[Docathon] Fix docathon content issues (#19444)

### Summary

Fixes #19258

Updated several documentation pages from the docathon issue:

- Fixed empty wrapper pages for desktop Core ML, desktop MPS, and
embedded Arm Ethos-U by pointing them to the correct backend overview
docs.
- Removed unnecessary single-entry Arm Ethos-U and Arm VGF tutorial
index pages and linked directly to the Getting Started tutorials.
- Fixed the empty Runtime subsection in the Arm VGF tutorial.
- Expanded the Backend Dialect section in the Export IR specification
with a short explanation and pointer to the detailed doc.
- Clarified the API Life Cycle “How to Mark API State” table.
- Fixed the `quantize_` typo in XNNPACK quantization docs.
- Updated Samsung Exynos navigation after Cadence and linked the Android
Samsung wrapper to the real Samsung overview page.
- Updated the Tools section Model Visualization link to point to
`visualize.md`.

### Test plan

- Ran `git diff --check`.
- Manually checked that old broken references and typos are no longer
present with `rg`.
- Ran a local Sphinx docs build:
```bash
LC_ALL=C python -m sphinx -b html docs/source /private/tmp/executorch-docs-html -W --keep-going
```

cc @mergennachin @AlannaBurke @digantdesai @freddan80 @per @zingo
@oscarandersson8218 @mansnils @Sebastian-Larsson @robell @rascani

Co-authored-by: Svetlana Karslioglu <svekars@meta.com>

Author: ozgecinko

docs/source/android-examples.md

@@ -2,9 +2,9 @@
 
 - [Working with LLMs - Android Examples](https://github.com/meta-pytorch/executorch-examples/blob/main/llm/android/LlamaDemo/README.md) - ExecuTorch Llama Android Demo App
 - [Demo Apps](https://github.com/meta-pytorch/executorch-examples/tree/main/dl3/android/DeepLabV3Demo#executorch-android-demo-app) -  DeepLab v3 model for image segmentation
-- {doc}`backends/arm-vgf/tutorials/arm-vgf-tutorials` — Export a simple PyTorch model for the ExecuTorch VGF backend
+- {doc}`backends/arm-vgf/tutorials/vgf-getting-started` — Export a simple PyTorch model for the ExecuTorch VGF backend
 
 ```{toctree}
 :hidden:
-backends/arm-vgf/tutorials/arm-vgf-tutorials
+backends/arm-vgf/tutorials/vgf-getting-started
 ```

docs/source/android-samsung-exynos.md

@@ -1 +1,2 @@
-```{include} backends-samsung-exynos.md
+```{include} backends/samsung/samsung-overview.md
+```

docs/source/api-life-cycle.md

@@ -80,78 +80,149 @@ communicate state to developers.
   <tr>
    <td><strong>Language</strong>
    </td>
+   <td><strong>API state</strong>
+   </td>
    <td><strong>Code</strong>
    </td>
    <td><strong>Documentation</strong>
    </td>
   </tr>
+  <tr>
+   <td>All
+   </td>
+   <td>Stable
+   </td>
+   <td>
+
+No annotation is required. APIs are considered stable if they are not marked as
+experimental or deprecated.
+
+   </td>
+   <td>
+
+No warning is required.
+
+   </td>
+  </tr>
+  <tr>
+   <td>All
+   </td>
+   <td>Deleted
+   </td>
+   <td>
+
+Remove the API from code after the deprecation period has passed.
+
+   </td>
+   <td>
+
+Remove references to the deleted API from documentation.
+
+   </td>
+  </tr>
   <tr>
    <td>Python
    </td>
+   <td>Deprecated
+   </td>
    <td>
 
 Use the
 <a href="https://github.com/pytorch/executorch/blob/main/exir/_warnings.py">executorch.exir._warnings.deprecated</a>
 decorator.
 
-<p>
+   </td>
+   <td>
+
+Use <code>.. warning::</code> in the docstring. Clearly point to the
+replacement API when one exists.
+
+   </td>
+  </tr>
+  <tr>
+   <td>Python
+   </td>
+   <td>Experimental
+   </td>
+   <td>
+
 Use the
 <a href="https://github.com/pytorch/executorch/blob/main/exir/_warnings.py">executorch.exir._warnings.experimental</a>
 decorator.
 
    </td>
    <td>
 
-Use <code>.. warning::</code> in the docstrings of deprecated and experimental
-APIs. See
+Use <code>.. warning::</code> in the docstring. State that the API is
+experimental and may change or be removed without notice. See
 <a href="https://github.com/pytorch/pytorch/blob/main/torch/nn/utils/stateless.py#L176">example
 usage</a>.
 
