Move examples/mediatek out from under the torch namespace

examples/mediatek/executor_runner/llama_runner/FileMemMapper.h

@@ -15,7 +15,7 @@
 #include <sys/stat.h>
 #include <unistd.h>
 
-namespace torch::executor {
+namespace example {
 
 class FileMemMapper { // Read-only mmap
  public:
@@ -97,4 +97,4 @@ class FileMemMapper { // Read-only mmap
   size_t mSize = 0;
 };
 
-} // namespace torch::executor
+} // namespace example

examples/mediatek/executor_runner/llama_runner/LlamaConfig.h

@@ -13,7 +13,7 @@
 
 #include "llm_helper/include/llm_types.h"
 
-namespace torch::executor {
+namespace example {
 
 using llm_helper::LLMType;
 
@@ -42,4 +42,4 @@ struct LlamaModelPaths {
   std::vector<std::string> gen_model_paths;
 };
 
-} // namespace torch::executor
+} // namespace example

examples/mediatek/executor_runner/llama_runner/LlamaModelChunk.cpp

@@ -26,7 +26,7 @@
 #include "llm_helper/include/mask_builder.h"
 #include "llm_helper/include/rotary_embedding.h"
 
-namespace torch::executor {
+namespace example {
 
 inline std::vector<size_t> getIndexRange(
     const size_t startIndex,
@@ -343,4 +343,4 @@ void LlamaModelChunk::InitCache() {
   }
 }
 
-} // namespace torch::executor
+} // namespace example

examples/mediatek/executor_runner/llama_runner/LlamaModelChunk.h

@@ -27,12 +27,12 @@
 #include "llm_helper/include/mask_builder.h"
 #include "llm_helper/include/rotary_embedding.h"
 
-namespace torch::executor {
+namespace example {
 
 using llm_helper::MaskBuilder;
 using llm_helper::RotaryEmbeddingMasterLut;
 
-using TensorShape = Span<const int32_t>;
+using TensorShape = executorch::runtime::Span<const int32_t>;
 using ModelIndexMap = std::unordered_map<size_t, size_t>;
 
 // Llama decoder chunk
@@ -135,4 +135,4 @@ class LlamaModelChunk : public ModelChunk {
   size_t mCurrentTokenIndex = 0;
 };
 
-} // namespace torch::executor
+} // namespace example

examples/mediatek/executor_runner/llama_runner/LlamaRuntime.cpp

@@ -18,7 +18,7 @@
 #include "llm_helper/include/rotary_embedding.h"
 #include "llm_helper/include/token_embedding.h"
 
-namespace torch::executor {
+namespace example {
 
 void LlamaRuntime::Initialize(
     const LlamaModelOptions& modelOptions,
@@ -201,4 +201,4 @@ const LlamaModelOptions& LlamaRuntime::GetModelOptions() const {
   return mModelOptions;
 }
 
-} // namespace torch::executor
+} // namespace example

examples/mediatek/executor_runner/llama_runner/LlamaRuntime.h

@@ -20,7 +20,7 @@
 #include "llm_helper/include/rotary_embedding.h"
 #include "llm_helper/include/token_embedding.h"
 
-namespace torch::executor {
+namespace example {
 
 class LlamaRuntime {
  public:
@@ -56,4 +56,4 @@ class LlamaRuntime {
   size_t mTokenIndex = 0;
 };
 
-} // namespace torch::executor
+} // namespace example

examples/mediatek/executor_runner/llama_runner/ModelChunk.cpp

@@ -23,9 +23,21 @@
 #define ENSURE_INIT \
   ET_CHECK_MSG(Initialized(), "Error: Model chunk not initialized.");
 
-namespace torch::executor {
-
-using util::FileDataLoader;
+namespace example {
+
+using executorch::aten::Tensor;
+using executorch::aten::TensorImpl;
+using executorch::extension::FileDataLoader;
+using executorch::runtime::Error;
+using executorch::runtime::HierarchicalAllocator;
+using executorch::runtime::MemoryAllocator;
+using executorch::runtime::MemoryManager;
+using executorch::runtime::Method;
+using executorch::runtime::MethodMeta;
+using executorch::runtime::Program;
+using executorch::runtime::Result;
+using executorch::runtime::Span;
+using executorch::runtime::Tag;
 
 static constexpr size_t kMethodAllocatorPoolSize = 4 * 1024U * 1024U; // 4MB
 
@@ -572,4 +584,4 @@ void ModelChunk::ReleaseModelInstance(void* modelInstance) {
   }
 }
 
