def _split_tensorwise(graph: Graph, op: Operator, v: Variable,
v_pair: Sequence[Variable], axis: Axis):
s1 = v_pair[0].shape_dict[axis]
s2 = v_pair[1].shape_dict[axis]
xs = dict(op.inputs)
ys = dict(op.outputs)
op.remove_all()
op_0 = op.copy()
op_1 = op.copy()
for key, x in xs.items():
if x == v:
x_0, x_1 = v_pair
else:
if axis in x.order.axes:
x_0, x_1 = SplitAxis(None, axis=axis, sections=[s1])(x)
else:
# splitting is not occurred
x_0 = x_1 = x
op_0.append_input(key, x_0)
op_1.append_input(key, x_1)
for key, y in ys.items():
if y == v:
y_0, y_1 = v_pair
else:
if axis in y.order.axes:
# TODO (Kiikurage)
# Attribute attached to "y" is not copied to neither "y_0" or "y_1"
y_0 = Variable([
s1 if a == axis else y.shape_dict[a] for a in y.order.axes
], y.order)
y_1 = Variable([
s2 if a == axis else y.shape_dict[a] for a in y.order.axes
], y.order)
y_new, = Concat(None, axis=axis)(y_0, y_1)
OptimizeRule.replace_variable(graph, y, y_new)
else:
raise UnexpectedAndPleaseReportError
op_0.append_output(key, y_0)
op_1.append_output(key, y_1)
def _split_tensorwise(graph: Graph, op: Operator, v: Variable,
v_pair: Sequence[Variable], axis: Axis):
s1 = v_pair[0].shape_dict[axis]
xs = dict(op.inputs)
ys = dict(op.outputs)
op.remove_all()
op_0 = op.copy()
op_1 = op.copy()
for key in xs.keys():
x = xs[key]
if x == v:
x_0, x_1 = v_pair
else:
if axis not in x.order.axes or x.shape_dict[axis] == 1:
# broadcasting
x_0 = x_1 = x
else:
x_0, x_1 = SplitAxis(None, axis=axis, sections=[s1])(x)
op_0.append_input(key, x_0)
op_1.append_input(key, x_1)
op_0.exec()
op_1.exec()
for key in ys.keys():
y = ys[key]
if y == v:
OptimizeRule.replace_variable(
graph, op_0.outputs[key].transpose_like(v_pair[0]), v_pair[0])
OptimizeRule.replace_variable(
graph, op_1.outputs[key].transpose_like(v_pair[1]), v_pair[1])
else:
y_0 = op_0.outputs[key]
y_1 = op_1.outputs[key]
y_new, = Concat(None, axis=axis)(y_0, y_1)
OptimizeRule.replace_variable(graph, y_new.transpose_like(y), y)