This function no longer makes sense, since its runtime parameter is
no longer used. Use directly _PyThreadState_GET() and
_PyInterpreterState_GET() instead.
his involves moving tp_dict, tp_bases, and tp_mro to PyInterpreterState, in the same way we did for tp_subclasses. Those three fields are effectively const for builtin static types (unlike tp_subclasses). In theory we only need to make their values immortal, along with their contents. However, that isn't such a simple proposition. (See gh-103823.) In the meantime the simplest solution is to move the fields into the interpreter.
One alternative is to statically allocate the values, but that's its own can of worms.
This is strictly about moving the "obmalloc" runtime state from
`_PyRuntimeState` to `PyInterpreterState`. Doing so improves isolation
between interpreters, specifically most of the memory (incl. objects)
allocated for each interpreter's use. This is important for a
per-interpreter GIL, but such isolation is valuable even without it.
FWIW, a per-interpreter obmalloc is the proverbial
canary-in-the-coalmine when it comes to the isolation of objects between
interpreters. Any object that leaks (unintentionally) to another
interpreter is highly likely to cause a crash (on debug builds at
least). That's a useful thing to know, relative to interpreter
isolation.
Core static types will continue to use the global value. All other types
will use the per-interpreter value. They all share the same range, where
the global types use values < 2^16 and each interpreter uses values
higher than that.
This speeds up `super()` (by around 85%, for a simple one-level
`super().meth()` microbenchmark) by avoiding allocation of a new
single-use `super()` object on each use.
We replace _PyRuntime.tstate_current with a thread-local variable. As part of this change, we add a _Py_thread_local macro in pyport.h (only for the core runtime) to smooth out the compiler differences. The main motivation here is in support of a per-interpreter GIL, but this change also provides some performance improvement opportunities.
Note that we do not provide a fallback to the thread-local, either falling back to the old tstate_current or to thread-specific storage (PyThread_tss_*()). If that proves problematic then we can circle back. I consider it unlikely, but will run the buildbots to double-check.
Also note that this does not change any of the code related to the GILState API, where it uses a thread state stored in thread-specific storage. I suspect we can combine that with _Py_tss_tstate (from here). However, that can be addressed separately and is not urgent (nor critical).
(While this change was mostly done independently, I did take some inspiration from earlier (~2020) work by @markshannon (main...markshannon:threadstate_in_tls) and @vstinner (#23976).)
This is the implementation of PEP683
Motivation:
The PR introduces the ability to immortalize instances in CPython which bypasses reference counting. Tagging objects as immortal allows up to skip certain operations when we know that the object will be around for the entire execution of the runtime.
Note that this by itself will bring a performance regression to the runtime due to the extra reference count checks. However, this brings the ability of having truly immutable objects that are useful in other contexts such as immutable data sharing between sub-interpreters.
* The majority of the monitoring code is in instrumentation.c
* The new instrumentation bytecodes are in bytecodes.c
* legacy_tracing.c adapts the new API to the old sys.setrace and sys.setprofile APIs
The function is like Py_AtExit() but for a single interpreter. This is a companion to the atexit module's register() function, taking a C callback instead of a Python one.
We also update the _xxinterpchannels module to use _Py_AtExit(), which is the motivating case. (This is inspired by pain points felt while working on gh-101660.)
Sharing mutable (or non-immortal) objects between interpreters is generally not safe. We can work around that but not easily.
There are two restrictions that are critical for objects that break interpreter isolation.
The first is that the object's state be guarded by a global lock. For now the GIL meets this requirement, but a granular global lock is needed once we have a per-interpreter GIL.
The second restriction is that the object (and, for a container, its items) be deallocated/resized only when the interpreter in which it was allocated is the current one. This is because every interpreter has (or will have, see gh-101660) its own object allocator. Deallocating an object with a different allocator can cause crashes.
The dict for the cache of module defs is completely internal, which simplifies what we have to do to meet those requirements. To do so, we do the following:
* add a mechanism for re-using a temporary thread state tied to the main interpreter in an arbitrary thread
* add _PyRuntime.imports.extensions.main_tstate`
* add _PyThreadState_InitDetached() and _PyThreadState_ClearDetached() (pystate.c)
* add _PyThreadState_BindDetached() and _PyThreadState_UnbindDetached() (pystate.c)
* make sure the cache dict (_PyRuntime.imports.extensions.dict) and its items are all owned by the main interpreter)
* add a placeholder using for a granular global lock
Note that the cache is only used for legacy extension modules and not for multi-phase init modules.
https://github.com/python/cpython/issues/100227
We can revisit the options for keeping it global later, if desired. For now the approach seems quite complex, so we've gone with the simpler isolation solution in the meantime.
https://github.com/python/cpython/issues/100227
This reverts commit 87be8d9.
This approach to keeping the interned strings safe is turning out to be too complex for my taste (due to obmalloc isolation). For now I'm going with the simpler solution, making the dict per-interpreter. We can revisit that later if we want a sharing solution.
This is effectively two changes. The first (the bulk of the change) is where we add _Py_AddToGlobalDict() (and _PyRuntime.cached_objects.main_tstate, etc.). The second (much smaller) change is where we update PyUnicode_InternInPlace() to use _Py_AddToGlobalDict() instead of calling PyDict_SetDefault() directly.
Basically, _Py_AddToGlobalDict() is a wrapper around PyDict_SetDefault() that should be used whenever we need to add a value to a runtime-global dict object (in the few cases where we are leaving the container global rather than moving it to PyInterpreterState, e.g. the interned strings dict). _Py_AddToGlobalDict() does all the necessary work to make sure the target global dict is shared safely between isolated interpreters. This is especially important as we move the obmalloc state to each interpreter (gh-101660), as well as, potentially, the GIL (PEP 684).
https://github.com/python/cpython/issues/100227
* Eliminate all remaining uses of Py_SIZE and Py_SET_SIZE on PyLongObject, adding asserts.
* Change layout of size/sign bits in longobject to support future addition of immortal ints and tagged medium ints.
* Add functions to hide some internals of long object, and for setting sign and digit count.
* Replace uses of IS_MEDIUM_VALUE macro with _PyLong_IsCompact().
Moving it valuable with a per-interpreter GIL. However, it is also useful without one, since it allows us to identify refleaks within a single interpreter or where references are escaping an interpreter. This becomes more important as we move the obmalloc state to PyInterpreterState.
https://github.com/python/cpython/issues/102304
Prior to this change, errors in _Py_NewInterpreterFromConfig() were always fatal. Instead, callers should be able to handle such errors and keep going. That's what this change supports. (This was an oversight in the original implementation of _Py_NewInterpreterFromConfig().) Note that the existing [fatal] behavior of the public Py_NewInterpreter() is preserved.
https://github.com/python/cpython/issues/98608
The essentially eliminates the global variable, with the associated benefits. This is also a precursor to isolating this bit of state to PyInterpreterState.
Folks that currently read _Py_RefTotal directly would have to start using _Py_GetGlobalRefTotal() instead.
https://github.com/python/cpython/issues/102304
