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.
* 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().
Make docstrings for `as_integer_ratio` consistent across types, and document that
the returned pair is always normalized (coprime integers, with positive denominator).
---------
Co-authored-by: Owain Davies <116417456+OTheDev@users.noreply.github.com>
Co-authored-by: Mark Dickinson <dickinsm@gmail.com>
Fix the behaviour of the `__sizeof__` method (and hence the results returned by `sys.getsizeof`) for subclasses of `int`. Previously, `int` subclasses gave identical results to the `int` base class, ignoring the presence of the instance dictionary.
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* Issue: gh-101266
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This PR fixes object allocation in long_subtype_new to ensure that there's at least one digit in all cases, and makes sure that the value of that digit is copied over from the source long.
Needs backport to 3.11, but not any further: the change to require at least one digit was only introduced for Python 3.11.
Fixes#101037.
Fixes behaviour where int (and subtypes like bool) __sizeof__ under-reports true size as it did not take into account the size 1 `ob_digit` array for the zero int.
Co-authored-by: Mark Dickinson <dickinsm@gmail.com>
This improves the lives of type annotation users of `float` - which type checkers implicitly treat as `int|float` because that is what most code actually wants. Before this change a `.is_integer()` method could not be assumed to exist on things annotated as `: float` due to the method not existing on both types.
* Properly decref on _pylong import error.
* Improve the error message on _pylong TypeError.
* Fix the assertion error in pydebug builds to be a TypeError.
* Tie the return value comments together.
These are minor followups to issues not caught among the reviewers on
https://github.com/python/cpython/pull/96673.
Add Python implementations of certain longobject.c functions. These use
asymptotically faster algorithms that can be used for operations on
integers with many digits. In those cases, the performance overhead of
the Python implementation is not significant since the asymptotic
behavior is what dominates runtime. Functions provided by this module
should be considered private and not part of any public API.
Co-author: Tim Peters <tim.peters@gmail.com>
Co-author: Mark Dickinson <dickinsm@gmail.com>
Co-author: Bjorn Martinsson
It had to live as a global outside of PyConfig for stable ABI reasons in
the pre-3.12 backports.
This removes the `_Py_global_config_int_max_str_digits` and gets rid of
the equivalent field in the internal `struct _is PyInterpreterState` as
code can just use the existing nested config struct within that.
Adds tests to verify unique settings and configs in subinterpreters.
This is a preliminary PR to refactor `PyLong_FromString` which is currently quite messy and has spaghetti like code that mixes up different concerns as well as duplicating logic.
In particular:
- `PyLong_FromString` now only handles sign, base and prefix detection and calls a new function `long_from_string_base` to parse the main body of the string.
- The `long_from_string_base` function handles all string validation and then calls `long_from_binary_base` or a new function `long_from_non_binary_base` to construct the actual `PyLong`.
- The existing `long_from_binary_base` function is simplified by factoring duplicated logic to `long_from_string_base`.
- The new function `long_from_non_binary_base` factors out much of the code from `PyLong_FromString` including in particular the quadratic algorithm reffered to in gh-95778 so that this can be seen separately from unrelated concerns such as string validation.
Converting a large enough `int` to a decimal string raises `ValueError` as expected. However, the raise comes _after_ the quadratic-time base-conversion algorithm has run to completion. For effective DOS prevention, we need some kind of check before entering the quadratic-time loop. Oops! =)
The quick fix: essentially we catch _most_ values that exceed the threshold up front. Those that slip through will still be on the small side (read: sufficiently fast), and will get caught by the existing check so that the limit remains exact.
The justification for the current check. The C code check is:
```c
max_str_digits / (3 * PyLong_SHIFT) <= (size_a - 11) / 10
```
In GitHub markdown math-speak, writing $M$ for `max_str_digits`, $L$ for `PyLong_SHIFT` and $s$ for `size_a`, that check is:
$$\left\lfloor\frac{M}{3L}\right\rfloor \le \left\lfloor\frac{s - 11}{10}\right\rfloor$$
From this it follows that
$$\frac{M}{3L} < \frac{s-1}{10}$$
hence that
$$\frac{L(s-1)}{M} > \frac{10}{3} > \log_2(10).$$
So
$$2^{L(s-1)} > 10^M.$$
But our input integer $a$ satisfies $|a| \ge 2^{L(s-1)}$, so $|a|$ is larger than $10^M$. This shows that we don't accidentally capture anything _below_ the intended limit in the check.
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* Issue: gh-95778
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Co-authored-by: Gregory P. Smith [Google LLC] <greg@krypto.org>
Integer to and from text conversions via CPython's bignum `int` type is not safe against denial of service attacks due to malicious input. Very large input strings with hundred thousands of digits can consume several CPU seconds.
This PR comes fresh from a pile of work done in our private PSRT security response team repo.
Signed-off-by: Christian Heimes [Red Hat] <christian@python.org>
Tons-of-polishing-up-by: Gregory P. Smith [Google] <greg@krypto.org>
Reviews via the private PSRT repo via many others (see the NEWS entry in the PR).
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* Issue: gh-95778
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I wrote up [a one pager for the release managers](https://docs.google.com/document/d/1KjuF_aXlzPUxTK4BMgezGJ2Pn7uevfX7g0_mvgHlL7Y/edit#). Much of that text wound up in the Issue. Backports PRs already exist. See the issue for links.
This is the first of several precursors to storing tp_subclasses (and tp_weaklist) on the interpreter state for static builtin types.
