Revert "Bratseth/emit float tensors in config"

16 files changed, 52 insertions, 73 deletions

diff --git a/config-model/src/main/java/com/yahoo/config/model/deploy/DeployState.java b/config-model/src/main/java/com/yahoo/config/model/deploy/DeployState.java
index cef0a5d4d8b..7a981cd6a53 100644
--- a/config-model/src/main/java/com/yahoo/config/model/deploy/DeployState.java
+++ b/config-model/src/main/java/com/yahoo/config/model/deploy/DeployState.java
@@ -376,17 +376,15 @@ public class DeployState implements ConfigDefinitionStore {
                     String searchName = builder.importReader(reader, readerName, logger);
                     String sdName = stripSuffix(readerName, ApplicationPackage.SD_NAME_SUFFIX);
                     names.put(searchName, sdName);
-                    if ( ! sdName.equals(searchName)) {
+                    if (!sdName.equals(searchName)) {
                         throw new IllegalArgumentException("Search definition file name ('" + sdName + "') and name of " +
                                                            "search element ('" + searchName +
                                                            "') are not equal for file '" + readerName + "'");
                     }
                 } catch (ParseException e) {
-                    throw new IllegalArgumentException("Could not parse search definition file '" +
-                                                       getSearchDefinitionRelativePath(reader.getName()) + "': " + e.getMessage(), e);
+                    throw new IllegalArgumentException("Could not parse search definition file '" + getSearchDefinitionRelativePath(reader.getName()) + "': " + e.getMessage(), e);
                 } catch (IOException e) {
-                    throw new IllegalArgumentException("Could not read search definition file '" +
-                                                       getSearchDefinitionRelativePath(reader.getName()) + "': " + e.getMessage(), e);
+                    throw new IllegalArgumentException("Could not read search definition file '" + getSearchDefinitionRelativePath(reader.getName()) + "': " + e.getMessage(), e);
                 } finally {
                     closeIgnoreException(reader.getReader());
                 }
diff --git a/config-model/src/main/java/com/yahoo/searchdefinition/SearchBuilder.java b/config-model/src/main/java/com/yahoo/searchdefinition/SearchBuilder.java
index 69cd6f09046..bd4daa58253 100644
--- a/config-model/src/main/java/com/yahoo/searchdefinition/SearchBuilder.java
+++ b/config-model/src/main/java/com/yahoo/searchdefinition/SearchBuilder.java
@@ -171,7 +171,6 @@ public class SearchBuilder {
         } catch (TokenMgrException e) {
             throw new ParseException("Unknown symbol: " + e.getMessage());
         } catch (ParseException pe) {
-            pe.printStackTrace();
             throw new ParseException(stream.formatException(Exceptions.toMessageString(pe)));
         }
         return importRawSearch(search);
diff --git a/config-model/src/main/javacc/SDParser.jj b/config-model/src/main/javacc/SDParser.jj
index b0af54812a4..571ad452b01 100644
--- a/config-model/src/main/javacc/SDParser.jj
+++ b/config-model/src/main/javacc/SDParser.jj
@@ -298,7 +298,6 @@ TOKEN :
 | < FASTSEARCH: "fast-search" >
 | < HUGE: "huge" >
 | < TENSOR_TYPE: "tensor(" (~["(",")"])+ ")" >
-| < TENSOR_TYPE_WITH_VALUE_TYPE: "tensor<" (["a"-"z","A"-"Z"])+ ">(" (~["(",")"])+ ")" >
 | < TENSOR_VALUE_SL: "value" (" ")* ":" (" ")* ("{"<BRACE_SL_LEVEL_1>) ("\n")? >
 | < TENSOR_VALUE_ML: "value" (<SEARCHLIB_SKIP>)? "{" (["\n"," "])* ("{"<BRACE_ML_LEVEL_1>) (["\n"," "])* "}" ("\n")? >
 | < COMPRESSION: "compression" >
@@ -842,7 +841,6 @@ DataType dataType() :
       | LOOKAHEAD(<MAP> <LESSTHAN>)                 ( mapType = mapDataType() { return mapType; } )
       | LOOKAHEAD(<ANNOTATIONREFERENCE> <LESSTHAN>) ( mapType = annotationRefDataType() { return mapType; } )
       | LOOKAHEAD(<TENSOR_TYPE>)                    ( tensorType = tensorType("Field type") { return DataType.getTensor(tensorType); } )
-      | LOOKAHEAD(<TENSOR_TYPE_WITH_VALUE_TYPE>)    ( tensorType = tensorType("Field type") { return DataType.getTensor(tensorType); } )
       | LOOKAHEAD(<REFERENCE>)                      ( <REFERENCE> <LESSTHAN> referenceType = referenceType() <GREATERTHAN> { return ReferenceDataType.createWithInferredId(referenceType); } )
       | ( typeName = identifier() ["[]" { isArrayOldStyle = true; }] )
     )
@@ -2407,7 +2405,7 @@ TensorType tensorType(String errorMessage) :
     String tensorTypeString;
 }
 {
-    ( <TENSOR_TYPE> | <TENSOR_TYPE_WITH_VALUE_TYPE> ) { tensorTypeString = token.image; }
+    <TENSOR_TYPE> { tensorTypeString = token.image; }
     {
         TensorType tensorType;
         try {
diff --git a/config-model/src/test/cfg/application/ml_models/searchdefinitions/test.sd b/config-model/src/test/cfg/application/ml_models/searchdefinitions/test.sd
index ab5e42f983d..d7b3cc76339 100644
--- a/config-model/src/test/cfg/application/ml_models/searchdefinitions/test.sd
+++ b/config-model/src/test/cfg/application/ml_models/searchdefinitions/test.sd
@@ -2,7 +2,7 @@
 search test {
 
     document test {
-        field argument type tensor<float>(d0[],d1[784]) {
+        field argument type tensor(d0[],d1[784]) {
             indexing: attribute
         }
     }
diff --git a/config-model/src/test/derived/tensor/attributes.cfg b/config-model/src/test/derived/tensor/attributes.cfg
index a72772276a8..4b54e67f8b8 100644
--- a/config-model/src/test/derived/tensor/attributes.cfg
+++ b/config-model/src/test/derived/tensor/attributes.cfg
@@ -17,7 +17,7 @@ attribute[].arity 8
 attribute[].lowerbound -9223372036854775808
 attribute[].upperbound 9223372036854775807
 attribute[].densepostinglistthreshold 0.4
-attribute[].tensortype "tensor<float>(x[2],y[1])"
+attribute[].tensortype "tensor(x[2],y[])"
 attribute[].imported false
 attribute[].name "f3"
 attribute[].datatype TENSOR
diff --git a/config-model/src/test/derived/tensor/documenttypes.cfg b/config-model/src/test/derived/tensor/documenttypes.cfg
index fba11509ad8..56818298eb8 100644
--- a/config-model/src/test/derived/tensor/documenttypes.cfg
+++ b/config-model/src/test/derived/tensor/documenttypes.cfg
@@ -29,7 +29,7 @@ documenttype[].datatype[].sstruct.field[].name "f2"
 documenttype[].datatype[].sstruct.field[].id 2080644671
 documenttype[].datatype[].sstruct.field[].id_v6 1424572148
 documenttype[].datatype[].sstruct.field[].datatype 21
-documenttype[].datatype[].sstruct.field[].detailedtype "tensor<float>(x[2],y[1])"
+documenttype[].datatype[].sstruct.field[].detailedtype "tensor(x[2],y[])"
 documenttype[].datatype[].sstruct.field[].name "f3"
 documenttype[].datatype[].sstruct.field[].id 1295091863
 documenttype[].datatype[].sstruct.field[].id_v6 1444109654
diff --git a/config-model/src/test/derived/tensor/rank-profiles.cfg b/config-model/src/test/derived/tensor/rank-profiles.cfg
index 45233f3e1ca..471343da63c 100644
--- a/config-model/src/test/derived/tensor/rank-profiles.cfg
+++ b/config-model/src/test/derived/tensor/rank-profiles.cfg
@@ -1,6 +1,6 @@
 rankprofile[].name "default"
 rankprofile[].fef.property[].name "vespa.type.attribute.f2"
-rankprofile[].fef.property[].value "tensor<float>(x[2],y[1])"
+rankprofile[].fef.property[].value "tensor(x[2],y[])"
 rankprofile[].fef.property[].name "vespa.type.attribute.f3"
 rankprofile[].fef.property[].value "tensor(x{})"
 rankprofile[].fef.property[].name "vespa.type.attribute.f4"
@@ -15,7 +15,7 @@ rankprofile[].fef.property[].value "0"
 rankprofile[].fef.property[].name "vespa.dump.ignoredefaultfeatures"
 rankprofile[].fef.property[].value "true"
 rankprofile[].fef.property[].name "vespa.type.attribute.f2"
-rankprofile[].fef.property[].value "tensor<float>(x[2],y[1])"
+rankprofile[].fef.property[].value "tensor(x[2],y[])"
 rankprofile[].fef.property[].name "vespa.type.attribute.f3"
 rankprofile[].fef.property[].value "tensor(x{})"
 rankprofile[].fef.property[].name "vespa.type.attribute.f4"
@@ -26,7 +26,7 @@ rankprofile[].fef.property[].value "rankingExpression(firstphase)"
 rankprofile[].fef.property[].name "rankingExpression(firstphase).rankingScript"
 rankprofile[].fef.property[].value "reduce(map(attribute(f4), f(x)(x * x)) + reduce(tensor(x[2],y[3])(random), count) * rename(attribute(f4), (x, y), (y, x)), sum)"
 rankprofile[].fef.property[].name "vespa.type.attribute.f2"
-rankprofile[].fef.property[].value "tensor<float>(x[2],y[1])"
+rankprofile[].fef.property[].value "tensor(x[2],y[])"
 rankprofile[].fef.property[].name "vespa.type.attribute.f3"
 rankprofile[].fef.property[].value "tensor(x{})"
 rankprofile[].fef.property[].name "vespa.type.attribute.f4"
@@ -37,7 +37,7 @@ rankprofile[].fef.property[].value "rankingExpression(firstphase)"
 rankprofile[].fef.property[].name "rankingExpression(firstphase).rankingScript"
 rankprofile[].fef.property[].value "reduce(reduce(join(attribute(f4), tensor(x[2],y[2],z[3])((x==y)*(y==z)), f(a,b)(a * b)), sum, x), sum)"
 rankprofile[].fef.property[].name "vespa.type.attribute.f2"
-rankprofile[].fef.property[].value "tensor<float>(x[2],y[1])"
+rankprofile[].fef.property[].value "tensor(x[2],y[])"
 rankprofile[].fef.property[].name "vespa.type.attribute.f3"
 rankprofile[].fef.property[].value "tensor(x{})"
 rankprofile[].fef.property[].name "vespa.type.attribute.f4"
@@ -52,7 +52,7 @@ rankprofile[].fef.property[].value "rankingExpression(firstphase)"
 rankprofile[].fef.property[].name "rankingExpression(firstphase).rankingScript"
