Merge branch 'release/0.6' into cherry-pick-10184-by-pytorch_bot_bot_

Author: shoumikhin

docs/source/using-executorch-building-from-source.md

@@ -113,7 +113,7 @@ Or alternatively, [install conda on your machine](https://conda.io/projects/cond
 > # From the root of the executorch repo:
 > ./install_executorch.sh --clean
 > git submodule sync
-> git submodule update --init
+> git submodule update --init --recursive
 > ```
 
 ## Build ExecuTorch C++ runtime from source

examples/demo-apps/apple_ios/LLaMA/docs/delegates/mps_README.md

@@ -85,7 +85,7 @@ Link your binary with the ExecuTorch runtime and any backends or kernels used by
 
 Note: To access logs, link against the Debug build of the ExecuTorch runtime, i.e., the executorch_debug framework. For optimal performance, always link against the Release version of the deliverables (those without the _debug suffix), which have all logging overhead removed.
 
-For more details integrating and Running ExecuTorch on Apple Platforms, checkout this [link](https://pytorch.org/executorch/main/apple-runtime.html).
+For more details integrating and Running ExecuTorch on Apple Platforms, checkout this [link](https://pytorch.org/executorch/main/using-executorch-ios.html).
 
 <p align="center">
 <img src="https://raw.githubusercontent.com/pytorch/executorch/refs/heads/main/docs/source/_static/img/ios_demo_app_swift_pm.png" alt="iOS LLaMA App Swift PM" style="width:600px">

examples/models/efficient_sam/README.md

@@ -32,7 +32,7 @@ python -m examples.xnnpack.aot_compiler -m efficient_sam
 
 # Performance
 
-Tests were conducted on an Apple M1 Pro chip using the instructions for building and running Executorch with [Core ML](https://pytorch.org/executorch/main/https://pytorch.org/executorch/main/backends-coreml#runtime-integration) and [XNNPACK](https://pytorch.org/executorch/main/tutorial-xnnpack-delegate-lowering#running-the-xnnpack-model-with-cmake) backends.
+Tests were conducted on an Apple M1 Pro chip using the instructions for building and running Executorch with [Core ML](https://pytorch.org/executorch/main/backends-coreml#runtime-integration) and [XNNPACK](https://pytorch.org/executorch/main/tutorial-xnnpack-delegate-lowering#running-the-xnnpack-model-with-cmake) backends.
 
 | Backend Configuration  | Average Inference Time (seconds) |
 | ---------------------- | -------------------------------- |