Android: consistent error types across all modules

extension/android/executorch_android/src/main/java/org/pytorch/executorch/ExecuTorchRuntime.java

@@ -36,12 +36,22 @@ public static ExecuTorchRuntime getRuntime() {
   /**
    * Validates that the given path points to a readable file.
    *
-   * @throws RuntimeException if the file does not exist or is not readable.
+   * @throws IllegalArgumentException if the path is null, does not exist, is not a file, or is not
+   *     readable.
    */
   public static void validateFilePath(String path, String description) {
+    if (path == null) {
+      throw new IllegalArgumentException("Cannot load " + description + ": path is null");
+    }
     File file = new File(path);
-    if (!file.canRead() || !file.isFile()) {
-      throw new RuntimeException("Cannot load " + description + " " + path);
+    if (!file.exists()) {
+      throw new IllegalArgumentException("Cannot load " + description + "!! " + path);
+    }
+    if (!file.isFile()) {
+      throw new IllegalArgumentException("Cannot load " + description + "!! " + path);
+    }
+    if (!file.canRead()) {
+      throw new IllegalArgumentException("Cannot load " + description + "!! " + path);
     }
   }
 

extension/android/executorch_android/src/main/java/org/pytorch/executorch/extension/asr/AsrModule.kt

@@ -11,6 +11,7 @@ package org.pytorch.executorch.extension.asr
 import java.io.Closeable
 import java.io.File
 import java.util.concurrent.atomic.AtomicLong
+import org.pytorch.executorch.ExecutorchRuntimeException
 import org.pytorch.executorch.annotations.Experimental
 
 /**
@@ -53,7 +54,10 @@ class AsrModule(
 
     val handle = nativeCreate(modelPath, tokenizerPath, dataPath, preprocessorPath)
     if (handle == 0L) {
-      throw RuntimeException("Failed to create native AsrModule")
+      throw ExecutorchRuntimeException(
+          ExecutorchRuntimeException.INTERNAL,
+          "Failed to create native AsrModule",
+      )
     }
     nativeHandle.set(handle)
   }
@@ -129,7 +133,7 @@ class AsrModule(
    * @param callback Optional callback to receive tokens as they are generated (can be null)
    * @return The complete transcribed text
    * @throws IllegalStateException if the module has been destroyed
-   * @throws RuntimeException if transcription fails (non-zero result code)
+   * @throws ExecutorchRuntimeException if transcription fails (error code carried in exception)
    */
   @JvmOverloads
   fun transcribe(
@@ -160,7 +164,7 @@ class AsrModule(
         )
 
     if (status != 0) {
-      throw RuntimeException("Transcription failed with error code: $status")
+      throw ExecutorchRuntimeException(status, "Transcription failed")
     }
 
     return result.toString()

extension/android/executorch_android/src/main/java/org/pytorch/executorch/training/SGD.java

@@ -93,7 +93,7 @@ public static SGD create(Map<String, Tensor> namedParameters, double learningRat
    */
   public void step(Map<String, Tensor> namedGradients) {
     if (!mHybridData.isValid()) {
-      throw new RuntimeException("Attempt to use a destroyed SGD optimizer");
+      throw new IllegalStateException("SGD optimizer has been destroyed");
     }
     stepNative(namedGradients);
   }

extension/android/executorch_android/src/main/java/org/pytorch/executorch/training/TrainingModule.java

@@ -8,12 +8,11 @@
 
 package org.pytorch.executorch.training;
 
-import android.util.Log;
 import com.facebook.jni.HybridData;
 import com.facebook.jni.annotations.DoNotStrip;
 import com.facebook.soloader.nativeloader.NativeLoader;
 import com.facebook.soloader.nativeloader.SystemDelegate;
-import java.util.HashMap;
+import java.io.Closeable;
 import java.util.Map;
 import org.pytorch.executorch.EValue;
 import org.pytorch.executorch.ExecuTorchRuntime;
@@ -26,7 +25,7 @@
  * <p>Warning: These APIs are experimental and subject to change without notice
  */
 @Experimental
-public class TrainingModule {
+public class TrainingModule implements Closeable {
 
   static {
     if (!NativeLoader.isInitialized()) {
@@ -37,6 +36,7 @@ public class TrainingModule {
   }
 
   private final HybridData mHybridData;
+  private boolean mDestroyed = false;
 