 rankprofile[].fef.property[].value "reduce(rankingExpression(joinedtensors), sum)"
 rankprofile[].fef.property[].name "vespa.type.attribute.f2"
-rankprofile[].fef.property[].value "tensor<float>(x[2],y[1])"
+rankprofile[].fef.property[].value "tensor(x[2],y[])"
 rankprofile[].fef.property[].name "vespa.type.attribute.f3"
 rankprofile[].fef.property[].value "tensor(x{})"
 rankprofile[].fef.property[].name "vespa.type.attribute.f4"
diff --git a/config-model/src/test/derived/tensor/tensor.sd b/config-model/src/test/derived/tensor/tensor.sd
index 3dee10b5f8a..e3e8cf43347 100644
--- a/config-model/src/test/derived/tensor/tensor.sd
+++ b/config-model/src/test/derived/tensor/tensor.sd
@@ -5,7 +5,7 @@ search tensor {
     field f1 type tensor(x[]) {
       indexing: summary
     }
-    field f2 type tensor<float>(x[2],y[1]) {
+    field f2 type tensor(x[2],y[]) {
       indexing: attribute
     }
     field f3 type tensor(x{}) {
diff --git a/config-model/src/test/java/com/yahoo/searchdefinition/processing/RankingExpressionWithOnnxTestCase.java b/config-model/src/test/java/com/yahoo/searchdefinition/processing/RankingExpressionWithOnnxTestCase.java
index 094068fc723..8944409e1e9 100644
--- a/config-model/src/test/java/com/yahoo/searchdefinition/processing/RankingExpressionWithOnnxTestCase.java
+++ b/config-model/src/test/java/com/yahoo/searchdefinition/processing/RankingExpressionWithOnnxTestCase.java
@@ -63,7 +63,7 @@ public class RankingExpressionWithOnnxTestCase {
     public void testOnnxReferenceWithConstantFeature() {
         RankProfileSearchFixture search = fixtureWith("constant(mytensor)",
                 "onnx('mnist_softmax.onnx')",
-                "constant mytensor { file: ignored\ntype: tensor<float>(d0[7],d1[784]) }",
+                "constant mytensor { file: ignored\ntype: tensor(d0[7],d1[784]) }",
                 null);
         search.assertFirstPhaseExpression(vespaExpression, "my_profile");
     }
@@ -73,7 +73,7 @@ public class RankingExpressionWithOnnxTestCase {
         String queryProfile = "<query-profile id='default' type='root'/>";
         String queryProfileType =
                 "<query-profile-type id='root'>" +
-                "  <field name='query(mytensor)' type='tensor&lt;float&gt;(d0[3],d1[784])'/>" +
+                "  <field name='query(mytensor)' type='tensor(d0[3],d1[784])'/>" +
                 "</query-profile-type>";
         StoringApplicationPackage application = new StoringApplicationPackage(applicationDir,
                 queryProfile,
@@ -93,7 +93,7 @@ public class RankingExpressionWithOnnxTestCase {
         RankProfileSearchFixture search = fixtureWith("attribute(mytensor)",
                 "onnx('mnist_softmax.onnx')",
                 null,
-                "field mytensor type tensor<float>(d0[],d1[784]) { indexing: attribute }",
+                "field mytensor type tensor(d0[],d1[784]) { indexing: attribute }",
                 "Placeholder",
                 application);
         search.assertFirstPhaseExpression(vespaExpression, "my_profile");
@@ -105,15 +105,15 @@ public class RankingExpressionWithOnnxTestCase {
         String queryProfile = "<query-profile id='default' type='root'/>";
         String queryProfileType =
                 "<query-profile-type id='root'>" +
-                "  <field name='query(mytensor)' type='tensor&lt;float&gt;(d0[3],d1[784],d2[10])'/>" +
+                "  <field name='query(mytensor)' type='tensor(d0[3],d1[784],d2[10])'/>" +
                 "</query-profile-type>";
         StoringApplicationPackage application = new StoringApplicationPackage(applicationDir,
                 queryProfile,
                 queryProfileType);
         RankProfileSearchFixture search = fixtureWith("sum(query(mytensor) * attribute(mytensor) * constant(mytensor),d2)",
                 "onnx('mnist_softmax.onnx')",
-                "constant mytensor { file: ignored\ntype: tensor<float>(d0[7],d1[784]) }",
-                "field mytensor type tensor<float>(d0[],d1[784]) { indexing: attribute }",
+                "constant mytensor { file: ignored\ntype: tensor(d0[7],d1[784]) }",
+                "field mytensor type tensor(d0[],d1[784]) { indexing: attribute }",
                 "Placeholder",
                 application);
         search.assertFirstPhaseExpression(vespaExpression, "my_profile");
@@ -122,7 +122,7 @@ public class RankingExpressionWithOnnxTestCase {
 
