_scalar_invalid_bitwise = [
(('float16',), int, 'float16'),
(('float32',), int, 'float32'),
(('float64',), int, 'float64'),
(('float64',), numpy.int8, 'float64'),
(('float16',), numpy.int64, 'float16'),
]
_params_bitwise = (
# Special shapes
chainer.testing.product({
'in_shapes': math_utils.shapes_combination_binary,
'in_dtypes,out_dtype': (
dtype_utils.make_same_in_out_dtypes(
2, chainerx.testing.nonfloat_dtypes)),
'input_lhs': ['random'],
'input_rhs': ['random'],
'is_module': [False],
})
# Dtype combinations
+ chainer.testing.product({
'in_shapes': [((2, 3), (2, 3))],
'in_dtypes,out_dtype': _in_out_dtypes_bitwise,
'input_lhs': ['random'],
'input_rhs': ['random'],
'is_module': [False],
})
# is_module
+ chainer.testing.product({
'in_shapes': [((2, 3), (2, 3))],
_in_out_dtypes_array_int_scalar + _in_out_dtypes_float_array_float_scalar)
_in_out_dtypes_float_arithmetic_scalar = (
_in_out_dtypes_int_array_float_scalar +
_in_out_dtypes_float_array_float_scalar)
_in_out_dtypes_inplace_float_arithmetic_scalar = (
_in_out_dtypes_float_array_float_scalar)
@op_utils.op_test(['native:0', 'cuda:0'])
@chainer.testing.parameterize(*(
# Special shapes
chainer.testing.product({
'shape': [(), (0, ), (1, ), (2, 0, 3), (1, 1, 1), (2, 3)],
'in_dtypes,out_dtype': (dtype_utils.make_same_in_out_dtypes(
1, chainerx.testing.numeric_dtypes)),
'input': ['random'],
'is_module': [False],
})
# is_module
+ chainer.testing.product({
'shape': [(2, 3)],
'in_dtypes,out_dtype': (dtype_utils.make_same_in_out_dtypes(
1, chainerx.testing.numeric_dtypes)),
'input': ['random'],
'is_module': [True, False],
})
# Special values
+ chainer.testing.product({
'shape': [(2, 3)],
'in_dtypes,out_dtype': (dtype_utils.make_same_in_out_dtypes(
'skip_backward_test': [True],
'skip_double_backward_test': [True],
})
))
class TestSqrt(math_utils.UnaryMathTestBase, op_utils.NumpyOpTest):
def func(self, xp, a):
return xp.sqrt(a)
@op_utils.op_test(['native:0', 'cuda:0'])
@chainer.testing.parameterize(*(
# Special shapes
chainer.testing.product({
'shape': [(), (0,), (1,), (2, 0, 3), (1, 1, 1), (2, 3)],
'in_dtypes,out_dtype': dtype_utils.make_same_in_out_dtypes(
1, chainerx.testing.numeric_dtypes),
'input': ['random'],
'contiguous': [None, 'C'],
})
# Special values
+ chainer.testing.product({
'shape': [(2, 3)],
'in_dtypes,out_dtype': dtype_utils.make_same_in_out_dtypes(
1, chainerx.testing.float_dtypes),
'input': [float('inf'), -float('inf'), float('nan')],
'skip_backward_test': [True],
'skip_double_backward_test': [True],
})
))
class TestSquare(math_utils.UnaryMathTestBase, op_utils.NumpyOpTest):
@op_utils.op_test(['native:0', 'cuda:0'])
@chainer.testing.parameterize(*(_make_inverse_trig_params('arctan')))
class TestArctan(math_utils.UnaryMathTestBase, op_utils.NumpyOpTest):
def func(self, xp, a):
return xp.arctan(a)
# Since the gradient of arctan2 is quite flaky.
# for smaller values especially `float16`.
@op_utils.op_test(['native:0', 'cuda:0'])
@chainer.testing.parameterize(*(
# Special shapes
chainer.testing.product({
'in_shapes':
math_utils.shapes_combination_binary,
'in_dtypes,out_dtype': (dtype_utils.make_same_in_out_dtypes(
2, chainerx.testing.float_dtypes)),
'input_lhs': [1],
'input_rhs': [2],
'skip_backward_test': [True],
'skip_double_backward_test': [True],
})
# Differentiable points
+ chainer.testing.product({
'in_shapes': [((2, 3), (2, 3))],
'in_dtypes,out_dtype': (dtype_utils.make_same_in_out_dtypes(
2, chainerx.testing.float_dtypes)),
'input_lhs': [-3, -0.75, 0.75, 3],
'input_rhs': [-3, -0.75, 0.75, 3],
})
# Mixed dtypes
+ chainer.testing.product({
@op_utils.op_test(['native:0', 'cuda:0'])
@chainer.testing.parameterize(*(chainer.testing.product({
'shape,axis': [
((), None),
((4, ), None),
((4, ), 0),
((4, 2), None),
((4, 2), 0),
((4, 2), 1),
((4, 2), -2),
((4, 3), (0, 1)),
((4, 3), (-2, -1)),
],
'in_dtypes,out_dtype':
(dtype_utils.make_same_in_out_dtypes(1, chainerx.testing.all_dtypes)),
'is_module': [True, False],
}) + chainer.testing.product({
'array,axis':
_minmax_params,
'in_dtypes,out_dtype':
(dtype_utils.make_same_in_out_dtypes(1, chainerx.testing.all_dtypes)),
'is_module': [True, False],
'skip_backward_test': [True],
'skip_double_backward_test': [True],
})))
class TestMax(math_utils.UnaryMathTestBase, op_utils.NumpyOpTest):
dodge_nondifferentiable = True
def generate_inputs(self):
