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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.DimensionRenamer;
import ai.vespa.rankingexpression.importer.OrderedTensorType;
import com.yahoo.tensor.TensorType;
import com.yahoo.tensor.functions.TensorFunction;
import com.yahoo.text.ExpressionFormatter;
import java.util.List;
import java.util.Optional;
public class MatMul extends IntermediateOperation {
public MatMul(String modelName, String nodeName, List<IntermediateOperation> inputs) {
super(modelName, nodeName, inputs);
}
@Override
protected OrderedTensorType lazyGetType() {
if ( ! allInputTypesPresent(2)) return null;
OrderedTensorType.Builder typeBuilder = new OrderedTensorType.Builder(resultValueType());
typeBuilder.add(inputs.get(0).type().get().dimensions().get(0));
typeBuilder.add(inputs.get(1).type().get().dimensions().get(1));
return typeBuilder.build();
}
@Override
protected TensorFunction lazyGetFunction() {
if ( ! allInputTypesPresent(2)) return null;
OrderedTensorType aType = inputs.get(0).type().get();
OrderedTensorType bType = inputs.get(1).type().get();
if (aType.type().rank() < 2 || bType.type().rank() < 2)
throw new IllegalArgumentException("Tensors in matmul must have rank of at least 2");
if (aType.type().rank() != bType.type().rank())
throw new IllegalArgumentException("Tensors in matmul must have the same rank");
Optional<TensorFunction> aFunction = inputs.get(0).function();
Optional<TensorFunction> bFunction = inputs.get(1).function();
if (!aFunction.isPresent() || !bFunction.isPresent()) {
return null;
}
return new com.yahoo.tensor.functions.Matmul(aFunction.get(), bFunction.get(), aType.dimensions().get(1).name());
}
@Override
public void addDimensionNameConstraints(DimensionRenamer renamer) {
if ( ! allInputTypesPresent(2)) return;
List<TensorType.Dimension> aDimensions = inputs.get(0).type().get().dimensions();
List<TensorType.Dimension> bDimensions = inputs.get(1).type().get().dimensions();
assertTwoDimensions(aDimensions, inputs.get(0), "first argument");
assertTwoDimensions(bDimensions, inputs.get(1), "second argument");
String aDim0 = aDimensions.get(0).name();
String aDim1 = aDimensions.get(1).name();
String bDim0 = bDimensions.get(0).name();
String bDim1 = bDimensions.get(1).name();
// The second dimension of a should have the same name as the first dimension of b
renamer.addConstraint(aDim1, bDim0, DimensionRenamer.Constraint.equal(false), this);
// The first dimension of a should have a different name than the second dimension of b
renamer.addConstraint(aDim0, bDim1, DimensionRenamer.Constraint.lessThan(false), this);
// For efficiency, the dimensions to join over should be innermost - soft constraint
renamer.addConstraint(aDim0, aDim1, DimensionRenamer.Constraint.lessThan(true), this);
renamer.addConstraint(bDim0, bDim1, DimensionRenamer.Constraint.greaterThan(true), this);
}
private void assertTwoDimensions(List<TensorType.Dimension> dimensions, IntermediateOperation supplier, String inputDescription) {
if (dimensions.size() >= 2) return;
throw new IllegalArgumentException("Expected 2 dimensions in the " + inputDescription + " to " + this +
" but got just " + dimensions + " from\n" +
ExpressionFormatter.inTwoColumnMode(70, 50).format(supplier.toFullString()));
}
@Override
public MatMul withInputs(List<IntermediateOperation> inputs) {
return new MatMul(modelName(), name(), inputs);
}
@Override
public String operationName() { return "MatMul"; }
}
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