[pt2] recursive IR check (#98887)

Summary:
Pull Request resolved: #98887

IR check needs to be recursive to accommodate Tuple[Tensor, Tuple[Tensor]] schema

Test Plan:
Run the repro cmd and make sure it no longer fails
  TORCH_SHOW_CPP_STACKTRACES=1 TORCH_LOGS="+dynamo,aot,inductor" buck2 run mode/opt scripts/ml_model_exploration/coffee:defi_local -- --baseline_model_entity_id 421946503 --meta_ids '{"union_meta":422685721}' -g -t -l --model_type mimo_ctr_mbl_feed

Differential Revision: D44809096

fbshipit-source-id: 4a1741a7e47619fc0591b6cbacfc13816d990936

torch/_inductor/ir.py

@@ -87,9 +87,13 @@
 
 
 def validate_ir(node_or_nodes):
-    def _check_tensorbox(node):
+    def _check_tensorbox(node_or_nodes):
         # Could expand this to check deeper properties
         # (e.g. TensorBox points to View or StorageBox)
+        if isinstance(node_or_nodes, (List, Tuple)):
+            for node in node_or_nodes:
+                _check_tensorbox(node)
+
         assert isinstance(
             node,
             (
@@ -103,11 +107,7 @@ def _check_tensorbox(node):
         ), f"Found {type(node)}, which is not a supported top level IR node. See [Note: Inductor IR]"
 
     # Be picky about the accepted data structure (don't use pytree here)
-    if isinstance(node_or_nodes, (List, Tuple)):
-        for node in node_or_nodes:
-            _check_tensorbox(node)
-    else:
-        _check_tensorbox(node_or_nodes)
+    _check_tensorbox(node_or_nodes)
 
 
 def inverse_reorder(order):