Switch docs to 0.6 branch (#10212)

Author: shoumikhin

Package.swift

@@ -15,7 +15,7 @@
 //
 // For details on building frameworks locally or using prebuilt binaries,
 // see the documentation:
-// https://pytorch.org/executorch/main/using-executorch-ios.html
+// https://pytorch.org/executorch/0.6/using-executorch-ios.html
 
 import PackageDescription
 

README-wheel.md

@@ -14,10 +14,10 @@ to run ExecuTorch `.pte` files, with some restrictions:
   operators](https://pytorch.org/executorch/stable/ir-ops-set-definition.html)
   are linked into the prebuilt module
 * Only the [XNNPACK backend
-  delegate](https://pytorch.org/executorch/main/native-delegates-executorch-xnnpack-delegate.html)
+  delegate](https://pytorch.org/executorch/0.6/backends-xnnpack)
   is linked into the prebuilt module.
-* \[macOS only] [Core ML](https://pytorch.org/executorch/main/build-run-coreml.html)
-  and [MPS](https://pytorch.org/executorch/main/build-run-mps.html) backend
+* \[macOS only] [Core ML](https://pytorch.org/executorch/0.6/backends-coreml)
+  and [MPS](https://pytorch.org/executorch/0.6/backends-mps) backend
   delegates are also linked into the prebuilt module.
 
 Please visit the [ExecuTorch website](https://pytorch.org/executorch/) for
@@ -30,7 +30,7 @@ tutorials and documentation. Here are some starting points:
   * Learn how to use ExecuTorch to export and accelerate a large-language model
     from scratch.
 * [Exporting to
-  ExecuTorch](https://pytorch.org/executorch/main/tutorials/export-to-executorch-tutorial.html)
+  ExecuTorch](https://pytorch.org/executorch/0.6/tutorials/export-to-executorch-tutorial.html)
   * Learn the fundamentals of exporting a PyTorch `nn.Module` to ExecuTorch, and
     optimizing its performance using quantization and hardware delegation.
 * Running LLaMA on

backends/cadence/README.md

@@ -6,7 +6,7 @@
 
 ## Tutorial
 
-Please follow the [tutorial](https://pytorch.org/executorch/main/backends-cadence) for more information on how to run models on Cadence/Xtensa DSPs.
+Please follow the [tutorial](https://pytorch.org/executorch/0.6/backends-cadence) for more information on how to run models on Cadence/Xtensa DSPs.
 
 ## Directory Structure
 

backends/qualcomm/README.md

@@ -8,7 +8,7 @@ This backend is implemented on the top of
 [Qualcomm AI Engine Direct SDK](https://developer.qualcomm.com/software/qualcomm-ai-engine-direct-sdk).
 Please follow [tutorial](../../docs/source/backends-qualcomm.md) to setup environment, build, and run executorch models by this backend (Qualcomm AI Engine Direct is also referred to as QNN in the source and documentation).
 
-A website version of the tutorial is [here](https://pytorch.org/executorch/main/backends-qualcomm).
+A website version of the tutorial is [here](https://pytorch.org/executorch/0.6/backends-qualcomm).
 
 ## Delegate Options
 

backends/xnnpack/README.md

@@ -132,5 +132,5 @@ create an issue on [github](https://www.github.com/pytorch/executorch/issues).
 
 ## See Also
 For more information about the XNNPACK Backend, please check out the following resources:
-- [XNNPACK Backend](https://pytorch.org/executorch/main/backends-xnnpack)
-- [XNNPACK Backend Internals](https://pytorch.org/executorch/main/backend-delegates-xnnpack-reference)
+- [XNNPACK Backend](https://pytorch.org/executorch/0.6/backends-xnnpack)
+- [XNNPACK Backend Internals](https://pytorch.org/executorch/0.6/backend-delegates-xnnpack-reference)

docs/source/index.md

@@ -79,7 +79,7 @@ ExecuTorch provides support for:
 - [Executorch Runtime API Reference](executorch-runtime-api-reference)
 - [Runtime Python API Reference](runtime-python-api-reference)
 - [API Life Cycle](api-life-cycle)
-- [Javadoc](https://pytorch.org/executorch/main/javadoc/)
+- [Javadoc](https://pytorch.org/executorch/0.6/javadoc/)
 #### Quantization
 - [Overview](quantization-overview)
 #### Kernel Library
@@ -208,7 +208,7 @@ export-to-executorch-api-reference
 executorch-runtime-api-reference
 runtime-python-api-reference
 api-life-cycle
-Javadoc <https://pytorch.org/executorch/main/javadoc/>
+Javadoc <https://pytorch.org/executorch/0.6/javadoc/>
 ```
 