   @DoNotStrip
   private static native HybridData initHybrid(String moduleAbsolutePath, String dataAbsolutePath);
@@ -45,6 +45,10 @@ private TrainingModule(String moduleAbsolutePath, String dataAbsolutePath) {
     mHybridData = initHybrid(moduleAbsolutePath, dataAbsolutePath);
   }
 
+  private void checkNotDestroyed() {
+    if (mDestroyed) throw new IllegalStateException("TrainingModule has been destroyed");
+  }
+
   /**
    * Loads a serialized ExecuTorch Training Module from the specified path on the disk.
    *
@@ -78,35 +82,33 @@ public static TrainingModule load(final String modelPath) {
    * @return return value(s) from the method.
    */
   public EValue[] executeForwardBackward(String methodName, EValue... inputs) {
-    if (!mHybridData.isValid()) {
-      Log.e("ExecuTorch", "Attempt to use a destroyed module");
-      return new EValue[0];
-    }
+    checkNotDestroyed();
     return executeForwardBackwardNative(methodName, inputs);
   }
 
   @DoNotStrip
   private native EValue[] executeForwardBackwardNative(String methodName, EValue... inputs);
 
   public Map<String, Tensor> namedParameters(String methodName) {
-    if (!mHybridData.isValid()) {
-      Log.e("ExecuTorch", "Attempt to use a destroyed module");
-      return new HashMap<String, Tensor>();
-    }
+    checkNotDestroyed();
     return namedParametersNative(methodName);
   }
 
   @DoNotStrip
   private native Map<String, Tensor> namedParametersNative(String methodName);
 
   public Map<String, Tensor> namedGradients(String methodName) {
-    if (!mHybridData.isValid()) {
-      Log.e("ExecuTorch", "Attempt to use a destroyed module");
-      return new HashMap<String, Tensor>();
-    }
+    checkNotDestroyed();
     return namedGradientsNative(methodName);
   }
 
   @DoNotStrip
   private native Map<String, Tensor> namedGradientsNative(String methodName);
+
+  @Override
+  public void close() {
+    if (mDestroyed) return;
+    mDestroyed = true;
+    mHybridData.resetNative();
+  }
 }

extension/android/jni/jni_layer.cpp

@@ -284,8 +284,18 @@ class ExecuTorchJni : public facebook::jni::HybridClass<ExecuTorchJni> {
 #else
     auto etdump_gen = nullptr;
 #endif
-    module_ = std::make_unique<Module>(
-        modelPath->toStdString(), load_mode, std::move(etdump_gen));
+    try {
+      module_ = std::make_unique<Module>(
+          modelPath->toStdString(), load_mode, std::move(etdump_gen));
+    } catch (const std::exception& e) {
+      executorch::jni_helper::throwExecutorchException(
+          static_cast<uint32_t>(Error::Internal),
+          std::string("Failed to create Module: ") + e.what());
+    } catch (...) {
+      executorch::jni_helper::throwExecutorchException(
+          static_cast<uint32_t>(Error::Internal),
+          "Failed to create Module: unknown native error");
+    }
 
 #ifdef ET_USE_THREADPOOL
     // Default to using cores/2 threadpool threads. The long-term plan is to

extension/android/jni/jni_layer_llama.cpp

@@ -149,103 +149,117 @@ class ExecuTorchLlmJni : public facebook::jni::HybridClass<ExecuTorchLlmJni> {
       jint num_bos = 0,
       jint num_eos = 0,
       jint load_mode = 1) {
-    temperature_ = temperature;
-    num_bos_ = num_bos;
-    num_eos_ = num_eos;
+    try {
+      temperature_ = temperature;
+      num_bos_ = num_bos;
+      num_eos_ = num_eos;
 #if defined(ET_USE_THREADPOOL)
-    // Reserve 1 thread for the main thread.
-    int32_t num_performant_cores =
-        ::executorch::extension::cpuinfo::get_num_performant_cores() - 1;
-    if (num_performant_cores > 0) {
-      ET_LOG(Info, "Resetting threadpool to %d threads", num_performant_cores);
-      ::executorch::extension::threadpool::get_threadpool()
-          ->_unsafe_reset_threadpool(num_performant_cores);
-    }
+      // Reserve 1 thread for the main thread.
+      int32_t num_performant_cores =
+          ::executorch::extension::cpuinfo::get_num_performant_cores() - 1;
+      if (num_performant_cores > 0) {
+        ET_LOG(
+            Info, "Resetting threadpool to %d threads", num_performant_cores);
+        ::executorch::extension::threadpool::get_threadpool()
+            ->_unsafe_reset_threadpool(num_performant_cores);
+      }
 #endif
 
-    model_type_category_ = model_type_category;
-    auto cpp_load_mode = load_mode_from_int(load_mode);
-    std::vector<std::string> data_files_vector;
-    if (model_type_category == MODEL_TYPE_CATEGORY_MULTIMODAL) {
-      runner_ = llm::create_multimodal_runner(
-          model_path->toStdString().c_str(),
-          llm::load_tokenizer(tokenizer_path->toStdString()),
-          std::nullopt,
-          cpp_load_mode);
-    } else if (model_type_category == MODEL_TYPE_CATEGORY_LLM) {
-      if (data_files != nullptr) {
-        // Convert Java List<String> to C++ std::vector<string>
-        auto list_class = facebook::jni::findClassStatic("java/util/List");
-        auto size_method = list_class->getMethod<jint()>("size");
-        auto get_method =
-            list_class->getMethod<facebook::jni::local_ref<jobject>(jint)>(
-                "get");
-
-        jint size = size_method(data_files);
-        for (jint i = 0; i < size; ++i) {
-          auto str_obj = get_method(data_files, i);
-          auto jstr = facebook::jni::static_ref_cast<jstring>(str_obj);
-          data_files_vector.push_back(jstr->toStdString());
+      model_type_category_ = model_type_category;
+      auto cpp_load_mode = load_mode_from_int(load_mode);
+      std::vector<std::string> data_files_vector;
+      if (model_type_category == MODEL_TYPE_CATEGORY_MULTIMODAL) {
+        runner_ = llm::create_multimodal_runner(
+            model_path->toStdString().c_str(),
+            llm::load_tokenizer(tokenizer_path->toStdString()),
+            std::nullopt,
+            cpp_load_mode);
+      } else if (model_type_category == MODEL_TYPE_CATEGORY_LLM) {
+        if (data_files != nullptr) {
+          // Convert Java List<String> to C++ std::vector<string>
+          auto list_class = facebook::jni::findClassStatic("java/util/List");
+          auto size_method = list_class->getMethod<jint()>("size");
+          auto get_method =
+              list_class->getMethod<facebook::jni::local_ref<jobject>(jint)>(
+                  "get");
+
+          jint size = size_method(data_files);
+          for (jint i = 0; i < size; ++i) {
+            auto str_obj = get_method(data_files, i);
+            auto jstr = facebook::jni::static_ref_cast<jstring>(str_obj);
+            data_files_vector.push_back(jstr->toStdString());
+          }
         }
-      }
-      runner_ = executorch::extension::llm::create_text_llm_runner(
-          model_path->toStdString(),
-          llm::load_tokenizer(tokenizer_path->toStdString()),
-          data_files_vector,
-          /*temperature=*/-1.0f,
-          /*event_tracer=*/nullptr,
-          /*method_name=*/"forward",
-          cpp_load_mode);
+        runner_ = executorch::extension::llm::create_text_llm_runner(
+            model_path->toStdString(),
+            llm::load_tokenizer(tokenizer_path->toStdString()),
+            data_files_vector,
+            /*temperature=*/-1.0f,
+            /*event_tracer=*/nullptr,
+            /*method_name=*/"forward",
+            cpp_load_mode);
 #if defined(EXECUTORCH_BUILD_QNN)
-    } else if (model_type_category == MODEL_TYPE_QNN_LLAMA) {
-      std::unique_ptr<executorch::extension::Module> module =
-          std::make_unique<executorch::extension::Module>(
-              model_path->toStdString().c_str(),
-              data_files_vector,
-              cpp_load_mode);
-      std::string decoder_model = "llama3"; // use llama3 for now
-      // Using 8bit as default since this meta is introduced with 16bit kv io
-      // support and older models only have 8bit kv io.
-      example::KvBitWidth kv_bitwidth = example::KvBitWidth::kWidth8;
-      if (module->method_names()->count("get_kv_io_bit_width") > 0) {
-        kv_bitwidth = static_cast<example::KvBitWidth>(
-            module->get("get_kv_io_bit_width").get().toScalar().to<int64_t>());
-      }
+      } else if (model_type_category == MODEL_TYPE_QNN_LLAMA) {
+        std::unique_ptr<executorch::extension::Module> module =
+            std::make_unique<executorch::extension::Module>(
+                model_path->toStdString().c_str(),
+                data_files_vector,
+                cpp_load_mode);
+        std::string decoder_model = "llama3"; // use llama3 for now
+        // Using 8bit as default since this meta is introduced with 16bit kv io
+        // support and older models only have 8bit kv io.
+        example::KvBitWidth kv_bitwidth = example::KvBitWidth::kWidth8;
+        if (module->method_names()->count("get_kv_io_bit_width") > 0) {
+          kv_bitwidth = static_cast<example::KvBitWidth>(
+              module->get("get_kv_io_bit_width")
+                  .get()
+                  .toScalar()
+                  .to<int64_t>());
+        }
 
