Revert "Bratseth/handle large constants take 2"

2 files changed, 15 insertions, 58 deletions

diff --git a/model-evaluation/src/test/java/ai/vespa/models/evaluation/ModelsEvaluatorTest.java b/model-evaluation/src/test/java/ai/vespa/models/evaluation/ModelsEvaluatorTest.java
index d94e5b2af1b..60cf0d25ded 100644
--- a/model-evaluation/src/test/java/ai/vespa/models/evaluation/ModelsEvaluatorTest.java
+++ b/model-evaluation/src/test/java/ai/vespa/models/evaluation/ModelsEvaluatorTest.java
@@ -3,10 +3,8 @@ package ai.vespa.models.evaluation;
 
 import com.yahoo.config.subscription.ConfigGetter;
 import com.yahoo.config.subscription.FileSource;
-import com.yahoo.path.Path;
 import com.yahoo.tensor.Tensor;
 import com.yahoo.vespa.config.search.RankProfilesConfig;
-import com.yahoo.vespa.config.search.core.RankingConstantsConfig;
 import org.junit.Test;
 
 import java.io.File;
@@ -20,9 +18,15 @@ public class ModelsEvaluatorTest {
 
     private static final double delta = 0.00000000001;
 
+    private ModelsEvaluator createModels() {
+        String configPath = "src/test/resources/config/rankexpression/rank-profiles.cfg";
+        RankProfilesConfig config = new ConfigGetter<>(new FileSource(new File(configPath)), RankProfilesConfig.class).getConfig("");
+        return new ModelsEvaluator(config);
+    }
+
     @Test
     public void testTensorEvaluation() {
-        ModelsEvaluator models = createModels("src/test/resources/config/rankexpression/");
+        ModelsEvaluator models = createModels();
         FunctionEvaluator function = models.evaluatorOf("macros", "fourtimessum");
         function.bind("var1", Tensor.from("{{x:0}:3,{x:1}:5}"));
         function.bind("var2", Tensor.from("{{x:0}:7,{x:1}:11}"));
@@ -31,7 +35,7 @@ public class ModelsEvaluatorTest {
 
     @Test
     public void testEvaluationDependingOnMacroTakingArguments() {
-        ModelsEvaluator models = createModels("src/test/resources/config/rankexpression/");
+        ModelsEvaluator models = createModels();
         FunctionEvaluator function = models.evaluatorOf("macros", "secondphase");
         function.bind("match", 3);
         function.bind("rankBoost", 5);
@@ -42,14 +46,6 @@ public class ModelsEvaluatorTest {
     // TODO: Test that binding nonexisting variable doesn't work
     // TODO: Test that rebinding doesn't work
     // TODO: Test with nested macros
-
-    private ModelsEvaluator createModels(String path) {
-        Path configDir = Path.fromString(path);
-        RankProfilesConfig config = new ConfigGetter<>(new FileSource(configDir.append("rank-profiles.cfg").toFile()),
-                                                       RankProfilesConfig.class).getConfig("");
-        RankingConstantsConfig constantsConfig = new ConfigGetter<>(new FileSource(configDir.append("ranking-constants.cfg").toFile()),
-                                                                    RankingConstantsConfig.class).getConfig("");
-        return new ModelsEvaluator(config, constantsConfig);
-    }
+    // TODO: Test TF/ONNX model
 
 }
diff --git a/model-evaluation/src/test/java/ai/vespa/models/evaluation/RankProfilesImporterTest.java b/model-evaluation/src/test/java/ai/vespa/models/evaluation/RankProfilesImporterTest.java
index 84e01e58280..d45372fc7da 100644
--- a/model-evaluation/src/test/java/ai/vespa/models/evaluation/RankProfilesImporterTest.java
+++ b/model-evaluation/src/test/java/ai/vespa/models/evaluation/RankProfilesImporterTest.java
@@ -3,10 +3,8 @@ package ai.vespa.models.evaluation;
 
 import com.yahoo.config.subscription.ConfigGetter;
 import com.yahoo.config.subscription.FileSource;
-import com.yahoo.path.Path;
 import com.yahoo.searchlib.rankingexpression.ExpressionFunction;
 import com.yahoo.vespa.config.search.RankProfilesConfig;
-import com.yahoo.vespa.config.search.core.RankingConstantsConfig;
 import org.junit.Test;
 
 import java.io.File;
@@ -23,41 +21,14 @@ import static org.junit.Assert.assertNotNull;
 public class RankProfilesImporterTest {
 
