Merge pull request #6111 from vespa-engine/lesters/revert-revert-onnx

Refactor ONNX and TF import to use same code base

2 files changed, 16 insertions, 32 deletions

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 1c54d12d8b3..d9beab6e2f2 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
@@ -37,15 +37,6 @@ public class RankingExpressionWithOnnxTestCase {
     }
 
     @Test
-    public void testOnnxReference() throws ParseException {
-        RankProfileSearchFixture search = fixtureWith("tensor(d0[2],d1[784])(0.0)",
-                "onnx('mnist_softmax.onnx')");
-        search.assertFirstPhaseExpression(vespaExpression, "my_profile");
-        assertLargeConstant("mnist_softmax_onnx_Variable_1", search, Optional.of(10L));
-        assertLargeConstant("mnist_softmax_onnx_Variable", search, Optional.of(7840L));
-    }
-
-    @Test
     public void testOnnxReferenceWithConstantFeature() {
         RankProfileSearchFixture search = fixtureWith("constant(mytensor)",
                 "onnx('mnist_softmax.onnx')",
@@ -122,13 +113,6 @@ public class RankingExpressionWithOnnxTestCase {
     }
 
     @Test
-    public void testOnnxReferenceSpecifyingOutput() {
-        RankProfileSearchFixture search = fixtureWith("tensor(d0[2],d1[784])(0.0)",
-                "onnx('mnist_softmax.onnx', 'add')");
-        search.assertFirstPhaseExpression(vespaExpression, "my_profile");
-    }
-
-    @Test
     public void testOnnxReferenceMissingMacro() throws ParseException {
         try {
             RankProfileSearchFixture search = new RankProfileSearchFixture(
@@ -145,7 +129,7 @@ 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 Placeholder 'Placeholder' of type tensor(d0[],d1[784]) but this macro is " +
+                            "Model refers input 'Placeholder' of type tensor(d0[],d1[784]) but this macro is " +
                             "not present in rank profile 'my_profile'",
                     Exceptions.toMessageString(expected));
         }
@@ -163,8 +147,8 @@ 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' of type tensor(d0[],d1[784]) which must be produced " +
-                            "by a macro in the rank profile, but this macro produces type tensor(d0[2],d5[10])",
+                            "Model refers input 'Placeholder'. The required type of this is tensor(d0[],d1[784]), " +
+                            "but this macro returns tensor(d0[2],d5[10])",
                     Exceptions.toMessageString(expected));
         }
     }
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 d288a396732..7228af2b0de 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
@@ -162,7 +162,7 @@ public class RankingExpressionWithTensorFlowTestCase {
         catch (IllegalArgumentException expected) {
             assertEquals("Rank profile 'my_profile' is invalid: Could not use tensorflow model from " +
                          "tensorflow('mnist_softmax/saved'): " +
-                         "Model refers placeholder 'Placeholder' of type tensor(d0[],d1[784]) but this macro is " +
+                         "Model refers input 'Placeholder' of type tensor(d0[],d1[784]) but this macro is " +
                          "not present in rank profile 'my_profile'",
                          Exceptions.toMessageString(expected));
         }
@@ -179,7 +179,7 @@ public class RankingExpressionWithTensorFlowTestCase {
         catch (IllegalArgumentException expected) {
             assertEquals("Rank profile 'my_profile' is invalid: Could not use tensorflow model from " +
                          "tensorflow('mnist_softmax/saved'): " +
-                         "Model refers placeholder 'Placeholder'. The required type of this is tensor(d0[],d1[784]), " +
+                         "Model refers input 'Placeholder'. The required type of this is tensor(d0[],d1[784]), " +
                          "but this macro returns tensor(d0[2],d5[10])",
                          Exceptions.toMessageString(expected));
         }
@@ -305,9 +305,9 @@ public class RankingExpressionWithTensorFlowTestCase {
 
     @Test
     public void testMacroGeneration() {
-        final String expression = "join(join(reduce(join(join(join(tf_macro_mnist_saved_dnn_hidden2_add, reduce(constant(mnist_saved_dnn_hidden2_Const), sum, d2), f(a,b)(a * b)), tf_macro_mnist_saved_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])(1.0), f(a,b)(a * b))";
+        final String expression = "join(join(reduce(join(join(join(imported_ml_macro_mnist_saved_dnn_hidden2_add, reduce(constant(mnist_saved_dnn_hidden2_Const), sum, d2), f(a,b)(a * b)), imported_ml_macro_mnist_saved_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])(1.0), 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_mnist_saved_dnn_hidden1_add, 0.009999999776482582, f(a,b)(a * b)), tf_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))";
+        final String macroExpression2 = "join(reduce(join(join(join(imported_ml_macro_mnist_saved_dnn_hidden1_add, 0.009999999776482582, f(a,b)(a * b)), 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))";
 
