Android Refactor generate() (pytorch#14183)

### Summary
We will no longer pass startPos, bos, eos args. User doesn't need to
know that.

For generate_from_pos with image and text prompts, it will add input
prompts first, and then invoke generate()

### Test plan
CI

Author: kirklandsign

examples/demo-apps/android/LlamaDemo/app/src/main/java/com/example/executorchllamademo/MainActivity.java

@@ -81,7 +81,6 @@ public class MainActivity extends AppCompatActivity implements Runnable, LlmCall
   private Runnable memoryUpdater;
   private boolean mThinkMode = false;
   private int promptID = 0;
-  private long startPos = 0;
   private static final int CONVERSATION_HISTORY_MESSAGE_LOOKBACK = 2;
   private Executor executor;
 
@@ -178,7 +177,8 @@ private void setLocalModel(String modelPath, String tokenizerPath, float tempera
 
       if (mCurrentSettingsFields.getModelType() == ModelType.LLAVA_1_5) {
         ETLogging.getInstance().log("Llava start prefill prompt");
-        startPos = mModule.prefillPrompt(PromptFormat.getLlavaPresetPrompt(), 0, 1, 0);
+        mModule.resetContext();
+        mModule.prefillPrompt(PromptFormat.getLlavaPresetPrompt());
         ETLogging.getInstance().log("Llava completes prefill prompt");
       }
     }
@@ -645,13 +645,11 @@ private void showMediaPreview(List<Uri> uris) {
               ETLogging.getInstance().log("Starting runnable prefill image");
               ETImage img = processedImageList.get(0);
               ETLogging.getInstance().log("Llava start prefill image");
-              startPos =
-                  mModule.prefillImages(
-                      img.getInts(),
-                      img.getWidth(),
-                      img.getHeight(),
-                      ModelUtils.VISION_MODEL_IMAGE_CHANNELS,
-                      startPos);
+              mModule.prefillImages(
+                  img.getInts(),
+                  img.getWidth(),
+                  img.getHeight(),
+                  ModelUtils.VISION_MODEL_IMAGE_CHANNELS);
             };
         executor.execute(runnable);
       }

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

@@ -125,9 +125,7 @@ public int generate(String prompt, LlmCallback llmCallback, boolean echo) {
    * @param llmCallback callback object to receive results
    * @param echo indicate whether to echo the input prompt or not (text completion vs chat)
    */
-  public int generate(String prompt, int seqLen, LlmCallback llmCallback, boolean echo) {
-    return generate(null, 0, 0, 0, prompt, seqLen, llmCallback, echo);
-  }
+  public native int generate(String prompt, int seqLen, LlmCallback llmCallback, boolean echo);
 
   /**
    * Start generating tokens from the module.
@@ -154,16 +152,19 @@ public int generate(String prompt, LlmGenerationConfig config, LlmCallback llmCa
    * @param llmCallback callback object to receive results.
    * @param echo indicate whether to echo the input prompt or not (text completion vs chat)
    */
-  @DoNotStrip
-  public native int generate(
+  public int generate(
       int[] image,
       int width,
       int height,
       int channels,
       String prompt,
       int seqLen,
       LlmCallback llmCallback,
-      boolean echo);
+      boolean echo) {
+    prefillPrompt(prompt);
+    prefillImages(image, width, height, channels);
+    return generate("", llmCallback, echo);
+  }
 
