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PEP 630: Format and copyedit prior to conversion into a docs HOWTO guide (GH-2459)

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@ -9,35 +9,36 @@ Created: 25-Aug-2020
Post-History: 16-Jul-2020
Isolating Extension Modules
===========================
.. highlight:: c
Abstract
--------
========
Traditionally, state of Python extension modules was kept in C
Traditionally, state belonging to 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.
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, potentially
possible. This transition involves allocating space for that state, potentially
switching from static types to heap types, and—perhaps most
importantly—accessing per-module state from code.
About this document
-------------------
About This Document
===================
As an :pep:`informational PEP <1#pep-types>`,
this document does not introduce any changes: those should be done in
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, and instructs
early adopters on how to use the finished features.
Once support is reasonably complete, the text can be moved to Python's
documentation as a HOWTO. Meanwhile, in the spirit of documentation-driven
development, gaps identified in this text can show where to focus
the effort, and the text can be updated as new features are implemented
Once support is reasonably complete, this content can be moved to Python's
documentation as a `HOWTO <https://docs.python.org/3/howto/index.html>`__.
Meanwhile, in the spirit of documentation-driven development,
gaps identified in this PEP can show where to focus the effort,
and it can be updated as new features are implemented.
Whenever this PEP mentions *extension modules*, the advice also
applies to *built-in* modules.
@ -52,7 +53,7 @@ applies to *built-in* modules.
PEPs related to this effort are:
- :pep:`384` -- *Defining a Stable ABI*, which added C API for creating
- :pep:`384` -- *Defining a Stable ABI*, which added a C API for creating
heap types
- :pep:`489` -- *Multi-phase extension module initialization*
- :pep:`573` -- *Module State Access from C Extension Methods*
@ -64,8 +65,9 @@ specific to CPython.
As with any Informational PEP, this text does not necessarily represent
a Python community consensus or recommendation.
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
@ -76,13 +78,13 @@ 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
- 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”.
assuming 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,
@ -90,34 +92,36 @@ 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.
more than one interpreter in the same process.
Unfortunately, *per-interpreter* state is not easy to achieve: extension
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
will then create its own module object, keeping the data separate. For
testing the isolation, multiple module objects corresponding to a single
extension 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 will initialize 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.
a module is just like any other ``PyObject *``; there are no "on
interpreter shutdown" hooks to think—or forget—about.
Goal: Easy-to-use Module State
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Goal: Easy-to-Use Module State
------------------------------
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
:pep:`489` and :pep:`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.
@ -128,20 +132,22 @@ per-thread or per-task state. The goal is to treat these as exceptional
cases: they should be possible, but extension authors will need to
think more carefully about them.
Non-goals: Speedups and the GIL
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-------------------------------
There is some effort to speed up CPython on multi-core CPUs by making the GIL
per-interpreter. While isolating interpreters helps that effort,
defaulting to per-module state will be beneficial even if no speedup is
achieved, as it makes supporting multiple interpreters safer by default.
How to make modules safe with multiple interpreters
---------------------------------------------------
Making 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 to convert an existing module.
write such a module, or to convert an existing one.
Note that support is a work in progress; the API for some features your
module needs might not yet be ready.
@ -149,15 +155,17 @@ module needs might not yet be ready.
A full example module is available as
`xxlimited <https://github.com/python/cpython/blob/master/Modules/xxlimited.c>`__.
This section assumes that “*you*” are an extension module author.
This section assumes that "*you*" are an extension module author.
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::
For example:
.. code-block:: pycon
>>> import sys
>>> import binascii
@ -171,7 +179,7 @@ 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
possible (see `Managing Global State`_), but they will need more
thought and attention to edge cases than code that follows this rule of
thumb.
@ -179,14 +187,18 @@ 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::
exceptions with other similar modules. Continuing from the
`example above <Isolated Module Objects_>`__,
note that ``old_binascii.Error`` and ``binascii.Error`` are
separate objects. In the following code, the exception is *not* caught:
.. code-block:: pycon
>>> old_binascii.Error == binascii.Error
False
@ -194,7 +206,7 @@ separate objects. In the following code, the exception is *not* caught::
... 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
@ -203,14 +215,15 @@ 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 behave intuitively. Mutating ``sys.modules`` “manually” counts
hacks behave intuitively. Mutating ``sys.modules`` "manually" counts
as a hack.
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).
to the entire process (or something else "more global" than a module).
For example:
- The ``readline`` module manages *the* terminal.
@ -226,14 +239,15 @@ 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 “Opt-Out:
Limiting to One Module Object per Process” below.
module from being loaded more than once per process—see
`Opt-Out: Limiting to One Module Object per Process`_.
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-initialization>`__
To use per-module state, use `multi-phase extension module initialization
<https://docs.python.org/3/c-api/module.html#multi-phase-initialization>`__
introduced in :pep:`489`. This signals that your module supports multiple
interpreters correctly.
@ -242,8 +256,8 @@ 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, but excluding static types)
and settings (e.g. ``csv``'s
`field_size_limit <https://docs.python.org/3.8/library/csv.html#csv.field_size_limit>`__)
and settings (e.g. ``csv``'s `field_size_limit
<https://docs.python.org/3/library/csv.html#csv.field_size_limit>`__)
which the C code needs to function.