-</ul>
    </td>
   </tr>
   <tr>
    <td>C++
    </td>
+   <td>Deprecated
+   </td>
    <td>
 
 Use the <code>ET_DEPRECATED</code> annotation macro. See <a href="https://github.com/pytorch/executorch/blob/main/runtime/executor/program.h#L92">example usage</a>.
 
-<p>
-<p>
-Use the <code>ET_EXPERIMENTAL</code> annotation macro.
-</ul>
    </td>
    <td>
 
 Start Doxygen comments with <code>DEPRECATED:</code> See
 <a href="https://github.com/pytorch/executorch/blob/main/runtime/executor/program.h#L164">example
 usage</a>.
 
-<p>
-<p>
+   </td>
+  </tr>
+  <tr>
+   <td>C++
+   </td>
+   <td>Experimental
+   </td>
+   <td>
+
+Use the <code>ET_EXPERIMENTAL</code> annotation macro.
+
+   </td>
+   <td>
+
 Start Doxygen comments with <code>EXPERIMENTAL:</code>.
+
    </td>
   </tr>
   <tr>
    <td>Java
    </td>
+   <td>Deprecated
+   </td>
    <td>
 
 Use <a href="https://docs.oracle.com/javase/9/docs/api/java/lang/Deprecated.html">java.lang.Deprecated</a>.
 
-<p>
-<p>
-
-Use <a href="https://cs.android.com/androidx/platform/frameworks/support/+/androidx-main:docs/api_guidelines/annotations.md">androidx.annotation.RequiresOptIn</a>.
-
    </td>
    <td>
 <p>
 <pre><code>/**
 * @deprecated Use {@link #newMethod()} instead.
 */
 </code></pre>
+   </td>
+  </tr>
+  <tr>
+   <td>Java
+   </td>
+   <td>Experimental
+   </td>
+   <td>
+
+Use <a href="https://cs.android.com/androidx/platform/frameworks/support/+/androidx-main:docs/api_guidelines/annotations.md">androidx.annotation.RequiresOptIn</a>.
+
+   </td>
+   <td>
 <p>
 <pre><code>/**
 * Warning: This API is experimental.
@@ -161,12 +232,11 @@ Use <a href="https://cs.android.com/androidx/platform/frameworks/support/+/andro
   <tr>
    <td>Objective-C
    </td>
+   <td>Deprecated
+   </td>
    <td>
 <p>
 <code>__attribute__((deprecated("Use newMethod instead")));</code>
-<p>
-<p>
-<code>__attribute__((deprecated("This API is experimental and may change without notice.")));</code>
    </td>
    <td>
 <p>
@@ -175,6 +245,18 @@ Use <a href="https://cs.android.com/androidx/platform/frameworks/support/+/andro
 * @deprecated Use `newMethod` instead.
 */
 </code></pre>
+   </td>
+  </tr>
+  <tr>
+   <td>Objective-C
+   </td>
+   <td>Experimental
+   </td>
+   <td>
+<p>
+<code>__attribute__((deprecated("This API is experimental and may change without notice.")));</code>
+   </td>
+   <td>
 <p>
 <pre><code>
 /**
@@ -186,17 +268,28 @@ Use <a href="https://cs.android.com/androidx/platform/frameworks/support/+/andro
   <tr>
    <td>Swift
    </td>
+   <td>Deprecated
+   </td>
    <td>
 <p>
 <code>@available(*, deprecated, message: "Use newMethod instead")</code>
+   </td>
+   <td>
 <p>
+<code>/// - Warning: Deprecated. Use `newMethod()` instead.</code>
+   </td>
+  </tr>
+  <tr>
+   <td>Swift
+   </td>
+   <td>Experimental
+   </td>
+   <td>
 <p>
 <code>@available(*, message: "This API is experimental")</code>
    </td>
    <td>
 <p>
-<code>/// - Warning: Deprecated. Use `newMethod()` instead.</code>
-<p>
 <code>/// - Warning: This API is experimental.</code>
    </td>
   </tr>

docs/source/backends-overview.md

@@ -65,4 +65,5 @@ backends/arm-vgf/arm-vgf-overview
 build-run-openvino
 backends/nxp/nxp-overview
 backends-cadence
-backends/samsung/samsung-overview
+Samsung Exynos Backend <backends/samsung/samsung-overview>
+```

docs/source/backends/arm-ethos-u/arm-ethos-u-overview.md

@@ -209,7 +209,7 @@ ExecuTorch for the Ethos-U backend, you automatically install the compiler conta
 
