def test_concat_arrays_to_chainerx(self):
device = 'native:0'
self.check_concat_arrays(
self.int_arrays, device,
backend.ChainerxDevice(chainerx.get_device(device)), numpy.int64)
self.check_concat_arrays(
self.float_arrays, device,
backend.ChainerxDevice(chainerx.get_device(device)), numpy.float64)
def test_ascontiguousarray_from_chainerx_array_device():
with chainerx.using_device(chainerx.get_device('native:0')):
dev = chainerx.get_device('native:1') # Non default one
assert chainerx.get_default_device() is not dev
a = chainerx.arange(10, device=dev)
b = chainerx.ascontiguousarray(a)
assert b.is_contiguous is True
assert b.device is dev
def test_chainerx_get_device():
context = chainerx.Context()
with chainerx.context_scope(context):
device = chainerx.get_device('native:0')
assert device.context is context
assert device.name == 'native:0'
assert device is chainerx.get_device('native', 0)
assert device is chainerx.get_device(device)
assert chainerx.get_default_device() is chainerx.get_device()
def test_chainerx_get_device():
context = chainerx.Context()
with chainerx.context_scope(context):
device = chainerx.get_device('native:0')
assert device.context is context
assert device.name == 'native:0'
assert device is chainerx.get_device('native', 0)
assert device is chainerx.get_device(device)
assert chainerx.get_default_device() is chainerx.get_device()
def from_fallback_device(device):
# TODO(niboshi): Write unit test
assert isinstance(device, _backend.Device)
if isinstance(device, _cpu.CpuDevice):
return ChainerxDevice(chainerx.get_device('native', 0))
if isinstance(device, cuda.GpuDevice):
return ChainerxDevice(chainerx.get_device('cuda',
device.device.id))
raise RuntimeError('Only CPU or GPU devices are allowed. '
'Actual: {}'.format(device))
def from_fallback_device(device):
# TODO(niboshi): Write unit test
assert isinstance(device, _backend.Device)
if isinstance(device, _cpu.CpuDevice):
return ChainerxDevice(chainerx.get_device('native', 0))
if isinstance(device, cuda.GpuDevice):
return ChainerxDevice(
chainerx.get_device('cuda', device.device.id))
raise RuntimeError(
'Only CPU or GPU devices are allowed. '
'Actual: {}'.format(device))
def test_concat_arrays_to_chainerx(self):
device = 'native:0'
self.check_concat_arrays(
self.int_arrays,
device,
backend.ChainerxDevice(chainerx.get_device(device)),
numpy.int64)
self.check_concat_arrays(
self.float_arrays,
device,
backend.ChainerxDevice(chainerx.get_device(device)),
numpy.float64)
def _get_device(device_spec):
# Called from chainer.backend.get_device
if not chainerx.is_available():
return None
if isinstance(device_spec, chainerx.Device):
return ChainerxDevice(device_spec)
if isinstance(device_spec, str):
return ChainerxDevice(chainerx.get_device(device_spec))
if (isinstance(device_spec, tuple) and len(device_spec) >= 1
and isinstance(device_spec[0], str)):
return ChainerxDevice(chainerx.get_device(*device_spec))
return None
def _get_device(device_spec):
# Called from chainer.backend.get_device
if not chainerx.is_available():
return None
if isinstance(device_spec, chainerx.Device):
return ChainerxDevice(device_spec)
if isinstance(device_spec, str):
return ChainerxDevice(chainerx.get_device(device_spec))
if (isinstance(device_spec, tuple) and len(device_spec) >= 1
and isinstance(device_spec[0], str)):
return ChainerxDevice(chainerx.get_device(*device_spec))
return None
def _array_to_chainerx(array, device=None):
# If device is None, appropriate device is chosen according to the input
# arrays.
assert device is None or isinstance(device, chainerx.Device)
if array is None:
return None
if array.dtype not in chainerx.all_dtypes:
raise TypeError(
'Dtype {} is not supported in ChainerX.'.format(array.dtype.name))
if isinstance(array, chainerx.ndarray):
if device is None:
return array
if device is array.device:
return array
return array.to_device(device)
if isinstance(array, numpy.ndarray):
if device is None:
device = chainerx.get_device('native', 0)
return chainerx.array(array, device=device, copy=False)
if isinstance(array, cuda.ndarray):
if device is None:
device = chainerx.get_device('cuda', array.device.id)
elif device.backend.name != 'cuda':
