def _convert_transpose(converter: ONNXConverter, onnx_op: INodeProto):
x = converter.get_variable(onnx_op.input[0])
attrs = attribute_dict(onnx_op)
y, = Transpose(None)(x)
perm = list(attrs["perm"].ints if "perm" in
attrs else reversed(range(x.ndim)))
y.change_order(Order([x.order.axes[i] for i in perm]))
converter.set_variable(onnx_op.output[0], y)
def _replace_input(op: Operator, var_name: str, target_orders: Union[Order, List[Order]]):
orig_var = op.inputs[var_name]
if isinstance(target_orders, Order):
target_orders = [target_orders]
if orig_var.order in target_orders:
return False
trans, = Transpose(None)(orig_var)
trans.change_order(target_orders[0])
op.remove_input(orig_var)
op.append_input(var_name, trans)
return True
def _replace_input(op: Operator, var_name: str,
target_orders: Union[Order, List[Order]]):
v = op.inputs[var_name]
if isinstance(target_orders, Order):
target_orders = [target_orders]
if v.order in target_orders:
return False
v_new, = Transpose(None)(v)
op.replace_input(v, v_new, with_assert=False)
v_new.change_order(target_orders[0])
return True
def transpose_handler(converter: TensorFlowConverter, tf_op: "tf.Operation"):
x = converter.get_variable(tf_op.inputs[0])
indices = converter.get_variable(tf_op.inputs[1])
if not isinstance(indices, ConstantVariable):
raise NotImplementedError(
"[TensorFlowConverter] Operator 'Transpose' with dynamic indices is not supported yet."
)
indices = indices.data.astype(int).flatten().tolist() # type: List[int]
y, = Transpose(None)(x)
y.change_order(Order([x.order.axes[i] for i in indices]))
converter.set_variable(tf_op.outputs[0], y)
def optimize(self, graph: Graph) -> Tuple[Graph, bool]:
flag_changed = False
for op in traverse.filter_nodes(traverse.listup_operators(graph),
Linear):
x = op.inputs["x"]
w = op.inputs["w"]
y = op.outputs["y"]
flag_changed = True
op.remove_all()
a_k = Axis.C
a_n = w.order.axes[0] if w.order.axes[1] == a_k else w.order.axes[1]
axes_m = [a for a in x.order.axes if a != a_k]
K = x.shape_dict[a_k]
M = x.size // K
N = w.shape_dict[a_n]
x, = Transpose(None)(x)
x.change_order(Order([a_k] + axes_m))
w, = Transpose(None)(w)
w.change_order(Order([a_k, a_n]))
new_y, = Sgemm(None,
M=M,
N=N,
K=K,
out_shape=[x.shape_dict[a] for a in axes_m] + [N],
out_order=Order(axes_m + [a_n]),
transpose_A=False,
transpose_B=True)(x, w)
new_y, = Transpose(None)(new_y)
OptimizeRule.replace_variable(graph, new_y, y)
return graph, flag_changed
def optimize(self, graph: Graph) -> Tuple[Graph, bool]:
flag_changed = False
for op in traverse.listup_operators(graph):
if isinstance(op, Reshape):
flag_changed |= _replace_input(op, "x",
op.parameters["in_order"])
flag_changed |= _replace_output(op, "y",
op.parameters["out_order"])
continue
elif isinstance(op, (Convolution2D, MaxPooling2D, AveragePooling2D,
Deconvolution2D, Space2Depth, Depth2Space)):
flag_changed |= _replace_input(op, "x", OrderNHWC)
flag_changed |= _replace_output(op, "y", OrderNHWC)
continue
elif isinstance(op, Softmax):
x = op.inputs["x"]
y = op.outputs["y"]
target_axis = op.parameters["axis"]
if not (x.ndim == 2
and x.order.axes_dict[target_axis] == x.ndim - 1):
"""
Before)
| x | | y |
|-----| -{softmax}-> |-----|
| XYZ | axis=Y | XYZ |
After)
| x | | hx1 | | hx2 | | hy1 | | hy2 | | y |
|-----| -{transpose}-> |-----| -{reshape}-> |-----| -{softmax}-> |-----| -{reshape}-> |-----| -{transpose}-> |-----|
| XYZ | | XZY | | NC | axis=C | NC | | XZY | | XYZ |
: :
order_nd = XZY order_2d = NC
"""
op.remove_all()
axes_nd = list(x.order.axes)
axes_nd.remove(target_axis)
axes_nd.append(target_axis)
order_nd = Order(axes_nd)
shape_nd = tuple([x.shape_dict[axis] for axis in axes_nd])
order_2d = OrderNC
shape_2d = tuple([
x.size // x.shape_dict[target_axis],
x.shape_dict[target_axis]
])
if x.order == order_nd:
hx1 = x
else:
hx1, = Transpose(None)(x)
hx1.change_order(order_nd)
flag_changed = True
if hx1.order == order_2d and hx1.shape == shape_2d:
hx2 = hx1
else:
hx2, = Reshape(None,
in_order=hx1.order,
out_order=order_2d,
out_shape=shape_2d)(hx1)
flag_changed = True
hy1, = Softmax(None, axis=Axis.C)(hx2)
if hy1.order == order_nd and hy1.shape == shape_nd:
hy2 = hy1
else:
hy2, = Reshape(None,
in_order=hy1.order,
out_order=order_nd,
out_shape=shape_nd)(hy1)
flag_changed = True
if hy2.order == y.order:
y_dummy = hy2
else:
y_dummy, = Transpose(None)(hy2)
y_dummy.change_order(y.order)
flag_changed = True
y_dummy.replace(y)
continue
return graph, flag_changed
def optimize(self, graph: Graph) -> Tuple[Graph, bool]:
flag_changed = False
matches = traverse.search_sub_structure(
graph, [Sgemm, Variable, ElementwiseMul])
while len(matches) > 0:
match = matches.pop()
sgemm = match[0] # type: Sgemm
elementwise_mul = match[2] # type: ElementwiseMul
out_order = sgemm.parameters["out_order"]
out_shape = sgemm.parameters["out_shape"]
axis_k = Axis('AxisK')
if not isinstance(sgemm.inputs["A"],
ConstantVariable) and not isinstance(
sgemm.inputs["B"], ConstantVariable):
# neither x nor w1 is constant
continue
elif isinstance(sgemm.inputs["A"], ConstantVariable):
w1 = sgemm.inputs["A"] # type: ConstantVariable
if sgemm.transpose_A:
# w1.shape = (M, K)
shape = []
axes = []
for axis, size in zip(out_order.axes, out_shape):
shape.append(size)
axes.append(axis)
if mul(shape) >= sgemm.M:
break
if mul(shape) != sgemm.M:
# output axes are derived from both w1 and x
continue
w1_virtual_order = Order(axes + [axis_k])
w1_virtual_shape = shape + [sgemm.K]
else:
# w1.shape = (K, M)
shape = [sgemm.K]
axes = [axis_k]
for axis, size in zip(out_order.axes, out_shape):
shape.append(size)
axes.append(axis)
if mul(shape) >= w1.size:
break
if mul(shape) != w1.size:
# output axes are derived from both w1 and x
continue
w1_virtual_order = Order(axes)
w1_virtual_shape = shape
else:
w1 = sgemm.inputs["B"] # type: ConstantVariable
if sgemm.transpose_B:
# w1.shape = (K, N)
shape = []
axes = []
for axis, size in reversed(
list(zip(out_order.axes, out_shape))):
shape.insert(0, size)
axes.insert(0, axis)
if mul(shape) >= sgemm.N:
break
if mul(shape) != sgemm.N:
# output axes are derived from both w1 and x
continue
w1_virtual_order = Order([axis_k] + axes)
w1_virtual_shape = [sgemm.K] + shape
else:
# w1.shape = (N, K)
shape = [sgemm.K]
axes = [axis_k]
for axis, size in reversed(
list(zip(out_order.axes, out_shape))):
