From 9a6554a1b6cfeeb27c58fc3bc9ec72be03a6a1cf Mon Sep 17 00:00:00 2001
From: Graham Markall <gmarkall@nvidia.com>
Date: Thu, 24 Nov 2022 15:41:24 +0000
Subject: [PATCH 01/13] CUDA intrinsics tests: correct np.float -> np.float16

I believe this was written in error and should always have been float16.
---
 numba/cuda/tests/cudapy/test_intrinsics.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

--- a/numba/cuda/tests/cudapy/test_intrinsics.py
+++ b/numba/cuda/tests/cudapy/test_intrinsics.py
@@ -619,7 +619,7 @@
         arg2 = np.float16(4.)
         compiled[1, 1](ary, arg1, arg2)
         np.testing.assert_allclose(ary[0], arg2)
-        arg1 = np.float(5.)
+        arg1 = np.float16(5.)
         compiled[1, 1](ary, arg1, arg2)
         np.testing.assert_allclose(ary[0], arg1)
 
@@ -631,7 +631,7 @@
         arg2 = np.float16(4.)
         compiled[1, 1](ary, arg1, arg2)
         np.testing.assert_allclose(ary[0], arg1)
-        arg1 = np.float(5.)
+        arg1 = np.float16(5.)
         compiled[1, 1](ary, arg1, arg2)
         np.testing.assert_allclose(ary[0], arg2)
 
--- a/numba/tests/test_linalg.py
+++ b/numba/tests/test_linalg.py
@@ -1122,6 +1122,32 @@
     Tests for np.linalg.svd.
     """
 
+    # This checks that A ~= U*S*V**H, i.e. SV decomposition ties out.  This is
+    # required as NumPy uses only double precision LAPACK routines and
+    # computation of SVD is numerically sensitive. Numba uses type-specific
+    # routines and therefore sometimes comes out with a different answer to
+    # NumPy (orthonormal bases are not unique, etc.).
+
+    def check_reconstruction(self, a, got, expected):
+        u, sv, vt = got
+
+        # Check they are dimensionally correct
+        for k in range(len(expected)):
+            self.assertEqual(got[k].shape, expected[k].shape)
+
+        # Columns in u and rows in vt dictates the working size of s
+        s = np.zeros((u.shape[1], vt.shape[0]))
+        np.fill_diagonal(s, sv)
+
+        rec = np.dot(np.dot(u, s), vt)
+        resolution = np.finfo(a.dtype).resolution
+        np.testing.assert_allclose(
+            a,
+            rec,
+            rtol=10 * resolution,
+            atol=100 * resolution  # zeros tend to be fuzzy
+        )
+
     @needs_lapack
     def test_linalg_svd(self):
         """
@@ -1150,34 +1176,8 @@
                     # plain match failed, test by reconstruction
                     use_reconstruction = True
 
-            # if plain match fails then reconstruction is used.
-            # this checks that A ~= U*S*V**H
-            # i.e. SV decomposition ties out
-            # this is required as numpy uses only double precision lapack
-            # routines and computation of svd is numerically
-            # sensitive, numba using the type specific routines therefore
-            # sometimes comes out with a different answer (orthonormal bases
-            # are not unique etc.).
             if use_reconstruction:
-                u, sv, vt = got
-
-                # check they are dimensionally correct
-                for k in range(len(expected)):
-                    self.assertEqual(got[k].shape, expected[k].shape)
-
-                # regardless of full_matrices cols in u and rows in vt
-                # dictates the working size of s
-                s = np.zeros((u.shape[1], vt.shape[0]))
-                np.fill_diagonal(s, sv)
-
-                rec = np.dot(np.dot(u, s), vt)
-                resolution = np.finfo(a.dtype).resolution
-                np.testing.assert_allclose(
-                    a,
-                    rec,
-                    rtol=10 * resolution,
-                    atol=100 * resolution  # zeros tend to be fuzzy
-                )
+                self.check_reconstruction(a, got, expected)
 
             # Ensure proper resource management
             with self.assertNoNRTLeak():
@@ -1238,8 +1238,11 @@
         got = func(X, False)
         np.testing.assert_allclose(X, X_orig)
 
-        for e_a, g_a in zip(expected, got):
-            np.testing.assert_allclose(e_a, g_a)
+        try:
+            for e_a, g_a in zip(expected, got):
+                np.testing.assert_allclose(e_a, g_a)
+        except AssertionError:
+            self.check_reconstruction(X, got, expected)
 
 
 class TestLinalgQr(TestLinalgBase):
--- a/numba/tests/test_comprehension.py
+++ b/numba/tests/test_comprehension.py
@@ -11,6 +11,7 @@
 from numba.core import types, utils
 from numba.core.errors import TypingError, LoweringError
 from numba.core.types.functions import _header_lead
+from numba.np.numpy_support import numpy_version
 from numba.tests.support import tag, _32bit, captured_stdout
 
 
@@ -360,6 +361,7 @@
         self.check(comp_nest_with_array_conditional, 5,
                    assert_allocate_list=True)
 
+    @unittest.skipUnless(numpy_version < (1, 24), 'Removed in NumPy 1.24')
     def test_comp_nest_with_dependency(self):
         def comp_nest_with_dependency(n):
             l = np.array([[i * j for j in range(i+1)] for i in range(n)])
--- a/numba/stencils/stencilparfor.py
+++ b/numba/stencils/stencilparfor.py
@@ -21,6 +21,7 @@
                             find_callname, require, find_const, GuardException)
 from numba.core.errors import NumbaValueError
 from numba.core.utils import OPERATORS_TO_BUILTINS
+from numba.np import numpy_support
 
 
 def _compute_last_ind(dim_size, index_const):
@@ -264,7 +265,11 @@
             dtype_g_np_assign = ir.Assign(dtype_g_np, dtype_g_np_var, loc)
             init_block.body.append(dtype_g_np_assign)
 
-            dtype_np_attr_call = ir.Expr.getattr(dtype_g_np_var, return_type.dtype.name, loc)
+            return_type_name = numpy_support.as_dtype(
+                               return_type.dtype).type.__name__
+            if return_type_name == 'bool':
+                return_type_name = 'bool_'
+            dtype_np_attr_call = ir.Expr.getattr(dtype_g_np_var, return_type_name, loc)
             dtype_attr_var = ir.Var(scope, mk_unique_var("$np_attr_attr"), loc)
             self.typemap[dtype_attr_var.name] = types.functions.NumberClass(return_type.dtype)
             dtype_attr_assign = ir.Assign(dtype_np_attr_call, dtype_attr_var, loc)
--- a/numba/tests/test_mathlib.py
+++ b/numba/tests/test_mathlib.py
@@ -516,7 +516,7 @@
             with warnings.catch_warnings():
                 warnings.simplefilter("error", RuntimeWarning)
                 self.assertRaisesRegexp(RuntimeWarning,
-                                        'overflow encountered in .*_scalars',
+                                        'overflow encountered in .*scalar',
                                         naive_hypot, val, val)
 
     def test_hypot_npm(self):
--- a/numba/tests/test_array_methods.py
+++ b/numba/tests/test_array_methods.py
@@ -1193,7 +1193,7 @@
         pyfunc = array_sum_dtype_kws
         cfunc = jit(nopython=True)(pyfunc)
         all_dtypes = [np.float64, np.float32, np.int64, np.int32, np.uint32,
-                      np.uint64, np.complex64, np.complex128, TIMEDELTA_M]
+                      np.uint64, np.complex64, np.complex128]
         all_test_arrays = [
             [np.ones((7, 6, 5, 4, 3), arr_dtype),
              np.ones(1, arr_dtype),
@@ -1207,8 +1207,7 @@
                       np.dtype('uint32'): [np.float64, np.int64, np.float32],
                       np.dtype('uint64'): [np.float64, np.int64],
                       np.dtype('complex64'): [np.complex64, np.complex128],
-                      np.dtype('complex128'): [np.complex128],
-                      np.dtype(TIMEDELTA_M): [np.dtype(TIMEDELTA_M)]}
+                      np.dtype('complex128'): [np.complex128]}
 
         for arr_list in all_test_arrays:
             for arr in arr_list:
@@ -1216,15 +1215,15 @@
                     subtest_str = ("Testing np.sum with {} input and {} output"
                                    .format(arr.dtype, out_dtype))
                     with self.subTest(subtest_str):
-                        self.assertPreciseEqual(pyfunc(arr, dtype=out_dtype),
-                                                cfunc(arr, dtype=out_dtype))
+                            self.assertPreciseEqual(pyfunc(arr, dtype=out_dtype),
+                                                    cfunc(arr, dtype=out_dtype))
 
     def test_sum_axis_dtype_kws(self):
         """ test sum with axis and dtype parameters over a whole range of dtypes """
         pyfunc = array_sum_axis_dtype_kws
         cfunc = jit(nopython=True)(pyfunc)
         all_dtypes = [np.float64, np.float32, np.int64, np.int32, np.uint32,
-                      np.uint64, np.complex64, np.complex128, TIMEDELTA_M]
+                      np.uint64, np.complex64, np.complex128]
         all_test_arrays = [
             [np.ones((7, 6, 5, 4, 3), arr_dtype),
              np.ones(1, arr_dtype),
@@ -1238,9 +1237,7 @@
                       np.dtype('uint32'): [np.float64, np.int64, np.float32],
                       np.dtype('uint64'): [np.float64, np.uint64],
                       np.dtype('complex64'): [np.complex64, np.complex128],
-                      np.dtype('complex128'): [np.complex128],
-                      np.dtype(TIMEDELTA_M): [np.dtype(TIMEDELTA_M)],
-                      np.dtype(TIMEDELTA_Y): [np.dtype(TIMEDELTA_Y)]}
+                      np.dtype('complex128'): [np.complex128]}
 
         for arr_list in all_test_arrays:
             for arr in arr_list:
--- a/numba/tests/test_np_functions.py
+++ b/numba/tests/test_np_functions.py
@@ -932,11 +932,11 @@
             yield np.inf, None
             yield np.PINF, None
             yield np.asarray([-np.inf, 0., np.inf]), None
-            yield np.NINF, np.zeros(1, dtype=np.bool)
-            yield np.inf, np.zeros(1, dtype=np.bool)
-            yield np.PINF, np.zeros(1, dtype=np.bool)
+            yield np.NINF, np.zeros(1, dtype=np.bool_)
+            yield np.inf, np.zeros(1, dtype=np.bool_)
+            yield np.PINF, np.zeros(1, dtype=np.bool_)
             yield np.NINF, np.empty(12)
-            yield np.asarray([-np.inf, 0., np.inf]), np.zeros(3, dtype=np.bool)
+            yield np.asarray([-np.inf, 0., np.inf]), np.zeros(3, dtype=np.bool_)
 
         pyfuncs = [isneginf, isposinf]
         for pyfunc in pyfuncs:
@@ -4775,6 +4775,7 @@
         eval(compile(funcstr, '<string>', 'exec'))
         return locals()['foo']
 
+    @unittest.skipIf(numpy_version >= (1, 24), "NumPy < 1.24 required")
     def test_MachAr(self):
         attrs = ('ibeta', 'it', 'machep', 'eps', 'negep', 'epsneg', 'iexp',
                  'minexp', 'xmin', 'maxexp', 'xmax', 'irnd', 'ngrd',
@@ -4817,7 +4818,8 @@
             cfunc = jit(nopython=True)(iinfo)
             cfunc(np.float64(7))
 
-    @unittest.skipUnless(numpy_version >= (1, 22), "Needs NumPy >= 1.22")
+    @unittest.skipUnless((1, 22) <= numpy_version < (1, 24),
+                         "Needs NumPy >= 1.22, < 1.24")
     @TestCase.run_test_in_subprocess
     def test_np_MachAr_deprecation_np122(self):
         # Tests that Numba is replaying the NumPy 1.22 deprecation warning
--- a/numba/np/arraymath.py
+++ b/numba/np/arraymath.py
@@ -4177,6 +4177,10 @@
 # This module is imported under the compiler lock which should deal with the
 # lack of thread safety in the warning filter.
 def _gen_np_machar():
+    # NumPy 1.24 removed np.MachAr
+    if numpy_version >= (1, 24):
+        return
+
     np122plus = numpy_version >= (1, 22)
     w = None
     with warnings.catch_warnings(record=True) as w:
--- a/numba/np/ufunc/_internal.c
+++ b/numba/np/ufunc/_internal.c
@@ -285,9 +285,7 @@
     PyCFunctionWithKeywords ufunc_accumulate;
     PyCFunctionWithKeywords ufunc_reduceat;
     PyCFunctionWithKeywords ufunc_outer;
-#if NPY_API_VERSION >= 0x00000008
     PyCFunction ufunc_at;
-#endif
 } ufunc_dispatch;
 
 static int
@@ -303,10 +301,8 @@
             if (strncmp(crnt_name, "accumulate", 11) == 0) {
                 ufunc_dispatch.ufunc_accumulate =
                     (PyCFunctionWithKeywords)crnt->ml_meth;
-#if NPY_API_VERSION >= 0x00000008
             } else if (strncmp(crnt_name, "at", 3) == 0) {
                 ufunc_dispatch.ufunc_at = crnt->ml_meth;
-#endif
             } else {
                 result = -1;
             }
@@ -326,10 +322,15 @@
             } else if (strncmp(crnt_name, "reduceat", 9) == 0) {
                 ufunc_dispatch.ufunc_reduceat =
                     (PyCFunctionWithKeywords)crnt->ml_meth;
+            } else if (strncmp(crnt_name, "resolve_dtypes", 15) == 0) {
+              /* Ignored */
             } else {
                 result = -1;
             }
             break;
+        case '_':
+            // We ignore private methods
+            break;
         default:
             result = -1; /* Unknown method */
         }
@@ -341,6 +342,8 @@
                 *numpy_uses_fastcall = crnt->ml_flags & METH_FASTCALL;
             }
             else if (*numpy_uses_fastcall != (crnt->ml_flags & METH_FASTCALL)) {
+                PyErr_SetString(PyExc_RuntimeError,
+                    "ufunc.at() flags do not match numpy_uses_fastcall");
                 return -1;
             }
         }
@@ -351,11 +354,13 @@
                   && (ufunc_dispatch.ufunc_accumulate != NULL)
                   && (ufunc_dispatch.ufunc_reduceat != NULL)
                   && (ufunc_dispatch.ufunc_outer != NULL)
-#if NPY_API_VERSION >= 0x00000008
                   && (ufunc_dispatch.ufunc_at != NULL)
-#endif
                   );
+    } else {
+        char const * const fmt = "Unexpected ufunc method %s()";
+        PyErr_Format(PyExc_RuntimeError, fmt, crnt_name);
     }
+
     return result;
 }
 
@@ -425,13 +430,11 @@
 }
 
 
-#if NPY_API_VERSION >= 0x00000008
 static PyObject *
 dufunc_at(PyDUFuncObject * self, PyObject * args)
 {
     return ufunc_dispatch.ufunc_at((PyObject*)self->ufunc, args);
 }
-#endif
 
 static PyObject *
 dufunc__compile_for_args(PyDUFuncObject * self, PyObject * args,
@@ -609,11 +612,9 @@
     {"outer",
         (PyCFunction)dufunc_outer,
         METH_VARARGS | 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,
@@ -643,11 +644,9 @@
     {"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,
@@ -791,9 +790,7 @@
     if (PyModule_AddIntMacro(m, PyUFunc_One)
         || PyModule_AddIntMacro(m, PyUFunc_Zero)
         || PyModule_AddIntMacro(m, PyUFunc_None)
-#if NPY_API_VERSION >= 0x00000007
         || PyModule_AddIntMacro(m, PyUFunc_ReorderableNone)
-#endif
         )
         return MOD_ERROR_VAL;
 
--- a/docs/source/user/installing.rst
+++ b/docs/source/user/installing.rst
@@ -5,7 +5,7 @@
 Compatibility
 -------------
 
-Numba is compatible with Python 3.7--3.10, and Numpy versions 1.18 up to 1.23.
+Numba is compatible with Python 3.7--3.10, and Numpy versions 1.21--1.24.
 
 Our supported platforms are:
 
@@ -276,7 +276,7 @@
 +----------++--------------+---------------------------+----------------------------+------------------------------+-------------------+-----------------------------+
 | Numba     | Release date | Python                    | NumPy                      | llvmlite                     | LLVM              | TBB                         |
 +===========+==============+===========================+============================+==============================+===================+=============================+
-| 0.57.x    | TBC          | 3.8.x <= version < 3.12   | 1.19 <= version < 1.24     | 0.40.x                       | 11.x              | 2021.x                      |
+| 0.57.0    | TBC          | 3.8.x <= version < 3.12   | 1.21 <= version < 1.24     | 0.40.x                       | 11.x              | 2021.x                      |
 +-----------+--------------+---------------------------+----------------------------+------------------------------+-------------------+-----------------------------+
 | 0.56.4    | 2022-11-03   | 3.7.x <= version < 3.11   | 1.18 <= version < 1.24     | 0.39.x                       | 11.x              | 2021.x                      |
 +-----------+--------------+---------------------------+----------------------------+------------------------------+-------------------+-----------------------------+
--- a/setup.py
+++ b/setup.py
@@ -24,7 +24,7 @@
 max_python_version = "3.11"  # exclusive
 min_numpy_build_version = "1.11"
 min_numpy_run_version = "1.18"
-max_numpy_run_version = "1.24"
+max_numpy_run_version = "1.25"
 min_llvmlite_version = "0.39.0dev0"
 max_llvmlite_version = "0.40"
 
--- a/numba/__init__.py
+++ b/numba/__init__.py
@@ -147,8 +147,8 @@
 
     if numpy_version < (1, 18):
         raise ImportError("Numba needs NumPy 1.18 or greater")
-    elif numpy_version > (1, 23):
-        raise ImportError("Numba needs NumPy 1.23 or less")
+    elif numpy_version > (1, 24):
+        raise ImportError("Numba needs NumPy 1.24 or less")
 
     try:
         import scipy