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// Copyright 2018 Yahoo Holdings. 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 ai.vespa.rankingexpression.importer.DimensionRenamer;
import com.yahoo.searchlib.rankingexpression.evaluation.StringValue;
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.ComparisonNode;
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.Function;
import com.yahoo.searchlib.rankingexpression.rule.FunctionNode;
import com.yahoo.searchlib.rankingexpression.rule.GeneratorLambdaFunctionNode;
import com.yahoo.searchlib.rankingexpression.rule.ReferenceNode;
import com.yahoo.searchlib.rankingexpression.rule.TensorFunctionNode;
import com.yahoo.searchlib.rankingexpression.rule.TruthOperator;
import com.yahoo.tensor.Tensor;
import com.yahoo.tensor.TensorType;
import com.yahoo.tensor.functions.Generate;
import com.yahoo.tensor.functions.Reduce;
import com.yahoo.tensor.functions.Rename;
import com.yahoo.tensor.functions.ScalarFunctions;
import com.yahoo.tensor.functions.TensorFunction;
import java.util.ArrayList;
import java.util.List;
import java.util.Optional;
import java.util.stream.Collectors;
import static com.yahoo.searchlib.rankingexpression.rule.TensorFunctionNode.wrapScalar;
public class Reshape extends IntermediateOperation {
private final AttributeMap attributeMap;
public Reshape(String modelName, String nodeName, List<IntermediateOperation> inputs, AttributeMap attributeMap) {
super(modelName, nodeName, inputs);
this.attributeMap = attributeMap;
}
@Override
protected OrderedTensorType lazyGetType() {
// required as we use tensor create
inputs.get(0).exportAsRankingFunction = true;
if (inputs.size() == 2) {
return typeWithShapeAsInput();
} else if (inputs.size() == 1) {
return typeWithShapeAsAttribute();
}
throw new IllegalArgumentException("Expected 2 or 3 inputs for '" + name + "', got " + inputs.size());
}
private OrderedTensorType typeWithShapeAsInput() {
IntermediateOperation newShape = inputs.get(1);
if (newShape.getConstantValue().isEmpty())
throw new IllegalArgumentException("Reshape " + name + ": Shape input must be a constant.");
OrderedTensorType inputType = inputs.get(0).type().get();
Tensor shape = newShape.getConstantValue().get().asTensor();
List<Integer> dimSizes = new ArrayList<>(shape.type().rank());
shape.valueIterator().forEachRemaining(v -> dimSizes.add(v.intValue()));
// first pass - set 0 values, meaning that size is retained from input
for (int i = 0; i < dimSizes.size(); ++i) {
if (dimSizes.get(i) == 0) {
if (i >= inputType.dimensions().size()) {
throw new IllegalArgumentException("Reshape " + name + ": 0 value for dimension not found in input");
}
dimSizes.set(i, inputType.dimensions().get(i).size().get().intValue());
}
}
// second pass - set any -1 value, meaning that the dimension size should be expanded to fill the tensor
for (int i = 0; i < dimSizes.size(); ++i) {
if (dimSizes.get(i) < 0) {
int shapeSize = dimSizes.stream().reduce(1, (a, b) -> a * b);
int tensorSize = OrderedTensorType.tensorSize(inputType.type()).intValue();
dimSizes.set(i, -1 * tensorSize / (shapeSize == 0 ? -1 : shapeSize));
}
}
return buildOutputType(dimSizes);
}
private OrderedTensorType typeWithShapeAsAttribute() {
if (attributeMap.getList("shape").isEmpty() || attributeMap.getList("shape").get().size() == 0)
throw new IllegalArgumentException("Reshape in " + name + ": Shape attribute is empty.");
OrderedTensorType inputType = inputs.get(0).type().get();
List<Value> shape = attributeMap.getList("shape").get();
List<Integer> dimSizes = new ArrayList<>(shape.size());
for (Value v : shape) {
int size = (int) v.asDouble();
if (size < 0) {
int shapeSize = (int) shape.stream().mapToDouble(Value::asDouble).reduce(1, (a, b) -> a * b);
int tensorSize = OrderedTensorType.tensorSize(inputType.type()).intValue();
size = -1 * shapeSize / tensorSize;
}
dimSizes.add(size);
}
return buildOutputType(dimSizes);
}
private OrderedTensorType buildOutputType(List<Integer> dimSizes) {
OrderedTensorType.Builder outputTypeBuilder = new OrderedTensorType.Builder(resultValueType());
for (int i = 0; i < dimSizes.size(); ++i) {
outputTypeBuilder.add(TensorType.Dimension.indexed(String.format("%s_%d", vespaName(), i), dimSizes.get(i)));
}
return outputTypeBuilder.build();
}
@Override
protected TensorFunction lazyGetFunction() {
if ( ! inputs.stream().map(IntermediateOperation::type).allMatch(Optional::isPresent) ) return null;
if ( ! inputs.stream().map(IntermediateOperation::function).allMatch(Optional::isPresent) ) return null;
OrderedTensorType inputType = inputs.get(0).type().get();
TensorFunction inputFunction = inputs.get(0).function().get();
return reshape(inputFunction, inputType, type);
}
@Override
public void addDimensionNameConstraints(DimensionRenamer renamer) {
addConstraintsFrom(type, renamer);
}
@Override
public Reshape withInputs(List<IntermediateOperation> inputs) {
return new Reshape(modelName(), name(), inputs, attributeMap);
}
