capi-sig December 2020

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3 months, 2 weeks

Pre-PEP: Maintaining the Stable ABI

by Petr Viktorin

Hello. I am writing a PEP to describe how I'd like to "resurrect" and maintain the Stable ABI & Limited API going forward. I assume this'll be my main focus for CPython in 2021. I'm not as far along as I hoped to be at the end of 2020, but I guess it's time to request comments. If you have any thoughts, arguments or improvements I'd be happy to hear them! I'm including the text below; a rendered and forkable/pull-request-able version is at https://github.com/encukou/abi3/blob/main/PEP.rst Have a great start of 2021! --- PEP: 9999 Title: Maintaining the Stable ABI Author: Petr Viktorin <encukou(a)gmail.com> Discussions-To: Status: Draft Type: Standards Track Content-Type: text/x-rst Created: 08-Dec-2020 Abstract ======== [A short (~200 word) description of the technical issue being addressed.] XXX: Abstract should be written last Motivation ========== :pep:`384` defined a limited API and stable ABI, which allows extenders and embedders of CPython to compile extension modules that are binary-compatible with any subsequent version of 3.x. In theory, this brings many advantages: * A module can be built only once per platform and support multiple versions of Python, reducing time, power and maintainer attention needed for builds. * Binary wheels using the stable ABI work with new versions of CPython throughout the pre-release period, and can be tested in environments where building from source is not practical. As a welcome side effect of the stable ABI's hiding of implementation details is that it is becoming a viable target for alternate Python implementations that need to implement (parts of) the C API. However, in hindsignt, PEP 384 and its implementation has several issues: * There is no process keep the ABI up to date. * Contents of the limited API are not listed explicitly, making it unclear if a particular member (e.g. function, structure) is a part of it. * There is no way to deprecate parts of the limited API. This PEP defines the limited API more clearly and introducess process designed to make the API more useful. Additionally, PEP 384 defines a *limited API* as a way to build against the stable ABI. This PEP defines the limited API more robustly. Rationale ========= This PEP contains a lot of clarifications and definitions, but just one big technical change: the stable ABI will be explicitly listed in a human-maintained “manifest” file. There have been efforts to collect such lists automatically, e.g. by scanning the symbols exported from Python. This might seem to be easier to maintain by our volunteer team. However, designing a future-proof API is not a trivial task. The cost of updating an explicit manifest is small compared to the overall work that should go into changing API that will need to be suppported forever (or until Python 3 reaches end of life, if that comes sooner). This PEP proposes automatically generating things *from* the manifest: initially documentation and DLL contents, with later possibilities for also automating tests. Stable ABI vs. Limited API ========================== :pep:`384` and this document deal with the *Limited API* and the *Stable ABI*, two related but distinct concepts. This section clarifies what they mean and defines some of their semantics (either pre-existing or newly proposed here). The word “Extensions” is used as a shorthand for all code that uses the Python API, e.g. extension modules or software that embeds Python. Stable ABI ---------- The CPython *Stable ABI* is a promise that extensions built with a specific Cpython version will be usable with any newer interpreter of the same major version, on the same platform and with the same compiler & settings. For example, a extension built with CPython 3.10 Stable ABI will be usable with CPython 3.11, 3.12, and so on, but not necessarily with 4.0. The Stable ABI is not generally forward-compatible: an extension built and tested with CPython 3.10 will not generally be compatible with CPython 3.9. .. note:: For example, starting in Python 3.10, the `Py_tp_doc` slot may be set to `NULL`, while in older versions, a `NULL` value will likely crash the interpreter. The Stable ABI trades performance for its stability. For example, many functions in the stable ABI are available as faster macros to extensions that are built for a specific CPython version. Future Python sversions may deprecate some members of the Stable ABI. Such members will still work, but may suffer from issues like reduced performance or, in the most extreme cases, memory/resource leaks. Limited API ----------- Stable ABI guarantee holds for extensions compiled from code that restricts itself to the *Limited API*, a subset of CPython's C API. The limited API is used when preprocessor macro `Py_LIMITED_API` is defined to either `3` or the current `PYTHON_API_VERSION`. The Limited API is not guaranteed to be *stable*. In the future, parts of the limited API may be deprecated. They may even be removed, as long as the *stable ABI* is kept stable and Python's general backwards compatibility policy, :pep:`387`, is followed. .. note:: For example, a function declaration might be removed from public header files but kept in the library. This is currently a possibility for the future; this PEP does not to propose a concrete process for deprecations and removals. The goal is for the limited API to cover everything needed to interact with the interpreter. There main reasons to not include a public API in the limited subset should be that it needs implementation details that change between CPython versions, like struct memory layouts, for performance reasons. The limited API is not limited to CPython; other implementations are encouraged to implement it and help drive its design. Specification ============= To make the stable ABI more useful and stable, the following changes are proposed. Stable ABI Manifest ------------------- All members of the stable ABI – functions, typedefs, structs, struct fields, data values etc. – will be explicitly listed in a single "manifest" file, along with the Limited API version they were added in. Struct fields that users of the stable ABI are allowed to access will be listed explicitly. Members that are not part of the Limited API, but are part of the Stable ABI (e.g. ``PyObject.ob_type``, which is accessible by the ``Py_TYPE`` macro), will be annotated as such. Notes saying “Part of the stable ABI” will be added to Python's documentation automatically, in a way similar to the notes on functions that return borrowed references. Source for the Windows shared library `python3.dll` will be generated from the stable ABI definition. The format of the manifest will be subject