-
wchar_t
*
Py_GetExecPrefix
(
)
¶
-
属于
稳定 ABI (应用程序二进制接口)
.
返回
exec-prefix
for installed platform-
dependent
files. This is derived through a number of complicated rules from the program name set with
PyConfig.program_name
and some environment variables; for example, if the program name is
'/usr/local/bin/python'
, the exec-prefix is
'/usr/local'
. The returned string points into static storage; the caller should not modify its value. This corresponds to the
exec_prefix
variable in the top-level
Makefile
和
--exec-prefix
自变量到
configure
script at build time. The value is available to Python code as
sys.base_exec_prefix
. It is only useful on Unix.
Background: The exec-prefix differs from the prefix when platform dependent files (such as executables and shared libraries) are installed in a different directory tree. In a typical installation, platform dependent files may be installed in the
/usr/local/plat
subtree while platform independent may be installed in
/usr/local
.
Generally speaking, a platform is a combination of hardware and software families, e.g. Sparc machines running the Solaris 2.x operating system are considered the same platform, but Intel machines running Solaris 2.x are another platform, and Intel machines running Linux are yet another platform. Different major revisions of the same operating system generally also form different platforms. Non-Unix operating systems are a different story; the installation strategies on those systems are so different that the prefix and exec-prefix are meaningless, and set to the empty string. Note that compiled Python bytecode files are platform independent (but not independent from the Python version by which they were compiled!).
System administrators will know how to configure the
mount
or
automount
programs to share
/usr/local
between platforms while having
/usr/local/plat
be a different filesystem for each platform.
This function should not be called before
Py_Initialize()
，否则返回
NULL
.
3.10 版改变：
It now returns
NULL
if called before
Py_Initialize()
.
Deprecated since version 3.13, will be removed in version 3.15:
Get
sys.base_exec_prefix
instead, or
sys.exec_prefix
if
虚拟环境
need to be handled.
线程状态和 GIL (全局解释器锁)
¶
The Python interpreter is not fully thread-safe. In order to support multi-threaded Python programs, there’s a global lock, called the
全局解释器锁
or
GIL
, that must be held by the current thread before it can safely access Python objects. Without the lock, even the simplest operations could cause problems in a multi-threaded program: for example, when two threads simultaneously increment the reference count of the same object, the reference count could end up being incremented only once instead of twice.
Therefore, the rule exists that only the thread that has acquired the
GIL
may operate on Python objects or call Python/C API functions. In order to emulate concurrency of execution, the interpreter regularly tries to switch threads (see
sys.setswitchinterval()
). The lock is also released around potentially blocking I/O operations like reading or writing a file, so that other Python threads can run in the meantime.
The Python interpreter keeps some thread-specific bookkeeping information inside a data structure called
PyThreadState
. There’s also one global variable pointing to the current
PyThreadState
: it can be retrieved using
PyThreadState_Get()
.
从扩展代码释放 GIL
¶
大多数扩展代码操纵
GIL
拥有以下简单结构：
Save the thread state in a local variable.
Release the global interpreter lock.
... Do some blocking I/O operation ...
Reacquire the global interpreter lock.
Restore the thread state from the local variable.
这很常见，所以存在一对宏来简化它：
Py_BEGIN_ALLOW_THREADS
... Do some blocking I/O operation ...
Py_END_ALLOW_THREADS
The
Py_BEGIN_ALLOW_THREADS
macro opens a new block and declares a hidden local variable; the
Py_END_ALLOW_THREADS
macro closes the block.
The block above expands to the following code:
PyThreadState *_save;
_save = PyEval_SaveThread();
... Do some blocking I/O operation ...
PyEval_RestoreThread(_save);
Here is how these functions work: the global interpreter lock is used to protect the pointer to the current thread state. When releasing the lock and saving the thread state, the current thread state pointer must be retrieved before the lock is released (since another thread could immediately acquire the lock and store its own thread state in the global variable). Conversely, when acquiring the lock and restoring the thread state, the lock must be acquired before storing the thread state pointer.
注意
Calling system I/O functions is the most common use case for releasing the GIL, but it can also be useful before calling long-running computations which don’t need access to Python objects, such as compression or cryptographic functions operating over memory buffers. For example, the standard
zlib
and
hashlib
modules release the GIL when compressing or hashing data.
非 Python 创建线程
¶
When threads are created using the dedicated Python APIs (such as the
threading
module), a thread state is automatically associated to them and the code showed above is therefore correct. However, when threads are created from C (for example by a third-party library with its own thread management), they don’t hold the GIL, nor is there a thread state structure for them.
If you need to call Python code from these threads (often this will be part of a callback API provided by the aforementioned third-party library), you must first register these threads with the interpreter by creating a thread state data structure, then acquiring the GIL, and finally storing their thread state pointer, before you can start using the Python/C API. When you are done, you should reset the thread state pointer, release the GIL, and finally free the thread state data structure.
The
PyGILState_Ensure()
and
PyGILState_Release()
functions do all of the above automatically. The typical idiom for calling into Python from a C thread is:
PyGILState_STATE gstate;
gstate = PyGILState_Ensure();
/* Perform Python actions here. */
result = CallSomeFunction();
/* evaluate result or handle exception */
/* Release the thread. No Python API allowed beyond this point. */
PyGILState_Release(gstate);
注意，
PyGILState_*
functions assume there is only one global interpreter (created automatically by
Py_Initialize()
). Python supports the creation of additional interpreters (using
Py_NewInterpreter()
), but mixing multiple interpreters and the
PyGILState_*
API is unsupported.
谨慎 fork()
¶
Another important thing to note about threads is their behaviour in the face of the C
fork()
call. On most systems with
fork()
, after a process forks only the thread that issued the fork will exist. This has a concrete impact both on how locks must be handled and on all stored state in CPython’s runtime.
The fact that only the “current” thread remains means any locks held by other threads will never be released. Python solves this for
os.fork()
by acquiring the locks it uses internally before the fork, and releasing them afterwards. In addition, it resets any
锁对象
in the child. When extending or embedding Python, there is no way to inform Python of additional (non-Python) locks that need to be acquired before or reset after a fork. OS facilities such as
pthread_atfork()
would need to be used to accomplish the same thing. Additionally, when extending or embedding Python, calling
fork()
directly rather than through
os.fork()
(and returning to or calling into Python) may result in a deadlock by one of Python’s internal locks being held by a thread that is defunct after the fork.
