cupy.testing.for_all_dtypes#

cupy.testing.for_all_dtypes(name='dtype', no_float16=False, no_bool=False, no_complex=False)[source]#

Decorator that checks the fixture with all dtypes.

Parameters:

name (str) – Argument name to which specified dtypes are passed.
no_float16 (bool) – If True, numpy.float16 is omitted from candidate dtypes.
no_bool (bool) – If True, numpy.bool_ is omitted from candidate dtypes.
no_complex (bool) – If True, numpy.complex64 and numpy.complex128 are omitted from candidate dtypes.

dtypes to be tested: numpy.complex64 (optional), numpy.complex128 (optional), numpy.float16 (optional), numpy.float32, numpy.float64, numpy.dtype('b'), numpy.dtype('h'), numpy.dtype('i'), numpy.dtype('l'), numpy.dtype('q'), numpy.dtype('B'), numpy.dtype('H'), numpy.dtype('I'), numpy.dtype('L'), numpy.dtype('Q'), and numpy.bool_ (optional).

The usage is as follows. This test fixture checks if cPickle successfully reconstructs cupy.ndarray for various dtypes. dtype is an argument inserted by the decorator.

>>> import unittest
>>> from cupy import testing
>>> class TestNpz(unittest.TestCase):
...
...     @testing.for_all_dtypes()
...     def test_pickle(self, dtype):
...         a = testing.shaped_arange((2, 3, 4), dtype=dtype)
...         s = pickle.dumps(a)
...         b = pickle.loads(s)
...         testing.assert_array_equal(a, b)

Typically, we use this decorator in combination with decorators that check consistency between NumPy and CuPy like cupy.testing.numpy_cupy_allclose(). The following is such an example.

>>> import unittest
>>> from cupy import testing
>>> class TestMean(unittest.TestCase):
...
...     @testing.for_all_dtypes()
...     @testing.numpy_cupy_allclose()
...     def test_mean_all(self, xp, dtype):
...         a = testing.shaped_arange((2, 3), xp, dtype)
...         return a.mean()