C API Extension Support for Free Threading
******************************************

Starting with the 3.13 release, CPython has support for running with
the *global interpreter lock* (GIL) disabled in a configuration called
*free threading*.  This document describes how to adapt C API
extensions to support free threading.


Identifying the Free-Threaded Build in C
========================================

The CPython C API exposes the "Py_GIL_DISABLED" macro: in the free-
threaded build it's defined to "1", and in the regular build it's not
defined. You can use it to enable code that only runs under the free-
threaded build:

   #ifdef Py_GIL_DISABLED
   /* code that only runs in the free-threaded build */
   #endif

Nota:

  On Windows, this macro is not defined automatically, but must be
  specified to the compiler when building. The
  "sysconfig.get_config_var()" function can be used to determine
  whether the current running interpreter had the macro defined.


Module Initialization
=====================

Extension modules need to explicitly indicate that they support
running with the GIL disabled; otherwise importing the extension will
raise a warning and enable the GIL at runtime.

There are two ways to indicate that an extension module supports
running with the GIL disabled depending on whether the extension uses
multi-phase or single-phase initialization.


Multi-Phase Initialization
--------------------------

Extensions that use multi-phase initialization (functions like
"PyModuleDef_Init()", "PyModExport_*" export hook,
"PyModule_FromSlotsAndSpec()") should add a "Py_mod_gil" slot in the
module definition. If your extension supports older versions of
CPython, you should guard the slot with a "PY_VERSION_HEX" check.

   static struct PyModuleDef_Slot module_slots[] = {
       ...
   #if PY_VERSION_HEX >= 0x030D0000
       {Py_mod_gil, Py_MOD_GIL_NOT_USED},
   #endif
       {0, NULL}
   };


Single-Phase Initialization
---------------------------

Extensions that use legacy single-phase initialization (that is,
"PyModule_Create()") should call "PyUnstable_Module_SetGIL()" to
indicate that they support running with the GIL disabled.  The
function is only defined in the free-threaded build, so you should
guard the call with "#ifdef Py_GIL_DISABLED" to avoid compilation
errors in the regular build.

   static struct PyModuleDef moduledef = {
       PyModuleDef_HEAD_INIT,
       ...
   };

   PyMODINIT_FUNC
   PyInit_mymodule(void)
   {
       PyObject *m = PyModule_Create(&moduledef);
       if (m == NULL) {
           return NULL;
       }
   #ifdef Py_GIL_DISABLED
       PyUnstable_Module_SetGIL(m, Py_MOD_GIL_NOT_USED);
   #endif
       return m;
   }


General API Guidelines
======================

Most of the C API is thread-safe, but there are some exceptions.

* **Struct Fields**: Accessing fields in Python C API objects or
  structs directly is not thread-safe if the field may be concurrently
  modified.

* **Macros**: Accessor macros like "PyList_GET_ITEM",
  "PyList_SET_ITEM", and macros like "PySequence_Fast_GET_SIZE" that
  use the object returned by "PySequence_Fast()" do not perform any
  error checking or locking. These macros are not thread-safe if the
  container object may be modified concurrently.

* **Borrowed References**: C API functions that return *borrowed
  references* may not be thread-safe if the containing object is
  modified concurrently.  See the section on borrowed references for
  more information.


Container Thread Safety
-----------------------

Containers like "PyListObject", "PyDictObject", and "PySetObject"
perform internal locking in the free-threaded build.  For example, the
"PyList_Append()" will lock the list before appending an item.