PyOS_AfterFork_Child()
tries to reset the necessary locks, but is not always able to.
The fact that all other threads go away also means that CPython’s runtime state there must be cleaned up properly, which
os.fork()
does. This means finalizing all other
PyThreadState
objects belonging to the current interpreter and all other
PyInterpreterState
objects. Due to this and the special nature of the
“main” interpreter
,
fork()
should only be called in that interpreter’s “main” thread, where the CPython global runtime was originally initialized. The only exception is if
exec()
will be called immediately after.
高级 API
¶
These are the most commonly used types and functions when writing C extension code, or when embedding the Python interpreter:
-
type
PyInterpreterState
¶
-
属于
Limited API
(as an opaque struct).
This data structure represents the state shared by a number of cooperating threads. Threads belonging to the same interpreter share their module administration and a few other internal items. There are no public members in this structure.
Threads belonging to different interpreters initially share nothing, except process state like available memory, open file descriptors and such. The global interpreter lock is also shared by all threads, regardless of to which interpreter they belong.
-
type
PyThreadState
¶
-
属于
Limited API
(as an opaque struct).
This data structure represents the state of a single thread. The only public data member is:
-
PyInterpreterState
*
interp
¶
-
This thread’s interpreter state.
-
void
PyEval_InitThreads
(
)
¶
-
属于
稳定 ABI (应用程序二进制接口)
.
弃用什么都不做的函数。
In Python 3.6 and older, this function created the GIL if it didn’t exist.
3.9 版改变：
函数现在什么都不做。
3.7 版改变：
This function is now called by
Py_Initialize()
, so you don’t have to call it yourself anymore.
3.2 版改变：
This function cannot be called before
Py_Initialize()
不再。
Deprecated since version 3.9.
-
PyThreadState
*
PyEval_SaveThread
(
)
¶
-
属于
稳定 ABI (应用程序二进制接口)
.
Release the global interpreter lock (if it has been created) and reset the thread state to
NULL
, returning the previous thread state (which is not
NULL
). If the lock has been created, the current thread must have acquired it.
-
void
PyEval_RestoreThread
(
PyThreadState
*
tstate
)
¶
-
属于
稳定 ABI (应用程序二进制接口)
.
Acquire the global interpreter lock (if it has been created) and set the thread state to
tstate
, which must not be
NULL
. If the lock has been created, the current thread must not have acquired it, otherwise deadlock ensues.
注意
Calling this function from a thread when the runtime is finalizing will terminate the thread, even if the thread was not created by Python. You can use
Py_IsFinalizing()
or
sys.is_finalizing()
to check if the interpreter is in process of being finalized before calling this function to avoid unwanted termination.
-
PyThreadState
*
PyThreadState_Get
(
)
¶
-
属于
稳定 ABI (应用程序二进制接口)
.
Return the current thread state. The global interpreter lock must be held. When the current thread state is
NULL
, this issues a fatal error (so that the caller needn’t check for
NULL
).
另请参阅
PyThreadState_GetUnchecked()
.
-
PyThreadState
*
PyThreadState_GetUnchecked
(
)
¶
-
类似于
PyThreadState_Get()
, but don’t kill the process with a fatal error if it is NULL. The caller is responsible to check if the result is NULL.
Added in version 3.13:
In Python 3.5 to 3.12, the function was private and known as
_PyThreadState_UncheckedGet()
.
-
PyThreadState
*
PyThreadState_Swap
(
PyThreadState
*
tstate
)
¶
-
属于
稳定 ABI (应用程序二进制接口)
.
Swap the current thread state with the thread state given by the argument
tstate
, which may be
NULL
. The global interpreter lock must be held and is not released.
The following functions use thread-local storage, and are not compatible with sub-interpreters:
-
PyGILState_STATE
PyGILState_Ensure
(
)
¶
-
属于
稳定 ABI (应用程序二进制接口)
.
Ensure that the current thread is ready to call the Python C API regardless of the current state of Python, or of the global interpreter lock. This may be called as many times as desired by a thread as long as each call is matched with a call to
PyGILState_Release()
. In general, other thread-related APIs may be used between
PyGILState_Ensure()
and
PyGILState_Release()
calls as long as the thread state is restored to its previous state before the Release(). For example, normal usage of the
Py_BEGIN_ALLOW_THREADS
and
Py_END_ALLOW_THREADS
macros is acceptable.
The return value is an opaque “handle” to the thread state when
PyGILState_Ensure()
was called, and must be passed to
PyGILState_Release()
to ensure Python is left in the same state. Even though recursive calls are allowed, these handles
cannot
be shared - each unique call to
PyGILState_Ensure()
must save the handle for its call to
PyGILState_Release()
.
When the function returns, the current thread will hold the GIL and be able to call arbitrary Python code. Failure is a fatal error.
注意
Calling this function from a thread when the runtime is finalizing will terminate the thread, even if the thread was not created by Python. You can use
Py_IsFinalizing()
or
sys.is_finalizing()
to check if the interpreter is in process of being finalized before calling this function to avoid unwanted termination.
-
void
PyGILState_Release
(
PyGILState_STATE
)
¶
-
属于
稳定 ABI (应用程序二进制接口)
.
Release any resources previously acquired. After this call, Python’s state will be the same as it was prior to the corresponding
PyGILState_Ensure()
call (but generally this state will be unknown to the caller, hence the use of the GILState API).
每次调用
PyGILState_Ensure()
must be matched by a call to
PyGILState_Release()
on the same thread.
-
PyThreadState
*
PyGILState_GetThisThreadState
(
)
¶
-
属于
稳定 ABI (应用程序二进制接口)
.
Get the current thread state for this thread. May return
NULL
if no GILState API has been used on the current thread. Note that the main thread always has such a thread-state, even if no auto-thread-state call has been made on the main thread. This is mainly a helper/diagnostic function.
-
int
PyGILState_Check
(
)
¶
-
返回
1
if the current thread is holding the GIL and
0
otherwise. This function can be called from any thread at any time. Only if it has had its Python thread state initialized and currently is holding the GIL will it return
1
. This is mainly a helper/diagnostic function. It can be useful for example in callback contexts or memory allocation functions when knowing that the GIL is locked can allow the caller to perform sensitive actions or otherwise behave differently.
Added in version 3.4.
The following macros are normally used without a trailing semicolon; look for example usage in the Python source distribution.
