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python-numba/numpy21.patch

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From 5f2a3d60fc9c1e25bec9fd6de0b0b8bae6f142da Mon Sep 17 00:00:00 2001
From: kc611 <ckaustubhm06@gmail.com>
Date: Mon, 30 Sep 2024 23:08:18 +0530
Subject: [PATCH 01/22] Added NumPy 2.1 Support
---
numba/cpython/randomimpl.py | 6 ++-
numba/cuda/tests/cudapy/test_debug.py | 2 +-
numba/np/arrayobj.py | 6 +--
numba/np/math/numbers.py | 5 +++
numba/np/npyfuncs.py | 58 +++++++++++++++++----------
numba/np/old_arraymath.py | 2 +
numba/np/random/old_distributions.py | 4 +-
numba/np/ufunc_db.py | 36 +++++++++++++++++
numba/tests/test_array_methods.py | 19 ++++++---
9 files changed, 105 insertions(+), 33 deletions(-)
Index: numba-0.60.0/numba/cpython/randomimpl.py
===================================================================
--- numba-0.60.0.orig/numba/cpython/randomimpl.py
+++ numba-0.60.0/numba/cpython/randomimpl.py
@@ -17,7 +17,7 @@ from numba.core.imputils import (Registr
from numba.core.typing import signature
from numba.core import types, cgutils
from numba.core.errors import NumbaTypeError
-
+from numba.np.random._constants import LONG_MAX
registry = Registry('randomimpl')
lower = registry.lower
@@ -1798,6 +1798,10 @@ def zipf_impl(a):
U = 1.0 - np.random.random()
V = np.random.random()
X = int(math.floor(U ** (-1.0 / am1)))
+
+ if (X > LONG_MAX or X < 1.0):
+ continue
+
T = (1.0 + 1.0 / X) ** am1
if X >= 1 and V * X * (T - 1.0) / (b - 1.0) <= (T / b):
return X
Index: numba-0.60.0/numba/np/arrayobj.py
===================================================================
--- numba-0.60.0.orig/numba/np/arrayobj.py
+++ numba-0.60.0/numba/np/arrayobj.py
@@ -1932,17 +1932,23 @@ def numpy_geomspace(start, stop, num=50)
raise ValueError('Geometric sequence cannot include zero')
start = result_dtype(start)
stop = result_dtype(stop)
- both_imaginary = (start.real == 0) & (stop.real == 0)
- both_negative = (np.sign(start) == -1) & (np.sign(stop) == -1)
- out_sign = 1
- if both_imaginary:
- start = start.imag
- stop = stop.imag
- out_sign = 1j
- if both_negative:
- start = -start
- stop = -stop
- out_sign = -out_sign
+ if numpy_version < (2, 0):
+ both_imaginary = (start.real == 0) & (stop.real == 0)
+ both_negative = (np.sign(start) == -1) & (np.sign(stop) == -1)
+ out_sign = 1
+ if both_imaginary:
+ start = start.imag
+ stop = stop.imag
+ out_sign = 1j
+ if both_negative:
+ start = -start
+ stop = -stop
+ out_sign = -out_sign
+ else:
+ out_sign = np.sign(start)
+ start /= out_sign
+ stop /= out_sign
+
logstart = np.log10(start)
logstop = np.log10(stop)
result = np.logspace(logstart, logstop, num)
@@ -2144,11 +2150,18 @@ def array_reshape_vararg(context, builde
return array_reshape(context, builder, new_sig, new_args)
-@overload(np.reshape)
-def np_reshape(a, newshape):
- def np_reshape_impl(a, newshape):
- return a.reshape(newshape)
- return np_reshape_impl
+if numpy_version < (2, 1):
+ @overload(np.reshape)
+ def np_reshape(a, newshape):
+ def np_reshape_impl(a, newshape):
+ return a.reshape(newshape)
+ return np_reshape_impl
+else:
+ @overload(np.reshape)
+ def np_reshape(a, shape):
+ def np_reshape_impl(a, shape):
+ return a.reshape(shape)
+ return np_reshape_impl
@overload(np.resize)
Index: numba-0.60.0/numba/np/math/numbers.py
===================================================================
--- numba-0.60.0.orig/numba/np/math/numbers.py
+++ numba-0.60.0/numba/np/math/numbers.py
@@ -397,6 +397,11 @@ def int_abs_impl(context, builder, sig,
return impl_ret_untracked(context, builder, sig.return_type, res)
+def identity_impl(context, builder, sig, args):
+ [x] = args
+ return impl_ret_untracked(context, builder, sig.return_type, x)
+
+
def uint_abs_impl(context, builder, sig, args):
[x] = args
return impl_ret_untracked(context, builder, sig.return_type, x)
