Prefix constants by model name

Large constants are cross rank profiles. This avoids name
conflicts when multiple models are used. It is not strictly
necessary because the user can always disambiguate when chosing
names, but there is a scenario where conflicts are plausible and
leaving this to users is inconvenient: Multiple versions of the
"same" model are tested in different rank profiles.

3 files changed, 49 insertions, 45 deletions

diff --git a/config-model/src/main/java/com/yahoo/searchdefinition/expressiontransforms/TensorFlowFeatureConverter.java b/config-model/src/main/java/com/yahoo/searchdefinition/expressiontransforms/TensorFlowFeatureConverter.java
index df48321d87a..83eb25d9dc2 100644
--- a/config-model/src/main/java/com/yahoo/searchdefinition/expressiontransforms/TensorFlowFeatureConverter.java
+++ b/config-model/src/main/java/com/yahoo/searchdefinition/expressiontransforms/TensorFlowFeatureConverter.java
@@ -53,7 +53,6 @@ import java.util.List;
 import java.util.Map;
 import java.util.Optional;
 import java.util.Set;
-import java.util.logging.Logger;
 import java.util.stream.Collectors;
 
 /**
@@ -63,11 +62,8 @@ import java.util.stream.Collectors;
  *
  * @author bratseth
  */
-// TODO: Avoid name conflicts across models for constants
 public class TensorFlowFeatureConverter extends ExpressionTransformer<RankProfileTransformContext> {
 
-    private static final Logger log = Logger.getLogger(TensorFlowFeatureConverter.class.getName());
-
     private final TensorFlowImporter tensorFlowImporter = new TensorFlowImporter();
 
     /** A cache of imported models indexed by model path. This avoids importing the same model multiple times. */
@@ -87,8 +83,7 @@ public class TensorFlowFeatureConverter extends ExpressionTransformer<RankProfil
         if ( ! feature.getName().equals("tensorflow")) return feature;
 
         try {
-            ModelStore store = new ModelStore(context.rankProfile().getSearch().sourceApplication(),
-                                              feature.getArguments());
+            ModelStore store = new ModelStore(context.rankProfile().getSearch().sourceApplication(), feature.getArguments());
             if ( ! store.hasStoredModel()) // not converted yet - access TensorFlow model files
                 return transformFromTensorFlowModel(store, context.rankProfile(), context.queryProfiles());
             else
@@ -103,7 +98,8 @@ public class TensorFlowFeatureConverter extends ExpressionTransformer<RankProfil
                                                         RankProfile profile,
                                                         QueryProfileRegistry queryProfiles) {
         TensorFlowModel model = importedModels.computeIfAbsent(store.arguments().modelPath(),
-                                                               k -> tensorFlowImporter.importModel(store.tensorFlowModelDir()));
+                                                               k -> tensorFlowImporter.importModel(store.arguments().modelName(),
+                                                                                                   store.tensorFlowModelDir()));
 
         // Add constants
         Set<String> constantsReplacedByMacros = new HashSet<>();
@@ -213,9 +209,7 @@ public class TensorFlowFeatureConverter extends ExpressionTransformer<RankProfil
             constantsReplacedByMacros.add(constantName); // will replace constant(constantName) by constantName later
         }
         else {
-
             Path constantPath = store.writeLargeConstant(constantName, constantValue);
-
             if ( ! profile.getSearch().getRankingConstants().containsKey(constantName)) {
                 profile.getSearch().addRankingConstant(new RankingConstant(constantName, constantValue.type(),
                                                                            constantPath.toString()));
@@ -310,7 +304,7 @@ public class TensorFlowFeatureConverter extends ExpressionTransformer<RankProfil
             RankProfile.Macro macro = profile.getMacros().get(macroName);
             if (macro == null) {
                 throw new IllegalArgumentException("Model refers to generated macro '" + macroName +
-                        "but this macro is not present in " + profile);
+                                                   "but this macro is not present in " + profile);
             }
             RankingExpression macroExpression = macro.getRankingExpression();
             macroExpression.setRoot(reduceBatchDimensionsAtInput(macroExpression.getRoot(), model, typeContext));
@@ -465,6 +459,8 @@ public class TensorFlowFeatureConverter extends ExpressionTransformer<RankProfil
             this.arguments = new FeatureArguments(arguments);
         }
 
+
+
         public FeatureArguments arguments() { return arguments; }
 
         public boolean hasStoredModel() {
@@ -651,6 +647,9 @@ public class TensorFlowFeatureConverter extends ExpressionTransformer<RankProfil
             output = optionalArgument(2, arguments);
         }
 
