PEP: 620 Title: Hide implementation details from the C API Author:
Victor Stinner <vstinner@python.org> Status: Withdrawn Type: Standards
Track Content-Type: text/x-rst Created: 19-Jun-2020 Python-Version: 3.12

Abstract

Introduce C API incompatible changes to hide implementation details.

Once most implementation details will be hidden, evolution of CPython
internals would be less limited by C API backward compatibility issues.
It will be way easier to add new features.

It becomes possible to experiment with more advanced optimizations in
CPython than just micro-optimizations, like tagged pointers.

Define a process to reduce the number of broken C extensions.

The implementation of this PEP is expected to be done carefully over
multiple Python versions. It already started in Python 3.7 and most
changes are already completed. The Process to reduce the number of
broken C extensions dictates the rhythm.

PEP withdrawn

This PEP was withdrawn by its author since the scope is too broad and
the work is distributed over multiple Python versions, which makes it
difficult to make a decision on the overall PEP. It was split into new
PEPs with narrower and better defined scopes, like PEP 670.

Motivation

The C API blocks CPython evolutions

Adding or removing members of C structures is causing multiple backward
compatibility issues.

Adding a new member breaks the stable ABI (PEP 384), especially for
types declared statically (e.g. static PyTypeObject MyType = {...};). In
Python 3.4, the PEP 442 "Safe object finalization" added the tp_finalize
member at the end of the PyTypeObject structure. For ABI backward
compatibility, a new Py_TPFLAGS_HAVE_FINALIZE type flag was required to
announce if the type structure contains the tp_finalize member. The flag
was removed in Python 3.8 (bpo-32388).

The PyTypeObject.tp_print member, deprecated since Python 3.0 released
in 2009, has been removed in the Python 3.8 development cycle. But the
change broke too many C extensions and had to be reverted before 3.8
final release. Finally, the member was removed again in Python 3.9.

C extensions rely on the ability to access structure members, indirectly
through the C API, or even directly. Modifying structures like
PyListObject cannot be even considered.

The PyTypeObject structure is the one which evolved the most, simply
because there was no other way to evolve CPython than modifying it.

A C extension can technically dereference a PyObject* pointer and access
PyObject members. This prevents experiments like tagged pointers
(storing small values as PyObject* which does not point to a valid
PyObject structure).

Replacing Python garbage collector with a tracing garbage collector
would also need to remove PyObject.ob_refcnt reference counter, whereas
currently Py_INCREF() and Py_DECREF() macros access directly to
PyObject.ob_refcnt.

Same CPython design since 1990: structures and reference counting

When the CPython project was created, it was written with one principle:
keep the implementation simple enough so it can be maintained by a
single developer. CPython complexity grew a lot and many
micro-optimizations have been implemented, but CPython core design has
not changed.

Members of PyObject and PyTupleObject structures have not changed since
the "Initial revision" commit (1990):

    #define OB_HEAD \
        unsigned int ob_refcnt; \
        struct _typeobject *ob_type;

    typedef struct _object {
        OB_HEAD
    } object;

    typedef struct {
        OB_VARHEAD
        object *ob_item[1];
    } tupleobject;

Only names changed: object was renamed to PyObject and tupleobject was
renamed to PyTupleObject.

CPython still tracks Python objects lifetime using reference counting
internally and for third party C extensions (through the Python C API).

All Python objects must be allocated on the heap and cannot be moved.

Why is PyPy more efficient than CPython?

The PyPy project is a Python implementation which is 4.2x faster than
CPython on average. PyPy developers chose to not fork CPython, but start
from scratch to have more freedom in terms of optimization choices.

PyPy does not use reference counting, but a tracing garbage collector
which moves objects. Objects can be allocated on the stack (or even not
at all), rather than always having to be allocated on the heap.

Objects layouts are designed with performance in mind. For example, a
list strategy stores integers directly as integers, rather than objects.

Moreover, PyPy also has a JIT compiler which emits fast code thanks to
the efficient PyPy design.

PyPy bottleneck: the Python C API

While PyPy is way more efficient than CPython to run pure Python code,
it is as efficient or slower than CPython to run C extensions.

Since the C API requires PyObject* and allows to access directly
structure members, PyPy has to associate a CPython object to PyPy
objects and maintain both consistent. Converting a PyPy object to a
CPython object is inefficient. Moreover, reference counting also has to
be implemented on top of PyPy tracing garbage collector.

These conversions are required because the Python C API is too close to
the CPython implementation: there is no high-level abstraction. For
example, structures members are part of the public C API and nothing
prevents a C extension to get or set directly PyTupleObject.ob_item[0]
(the first item of a tuple).

See Inside cpyext: Why emulating CPython C API is so Hard (Sept 2018) by
Antonio Cuni for more details.

Rationale

Hide implementation details

Hiding implementation details from the C API has multiple advantages:

-   It becomes possible to experiment with more advanced optimizations
    in CPython than just micro-optimizations. For example, tagged
    pointers, and replace the garbage collector with a tracing garbage
    collector which can move objects.
-   Adding new features in CPython becomes easier.
-   PyPy should be able to avoid conversions to CPython objects in more
    cases: keep efficient PyPy objects.
-   It becomes easier to implement the C API for a new Python
    implementation.
-   More C extensions will be compatible with Python implementations
    other than CPython.

Relationship with the limited C API

The PEP 384 "Defining a Stable ABI" is implemented in Python 3.4. It
introduces the "limited C API": a subset of the C API. When the limited
C API is used, it becomes possible to build a C extension only once and
use it on multiple Python versions: that's the stable ABI.

The main limitation of the PEP 384 is that C extensions have to opt-in
for the limited C API. Only very few projects made this choice, usually
to ease distribution of binaries, especially on Windows.

This PEP moves the C API towards the limited C API.

Ideally, the C API will become the limited C API and all C extensions
will use the stable ABI, but this is out of this PEP scope.

Specification

Summary

-   (Completed) Reorganize the C API header files: create
    Include/cpython/ and Include/internal/ subdirectories.
-   (Completed) Move private functions exposing implementation details
    to the internal C API.
-   (Completed) Convert macros to static inline functions.
-   (Completed) Add new functions Py_SET_TYPE(), Py_SET_REFCNT() and
    Py_SET_SIZE(). The Py_TYPE(), Py_REFCNT() and Py_SIZE() macros
    become functions which cannot be used as l-value.
-   (Completed) New C API functions must not return borrowed references.
-   (In Progress) Provide pythoncapi_compat.h header file.
-   (In Progress) Make structures opaque, add getter and setter
    functions.
-   (Not Started) Deprecate PySequence_Fast_ITEMS().
-   (Not Started) Convert PyTuple_GET_ITEM() and PyList_GET_ITEM()
    macros to static inline functions.

Reorganize the C API header files

The first consumer of the C API was Python itself. There is no clear
separation between APIs which must not be used outside Python, and API
which are public on purpose.

Header files must be reorganized in 3 API:

-   Include/ directory is the limited C API: no implementation details,
    structures are opaque. C extensions using it get a stable ABI.
-   Include/cpython/ directory is the CPython C API: less "portable"
    API, depends more on the Python version, expose some implementation
    details, few incompatible changes can happen.
-   Include/internal/ directory is the internal C API: implementation
    details, incompatible changes are likely at each Python release.

The creation of the Include/cpython/ directory is fully backward
compatible. Include/cpython/ header files cannot be included directly
and are included automatically by Include/ header files when the
Py_LIMITED_API macro is not defined.

The internal C API is installed and can be used for specific usage like
debuggers and profilers which must access structures members without
executing code. C extensions using the internal C API are tightly
coupled to a Python version and must be recompiled at each Python
version.

STATUS: Completed (in Python 3.8)

The reorganization of header files started in Python 3.7 and was
completed in Python 3.8:

-   bpo-35134: Add a new Include/cpython/ subdirectory for the "CPython
    API" with implementation details.
-   bpo-35081: Move internal headers to Include/internal/

Move private functions to the internal C API

Private functions which expose implementation details must be moved to
the internal C API.

