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Accepting request 932339 from devel:languages:python:numeric

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OBS-URL: https://build.opensuse.org/request/show/932339
OBS-URL: https://build.opensuse.org/package/show/openSUSE:Factory/python-numba?expand=0&rev=29
This commit is contained in:
Dominique Leuenberger 2021-11-20 08:37:53 +00:00 committed by Git OBS Bridge
commit 0d6025847d
9 changed files with 372 additions and 168 deletions
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10
fix-max-name-size.patch
View File

@ -8,11 +8,11 @@ Fix for #3876 without needing to patch LLVM.
numba/__init__.py | 5 +++++
1 file changed, 5 insertions(+)
diff --git a/numba/__init__.py b/numba/__init__.py
index c62ad06289..114e9a597e 100644
--- a/numba/__init__.py
+++ b/numba/__init__.py
@@ -102,6 +102,11 @@ def _ensure_llvm():
Index: numba-0.54.1/numba/__init__.py
===================================================================
--- numba-0.54.1.orig/numba/__init__.py
+++ numba-0.54.1/numba/__init__.py
@@ -119,6 +119,11 @@ def _ensure_llvm():
"Please update llvmlite." %
(_min_llvm_version + llvm_version_info))
raise ImportError(msg)

3
numba-0.53.0.tar.gz
View File

@ -1,3 +0,0 @@
version https://git-lfs.github.com/spec/v1
oid sha256:55c11d7edbba2ba715f2b56f5294cad55cfd87bff98e2627c3047c2d5cc52d16
size 2212284

3
numba-0.54.1.tar.gz Normal file
View File

@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:f9dfc803c864edcc2381219b800abf366793400aea55e26d4d5b7d953e14f43f
size 2232957

72
numba-pr6851-llvm-timings.patch
View File

@ -1,72 +0,0 @@
diff --git a/numba/misc/llvm_pass_timings.py b/numba/misc/llvm_pass_timings.py
index 205bb3396..b3263dd45 100644
--- a/numba/misc/llvm_pass_timings.py
+++ b/numba/misc/llvm_pass_timings.py
@@ -239,12 +239,14 @@ class ProcessedPassTimings:
missing[k] = 0.0
# parse timings
n = r"\s*((?:[0-9]+\.)?[0-9]+)"
- pat = f"\\s+{n}\\s*\\({n}%\\)" * (len(headers) - 1) + r"\s*(.*)"
+ pat = f"\\s+(?:{n}\\s*\\({n}%\\)|-+)" * (len(headers) - 1)
+ pat += r"\s*(.*)"
for ln in line_iter:
m = re.match(pat, ln)
if m is not None:
raw_data = list(m.groups())
- data = {k: float(v) for k, v in zip(attrs, raw_data)}
+ data = {k: float(v) if v is not None else 0.0
+ for k, v in zip(attrs, raw_data)}
data.update(missing)
pass_name = raw_data[-1]
rec = PassTimingRecord(
diff --git a/numba/tests/test_llvm_pass_timings.py b/numba/tests/test_llvm_pass_timings.py
index a7e9135cd..25b77e2c5 100644
--- a/numba/tests/test_llvm_pass_timings.py
+++ b/numba/tests/test_llvm_pass_timings.py
@@ -5,6 +5,30 @@ from numba.tests.support import TestCase, override_config
from numba.misc import llvm_pass_timings as lpt
+timings_raw1 = """
+===-------------------------------------------------------------------------===
+ ... Pass execution timing report ...
+===-------------------------------------------------------------------------===
+ Total Execution Time: 0.0001 seconds (0.0001 wall clock)
+
+ ---User Time--- --System Time-- --User+System-- ---Wall Time--- --- Name ---
+ 0.0001 ( 90.1%) 0.0001 ( 90.1%) 0.0001 ( 90.1%) 0.0001 ( 90.1%) A1
+ 0.0000 ( 9.9%) 0.0000 ( 9.9%) 0.0000 ( 9.9%) 0.0000 ( 9.9%) A2
+ 0.0001 (100.0%) 0.0001 (100.0%) 0.0001 (100.0%) 0.0001 (100.0%) Total
+
+""" # noqa: E501
+
+timings_raw2 = """
+ Total Execution Time: 0.0001 seconds (0.0001 wall clock)
+
+ ---User Time--- --System Time-- --User+System-- ---Wall Time--- --- Name ---
+ 0.0001 ( 90.1%) ----- 0.0001 ( 90.1%) 0.0001 ( 90.1%) A1
+ 0.0000 ( 9.9%) ----- 0.0000 ( 9.9%) 0.0000 ( 9.9%) A2
+ 0.0001 (100.0%) ----- 0.0001 (100.0%) 0.0001 (100.0%) Total
+
+""" # noqa: E501
+
+
class TestLLVMPassTimings(TestCase):
def test_usage(self):
@@ -61,6 +85,15 @@ class TestLLVMPassTimings(TestCase):
self.assertGreaterEqual(last, cur)
cur = last
+ def test_parse_raw(self):
+ timings1 = lpt.ProcessedPassTimings(timings_raw1)
+ self.assertAlmostEqual(timings1.get_total_time(), 0.0001)
+ self.assertIsInstance(timings1.summary(), str)
+
+ timings2 = lpt.ProcessedPassTimings(timings_raw2)
+ self.assertAlmostEqual(timings2.get_total_time(), 0.0001)
+ self.assertIsInstance(timings2.summary(), str)
+
class TestLLVMPassTimingsDisabled(TestCase):
def test_disabled_behavior(self):

