def to_gpu(self, device=None):
with cuda._get_device(device):
super(Chain, self).to_gpu()
d = self.__dict__
for name in self._children:
d[name].to_gpu()
return self
def to_gpu(self, device=None):
"""Copies parameter variables and persistent values to GPU.
This method does not handle non-registered attributes. If some of such
attributes must be copied to GPU, the link implementation must
override this method to do so.
Args:
device: Target device specifier. If omitted, the current device is
used.
Returns: self
"""
cuda.check_cuda_available()
if not self._cpu:
return self
d = self.__dict__
with cuda._get_device(device):
for name in self._params:
d[name].to_gpu()
for name in self._persistent:
value = d[name]
if isinstance(value, intel64.mdarray):
value = numpy.array(value)
if isinstance(value, numpy.ndarray):
d[name] = cuda.to_gpu(value)
self._device_id = cuda.cupy.cuda.get_device_id()
self._cpu = False
return self
def to_gpu(self, device=None):
"""Copies parameter variables and persistent values to GPU.
This method does not handle non-registered attributes. If some of such
attributes must be copied to GPU, the link implementation must
override this method to do so.
Args:
device: Target device specifier. If omitted, the current device is
used.
Returns: self
"""
cuda.check_cuda_available()
if not self._cpu:
return self
d = self.__dict__
with cuda._get_device(device):
for name in self._params:
d[name].to_gpu()
for name in self._persistent:
value = d[name]
if isinstance(value, intel64.mdarray):
value = numpy.array(value)
if isinstance(value, numpy.ndarray):
d[name] = cuda.to_gpu(value)
self._device_id = cuda.cupy.cuda.get_device_id()
self._cpu = False
return self
def to_gpu(self, device=None):
with cuda._get_device(device):
super(Chain, self).to_gpu()
d = self.__dict__
for name in self._children:
d[name].to_gpu()
return self
def to_gpu(self, device=None):
with cuda._get_device(device):
super(ChainList, self).to_gpu()
for link in self._children:
link.to_gpu()
return self
def to_gpu(self, device=None):
with cuda._get_device(device):
super(NegativeSampling, self).to_gpu()
self.sampler.to_gpu()
def to_gpu(self, device=None):
with cuda._get_device(device):
super(BinaryHierarchicalSoftmax, self).to_gpu(device)
self._func.to_gpu(device)
return self
def to_gpu(self, device=None):
with cuda._get_device(device):
self.paths = cuda.to_gpu(self.paths)
self.codes = cuda.to_gpu(self.codes)
self.begins = cuda.to_gpu(self.begins)
return self
def to_gpu(self, device=None):
with cuda._get_device(device):
super(ChainList, self).to_gpu()
for link in self._children:
link.to_gpu()
return self
def to_gpu(self, device=None):
with cuda._get_device(device):
super(BinaryHierarchicalSoftmax, self).to_gpu(device)
self._func.to_gpu(device)
return self
def to_gpu(self, device=None):
with cuda._get_device(device):
self.paths = cuda.to_gpu(self.paths)
self.codes = cuda.to_gpu(self.codes)
self.begins = cuda.to_gpu(self.begins)
return self
def to_gpu(self, device=None):
with cuda._get_device(device):
super(NegativeSampling, self).to_gpu()
self.sampler.to_gpu()
def to_gpu(self, device=None):
with cuda._get_device(device):
super(BlackOut, self).to_gpu()
self.sampler.to_gpu()
def to_gpu(self, device=None):
with cuda._get_device(device):
super(BlackOut, self).to_gpu()
self.sampler.to_gpu()