to change whenever needed. It should be consumed only by scripts in the CPython repository. If a more public list is needed, a script can be added to generate it. Contents of the Stable ABI -------------------------- The initial stable ABI manifest will include: * The Stable ABI specified in :pep:`384`. * All functions listed in ``PC/python3dll.c``. * All structs (struct typedefs) which these functions return or take as arguments. (Fields of such structs will not necessarily be added.) * New type slots, such as ``Py_am_aiter``. * The type flags ``Py_TPFLAGS_DEFAULT``, ``Py_TPFLAGS_BASETYPE``, ``Py_TPFLAGS_HAVE_GC``, ``Py_TPFLAGS_METHOD_DESCRIPTOR``. * The calling conventions ``METH_*`` (except deprecated ones). * All API needed by macros is the stable ABI (usually annotated as not being part of the limited API). Additional items may be aded to the initial manifest according to the checklist below. Testing the Stable ABI ---------------------- An automatically generated test module will be added to ensure that all members of the stable ABI are available at compile time For each function in the stable ABI, a test will be added that calls the function using `ctypes`. (Where calling is not practical, such as with functions related to intepreter initialization and shutdown, the test will only look the function up.) This should prevent regressions when a function is converted to a macro, which keeps the same API but breaks the ABI. An check will be added to ensure all functions in the stable ABI are tested this way. Changing the Limited API ------------------------ A checklist for changing the limited API, including new members (structs, functions or values), will be added to the `Devguide`_. The checklist will 1) mention best practices and common pitfalls in Python C API design and 2) guide the developer around the files that need changing and scripts that need running when the limited API is changed. Below is the initial proposal for the checklist. After the PEP is accepted, see the Devguide for the current version. Note that the checklist applies to new additions; not the existing limited API. Design considerations: * Make sure the change does not break the Stable ABI of any version of Python since 3.5. * Make sure no exposed names are private (i.e. begin with an underscore). * Make sure the new API is well documented. * Make sure the types of all parameters and return values of the added function(s) and all fields of the added struct(s) are be part of the limited API (or standard C). * Make sure the new API and its intended use follows standard C, not just features of currently suppoerted platforms. * Do not cast a function pointer to ``void*`` (a data pointer) or vice versa. * Make sure the new API follows reference counting conventions. (Following them makes the API easier to reason about, and easier use in other Python implementations.) * Do not return borrowed references from functions. * Do not steal references to function arguments. * Make sure the ownership rules and lifetimes of all applicable struct fields, arguments and return values are well defined. * Think about ease of use for the user. (In C, ease of use itself is not very important; what *is* important is reducing boilerplate code needed to use the API. Bugs like to hide in boiler plates.) * If a function will be often called with specific value for an argument, consider making it default (assumed when ``NULL`` is passed in). * Think about future extensions: for example, if it's possible that future Python versions will need to add a new field to your struct, how will that be done? * Make as few assumptions as possible about details that might change in future CPython versions or differ across C API implementations: * The GIL * Garbage collection * Layout of PyObject and other structs If following these guidelines would hurt performance, add a fast function (or macro) to the non-limited API and a stable equivalent to the limited API. If anything is unclear, or you have a good reason to break the guidelines, consider discussing the change at the `capi-sig`_ mailing list. .. _capi-sig: https://mail.python.org/mailman3/lists/capi-sig.python.org/ Procedure: * Move the declaration to a header file directly under ``Include/`` and ``#if !defined(Py_LIMITED_API) || Py_LIMITED_API+0 >= 0x03yy0000`` (with the ``yy`` corresponding to Python version). * Make an entry in the stable ABI list. (XXX: mention filename) * For functions, add a test that calls the function using ctypes (XXX: mention filename). * Regenerate the autogenerated files. (XXX: specific instructions) Advice for Extenders and Embedders ---------------------------------- The following notes will be added to documentation. Extension authors should test with all Python versions they support, and preferably build with the lowest such version. Compiling with ``Py_LIMITED_API`` defined is *not* a guarantee that your code conforms to the limited API or the stable ABI. It only covers definitions, but an API also includes other issues, such as expected semantics. Examples of issues that ``Py_LIMITED_API`` does not guard against are: * Calling a function with invalid arguments * An function that started accepting ``NULL`` values for an argument in Python 3.9 will fail if ``NULL`` is passed to it under Python 3.8. Only testing with 3.8 (or lower versions) will uncover this issue. * Some structs include a few fields that are part of the stable ABI and other fields that aren't. ``Py_LIMITED_API`` does not filter out such “private” fields. * Using something that is not documented as part of the stable ABI, but exposed even with ``Py_LIMITED_API`` defined. Despite the team's best efforts, such issues may happen. Backwards Compatibility ======================= The PEP aims at full compatibility with the existing stable ABI and limited API, but defines them terms more explicitly. It might not be consistent with some interpretations of what the existing stable ABI/limited API is. Security Implications ===================== None known. How to Teach This ================= Technical documentation will be provided. It will be aimed at experienced users familiar with C. Reference Implementation ======================== None so far. Rejected Ideas ============== While this PEP acknowledges that parts of the limited API might be deprecated or removed in the future, a process to do this is not in scope, and is left to a possible future PEP. Open Issues =========== None so far. References ========== Copyright ========= This document is placed in the public domain or under the CC0-1.0-Universal license, whichever is more permissive. .. _Devguide: https://devguide.python.org/ .. Local Variables: mode: indented-text indent-tabs-mode: nil sentence-end-double-space: t fill-column: 70 coding: utf-8 End:

