Clean up additional doc placeholders and fix broken links

GregoryComer · GregoryComer · commit 7d72c5ac54b1 · 2025-02-21T15:42:15.000-08:00
diff --git a/docs/source/backends-arm-ethos-u.md b/docs/source/backends-arm-ethos-u.md
@@ -7,8 +7,8 @@
 :::{grid-item-card}  Tutorials we recommend you complete before this:
 :class-card: card-prerequisites
 * [Introduction to ExecuTorch](./intro-how-it-works.md)
-* [Setting up ExecuTorch](./getting-started-setup.md)
-* [Building ExecuTorch with CMake](./runtime-build-and-cross-compilation.md)
+* [Getting Started](./getting-started.md)
+* [Building ExecuTorch with CMake](./using-executorch-building-from-source.md)
 :::
 
 :::{grid-item-card}  What you will learn in this tutorial:
@@ -280,7 +280,7 @@ The `generate_pte_file` function in `run.sh` script produces the `.pte` files ba
 
 ExecuTorch's CMake build system produces a set of build pieces which are critical for us to include and run the ExecuTorch runtime with-in the bare-metal environment we have for Corstone FVPs from Ethos-U SDK.
 
-[This](./runtime-build-and-cross-compilation.md) document provides a detailed overview of each individual build piece. For running either variant of the `.pte` file, we will need a core set of libraries. Here is a list,
+[This](./using-executorch-building-from-source.md) document provides a detailed overview of each individual build piece. For running either variant of the `.pte` file, we will need a core set of libraries. Here is a list,
 
 - `libexecutorch.a`
 - `libportable_kernels.a`
diff --git a/docs/source/backends-cadence.md b/docs/source/backends-cadence.md
@@ -17,9 +17,9 @@ On top of being able to run on the Xtensa HiFi4 DSP, another goal of this tutori
 :::
 :::{grid-item-card}  Tutorials we recommend you complete before this:
 :class-card: card-prerequisites
-* [Introduction to ExecuTorch](intro-how-it-works.md)
-* [Setting up ExecuTorch](getting-started-setup.md)
-* [Building ExecuTorch with CMake](runtime-build-and-cross-compilation.md)
+* [Introduction to ExecuTorch](./intro-how-it-works.md)
+* [Getting Started](./getting-started.md)
+* [Building ExecuTorch with CMake](./using-executorch-building-from-source.md)
 :::
 ::::
 
diff --git a/docs/source/backends-coreml.md b/docs/source/backends-coreml.md
@@ -11,9 +11,9 @@ Core ML delegate uses Core ML APIs to enable running neural networks via Apple's
 :::
 :::{grid-item-card}  Tutorials we recommend you complete before this:
 :class-card: card-prerequisites
-* [Introduction to ExecuTorch](intro-how-it-works.md)
-* [Setting up ExecuTorch](getting-started-setup.md)
-* [Building ExecuTorch with CMake](runtime-build-and-cross-compilation.md)
+* [Introduction to ExecuTorch](./intro-how-it-works.md)
+* [Getting Started](./getting-started.md)
+* [Building ExecuTorch with CMake](./using-executorch-building-from-source.md)
 * [ExecuTorch iOS Demo App](demo-apps-ios.md)
 :::
 ::::
diff --git a/docs/source/backends-mediatek.md b/docs/source/backends-mediatek.md
@@ -11,9 +11,9 @@ MediaTek backend empowers ExecuTorch to speed up PyTorch models on edge devices
 :::
 :::{grid-item-card}  Tutorials we recommend you complete before this:
 :class-card: card-prerequisites
-* [Introduction to ExecuTorch](intro-how-it-works.md)
-* [Setting up ExecuTorch](getting-started-setup.md)
-* [Building ExecuTorch with CMake](runtime-build-and-cross-compilation.md)
+* [Introduction to ExecuTorch](./intro-how-it-works.md)
+* [Getting Started](./getting-started.md)
+* [Building ExecuTorch with CMake](./using-executorch-building-from-source.md)
 :::
 ::::
 
@@ -91,4 +91,4 @@ cd executorch
 
    ```bash
    export LD_LIBRARY_PATH=<path_to_usdk>:<path_to_neuron_backend>:$LD_LIBRARY_PATH
-   ```
+   ```
diff --git a/docs/source/backends-mps.md b/docs/source/backends-mps.md
@@ -12,9 +12,9 @@ The MPS backend device maps machine learning computational graphs and primitives
 :::
 :::{grid-item-card}  Tutorials we recommend you complete before this:
 :class-card: card-prerequisites
-* [Introduction to ExecuTorch](intro-how-it-works.md)
-* [Setting up ExecuTorch](getting-started-setup.md)
-* [Building ExecuTorch with CMake](runtime-build-and-cross-compilation.md)
+* [Introduction to ExecuTorch](./intro-how-it-works.md)
+* [Getting Started](./getting-started.md)
+* [Building ExecuTorch with CMake](./using-executorch-building-from-source.md)
 * [ExecuTorch iOS Demo App](demo-apps-ios.md)
 * [ExecuTorch iOS LLaMA Demo App](llm/llama-demo-ios.md)
 :::
diff --git a/docs/source/backends-overview.md b/docs/source/backends-overview.md
@@ -8,7 +8,7 @@ As part of the .pte file creation process, ExecuTorch identifies portions of the
 
 ### Available Backends
 
-Commonly used hardware backends are listed below. For mobile, consider using XNNPACK for Android and XNNPACK or Core ML for iOS. To create a .pte file for a specific backend, pass the appropriate partitioner class to `to_edge_transform_and_lower`. See the appropriate backend documentation and the [Export and Lowering](#export-and-lowering) section below for more information. 
+Commonly used hardware backends are listed below. For mobile, consider using XNNPACK for Android and XNNPACK or Core ML for iOS. To create a .pte file for a specific backend, pass the appropriate partitioner class to `to_edge_transform_and_lower`. See the appropriate backend documentation for more information.
 
 - [XNNPACK (Mobile CPU)](backends-xnnpack.md)
 - [Core ML (iOS)](backends-coreml.md)
@@ -17,4 +17,4 @@ Commonly used hardware backends are listed below. For mobile, consider using XNN
 - [Qualcomm NPU](backends-qualcomm.md)
 - [MediaTek NPU](backends-mediatek.md)
 - [Arm Ethos-U NPU](backends-arm-ethos-u.md)
-- [Cadence DSP](backends-cadence.md)
+- [Cadence DSP](backends-cadence.md)
diff --git a/docs/source/backends-qualcomm.md b/docs/source/backends-qualcomm.md
@@ -14,9 +14,9 @@ Qualcomm AI Engine Direct is also referred to as QNN in the source and documenta
 :::
 :::{grid-item-card}  Tutorials we recommend you complete before this:
 :class-card: card-prerequisites
-* [Introduction to ExecuTorch](intro-how-it-works.md)
-* [Setting up ExecuTorch](getting-started-setup.md)
-* [Building ExecuTorch with CMake](runtime-build-and-cross-compilation.md)
+* [Introduction to ExecuTorch](./intro-how-it-works.md)
+* [Getting Started](./getting-started.md)
+* [Building ExecuTorch with CMake](./using-executorch-building-from-source.md)
 :::
 ::::
 
diff --git a/docs/source/getting-started.md b/docs/source/getting-started.md
@@ -29,7 +29,9 @@ The following are required to install the ExecuTorch host libraries, needed to e
 <hr/>
 
 ## Preparing the Model
-Exporting is the process of taking a PyTorch model and converting it to the .pte file format used by the ExecuTorch runtime. This is done using Python APIs. PTE files for common models, such as Llama 3, can be found on HuggingFace under [ExecuTorch Community](https://huggingface.co/executorch-community). These models have been exported and lowered for ExecuTorch, and can be directly deployed without needing to go through the lowering process.
+Exporting is the process of taking a PyTorch model and converting it to the .pte file format used by the ExecuTorch runtime. This is done using Python APIs. PTE files for common models, such as Llama 3.2, can be found on HuggingFace under [ExecuTorch Community](https://huggingface.co/executorch-community). These models have been exported and lowered for ExecuTorch, and can be directly deployed without needing to go through the lowering process.
+
+A complete example of exporting, lowering, and verifying MobileNet V2 is available as a [Colab notebook](https://colab.research.google.com/drive/1qpxrXC3YdJQzly3mRg-4ayYiOjC6rue3?usp=sharing).
 
 ### Requirements
 - A PyTorch model.
@@ -39,7 +41,7 @@ Exporting is the process of taking a PyTorch model and converting it to the .pte
 ### Selecting a Backend
 ExecuTorch provides hardware acceleration for a wide variety of hardware. The most commonly used backends are XNNPACK, for Arm and x86 CPU, Core ML (for iOS), Vulkan (for Android GPUs), and Qualcomm (for Qualcomm-powered Android phones).
 
