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// Copyright Vespa.ai. 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.searchlib.rankingexpression.evaluation.DoubleValue;
import com.yahoo.searchlib.rankingexpression.rule.OperationNode;
import com.yahoo.searchlib.rankingexpression.rule.Operator;
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.Slice;
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;
/*
* Onnx gather is the same as Numpy take.
*/
public class Gather extends IntermediateOperation {
private final AttributeMap attributeMap;
private int axis;
public Gather(String modelName, String nodeName, List<IntermediateOperation> inputs, AttributeMap attributeMap) {
super(modelName, nodeName, inputs);
this.attributeMap = attributeMap;
}
@Override
protected OrderedTensorType lazyGetType() {
if ( ! allInputTypesPresent(2)) return null;
OrderedTensorType dataType = inputs.get(0).type().get();
OrderedTensorType indicesType = inputs.get(1).type().get();
axis = (int) attributeMap.get("axis").orElse(DoubleValue.zero).asDouble();
if (axis < 0)
axis = dataType.rank() + axis;
OrderedTensorType.Builder typeBuilder = new OrderedTensorType.Builder(resultValueType());
for (int i = 0; i < axis; ++i) {
addDimension(i, dataType.dimensions().get(i).size().orElse(-1L), typeBuilder);
}
for (int i = 0; i < indicesType.rank(); ++i) {
addDimension(i + axis, indicesType.dimensions().get(i).size().orElse(-1L), typeBuilder);
}
for (int i = axis + 1; i < dataType.rank(); ++i) {
addDimension(i + indicesType.rank(), dataType.dimensions().get(i).size().orElse(-1L), typeBuilder);
}
inputs.get(0).exportAsRankingFunction = true;
inputs.get(1).exportAsRankingFunction = true;
return typeBuilder.build();
}
private void addDimension(int dimensionIndex, long size, OrderedTensorType.Builder typeBuilder) {
String name = String.format("%s_%d", vespaName(), dimensionIndex);
typeBuilder.add(TensorType.Dimension.indexed(name, size));
}
@Override
protected TensorFunction<Reference> lazyGetFunction() {
if ( ! allInputFunctionsPresent(2)) return null;
IntermediateOperation data = inputs.get(0);
IntermediateOperation indices = inputs.get(1);
OrderedTensorType dataType = data.type().get();
OrderedTensorType indicesType = indices.type().get();
String dataFunctionName = data.rankingExpressionFunctionName();
String indicesFunctionName = indices.rankingExpressionFunctionName();
List<Slice.DimensionValue<Reference>> dataSliceDimensions = new ArrayList<>();
for (int i = 0; i < axis; ++i) {
addSliceDimension(dataSliceDimensions, dataType.dimensions().get(i).name(), i);
}
if (indicesType.rank() == 0 && indices.isConstant()) {
double constantValue = indices.getConstantValue().get().asDouble();
ExpressionNode indexExpression = new ConstantNode(new DoubleValue(constantValue));
if (constantValue < 0) {
ExpressionNode axisSize = new ConstantNode(new DoubleValue(dataType.dimensions().get(axis).size().get()));
indexExpression = new EmbracedNode(new OperationNode(indexExpression, Operator.plus, axisSize));
}
addSliceDimension(dataSliceDimensions, dataType.dimensions().get(axis).name(), indexExpression);
} else {
List<Slice.DimensionValue<Reference>> indicesSliceDimensions = new ArrayList<>();
for (int i = 0; i < indicesType.rank(); ++i) {
addSliceDimension(indicesSliceDimensions, indicesType.dimensions().get(i).name(), axis + i);
}
ExpressionNode sliceExpression = createSliceExpression(indicesSliceDimensions, indicesFunctionName);
ExpressionNode indexExpression = createIndexExpression(dataType, sliceExpression);
addSliceDimension(dataSliceDimensions, dataType.dimensions().get(axis).name(), indexExpression);
}
for (int i = axis + 1; i < dataType.rank(); ++i) {
addSliceDimension(dataSliceDimensions, dataType.dimensions().get(i).name(), i + indicesType.rank() - 1);
}
ExpressionNode sliceExpression = createSliceExpression(dataSliceDimensions, dataFunctionName);
return Generate.bound(type.type(), wrapScalar(sliceExpression));
}
private ExpressionNode createSliceExpression(List<Slice.DimensionValue<Reference>> dimensionValues, String referenceName) {
TensorFunction<Reference> inputIndices = new TensorFunctionNode.ExpressionTensorFunction(new ReferenceNode(referenceName));
if (dimensionValues.isEmpty()) {
return new TensorFunctionNode(inputIndices);
}
Slice<Reference> sliceIndices = new Slice<>(inputIndices, dimensionValues);
return new TensorFunctionNode(sliceIndices);
}
/** to support negative indexing */
private ExpressionNode createIndexExpression(OrderedTensorType dataType, ExpressionNode slice) {
ExpressionNode axisSize = new ConstantNode(new DoubleValue(dataType.dimensions().get(axis).size().get()));
ExpressionNode plus = new EmbracedNode(new OperationNode(slice, Operator.plus, axisSize));
ExpressionNode mod = new OperationNode(plus, Operator.modulo, axisSize);
return mod;
}
private void addSliceDimension(List<Slice.DimensionValue<Reference>> dimensionValues, String dimensionName, ExpressionNode expr) {
dimensionValues.add(new Slice.DimensionValue<>(Optional.of(dimensionName), wrapScalar(new EmbracedNode(expr))));
}
private void addSliceDimension(List<Slice.DimensionValue<Reference>> dimensionValues, String dimensionName, int dimensionIndex) {
String outputDimensionName = type.dimensions().get(dimensionIndex).name();
addSliceDimension(dimensionValues, dimensionName, new ReferenceNode(outputDimensionName));
}
@Override
public void addDimensionNameConstraints(DimensionRenamer renamer) {
if ( ! allInputTypesPresent(2)) return;
for (int i = 0; i < type.dimensions().size(); i++) {
renamer.addDimension(type.dimensions().get(i).name());
for (int j = i + 1; j < type.dimensions().size(); j++) {
renamer.addConstraint(type.dimensions().get(i).name(), type.dimensions().get(j).name(),
DimensionRenamer.Constraint.lessThan(), this);
}
}
OrderedTensorType dataType = inputs.get(0).type().get();
OrderedTensorType indicesType = inputs.get(1).type().get();
for (int i = 0; i < axis; ++i) {
renamer.addConstraint(type.dimensions().get(i).name(),
dataType.dimensions().get(i).name(),
DimensionRenamer.Constraint.equal(), this);
}
for (int i = 0; i < indicesType.rank(); ++i) {
renamer.addConstraint(type.dimensions().get(i + axis).name(),
indicesType.dimensions().get(i).name(),
DimensionRenamer.Constraint.equal(), this);
}
for (int i = axis + 1; i < dataType.rank(); ++i) {
renamer.addConstraint(type.dimensions().get(i + indicesType.rank() - 1).name(),
dataType.dimensions().get(i).name(),
DimensionRenamer.Constraint.equal(), this);
}
}
@Override
public Gather withInputs(List<IntermediateOperation> inputs) {
return new Gather(modelName(), name(), inputs, attributeMap);
}
@Override
public String operationName() { return "Gather"; }
}
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