Integrating and Running ExecuTorch on Apple Platforms¶

Author: Anthony Shoumikhin

The ExecuTorch Runtime for iOS and macOS is distributed as a collection of prebuilt .xcframework binary targets. These targets are compatible with both iOS and macOS devices and simulators and are available in both release and debug modes:

executorch - Main Runtime components
backend_coreml - Core ML backend
backend_mps - MPS backend
backend_xnnpack - XNNPACK backend
kernels_custom - Custom kernels
kernels_optimized - Optimized kernels
kernels_portable - Portable kernels (naive implementation used as a reference)
kernels_quantized - Quantized kernels

Link your binary with the ExecuTorch runtime and any backends or kernels used by the exported ML model. It is recommended to link the core runtime to the components that use ExecuTorch directly, and link kernels and backends against the main app target.

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.

Integration¶

Setup¶

CMake¶

Building the Xcode project requires CMake. Installing via homebrew does not typically work; instead, install the packaged application and commandline tools globally:

Download the macOS .dmg installer from https://cmake.org/download
Open the .dmg
Drag the CMake app to the /Applications folder
In a terminal, install the command line tools: sudo /Applications/CMake.app/Contents/bin/cmake-gui --install

Swift Package Manager¶

The prebuilt ExecuTorch runtime, backend, and kernels are available as a Swift PM package.

Xcode¶

In 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, e.g., “0.4.0”, or just use the “latest” branch name for the latest stable build.

Then select which ExecuTorch framework should link against which target.

Click the screenshot below to watch the demo video on how to add the package and run a simple ExecuTorch model on iOS.

Integrating and Running ExecuTorch on Apple Platforms

CLI¶

Add a package and target dependencies on ExecuTorch to your package file like this:

// swift-tools-version:5.0
import PackageDescription

let package = Package(
  name: "YourPackageName",
  products: [
    .library(name: "YourPackageName", targets: ["YourTargetName"]),
  ],
  dependencies: [
    // Use "latest" branch name for the latest stable build.
    .package(url: "https://github.com/pytorch/executorch.git", .branch("0.4.0"))
  ],
  targets: [
    .target(
      name: "YourTargetName",
      dependencies: [
        .product(name: "executorch", package: "executorch"),
        .product(name: "xnnpack_backend", package: "executorch")
      ]),
  ]
)

Then check if everything works correctly:

cd path/to/your/package

swift package resolve

# or just build it
swift build

Local Build¶

Another way to integrate the ExecuTorch runtime is to build the necessary components from sources locally and link against them. This route is more involved but certainly doable.

Install Xcode 15+ and Command Line Tools:

xcode-select --install

Clone ExecuTorch:

git clone https://github.com/pytorch/executorch.git --recursive --depth 1
cd executorch

Set up Python 3.10+ and activate a virtual environment:

python3 -m venv .venv
source .venv/bin/activate

Install Cmake and other helpful PyPI packages:

pip install --upgrade cmake pip zstd

Install the required dependencies, including those needed for the backends like Core ML or MPS, if you plan to build them as well:

./install_requirements.sh

# Optional dependencies for Core ML backend.
./backends/apple/coreml/scripts/install_requirements.sh

# And MPS backend.
./backends/apple/mps/install_requirements.sh

Use the provided script to build .xcframeworks:

./build/build_apple_frameworks.sh --help

For example, the following invocation will build the ExecuTorch Runtime and all currently available kernels and backends for the Apple platform:

./build/build_apple_frameworks.sh --coreml --custom --mps --optimized --portable --quantized --xnnpack

After the build finishes successfully, the resulting frameworks can be found in the cmake-out directory. Copy them to your project and link them against your targets.

Runtime API¶

Check out the C++ Runtime API Tutorial to learn more about how to load and run an exported model. It is recommended to use the C++ API for macOS or iOS, wrapped with Objective-C++ and Swift code if needed to expose it for other components. Please refer to the Demo App as an example of such a setup.

