docs(android): add Java API Reference (Javadoc) link, sidebar entry, …

docs/source/android-section.md

@@ -16,8 +16,16 @@ Deploy ExecuTorch on Android devices with hardware acceleration support.
 
 - {doc}`android-examples` — Explore Android Examples & Demos
 
+## API Reference
+
+- [Java API Reference (Javadoc)](https://pytorch.org/executorch/main/javadoc/index.html) — Full Java class and method reference
+
 ```{toctree}
+:maxdepth: 1
 :hidden:
+
 using-executorch-android
 android-backends
 android-examples
+Java API Reference (Javadoc) <https://pytorch.org/executorch/main/javadoc/index.html>
+```

docs/source/using-executorch-android.md

@@ -21,6 +21,7 @@ All ExecuTorch Android libraries are packaged into an Android library (AAR), exe
 The AAR artifact contains the Java library for users to integrate with their Java/Kotlin application code, as well as the corresponding JNI library (.so file), which is loaded by the Java code during initialization.
 
 - [Java library](https://github.com/pytorch/executorch/tree/main/extension/android/executorch_android/src/main/java/org/pytorch/executorch)
+- [Java API Reference (Javadoc)](https://pytorch.org/executorch/main/javadoc/index.html)
 - JNI contains the JNI binding for the corresponding Java code, and ExecuTorch native library, including
   - Core ExecuTorch runtime libraries
   - XNNPACK backend
@@ -240,4 +241,4 @@ using ExecuTorch AAR package.
 
 ## Java API reference
 
-Please see [Java API reference](https://pytorch.org/executorch/main/javadoc/).
+Please see [Java API reference](https://pytorch.org/executorch/main/javadoc/index.html).

extension/android/executorch_android/build.gradle

@@ -114,3 +114,17 @@ repositories {
         url "https://central.sonatype.com/repository/maven-snapshots/"
     }
 }
+
+android.libraryVariants.all { variant ->
+    task("generate${variant.name.capitalize()}Javadoc", type: Javadoc) {
+        source = variant.javaCompileProvider.get().source
+        classpath += project.files(android.getBootClasspath().join(File.pathSeparator))
+        classpath += variant.javaCompileProvider.get().classpath
+        options {
+            overview = "src/main/javadoc/overview.html"
+            windowTitle = "ExecuTorch Android Java API"
+            docTitle = "ExecuTorch Android Java API"
+            links("https://docs.oracle.com/en/java/javase/11/docs/api/")
+        }
+    }
+}

extension/android/executorch_android/src/main/java/org/pytorch/executorch/extension/llm/package-info.java

@@ -1,2 +1,49 @@
-/** Extension for LLM related use cases for ExecuTorch Android Java/JNI package. */
+/**
+ * ExecuTorch LLM extension for Android.
+ *
+ * <p>This package provides Java bindings for running large language models (LLMs)
+ * on Android using ExecuTorch. It supports text generation, tokenization,
+ * and streaming token callbacks.
+ *
+ * <h2>Quick Start</h2>
+ *
+ * <pre>{@code
+ * import org.pytorch.executorch.extension.llm.LlmModule;
+ *
+ * // Load a Llama model
+ * LlmModule llm = new LlmModule(
+ *     "/data/local/tmp/llama.pte",
+ *     "/data/local/tmp/tokenizer.bin",
+ *     0.8f
+ * );
+ * llm.load();
+ *
+ * // Generate text token by token
+ * llm.generate("Hello, my name is", 200, new LlmCallback() {
+ *     public void onResult(String token) {
+ *         System.out.print(token);
+ *     }
+ *     public void onStats(String stats) {
+ *         System.out.println("\nStats: " + stats);
+ *     }
+ * });
+ * }</pre>
+ *
+ * <h2>Key Classes</h2>
+ *
+ * <ul>
+ *   <li>{@link org.pytorch.executorch.extension.llm.LlmModule} — load and run an LLM</li>
+ *   <li>{@link org.pytorch.executorch.extension.llm.LlmModuleConfig} — configure model paths and settings</li>
+ *   <li>{@link org.pytorch.executorch.extension.llm.LlmGenerationConfig} — control generation (temperature, seq length)</li>
+ * </ul>
+ *
+ * <h2>More Resources</h2>
+ *
+ * <ul>
+ *   <li><a href="https://github.com/pytorch/executorch/tree/main/examples/demo-apps/android/LlamaDemo">
+ *       Llama Android Demo App</a> — full working app with UI</li>
+ *   <li><a href="https://pytorch.org/executorch/main/using-executorch-android.html">
+ *       Using ExecuTorch on Android</a></li>
+ * </ul>
+ */
 package org.pytorch.executorch.extension.llm;

extension/android/executorch_android/src/main/java/org/pytorch/executorch/package-info.java

@@ -1,2 +1,59 @@
-/** ExecuTorch Android Java/JNI package. This is the main package for generic use cases. */
+/**
+ * ExecuTorch Android Java API.
+ *
+ * <p>This package provides Java bindings for running ExecuTorch models on Android.
+ * Use these classes to load a {@code .pte} model file and run inference directly
