The designated initializer syntax in static inline functions in pycore_backoff.h
causes problems for C++ or MSVC users who aren't yet using C++20.
While internal, pycore_backoff.h is included (indirectly, via pycore_code.h)
by some key 3rd party software that does so for speed.
This is *not* sufficient for the final 3.13 release, but it will do for beta 1:
- What's new entry
- Updated changelog entry (news blurb)
- Mention the proxy for f_globals in the datamodel and Python frame object docs
This doesn't have any C API details (what's new refers to the PEP).
Use the new public Raw functions:
* _PyTime_PerfCounterUnchecked() with PyTime_PerfCounterRaw()
* _PyTime_TimeUnchecked() with PyTime_TimeRaw()
* _PyTime_MonotonicUnchecked() with PyTime_MonotonicRaw()
Remove internal functions:
* _PyTime_PerfCounterUnchecked()
* _PyTime_TimeUnchecked()
* _PyTime_MonotonicUnchecked()
* Fix mangle_from_ default value in email.policy.Policy.__doc__
The docstring says it defaults to True, but it actually defaults
to False. Only the Compat32 subclass overrides that.
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Co-authored-by: Nikita Sobolev <mail@sobolevn.me>
For converting large ints to strings, CPython invokes a function in _pylong.py,
which uses the decimal module to implement an asymptotically waaaaay
sub-quadratic algorithm. But if the C decimal module isn't available, CPython
uses _pydecimal.py instead. Which in turn frequently does str(int). If the int
is very large, _pylong ends up doing the work, which in turn asks decimal to do
"big" arithmetic, which in turn calls str(big_int), which in turn ... it can
become infinite mutual recursion.
This change introduces a different int->str function that doesn't use decimal.
It's asymptotically worse, "Karatsuba time" instead of quadratic time, so
still a huge improvement. _pylong switches to that when the C decimal isn't
available. It is also used for not too large integers (less than 450_000 bits),
where it is faster (up to 2 times for 30_000 bits) than the asymptotically
better implementation that uses the C decimal.
Co-authored-by: Tim Peters <tim.peters@gmail.com>
* Initial stab.
* Test the tentative fix. Hangs "forever" without this change.
* Move the new test to a better spot.
* New comment to explain why _convert_to_str allows any poewr of 10.
* Fixed a comment, and fleshed out an existing test that appeared unfinished.
* Added temporary asserts. Or maybe permanent ;-)
* Update Lib/_pydecimal.py
Co-authored-by: Serhiy Storchaka <storchaka@gmail.com>
* Remove the new _convert_to_str().
Serhiy and I independently concluded that exact powers of 10
aren't possible in these contexts, so just checking the
string length is sufficient.
* At least for now, add the asserts to the other block too.
* 📜🤖 Added by blurb_it.
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Co-authored-by: Serhiy Storchaka <storchaka@gmail.com>
Co-authored-by: blurb-it[bot] <43283697+blurb-it[bot]@users.noreply.github.com>
We have only been tracking each module's PyModuleDef. However, there are some problems with that. For example, in some cases we load single-phase init extension modules from def->m_base.m_init or def->m_base.m_copy, but if multiple modules share a def then we can end up with unexpected behavior.
With this change, we track the following:
* PyModuleDef (same as before)
* for some modules, its init function or a copy of its __dict__, but specific to that module
* whether it is a builtin/core module or a "dynamic" extension
* the interpreter (ID) that owns the cached __dict__ (only if cached)
This also makes it easier to remember the module's kind (e.g. single-phase init) and if loading it previously failed, which I'm doing separately.
* Add CALL_PY_GENERAL, CALL_BOUND_METHOD_GENERAL and call CALL_NON_PY_GENERAL specializations.
* Remove CALL_PY_WITH_DEFAULTS specialization
* Use CALL_NON_PY_GENERAL in more cases when otherwise failing to specialize
Use _PyDeadline_Init() and _PyDeadline_Get() in
EnterNonRecursiveMutex() of thread_nt.h.
_PyDeadline_Get() uses the monotonic clock which is now the same as
the perf counter clock on all platforms. So this change does not
cause any behavior change. It just reuses existing helper functions.
Pablo Galindo Salgado
Xie Yanbo
Serhiy Storchaka
Tian Gao
Alyssa Coghlan
Lysandros Nikolaou
Erlend E. Aasland
Pieter Eendebak
Terry Jan Reedy
Nikita Sobolev
Russell Keith-Magee
Gregory P. Smith
Barney Gale
Guido van Rossum
Raymond Hettinger
Ryan Batchelder
Victor Stinner
Hugo van Kemenade
wim glenn
Tim Peters
Davide Rizzo
Eric Snow
Ken Jin
dependabot[bot]
Mark Shannon
Irit Katriel