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.
---------
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.
This interns the strings for `co_filename`, `co_name`, and `co_qualname`
on codeobjects in the free-threaded build. This partially addresses a
reference counting bottleneck when creating closures concurrently. The
closures take the name and qualified name from the code object.
Moving this code under the `pathlib` package makes it quite a lot easier
to backport in the `pathlib-abc` PyPI package. It was a bit foolish of me
to add it to `glob` in the first place.
Also add `translate()` to `__all__` in `glob`. This function is new in
3.13, so there's no NEWS needed.
This PR adds the ability to enable the GIL if it was disabled at
interpreter startup, and modifies the multi-phase module initialization
path to enable the GIL when loading a module, unless that module's spec
includes a slot indicating it can run safely without the GIL.
PEP 703 called the constant for the slot `Py_mod_gil_not_used`; I went
with `Py_MOD_GIL_NOT_USED` for consistency with gh-104148.
A warning will be issued up to once per interpreter for the first
GIL-using module that is loaded. If `-v` is given, a shorter message
will be printed to stderr every time a GIL-using module is loaded
(including the first one that issues a warning).
This is unsupported. Note that `skip_unless_reliable_fork()` checks for
the conditions used by the decorators that were removed, along with checking
for TSAN.
The function returns `True` or `False` depending on whether the GIL is
currently enabled. In the default build, it always returns `True`
because the GIL is always enabled.
Most module names are interned and immortalized, but the main
module was not. This partially addresses a scaling bottleneck in the
free-threaded when creating closure concurrently in the main module.
The module itself is a thin wrapper around calls to functions in
`Python/codecs.c`, so that's where the meaningful changes happened:
- Move codecs-related state that lives on `PyInterpreterState` to a
struct declared in `pycore_codecs.h`.
- In free-threaded builds, add a mutex to `codecs_state` to synchronize
operations on `search_path`. Because `search_path_mutex` is used as a
normal mutex and not a critical section, we must be extremely careful
with operations called while holding it.
- The codec registry is explicitly initialized as part of
`_PyUnicode_InitEncodings` to simplify thread-safety.
The `time.sleep()` call should happen before the GC to give the worker
threads time to clean-up their remaining references to objs.
Additionally, use `support.gc_collect()` instead of `gc.collect()`
just in case the extra GC calls matter.
Serhiy Storchaka
Tian Gao
Pablo Galindo Salgado
Tim Peters
Davide Rizzo
Eric Snow
Ken Jin
Nikita Sobolev
dependabot[bot]
wim glenn
Mark Shannon
Irit Katriel
Victor Stinner
Raymond Hettinger
Brandt Bucher
Sam Gross
Barney Gale
mpage
Savannah Ostrowski
Erlend E. Aasland
Ned Batchelder
Brett Simmers
Carl Meyer
Alexander Kanavin
Jelle Zijlstra
Nice Zombies
Dino Viehland
Steve Dower
infohash