chore: unify naming of the errors

Author: chmjkb

docs/docs/03-typescript-api/04-error-handling.md

@@ -4,16 +4,16 @@ title: Error handling
 
 ## Overview
 
-In order to handle different types of errors, you can use `instanceof` with our exported class `ExecutorchError` and its `code` property. This allows you to check what exactly went wrong and act accordingly.
+In order to handle different types of errors, you can use `instanceof` with our exported class `RnExecutorchError` and its `code` property. This allows you to check what exactly went wrong and act accordingly.
 
 This example uses the `LLMModule`, and then tries to change its `generationConfig`. As the topp param has to be a value between 0 and 1 (inclusive), the `.configure()` method will throw an error with a code InvalidConfig.
 
 ```typescript
 import {
   LLMModule,
   LLAMA3_2_1B_QLORA,
-  ExecutorchError,
-  ETErrorCode,
+  RnExecutorchError,
+  RnExecutorchErrorCode,
 } from 'react-native-executorch';
 
 const llm = new LLMModule({
@@ -28,8 +28,8 @@ try {
   await llm.configure({ topp: 1.5 }); // This will throw InvalidConfig error
 } catch (err) {
   if (
-    err instanceof ExecutorchError &&
-    err.code === ETErrorCode.InvalidConfig
+    err instanceof RnExecutorchError &&
+    err.code === RnExecutorchErrorCode.InvalidConfig
   ) {
     console.error('Invalid configuration:', err.message);
     // Handle the invalid config - set default values
@@ -43,11 +43,11 @@ try {
 try {
   await llm.sendMessage('Hello, World!');
 } catch (err) {
-  if (err instanceof ExecutorchError) {
-    if (err.code === ETErrorCode.ModuleNotLoaded) {
+  if (err instanceof RnExecutorchError) {
+    if (err.code === RnExecutorchErrorCode.ModuleNotLoaded) {
       console.error('Model not loaded:', err.message);
       // Load the model first
-    } else if (err.code === ETErrorCode.ModelGenerating) {
+    } else if (err.code === RnExecutorchErrorCode.ModelGenerating) {
       console.error('Model is already generating:', err.message);
       // Wait for current generation to complete
     } else {
@@ -68,7 +68,7 @@ llm.delete();
 
 ## Reference
 
-All errors in React Native ExecuTorch inherit from `ExecutorchError` and include a `code` property from the `ETErrorCode` enum. Below is a comprehensive list of all possible errors, organized by category.
+All errors in React Native ExecuTorch inherit from `RnExecutorchError` and include a `code` property from the `RnExecutorchErrorCode` enum. Below is a comprehensive list of all possible errors, organized by category.
 
 ### Module State Errors
 

packages/react-native-executorch/common/rnexecutorch/Error.h

@@ -10,7 +10,7 @@
 namespace rnexecutorch {
 
 using ErrorVariant =
-    std::variant<RnExecutorchInternalError, executorch::runtime::Error>;
+    std::variant<RnExecutorchErrorCode, executorch::runtime::Error>;
 
 class RnExecutorchError : public std::runtime_error {
 public:
@@ -25,11 +25,11 @@ class RnExecutorchError : public std::runtime_error {
         errorCode);
   }
 
-  bool isInternalError() const noexcept {
-    return std::holds_alternative<RnExecutorchInternalError>(errorCode);
+  bool isRnExecutorchError() const noexcept {
+    return std::holds_alternative<RnExecutorchErrorCode>(errorCode);
   }
 
-  bool isExecuTorchError() const noexcept {
+  bool isExecuTorchRuntimeError() const noexcept {
     return std::holds_alternative<executorch::runtime::Error>(errorCode);
   }
 };

packages/react-native-executorch/common/rnexecutorch/ErrorCodes.h

@@ -7,7 +7,7 @@
 
 namespace rnexecutorch {
 
-enum class RnExecutorchInternalError : int32_t {
+enum class RnExecutorchErrorCode : int32_t {
   /**
    * An umbrella-error that is thrown usually when something unexpected happens,
    * for example a 3rd-party library error.

packages/react-native-executorch/common/rnexecutorch/TokenizerModule.cpp

@@ -18,7 +18,7 @@ void TokenizerModule::ensureTokenizerLoaded(
     const std::string &methodName) const {
   if (!tokenizer) {
     throw RnExecutorchError(
-        RnExecutorchInternalError::ModuleNotLoaded,
+        RnExecutorchErrorCode::ModuleNotLoaded,
         methodName + " function was called on an uninitialized tokenizer!");
   }
 }

packages/react-native-executorch/common/rnexecutorch/data_processing/FileUtils.h

@@ -18,7 +18,7 @@ inline std::string getTimeID() {
 inline std::string loadBytesFromFile(const std::string &path) {
   std::ifstream fs(path, std::ios::in | std::ios::binary);