-For mobile use cases, consider using XNNPACK for Android and Core ML or XNNPACK for iOS as a first step. See [Hardware Backends](using-executorch-export.md#hardware-backends) for more information.
+For mobile use cases, consider using XNNPACK for Android and Core ML or XNNPACK for iOS as a first step. See [Hardware Backends](backends-overview.md) for more information.
 
 ### Exporting
 Exporting is done using Python APIs. ExecuTorch provides a high degree of customization during the export process, but the typical flow is as follows:
@@ -50,13 +52,13 @@ model = MyModel() # The PyTorch model to export
 example_inputs = (torch.randn(1,3,64,64),) # A tuple of inputs
 
 et_program =
- executorch.exir.to_edge_transform_and_lower(
- torch.export.export(model, example_inputs)
+    executorch.exir.to_edge_transform_and_lower(
+    torch.export.export(model, example_inputs)
 partitioner=[XnnpackPartitioner()]
 ).to_executorch()
 
 with open(“model.pte”, “wb”) as f:
-	f.write(et_program.buffer)
+    f.write(et_program.buffer)
 ```
 
 If the model requires varying input sizes, you will need to specify the varying dimensions and bounds as part of the `export` call. See [Model Export and Lowering](using-executorch-export.md) for more information.
@@ -119,7 +121,7 @@ import org.pytorch.executorch.Tensor;
 
 // …
 
-Module model = Module.load(“/path/to/model.pte”)
+Module model = Module.load(“/path/to/model.pte”);
 
 Tensor input_tensor = Tensor.fromBlob(float_data, new long[] { 1, 3, height, width });
 EValue input_evalue = EValue.from(input_tensor);
@@ -204,4 +206,4 @@ ExecuTorch provides a high-degree of customizability to support diverse hardware
 - [Using ExecuTorch with C++](using-executorch-cpp.md) for embedded and mobile native development.
 - [Profiling and Debugging](using-executorch-troubleshooting.md) for developer tooling and debugging.
 - [API Reference](export-to-executorch-api-reference.md) for a full description of available APIs.
-- [Examples](https://github.com/pytorch/executorch/tree/main/examples) for demo apps and example code.
+- [Examples](https://github.com/pytorch/executorch/tree/main/examples) for demo apps and example code.
diff --git a/docs/source/index.rst b/docs/source/index.rst
@@ -91,8 +91,8 @@ Topics in this section will help you get started with ExecuTorch.
    using-executorch-cpp
    using-executorch-runtime-integration
    using-executorch-troubleshooting
-   using-executorch-faqs
    using-executorch-building-from-source
+   using-executorch-faqs
 
 .. toctree::
    :glob:
@@ -140,6 +140,9 @@ Topics in this section will help you get started with ExecuTorch.
    :hidden:
 
    runtime-overview
+   extension-module
+   extension-tensor
+   running-a-model-cpp-tutorial
    runtime-backend-delegate-implementation-and-linking
    runtime-platform-abstraction-layer
    portable-cpp-programming
diff --git a/docs/source/llm/build-run-llama3-qualcomm-ai-engine-direct-backend.md b/docs/source/llm/build-run-llama3-qualcomm-ai-engine-direct-backend.md
@@ -5,7 +5,7 @@ This tutorial demonstrates how to export Llama 3 8B Instruct for Qualcomm AI Eng
 ## Prerequisites
 
 - Set up your ExecuTorch repo and environment if you haven’t done so by following [the Setting up ExecuTorch](../getting-started-setup.md) to set up the repo and dev environment.
-- Read [the Building and Running ExecuTorch with Qualcomm AI Engine Direct Backend page](../build-run-qualcomm-ai-engine-direct-backend.md) to understand how to export and run a model with Qualcomm AI Engine Direct Backend on Qualcomm device.
+- Read [the Building and Running ExecuTorch with Qualcomm AI Engine Direct Backend page](../backends-qualcomm.md) to understand how to export and run a model with Qualcomm AI Engine Direct Backend on Qualcomm device.
 - Follow [the README for executorch llama](https://github.com/pytorch/executorch/tree/main/examples/models/llama) to know how to run a llama model on mobile via ExecuTorch.
 - A Qualcomm device with 16GB RAM
   - We are continuing to optimize our memory usage to ensure compatibility with lower memory devices.
diff --git a/docs/source/llm/getting-started.md b/docs/source/llm/getting-started.md
@@ -592,8 +592,8 @@ I'm not sure if you've heard of the "Curse of the Dragon" or not, but it's a ver
 The delegated model should be noticeably faster compared to the non-delegated model.
 
 For more information regarding backend delegateion, see the ExecuTorch guides
-for the [XNNPACK Backend](../tutorial-xnnpack-delegate-lowering.md),  [Core ML
-Backend](../build-run-coreml.md) and [Qualcomm AI Engine Direct Backend](build-run-llama3-qualcomm-ai-engine-direct-backend.md).
+for the [XNNPACK Backend](../backends-xnnpack.md),  [Core ML
+Backend](../backends-coreml.md) and [Qualcomm AI Engine Direct Backend](build-run-llama3-qualcomm-ai-engine-direct-backend.md).
 
 ## Quantization
 
diff --git a/docs/source/runtime-overview.md b/docs/source/runtime-overview.md
@@ -157,7 +157,7 @@ For more details about the ExecuTorch runtime, please see:
 
 * [Detailed Runtime APIs Tutorial](running-a-model-cpp-tutorial.md)
 * [Simplified Runtime APIs Tutorial](extension-module.md)
-* [Runtime Build and Cross Compilation](runtime-build-and-cross-compilation.md)
+* [Building from Source](using-executorch-building-from-source.md)
 * [Runtime Platform Abstraction Layer](runtime-platform-abstraction-layer.md)
 * [Runtime Profiling](runtime-profiling.md)
 * [Backends and Delegates](compiler-delegate-and-partitioner.md)
diff --git a/docs/source/tutorial-xnnpack-delegate-lowering.md b/docs/source/tutorial-xnnpack-delegate-lowering.md
@@ -12,7 +12,7 @@ In this tutorial, you will learn how to export an XNNPACK lowered Model and run
 :class-card: card-prerequisites
 * [Setting up ExecuTorch](./getting-started-setup.md)
 * [Model Lowering Tutorial](./tutorials/export-to-executorch-tutorial)
-* [ExecuTorch XNNPACK Delegate](./native-delegates-executorch-xnnpack-delegate.md)
+* [ExecuTorch XNNPACK Delegate](./backends-xnnpack.md)
 :::
 ::::
 
diff --git a/docs/source/using-executorch-building-from-source.md b/docs/source/using-executorch-building-from-source.md
@@ -159,7 +159,7 @@ xcode-select --install
 ```
 