Once linked against the executorch runtime framework, the target can now import all ExecuTorch public headers. For example, in Objective-C++:

#import <ExecuTorch/ExecuTorch.h>
#import <executorch/extension/module/module.h>

Or in Swift:

import ExecuTorch

Note: Importing the ExecuTorch umbrella header (or ExecuTorch module in Swift) provides access to the logging API only. You still need to import the other runtime headers explicitly as needed, e.g., module.h. There is no support for other runtime APIs in Objective-C or Swift beyond logging described below.

Logging¶

We provide extra APIs for logging in Objective-C and Swift as a lightweight wrapper of the internal ExecuTorch machinery. To use it, just import the main framework header in Objective-C. Then use the ExecuTorchLog interface (or the Log class in Swift) to subscribe your own implementation of the ExecuTorchLogSink protocol (or LogSink in Swift) to listen to log events.

#import <ExecuTorch/ExecuTorch.h>
#import <os/log.h>

@interface MyClass : NSObject<ExecuTorchLogSink>
@end

@implementation MyClass

- (instancetype)init {
  self = [super init];
  if (self) {
#if DEBUG
    [ExecuTorchLog.sharedLog addSink:self];
#endif
  }
  return self;
}

- (void)dealloc {
#if DEBUG
  [ExecuTorchLog.sharedLog removeSink:self];
#endif
}

#if DEBUG
- (void)logWithLevel:(ExecuTorchLogLevel)level
           timestamp:(NSTimeInterval)timestamp
            filename:(NSString *)filename
                line:(NSUInteger)line
             message:(NSString *)message {
  NSString *logMessage = [NSString stringWithFormat:@"%@:%lu %@", filename, (unsigned long)line, message];
  switch (level) {
    case ExecuTorchLogLevelDebug:
      os_log_with_type(OS_LOG_DEFAULT, OS_LOG_TYPE_DEBUG, "%{public}@", logMessage);
      break;
    case ExecuTorchLogLevelInfo:
      os_log_with_type(OS_LOG_DEFAULT, OS_LOG_TYPE_INFO, "%{public}@", logMessage);
      break;
    case ExecuTorchLogLevelError:
      os_log_with_type(OS_LOG_DEFAULT, OS_LOG_TYPE_ERROR, "%{public}@", logMessage);
      break;
    case ExecuTorchLogLevelFatal:
      os_log_with_type(OS_LOG_DEFAULT, OS_LOG_TYPE_FAULT, "%{public}@", logMessage);
      break;
    default:
      os_log(OS_LOG_DEFAULT, "%{public}@", logMessage);
      break;
  }
}
#endif

@end

Swift version:

import ExecuTorch
import os.log

public class MyClass {
  public init() {
    #if DEBUG
    Log.shared.add(sink: self)
    #endif
  }
  deinit {
    #if DEBUG
    Log.shared.remove(sink: self)
    #endif
  }
}

#if DEBUG
extension MyClass: LogSink {
  public func log(level: LogLevel, timestamp: TimeInterval, filename: String, line: UInt, message: String) {
    let logMessage = "\(filename):\(line) \(message)"
    switch level {
    case .debug:
      os_log(.debug, "%{public}@", logMessage)
    case .info:
      os_log(.info, "%{public}@", logMessage)
    case .error:
      os_log(.error, "%{public}@", logMessage)
    case .fatal:
      os_log(.fault, "%{public}@", logMessage)
    default:
      os_log("%{public}@", logMessage)
    }
  }
}
#endif

Note: In the example, the logs are intentionally stripped out when the code is not built for Debug mode, i.e., the DEBUG macro is not defined or equals zero.

Troubleshooting¶

Missing operator or backend¶

If after linking against a certain ExecuTorch library you still get an unregistered kernel or backend error at runtime, you may need to use the -all_load or -force_load $(BUILT_PRODUCTS_DIR)/<library_name> linker flags to forcefully register the corresponding components at app start, e.g., -force_load $(BUILT_PRODUCTS_DIR)/libkernels_portable-ios-release.a.

Swift PM¶

If Swift PM complains about a checksum mismatch for the package contents, try cleaning the cache:

rm -rf ~/Library/Caches/org.swift.swiftpm