"PyDict_Next"
~~~~~~~~~~~~~

A notable exception is "PyDict_Next()", which does not lock the
dictionary.  You should use "Py_BEGIN_CRITICAL_SECTION" to protect the
dictionary while iterating over it if the dictionary may be
concurrently modified:

   Py_BEGIN_CRITICAL_SECTION(dict);
   PyObject *key, *value;
   Py_ssize_t pos = 0;
   while (PyDict_Next(dict, &pos, &key, &value)) {
       ...
   }
   Py_END_CRITICAL_SECTION();


Borrowed References
===================

Some C API functions return *borrowed references*. These APIs are not
thread-safe if the containing object is modified concurrently.  For
example, it's not safe to use "PyList_GetItem()" if the list may be
modified concurrently.

The following table lists some borrowed reference APIs and their
replacements that return *strong references*.

+-------------------------------------+-------------------------------------+
| Borrowed reference API              | Strong reference API                |
|=====================================|=====================================|
| "PyList_GetItem()"                  | "PyList_GetItemRef()"               |
+-------------------------------------+-------------------------------------+
| "PyList_GET_ITEM()"                 | "PyList_GetItemRef()"               |
+-------------------------------------+-------------------------------------+
| "PyDict_GetItem()"                  | "PyDict_GetItemRef()"               |
+-------------------------------------+-------------------------------------+
| "PyDict_GetItemWithError()"         | "PyDict_GetItemRef()"               |
+-------------------------------------+-------------------------------------+
| "PyDict_GetItemString()"            | "PyDict_GetItemStringRef()"         |
+-------------------------------------+-------------------------------------+
| "PyDict_SetDefault()"               | "PyDict_SetDefaultRef()"            |
+-------------------------------------+-------------------------------------+
| "PyDict_Next()"                     | none (see PyDict_Next)              |
+-------------------------------------+-------------------------------------+
| "PyWeakref_GetObject()"             | "PyWeakref_GetRef()"                |
+-------------------------------------+-------------------------------------+
| "PyWeakref_GET_OBJECT()"            | "PyWeakref_GetRef()"                |
+-------------------------------------+-------------------------------------+
| "PyImport_AddModule()"              | "PyImport_AddModuleRef()"           |
+-------------------------------------+-------------------------------------+
| "PyCell_GET()"                      | "PyCell_Get()"                      |
+-------------------------------------+-------------------------------------+

Not all APIs that return borrowed references are problematic.  For
example, "PyTuple_GetItem()" is safe because tuples are immutable.
Similarly, not all uses of the above APIs are problematic.  For
example, "PyDict_GetItem()" is often used for parsing keyword argument
dictionaries in function calls; those keyword argument dictionaries
are effectively private (not accessible by other threads), so using
borrowed references in that context is safe.

Some of these functions were added in Python 3.13.  You can use the
pythoncapi-compat package to provide implementations of these
functions for older Python versions.


Memory Allocation APIs
======================

Python's memory management C API provides functions in three different
allocation domains: "raw", "mem", and "object". For thread-safety, the
free-threaded build requires that only Python objects are allocated
using the object domain, and that all Python objects are allocated
using that domain.  This differs from the prior Python versions, where
this was only a best practice and not a hard requirement.

Nota:

  Search for uses of "PyObject_Malloc()" in your extension and check
  that the allocated memory is used for Python objects. Use
  "PyMem_Malloc()" to allocate buffers instead of "PyObject_Malloc()".


Thread State and GIL APIs
=========================

Python provides a set of functions and macros to manage thread state
and the GIL, such as:

* "PyThreadState_Ensure()", "PyThreadState_EnsureFromView()", and
  "PyThreadState_Release()"

* "PyGILState_Ensure()" and "PyGILState_Release()"

* "PyEval_SaveThread()" and "PyEval_RestoreThread()"

* "Py_BEGIN_ALLOW_THREADS" and "Py_END_ALLOW_THREADS"

These functions should still be used in the free-threaded build to
manage thread state even when the *GIL* is disabled.  For example, if
you create a thread outside of Python, you must call
"PyThreadState_Ensure()" before calling into the Python API to ensure
that the thread has a valid Python thread state.

You should continue to call "PyEval_SaveThread()" or
"Py_BEGIN_ALLOW_THREADS" around blocking operations, such as I/O or
lock acquisitions, to allow other threads to run the *cyclic garbage
collector*.


Protecting Internal Extension State
===================================

Your extension may have internal state that was previously protected
by the GIL.  You may need to add locking to protect this state.  The
approach will depend on your extension, but some common patterns
include:

* **Caches**: global caches are a common source of shared state.
  Consider using a lock to protect the cache or disabling it in the
  free-threaded build if the cache is not critical for performance.

* **Global State**: global state may need to be protected by a lock or
  moved to thread local storage. C11 and C++11 provide the
  "thread_local" or "_Thread_local" for thread-local storage.


Critical Sections
=================

In the free-threaded build, CPython provides a mechanism called
"critical sections" to protect data that would otherwise be protected
by the GIL. While extension authors may not interact with the internal
critical section implementation directly, understanding their behavior
is crucial when using certain C API functions or managing shared state
in the free-threaded build.


What Are Critical Sections?
---------------------------

Conceptually, critical sections act as a deadlock avoidance layer
built on top of simple mutexes. Each thread maintains a stack of
active critical sections. When a thread needs to acquire a lock
associated with a critical section (e.g., implicitly when calling a
thread-safe C API function like "PyDict_SetItem()", or explicitly
using macros), it attempts to acquire the underlying mutex.


Using Critical Sections
-----------------------

The primary APIs for using critical sections are:

* "Py_BEGIN_CRITICAL_SECTION" and "Py_END_CRITICAL_SECTION" - For
  locking a single object

* "Py_BEGIN_CRITICAL_SECTION2" and "Py_END_CRITICAL_SECTION2" - For
  locking two objects simultaneously

