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python-numba/numba-pr7483-numpy1_21.patch
Matej Cepl 2704467ddf Accepting request 945125 from home:bnavigator:branches:devel:languages:python:numeric 
- Numba <0.55 is not compatible with Python 3.10 or NumPy 1.22
  gh#numba/numba#7557
- Add test skip to numba-pr7483-numpy1_21.patch due to numpy update
  gh#numpy/numpy#20376

OBS-URL: https://build.opensuse.org/request/show/945125
OBS-URL: https://build.opensuse.org/package/show/devel:languages:python:numeric/python-numba?expand=0&rev=50
2022-01-10 17:36:04 +00:00

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From 3a2c5042fa85ac9b8fe398d605d6d373d27f42bb Mon Sep 17 00:00:00 2001
From: Stuart Archibald <stuartarchibald@users.noreply.github.com>
Date: Thu, 14 Oct 2021 09:35:24 +0100
Subject: [PATCH 1/7] Update build matrix for NumPy 1.21 as per NEP-029.
As title.
---
azure-pipelines.yml | 68 ++++++++++++++--------------
buildscripts/azure/azure-windows.yml | 8 ++--
2 files changed, 38 insertions(+), 38 deletions(-)
Index: numba-0.54.1/setup.py
===================================================================
--- numba-0.54.1.orig/setup.py
+++ numba-0.54.1/setup.py
@@ -22,8 +22,7 @@ except ImportError:
min_python_version = "3.7"
max_python_version = "3.10" # exclusive
min_numpy_build_version = "1.11"
-min_numpy_run_version = "1.17"
-max_numpy_run_version = "1.21"
+min_numpy_run_version = "1.18"
min_llvmlite_version = "0.37.0rc1"
max_llvmlite_version = "0.38"
@@ -360,7 +359,7 @@ packages = find_packages(include=["numba
build_requires = ['numpy >={}'.format(min_numpy_build_version)]
install_requires = [
'llvmlite >={},<{}'.format(min_llvmlite_version, max_llvmlite_version),
- 'numpy >={},<{}'.format(min_numpy_run_version, max_numpy_run_version),
+ 'numpy >={}'.format(min_numpy_run_version),
'setuptools',
]
Index: numba-0.54.1/numba/__init__.py
===================================================================
--- numba-0.54.1.orig/numba/__init__.py
+++ numba-0.54.1/numba/__init__.py
@@ -137,10 +137,8 @@ def _ensure_critical_deps():
if PYVERSION < (3, 7):
raise ImportError("Numba needs Python 3.7 or greater")
- if numpy_version < (1, 17):
- raise ImportError("Numba needs NumPy 1.17 or greater")
- elif numpy_version > (1, 20):
- raise ImportError("Numba needs NumPy 1.20 or less")
+ if numpy_version < (1, 18):
+ raise ImportError("Numba needs NumPy 1.18 or greater")
try:
import scipy
Index: numba-0.54.1/README.rst
===================================================================
--- numba-0.54.1.orig/README.rst
+++ numba-0.54.1/README.rst
@@ -50,7 +50,7 @@ Dependencies
* Python versions: 3.7-3.9
* llvmlite 0.37.*
-* NumPy >=1.17,<1.21 (can build with 1.11 for ABI compatibility).
+* NumPy >=1.18 (can build with 1.11 for ABI compatibility).
Optionally:
Index: numba-0.54.1/docs/source/user/5minguide.rst
===================================================================
--- numba-0.54.1.orig/docs/source/user/5minguide.rst
+++ numba-0.54.1/docs/source/user/5minguide.rst
@@ -18,7 +18,7 @@ Out of the box Numba works with the foll
support on M1/Arm64.
* GPUs: Nvidia CUDA.
* CPython
-* NumPy 1.17 - latest
+* NumPy 1.18 - latest
How do I get it?
----------------
Index: numba-0.54.1/numba/np/ufunc/_internal.c
===================================================================
--- numba-0.54.1.orig/numba/np/ufunc/_internal.c
+++ numba-0.54.1/numba/np/ufunc/_internal.c
@@ -276,6 +276,7 @@ static PyMemberDef dufunc_members[] = {
*/
static struct _ufunc_dispatch {
+ /* Note that the following may also hold `_PyCFunctionFastWithKeywords` */
PyCFunctionWithKeywords ufunc_reduce;
PyCFunctionWithKeywords ufunc_accumulate;
PyCFunctionWithKeywords ufunc_reduceat;
@@ -286,7 +287,7 @@ static struct _ufunc_dispatch {
} ufunc_dispatch;
static int
-init_ufunc_dispatch(void)
+init_ufunc_dispatch(int *numpy_uses_fastcall)
{
int result = 0;
PyMethodDef * crnt = PyUFunc_Type.tp_methods;
@@ -329,6 +330,16 @@ init_ufunc_dispatch(void)
result = -1; /* Unknown method */
}
if (result < 0) break;
+
+ /* Check whether NumPy uses fastcall (ufunc.at never uses it) */
+ if (strncmp(crnt_name, "at", 3) != 0) {
+ if (*numpy_uses_fastcall == -1) {
+ *numpy_uses_fastcall = crnt->ml_flags & METH_FASTCALL;
+ }
+ else if (*numpy_uses_fastcall != (crnt->ml_flags & METH_FASTCALL)) {
+ return -1;
+ }
+ }
}
if (result == 0) {
/* Sanity check. */
@@ -344,6 +355,7 @@ init_ufunc_dispatch(void)
return result;
}
+
static PyObject *
dufunc_reduce(PyDUFuncObject * self, PyObject * args, PyObject *kws)
{
@@ -368,6 +380,47 @@ dufunc_outer(PyDUFuncObject * self, PyOb
return ufunc_dispatch.ufunc_outer((PyObject*)self->ufunc, args, kws);
}
+
+/*
+ * The following are the vectorcall versions of the above, since NumPy
+ * uses the FASTCALL/Vectorcall protocol starting with version 1.21.