 ```{toctree}

docs/source/llm/getting-started.md

@@ -159,7 +159,7 @@ example_inputs = (torch.randint(0, 100, (1, model.config.block_size), dtype=torc
 # long as they adhere to the rules specified in the dynamic shape configuration.
 # Here we set the range of 0th model input's 1st dimension as
 # [0, model.config.block_size].
-# See https://pytorch.org/executorch/main/concepts#dynamic-shapes
+# See https://pytorch.org/executorch/0.6/concepts#dynamic-shapes
 # for details about creating dynamic shapes.
 dynamic_shape = (
     {1: torch.export.Dim("token_dim", max=model.config.block_size)},
@@ -478,7 +478,7 @@ example_inputs = (
 # long as they adhere to the rules specified in the dynamic shape configuration.
 # Here we set the range of 0th model input's 1st dimension as
 # [0, model.config.block_size].
-# See https://pytorch.org/executorch/main/concepts.html#dynamic-shapes
+# See https://pytorch.org/executorch/0.6/concepts.html#dynamic-shapes
 # for details about creating dynamic shapes.
 dynamic_shape = (
     {1: torch.export.Dim("token_dim", max=model.config.block_size - 1)},

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](https://pytorch.org/executorch/0.6/concepts.html#memory-planning) pass of ExecuTorch, memory allocation information is stored on the nodes of the [`ExportedProgram`](https://pytorch.org/executorch/0.6/concepts.html#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()](https://pytorch.org/executorch/0.6/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.
 
 ```python
 from executorch.util.activation_memory_profiler import generate_memory_trace
@@ -13,7 +13,7 @@ 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`](https://pytorch.org/executorch/0.6/export-to-executorch-api-reference.html#executorch.exir.ExecutorchProgramManager), returned by [to_executorch()](https://pytorch.org/executorch/0.6/export-to-executorch-api-reference.html#executorch.exir.EdgeProgramManager.to_executorch).
 * Set `enable_memory_offsets` to `True` to show the location of each tensor on the memory space.
 
 ## Chrome Trace
@@ -27,4 +27,4 @@ Note that, since we are repurposing the Chrome trace tool, the axes in this cont
 * 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](https://pytorch.org/executorch/0.6/compiler-memory-planning.html)

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](https://pytorch.org/executorch/0.6/using-executorch-building-from-source.html) 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.
 

docs/source/using-executorch-android.md

@@ -2,7 +2,7 @@
 
 To use from Android, ExecuTorch provides Java/Kotlin API bindings and Android platform integration, available as an AAR file.
 
-Note: This page covers Android app integration through the AAR library. The ExecuTorch C++ APIs can also be used from Android native, and the documentation can be found on [this page about cross compilation](https://pytorch.org/executorch/main/using-executorch-building-from-source.html#cross-compilation).
+Note: This page covers Android app integration through the AAR library. The ExecuTorch C++ APIs can also be used from Android native, and the documentation can be found on [this page about cross compilation](https://pytorch.org/executorch/0.6/using-executorch-building-from-source.html#cross-compilation).
 
 ## Installation
 
@@ -41,8 +41,8 @@ dependencies {
 Note: If you want to use release v0.5.0, please use dependency `org.pytorch:executorch-android:0.5.1`.
 
 Click the screenshot below to watch the *demo video* on how to add the package and run a simple ExecuTorch model with Android Studio.
-<a href="https://pytorch.org/executorch/main/_static/img/android_studio.mp4">
-  <img src="https://pytorch.org/executorch/main/_static/img/android_studio.jpeg" width="800" alt="Integrating and Running ExecuTorch on Android">
+<a href="https://pytorch.org/executorch/0.6/_static/img/android_studio.mp4">
+  <img src="https://pytorch.org/executorch/0.6/_static/img/android_studio.jpeg" width="800" alt="Integrating and Running ExecuTorch on Android">
 </a>
 
 ## Using AAR file directly
@@ -130,17 +130,17 @@ Set environment variable `EXECUTORCH_CMAKE_BUILD_TYPE` to `Release` or `Debug` b
 
 #### Using MediaTek backend
 
-To use [MediaTek backend](https://pytorch.org/executorch/main/backends-mediatek.html),
+To use [MediaTek backend](https://pytorch.org/executorch/0.6/backends-mediatek.html),
 after installing and setting up the SDK, set `NEURON_BUFFER_ALLOCATOR_LIB` and `NEURON_USDK_ADAPTER_LIB` to the corresponding path.
 
 #### Using Qualcomm AI Engine Backend
 
-To use [Qualcomm AI Engine Backend](https://pytorch.org/executorch/main/backends-qualcomm.html#qualcomm-ai-engine-backend),
+To use [Qualcomm AI Engine Backend](https://pytorch.org/executorch/0.6/backends-qualcomm.html#qualcomm-ai-engine-backend),
 after installing and setting up the SDK, set `QNN_SDK_ROOT` to the corresponding path.
 
 #### Using Vulkan Backend
 
-To use [Vulkan Backend](https://pytorch.org/executorch/main/backends-vulkan.html#vulkan-backend),
+To use [Vulkan Backend](https://pytorch.org/executorch/0.6/backends-vulkan.html#vulkan-backend),
 set `EXECUTORCH_BUILD_VULKAN` to `ON`.
 
 ## Android Backends
@@ -195,4 +195,4 @@ 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/0.6/javadoc/).