Merge branch 'main' into fix_logging

Author: metascroy

CONTRIBUTING.md

@@ -58,7 +58,7 @@ executorch
 │   ├── <a href="exir/verification">verification</a> - IR verification.
 ├── <a href="extension">extension</a> - Extensions built on top of the runtime.
 │   ├── <a href="extension/android">android</a> - ExecuTorch wrappers for Android apps. Please refer to the <a href="docs/source/using-executorch-android.md">Android documentation</a> and <a href="https://pytorch.org/executorch/main/javadoc/">Javadoc</a> for more information.
-│   ├── <a href="extension/apple">apple</a> - ExecuTorch wrappers for iOS apps. Please refer to the <a href="docs/source/using-executorch-ios.md">iOS documentation</a> and <a href="https://pytorch.org/executorch/stable/apple-runtime.html">how to integrate into Apple platform</a> for more information.
+│   ├── <a href="extension/apple">apple</a> - ExecuTorch wrappers for iOS apps. Please refer to the <a href="docs/source/using-executorch-ios.md">iOS documentation</a> and <a href="https://pytorch.org/executorch/main/using-executorch-ios.html">how to integrate into Apple platform</a> for more information.
 │   ├── <a href="extension/aten_util">aten_util</a> - Converts to and from PyTorch ATen types.
 │   ├── <a href="extension/data_loader">data_loader</a> - 1st party data loader implementations.
 │   ├── <a href="extension/evalue_util">evalue_util</a> - Helpers for working with EValue objects.

docs/source/build-run-openvino.md

@@ -61,7 +61,7 @@ For more information about OpenVINO build, refer to the [OpenVINO Build Instruct
 
 Follow the steps below to setup your build environment:
 
-1. **Setup ExecuTorch Environment**: Refer to the [Environment Setup](https://pytorch.org/executorch/stable/getting-started-setup#environment-setup) guide for detailed instructions on setting up the ExecuTorch environment.
+1. **Setup ExecuTorch Environment**: Refer to the [Environment Setup](getting-started-setup.md#environment-setup) guide for detailed instructions on setting up the ExecuTorch environment.
 
 2. **Setup OpenVINO Backend Environment**
 - Install the dependent libs. Ensure that you are inside `executorch/backends/openvino/` directory

docs/source/memory-planning-inspection.md

@@ -1,9 +1,9 @@
 # Memory Planning Inspection in ExecuTorch
 
-After the [Memory Planning](https://pytorch.org/executorch/main/concepts.html#memory-planning) pass of ExecuTorch, memory allocation information is stored on the nodes of the [`ExportedProgram`](https://pytorch.org/executorch/main/concepts.html#exportedprogram). Here, we present a tool designed to inspect memory allocation and visualize all active tensor objects.
+After the [Memory Planning](concepts.md#memory-planning) pass of ExecuTorch, memory allocation information is stored on the nodes of the [`ExportedProgram`](concepts.md#exportedprogram). Here, we present a tool designed to inspect memory allocation and visualize all active tensor objects.
 
 ## Usage
-User should add this code after they call [to_executorch()](https://pytorch.org/executorch/main/export-to-executorch-api-reference.html#executorch.exir.EdgeProgramManager.to_executorch), and it will write memory allocation information stored on the nodes to the file path "memory_profile.json". The file is compatible with the Chrome trace viewer; see below for more information about interpreting the results.
+User should add this code after they call [to_executorch()](export-to-executorch-api-reference.rst#executorch.exir.EdgeProgramManager.to_executorch), and it will write memory allocation information stored on the nodes to the file path "memory_profile.json". The file is compatible with the Chrome trace viewer; see below for more information about interpreting the results.
 
 ```python
 from executorch.util.activation_memory_profiler import generate_memory_trace
@@ -13,18 +13,18 @@ generate_memory_trace(
     enable_memory_offsets=True,
 )
 ```
-* `prog` is an instance of [`ExecuTorchProgramManager`](https://pytorch.org/executorch/main/export-to-executorch-api-reference.html#executorch.exir.ExecutorchProgramManager), returned by [to_executorch()](https://pytorch.org/executorch/main/export-to-executorch-api-reference.html#executorch.exir.EdgeProgramManager.to_executorch).
+* `prog` is an instance of [`ExecuTorchProgramManager`](export-to-executorch-api-reference.rst#executorch.exir.ExecutorchProgramManager), returned by [to_executorch()](export-to-executorch-api-reference.rst#executorch.exir.EdgeProgramManager.to_executorch).
 * Set `enable_memory_offsets` to `True` to show the location of each tensor on the memory space.
 
 ## Chrome Trace
 Open a Chrome browser tab and navigate to <chrome://tracing/>. Upload the generated `.json` to view.
 Example of a [MobileNet V2](https://pytorch.org/vision/main/models/mobilenetv2.html) model:
 
-![Memory planning Chrome trace visualization](/_static/img/memory_planning_inspection.png)
+![Memory planning Chrome trace visualization](_static/img/memory_planning_inspection.png)
 
 Note that, since we are repurposing the Chrome trace tool, the axes in this context may have different meanings compared to other Chrome trace graphs you may have encountered previously:
 * The horizontal axis, despite being labeled in seconds (s), actually represents megabytes (MBs).
 * The vertical axis has a 2-level hierarchy. The first level, "pid", represents memory space. For CPU, everything is allocated on one "space"; other backends may have multiple. In the second level, each row represents one time step. Since nodes will be executed sequentially, each node represents one time step, thus you will have as many nodes as there are rows.
 
 ## Further Reading
-* [Memory Planning](https://pytorch.org/executorch/main/compiler-memory-planning.html)
+* [Memory Planning](compiler-memory-planning.md)

docs/source/new-contributor-guide.md

@@ -129,7 +129,7 @@ Before you can start writing any code, you need to get a copy of ExecuTorch code
     git push                  # push updated local main to your GitHub fork
     ```
 
-6. [Build the project](https://pytorch.org/executorch/main/using-executorch-building-from-source.html) and [run the tests](https://github.com/pytorch/executorch/blob/main/CONTRIBUTING.md#testing).
+6. [Build the project](using-executorch-building-from-source.md) and [run the tests](https://github.com/pytorch/executorch/blob/main/CONTRIBUTING.md#testing).
 
     Unfortunately, this step is too long to detail here. If you get stuck at any point, please feel free to ask for help on our [Discord server](https://discord.com/invite/Dh43CKSAdc) — we're always eager to help newcomers get onboarded.