-
Py_BEGIN_ALLOW_THREADS
¶
-
属于
稳定 ABI (应用程序二进制接口)
.
This macro expands to
{ PyThreadState *_save; _save = PyEval_SaveThread();
. Note that it contains an opening brace; it must be matched with a following
Py_END_ALLOW_THREADS
macro. See above for further discussion of this macro.
-
Py_END_ALLOW_THREADS
¶
-
属于
稳定 ABI (应用程序二进制接口)
.
This macro expands to
PyEval_RestoreThread(_save); }
. Note that it contains a closing brace; it must be matched with an earlier
Py_BEGIN_ALLOW_THREADS
macro. See above for further discussion of this macro.
-
Py_BLOCK_THREADS
¶
-
属于
稳定 ABI (应用程序二进制接口)
.
This macro expands to
PyEval_RestoreThread(_save);
: it is equivalent to
Py_END_ALLOW_THREADS
without the closing brace.
-
Py_UNBLOCK_THREADS
¶
-
属于
稳定 ABI (应用程序二进制接口)
.
This macro expands to
_save = PyEval_SaveThread();
: it is equivalent to
Py_BEGIN_ALLOW_THREADS
without the opening brace and variable declaration.
低级 API
¶
All of the following functions must be called after
Py_Initialize()
.
3.7 版改变：
Py_Initialize()
现在初始化
GIL
.
-
PyInterpreterState
*
PyInterpreterState_New
(
)
¶
-
属于
稳定 ABI (应用程序二进制接口)
.
Create a new interpreter state object. The global interpreter lock need not be held, but may be held if it is necessary to serialize calls to this function.
引发
审计事件
cpython.PyInterpreterState_New
不带自变量。
-
void
PyInterpreterState_Clear
(
PyInterpreterState
*
interp
)
¶
-
属于
稳定 ABI (应用程序二进制接口)
.
Reset all information in an interpreter state object. The global interpreter lock must be held.
引发
审计事件
cpython.PyInterpreterState_Clear
不带自变量。
-
void
PyInterpreterState_Delete
(
PyInterpreterState
*
interp
)
¶
-
属于
稳定 ABI (应用程序二进制接口)
.
Destroy an interpreter state object. The global interpreter lock need not be held. The interpreter state must have been reset with a previous call to
PyInterpreterState_Clear()
.
-
PyThreadState
*
PyThreadState_New
(
PyInterpreterState
*
interp
)
¶
-
属于
稳定 ABI (应用程序二进制接口)
.
Create a new thread state object belonging to the given interpreter object. The global interpreter lock need not be held, but may be held if it is necessary to serialize calls to this function.
-
void
PyThreadState_Clear
(
PyThreadState
*
tstate
)
¶
-
属于
稳定 ABI (应用程序二进制接口)
.
Reset all information in a thread state object. The global interpreter lock must be held.
3.9 版改变：
This function now calls the
PyThreadState.on_delete
callback. Previously, that happened in
PyThreadState_Delete()
.
Changed in version 3.13:
The
PyThreadState.on_delete
callback was removed.
-
void
PyThreadState_Delete
(
PyThreadState
*
tstate
)
¶
-
属于
稳定 ABI (应用程序二进制接口)
.
Destroy a thread state object. The global interpreter lock need not be held. The thread state must have been reset with a previous call to
PyThreadState_Clear()
.
-
void
PyThreadState_DeleteCurrent
(
void
)
¶
-
Destroy the current thread state and release the global interpreter lock. Like
PyThreadState_Delete()
, the global interpreter lock must be held. The thread state must have been reset with a previous call to
PyThreadState_Clear()
.
-
PyFrameObject
*
PyThreadState_GetFrame
(
PyThreadState
*
tstate
)
¶
-
属于
稳定 ABI (应用程序二进制接口)
since version 3.10.
Get the current frame of the Python thread state
tstate
.
返回
强引用
。返回
NULL
if no frame is currently executing.
另请参阅
PyEval_GetFrame()
.
tstate
不得为
NULL
.
Added in version 3.9.
-
uint64_t
PyThreadState_GetID
(
PyThreadState
*
tstate
)
¶
-
属于
稳定 ABI (应用程序二进制接口)
since version 3.10.
Get the unique thread state identifier of the Python thread state
tstate
.
tstate
不得为
NULL
.
Added in version 3.9.
-
PyInterpreterState
*
PyThreadState_GetInterpreter
(
PyThreadState
*
tstate
)
¶
-
属于
稳定 ABI (应用程序二进制接口)
since version 3.10.
Get the interpreter of the Python thread state
tstate
.
tstate
不得为
NULL
.
Added in version 3.9.
-
void
PyThreadState_EnterTracing
(
PyThreadState
*
tstate
)
¶
-
Suspend tracing and profiling in the Python thread state
tstate
.
Resume them using the
PyThreadState_LeaveTracing()
函数。
Added in version 3.11.
-
void
PyThreadState_LeaveTracing
(
PyThreadState
*
tstate
)
¶
-
Resume tracing and profiling in the Python thread state
tstate
suspended by the
PyThreadState_EnterTracing()
函数。
另请参阅
PyEval_SetTrace()
and
PyEval_SetProfile()
函数。
Added in version 3.11.
-
PyInterpreterState
*
PyInterpreterState_Get
(
void
)
¶
-
属于
稳定 ABI (应用程序二进制接口)
since version 3.9.
Get the current interpreter.
Issue a fatal error if there no current Python thread state or no current interpreter. It cannot return NULL.
调用者必须保持 GIL (全局解释器锁)。
Added in version 3.9.
-
int64_t
PyInterpreterState_GetID
(
PyInterpreterState
*
interp
)
¶
-
属于
稳定 ABI (应用程序二进制接口)
since version 3.7.
Return the interpreter’s unique ID. If there was any error in doing so then
-1
is returned and an error is set.
调用者必须保持 GIL (全局解释器锁)。
3.7 版添加。
-
PyObject
*
PyInterpreterState_GetDict
(
PyInterpreterState
*
interp
)
¶
-
属于
稳定 ABI (应用程序二进制接口)
since version 3.8.
Return a dictionary in which interpreter-specific data may be stored. If this function returns
NULL
then no exception has been raised and the caller should assume no interpreter-specific dict is available.
This is not a replacement for
PyModule_GetState()
, which extensions should use to store interpreter-specific state information.
Added in version 3.8.
