Building from Source

ExecuTorch uses CMake as the primary build system. Even if you don’t use CMake directly, CMake can emit scripts for other format like Make, Ninja or Xcode. For information, see cmake-generators(7).

System Requirements

Operating System

We’ve tested these instructions on the following systems, although they should also work in similar environments.

Linux (x86_64)

• CentOS 8+

• Ubuntu 20.04.6 LTS+

• RHEL 8+

macOS (x86_64/M1/M2)

• Big Sur (11.0)+

Windows (x86_64)

• Windows Subsystem for Linux (WSL) with any of the Linux options

Software

• conda or another virtual environment manager

  • We recommend conda as it provides cross-language support and integrates smoothly with pip (Python’s built-in package manager)

  • Otherwise, Python’s built-in virtual environment manager python venv is a good alternative.

• g++ version 7 or higher, clang++ version 5 or higher, or another C++17-compatible toolchain.

Note that the cross-compilable core runtime code supports a wider range of toolchains, down to C++17. See the Runtime Overview for portability details.

Environment Setup

Create a Virtual Environment

Install conda on your machine. Then, create a virtual environment to manage our dependencies.

# Create and activate a conda environment named "executorch"
conda create -yn executorch python=3.10.0
conda activate executorch

Clone ExecuTorch

# Clone the ExecuTorch repo from GitHub
# 'main' branch is the primary development branch where you see the latest changes.
# 'viable/strict' contains all of the commits on main that pass all of the necessary CI checks.
git clone --branch viable/strict https://github.com/pytorch/executorch.git
cd executorch

# Update and pull submodules
git submodule sync
git submodule update --init

Install ExecuTorch pip package from Source

# Install ExecuTorch pip package and its dependencies, as well as
# development tools like CMake.
# If developing on a Mac, make sure to install the Xcode Command Line Tools first.
./install_executorch.sh

Use the --pybind flag to install with pybindings and dependencies for other backends.

./install_executorch.sh --pybind <coreml | mps | xnnpack>

# Example: pybindings with CoreML *only*
./install_executorch.sh --pybind coreml

# Example: pybinds with CoreML *and* XNNPACK
./install_executorch.sh --pybind coreml xnnpack

By default, ./install_executorch.sh command installs pybindings for XNNPACK. To disable any pybindings altogether:

./install_executorch.sh --pybind off

For development mode, run the command with --editable, which allows us to modify Python source code and see changes reflected immediately.

./install_executorch.sh --editable [--pybind xnnpack]

# Or you can directly do the following if dependencies are already installed
# either via a previous invocation of `./install_executorch.sh` or by explicitly installing requirements via `./install_requirements.sh` first.
pip install -e .

NOTE: Cleaning the build system

When fetching a new version of the upstream repo (via git fetch or git pull) it is a good idea to clean the old build artifacts. The build system does not currently adapt well to changes in build dependencies.

You should also update and pull the submodules again, in case their versions have changed.

# From the root of the executorch repo:
./install_executorch.sh --clean
git submodule sync
git submodule update --init

Build ExecuTorch C++ runtime from source

ExecuTorch’s CMake build system covers the pieces of the runtime that are likely to be useful to embedded systems users.

• libexecutorch.a: The core of the ExecuTorch runtime. Does not contain any operator/kernel definitions or backend definitions.

• libportable_kernels.a: The implementations of ATen-compatible operators, following the signatures in //kernels/portable/functions.yaml.

• libportable_kernels_bindings.a: Generated code that registers the contents of libportable_kernels.a with the runtime.

  • NOTE: This must be linked into your application with a flag like -Wl,-force_load or -Wl,--whole-archive. It contains load-time functions that automatically register the kernels, but linkers will often prune those functions by default because there are no direct calls to them.

• executor_runner: An example tool that runs a .pte program file using all 1 values as inputs, and prints the outputs to stdout. It is linked with libportable_kernels.a, so the program may use any of the operators it implements.

Configure the CMake build

Follow these steps after cloning or pulling the upstream repo, since the build dependencies may have changed.

# cd to the root of the executorch repo
cd executorch

# Clean and configure the CMake build system. It's good practice to do this
# whenever cloning or pulling the upstream repo.
./install_executorch.sh --clean
(mkdir cmake-out && cd cmake-out && cmake ..)

Once this is done, you don’t need to do it again until you pull from the upstream repo again, or if you modify any CMake-related files.

