diff --git a/docker/finn_entrypoint.sh b/docker/finn_entrypoint.sh
index 035bba3b53d85a8457eff1e7c1a23e0efff60caa..7ba0aeabbd9bd83c7f33e54cfde626070dcc5ec5 100644
--- a/docker/finn_entrypoint.sh
+++ b/docker/finn_entrypoint.sh
@@ -17,7 +17,7 @@ BREVITAS_COMMIT=215cf44c76d562339fca368c8c3afee3110033e8
BREVITAS_EXAMPLES_COMMIT=2059f96bd576bf71f32c757e7f92617a70190c90
CNPY_COMMIT=4e8810b1a8637695171ed346ce68f6984e585ef4
HLSLIB_COMMIT=6b88db826bb023937506913a23d964775a7606af
-PYVERILATOR_COMMIT=fb1afefa5b207acf6fec28f8abb72a862f2ca1d2
+PYVERILATOR_COMMIT=1d89cb0d4e0c97469cc6352c611f876ec13edfa6
PYNQSHELL_COMMIT=0c82a61b0ec1a07fa275a14146233824ded7a13d
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/src/finn/core/modelwrapper.py b/src/finn/core/modelwrapper.py
index e99a6ef4cd40d6323d77354d3c9b4be341d7649c..dc5b36920a5639933463d682dc66fb8bc15b35f2 100644
--- a/src/finn/core/modelwrapper.py
+++ b/src/finn/core/modelwrapper.py
@@ -288,6 +288,46 @@ class ModelWrapper:
except ValueError:
return None
+ def find_consumers(self, tensor_name):
+ """Finds and returns a list of the nodes that consume tensor with
+ given name."""
+ consumers = []
+ for n in self._model_proto.graph.node:
+ for inp_tensor in n.input:
+ if inp_tensor == tensor_name:
+ consumers.append(n)
+ if consumers != []:
+ return consumers
+ else:
+ return None
+
+ def find_direct_successors(self, node):
+ """Finds and returns a list of the nodes that are successors of
+ given node."""
+ successors = []
+ for outp_tensor in node.output:
+ tensor_consumer_list = self.find_consumers(outp_tensor)
+ if tensor_consumer_list is not None:
+ for consumer in tensor_consumer_list:
+ successors.append(consumer)
+ if successors != []:
+ return successors
+ else:
+ return None
+
+ def find_direct_predecessors(self, node):
+ """Finds and returns a list of the nodes that are predecessors of
+ given node."""
+ predecessors = []
+ for inp_tensor in node.input:
+ producer = self.find_producer(inp_tensor)
+ if producer is not None:
+ predecessors.append(producer)
+ if predecessors != []:
+ return predecessors
+ else:
+ return None
+
def get_all_tensor_names(self):
"""Returns a list of all (input, output and value_info) tensor names
in the graph."""
@@ -383,3 +423,14 @@ class ModelWrapper:
def get_non_finn_nodes(self):
"""Returns a list of nodes where domain != 'finn'."""
return list(filter(lambda x: x.domain != "finn", self.graph.node))
+
+ def get_node_index(self, node):
+ """Returns current index of given node."""
+ n_ind = 0
+ try:
+ for n in self.graph.node:
+ if n == node:
+ return n_ind
+ n_ind += 1
+ except ValueError:
+ return None
diff --git a/src/finn/core/onnx_exec.py b/src/finn/core/onnx_exec.py
index 172ba25b223fd087df134add460a42d0a9935e0e..44787e1d26049e6075e2222316b45ab3898acbc7 100644
--- a/src/finn/core/onnx_exec.py
+++ b/src/finn/core/onnx_exec.py
@@ -38,6 +38,7 @@ from finn.core.modelwrapper import ModelWrapper
from finn.core.remote_exec import remote_exec
from finn.core.rtlsim_exec import rtlsim_exec
from finn.custom_op.registry import getCustomOp
+import finn.analysis.topology as ta
def execute_node(node, context, graph):
@@ -121,6 +122,11 @@ def execute_onnx(model, input_dict, return_full_exec_context=False):
if not model.check_all_tensor_shapes_specified():
raise Exception("Found unspecified tensor shapes, try infer_shapes")
+ ret = model.analysis(ta.nodes_topologically_sorted)
+ assert (
+ ret["nodes_topologically_sorted"] is True
+ ), """Nodes must be
+ topologically sorted."""
