Don't reuse runtime between methods

Caching evaluators between test methods may have unwanted side effects

1 files changed, 18 insertions, 21 deletions

diff --git a/model-integration/src/test/java/ai/vespa/modelintegration/evaluator/OnnxEvaluatorTest.java b/model-integration/src/test/java/ai/vespa/modelintegration/evaluator/OnnxEvaluatorTest.java
index 5aba54de11b..b2d76baa566 100644
--- a/model-integration/src/test/java/ai/vespa/modelintegration/evaluator/OnnxEvaluatorTest.java
+++ b/model-integration/src/test/java/ai/vespa/modelintegration/evaluator/OnnxEvaluatorTest.java
@@ -5,30 +5,23 @@ package ai.vespa.modelintegration.evaluator;
 import com.yahoo.tensor.Tensor;
 import com.yahoo.tensor.TensorType;
 import org.junit.Test;
-import org.junit.jupiter.api.BeforeAll;
 
 import java.util.HashMap;
 import java.util.Map;
 
 import static org.junit.Assert.assertEquals;
 import static org.junit.Assert.assertTrue;
-import static org.junit.Assume.assumeNotNull;
+import static org.junit.Assume.assumeTrue;
 
 /**
  * @author lesters
  */
 public class OnnxEvaluatorTest {
 
-    private static OnnxRuntime runtime;
-
-    @BeforeAll
-    public static void beforeAll() {
-        if (OnnxRuntime.isRuntimeAvailable()) runtime = new OnnxRuntime();
-    }
-
     @Test
     public void testSimpleModel() {
-        assumeNotNull(runtime);
+        assumeTrue(OnnxRuntime.isRuntimeAvailable());
+        var runtime = new OnnxRuntime();
         OnnxEvaluator evaluator = runtime.evaluatorOf("src/test/models/onnx/simple/simple.onnx");
 
         // Input types
@@ -53,7 +46,8 @@ public class OnnxEvaluatorTest {
 
     @Test
     public void testBatchDimension() {
-        assumeNotNull(runtime);
+        assumeTrue(OnnxRuntime.isRuntimeAvailable());
+        var runtime = new OnnxRuntime();
         OnnxEvaluator evaluator = runtime.evaluatorOf("src/test/models/onnx/pytorch/one_layer.onnx");
 
         // Input types
@@ -72,21 +66,23 @@ public class OnnxEvaluatorTest {
 
     @Test
     public void testMatMul() {
-        assumeNotNull(runtime);
+        assumeTrue(OnnxRuntime.isRuntimeAvailable());
+        var runtime = new OnnxRuntime();
         String expected = "tensor<float>(d0[2],d1[4]):[38,44,50,56,83,98,113,128]";
         String input1 = "tensor<float>(d0[2],d1[3]):[1,2,3,4,5,6]";
         String input2 = "tensor<float>(d0[3],d1[4]):[1,2,3,4,5,6,7,8,9,10,11,12]";
-        assertEvaluate("simple/matmul.onnx", expected, input1, input2);
+        assertEvaluate(runtime, "simple/matmul.onnx", expected, input1, input2);
     }
 
     @Test
     public void testTypes() {
-        assumeNotNull(runtime);
-        assertEvaluate("add_double.onnx", "tensor(d0[1]):[3]", "tensor(d0[1]):[1]", "tensor(d0[1]):[2]");
-        assertEvaluate("add_float.onnx", "tensor<float>(d0[1]):[3]", "tensor<float>(d0[1]):[1]", "tensor<float>(d0[1]):[2]");
-        assertEvaluate("add_int64.onnx", "tensor<double>(d0[1]):[3]", "tensor<double>(d0[1]):[1]", "tensor<double>(d0[1]):[2]");
-        assertEvaluate("cast_int8_float.onnx", "tensor<float>(d0[1]):[-128]", "tensor<int8>(d0[1]):[128]");
-        assertEvaluate("cast_float_int8.onnx", "tensor<int8>(d0[1]):[-1]", "tensor<float>(d0[1]):[255]");
+        assumeTrue(OnnxRuntime.isRuntimeAvailable());
+        var runtime = new OnnxRuntime();
+        assertEvaluate(runtime, "add_double.onnx", "tensor(d0[1]):[3]", "tensor(d0[1]):[1]", "tensor(d0[1]):[2]");
+        assertEvaluate(runtime, "add_float.onnx", "tensor<float>(d0[1]):[3]", "tensor<float>(d0[1]):[1]", "tensor<float>(d0[1]):[2]");
+        assertEvaluate(runtime, "add_int64.onnx", "tensor<double>(d0[1]):[3]", "tensor<double>(d0[1]):[1]", "tensor<double>(d0[1]):[2]");
+        assertEvaluate(runtime, "cast_int8_float.onnx", "tensor<float>(d0[1]):[-128]", "tensor<int8>(d0[1]):[128]");
+        assertEvaluate(runtime, "cast_float_int8.onnx", "tensor<int8>(d0[1]):[-1]", "tensor<float>(d0[1]):[255]");
 
         // ONNX Runtime 1.8.0 does not support much of bfloat16 yet
         // assertEvaluate("cast_bfloat16_float.onnx", "tensor<float>(d0[1]):[1]", "tensor<bfloat16>(d0[1]):[1]");
@@ -94,7 +90,8 @@ public class OnnxEvaluatorTest {
 
     @Test
     public void testNotIdentifiers() {
-        assumeNotNull(runtime);
+        assumeTrue(OnnxRuntime.isRuntimeAvailable());
+        var runtime = new OnnxRuntime();
         OnnxEvaluator evaluator = runtime.evaluatorOf("src/test/models/onnx/badnames.onnx");
         var inputInfo = evaluator.getInputInfo();
         var outputInfo = evaluator.getOutputInfo();
@@ -159,7 +156,7 @@ public class OnnxEvaluatorTest {
         assertEquals(3, allResults.size());
     }
 
-    private void assertEvaluate(String model, String output, String... input) {
+    private void assertEvaluate(OnnxRuntime runtime, String model, String output, String... input) {
         OnnxEvaluator evaluator = runtime.evaluatorOf("src/test/models/onnx/" + model);
         Map<String, Tensor> inputs = new HashMap<>();
         for (int i = 0; i < input.length; ++i) {