Dimension.indexed(inputDimension.name(), dimSize));
}
return typeBuilder.build();
}
@Override
protected TensorFunction<Reference> lazyGetFunction() {
if (!allInputFunctionsPresent(1)) return null;
IntermediateOperation input = inputs.get(0);
OrderedTensorType inputType = input.type().get();
String inputFunctionName = input.rankingExpressionFunctionName();
List<com.yahoo.tensor.functions.Slice.DimensionValue<Reference>> dimensionValues = new ArrayList<>();
for (int i = 0; i < inputType.rank(); ++i) {
String inputDimensionName = inputType.dimensions().get(i).name();
ExpressionNode reference = new ReferenceNode(inputDimensionName);
ExpressionNode offset = new ArithmeticNode(reference, ArithmeticOperator.PLUS, new ConstantNode(new DoubleValue(i == axis ? start : 0)));
dimensionValues.add(new com.yahoo.tensor.functions.Slice.DimensionValue<>(Optional.of(inputDimensionName), wrapScalar(new EmbracedNode(offset))));
}
TensorFunction<Reference> inputIndices = new TensorFunctionNode.ExpressionTensorFunction(new ReferenceNode(inputFunctionName));
com.yahoo.tensor.functions.Slice<Reference> sliceIndices = new com.yahoo.tensor.functions.Slice<>(inputIndices, dimensionValues);
ExpressionNode sliceExpression = new TensorFunctionNode(sliceIndices);
TensorFunction<Reference> generate = Generate.bound(type.type(), wrapScalar(sliceExpression));
return generate;
}
@Override
public Split withInputs(List<IntermediateOperation> inputs) {
return new Split(modelName(), name(), inputs, attributes, output);
}
@Override
public String operationName() { return "Split"; }
}
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