Add input() and add_output() methods to GraphBuilder

onnxscript/_internal/builder.py

@@ -152,6 +152,52 @@ def initializer(
         self._graph.register_initializer(value)
         return value
 
+    def input(
+        self,
+        name: str,
+        dtype: ir.DataType | None = None,
+        shape: ir.Shape | Sequence[int | str | None] | None = None,
+        *,
+        type: ir.TypeProtocol | None = None,
+        const_value: ir.TensorProtocol | None = None,
+        metadata_props: dict[str, str] | None = None,
+    ) -> ir.Value:
+        """Create an input to the graph and return the corresponding ir.Value.
+
+        Args:
+            name: The name of the value.
+            dtype: The data type of the TensorType of the value. This is used only when type is None.
+            shape: The shape of the value.
+            type: The type of the value. Only one of dtype and type can be specified.
+            const_value: The constant tensor that initializes the value. Supply this argument
+                when you want to create an initializer. The type and shape can be obtained from the tensor.
+            metadata_props: The metadata properties that will be serialized to the ONNX proto.
+
+        Returns:
+            A Value object.
+        """
+        value = ir.val(
+            name=name,
+            dtype=dtype,
+            shape=shape,
+            type=type,
+            const_value=const_value,
+            metadata_props=metadata_props,
+        )
+        self._graph.inputs.append(value)
+        return value
+
+    def add_output(self, value: ir.Value, name: str | None) -> None:
+        """Add an output to the graph.
+
+        Args:
+            value: The ir.Value to add as an output.
+            name: The name to assign to the output value. If None, no renaming is done.
+        """
+        if name:
+            value.name = name
+        self._graph.outputs.append(value)
+
     def _input_to_ir_value(
         self, value: VALUE_LIKE, like_type: ir.Value | None = None
     ) -> ir.Value:

onnxscript/_internal/builder_test.py

@@ -541,6 +541,65 @@ def test_output_names_are_unique_for_same_op_type(self):
         names = [t1.name, t2.name, t3.name]
         self.assertEqual(len(set(names)), 3)
 
+    def test_input_creates_and_registers_graph_input(self):
+        """Test that GraphBuilder.input creates and appends a graph input value."""
+        graph = ir.Graph(
+            name="test_model",
+            inputs=[],
+            outputs=[],
+            nodes=[],
+            opset_imports={"": _default_opset_version},
+        )
+        graph_builder = builder.GraphBuilder(graph)
+
+        value = graph_builder.input("data", dtype=ir.DataType.FLOAT, shape=[2, 3])
+
+        self.assertEqual(value.name, "data")
+        self.assertEqual(value.type.dtype, ir.DataType.FLOAT)
+        self.assertEqual(list(value.shape), [2, 3])
+        self.assertEqual(len(graph.inputs), 1)
+        self.assertIs(graph.inputs[0], value)
+
+    def test_add_output_renames_and_registers_output(self):
+        """Test that GraphBuilder.add_output renames (optionally) and appends outputs."""
+        graph = ir.Graph(
+            name="test_model",
+            inputs=[],
+            outputs=[],
+            nodes=[],
+            opset_imports={"": _default_opset_version},
+        )
+        graph_builder = builder.GraphBuilder(graph)
+
+        output = ir.Value(name="old_name")
+        graph_builder.add_output(output, "new_name")
+
+        self.assertEqual(output.name, "new_name")
+        self.assertEqual(len(graph.outputs), 1)
+        self.assertIs(graph.outputs[0], output)
+
+    def test_initializer_qualification_behavior(self):
+        """Test that GraphBuilder.initializer qualifies names unless explicitly disabled."""
+        graph = ir.Graph(
+            name="test_model",
+            inputs=[],
+            outputs=[],
+            nodes=[],
+            opset_imports={"": _default_opset_version},
+        )
+        graph_builder = builder.GraphBuilder(graph)
+
+        graph_builder.push_module("layer1")
+        qualified = graph_builder.initializer(ir.tensor([1.0], name="w"), name="weight")
+        unqualified = graph_builder.initializer(
+            ir.tensor([2.0], name="b"), name="bias", qualify=False
+        )
+
+        self.assertEqual(qualified.name, "layer1.weight")
+        self.assertEqual(unqualified.name, "bias")
+        self.assertIn("layer1.weight", graph.initializers)
+        self.assertIn("bias", graph.initializers)
+
     def test_multi_output_names_are_unique(self):
         """Test that multi-output ops produce unique names with counter suffix."""
         op, x, y = _create_builder_with_inputs()