Using a macro as a an l-value

In the Python C API, some functions are implemented as macro because writing a macro is simpler than writing a regular function. If a macro exposes directly a structure member, it is technically possible to use this macro to not only get the structure member but also set it.

Example with the Python 3.10 Py_TYPE() macro:

#define Py_TYPE(ob) (((PyObject *)(ob))->ob_type)

This macro can be used as a r-value to get an object type:

type = Py_TYPE(object);

It can also be used as an l-value to set an object type:

Py_TYPE(object) = new_type;

It is also possible to set an object reference count and an object size using Py_REFCNT() and Py_SIZE() macros.

Setting directly an object attribute relies on the current exact CPython implementation. Implementing this feature in other Python implementations can make their C API implementation less efficient.

CPython nogil fork

Sam Gross forked Python 3.9 to remove the GIL: the nogil branch. This fork has no PyObject.ob_refcnt member, but a more elaborated implementation for reference counting, and so the Py_REFCNT(obj) = new_refcnt; code fails with a compiler error.

Merging the nogil fork into the upstream CPython main branch requires first to fix this C API compatibility issue. It is a concrete example of a Python optimization blocked indirectly by the C API.

This issue was already fixed in Python 3.10: the Py_REFCNT() macro has been already modified to disallow using it as an l-value.

These statements are endorsed by Sam Gross (nogil developer).

HPy project

The HPy project is a brand new C API for Python using only handles and function calls: handles are opaque, structure members cannot be accessed directly, and pointers cannot be dereferenced.

Searching and replacing Py_SET_SIZE() is easier and safer than searching and replacing some strange macro uses of Py_SIZE(). Py_SIZE() can be semi-mechanically replaced by HPy_Length(), whereas seeing Py_SET_SIZE() would immediately make clear that the code needs bigger changes in order to be ported to HPy (for example by using HPyTupleBuilder or HPyListBuilder).

The fewer internal details exposed via macros, the easier it will be for HPy to provide direct equivalents. Any macro that references “non-public” interfaces effectively exposes those interfaces publicly.

These statements are endorsed by Antonio Cuni (HPy developer).

GraalVM Python

In GraalVM, when a Python object is accessed by the Python C API, the C API emulation layer has to wrap the GraalVM objects into wrappers that expose the internal structure of the CPython structures (PyObject, PyLongObject, PyTypeObject, etc). This is because when the C code accesses it directly or via macros, all GraalVM can intercept is a read at the struct offset, which has to be mapped back to the representation in GraalVM. The smaller the “effective” number of exposed struct members (by replacing macros with functions), the simpler GraalVM wrappers can be.

This PEP alone is not enough to get rid of the wrappers in GraalVM, but it is a step towards this long term goal. GraalVM already supports HPy which is a better solution in the long term.

These statements are endorsed by Tim Felgentreff (GraalVM Python developer).

Disallow using macros as l-values

The following 65 macros are modified to disallow using them as l-values.

Port C extensions to Python 3.11

In practice, the majority of projects affected by these PEP only have to make two changes:

• Replace Py_TYPE(obj) = new_type with Py_SET_TYPE(obj, new_type).
• Replace Py_SIZE(obj) = new_size with Py_SET_SIZE(obj, new_size).

The pythoncapi_compat project can be used to update automatically C extensions: add Python 3.11 support without losing support with older Python versions. The project provides a header file which provides Py_SET_REFCNT(), Py_SET_TYPE() and Py_SET_SIZE() functions to Python 3.8 and older.

PyTuple_GET_ITEM() and PyList_GET_ITEM() are left unchanged

The PyTuple_GET_ITEM() and PyList_GET_ITEM() macros are left unchanged.

The code patterns &PyTuple_GET_ITEM(tuple, 0) and &PyList_GET_ITEM(list, 0) are still commonly used to get access to the inner PyObject** array.

Changing these macros is out of the scope of this PEP.

PyDescr_NAME() and PyDescr_TYPE() are left unchanged

The PyDescr_NAME() and PyDescr_TYPE() macros are left unchanged.

These macros give access to PyDescrObject.d_name and PyDescrObject.d_type members. They can be used as l-values to set these members.

The SWIG project uses these macros as l-values to set these members. It would be possible to modify SWIG to prevent setting PyDescrObject structure members directly, but it is not really worth it since the PyDescrObject structure is not performance critical and is unlikely to change soon.

See the bpo-46538 “[C API] Make the PyDescrObject structure opaque: PyDescr_NAME() and PyDescr_TYPE()” issue for more details.

