Merge pull request #7155 from vespa-engine/bratseth/expose-type-information

Bratseth/expose type information

3 files changed, 50 insertions, 23 deletions

diff --git a/config-model/src/main/java/com/yahoo/searchdefinition/processing/RankingExpressionTypeResolver.java b/config-model/src/main/java/com/yahoo/searchdefinition/processing/RankingExpressionTypeResolver.java
index 4c8b5910b78..3d1ef48c9dd 100644
--- a/config-model/src/main/java/com/yahoo/searchdefinition/processing/RankingExpressionTypeResolver.java
+++ b/config-model/src/main/java/com/yahoo/searchdefinition/processing/RankingExpressionTypeResolver.java
@@ -6,6 +6,7 @@ import com.yahoo.search.query.profile.QueryProfileRegistry;
 import com.yahoo.searchdefinition.RankProfile;
 import com.yahoo.searchdefinition.RankProfileRegistry;
 import com.yahoo.searchdefinition.Search;
+import com.yahoo.searchlib.rankingexpression.ExpressionFunction;
 import com.yahoo.searchlib.rankingexpression.RankingExpression;
 import com.yahoo.searchlib.rankingexpression.Reference;
 import com.yahoo.searchlib.rankingexpression.rule.ExpressionNode;
@@ -60,7 +61,7 @@ public class RankingExpressionTypeResolver extends Processor {
     private void resolveTypesIn(RankProfile profile, boolean validate) {
         TypeContext<Reference> context = profile.typeContext(queryProfiles);
         for (Map.Entry<String, RankProfile.RankingExpressionFunction> function : profile.getFunctions().entrySet()) {
-            if ( ! function.getValue().function().arguments().isEmpty()) continue;
+            if (hasUntypedArguments(function.getValue().function())) continue;
             TensorType type = resolveType(function.getValue().function().getBody(),
                                           "function '" + function.getKey() + "'",
                                           context);
@@ -74,6 +75,10 @@ public class RankingExpressionTypeResolver extends Processor {
         }
     }
 
+    private boolean hasUntypedArguments(ExpressionFunction function) {
+        return function.arguments().size() > function.argumentTypes().size();
+    }
+
     private TensorType resolveType(RankingExpression expression, String expressionDescription, TypeContext context) {
         if (expression == null) return null;
         return resolveType(expression.getRoot(), expressionDescription, context);
diff --git a/config-model/src/test/java/com/yahoo/vespa/model/ml/MlModelsTest.java b/config-model/src/test/java/com/yahoo/vespa/model/ml/MlModelsTest.java
index 9ed82b9eef5..d2d5ecbd5aa 100644
--- a/config-model/src/test/java/com/yahoo/vespa/model/ml/MlModelsTest.java
+++ b/config-model/src/test/java/com/yahoo/vespa/model/ml/MlModelsTest.java
@@ -54,7 +54,7 @@ public class MlModelsTest {
         assertEquals("test", config.rankprofile(2).name());
         RankProfilesConfig.Rankprofile.Fef test = config.rankprofile(2).fef();
 
-        // Compare string content in a denser for that config:
+        // Compare profile content in a denser format than config:
         StringBuilder b = new StringBuilder();
         for (RankProfilesConfig.Rankprofile.Fef.Property p : test.property())
             b.append(p.name()).append(": ").append(p.value()).append("\n");
diff --git a/config-model/src/test/java/com/yahoo/vespa/model/ml/ModelEvaluationTest.java b/config-model/src/test/java/com/yahoo/vespa/model/ml/ModelEvaluationTest.java
index 22bba9dd079..10de10bcdfe 100644
--- a/config-model/src/test/java/com/yahoo/vespa/model/ml/ModelEvaluationTest.java
+++ b/config-model/src/test/java/com/yahoo/vespa/model/ml/ModelEvaluationTest.java
@@ -36,6 +36,27 @@ import static org.junit.Assert.assertTrue;
  */
 public class ModelEvaluationTest {
 
+    /** Tests that we do not load models (which would waste memory) when not requested */
+    @Test
+    public void testMl_serving_not_activated() {
+        Path appDir = Path.fromString("src/test/cfg/application/ml_serving_not_activated");
+        try {
+            ImportedModelTester tester = new ImportedModelTester("ml_serving", appDir);
+            VespaModel model = tester.createVespaModel();
+            ContainerCluster cluster = model.getContainerClusters().get("container");
+            assertNull(cluster.getComponentsMap().get(new ComponentId(ModelsEvaluator.class.getName())));
+
+            RankProfilesConfig.Builder b = new RankProfilesConfig.Builder();
+            cluster.getConfig(b);
+            RankProfilesConfig config = new RankProfilesConfig(b);
+
+            assertEquals(0, config.rankprofile().size());
+        }
+        finally {
+            IOUtils.recursiveDeleteDir(appDir.append(ApplicationPackage.MODELS_GENERATED_DIR).toFile());
+        }
+    }
+
     @Test
     public void testMl_serving() throws IOException {
         Path appDir = Path.fromString("src/test/cfg/application/ml_serving");
@@ -58,27 +79,6 @@ public class ModelEvaluationTest {
         }
     }
 
