Docs: Convert PEP 630 (Isolating Extension Modules) to a HOWTO (GH-94489)

Co-authored-by: Stanley <46876382+slateny@users.noreply.github.com>
Co-authored-by: Alex Waygood <Alex.Waygood@Gmail.com>

2 files changed, 537 insertions, 0 deletions

diff --git a/Doc/howto/index.rst b/Doc/howto/index.rst
index eae8f14..8a378e6 100644
--- a/Doc/howto/index.rst
+++ b/Doc/howto/index.rst
@@ -31,4 +31,5 @@ Currently, the HOWTOs are:
    clinic.rst
    instrumentation.rst
    annotations.rst
+   isolating-extensions.rst
 
diff --git a/Doc/howto/isolating-extensions.rst b/Doc/howto/isolating-extensions.rst
new file mode 100644
index 0000000..8ee7e5e
--- /dev/null
+++ b/Doc/howto/isolating-extensions.rst
@@ -0,0 +1,536 @@
+.. highlight:: c
+
+***************************
+Isolating Extension Modules
+***************************
+
+.. topic:: Abstract
+
+    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 shows a safer way:
+    per-module state.
+
+    The document also describes how to switch to per-module state where
+    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.
+
+
+Who should read this
+====================
+
+This guide is written for maintainers of :ref:`C-API <c-api-index>` extensions
+who would like to make that extension safer to use in applications where
+Python itself is used as a library.
+
+
+Background
+==========
+
+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 :c:func:`Py_InitializeEx`/:c:func:`Py_FinalizeEx`
+   cycles, and
+-  in parallel, managing "sub-interpreters" using
+   :c:func:`Py_NewInterpreter`/:c:func:`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 include
+assuming a process-wide "main Python interpreter".
+
+Historically, Python extension modules don'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 in the same process.
+
+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.
+
+Enter 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.
+This means that C-level data is be attached to a *module object*.
+Each interpreter creates 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 :c:expr:`PyObject *`; there are no "on
+interpreter shutdown" hooks to think—or forget—about.
+
+Note that there are use cases for different kinds of "globals":
+per-process, per-interpreter, per-thread or per-task state.
+With per-module state as the default, these are still possible,
+but you should treat them as exceptional cases:
+if you need them, you should give them additional care and testing.
+(Note that this guide does not cover them.)
+
+
+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:
+
+.. code-block:: 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.
+Since this just is a rule of thumb, exceptions are possible
+(see `Managing Global State`_), but they will need more
+thought and attention to edge cases.
+
+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 <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
+   >>> 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.
+
+
+Making Modules Safe with Multiple Interpreters
+==============================================
+
+
+Managing Global State
+---------------------
+
+Sometimes, the state associated with 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`_.
+
+
+Managing Per-Module State
+-------------------------
+
+To use per-module state, use
+:ref:`multi-phase extension module initialization <multi-phase-initialization>`.
+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, but excluding static types)
+and settings (e.g. ``csv``'s :py:data:`~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 usually means error- and type-checking at the C level,
+   which is easy to get wrong and hard to test sufficiently.
+
+   However, if module state is not needed in C code, storing it in
+   ``__dict__`` only is a good idea.
+
+If the module state includes ``PyObject`` pointers, the module object
+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.
+
+An example of a module with per-module state is currently available as
+`xxlimited <https://github.com/python/cpython/blob/master/Modules/xxlimited.c>`__;
+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
+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, you can use ``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::
+   ``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,
+``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``.
+
+.. impl-detail::
+   Sharing truly immutable objects between interpreters is fine,
+   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.
+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
+``class`` statement.
+
+For new modules, using heap types by default is a good rule of thumb.
+
+
+Changing Static Types to Heap Types
+-----------------------------------
+
+Static types can be converted to heap types, but note that
+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.
+So, when rewriting the class definition in a new API,
+you are likely to unintentionally change a few details (e.g. pickleability
+or inherited slots).
+Always test the details that are important to you.
+
+Watch out for the following two points in particular (but note that this is not
+a comprehensive list):
+
+* Unlike static types, heap type objects are mutable by default.
+  Use the :c:data:`Py_TPFLAGS_IMMUTABLETYPE` flag to prevent mutability.
