upgrade djl version to 0.28.0 (opensearch-project#2578) (opensearch-project#2580)

* upgrade djl version to latest 0.28.0

Signed-off-by: Bhavana Ramaram <rbhavna@amazon.com>

* force onnxruntime_gpu to 1.16.3

Signed-off-by: Yaliang Wu <ylwu@amazon.com>

---------

Signed-off-by: Bhavana Ramaram <rbhavna@amazon.com>
Signed-off-by: Yaliang Wu <ylwu@amazon.com>
Co-authored-by: Yaliang Wu <ylwu@amazon.com>
(cherry picked from commit 01c85cb)

Co-authored-by: Bhavana Ramaram <rbhavna@amazon.com>

Author: opensearch-trigger-bot[bot]

ml-algorithms/build.gradle

@@ -42,22 +42,22 @@ dependencies {
     testImplementation group: 'org.mockito', name: 'mockito-core', version: '5.7.0'
     implementation group: 'com.google.guava', name: 'guava', version: '32.1.2-jre'
     implementation group: 'com.google.code.gson', name: 'gson', version: '2.10.1'
-    implementation platform("ai.djl:bom:0.21.0")
-    implementation group: 'ai.djl.pytorch', name: 'pytorch-model-zoo', version: '0.21.0'
+    implementation platform("ai.djl:bom:0.28.0")
+    implementation group: 'ai.djl.pytorch', name: 'pytorch-model-zoo'
     implementation group: 'ai.djl', name: 'api'
     implementation group: 'ai.djl.huggingface', name: 'tokenizers'
-    implementation("ai.djl.onnxruntime:onnxruntime-engine:0.21.0") {
+    implementation("ai.djl.onnxruntime:onnxruntime-engine") {
         exclude group: "com.microsoft.onnxruntime", module: "onnxruntime"
     }
     def os = DefaultNativePlatform.currentOperatingSystem
     //arm/macos doesn't support GPU
     if (os.macOsX || System.getProperty("os.arch") == "aarch64") {
         dependencies {
-            implementation "com.microsoft.onnxruntime:onnxruntime:1.14.0"
+            implementation "com.microsoft.onnxruntime:onnxruntime:1.16.3!!"
         }
     } else {
         dependencies {
-            implementation "com.microsoft.onnxruntime:onnxruntime_gpu:1.14.0"
+            implementation "com.microsoft.onnxruntime:onnxruntime_gpu:1.16.3!!"
         }
     }
 

ml-algorithms/src/main/java/org/opensearch/ml/engine/algorithms/DLModel.java

@@ -253,6 +253,7 @@ protected void loadModel(
                 ClassLoader contextClassLoader = Thread.currentThread().getContextClassLoader();
                 try {
                     System.setProperty("PYTORCH_PRECXX11", "true");
+                    System.setProperty("PYTORCH_VERSION", "1.13.1");
                     System.setProperty("DJL_CACHE_DIR", mlEngine.getMlCachePath().toAbsolutePath().toString());
                     // DJL will read "/usr/java/packages/lib" if don't set "java.library.path". That will throw
                     // access denied exception

ml-algorithms/src/main/java/org/opensearch/ml/engine/algorithms/DLModelExecute.java

@@ -131,6 +131,7 @@ private void loadModel(File modelZipFile, String modelId, String modelName, Stri
                 ClassLoader contextClassLoader = Thread.currentThread().getContextClassLoader();
                 try {
                     System.setProperty("PYTORCH_PRECXX11", "true");
+                    System.setProperty("PYTORCH_VERSION", "1.13.1");
                     System.setProperty("DJL_CACHE_DIR", mlEngine.getMlCachePath().toAbsolutePath().toString());
                     // DJL will read "/usr/java/packages/lib" if don't set "java.library.path". That will throw
                     // access denied exception

ml-algorithms/src/test/java/org/opensearch/ml/engine/algorithms/question_answering/QuestionAnsweringModelTest.java

@@ -163,15 +163,15 @@ public void initModel_predict_ONNX_QuestionAnswering() throws URISyntaxException
             .modelFormat(MLModelFormat.ONNX)
             .name("test_model_name")
             .modelId("test_model_id")
-            .algorithm(FunctionName.TEXT_SIMILARITY)
+            .algorithm(FunctionName.QUESTION_ANSWERING)
             .version("1.0.0")
             .modelState(MLModelState.TRAINED)
             .build();
         modelZipFile = new File(getClass().getResource("question_answering_onnx.zip").toURI());
         params.put(MODEL_ZIP_FILE, modelZipFile);
 
         questionAnsweringModel.initModel(model, params, encryptor);
-        MLInput mlInput = MLInput.builder().algorithm(FunctionName.TEXT_SIMILARITY).inputDataset(inputDataSet).build();
+        MLInput mlInput = MLInput.builder().algorithm(FunctionName.QUESTION_ANSWERING).inputDataset(inputDataSet).build();
         ModelTensorOutput output = (ModelTensorOutput) questionAnsweringModel.predict(mlInput);
         List<ModelTensors> mlModelOutputs = output.getMlModelOutputs();
         assertEquals(1, mlModelOutputs.size());