Index: numba-0.60.0/numba/np/npyfuncs.py
===================================================================
--- numba-0.60.0.orig/numba/np/npyfuncs.py
+++ numba-0.60.0/numba/np/npyfuncs.py
@@ -16,6 +16,7 @@ from numba.core import typing, types, er
from numba.core.extending import register_jitable
from numba.np import npdatetime
from numba.np.math import cmathimpl, mathimpl, numbers
+from numba.np.numpy_support import numpy_version
# some NumPy constants. Note that we could generate some of them using
# the math library, but having the values copied from npy_math seems to
@@ -580,29 +581,42 @@ def np_complex_sign_impl(context, builde
# equivalent to complex sign in NumPy's sign
# but implemented via selects, balancing the 4 cases.
_check_arity_and_homogeneity(sig, args, 1)
- op = args[0]
- ty = sig.args[0]
- float_ty = ty.underlying_float
- ZERO = context.get_constant(float_ty, 0.0)
- ONE = context.get_constant(float_ty, 1.0)
- MINUS_ONE = context.get_constant(float_ty, -1.0)
- NAN = context.get_constant(float_ty, float('nan'))
- result = context.make_complex(builder, ty)
- result.real = ZERO
- result.imag = ZERO
-
- cmp_sig = typing.signature(types.boolean, *[ty] * 2)
- cmp_args = [op, result._getvalue()]
- arg1_ge_arg2 = np_complex_ge_impl(context, builder, cmp_sig, cmp_args)
- arg1_eq_arg2 = np_complex_eq_impl(context, builder, cmp_sig, cmp_args)
- arg1_lt_arg2 = np_complex_lt_impl(context, builder, cmp_sig, cmp_args)
-
- real_when_ge = builder.select(arg1_eq_arg2, ZERO, ONE)
- real_when_nge = builder.select(arg1_lt_arg2, MINUS_ONE, NAN)
- result.real = builder.select(arg1_ge_arg2, real_when_ge, real_when_nge)
+ if numpy_version >= (2, 0):
+ # NumPy >= 2.0.0
+ def complex_sign(z):
+ abs = math.hypot(z.real, z.imag)
+ if abs == 0:
+ return 0 + 0j
+ else:
+ return z / abs
+
+ res = context.compile_internal(builder, complex_sign, sig, args)
+ return impl_ret_untracked(context, builder, sig.return_type, res)
+ else:
+ op = args[0]
+ ty = sig.args[0]
+ result = context.make_complex(builder, ty)
+ float_ty = ty.underlying_float
+
+ ZERO = context.get_constant(float_ty, 0.0)
+ ONE = context.get_constant(float_ty, 1.0)
+ MINUS_ONE = context.get_constant(float_ty, -1.0)
+ NAN = context.get_constant(float_ty, float('nan'))
+
+ result.real = ZERO
+ result.imag = ZERO
+ cmp_sig = typing.signature(types.boolean, *[ty] * 2)
+ cmp_args = [op, result._getvalue()]
+ arg1_ge_arg2 = np_complex_ge_impl(context, builder, cmp_sig, cmp_args)
+ arg1_eq_arg2 = np_complex_eq_impl(context, builder, cmp_sig, cmp_args)
+ arg1_lt_arg2 = np_complex_lt_impl(context, builder, cmp_sig, cmp_args)
+
+ real_when_ge = builder.select(arg1_eq_arg2, ZERO, ONE)
+ real_when_nge = builder.select(arg1_lt_arg2, MINUS_ONE, NAN)
+ result.real = builder.select(arg1_ge_arg2, real_when_ge, real_when_nge)
- return result._getvalue()
+ return result._getvalue()
########################################################################
Index: numba-0.60.0/numba/np/ufunc_db.py
===================================================================
