Merge pull request easybuilders#19419 from Flamefire/20231214094225_n…

…ew_pr_SciPy-bundle202307

Fix numpy build on Sapphire Rapids CPUs in SciPy-bundle-2023.07-gfbf-2023a

easybuild/easyconfigs/s/SciPy-bundle/SciPy-bundle-2023.07-gfbf-2023a.eb

@@ -29,11 +29,24 @@ use_pip = True
 # order is important!
 exts_list = [
     ('numpy', '1.25.1', {
-        'patches': ['numpy-1.22.3_disable-broken-override-test.patch'],
+        'patches': [
+            'numpy-1.22.3_disable-broken-override-test.patch',
+            ('numpy-1.25.1_fix-duplicate-avx512-symbols.patch', 'numpy/core/src/npysort/x86-simd-sort'),
+            'numpy-1.25.1_fix-undefined-avx512-reference.patch',
+            'numpy-1.25.1_fix-test_features.patch',
+            'numpy-1.25.1_fix-test_half.patch',
+        ],
         'checksums': [
             {'numpy-1.25.1.tar.gz': '9a3a9f3a61480cc086117b426a8bd86869c213fc4072e606f01c4e4b66eb92bf'},
             {'numpy-1.22.3_disable-broken-override-test.patch':
              '9c589bb073b28b25ff45eb3c63c57966aa508dd8b318d0b885b6295271e4983c'},
+            {'numpy-1.25.1_fix-duplicate-avx512-symbols.patch':
+             '8e32087c279b7193ae3507953480601200c9eff021819f3001d78c232c5852e6'},
+            {'numpy-1.25.1_fix-undefined-avx512-reference.patch':
+             'c4b66da93bf36071663f122de1ae668386cc6ab0154d21fa3e14ed7ddfe2a72c'},
+            {'numpy-1.25.1_fix-test_features.patch':
+             '1c05ee5d105fe2f824416dd6dd5c64ed0c1cd710a002b4e6dbfafff19203adc5'},
+            {'numpy-1.25.1_fix-test_half.patch': '341b99ae1801feebf382c92591794eeefdf451bc34b98f20aa985ea897488951'},
         ],
     }),
     ('ply', '3.11', {
@@ -52,6 +65,8 @@ exts_list = [
         'checksums': ['5ab283b9857211d61b53318b7c792cf68e798e765ee17c27ade9f6c924235731'],
     }),
     ('scipy', '1.11.1', {
+        'enable_slow_tests': True,
+        'ignore_test_result': False,
         'patches': [
             'scipy-1.11.1_disable-tests.patch',
             'scipy-1.11.1_xfail-aarch64_test_maxiter_worsening.patch',
@@ -62,8 +77,6 @@ exts_list = [
             {'scipy-1.11.1_xfail-aarch64_test_maxiter_worsening.patch':
              '918c8e6fa8215d459126f267764c961bde729ea4a116c7f6287cddfdc58ffcea'},
         ],
-        'enable_slow_tests': True,
-        'ignore_test_result': False,
     }),
     ('numexpr', '2.8.4', {
         'checksums': ['d5432537418d18691b9115d615d6daa17ee8275baef3edf1afbbf8bc69806147'],
@@ -82,8 +95,8 @@ exts_list = [
         'checksums': ['7a28eb2a9774d00c7bc92411c19a89209d5da7c4c9a9e227be8330a23a25b91f'],
     }),
     ('deap', '1.4.0', {
-        'checksums': ['ffef2921932a0edbe634fcb6d156189e7a364bf638a2af4ae5d59931a9a4c8cc'],
         'modulename': 'deap.base',
+        'checksums': ['ffef2921932a0edbe634fcb6d156189e7a364bf638a2af4ae5d59931a9a4c8cc'],
     }),
 ]
 

easybuild/easyconfigs/s/SciPy-bundle/numpy-1.25.1_fix-test_half.patch

@@ -0,0 +1,69 @@
+test_half_conversions fails when there is hardware support for Float16 (e.g. AVX512) as NaNs are not kept the same.
+Error looks like:
+        # Convert from float64 back to float16
+        b = np.array(self.all_f64, dtype=float16)
+        bv = b.view(dtype=uint16)
+        fv = self.all_f16.view(dtype=uint16)
+>       assert_equal(self.all_f16.view(dtype=uint16),
+                     b.view(dtype=uint16))
+...
+E           AssertionError: 
+E           Arrays are not equal
+E           
+E           Mismatched elements: 1022 / 65536 (1.56%)
+E           Max absolute difference: 512
+E           Max relative difference: 0.01587252
+E            x: array([    0,     1,     2, ..., 65533, 65534, 65535], dtype=uint16)
+E            y: array([    0,     1,     2, ..., 65533, 65534, 65535], dtype=uint16)
+
+Deeper investigation shows a difference of exactly 512 in indices 31745-32255 & 64513-65023
+
+Fix using https://github.com/numpy/numpy/commit/7a84442b1caa4904a9b8e58bd6b93045b4ad350f
+from Sayed Adel <[email protected]>
+
+Author: Alexander Grund (TU Dresden)
+
+diff --git a/numpy/core/tests/test_half.py b/numpy/core/tests/test_half.py
+index ca849ad52..3e72eba89 100644
+--- a/numpy/core/tests/test_half.py
++++ b/numpy/core/tests/test_half.py
+@@ -21,8 +21,11 @@ def setup_method(self):
+         # An array of all possible float16 values
+         self.all_f16 = np.arange(0x10000, dtype=uint16)
+         self.all_f16.dtype = float16
+-        self.all_f32 = np.array(self.all_f16, dtype=float32)
+-        self.all_f64 = np.array(self.all_f16, dtype=float64)
++
++        # NaN value can cause an invalid FP exception if HW is been used
++        with np.errstate(invalid='ignore'):
++            self.all_f32 = np.array(self.all_f16, dtype=float32)
++            self.all_f64 = np.array(self.all_f16, dtype=float64)
+
+         # An array of all non-NaN float16 values, in sorted order
+         self.nonan_f16 = np.concatenate(
+@@ -44,14 +47,19 @@ def test_half_conversions(self):
+         # value is preserved when converting to/from other floats.
+
+         # Convert from float32 back to float16
+-        b = np.array(self.all_f32, dtype=float16)
+-        assert_equal(self.all_f16.view(dtype=uint16),
+-                     b.view(dtype=uint16))
++        with np.errstate(invalid='ignore'):
++            b = np.array(self.all_f32, dtype=float16)
++        # avoid testing NaNs due to differ bits wither Q/SNaNs
++        b_nn = b == b
++        assert_equal(self.all_f16[b_nn].view(dtype=uint16),
++                     b[b_nn].view(dtype=uint16))
+
+         # Convert from float64 back to float16
+-        b = np.array(self.all_f64, dtype=float16)
+-        assert_equal(self.all_f16.view(dtype=uint16),
+-                     b.view(dtype=uint16))
++        with np.errstate(invalid='ignore'):
++            b = np.array(self.all_f64, dtype=float16)
++        b_nn = b == b
++        assert_equal(self.all_f16[b_nn].view(dtype=uint16),
++                     b[b_nn].view(dtype=uint16))
+
+         # Convert float16 to longdouble and back
+         # This doesn't necessarily preserve the extra NaN bits,