-
typedef
PyObject
*
(
*
_PyFrameEvalFunction
)
(
PyThreadState
*
tstate
,
_PyInterpreterFrame
*
frame
,
int
throwflag
)
¶
-
Type of a frame evaluation function.
The
throwflag
parameter is used by the
throw()
method of generators: if non-zero, handle the current exception.
3.9 版改变：
The function now takes a
tstate
参数。
3.11 版改变：
The
frame
parameter changed from
PyFrameObject*
to
_PyInterpreterFrame*
.
-
_PyFrameEvalFunction
_PyInterpreterState_GetEvalFrameFunc
(
PyInterpreterState
*
interp
)
¶
-
Get the frame evaluation function.
见
PEP 523
“Adding a frame evaluation API to CPython”.
Added in version 3.9.
-
void
_PyInterpreterState_SetEvalFrameFunc
(
PyInterpreterState
*
interp
,
_PyFrameEvalFunction
eval_frame
)
¶
-
Set the frame evaluation function.
见
PEP 523
“Adding a frame evaluation API to CPython”.
Added in version 3.9.
-
PyObject
*
PyThreadState_GetDict
(
)
¶
-
返回值：借位引用。
属于
稳定 ABI (应用程序二进制接口)
.
Return a dictionary in which extensions can store thread-specific state information. Each extension should use a unique key to use to store state in the dictionary. It is okay to call this function when no current thread state is available. If this function returns
NULL
, no exception has been raised and the caller should assume no current thread state is available.
-
int
PyThreadState_SetAsyncExc
(
unsigned
long
id
,
PyObject
*
exc
)
¶
-
属于
稳定 ABI (应用程序二进制接口)
.
Asynchronously raise an exception in a thread. The
id
argument is the thread id of the target thread;
exc
is the exception object to be raised. This function does not steal any references to
exc
. To prevent naive misuse, you must write your own C extension to call this. Must be called with the GIL held. Returns the number of thread states modified; this is normally one, but will be zero if the thread id isn’t found. If
exc
is
NULL
, the pending exception (if any) for the thread is cleared. This raises no exceptions.
3.7 版改变：
The type of the
id
parameter changed from
long
to
unsigned
long
.
-
void
PyEval_AcquireThread
(
PyThreadState
*
tstate
)
¶
-
属于
稳定 ABI (应用程序二进制接口)
.
Acquire the global interpreter lock and set the current thread state to
tstate
, which must not be
NULL
. The lock must have been created earlier. If this thread already has the lock, deadlock ensues.
注意
Calling this function from a thread when the runtime is finalizing will terminate the thread, even if the thread was not created by Python. You can use
Py_IsFinalizing()
or
sys.is_finalizing()
to check if the interpreter is in process of being finalized before calling this function to avoid unwanted termination.
3.8 版改变：
Updated to be consistent with
PyEval_RestoreThread()
,
Py_END_ALLOW_THREADS()
，和
PyGILState_Ensure()
, and terminate the current thread if called while the interpreter is finalizing.
PyEval_RestoreThread()
is a higher-level function which is always available (even when threads have not been initialized).
-
void
PyEval_ReleaseThread
(
PyThreadState
*
tstate
)
¶
-
属于
稳定 ABI (应用程序二进制接口)
.
Reset the current thread state to
NULL
and release the global interpreter lock. The lock must have been created earlier and must be held by the current thread. The
tstate
argument, which must not be
NULL
, is only used to check that it represents the current thread state — if it isn’t, a fatal error is reported.
PyEval_SaveThread()
is a higher-level function which is always available (even when threads have not been initialized).
子解释器支持
¶
While in most uses, you will only embed a single Python interpreter, there are cases where you need to create several independent interpreters in the same process and perhaps even in the same thread. Sub-interpreters allow you to do that.
The “main” interpreter is the first one created when the runtime initializes. It is usually the only Python interpreter in a process. Unlike sub-interpreters, the main interpreter has unique process-global responsibilities like signal handling. It is also responsible for execution during runtime initialization and is usually the active interpreter during runtime finalization. The
PyInterpreterState_Main()
function returns a pointer to its state.
You can switch between sub-interpreters using the
PyThreadState_Swap()
function. You can create and destroy them using the following functions:
-
type
PyInterpreterConfig
¶
-
Structure containing most parameters to configure a sub-interpreter. Its values are used only in
Py_NewInterpreterFromConfig()
and never modified by the runtime.
3.12 版添加。
Structure fields:
-
int
use_main_obmalloc
¶
-
若这为
0
then the sub-interpreter will use its own “object” allocator state. Otherwise it will use (share) the main interpreter’s.
若这为
0
then
check_multi_interp_extensions
必须为
1
(non-zero). If this is
1
then
gil
不得为
PyInterpreterConfig_OWN_GIL
.
-
int
allow_fork
¶
-
若这为
0
then the runtime will not support forking the process in any thread where the sub-interpreter is currently active. Otherwise fork is unrestricted.
注意，
subprocess
module still works when fork is disallowed.
-
int
allow_exec
¶
-
若这为
0
then the runtime will not support replacing the current process via exec (e.g.
os.execv()
) in any thread where the sub-interpreter is currently active. Otherwise exec is unrestricted.
注意，
subprocess
module still works when exec is disallowed.
-
int
allow_threads
¶
-
若这为
0
then the sub-interpreter’s
threading
module won’t create threads. Otherwise threads are allowed.
-
int
allow_daemon_threads
¶
-
若这为
0
then the sub-interpreter’s
threading
module won’t create daemon threads. Otherwise daemon threads are allowed (as long as
allow_threads
is non-zero).
-
int
check_multi_interp_extensions
¶
-
若这为
0
then all extension modules may be imported, including legacy (single-phase init) modules, in any thread where the sub-interpreter is currently active. Otherwise only multi-phase init extension modules (see
PEP 489
) may be imported. (Also see
Py_mod_multiple_interpreters
)。
This must be
1
(non-zero) if
use_main_obmalloc
is
0
.
-
int
gil
¶
-
This determines the operation of the GIL for the sub-interpreter. It may be one of the following:
-
PyInterpreterConfig_DEFAULT_GIL
¶
-
Use the default selection (
PyInterpreterConfig_SHARED_GIL
).
-
PyInterpreterConfig_SHARED_GIL
¶
-
Use (share) the main interpreter’s GIL.
-
PyInterpreterConfig_OWN_GIL
¶
-
Use the sub-interpreter’s own GIL.
