Fix for Python 3.10.0b1+
Fixes https://bugzilla.redhat.com/show_bug.cgi?id=1958052
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Miro Hrončok
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126
ad2a73c18d.patch
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126
ad2a73c18d.patch
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From ad2a73c18dcff95d844c382c94ab7f73b5571cf3 Mon Sep 17 00:00:00 2001
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From: Sebastian Berg <sebastian@sipsolutions.net>
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Date: Tue, 4 May 2021 17:43:26 -0500
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Subject: [PATCH] MAINT: Adjust NumPy float hashing to Python's slightly
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changed hash
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This is necessary, since we use the Python double hash and the
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semi-private function to calculate it in Python has a new signature
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to return the identity-hash when the value is NaN.
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closes gh-18833, gh-18907
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---
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numpy/core/src/common/npy_pycompat.h | 16 ++++++++++
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numpy/core/src/multiarray/scalartypes.c.src | 13 ++++----
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numpy/core/tests/test_scalarmath.py | 34 +++++++++++++++++++++
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3 files changed, 57 insertions(+), 6 deletions(-)
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diff --git a/numpy/core/src/common/npy_pycompat.h b/numpy/core/src/common/npy_pycompat.h
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index aa0b5c1224d3..9e94a971090a 100644
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--- a/numpy/core/src/common/npy_pycompat.h
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+++ b/numpy/core/src/common/npy_pycompat.h
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@@ -3,4 +3,20 @@
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#include "numpy/npy_3kcompat.h"
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+
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+/*
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+ * In Python 3.10a7 (or b1), python started using the identity for the hash
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+ * when a value is NaN. See https://bugs.python.org/issue43475
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+ */
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+#if PY_VERSION_HEX > 0x030a00a6
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+#define Npy_HashDouble _Py_HashDouble
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+#else
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+static NPY_INLINE Py_hash_t
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+Npy_HashDouble(PyObject *NPY_UNUSED(identity), double val)
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+{
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+ return _Py_HashDouble(val);
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+}
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+#endif
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+
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+
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#endif /* _NPY_COMPAT_H_ */
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diff --git a/numpy/core/src/multiarray/scalartypes.c.src b/numpy/core/src/multiarray/scalartypes.c.src
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index a001500b0a97..9930f7791d6e 100644
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--- a/numpy/core/src/multiarray/scalartypes.c.src
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+++ b/numpy/core/src/multiarray/scalartypes.c.src
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@@ -3172,7 +3172,7 @@ static npy_hash_t
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static npy_hash_t
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@lname@_arrtype_hash(PyObject *obj)
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{
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- return _Py_HashDouble((double) PyArrayScalar_VAL(obj, @name@));
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+ return Npy_HashDouble(obj, (double)PyArrayScalar_VAL(obj, @name@));
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}
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/* borrowed from complex_hash */
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@@ -3180,14 +3180,14 @@ static npy_hash_t
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c@lname@_arrtype_hash(PyObject *obj)
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{
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npy_hash_t hashreal, hashimag, combined;
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- hashreal = _Py_HashDouble((double)
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- PyArrayScalar_VAL(obj, C@name@).real);
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+ hashreal = Npy_HashDouble(
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+ obj, (double)PyArrayScalar_VAL(obj, C@name@).real);
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if (hashreal == -1) {
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return -1;
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}
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- hashimag = _Py_HashDouble((double)
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- PyArrayScalar_VAL(obj, C@name@).imag);
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+ hashimag = Npy_HashDouble(
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+ obj, (double)PyArrayScalar_VAL(obj, C@name@).imag);
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if (hashimag == -1) {
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return -1;
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}
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@@ -3202,7 +3202,8 @@ c@lname@_arrtype_hash(PyObject *obj)
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static npy_hash_t
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half_arrtype_hash(PyObject *obj)
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{
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- return _Py_HashDouble(npy_half_to_double(PyArrayScalar_VAL(obj, Half)));
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+ return Npy_HashDouble(
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+ obj, npy_half_to_double(PyArrayScalar_VAL(obj, Half)));
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}
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static npy_hash_t
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diff --git a/numpy/core/tests/test_scalarmath.py b/numpy/core/tests/test_scalarmath.py
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index d91b4a39146d..09a734284a76 100644
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--- a/numpy/core/tests/test_scalarmath.py
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+++ b/numpy/core/tests/test_scalarmath.py
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@@ -707,3 +707,37 @@
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shift_arr = np.array([shift]*32, dtype=dt)
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res_arr = op(val_arr, shift_arr)
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assert_equal(res_arr, res_scl)
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+
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+
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+class TestHash:
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+ @pytest.mark.parametrize("type_code", np.typecodes['AllInteger'])
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+ def test_integer_hashes(self, type_code):
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+ scalar = np.dtype(type_code).type
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+ for i in range(128):
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+ assert hash(i) == hash(scalar(i))
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+
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+ @pytest.mark.parametrize("type_code", np.typecodes['AllFloat'])
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+ def test_float_and_complex_hashes(self, type_code):
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+ scalar = np.dtype(type_code).type
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+ for val in [np.pi, np.inf, 3, 6.]:
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+ numpy_val = scalar(val)
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+ # Cast back to Python, in case the NumPy scalar has less precision
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+ if numpy_val.dtype.kind == 'c':
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+ val = complex(numpy_val)
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+ else:
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+ val = float(numpy_val)
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+ assert val == numpy_val
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+ print(repr(numpy_val), repr(val))
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+ assert hash(val) == hash(numpy_val)
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+
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+ if hash(float(np.nan)) != hash(float(np.nan)):
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+ # If Python distinguises different NaNs we do so too (gh-18833)
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+ assert hash(scalar(np.nan)) != hash(scalar(np.nan))
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+
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+ @pytest.mark.parametrize("type_code", np.typecodes['Complex'])
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+ def test_complex_hashes(self, type_code):
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+ # Test some complex valued hashes specifically:
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+ scalar = np.dtype(type_code).type
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+ for val in [np.pi+1j, np.inf-3j, 3j, 6.+1j]:
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+ numpy_val = scalar(val)
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+ assert hash(complex(numpy_val)) == hash(numpy_val)
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@ -30,6 +30,11 @@ URL: http://www.numpy.org/
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Source0: https://github.com/%{name}/%{name}/releases/download/v%{version}/%{name}-%{version}.tar.gz
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Source1: https://numpy.org/doc/1.19/numpy-html.zip
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# Adjust NumPy float hashing to Python's slightly changed hash (Python 3.10.0b1+)
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# Merged upstream, rebased slightly
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# https://github.com/numpy/numpy/commit/ad2a73c18d.patch
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Patch1: ad2a73c18d.patch
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%description
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NumPy is a general-purpose array-processing package designed to
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efficiently manipulate large multi-dimensional arrays of arbitrary
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