+        /** Returns modelPath with slashes replaced by underscores */
+        public String modelName() { return modelPath.toString().replace('/', '_'); }
+
         /** Returns relative path to this model below the "models/" dir in the application package */
         public Path modelPath() { return modelPath; }
         public Optional<String> signature() { return signature; }
diff --git a/config-model/src/main/java/com/yahoo/vespa/model/content/storagecluster/FileStorProducer.java b/config-model/src/main/java/com/yahoo/vespa/model/content/storagecluster/FileStorProducer.java
index b678f624dc7..b4faa6eeb7e 100644
--- a/config-model/src/main/java/com/yahoo/vespa/model/content/storagecluster/FileStorProducer.java
+++ b/config-model/src/main/java/com/yahoo/vespa/model/content/storagecluster/FileStorProducer.java
@@ -13,6 +13,7 @@ import java.util.List;
  * Serves stor-filestor for storage clusters.
  */
 public class FileStorProducer implements StorFilestorConfig.Producer {
+
     public static class Builder {
         protected FileStorProducer build(ContentCluster parent, ModelElement clusterElem) {
             return new FileStorProducer(parent, getThreads(clusterElem));
diff --git a/config-model/src/test/java/com/yahoo/searchdefinition/processing/RankingExpressionWithTensorFlowTestCase.java b/config-model/src/test/java/com/yahoo/searchdefinition/processing/RankingExpressionWithTensorFlowTestCase.java
index 90a20746a0a..ff2da744d46 100644
--- a/config-model/src/test/java/com/yahoo/searchdefinition/processing/RankingExpressionWithTensorFlowTestCase.java
+++ b/config-model/src/test/java/com/yahoo/searchdefinition/processing/RankingExpressionWithTensorFlowTestCase.java
@@ -42,7 +42,7 @@ import static org.junit.Assert.*;
 public class RankingExpressionWithTensorFlowTestCase {
 
     private final Path applicationDir = Path.fromString("src/test/integration/tensorflow/");
-    private final String vespaExpression = "join(reduce(join(rename(Placeholder, (d0, d1), (d0, d2)), constant(layer_Variable_read), f(a,b)(a * b)), sum, d2), constant(layer_Variable_1_read), f(a,b)(a + b))";
+    private final String vespaExpression = "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))";
 
     @After
     public void removeGeneratedConstantTensorFiles() {
@@ -54,8 +54,8 @@ public class RankingExpressionWithTensorFlowTestCase {
         RankProfileSearchFixture search = fixtureWith("tensor(d0[2],d1[784])(0.0)",
                                                       "tensorflow('mnist_softmax/saved')");
         search.assertFirstPhaseExpression(vespaExpression, "my_profile");
-        assertLargeConstant("layer_Variable_1_read", search, Optional.of(10L));
-        assertLargeConstant("layer_Variable_read", search, Optional.of(7840L));
+        assertLargeConstant("mnist_softmax_saved_layer_Variable_1_read", search, Optional.of(10L));
+        assertLargeConstant("mnist_softmax_saved_layer_Variable_read", search, Optional.of(7840L));
     }
 
     @Test
@@ -65,8 +65,8 @@ public class RankingExpressionWithTensorFlowTestCase {
                                                       "constant mytensor { file: ignored\ntype: tensor(d0[7],d1[784]) }",
                                                       null);
         search.assertFirstPhaseExpression(vespaExpression, "my_profile");
-        assertLargeConstant("layer_Variable_1_read", search, Optional.of(10L));
-        assertLargeConstant("layer_Variable_read", search, Optional.of(7840L));
+        assertLargeConstant("mnist_softmax_saved_layer_Variable_1_read", search, Optional.of(10L));
+        assertLargeConstant("mnist_softmax_saved_layer_Variable_read", search, Optional.of(7840L));
     }
 
     @Test
@@ -85,8 +85,8 @@ public class RankingExpressionWithTensorFlowTestCase {
                                                       "Placeholder",
                                                       application);
         search.assertFirstPhaseExpression(vespaExpression, "my_profile");
-        assertLargeConstant("layer_Variable_1_read", search, Optional.of(10L));
-        assertLargeConstant("layer_Variable_read", search, Optional.of(7840L));
+        assertLargeConstant("mnist_softmax_saved_layer_Variable_1_read", search, Optional.of(10L));
+        assertLargeConstant("mnist_softmax_saved_layer_Variable_read", search, Optional.of(7840L));
     }
 