8 months, 3 weeks

Pre-PEP: Isolating Extension Modules

by Petr Viktorin

Hello, Several people asked me to put the long-term effort re. PEPs 489 and 573 in words, so I've written a document that I'd like to submit as an informational PEP and/or a HOWTO. A rendered version is available at: https://hackmd.io/@encukou/module-state It's in Markdown: I've gotten so used to MD that translating to ReST once sounds easier than drafting in ReST. If you have a HackMD account and would like to co-author or fix typos directly, I can give you access. Isolating Extension Modules =========================== Abstract -------- Traditionally, state of Python extension modules was kept in C `static` variables, which have process-wide scope. This document describes problems of such per-process state, and efforts to make per-module state, a better default, possible and easy to use. The document also describes how to switch to per-module state where possible. The switch involves allocating space for that state, switching from static types to heap types, and—perhaps most importantly—accessing per-module state from code. About this document ------------------- This document does not introduce any changes: those should be done in their own PEPs (or issues, if small enough). Rather, it covers the motivation behind an effort that spans multiple releases. Once support is reasonably complete, the text can be moved to a [HOWTO](https://docs.python.org/3/howto/index.html) in the documentation. Meanwhile, in the spirit of documentation-driven development, gaps in the text show where to focus the effort. Whenever the document mentions *extension modules*, the advice also applies to *built-in* modules, such as the C parts of the standard library. The standard library is expected to switch to per-module state early. PEPs related to this effort are: * PEP 384 -- *Defining a Stable ABI*, which added C API for creating heap types * PEP 489 -- *Multi-phase extension module initialization* * PEP 573 -- *Module State Access from C Extension Methods* This document is concerned with Python's public C API. Nothing is specific to CPython. Motivation ---------- An *interpreter* is the context in which Python code runs. It contains configuration (e.g. the import path) and runtime state (e.g. the set of imported modules). Python supports running multiple interpreters in one process. There are two cases to think about. Users may run interpreters: * in sequence, with several `Py_InitializeEx`/`Py_FinalizeEx` cycles, and * in parallel, managing “sub-interpreters” using `Py_NewInterpreter`/`Py_EndInterpreter`. Both cases (and combinations of them) would be most useful when embedding Python within a library. Libraries generally shouldn't make assumptions about the application that uses them, which includes assumptions about a process-wide “main Python interpreter”. Currently, CPython doesn't handle this use case well. Many extension modules (and even some stdlib modules) use *per-process* global state, because C `static` variables are extremely easy to use. Thus, data that should be specific to an interpreter ends up being shared between interpreters. Unless the extension developer is careful, it is very easy to introduce edge cases that lead to crashes when a module is loaded in more than one interpreter. Unfortunately, *per-interpreter* state is not easy to achieve: extension authors tend to not keep multiple interpreters in mind when developing, and it is currently cumbersome to test the behavior. Rationale for Per-module State ------------------------------ Instead of focusing on per-interpreter state, Python's C API is evolving to better support the more granular *per-module* state. By default, C-level data will be attached to a *module object*. Each interpreter will then create its own module object, keeping data separate. For testing the isolation, multiple module objects can even be loaded in a single interpreter. Per-module state provides an easy way to think about lifetime and resource ownership: the extension module author will set up when a module object is created, and clean up when it's freed. In this regard, a module is just like any other `PyObject *`; there are no “on interpreter shutdown” hooks to think about (or forget about). ### Easy-to-use Module State as a Goal It is currently cumbersome or impossible to do everything the C API offers while keeping modules isolated. Enabled by PEP 384, changes in PEPs 489 and 573 (and future planned ones) aim to first make it *possible* to build modules this way, and then to make it *easy* to write new modules this way and to convert old ones, so that it can become a natural default. Even if per-module state becomes the default, there will be use cases for different levels of encapsulation: per-process, per-interpreter, per-thread or per-task state. The goal is to treat these as exceptional cases: extension authors will need to think more carefully about them. ### Non-goals: Speedups and the GIL There is some effort to speed up CPython by making the GIL per-interpreter. While isolating interpreters helps that effort, defaulting to per-module state will be beneficial even if no speed-up is achieved. How to make modules safe with multiple interpreters --------------------------------------------------- There are many ways to correctly support multiple interpreters in extension modules. The rest of this text describes the preferred way to write such a module, or convert an existing module. Note that support is a work in progress; the API for some features your module needs might not yet be ready. A full example