     @Test
     public void testNestedOnnxReference() {
-        RankProfileSearchFixture search = fixtureWith("tensor<float>(d0[2],d1[784])(0.0)",
+        RankProfileSearchFixture search = fixtureWith("tensor(d0[2],d1[784])(0.0)",
                 "5 + sum(onnx('mnist_softmax.onnx'))");
         search.assertFirstPhaseExpression("5 + reduce(" + vespaExpression + ", sum)", "my_profile");
     }
@@ -145,7 +145,7 @@ public class RankingExpressionWithOnnxTestCase {
         catch (IllegalArgumentException expected) {
             assertEquals("Rank profile 'my_profile' is invalid: Could not use Onnx model from " +
                             "onnx('mnist_softmax.onnx'): " +
-                            "Model refers input 'Placeholder' of type tensor<float>(d0[],d1[784]) but this function is " +
+                            "Model refers input 'Placeholder' of type tensor(d0[],d1[784]) but this function is " +
                             "not present in rank profile 'my_profile'",
                     Exceptions.toMessageString(expected));
         }
@@ -154,7 +154,7 @@ public class RankingExpressionWithOnnxTestCase {
     @Test
     public void testOnnxReferenceWithWrongFunctionType() {
         try {
-            RankProfileSearchFixture search = fixtureWith("tensor<float>(d0[2],d5[10])(0.0)",
+            RankProfileSearchFixture search = fixtureWith("tensor(d0[2],d5[10])(0.0)",
                     "onnx('mnist_softmax.onnx')");
             search.assertFirstPhaseExpression(vespaExpression, "my_profile");
             fail("Expecting exception");
@@ -162,8 +162,8 @@ public class RankingExpressionWithOnnxTestCase {
         catch (IllegalArgumentException expected) {
             assertEquals("Rank profile 'my_profile' is invalid: Could not use Onnx model from " +
                             "onnx('mnist_softmax.onnx'): " +
-                            "Model refers input 'Placeholder'. The required type of this is tensor<float>(d0[],d1[784]), " +
-                            "but this function returns tensor<float>(d0[2],d5[10])",
+                            "Model refers input 'Placeholder'. The required type of this is tensor(d0[],d1[784]), " +
+                            "but this function returns tensor(d0[2],d5[10])",
                     Exceptions.toMessageString(expected));
         }
     }
@@ -186,7 +186,7 @@ public class RankingExpressionWithOnnxTestCase {
 
