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 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.

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.

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), 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.

Managing Per-Module State

To use per-module state, use multi-phase extension module 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, but excluding static types) and settings (e.g. csv’s field_size_limit) which the C code needs to function.

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; 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
}

Defining Heap Types

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.

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 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 <https://docs.python.org/3/c-api/typeobj.html#c.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 PyType_FromModuleAndSpec(), 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;
}

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 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 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.

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 and the corresponding 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 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

Slot methods – the fast C equivalents for special methods, such as nb_add for __add__ or tp_new for initialization – have a very simple API that doesn’t allow passing in the defining class, unlike with PyCMethod. The same goes for getters and setters defined with PyGetSetDef.

To access the module state in these cases, use the PyType_GetModuleByDef function, and pass in the module definition. Once you have the module, call 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 MRO (i.e. all superclasses) for the first superclass that has a corresponding 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 to the module object.

Usually this is not an issue, because types created with 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.

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 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 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.

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”.