Stateless REST API: short forms for sparse and mixed tensors

3 files changed, 47 insertions, 4 deletions

diff --git a/model-evaluation/src/main/java/ai/vespa/models/handler/ModelsEvaluationHandler.java b/model-evaluation/src/main/java/ai/vespa/models/handler/ModelsEvaluationHandler.java
index bbd9962be77..e7e453d8fe3 100644
--- a/model-evaluation/src/main/java/ai/vespa/models/handler/ModelsEvaluationHandler.java
+++ b/model-evaluation/src/main/java/ai/vespa/models/handler/ModelsEvaluationHandler.java
@@ -20,6 +20,7 @@ import java.io.IOException;
 import java.io.OutputStream;
 import java.net.URI;
 import java.nio.charset.Charset;
+import java.nio.charset.StandardCharsets;
 import java.util.Arrays;
 import java.util.Map;
 import java.util.Optional;
@@ -90,8 +91,11 @@ public class ModelsEvaluationHandler extends ThreadedHttpRequestHandler {
         Tensor result = evaluator.evaluate();
 
         Optional<String> format = property(request, "format");
-        if (format.isPresent() && format.get().equalsIgnoreCase("short") && result instanceof IndexedTensor) {
-            return new Response(200, JsonFormat.encodeShortForm((IndexedTensor) result));
+        if (format.isPresent() && format.get().equalsIgnoreCase("short")) {
+            return new Response(200, JsonFormat.encodeShortForm(result));
+        }
+        else if (format.isPresent() && format.get().equalsIgnoreCase("literal")) {
+            return new Response(200, result.toString().getBytes(StandardCharsets.UTF_8));
         }
         return new Response(200, JsonFormat.encode(result));
     }
diff --git a/model-evaluation/src/test/java/ai/vespa/models/handler/ModelsEvaluationHandlerTest.java b/model-evaluation/src/test/java/ai/vespa/models/handler/ModelsEvaluationHandlerTest.java
index 8034be6bb22..3a900b0e815 100644
--- a/model-evaluation/src/test/java/ai/vespa/models/handler/ModelsEvaluationHandlerTest.java
+++ b/model-evaluation/src/test/java/ai/vespa/models/handler/ModelsEvaluationHandlerTest.java
@@ -48,7 +48,7 @@ public class ModelsEvaluationHandlerTest {
     public void testListModels() {
         String url = "http://localhost/model-evaluation/v1";
         String expected =
-                "{\"mnist_softmax\":\"http://localhost/model-evaluation/v1/mnist_softmax\",\"mnist_saved\":\"http://localhost/model-evaluation/v1/mnist_saved\",\"mnist_softmax_saved\":\"http://localhost/model-evaluation/v1/mnist_softmax_saved\",\"xgboost_2_2\":\"http://localhost/model-evaluation/v1/xgboost_2_2\",\"lightgbm_regression\":\"http://localhost/model-evaluation/v1/lightgbm_regression\"}";
+                "{\"mnist_softmax\":\"http://localhost/model-evaluation/v1/mnist_softmax\",\"mnist_saved\":\"http://localhost/model-evaluation/v1/mnist_saved\",\"mnist_softmax_saved\":\"http://localhost/model-evaluation/v1/mnist_softmax_saved\",\"vespa_model\":\"http://localhost/model-evaluation/v1/vespa_model\",\"xgboost_2_2\":\"http://localhost/model-evaluation/v1/xgboost_2_2\",\"lightgbm_regression\":\"http://localhost/model-evaluation/v1/lightgbm_regression\"}";
         handler.assertResponse(url, 200, expected);
     }
 
@@ -56,7 +56,7 @@ public class ModelsEvaluationHandlerTest {
     public void testListModelsWithDifferentHost() {
         String url = "http://localhost/model-evaluation/v1";
         String expected =
-                "{\"mnist_softmax\":\"http://localhost:8088/model-evaluation/v1/mnist_softmax\",\"mnist_saved\":\"http://localhost:8088/model-evaluation/v1/mnist_saved\",\"mnist_softmax_saved\":\"http://localhost:8088/model-evaluation/v1/mnist_softmax_saved\",\"xgboost_2_2\":\"http://localhost:8088/model-evaluation/v1/xgboost_2_2\",\"lightgbm_regression\":\"http://localhost:8088/model-evaluation/v1/lightgbm_regression\"}";
+                "{\"mnist_softmax\":\"http://localhost:8088/model-evaluation/v1/mnist_softmax\",\"mnist_saved\":\"http://localhost:8088/model-evaluation/v1/mnist_saved\",\"mnist_softmax_saved\":\"http://localhost:8088/model-evaluation/v1/mnist_softmax_saved\",\"vespa_model\":\"http://localhost:8088/model-evaluation/v1/vespa_model\",\"xgboost_2_2\":\"http://localhost:8088/model-evaluation/v1/xgboost_2_2\",\"lightgbm_regression\":\"http://localhost:8088/model-evaluation/v1/lightgbm_regression\"}";
         handler.assertResponse(url, 200, expected, Map.of("Host", "localhost:8088"));
     }
 