若这为
PyInterpreterConfig_OWN_GIL
then
PyInterpreterConfig.use_main_obmalloc
必须为
0
.
-
PyStatus
Py_NewInterpreterFromConfig
(
PyThreadState
*
*
tstate_p
,
const
PyInterpreterConfig
*
config
)
¶
-
Create a new sub-interpreter. This is an (almost) totally separate environment for the execution of Python code. In particular, the new interpreter has separate, independent versions of all imported modules, including the fundamental modules
builtins
,
__main__
and
sys
. The table of loaded modules (
sys.modules
) and the module search path (
sys.path
) are also separate. The new environment has no
sys.argv
variable. It has new standard I/O stream file objects
sys.stdin
,
sys.stdout
and
sys.stderr
(however these refer to the same underlying file descriptors).
给定
config
controls the options with which the interpreter is initialized.
Upon success,
tstate_p
will be set to the first thread state created in the new sub-interpreter. This thread state is made in the current thread state. Note that no actual thread is created; see the discussion of thread states below. If creation of the new interpreter is unsuccessful,
tstate_p
被设为
NULL
; no exception is set since the exception state is stored in the current thread state and there may not be a current thread state.
Like all other Python/C API functions, the global interpreter lock must be held before calling this function and is still held when it returns. Likewise a current thread state must be set on entry. On success, the returned thread state will be set as current. If the sub-interpreter is created with its own GIL then the GIL of the calling interpreter will be released. When the function returns, the new interpreter’s GIL will be held by the current thread and the previously interpreter’s GIL will remain released here.
3.12 版添加。
Sub-interpreters are most effective when isolated from each other, with certain functionality restricted:
PyInterpreterConfig config = {
.use_main_obmalloc = 0,
.allow_fork = 0,
.allow_exec = 0,
.allow_threads = 1,
.allow_daemon_threads = 0,
.check_multi_interp_extensions = 1,
.gil = PyInterpreterConfig_OWN_GIL,
};
PyThreadState *tstate = NULL;
PyStatus status = Py_NewInterpreterFromConfig(&tstate, &config);
if (PyStatus_Exception(status)) {
Py_ExitStatusException(status);
}
Note that the config is used only briefly and does not get modified. During initialization the config’s values are converted into various
PyInterpreterState
values. A read-only copy of the config may be stored internally on the
PyInterpreterState
.
Extension modules are shared between (sub-)interpreters as follows:
-
For modules using multi-phase initialization, e.g.
PyModule_FromDefAndSpec()
, a separate module object is created and initialized for each interpreter. Only C-level static and global variables are shared between these module objects.
-
For modules using single-phase initialization, e.g.
PyModule_Create()
, the first time a particular extension is imported, it is initialized normally, and a (shallow) copy of its module’s dictionary is squirreled away. When the same extension is imported by another (sub-)interpreter, a new module is initialized and filled with the contents of this copy; the extension’s
init
function is not called. Objects in the module’s dictionary thus end up shared across (sub-)interpreters, which might cause unwanted behavior (see
Bug 和告诫
below).
Note that this is different from what happens when an extension is imported after the interpreter has been completely re-initialized by calling
Py_FinalizeEx()
and
Py_Initialize()
; in that case, the extension’s
initmodule
function
is
called again. As with multi-phase initialization, this means that only C-level static and global variables are shared between these modules.
-
PyThreadState
*
Py_NewInterpreter
(
void
)
¶
-
属于
稳定 ABI (应用程序二进制接口)
.
Create a new sub-interpreter. This is essentially just a wrapper around
Py_NewInterpreterFromConfig()
with a config that preserves the existing behavior. The result is an unisolated sub-interpreter that shares the main interpreter’s GIL, allows fork/exec, allows daemon threads, and allows single-phase init modules.
-
void
Py_EndInterpreter
(
PyThreadState
*
tstate
)
¶
-
属于
稳定 ABI (应用程序二进制接口)
.
Destroy the (sub-)interpreter represented by the given thread state. The given thread state must be the current thread state. See the discussion of thread states below. When the call returns, the current thread state is
NULL
. All thread states associated with this interpreter are destroyed. The global interpreter lock used by the target interpreter must be held before calling this function. No GIL is held when it returns.
Py_FinalizeEx()
will destroy all sub-interpreters that haven’t been explicitly destroyed at that point.
A Per-Interpreter GIL
¶
使用
Py_NewInterpreterFromConfig()
you can create a sub-interpreter that is completely isolated from other interpreters, including having its own GIL. The most important benefit of this isolation is that such an interpreter can execute Python code without being blocked by other interpreters or blocking any others. Thus a single Python process can truly take advantage of multiple CPU cores when running Python code. The isolation also encourages a different approach to concurrency than that of just using threads. (See
PEP 554
)。
Using an isolated interpreter requires vigilance in preserving that isolation. That especially means not sharing any objects or mutable state without guarantees about thread-safety. Even objects that are otherwise immutable (e.g.
None
,
(1, 5)
) can’t normally be shared because of the refcount. One simple but less-efficient approach around this is to use a global lock around all use of some state (or object). Alternately, effectively immutable objects (like integers or strings) can be made safe in spite of their refcounts by making them
immortal
. In fact, this has been done for the builtin singletons, small integers, and a number of other builtin objects.
If you preserve isolation then you will have access to proper multi-core computing without the complications that come with free-threading. Failure to preserve isolation will expose you to the full consequences of free-threading, including races and hard-to-debug crashes.
Aside from that, one of the main challenges of using multiple isolated interpreters is how to communicate between them safely (not break isolation) and efficiently. The runtime and stdlib do not provide any standard approach to this yet. A future stdlib module would help mitigate the effort of preserving isolation and expose effective tools for communicating (and sharing) data between interpreters.
3.12 版添加。
Bug 和告诫
¶
Because sub-interpreters (and the main interpreter) are part of the same process, the insulation between them isn’t perfect — for example, using low-level file operations like
os.close()
they can (accidentally or maliciously) affect each other’s open files. Because of the way extensions are shared between (sub-)interpreters, some extensions may not work properly; this is especially likely when using single-phase initialization or (static) global variables. It is possible to insert objects created in one sub-interpreter into a namespace of another (sub-)interpreter; this should be avoided if possible.
Special care should be taken to avoid sharing user-defined functions, methods, instances or classes between sub-interpreters, since import operations executed by such objects may affect the wrong (sub-)interpreter’s dictionary of loaded modules. It is equally important to avoid sharing objects from which the above are reachable.