module is (XXX currently available in [a fork on GitHub](https://github.com/encukou/cpython/blob/xxlimited-facelift/Modules/…; later it should be in the CPython source tree). ### Isolated Module Objects The key point to keep in mind when developing an extension module is that several module objects can be created from a single shared library. For example: ```pycon >>> import sys >>> import binascii >>> old_binascii = binascii >>> del sys.modules['binascii'] >>> import binascii # create a new module object >>> old_binascii == binascii False ``` As a rule of thumb, the two modules should be completely independent. All objects and state specific to the module should be encapsulated within the module object, not shared with other module objects, and cleaned up when the module object is deallocated. Exceptions are possible (see “Managing global state” below) but they will need more thought and attention to edge cases than code that follows this rule of thumb. While some modules could do with less stringent restrictions, isolated modules make it easier to set clear expectations (and guidelines) that work across a variety of use cases. ### Surprising Edge Cases Note that isolated modules do create some surprising edge cases. Most notably, each module object will typically not share its classes and exceptions with other similar modules. Continuing from the example above, note that `old_binascii.Error` and `binascii.Error` are separate objects. In the following code, the exception is *not* caught: ```pycon >>> old_binascii.Error == binascii.Error False >>> try: ... old_binascii.unhexlify(b'qwertyuiop') ... except binascii.Error: ... print('boo') ... Traceback (most recent call last): File "<stdin>", line 2, in <module> binascii.Error: Non-hexadecimal digit found ``` This is expected. Notice that pure-Python modules behave the same way: it is a part of how Python works. The goal is to make extension modules safe at the C level, not to make hacks (like mutating `sys.modules` or sharing objects across interpreters) behave intuitively. ### Managing Global State Sometimes, state of a Python module is not specific to that module, but to the entire process (or something else “more global” than a module). For example: * The `readline` module provides access to *the* terminal. * A module running on a circuit board wants to control *the* on-board LED. In these cases, the Python module should provide *access* to the global state, rather than *own* it. If possible, write the module so that multiple copies of it can access the state independently (along with other libraries, whether for Python or other languages). If that is not possible, consider explicit locking. If it is necessary to use process-global state, the simplest way to avoid issues with multiple interpreters is to explicitly prevent a module from being loaded more than once per process—see “An Opt-Out Method” below. ### Managing Per-module state To use per-module state, use [multi-phase extension module initialization](https://docs.python.org/3/c-api/module.html#multi-phase-ini… introduced in PEP 489. This signals that your module supports multiple interpreters correctly. Set `PyModuleDef.m_size` to a positive number to request that many bytes of storage local to the module. Usually, this will be set to the size of some module-specific `struct`, which can store all of the module's C-level state. In particular, it is where you should put pointers to classes (including exceptions) and settings (e.g. `csv.field_size_limit`) which the C code needs to function. > [Note] > Another option is to store state in the module's `__dict__`, but you must avoid crashing when users modify `__dict__` from Python code. > This means error- and type-checking at the C level, which is easy to get wrong and hard to test sufficiently. If the module state includes `PyObject` pointers, the module object must hold references to those objects and implement module-level hooks `m_traverse`, `m_clear`, `m_free`. These work like `tp_traverse`, `tp_clear`, `tp_free` of a class. Adding them will require some work and make the code longer; this is the price for modules which can be unloaded cleanly. An example of a module with per-module state is (XXX currently available in [a fork on GitHub](https://github.com/encukou/cpython/blob/xxlimited-facelift/Modules/…; later it should be in the CPython source tree), with module initialization is at the bottom of the file. ### Opt-Out: Limiting to One Module Object per Process A non-negative `PyModuleDef.m_size` signals that a module supports multiple interpreters correctly. If this is not yet the case for your module, you can explicitly make your module loadable only once per process. Raising an exception is better than mysteriously crashing in C code! For example: ```c static int loaded = 0; static int exec_module(PyObject* module) { if (loaded) { PyErr_SetString(PyExc_ImportError, "cannot load module more than once per process"); return -1; } loaded = 1; ... // rest of initialization } ``` When your module becomes ready, you can remove this check. ### Module state access from functions Accessing the state from module-level functions is straightforward. Functions get the module object as their first argument; for extracting the state there is `PyModule_GetState`: ```c static PyObject * func(PyObject *module, PyObject *args) { my_struct *state = (my_struct*)PyModule_GetState(module); if (state == NULL) { return NULL; } ... // rest of logic } ``` (Note that `PyModule_GetState` may return NULL without seting an exception if there is no module state, i.e. `PyModuleDef.m_size` was