diff --git a/src/finn/transformation/streamline/reorder.py b/src/finn/transformation/streamline/reorder.py
index ec58084df1589aaa4db5e154832b3fbc4eddb9de..0b6259a61d3eb67b7b38d4c6939019ce2893a875 100644
--- a/src/finn/transformation/streamline/reorder.py
+++ b/src/finn/transformation/streamline/reorder.py
@@ -444,3 +444,90 @@ class MakeMaxPoolNHWC(Transformation):
graph.node.insert(node_ind - 1, consumer)
graph_modified = True
return (model, graph_modified)
+
+
+class MoveOpPastFork(Transformation):
+ """Move node operations past graph forks. Used when a node before a fork
+ can be merged with nodes in the branches
+ """
+
+ def __init__(self, op_name_list):
+ super().__init__()
+ self.ops_to_move = op_name_list
+
+ def apply(self, model):
+ graph = model.graph
+ graph_modified = False
+ nodes = [n for n in graph.node]
+ node_ind = 0
+ for n in nodes:
+ node_ind += 1
+ if (
+ n.op_type in self.ops_to_move
+ and model.is_fork_node(n)
+ and not model.is_join_node(n)
+ ):
+
+ # Restrict this transform to operations with constant parameters
+ # Assuming parameters is in input 1
+ op_init_param = model.get_initializer(n.input[1])
+ if op_init_param is None:
+ continue
+
+ # Check case when branches are empty and go
+ # to the same node
+ consumers = model.find_consumers(n.output[0])
+ unique_consumer = True
+ for consum_node in consumers[1:]:
+ if consumers[0] != consum_node:
+ unique_consumer = False
+ break
+
+ if unique_consumer:
+ continue
+
+ for consumer_node in consumers[1:]:
+ # create new node
+ new_param_name = model.make_new_valueinfo_name()
+ new_output_tensor_name = model.make_new_valueinfo_name()
+ new_node = oh.make_node(
+ n.op_type,
+ [n.input[0], new_param_name],
+ [new_output_tensor_name],
+ )
+ graph.node.insert(node_ind, new_node)
+ node_ind += 1
+ model.set_initializer(new_param_name, op_init_param)
+
+ # change consumer input tensor
+ graph.node.remove(consumer_node)
+ for idx, consumer_input in enumerate(consumer_node.input):
+ if consumer_input == n.output[0]:
+ consumer_node.input[idx] = new_output_tensor_name
+ break
+ else:
+ raise Exception(
+ "Consumer should have the current node output as input"
+ )
+
+ graph.node.insert(node_ind, consumer_node)
+
+ graph_modified = True
+
+ model = model.transform(InferShapes())
+ return (model, graph_modified)
+
+
+class MoveAddPastFork(MoveOpPastFork):
+ def __init__(self):
+ super().__init__(["Add"])
+
+
+class MoveMulPastFork(MoveOpPastFork):
+ def __init__(self):
+ super().__init__(["Mul"])
+
+
+class MoveLinearPastFork(MoveOpPastFork):
+ def __init__(self):
+ super().__init__(["Add", "Mul"])
diff --git a/tests/transformation/test_move_past_fork.py b/tests/transformation/test_move_past_fork.py
new file mode 100644
index 0000000000000000000000000000000000000000..f3d37bd60c9e2580ca4499daafa8693f39fec810
--- /dev/null
+++ b/tests/transformation/test_move_past_fork.py
@@ -0,0 +1,79 @@
+from onnx import TensorProto, helper
+import numpy as np
+
+import finn.core.onnx_exec as oxe
+from finn.core.modelwrapper import ModelWrapper
+from finn.transformation.streamline.reorder import MoveLinearPastFork
+from finn.transformation.infer_shapes import InferShapes
+
+import pytest
+
+
+@pytest.mark.parametrize("ch", [64, 1])
+# ifmdim
+@pytest.mark.parametrize("ifmdim", [-1, 7])
+def test_move_past_fork(ch, ifmdim):
+ # generate test vectors of correct shape
+ if ifmdim == -1:
+ input_shape = (1, ch)
+ else:
+ input_shape = (1, ch, ifmdim, ifmdim)
+
+ top_in = helper.make_tensor_value_info("top_in", TensorProto.FLOAT, input_shape)
+ top_out = helper.make_tensor_value_info("top_out", TensorProto.FLOAT, input_shape)
+
+ num_of_params = 8
+ value_info = []
+ for i in range(num_of_params):
+ value_info += [
+ helper.make_tensor_value_info("p" + str(i), TensorProto.FLOAT, input_shape)
+ ]
+
+ add_1_to_move = helper.make_node("Add", ["top_in", "p0"], ["fork1"])
+ mul_1_to_move = helper.make_node("Mul", ["t5", "p4"], ["fork2"])
+ add_2_to_move = helper.make_node("Add", ["fork2", "p5"], ["t6"])
+ mul_1_not_to_move = helper.make_node("Mul", ["t8", "p7"], ["fork3"])
+ modelproto = helper.make_model(
+ helper.make_graph(
+ name="test",
+ inputs=[top_in],
+ outputs=[top_out],
+ value_info=value_info,
+ nodes=[
+ # fork1
+ add_1_to_move,
+ helper.make_node("Mul", ["fork1", "p1"], ["t2"]),
+ helper.make_node("Mul", ["fork1", "p2"], ["t3"]),
+ helper.make_node("Add", ["t2", "t3"], ["t4"]),
+ helper.make_node("Add", ["t4", "p3"], ["t5"]),
+ # fork2
+ mul_1_to_move,
+ add_2_to_move,
+ helper.make_node("Add", ["fork2", "p6"], ["t7"]),
+ helper.make_node("Add", ["t6", "t7"], ["t8"]),
+ # empty branches: do nothing
+ mul_1_not_to_move,
+ helper.make_node("Add", ["fork3", "fork3"], ["top_out"]),
+ ],
+ )
+ )
+ model = ModelWrapper(modelproto)
+ model = model.transform(InferShapes())
+
+ np.random.seed(0)
+ for i in range(num_of_params):
+ model.set_initializer(
+ "p" + str(i), np.random.rand(*input_shape).astype(np.float32)
+ )
+
+ # Transform
+ new_model = model.transform(MoveLinearPastFork())
+ inp_dict = {"top_in": np.random.rand(*input_shape).astype(np.float32)}
+
+ # Test
+ assert oxe.compare_execution(model, new_model, inp_dict)
+ assert not new_model.is_fork_node(add_1_to_move)
+ assert not new_model.is_fork_node(mul_1_to_move)
+ assert not new_model.is_fork_node(add_2_to_move)
+ assert new_model.is_fork_node(mul_1_not_to_move)
+ assert len(new_model.graph.node) == 14