This Python module, named portable_lib, provides a set of functions and classes for loading and executing bundled programs. To install it, run the fullowing command:

./install_executorch.sh

# ...or use pip directly
pip install . --no-build-isolation

Not all backends are built into the pip wheel by default. You can link these missing/experimental backends by turning on the corresponding cmake flag. For example, to include the MPS backend:

CMAKE_ARGS="-DEXECUTORCH_BUILD_MPS=ON" ./install_executorch.sh

• _load_for_executorch(path: str, enable_etdump: bool = False): Load a module from a file.
• _load_for_executorch_from_buffer(buffer: str, enable_etdump: bool = False): Load a module from a buffer.
• _load_for_executorch_from_bundled_program(ptr: str, enable_etdump: bool = False): Load a module from a bundled program.
• _load_bundled_program_from_buffer(buffer: str, non_const_pool_size: int = kDEFAULT_BUNDLED_INPUT_POOL_SIZE): Load a bundled program from a buffer.
• _dump_profile_results(): Dump profile results.
• _get_operator_names(): Get operator names.
• _create_profile_block(): Create a profile block.
• _reset_profile_results(): Reset profile results.

• load_bundled_input(): Load bundled input.
• verify_result_with_bundled_expected_output(bundle: str, method_name: str, testset_idx: int, rtol: float = 1e-5, atol: float = 1e-8): Verify result with bundled expected output.
• plan_execute(): Plan and execute.
• run_method(): Run method.
• forward(): Forward. This takes a pytree-flattend PyTorch-tensor-based input.
• has_etdump(): Check if etdump is available.
• write_etdump_result_to_file(): Write etdump result to a file.
• __call__(): Call method.

This class is currently empty and serves as a placeholder for future methods and attributes.

All functions and methods are guarded by a call guard that redirects cout and cerr to the Python environment.