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");
  }
  }