import os
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import sys
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from ctypes import POINTER, c_double, c_float, c_int, c_longlong, cast, pointer
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from os import path
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import pytest
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from numpy._core._multiarray_umath import __cpu_features__
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import numpy as np
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from numpy.testing import assert_array_max_ulp
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from numpy.testing._private.utils import _glibc_older_than
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UNARY_UFUNCS = [obj for obj in np._core.umath.__dict__.values() if
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isinstance(obj, np.ufunc)]
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UNARY_OBJECT_UFUNCS = [uf for uf in UNARY_UFUNCS if "O->O" in uf.types]
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# Remove functions that do not support `floats`
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UNARY_OBJECT_UFUNCS.remove(np.invert)
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UNARY_OBJECT_UFUNCS.remove(np.bitwise_count)
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IS_AVX = __cpu_features__.get('AVX512F', False) or \
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(__cpu_features__.get('FMA3', False) and __cpu_features__.get('AVX2', False))
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IS_AVX512FP16 = __cpu_features__.get('AVX512FP16', False)
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# only run on linux with AVX, also avoid old glibc (numpy/numpy#20448).
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runtest = (sys.platform.startswith('linux')
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and IS_AVX and not _glibc_older_than("2.17"))
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platform_skip = pytest.mark.skipif(not runtest,
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reason="avoid testing inconsistent platform "
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"library implementations")
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# convert string to hex function taken from:
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# https://stackoverflow.com/questions/1592158/convert-hex-to-float #
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def convert(s, datatype="np.float32"):
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i = int(s, 16) # convert from hex to a Python int
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if (datatype == "np.float64"):
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cp = pointer(c_longlong(i)) # make this into a c long long integer
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fp = cast(cp, POINTER(c_double)) # cast the int pointer to a double pointer
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else:
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cp = pointer(c_int(i)) # make this into a c integer
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fp = cast(cp, POINTER(c_float)) # cast the int pointer to a float pointer
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return fp.contents.value # dereference the pointer, get the float
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str_to_float = np.vectorize(convert)
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class TestAccuracy:
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@platform_skip
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def test_validate_transcendentals(self):
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with np.errstate(all='ignore'):
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data_dir = path.join(path.dirname(__file__), 'data')
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files = os.listdir(data_dir)
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files = list(filter(lambda f: f.endswith('.csv'), files))
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for filename in files:
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filepath = path.join(data_dir, filename)
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with open(filepath) as fid:
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file_without_comments = (
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r for r in fid if r[0] not in ('$', '#')
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)
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data = np.genfromtxt(file_without_comments,
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dtype=('|S39', '|S39', '|S39', int),
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names=('type', 'input', 'output', 'ulperr'),
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delimiter=',',
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skip_header=1)
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npname = path.splitext(filename)[0].split('-')[3]
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npfunc = getattr(np, npname)
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for datatype in np.unique(data['type']):
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data_subset = data[data['type'] == datatype]
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inval = np.array(str_to_float(data_subset['input'].astype(str), data_subset['type'].astype(str)), dtype=eval(datatype))
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outval = np.array(str_to_float(data_subset['output'].astype(str), data_subset['type'].astype(str)), dtype=eval(datatype))
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perm = np.random.permutation(len(inval))
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inval = inval[perm]
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outval = outval[perm]
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maxulperr = data_subset['ulperr'].max()
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assert_array_max_ulp(npfunc(inval), outval, maxulperr)
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@pytest.mark.skipif(IS_AVX512FP16,
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reason="SVML FP16 have slightly higher ULP errors")
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@pytest.mark.parametrize("ufunc", UNARY_OBJECT_UFUNCS)
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def test_validate_fp16_transcendentals(self, ufunc):
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with np.errstate(all='ignore'):
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arr = np.arange(65536, dtype=np.int16)
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datafp16 = np.frombuffer(arr.tobytes(), dtype=np.float16)
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datafp32 = datafp16.astype(np.float32)
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assert_array_max_ulp(ufunc(datafp16), ufunc(datafp32),
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maxulp=1, dtype=np.float16)
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@pytest.mark.skipif(not IS_AVX512FP16,
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reason="lower ULP only apply for SVML FP16")
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def test_validate_svml_fp16(self):
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max_ulp_err = {
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"arccos": 2.54,
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"arccosh": 2.09,
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"arcsin": 3.06,
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"arcsinh": 1.51,
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"arctan": 2.61,
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"arctanh": 1.88,
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"cbrt": 1.57,
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"cos": 1.43,
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"cosh": 1.33,
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"exp2": 1.33,
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"exp": 1.27,
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"expm1": 0.53,
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"log": 1.80,
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"log10": 1.27,
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"log1p": 1.88,
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"log2": 1.80,
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"sin": 1.88,
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"sinh": 2.05,
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"tan": 2.26,
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"tanh": 3.00,
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}
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with np.errstate(all='ignore'):
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arr = np.arange(65536, dtype=np.int16)
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datafp16 = np.frombuffer(arr.tobytes(), dtype=np.float16)
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datafp32 = datafp16.astype(np.float32)
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for func in max_ulp_err:
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ufunc = getattr(np, func)
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ulp = np.ceil(max_ulp_err[func])
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assert_array_max_ulp(ufunc(datafp16), ufunc(datafp32),
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maxulp=ulp, dtype=np.float16)
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