def _remove_binary_elementwise(graph: Graph, op: Operator, v: Variable):
"""
before)
x1 -+
+-{op}- y -
x2 -+
after)
v -
Args:
graph: the graph
op: the operator which will be removed
v: variable with which output variable is replaced
"""
y = op.outputs["y"]
op.remove_all()
y.change_order(v.order)
v.replace(y)
if v in graph.inputs:
if y in graph.outputs:
index = graph.outputs.index(y)
graph.outputs.remove(y)
graph.outputs.insert(index, v)
else:
y.replace(v)
else:
v.replace(y)
def _remove_unary_operator(graph: Graph, op: Operator):
x = list(op.inputs.values())[0]
y = list(op.outputs.values())[0]
op.remove_all()
if x.order == y.order and x.shape == y.shape:
x.change_order(y.order)
if y in graph.outputs:
index = graph.outputs.index(y)
graph.outputs.remove(y)
graph.outputs.insert(index, x)
else:
y.replace(x)
else:
if y in graph.outputs:
index = graph.outputs.index(y)
graph.outputs.remove(y)
graph.outputs.insert(index, x)
for op2 in list(y.input_to):
name = op2.get_input_name(y)
op2.remove_input(y)
op2.append_input(name, x)
def _optimize_ScalarAdd_ScalarMul(op1: ScalarAdd, op2: Operator):
if not isinstance(op2, ScalarMul):
return False
x0 = op1.inputs["x0"]
y2 = op2.outputs["y"]
op2.remove_all()
op1.remove_all()
y = (x0 * op2.value) + (op1.value * op2.value)
y.replace(y2)
return True
def _optimize_ElementwiseMul_ScalarMul(op1: ElementwiseMul,
c1: ConstantVariable, v1: Variable,
op2: Operator):
if not isinstance(op2, ScalarMul):
return False
y2 = op2.outputs["y"]
op1.remove_all()
op2.remove_all()
y = v1 * (c1 * op2.value)
y.replace(y2)
return True
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 _remove_binary_elementwise(graph: Graph, op: Operator, v: Variable):
y = op.outputs["y"]
op.remove_all()
y.change_order(v.order)
v.replace(y)
if v in graph.inputs:
if y in graph.outputs:
index = graph.outputs.index(y)
graph.outputs.remove(y)
graph.outputs.insert(index, v)
else:
y.replace(v)
else:
v.replace(y)
def _optimize_ScalarAdd_ElementwiseAdd(op1: ScalarAdd, op2: Operator):
if not isinstance(op2, ElementwiseAdd):
return False
x0 = op1.inputs["x0"]
y1 = op1.outputs["y"]
if y1 == op2.inputs["x0"]:
w = op2.inputs["x1"]
else:
w = op2.inputs["x0"]
y2 = op2.outputs["y"]
op2.remove_all()
op1.remove_all()
y = (x0 + w) + op1.value
y.replace(y2)
return True
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)
def _remove_binary_elementwise(graph: Graph, op: Operator, v: Variable):
"""
before)
x1 -+
+-{op}- y -
x2 -+
after)
v -
Args:
graph: the graph
op: the operator which will be removed
v: variable with which output variable is replaced
"""
y = op.outputs["y"]
op.remove_all()
OptimizeRule.replace_variable(graph, v, y, with_assert=False)
def test_remove_all():
op = Operator("op")
v1 = Variable((1, 2, 3, 4), OrderNHWC)
v2 = Variable((1, 2, 3, 4), OrderNHWC)
v3 = Variable((1, 2, 3, 4), OrderNHWC)
v4 = Variable((1, 2, 3, 4), OrderNHWC)
op.append_input("v1", v1)
op.append_input("v2", v2)
op.append_output("v3", v3)
op.append_output("v4", v4)
op.remove_all()
assert len(op.inputs) == 0
assert len(op.outputs) == 0
assert v1.input_to == set()
assert v2.input_to == set()
assert v3.output_from is None
assert v4.output_from is None
def _optimize_ElementwiseMul_ElementwiseMul(op1: ElementwiseMul,
c1: ConstantVariable, v1: Variable,
op2: Operator):
if not isinstance(op2, ElementwiseMul):
return False
x0 = op2.inputs["x0"]
x1 = op2.inputs["x1"]
y2 = op2.outputs["y"]
if isinstance(x0, ConstantVariable):
c2 = x0
elif isinstance(x1, ConstantVariable):
c2 = x1
else:
return False
op2.remove_all()
op1.remove_all()
y = v1 * (c1 * c2)
y.replace(y2)
return True
def remove_operator(op: Operator):
x = op.inputs["x0"]
y = op.outputs["y"]
op.remove_all()
x.replace(y)
def _remove_unary_operator(graph: Graph, op: Operator):
x = list(op.inputs.values())[0]
y = list(op.outputs.values())[0]
op.remove_all()
OptimizeRule.replace_variable(graph, y, x, with_assert=False)