These macros must be used in matching pairs and must appear in the
same C scope, since they establish a new local scope.  These macros
are no-ops in non-free-threaded builds, so they can be safely added to
code that needs to support both build types.

A common use of a critical section would be to lock an object while
accessing an internal attribute of it.  For example, if an extension
type has an internal count field, you could use a critical section
while reading or writing that field:

   // read the count, returns new reference to internal count value
   PyObject *result;
   Py_BEGIN_CRITICAL_SECTION(obj);
   result = Py_NewRef(obj->count);
   Py_END_CRITICAL_SECTION();
   return result;

   // write the count, consumes reference from new_count
   Py_BEGIN_CRITICAL_SECTION(obj);
   obj->count = new_count;
   Py_END_CRITICAL_SECTION();


How Critical Sections Work
--------------------------

Unlike traditional locks, critical sections do not guarantee exclusive
access throughout their entire duration. If a thread would block while
holding a critical section (e.g., by acquiring another lock or
performing I/O), the critical section is temporarily suspended—all
locks are released—and then resumed when the blocking operation
completes.

This behavior is similar to what happens with the GIL when a thread
makes a blocking call. The key differences are:

* Critical sections operate on a per-object basis rather than globally

* Critical sections follow a stack discipline within each thread (the
  "begin" and "end" macros enforce this since they must be paired and
  within the same scope)

* Critical sections automatically release and reacquire locks around
  potential blocking operations


Deadlock Avoidance
------------------

Critical sections help avoid deadlocks in two ways:

1. If a thread tries to acquire a lock that's already held by another
   thread, it first suspends all of its active critical sections,
   temporarily releasing their locks

2. When the blocking operation completes, only the top-most critical
   section is reacquired first

This means you cannot rely on nested critical sections to lock
multiple objects at once, as the inner critical section may suspend
the outer ones. Instead, use "Py_BEGIN_CRITICAL_SECTION2" to lock two
objects simultaneously.

Note that the locks described above are only "PyMutex" based locks.
The critical section implementation does not know about or affect
other locking mechanisms that might be in use, like POSIX mutexes.
Also note that while blocking on any "PyMutex" causes the critical
sections to be suspended, only the mutexes that are part of the
critical sections are released.  If "PyMutex" is used without a
critical section, it will not be released and therefore does not get
the same deadlock avoidance.


Important Considerations
------------------------

* Critical sections may temporarily release their locks, allowing
  other threads to modify the protected data. Be careful about making
  assumptions about the state of the data after operations that might
  block.

* Because locks can be temporarily released (suspended), entering a
  critical section does not guarantee exclusive access to the
  protected resource throughout the section's duration. If code within
  a critical section calls another function that blocks (e.g.,
  acquires another lock, performs blocking I/O), all locks held by the
  thread via critical sections will be released. This is similar to
  how the GIL can be released during blocking calls.

* Only the lock(s) associated with the most recently entered (top-
  most) critical section are guaranteed to be held at any given time.
  Locks for outer, nested critical sections might have been suspended.

* You can lock at most two objects simultaneously with these APIs. If
  you need to lock more objects, you'll need to restructure your code.

* While critical sections will not deadlock if you attempt to lock the
  same object twice, they are less efficient than purpose-built
  reentrant locks for this use case.

* When using "Py_BEGIN_CRITICAL_SECTION2", the order of the objects
  doesn't affect correctness (the implementation handles deadlock
  avoidance), but it's good practice to always lock objects in a
  consistent order.

* Remember that the critical section macros are primarily for
  protecting access to *Python objects* that might be involved in
  internal CPython operations susceptible to the deadlock scenarios
  described above. For protecting purely internal extension state,
  standard mutexes or other synchronization primitives might be more
  appropriate.


Per-Object Locks ("ob_mutex")
-----------------------------

In the free-threaded build, each Python object contains a "ob_mutex"
field of type "PyMutex".  This mutex is **reserved for use by the
critical section API** ("Py_BEGIN_CRITICAL_SECTION" /
"Py_END_CRITICAL_SECTION").

Avvertimento:

  Do **not** lock "ob_mutex" directly with
  "PyMutex_Lock(&obj->ob_mutex)". Mixing direct "PyMutex_Lock" calls
  with the critical section API on the same mutex can cause deadlocks.

Even if your own code never uses critical sections on a particular
object type, **CPython internals may use the critical section API on
any Python object**.

If your extension type needs its own lock, add a separate "PyMutex"
field (or another synchronization primitive) to your object struct.
"PyMutex" is very lightweight, so there is negligible cost to having
an additional one.


Building Extensions for the Free-Threaded Build
===============================================

C API extensions need to be built specifically for the free-threaded
build. The wheels, shared libraries, and binaries are indicated by a
"t" suffix.

* pypa/manylinux supports the free-threaded build, with the "t"
  suffix, such as "python3.14t".

* pypa/cibuildwheel supports building wheels for the free-threaded
  build of Python 3.14 and newer.


Limited C API and Stable ABI
----------------------------

The free-threaded build does not currently support the Limited C API
or the stable ABI.  If you use setuptools to build your extension and
currently set "py_limited_api=True" you can use "py_limited_api=not
sysconfig.get_config_var("Py_GIL_DISABLED")" to opt out of the limited
API when building with the free-threaded build.

Nota:

  You will need to build separate wheels specifically for the free-
  threaded build.  If you currently use the stable ABI, you can
  continue to build a single wheel for multiple non-free-threaded
  Python versions.


Windows
-------

Due to a limitation of the official Windows installer, you will need
to manually define "Py_GIL_DISABLED=1" when building extensions from
source.

Vedi anche:

  Porting Extension Modules to Support Free-Threading: A community-
  maintained porting guide for extension authors.