     @Test
     public void testImportingFromStoredExpressions() throws IOException {
-        RankProfileSearchFixture search = fixtureWith("tensor<float>(d0[2],d1[784])(0.0)",
+        RankProfileSearchFixture search = fixtureWith("tensor(d0[2],d1[784])(0.0)",
                 "onnx('mnist_softmax.onnx')");
         search.assertFirstPhaseExpression(vespaExpression, "my_profile");
 
@@ -197,7 +197,7 @@ public class RankingExpressionWithOnnxTestCase {
             IOUtils.copyDirectory(applicationDir.append(ApplicationPackage.MODELS_GENERATED_DIR).toFile(),
                     storedApplicationDirectory.append(ApplicationPackage.MODELS_GENERATED_DIR).toFile());
             StoringApplicationPackage storedApplication = new StoringApplicationPackage(storedApplicationDirectory);
-            RankProfileSearchFixture searchFromStored = fixtureWith("tensor<float>(d0[2],d1[784])(0.0)",
+            RankProfileSearchFixture searchFromStored = fixtureWith("tensor(d0[2],d1[784])(0.0)",
                     "onnx('mnist_softmax.onnx')",
                     null,
                     null,
@@ -217,10 +217,10 @@ public class RankingExpressionWithOnnxTestCase {
         String rankProfile =
                 "  rank-profile my_profile {\n" +
                         "    function Placeholder() {\n" +
-                        "      expression: tensor<float>(d0[2],d1[784])(0.0)\n" +
+                        "      expression: tensor(d0[2],d1[784])(0.0)\n" +
                         "    }\n" +
                         "    function " + name + "_Variable() {\n" +
-                        "      expression: tensor<float>(d1[10],d2[784])(0.0)\n" +
+                        "      expression: tensor(d1[10],d2[784])(0.0)\n" +
                         "    }\n" +
                         "    first-phase {\n" +
                         "      expression: onnx('mnist_softmax.onnx')" +
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 d66f376ed6a..c19f1a769de 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
@@ -63,9 +63,9 @@ public class MlModelsTest {
 
