Add helper to build in-tree extensions. (#2831)

For single-file extensions, a convenient pattern offered by cython
is to place the source files directly in the python source tree
(`foo/__init__.py`, `foo/ext.pyx`), deriving the package names from
their filesystem location.  Adapt this pattern for pybind11, using an
`intree_extensions` helper, which should be thought of as the moral
equivalent to `cythonize`.

Differences with cythonize: I chose not to include globbing support
(`intree_extensions(glob.glob("**/*.cpp"))` seems sufficient), nor to
provide extension-customization kwargs (directly setting the attributes
on the resulting Pybind11Extension objects seems sufficient).

We could choose to have `intree_extension` (singular instead) and make
users write `[*map(intree_extension, glob.glob("**/*.cpp"))]`; no strong
opinion here.

Co-authored-by: Aaron Gokaslan <skylion.aaron@gmail.com>
This commit is contained in:
Antony Lee
2021-07-13 23:21:55 +02:00
committed by GitHub
parent 2b7985e548
commit 1be0a0a610
4 changed files with 95 additions and 1 deletions

View File

@@ -70,6 +70,19 @@ that is supported via a ``build_ext`` command override; it will only affect
ext_modules=ext_modules
)
If you have single-file extension modules that are directly stored in the
Python source tree (``foo.cpp`` in the same directory as where a ``foo.py``
would be located), you can also generate ``Pybind11Extensions`` using
``setup_helpers.intree_extensions``: ``intree_extensions(["path/to/foo.cpp",
...])`` returns a list of ``Pybind11Extensions`` which can be passed to
``ext_modules``, possibly after further customizing their attributes
(``libraries``, ``include_dirs``, etc.). By doing so, a ``foo.*.so`` extension
module will be generated and made available upon installation.
``intree_extension`` will automatically detect if you are using a ``src``-style
layout (as long as no namespace packages are involved), but you can also
explicitly pass ``package_dir`` to it (as in ``setuptools.setup``).
Since pybind11 does not require NumPy when building, a light-weight replacement
for NumPy's parallel compilation distutils tool is included. Use it like this: