Use well defined order where we output text and generate config. Makes config stable and simple tests predictable.

4 files changed, 11 insertions, 11 deletions

diff --git a/config-model/src/main/java/com/yahoo/schema/derived/RawRankProfile.java b/config-model/src/main/java/com/yahoo/schema/derived/RawRankProfile.java
index 59f4035f34f..14ee60bb9a6 100644
--- a/config-model/src/main/java/com/yahoo/schema/derived/RawRankProfile.java
+++ b/config-model/src/main/java/com/yahoo/schema/derived/RawRankProfile.java
@@ -20,7 +20,6 @@ import com.yahoo.searchlib.rankingexpression.Reference;
 import com.yahoo.searchlib.rankingexpression.parser.ParseException;
 import com.yahoo.searchlib.rankingexpression.rule.ReferenceNode;
 import com.yahoo.searchlib.rankingexpression.rule.SerializationContext;
-import com.yahoo.tensor.TensorType;
 import com.yahoo.vespa.config.search.RankProfilesConfig;
 
 import java.nio.ByteBuffer;
@@ -196,7 +195,7 @@ public class RawRankProfile implements RankProfilesConfig.Producer {
             rankProperties = new ArrayList<>(compiled.getRankProperties());
 
             Map<String, RankProfile.RankingExpressionFunction> functions = compiled.getFunctions();
-            List<ExpressionFunction> functionExpressions = functions.values().stream().map(f -> f.function()).collect(Collectors.toList());
+            List<ExpressionFunction> functionExpressions = functions.values().stream().map(RankProfile.RankingExpressionFunction::function).collect(Collectors.toList());
             Map<String, String> functionProperties = new LinkedHashMap<>();
             SerializationContext functionSerializationContext = new SerializationContext(functionExpressions,
                                                                                          Map.of(),
@@ -248,8 +247,8 @@ public class RawRankProfile implements RankProfilesConfig.Producer {
                 String expressionString = e.getValue().function().getBody().getRoot().toString(context).toString();
 
                 context.addFunctionSerialization(propertyName, expressionString);
-                for (Map.Entry<String, TensorType> argumentType : e.getValue().function().argumentTypes().entrySet())
-                    context.addArgumentTypeSerialization(e.getKey(), argumentType.getKey(), argumentType.getValue());
+                e.getValue().function().argumentTypes().entrySet().stream().sorted(Map.Entry.comparingByKey())
+                        .forEach(argumentType -> context.addArgumentTypeSerialization(e.getKey(), argumentType.getKey(), argumentType.getValue()));
                 if (e.getValue().function().returnType().isPresent())
                     context.addFunctionTypeSerialization(e.getKey(), e.getValue().function().returnType().get());
                 // else if (e.getValue().function().arguments().isEmpty()) TODO: Enable this check when we resolve all types
diff --git a/config-model/src/main/java/com/yahoo/schema/processing/RankingExpressionTypeResolver.java b/config-model/src/main/java/com/yahoo/schema/processing/RankingExpressionTypeResolver.java
index 3e7a1f7613b..871b79a7737 100644
--- a/config-model/src/main/java/com/yahoo/schema/processing/RankingExpressionTypeResolver.java
+++ b/config-model/src/main/java/com/yahoo/schema/processing/RankingExpressionTypeResolver.java
@@ -73,7 +73,7 @@ public class RankingExpressionTypeResolver extends Processor {
             for (String argument : expressionFunction.arguments()) {
                 Reference ref = Reference.fromIdentifier(argument);
                 if (context.getType(ref).equals(TensorType.empty)) {
-                    context.setType(ref, expressionFunction.argumentTypes().get(argument));
+                    context.setType(ref, expressionFunction.getArgumentType(argument));
                 }
             }
             context.forgetResolvedTypes();
diff --git a/config-model/src/test/java/com/yahoo/schema/processing/VespaMlModelTestCase.java b/config-model/src/test/java/com/yahoo/schema/processing/VespaMlModelTestCase.java
index dc72df9fc78..01e80e0f47a 100644
--- a/config-model/src/test/java/com/yahoo/schema/processing/VespaMlModelTestCase.java
+++ b/config-model/src/test/java/com/yahoo/schema/processing/VespaMlModelTestCase.java
@@ -28,11 +28,12 @@ public class VespaMlModelTestCase {
             "constant(constant1).type : tensor(x[3])\n" +
             "constant(constant1).value : tensor(x[3]):[0.5, 1.5, 2.5]\n" +
             "rankingExpression(foo1).rankingScript : reduce(reduce(input1 * input2, sum, name) * constant(constant1), max, x) * 3.0\n" +
-            "rankingExpression(foo1).input2.type : tensor(x[3])\n" +
             "rankingExpression(foo1).input1.type : tensor(name{},x[3])\n" +
+            "rankingExpression(foo1).input2.type : tensor(x[3])\n" +
             "rankingExpression(foo2).rankingScript : reduce(reduce(input1 * input2, sum, name) * constant(constant1asLarge), max, x) * 3.0\n" +
-            "rankingExpression(foo2).input2.type : tensor(x[3])\n" +
-            "rankingExpression(foo2).input1.type : tensor(name{},x[3])\n";
+            "rankingExpression(foo2).input1.type : tensor(name{},x[3])\n" +
+            "rankingExpression(foo2).input2.type : tensor(x[3])\n";
+
 
     /** The model name */
     private final String name = "example";
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 f4d37cc4b35..caf0d22d44e 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
@@ -153,8 +153,8 @@ public class ModelEvaluationTest {
         assertNotNull(evaluator.evaluatorOf("add_mul", "default.output2"));
         assertNotNull(evaluator.evaluatorOf("add_mul", "default", "output1"));
         assertNotNull(evaluator.evaluatorOf("add_mul", "default", "output2"));
-        assertEquals(TensorType.fromSpec("tensor<float>(d0[1])"), add_mul.functions().get(0).argumentTypes().get("input1"));
-        assertEquals(TensorType.fromSpec("tensor<float>(d0[1])"), add_mul.functions().get(0).argumentTypes().get("input2"));
+        assertEquals(TensorType.fromSpec("tensor<float>(d0[1])"), add_mul.functions().get(0).getArgumentType("input1"));
+        assertEquals(TensorType.fromSpec("tensor<float>(d0[1])"), add_mul.functions().get(0).getArgumentType("input2"));
 
         Model sqrt = evaluator.models().get("sqrt");
         assertNotNull(sqrt);
@@ -163,7 +163,7 @@ public class ModelEvaluationTest {
         assertNotNull(sqrt.evaluatorOf("out_layer_1_1"));  // converted from "out/layer/1:1"
         assertNotNull(evaluator.evaluatorOf("sqrt"));
         assertNotNull(evaluator.evaluatorOf("sqrt", "out_layer_1_1"));
-        assertEquals(TensorType.fromSpec("tensor<float>(d0[1])"), sqrt.functions().get(0).argumentTypes().get("input"));
+        assertEquals(TensorType.fromSpec("tensor<float>(d0[1])"), sqrt.functions().get(0).getArgumentType("input"));
     }
 
     private final String profile =