-      if (kv_bitwidth == example::KvBitWidth::kWidth8) {
-        runner_ = std::make_unique<example::Runner<uint8_t>>(
-            std::move(module),
-            decoder_model.c_str(),
-            model_path->toStdString().c_str(),
-            tokenizer_path->toStdString().c_str(),
-            "",
-            "",
-            temperature_);
-      } else if (kv_bitwidth == example::KvBitWidth::kWidth16) {
-        runner_ = std::make_unique<example::Runner<uint16_t>>(
-            std::move(module),
-            decoder_model.c_str(),
-            model_path->toStdString().c_str(),
-            tokenizer_path->toStdString().c_str(),
-            "",
-            "",
-            temperature_);
-      } else {
-        ET_CHECK_MSG(
-            false,
-            "Unsupported kv bitwidth: %ld",
-            static_cast<int64_t>(kv_bitwidth));
-      }
-      model_type_category_ = MODEL_TYPE_CATEGORY_LLM;
+        if (kv_bitwidth == example::KvBitWidth::kWidth8) {
+          runner_ = std::make_unique<example::Runner<uint8_t>>(
+              std::move(module),
+              decoder_model.c_str(),
+              model_path->toStdString().c_str(),
+              tokenizer_path->toStdString().c_str(),
+              "",
+              "",
+              temperature_);
+        } else if (kv_bitwidth == example::KvBitWidth::kWidth16) {
+          runner_ = std::make_unique<example::Runner<uint16_t>>(
+              std::move(module),
+              decoder_model.c_str(),
+              model_path->toStdString().c_str(),
+              tokenizer_path->toStdString().c_str(),
+              "",
+              "",
+              temperature_);
+        } else {
+          ET_CHECK_MSG(
+              false,
+              "Unsupported kv bitwidth: %ld",
+              static_cast<int64_t>(kv_bitwidth));
+        }
+        model_type_category_ = MODEL_TYPE_CATEGORY_LLM;
 #endif
 #if defined(EXECUTORCH_BUILD_MEDIATEK)
-    } else if (model_type_category == MODEL_TYPE_MEDIATEK_LLAMA) {
-      runner_ = std::make_unique<MTKLlamaRunner>(
-          model_path->toStdString().c_str(),
-          tokenizer_path->toStdString().c_str());
-      // Interpret the model type as LLM
-      model_type_category_ = MODEL_TYPE_CATEGORY_LLM;
+      } else if (model_type_category == MODEL_TYPE_MEDIATEK_LLAMA) {
+        runner_ = std::make_unique<MTKLlamaRunner>(
+            model_path->toStdString().c_str(),
+            tokenizer_path->toStdString().c_str());
+        // Interpret the model type as LLM
+        model_type_category_ = MODEL_TYPE_CATEGORY_LLM;
 #endif
+      }
+    } catch (const std::exception& e) {
+      executorch::jni_helper::throwExecutorchException(
+          static_cast<uint32_t>(Error::Internal),
+          std::string("Failed to create LlmModule: ") + e.what());
+    } catch (...) {
+      executorch::jni_helper::throwExecutorchException(
+          static_cast<uint32_t>(Error::Internal),
+          "Failed to create LlmModule: unknown native error");
     }
   }