JNI support for multiple ptd files (pytorch#14769)

This PR was created by the merge bot to help merge the original PR into
the main branch.
ghstack PR number: pytorch#14168 by
@lucylq
^ Please use this as the source of truth for the PR details, comments,
and reviews
ghstack PR base:
https://github.com/pytorch/executorch/tree/gh/lucylq/113/base
ghstack PR head:
https://github.com/pytorch/executorch/tree/gh/lucylq/113/head
Merge bot PR base:
https://github.com/pytorch/executorch/tree/gh/lucylq/111/orig
Merge bot PR head:
https://github.com/pytorch/executorch/tree/gh/lucylq/113/orig
Differential Revision:
[D82072929](https://our.internmc.facebook.com/intern/diff/D82072929/)
@diff-train-skip-merge

---------

Co-authored-by: lucylq <[email protected]>

Author: pytorchbot

extension/android/executorch_android/src/main/java/org/pytorch/executorch/extension/llm/LlmModule.java

@@ -11,6 +11,7 @@
 import com.facebook.jni.HybridData;
 import com.facebook.jni.annotations.DoNotStrip;
 import java.io.File;
+import java.util.List;
 import org.pytorch.executorch.ExecuTorchRuntime;
 import org.pytorch.executorch.annotations.Experimental;
 
@@ -32,14 +33,22 @@ public class LlmModule {
 
   @DoNotStrip
   private static native HybridData initHybrid(
-      int modelType, String modulePath, String tokenizerPath, float temperature, String dataPath);
+      int modelType,
+      String modulePath,
+      String tokenizerPath,
+      float temperature,
+      List<String> dataFiles);
 
   /**
    * Constructs a LLM Module for a model with given type, model path, tokenizer, temperature, and
-   * data path.
+   * dataFiles.
    */
   public LlmModule(
-      int modelType, String modulePath, String tokenizerPath, float temperature, String dataPath) {
+      int modelType,
+      String modulePath,
+      String tokenizerPath,
+      float temperature,
+      List<String> dataFiles) {
     ExecuTorchRuntime runtime = ExecuTorchRuntime.getRuntime();
 
     File modelFile = new File(modulePath);
@@ -50,25 +59,35 @@ public LlmModule(
     if (!tokenizerFile.canRead() || !tokenizerFile.isFile()) {
       throw new RuntimeException("Cannot load tokenizer path " + tokenizerPath);
     }
-    mHybridData = initHybrid(modelType, modulePath, tokenizerPath, temperature, dataPath);
+
+    mHybridData = initHybrid(modelType, modulePath, tokenizerPath, temperature, dataFiles);
+  }
+
+  /**
+   * Constructs a LLM Module for a model with given type, model path, tokenizer, temperature, and
+   * data path.
+   */
+  public LlmModule(
+      int modelType, String modulePath, String tokenizerPath, float temperature, String dataPath) {
+    this(modelType, modulePath, tokenizerPath, temperature, List.of(dataPath));
   }
 
   /** Constructs a LLM Module for a model with given model path, tokenizer, temperature. */
   public LlmModule(String modulePath, String tokenizerPath, float temperature) {
-    this(MODEL_TYPE_TEXT, modulePath, tokenizerPath, temperature, null);
+    this(MODEL_TYPE_TEXT, modulePath, tokenizerPath, temperature, List.of());
   }
 
   /**
    * Constructs a LLM Module for a model with given model path, tokenizer, temperature and data
    * path.
    */
   public LlmModule(String modulePath, String tokenizerPath, float temperature, String dataPath) {
-    this(MODEL_TYPE_TEXT, modulePath, tokenizerPath, temperature, dataPath);
+    this(MODEL_TYPE_TEXT, modulePath, tokenizerPath, temperature, List.of(dataPath));
   }
 
   /** Constructs a LLM Module for a model with given path, tokenizer, and temperature. */
   public LlmModule(int modelType, String modulePath, String tokenizerPath, float temperature) {
-    this(modelType, modulePath, tokenizerPath, temperature, null);
+    this(modelType, modulePath, tokenizerPath, temperature, List.of());
   }
 
   /** Constructs a LLM Module for a model with the given LlmModuleConfig */

extension/android/jni/jni_layer_llama.cpp

@@ -140,21 +140,21 @@ class ExecuTorchLlmJni : public facebook::jni::HybridClass<ExecuTorchLlmJni> {
       facebook::jni::alias_ref<jstring> model_path,
       facebook::jni::alias_ref<jstring> tokenizer_path,
       jfloat temperature,
-      facebook::jni::alias_ref<jstring> data_path) {
+      facebook::jni::alias_ref<jobject> data_files) {
     return makeCxxInstance(
         model_type_category,
         model_path,
         tokenizer_path,
         temperature,
-        data_path);
+        data_files);
   }
 
   ExecuTorchLlmJni(
       jint model_type_category,
       facebook::jni::alias_ref<jstring> model_path,
       facebook::jni::alias_ref<jstring> tokenizer_path,
       jfloat temperature,
-      facebook::jni::alias_ref<jstring> data_path = nullptr) {
+      facebook::jni::alias_ref<jobject> data_files = nullptr) {
     temperature_ = temperature;
 #if defined(ET_USE_THREADPOOL)
     // Reserve 1 thread for the main thread.
@@ -173,26 +173,39 @@ class ExecuTorchLlmJni : public facebook::jni::HybridClass<ExecuTorchLlmJni> {
           model_path->toStdString().c_str(),
           llm::load_tokenizer(tokenizer_path->toStdString()));
     } else if (model_type_category == MODEL_TYPE_CATEGORY_LLM) {
-      std::optional<const std::string> data_path_str = data_path
-          ? std::optional<const std::string>{data_path->toStdString()}
-          : std::nullopt;
+      std::vector<std::string> data_files_vector;
+      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_path_str);
+          data_files_vector);
 #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_set,
           executorch::extension::Module::LoadMode::MmapUseMlockIgnoreErrors);
       std::string decoder_model = "llama3"; // use llama3 for now
       runner_ = std::make_unique<example::Runner<uint16_t>>( // QNN runner
           std::move(module),
           decoder_model.c_str(),
           model_path->toStdString().c_str(),
           tokenizer_path->toStdString().c_str(),
-          data_path->toStdString().c_str(),
           "");
       model_type_category_ = MODEL_TYPE_CATEGORY_LLM;
 #endif