     private final String testProfile =
             "rankingExpression(input).rankingScript: attribute(argument)\n" +
-            "rankingExpression(input).type: tensor<float>(d0[],d1[784])\n" +
+            "rankingExpression(input).type: tensor(d0[],d1[784])\n" +
             "rankingExpression(Placeholder).rankingScript: attribute(argument)\n" +
-            "rankingExpression(Placeholder).type: tensor<float>(d0[],d1[784])\n" +
+            "rankingExpression(Placeholder).type: tensor(d0[],d1[784])\n" +
             "rankingExpression(imported_ml_function_mnist_saved_dnn_hidden1_add).rankingScript: join(reduce(join(rename(rankingExpression(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(mnist_tensorflow).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)(1.0507009873554805 * if (a >= 0, a, 1.6732632423543772 * (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(mnist_softmax_tensorflow).rankingScript: join(reduce(join(rename(rankingExpression(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))\n" +
@@ -73,6 +73,6 @@ public class MlModelsTest {
             "rankingExpression(my_xgboost).rankingScript: if (f29 < -0.1234567, if (f56 < -0.242398, 1.71218, -1.70044), if (f109 < 0.8723473, -1.94071, 1.85965)) + if (f60 < -0.482947, if (f29 < -4.2387498, 0.784718, -0.96853), -6.23624)\n" +
             "vespa.rank.firstphase: rankingExpression(firstphase)\n" +
             "rankingExpression(firstphase).rankingScript: rankingExpression(mnist_tensorflow) + rankingExpression(mnist_softmax_tensorflow) + rankingExpression(mnist_softmax_onnx) + rankingExpression(my_xgboost)\n" +
-            "vespa.type.attribute.argument: tensor<float>(d0[],d1[784])\n";
+            "vespa.type.attribute.argument: tensor(d0[],d1[784])\n";
 
 }
diff --git a/model-integration/src/test/java/ai/vespa/rankingexpression/importer/onnx/OnnxMnistSoftmaxImportTestCase.java b/model-integration/src/test/java/ai/vespa/rankingexpression/importer/onnx/OnnxMnistSoftmaxImportTestCase.java
index 35c853bd746..07814687dc6 100644
--- a/model-integration/src/test/java/ai/vespa/rankingexpression/importer/onnx/OnnxMnistSoftmaxImportTestCase.java
+++ b/model-integration/src/test/java/ai/vespa/rankingexpression/importer/onnx/OnnxMnistSoftmaxImportTestCase.java
@@ -31,13 +31,13 @@ public class OnnxMnistSoftmaxImportTestCase {
 
         Tensor constant0 = Tensor.from(model.largeConstants().get("test_Variable"));
         assertNotNull(constant0);
-        assertEquals(new TensorType.Builder(TensorType.Value.FLOAT).indexed("d2", 784).indexed("d1", 10).build(),
+        assertEquals(new TensorType.Builder().indexed("d2", 784).indexed("d1", 10).build(),
                      constant0.type());
         assertEquals(7840, constant0.size());
 
         Tensor constant1 = Tensor.from(model.largeConstants().get("test_Variable_1"));
         assertNotNull(constant1);
-        assertEquals(new TensorType.Builder(TensorType.Value.FLOAT).indexed("d1", 10).build(), constant1.type());
+        assertEquals(new TensorType.Builder().indexed("d1", 10).build(), constant1.type());
         assertEquals(10, constant1.size());
 
         // Check inputs
@@ -52,7 +52,7 @@ public class OnnxMnistSoftmaxImportTestCase {
                      output.expression());
         assertEquals(TensorType.fromSpec("tensor<float>(d0[],d1[784])"),
                      model.inputs().get(model.defaultSignature().inputs().get("Placeholder")));
-        assertEquals("{Placeholder=tensor<float>(d0[],d1[784])}", output.argumentTypes().toString());
+        assertEquals("{Placeholder=tensor(d0[],d1[784])}", output.argumentTypes().toString());
     }
 