graph = model.graph
# first, we need to make sure that every variable required by the graph has
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
diff --git a/tests/brevitas/test_brevitas_act_export.py b/tests/brevitas/test_brevitas_act_export.py
index 08c4a99151d1105ad4258a8d7d6c19cc72da7a99..0415d70bfe6a543b4547f07dd99ff2525bef5994 100644
--- a/tests/brevitas/test_brevitas_act_export.py
+++ b/tests/brevitas/test_brevitas_act_export.py
@@ -1,3 +1,4 @@
+import os
import numpy as np
import torch
import brevitas.onnx as bo
@@ -41,3 +42,4 @@ def test_brevitas_act_export(abits, narrow_range, max_val):
inp_tensor = torch.from_numpy(inp_tensor).float()
expected = b_act.forward(inp_tensor).detach().numpy()
assert np.isclose(produced, expected, atol=1e-3).all()
+ os.remove(export_onnx_path)
diff --git a/tests/core/test_modelwrapper.py b/tests/core/test_modelwrapper.py
index 942eda19ca4c2cdbded9f906a5e7772f50acbd6e..4d2029093f09a705eb562a7f706f21b64172e435 100644
--- a/tests/core/test_modelwrapper.py
+++ b/tests/core/test_modelwrapper.py
@@ -27,8 +27,8 @@
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
import os
+import onnx
from collections import Counter
-
import brevitas.onnx as bo
import numpy as np
@@ -68,3 +68,56 @@ def test_modelwrapper():
out_prod = model.find_producer(l0_inp_tensor_name)
assert out_prod.op_type == "Sign"
os.remove(export_onnx_path)
+
+
+def test_modelwrapper_graph_order():
+ # create small network with properties to be tested
+ Neg_node = onnx.helper.make_node("Neg", inputs=["in1"], outputs=["neg1"],)
+ Round_node = onnx.helper.make_node("Round", inputs=["neg1"], outputs=["round1"],)
+
+ Ceil_node = onnx.helper.make_node("Ceil", inputs=["neg1"], outputs=["ceil1"],)
+ Add_node = onnx.helper.make_node(
+ "Add", inputs=["round1", "ceil1"], outputs=["out1"],
+ )
+
+ in1 = onnx.helper.make_tensor_value_info("in1", onnx.TensorProto.FLOAT, [4, 4])
+ out1 = onnx.helper.make_tensor_value_info("out1", onnx.TensorProto.FLOAT, [4, 4])
+
+ graph = onnx.helper.make_graph(
+ nodes=[Neg_node, Round_node, Ceil_node, Add_node],
+ name="simple_graph",
+ inputs=[in1],
+ outputs=[out1],
+ value_info=[
+ onnx.helper.make_tensor_value_info("neg1", onnx.TensorProto.FLOAT, [4, 4]),
+ onnx.helper.make_tensor_value_info(
+ "round1", onnx.TensorProto.FLOAT, [4, 4]
+ ),
+ onnx.helper.make_tensor_value_info("ceil1", onnx.TensorProto.FLOAT, [4, 4]),
+ ],
+ )
+
+ onnx_model = onnx.helper.make_model(graph, producer_name="simple-model")
+ model = ModelWrapper(onnx_model)
+
+ # test graph order functions
+ assert model.find_consumers("in1") == [Neg_node]
+ assert model.find_consumers("neg1") == [Round_node, Ceil_node]
+ assert model.find_consumers("round1") == [Add_node]
+ assert model.find_consumers("ceil1") == [Add_node]
+ assert model.find_consumers("out1") is None
+
+ assert model.find_direct_successors(Neg_node) == [Round_node, Ceil_node]
+ assert model.find_direct_successors(Round_node) == [Add_node]
+ assert model.find_direct_successors(Ceil_node) == [Add_node]
+ assert model.find_direct_successors(Add_node) is None
+
+ assert model.find_direct_predecessors(Neg_node) is None
+ assert model.find_direct_predecessors(Round_node) == [Neg_node]
+ assert model.find_direct_predecessors(Ceil_node) == [Neg_node]
+ assert model.find_direct_predecessors(Add_node) == [Round_node, Ceil_node]
+
+ assert model.get_node_index(Neg_node) == 0
+ assert model.get_node_index(Round_node) == 1
+ assert model.get_node_index(Ceil_node) == 2
+ assert model.get_node_index(Add_node) == 3
diff --git a/tests/transformation/test_topk_insert.py b/tests/transformation/test_topk_insert.py
index ac32c30edbbf466b2b441bcc92975a7d50f42bda..1af0f255d8fb1af8a6e571518f18d831aa71298b 100644
--- a/tests/transformation/test_topk_insert.py
+++ b/tests/transformation/test_topk_insert.py
@@ -1,3 +1,4 @@
+import os
import onnx
from finn.util.test import get_test_model_trained
import brevitas.onnx as bo
@@ -56,3 +57,4 @@ def test_topk_insert(k):
output_pysim_topk = output_pysim_topk.astype(np.int).flatten()
assert np.array_equal(output_golden_topk, output_pysim_topk)
+ os.remove(export_onnx_path)