If a C extension relies on a CPython private function which exposes
CPython implementation details, other Python implementations have to
re-implement this private function to support this C extension.

STATUS: Completed (in Python 3.9)

Private functions moved to the internal C API in Python 3.8:

-   _PyObject_GC_TRACK(), _PyObject_GC_UNTRACK()

Macros and functions excluded from the limited C API in Python 3.9:

-   _PyObject_SIZE(), _PyObject_VAR_SIZE()
-   PyThreadState_DeleteCurrent()
-   PyFPE_START_PROTECT(), PyFPE_END_PROTECT()
-   _Py_NewReference(), _Py_ForgetReference()
-   _PyTraceMalloc_NewReference()
-   _Py_GetRefTotal()

Private functions moved to the internal C API in Python 3.9:

-   GC functions like _Py_AS_GC(), _PyObject_GC_IS_TRACKED() and
    _PyGCHead_NEXT()
-   _Py_AddToAllObjects() (not exported)
-   _PyDebug_PrintTotalRefs(), _Py_PrintReferences(),
    _Py_PrintReferenceAddresses() (not exported)

Public "clear free list" functions moved to the internal C API and
renamed to private functions in Python 3.9:

-   PyAsyncGen_ClearFreeLists()
-   PyContext_ClearFreeList()
-   PyDict_ClearFreeList()
-   PyFloat_ClearFreeList()
-   PyFrame_ClearFreeList()
-   PyList_ClearFreeList()
-   PyTuple_ClearFreeList()
-   Functions simply removed:
    -   PyMethod_ClearFreeList() and PyCFunction_ClearFreeList(): bound
        method free list removed in Python 3.9.
    -   PySet_ClearFreeList(): set free list removed in Python 3.4.
    -   PyUnicode_ClearFreeList(): Unicode free list removed in Python
        3.3.

Convert macros to static inline functions

Converting macros to static inline functions has multiple advantages:

-   Functions have well defined parameter types and return type.
-   Functions can use variables with a well defined scope (the
    function).
-   Debugger can be put breakpoints on functions and profilers can
    display the function name in the call stacks. In most cases, it
    works even when a static inline function is inlined.
-   Functions don't have macros pitfalls.

Converting macros to static inline functions should only impact very few
C extensions that use macros in unusual ways.

For backward compatibility, functions must continue to accept any type,
not only PyObject*, to avoid compiler warnings, since most macros cast
their parameters to PyObject*.

Python 3.6 requires C compilers to support static inline functions: the
PEP 7 requires a subset of C99.

STATUS: Completed (in Python 3.9)

Macros converted to static inline functions in Python 3.8:

-   Py_INCREF(), Py_DECREF()
-   Py_XINCREF(), Py_XDECREF()
-   PyObject_INIT(), PyObject_INIT_VAR()
-   _PyObject_GC_TRACK(), _PyObject_GC_UNTRACK(), _Py_Dealloc()

Macros converted to regular functions in Python 3.9:

-   Py_EnterRecursiveCall(), Py_LeaveRecursiveCall() (added to the
    limited C API)
-   PyObject_INIT(), PyObject_INIT_VAR()
-   PyObject_GET_WEAKREFS_LISTPTR()
-   PyObject_CheckBuffer()
-   PyIndex_Check()
-   PyObject_IS_GC()
-   PyObject_NEW() (alias to PyObject_New()), PyObject_NEW_VAR() (alias
    to PyObject_NewVar())
-   PyType_HasFeature() (always call PyType_GetFlags())
-   Py_TRASHCAN_BEGIN_CONDITION() and Py_TRASHCAN_END() macros now call
    functions which hide implementation details, rather than accessing
    directly members of the PyThreadState structure.

Make structures opaque

The following structures of the C API become opaque:

-   PyInterpreterState
-   PyThreadState
-   PyGC_Head
-   PyTypeObject
-   PyObject and PyVarObject
-   PyTypeObject
-   All types which inherit from PyObject or PyVarObject

C extensions must use getter or setter functions to get or set structure
members. For example, tuple->ob_item[0] must be replaced with
PyTuple_GET_ITEM(tuple, 0).