# cupy to non-cuda backend
# TODO(niboshi): Remove conversion to numpy when both CuPy and
# ChainerX support the array interface.
array = _cpu._to_cpu(array)
return chainerx.array(array, device=device, copy=False)
elif device.index != array.device.id:
# cupy to cuda backend but different device
array = cuda.to_gpu(array, device=device.index)
# cupy to cuda backend with the same device
return chainerx._core._fromrawpointer(
array.data.mem.ptr,
array.shape,
array.dtype,
array.strides,
device,
array.data.ptr - array.data.mem.ptr,
array)
if isinstance(array, intel64.mdarray):
return _array_to_chainerx(numpy.array(array), device)
if numpy.isscalar(array):
return chainerx.asarray(array)
raise TypeError(
'Array cannot be converted into chainerx.ndarray'
'\nActual type: {0}.'.format(type(array)))
def from_fallback_device(device):
"""Returns a :class:`~chainer.backend.ChainerxDevice` corresponding \
to the fallback device.
.. seealso::
:data:`~chainer.backend.ChainerxDevice.fallback_device`
"""
assert isinstance(device, _backend.Device)
if isinstance(device, _cpu.CpuDevice):
return ChainerxDevice(chainerx.get_device('native', 0))
if isinstance(device, cuda.GpuDevice):
return ChainerxDevice(chainerx.get_device('cuda',
device.device.id))
raise RuntimeError('Only CPU or GPU devices are allowed. '
'Actual: {}'.format(device))
def test_init(self, backend_config):
name = backend_config.chainerx_device
chx_device = chainerx.get_device(name)
device = backend.ChainerxDevice(chx_device)
self.check_device(device, backend_config)
assert device.device is chx_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 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_backward_default_device(self):
# Default device in backward should be determined by arrays,
# otherwise, creation routines in backward do not create new arrays
# on the proper device.
device = chainerx.get_device('cuda:0')
shape = (2, 3)
dtype = numpy.float32
x1 = chainerx.full(shape, 3, dtype, device=device)
x2 = chainerx.full(shape, 5, dtype, device=device).require_grad()
backward_call_new_array = []
def backward_call_callback(call_arg):
backward_call_new_array.append(chainerx.empty(shape, dtype))
with chainerx.using_device('native:0'):
# forward
func = self.SimpleFunctionNode(backward_call_callback)
y1, y2 = func.apply((x1, x2))
# backward
y2.backward()
assert backward_call_new_array[0].device is device
def test_backward_default_device(self):
# Default device in backward should be determined by arrays,
# otherwise, creation routines in backward do not create new arrays
# on the proper device.
device = chainerx.get_device('cuda:0')
shape = (2, 3)
dtype = numpy.float32
x1 = chainerx.full(shape, 3, dtype, device=device)
x2 = chainerx.full(shape, 5, dtype, device=device).require_grad()
backward_call_new_array = []
def backward_call_callback(call_arg):
backward_call_new_array.append(chainerx.empty(shape, dtype))
with chainerx.using_device('native:0'):
# forward
func = self.SimpleFunctionNode(backward_call_callback)
y1, y2 = func.apply((x1, x2))
# backward
y2.backward()
assert backward_call_new_array[0].device is device
def _from_cupy(array):
assert cupy is not None
assert isinstance(array, cupy.ndarray)
device = chainerx.get_device('cuda', array.device.id)
return chainerx._core._fromrawpointer(array.data.mem.ptr, array.shape,
array.dtype, array.strides, device,
array.data.ptr - array.data.mem.ptr,
array)
def test_frombuffer_from_device_buffer(device):
dtype = 'int32'
device_buffer = chainerx.testing._DeviceBuffer(
[1, 2, 3, 4, 5, 6], (2, 3), dtype)
a = chainerx.frombuffer(device_buffer, dtype)
e = chainerx.array([1, 2, 3, 4, 5, 6], dtype)
chainerx.testing.assert_array_equal_ex(e, a)
assert a.device is chainerx.get_device(device)
def test_frombuffer_from_device_buffer(device):
dtype = 'int32'
device_buffer = chainerx.testing._DeviceBuffer(
[1, 2, 3, 4, 5, 6], (2, 3), dtype)
a = chainerx.frombuffer(device_buffer, dtype)
e = chainerx.array([1, 2, 3, 4, 5, 6], dtype)
chainerx.testing.assert_array_equal_ex(e, a)
assert a.device is chainerx.get_device(device)
def test_from_fallback_device(self, backend_config):
# Preparation: it depends on ChainerxDevice.fallback_device
tmp_device = backend.ChainerxDevice(
chainerx.get_device(backend_config.chainerx_device))
fallback_device = tmp_device.fallback_device
# Test
device = backend.ChainerxDevice.from_fallback_device(fallback_device)
self.check_device(device, backend_config)
assert device.fallback_device == fallback_device
def check_device_spec_chainerx(self, device_spec, expected_device_name):
device = backend.get_device(device_spec)
assert isinstance(device, backend.ChainerxDevice)
assert device.xp is chainerx
assert isinstance(device.device, chainerx.Device)
assert device.device.name == expected_device_name
with backend.using_device(device_spec):
# TODO(niboshi): Test the Chainer default device
assert (chainerx.get_default_device() == chainerx.get_device(
expected_device_name))
def _from_cupy(array):
assert cupy is not None
assert isinstance(array, cupy.ndarray)
device = chainerx.get_device('cuda', array.device.id)
return chainerx._core._fromrawpointer(
array.data.mem.ptr,
array.shape,
array.dtype,
array.strides,
device,
array.data.ptr - array.data.mem.ptr,
array)
def check_device_spec_chainerx(self, device_spec, expected_device_name):
device = backend.get_device(device_spec)
assert isinstance(device, backend.ChainerxDevice)
assert device.xp is chainerx
assert isinstance(device.device, chainerx.Device)
assert device.device.name == expected_device_name
with backend.using_device(device_spec):
# TODO(niboshi): Test the Chainer default device
assert (
chainerx.get_default_device()
== chainerx.get_device(expected_device_name))
def device(self):
if self._device is None:
if self.use_cuda:
device = backend.GpuDevice.from_device_id(self.cuda_device)
elif self.use_chainerx:
device = backend.ChainerxDevice(
chainerx.get_device(self.chainerx_device))
elif self.use_ideep != 'never':
device = backend.Intel64Device()
else:
device = backend.CpuDevice()
self._device = device
return self._device
def test_to_device():
a = chainerx.ones((2,), chainerx.float32, device='native:0')
dst_device = chainerx.get_device('native:1')
b0 = a.to_device(dst_device) # by device instance
assert b0.device is dst_device
chainerx.testing.assert_array_equal_ex(a, b0)
b1 = a.to_device('native:1') # by device name
assert b1.device is dst_device
chainerx.testing.assert_array_equal_ex(a, b1)
b2 = a.to_device('native', 1) # by backend name and index
assert b2.device is dst_device
chainerx.testing.assert_array_equal_ex(a, b2)
def test_to_device():
a = chainerx.ones((2,), chainerx.float32, device='native:0')
dst_device = chainerx.get_device('native:1')
b0 = a.to_device(dst_device) # by device instance
assert b0.device is dst_device
chainerx.testing.assert_array_equal_ex(a, b0)
b1 = a.to_device('native:1') # by device name
assert b1.device is dst_device
chainerx.testing.assert_array_equal_ex(a, b1)
b2 = a.to_device('native', 1) # by backend name and index
assert b2.device is dst_device
chainerx.testing.assert_array_equal_ex(a, b2)
def test_array_from_chainerx_array_with_device(
src_dtype, dst_dtype, copy, device, dst_device_spec):
t = array_utils.create_dummy_ndarray(
chainerx, (2,), src_dtype, device=device)
a = chainerx.array(t, dtype=dst_dtype, copy=copy, device=dst_device_spec)
dst_device = chainerx.get_device(dst_device_spec)
if not copy and src_dtype == dst_dtype and device is dst_device:
assert t is a
else:
assert t is not a
chainerx.testing.assert_array_equal_ex(
a, t.to_device(dst_device).astype(dst_dtype))
assert a.dtype == chainerx.dtype(dst_dtype)
assert a.device is dst_device
def test_array_from_chainerx_array_with_device(
src_dtype, dst_dtype, copy, device, dst_device_spec):
t = array_utils.create_dummy_ndarray(
chainerx, (2,), src_dtype, device=device)
a = chainerx.array(t, dtype=dst_dtype, copy=copy, device=dst_device_spec)
dst_device = chainerx.get_device(dst_device_spec)
if not copy and src_dtype == dst_dtype and device is dst_device:
assert t is a
else:
assert t is not a
chainerx.testing.assert_array_equal_ex(
a, t.to_device(dst_device).astype(dst_dtype))
assert a.dtype == chainerx.dtype(dst_dtype)
assert a.device is dst_device
def test_frombuffer_from_numpy_array(device):
obj = array_utils.create_dummy_ndarray(
numpy, (2, 3), 'int32', padding=False)
a_chx = chainerx.frombuffer(obj, obj.dtype)
a_np = numpy.frombuffer(obj, obj.dtype)
chainerx.testing.assert_array_equal_ex(a_np, a_chx)
assert a_chx.device is chainerx.get_device(device)
# test buffer is shared
obj += obj
chainerx.testing.assert_array_equal_ex(obj.ravel(), a_chx)
# test possibly freed memory
obj_copy = obj.copy()
del obj
chainerx.testing.assert_array_equal_ex(obj_copy.ravel(), a_chx)
def test_frombuffer_from_numpy_array(device):
obj = array_utils.create_dummy_ndarray(
numpy, (2, 3), 'int32', padding=False)
a_chx = chainerx.frombuffer(obj, obj.dtype)
a_np = numpy.frombuffer(obj, obj.dtype)
chainerx.testing.assert_array_equal_ex(a_np, a_chx)
assert a_chx.device is chainerx.get_device(device)
# test buffer is shared
obj += obj
chainerx.testing.assert_array_equal_ex(obj.ravel(), a_chx)
# test possibly freed memory
obj_copy = obj.copy()
del obj
chainerx.testing.assert_array_equal_ex(obj_copy.ravel(), a_chx)
def _check_array_from_chainerx_array_with_dtype(
shape, src_dtype, dst_dtype_spec, copy, device=None):
t = array_utils.create_dummy_ndarray(
chainerx, shape, src_dtype, device=device)
a = chainerx.array(t, dtype=dst_dtype_spec, copy=copy)
src_dtype = chainerx.dtype(src_dtype)
dst_dtype = src_dtype if dst_dtype_spec is None else chainerx.dtype(
dst_dtype_spec)
device = chainerx.get_device(device)
if (not copy
and src_dtype == dst_dtype
and device is chainerx.get_default_device()):
assert t is a
else:
assert t is not a
chainerx.testing.assert_array_equal_ex(a, t.astype(dst_dtype))
assert a.dtype == dst_dtype
assert a.device is chainerx.get_default_device()
def _check_array_from_chainerx_array_with_dtype(
shape, src_dtype, dst_dtype_spec, copy, device=None):
t = array_utils.create_dummy_ndarray(
chainerx, shape, src_dtype, device=device)
a = chainerx.array(t, dtype=dst_dtype_spec, copy=copy)
src_dtype = chainerx.dtype(src_dtype)
dst_dtype = src_dtype if dst_dtype_spec is None else chainerx.dtype(
dst_dtype_spec)
device = chainerx.get_device(device)
if (not copy
and src_dtype == dst_dtype
and device is chainerx.get_default_device()):
assert t is a
else:
assert t is not a
chainerx.testing.assert_array_equal_ex(a, t.astype(dst_dtype))
assert a.dtype == dst_dtype
assert a.device is chainerx.get_default_device()
def test_asarray_from_chainerx_array_with_device(src_dtype, dst_dtype, device,
dst_device_spec):
t = array_utils.create_dummy_ndarray(chainerx, (2, ),
src_dtype,
device=device)
a = chainerx.asarray(t, dtype=dst_dtype, device=dst_device_spec)
dst_device = chainerx.get_device(dst_device_spec)
if ((dst_dtype is None or src_dtype == dst_dtype)
and (dst_device_spec is None or device is dst_device)):
assert t is a
else:
assert t is not a
if dst_dtype is None:
dst_dtype = t.dtype
chainerx.testing.assert_array_equal_ex(
a,
t.to_device(dst_device).astype(dst_dtype))
assert a.dtype == chainerx.dtype(dst_dtype)
assert a.device is dst_device
if xp is numpy and isinstance(dtype_spec, chainerx.dtype):
dtype_spec = dtype_spec.name
# Skip nan/inf -> integer conversion that would cause a cast error.
if (not _is_all_finite(obj)
and dtype_spec not in (None, Unspecified)
and chainerx.dtype(dtype_spec).kind not in ('f', 'c')):
return chainerx.testing.ignore()
if dtype_spec is Unspecified:
return xp.array(obj)
else:
return xp.array(obj, dtype_spec)
@pytest.mark.parametrize('obj', _array_params(_array_params_list))
@pytest.mark.parametrize(
'device', [None, 'native:1', chainerx.get_device('native:1')])
def test_array_from_python_tuple_or_list_with_device(obj, device):
a = chainerx.array(obj, 'float32', device=device)
b = chainerx.array(obj, 'float32')
chainerx.testing.assert_array_equal_ex(a, b)
array_utils.check_device(a, device)
def _check_array_from_numpy_array(a_chx, a_np, device=None):
assert a_chx.offset == 0
array_utils.check_device(a_chx, device)
# recovered data should be equal
a_np_recovered = chainerx.to_numpy(a_chx)
chainerx.testing.assert_array_equal_ex(
a_chx, a_np_recovered, strides_check=False)
def test_dummy_ndarray_device_spec(device_spec):
a = array_utils.create_dummy_ndarray(
chainerx, (2, 3), 'float32', device=device_spec)
assert a.device is chainerx.get_device(device_spec)
def test_concat_tuples_to_chainerx(self, backend_config):
device = chainerx.get_device('native:0')
arrays = self.get_tuple_arrays_to_concat(backend_config)
self.check_concat_tuples(
arrays, device, backend.ChainerxDevice(device))
def get_device(device_spec: types.DeviceSpec) -> Device:
"""Returns a device object.
Args:
device_spec (object): Device specifier.
If a :class:`chainer.backend.Device` instance is given, it is
returned intact. Otherwise the following values are supported:
* ChainerX devices
* A string representing a device.
(ex. ``'native:0'``, ``'native'``)
* A :class:`chainerx.Device` object.
* CuPy
* A string starts with ``'@cupy:'``.
(ex. ``'@cupy:0'``)
* A :class:`cupy.cuda.Device` object.
* NumPy
* The string ``'@numpy'``.
* NumPy with Intel Architecture
* The string ``'@intel64'``.
"""
if isinstance(device_spec, Device):
return device_spec
if isinstance(device_spec, cuda._integer_types):
return _get_device_cupy_or_numpy(device_spec)
if chainerx.is_available() and isinstance(device_spec, chainerx.Device):
return _chainerx.ChainerxDevice(device_spec)
if cuda.available and isinstance(device_spec, cuda.Device):
return cuda.GpuDevice(device_spec)
if isinstance(device_spec, six.string_types):
# '-1', '0', '1', ...
try:
int_device_spec = int(device_spec)
except ValueError:
pass
else:
return _get_device_cupy_or_numpy(int_device_spec)
if device_spec.startswith('@'):
# '@module:...'
mod_name, colon, precise_spec = device_spec[1:].partition(':')
if mod_name == 'numpy':
if not colon:
return _cpu.CpuDevice()
elif mod_name == 'cupy':
if colon:
return cuda.GpuDevice.from_device_id(int(precise_spec))
elif mod_name == 'intel64':
if not colon:
return intel64.Intel64Device()
raise ValueError(
'Device specifiers starting with \'@\' must be followed by'
' a module name and depending on the module, module specific'
' precise device specifiers. Actual: {}'.format(device_spec))
else:
# String device specifier without '@' prefix is assumed to be a
# ChainerX device.
if not chainerx.is_available():
raise RuntimeError(
'Tried to parse ChainerX device specifier \'{}\', '
'but ChainerX is not available. '
'Note that device specifiers without \'@\' prefix are '
'assumed to be ChainerX device '
'specifiers.'.format(device_spec))
return _chainerx.ChainerxDevice(chainerx.get_device(device_spec))
raise TypeError(
'Device specifier must be a backend.Device, cuda.Device,'
' chainerx.Device, integer or a string. Actual: {}'.format(
type(device_spec)))
def test_init_chainerx_with_cuda(self):
device = chainerx.get_device('cuda:0')
array = self._generate_array(chainerx, 'float64', device)
assert array.device is device
def check_device(a, device=None):
if device is None:
device = chainerx.get_default_device()
elif isinstance(device, str):
device = chainerx.get_device(device)
assert a.device is device
def test_chainerx_device(self):
chainerx_device = chainerx.get_device('native:0')
self.check_device_spec_chainerx(chainerx_device, 'native:0')
if xp is numpy and isinstance(dtype_spec, chainerx.dtype):
dtype_spec = dtype_spec.name
# Skip nan/inf -> integer conversion that would cause a cast error.
if (not _is_all_finite(obj)
and dtype_spec not in (None, Unspecified)
and chainerx.dtype(dtype_spec).kind not in ('f', 'c')):
return chainerx.testing.ignore()
if dtype_spec is Unspecified:
return xp.array(obj)
else:
return xp.array(obj, dtype_spec)
@pytest.mark.parametrize('obj', _array_params(_array_params_list))
@pytest.mark.parametrize(
'device', [None, 'native:1', chainerx.get_device('native:1')])
def test_array_from_python_tuple_or_list_with_device(obj, device):
a = chainerx.array(obj, 'float32', device=device)
b = chainerx.array(obj, 'float32')
chainerx.testing.assert_array_equal_ex(a, b)
array_utils.check_device(a, device)
def _check_array_from_numpy_array(a_chx, a_np, device=None):
assert a_chx.offset == 0
array_utils.check_device(a_chx, device)
# recovered data should be equal
a_np_recovered = chainerx.to_numpy(a_chx)
chainerx.testing.assert_array_equal_ex(
a_chx, a_np_recovered, strides_check=False)
def _get_chainerx_device(device_spec):
# Returns chainerx.Device
if isinstance(device_spec, chainerx.Device):
return device_spec
return chainerx.get_device(device_spec)
def test_init_chainerx_with_cuda(self):
device = chainerx.get_device('cuda:0')
array = self._generate_array(chainerx, 'float64', device)
assert array.device is device
assert array._debug_flat_data == expected_data_list
assert array.is_contiguous
array_utils.check_device(array, device)
@chainerx.testing.parametrize_dtype_specifier('dtype_spec')
def test_init(shape, dtype_spec):
array = chainerx.ndarray(shape, dtype_spec)
_check_array(array, dtype_spec, shape)
@pytest.mark.parametrize(
'device',
[None, 'native:1', chainerx.get_device('native:1')])
@chainerx.testing.parametrize_dtype_specifier('dtype_spec')
def test_init_with_device(shape, dtype_spec, device):
array = chainerx.ndarray(shape, dtype_spec, device=device)
_check_array(array, dtype_spec, shape, device=device)
@pytest.mark.parametrize('value', [
0, 1, -1, 0.1, 0.9, -0.1, -0.9, 1.1, -1.1, 1.9, -1.9, True, False,
float('inf'), -float('inf'),
float('nan'), -0.0
])
@pytest.mark.parametrize('shape', [(), (1, ), (1, 1, 1)])
@pytest.mark.parametrize_device(['native:0', 'cuda:0'])
def test_cast_scalar(device, value, shape, dtype):
np_dtype = numpy.dtype(dtype)
def test_init_chainerx_with_default_device(self):
device = chainerx.get_device('native:1')
with chainerx.using_device(device):
array = self._generate_array(chainerx, 'float64')
assert array.device is device
def test_concat_tuples_to_chainerx(self, backend_config):
device = chainerx.get_device('native:0')
arrays = self.get_tuple_arrays_to_concat(backend_config)
self.check_concat_tuples(
arrays, device, backend.ChainerxDevice(device))
def test_chainerx_device(self):
chainerx_device = chainerx.get_device('native:0')
self.check_device_spec_chainerx(chainerx_device, 'native:0')
def test_init_chainerx_with_default_device(self):
device = chainerx.get_device('native:1')
with chainerx.using_device(device):
array = self._generate_array(chainerx, 'float64')
assert array.device is device
def _get_chainerx_device(device_spec):
# Returns chainerx.Device
if isinstance(device_spec, chainerx.Device):
return device_spec
return chainerx.get_device(device_spec)
def test_dummy_ndarray_device_spec(device_spec):
a = array_utils.create_dummy_ndarray(chainerx, (2, 3),
'float32',
device=device_spec)
assert a.device is chainerx.get_device(device_spec)
def get_device(device_spec):
# type: (types.DeviceSpec) -> Device
"""Returns a device object.
