def _chainerx_apply_fallback_postprocess(
self, chainerx_in_data, inputs, outputs):
# TODO(hvy): Take configuration.config.enable_backprop into
# account?
chainerx_out_data = backend.to_chainerx(outputs)
# Insert a ChainerX op-node that calls FunctionNode.backward in
# backprop. Note that chainerx_out_data may not require gradients.
chainerx._core._function_node_forward(
self, chainerx_in_data, chainerx_out_data,
[] if self._input_indexes_to_retain is None
else self._input_indexes_to_retain,
[] if self._output_indexes_to_retain is None
else self._output_indexes_to_retain)
self.inputs = tuple([
None if x is None
else variable._ChainerxVariableNodeProps(x) for x in inputs])
ret = tuple([
_to_variable_with_chainerx_fallback_array(
chainerx_out_array, out_array)
for chainerx_out_array, out_array
in six.moves.zip(chainerx_out_data, outputs)])
return ret
def _chainerx_apply_fallback_postprocess(
self, chainerx_in_data, inputs, outputs):
# TODO(hvy): Take configuration.config.enable_backprop into
# account?
chainerx_out_data = backend.to_chainerx(outputs)
# Insert a ChainerX op-node that calls FunctionNode.backward in
# backprop. Note that chainerx_out_data may not require gradients.
chainerx._core._function_node_forward(
self, chainerx_in_data, chainerx_out_data,
[] if self._input_indexes_to_retain is None
else self._input_indexes_to_retain,
[] if self._output_indexes_to_retain is None
else self._output_indexes_to_retain)
self.inputs = tuple(
[variable._ChainerxVariableNodeProps(x) for x in inputs])
ret = tuple([
_to_variable_with_chainerx_fallback_array(
chainerx_out_array, out_array)
for chainerx_out_array, out_array
in six.moves.zip(chainerx_out_data, outputs)])
return ret