zero. In your own module, you're in control of `m_size`, so this is easy to prevent.) ### Heap types Traditionally, types defined in C code were static, that is, `static PyTypeObject` structures defined directly in code and initialized using `PyType_Ready()`. Such types are necessarily shared across the process. Sharing them between module objects requires paying attention to any state they own or access. To limit the possible issues, static types are immutable at the Python level: for example, you can't set `str.myattribute = 123`. > [Note] > Sharing truly immutable objects between interpreters is fine, as long as they don't provide access to mutable objects. > But, every Python object has a mutable implementation detail: the reference count. > Changes to the refcount are guarded by the GIL. > Thus, code that shares any Python objects across interpreters implicitly depends on CPython's current, process-wide GIL. An alternative to static types is *heap-allocated types*, or heap types for short. These correspond more closely to classes created by Python’s `class` statement. Heap types can be created by filling a `PyType_Spec` structure, a description or “blueprint” of a class, and calling `PyType_FromModuleAndSpec()` to construct a new class object. > [note] > Other functions, like `PyType_FromSpec()`, can also create heap types, > but `PyType_FromModuleAndSpec()` associates the module with the class, granting access > to the module state to methods. The class should generally be stored in *both* the module state (for safe access from C) and the module's `__dict__` (for access from Python code). ### Module State Access from Classes If you have a type object defined with `PyType_FromModuleAndSpec()`, call `PyType_GetModule` to get the associated module, then `PyModule_GetState` to get the module's state. These steps can be combined with `PyType_GetModuleState` to save typing some tedious error-handling boilerplate: ```c my_struct *state = (my_struct*)PyType_GetModuleState(type); if (state === NULL) { return NULL; } ``` ### Module State Access from Regular Methods Accessing the module-level state from methods of a class is somewhat more complicated, but possible thanks to changes introduced in PEP 573. To get the state, you need to first get the *defining class*, and then get the module state from it. The largest roadblock is getting *the class a method was defined in*, or that method's “defining class” for short. The defining class can have a reference to the module it is part of. It is important to not confuse the defining class with `Py_TYPE(self)`. If the method is called on a *subclass* of your type, `Py_TYPE(self)` will refer to that subclass, which may be defined in different module than yours. In the following Python example, the defining class of the method `Base.get_defining_class` is `Base`, even if `type(self) == Sub`: ```python class Base: def get_defining_class(self): return __class__ class Sub(Base): pass ``` To get its “defining class”, a method must use the `METH_METHOD | METH_FASTCALL | METH_KEYWORDS` [calling convention](https://docs.python.org/3.9/c-api/structures.html?highlight=met… and the corresponding [PyCMethod signature](https://docs.python.org/3.9/c-api/structures.html#c.PyCMethod): ```c PyObject *PyCMethod( PyObject *self, // object the method was called on PyTypeObject *defining_class, // defining class PyObject *const *args, // C array of arguments Py_ssize_t nargs, // length of "args" PyObject *kwnames) // NULL, or dict of keyword arguments ``` Once you have the defining class, call `PyType_GetModuleState` to get the state of its associated module. For example: ```c static PyObject * example_method(PyObject *self, PyTypeObject *defining_class, PyObject *const *args, Py_ssize_t nargs, PyObject *kwnames) { my_struct *state = (my_struct*)PyType_GetModuleState(defining_class); if (state === NULL) { return NULL; } ... // rest of logic } PyDoc_STRVAR(example_method_doc, "..."); static PyMethodDef my_methods[] = { {"example_method", (PyCFunction)(void(*)(void))example_method, METH_METHOD|METH_FASTCALL|METH_KEYWORDS, example_method_doc} {NULL}, } ``` Open Issues ----------- Several issues around per-module state and heap types are still open. Discussions about improving the situation are best held on the [capi-sig mailing list](https://mail.python.org/mailman3/lists/capi-sig.python.org/). ### Module State Access from Slot Methods, Getters and Setters Currently (as of Python 3.9), there is no API to access the module state from: * slot methods (meaning type slots, such as `tp_new`, `nb_add` or `tp_iternext`) * getters and setters defined with `tp_getset` ### Type Checking Currently (as of Python 3.9), there is no good API to write `Py*_Check` functions (like `PyUnicode_Check` exists for `str`, a static type) for heap types. This check ensures whether instances have a particular C layout. ### Metaclasses Currently (as of Python 3.9), there is no good API to specify the *metaclass* of a heap type, that is, the `ob_type` field of the type object. ### Per-Class scope It is also not possible to attach state to *types*. While `PyHeapTypeObject` is a variable-size object (`PyVarObject`), but its variable-size storage is currently consumed by slots. There will also be issues if several classes in an inheritance hierarchy need state. Copyright --------- This document is placed in the public domain or under the CC0-1.0-Universal license, whichever is more permissive. .. Local Variables: mode: indented-text indent-tabs-mode: nil sentence-end-double-space: t fill-column: 70 coding: utf-8 End:

10 months, 2 weeks

New script: add Python 3.10 support to your C extensions without losing Python 3.6 support

by Victor Stinner

Hi, I wrote a new script which adds Python 3.10 support to your C extensions without losing Python 3.6 support: https://github.com/pythoncapi/pythoncapi_compat For example, it replaces "op->ob_type" with "Py_TYPE(op)" and replaces "frame->f_back" with "_PyFrame_GetBackBorrow(frame)". It relies on the pythoncapi_compat.h header file that I wrote to implement Python 3.9 and Python 3.10 on old Python versions. Examples: Py_NewRef() and PyThreadState_GetFrame(). The _PyFrame_GetBackBorrow() function doesn't exist in the Python C API, it's only provided by pythoncapi_compat.h to ease the migration of C extensions. I advise you to replace _PyFrame_GetBackBorrow() (borrowed reference) with PyFrame_GetBack() (strong reference). The PyFrame_GetBack() function was added to Python 3.9 and pythoncapi_compat.h provides it on older Python versions. This project is related to my PEP 620 "Hide implementation details from the C API" which tries to make the C API more abstract to later allow to implement new optimization in CPython and to make other Python implementations like PyPy faster when running C extensions. This project only targets extension modules written in C by using directly the "Python.h" API. I advise you to use Cython or HPy to no longer be bothered with incompatible C API changes at every Python release ;-) * https://cython.org/ * https://hpy.readthedocs.io/ I hope that my script will facilitate migration of C extensions to HPy. Victor -- Night gathers, and now my watch begins. It shall not end until my death.

10 months, 3 weeks

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capi-sig December 2020