         RankProfileSearchFixture search = fixtureWith("tensor(d0[1],d1[784])(0.0)",
                                     "tensorflow('mnist/saved')",
@@ -316,15 +316,15 @@ public class RankingExpressionWithTensorFlowTestCase {
                                                       "input",
                                                       new StoringApplicationPackage(applicationDir));
         search.assertFirstPhaseExpression(expression, "my_profile");
-        search.assertMacro(macroExpression1, "tf_macro_mnist_saved_dnn_hidden1_add", "my_profile");
-        search.assertMacro(macroExpression2, "tf_macro_mnist_saved_dnn_hidden2_add", "my_profile");
+        search.assertMacro(macroExpression1, "imported_ml_macro_mnist_saved_dnn_hidden1_add", "my_profile");
+        search.assertMacro(macroExpression2, "imported_ml_macro_mnist_saved_dnn_hidden2_add", "my_profile");
     }
 
     @Test
     public void testImportingFromStoredExpressionsWithSmallConstants() throws IOException {
-        final String expression = "join(join(reduce(join(join(join(tf_macro_mnist_saved_dnn_hidden2_add, reduce(constant(mnist_saved_dnn_hidden2_Const), sum, d2), f(a,b)(a * b)), tf_macro_mnist_saved_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])(1.0), f(a,b)(a * b))";
+        final String expression = "join(join(reduce(join(join(join(imported_ml_macro_mnist_saved_dnn_hidden2_add, reduce(constant(mnist_saved_dnn_hidden2_Const), sum, d2), f(a,b)(a * b)), imported_ml_macro_mnist_saved_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])(1.0), 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_mnist_saved_dnn_hidden1_add, 0.009999999776482582, f(a,b)(a * b)), tf_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))";
+        final String macroExpression2 = "join(reduce(join(join(join(imported_ml_macro_mnist_saved_dnn_hidden1_add, 0.009999999776482582, f(a,b)(a * b)), 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))";
 
         StoringApplicationPackage application = new StoringApplicationPackage(applicationDir);
         RankProfileSearchFixture search = fixtureWith("tensor(d0[1],d1[784])(0.0)",
@@ -335,8 +335,8 @@ public class RankingExpressionWithTensorFlowTestCase {
                 application);
         search.assertFirstPhaseExpression(expression, "my_profile");
         assertSmallConstant("mnist_saved_dnn_hidden1_mul_x", TensorType.fromSpec("tensor()"), search);
-        search.assertMacro(macroExpression1, "tf_macro_mnist_saved_dnn_hidden1_add", "my_profile");
-        search.assertMacro(macroExpression2, "tf_macro_mnist_saved_dnn_hidden2_add", "my_profile");
+        search.assertMacro(macroExpression1, "imported_ml_macro_mnist_saved_dnn_hidden1_add", "my_profile");
+        search.assertMacro(macroExpression2, "imported_ml_macro_mnist_saved_dnn_hidden2_add", "my_profile");
 
         // 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");
@@ -353,8 +353,8 @@ public class RankingExpressionWithTensorFlowTestCase {
                     storedApplication);
             searchFromStored.assertFirstPhaseExpression(expression, "my_profile");
             assertSmallConstant("mnist_saved_dnn_hidden1_mul_x", TensorType.fromSpec("tensor()"), search);
-            searchFromStored.assertMacro(macroExpression1, "tf_macro_mnist_saved_dnn_hidden1_add", "my_profile");
-            searchFromStored.assertMacro(macroExpression2, "tf_macro_mnist_saved_dnn_hidden2_add", "my_profile");
+            searchFromStored.assertMacro(macroExpression1, "imported_ml_macro_mnist_saved_dnn_hidden1_add", "my_profile");
+            searchFromStored.assertMacro(macroExpression2, "imported_ml_macro_mnist_saved_dnn_hidden2_add", "my_profile");
         }
         finally {
             IOUtils.recursiveDeleteDir(storedApplicationDirectory.toFile());
@@ -465,7 +465,7 @@ public class RankingExpressionWithTensorFlowTestCase {
 
     }
 
-    public static class StoringApplicationPackageFile extends ApplicationFile {
+    static class StoringApplicationPackageFile extends ApplicationFile {
 
         /** The path to the application package root */
         private final Path root;