executorch-sys

For a general description of the project, see the the executorch crate.

Build

To build the library, you need to build the C++ library yourself first. Currently the supported Cpp executorch version is 1.0.1 (or 1.0.0). The C++ library allow for great flexibility with many flags, customizing which modules, kernels, and extensions are built. Multiple static libraries are built, and the Rust library links to them. In the following example we build the C++ library with the necessary flags to run example hello_world:

# Clone the C++ library
cd ${EXECUTORCH_CPP_DIR}
git clone --depth 1 --branch v1.0.1 https://github.com/pytorch/executorch.git .
git submodule sync --recursive
git submodule update --init --recursive

# Install requirements
./install_requirements.sh

# Build C++ library
mkdir cmake-out && cd cmake-out
cmake \
    -DDEXECUTORCH_SELECT_OPS_LIST=aten::add.out \
    -DEXECUTORCH_BUILD_EXECUTOR_RUNNER=OFF \
    -DEXECUTORCH_BUILD_EXTENSION_RUNNER_UTIL=OFF \
    -DEXECUTORCH_BUILD_PORTABLE_OPS=ON \
    -DEXECUTORCH_BUILD_EXTENSION_DATA_LOADER=ON \
    -DEXECUTORCH_BUILD_EXTENSION_FLAT_TENSOR=ON \
    -DEXECUTORCH_BUILD_EXTENSION_NAMED_DATA_MAP=ON \
    -DEXECUTORCH_BUILD_EXTENSION_MODULE=ON \
    -DEXECUTORCH_BUILD_EXTENSION_TENSOR=ON \
    -DEXECUTORCH_ENABLE_PROGRAM_VERIFICATION=ON \
    -DEXECUTORCH_ENABLE_LOGGING=ON \
    ..
make -j

# Run example
# We set EXECUTORCH_RS_EXECUTORCH_LIB_DIR to the path of the C++ build output
cd ${EXECUTORCH_RS_DIR}/examples/hello_world
python export_model.py
EXECUTORCH_RS_EXECUTORCH_LIB_DIR=${EXECUTORCH_CPP_DIR}/cmake-out cargo run

The executorch crate will always look for the following static libraries:

• libexecutorch.a
• libexecutorch_core.a

Additional libs are required if feature flags are enabled. For example the libextension_data_loader.a is required if the data-loader feature is enabled, and libextension_tensor.a is required if the tensor-ptr feature is enabled. See the feature flags section for more info.

The static libraries of the kernels implementations are required only if your model uses them, and they should be linked manually by the binary that uses the executorch crate. For example, the hello_world example uses a model with a single addition operation, so it compile the C++ library with DEXECUTORCH_SELECT_OPS_LIST=aten::add.out and contain the following lines in its build.rs:

println!("cargo::rustc-link-lib=static:+whole-archive=portable_kernels");
println!("cargo::rustc-link-lib=static:+whole-archive=portable_ops_lib");

let libs_dir = std::env::var("EXECUTORCH_RS_EXECUTORCH_LIB_DIR").unwrap();
println!("cargo::rustc-link-search=native={libs_dir}/kernels/portable/");

Note that the ops and kernels libs are linked with +whole-archive to ensure that all symbols are included in the binary.

The EXECUTORCH_RS_EXECUTORCH_LIB_DIR environment variable should be set to the path of the C++ build output. If its not provided, its the responsibility of the binary to add the libs directories to the linker search path, and the crate will just link to the static libraries using cargo::rustc-link-lib=....

If you want to link to executorch libs yourself, set the environment variable EXECUTORCH_RS_LINK to 0, and the crate will not link to any library and not modify the linker search path.

The crate contains a small C/C++ bridge that uses the headers of the C++ library, and it is compiled using the cc crate (and the cxx crate, that uses cc under the hood). If custom compiler flags (for example -DET_MIN_LOG_LEVEL=Debug) are used when compiling the C++ library, you should set the matching environment variables that cc reads during cargo build (for example CFLAGS=-DET_MIN_LOG_LEVEL=Debug CXXFLAGS=-DET_MIN_LOG_LEVEL=Debug), see the cc docs.

Cargo Features

• data-loader

  Includes the FileDataLoader and MmapDataLoader structs. Without this feature the only available data loader is BufferDataLoader. The libextension_data_loader.a static library is required, compile C++ executorch with EXECUTORCH_BUILD_EXTENSION_DATA_LOADER=ON.

• module

  Includes the Module struct, a high-level API for loading and executing PyTorch models. It is an alternative to the lower-level Program API, which is more suitable for embedded systems. The libextension_module_static.a static library is required, compile C++ executorch with EXECUTORCH_BUILD_EXTENSION_MODULE=ON. Also includes the std, data-loader and flat-tensor features.

• tensor-ptr

  Includes a few functions creating cxx::SharedPtr<Tensor> pointers, that manage the lifetime of the tensor object alongside the lifetimes of the data buffer and additional metadata. The libextension_tensor.a static library is required, compile C++ executorch with EXECUTORCH_BUILD_EXTENSION_TENSOR=ON. Also includes the std feature.

• flat-tensor

  Includes the FlatTensorDataMap struct that can read .ptd files with external tensors for models. The libextension_flat_tensor.a static library is required, compile C++ executorch with EXECUTORCH_BUILD_EXTENSION_FLAT_TENSOR=ON.

• etdump

  Includes the ETDumpGen struct, an implementation of an EventTracer, used for debugging and profiling. The libetdump.a static library is required, compile C++ executorch with EXECUTORCH_BUILD_DEVTOOLS=ON and EXECUTORCH_ENABLE_EVENT_TRACER=ON. In addition, the flatcc (or flatcc_d) library is required, available at {CMAKE_DIR}/third-party/flatcc_ep/lib/, and should be linked by the user.

• std

  Enable the standard library. This feature is enabled by default, but can be disabled to build executorch in a no_std environment. NOTE: no_std is still WIP, see https://github.com/pytorch/executorch/issues/4561

By default the std feature is enabled.