Also note that combining this functionality with
PyGILState_*
APIs is delicate, because these APIs assume a bijection between Python thread states and OS-level threads, an assumption broken by the presence of sub-interpreters. It is highly recommended that you don’t switch sub-interpreters between a pair of matching
PyGILState_Ensure()
and
PyGILState_Release()
calls. Furthermore, extensions (such as
ctypes
) using these APIs to allow calling of Python code from non-Python created threads will probably be broken when using sub-interpreters.
异步通知
¶
A mechanism is provided to make asynchronous notifications to the main interpreter thread. These notifications take the form of a function pointer and a void pointer argument.
-
int
Py_AddPendingCall
(
int
(
*
func
)
(
void
*
)
,
void
*
arg
)
¶
-
属于
稳定 ABI (应用程序二进制接口)
.
Schedule a function to be called from the main interpreter thread. On success,
0
is returned and
func
is queued for being called in the main thread. On failure,
-1
is returned without setting any exception.
When successfully queued,
func
将是
eventually
called from the main interpreter thread with the argument
arg
. It will be called asynchronously with respect to normally running Python code, but with both these conditions met:
func
must return
0
当成功时，或
-1
on failure with an exception set.
func
won’t be interrupted to perform another asynchronous notification recursively, but it can still be interrupted to switch threads if the global interpreter lock is released.
This function doesn’t need a current thread state to run, and it doesn’t need the global interpreter lock.
To call this function in a subinterpreter, the caller must hold the GIL. Otherwise, the function
func
can be scheduled to be called from the wrong interpreter.
警告
This is a low-level function, only useful for very special cases. There is no guarantee that
func
will be called as quick as possible. If the main thread is busy executing a system call,
func
won’t be called before the system call returns. This function is generally
not
suitable for calling Python code from arbitrary C threads. Instead, use the
PyGILState API
.
Added in version 3.1.
3.9 版改变：
If this function is called in a subinterpreter, the function
func
is now scheduled to be called from the subinterpreter, rather than being called from the main interpreter. Each subinterpreter now has its own list of scheduled calls.
剖分析和跟踪
¶
The Python interpreter provides some low-level support for attaching profiling and execution tracing facilities. These are used for profiling, debugging, and coverage analysis tools.
This C interface allows the profiling or tracing code to avoid the overhead of calling through Python-level callable objects, making a direct C function call instead. The essential attributes of the facility have not changed; the interface allows trace functions to be installed per-thread, and the basic events reported to the trace function are the same as had been reported to the Python-level trace functions in previous versions.
-
typedef
int
(
*
Py_tracefunc
)
(
PyObject
*
obj
,
PyFrameObject
*
frame
,
int
what
,
PyObject
*
arg
)
¶
-
The type of the trace function registered using
PyEval_SetProfile()
and
PyEval_SetTrace()
. The first parameter is the object passed to the registration function as
obj
,
frame
is the frame object to which the event pertains,
what
is one of the constants
PyTrace_CALL
,
PyTrace_EXCEPTION
,
PyTrace_LINE
,
PyTrace_RETURN
,
PyTrace_C_CALL
,
PyTrace_C_EXCEPTION
,
PyTrace_C_RETURN
，或
PyTrace_OPCODE
，和
arg
depends on the value of
what
:
-
int
PyTrace_CALL
¶
-
值对于
what
parameter to a
Py_tracefunc
function when a new call to a function or method is being reported, or a new entry into a generator. Note that the creation of the iterator for a generator function is not reported as there is no control transfer to the Python bytecode in the corresponding frame.
-
int
PyTrace_EXCEPTION
¶
-
值对于
what
parameter to a
Py_tracefunc
function when an exception has been raised. The callback function is called with this value for
what
when after any bytecode is processed after which the exception becomes set within the frame being executed. The effect of this is that as exception propagation causes the Python stack to unwind, the callback is called upon return to each frame as the exception propagates. Only trace functions receives these events; they are not needed by the profiler.
-
int
PyTrace_LINE
¶
-
The value passed as the
what
parameter to a
Py_tracefunc
function (but not a profiling function) when a line-number event is being reported. It may be disabled for a frame by setting
f_trace_lines
to
0
on that frame.
-
int
PyTrace_RETURN
¶
-
The value for the
what
参数用于
Py_tracefunc
functions when a call is about to return.
-
int
PyTrace_C_CALL
¶
-
The value for the
what
参数用于
Py_tracefunc
functions when a C function is about to be called.
-
int
PyTrace_C_EXCEPTION
¶
-
The value for the
what
参数用于
Py_tracefunc
functions when a C function has raised an exception.
-
int
PyTrace_C_RETURN
¶
-
The value for the
what
参数用于
Py_tracefunc
functions when a C function has returned.
-
int
PyTrace_OPCODE
¶
-
The value for the
what
参数用于
Py_tracefunc
functions (but not profiling functions) when a new opcode is about to be executed. This event is not emitted by default: it must be explicitly requested by setting
f_trace_opcodes
to
1
on the frame.
-
void
PyEval_SetProfile
(
Py_tracefunc
func
,
PyObject
*
obj
)
¶
-
Set the profiler function to
func
。
obj
parameter is passed to the function as its first parameter, and may be any Python object, or
NULL
. If the profile function needs to maintain state, using a different value for
obj
for each thread provides a convenient and thread-safe place to store it. The profile function is called for all monitored events except
PyTrace_LINE
PyTrace_OPCODE
and
PyTrace_EXCEPTION
.
另请参阅
sys.setprofile()
函数。
The caller must hold the
GIL
.
-
void
PyEval_SetProfileAllThreads
(
Py_tracefunc
func
,
PyObject
*
obj
)
¶
-
像
PyEval_SetProfile()
but sets the profile function in all running threads belonging to the current interpreter instead of the setting it only on the current thread.
The caller must hold the
GIL
.
As
PyEval_SetProfile()
, this function ignores any exceptions raised while setting the profile functions in all threads.
3.12 版添加。
-
void
PyEval_SetTrace
(
Py_tracefunc
func
,
PyObject
*
obj
)
¶
-
Set the tracing function to
func
. This is similar to
PyEval_SetProfile()
, except the tracing function does receive line-number events and per-opcode events, but does not receive any event related to C function objects being called. Any trace function registered using
PyEval_SetTrace()
will not receive
PyTrace_C_CALL
,
PyTrace_C_EXCEPTION
or
PyTrace_C_RETURN
as a value for the
what
参数。
另请参阅
sys.settrace()
函数。
The caller must hold the
GIL
.