@@ -214,6 +214,36 @@ public class ModelsEvaluationHandlerTest {
     }
 
     @Test
+    public void testVespaModelShortOutput() {
+        Map<String, String> properties = new HashMap<>();
+        properties.put("format", "short");
+        String url = "http://localhost/model-evaluation/v1/vespa_model/";
+        handler.assertResponse(url + "test_mapped/eval", properties, 200,
+                "{\"type\":\"tensor(d0{})\",\"value\":{\"a\":1.0,\"b\":2.0}}");
+        handler.assertResponse(url + "test_indexed/eval", properties, 200,
+                "{\"type\":\"tensor(d0[2],d1[3])\",\"value\":[[1.0,2.0,3.0],[4.0,5.0,6.0]]}");
+        handler.assertResponse(url + "test_mixed/eval", properties, 200,
+                "{\"type\":\"tensor(x{},y[3])\",\"value\":{\"a\":[1.0,2.0,3.0],\"b\":[4.0,5.0,6.0]}}");
+        handler.assertResponse(url + "test_mixed_2/eval", properties, 200,
+                "{\"type\":\"tensor(a[2],b[2],c{},d[2])\",\"value\":{\"a\":[[[1.0,2.0],[3.0,4.0]],[[5.0,6.0],[7.0,8.0]]],\"b\":[[[1.0,2.0],[3.0,4.0]],[[5.0,6.0],[7.0,8.0]]]}}");
+    }
+
+    @Test
+    public void testVespaModelLiteralOutput() {
+        Map<String, String> properties = new HashMap<>();
+        properties.put("format", "literal");
+        String url = "http://localhost/model-evaluation/v1/vespa_model/";
+        handler.assertResponse(url + "test_mapped/eval", properties, 200,
+                "tensor(d0{}):{a:1.0,b:2.0}");
+        handler.assertResponse(url + "test_indexed/eval", properties, 200,
+                "tensor(d0[2],d1[3]):[[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]]");
+        handler.assertResponse(url + "test_mixed/eval", properties, 200,
+                "tensor(x{},y[3]):{a:[1.0, 2.0, 3.0],b:[4.0, 5.0, 6.0]}");
+        handler.assertResponse(url + "test_mixed_2/eval", properties, 200,
+                "tensor(a[2],b[2],c{},d[2]):{a:[[[1.0, 2.0], [3.0, 4.0]], [[5.0, 6.0], [7.0, 8.0]]],b:[[[1.0, 2.0], [3.0, 4.0]], [[5.0, 6.0], [7.0, 8.0]]]}");
+    }
+
+    @Test
     public void testMnistSavedEvaluateSpecificFunction() {
         Map<String, String> properties = new HashMap<>();
         properties.put("input", inputTensor());
diff --git a/model-evaluation/src/test/resources/config/models/rank-profiles.cfg b/model-evaluation/src/test/resources/config/models/rank-profiles.cfg
index 385115b7cd4..4877a24f171 100644
--- a/model-evaluation/src/test/resources/config/models/rank-profiles.cfg
+++ b/model-evaluation/src/test/resources/config/models/rank-profiles.cfg
@@ -29,3 +29,12 @@ rankprofile[3].fef.property[4].value "tensor(d1[10])"
 rankprofile[4].name "lightgbm_regression"
 rankprofile[4].fef.property[0].name "rankingExpression(lightgbm_regression).rankingScript"
 rankprofile[4].fef.property[0].value "if (!(numerical_2 >= 0.46643291586559305), 2.1594397038037663, if (categorical_2 in ["k", "l", "m"], 2.235297305276056, 2.1792953471546546)) + if (categorical_1 in ["d", "e"], 0.03070842919354316, if (!(numerical_1 >= 0.5102250691730842), -0.04439151147520909, 0.005117411709368601)) + if (!(numerical_2 >= 0.668665477622446), if (!(numerical_2 >= 0.008118820676863816), -0.15361238490967524, -0.01192330846157292), 0.03499044894987518) + if (!(numerical_1 >= 0.5201391072644542), -0.02141000620783247, if (categorical_1 in ["a", "b"], -0.004121485787596721, 0.04534090904886873)) + if (categorical_2 in ["k", "l", "m"], if (!(numerical_2 >= 0.27283279016959255), -0.01924803254356527, 0.03643772842347651), -0.02701711918923075)"
+rankprofile[5].name "vespa_model"
+rankprofile[5].fef.property[0].name "rankingExpression(test_mapped).rankingScript"
+rankprofile[5].fef.property[0].value "tensor(d0{}):{a:1, b:2}"
+rankprofile[5].fef.property[1].name "rankingExpression(test_indexed).rankingScript"
+rankprofile[5].fef.property[1].value "tensor(d0[2],d1[3]):[[1,2,3],[4,5,6]]"
+rankprofile[5].fef.property[2].name "rankingExpression(test_mixed).rankingScript"
+rankprofile[5].fef.property[2].value "tensor(x{},y[3]):{a:[1,2,3], b:[4,5,6]}"
+rankprofile[5].fef.property[3].name "rankingExpression(test_mixed_2).rankingScript"
+rankprofile[5].fef.property[3].value "tensor(a[2],b[2],c{},d[2]):{a:[[[1,2], [3,4]], [[5,6], [7,8]]], b:[[[1,2], [3,4]], [[5,6], [7,8]]] }"