diff --git a/test/fx/test_fx_split.py b/test/fx/test_fx_split.py
index ff776fe1adcde..fa8910ed61e2b 100644
--- a/test/fx/test_fx_split.py
+++ b/test/fx/test_fx_split.py
@@ -150,3 +150,76 @@ def test_split_by_tags(self) -> None:
             },
             f"{orig_to_split_fqn_mapping=}",
         )
+
+class TestSplitOutputType(TestCase):
+    class TestModule(torch.nn.Module):
+        def __init__(self):
+            super().__init__()
+            self.conv = torch.nn.Conv2d(3, 16, 3, stride=1, bias=True)
+            self.relu = torch.nn.ReLU()
+
+        def forward(self, x):
+            conv = self.conv(x)
+            conv = conv * 0.5
+            relu = self.relu(conv)
+            return relu
+
+    @staticmethod
+    def trace_and_tag(
+        module: torch.nn.Module, inputs: torch.Tensor, tags: List[str]
+    ) -> Tuple[torch.fx.GraphModule, Dict[str, List[str]]]:
+        """
+        Test simple gm consists of nodes with tag (only show call_module nodes here):
+            conv - tag: "red"
+            mul - tag: "blue"
+            relu - tag: "green"
+
+        At the beginning we have:
+            gm:
+                conv
+                mul
+                relu
+
+        split_gm = split_by_tags(gm, tags)
+
+        Then we have:
+            split_gm:
+                red:
+                    conv
+                blue:
+                    mul
+                green:
+                    relu
+        """
+        tag_node = defaultdict(list)
+        gm: torch.fx.GraphModule = torch.export.export(module, (inputs,)).module()
+        # Add tag to all nodes and build dictionary record tag to call_module nodes
+        for node in gm.graph.nodes:
+            if "conv" in node.name:
+                node.tag = tags[0]
+                tag_node[tags[0]].append(node.name)
+            elif "mul" in node.name:
+                node.tag = tags[1]
+                tag_node[tags[1]].append(node.name)
+            else:
+                node.tag = tags[2]
+                if node.op == "call_module":
+                    tag_node[tags[2]].append(node.name)
+        return gm, tag_node
+
+    def test_split_by_tags(self) -> None:
+        tags = ["red", "blue", "green"]
+        module = TestSplitOutputType.TestModule()
+
+        inputs = torch.randn((1, 3, 224, 224))
+
+        gm, tag_node = TestSplitOutputType.trace_and_tag(module, inputs, tags)
+        split_gm, orig_to_split_fqn_mapping = split_by_tags(
+            gm, tags, return_fqn_mapping=True
+        )
+
+        gm_output = module(inputs)
+        split_gm_output = split_gm(inputs)
+
+        self.assertTrue(type(gm_output) == type(split_gm_output))
+        self.assertTrue(torch.equal(gm_output, split_gm_output))