shape.insert(0, size)
axes.insert(0, axis)
if mul(shape) >= w1.size:
break
if mul(shape) != w1.size:
# output axes are derived from both w1 and x
continue
w1_virtual_order = Order(axes)
w1_virtual_shape = shape
h = sgemm.outputs["C"] # type: Variable
x0 = elementwise_mul.inputs["x0"]
x1 = elementwise_mul.inputs["x1"]
if h == x1:
if not isinstance(x0, ConstantVariable):
# w2 is not constant
continue
w2 = x0 # type: ConstantVariable
else:
if not isinstance(x1, ConstantVariable):
# w2 is not constant
continue
w2 = x1 # type: ConstantVariable
y = elementwise_mul.outputs["y"] # type: Variable
if not all(axis in w1_virtual_order.axes
for axis in w2.order.axes):
# w2's axes are derived from both w1 and x
continue
elementwise_mul.remove_all()
y_dummy, = Transpose(None)(h)
y_dummy.change_order(y.order)
y_dummy.replace(y)
w2.change_order(w1_virtual_order)
w_new = ConstantVariable(
w1.data.reshape(w1_virtual_shape),
w1_virtual_order) * w2 # type: ConstantVariable
w1.data = w_new.data.reshape(w1.shape)
flag_changed = True
matches = traverse.search_sub_structure(
graph, [Sgemm, Variable, ElementwiseMul])
return graph, flag_changed
def optimize(self, graph: Graph) -> Tuple[Graph, bool]:
flag_changed = False
for op in traverse.listup_operators(graph):
if isinstance(op, Transpose):
x = op.inputs["x0"]
y = op.outputs["y"]
if x.order == y.order:
op.remove_all()
x.replace(y)
flag_changed = True
if all(isinstance(op2, (Elementwise, SplitAxis)) for op2 in y.input_to):
op.remove_all()
for op2 in list(y.input_to):
name = op2._get_input_name(y)
op2.remove_input(y)
op2.append_input(name, x)
elif isinstance(op, Reshape):
flag_changed |= _replace_input(op, "x", op.parameters["in_order"])
flag_changed |= _replace_output(op, "y", op.parameters["out_order"])
elif isinstance(op, (Convolution2D, MaxPooling2D, AveragePooling2D, Deconvolution2D)):
flag_changed |= _replace_input(op, "x", OrderNHWC)
flag_changed |= _replace_output(op, "y", OrderNHWC)
elif isinstance(op, Softmax):
x = op.inputs["x"]
y = op.outputs["y"]
if x.ndim > 2:
"""
Before)
| x | | y |
|------| -{softmax}-> |------|
| NCHW | | NCHW |
After)
| x | | hx1 | | hx2 | | hy1 | | hy2 | | y |
|------| -{transpose}-> |------| -{reshape}-> |-----| -{softmax}-> |-----| -{reshape}-> |------| -{transpose}-> |------|
| NCHW | | NHWC | | NC | | NC | | NHWC | | NCHW |
"""
op.remove_all()
target_axis = op.parameters["axis"]
axes_nd = list(x.order.axes)
axes_nd.remove(target_axis)
axes_nd.append(target_axis)
order_nd = Order(axes_nd)
shape_nd = [x.shape_dict[axis] for axis in axes_nd]
order_2d = OrderNC
shape_2d = [x.size // x.shape_dict[target_axis], x.shape_dict[target_axis]]
hx1, = Transpose(None)(x)
hx1.change_order(order_nd)
hx2, = Reshape(None, in_order=hx1.order, out_order=order_2d, out_shape=shape_2d)(hx1)
hy1, = Softmax(None, axis=Axis.C)(hx2)
hy2, = Reshape(None, in_order=hy1.order, out_order=order_nd, out_shape=shape_nd)(hy1)
y_dummy, = Transpose(None)(hy2)
y_dummy.change_order(y.order)
y_dummy.replace(y)
flag_changed = True
else:
flag_changed |= _replace_input(op, "x", OrderNC)
flag_changed |= _replace_output(op, "y", OrderNC)
return graph, flag_changed
