Create the convenience methods i() and o() on Node

src/onnx_ir/_core.py

@@ -1546,6 +1546,56 @@
             "Directly mutating the input sequence is unsupported. Please use Node.replace_input_with() instead."
         )
 
+    @property
+    def outputs(self) -> Sequence[Value]:
+        """The output values of the node.
+
+        The outputs are immutable. To change the outputs, create a new node and
+        replace the inputs of the using nodes of this node's outputs by calling
+        :meth:`replace_input_with` on the using nodes of this node's outputs.
+        """
+        return self._outputs
+
+    @outputs.setter
+    def outputs(self, _: Sequence[Value]) -> None:
+        raise AttributeError("outputs is immutable. Please create a new node instead.")
+
+    def i(self, index: int = 0) -> Value | None:
+        """Get the input value at the given index.
+
+        This is a convenience method that is equivalent to ``node.inputs[index]``.
+
+        The following is equivalent::
+
+            node.inputs[0] == node.i(0) == node.i()  # Default index is 0
+            node.inputs[index] == node.i(index)
+
+        Returns:
+            The input value at the given index.
+
+        Raises:
+            IndexError: If the index is out of range.
+        """
+        return self.inputs[index]
+
+    def o(self, index: int = 0) -> Value:
+        """Get the output value at the given index.
+
+        This is a convenience method that is equivalent to ``node.outputs[index]``.
+
+        The following is equivalent::
+
+            node.outputs[0] == node.o(0) == node.o()  # Default index is 0
+            node.outputs[index] == node.o(index)
+
+        Returns:
+            The output value at the given index.
+
+        Raises:
+            IndexError: If the index is out of range.
+        """
+        return self.outputs[index]
+
     def predecessors(self) -> Sequence[Node]:
         """Return the predecessor nodes of the node, deduplicated, in a deterministic order."""
         # Use the ordered nature of a dictionary to deduplicate the nodes
@@ -1616,20 +1666,6 @@
             raise ValueError("The node to append to does not belong to any graph.")
         self._graph.insert_after(self, nodes)
 
-    @property
-    def outputs(self) -> Sequence[Value]:
-        """The output values of the node.
-
-        The outputs are immutable. To change the outputs, create a new node and
-        replace the inputs of the using nodes of this node's outputs by calling
-        :meth:`replace_input_with` on the using nodes of this node's outputs.
-        """
-        return self._outputs
-
-    @outputs.setter
-    def outputs(self, _: Sequence[Value]) -> None:
-        raise AttributeError("outputs is immutable. Please create a new node instead.")
-
     @property
     def attributes(self) -> _graph_containers.Attributes:
         """The attributes of the node."""

src/onnx_ir/external_data_test.py

@@ -173,13 +173,13 @@ def _simple_model(self) -> ir.Model:
         node_1 = ir.Node(
             "",
             "Op_1",
-            inputs=[node_0.outputs[0]],
+            inputs=[node_0.o()],
             num_outputs=1,
             name="node_1",
         )
         graph = ir.Graph(
             inputs=node_0.inputs,  # type: ignore
-            outputs=[node_1.outputs[0]],
+            outputs=[node_1.o()],
             initializers=[
                 ir.Value(name="tensor1", const_value=tensor1),
                 ir.Value(name="tensor2", const_value=tensor2),

