Decouple LlavaRunner from multimodal runner

Summary: Make sure upcoming changes to `MultimodalRunner` class doesn't break existing `LlavaRunner`. Eventually `LlavaRunner` will be deprecated and we will use `MultimodalRunner` for Llava demo and app integration.

Reviewed By: jackzhxng

Differential Revision: D78997241

Author: larryliu0820

examples/models/llava/runner/llava_image_prefiller.h

@@ -15,11 +15,10 @@
 
 namespace example {
 
-class ET_EXPERIMENTAL LlavaImagePrefiller
-    : public ::executorch::extension::llm::ImagePrefiller {
+class ET_EXPERIMENTAL LlavaImagePrefiller {
  public:
   explicit LlavaImagePrefiller(::executorch::extension::Module* module)
-      : ImagePrefiller(module){};
+      : module_(module){};
   /**
    * Prefill an LLM Module with the given image input.
    * @param image The image input to LLaVa.
@@ -28,7 +27,7 @@ class ET_EXPERIMENTAL LlavaImagePrefiller
    */
   inline ::executorch::runtime::Result<executorch::aten::Tensor> prefill(
       ::executorch::extension::llm::Image& image,
-      int64_t& start_pos) override {
+      int64_t& start_pos) {
     auto image_tensor = executorch::extension::from_blob(
         image.data.data(),
         {3, image.height, image.width},
@@ -59,7 +58,7 @@ class ET_EXPERIMENTAL LlavaImagePrefiller
    * Load the Module for image prefill purpose.
    * @return The error code.
    */
-  inline ::executorch::runtime::Error load() override {
+  inline ::executorch::runtime::Error load() {
     if (is_method_loaded()) {
       return ::executorch::runtime::Error::Ok;
     }
@@ -72,7 +71,7 @@ class ET_EXPERIMENTAL LlavaImagePrefiller
    * Check if the required methods in the Module is loaded.
    * @return True if the Module is loaded, false otherwise.
    */
-  inline bool is_method_loaded() override {
+  inline bool is_method_loaded() {
     ::executorch::runtime::Result<std::unordered_set<std::string>> methods_res =
         module_->method_names();
     if (methods_res.error() != ::executorch::runtime::Error::Ok) {
@@ -88,16 +87,20 @@ class ET_EXPERIMENTAL LlavaImagePrefiller
       ET_CHECK_MSG(
           methods_exist,
           "Missing required methods (%s, %s) in the model",
-          kImageEncoderMethod.c_str(),
-          kTextModelMethod.c_str());
+          kImageEncoderMethod,
+          kTextModelMethod);
     }
     bool methods_loaded = module_->is_method_loaded(kImageEncoderMethod) &&
         module_->is_method_loaded(kTextModelMethod);
     return methods_loaded;
   }
 
-  inline static const std::string kImageEncoderMethod = "image_encoder";
-  inline static const std::string kTextModelMethod = "text_model";
+  inline static constexpr auto kImageEncoderMethod = "image_encoder";
+  inline static constexpr auto kTextModelMethod = "text_model";
+
+ private:
+  ::executorch::extension::Module* module_;
+
 };
 
 } // namespace example

examples/models/llava/runner/llava_runner.h

@@ -10,29 +10,50 @@
 // processing logic.
 #pragma once
 
+#include <executorch/examples/models/llava/runner/llava_image_prefiller.h>
+#include <executorch/extension/llm/runner/image.h>
+#include <executorch/extension/llm/runner/io_manager/io_manager.h>
+#include <executorch/extension/llm/runner/irunner.h>
+#include <executorch/extension/llm/runner/stats.h>
+#include <executorch/extension/llm/runner/text_decoder_runner.h>
+#include <executorch/extension/llm/runner/text_prefiller.h>
+#include <executorch/extension/llm/runner/text_token_generator.h>
+#include <executorch/extension/module/module.h>
 #include <cstdint>
 #include <functional>
 #include <memory>
 #include <string>
-#include <type_traits>
-#include <unordered_map>
 
