Merge branch 'main' into use-importlib

CHANGELOG.rst

@@ -7,12 +7,13 @@
 Change log
 ==========
 
-0.9.1 (2023-xx-xx)
+0.9.1 (2023-10-05)
 ------------------
 
 **Bug fix**
 
 - The node-adaption no longer fails if faced with a node that has repeating inputs.
+- Forego version adaption of inlined models if no nodes are from the default domain.
 
 
 0.9.0 (2023-06-12)

src/spox/_adapt.py

@@ -73,6 +73,10 @@ def adapt_inline(
     source_version = max({v for d, v in node.opset_req if d in ("", "ai.onnx")})
     target_version = target_opsets[""]
 
+    # convert_version fails if the inlined model does not import the default domain
+    seen_domains = {prot.domain for prot in protos}
+    if not seen_domains & {"", "ai.onnx"}:
+        return protos
     if source_version != target_version:
         target_model = onnx.version_converter.convert_version(
             node.model, target_version

tests/test_adapt.py

@@ -149,3 +149,87 @@ def test_adapt_node_with_repeating_input_names():
     c = op19.identity(a)
 
     build({"a": a}, {"b": b, "c": c})
+
+
+def test_inline_model_custom_node_only():
+    """Inline a model which only consists of a custom node.
+
+    Such models do not import from the default domain.
+    """
+    domain = "foo.ai"
+    node = onnx.helper.make_node("FooOp", ["a"], ["b"], domain=domain)
+    value_infos_input = [
+        onnx.helper.make_value_info(
+            "a", onnx.helper.make_tensor_type_proto(onnx.TensorProto.STRING, ("N",))
+        ),
+    ]
+    value_infos_output = [
+        onnx.helper.make_value_info(
+            "b", onnx.helper.make_tensor_type_proto(onnx.TensorProto.STRING, ("N",))
+        )
+    ]
+
+    model = onnx.helper.make_model(
+        onnx.helper.make_graph(
+            [node],
+            "graph",
+            value_infos_input,
+            value_infos_output,
+        ),
+        opset_imports=[onnx.helper.make_opsetid(domain, 1)],
+    )
+
+    # Ensure that our model is valid
+    onnx.checker.check_model(model, full_check=True)
+
+    (a,) = arguments(data=Tensor(numpy.str_, ("N",)))
+    (b,) = inline(model)(a).values()
+
+    # Add another node to the model to trigger the adaption logic
+    c = op18.identity(b)
+    build({"a": a}, {"c": c})
+
+
+@pytest.mark.skip(
+    reason="Adapting custom nodes (including their subgraphs) is currently not supported"
+)
+def test_inline_model_custom_node_nested(old_squeeze: onnx.ModelProto):
+    """A singleton custom node with a old standard node in its attribute."""
+    domain = "foo.ai"
+
+    node = onnx.helper.make_node(
+        "FooOp", ["a"], ["b"], domain=domain, **{"nested_graph": old_squeeze.graph}
+    )
+    value_infos_input = [
+        onnx.helper.make_value_info(
+            "a", onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, ("N",))
+        ),
+    ]
+    value_infos_output = [
+        onnx.helper.make_value_info(
+            "b", onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, ("N",))
+        )
+    ]
+
+    model = onnx.helper.make_model(
+        onnx.helper.make_graph(
+            [node],
+            "graph",
+            value_infos_input,
+            value_infos_output,
+        ),
+        opset_imports=[
+            onnx.helper.make_opsetid(domain, 1),
+            onnx.helper.make_opsetid("", 12),
+        ],
+    )
+
+    # Ensure that our model is valid
+    onnx.checker.check_model(model, full_check=True)
+
+    (a,) = arguments(data=Tensor(numpy.float32, ("N",)))
+    (b,) = inline(model)(a).values()
+
+    # Add another node to the model to trigger the adaption logic
+    c = op18.identity(b)
+    build({"a": a}, {"c": c})