src/onnx_ir/passes/common/clear_metadata_and_docstring_test.py

@@ -30,25 +30,41 @@ def test_pass_with_clear_metadata_and_docstring(self):
         )
         mul_node = ir.node(
             "Mul",
-            inputs=[add_node.outputs[0], inputs[1]],
+            inputs=[add_node.o(), inputs[1]],
             num_outputs=1,
             metadata_props={"mul_key": "mul_value"},
             doc_string="This is a Mul node",
         )
-        func_inputs = [
-            ir.Value(
-                name="input_a", type=ir.TensorType(ir.DataType.FLOAT), shape=ir.Shape((2, 3))
-            ),
-            ir.Value(
-                name="input_b", type=ir.TensorType(ir.DataType.FLOAT), shape=ir.Shape((2, 3))
-            ),
-        ]
         function = ir.Function(
             graph=ir.Graph(
                 name="my_function",
-                inputs=func_inputs,
-                outputs=mul_node.outputs,
-                nodes=[add_node, mul_node],
+                inputs=[
+                    input_a := ir.Value(
+                        name="input_a",
+                        type=ir.TensorType(ir.DataType.FLOAT),
+                        shape=ir.Shape((2, 3)),
+                    ),
+                    input_b := ir.Value(
+                        name="input_b",
+                        type=ir.TensorType(ir.DataType.FLOAT),
+                        shape=ir.Shape((2, 3)),
+                    ),
+                ],
+                nodes=[
+                    add_node_func := ir.node(
+                        "Add",
+                        inputs=[input_a, input_b],
+                        metadata_props={"add_key": "add_value"},
+                        doc_string="This is an Add node",
+                    ),
+                    mul_node_func := ir.node(
+                        "Mul",
+                        inputs=[add_node_func.o(), input_b],
+                        metadata_props={"mul_key": "mul_value"},
+                        doc_string="This is a Mul node",
+                    ),
+                ],
+                outputs=mul_node_func.outputs,
                 opset_imports={"": 20},
                 doc_string="This is a function docstring",
                 metadata_props={"function_key": "function_value"},
@@ -57,6 +73,14 @@ def test_pass_with_clear_metadata_and_docstring(self):
             domain="my_domain",
             attributes=[],
         )
+        func_node = ir.node(
+            "my_function",
+            inputs=[inputs[0], mul_node.o()],
+            domain="my_domain",
+            metadata_props={"mul_key": "mul_value"},
+            doc_string="This is a Mul node",
+        )
+        # TODO(justinchuby): This graph is broken. The output of the function cannot be a input to a node
         # Create a model with the graph and function
         constant_tensor = ir.tensor(np.random.rand(2, 3).astype(ir.DataType.FLOAT.numpy()))
         const_node = ir.node(
@@ -69,7 +93,7 @@ def test_pass_with_clear_metadata_and_docstring(self):
         )
         sub_node = ir.node(
             "Sub",
-            inputs=[function.outputs[0], const_node.outputs[0]],
+            inputs=[func_node.o(), const_node.o()],
             num_outputs=1,
             metadata_props={"sub_key": "sub_value"},
             doc_string="This is a Sub node",

src/onnx_ir/passes/common/constant_manipulation.py

@@ -41,7 +41,7 @@ def call(self, model: ir.Model) -> ir.passes.PassResult:
             assert node.graph is not None
             if node.op_type != "Constant" or node.domain not in ("", "onnx.ai"):
                 continue
-            if node.outputs[0].is_graph_output():
+            if node.o().is_graph_output():
                 logger.debug(
                     "Constant node '%s' is used as output, so it can't be lifted.", node.name
                 )
@@ -54,7 +54,7 @@ def call(self, model: ir.Model) -> ir.passes.PassResult:
                 continue
 
             attr_name, attr_value = next(iter(node.attributes.items()))
-            initializer_name = node.outputs[0].name
+            initializer_name = node.o().name
             assert initializer_name is not None
             assert isinstance(attr_value, ir.Attr)
             tensor = self._constant_node_attribute_to_tensor(
@@ -73,7 +73,7 @@ def call(self, model: ir.Model) -> ir.passes.PassResult:
             assert node.graph is not None
             node.graph.register_initializer(initializer)
             # Replace the constant node with the initializer
-            ir.convenience.replace_all_uses_with(node.outputs[0], initializer)
+            ir.convenience.replace_all_uses_with(node.o(), initializer)
             node.graph.remove(node, safe=True)
             count += 1
             logger.debug(

src/onnx_ir/passes/common/constant_manipulation_test.py

@@ -36,8 +36,8 @@ def test_pass_with_lifting_float_and_int_constants_to_initializers(
         const_node = ir.node(
             "Constant", inputs=[], attributes={"value": constant_tensor}, num_outputs=1
         )
-        add_node = ir.node("Add", inputs=[inputs[0], const_node.outputs[0]])
-        mul_node = ir.node("Mul", inputs=[add_node.outputs[0], inputs[1]])
+        add_node = ir.node("Add", inputs=[inputs[0], const_node.o()])
+        mul_node = ir.node("Mul", inputs=[add_node.o(), inputs[1]])
 