To be able to move away from reference counting, PyObject must become
opaque. Currently, the reference counter PyObject.ob_refcnt is exposed
in the C API. All structures must become opaque, since they "inherit"
from PyObject. For, PyFloatObject inherits from PyObject:

    typedef struct {
        PyObject ob_base;
        double ob_fval;
    } PyFloatObject;

Making PyObject fully opaque requires converting Py_INCREF() and
Py_DECREF() macros to function calls. This change has an impact on
performance. It is likely to be one of the very last changes when making
structures opaque.

Making PyTypeObject structure opaque breaks C extensions declaring types
statically (e.g. static PyTypeObject MyType = {...};). C extensions must
use PyType_FromSpec() to allocate types on the heap instead. Using heap
types has other advantages like being compatible with subinterpreters.
Combined with PEP 489 "Multi-phase extension module initialization", it
makes a C extension behavior closer to a Python module, like allowing to
create more than one module instance.

Making PyThreadState structure opaque requires adding getter and setter
functions for members used by C extensions.

STATUS: In Progress (started in Python 3.8)

The PyInterpreterState structure was made opaque in Python 3.8
(bpo-35886) and the PyGC_Head structure (bpo-40241) was made opaque in
Python 3.9.

Issues tracking the work to prepare the C API to make following
structures opaque:

-   PyObject: bpo-39573
-   PyTypeObject: bpo-40170
-   PyFrameObject: bpo-40421
    -   Python 3.9 adds PyFrame_GetCode() and PyFrame_GetBack() getter
        functions, and moves PyFrame_GetLineNumber to the limited C API.
-   PyThreadState: bpo-39947
    -   Python 3.9 adds 3 getter functions: PyThreadState_GetFrame(),
        PyThreadState_GetID(), PyThreadState_GetInterpreter().

Disallow using Py_TYPE() as l-value

The Py_TYPE() function gets an object type, its PyObject.ob_type member.
It is implemented as a macro which can be used as an l-value to set the
type: Py_TYPE(obj) = new_type. This code relies on the assumption that
PyObject.ob_type can be modified directly. It prevents making the
PyObject structure opaque.

New setter functions Py_SET_TYPE(), Py_SET_REFCNT() and Py_SET_SIZE()
are added and must be used instead.

The Py_TYPE(), Py_REFCNT() and Py_SIZE() macros must be converted to
static inline functions which can not be used as l-value.

For example, the Py_TYPE() macro:

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

becomes:

    #define _PyObject_CAST_CONST(op) ((const PyObject*)(op))

    static inline PyTypeObject* _Py_TYPE(const PyObject *ob) {
        return ob->ob_type;
    }

    #define Py_TYPE(ob) _Py_TYPE(_PyObject_CAST_CONST(ob))

STATUS: Completed (in Python 3.10)

New functions Py_SET_TYPE(), Py_SET_REFCNT() and Py_SET_SIZE() were
added to Python 3.9.

In Python 3.10, Py_TYPE(), Py_REFCNT() and Py_SIZE() can no longer be
used as l-value and the new setter functions must be used instead.

New C API functions must not return borrowed references

When a function returns a borrowed reference, Python cannot track when
the caller stops using this reference.

For example, if the Python list type is specialized for small integers,
store directly "raw" numbers rather than Python objects,
PyList_GetItem() has to create a temporary Python object. The problem is
to decide when it is safe to delete the temporary object.

The general guidelines is to avoid returning borrowed references for new
C API functions.

No function returning borrowed references is scheduled for removal by
this PEP.

STATUS: Completed (in Python 3.9)

In Python 3.9, new C API functions returning Python objects only return
strong references:

-   PyFrame_GetBack()
-   PyFrame_GetCode()
-   PyObject_CallNoArgs()
-   PyObject_CallOneArg()
-   PyThreadState_GetFrame()

Avoid functions returning PyObject**

The PySequence_Fast_ITEMS() function gives a direct access to an array
of PyObject* objects. The function is deprecated in favor of
PyTuple_GetItem() and PyList_GetItem().

PyTuple_GET_ITEM() can be abused to access directly the
PyTupleObject.ob_item member:

    PyObject **items = &PyTuple_GET_ITEM(0);

The PyTuple_GET_ITEM() and PyList_GET_ITEM() macros are converted to
static inline functions to disallow that.

STATUS: Not Started

New pythoncapi_compat.h header file

Making structures opaque requires modifying C extensions to use getter
and setter functions. The practical issue is how to keep support for old
Python versions which don't have these functions.

For example, in Python 3.10, it is no longer possible to use Py_TYPE()
as an l-value. The new Py_SET_TYPE() function must be used instead:

    #if PY_VERSION_HEX >= 0x030900A4
        Py_SET_TYPE(&MyType, &PyType_Type);
    #else
        Py_TYPE(&MyType) = &PyType_Type;
    #endif

This code may ring a bell to developers who ported their Python code
base from Python 2 to Python 3.

Python will distribute a new pythoncapi_compat.h header file which
provides new C API functions to old Python versions. Example:

    #if PY_VERSION_HEX < 0x030900A4
    static inline void
    _Py_SET_TYPE(PyObject *ob, PyTypeObject *type)
    {
        ob->ob_type = type;
    }
    #define Py_SET_TYPE(ob, type) _Py_SET_TYPE((PyObject*)(ob), type)
    #endif  // PY_VERSION_HEX < 0x030900A4

Using this header file, Py_SET_TYPE() can be used on old Python versions
as well.

Developers can copy this file in their project, or even to only
copy/paste the few functions needed by their C extension.

STATUS: In Progress (implemented but not distributed by CPython yet)

The pythoncapi_compat.h header file is currently developed at:
https://github.com/pythoncapi/pythoncapi_compat

Process to reduce the number of broken C extensions

Process to reduce the number of broken C extensions when introducing C
API incompatible changes listed in this PEP:

-   Estimate how many popular C extensions are affected by the
    incompatible change.
-   Coordinate with maintainers of broken C extensions to prepare their
    code for the future incompatible change.
-   Introduce the incompatible changes in Python. The documentation must
    explain how to port existing code. It is recommended to merge such
    changes at the beginning of a development cycle to have more time
    for tests.
-   Changes which are the most likely to break a large number of C
    extensions should be announced on the capi-sig mailing list to
    notify C extensions maintainers to prepare their project for the
    next Python.
-   If the change breaks too many projects, reverting the change should
    be discussed, taking in account the number of broken packages, their
    importance in the Python community, and the importance of the
    change.

The coordination usually means reporting issues to the projects, or even
proposing changes. It does not require waiting for a new release
including fixes for every broken project.

Since more and more C extensions are written using Cython, rather
directly using the C API, it is important to ensure that Cython is
prepared in advance for incompatible changes. It gives more time for C
extension maintainers to release a new version with code generated with
the updated Cython (for C extensions distributing the code generated by
Cython).

Future incompatible changes can be announced by deprecating a function
in the documentation and by annotating the function with
Py_DEPRECATED(). But making a structure opaque and preventing the usage
of a macro as l-value cannot be deprecated with Py_DEPRECATED().

The important part is coordination and finding a balance between CPython
evolutions and backward compatibility. For example, breaking a random,
old, obscure and unmaintained C extension on PyPI is less severe than
breaking numpy.

If a change is reverted, we move back to the coordination step to better
prepare the change. Once more C extensions are ready, the incompatible
change can be reconsidered.

Version History

-   Version 3, June 2020: PEP rewritten from scratch. Python now
    distributes a new pythoncapi_compat.h header and a process is
    defined to reduce the number of broken C extensions when introducing
    C API incompatible changes listed in this PEP.
-   Version 2, April 2020: PEP: Modify the C API to hide implementation
    details.
-   Version 1, July 2017: PEP: Hide implementation details in the C API
    sent to python-ideas

Copyright

This document has been placed in the public domain.