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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 com.yahoo.searchlib.rankingexpression.integration.ml;
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.integration.ml.importer.tensorflow.TensorConverter;
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 org.tensorflow.SavedModelBundle;
import org.tensorflow.Session;
import java.nio.FloatBuffer;
import java.util.List;
import static org.junit.Assert.assertEquals;
/**
* Helper for TensorFlow import tests: Imports a model and provides asserts on it.
* This currently assumes the TensorFlow model takes a single input of type tensor(d0[1],d1[784])
*
* @author bratseth
*/
public class TestableTensorFlowModel {
private SavedModelBundle tensorFlowModel;
private ImportedModel model;
// Sizes of the input vector
private final int d0Size = 1;
private final int d1Size = 784;
public TestableTensorFlowModel(String modelName, String modelDir) {
tensorFlowModel = SavedModelBundle.load(modelDir, "serve");
model = new TensorFlowImporter().importModel(modelName, modelDir, tensorFlowModel);
}
public ImportedModel get() { return model; }
/** Compare that summing the tensors produce the same result to within some tolerance delta */
public void assertEqualResultSum(String inputName, String operationName, double delta) {
Tensor tfResult = tensorFlowExecute(tensorFlowModel, inputName, operationName);
Context context = contextFrom(model);
Tensor placeholder = placeholderArgument();
context.put(inputName, new TensorValue(placeholder));
model.functions().forEach((k, v) -> evaluateFunction(context, model, k));
RankingExpression expression = model.expressions().get(operationName);
ExpressionOptimizer optimizer = new ExpressionOptimizer();
optimizer.optimize(expression, (ContextIndex)context);
Tensor vespaResult = expression.evaluate(context).asTensor();
assertEquals("Operation '" + operationName + "' produces equal results",
tfResult.sum().asDouble(), vespaResult.sum().asDouble(), delta);
}
/** Compare tensors 100% exactly */
public void assertEqualResult(String inputName, String operationName) {
Tensor tfResult = tensorFlowExecute(tensorFlowModel, inputName, operationName);
Context context = contextFrom(model);
Tensor placeholder = placeholderArgument();
context.put(inputName, new TensorValue(placeholder));
model.functions().forEach((k, v) -> evaluateFunction(context, model, k));
RankingExpression expression = model.expressions().get(operationName);
ExpressionOptimizer optimizer = new ExpressionOptimizer();
optimizer.optimize(expression, (ContextIndex)context);
Tensor vespaResult = expression.evaluate(context).asTensor();
assertEquals("Operation '" + operationName + "' produces equal results", tfResult, vespaResult);
}
private Tensor tensorFlowExecute(SavedModelBundle model, String inputName, String operationName) {
Session.Runner runner = model.session().runner();
FloatBuffer fb = FloatBuffer.allocate(d0Size * d1Size);
for (int i = 0; i < d1Size; ++i) {
fb.put(i, (float)(i * 1.0 / d1Size));
}
org.tensorflow.Tensor<?> placeholder = org.tensorflow.Tensor.create(new long[]{ d0Size, d1Size }, fb);
runner.feed(inputName, placeholder);
List<org.tensorflow.Tensor<?>> results = runner.fetch(operationName).run();
assertEquals(1, results.size());
return TensorConverter.toVespaTensor(results.get(0));
}
static Context contextFrom(ImportedModel result) {
TestableModelContext context = new TestableModelContext();
result.largeConstants().forEach((name, tensor) -> context.put("constant(" + name + ")", new TensorValue(tensor)));
result.smallConstants().forEach((name, tensor) -> context.put("constant(" + name + ")", new TensorValue(tensor)));
return context;
}
private Tensor placeholderArgument() {
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 = 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);
}
}
}
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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