         model = ir.Model(
             graph=ir.Graph(
@@ -92,21 +92,21 @@ def test_pass_with_lifting_constants_to_initializers_within_subgraph(
         )
         # then branch adds the constant to the input
         # else branch multiplies the input by the constant
-        add_node = ir.node("Add", inputs=[input_value, then_const_node.outputs[0]])
+        add_node = ir.node("Add", inputs=[input_value, then_const_node.o()])
         then_graph = ir.Graph(
             inputs=[],
-            outputs=[add_node.outputs[0]],
+            outputs=[add_node.o()],
             nodes=[then_const_node, add_node],
             opset_imports={"": 20},
         )
         else_constant_tensor = ir.tensor(np.random.rand(2, 3).astype(np.float32))
         else_const_node = ir.node(
             "Constant", inputs=[], attributes={"value": else_constant_tensor}, num_outputs=1
         )
-        mul_node = ir.node("Mul", inputs=[input_value, else_const_node.outputs[0]])
+        mul_node = ir.node("Mul", inputs=[input_value, else_const_node.o()])
         else_graph = ir.Graph(
             inputs=[],
-            outputs=[mul_node.outputs[0]],
+            outputs=[mul_node.o()],
             nodes=[else_const_node, mul_node],
             opset_imports={"": 20},
         )
@@ -178,15 +178,13 @@ def test_pass_with_lifting_constants_to_initializers_with_floats_ints_strings(
             attributes={constant_attribute: constant_value},
             num_outputs=1,
         )
-        identity_node_constant = ir.node(
-            "Identity", inputs=[const_node.outputs[0]], num_outputs=1
-        )
+        identity_node_constant = ir.node("Identity", inputs=[const_node.o()], num_outputs=1)
         identity_node_input = ir.node("Identity", inputs=[input_value], num_outputs=1)
 
         model = ir.Model(
             graph=ir.Graph(
                 inputs=[input_value],
-                outputs=[identity_node_input.outputs[0], identity_node_constant.outputs[0]],
+                outputs=[identity_node_input.o(), identity_node_constant.o()],
                 nodes=[identity_node_input, const_node, identity_node_constant],
                 opset_imports={"": 20},
             ),
@@ -232,7 +230,7 @@ def test_not_lifting_constants_to_initializers_when_it_is_output(self):
         model = ir.Model(
             graph=ir.Graph(
                 inputs=[input_value],
-                outputs=[identity_node_input.outputs[0], const_node.outputs[0]],
+                outputs=[identity_node_input.o(), const_node.o()],
                 nodes=[identity_node_input, const_node],
                 opset_imports={"": 20},
             ),
@@ -272,7 +270,7 @@ def test_pass_with_lifting_constants_to_initializers_within_subgraph(
         add_node = ir.node("Add", inputs=[input_value, then_initializer_value])
         then_graph = ir.Graph(
             inputs=[],
-            outputs=[add_node.outputs[0]],
+            outputs=[add_node.o()],
             nodes=[add_node],
             opset_imports={"": 20},
             initializers=[then_initializer_value],
@@ -287,7 +285,7 @@ def test_pass_with_lifting_constants_to_initializers_within_subgraph(
         mul_node = ir.node("Mul", inputs=[input_value, else_initializer_value])
         else_graph = ir.Graph(
             inputs=[],
-            outputs=[mul_node.outputs[0]],
+            outputs=[mul_node.o()],
             nodes=[mul_node],
             opset_imports={"": 20},
             initializers=[else_initializer_value],
@@ -351,7 +349,7 @@ def test_pass_does_not_lift_initialized_inputs_in_subgraph(
             # The initializer is also an input. We don't lift it to the main graph
             # to preserve the graph signature
             inputs=[then_initializer_value],
-            outputs=[add_node.outputs[0]],
+            outputs=[add_node.o()],
             nodes=[add_node],
             opset_imports={"": 20},
             initializers=[then_initializer_value],
@@ -366,7 +364,7 @@ def test_pass_does_not_lift_initialized_inputs_in_subgraph(
         mul_node = ir.node("Mul", inputs=[input_value, else_initializer_value])
         else_graph = ir.Graph(
             inputs=[],
-            outputs=[mul_node.outputs[0]],
+            outputs=[mul_node.o()],
             nodes=[mul_node],
             opset_imports={"": 20},
             initializers=[else_initializer_value],