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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.tensor.functions.Join;
import com.yahoo.tensor.functions.Map;
import com.yahoo.tensor.functions.Reduce;
import com.yahoo.tensor.functions.ScalarFunctions;
import com.yahoo.tensor.functions.TensorFunction;
import java.util.ArrayList;
import java.util.Collections;
import java.util.List;
/**
* Convert imported 'softmax' operation to the Vespa softmax ranking function.
*
* @author lesters
*/
public class Softmax extends IntermediateOperation {
private final AttributeMap attributeMap;
public Softmax(String modelName, String nodeName, List<IntermediateOperation> inputs, AttributeMap attributeMap) {
super(modelName, nodeName, inputs);
this.attributeMap = attributeMap;
insert(new SoftmaxPartialOperation(modelName, nodeName, null), 0); // inputs are fixed in insert
}
@Override
protected OrderedTensorType lazyGetType() {
if ( ! allInputTypesPresent(1)) return null;
return inputs.get(0).type().get();
}
@Override
protected TensorFunction<Reference> lazyGetFunction() {
if ( ! allInputFunctionsPresent(1)) return null;
List<String> reduceDimensions = reduceDimensions();
TensorFunction<Reference> input = inputs.get(0).function().get();
TensorFunction<Reference> sum = new Reduce<>(input, Reduce.Aggregator.sum, reduceDimensions);
TensorFunction<Reference> div = new Join<>(input, sum, ScalarFunctions.divide());
return div;
}
@Override
public Softmax withInputs(List<IntermediateOperation> inputs) {
return new Softmax(modelName(), name(), inputs, attributeMap);
}
@Override
public String operationName() { return "SoftMax"; }
private List<String> reduceDimensions() {
OrderedTensorType inputType = inputs.get(0).type().get();
int axis = inputType.rank() == 1 ? 0 : 1; // assumption: first dimension is batch dimension
if (attributeMap.get("axis").isPresent()) {
axis = (int)attributeMap.get("axis").get().asDouble();
}
if (axis < 0) {
axis = inputType.rank() + axis;
}
List<String> reduceDimensions = new ArrayList<>();
for (int i = axis; i < inputType.rank(); ++i) {
reduceDimensions.add(inputType.dimensions().get(i).name()); // Do softmax over all dimensions except batch dimension
}
return reduceDimensions;
}
/*
* Operation to insert between input and this softmax to avoid double calculation
* Note that this partial operation should be removed when we have a specific
* softmax optimization in the backend, as this way of splitting the calculation
* makes the full softmax expression impossible to recognize.
*/
private class SoftmaxPartialOperation extends IntermediateOperation {
private SoftmaxPartialOperation(String modelName, String nodeName, List<IntermediateOperation> inputs) {
super(modelName, nodeName + "_partial" , inputs != null ? inputs : Collections.emptyList());
}
@Override
protected OrderedTensorType lazyGetType() {
if ( ! allInputTypesPresent(1)) return null;
// input is referenced twice due to overflow avoidance, so make sure it is exported as a ranking function
inputs.get(0).exportAsRankingFunction = true;
// this should also be it's own function since we use it twice
exportAsRankingFunction = true;
return inputs.get(0).type().get();
}
@Override
protected TensorFunction<Reference> lazyGetFunction() {
if ( ! allInputFunctionsPresent(1)) return null;
List<String> reduceDimensions = reduceDimensions();
TensorFunction<Reference> input = inputs.get(0).function().get();
TensorFunction<Reference> max = new Reduce<>(input, Reduce.Aggregator.max, reduceDimensions);
TensorFunction<Reference> cap = new Join<>(input, max, ScalarFunctions.subtract()); // to avoid overflow
TensorFunction<Reference> exp = new Map<>(cap, ScalarFunctions.exp());
return exp;
}
@Override
public SoftmaxPartialOperation withInputs(List<IntermediateOperation> inputs) {
return new SoftmaxPartialOperation(modelName(), name(), inputs);
}
@Override
public String operationName() { return "SoftMaxPartial"; }
}
}
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