diff --git a/src/finn/analysis/topology.py b/src/finn/analysis/topology.py
index c825a221ec178ee89b4e3747c982e59a3005cadd..acdb8ed7fcf41fd041c3601b2ee4fe67b6dc5f19 100644
--- a/src/finn/analysis/topology.py
+++ b/src/finn/analysis/topology.py
@@ -79,3 +79,26 @@ def node_inputs_in_expected_order(model):
if n.op_type != "Add":
all_OK = all_OK and (model.get_initializer(n.input[1]) is not None)
return {"node_inputs_in_expected_order": all_OK}
+
+
+def nodes_topologically_sorted(model):
+ """Verifies that graph.node is topologically sorted. This is required by the
+ ONNX specification.
+
+ Returns {"nodes_topologically_sorted": Bool}."""
+
+ # get successors of every node and check that
+ # successor index > current node index
+
+ all_OK = True
+ for n in model.graph.node:
+ successors = model.find_direct_successors(n)
+ if successors is not None:
+ for successor in successors:
+ # check the condition by checking the antithesis
+ index_n = model.get_node_index(n)
+ index_suc = model.get_node_index(successor)
+ if index_n > index_suc:
+ all_OK = False
+
+ return {"nodes_topologically_sorted": all_OK}
diff --git a/tests/analysis/test_topology_checks.py b/tests/analysis/test_topology_checks.py
index 41fbdb6cac8e81d6b1e3eed54a71d0e1d43c3adc..7f7f800da05e38fefa9350928ab6ddc94acbe2b6 100644
--- a/tests/analysis/test_topology_checks.py
+++ b/tests/analysis/test_topology_checks.py
@@ -26,11 +26,13 @@
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+import os
from pkgutil import get_data
import onnx.helper as oh
from onnx import TensorProto
-
+import brevitas.onnx as bo
+from finn.util.test import get_test_model_trained
import finn.analysis.topology as ta
from finn.core.modelwrapper import ModelWrapper
from finn.transformation.infer_shapes import InferShapes
@@ -88,3 +90,116 @@ def test_node_inputs_in_expected_order():
# this model has an (unnecessary) dynamic reshape for its weight tensor
# and so it fails the check
assert ret["node_inputs_in_expected_order"] is False
+
+
+def test_nodes_topologically_sorted():
+ # test analysis pass (nodes_topologically_sorted) with different models
+
+ # test with data/onnx/finn-hls-model/tfc_w1_a1_after_conv_to_hls.onnx
+ raw_m = get_data(
+ "finn", "data/onnx/finn-hls-model/tfc_w1_a1_after_conv_to_hls.onnx"
+ )
+ model = ModelWrapper(raw_m)
+ ret = model.analysis(ta.nodes_topologically_sorted)
+ assert ret["nodes_topologically_sorted"] is True
+
+ # remove first node and add it at the end
+ graph = model.graph
+ first_node = graph.node[0]
+ graph.node.remove(first_node)
+ graph.node.append(first_node)
+ ret = model.analysis(ta.nodes_topologically_sorted)
+ assert ret["nodes_topologically_sorted"] is False
+
+ # test with data/onnx/mnist-conv/model.onnx
+ raw_m = get_data("finn", "data/onnx/mnist-conv/model.onnx")
+ model = ModelWrapper(raw_m)
+ ret = model.analysis(ta.nodes_topologically_sorted)
+ assert ret["nodes_topologically_sorted"] is True
+
+ # remove first node and add it at the end
+ graph = model.graph
+ first_node = graph.node[0]
+ graph.node.remove(first_node)
+ graph.node.append(first_node)
+ ret = model.analysis(ta.nodes_topologically_sorted)
+ assert ret["nodes_topologically_sorted"] is False
+
+ # test with manually created small network
+ Neg_node = oh.make_node("Neg", inputs=["in1"], outputs=["neg1"])
+ Round_node = oh.make_node("Round", inputs=["neg1"], outputs=["round1"])
+
+ Ceil_node = oh.make_node("Ceil", inputs=["neg1"], outputs=["ceil1"])
+ Add_node = oh.make_node("Add", inputs=["round1", "ceil1"], outputs=["out1"])
+
+ in1 = oh.make_tensor_value_info("in1", TensorProto.FLOAT, [4, 4])
+ out1 = oh.make_tensor_value_info("out1", TensorProto.FLOAT, [4, 4])
+
+ graph = oh.make_graph(
+ nodes=[Neg_node, Round_node, Ceil_node, Add_node],
+ name="simple_graph",
+ inputs=[in1],
+ outputs=[out1],
+ value_info=[
+ oh.make_tensor_value_info("neg1", TensorProto.FLOAT, [4, 4]),
+ oh.make_tensor_value_info("round1", TensorProto.FLOAT, [4, 4]),
+ oh.make_tensor_value_info("ceil1", TensorProto.FLOAT, [4, 4]),
+ ],
+ )
+
+ onnx_model = oh.make_model(graph, producer_name="simple-model")
+ model = ModelWrapper(onnx_model)
+
+ ret = model.analysis(ta.nodes_topologically_sorted)
+ assert ret["nodes_topologically_sorted"] is True
+
+ # create same graph but with "wrong" node order
+ graph = oh.make_graph(
+ nodes=[Round_node, Ceil_node, Neg_node, Add_node],
+ name="simple_graph",
+ inputs=[in1],
+ outputs=[out1],
+ value_info=[
+ oh.make_tensor_value_info("neg1", TensorProto.FLOAT, [4, 4]),
+ oh.make_tensor_value_info("round1", TensorProto.FLOAT, [4, 4]),
+ oh.make_tensor_value_info("ceil1", TensorProto.FLOAT, [4, 4]),
+ ],
+ )
+
+ onnx_model = oh.make_model(graph, producer_name="simple-model")
+ model = ModelWrapper(onnx_model)
+
+ ret = model.analysis(ta.nodes_topologically_sorted)
+ assert ret["nodes_topologically_sorted"] is False
+
+ # test with data/onnx/finn-hls-model/finn-hls-onnx-model.onnx
+ raw_m = get_data("finn", "data/onnx/finn-hls-model/finn-hls-onnx-model.onnx")
+ model = ModelWrapper(raw_m)
+ ret = model.analysis(ta.nodes_topologically_sorted)
+ assert ret["nodes_topologically_sorted"] is True
+
+ # remove first node and add it at the end
+ graph = model.graph
+ first_node = graph.node[0]
+ graph.node.remove(first_node)
+ graph.node.append(first_node)
+ ret = model.analysis(ta.nodes_topologically_sorted)
+ assert ret["nodes_topologically_sorted"] is False
+
+ # test with cnv_w1a1
+ build_dir = "/tmp/" + os.environ["FINN_INST_NAME"]
+ cnv = get_test_model_trained("CNV", 1, 1)
+ bo.export_finn_onnx(
+ cnv, (1, 3, 32, 32), build_dir + "/end2end_cnv_w1a1_export.onnx"
+ )
+ model = ModelWrapper(build_dir + "/end2end_cnv_w1a1_export.onnx")
+ ret = model.analysis(ta.nodes_topologically_sorted)
+ assert ret["nodes_topologically_sorted"] is True
+
+ # remove first node and add it at the end
+ graph = model.graph
+ first_node = graph.node[0]
+ graph.node.remove(first_node)
+ graph.node.append(first_node)
+ ret = model.analysis(ta.nodes_topologically_sorted)
+ assert ret["nodes_topologically_sorted"] is False