230
numba-pr7483-numpy1_21.patch Normal file
View File

@ -0,0 +1,230 @@
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
@@ -275,6 +275,7 @@ 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;
@@ -285,7 +286,7 @@ 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;
@@ -328,6 +329,16 @@ 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. */
@@ -343,6 +354,7 @@ init_ufunc_dispatch(void)
return result;
}
+
static PyObject *
dufunc_reduce(PyDUFuncObject * self, PyObject * args, PyObject *kws)
{
@@ -367,6 +379,47 @@ 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)
@@ -567,6 +620,41 @@ 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)
{
@@ -680,8 +768,15 @@ 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);

52
packaging-ignore-setuptools-deprecation.patch
View File

@ -1,52 +0,0 @@
From 4811aeceb2dde05124697909b87cf4ad2ae07c65 Mon Sep 17 00:00:00 2001
From: Stuart Archibald <stuartarchibald@users.noreply.github.com>
Date: Wed, 17 Mar 2021 12:29:16 +0000
Subject: [PATCH 1/2] Ignore warnings from packaging module when testing import
behaviour.
As title.
Fixes #6831
---
numba/tests/test_import.py | 4 +++-
1 file changed, 3 insertions(+), 1 deletion(-)
diff --git a/numba/tests/test_import.py b/numba/tests/test_import.py
index 7cc9082046..a46ad2df6c 100644
--- a/numba/tests/test_import.py
+++ b/numba/tests/test_import.py
@@ -58,7 +58,9 @@ def test_no_accidental_warnings(self):
# checks that importing Numba isn't accidentally triggering warnings due
# to e.g. deprecated use of import locations from Python's stdlib
code = "import numba"
- flags = ["-Werror",]
+ # See: https://github.com/numba/numba/issues/6831
+ # bug in setuptools/packaging causing a deprecation warning
+ flags = ["-Werror", "-Wignore::DeprecationWarning:packaging:"]
self.run_in_subproc(code, flags)
def test_import_star(self):
From 94fc06e77b648b1cb5021cd6d460aa42808a0393 Mon Sep 17 00:00:00 2001
From: Stuart Archibald <stuartarchibald@users.noreply.github.com>
Date: Wed, 17 Mar 2021 16:42:14 +0000
Subject: [PATCH 2/2] Respond to feedback
As title]
---
numba/tests/test_import.py | 2 +-
1 file changed, 1 insertion(+), 1 deletion(-)
diff --git a/numba/tests/test_import.py b/numba/tests/test_import.py
index a46ad2df6c..a0ca5725eb 100644
--- a/numba/tests/test_import.py
+++ b/numba/tests/test_import.py
@@ -60,7 +60,7 @@ def test_no_accidental_warnings(self):
code = "import numba"
# See: https://github.com/numba/numba/issues/6831
# bug in setuptools/packaging causing a deprecation warning
- flags = ["-Werror", "-Wignore::DeprecationWarning:packaging:"]
+ flags = ["-Werror", "-Wignore::DeprecationWarning:packaging.version:"]
self.run_in_subproc(code, flags)
def test_import_star(self):

