[ Context ] Add Engine Class to manage Contexts

In order to extend the Context more easily, this PR adds Engine Class to manage the Contexts. It adds Context Class as well which is base class of all the Context. . add Engine Class . add Context Class . set default Context is app contexts . Pluggable support in Engine . some more code optimization & test codes requires. **Self evaluation:** 1. Build test: [X]Passed [ ]Failed [ ]Skipped 2. Run test: [X]Passed [ ]Failed [ ]Skipped Signed-off-by: jijoong.moon <[email protected]>
nnstreamer · Jan 2, 2025 · dbbde82 · dbbde82
1 parent 869d348
commit dbbde82
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Showing 36 changed files with 898 additions and 262 deletions.
diff --git a/Applications/Custom/LayerClient/jni/main.cpp b/Applications/Custom/LayerClient/jni/main.cpp
@@ -20,6 +20,7 @@
 
 /// @todo Migrate this to api
 #include <app_context.h>
+#include <engine.h>
 
 #include <mae_loss.h>
 #include <pow.h>
@@ -195,13 +196,15 @@ int main(int argc, char *argv[]) {
   }
 
   try {
-    auto &app_context = nntrainer::AppContext::Global();
+    auto &ct_engine = nntrainer::Engine::Global();
+    auto app_context = static_cast<nntrainer::AppContext *>(
+      ct_engine.getRegisteredContext("cpu"));
     /// registering custom layer here
     /// registerFactory excepts a function that returns unique_ptr<Layer> from
     /// std::vector<std::string> ml::train::createLayer<T> is a templated
     /// function for generic usage
-    app_context.registerFactory(nntrainer::createLayer<custom::PowLayer>);
-    app_context.registerFactory(nntrainer::createLayer<custom::MaeLossLayer>);
+    app_context->registerFactory(nntrainer::createLayer<custom::PowLayer>);
+    app_context->registerFactory(nntrainer::createLayer<custom::MaeLossLayer>);
   } catch (std::invalid_argument &e) {
     std::cerr << "failed to register factory, reason: " << e.what()
               << std::endl;

diff --git a/Applications/LLaMA/jni/main.cpp b/Applications/LLaMA/jni/main.cpp
@@ -26,6 +26,7 @@
 
 #include <app_context.h>
 #include <custom_multi_head_attention_layer.h>
+#include <engine.h>
 #include <rms_norm.h>
 #include <rotary_embedding.h>
 #include <swiglu.h>
@@ -723,18 +724,19 @@ int main(int argc, char *argv[]) {
 #else
   std::string text = "This is smaple input for LLaMA.";
 #endif
-
-  auto &app_context = nntrainer::AppContext::Global();
+  auto &ct_engine = nntrainer::Engine::Global();
+  auto app_context =
+    static_cast<nntrainer::AppContext *>(ct_engine.getRegisteredContext("cpu"));
   try {
-    app_context.registerFactory(nntrainer::createLayer<custom::SwiGLULayer>);
+    app_context->registerFactory(nntrainer::createLayer<custom::SwiGLULayer>);
   } catch (std::invalid_argument &e) {
     std::cerr << "failed to register factory, reason: " << e.what()
               << std::endl;
     return 1;
   }
 
   try {
-    app_context.registerFactory(nntrainer::createLayer<custom::RMSNormLayer>);
+    app_context->registerFactory(nntrainer::createLayer<custom::RMSNormLayer>);
   } catch (std::invalid_argument &e) {
     std::cerr << "failed to register factory, reason: " << e.what()
               << std::endl;

diff --git a/Applications/SimpleShot/task_runner.cpp b/Applications/SimpleShot/task_runner.cpp
@@ -18,6 +18,7 @@
 #include <unistd.h>
 
 #include <app_context.h>
+#include <engine.h>
 #include <model.h>
 #include <nntrainer-api-common.h>
 
@@ -183,7 +184,9 @@ std::unique_ptr<ml::train::Model> createModel(const std::string &backbone,
  * @return int
  */
 int main(int argc, char **argv) {
-  auto &app_context = nntrainer::AppContext::Global();
+  auto &ct_engine = nntrainer::Engine::Global();
+  auto app_context =
+    static_cast<nntrainer::AppContext *>(ct_engine.getRegisteredContext("cpu"));
 
   if (argc != 6 && argc != 5) {
     std::cout
@@ -221,7 +224,7 @@ int main(int argc, char **argv) {
   std::string val_path = app_path + "/tasks/" + argv[4];
 
   try {
-    app_context.registerFactory(
+    app_context->registerFactory(
       nntrainer::createLayer<simpleshot::layers::CenteringLayer>);
   } catch (std::exception &e) {
     std::cerr << "registering factory failed: " << e.what();

diff --git a/Applications/TransferLearning/CIFAR_Classification/jni/main.cpp b/Applications/TransferLearning/CIFAR_Classification/jni/main.cpp
@@ -39,7 +39,7 @@
 #include <stdlib.h>
 #include <time.h>
 
-#include <app_context.h>
+#include <engine.h>
 #include <neuralnet.h>
 #include <tensor.h>
 