   if (fs.fail()) {
-    throw RnExecutorchError(RnExecutorchInternalError::FileReadFailed,
+    throw RnExecutorchError(RnExecutorchErrorCode::FileReadFailed,
                             "Failed to open tokenizer file!");
   }
   std::string data;

packages/react-native-executorch/common/rnexecutorch/data_processing/ImageProcessing.cpp

@@ -68,7 +68,7 @@ std::string saveToTempFile(const cv::Mat &image) {
   std::filesystem::path filePath = tempDir / filename;
 
   if (!cv::imwrite(filePath.string(), image)) {
-    throw RnExecutorchError(RnExecutorchInternalError::FileWriteFailed,
+    throw RnExecutorchError(RnExecutorchErrorCode::FileWriteFailed,
                             "Failed to save the image: " + filePath.string());
   }
 
@@ -87,7 +87,7 @@ cv::Mat readImage(const std::string &imageURI) {
       ++segmentIndex;
     }
     if (segmentIndex != 1) {
-      throw RnExecutorchError(RnExecutorchInternalError::FileReadFailed,
+      throw RnExecutorchError(RnExecutorchErrorCode::FileReadFailed,
                               "Read image error: invalid base64 URI");
     }
     auto data = base64_decode(stringData);
@@ -104,12 +104,12 @@ cv::Mat readImage(const std::string &imageURI) {
         cv::Mat(1, imageData.size(), CV_8UC1, (void *)imageData.data()),
         cv::IMREAD_COLOR);
   } else {
-    throw RnExecutorchError(RnExecutorchInternalError::FileReadFailed,
+    throw RnExecutorchError(RnExecutorchErrorCode::FileReadFailed,
                             "Read image error: unknown protocol");
   }
 
   if (image.empty()) {
-    throw RnExecutorchError(RnExecutorchInternalError::FileReadFailed,
+    throw RnExecutorchError(RnExecutorchErrorCode::FileReadFailed,
                             "Read image error: invalid argument");
   }
 
@@ -225,7 +225,7 @@ readImageToTensor(const std::string &path,
                   "Unexpected tensor size, expected at least 2 dimentions "
                   "but got: %zu.",
                   tensorDims.size());
-    throw RnExecutorchError(RnExecutorchInternalError::UnexpectedNumInputs,
+    throw RnExecutorchError(RnExecutorchErrorCode::UnexpectedNumInputs,
                             errorMessage);
   }
   cv::Size tensorSize = cv::Size(tensorDims[tensorDims.size() - 1],

packages/react-native-executorch/common/rnexecutorch/data_processing/Numerical.cpp

@@ -38,7 +38,7 @@ void softmaxWithTemperature(std::span<float> input, float temperature) {
 
   if (temperature <= 0.0F) {
     throw RnExecutorchError(
-        RnExecutorchInternalError::InvalidConfig,
+        RnExecutorchErrorCode::InvalidConfig,
         "Temperature must be greater than 0 for softmax with temperature!");
   }
 
@@ -78,7 +78,7 @@ std::vector<float> meanPooling(std::span<const float> modelOutput,
        << "by the size of attention mask but got size: " << modelOutput.size()
        << " for model output and size: " << attnMask.size()
        << " for attention mask";
-    throw RnExecutorchError(RnExecutorchInternalError::InvalidConfig, ss.str());
+    throw RnExecutorchError(RnExecutorchErrorCode::InvalidConfig, ss.str());
   }
 
   auto attnMaskLength = attnMask.size();

packages/react-native-executorch/common/rnexecutorch/data_processing/gzip.cpp

@@ -18,7 +18,7 @@ size_t deflateSize(const std::string &input) {
   if (::deflateInit2(&strm, Z_DEFAULT_COMPRESSION, Z_DEFLATED,
                      MAX_WBITS + kGzipWrapper, kMemLevel,
                      Z_DEFAULT_STRATEGY) != Z_OK) {
-    throw RnExecutorchError(RnExecutorchInternalError::UnknownError,
+    throw RnExecutorchError(RnExecutorchErrorCode::UnknownError,
                             "deflateInit2 failed");
   }
 
@@ -37,7 +37,7 @@ size_t deflateSize(const std::string &input) {
     ret = ::deflate(&strm, strm.avail_in ? Z_NO_FLUSH : Z_FINISH);
     if (ret == Z_STREAM_ERROR) {
       ::deflateEnd(&strm);
-      throw RnExecutorchError(RnExecutorchInternalError::UnknownError,
+      throw RnExecutorchError(RnExecutorchErrorCode::UnknownError,
                               "deflate stream error");
     }
 

packages/react-native-executorch/common/rnexecutorch/models/BaseModel.cpp

@@ -31,7 +31,7 @@ BaseModel::BaseModel(const std::string &modelSource,
 std::vector<int32_t> BaseModel::getInputShape(std::string method_name,
                                               int32_t index) const {
   if (!module_) {
-    throw RnExecutorchError(RnExecutorchInternalError::ModuleNotLoaded,