     @Test
-    public void testImportingModels() {
-        Map<String, Model> models = createModels("src/test/resources/config/models/");
-
-        assertEquals(4, models.size());
-
-        Model xgboost = models.get("xgboost_2_2");
-        assertFunction("xgboost_2_2",
-                       "(optimized sum of condition trees of size 192 bytes)",
-                       xgboost);
-
-        Model onnxMnistSoftmax = models.get("mnist_softmax");
-        assertFunction("default.add",
-                       "join(reduce(join(rename(Placeholder, (d0, d1), (d0, d2)), constant(mnist_softmax_Variable), f(a,b)(a * b)), sum, d2), constant(mnist_softmax_Variable_1), f(a,b)(a + b))",
-                       onnxMnistSoftmax);
-        assertEquals("tensor(d1[10],d2[784])",
-                     onnxMnistSoftmax.evaluatorOf("default.add").context().get("constant(mnist_softmax_Variable)").type().toString());
-
-        Model tfMnistSoftmax = models.get("mnist_softmax_saved");
-        assertFunction("serving_default.y",
-                       "join(reduce(join(rename(Placeholder, (d0, d1), (d0, d2)), constant(mnist_softmax_saved_layer_Variable_read), f(a,b)(a * b)), sum, d2), constant(mnist_softmax_saved_layer_Variable_1_read), f(a,b)(a + b))",
-                       tfMnistSoftmax);
-
-        Model tfMnist = models.get("mnist_saved");
-        assertFunction("serving_default.y",
-                       "join(reduce(join(map(join(reduce(join(join(join(rankingExpression(imported_ml_macro_mnist_saved_dnn_hidden1_add), 0.009999999776482582, f(a,b)(a * b)), rankingExpression(imported_ml_macro_mnist_saved_dnn_hidden1_add), f(a,b)(max(a,b))), constant(mnist_saved_dnn_hidden2_weights_read), f(a,b)(a * b)), sum, d3), constant(mnist_saved_dnn_hidden2_bias_read), f(a,b)(a + b)), f(a)(1.050701 * if (a >= 0, a, 1.673263 * (exp(a) - 1)))), constant(mnist_saved_dnn_outputs_weights_read), f(a,b)(a * b)), sum, d2), constant(mnist_saved_dnn_outputs_bias_read), f(a,b)(a + b))",
-                       tfMnist);
-    }
-
-    @Test
-    public void testImportingRankExpressions() {
-        Map<String, Model> models = createModels("src/test/resources/config/rankexpression/");
-
+    public void testImporting() {
+        String configPath = "src/test/resources/config/rankexpression/rank-profiles.cfg";
+        RankProfilesConfig config = new ConfigGetter<>(new FileSource(new File(configPath)), RankProfilesConfig.class).getConfig("");
+        Map<String, Model> models = new RankProfilesConfigImporter().importFrom(config);
         assertEquals(18, models.size());
 
         Model macros = models.get("macros");
+        assertNotNull(macros);
         assertEquals("macros", macros.name());
         assertEquals(4, macros.functions().size());
         assertFunction("fourtimessum", "4 * (var1 + var2)", macros);
@@ -73,9 +44,8 @@ public class RankProfilesImporterTest {
     }
 
     private void assertFunction(String name, String expression, Model model) {
-        assertNotNull("Model is present in config", model);
         ExpressionFunction function = model.function(name);
-        assertNotNull("Function '" + name + "' is in " + model, function);
+        assertNotNull(function);
         assertEquals(name, function.getName());
         assertEquals(expression, function.getBody().getRoot().toString());
     }
@@ -87,13 +57,4 @@ public class RankProfilesImporterTest {
         assertEquals(expression, function.getBody().getRoot().toString());
     }
 
-    private Map<String, Model> createModels(String path) {
-        Path configDir = Path.fromString(path);
-        RankProfilesConfig config = new ConfigGetter<>(new FileSource(configDir.append("rank-profiles.cfg").toFile()),
-                                                       RankProfilesConfig.class).getConfig("");
-        RankingConstantsConfig constantsConfig = new ConfigGetter<>(new FileSource(configDir.append("ranking-constants.cfg").toFile()),
-                                                                    RankingConstantsConfig.class).getConfig("");
-        return new RankProfilesConfigImporter().importFrom(config, constantsConfig);
-    }
-
 }