Function load_inline

torch/utils/cpp_extension.py:1497–1647

r''' Load a PyTorch C++ extension just-in-time (JIT) from string sources. This function behaves exactly like :func:`load`, but takes its sources as strings rather than filenames. These strings are stored to files in the build directory, after which the behavior of :func:`load_inline

(name,
                cpp_sources,
                cuda_sources=None,
                functions=None,
                extra_cflags=None,
                extra_cuda_cflags=None,
                extra_ldflags=None,
                extra_include_paths=None,
                build_directory=None,
                verbose=False,
                with_cuda=None,
                is_python_module=True,
                with_pytorch_error_handling=True,
                keep_intermediates=True,
                use_pch=False)

    _remove_if_file_exists(head_file_signature)

def load_inline(name,
                cpp_sources,
                cuda_sources=None,
                functions=None,
                extra_cflags=None,
                extra_cuda_cflags=None,
                extra_ldflags=None,
                extra_include_paths=None,
                build_directory=None,
                verbose=False,
                with_cuda=None,
                is_python_module=True,
                with_pytorch_error_handling=True,
                keep_intermediates=True,
                use_pch=False):
    r'''
    Load a PyTorch C++ extension just-in-time (JIT) from string sources.

    This function behaves exactly like :func:`load`, but takes its sources as
    strings rather than filenames. These strings are stored to files in the
    build directory, after which the behavior of :func:`load_inline` is
    identical to :func:`load`.

    See `the
    tests <https://github.com/pytorch/pytorch/blob/master/test/test_cpp_extensions_jit.py>`_
    for good examples of using this function.

    Sources may omit two required parts of a typical non-inline C++ extension:
    the necessary header includes, as well as the (pybind11) binding code. More
    precisely, strings passed to ``cpp_sources`` are first concatenated into a
    single ``.cpp`` file. This file is then prepended with ``#include
    <torch/extension.h>``.

    Furthermore, if the ``functions`` argument is supplied, bindings will be
    automatically generated for each function specified. ``functions`` can
    either be a list of function names, or a dictionary mapping from function
    names to docstrings. If a list is given, the name of each function is used
    as its docstring.

    The sources in ``cuda_sources`` are concatenated into a separate ``.cu``
    file and  prepended with ``torch/types.h``, ``cuda.h`` and
    ``cuda_runtime.h`` includes. The ``.cpp`` and ``.cu`` files are compiled
    separately, but ultimately linked into a single library. Note that no
    bindings are generated for functions in ``cuda_sources`` per  se. To bind
    to a CUDA kernel, you must create a C++ function that calls it, and either
    declare or define this C++ function in one of the ``cpp_sources`` (and
    include its name in ``functions``).

    See :func:`load` for a description of arguments omitted below.

    Args:
        cpp_sources: A string, or list of strings, containing C++ source code.
        cuda_sources: A string, or list of strings, containing CUDA source code.
        functions: A list of function names for which to generate function
            bindings. If a dictionary is given, it should map function names to
            docstrings (which are otherwise just the function names).
        with_cuda: Determines whether CUDA headers and libraries are added to
            the build. If set to ``None`` (default), this value is