CMake build options

The release build offers optimizations intended to improve performance and reduce binary size. It disables program verification and executorch logging, and adds optimizations flags.

-DCMAKE_BUILD_TYPE=Release

To further optimize the release build for size, use both:

-DCMAKE_BUILD_TYPE=Release \
-DOPTIMIZE_SIZE=ON

See CMakeLists.txt

Build the runtime components

Build all targets with

# cd to the root of the executorch repo
cd executorch

# Build using the configuration that you previously generated under the
# `cmake-out` directory.
#
# NOTE: The `-j` argument specifies how many jobs/processes to use when
# building, and tends to speed up the build significantly. It's typical to use
# "core count + 1" as the `-j` value.
cmake --build cmake-out -j9

Use an example binary executor_runner to execute a .pte file

First, generate an add.pte or other ExecuTorch program file using the instructions as described in Preparing a Model.

Then, pass it to the command line tool:

./cmake-out/executor_runner --model_path path/to/model.pte

You should see the message “Model executed successfully” followed by the output values.

I 00:00:00.000526 executorch:executor_runner.cpp:82] Model file add.pte is loaded.
I 00:00:00.000595 executorch:executor_runner.cpp:91] Using method forward
I 00:00:00.000612 executorch:executor_runner.cpp:138] Setting up planned buffer 0, size 48.
I 00:00:00.000669 executorch:executor_runner.cpp:161] Method loaded.
I 00:00:00.000685 executorch:executor_runner.cpp:171] Inputs prepared.
I 00:00:00.000764 executorch:executor_runner.cpp:180] Model executed successfully.
I 00:00:00.000770 executorch:executor_runner.cpp:184] 1 outputs:
Output 0: tensor(sizes=[1], [2.])

Cross compilation

Following are instruction on how to perform cross compilation for Android and iOS.

Android

Building executor_runner shell binary

• Prerequisite: Android NDK, choose one of the following:

  • Option 1: Download Android Studio by following the instructions to install ndk.

  • Option 2: Download Android NDK directly from here.

Assuming Android NDK is available, run:

# Run the following lines from the `executorch/` folder
./install_executorch.sh --clean
mkdir cmake-android-out && cd cmake-android-out

# point -DCMAKE_TOOLCHAIN_FILE to the location where ndk is installed
cmake -DCMAKE_TOOLCHAIN_FILE=/Users/{user_name}/Library/Android/sdk/ndk/27.2.12479018/build/cmake/android.toolchain.cmake  -DANDROID_ABI=arm64-v8a ..

cd  ..
cmake --build  cmake-android-out  -j9

adb shell mkdir -p /data/local/tmp/executorch
# push the binary to an Android device
adb push  cmake-android-out/executor_runner  /data/local/tmp/executorch
# push the model file
adb push  add.pte  /data/local/tmp/executorch

adb shell  "/data/local/tmp/executorch/executor_runner --model_path /data/local/tmp/executorch/add.pte"

Building AAR for app integration from source

• Prerequisite: Android NDK from the previous section, and Android SDK (Android Studio is recommended).

Assuming Android NDK and SDK is available, run:

export ANDROID_ABIS=arm64-v8a
export BUILD_AAR_DIR=aar-out
mkdir -p $BUILD_AAR_DIR
sh scripts/build_android_library.sh

This script will build the AAR, which contains the Java API and its corresponding JNI library. Please see this documentation for usage.

iOS

For iOS we’ll build frameworks instead of static libraries, that will also contain the public headers inside.

1. Install Xcode from the Mac App Store and then install the Command Line Tools using the terminal:

xcode-select --install

2. Build the frameworks:

./scripts/build_apple_frameworks.sh

Run the above command with --help flag to learn more on how to build additional backends (like Core ML, MPS or XNNPACK), etc. Note, some backends may require additional dependencies and certain versions of Xcode and iOS.

3. Copy over the generated .xcframework bundles to your Xcode project, link them against your targets and don’t forget to add an extra linker flag -all_load.

Check out the iOS Demo App tutorial for more info.

Next steps

You have successfully cross-compiled executor_runner binary to iOS and Android platforms. You can start exploring advanced features and capabilities. Here is a list of sections you might want to read next:

• Selective build to build the runtime that links to only kernels used by the program, which can provide significant binary size savings.

• Tutorials on building Android and iOS demo apps.

• Tutorials on deploying applications to embedded devices such as ARM Cortex-M/Ethos-U and XTensa HiFi DSP.