106
python-numba.changes
View File

@ -1,3 +1,109 @@
-------------------------------------------------------------------
Thu Nov 18 18:42:21 UTC 2021 - Ben Greiner <code@bnavigator.de>
- Update to 0.54.1
* This is a bugfix release for 0.54.0. It fixes a regression in
structured array type handling, a potential leak on
initialization failure in the CUDA target, a regression caused
by Numba’s vendored cloudpickle module resetting dynamic
classes and a few minor testing/infrastructure related
problems.
- Release summary for 0.54.0
* This release includes a significant number of new features,
important refactoring, critical bug fixes and a number of
dependency upgrades.
* Python language support enhancements:
- Basic support for f-strings.
- dict comprehensions are now supported.
- The sum built-in function is implemented.
* NumPy features/enhancements, The following functions are now
supported:
- np.clip
- np.iscomplex
- np.iscomplexobj
- np.isneginf
- np.isposinf
- np.isreal
- np.isrealobj
- np.isscalar
- np.random.dirichlet
- np.rot90
- np.swapaxes
* Also np.argmax has gained support for the axis keyword argument
and it’s now possible to use 0d NumPy arrays as scalars in
__setitem__ calls.
Internal changes:
* Debugging support through DWARF has been fixed and enhanced.
* Numba now optimises the way in which locals are emitted to help
reduce time spend in LLVM’s SROA passes.
CUDA target changes:
* Support for emitting lineinfo to be consumed by profiling tools
such as Nsight Compute
* Improved fastmath code generation for various trig, division,
and other functions
* Faster compilation using lazy addition of libdevice to compiled
units
* Support for IPC on Windows
* Support for passing tuples to CUDA ufuncs
* Performance warnings:
- When making implicit copies by calling a kernel on arrays in
host memory
- When occupancy is poor due to kernel or ufunc/gufunc
configuration
* Support for implementing warp-aggregated intrinsics:
- Using support for more CUDA functions: activemask(),
lanemask_lt()
- The ffs() function now works correctly!
* Support for @overload in the CUDA target
Intel kindly sponsored research and development that lead to a
number of new features and internal support changes:
* Dispatchers can now be retargetted to a new target via a user
defined context manager.
* Support for custom NumPy array subclasses has been added
(including an overloadable memory allocator).
* An inheritance based model for targets that permits targets to
share @overload implementations.
* Per function compiler flags with inheritance behaviours.
* The extension API now has support for overloading class methods
via the @overload_classmethod decorator.
Deprecations:
* The ROCm target (for AMD ROC GPUs) has been moved to an
“unmaintained” status and a seperate repository stub has been
created for it at: https://github.com/numba/numba-rocm
CUDA target deprecations and breaking changes:
* Relaxed strides checking is now the default when computing the
contiguity of device arrays.
* The inspect_ptx() method is deprecated. For use cases that
obtain PTX for further compilation outside of Numba, use
compile_ptx() instead.
* Eager compilation of device functions (the case when
device=True and a signature is provided) is deprecated.
Version support/dependency changes:
* LLVM 11 is now supported on all platforms via llvmlite.
* The minimum supported Python version is raised to 3.7.
* NumPy version 1.20 is supported.
* The minimum supported NumPy version is raised to 1.17 for
runtime (compilation however remains compatible with NumPy
1.11).
* Vendor cloudpickle v1.6.0 – now used for all pickle operations.
* TBB >= 2021 is now supported and all prior versions are
unsupported (not easily possible to maintain the ABI breaking
changes).
- Full release notes;
https://numba.readthedocs.io/en/0.54.1/release-notes.html
- Drop patches merged upstream:
* packaging-ignore-setuptools-deprecation.patch
* numba-pr6851-llvm-timings.patch
- Refresh skip-failing-tests.patch, fix-max-name-size.patch
- Add numba-pr7483-numpy1_21.patch gh#numba/numba#7176,
gh#numba/numba#7483
-------------------------------------------------------------------
Wed Mar 17 16:51:46 UTC 2021 - Ben Greiner <code@bnavigator.de>