+* Heap types inherit :c:member:`~PyTypeObject.tp_new` by default,
+  so it may become possible to instantiate them from Python code.
+  You can prevent this with the :c:data:`Py_TPFLAGS_DISALLOW_INSTANTIATION` flag.
+
+
+Defining Heap Types
+-------------------
+
+Heap types can be created by filling a :c:struct:`PyType_Spec` structure, a
+description or "blueprint" of a class, and calling
+:c:func:`PyType_FromModuleAndSpec` to construct a new class object.
+
+.. note::
+   Other functions, like :c:func:`PyType_FromSpec`, can also create
+   heap types, but :c:func:`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).
+
+
+Garbage-Collection Protocol
+---------------------------
+
+Instances of heap types hold a reference to their type.
+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.
+
+To avoid memory leaks, instances of heap types must implement the
+garbage collection protocol.
+That is, heap types should:
+
+- Have the :c:data:`Py_TPFLAGS_HAVE_GC` flag.
+- Define a traverse function using ``Py_tp_traverse``, which
+  visits the type (e.g. using :c:expr:`Py_VISIT(Py_TYPE(self))`).
+
+Please refer to the :ref:`the documentation <type-structs>` of
+:c:data:`Py_TPFLAGS_HAVE_GC` and :c:member:`~PyTypeObject.tp_traverse`
+for additional considerations.
+
+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``.
+
+For example, if your traverse function includes::
+
+   base->tp_traverse(self, visit, arg)
+
+...and ``base`` may be a static type, then it should also include::
+
+    if (base->tp_flags & Py_TPFLAGS_HEAPTYPE) {
+        // a heap type's tp_traverse already visited Py_TYPE(self)
+    } else {
+        Py_VISIT(Py_TYPE(self));
+    }
+
+It is not necessary to handle the type's reference count in ``tp_new``
+and ``tp_clear``.
+
+
+Module State Access from Classes
+--------------------------------
+
+If you have a type object defined with :c:func:`PyType_FromModuleAndSpec`,
+you can call :c:func:`PyType_GetModule` to get the associated module, and then
+:c:func:`PyModule_GetState` to get the module's state.
+
+To save a some tedious error-handling boilerplate code, you can combine
+these two steps with :c:func:`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 is possible thanks to API introduced in Python 3.9.
+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 :c:expr:`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``:
+
+   .. code-block:: python
+
+      class Base:
+          def get_type_of_self(self):
+              return type(self)
+
+          def get_defining_class(self):
+              return __class__
+
+      class Sub(Base):
+          pass
+
+For a method to get its "defining class", it must use the
+:c:data:`METH_METHOD | METH_FASTCALL | METH_KEYWORDS`
+:c:type:`calling convention <PyMethodDef>`
+and the corresponding :c:type:`PyCMethod` signature::
+
+   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 :c:func:`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},
+   }
+
+
+Module State Access from Slot Methods, Getters and Setters
+----------------------------------------------------------
+
+.. note::
+
+   This is new in Python 3.11.
+
+   .. After adding to limited API:
+
+      If you use the `limited API <https://docs.python.org/3/c-api/stable.html>__,
+      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
+:c:member:`~PyNumberMethods.nb_add` for :py:attr:`~object.__add__` or
+:c:member:`~PyType.tp_new` for initialization—have a very simple API that
+doesn't allow passing in the defining class, unlike with :c:type:`PyCMethod`.
+The same goes for getters and setters defined with
+:c:type:`PyGetSetDef`.
+
+To access the module state in these cases, use the
+:c:func:`PyType_GetModuleByDef` function, and pass in the module definition.
+Once you have the module, call :c:func:`PyModule_GetState`
+to get the state::
+
+    PyObject *module = PyType_GetModuleByDef(Py_TYPE(self), &module_def);
+    my_struct *state = (my_struct*)PyModule_GetState(module);
+    if (state === NULL) {
+        return NULL;
+    }
+
+``PyType_GetModuleByDef`` works by searching the
+:term:`method resolution order` (i.e. all superclasses) for the first
+superclass that has a corresponding module.
+
+.. note::
+
+   In very exotic cases (inheritance chains spanning multiple modules
+   created from the same definition), ``PyType_GetModuleByDef`` might not
+   return the module of the true defining class. However, it will always
+   return a module with the same definition, ensuring a compatible
+   C memory layout.
+
+
+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
+to the module object.
+
+Usually this is not an issue, because types created with
+:c:func:`PyType_FromModuleAndSpec`, and their instances, hold a reference
+to the module.
+However, you must be careful in reference counting when you reference
+module state from other places, such as callbacks for external
+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/>`__.
+
+
+Per-Class Scope
+---------------
+
+It is currently (as of Python 3.11) not possible to attach state to individual
+*types* without relying on CPython implementation details (which may change
+in the future—perhaps, ironically, to allow a proper solution for
+per-class scope).
+
+
+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.