@@ -375,7 +375,7 @@ int main(int argc, char *argv[]) {
 
   /// @todo add api version of this
   try {
-    nntrainer::AppContext::Global().setWorkingDirectory(data_path);
+    nntrainer::Engine::Global().setWorkingDirectory(data_path);
   } catch (std::invalid_argument &e) {
     std::cerr << "setting data_path failed, pwd is used instead";
   }

diff --git a/Applications/TransferLearning/CIFAR_Classification/jni/main_func.cpp b/Applications/TransferLearning/CIFAR_Classification/jni/main_func.cpp
@@ -39,7 +39,7 @@
 
 #include <bitmap_helpers.h>
 
-#include <app_context.h>
+#include <engine.h>
 #define TRAINING true
 
 /**
@@ -254,7 +254,7 @@ int main(int argc, char *argv[]) {
 
   /// @todo add api version of this
   try {
-    nntrainer::AppContext::Global().setWorkingDirectory(data_path);
+    nntrainer::Engine::Global().setWorkingDirectory(data_path);
   } catch (std::invalid_argument &e) {
     std::cerr << "setting data_path failed, pwd is used instead";
   }

diff --git a/Applications/YOLOv2/jni/main.cpp b/Applications/YOLOv2/jni/main.cpp
@@ -21,6 +21,7 @@
 
 #include <app_context.h>
 #include <det_dataloader.h>
+#include <engine.h>
 #include <layer.h>
 #include <model.h>
 #include <optimizer.h>
@@ -285,9 +286,13 @@ int main(int argc, char *argv[]) {
             << std::endl;
 
   try {
-    auto &app_context = nntrainer::AppContext::Global();
-    app_context.registerFactory(nntrainer::createLayer<custom::ReorgLayer>);
-    app_context.registerFactory(
+    auto &ct_engine = nntrainer::Engine::Global();
+
+    auto app_context = static_cast<nntrainer::AppContext *>(
+      ct_engine.getRegisteredContext("cpu"));
+
+    app_context->registerFactory(nntrainer::createLayer<custom::ReorgLayer>);
+    app_context->registerFactory(
       nntrainer::createLayer<custom::YoloV2LossLayer>);
   } catch (std::invalid_argument &e) {
     std::cerr << "failed to register factory, reason: " << e.what()

diff --git a/Applications/YOLOv3/jni/main.cpp b/Applications/YOLOv3/jni/main.cpp
@@ -20,6 +20,7 @@
 
 #include <app_context.h>
 #include <det_dataloader.h>
+#include <engine.h>
 #include <layer.h>
 #include <model.h>
 #include <optimizer.h>
@@ -402,17 +403,22 @@ int main(int argc, char *argv[]) {
             << std::endl;
 
   try {
-    auto &app_context = nntrainer::AppContext::Global();
-    app_context.registerFactory(nntrainer::createLayer<custom::UpsampleLayer>);
+    auto &ct_engine = nntrainer::Engine::Global();
+    auto app_context = static_cast<nntrainer::AppContext *>(
+      ct_engine.getRegisteredContext("cpu"));
+
+    app_context->registerFactory(nntrainer::createLayer<custom::UpsampleLayer>);
   } catch (std::invalid_argument &e) {
     std::cerr << "failed to register factory, reason: " << e.what()
               << std::endl;
     return 1;
   }
 
   try {
-    auto &app_context = nntrainer::AppContext::Global();
-    app_context.registerFactory(
+    auto &ct_engine = nntrainer::Engine::Global();
+    auto app_context = static_cast<nntrainer::AppContext *>(
+      ct_engine.getRegisteredContext("cpu"));
+    app_context->registerFactory(
       nntrainer::createLayer<custom::YoloV3LossLayer>);
   } catch (std::invalid_argument &e) {
     std::cerr << "failed to register yolov3 loss, reason: " << e.what()

diff --git a/api/ccapi/src/factory.cpp b/api/ccapi/src/factory.cpp
@@ -15,9 +15,9 @@
 #include <string>
 #include <vector>
 
-#include <app_context.h>
 #include <databuffer.h>
 #include <databuffer_factory.h>
+#include <engine.h>
 #include <layer.h>
 #include <model.h>
 #include <neuralnet.h>
@@ -127,8 +127,8 @@ createDataset(DatasetType type, datagen_cb cb, void *user_data,
 std::unique_ptr<ml::train::LearningRateScheduler>
 createLearningRateScheduler(const LearningRateSchedulerType &type,
                             const std::vector<std::string> &properties) {
-  auto &ac = nntrainer::AppContext::Global();
-  return ac.createObject<ml::train::LearningRateScheduler>(type, properties);
+  auto &eg = nntrainer::Engine::Global();
+  return eg.createLearningRateSchedulerObject(type, properties);
 }
 