-#include <executorch/extension/llm/runner/multimodal_runner.h>
+using executorch::extension::Module;
+using executorch::extension::llm::ImagePrefiller;
+using executorch::extension::llm::IOManager;
+using executorch::extension::llm::Stats;
+using executorch::extension::llm::TextDecoderRunner;
+using executorch::extension::llm::TextPrefiller;
+using executorch::extension::llm::TextTokenGenerator;
 
 namespace example {
 
-class ET_EXPERIMENTAL LlavaRunner
-    : public ::executorch::extension::llm::MultimodalRunner {
+class ET_EXPERIMENTAL LlavaRunner {
  public:
   explicit LlavaRunner(
       const std::string& model_path,
       const std::string& tokenizer_path,
       const float temperature = 0.8f)
-      : MultimodalRunner(model_path, tokenizer_path, temperature){};
+      : temperature_(temperature),
+        module_(std::make_unique<Module>(model_path, Module::LoadMode::File)),
+        io_manager_(std::make_unique<IOManager>()),
+        tokenizer_path_(tokenizer_path) {
+    ET_LOG(
+        Info,
+        "Creating Llava runner: model_path=%s, tokenizer_path=%s",
+        model_path.c_str(),
+        tokenizer_path.c_str());
+  }
 
-  bool is_loaded() override;
+  bool is_loaded();
 
-  ::executorch::runtime::Error load() override;
+  ::executorch::runtime::Error load();
 
   ::executorch::runtime::Error generate(
       std::vector<::executorch::extension::llm::Image> images,
@@ -41,17 +62,17 @@ class ET_EXPERIMENTAL LlavaRunner
       std::function<void(const std::string&)> token_callback = {},
       std::function<void(const ::executorch::extension::llm::Stats&)>
           stats_callback = {},
-      bool echo = true) override;
+      bool echo = true);
 
   ::executorch::runtime::Error prefill_images(
       std::vector<::executorch::extension::llm::Image>& images,
-      int64_t& start_pos) override;
+      int64_t& start_pos);
 
   ::executorch::runtime::Result<uint64_t> prefill_prompt(
       const std::string& prompt,
       int64_t& start_pos,
       int8_t bos = 0,
-      int8_t eos = 0) override;
+      int8_t eos = 0);
 
   ::executorch::runtime::Error generate_from_pos(
       const std::string& prompt,
@@ -60,9 +81,26 @@ class ET_EXPERIMENTAL LlavaRunner
       std::function<void(const std::string&)> token_callback = {},
       std::function<void(const ::executorch::extension::llm::Stats&)>
           stats_callback = {},
-      bool echo = true) override;
+      bool echo = true);
 
  private:
+  // metadata
+  float temperature_;
+
+  // model
+  std::unordered_set<std::string> model_methods_;
+  std::unique_ptr<Module> module_;
+  std::unique_ptr<TextDecoderRunner> text_decoder_runner_;
+  std::unique_ptr<TextPrefiller> text_prefiller_;
+  std::unique_ptr<LlavaImagePrefiller> image_prefiller_;
+  std::unique_ptr<IOManager> io_manager_;
+  std::unique_ptr<TextTokenGenerator> text_token_generator_;
+  std::string tokenizer_path_;
+  std::unique_ptr<::tokenizers::Tokenizer> tokenizer_;
+
+  // stats
+  Stats stats_;
+
   inline static const char* kPresetPrompt =
       "A chat between a curious human and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the human's questions. USER: ";
 };

examples/models/llava/runner/targets.bzl

@@ -20,7 +20,7 @@ def define_common_targets():
             "//executorch/kernels/quantized:generated_lib",
             "//executorch/runtime/core/exec_aten:lib",
             "//executorch/runtime/core/exec_aten/util:tensor_util",
-            "//executorch/configurations:optimized_native_cpu_ops", 
+            "//executorch/configurations:optimized_native_cpu_ops",
             "//executorch/extension/llm/custom_ops:custom_ops",
             "//pytorch/tokenizers:llama2c_tokenizer",
         ],

extension/android/jni/jni_layer_llama.cpp

@@ -115,7 +115,7 @@ class ExecuTorchLlmJni : public facebook::jni::HybridClass<ExecuTorchLlmJni> {
   float temperature_ = 0.0f;
   int model_type_category_;
   std::unique_ptr<llm::IRunner> runner_;
-  std::unique_ptr<llm::MultimodalRunner> multi_modal_runner_;
+  std::unique_ptr<example::LlavaRunner> multi_modal_runner_;
 
  public:
   constexpr static auto kJavaDescriptor =