+ * The only NumPy versions supporting vectorcall use Python 3.7 or higher.
+ */
+static PyObject *
+dufunc_reduce_fast(PyDUFuncObject * self,
+ PyObject *const *args, Py_ssize_t len_args, PyObject *kwnames)
+{
+ return ((_PyCFunctionFastWithKeywords)ufunc_dispatch.ufunc_reduce)(
+ (PyObject*)self->ufunc, args, len_args, kwnames);
+}
+
+static PyObject *
+dufunc_reduceat_fast(PyDUFuncObject * self,
+ PyObject *const *args, Py_ssize_t len_args, PyObject *kwnames)
+{
+ return ((_PyCFunctionFastWithKeywords)ufunc_dispatch.ufunc_reduceat)(
+ (PyObject*)self->ufunc, args, len_args, kwnames);
+}
+
+
+static PyObject *
+dufunc_accumulate_fast(PyDUFuncObject * self,
+ PyObject *const *args, Py_ssize_t len_args, PyObject *kwnames)
+{
+ return ((_PyCFunctionFastWithKeywords)ufunc_dispatch.ufunc_accumulate)(
+ (PyObject*)self->ufunc, args, len_args, kwnames);
+}
+
+
+static PyObject *
+dufunc_outer_fast(PyDUFuncObject * self,
+ PyObject *const *args, Py_ssize_t len_args, PyObject *kwnames)
+{
+ return ((_PyCFunctionFastWithKeywords)ufunc_dispatch.ufunc_outer)(
+ (PyObject*)self->ufunc, args, len_args, kwnames);
+}
+
+
#if NPY_API_VERSION >= 0x00000008
static PyObject *
dufunc_at(PyDUFuncObject * self, PyObject * args)
@@ -568,6 +621,41 @@ static struct PyMethodDef dufunc_methods
{NULL, NULL, 0, NULL} /* sentinel */
};
+
+/*
+ * If Python is new enough, NumPy may use fastcall. In that case we have to
+ * also use fastcall for simplicity and speed.
+ */
+static struct PyMethodDef dufunc_methods_fast[] = {
+ {"reduce",
+ (PyCFunction)dufunc_reduce_fast,
+ METH_FASTCALL | METH_KEYWORDS, NULL },
+ {"accumulate",
+ (PyCFunction)dufunc_accumulate_fast,
+ METH_FASTCALL | METH_KEYWORDS, NULL },
+ {"reduceat",
+ (PyCFunction)dufunc_reduceat_fast,
+ METH_FASTCALL | METH_KEYWORDS, NULL },
+ {"outer",
+ (PyCFunction)dufunc_outer_fast,
+ METH_FASTCALL | METH_KEYWORDS, NULL},
+#if NPY_API_VERSION >= 0x00000008
+ {"at",
+ (PyCFunction)dufunc_at,
+ METH_VARARGS, NULL},
+#endif
+ {"_compile_for_args",
+ (PyCFunction)dufunc__compile_for_args,
+ METH_VARARGS | METH_KEYWORDS,
+ "Abstract method: subclasses should overload _compile_for_args() to compile the ufunc at the given arguments' types."},
+ {"_add_loop",
+ (PyCFunction)dufunc__add_loop,
+ METH_VARARGS,
+ NULL},
+ {NULL, NULL, 0, NULL} /* sentinel */
+};
+
+
static PyObject *
dufunc_getfrozen(PyDUFuncObject * self, void * closure)
{
@@ -681,8 +769,15 @@ MOD_INIT(_internal)
return MOD_ERROR_VAL;
PyDUFunc_Type.tp_new = PyType_GenericNew;
- if (init_ufunc_dispatch() <= 0)
+
+ int numpy_uses_fastcall = -1;
+ if (init_ufunc_dispatch(&numpy_uses_fastcall) <= 0)
return MOD_ERROR_VAL;
+
+ if (numpy_uses_fastcall) {
+ PyDUFunc_Type.tp_methods = dufunc_methods_fast;
+ }
+
if (PyType_Ready(&PyDUFunc_Type) < 0)
return MOD_ERROR_VAL;
Py_INCREF(&PyDUFunc_Type);
Index: numba-0.54.1/numba/tests/test_array_reductions.py
===================================================================
--- numba-0.54.1.orig/numba/tests/test_array_reductions.py
+++ numba-0.54.1/numba/tests/test_array_reductions.py
@@ -725,6 +725,7 @@ class TestArrayReductions(MemoryLeakMixi
self.check_npdatetime(array_argmax)
self.check_nptimedelta(array_argmax)
+ @unittest.skip("https://github.com/numpy/numpy/issues/20376")
def test_median_npdatetime(self):
self.check_nptimedelta(array_median_global)
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