ETRecord ser/de handling "None" outputs and more (#3039)

Summary:

For the ease of communication, let me assign nicknames to the files related to this diff:
* File A: *caffe2/torch/_export/serde/serialize.py*
* File B: *executorch/exir/serde/serialize.py*
* File C: *executorch/exir/serde/export_serialize.py*

Recently, we noticed that error `torch._export.serde.serialize.SerializeError: Unable to deserialize output node Argument(as_none=[])` (P1210590561) was thrown from File B when deserializing ETRecord. It's possible that the error has been there since the beginning, but we've just never tested that logic path. 

In this diff, I made a fix on File B to resolve this particular issue. Also adding handling for "None" output case in sdk logic. ***Keep on reading if you don't think the code changes make sense:***

I explored the history of file changes. In chronological order:
1. D48258552, `deserialize_graph_output()` was copied from File A to File B, with some modifications made. The `deserialize_graph_output()` in File B overrides that in File A due to polymorphism. 
2. D52446586, File C was created by ***copying*** File A. As a result of this diff, the `deserialize_graph_output()` in File B now overrides that in File C.
3. Also in D52446586, the `deserialize_graph_output()` in File A had some significant changes; File C got the new version of `deserialize_graph_output()`. But this diff didn't update the `deserialize_graph_output()` in File B. 
4. D55391674 added the handling for "None" outputs to File A. 


This diff brings (parts of) File C up-to-date with File A, and make `deserialize_graph_output()` in File B properly overrides that in File A.

In the future, we should figure out how to keep File C and File A in sync. Recently, File C was broken because it didn't stay in sync with File A in D54855251 and had to be fixed by D55776877. There will be a design review session this Friday to discuss consolidating the serialization code for edge and export.

Reviewed By: tarun292

Differential Revision: D56091104

Author: Olivia-liu

exir/serde/export_serialize.py

@@ -1190,13 +1190,17 @@ def deserialize_tensor_meta(
                 ),
             )
 
-    def deserialize_graph_output(self, output) -> torch.fx.Node:
+    def deserialize_graph_output(self, output) -> Optional[Union[torch.fx.Node, int]]:
         if output.type == "as_tensor":
             return self.serialized_name_to_node[output.as_tensor.name]
         elif output.type == "as_sym_int":
             return self.serialized_name_to_node[output.as_sym_int.as_name]
         elif output.type == "as_sym_bool":
             return self.serialized_name_to_node[output.as_sym_bool.as_name]
+        elif output.type == "as_int":
+            return output.as_int
+        elif output.type == "as_none":
+            return None
         else:
             raise SerializeError(f"Unable to deserialize output node {output}")
 
@@ -1249,7 +1253,8 @@ def deserialize_graph(self, serialized_graph: Graph) -> torch.fx.Graph:
             output_node.meta["val"] = output_node.args[0].meta["val"]
         else:
             output_node.meta["val"] = tuple(
-                arg.meta["val"] for arg in output_node.args[0]
+                arg.meta["val"] if isinstance(arg, torch.fx.Node) else arg
+                for arg in output_node.args[0]
             )
 
         return self.graph

exir/serde/serialize.py

@@ -40,7 +40,6 @@
     SCHEMA_VERSION,
 )
 from torch._export.serde.schema import SchemaVersion
-from torch._export.serde.serialize import SerializeError
 from torch._export.serde.union import _Union
 from torch._export.verifier import load_verifier
 from torch.fx.experimental import symbolic_shapes
@@ -484,23 +483,22 @@ def deserialize_metadata(self, metadata: Dict[str, str]) -> Dict[str, Any]:
 
         return res
 
-    def deserialize_graph_output(self, output: schema.Argument) -> torch.fx.Node:
-        if isinstance(output.value, schema.TensorArgument):
-            if output.value.name in self.state_dict:  # TODO(T157676982)
-                val = self.state_dict[output.value.name]
-                setattr(self.module, output.value.name, val)
-                node = self.graph.create_node(
-                    "get_attr",
-                    output.value.name,
-                    name=output.value.name,
-                )
-                node.meta = {"val": ""}
-                return node
-            return self.serialized_name_to_node[output.value.name]
-        elif isinstance(output.value, (schema.SymIntArgument, schema.SymBoolArgument)):
-            return self.serialized_name_to_node[output.value.as_name]
-        else:
-            raise SerializeError(f"Unable to deserialize output node {output}")
+    def deserialize_graph_output(
+        self, output: schema.Argument
+    ) -> Optional[Union[torch.fx.Node, int]]:
+        if (
+            output.type == "as_tensor" and output.value.name in self.state_dict
+        ):  # TODO(T157676982)
+            val = self.state_dict[output.value.name]
+            setattr(self.module, output.value.name, val)
+            node = self.graph.create_node(
+                "get_attr",
+                output.value.name,
+                name=output.value.name,
+            )
+            node.meta = {"val": ""}
+            return node
+        return super().deserialize_graph_output(output)
 
     # pyre-ignore
     def deserialize_alloc_inputs(self, serialized_inputs: List[schema.NamedArgument]):

sdk/debug_format/et_schema.py

@@ -260,7 +260,12 @@ def gen_operator_graph(
                 assert len(args) == 1
                 # Args of op=='output' is a wrapped list of return nodes ([ret_1, ret_2, ...], )
                 in_nodes = [
-                    nodes[FXOperatorGraph._get_node_name(ret)] for ret in args[0]
+                    (
+                        nodes[FXOperatorGraph._get_node_name(ret)]
+                        if ret is not None
+                        else []
+                    )
+                    for ret in args[0]
                 ]
                 node = ValueNode(
                     name,