-} // namespace torch::executor
+} // namespace example

examples/mediatek/executor_runner/llama_runner/ModelChunk.h

@@ -16,7 +16,7 @@
 
 #include "MultiModelLoader.h"
 
-namespace torch::executor {
+namespace example {
 
 struct BufferInfo {
   void* data = nullptr;
@@ -91,7 +91,7 @@ class ModelChunk : protected MultiTokenSizeModelLoader {
   // Release allocated buffers for model IOs
   void ReleaseIoBuffers();
 
-  Method& GetModelMethod();
+  executorch::runtime::Method& GetModelMethod();
 
  private:
   // Override the virtual functions
@@ -119,4 +119,4 @@ class ModelChunk : protected MultiTokenSizeModelLoader {
   std::unordered_map<size_t, size_t> mModelOutToInIndexLinks;
 };
 
-} // namespace torch::executor
+} // namespace example

examples/mediatek/executor_runner/llama_runner/MultiModelLoader.cpp

@@ -16,7 +16,7 @@
 #include <unordered_map>
 #include <vector>
 
-namespace torch::executor {
+namespace example {
 
 template <typename IdType>
 void MultiModelLoader<IdType>::LoadModels() {
@@ -174,4 +174,4 @@ std::string MultiModelLoader<IdType>::GetIdString(const IdType& id) {
 template class MultiModelLoader<int>;
 template class MultiModelLoader<size_t>;
 
-} // namespace torch::executor
+} // namespace example

examples/mediatek/executor_runner/llama_runner/MultiModelLoader.h

@@ -12,7 +12,7 @@
 #include <unordered_map>
 #include <vector>
 
-namespace torch::executor {
+namespace example {
 
 template <typename IdType = size_t>
 class MultiModelLoader {
@@ -92,4 +92,4 @@ class MultiModelLoader {
   IdType mCurrentModelId = 0;
 };
 
-} // namespace torch::executor
+} // namespace example

examples/mediatek/executor_runner/llama_runner/Utils.h

@@ -18,7 +18,7 @@
 #include <string_view>
 #include <vector>
 
-namespace torch::executor {
+namespace example {
 namespace utils {
 
 class Timer {
@@ -113,4 +113,4 @@ static std::string to_string(const std::vector<T> vec) {
 }
 
 } // namespace utils
-} // namespace torch::executor
+} // namespace example

examples/mediatek/executor_runner/llama_runner/llm_helper/include/llm_types.h

@@ -11,7 +11,7 @@
 #include <stddef.h>
 #include <strings.h>
 
-namespace torch::executor {
+namespace example {
 namespace llm_helper {
 
 typedef enum { INT4, INT8, INT16, FP16, INT32, FP32, INVALID } LLMType;
@@ -72,4 +72,4 @@ inline const char* getLLMTypeName(const LLMType llm_type) {
 }
 
 } // namespace llm_helper
-} // namespace torch::executor
+} // namespace example

examples/mediatek/executor_runner/llama_runner/llm_helper/include/mask_builder.h

@@ -12,7 +12,7 @@
 
 #include <string>
 
-namespace torch::executor {
+namespace example {
 namespace llm_helper {
 
 class MaskBuilder {
@@ -76,4 +76,4 @@ class MaskBuilder {
 };
 
 } // namespace llm_helper
-} // namespace torch::executor
+} // namespace example

examples/mediatek/executor_runner/llama_runner/llm_helper/include/rotary_embedding.h

@@ -13,7 +13,7 @@
 #include <string>
 #include <vector>
 
-namespace torch::executor {
+namespace example {
 namespace llm_helper {
 
 class RotaryEmbeddingMasterLut {
@@ -77,4 +77,4 @@ class RotaryEmbeddingMasterLut {
 };
 
