Accepting request 946610 from devel:languages:python:numeric

OBS-URL: https://build.opensuse.org/request/show/946610
OBS-URL: https://build.opensuse.org/package/show/openSUSE:Factory/python-numba?expand=0&rev=31

numba-0.54.1.tar.gz

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

numba-0.55.0.tar.gz

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b221ebd997662a1dcb77e4f09140e022fbfe7577ad0781fc2e0214135dba6cbd
+size 2300580

numba-pr7483-numpy1_21.patch

@@ -1,242 +0,0 @@
-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)
- 

numba-pr7748-random32bitwidth.patch

@@ -0,0 +1,58 @@
+From e6df66d52152156ba8bcda64b37f4995bda72d2f Mon Sep 17 00:00:00 2001
+From: Graham Markall <gmarkall@nvidia.com>
+Date: Fri, 14 Jan 2022 21:24:40 +0000
+Subject: [PATCH] Fix #7713: Ensure _prng_random_hash return has correct
+ bitwidth
+
+When the hash width is 32 bits, get_next_int32 needs to be used because
+that returns a 32-bit integer, as opposed to get_next_int, which returns
+a 64-bit integer regardless of the supplied bitwidth.
+
+This commit also alters the signature of _prng_random_hash to use
+_Py_hash_t as the return type - it will be equal to types.intp, but the
+intent should be clearer.
+---
+ numba/cpython/hashing.py | 21 +++++++++++++++++----
+ 1 file changed, 17 insertions(+), 4 deletions(-)
+
+diff --git a/numba/cpython/hashing.py b/numba/cpython/hashing.py
+index 5a8673b04b..227fc4e1c5 100644
+--- a/numba/cpython/hashing.py
++++ b/numba/cpython/hashing.py
+@@ -18,7 +18,8 @@
+ from numba.core import errors
+ from numba.core import types, utils
+ from numba.core.unsafe.bytes import grab_byte, grab_uint64_t
+-from numba.cpython.randomimpl import get_state_ptr, get_next_int, const_int
++from numba.cpython.randomimpl import (const_int, get_next_int, get_next_int32,
++                                      get_state_ptr)
+ 
+ _py38_or_later = utils.PYVERSION >= (3, 8)
+ _py310_or_later = utils.PYVERSION >= (3, 10)
+@@ -137,11 +138,23 @@ def _prng_random_hash(tyctx):
+ 
+     def impl(cgctx, builder, signature, args):
+         state_ptr = get_state_ptr(cgctx, builder, "internal")
+-        bits = const_int(types.intp.bitwidth)
+-        value = get_next_int(cgctx, builder, state_ptr, bits, False)
++        bits = const_int(_hash_width)
++
++        # Why not just use get_next_int() with the correct bitwidth?
++        # get_next_int() always returns an i64, because the bitwidth it is
++        # passed may not be a compile-time constant, so it needs to allocate
++        # the largest unit of storage that may be required. Therefore, if the
++        # hash width is 32, then we need to use get_next_int32() to ensure we
++        # don't return a wider-than-expected hash, even if everything above
++        # the low 32 bits would have been zero.
++        if _hash_width == 32:
++            value = get_next_int32(cgctx, builder, state_ptr)
++        else:
++            value = get_next_int(cgctx, builder, state_ptr, bits, False)
++
+         return value
+ 
+-    sig = types.intp()
++    sig = _Py_hash_t()
+     return sig, impl
+ 
+ 

python-numba.changes

@@ -1,3 +1,73 @@
+-------------------------------------------------------------------
+Fri Jan 14 16:55:37 UTC 2022 - Ben Greiner <code@bnavigator.de>
+
+- Update to 0.55.0
+  * This release includes a significant number important dependency
+    upgrades along with a number of new features and bug fixes.
+  * NOTE: Due to NumPy CVE-2021-33430 this release has bypassed the
+    usual release process so as to promptly provide a Numba release
+    that supports NumPy 1.21. A single release candidate (RC1) was
+    made and a few issues were reported, these are summarised as
+    follows and will be fixed in a subsequent 0.55.1 release.
+  * Known issues with this release:
+    - Incorrect result copying array-typed field of structured
+      array (#7693)
+    - Two issues in DebugInfo generation (#7726, #7730)
+    - Compilation failure for hash of floating point values on 32
+      bit Windows when using Python 3.10 (#7713).
+  * Support for Python 3.10
+  * Support for NumPy 1.21
+  * The minimum supported NumPy version is raised to 1.18 for
+    runtime (compilation however remains compatible with NumPy
+    1.11).
+  * Experimental support for isinstance.
+  * The following functions are now supported:
+    - np.broadcast_to
+    - np.float_power
+    - np.cbrt
+    - np.logspace
+    - np.take_along_axis
+    - np.average
+    - np.argmin gains support for the axis kwarg.
+    - np.ndarray.astype gains support for types expressed as
+      literal strings.
+  * For users of the Numba extension API, Numba now has a new error
+    handling mode whereby it will treat all exceptions that do not
+    inherit from numba.errors.NumbaException as a “hard error” and
+    immediately unwind the stack. This makes it much easier to
+    debug when writing @overloads etc from the extension API as
+    there’s now no confusion between Python errors and Numba
+    errors. This feature can be enabled by setting the environment
+    variable: NUMBA_CAPTURED_ERRORS='new_style'.
+  * The threading layer selection priority can now be changed via
+    the environment variable NUMBA_THREADING_LAYER_PRIORITY.
+  * Support for NVIDIA’s CUDA Python bindings.
+  * Support for 16-bit floating point numbers and their basic
+    operations via intrinsics.
+  * Streams are provided in the Stream.async_done result, making it
+    easier to implement asynchronous work queues.
+  * Support for structured types in device arrays, character
+    sequences in NumPy arrays, and some array operations on nested
+    arrays.
+  * Much underlying refactoring to align the CUDA target more
+    closely with the CPU target, which lays the groudwork for
+    supporting the high level extension API in CUDA in future
+    releases.
+  * Intel also kindly sponsored research and development into
+    native debug (DWARF) support and handling per-function
+    compilation flags:
+  * Line number/location tracking is much improved.
+  * Numba’s internal representation of containers (e.g. tuples,
+    arrays) are now encoded as structures.
+  * Numba’s per-function compilation flags are encoded into the ABI
+    field of the mangled name of the function such that it’s
+    possible to compile and differentiate between versions of the
+    same function with different flags set.
+  * There are no new general deprecations.
+  * There are no new CUDA target deprecations.
+- Drop numba-pr7483-numpy1_21.patch
+- Add numba-pr7748-random32bitwidth.patch -- gh#numba/numba#7748
+
 -------------------------------------------------------------------
 Sat Jan  8 22:19:07 UTC 2022 - Ben Greiner <code@bnavigator.de>
 

python-numba.spec

@@ -16,9 +16,8 @@
 #
 
 
-%{?!python_module:%define python_module() python-%{**} python3-%{**}}
+%{?!python_module:%define python_module() python3-%{**}}
 %define skip_python2 1
-%define skip_python310 1
 %define plainpython python
 %global flavor @BUILD_FLAVOR@%{nil}
 %if "%{flavor}" == "test"
@@ -29,7 +28,7 @@
 %bcond_with test
 %endif
 Name:           python-numba%{psuffix}
-Version:        0.54.1
+Version:        0.55.0
 Release:        0
 Summary:        NumPy-aware optimizing compiler for Python using LLVM
 License:        BSD-2-Clause
@@ -37,26 +36,25 @@ 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 support numpy 1.21 -- gh#numba/numba#7176, gh#numba/numba#7483, gh#numpy/numpy#20376
+# PATCH-FIX-UPSTREAM numba-pr7748-random32bitwidth.patch -- gh#numba/numba#7748
-Patch1:         numba-pr7483-numpy1_21.patch
+Patch1:         numba-pr7748-random32bitwidth.patch
 # PATCH-FIX-OPENSUSE skip tests failing due to OBS specifics
 Patch3:         skip-failing-tests.patch
-BuildRequires:  %{python_module devel >= 3.7 with %python-devel < 3.10}
+BuildRequires:  %{python_module devel >= 3.7}
 BuildRequires:  %{python_module numpy-devel >= 1.18 with %python-numpy-devel < 1.22}
 BuildRequires:  %{python_module setuptools}
 BuildRequires:  fdupes
 BuildRequires:  gcc-c++
 BuildRequires:  python-rpm-macros
 BuildRequires:  tbb-devel >= 2021
-Requires:       python-llvmlite < 0.38
+Requires:       (python-llvmlite >= 0.38 with python-llvmlite < 0.39)
-Requires:       python-llvmlite >= 0.37
-Requires:       python-scipy >= 0.16
 Requires:       (python-numpy >= 1.18 with python-numpy < 1.22)
 Requires(post): update-alternatives
 Requires(preun):update-alternatives
 Recommends:     python-Jinja2
 Recommends:     python-Pygments
 Recommends:     python-cffi
+Recommends:     python-scipy > 1.0
 Recommends:     python-tbb
 %if %{with test}
 BuildRequires:  %{python_module Jinja2}
@@ -69,7 +67,7 @@ BuildRequires:  %{python_module numba-devel >= %{version}}
 BuildRequires:  %{python_module pip}
 BuildRequires:  %{python_module psutil}
 BuildRequires:  %{python_module pytest}
-BuildRequires:  %{python_module scipy >= 0.16}
+BuildRequires:  %{python_module scipy >= 1.0}
 BuildRequires:  %{python_module tbb}
 %endif
 %python_subpackages
@@ -102,17 +100,13 @@ Requires:       %{plainpython}(abi) = %{python_version}
 This package contains files for developing applications using numba.
 
 %prep
-%setup -q -n numba-%{version}
+%autosetup -p1 -n numba-%{version}
-%autopatch -p1
+sed -i -e '1{/env python/ d}' numba/misc/appdirs.py
 
-# timeouts randomly in OBS
+# random timeouts 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
-# 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}
@@ -136,13 +130,14 @@ sed 's|^%{buildroot}|%%exclude |' devel-files0-%{$python_bin_suffix}.files > dev
 
 %check
 %if %{with test}
-mv numba numba_temp
+# test the installed package, not the source without compiled modules
-export NUMBA_PARALLEL_DIAGNOSTICS=1
+mkdir emtpytestdir
-%{python_expand # test the installed package
+pushd emtpytestdir
+%{python_expand # numbatests: check specific tests with `osc build -M test --define="numbatests <testnames>"`
 %{_bindir}/numba-%{$python_bin_suffix} -s
-$python -m numba.runtests -v -b --exclude-tags='long_running' -m %{_smp_build_ncpus} -- numba.tests
+$python -m numba.runtests -v -b --exclude-tags='long_running' -m %{_smp_build_ncpus} -- %{?!numbatests:numba.tests}%{?numbatests}
 }
-mv numba_temp numba
+popd
 %endif
 
 %if !%{with test}