public TensorFunction reshape(TensorFunction inputFunction, OrderedTensorType inputType, OrderedTensorType outputType) {
if ( ! OrderedTensorType.tensorSize(inputType.type()).equals(OrderedTensorType.tensorSize(outputType.type())))
throw new IllegalArgumentException("New and old shape of tensor must have the same size when reshaping");
IntermediateOperation input = inputs.get(0);
String inputFunctionName = input.rankingExpressionFunctionName();
List<com.yahoo.tensor.functions.Slice.DimensionValue<Reference>> dimensionValues = new ArrayList<>();
// ala (d0 * 2 + d1)
ExpressionNode unrolled = new EmbracedNode(unrollTensorExpression(outputType));
long innerSize = 1;
for (int dim = 0; dim < inputType.rank(); ++dim) {
innerSize *= inputType.dimensions().get(dim).size().get();
}
for (int dim = 0; dim < inputType.rank(); ++dim) {
String inputDimensionName = inputType.dimensions().get(dim).name();
long inputDimensionSize = inputType.dimensions().get(dim).size().get();
long previousInnerSize = innerSize;
innerSize /= inputDimensionSize;
ExpressionNode inputDimensionExpression;
if (inputDimensionSize == 1) {
inputDimensionExpression = new EmbracedNode(new ConstantNode(DoubleValue.zero));
} else if (dim == (inputType.rank() - 1)) {
ExpressionNode size = new ConstantNode(new DoubleValue(inputDimensionSize));
ExpressionNode div = new ArithmeticNode(unrolled, ArithmeticOperator.MODULO, size);
inputDimensionExpression = new EmbracedNode(div);
} else {
ExpressionNode size = new ConstantNode(new DoubleValue(innerSize));
ExpressionNode previousSize = new ConstantNode(new DoubleValue(previousInnerSize));
ExpressionNode mod = new ArithmeticNode(unrolled, ArithmeticOperator.MODULO, previousSize);
ExpressionNode div = new ArithmeticNode(new EmbracedNode(mod), ArithmeticOperator.DIVIDE, size);
inputDimensionExpression = new EmbracedNode(new FunctionNode(Function.floor, div));
}
dimensionValues.add(new com.yahoo.tensor.functions.Slice.DimensionValue<>(Optional.of(inputDimensionName), wrapScalar(inputDimensionExpression)));
}
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 generate = Generate.bound(outputType.type(), wrapScalar(sliceExpression));
return generate;
/*
// Conceptually, reshaping consists on unrolling a tensor to an array using the dimension order,
// then use the dimension order of the new shape to roll back into a tensor.
// Here we create a transformation tensor that is multiplied with the from tensor to map into
// the new shape. We have to introduce temporary dimension names and rename back if dimension names
// in the new and old tensor type overlap.
// Todo: change this to use tensor generate when available
List<String> from = new ArrayList<>();
List<String> to = new ArrayList<>();
boolean dimensionNamesOverlap = dimensionNamesOverlap(inputType, outputType);
if (dimensionNamesOverlap) {
OrderedTensorType.Builder builder = new OrderedTensorType.Builder(outputType.type().valueType());
for (int i = 0; i < outputType.rank(); ++i) {
TensorType.Dimension dim = outputType.dimensions().get(i);
from.add(dim.name());
to.add("temp_" + dim.name());
builder.add(dim.withName("temp_" + dim.name()));
}
outputType = builder.build();
}
ExpressionNode unrollFrom = unrollTensorExpression(inputType);
ExpressionNode unrollTo = unrollTensorExpression(outputType);
ExpressionNode transformExpression = new ComparisonNode(new EmbracedNode(unrollFrom), TruthOperator.EQUAL, new EmbracedNode(unrollTo));
TensorType transformationType = new TensorType.Builder(inputType.type(), outputType.type()).build();
Generate transformTensor = new Generate(transformationType,
new GeneratorLambdaFunctionNode(transformationType, transformExpression).asLongListToDoubleOperator());
TensorFunction result = new Reduce(new com.yahoo.tensor.functions.Join(inputFunction, transformTensor, ScalarFunctions.multiply()),
Reduce.Aggregator.sum,
inputType.dimensions().stream().map(TensorType.Dimension::name).collect(Collectors.toList()));
if (dimensionNamesOverlap) {
result = new Rename(result, to, from);
}
return result;
*/
}
/*
private static boolean dimensionNamesOverlap(OrderedTensorType a, OrderedTensorType b) {
return a.dimensionNames().stream().anyMatch(d -> b.type().indexOfDimension(d).isPresent());
}
*/
private static ExpressionNode unrollTensorExpression(OrderedTensorType type) {
if (type.rank() == 0)
return new ConstantNode(DoubleValue.zero);
List<ExpressionNode> children = new ArrayList<>();
List<ArithmeticOperator> operators = new ArrayList<>();
int size = 1;
for (int i = type.dimensions().size() - 1; i >= 0; --i) {
TensorType.Dimension dimension = type.dimensions().get(i);
children.add(0, new ReferenceNode(dimension.name()));
if (size > 1) {
operators.add(0, ArithmeticOperator.MULTIPLY);
children.add(0, new ConstantNode(new DoubleValue(size)));
}
size *= OrderedTensorType.dimensionSize(dimension);
if (i > 0) {
operators.add(0, ArithmeticOperator.PLUS);
}
}
return new ArithmeticNode(children, operators);
}
@Override
public String operationName() { return "Reshape"; }
}
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