Update iOS SwiftPM docs for ExecuTorch 1.0.0

docs/source/getting-started.md

@@ -158,7 +158,7 @@ For a full example of running a model on Android, see the [DeepLabV3AndroidDemo]
 #### Installation
 ExecuTorch supports both iOS and macOS via C++, as well as hardware backends for CoreML, MPS, and CPU. The iOS runtime library is provided as a collection of .xcframework targets and are made available as a Swift PM package.
 
-To get started with Xcode, go to File > Add Package Dependencies. Paste the URL of the ExecuTorch repo into the search bar and select it. Make sure to change the branch name to the desired ExecuTorch version in format “swiftpm-”, (e.g. “swiftpm-0.6.0”).  The ExecuTorch dependency can also be added to the package file manually. See [Using ExecuTorch on iOS](using-executorch-ios.md) for more information.
+To get started with Xcode, go to `File > Add Package Dependencies`. Paste the URL of the ExecuTorch repo into the search bar and select it. Make sure to change the dependency rule to a branch named for the desired ExecuTorch version (e.g., `swiftpm-1.0.0`). The ExecuTorch dependency can also be added to the package file manually. See [Using ExecuTorch on iOS](using-executorch-ios.md) for more information.
 
 #### Runtime APIs
 Models can be loaded and run from Objective-C using the C++ APIs.

docs/source/using-executorch-ios.md

@@ -28,11 +28,11 @@ The prebuilt ExecuTorch runtime, backend, and kernels are available as a [Swift
 
 #### Xcode
 
-In Xcode, go to `File > Add Package Dependencies`. Paste the URL of the [ExecuTorch repo](https://github.com/pytorch/executorch) into the search bar and select it. Make sure to change the branch name to the desired ExecuTorch version in format "swiftpm-<version>", (e.g. "swiftpm-1.0.0"), or a branch name in format "swiftpm-<version>.<year_month_date>" (e.g. "swiftpm-1.1.0-20251101") for a [nightly build](https://ossci-ios.s3.amazonaws.com/list.html) on a specific date.
+In Xcode, go to `File > Add Package Dependencies`. Paste the URL of the [ExecuTorch repo](https://github.com/pytorch/executorch) into the search bar and select it. Change the dependency rule to a branch named for the desired ExecuTorch version (e.g., `swiftpm-1.0.0`), or use a branch name in the format `swiftpm-<version>.<year_month_day>` for a [nightly build](https://ossci-ios.s3.amazonaws.com/list.html) on a specific date (e.g., `swiftpm-1.1.0.20251101`).
 
 ![](_static/img/swiftpm_xcode1.png)
 
-Then select which ExecuTorch framework should link against which target.
+Then select the ExecuTorch products that your app needs. For a simple app using the XNNPACK backend, link the app target against `executorch`, `backend_xnnpack`, and `kernels_optimized`. The `executorch` product provides the core runtime; for Core ML, MPS, or other execution paths, choose the matching backend and kernel products from the product list above.
 
 ![](_static/img/swiftpm_xcode2.png)