.. note::
@ -253,9 +267,9 @@ which the C code needs to function.
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
must hold references to those objects and implement the module-level hooks
``m_traverse``, ``m_clear`` and ``m_free``. These work like
``tp_traverse``, ``tp_clear`` and ``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.
@ -265,7 +279,7 @@ example module initialization shown 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
@ -286,12 +300,13 @@ process. For example::
// ... rest of initialization
}
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``::
the state, you can use ``PyModule_GetState``::
static PyObject *
func(PyObject *module, PyObject *args)
@ -303,15 +318,17 @@ the state there is ``PyModule_GetState``::
// ... rest of logic
}
(Note that ``PyModule_GetState`` may return NULL without setting 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.)
.. note::
``PyModule_GetState`` may return NULL without setting 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 are *static*, that is,
Heap Types
==========
Traditionally, types defined in C code are *static*; that is,
``static PyTypeObject`` structures defined directly in code and
initialized using ``PyType_Ready()``.
@ -322,23 +339,23 @@ 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.
as long as they don't provide access to mutable objects.
However, in CPython, 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.
Because they are immutable and process-global, static types cannot access
“their” module state.
"their" module state.
If any method of such a type requires access to module state,
the type must be converted to a *heap-allocated type*, or *heap type*
for short. These correspond more closely to classes created by Python’s
for short. These correspond more closely to classes created by Python's
``class`` statement.
For new modules, using heap types by default is a good rule of thumb.
Static types can be converted to heap types, but note that
the heap type API was not designed for “lossless” conversion
the heap type API was not designed for "lossless" conversion
from static types -- that is, creating a type that works exactly like a given
static type. Unlike static types, heap type objects are mutable by default.
Also, when rewriting the class definition in a new API,
@ -347,10 +364,10 @@ or inherited slots). Always test the details that are important to you.
Defining Heap Types
~~~~~~~~~~~~~~~~~~~
-------------------
Heap types can be created by filling a ``PyType_Spec`` structure, a
description or “blueprint” of a class, and calling
description or "blueprint" of a class, and calling
``PyType_FromModuleAndSpec()`` to construct a new class object.
.. note::
@ -364,10 +381,10 @@ Python code).
Garbage Collection Protocol
~~~~~~~~~~~~~~~~~~~~~~~~~~~
---------------------------
Instances of heap types hold a reference to their type.
This ensures that the type isn't destroyed before its instance,
This ensures that the type isn't destroyed before all its instances are,
but may result in reference cycles that need to be broken by the
garbage collector.
@ -375,14 +392,18 @@ To avoid memory leaks, instances of heap types must implement the
garbage collection protocol.
That is, heap types should:
- Have the ``Py_TPFLAGS_HAVE_GC`` flag,
- Have the ``Py_TPFLAGS_HAVE_GC`` flag.
- Define a traverse function using ``Py_tp_traverse``, which
visits the type (e.g. using ``Py_VISIT(Py_TYPE(self));``).
Please refer to the documentation of ``Py_TPFLAGS_HAVE_GC`` and
``tp_traverse`` for additional considerations.
Please refer to the `documentation
<https://docs.python.org/3/c-api/typeobj.html>`__ of `Py_TPFLAGS_HAVE_GC
<https://docs.python.org/3/c-api/typeobj.html#Py_TPFLAGS_HAVE_GC>`__ and
`tp_traverse
<https://docs.python.org/3/c-api/typeobj.html#c.PyTypeObject.tp_traverse>`
for additional considerations.
If your traverse function delegates to ``tp_traverse`` of its base class
If your traverse function delegates to the ``tp_traverse`` of its base class
(or another type), ensure that ``Py_TYPE(self)`` is visited only once.
Note that only heap type are expected to visit the type in ``tp_traverse``.
@ -403,30 +424,31 @@ and ``tp_clear``.
Module State Access from Classes
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
--------------------------------
If you have a type object defined with ``PyType_FromModuleAndSpec()``,
you can call ``PyType_GetModule`` to get the associated module, then
you can call ``PyType_GetModule`` to get the associated module, and then
``PyModule_GetState`` to get the module's state.
To save a some tedious error-handling boilerplate code, you can combine
these two steps with ``PyType_GetModuleState``, resulting in::
my_struct *state = (my_struct*)PyType_GetModuleState(type);
if (state === NULL) {
return NULL;
}
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`.
Accessing the module-level state from methods of a class is somewhat more
complicated, but is possible thanks to the 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
that method's "defining class" for short. The defining class can have a
reference to the module it is part of.
Do not confuse the defining class with ``Py_TYPE(self)``. If the method
@ -436,7 +458,9 @@ that subclass, which may be defined in different module than yours.
.. note::
The following Python code can illustrate the concept.
