[ET-VK] Don't specify memory layouts when testing

As title. Now that we have a memory metadata tagging pass that automatically determines the optimal memory layout to use for operators, there is no need to specify what memory layout to test in the Python export tests.

There were some issues with the memory metadata tagging pass when dealing with nodes that contain tensor lists, which have been fixed as part of this diff as well.

Differential Revision: [D67180897](https://our.internmc.facebook.com/intern/diff/D67180897/)

ghstack-source-id: 258028337
Pull Request resolved: #7322

Author: SS-JIA

backends/vulkan/_passes/tag_memory_meta_pass.py

@@ -23,8 +23,6 @@
 
 from executorch.exir.pass_base import ExportPass, PassResult
 
-from torch._subclasses.fake_tensor import FakeTensor
-
 from torch.fx.passes.tools_common import NodeList
 from torch.fx.passes.utils.fuser_utils import topo_sort
 
@@ -138,9 +136,7 @@ def propose_node_storage(
                 return storage
 
         for arg in node.args:
-            if isinstance(arg, torch.fx.Node) and isinstance(
-                arg.meta["val"], FakeTensor
-            ):
+            if isinstance(arg, torch.fx.Node) and utils.is_tensor_node(arg):
                 storage = utils.get_node_storage_type(arg)
                 if storage is not None and storage in valid_storage_types:
                     return storage
@@ -178,9 +174,7 @@ def propose_node_layout(
                 return layout
 
         for arg in node.args:
-            if isinstance(arg, torch.fx.Node) and isinstance(
-                arg.meta["val"], FakeTensor
-            ):
+            if isinstance(arg, torch.fx.Node) and utils.is_tensor_node(arg):
                 layout = utils.get_node_memory_layout(arg)
                 if layout is not None and layout in valid_layouts:
                     return layout
@@ -202,14 +196,19 @@ def should_annotate(self, node) -> bool:
         if not isinstance(node, torch.fx.Node):
             return False
 
-        if not isinstance(node.meta["val"], FakeTensor):
+        if not utils.is_tensor_node(node):
             return False
 
         # Storage type and memory layout for tensorref will be determined at runtime
         # so there's no use in setting those attributes ahead of time.
         if node.meta.get("vkdg_tensorref", False):
             return False
 
+        # Skip annotating output node. The output tensors should be annotated by the
+        # time the output node is observed.
+        if node.op == "output":
+            return False
+
         return True
 
     def should_delay_annotation(self, node: torch.fx.Node) -> bool: