def test_check_device_fail(shape, device, compare_device_spec):
dtype = 'float32'
a = chainerx.empty(shape, dtype, device=device)
with chainerx.using_device('native:1'):
with pytest.raises(AssertionError):
array_utils.check_device(a, compare_device_spec)
def test_empty_like_with_device(device):
t = chainerx.empty((2,), 'float32')
a = chainerx.empty_like(t, device)
b = chainerx.empty_like(t)
array_utils.check_device(a, device)
assert a.dtype == b.dtype
assert a.shape == b.shape
def test_empty_like_with_device(device):
t = chainerx.empty((2,), 'float32')
a = chainerx.empty_like(t, device)
b = chainerx.empty_like(t)
array_utils.check_device(a, device)
assert a.dtype == b.dtype
assert a.shape == b.shape
def test_check_device_fail(shape, device, compare_device_spec):
dtype = 'float32'
a = chainerx.empty(shape, dtype, device=device)
with chainerx.using_device('native:1'):
with pytest.raises(AssertionError):
array_utils.check_device(a, compare_device_spec)
def test_check_device_device_spec(shape, device_spec):
dtype = 'float32'
a = chainerx.empty(shape, dtype, device=device_spec)
device = chainerx.get_device(device_spec)
array_utils.check_device(a, device_spec)
array_utils.check_device(a, device)
def test_check_device_device_spec(shape, device_spec):
dtype = 'float32'
a = chainerx.empty(shape, dtype, device=device_spec)
device = chainerx.get_device(device_spec)
array_utils.check_device(a, device_spec)
array_utils.check_device(a, device)
def generate_array(initializer, shape, xp, dtype=None, device=None):
# type: (types.AbstractInitializer, types.ShapeSpec, types.Xp, types.DTypeSpec, types.DeviceSpec) -> types.NdArray # NOQA
"""Return initialized array.
The algorithms used to make the new values depend on the
concrete derived classes. If the initializer has the ``dtype`` attribute,
it is used to construct the array. Otherwise, ``chainer.config.dtype`` is
used instead. See :ref:`configuration` for the dtype config.
Args:
initializer: A callable object that takes :ref:`ndarray` and edits its
value.
shape (int or tuple of int): Shape of the initialized array.
xp (module): :mod:`cupy`, :mod:`numpy`, or :mod:`chainerx`.
dtype: Dtype specifier. If omitted, ``initializer.dtype`` is used.
device: Target device specifier. If omitted, the current device is
used for :mod:`cupy`, and the default device is used for
:mod:`chainerx`.
Returns:
:ref:`ndarray`: An initialized array.
"""
dtype_attr = getattr(initializer, 'dtype', None)
if dtype is not None and dtype_attr is not None \
and numpy.dtype(dtype) != numpy.dtype(dtype_attr):
raise ValueError('dtype mismatch: {} != {}'.format(dtype, dtype_attr))
if dtype is None:
dtype = dtype_attr
dtype = chainer.get_dtype(dtype)
if device is None:
backend_device = backend._guess_device_from_array_module(xp)
else:
backend_device = chainer.get_device(device)
if xp != backend_device.xp:
raise ValueError('xp and device arguments are inconsistent.')
if xp is chainerx:
# Initialize with NumPy/CuPy array that shares memory with the
# ChainerX array.
# TODO(sonots): Directly use initializer after ChainerX
# supports random.
chx_device = backend_device.device
array = chainerx.empty(shape, dtype=dtype, device=chx_device)
fallback_device = backend_device.fallback_device
with chainer.using_device(fallback_device):
initializer(fallback_device.send(array))
return array
with chainer.using_device(backend_device):
array = xp.empty(shape, dtype=dtype)
initializer(array)
return array
def backward_call_callback(call_arg):
backward_call_new_array.append(chainerx.empty(shape, dtype))
def test_full_like_with_device(device):
t = chainerx.empty((2,), 'float32')
a = chainerx.full_like(t, 1, device)
b = chainerx.full_like(t, 1)
array_utils.check_device(a, device)
chainerx.testing.assert_array_equal_ex(a, b)
def test_ones_like_with_device(shape, device):
t = chainerx.empty((2,), 'float32')
a = chainerx.ones_like(t, device)
b = chainerx.ones_like(t)
array_utils.check_device(a, device)
chainerx.testing.assert_array_equal_ex(a, b)
def test_check_device(shape, device):
dtype = 'float32'
a = chainerx.empty(shape, dtype, device=device)
array_utils.check_device(a, device.name)
array_utils.check_device(a, device)
def backward_call_callback(call_arg):
backward_call_new_array.append(chainerx.empty(shape, dtype))
def generate_array(initializer, shape, xp, dtype=None, device=None):
# type: (types.AbstractInitializer, types.ShapeSpec, types.Xp, types.DTypeSpec, types.DeviceSpec) -> types.NdArray # NOQA
"""Return initialized array.
The algorithms used to make the new values depend on the
concrete derived classes. If the initializer has the ``dtype`` attribute,
it is used to construct the array. Otherwise, ``chainer.config.dtype`` is
used instead. See :ref:`configuration` for the dtype config.
Args:
initializer: A callable object that takes :ref:`ndarray` and edits its
value.
shape (tuple): Shape of a return array.
xp (module): :mod:`cupy`, :mod:`numpy`, or :mod:`chainerx`.
dtype: Dtype specifier. If omitted, ``initializer.dtype`` is used.
device: Target device specifier. If omitted, the current device is
used for :mod:`cupy`, and the default device is used for
:mod:`chainerx`.