--- numba-0.60.0.orig/numba/np/ufunc_db.py
+++ numba-0.60.0/numba/np/ufunc_db.py
@@ -583,16 +583,58 @@ def _fill_ufunc_db(ufunc_db):
'f->f': npyfuncs.np_real_floor_impl,
'd->d': npyfuncs.np_real_floor_impl,
}
+ if numpy_version >= (2, 1):
+ ufunc_db[np.floor].update({
+ '?->?': numbers.identity_impl,
+ 'b->b': numbers.identity_impl,
+ 'B->B': numbers.identity_impl,
+ 'h->h': numbers.identity_impl,
+ 'H->H': numbers.identity_impl,
+ 'i->i': numbers.identity_impl,
+ 'I->I': numbers.identity_impl,
+ 'l->l': numbers.identity_impl,
+ 'L->L': numbers.identity_impl,
+ 'q->q': numbers.identity_impl,
+ 'Q->Q': numbers.identity_impl,
+ })
ufunc_db[np.ceil] = {
'f->f': npyfuncs.np_real_ceil_impl,
'd->d': npyfuncs.np_real_ceil_impl,
}
+ if numpy_version >= (2, 1):
+ ufunc_db[np.ceil].update({
+ '?->?': numbers.identity_impl,
+ 'b->b': numbers.identity_impl,
+ 'B->B': numbers.identity_impl,
+ 'h->h': numbers.identity_impl,
+ 'H->H': numbers.identity_impl,
+ 'i->i': numbers.identity_impl,
+ 'I->I': numbers.identity_impl,
+ 'l->l': numbers.identity_impl,
+ 'L->L': numbers.identity_impl,
+ 'q->q': numbers.identity_impl,
+ 'Q->Q': numbers.identity_impl,
+ })
ufunc_db[np.trunc] = {
'f->f': npyfuncs.np_real_trunc_impl,
'd->d': npyfuncs.np_real_trunc_impl,
}
+ if numpy_version >= (2, 1):
+ ufunc_db[np.trunc].update({
+ '?->?': numbers.identity_impl,
+ 'b->b': numbers.identity_impl,
+ 'B->B': numbers.identity_impl,
+ 'h->h': numbers.identity_impl,
+ 'H->H': numbers.identity_impl,
+ 'i->i': numbers.identity_impl,
+ 'I->I': numbers.identity_impl,
+ 'l->l': numbers.identity_impl,
+ 'L->L': numbers.identity_impl,
+ 'q->q': numbers.identity_impl,
+ 'Q->Q': numbers.identity_impl,
+ })
ufunc_db[np.fabs] = {
'f->f': npyfuncs.np_real_fabs_impl,
Index: numba-0.60.0/numba/tests/test_array_methods.py
===================================================================
--- numba-0.60.0.orig/numba/tests/test_array_methods.py
+++ numba-0.60.0/numba/tests/test_array_methods.py
@@ -774,13 +774,20 @@ class TestArrayMethods(MemoryLeakMixin,
check_arr(arr.reshape((2, 3, 4)))
check_arr(arr.reshape((2, 3, 4)).T)
check_arr(arr.reshape((2, 3, 4))[::2])
- for v in (0.0, 1.5, float('nan')):
- arr = np.array([v]).reshape(())
- check_arr(arr)
arr = np.array(["Hello", "", "world"])
check_arr(arr)
+ for v in (0.0, 1.5, float('nan')):
+ arr = np.array([v]).reshape(())
+ if numpy_version < (2, 1):
+ check_arr(arr)
+ else:
+ with self.assertRaises(ValueError) as raises:
+ njit((typeof(arr),))(pyfunc)
+ self.assertEqual(str(raises.exception),
+ "Calling nonzero on 0d arrays is not allowed. Use np.atleast_1d(scalar).nonzero() instead.")
+
def test_array_nonzero(self):
self.check_nonzero(array_nonzero)
Index: numba-0.60.0/docs/upcoming_changes/9741.highlight.rst
===================================================================
--- /dev/null
+++ numba-0.60.0/docs/upcoming_changes/9741.highlight.rst
@@ -0,0 +1,4 @@
+Added Support for NumPy 2.1
+---------------------------
+
+This release adds support for NumPy 2.1 (excluding the NEP-050 semantics).