+ * from your Java or Kotlin Android app — no C++ required.
+ *
+ * <h2>Quick Start</h2>
+ *
+ * <p><b>Step 1.</b> Add the dependency to your {@code app/build.gradle.kts}:
+ *
+ * <pre>{@code
+ * dependencies {
+ *     implementation("org.pytorch:executorch-android:${executorch_version}")
+ * }
+ * }</pre>
+ *
+ * <p><b>Step 2.</b> Load your model and run inference:
+ *
+ * <pre>{@code
+ * import org.pytorch.executorch.EValue;
+ * import org.pytorch.executorch.Module;
+ * import org.pytorch.executorch.Tensor;
+ *
+ * // Load your exported .pte model file
+ * Module module = Module.load("/data/local/tmp/model.pte");
+ *
+ * // Build an input tensor  e.g. a 1x3x224x224 image
+ * float[] inputData = new float[1 * 3 * 224 * 224];
+ * Tensor inputTensor = Tensor.fromBlob(inputData, new long[]{1, 3, 224, 224});
+ *
+ * // Run inference
+ * EValue[] output = module.forward(EValue.from(inputTensor));
+ *
+ * // Read the result
+ * float[] scores = output[0].toTensor().getDataAsFloatArray();
+ * }</pre>
+ *
+ * <h2>Key Classes</h2>
+ *
+ * <ul>
+ *   <li>{@link org.pytorch.executorch.Module} — load and run a {@code .pte} model</li>
+ *   <li>{@link org.pytorch.executorch.Tensor} — create input tensors and read outputs</li>
+ *   <li>{@link org.pytorch.executorch.EValue} — wrap inputs and unwrap outputs</li>
+ *   <li>{@link org.pytorch.executorch.DType} — supported data types (FLOAT, INT32, etc.)</li>
+ * </ul>
+ *
+ * <h2>More Resources</h2>
+ *
+ * <ul>
+ *   <li><a href="https://pytorch.org/executorch/main/using-executorch-android.html">
+ *       Using ExecuTorch on Android</a> — full setup guide, AAR install, build from source</li>
+ *   <li><a href="https://github.com/pytorch/executorch/tree/main/examples/demo-apps/android">
+ *       Android Demo Apps</a> — working example apps you can build and run immediately</li>
+ *   <li><a href="https://pytorch.org/executorch/main/cross-compilation-for-android.html">
+ *       Cross Compilation for Android</a> — using C++ APIs from Android native code</li>
+ * </ul>
+ */
 package org.pytorch.executorch;

extension/android/executorch_android/src/main/javadoc/overview.html

@@ -0,0 +1,89 @@
+<!DOCTYPE html>
+<html lang="en">
+<head>
+  <title>ExecuTorch Android Java API</title>
+</head>
+<body>
+
+<p>
+  The ExecuTorch Android Java API lets you run PyTorch models on Android
+  devices using a simple Java or Kotlin interface.
+</p>
+
+<p>
+  ExecuTorch is PyTorch's solution for on-device AI — from smartphones to
+  microcontrollers. The Java API wraps the native ExecuTorch runtime and gives
+  you clean Java classes to load models, build tensors, and run inference.
+</p>
+
+<h2>Quick Start</h2>
+
+<p>Add the library to your app:</p>
+
+<pre>
+// app/build.gradle.kts
+dependencies {
+    implementation("org.pytorch:executorch-android:${executorch_version}")
+}
+</pre>
+
+<p>Load a model and run inference:</p>
+
+<pre>
+import org.pytorch.executorch.EValue;
+import org.pytorch.executorch.Module;
+import org.pytorch.executorch.Tensor;
+
+// Load your exported .pte model
+Module module = Module.load("/data/local/tmp/model.pte");
+
+// Create an input tensor (1x3x224x224 image)
+float[] data = new float[1 * 3 * 224 * 224];
+Tensor input = Tensor.fromBlob(data, new long[]{1, 3, 224, 224});
+
+// Run inference
+EValue[] output = module.forward(EValue.from(input));
+float[] scores = output[0].toTensor().getDataAsFloatArray();
+</pre>
+
+<h2>Packages</h2>
+
+<table>
+  <tr>
+    <td><b>org.pytorch.executorch</b></td>
+    <td>Core API. Contains Module to load and run models, Tensor for tensor operations,
+        and EValue to wrap inputs and outputs.</td>
+  </tr>
+  <tr>
+    <td><b>org.pytorch.executorch.extension.llm</b></td>
+    <td>LLM extension. Contains LlmModule for running large language models like Llama
+        with streaming token generation.</td>
+  </tr>
+  <tr>
+    <td><b>org.pytorch.executorch.annotations</b></td>
+    <td>API annotations. Experimental marks APIs that may change in future releases.</td>
+  </tr>
+</table>
+
+<h2>Resources</h2>
+
+<ul>
+  <li>
+    <a href="https://pytorch.org/executorch/main/using-executorch-android.html">
+      Using ExecuTorch on Android
+    </a> — setup guide, Maven install, build from source
+  </li>
+  <li>
+    <a href="https://github.com/pytorch/executorch/tree/main/examples/demo-apps/android">
+      Android Demo Apps
+    </a> — working example apps
+  </li>
+  <li>
+    <a href="https://pytorch.org/executorch/main/cross-compilation-for-android.html">
+      Cross Compilation for Android
+    </a> — using C++ APIs from native Android code
+  </li>
+</ul>
+
+</body>
+</html>