 } // namespace llm_helper
-} // namespace torch::executor
+} // namespace example

examples/mediatek/executor_runner/llama_runner/llm_helper/include/token_embedding.h

@@ -13,7 +13,7 @@
 #include <string>
 #include <vector>
 
-namespace torch::executor {
+namespace example {
 
 class FileMemMapper;
 
@@ -49,4 +49,4 @@ class TokenEmbeddingLut {
 };
 
 } // namespace llm_helper
-} // namespace torch::executor
+} // namespace example

examples/mediatek/executor_runner/llama_runner/llm_helper/mask_builder.cpp

@@ -11,7 +11,7 @@
 #include <executorch/runtime/platform/assert.h>
 #include <executorch/runtime/platform/log.h>
 
-namespace torch::executor {
+namespace example {
 namespace llm_helper {
 
 // Define mask values for different types
@@ -260,4 +260,4 @@ bool MaskBuilder::adjustMaskForPadding(const size_t tokenBatchSize) {
 }
 
 } // namespace llm_helper
-} // namespace torch::executor
+} // namespace example

examples/mediatek/executor_runner/llama_runner/llm_helper/rotary_embedding.cpp

@@ -16,7 +16,7 @@
 #include <fstream>
 #include <type_traits>
 
-namespace torch::executor {
+namespace example {
 namespace llm_helper {
 
 RotaryEmbeddingMasterLut::RotaryEmbeddingMasterLut(
@@ -394,4 +394,4 @@ size_t RotaryEmbeddingMasterLut::getRotEmbedLength() const {
 }
 
 } // namespace llm_helper
-} // namespace torch::executor
+} // namespace example

examples/mediatek/executor_runner/llama_runner/llm_helper/token_embedding.cpp

@@ -19,7 +19,7 @@
 
 namespace fs = std::filesystem;
 
-namespace torch::executor {
+namespace example {
 namespace llm_helper {
 
 TokenEmbeddingLut::TokenEmbeddingLut(
@@ -90,4 +90,4 @@ void TokenEmbeddingLut::lookupEmbedding(const std::vector<uint64_t>& tokens) {
 }
 
 } // namespace llm_helper
-} // namespace torch::executor
+} // namespace example

examples/mediatek/executor_runner/mtk_executor_runner.cpp

@@ -41,11 +41,21 @@ DEFINE_string(
     "Model serialized in flatbuffer format.");
 DEFINE_int32(iteration, 1, "Iterations of inference.");
 
-using namespace torch::executor;
-using torch::executor::util::FileDataLoader;
+using executorch::extension::FileDataLoader;
+using executorch::extension::prepare_input_tensors;
+using executorch::runtime::Error;
+using executorch::runtime::EValue;
+using executorch::runtime::HierarchicalAllocator;
+using executorch::runtime::MemoryAllocator;
+using executorch::runtime::MemoryManager;
+using executorch::runtime::Method;
+using executorch::runtime::MethodMeta;
+using executorch::runtime::Program;
+using executorch::runtime::Result;
+using executorch::runtime::Span;
 
 int main(int argc, char** argv) {
-  runtime_init();
+  executorch::runtime::runtime_init();
 
   gflags::ParseCommandLineFlags(&argc, &argv, true);
   if (argc != 1) {
@@ -158,7 +168,7 @@ int main(int argc, char** argv) {
   // Allocate input tensors and set all of their elements to 1. The `inputs`
   // variable owns the allocated memory and must live past the last call to
   // `execute()`.
-  auto inputs = util::prepare_input_tensors(*method);
+  auto inputs = prepare_input_tensors(*method);
   ET_CHECK_MSG(
       inputs.ok(),
       "Could not prepare inputs: 0x%" PRIx32,
@@ -196,7 +206,7 @@ int main(int argc, char** argv) {
   status = method->get_outputs(outputs.data(), outputs.size());
   ET_CHECK(status == Error::Ok);
   // Print the first and last 100 elements of long lists of scalars.
-  std::cout << torch::executor::util::evalue_edge_items(100);
+  std::cout << executorch::extension::evalue_edge_items(100);
   for (int i = 0; i < outputs.size(); ++i) {
     std::cout << "Output " << i << ": " << outputs[i] << std::endl;
   }