10000 MAINT: Remove any promotion-state switching logic by seberg · Pull Request #27156 · numpy/numpy · GitHub
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MAINT: Remove any promotion-state switching logic #27156

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Merged
merged 4 commits into from
Aug 31, 2024
Merged

MAINT: Remove any promotion-state switching logic #27156

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MAINT: Remove any promotion-state switching logic
seberg Aug 9, 2024
8255ea4
DOC: Add release note for promotion state removal
seberg Aug 9, 2024
5a6435c
DOC: Mention semi-private removed functions and tweak docs
seberg Aug 9, 2024
65ffbdd
Adress Marten's last comment
seberg Aug 31, 2024
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MAINT: Remove any promotion-state switching logic 
This is the first level, no following code simplifications, just
straight up deletions of any branching.

I kept a UserWarning, in case someone had the bad idea to permanently
set the environment variable and think they can rely on it.
Although would be happy to just delete that as well.
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@seberg
seberg committed Aug 9, 2024
commit 8761011922e2855b595e5831f597b62bb58143ee
11 changes: 7 additions & 4 deletions numpy/__init__.py
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Expand Up @@ -120,8 +120,8 @@

from . import _core
from ._core import (
False_, ScalarType, True_, _get_promotion_state, _no_nep50_warning,
_set_promotion_state, abs, absolute, acos, acosh, add, all, allclose,
False_, ScalarType, True_,
abs, absolute, acos, acosh, add, all, allclose,
amax, amin, any, arange, arccos, arccosh, arcsin, arcsinh,
arctan, arctan2, arctanh, argmax, argmin, argpartition, argsort,
argwhere, around, array, array2string, array_equal, array_equiv,
Expand Down Expand Up @@ -529,8 +529,11 @@ def hugepage_setup():
_core.multiarray._multiarray_umath._reload_guard()

# TODO: Remove the environment variable entirely now that it is "weak"
_core._set_promotion_state(
os.environ.get("NPY_PROMOTION_STATE", "weak"))
if (os.environ.get("NPY_PROMOTION_STATE", "weak") != "weak"):
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Assign "weak" to a variable like DEFAULT_PROMOTION_STATE for better maintainability and readability.

warnings.warn(
"NPY_PROMOTION_STATE was a temporary feature for NumPy 2.0 "
"transition and is ignored after NumPy 2.2.",
UserWarning, stacklevel=2)

# Tell PyInstaller where to find hook-numpy.py
def _pyinstaller_hooks_dir():
Expand Down
5 changes: 0 additions & 5 deletions numpy/__init__.pyi
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Expand Up @@ -3533,11 +3533,6 @@ class errstate:
) -> None: ...
def __call__(self, func: _CallType) -> _CallType: ...

@contextmanager
def _no_nep50_warning() -> Generator[None, None, None]: ...
def _get_promotion_state() -> str: ...
def _set_promotion_state(state: str, /) -> None: ...

_ScalarType_co = TypeVar("_ScalarType_co", bound=generic, covariant=True)

class ndenumerate(Generic[_ScalarType_co]):
Expand Down
16 changes: 6 additions & 10 deletions numpy/_core/_methods.py
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Expand Up @@ -14,7 +14,6 @@
from numpy._core.multiarray import asanyarray
from numpy._core import numerictypes as nt
from numpy._core import _exceptions
from numpy._core._ufunc_config import _no_nep50_warning
from numpy._globals import _NoValue

# save those O(100) nanoseconds!
Expand Down Expand Up @@ -135,9 +134,8 @@ def _mean(a, axis=None, dtype=None, out=None, keepdims=False, *, where=True):

ret = umr_sum(arr, axis, dtype, out, keepdims, where=where)
if isinstance(ret, mu.ndarray):
with _no_nep50_warning():
ret = um.true_divide(
ret, rcount, out=ret, casting='unsafe', subok=False)
ret = um.true_divide(
ret, rcount, out=ret, casting='unsafe', subok=False)
if is_float16_result and out is None:
ret = arr.dtype.type(ret)
elif hasattr(ret, 'dtype'):
Expand Down Expand Up @@ -180,9 +178,8 @@ def _var(a, axis=None, dtype=None, out=None, ddof=0, keepdims=False, *,
# matching rcount to arrmean when where is specified as array
div = rcount.reshape(arrmean.shape)
if isinstance(arrmean, mu.ndarray):
with _no_nep50_warning():
arrmean = um.true_divide(arrmean, div, out=arrmean,
casting='unsafe', subok=False)
arrmean = um.true_divide(arrmean, div, out=arrmean,
casting='unsafe', subok=False)
elif hasattr(arrmean, "dtype"):
arrmean = arrmean.dtype.type(arrmean / rcount)
else:
Expand Down Expand Up @@ -212,9 +209,8 @@ def _var(a, axis=None, dtype=None, out=None, ddof=0, keepdims=False, *,

# divide by degrees of freedom
if isinstance(ret, mu.ndarray):
with _no_nep50_warning():
ret = um.true_divide(
ret, rcount, out=ret, casting='unsafe', subok=False)
ret = um.true_divide(
ret, rcount, out=ret, casting='unsafe', subok=False)
elif hasattr(ret, 'dtype'):
ret = ret.dtype.type(ret / rcount)
else:
Expand Down
21 changes: 1 addition & 20 deletions numpy/_core/_ufunc_config.py
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Expand Up @@ -14,7 +14,7 @@

__all__ = [
"seterr", "geterr", "setbufsize", "getbufsize", "seterrcall", "geterrcall",
"errstate", '_no_nep50_warning'
"errstate"
]


Expand Down Expand Up @@ -482,22 +482,3 @@ def inner(*args, **kwargs):
_extobj_contextvar.reset(_token)

return inner


NO_NEP50_WARNING = contextvars.ContextVar("_no_nep50_warning", default=False)

@set_module('numpy')
@contextlib.contextmanager
def _no_nep50_warning():
"""
Context manager to disable NEP 50 warnings. This context manager is
only relevant if the NEP 50 warnings are enabled globally (which is not
thread/context safe).