   /**
    * Prefill an LLaVA Module with the given images input.
@@ -172,16 +173,12 @@ public native int generate(
    * @param width Input image width
    * @param height Input image height
    * @param channels Input image number of channels
-   * @param startPos The starting position in KV cache of the input in the LLM.
    * @return 0, as the updated starting position in KV cache of the input in the LLM is no longer
    *     exposed to user.
    * @throws RuntimeException if the prefill failed
    */
   @Deprecated
-  public long prefillImages(int[] image, int width, int height, int channels, long startPos) {
-    if (startPos == 0) {
-      resetContext();
-    }
+  public long prefillImages(int[] image, int width, int height, int channels) {
     int nativeResult = appendImagesInput(image, width, height, channels);
     if (nativeResult != 0) {
       throw new RuntimeException("Prefill failed with error code: " + nativeResult);
@@ -195,28 +192,21 @@ public long prefillImages(int[] image, int width, int height, int channels, long
    * Prefill an LLaVA Module with the given text input.
    *
    * @param prompt The text prompt to LLaVA.
-   * @param startPos The starting position in KV cache of the input in the LLM. It's passed as
-   *     reference and will be updated inside this function.
-   * @param bos The number of BOS (begin of sequence) token.
-   * @param eos The number of EOS (end of sequence) token.
    * @return 0, as the updated starting position in KV cache of the input in the LLM is no longer
    *     exposed to user.
    * @throws RuntimeException if the prefill failed
    */
   @Deprecated
-  public long prefillPrompt(String prompt, long startPos, int bos, int eos) {
-    if (startPos == 0) {
-      resetContext();
-    }
-    int nativeResult = appendTextInput(prompt, bos, eos);
+  public long prefillPrompt(String prompt) {
+    int nativeResult = appendTextInput(prompt);
     if (nativeResult != 0) {
       throw new RuntimeException("Prefill failed with error code: " + nativeResult);
     }
     return 0;
   }
 
-  // returns a tuple of (status, updated startPos)
-  private native int appendTextInput(String prompt, int bos, int eos);
+  // returns status
+  private native int appendTextInput(String prompt);
 
   /**
    * Reset the context of the LLM. This will clear the KV cache and reset the state of the LLM.

extension/android/jni/jni_layer_llama.cpp

@@ -208,29 +208,15 @@ class ExecuTorchLlmJni : public facebook::jni::HybridClass<ExecuTorchLlmJni> {
   }
 
   jint generate(
-      facebook::jni::alias_ref<jintArray> image,
-      jint width,
-      jint height,
-      jint channels,
       facebook::jni::alias_ref<jstring> prompt,
       jint seq_len,
       facebook::jni::alias_ref<ExecuTorchLlmCallbackJni> callback,
       jboolean echo) {
     if (model_type_category_ == MODEL_TYPE_CATEGORY_MULTIMODAL) {
       std::vector<llm::MultimodalInput> inputs = prefill_inputs_;
       prefill_inputs_.clear();
-      inputs.emplace_back(llm::MultimodalInput{prompt->toStdString()});
-      auto image_size = image->size();
-      std::vector<llm::Image> images;
-      if (image_size != 0) {
-        std::vector<jint> image_data_jint(image_size);
-        std::vector<uint8_t> image_data(image_size);
-        image->getRegion(0, image_size, image_data_jint.data());
-        for (int i = 0; i < image_size; i++) {
-          image_data[i] = image_data_jint[i];
-        }
-        llm::Image image_runner{image_data, width, height, channels};
-        inputs.emplace_back(llm::MultimodalInput{std::move(image_runner)});
+      if (!prompt->toStdString().empty()) {
+        inputs.emplace_back(llm::MultimodalInput{prompt->toStdString()});
       }
       executorch::extension::llm::GenerationConfig config{
           .echo = static_cast<bool>(echo),
@@ -257,23 +243,23 @@ class ExecuTorchLlmJni : public facebook::jni::HybridClass<ExecuTorchLlmJni> {
     return 0;
   }
 
-  // Returns a tuple of (error, start_pos)
+  // Returns status_code
   // Contract is valid within an AAR (JNI + corresponding Java code)
-  // If the first element is not Error::Ok, the other element is undefined.
-  jint append_text_input(
-      facebook::jni::alias_ref<jstring> prompt,
-      jint bos,
-      jint eos) {
+  jint append_text_input(facebook::jni::alias_ref<jstring> prompt) {
     prefill_inputs_.emplace_back(llm::MultimodalInput{prompt->toStdString()});
     return 0;
   }
 
+  // Returns status_code
   jint append_images_input(
       facebook::jni::alias_ref<jintArray> image,
       jint width,
       jint height,
       jint channels) {
     std::vector<llm::Image> images;
+    if (image == nullptr) {
+      return static_cast<jint>(Error::EndOfMethod);
+    }
     auto image_size = image->size();
     if (image_size != 0) {
       std::vector<jint> image_data_jint(image_size);