     @Test
@@ -99,8 +99,8 @@ public class RankingExpressionWithTensorFlowTestCase {
                                                       "Placeholder",
                                                       application);
         search.assertFirstPhaseExpression(vespaExpression, "my_profile");
-        assertLargeConstant("layer_Variable_1_read", search, Optional.of(10L));
-        assertLargeConstant("layer_Variable_read", search, Optional.of(7840L));
+        assertLargeConstant("mnist_softmax_saved_layer_Variable_1_read", search, Optional.of(10L));
+        assertLargeConstant("mnist_softmax_saved_layer_Variable_read", search, Optional.of(7840L));
     }
 
     @Test
@@ -119,8 +119,8 @@ public class RankingExpressionWithTensorFlowTestCase {
                                                       "Placeholder",
                                                       application);
         search.assertFirstPhaseExpression(vespaExpression, "my_profile");
-        assertLargeConstant("layer_Variable_1_read", search, Optional.of(10L));
-        assertLargeConstant("layer_Variable_read", search, Optional.of(7840L));
+        assertLargeConstant("mnist_softmax_saved_layer_Variable_1_read", search, Optional.of(10L));
+        assertLargeConstant("mnist_softmax_saved_layer_Variable_read", search, Optional.of(7840L));
     }
 
     @Test
@@ -128,8 +128,8 @@ public class RankingExpressionWithTensorFlowTestCase {
         RankProfileSearchFixture search = fixtureWith("tensor(d0[2],d1[784])(0.0)",
                                                       "5 + sum(tensorflow('mnist_softmax/saved'))");
         search.assertFirstPhaseExpression("5 + reduce(" + vespaExpression + ", sum)", "my_profile");
-        assertLargeConstant("layer_Variable_1_read", search, Optional.of(10L));
-        assertLargeConstant("layer_Variable_read", search, Optional.of(7840L));
+        assertLargeConstant("mnist_softmax_saved_layer_Variable_1_read", search, Optional.of(10L));
+        assertLargeConstant("mnist_softmax_saved_layer_Variable_read", search, Optional.of(7840L));
     }
 
     @Test
@@ -224,8 +224,8 @@ public class RankingExpressionWithTensorFlowTestCase {
                                                       "tensorflow('mnist_softmax/saved')");
         search.assertFirstPhaseExpression(vespaExpression, "my_profile");
 
-        assertLargeConstant("layer_Variable_1_read", search, Optional.of(10L));
-        assertLargeConstant("layer_Variable_read", search, Optional.of(7840L));
+        assertLargeConstant("mnist_softmax_saved_layer_Variable_1_read", search, Optional.of(10L));
+        assertLargeConstant("mnist_softmax_saved_layer_Variable_read", search, Optional.of(7840L));
 
         // At this point the expression is stored - copy application to another location which do not have a models dir
         Path storedApplicationDirectory = applicationDir.getParentPath().append("copy");
@@ -243,8 +243,8 @@ public class RankingExpressionWithTensorFlowTestCase {
             searchFromStored.assertFirstPhaseExpression(vespaExpression, "my_profile");
             // Verify that the constants exists, but don't verify the content as we are not
             // simulating file distribution in this test
-            assertLargeConstant("layer_Variable_1_read", searchFromStored, Optional.empty());
-            assertLargeConstant("layer_Variable_read", searchFromStored, Optional.empty());
+            assertLargeConstant("mnist_softmax_saved_layer_Variable_1_read", searchFromStored, Optional.empty());
+            assertLargeConstant("mnist_softmax_saved_layer_Variable_read", searchFromStored, Optional.empty());
         }
         finally {
             IOUtils.recursiveDeleteDir(storedApplicationDirectory.toFile());
@@ -258,7 +258,7 @@ public class RankingExpressionWithTensorFlowTestCase {
                 "    macro Placeholder() {\n" +
                 "      expression: tensor(d0[2],d1[784])(0.0)\n" +
                 "    }\n" +
-                "    macro layer_Variable_read() {\n" +
+                "    macro mnist_softmax_saved_layer_Variable_read() {\n" +
                 "      expression: tensor(d1[10],d2[784])(0.0)\n" +
                 "    }\n" +
                 "    first-phase {\n" +
@@ -268,13 +268,13 @@ public class RankingExpressionWithTensorFlowTestCase {
 