35
python-numba.spec
View File

@ -1,5 +1,5 @@
#
# spec file for package python-numba-test
# spec file
#
# Copyright (c) 2021 SUSE LLC
#
@ -18,7 +18,6 @@
%{?!python_module:%define python_module() python-%{**} python3-%{**}}
%define skip_python2 1
# NEP 29: python36-numpy and -scipy no longer in TW
%define skip_python36 1
%global flavor @BUILD_FLAVOR@%{nil}
%if "%{flavor}" == "test"
@ -29,7 +28,7 @@
%bcond_with test
%endif
Name: python-numba%{psuffix}
Version: 0.53.0
Version: 0.54.1
Release: 0
Summary: NumPy-aware optimizing compiler for Python using LLVM
License: BSD-2-Clause
@ -37,22 +36,20 @@ URL: https://numba.pydata.org/
Source: https://files.pythonhosted.org/packages/source/n/numba/numba-%{version}.tar.gz
# PATCH-FIX-UPSTREAM fix-max-name-size.patch -- fix for gh#numba/numba#3876 -- from gh#numba/numba#4373
Patch0: fix-max-name-size.patch
# PATCH-FIX-UPSTREAM packaging-ignore-setuptools-deprecation.patch -- gh#numba/numba#6837
Patch1: https://github.com/numba/numba/pull/6837.patch#/packaging-ignore-setuptools-deprecation.patch
# PATCH-FIX-USPTREAM ignore empty system time column on llvm timings -- gh#numba/numba#6851
Patch2: numba-pr6851-llvm-timings.patch
# PATCH-FIX-UPSTREAM support numpy 1.21 -- gh#numba/numba#7176, gh#numba/numba#7483
Patch1: numba-pr7483-numpy1_21.patch
# PATCH-FIX-OPENSUSE skip tests failing due to OBS specifics
Patch3: skip-failing-tests.patch
BuildRequires: %{python_module devel}
BuildRequires: %{python_module numpy-devel >= 1.15}
BuildRequires: %{python_module devel >= 3.7}
BuildRequires: %{python_module numpy-devel >= 1.18}
BuildRequires: %{python_module setuptools}
BuildRequires: fdupes
BuildRequires: gcc-c++
BuildRequires: python-rpm-macros
BuildRequires: tbb-devel
Requires: python-llvmlite < 0.37
Requires: python-llvmlite >= 0.36
Requires: python-numpy >= 1.15
BuildRequires: tbb-devel >= 2021
Requires: python-llvmlite < 0.38
Requires: python-llvmlite >= 0.37
Requires: python-numpy >= 1.18
Requires: python-scipy >= 0.16
Requires(post): update-alternatives
Requires(preun):update-alternatives
@ -106,24 +103,20 @@ This package contains files for developing applications using numba.
%setup -q -n numba-%{version}
%autopatch -p1
# Incompatilbe numpy versions (?)
# Check these with every version update! (Last check 0.53)
# https://github.com/numba/numba/issues/5251
# https://numba.discourse.group/t/helping-test-numba-0-53-0-rc/519/34
rm numba/tests/test_np_functions.py
rm numba/tests/test_array_reductions.py
# timeouts randomly in OBS
rm numba/tests/test_typedlist.py
# if we reduced the amount of tests too much:
#sed -i -e '/check_testsuite_size/ s/5000/3000/' numba/tests/test_runtests.py
# sed -i -e '/check_testsuite_size/ s/5000/3000/' numba/tests/test_runtests.py
# our setup imports distutils. Not sure why, but should not be a problem.
sed -i -e "/def test_laziness/,/def/ {/'distutils',/ d}" numba/tests/test_import.py
sed -i -e '1{/env python/ d}' numba/misc/appdirs.py
%build
%if !%{with test}
export CFLAGS="%{optflags} -fPIC"
%python_build
%endif
%install
%if !%{with test}
@ -154,7 +147,7 @@ mv numba_temp numba
%post
%{python_install_alternative numba pycc}
%preun
%postun
%python_uninstall_alternative numba
%files %{python_files} -f devel-files-exclude-%{python_bin_suffix}.files