     @Test
diff --git a/searchlib/src/main/javacc/RankingExpressionParser.jj b/searchlib/src/main/javacc/RankingExpressionParser.jj
index ea65a508047..c83de4ced0a 100755
--- a/searchlib/src/main/javacc/RankingExpressionParser.jj
+++ b/searchlib/src/main/javacc/RankingExpressionParser.jj
@@ -617,7 +617,7 @@ TensorType.Value optionalTensorValueTypeParameter() :
 }
 {
     ( <LT> valueType = identifier() <GT> )?
-    { return TensorType.Value.fromId(valueType); }
+    { return TensorTypeParser.toValueType(valueType); }
 }
 
 // NOTE: Only indexed bound dimensions are parsed currently, as that is what we need
diff --git a/vespajlib/abi-spec.json b/vespajlib/abi-spec.json
index 65fbf49d334..4f81f3baea8 100644
--- a/vespajlib/abi-spec.json
+++ b/vespajlib/abi-spec.json
@@ -1346,11 +1346,8 @@
     "methods": [
       "public static com.yahoo.tensor.TensorType$Value[] values()",
       "public static com.yahoo.tensor.TensorType$Value valueOf(java.lang.String)",
-      "public java.lang.String id()",
-      "public boolean isEqualOrLargerThan(com.yahoo.tensor.TensorType$Value)",
       "public static com.yahoo.tensor.TensorType$Value largestOf(java.util.List)",
-      "public static com.yahoo.tensor.TensorType$Value largestOf(com.yahoo.tensor.TensorType$Value, com.yahoo.tensor.TensorType$Value)",
-      "public static com.yahoo.tensor.TensorType$Value fromId(java.lang.String)"
+      "public static com.yahoo.tensor.TensorType$Value largestOf(com.yahoo.tensor.TensorType$Value, com.yahoo.tensor.TensorType$Value)"
     ],
     "fields": [
       "public static final enum com.yahoo.tensor.TensorType$Value DOUBLE",
@@ -1393,7 +1390,8 @@
     ],
     "methods": [
       "public void <init>()",
-      "public static com.yahoo.tensor.TensorType fromSpec(java.lang.String)"
+      "public static com.yahoo.tensor.TensorType fromSpec(java.lang.String)",
+      "public static com.yahoo.tensor.TensorType$Value toValueType(java.lang.String)"
     ],
     "fields": []
   },
diff --git a/vespajlib/src/main/java/com/yahoo/tensor/TensorType.java b/vespajlib/src/main/java/com/yahoo/tensor/TensorType.java
index 7f73ef41032..b1c7a2341c0 100644
--- a/vespajlib/src/main/java/com/yahoo/tensor/TensorType.java
+++ b/vespajlib/src/main/java/com/yahoo/tensor/TensorType.java
@@ -29,17 +29,7 @@ public class TensorType {
     public enum Value {
 
         // Types added must also be added to TensorTypeParser.parseValueTypeSpec, serialization, and largestOf below
-        DOUBLE("double"), FLOAT("float");
-
-        private final String id;
-
-        Value(String id) { this.id = id; }
-
-        public String id() { return id; }
-
-        public boolean isEqualOrLargerThan(TensorType.Value other) {
-            return this == other || largestOf(this, other) == this;
-        }
+        DOUBLE, FLOAT;
 
         public static Value largestOf(List<Value> values) {
             if (values.isEmpty()) return Value.DOUBLE; // Default
@@ -58,15 +48,6 @@ public class TensorType {
             return FLOAT;
         }
 
-        public static Value fromId(String valueTypeString) {
-            switch (valueTypeString) {
-                case "double" : return Value.DOUBLE;
-                case "float" : return Value.FLOAT;
-                default : throw new IllegalArgumentException("Value type must be either 'double' or 'float'" +
-                                                             " but was '" + valueTypeString + "'");
-            }
-        }
-
     };
 