 **→{doc}`/backends/arm-ethos-u/arm-ethos-u-troubleshooting` — Troubleshooting and common issues.**
 
-**→{doc}`/backends/arm-ethos-u/tutorials/arm-ethos-u-tutorials` — Tutorials.**
+**→{doc}`/backends/arm-ethos-u/tutorials/ethos-u-getting-started` — Getting started tutorial.**
 
 **→{doc}`/backends/arm-ethos-u/U55_op_support` — Ethos-U55 supported operators.**
 
@@ -224,7 +224,7 @@ ExecuTorch for the Ethos-U backend, you automatically install the compiler conta
 arm-ethos-u-partitioner
 arm-ethos-u-quantization
 arm-ethos-u-troubleshooting
-tutorials/arm-ethos-u-tutorials
+tutorials/ethos-u-getting-started
 U55_op_support
 U85_op_support
 ```

docs/source/backends/arm-ethos-u/tutorials/arm-ethos-u-tutorials.md

@@ -129,7 +129,7 @@ described in the rest of this guide but with a concrete end-to-end sample.
 
 **→{doc}`/backends/arm-vgf/arm-vgf-troubleshooting` — Debug common issues.**
 
-**→{doc}`/backends/arm-vgf/tutorials/arm-vgf-tutorials` — Tutorials.**
+**→{doc}`/backends/arm-vgf/tutorials/vgf-getting-started` — Getting started tutorial.**
 
 **→{doc}`/backends/arm-vgf/VGF_op_support` — VGF supported operators.**
 
@@ -142,6 +142,6 @@ described in the rest of this guide but with a concrete end-to-end sample.
 arm-vgf-partitioner
 arm-vgf-quantization
 arm-vgf-troubleshooting
-tutorials/arm-vgf-tutorials
+tutorials/vgf-getting-started
 VGF_op_support
 ```

docs/source/backends/arm-vgf/arm-vgf-overview.md

@@ -168,9 +168,9 @@ To produce a pte file equivalent to the one above, run
 `python -m backends.arm.scripts.aot_arm_compiler --model_name=add --delegate --quantize --output=simple_example.pte --target=vgf`
 ```
 
-### Runtime:
+## Runtime
 
-## Build executor runtime
+### Build executor runtime
 
 After the AOT compilation flow is done, we can build the executor runner target. For this tutorial, the default runner can be used. Build it with the following configuration:
 
@@ -200,7 +200,7 @@ The block diagram below demonstrates, at the high level, how the various build a
 ![](arm-delegate-runtime-build.svg)
 
 
-## Deploying and running on device
+### Deploying and running on device
 
 Since we are using the Vulkan emulation layer, we can run the executor runner with the VGF delegate on the host machine:
 

docs/source/backends/arm-vgf/tutorials/arm-vgf-tutorials.md

@@ -61,7 +61,7 @@ See [PyTorch 2 Export Post Training Quantization](https://docs.pytorch.org/ao/ma
 
 The XNNPACK backend also supports quantizing models with the [torchao](https://github.com/pytorch/ao) quantize_ API.  This is most commonly used for LLMs, requiring more advanced quantization.  Since quantize_ is not backend aware, it is important to use a config that is compatible with CPU/XNNPACK:
 
-* Quantize embeedings with `IntxWeightOnlyConfig` (with weight_dtype torch.int2, torch.int4, or torch.int8, using PerGroup or PerAxis granularity)
+* Quantize embeddings with `IntxWeightOnlyConfig` (with weight_dtype torch.int2, torch.int4, or torch.int8, using PerGroup or PerAxis granularity)
 * Quantize linear layers with 4 bit weight and 8bit dynamic activation, use `Int8DynamicActivationIntxWeightConfig` (with weight_dtype=torch.int4, using PerGroup or PerAxis granularity)
 
 Below is a simple example, but a more detailed tutorial including accuracy evaluation on popular LLM benchmarks can be found in the [torchao documentation](https://docs.pytorch.org/ao/main/serving.html#mobile-deployment-with-executorch).
@@ -79,13 +79,13 @@ embedding_config = IntxWeightOnlyConfig(
     weight_dtype=torch.int8,
     granularity=PerAxis(0),
 )
-qunatize_(
+quantize_(
     eager_model,
     lambda m, fqn: isinstance(m, torch.nn.Embedding),
 )
 
 
-# Quatize linear layers with 8-bit dynamic activations and 4-bit weights
+# Quantize linear layers with 8-bit dynamic activations and 4-bit weights
 linear_config = Int8DynamicActivationIntxWeightConfig(
     weight_dtype=torch.int4,
     weight_granularity=PerGroup(32),