 Run the above command with `--help` flag to learn more on how to build additional backends
-(like [Core ML](build-run-coreml.md), [MPS](build-run-mps.md) or XNNPACK), etc.
+(like [Core ML](backends-coreml.md), [MPS](backends-mps.md) or XNNPACK), etc.
 Note, some backends may require additional dependencies and certain versions of Xcode and iOS.
 
 3. Copy over the generated `.xcframework` bundles to your Xcode project, link them against
@@ -172,6 +172,6 @@ Check out the [iOS Demo App](demo-apps-ios.md) tutorial for more info.
 
 You have successfully cross-compiled `executor_runner` binary to iOS and Android platforms. You can start exploring advanced features and capabilities. Here is a list of sections you might want to read next:
 
-* [Selective build](./kernel-library-selective_build) to build the runtime that links to only kernels used by the program, which can provide significant binary size savings.
+* [Selective build](kernel-library-selective-build.md) to build the runtime that links to only kernels used by the program, which can provide significant binary size savings.
 * Tutorials on building [Android](./demo-apps-android.md) and [iOS](./demo-apps-ios.md) demo apps.
-* Tutorials on deploying applications to embedded devices such as [ARM Cortex-M/Ethos-U](./executorch-arm-delegate-tutorial.md) and [XTensa HiFi DSP](./build-run-xtensa.md).
+* Tutorials on deploying applications to embedded devices such as [ARM Cortex-M/Ethos-U](backends-arm-ethos-u.md) and [XTensa HiFi DSP](./backends-cadence.md).
diff --git a/docs/source/using-executorch-cpp.md b/docs/source/using-executorch-cpp.md
@@ -36,9 +36,29 @@ For more information on the Module class, see [Running an ExecuTorch Model Using
 
 Running a model using the low-level runtime APIs allows for a high-degree of control over memory allocation, placement, and loading. This allows for advanced use cases, such as placing allocations in specific memory banks or loading a model without a file system. For an end to end example using the low-level runtime APIs, see [Running an ExecuTorch Model in C++ Tutorial](running-a-model-cpp-tutorial.md).
 