``Base.get_defining_class`` returns ``Base`` even
if ``type(self) == Sub``::
if ``type(self) == Sub``:
.. code-block:: python
class Base:
def get_defining_class(self):
@ -445,12 +469,11 @@ that subclass, which may be defined in different module than yours.
class Sub(Base):
pass
For a method to get its “defining class”, it must use the
``METH_METHOD | METH_FASTCALL | METH_KEYWORDS`` `calling
convention <https://docs.python.org/3.9/c-api/structures.html?highlight=meth_o#c.PyMethodDef>`__
and the corresponding `PyCMethod
signature <https://docs.python.org/3.9/c-api/structures.html#c.PyCMethod>`__::
For a method to get its "defining class", it must use the
``METH_METHOD | METH_FASTCALL | METH_KEYWORDS`` `calling convention
<https://docs.python.org/3/c-api/structures.html#c.PyMethodDef>`__
and the corresponding `PyCMethod signature
<https://docs.python.org/3/c-api/structures.html#c.PyCMethod>`__::
PyObject *PyCMethod(
PyObject *self, // object the method was called on
@ -488,8 +511,9 @@ For example::
{NULL},
}
Module State Access from Slot Methods, Getters and Setters
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
----------------------------------------------------------
.. note::
@ -501,18 +525,20 @@ Module State Access from Slot Methods, Getters and Setters
you must update ``Py_LIMITED_API`` to ``0x030b0000``, losing ABI
compatibility with earlier versions.
Slot methods -- the fast C equivalents for special methods, such as
`nb_add <https://docs.python.org/3/c-api/typeobj.html#c.PyNumberMethods.nb_add>`__
for ``__add__`` or `tp_new <https://docs.python.org/3/c-api/typeobj.html#c.PyTypeObject.tp_new>`__
Slot methods -- the fast C equivalents for special methods, such as `nb_add
<https://docs.python.org/3/c-api/typeobj.html#c.PyNumberMethods.nb_add>`__
for ``__add__`` or `tp_new
<https://docs.python.org/3/c-api/typeobj.html#c.PyTypeObject.tp_new>`__
for initialization -- have a very simple API that doesn't allow
passing in the defining class as in ``PyCMethod``.
passing in the defining class, unlike with ``PyCMethod``.
The same goes for getters and setters defined with
`PyGetSetDef <https://docs.python.org/3/c-api/structures.html#c.PyGetSetDef>`__.
To access the module state in these cases, use the
`PyType_GetModuleByDef <https://docs.python.org/typeobj.html#c.PyType_GetModuleByDef>`__
To access the module state in these cases, use the `PyType_GetModuleByDef
<https://docs.python.org/3/c-api/typeobj.html#c.PyType_GetModuleByDef>`__
function, and pass in the module definition.
Once you have the module, call `PyModule_GetState <https://docs.python.org/3/c-api/module.html?highlight=pymodule_getstate#c.PyModule_GetState>`__
Once you have the module, call `PyModule_GetState
<https://docs.python.org/3/c-api/module.html#c.PyModule_GetState>`__
to get the state::
PyObject *module = PyType_GetModuleByDef(Py_TYPE(self), &module_def);
@ -521,7 +547,8 @@ to get the state::
return NULL;
}
``PyType_GetModuleByDef`` works by searching the `MRO <https://docs.python.org/3/glossary.html#term-method-resolution-order>`__
``PyType_GetModuleByDef`` works by searching the `MRO
<https://docs.python.org/3/glossary.html#term-method-resolution-order>`__
(i.e. all superclasses) for the first superclass that has a corresponding
module.
@ -535,7 +562,7 @@ module.
Lifetime of the Module State
~~~~~~~~~~~~~~~~~~~~~~~~~~~~
----------------------------
When a module object is garbage-collected, its module state is freed.
For each pointer to (a part of) the module state, you must hold a reference
@ -550,30 +577,33 @@ libraries.
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/>`__.
Type Checking
~~~~~~~~~~~~~
-------------
Currently (as of Python 3.10), heap types have no good API to write
``Py*_Check`` functions (like ``PyUnicode_Check`` exists for ``str``, a
static type), and so it is not easy to ensure whether instances have a
static type), and so it is not easy to ensure that instances have a
particular C layout.
Metaclasses
~~~~~~~~~~~
-----------
Currently (as of Python 3.10), there is no good API to specify the
*metaclass* of a heap type, that is, the ``ob_type`` field of the type
*metaclass* of a heap type; that is, the ``ob_type`` field of the type
object.
Per-Class scope
~~~~~~~~~~~~~~~
Per-Class Scope
---------------
It is also not possible to attach state to *types*. While
``PyHeapTypeObject`` is a variable-size object (``PyVarObject``),
@ -581,26 +611,18 @@ its variable-size storage is currently consumed by slots. Fixing this
is complicated by the fact that several classes in an inheritance
hierarchy may need to reserve some state.
Lossless conversion to heap types
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
The heap type API was not designed for “lossless” conversion from static types,
Lossless Conversion to Heap Types
---------------------------------
The heap type API was not designed for "lossless" conversion from static types;
that is, creating a type that works exactly like a given static type.
The best way to address it would probably be to write a guide that covers
known “gotchas”.
known "gotchas".
Copyright
---------
=========
This document is placed in the public domain or under the
CC0-1.0-Universal license, whichever is more permissive.
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