+    throw RnExecutorchError(RnExecutorchErrorCode::ModuleNotLoaded,
                             "Model not loaded: Cannot get input shape");
   }
 
@@ -58,7 +58,7 @@ std::vector<int32_t> BaseModel::getInputShape(std::string method_name,
 std::vector<std::vector<int32_t>>
 BaseModel::getAllInputShapes(std::string methodName) const {
   if (!module_) {
-    throw RnExecutorchError(RnExecutorchInternalError::ModuleNotLoaded,
+    throw RnExecutorchError(RnExecutorchErrorCode::ModuleNotLoaded,
                             "Model not loaded: Cannot get all input shapes");
   }
 
@@ -91,7 +91,7 @@ BaseModel::getAllInputShapes(std::string methodName) const {
 std::vector<JSTensorViewOut>
 BaseModel::forwardJS(std::vector<JSTensorViewIn> tensorViewVec) const {
   if (!module_) {
-    throw RnExecutorchError(RnExecutorchInternalError::ModuleNotLoaded,
+    throw RnExecutorchError(RnExecutorchErrorCode::ModuleNotLoaded,
                             "Model not loaded: Cannot perform forward pass");
   }
   std::vector<executorch::runtime::EValue> evalues;
@@ -141,7 +141,7 @@ BaseModel::forwardJS(std::vector<JSTensorViewIn> tensorViewVec) const {
 Result<executorch::runtime::MethodMeta>
 BaseModel::getMethodMeta(const std::string &methodName) const {
   if (!module_) {
-    throw RnExecutorchError(RnExecutorchInternalError::ModuleNotLoaded,
+    throw RnExecutorchError(RnExecutorchErrorCode::ModuleNotLoaded,
                             "Model not loaded: Cannot get method meta");
   }
   return module_->method_meta(methodName);
@@ -150,7 +150,7 @@ BaseModel::getMethodMeta(const std::string &methodName) const {
 Result<std::vector<EValue>>
 BaseModel::forward(const EValue &input_evalue) const {
   if (!module_) {
-    throw RnExecutorchError(RnExecutorchInternalError::ModuleNotLoaded,
+    throw RnExecutorchError(RnExecutorchErrorCode::ModuleNotLoaded,
                             "Model not loaded: Cannot perform forward pass");
   }
   return module_->forward(input_evalue);
@@ -159,7 +159,7 @@ BaseModel::forward(const EValue &input_evalue) const {
 Result<std::vector<EValue>>
 BaseModel::forward(const std::vector<EValue> &input_evalues) const {
   if (!module_) {
-    throw RnExecutorchError(RnExecutorchInternalError::ModuleNotLoaded,
+    throw RnExecutorchError(RnExecutorchErrorCode::ModuleNotLoaded,
                             "Model not loaded: Cannot perform forward pass");
   }
   return module_->forward(input_evalues);
@@ -169,7 +169,7 @@ Result<std::vector<EValue>>
 BaseModel::execute(const std::string &methodName,
                    const std::vector<EValue> &input_value) const {
   if (!module_) {
-    throw RnExecutorchError(RnExecutorchInternalError::ModuleNotLoaded,
+    throw RnExecutorchError(RnExecutorchErrorCode::ModuleNotLoaded,
                             "Model not loaded, cannot run execute");
   }
   return module_->execute(methodName, input_value);

packages/react-native-executorch/common/rnexecutorch/models/classification/Classification.cpp

@@ -15,7 +15,7 @@ Classification::Classification(const std::string &modelSource,
     : BaseModel(modelSource, callInvoker) {
   auto inputShapes = getAllInputShapes();
   if (inputShapes.size() == 0) {
-    throw RnExecutorchError(RnExecutorchInternalError::UnexpectedNumInputs,
+    throw RnExecutorchError(RnExecutorchErrorCode::UnexpectedNumInputs,
                             "Model seems to not take any input tensors.");
   }
   std::vector<int32_t> modelInputShape = inputShapes[0];
@@ -25,7 +25,7 @@ Classification::Classification(const std::string &modelSource,
                   "Unexpected model input size, expected at least 2 dimentions "
                   "but got: %zu.",
                   modelInputShape.size());
-    throw RnExecutorchError(RnExecutorchInternalError::WrongDimensions,
+    throw RnExecutorchError(RnExecutorchErrorCode::WrongDimensions,
                             errorMessage);
   }
   modelImageSize = cv::Size(modelInputShape[modelInputShape.size() - 1],
@@ -59,7 +59,7 @@ Classification::postprocess(const Tensor &tensor) {
         "Unexpected classification output size, was expecting: %zu classes "
         "but got: %zu classes",
         constants::kImagenet1kV1Labels.size(), resultVec.size());
-    throw RnExecutorchError(RnExecutorchInternalError::InvalidModelOutput,
+    throw RnExecutorchError(RnExecutorchErrorCode::InvalidModelOutput,
                             errorMessage);
   }