1 files changed, 0 insertions, 116 deletions
diff --git a/model-integration/src/test/java/ai/vespa/rankingexpression/importer/tensorflow/BlogEvaluationBenchmark.java b/model-integration/src/test/java/ai/vespa/rankingexpression/importer/tensorflow/BlogEvaluationBenchmark.java
deleted file mode 100644
index 37104ab43db..00000000000
--- a/model-integration/src/test/java/ai/vespa/rankingexpression/importer/tensorflow/BlogEvaluationBenchmark.java
+++ /dev/null
@@ -1,116 +0,0 @@
-// 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.tensorflow;
-
-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.OptimizationReport;
-import com.yahoo.searchlib.rankingexpression.evaluation.TensorValue;
-import ai.vespa.rankingexpression.importer.ImportedModel;
-import com.yahoo.searchlib.rankingexpression.parser.ParseException;
-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;
-
-/**
- * Microbenchmark of imported ML model evaluation.
- *
- * @author lesters
- */
-public class BlogEvaluationBenchmark {
-
-    static final String modelDir = "src/test/models/tensorflow/blog/saved";
-
-    public static void main(String[] args) throws ParseException {
-        SavedModelBundle tensorFlowModel = SavedModelBundle.load(modelDir, "serve");
-        ImportedModel model = new TensorFlowImporter().importModel("blog", modelDir, tensorFlowModel);
-
-        Context context = TestableTensorFlowModel.contextFrom(model);
-        Tensor u = generateInputTensor();
-        Tensor d = generateInputTensor();
-        context.put("input_u", new TensorValue(u));
-        context.put("input_d", new TensorValue(d));
-
-        // Parse the ranking expression from imported string to force primitive tensor functions.
-        RankingExpression expression = new RankingExpression(model.expressions().get("y").getRoot().toString());
-        benchmarkJava(expression, context, 20, 200);
-
-        System.out.println("*** Optimizing expression ***");
-        ExpressionOptimizer optimizer = new ExpressionOptimizer();
-        OptimizationReport report = optimizer.optimize(expression, (ContextIndex)context);
-        System.out.println(report.toString());
-
-        benchmarkJava(expression, context, 2000, 20000);
-        benchmarkTensorFlow(tensorFlowModel, 2000, 20000);
-    }
-
-    private static void benchmarkJava(RankingExpression expression, Context context, int warmup, int iterations) {
-        System.out.println("*** Java evaluation - warmup ***");
-        evaluate(expression, context, warmup);
-        System.gc();
-        System.out.println("*** Java evaluation - " + iterations + " iterations ***");
-        double startTime = System.nanoTime();
-        evaluate(expression, context, iterations);
-        double endTime = System.nanoTime();
-        System.out.println("Model evaluation time is " + ((endTime-startTime) / (1000*1000)) + " ms");
-        System.out.println("Average model evaluation time is " + ((endTime-startTime) / (1000*1000)) / iterations + " ms");
-    }
-
-    private static double evaluate(RankingExpression expression, Context context, int iterations) {
-        double result = 0;
-        for (int i = 0 ; i < iterations; i++) {
-            result = expression.evaluate(context).asTensor().sum().asDouble();
-        }
-        return result;
-    }
-
-    private static Tensor generateInputTensor() {
-        Tensor.Builder b = Tensor.Builder.of(new TensorType.Builder().indexed("d0", 1).indexed("d1", 128).build());
-        for (int d0 = 0; d0 < 1; d0++)
-            for (int d1 = 0; d1 < 128; d1++)
-                b.cell(d1 * 1.0 / 128, d0, d1);
-        return b.build();
-    }
-
-    private static void benchmarkTensorFlow(SavedModelBundle tensorFlowModel, int warmup, int iterations) {
-        org.tensorflow.Tensor<?> u = generateInputTensorFlow();
-        org.tensorflow.Tensor<?> d = generateInputTensorFlow();
-
-        System.out.println("*** TensorFlow evaluation - warmup ***");
-        evaluateTensorflow(tensorFlowModel, u, d, warmup);
-
-        System.gc();
-        System.out.println("*** TensorFlow evaluation - " + iterations + " iterations ***");
-        double startTime = System.nanoTime();
-        evaluateTensorflow(tensorFlowModel, u, d, iterations);
-        double endTime = System.nanoTime();
-        System.out.println("Model evaluation time is " + ((endTime-startTime) / (1000*1000) + " ms"));
-        System.out.println("Average model evaluation time is " + ((endTime-startTime) / (1000*1000)) / iterations + " ms");
-    }
-
-    private static double evaluateTensorflow(SavedModelBundle tensorFlowModel, org.tensorflow.Tensor<?> u, org.tensorflow.Tensor<?> d, int iterations) {
-        double result = 0;
-        for (int i = 0 ; i < iterations; i++) {
-            Session.Runner runner = tensorFlowModel.session().runner();
-            runner.feed("input_u", u);
-            runner.feed("input_d", d);
-            List<org.tensorflow.Tensor<?>> results = runner.fetch("y").run();
-            result = TensorConverter.toVespaTensor(results.get(0)).sum().asDouble();
-        }
-        return result;
-    }
-
-    private static org.tensorflow.Tensor<?> generateInputTensorFlow() {
-        FloatBuffer fb = FloatBuffer.allocate(1 * 128);
-        for (int i = 0; i < 128; ++i) {
-            fb.put(i, (float)(i * 1.0 / 128));
-        }
-        return org.tensorflow.Tensor.create(new long[]{ 1, 128 }, fb);
-    }
-
-}