29
skip-failing-tests.patch
View File

@ -1,39 +1,38 @@
Index: numba-0.53.0/numba/tests/test_parfors.py
Index: numba-0.54.0/numba/tests/test_parfors.py
===================================================================
--- numba-0.53.0.orig/numba/tests/test_parfors.py
+++ numba-0.53.0/numba/tests/test_parfors.py
@@ -1649,7 +1649,7 @@ class TestParfors(TestParforsBase):
msg = ("The reshape API may only include one negative argument.")
self.assertIn(msg, str(raised.exception))
--- numba-0.54.0.orig/numba/tests/test_parfors.py
+++ numba-0.54.0/numba/tests/test_parfors.py
@@ -1146,6 +1146,7 @@ class TestParforNumPy(TestParforsBase):
self.check_variants(test_impl2, data_gen)
self.count_parfors_variants(test_impl2, data_gen)
- @skip_parfors_unsupported
+ @unittest.skip("Fails on type check in OBS")
def test_ndarray_fill(self):
def test_impl(x):
x.fill(7.0)
@@ -2842,7 +2842,7 @@ class TestParforsVectorizer(TestPrangeBa
@@ -3890,7 +3891,7 @@ class TestParforsVectorizer(TestPrangeBa
# to check vsqrtpd operates on zmm
match_vsqrtpd_on_zmm = re.compile('\n\s+vsqrtpd\s+.*zmm.*\n')
- @linux_only
+ @unittest.skip("Our x86_64 asm is most probably different from the Travis one.")
+ @unittest.skip("Our x86_64 asm is most probably different from the upstream one.")
def test_vectorizer_fastmath_asm(self):
""" This checks that if fastmath is set and the underlying hardware
is suitable, and the function supplied is amenable to fastmath based
@@ -2885,7 +2885,7 @@ class TestParforsVectorizer(TestPrangeBa
@@ -3933,7 +3934,7 @@ class TestParforsVectorizer(TestPrangeBa
# check no zmm addressing is present
self.assertTrue('zmm' not in v)
- @linux_only
+ @unittest.skip("Our x86_64 asm is most probably different from the Travis one.")
+ @unittest.skip("Our x86_64 asm is most probably different from the upstream one.")
def test_unsigned_refusal_to_vectorize(self):
""" This checks that if fastmath is set and the underlying hardware
is suitable, and the function supplied is amenable to fastmath based
Index: numba-0.53.0/numba/tests/test_parfors_passes.py
Index: numba-0.54.0/numba/tests/test_parfors_passes.py
===================================================================
--- numba-0.53.0.orig/numba/tests/test_parfors_passes.py
+++ numba-0.53.0/numba/tests/test_parfors_passes.py
@@ -512,6 +512,7 @@ class TestConvertLoopPass(BaseTest):
--- numba-0.54.0.orig/numba/tests/test_parfors_passes.py
+++ numba-0.54.0/numba/tests/test_parfors_passes.py
@@ -516,6 +516,7 @@ class TestConvertLoopPass(BaseTest):
str(raises.exception),
)