[IR] Do not serialize the trailing outputs that have empty names (#1905)

onnxscript/ir/serde.py

@@ -1036,7 +1036,12 @@ def _should_create_value_info_for_value(value: _protocols.ValueProtocol) -> bool
         True if value info should be created for the value.
     """
     # No need to serialize value info if it is not set
-    return not (value.shape is None and value.type is None)
+    if value.shape is None and value.type is None:
+        return False
+    if not value.name:
+        logger.debug("Did not serialize '%s' because its name is empty", value)
+        return False
+    return True
 
 
 def _serialize_experimental_value_info_for_function_ir9_into(
@@ -1269,6 +1274,23 @@ def serialize_node(node: _protocols.NodeProtocol) -> onnx.NodeProto:
     return node_proto
 
 
+def _remove_trailing_outputs(
+    outputs: Sequence[_protocols.ValueProtocol],
+) -> Sequence[_protocols.ValueProtocol]:
+    """Remove trailing outputs that have empty names.
+
+    Args:
+        outputs: The outputs to remove trailing outputs from.
+
+    Returns:
+        The outputs with trailing outputs removed.
+    """
+    for i, output in enumerate(reversed(outputs)):
+        if output.name:
+            return outputs[: len(outputs) - i]
+    return []
+
+
 @_capture_errors(lambda node_proto, from_: repr(from_))
 def serialize_node_into(node_proto: onnx.NodeProto, from_: _protocols.NodeProtocol) -> None:
     node_proto.op_type = from_.op_type
@@ -1288,8 +1310,11 @@ def serialize_node_into(node_proto: onnx.NodeProto, from_: _protocols.NodeProtoc
             node_proto.input.append("")
         else:
             node_proto.input.append(input_.name)
-    for output in from_.outputs:
+
+    # Do not include the trailing outputs that have empty names
+    for output in _remove_trailing_outputs(from_.outputs):
         node_proto.output.append(output.name)
+
     for attr in from_.attributes.values():
         if isinstance(attr, _core.Attr):
             serialize_attribute_into(node_proto.attribute.add(), from_=attr)

onnxscript/optimizer/_remove_unused_test.py

@@ -11,6 +11,8 @@
 
 @parameterized.parameterized_class(("using_ir",), [(False,), (True,)])
 class RemoveUnusedTest(unittest.TestCase):
+    using_ir: bool
+
     def remove_unused_nodes(self, model: onnx.ModelProto):
         if self.using_ir:
             model_ir = ir.serde.deserialize_model(model)
@@ -81,11 +83,7 @@ def test_remove_unused_optional_outputs_maxpool(self):
         model = self.remove_unused_nodes(model)
         self.assertEqual(len(model.graph.node), 1)
         self.assertEqual(model.graph.node[0].op_type, "MaxPool")
-        if self.using_ir:
-            expected_outputs = ["z", ""]
-        else:
-            expected_outputs = ["z"]
-        self.assertEqual(model.graph.node[0].output, expected_outputs)
+        self.assertEqual(model.graph.node[0].output, ["z"])
 
     def test_remove_unused_optional_outputs_dropout_in_function(self):
         model = onnx.parser.parse_model(
@@ -110,11 +108,7 @@ def test_remove_unused_optional_outputs_dropout_in_function(self):
         self.assertEqual(len(model.functions), 1)
         self.assertEqual(len(model.functions[0].node), 1)
         self.assertEqual(model.functions[0].node[0].op_type, "MaxPool")
-        if self.using_ir:
-            expected_outputs = ["z", ""]
-        else:
-            expected_outputs = ["z"]
-        self.assertEqual(model.functions[0].node[0].output, expected_outputs)
+        self.assertEqual(model.functions[0].node[0].output, ["z"])
 
     def test_remove_used_optional_outputs_maxpool(self):
         model = onnx.parser.parse_model(
@@ -150,11 +144,7 @@ def test_remove_multiple_unused_optional_outputs_layernorm(self):
         model = self.remove_unused_nodes(model)
         self.assertEqual(len(model.graph.node), 3)
         self.assertEqual(model.graph.node[2].op_type, "LayerNormalization")
-        if self.using_ir:
-            expected_outputs = ["z", "", ""]
-        else:
-            expected_outputs = ["z"]
-        self.assertEqual(list(model.graph.node[2].output), expected_outputs)
+        self.assertEqual(list(model.graph.node[2].output), ["z"])
 
     def test_remove_trailing_unused_optional_outputs_layernorm(self):
         model = onnx.parser.parse_model(
@@ -173,11 +163,7 @@ def test_remove_trailing_unused_optional_outputs_layernorm(self):
         model = self.remove_unused_nodes(model)
         self.assertEqual(len(model.graph.node), 3)
         self.assertEqual(model.graph.node[2].op_type, "LayerNormalization")
-        if self.using_ir:
-            expected_outputs = ["z", "mean", ""]
-        else:
-            expected_outputs = ["z", "mean"]
-        self.assertEqual(list(model.graph.node[2].output), expected_outputs)
+        self.assertEqual(list(model.graph.node[2].output), ["z", "mean"])
 
     def test_avoid_remove_non_trailing_unused_optional_outputs_layernorm(self):
         model = onnx.parser.parse_model(
@@ -212,11 +198,7 @@ def test_remove_trailing_unused_optional_outputs_batchnorm(self):
         self.assertEqual(len(model.graph.node), 1)
         self.assertEqual(model.graph.node[0].op_type, "BatchNormalization")
         # Check that both the mean/var outputs are removed, and training_mode attribute is removed.
-        if self.using_ir:
-            expected_outputs = ["z", "", ""]
-        else:
-            expected_outputs = ["z"]
-        self.assertEqual(list(model.graph.node[0].output), expected_outputs)
+        self.assertEqual(list(model.graph.node[0].output), ["z"])
         self.assertEqual(len(model.graph.node[0].attribute), 0)
 
     def test_avoid_remove_used_optional_outputs_batchnorm(self):