 
         String vespaExpressionWithoutConstant =
-                "join(reduce(join(rename(Placeholder, (d0, d1), (d0, d2)), layer_Variable_read, f(a,b)(a * b)), sum, d2), constant(layer_Variable_1_read), f(a,b)(a + b))";
+                "join(reduce(join(rename(Placeholder, (d0, d1), (d0, d2)), 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))";
         RankProfileSearchFixture search = fixtureWith(rankProfile, new StoringApplicationPackage(applicationDir));
         search.assertFirstPhaseExpression(vespaExpressionWithoutConstant, "my_profile");
 
         assertNull("Constant overridden by macro is not added",
-                   search.search().getRankingConstants().get("layer_Variable_read"));
-        assertLargeConstant("layer_Variable_1_read", search, Optional.of(10L));
+                   search.search().getRankingConstants().get("mnist_softmax_saved_layer_Variable_read"));
+        assertLargeConstant("mnist_softmax_saved_layer_Variable_1_read", search, Optional.of(10L));
 
         // At this point the expression is stored - copy application to another location which do not have a models dir
         Path storedApplicationDirectory = applicationDir.getParentPath().append("copy");
@@ -286,8 +286,8 @@ public class RankingExpressionWithTensorFlowTestCase {
             RankProfileSearchFixture searchFromStored = fixtureWith(rankProfile, storedApplication);
             searchFromStored.assertFirstPhaseExpression(vespaExpressionWithoutConstant, "my_profile");
             assertNull("Constant overridden by macro is not added",
-                       searchFromStored.search().getRankingConstants().get("layer_Variable_read"));
-            assertLargeConstant("layer_Variable_1_read", searchFromStored, Optional.of(10L));
+                       searchFromStored.search().getRankingConstants().get("mnist_softmax_saved_layer_Variable_read"));
+            assertLargeConstant("mnist_softmax_saved_layer_Variable_1_read", searchFromStored, Optional.of(10L));
         }
         finally {
             IOUtils.recursiveDeleteDir(storedApplicationDirectory.toFile());
@@ -296,22 +296,26 @@ public class RankingExpressionWithTensorFlowTestCase {
 
     @Test
     public void testTensorFlowReduceBatchDimension() {
-        final String expression = "join(join(reduce(join(reduce(rename(Placeholder, (d0, d1), (d0, d2)), sum, d0), constant(layer_Variable_read), f(a,b)(a * b)), sum, d2), constant(layer_Variable_1_read), f(a,b)(a + b)), tensor(d0[1])(0.0), f(a,b)(a + b))";
+        final String expression = "join(join(reduce(join(reduce(rename(Placeholder, (d0, d1), (d0, d2)), sum, d0), 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)), tensor(d0[1])(0.0), f(a,b)(a + b))";
         RankProfileSearchFixture search = fixtureWith("tensor(d0[1],d1[784])(0.0)",
                 "tensorflow('mnist_softmax/saved')");
         search.assertFirstPhaseExpression(expression, "my_profile");
-        assertLargeConstant("layer_Variable_1_read", search, Optional.of(10L));
-        assertLargeConstant("layer_Variable_read", search, Optional.of(7840L));
+        assertLargeConstant("mnist_softmax_saved_layer_Variable_1_read", search, Optional.of(10L));
+        assertLargeConstant("mnist_softmax_saved_layer_Variable_read", search, Optional.of(7840L));
     }
 
     @Test
     public void testMacroGeneration() {
-        final String expression = "join(join(reduce(join(join(join(tf_macro_dnn_hidden2_add, reduce(constant(dnn_hidden2_Const), sum, d2), f(a,b)(a * b)), tf_macro_dnn_hidden2_add, f(a,b)(max(a,b))), constant(dnn_outputs_weights_read), f(a,b)(a * b)), sum, d2), constant(dnn_outputs_bias_read), f(a,b)(a + b)), tensor(d0[1])(0.0), f(a,b)(a + b))";
-        final String macroExpression1 = "join(reduce(join(reduce(rename(input, (d0, d1), (d0, d4)), sum, d0), constant(dnn_hidden1_weights_read), f(a,b)(a * b)), sum, d4), constant(dnn_hidden1_bias_read), f(a,b)(a + b))";
-        final String macroExpression2 = "join(reduce(join(join(join(tf_macro_dnn_hidden1_add, 0.009999999776482582, f(a,b)(a * b)), tf_macro_dnn_hidden1_add, f(a,b)(max(a,b))), constant(dnn_hidden2_weights_read), f(a,b)(a * b)), sum, d3), constant(dnn_hidden2_bias_read), f(a,b)(a + b))";
+        final String expression = "join(join(reduce(join(join(join(tf_macro_dnn_hidden2_add, reduce(constant(mnist_saved_dnn_hidden2_Const), sum, d2), f(a,b)(a * b)), tf_macro_dnn_hidden2_add, f(a,b)(max(a,b))), 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)), tensor(d0[1])(0.0), f(a,b)(a + b))";
+        final String macroExpression1 = "join(reduce(join(reduce(rename(input, (d0, d1), (d0, d4)), sum, d0), 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))";
+        final String macroExpression2 = "join(reduce(join(join(join(tf_macro_dnn_hidden1_add, 0.009999999776482582, f(a,b)(a * b)), tf_macro_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))";
 