This warning context manager itself is fully safe, however.
"""
token = NO_NEP50_WARNING.set(True)
try:
yield
finally:
NO_NEP50_WARNING.reset(token)
6 changes: 1 addition & 5 deletions numpy/_core/multiarray.py
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Expand Up @@ -17,7 +17,6 @@
_flagdict, from_dlpack, _place, _reconstruct,
_vec_string, _ARRAY_API, _monotonicity, _get_ndarray_c_version,
_get_madvise_hugepage, _set_madvise_hugepage,
_get_promotion_state, _set_promotion_state
)

__all__ = [
Expand All @@ -40,8 +39,7 @@
'normalize_axis_index', 'packbits', 'promote_types', 'putmask',
'ravel_multi_index', 'result_type', 'scalar', 'set_datetimeparse_function',
'set_typeDict', 'shares_memory', 'typeinfo',
'unpackbits', 'unravel_index', 'vdot', 'where', 'zeros',
'_get_promotion_state', '_set_promotion_state']
'unpackbits', 'unravel_index', 'vdot', 'where', 'zeros']

# For backward compatibility, make sure pickle imports
# these functions from here
Expand All @@ -67,8 +65,6 @@
nested_iters.__module__ = 'numpy'
promote_types.__module__ = 'numpy'
zeros.__module__ = 'numpy'
_get_promotion_state.__module__ = 'numpy'
_set_promotion_state.__module__ = 'numpy'
normalize_axis_index.__module__ = 'numpy.lib.array_utils'


Expand Down
9 changes: 4 additions & 5 deletions numpy/_core/numeric.py
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Expand Up @@ -17,8 +17,7 @@
empty, empty_like, flatiter, frombuffer, from_dlpack, fromfile, fromiter,
fromstring, inner, lexsort, matmul, may_share_memory, min_scalar_type,
ndarray, nditer, nested_iters, promote_types, putmask, result_type,
shares_memory, vdot, where, zeros, normalize_axis_index,
_get_promotion_state, _set_promotion_state, vecdot
shares_memory, vdot, where, zeros, normalize_axis_index, vecdot
)

from . import overrides
Expand All @@ -28,7 +27,7 @@
from .umath import (multiply, invert, sin, PINF, NAN)
from . import numerictypes
from ..exceptions import AxisError
from ._ufunc_config import errstate, _no_nep50_warning
from ._ufunc_config import errstate

bitwise_not = invert
ufunc = type(sin)
Expand All @@ -53,7 +52,7 @@
'identity', 'allclose', 'putmask',
'flatnonzero', 'inf', 'nan', 'False_', 'True_', 'bitwise_not',
'full', 'full_like', 'matmul', 'vecdot', 'shares_memory',
'may_share_memory', '_get_promotion_state', '_set_promotion_state']
'may_share_memory']


def _zeros_like_dispatcher(
Expand Down Expand Up @@ -2457,7 +2456,7 @@ def isclose(a, b, rtol=1.e-5, atol=1.e-8, equal_nan=False):
elif isinstance(y, int):
y = float(y)

with errstate(invalid='ignore'), _no_nep50_warning():
with errstate(invalid='ignore'):
result = (less_equal(abs(x-y), atol + rtol * abs(y))
& isfinite(y)
| (x == y))
Expand Down
39 changes: 4 additions & 35 deletions numpy/_core/src/multiarray/arraytypes.c.src
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Expand Up @@ -275,41 +275,10 @@ static int
#endif
) {
PyArray_Descr *descr = PyArray_DescrFromType(NPY_@TYPE@);
int promotion_state = get_npy_promotion_state();
if (promotion_state == NPY_USE_LEGACY_PROMOTION || (
promotion_state == NPY_USE_WEAK_PROMOTION_AND_WARN
&& !npy_give_promotion_warnings())) {
/*
* This path will be taken both for the "promotion" case such as
* `uint8_arr + 123` as well as the assignment case.
* The "legacy" path should only ever be taken for assignment
* (legacy promotion will prevent overflows by promoting up)
* so a normal deprecation makes sense.
* When weak promotion is active, we use "future" behavior unless
* warnings were explicitly opt-in.
*/
if (PyErr_WarnFormat(PyExc_DeprecationWarning, 1,
"NumPy will stop allowing conversion of out-of-bound "
"Python integers to integer arrays. The conversion "
"of %.100R to %S will fail in the future.\n"
"For the old behavior, usually:\n"
" np.array(value).astype(dtype)\n"
"will give the desired result (the cast overflows).",
obj, descr) < 0) {
Py_DECREF(descr);
return -1;
}
Py_DECREF(descr);
return 0;
}
else {
/* Live in the future, outright error: */
PyErr_Format(PyExc_OverflowError,
"Python integer %R out of bounds for %S", obj, descr);
Py_DECREF(descr);
return -1;
}
assert(0);
PyErr_Format(PyExc_OverflowError,
"Python integer %R out of bounds for %S", obj, descr);
Py_DECREF(descr);
return -1;
}
return 0;
}
Expand Down
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