Returns:
:ref:`ndarray`: An initialized array.
"""
dtype_attr = getattr(initializer, 'dtype', None)
if dtype is not None and dtype_attr is not None \
and numpy.dtype(dtype) != numpy.dtype(dtype_attr):
raise ValueError(
'dtype mismatch: {} != {}'.format(dtype, dtype_attr))
if dtype is None:
dtype = dtype_attr
dtype = chainer.get_dtype(dtype)
if device is None:
backend_device = backend._guess_device_from_array_module(xp)
else:
backend_device = chainer.get_device(device)
if xp != backend_device.xp:
raise ValueError('xp and device arguments are inconsistent.')
if xp is chainerx:
# Initialize with NumPy/CuPy array that shares memory with the
# ChainerX array.
# TODO(sonots): Directly use initializer after ChainerX
# supports random.
chx_device = backend_device.device # type: ignore
# TODO(okapies): remove 'type: ignore' when chainerx implements sequence support for empty() # NOQA
array = chainerx.empty(shape, dtype=dtype, device=chx_device) # type: ignore # NOQA
if chx_device.backend.name == 'native':
temp_array = _cpu._to_cpu(array)
temp_device = cuda.DummyDevice # type: cuda.Device
elif chx_device.backend.name == 'cuda':
temp_array = cuda.to_gpu(array, chx_device.index)
temp_device = cuda.Device(chx_device.index)
else:
raise RuntimeError('ChainerX backend: {} is not supported.'.format(
chx_device.backend.name))
with temp_device:
initializer(temp_array)
return array
with chainer.using_device(backend_device):
array = xp.empty(shape, dtype=dtype)
initializer(array)
return array
def generate_array(initializer, shape, xp, dtype=None, device=None):
# type: (types.AbstractInitializer, types.ShapeSpec, types.Xp, types.DTypeSpec, types.DeviceSpec) -> types.NdArray # NOQA
"""Return initialized array.
The algorithms used to make the new values depend on the
concrete derived classes. If the initializer has the ``dtype`` attribute,
it is used to construct the array. Otherwise, ``chainer.config.dtype`` is
used instead. See :ref:`configuration` for the dtype config.
Args:
initializer: A callable object that takes :class:`numpy.ndarray`
or :class:`cupy.ndarray` and edits its value.
shape (tuple): Shape of a return array.
xp (module): :mod:`cupy`, :mod:`numpy`, or :mod:`chainerx`.
dtype: Dtype specifier. If omitted, ``initializer.dtype`` is used.
device: Target device specifier. If omitted, the current device is
used for :mod:`cupy`, and the default device is used for
:mod:`chainerx`.
Returns:
numpy.ndarray, cupy.ndarray, or chainerx.ndarray: An initialized array.
"""
dtype_attr = getattr(initializer, 'dtype', None)
if dtype is not None and dtype_attr is not None \
and numpy.dtype(dtype) != numpy.dtype(dtype_attr):
raise ValueError(
'dtype mismatch: {} != {}'.format(dtype, dtype_attr))
if dtype is None:
dtype = dtype_attr
dtype = chainer.get_dtype(dtype)
if device is None:
if xp is cuda.cupy:
backend_device = chainer.get_device(cuda.Device())
elif xp is chainerx:
backend_device = chainer.get_device(chainerx.get_default_device())
else:
backend_device = chainer.get_device(numpy)
else:
backend_device = chainer.get_device(device)
if xp != backend_device.xp:
raise ValueError('xp and device arguments are inconsistent.')
if xp is chainerx:
# Initialize with NumPy/CuPy array that shares memory with the
# ChainerX array.
# TODO(sonots): Directly use initializer after ChainerX
# supports random.
chx_device = backend_device.device # type: ignore
# TODO(okapies): remove 'type: ignore' when chainerx implements sequence support for empty() # NOQA
array = chainerx.empty(shape, dtype=dtype, device=chx_device) # type: ignore # NOQA
if chx_device.backend.name == 'native':
temp_array = _cpu._to_cpu(array)
temp_device = cuda.DummyDevice # type: cuda.Device
elif chx_device.backend.name == 'cuda':
temp_array = cuda.to_gpu(array, chx_device.index)
temp_device = cuda.Device(chx_device.index)
else:
raise RuntimeError('ChainerX backend: {} is not supported.'.format(
chx_device.backend.name))
with temp_device:
initializer(temp_array)
return array
with chainer.using_device(backend_device):
array = xp.empty(shape, dtype=dtype)
initializer(array)
return array
def test_ones_like_with_device(shape, device):
t = chainerx.empty((2,), 'float32')
a = chainerx.ones_like(t, device)
b = chainerx.ones_like(t)
array_utils.check_device(a, device)
chainerx.testing.assert_array_equal_ex(a, b)
def test_full_like_with_device(device):
t = chainerx.empty((2,), 'float32')
a = chainerx.full_like(t, 1, device)
b = chainerx.full_like(t, 1)
array_utils.check_device(a, device)
chainerx.testing.assert_array_equal_ex(a, b)
def test_check_device(shape, device):
dtype = 'float32'
a = chainerx.empty(shape, dtype, device=device)
array_utils.check_device(a, device.name)
array_utils.check_device(a, device)