Index: numba-0.60.0/numba/tests/test_ufuncs.py
===================================================================
--- numba-0.60.0.orig/numba/tests/test_ufuncs.py
+++ numba-0.60.0/numba/tests/test_ufuncs.py
@@ -18,7 +18,6 @@ from numba.np import numpy_support
from numba.core.registry import cpu_target
from numba.core.base import BaseContext
from numba.np import ufunc_db
-from numba.tests.support import expected_failure_np2
is32bits = tuple.__itemsize__ == 4
iswindows = sys.platform.startswith('win32')
@@ -1696,8 +1695,6 @@ class TestLoopTypesComplex(_LoopTypesTes
TestLoopTypesComplex.autogenerate()
-expected_failure_np2(TestLoopTypesComplex.test_sign_F_F)
-expected_failure_np2(TestLoopTypesComplex.test_sign_D_D)
class TestLoopTypesDatetime(_LoopTypesTester):
Index: numba-0.60.0/numba/core/typing/arraydecl.py
===================================================================
--- numba-0.60.0.orig/numba/core/typing/arraydecl.py
+++ numba-0.60.0/numba/core/typing/arraydecl.py
@@ -415,6 +415,11 @@ class ArrayAttribute(AttributeTemplate):
def resolve_nonzero(self, ary, args, kws):
assert not args
assert not kws
+ if ary.ndim == 0 and numpy_version >= (2, 1):
+ raise ValueError(
+ "Calling nonzero on 0d arrays is not allowed."
+ " Use np.atleast_1d(scalar).nonzero() instead."
+ )
# 0-dim arrays return one result array
ndim = max(ary.ndim, 1)
retty = types.UniTuple(types.Array(types.intp, 1, 'C'), ndim)
Index: numba-0.60.0/numba/np/random/_constants.py
===================================================================
--- numba-0.60.0.orig/numba/np/random/_constants.py
+++ numba-0.60.0/numba/np/random/_constants.py
@@ -1,4 +1,5 @@
import numpy as np
+import ctypes
# These constants are directly obtained from:
# https://github.com/numpy/numpy/blob/caccd283941b0bade7b71056138ded5379b1625f/numpy/random/src/distributions/ziggurat_constants.h
@@ -1222,6 +1223,7 @@ UINT8_MAX = 255
UINT16_MAX = 65535
UINT32_MAX = 4294967295
UINT64_MAX = 18446744073709551615
+LONG_MAX = (1 << ( 8 * ctypes.sizeof(ctypes.c_long) - 1)) - 1
LS2PI = 0.91893853320467267
TWELFTH = 0.083333333333333333333333
Index: numba-0.60.0/numba/__init__.py
===================================================================
--- numba-0.60.0.orig/numba/__init__.py
+++ numba-0.60.0/numba/__init__.py
@@ -34,13 +34,13 @@ def _ensure_critical_deps():
import numpy as np
numpy_version = extract_version(np)
- if numpy_version < (1, 22):
- msg = (f"Numba needs NumPy 1.22 or greater. Got NumPy "
+ if numpy_version < (1, 24):
+ msg = (f"Numba needs NumPy 1.24 or greater. Got NumPy "
f"{numpy_version[0]}.{numpy_version[1]}.")
raise ImportError(msg)
- if numpy_version > (2, 0):
- msg = (f"Numba needs NumPy 2.0 or less. Got NumPy "
+ if numpy_version > (2, 1):
+ msg = (f"Numba needs NumPy 2.1 or less. Got NumPy "
f"{numpy_version[0]}.{numpy_version[1]}.")
raise ImportError(msg)
Index: numba-0.60.0/numba/np/random/distributions.py
===================================================================
--- a/numba/np/random/distributions.py
+++ b/numba/np/random/distributions.py
@@ -394,8 +394,10 @@ def random_geometric(bitgen, p):
def random_zipf(bitgen, a):
am1 = a - 1.0
b = pow(2.0, am1)
+ Umin = pow(INT64_MAX, -am1)
while 1:
- U = 1.0 - next_double(bitgen)
+ U01 = next_double(bitgen)
+ U = U01*Umin + (1 - U01)
V = next_double(bitgen)
X = np.floor(pow(U, -1.0 / am1))
if (X > INT64_MAX or X < 1.0):
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