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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.onnx;
import ai.vespa.rankingexpression.importer.ImportedModel;
import com.yahoo.searchlib.rankingexpression.RankingExpression;
import com.yahoo.searchlib.rankingexpression.evaluation.Context;
import com.yahoo.searchlib.rankingexpression.evaluation.ContextIndex;
import com.yahoo.searchlib.rankingexpression.evaluation.ExpressionOptimizer;
import com.yahoo.searchlib.rankingexpression.evaluation.MapContext;
import com.yahoo.searchlib.rankingexpression.evaluation.TensorValue;
import com.yahoo.searchlib.rankingexpression.rule.CompositeNode;
import com.yahoo.searchlib.rankingexpression.rule.ExpressionNode;
import com.yahoo.searchlib.rankingexpression.rule.ReferenceNode;
import com.yahoo.tensor.Tensor;
import com.yahoo.tensor.TensorType;
import java.util.Map;
public class TestableModel {
Tensor evaluateVespa(ImportedModel model, String operationName, Map<String, TensorType> inputs) {
Context context = contextFrom(model);
for (Map.Entry<String, TensorType> entry : inputs.entrySet()) {
Tensor argument = vespaInputArgument(1, entry.getValue().dimensions().get(1).size().get().intValue());
context.put(entry.getKey(), new TensorValue(argument));
}
model.functions().forEach((k, v) -> evaluateFunction(context, model, k));
RankingExpression expression = model.expressions().get(operationName);
ExpressionOptimizer optimizer = new ExpressionOptimizer();
optimizer.optimize(expression, (ContextIndex)context);
return expression.evaluate(context).asTensor();
}
private Tensor vespaInputArgument(int d0Size, int d1Size) {
Tensor.Builder b = Tensor.Builder.of(new TensorType.Builder().indexed("d0", d0Size).indexed("d1", d1Size).build());
for (int d0 = 0; d0 < d0Size; d0++)
for (int d1 = 0; d1 < d1Size; d1++)
b.cell(d1 * 1.0 / d1Size, d0, d1);
return b.build();
}
private void evaluateFunction(Context context, ImportedModel model, String functionName) {
if (!context.names().contains(functionName)) {
RankingExpression e = RankingExpression.from(model.functions().get(functionName));
evaluateFunctionDependencies(context, model, e.getRoot());
context.put(functionName, new TensorValue(e.evaluate(context).asTensor()));
}
}
private void evaluateFunctionDependencies(Context context, ImportedModel model, ExpressionNode node) {
if (node instanceof ReferenceNode) {
String name = node.toString();
if (model.functions().containsKey(name)) {
evaluateFunction(context, model, name);
}
}
else if (node instanceof CompositeNode) {
for (ExpressionNode child : ((CompositeNode)node).children()) {
evaluateFunctionDependencies(context, model, child);
}
}
}
static Context contextFrom(ImportedModel result) {
TestableModelContext context = new TestableModelContext();
result.largeConstantTensors().forEach((name, tensor) -> context.put("constant(" + name + ")", new TensorValue(tensor)));
result.smallConstantTensors().forEach((name, tensor) -> context.put("constant(" + name + ")", new TensorValue(tensor)));
return context;
}
private static class TestableModelContext extends MapContext implements ContextIndex {
@Override
public int size() {
return bindings().size();
}
@Override
public int getIndex(String name) {
throw new UnsupportedOperationException(this + " does not support index lookup by name");
}
}
}
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