-
void
PyEval_SetTraceAllThreads
(
Py_tracefunc
func
,
PyObject
*
obj
)
¶
-
像
PyEval_SetTrace()
but sets the tracing function in all running threads belonging to the current interpreter instead of the setting it only on the current thread.
The caller must hold the
GIL
.
As
PyEval_SetTrace()
, this function ignores any exceptions raised while setting the trace functions in all threads.
3.12 版添加。
Reference tracing
¶
3.13 版添加。
-
typedef
int
(
*
PyRefTracer
)
(
PyObject
*
,
int
event
,
void
*
data
)
¶
-
The type of the trace function registered using
PyRefTracer_SetTracer()
. The first parameter is a Python object that has been just created (when
event
被设为
PyRefTracer_CREATE
) or about to be destroyed (when
event
被设为
PyRefTracer_DESTROY
)。
data
argument is the opaque pointer that was provided when
PyRefTracer_SetTracer()
was called.
3.13 版添加。
-
int
PyRefTracer_CREATE
¶
-
The value for the
event
参数用于
PyRefTracer
functions when a Python object has been created.
-
int
PyRefTracer_DESTROY
¶
-
The value for the
event
参数用于
PyRefTracer
functions when a Python object has been destroyed.
-
int
PyRefTracer_SetTracer
(
PyRefTracer
tracer
,
void
*
data
)
¶
-
Register a reference tracer function. The function will be called when a new Python has been created or when an object is going to be destroyed. If
data
is provided it must be an opaque pointer that will be provided when the tracer function is called. Return
0
on success. Set an exception and return
-1
当出错时。
Not that tracer functions
不必
create Python objects inside or otherwise the call will be re-entrant. The tracer also
不必
clear any existing exception or set an exception. The GIL will be held every time the tracer function is called.
The GIL must be held when calling this function.
3.13 版添加。
-
PyRefTracer
PyRefTracer_GetTracer
(
void
*
*
data
)
¶
-
Get the registered reference tracer function and the value of the opaque data pointer that was registered when
PyRefTracer_SetTracer()
was called. If no tracer was registered this function will return NULL and will set the
data
pointer to NULL.
The GIL must be held when calling this function.
3.13 版添加。
高级调试器支持
¶
These functions are only intended to be used by advanced debugging tools.
-
PyInterpreterState
*
PyInterpreterState_Head
(
)
¶
-
Return the interpreter state object at the head of the list of all such objects.
-
PyInterpreterState
*
PyInterpreterState_Main
(
)
¶
-
Return the main interpreter state object.
-
PyInterpreterState
*
PyInterpreterState_Next
(
PyInterpreterState
*
interp
)
¶
-
Return the next interpreter state object after
interp
from the list of all such objects.
-
PyThreadState
*
PyInterpreterState_ThreadHead
(
PyInterpreterState
*
interp
)
¶
-
Return the pointer to the first
PyThreadState
object in the list of threads associated with the interpreter
interp
.
-
PyThreadState
*
PyThreadState_Next
(
PyThreadState
*
tstate
)
¶
-
Return the next thread state object after
tstate
from the list of all such objects belonging to the same
PyInterpreterState
对象。
线程局部存储支持
¶
The Python interpreter provides low-level support for thread-local storage (TLS) which wraps the underlying native TLS implementation to support the Python-level thread local storage API (
threading.local
). The CPython C level APIs are similar to those offered by pthreads and Windows: use a thread key and functions to associate a
void
*
value per thread.
The GIL does
not
need to be held when calling these functions; they supply their own locking.
注意，
Python.h
does not include the declaration of the TLS APIs, you need to include
pythread.h
to use thread-local storage.
注意
None of these API functions handle memory management on behalf of the
void
*
values. You need to allocate and deallocate them yourself. If the
void
*
values happen to be
PyObject
*
, these functions don’t do refcount operations on them either.
TSS (线程特定存储) API
¶
TSS API is introduced to supersede the use of the existing TLS API within the CPython interpreter. This API uses a new type
Py_tss_t
而不是
int
to represent thread keys.
3.7 版添加。
另请参阅
“A New C-API for Thread-Local Storage in CPython” (
PEP 539
)
-
type
Py_tss_t
¶
-
This data structure represents the state of a thread key, the definition of which may depend on the underlying TLS implementation, and it has an internal field representing the key’s initialization state. There are no public members in this structure.
当
Py_LIMITED_API
is not defined, static allocation of this type by
Py_tss_NEEDS_INIT
is allowed.
-
Py_tss_NEEDS_INIT
¶
-
This macro expands to the initializer for
Py_tss_t
variables. Note that this macro won’t be defined with
Py_LIMITED_API
.
动态分配
¶
Dynamic allocation of the
Py_tss_t
, required in extension modules built with
Py_LIMITED_API
, where static allocation of this type is not possible due to its implementation being opaque at build time.
-
Py_tss_t
*
PyThread_tss_alloc
(
)
¶
-
属于
稳定 ABI (应用程序二进制接口)
since version 3.7.
Return a value which is the same state as a value initialized with
Py_tss_NEEDS_INIT
，或
NULL
in the case of dynamic allocation failure.
-
void
PyThread_tss_free
(
Py_tss_t
*
key
)
¶
-
属于
稳定 ABI (应用程序二进制接口)
since version 3.7.
Free the given
key
allocated by
PyThread_tss_alloc()
, after first calling
PyThread_tss_delete()
to ensure any associated thread locals have been unassigned. This is a no-op if the
key
自变量为
NULL
.
注意
A freed key becomes a dangling pointer. You should reset the key to
NULL
.
方法
¶
参数
key
of these functions must not be
NULL
. Moreover, the behaviors of
PyThread_tss_set()
and
PyThread_tss_get()
are undefined if the given
Py_tss_t
has not been initialized by
PyThread_tss_create()
.
-
int
PyThread_tss_is_created
(
Py_tss_t
*
key
)
¶
-
属于
稳定 ABI (应用程序二进制接口)
since version 3.7.
Return a non-zero value if the given
Py_tss_t
has been initialized by
PyThread_tss_create()
.
-
int
PyThread_tss_create
(
Py_tss_t
*
key
)
¶
-
属于
稳定 ABI (应用程序二进制接口)
since version 3.7.