+## Building with C++
+
+ExecuTorch uses CMake as the primary build system. Inclusion of the module and tensor APIs are controlled by the `EXECUTORCH_BUILD_EXTENSION_MODULE` and `EXECUTORCH_BUILD_EXTENSION_TENSOR` CMake options. As these APIs may not be supported on embedded systems, they are disabled by default when building from source. The low-level API surface is always included. To link, add the `executorch` target as a CMake dependency, along with `executorch_module_static` and `executorch_tensor`, if desired.
+
+```
+# CMakeLists.txt
+add_subdirectory("executorch")
+...
+target_link_libraries(
+    my_target
+    PRIVATE executorch
+    executorch_module_static
+    executorch_tensor
+    optimized_native_cpu_ops_lib
+    xnnpack_backend)
+```
+
+See [Building from Source](using-executorch-building-from-source.md) for more information on the CMake build process.
+
 ## Next Steps
 
 - [Runtime API Reference](executorch-runtime-api-reference.md) for documentation on the available C++ runtime APIs.
 - [Running an ExecuTorch Model Using the Module Extension in C++](extension-module.md) for information on the high-level Module API.
 - [Managing Tensor Memory in C++](extension-tensor.md) for information on high-level tensor APIs.
-- [Running an ExecuTorch Model in C++ Tutorial](running-a-model-cpp-tutorial.md) for information on the low-level runtime APIs.
+- [Running an ExecuTorch Model in C++ Tutorial](running-a-model-cpp-tutorial.md) for information on the low-level runtime APIs.
+- [Building from Source](using-executorch-building-from-source.md) for information on CMake build integration.
diff --git a/docs/source/using-executorch-runtime-integration.md b/docs/source/using-executorch-runtime-integration.md
@@ -10,7 +10,7 @@ Logging is sent to STDOUT and STDERR by default on host platforms, and is redire
 
 To configure log level when building from source, specify `EXECUTORCH_ENABLE_LOGGING` as on or off and `EXECUTORCH_LOG_LEVEL` as one of debug, info, error, or fatal. Logging is enabled by default in debug builds and disabled in release. Log level defaults to info.
 
-See [Building from Source](TODO) for more information.
+See [Building from Source](using-executorch-building-from-source.md) for more information.
 
 ```
 cmake -b cmake-out -DEXECUTORCH_ENABLE_LOGGING=ON -DEXECUTORCH_LOG_LEVEL=DEBUG ...
@@ -50,4 +50,4 @@ The choice of kernel library is transparent to the user when using mobile pre-bu
 
 By default, ExecuTorch ships with all supported operator kernels, allowing it to run any supported model at any precision. This comes with a binary size of several megabytes, which may be undesirable for production use cases or resource constrained systems. To minimize binary size, ExecuTorch provides selective build functionality, in order to include only the operators needed to run specific models.
 
-Note the selective build only applies to the portable and optimized kernel libraries. Delegates do not participate in selective build and can be included or excluded by linking indivually. See [Kernel Library Selective Build](kernel-library-selective-build.md) for more information.
+Note the selective build only applies to the portable and optimized kernel libraries. Delegates do not participate in selective build and can be included or excluded by linking indivually. See [Kernel Library Selective Build](kernel-library-selective-build.md) for more information.
diff --git a/docs/source/using-executorch-troubleshooting.md b/docs/source/using-executorch-troubleshooting.md
@@ -1,6 +1,6 @@
 # Profiling and Debugging
 
-ExecuTorch 
+To faciliate model and runtime integration, ExecuTorch provides tools to profile model resource utilization, numerics, and more. This section describes the available troubleshooting tools and steps to resolve issues when integrating ExecuTorch.
 
 ## General Troubleshooting Steps
 
@@ -17,4 +17,4 @@ The ExecuTorch developer tools, or devtools, are a collection of tooling for tro
 - [Frequently Asked Questions](using-executorch-faqs.md) for solutions to commonly encountered questions and issues.
 - [Introduction to the ExecuTorch Developer Tools](runtime-profiling.md) for a high-level introduction to available developer tooling.
 - [Using the ExecuTorch Developer Tools to Profile a Model](tutorials/devtools-integration-tutorial.md) for information on runtime performance profiling.
-- [Inspector APIs](runtime-profiling.md) for reference material on trace inspector APIs.
+- [Inspector APIs](runtime-profiling.md) for reference material on trace inspector APIs.