Args:
device_spec (object): Device specifier. If a :class:`chainer.Device`
instance is given, it is returned intact. Otherwise the following
values are supported:
* ChainerX devices
* A string representing a device.
(ex. ``'native:0'``, ``'native'``)
* A :class:`chainerx.Device` object.
* CuPy
* A string starts with ``'@cupy:'``.
(ex. ``'@cupy:0'``)
* A :class:`cupy.cuda.Device` object.
* NumPy
* The string ``'@numpy'``.
* NumPy with Intel Architecture
* The string ``'@intel64'``.
"""
if isinstance(device_spec, Device):
return device_spec
if isinstance(device_spec, cuda._integer_types):
return _get_device_cupy_or_numpy(device_spec)
if chainerx.is_available() and isinstance(device_spec, chainerx.Device):
return _chainerx.ChainerxDevice(device_spec)
if cuda.available and isinstance(device_spec, cuda.Device):
return cuda.GpuDevice(device_spec)
if isinstance(device_spec, six.string_types):
# '-1', '0', '1', ...
try:
int_device_spec = int(device_spec)
except ValueError:
pass
else:
return _get_device_cupy_or_numpy(int_device_spec)
if device_spec.startswith('@'):
# '@module:...'
mod_name, colon, precise_spec = device_spec[1:].partition(':')
if mod_name == 'numpy':
if not colon:
return _cpu.CpuDevice()
elif mod_name == 'cupy':
if colon:
return cuda.GpuDevice.from_device_id(int(precise_spec))
elif mod_name == 'intel64':
if not colon:
return intel64.Intel64Device()
elif chainerx.is_available():
return _chainerx.ChainerxDevice(chainerx.get_device(device_spec))
raise ValueError('Invalid device specifier: {}'.format(device_spec))
def get_device(device_spec):
# type: (types.DeviceSpec) -> Device
"""Returns a device object.
Args:
device_spec (object): Device specifier.
If a :class:`chainer.backend.Device` instance is given, it is
returned intact. Otherwise the following values are supported:
* ChainerX devices
* A string representing a device.
(ex. ``'native:0'``, ``'native'``)
* A :class:`chainerx.Device` object.
* CuPy
* A string starts with ``'@cupy:'``.
(ex. ``'@cupy:0'``)
* A :class:`cupy.cuda.Device` object.
* NumPy
* The string ``'@numpy'``.
* NumPy with Intel Architecture
* The string ``'@intel64'``.
"""
if isinstance(device_spec, Device):
return device_spec
if isinstance(device_spec, cuda._integer_types):
return _get_device_cupy_or_numpy(device_spec)
if chainerx.is_available() and isinstance(device_spec, chainerx.Device):
return _chainerx.ChainerxDevice(device_spec)
if cuda.available and isinstance(device_spec, cuda.Device):
return cuda.GpuDevice(device_spec)
if isinstance(device_spec, six.string_types):
# '-1', '0', '1', ...
try:
int_device_spec = int(device_spec)
except ValueError:
pass
else:
return _get_device_cupy_or_numpy(int_device_spec)
if device_spec.startswith('@'):
# '@module:...'
mod_name, colon, precise_spec = device_spec[1:].partition(':')
if mod_name == 'numpy':
if not colon:
return _cpu.CpuDevice()
elif mod_name == 'cupy':
if colon:
return cuda.GpuDevice.from_device_id(int(precise_spec))
elif mod_name == 'intel64':
if not colon:
return intel64.Intel64Device()
elif chainerx.is_available():
return _chainerx.ChainerxDevice(chainerx.get_device(device_spec))
raise ValueError('Invalid device specifier: {}'.format(device_spec))