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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.searchlib.rankingexpression.evaluation.DoubleValue;
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.TensorFunctionNode;
import com.yahoo.tensor.TensorType;
import com.yahoo.tensor.functions.ScalarFunctions;
import com.yahoo.tensor.functions.TensorFunction;
import com.yahoo.text.ExpressionFormatter;
import java.util.List;
import java.util.Optional;
public class Gemm extends IntermediateOperation {
private final AttributeMap attributeMap;
private final float alpha, beta;
private final int transposeA, transposeB;
private final static DoubleValue zero = DoubleValue.frozen(0.0);
private final static DoubleValue one = DoubleValue.frozen(1.0);
public Gemm(String modelName, String nodeName, List<IntermediateOperation> inputs, AttributeMap attributeMap) {
super(modelName, nodeName, inputs);
this.attributeMap = attributeMap;
this.alpha = (float) attributeMap.get("alpha").orElse(one).asDouble();
this.beta = (float) attributeMap.get("beta").orElse(one).asDouble();
this.transposeA = (int) attributeMap.get("transA").orElse(zero).asDouble();
this.transposeB = (int) attributeMap.get("transB").orElse(zero).asDouble();
}
@Override
protected OrderedTensorType lazyGetType() {
if ( ! check2or3InputsPresent()) return null;
OrderedTensorType.Builder typeBuilder = new OrderedTensorType.Builder(resultValueType());
TensorType.Dimension dimA = inputs.get(0).type().get().dimensions().get(transposeA);
TensorType.Dimension dimB = inputs.get(1).type().get().dimensions().get(1 - transposeB);
typeBuilder.add(dimA);
typeBuilder.add(dimB);
OrderedTensorType result = typeBuilder.build();
// Input tensor C. The shape of C should be unidirectional "broadcastable" to (dimA, dimB).
if (inputs.size() == 3) {
List<TensorType.Dimension> cDimensions = inputs.get(2).type().get().dimensions();
if (cDimensions.size() == 2) {
TensorType.Dimension dimC0 = cDimensions.get(0);
TensorType.Dimension dimC1 = cDimensions.get(1);
if ( ! (dimA.size().get().equals(dimC0.size().get()) || dimC0.size().get() == 1) ) {
throw new IllegalArgumentException("GEMM: type of optional input C " + inputs.get(2).type().get() +
" is not compatible or not broadcastable to " + result.type());
}
if ( ! (dimB.size().get().equals(dimC1.size().get()) || dimC1.size().get() == 1) ) {
throw new IllegalArgumentException("GEMM: type of optional input C " + inputs.get(2).type().get() +
" is not compatible or not broadcastable to " + result.type());
}
} else if (cDimensions.size() == 1) {
TensorType.Dimension dimC0 = cDimensions.get(0);
if ( ! (dimB.size().get().equals(dimC0.size().get()) || dimC0.size().get() == 1) ) {
throw new IllegalArgumentException("GEMM: type of optional input C " + inputs.get(2).type().get() +
" is not compatible or not broadcastable to " + result.type());
}
} else {
throw new IllegalArgumentException("GEMM: optional input C has no dimensions.");
}
}
return result;
}
@Override
protected TensorFunction<Reference> lazyGetFunction() {
if ( ! check2or3InputsPresent()) 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 Gemm must have rank of exactly 2");
Optional<TensorFunction<Reference>> aFunction = inputs.get(0).function();
Optional<TensorFunction<Reference>> bFunction = inputs.get(1).function();
if (aFunction.isEmpty() || bFunction.isEmpty()) {
return null;
}
String joinDimension = aType.dimensions().get(1 - transposeA).name();
TensorFunction<Reference> AxB = new com.yahoo.tensor.functions.Matmul<>(aFunction.get(), bFunction.get(), joinDimension);
TensorFunction<Reference> alphaxAxB = new TensorFunctionNode.ExpressionTensorFunction(
new ArithmeticNode(
new TensorFunctionNode(AxB),
ArithmeticOperator.MULTIPLY,
new ConstantNode(new DoubleValue(alpha))));
if (inputs.size() == 3) {
Optional<TensorFunction<Reference>> cFunction = inputs.get(2).function();
TensorFunction<Reference> betaxC = new TensorFunctionNode.ExpressionTensorFunction(
new ArithmeticNode(
new TensorFunctionNode(cFunction.get()),
ArithmeticOperator.MULTIPLY,
new ConstantNode(new DoubleValue(beta))));
return new com.yahoo.tensor.functions.Join<>(alphaxAxB, betaxC, ScalarFunctions.add());
}
return alphaxAxB;
}
private boolean check2or3InputsPresent() {
if (inputs.size() != 2 && inputs.size() != 3) {
throw new IllegalArgumentException("Expected 2 or 3 inputs for '" + name + "', got " + inputs.size());
}
return inputs.stream().map(IntermediateOperation::type).allMatch(Optional::isPresent);
}
@Override
public void addDimensionNameConstraints(DimensionRenamer renamer) {
if ( ! check2or3InputsPresent()) 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(transposeA).name();
String aDim1 = aDimensions.get(1 - transposeA).name();
String bDim0 = bDimensions.get(transposeB).name();
String bDim1 = bDimensions.get(1 - transposeB).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);
// If c is given, should be unidirectionally broadcastable to tensor a * b:
// Tensor A and B both have exactly the same shape.
// Tensor A and B all have the same number of dimensions and the length of each dimensions is either a common length or B's length is 1.
// Tensor B has too few dimensions, and B can have its shapes prepended with a dimension of length 1 to satisfy property 2.
if (inputs.size() == 3) {
List<TensorType.Dimension> cDimensions = inputs.get(2).type().get().dimensions();
if (cDimensions.size() == 2) {
String cDim0 = cDimensions.get(0).name();
String cDim1 = cDimensions.get(1).name();
renamer.addConstraint(aDim0, cDim0, DimensionRenamer.Constraint.equal(false), this);
renamer.addConstraint(bDim1, cDim1, DimensionRenamer.Constraint.equal(false), this);
} else if (cDimensions.size() == 1) {
String cDim0 = cDimensions.get(0).name();
renamer.addConstraint(bDim1, cDim0, DimensionRenamer.Constraint.equal(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 Gemm withInputs(List<IntermediateOperation> inputs) {
return new Gemm(modelName(), name(), inputs, attributeMap);
}
@Override
public String operationName() { return "Gemm"; }
}
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