     /** The empty tensor type - which is the same as a double */
@@ -162,7 +143,7 @@ public class TensorType {
     }
 
     private boolean isConvertibleOrAssignableTo(TensorType generalization, boolean convertible, boolean considerName) {
-        if ( ! generalization.valueType().isEqualOrLargerThan(this.valueType) ) return false;
+        if ( this.valueType() != generalization.valueType()) return false; // TODO: This can be relaxed
         if (generalization.dimensions().size() != this.dimensions().size()) return false;
         for (int i = 0; i < generalization.dimensions().size(); i++) {
             Dimension thisDimension = this.dimensions().get(i);
@@ -184,9 +165,7 @@ public class TensorType {
 
     @Override
     public String toString() {
-        return "tensor" +
-               (valueType == Value.DOUBLE ? "" : "<" + valueType.id() + ">") +
-               "(" + dimensions.stream().map(Dimension::toString).collect(Collectors.joining(",")) + ")";
+        return "tensor(" + dimensions.stream().map(Dimension::toString).collect(Collectors.joining(",")) + ")";
     }
 
     @Override
@@ -251,7 +230,7 @@ public class TensorType {
 
     @Override
     public int hashCode() {
-        return Objects.hash(dimensions, valueType);
+        return dimensions.hashCode();
     }
 
     /**
diff --git a/vespajlib/src/main/java/com/yahoo/tensor/TensorTypeParser.java b/vespajlib/src/main/java/com/yahoo/tensor/TensorTypeParser.java
index ba23868381c..d5f77be0dd0 100644
--- a/vespajlib/src/main/java/com/yahoo/tensor/TensorTypeParser.java
+++ b/vespajlib/src/main/java/com/yahoo/tensor/TensorTypeParser.java
@@ -55,12 +55,21 @@ public class TensorTypeParser {
         return new TensorType.Builder(valueType, dimensions).build();
     }
 
+    public static TensorType.Value toValueType(String valueTypeString) {
+        switch (valueTypeString) {
+            case "double" : return TensorType.Value.DOUBLE;
+            case "float" : return TensorType.Value.FLOAT;
+            default : throw new IllegalArgumentException("Value type must be either 'double' or 'float'" +
+                                                         " but was '" + valueTypeString + "'");
+        }
+    }
+
     private static TensorType.Value parseValueTypeSpec(String valueTypeSpec, String fullSpecString) {
         if ( ! valueTypeSpec.startsWith("<") || ! valueTypeSpec.endsWith(">"))
             throw formatException(fullSpecString, Optional.of("Value type spec must be enclosed in <>"));
 
         try {
-            return TensorType.Value.fromId(valueTypeSpec.substring(1, valueTypeSpec.length() - 1));
+            return toValueType(valueTypeSpec.substring(1, valueTypeSpec.length() - 1));
         }
         catch (IllegalArgumentException e) {
             throw formatException(fullSpecString, e.getMessage());
diff --git a/vespajlib/src/test/java/com/yahoo/tensor/TensorTypeTestCase.java b/vespajlib/src/test/java/com/yahoo/tensor/TensorTypeTestCase.java
index a547f941d8e..d3bb702175a 100644
--- a/vespajlib/src/test/java/com/yahoo/tensor/TensorTypeTestCase.java
+++ b/vespajlib/src/test/java/com/yahoo/tensor/TensorTypeTestCase.java
@@ -96,8 +96,6 @@ public class TensorTypeTestCase {
         assertValueType(TensorType.Value.DOUBLE, "tensor(x[])");
         assertValueType(TensorType.Value.DOUBLE, "tensor<double>(x[])");
         assertValueType(TensorType.Value.FLOAT, "tensor<float>(x[])");
-        assertEquals("tensor(x[])", TensorType.fromSpec("tensor<double>(x[])").toString());
-        assertEquals("tensor<float>(x[])", TensorType.fromSpec("tensor<float>(x[])").toString());
     }
 
     private static void assertTensorType(String typeSpec) {