Test importing of ml models

3 files changed, 45 insertions, 4 deletions

diff --git a/model-evaluation/src/test/java/ai/vespa/models/evaluation/ModelsEvaluatorTest.java b/model-evaluation/src/test/java/ai/vespa/models/evaluation/ModelsEvaluatorTest.java
index 60cf0d25ded..23928c5b7e7 100644
--- a/model-evaluation/src/test/java/ai/vespa/models/evaluation/ModelsEvaluatorTest.java
+++ b/model-evaluation/src/test/java/ai/vespa/models/evaluation/ModelsEvaluatorTest.java
@@ -46,6 +46,5 @@ public class ModelsEvaluatorTest {
     // TODO: Test that binding nonexisting variable doesn't work
     // TODO: Test that rebinding doesn't work
     // TODO: Test with nested macros
-    // TODO: Test TF/ONNX model
 
 }
diff --git a/model-evaluation/src/test/java/ai/vespa/models/evaluation/RankProfilesImporterTest.java b/model-evaluation/src/test/java/ai/vespa/models/evaluation/RankProfilesImporterTest.java
index d45372fc7da..e0f5674e016 100644
--- a/model-evaluation/src/test/java/ai/vespa/models/evaluation/RankProfilesImporterTest.java
+++ b/model-evaluation/src/test/java/ai/vespa/models/evaluation/RankProfilesImporterTest.java
@@ -21,14 +21,41 @@ import static org.junit.Assert.assertNotNull;
 public class RankProfilesImporterTest {
 
     @Test
-    public void testImporting() {
+    public void testImportingModels() {
+        String configPath = "src/test/resources/config/models/rank-profiles.cfg";
+        RankProfilesConfig config = new ConfigGetter<>(new FileSource(new File(configPath)), RankProfilesConfig.class).getConfig("");
+        Map<String, Model> models = new RankProfilesConfigImporter().importFrom(config);
+        assertEquals(4, models.size());
+
+        Model xgboost = models.get("xgboost_2_2");
+        assertFunction("xgboost_2_2",
+                       "(optimized sum of condition trees of size 192 bytes)",
+                       xgboost);
+
+        Model onnxMnistSoftmax = models.get("mnist_softmax");
+        assertFunction("default.add",
+                       "join(reduce(join(rename(Placeholder, (d0, d1), (d0, d2)), constant(mnist_softmax_Variable), f(a,b)(a * b)), sum, d2), constant(mnist_softmax_Variable_1), f(a,b)(a + b))",
+                       onnxMnistSoftmax);
+
+        Model tfMnistSoftmax = models.get("mnist_softmax_saved");
+        assertFunction("serving_default.y",
+                       "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))",
+                       tfMnistSoftmax);
+
+        Model tfMnist = models.get("mnist_saved");
+        assertFunction("serving_default.y",
+                       "join(reduce(join(map(join(reduce(join(join(join(rankingExpression(imported_ml_macro_mnist_saved_dnn_hidden1_add), 0.009999999776482582, f(a,b)(a * b)), rankingExpression(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)), 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))",
+                       tfMnist);
+    }
+
+    @Test
+    public void testImportingRankExpressions() {
         String configPath = "src/test/resources/config/rankexpression/rank-profiles.cfg";
         RankProfilesConfig config = new ConfigGetter<>(new FileSource(new File(configPath)), RankProfilesConfig.class).getConfig("");
         Map<String, Model> models = new RankProfilesConfigImporter().importFrom(config);
         assertEquals(18, models.size());
 
         Model macros = models.get("macros");
-        assertNotNull(macros);
         assertEquals("macros", macros.name());
         assertEquals(4, macros.functions().size());
         assertFunction("fourtimessum", "4 * (var1 + var2)", macros);
@@ -44,8 +71,9 @@ public class RankProfilesImporterTest {
     }
 
     private void assertFunction(String name, String expression, Model model) {
+        assertNotNull("Model is present in config", model);
         ExpressionFunction function = model.function(name);
-        assertNotNull(function);
+        assertNotNull("Function '" + name + "' is in " + model, function);
         assertEquals(name, function.getName());
         assertEquals(expression, function.getBody().getRoot().toString());
     }
diff --git a/model-evaluation/src/test/resources/config/models/rank-profiles.cfg b/model-evaluation/src/test/resources/config/models/rank-profiles.cfg
new file mode 100644
index 00000000000..1cc36f75158
--- /dev/null
+++ b/model-evaluation/src/test/resources/config/models/rank-profiles.cfg
@@ -0,0 +1,14 @@
+rankprofile[0].name "mnist_saved"
+rankprofile[0].fef.property[0].name "rankingExpression(imported_ml_macro_mnist_saved_dnn_hidden1_add).rankingScript"
+rankprofile[0].fef.property[0].value "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))"
+rankprofile[0].fef.property[1].name "rankingExpression(serving_default.y).rankingScript"
+rankprofile[0].fef.property[1].value "join(reduce(join(map(join(reduce(join(join(join(rankingExpression(imported_ml_macro_mnist_saved_dnn_hidden1_add), 0.009999999776482582, f(a,b)(a * b)), rankingExpression(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)), 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))"
+rankprofile[1].name "xgboost_2_2"
+rankprofile[1].fef.property[0].name "rankingExpression(xgboost_2_2).rankingScript"
+rankprofile[1].fef.property[0].value "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)"
+rankprofile[2].name "mnist_softmax_saved"
+rankprofile[2].fef.property[0].name "rankingExpression(serving_default.y).rankingScript"
+rankprofile[2].fef.property[0].value "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))"
+rankprofile[3].name "mnist_softmax"
+rankprofile[3].fef.property[0].name "rankingExpression(default.add).rankingScript"
+rankprofile[3].fef.property[0].value "join(reduce(join(rename(Placeholder, (d0, d1), (d0, d2)), constant(mnist_softmax_Variable), f(a,b)(a * b)), sum, d2), constant(mnist_softmax_Variable_1), f(a,b)(a + b))"