Isolating Extension Modules

Who should read this

This guide is written for maintainers of C-API 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:

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 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:

>>> 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, 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.

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 multi-phase extension module 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 field_size_limit) which the C code needs to function.

참고

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; 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
}

참고

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.

CPython 구현 상세: 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):

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.

참고

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).

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 the documentation of Py_TPFLAGS_HAVE_GC and tp_traverse for additional considerations.

The API for defining heap types grew organically, leaving it somewhat awkward to use in its current state. The following sections will guide you through common issues.

tp_traverse in Python 3.8 and lower

The requirement to visit the type from tp_traverse was added in Python 3.9. If you support Python 3.8 and lower, the traverse function must not visit the type, so it must be more complicated:

static int my_traverse(PyObject *self, visitproc visit, void *arg)
{
    if (Py_Version >= 0x03090000) {
        Py_VISIT(Py_TYPE(self));
    }
    return 0;
}

Unfortunately, Py_Version was only added in Python 3.11. As a replacement, use:

Delegating tp_traverse

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 {
    if (Py_Version >= 0x03090000) {
        Py_VISIT(Py_TYPE(self));
    }
}

It is not necessary to handle the type’s reference count in tp_new and tp_clear.

Defining tp_dealloc

If your type has a custom tp_dealloc function, it needs to:

  • call PyObject_GC_UnTrack() before any fields are invalidated, and

  • decrement the reference count of the type.

To keep the type valid while tp_free is called, the type’s refcount needs to be decremented after the instance is deallocated. For example:

static void my_dealloc(PyObject *self)
{
    PyObject_GC_UnTrack(self);
    ...
    PyTypeObject *type = Py_TYPE(self);
    type->tp_free(self);
    Py_DECREF(type);
}

The default tp_dealloc function does this, so if your type does not override tp_dealloc you don’t need to add it.

Not overriding tp_free

The tp_free slot of a heap type must be set to PyObject_GC_Del(). This is the default; do not override it.

Avoiding PyObject_New

GC-tracked objects need to be allocated using GC-aware functions.

If you use use PyObject_New() or PyObject_NewVar():

  • Get and call type’s tp_alloc slot, if possible. That is, replace TYPE *o = PyObject_New(TYPE, typeobj) with:

    TYPE *o = typeobj->tp_alloc(typeobj, 0);

    Replace o = PyObject_NewVar(TYPE, typeobj, size) with the same, but use size instead of the 0.

  • If the above is not possible (e.g. inside a custom tp_alloc), call PyObject_GC_New() or PyObject_GC_NewVar():

    TYPE *o = PyObject_GC_New(TYPE, typeobj);

TYPE *o = PyObject_GC_NewVar(TYPE, typeobj, size);

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 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 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.

참고

The following Python code can illustrate the concept. Base.get_defining_class returns Base even if type(self) == Sub:

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 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

참고

This is new in Python 3.11.

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 method resolution order (i.e. all superclasses) for the first superclass that has a corresponding module.

참고

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 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.

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.