def forward(self, inputs):
# Retain all inputs by default in old-style functions.
self.retain_inputs(six.moves.range(len(inputs)))
if self._is_chainerx_fallback_mode:
with function_node._chainerx_attribute_fallback(
self._function, self.chainerx_device):
return self._function.forward(inputs)
else:
return self._function.forward(inputs)
def forward(self, inputs):
# Retain all inputs by default in old-style functions.
self.retain_inputs(six.moves.range(len(inputs)))
if self._is_chainerx_fallback_mode:
with function_node._chainerx_attribute_fallback(
self._function, self.chainerx_device):
return self._function.forward(inputs)
else:
return self._function.forward(inputs)
def backward(self, target_input_indexes, grad_outputs):
retained_inputs = self.get_retained_inputs()
inputs = [None] * len(self.inputs)
in_data = [None] * len(self.inputs)
for retained, i_in in six.moves.zip(retained_inputs,
self._input_indexes_to_retain):
inputs[i_in] = retained
in_data[i_in] = None if retained is None else retained.array
in_data = tuple(in_data)
grad_out_data = tuple(
[None if grad is None else grad.array for grad in grad_outputs])
is_chainerx_fallback_mode = self._is_chainerx_fallback_mode
if is_chainerx_fallback_mode:
# Convert input and output gradients to numpy/cupy
in_data = backend.from_chx(in_data)
grad_out_data = backend.from_chx(grad_out_data)
# Call Function.backward
with chainer.using_device(
backend.get_device_from_array(*(in_data + grad_out_data))):
if is_chainerx_fallback_mode:
# Enable attribute fallback
with function_node._chainerx_attribute_fallback(
self._function, self.chainerx_device):
gxs = self._function.backward(in_data, grad_out_data)
else:
gxs = self._function.backward(in_data, grad_out_data)
# Check gradients
for x, gx in six.moves.zip(self.inputs, gxs):
if gx is not None:
variable._check_grad_type(self, x, True, gx)
# Convert input gradients back to ChainerX
if is_chainerx_fallback_mode:
gxs = backend.to_chx(gxs)
ret = []
for i in target_input_indexes:
if gxs[i] is None:
g = None
else:
# Intentionally not passing requires_grad=False so that
# backprop routines can raise an error when a further backprop
# is attempted against this gradient variable.
g = variable.Variable(gxs[i])
if g.xp is not chainerx:
g.node._old_style_grad_generator = self._function.label
ret.append(g)
return tuple(ret)
def backward(self, target_input_indexes, grad_outputs):
retained_inputs = self.get_retained_inputs()
inputs = [None] * len(self.inputs)
in_data = [None] * len(self.inputs)
for retained, i_in in six.moves.zip(
retained_inputs, self._input_indexes_to_retain):
inputs[i_in] = retained
in_data[i_in] = None if retained is None else retained.array
in_data = tuple(in_data)
grad_out_data = tuple([None if grad is None else grad.data
for grad in grad_outputs])
is_chainerx_fallback_mode = self._is_chainerx_fallback_mode
if is_chainerx_fallback_mode:
# Convert input and output gradients to numpy/cupy
in_data = backend.from_chx(in_data)
grad_out_data = backend.from_chx(grad_out_data)
# Call Function.backward
with cuda.get_device_from_array(*(in_data + grad_out_data)):
if is_chainerx_fallback_mode:
# Enable attribute fallback
with function_node._chainerx_attribute_fallback(
self._function, self.chainerx_device):
gxs = self._function.backward(in_data, grad_out_data)
else:
gxs = self._function.backward(in_data, grad_out_data)
# Check gradients
for x, gx in six.moves.zip(self.inputs, gxs):
if gx is not None:
variable._check_grad_type(self, x, True, gx)
# Convert input gradients back to ChainerX
if is_chainerx_fallback_mode:
gxs = backend.to_chx(gxs)
ret = []
for i in target_input_indexes:
if gxs[i] is None:
g = None
else:
# Intentionally not passing requires_grad=False so that
# backprop routines can raise an error when a further backprop
# is attempted against this gradient variable.
g = variable.Variable(gxs[i])
if g.xp is not chainerx:
g.node._old_style_grad_generator = self._function.label
ret.append(g)
return tuple(ret)