 /**
@@ -137,8 +137,8 @@ createLearningRateScheduler(const LearningRateSchedulerType &type,
 std::unique_ptr<ml::train::LearningRateScheduler>
 createLearningRateScheduler(const std::string &type,
                             const std::vector<std::string> &properties) {
-  auto &ac = nntrainer::AppContext::Global();
-  return ac.createObject<ml::train::LearningRateScheduler>(type, properties);
+  auto &eg = nntrainer::Engine::Global();
+  return eg.createLearningRateSchedulerObject(type, properties);
 }
 
 std::string getVersion() {

diff --git a/debian/nntrainer-dev.install b/debian/nntrainer-dev.install
@@ -31,6 +31,8 @@
 /usr/include/nntrainer/loss_layer.h
 # custom layer kits
 /usr/include/nntrainer/app_context.h
+/usr/include/nntrainer/context.h
+/usr/include/nntrainer/engine.h
 # logger
 /usr/include/nntrainer/nntrainer_log.h
 /usr/include/nntrainer/nntrainer_logger.h

diff --git a/nntrainer/app_context.cpp b/nntrainer/app_context.cpp
@@ -409,12 +409,8 @@ static void registerer(AppContext &ac) noexcept {
 };
 
 AppContext &AppContext::Global() {
-  static AppContext instance;
-  /// in g++ there is a bug that hangs up if caller throws,
-  /// so registerer is noexcept although it'd better not
-  /// https://gcc.gnu.org/bugzilla/show_bug.cgi?id=70298
-  std::call_once(global_app_context_init_flag, registerer, std::ref(instance));
-  return instance;
+  registerer(*this);
+  return *this;
 }
 
 void AppContext::setWorkingDirectory(const std::string &base) {

diff --git a/nntrainer/app_context.h b/nntrainer/app_context.h
@@ -31,6 +31,7 @@
 #include <optimizer.h>
 #include <optimizer_devel.h>
 
+#include <context.h>
 #include <nntrainer_error.h>
 
 namespace nntrainer {
@@ -42,45 +43,26 @@ namespace {} // namespace
  * @class AppContext contains user-dependent configuration
  * @brief App
  */
-class AppContext {
+class AppContext : public Context {
 public:
-  using PropsType = std::vector<std::string>;
-
-  template <typename T> using PtrType = std::unique_ptr<T>;
-
-  template <typename T>
-  using FactoryType = std::function<PtrType<T>(const PropsType &)>;
-
-  template <typename T>
-  using PtrFactoryType = PtrType<T> (*)(const PropsType &);
-  template <typename T>
-  using StrIndexType = std::unordered_map<std::string, FactoryType<T>>;
-
-  /** integer to string key */
-  using IntIndexType = std::unordered_map<int, std::string>;
 
   /**
-   * This type contains tuple of
-   * 1) integer -> string index
-   * 2) string -> factory index
+   * @brief   Default constructor
    */
-  template <typename T>
-  using IndexType = std::tuple<StrIndexType<T>, IntIndexType>;
-
-  template <typename... Ts> using FactoryMap = std::tuple<IndexType<Ts>...>;
+  AppContext() = default;
 
   /**
-   * @brief   Default constructor
+   * @brief   Default destructor
    */
-  AppContext() = default;
+  ~AppContext() override = default;
 
   /**
    *
    * @brief Get Global app context.
    *
    * @return AppContext&
    */
-  static AppContext &Global();
+  AppContext &Global();
 
   /**
    * @brief Set Working Directory for a relative path. working directory is set
@@ -201,6 +183,44 @@ class AppContext {
                             const std::string &key = "",
                             const int int_key = -1);
 
+  std::unique_ptr<nntrainer::Layer>
+  createLayerObject(const std::string &type,
+                    const std::vector<std::string> &properties = {}) override {
+    return createObject<nntrainer::Layer>(type, properties);
+  }
+
+  std::unique_ptr<nntrainer::Optimizer> createOptimizerObject(
+    const std::string &type,
+    const std::vector<std::string> &properties = {}) override {
+    return createObject<nntrainer::Optimizer>(type, properties);
+  }
+
+  std::unique_ptr<ml::train::LearningRateScheduler>
+  createLearningRateSchedulerObject(
+    const std::string &type,
+    const std::vector<std::string> &properties = {}) override {
+    return createObject<ml::train::LearningRateScheduler>(type, properties);
+  }
+
+  std::unique_ptr<nntrainer::Layer>
+  createLayerObject(const int int_key,
+                    const std::vector<std::string> &properties = {}) override {
+    return createObject<nntrainer::Layer>(int_key, properties);
+  }
+
+  std::unique_ptr<nntrainer::Optimizer> createOptimizerObject(
+    const int int_key,
+    const std::vector<std::string> &properties = {}) override {
+    return createObject<nntrainer::Optimizer>(int_key, properties);
+  }
+
+  std::unique_ptr<ml::train::LearningRateScheduler>
+  createLearningRateSchedulerObject(
+    const int int_key,
+    const std::vector<std::string> &properties = {}) override {
+    return createObject<ml::train::LearningRateScheduler>(int_key, properties);
+  }
+
   /**
    * @brief Create an Object from the integer key
    *
@@ -271,6 +291,8 @@ class AppContext {
     throw std::invalid_argument("cannot create unknown object");
   }
 
+  std::string getName() override { return "cpu"; }
+
 private:
   FactoryMap<nntrainer::Optimizer, nntrainer::Layer,
              ml::train::LearningRateScheduler>