Py_TYPE() and Py_SIZE() macros

In May 2020, the Py_TYPE() and Py_SIZE() macros have been modified to disallow using them as l-values (Py_TYPE, Py_SIZE).

In November 2020, the change was reverted, since it broke too many third party projects.

In June 2021, once most third party projects were updated, a second attempt was done, but had to be reverted again , since it broke test_exceptions on Windows.

In September 2021, once test_exceptions has been fixed, Py_TYPE() and Py_SIZE() were finally changed.

In November 2021, this backward incompatible change got a Steering Council exception.

In October 2022, Python 3.11 got released with Py_TYPE() and Py_SIZE() incompatible changes.

Statistics

In total (projects on PyPI and not on PyPI), 34 projects are known to be affected by this PEP:

• 16 projects (47%) are already fixed
• 18 projects (53%) are not fixed yet (pending fix or have to regenerate their Cython code)

On September 1, 2022, the PEP affects 18 projects (0.4%) of the top 5000 PyPI projects:

• 15 projects (0.3%) have to regenerate their Cython code
• 3 projects (0.1%) have a pending fix

Top 5000 PyPI

Projects with a pending fix (3):

• datatable (1.0.0): fixed
• guppy3 (3.1.2): fixed
• scipy (1.9.3): need to update boost python

Moreover, 15 projects have to regenerate their Cython code.

Projects released with a fix (12):

• bitarray (1.6.2): commit
• Cython (0.29.20): commit
• immutables (0.15): commit
• mercurial (5.7): commit, bug report
• mypy (v0.930): commit
• numpy (1.22.1): commit, commit 2
• pycurl (7.44.1): commit
• PyGObject (3.42.0)
• pyside2 (5.15.1): bug report
• python-snappy (0.6.1): fixed
• recordclass (0.17.2): fixed
• zstd (1.5.0.3): commit

There are also two backport projects which are affected by this PEP:

• pickle5 (0.0.12): backport for Python <= 3.7
• pysha3 (1.0.2): backport for Python <= 3.5

They must not be used and cannot be used on Python 3.11.

Other affected projects

Other projects released with a fix (4):

• boost (1.78.0): commit
• breezy (3.2.1): bug report
• duplicity (0.8.18): commit
• gobject-introspection (1.70.0): MR

The HPy project

The hope with the HPy project is to provide a C API that is close to the original API—to make porting easy—and have it perform as close to the existing API as possible. At the same time, HPy is sufficiently removed to be a good “C extension API” (as opposed to a stable subset of the CPython implementation API) that does not leak implementation details. To ensure this latter property, the HPy project tries to develop everything in parallel for CPython, PyPy, and GraalVM Python.

HPy is still evolving very fast. Issues are still being solved while migrating NumPy, and work has begun on adding support for HPy to Cython. Work on pybind11 is starting soon. Tim Felgentreff believes by the time HPy has these users of the existing C API working, HPy should be in a state where it is generally useful and can be deemed stable enough that further development can follow a more stable process.

In the long run the HPy project would like to become a promoted API to write Python C extensions.

The HPy project is a good solution for the long term. It has the advantage of being developed outside Python and it doesn’t require any C API change.

The C API is here is stay for a few more years

The first concern about HPy is that right now, HPy is not mature nor widely used, and CPython still has to continue supporting a large amount of C extensions which are not likely to be ported to HPy soon.

The second concern is the inability to evolve CPython internals to implement new optimizations, and the inefficient implementation of the current C API in PyPy, GraalPython, etc. Sadly, HPy will only solve these problems when most C extensions will be fully ported to HPy: when it will become reasonable to consider dropping the “legacy” Python C API.

While porting a C extension to HPy can be done incrementally on CPython, it requires to modify a lot of code and takes time. Porting most C extensions to HPy is expected to take a few years.

This PEP proposes to make the C API “less bad” by fixing one problem which is clearily identified as causing practical issues: macros used as l-values. This PEP only requires updating a minority of C extensions, and usually only a few lines need to be changed in impacted extensions.

For example, NumPy 1.22 is made of 307,300 lines of C code, and adapting NumPy to the this PEP only modified 11 lines (use Py_SET_TYPE and Py_SET_SIZE) and adding 4 lines (to define Py_SET_TYPE and Py_SET_SIZE for Python 3.8 and older). The beginnings of the NumPy port to HPy already required modifying more lines than that.

Right now, it’s hard to bet which approach is the best: fixing the current C API, or focusing on HPy. It would be risky to only focus on HPy.