name : test_umath_accuracy.py import numpy as np import os from os import path import sys import pytest from ctypes import c_longlong, c_double, c_float, c_int, cast, pointer, POINTER from numpy.testing import assert_array_max_ulp from numpy.testing._private.utils import _glibc_older_than from numpy.core._multiarray_umath import __cpu_features__ UNARY_UFUNCS = [obj for obj in np.core.umath.__dict__.values() if isinstance(obj, np.ufunc)] UNARY_OBJECT_UFUNCS = [uf for uf in UNARY_UFUNCS if "O->O" in uf.types] UNARY_OBJECT_UFUNCS.remove(getattr(np, 'invert')) IS_AVX = __cpu_features__.get('AVX512F', False) or \ (__cpu_features__.get('FMA3', False) and __cpu_features__.get('AVX2', False)) # only run on linux with AVX, also avoid old glibc (numpy/numpy#20448). runtest = (sys.platform.startswith('linux') and IS_AVX and not _glibc_older_than("2.17")) platform_skip = pytest.mark.skipif(not runtest, reason="avoid testing inconsistent platform " "library implementations") # convert string to hex function taken from: # https://stackoverflow.com/questions/1592158/convert-hex-to-float # def convert(s, datatype="np.float32"): i = int(s, 16) # convert from hex to a Python int if (datatype == "np.float64"): cp = pointer(c_longlong(i)) # make this into a c long long integer fp = cast(cp, POINTER(c_double)) # cast the int pointer to a double pointer else: cp = pointer(c_int(i)) # make this into a c integer fp = cast(cp, POINTER(c_float)) # cast the int pointer to a float pointer return fp.contents.value # dereference the pointer, get the float str_to_float = np.vectorize(convert) class TestAccuracy: @platform_skip def test_validate_transcendentals(self): with np.errstate(all='ignore'): data_dir = path.join(path.dirname(__file__), 'data') files = os.listdir(data_dir) files = list(filter(lambda f: f.endswith('.csv'), files)) for filename in files: filepath = path.join(data_dir, filename) with open(filepath) as fid: file_without_comments = (r for r in fid if not r[0] in ('$', '#')) data = np.genfromtxt(file_without_comments, dtype=('|S39','|S39','|S39',int), names=('type','input','output','ulperr'), delimiter=',', skip_header=1) npname = path.splitext(filename)[0].split('-')[3] npfunc = getattr(np, npname) for datatype in np.unique(data['type']): data_subset = data[data['type'] == datatype] inval = np.array(str_to_float(data_subset['input'].astype(str), data_subset['type'].astype(str)), dtype=eval(datatype)) outval = np.array(str_to_float(data_subset['output'].astype(str), data_subset['type'].astype(str)), dtype=eval(datatype)) perm = np.random.permutation(len(inval)) inval = inval[perm] outval = outval[perm] maxulperr = data_subset['ulperr'].max() assert_array_max_ulp(npfunc(inval), outval, maxulperr) @pytest.mark.parametrize("ufunc", UNARY_OBJECT_UFUNCS) def test_validate_fp16_transcendentals(self, ufunc): with np.errstate(all='ignore'): arr = np.arange(65536, dtype=np.int16) datafp16 = np.frombuffer(arr.tobytes(), dtype=np.float16) datafp32 = datafp16.astype(np.float32) assert_array_max_ulp(ufunc(datafp16), ufunc(datafp32), maxulp=1, dtype=np.float16)