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PEP 630: Isolating Extension Modules (GH-1574)

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PEP: 630
Title: Isolating Extension Modules
Author: Petr Viktorin <encukou@gmail.com>
Discussions-To: capi-sig@python.org
Status: Draft
Type: Informational
Content-Type: text/x-rst
Created: 25-Aug-2020
Post-History: 16-Jul-2020
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
-------------------
As an `informational PEP <https://www.python.org/dev/peps/pep-0001/#pep-types>`__,
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
Whenever this PEP 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, which is not
offered by all implementations of Python. However, nothing in this PEP is
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
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 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 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.
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
*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: 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
---------------------------------------------------
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.
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 currently available in `a fork on
GitHub <https://github.com/encukou/cpython/blob/xxlimited-facelift/Modules/xxlimited.c>`__.
.. XXX: Later, it should be in the CPython source tree.
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::
>>> 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::
>>> 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 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).
For example:
- The ``readline`` module manages *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 “Opt-Out:
Limiting to One Module Object per Process” 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-initialization>`__
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``'s
`field_size_limit <https://docs.python.org/3.8/library/csv.html#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 currently available in
`a fork on GitHub <https://github.com/encukou/cpython/blob/xxlimited-facelift/Modules/xxlimited.c>`__;
later it should be in the CPython source tree, with module
initialization is at the bottom of the file.
.. XXX: Later, it should be in the CPython source tree.
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. For example::
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
}
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``::
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, allowing access to the module state from 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()``,
you can call ``PyType_GetModule`` to get the associated module, 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;
}
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.
Do 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.
.. note::
The following Python code. can illustrate the concept.
``Base.get_defining_class`` returns ``Base`` even
if ``type(self) == Sub``::
class Base:
def get_defining_class(self):
return __class__
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>`__::
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::
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), 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
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``),
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.
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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