FIX: beginner/onnx/onnx_registry_tutorial.py fails against 2.4 RC binaries

.jenkins/validate_tutorials_built.py

@@ -53,7 +53,6 @@
     "intermediate_source/flask_rest_api_tutorial",
     "intermediate_source/text_to_speech_with_torchaudio",
     "intermediate_source/tensorboard_profiler_tutorial", # reenable after 2.0 release.
-    "beginner_source/onnx/onnx_registry_tutorial", # reenable after 2941 is fixed.
     "intermediate_source/torch_export_tutorial" # reenable after 2940 is fixed.
 ]
 

beginner_source/onnx/onnx_registry_tutorial.py

@@ -99,7 +99,6 @@ def forward(self, input_x, input_y):
 # NOTE: All attributes must be annotated with type hints.
 @onnxscript.script(custom_aten)
 def custom_aten_add(input_x, input_y, alpha: float = 1.0):
-    alpha = opset18.CastLike(alpha, input_y)
     input_y = opset18.Mul(input_y, alpha)
     return opset18.Add(input_x, input_y)
 
@@ -130,9 +129,9 @@ def custom_aten_add(input_x, input_y, alpha: float = 1.0):
 # graph node name is the function name
 assert onnx_program.model_proto.graph.node[0].op_type == "custom_aten_add"
 # function node domain is empty because we use standard ONNX operators
-assert onnx_program.model_proto.functions[0].node[3].domain == ""
+assert {node.domain for node in onnx_program.model_proto.functions[0].node} == {""}
 # function node name is the standard ONNX operator name
-assert onnx_program.model_proto.functions[0].node[3].op_type == "Add"
+assert {node.op_type for node in onnx_program.model_proto.functions[0].node} == {"Add", "Mul", "Constant"}
 
 
 ######################################################################
@@ -231,33 +230,25 @@ def custom_aten_gelu(input_x, approximate: str = "none"):
 
 
 ######################################################################
-# Let's inspect the model and verify the model uses :func:`custom_aten_gelu` instead of
-# :class:`aten::gelu`. Note the graph has one graph nodes for
-# ``custom_aten_gelu``, and inside ``custom_aten_gelu``, there is a function
-# node for ``Gelu`` with namespace ``com.microsoft``.
+# Let's inspect the model and verify the model uses op_type ``Gelu``
+# from namespace ``com.microsoft``.
+#
+# .. note::
+#     :func:`custom_aten_gelu` does not exist in the graph because
+#     functions with fewer than three operators are inlined automatically.
 #
 
 # graph node domain is the custom domain we registered
 assert onnx_program.model_proto.graph.node[0].domain == "com.microsoft"
 # graph node name is the function name
-assert onnx_program.model_proto.graph.node[0].op_type == "custom_aten_gelu"
-# function node domain is the custom domain we registered
-assert onnx_program.model_proto.functions[0].node[0].domain == "com.microsoft"
-# function node name is the node name used in the function
-assert onnx_program.model_proto.functions[0].node[0].op_type == "Gelu"
+assert onnx_program.model_proto.graph.node[0].op_type == "Gelu"
 
 
 ######################################################################
-# The following diagram shows ``custom_aten_gelu_model`` ONNX graph using Netron:
+# The following diagram shows ``custom_aten_gelu_model`` ONNX graph using Netron,
+# we can see the ``Gelu`` node from module ``com.microsoft`` used in the function:
 #
 # .. image:: /_static/img/onnx/custom_aten_gelu_model.png
-#    :width: 70%
-#    :align: center
-#
-# Inside the ``custom_aten_gelu`` function, we can see the ``Gelu`` node from module
-# ``com.microsoft`` used in the function:
-#
-# .. image:: /_static/img/onnx/custom_aten_gelu_function.png
 #
 # That is all we need to do. As an additional step, we can use ONNX Runtime to run the model,
 # and compare the results with PyTorch.