def test_add_invalid_dtypes(device, dtypes, is_module):
(in_dtype1, in_dtype2), _ = dtypes
shape = (2, 3)
a = chainerx.array(array_utils.uniform(shape, in_dtype1))
b = chainerx.array(array_utils.uniform(shape, in_dtype2))
with pytest.raises(chainerx.DtypeError):
if is_module:
a + b
else:
chainerx.add(a, b)
def test_add_scalar(scalar, device, shape, dtype):
x_np = array_utils.create_dummy_ndarray(numpy, shape, dtype)
# Implicit casting in NumPy's multiply depends on the 'casting' argument,
# which is not yet supported (ChainerX always casts).
# Therefore, we explicitly cast the scalar to the dtype of the ndarray
# before the multiplication for NumPy.
expected = x_np + numpy.dtype(dtype).type(scalar)
x = chainerx.array(x_np)
chainerx.testing.assert_array_equal_ex(x + scalar, expected)
chainerx.testing.assert_array_equal_ex(scalar + x, expected)
chainerx.testing.assert_array_equal_ex(chainerx.add(x, scalar), expected)
chainerx.testing.assert_array_equal_ex(chainerx.add(scalar, x), expected)
def test_add_scalar(scalar, device, shape, dtype):
x_np = array_utils.create_dummy_ndarray(numpy, shape, dtype)
# Implicit casting in NumPy's multiply depends on the 'casting' argument,
# which is not yet supported (ChainerX always casts).
# Therefore, we explicitly cast the scalar to the dtype of the ndarray
# before the multiplication for NumPy.
expected = x_np + numpy.dtype(dtype).type(scalar)
x = chainerx.array(x_np)
scalar_chx = chainerx.Scalar(scalar, dtype)
chainerx.testing.assert_array_equal_ex(x + scalar, expected)
chainerx.testing.assert_array_equal_ex(x + scalar_chx, expected)
chainerx.testing.assert_array_equal_ex(scalar + x, expected)
chainerx.testing.assert_array_equal_ex(scalar_chx + x, expected)
chainerx.testing.assert_array_equal_ex(chainerx.add(x, scalar), expected)
chainerx.testing.assert_array_equal_ex(
chainerx.add(x, scalar_chx), expected)
chainerx.testing.assert_array_equal_ex(chainerx.add(scalar, x), expected)
chainerx.testing.assert_array_equal_ex(
chainerx.add(scalar_chx, x), expected)