-    /** Tests that we do not load models (which will waste memory) when not requested */
-    @Test
-    public void testMl_serving_not_activated() {
-        Path appDir = Path.fromString("src/test/cfg/application/ml_serving_not_activated");
-        try {
-            ImportedModelTester tester = new ImportedModelTester("ml_serving", appDir);
-            VespaModel model = tester.createVespaModel();
-            ContainerCluster cluster = model.getContainerClusters().get("container");
-            assertNull(cluster.getComponentsMap().get(new ComponentId(ModelsEvaluator.class.getName())));
-
-            RankProfilesConfig.Builder b = new RankProfilesConfig.Builder();
-            cluster.getConfig(b);
-            RankProfilesConfig config = new RankProfilesConfig(b);
-
-            assertEquals(0, config.rankprofile().size());
-        }
-        finally {
-            IOUtils.recursiveDeleteDir(appDir.append(ApplicationPackage.MODELS_GENERATED_DIR).toFile());
-        }
-    }
-
     private void assertHasMlModels(VespaModel model) {
         ContainerCluster cluster = model.getContainerClusters().get("container");
         assertNotNull(cluster.getComponentsMap().get(new ComponentId(ModelsEvaluator.class.getName())));
@@ -90,6 +90,7 @@ public class ModelEvaluationTest {
         RankProfilesConfig.Builder b = new RankProfilesConfig.Builder();
         cluster.getConfig(b);
         RankProfilesConfig config = new RankProfilesConfig(b);
+        System.out.println(config);
 
         RankingConstantsConfig.Builder cb = new RankingConstantsConfig.Builder();
         cluster.getConfig(cb);
@@ -102,6 +103,12 @@ public class ModelEvaluationTest {
         assertTrue(modelNames.contains("mnist_softmax"));
         assertTrue(modelNames.contains("mnist_softmax_saved"));
 
+        // Compare profile content in a denser format than config:
+        StringBuilder sb = new StringBuilder();
+        for (RankProfilesConfig.Rankprofile.Fef.Property p : findProfile("mnist_saved", config).property())
+            sb.append(p.name()).append(": ").append(p.value()).append("\n");
+        assertEquals(mnistProfile, sb.toString());
+
         ModelsEvaluator evaluator = new ModelsEvaluator(new ToleratingMissingConstantFilesRankProfilesConfigImporter(MockFileAcquirer.returnFile(null))
                                                                 .importFrom(config, constantsConfig));
 
@@ -136,6 +143,21 @@ public class ModelEvaluationTest {
         assertNotNull(tensorflow_mnist_softmax.evaluatorOf("serving_default", "y"));
     }
 
+    private final String mnistProfile =
+            "rankingExpression(imported_ml_function_mnist_saved_dnn_hidden1_add).rankingScript: join(reduce(join(rename(input, (d0, d1), (d0, d4)), constant(mnist_saved_dnn_hidden1_weights_read), f(a,b)(a * b)), sum, d4), constant(mnist_saved_dnn_hidden1_bias_read), f(a,b)(a + b))\n" +
+            "rankingExpression(imported_ml_function_mnist_saved_dnn_hidden1_add).type: tensor(d3[300])\n" +
+            "rankingExpression(serving_default.y).rankingScript: join(reduce(join(map(join(reduce(join(join(join(rankingExpression(imported_ml_function_mnist_saved_dnn_hidden1_add), 0.009999999776482582, f(a,b)(a * b)), rankingExpression(imported_ml_function_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))\n" +
+            "rankingExpression(serving_default.y).input.type: tensor(d0[],d1[784])\n" +
+            "rankingExpression(serving_default.y).type: tensor(d1[10])\n";
+
+    private RankProfilesConfig.Rankprofile.Fef findProfile(String name, RankProfilesConfig config) {
+        for (RankProfilesConfig.Rankprofile profile : config.rankprofile()) {
+            if (profile.name().equals(name))
+                return profile.fef();
+        }
+        throw new IllegalArgumentException("No profile named " + name);
+    }
+
     // We don't have function file distribution so just return empty tensor constants
     private static class ToleratingMissingConstantFilesRankProfilesConfigImporter extends RankProfilesConfigImporter {