Return a zero value on successful initialization of a TSS key. The behavior is undefined if the value pointed to by the
key
argument is not initialized by
Py_tss_NEEDS_INIT
. This function can be called repeatedly on the same key – calling it on an already initialized key is a no-op and immediately returns success.
-
void
PyThread_tss_delete
(
Py_tss_t
*
key
)
¶
-
属于
稳定 ABI (应用程序二进制接口)
since version 3.7.
Destroy a TSS key to forget the values associated with the key across all threads, and change the key’s initialization state to uninitialized. A destroyed key is able to be initialized again by
PyThread_tss_create()
. This function can be called repeatedly on the same key – calling it on an already destroyed key is a no-op.
-
int
PyThread_tss_set
(
Py_tss_t
*
key
,
void
*
值
)
¶
-
属于
稳定 ABI (应用程序二进制接口)
since version 3.7.
Return a zero value to indicate successfully associating a
void
*
value with a TSS key in the current thread. Each thread has a distinct mapping of the key to a
void
*
值。
-
void
*
PyThread_tss_get
(
Py_tss_t
*
key
)
¶
-
属于
稳定 ABI (应用程序二进制接口)
since version 3.7.
返回
void
*
value associated with a TSS key in the current thread. This returns
NULL
if no value is associated with the key in the current thread.
TLS (线程本地存储) API
¶
注意
This version of the API does not support platforms where the native TLS key is defined in a way that cannot be safely cast to
int
. On such platforms,
PyThread_create_key()
will return immediately with a failure status, and the other TLS functions will all be no-ops on such platforms.
Due to the compatibility problem noted above, this version of the API should not be used in new code.
-
int
PyThread_create_key
(
)
¶
-
属于
稳定 ABI (应用程序二进制接口)
.
-
void
PyThread_delete_key
(
int
key
)
¶
-
属于
稳定 ABI (应用程序二进制接口)
.
-
int
PyThread_set_key_value
(
int
key
,
void
*
值
)
¶
-
属于
稳定 ABI (应用程序二进制接口)
.
-
void
*
PyThread_get_key_value
(
int
key
)
¶
-
属于
稳定 ABI (应用程序二进制接口)
.
-
void
PyThread_delete_key_value
(
int
key
)
¶
-
属于
稳定 ABI (应用程序二进制接口)
.
-
void
PyThread_ReInitTLS
(
)
¶
-
属于
稳定 ABI (应用程序二进制接口)
.
同步原语
¶
The C-API provides a basic mutual exclusion lock.
-
type
PyMutex
¶
-
A mutual exclusion lock. The
PyMutex
should be initialized to zero to represent the unlocked state. For example:
实例化的
PyMutex
should not be copied or moved. Both the contents and address of a
PyMutex
are meaningful, and it must remain at a fixed, writable location in memory.
注意
A
PyMutex
currently occupies one byte, but the size should be considered unstable. The size may change in future Python releases without a deprecation period.
3.13 版添加。
-
void
PyMutex_Lock
(
PyMutex
*
m
)
¶
-
Lock mutex
m
. If another thread has already locked it, the calling thread will block until the mutex is unlocked. While blocked, the thread will temporarily release the
GIL
if it is held.
3.13 版添加。
-
void
PyMutex_Unlock
(
PyMutex
*
m
)
¶
-
Unlock mutex
m
. The mutex must be locked — otherwise, the function will issue a fatal error.
3.13 版添加。
Python Critical Section API
¶
The critical section API provides a deadlock avoidance layer on top of per-object locks for
free-threaded
CPython. They are intended to replace reliance on the
全局解释器锁
, and are no-ops in versions of Python with the global interpreter lock.
Critical sections avoid deadlocks by implicitly suspending active critical sections and releasing the locks during calls to
PyEval_SaveThread()
。当
PyEval_RestoreThread()
is called, the most recent critical section is resumed, and its locks reacquired. This means the critical section API provides weaker guarantees than traditional locks – they are useful because their behavior is similar to the
GIL
.
The functions and structs used by the macros are exposed for cases where C macros are not available. They should only be used as in the given macro expansions. Note that the sizes and contents of the structures may change in future Python versions.
注意
Operations that need to lock two objects at once must use
Py_BEGIN_CRITICAL_SECTION2
. You
cannot
use nested critical sections to lock more than one object at once, because the inner critical section may suspend the outer critical sections. This API does not provide a way to lock more than two objects at once.
用法范例：
static PyObject *
set_field(MyObject *self, PyObject *value)
{
Py_BEGIN_CRITICAL_SECTION(self);
Py_SETREF(self->field, Py_XNewRef(value));
Py_END_CRITICAL_SECTION();
Py_RETURN_NONE;
}
In the above example,
Py_SETREF
调用
Py_DECREF
, which can call arbitrary code through an object’s deallocation function. The critical section API avoids potential deadlocks due to reentrancy and lock ordering by allowing the runtime to temporarily suspend the critical section if the code triggered by the finalizer blocks and calls
PyEval_SaveThread()
.
-
Py_BEGIN_CRITICAL_SECTION
(
op
)
¶
-
Acquires the per-object lock for the object
op
and begins a critical section.
In the free-threaded build, this macro expands to:
{
PyCriticalSection _py_cs;
PyCriticalSection_Begin(&_py_cs, (PyObject*)(op))
In the default build, this macro expands to
{
.
3.13 版添加。
-
Py_END_CRITICAL_SECTION
(
)
¶
-
Ends the critical section and releases the per-object lock.
In the free-threaded build, this macro expands to:
PyCriticalSection_End(&_py_cs);
}
In the default build, this macro expands to
}
.
3.13 版添加。
-
Py_BEGIN_CRITICAL_SECTION2
(
a
,
b
)
¶
-
Acquires the per-objects locks for the objects
a
and
b
and begins a critical section. The locks are acquired in a consistent order (lowest address first) to avoid lock ordering deadlocks.
In the free-threaded build, this macro expands to:
{
PyCriticalSection2 _py_cs2;
PyCriticalSection2_Begin(&_py_cs2, (PyObject*)(a), (PyObject*)(b))
In the default build, this macro expands to
{
.
3.13 版添加。
-
Py_END_CRITICAL_SECTION2
(
)
¶
-
Ends the critical section and releases the per-object locks.
In the free-threaded build, this macro expands to:
PyCriticalSection2_End(&_py_cs2);
}
In the default build, this macro expands to
}
.
3.13 版添加。