         RankProfileSearchFixture search = fixtureWith("tensor(d0[1],d1[784])(0.0)",
-                "tensorflow('mnist/saved')", null, null, "input", new StoringApplicationPackage(applicationDir));
+                                    "tensorflow('mnist/saved')",
+                                                      null,
+                                                      null,
+                                                      "input",
+                                                      new StoringApplicationPackage(applicationDir));
         search.assertFirstPhaseExpression(expression, "my_profile");
         search.assertMacro(macroExpression1, "tf_macro_dnn_hidden1_add", "my_profile");
         search.assertMacro(macroExpression2, "tf_macro_dnn_hidden2_add", "my_profile");
@@ -319,9 +323,9 @@ public class RankingExpressionWithTensorFlowTestCase {
 
     @Test
     public void testImportingFromStoredExpressionsWithSmallConstants() throws IOException {
-        final String expression = "join(join(reduce(join(join(join(tf_macro_dnn_hidden2_add, reduce(constant(dnn_hidden2_Const), sum, d2), f(a,b)(a * b)), tf_macro_dnn_hidden2_add, f(a,b)(max(a,b))), constant(dnn_outputs_weights_read), f(a,b)(a * b)), sum, d2), constant(dnn_outputs_bias_read), f(a,b)(a + b)), tensor(d0[1])(0.0), f(a,b)(a + b))";
-        final String macroExpression1 = "join(reduce(join(reduce(rename(input, (d0, d1), (d0, d4)), sum, d0), constant(dnn_hidden1_weights_read), f(a,b)(a * b)), sum, d4), constant(dnn_hidden1_bias_read), f(a,b)(a + b))";
-        final String macroExpression2 = "join(reduce(join(join(join(tf_macro_dnn_hidden1_add, 0.009999999776482582, f(a,b)(a * b)), tf_macro_dnn_hidden1_add, f(a,b)(max(a,b))), constant(dnn_hidden2_weights_read), f(a,b)(a * b)), sum, d3), constant(dnn_hidden2_bias_read), f(a,b)(a + b))";
+        final String expression = "join(join(reduce(join(join(join(tf_macro_dnn_hidden2_add, reduce(constant(mnist_saved_dnn_hidden2_Const), sum, d2), f(a,b)(a * b)), tf_macro_dnn_hidden2_add, f(a,b)(max(a,b))), 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)), tensor(d0[1])(0.0), f(a,b)(a + b))";
+        final String macroExpression1 = "join(reduce(join(reduce(rename(input, (d0, d1), (d0, d4)), sum, d0), 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))";
+        final String macroExpression2 = "join(reduce(join(join(join(tf_macro_dnn_hidden1_add, 0.009999999776482582, f(a,b)(a * b)), tf_macro_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))";
 
         StoringApplicationPackage application = new StoringApplicationPackage(applicationDir);
         RankProfileSearchFixture search = fixtureWith("tensor(d0[1],d1[784])(0.0)",
@@ -331,7 +335,7 @@ public class RankingExpressionWithTensorFlowTestCase {
                 "input",
                 application);
         search.assertFirstPhaseExpression(expression, "my_profile");
-        assertSmallConstant("dnn_hidden2_Const", TensorType.fromSpec("tensor(d2[1])"), search);
+        assertSmallConstant("mnist_saved_dnn_hidden2_Const", TensorType.fromSpec("tensor(d2[1])"), search);
         search.assertMacro(macroExpression1, "tf_macro_dnn_hidden1_add", "my_profile");
         search.assertMacro(macroExpression2, "tf_macro_dnn_hidden2_add", "my_profile");
 
@@ -349,7 +353,7 @@ public class RankingExpressionWithTensorFlowTestCase {
                     "input",
                     storedApplication);
             searchFromStored.assertFirstPhaseExpression(expression, "my_profile");
-            assertSmallConstant("dnn_hidden2_Const", TensorType.fromSpec("tensor(d2[1])"), search);
+            assertSmallConstant("mnist_saved_dnn_hidden2_Const", TensorType.fromSpec("tensor(d2[1])"), search);
             searchFromStored.assertMacro(macroExpression1, "tf_macro_dnn_hidden1_add", "my_profile");
             searchFromStored.assertMacro(macroExpression2, "tf_macro_dnn_hidden2_add", "my_profile");
         }