We do the following:
* add `_PyStaticType_InitBuiltin()`
* add `_Py_TPFLAGS_STATIC_BUILTIN`
* set it on all static builtin types in `_PyStaticType_InitBuiltin()`
* shuffle some code around to be able to use _PyStaticType_InitBuiltin()
* rename `_PyStructSequence_InitType()` to `_PyStructSequence_InitBuiltinWithFlags()`
* add `_PyStructSequence_InitBuiltin()`.
Python 3.11 now uses C11 standard which adds static_assert()
to <assert.h>.
* In pytime.c, replace Py_BUILD_ASSERT() with preprocessor checks on
SIZEOF_TIME_T with #error.
* On macOS, py_mach_timebase_info() now accepts timebase members with
the same size than _PyTime_t.
* py_get_monotonic_clock() now saturates GetTickCount64() to
_PyTime_MAX: GetTickCount64() is unsigned, whereas _PyTime_t is
signed.
We're no longer using _Py_IDENTIFIER() (or _Py_static_string()) in any core CPython code. It is still used in a number of non-builtin stdlib modules.
The replacement is: PyUnicodeObject (not pointer) fields under _PyRuntimeState, statically initialized as part of _PyRuntime. A new _Py_GET_GLOBAL_IDENTIFIER() macro facilitates lookup of the fields (along with _Py_GET_GLOBAL_STRING() for non-identifier strings).
https://bugs.python.org/issue46541#msg411799 explains the rationale for this change.
The core of the change is in:
* (new) Include/internal/pycore_global_strings.h - the declarations for the global strings, along with the macros
* Include/internal/pycore_runtime_init.h - added the static initializers for the global strings
* Include/internal/pycore_global_objects.h - where the struct in pycore_global_strings.h is hooked into _PyRuntimeState
* Tools/scripts/generate_global_objects.py - added generation of the global string declarations and static initializers
I've also added a --check flag to generate_global_objects.py (along with make check-global-objects) to check for unused global strings. That check is added to the PR CI config.
The remainder of this change updates the core code to use _Py_GET_GLOBAL_IDENTIFIER() instead of _Py_IDENTIFIER() and the related _Py*Id functions (likewise for _Py_GET_GLOBAL_STRING() instead of _Py_static_string()). This includes adding a few functions where there wasn't already an alternative to _Py*Id(), replacing the _Py_Identifier * parameter with PyObject *.
The following are not changed (yet):
* stop using _Py_IDENTIFIER() in the stdlib modules
* (maybe) get rid of _Py_IDENTIFIER(), etc. entirely -- this may not be doable as at least one package on PyPI using this (private) API
* (maybe) intern the strings during runtime init
https://bugs.python.org/issue46541
Added new internal functions to compute mod without also computing the quotient.
The loops can be leaner then, which leads to modestly but reliably faster execution in contexts that know they don't need the quotient.
Code by Jeremiah Vivian (Pascual).
* bpo-46504: faster code for trial quotient in x_divrem()
This brings x_divrem() back into synch with x_divrem1(), which was changed
in bpo-46406 to generate faster code to find machine-word division
quotients and remainders. Modern processors compute both with a single
machine instruction, but convincing C to exploit that requires writing
_less_ "clever" C code.
The _curses module now creates its ncurses_version type as a heap
type using PyStructSequence_NewType(), rather than using a static
type.
* Move _PyStructSequence_FiniType() definition to pycore_structseq.h.
* test.pythoninfo: log curses.ncurses_version.
Add _PyStructSequence_FiniType() and _PyStaticType_Dealloc()
functions to finalize a structseq static type in Py_Finalize().
Currrently, these functions do nothing if Python is built in release
mode.
Clear static types:
* AsyncGenHooksType: sys.set_asyncgen_hooks()
* FlagsType: sys.flags
* FloatInfoType: sys.float_info
* Hash_InfoType: sys.hash_info
* Int_InfoType: sys.int_info
* ThreadInfoType: sys.thread_info
* UnraisableHookArgsType: sys.unraisablehook
* VersionInfoType: sys.version
* WindowsVersionType: sys.getwindowsversion()
x_mul()'s squaring code can do some redundant and/or useless
work at the end of each digit pass. A more careful analysis
of worst-case carries at various digit positions allows
making that code leaner.
* bpo-46218: Change long_pow() to sliding window algorithm
The primary motivation is to eliminate long_pow's reliance on that the number of bits in a long "digit" is a multiple of 5. Now it no longer cares how many bits are in a digit.
But the sliding window approach also allows cutting the precomputed table of small powers in half, which reduces initialization overhead enough that the approach pays off for smaller exponents too. Depending on exponent bit patterns, a sliding window may also be able to save some bigint multiplies (sometimes when at least 5 consecutive exponent bits are 0, regardless of their starting bit position modulo 5).
Note: boosting the window width to 6 didn't work well overall. It give marginal speed improvements for huge exponents, but the increased overhead (the small-power table needs twice as many entries) made it a loss for smaller exponents.
Co-authored-by: Oleg Iarygin <dralife@yandex.ru>
The array of small PyLong objects has been statically declared. Here I also statically initialize them. Consequently they are no longer initialized dynamically during runtime init.
I've also moved them under a new sub-struct in _PyRuntimeState, in preparation for static allocation and initialization of other global objects.
https://bugs.python.org/issue45953
This change is strictly renames and moving code around. It helps in the following ways:
* ensures type-related init functions focus strictly on one of the three aspects (state, objects, types)
* passes in PyInterpreterState * to all those functions, simplifying work on moving types/objects/state to the interpreter
* consistent naming conventions help make what's going on more clear
* keeping API related to a type in the corresponding header file makes it more obvious where to look for it
https://bugs.python.org/issue46008