[ET-VK][ez] Constrain out repsets individually (#15748)

Address the TODO comment in the `tag_memory_meta_pass.py` graph pass.

```
        # TODO(ssjia): For most ops, inputs and outputs must be synchronized, so there
        # is no need to constrain output repsets explicitly. Currently, the exceptions
        # (i.e. choose qparams) already define constrined repsets for the output, so
        # there is again no need to explicitly constrain the outputs. If an operator
        # appears later on that does not sync input and output representations, and
        # defines ambiguous repsets for the output tensor(s), then we will need to add
        # additional logic to this function to constrain the output repsets separately
        # from the input repsets.
```

This condition is now fulfilled with the below diff.

Differential Revision: [D86674164](https://our.internmc.facebook.com/intern/diff/D86674164/)

Author: pytorchbot

backends/vulkan/_passes/tag_memory_meta_pass.py

@@ -368,7 +368,7 @@ def constrain_op_arg_repset(self, arg_i: int, op_repsets: utils.OpRepSets) -> No
 
         arg_repset = op_repsets.get_arg_repset(arg_i)
         if arg_repset.is_constrained():
-            return arg_repset
+            return
 
         arg_node = op_repsets.op_node.args[arg_i]
 
@@ -378,21 +378,33 @@ def constrain_op_arg_repset(self, arg_i: int, op_repsets: utils.OpRepSets) -> No
         arg_repset = self.trace_node_users_to_constrain_repset(arg_node, arg_repset)
         op_repsets.try_constrain_with_arg_repset(arg_i, arg_repset)
 
+    def constrain_op_out_repset(self, op_repsets: utils.OpRepSets) -> None:
+        """
+        Similar to the `constrain_op_arg_repset` function, but for the output repset of
+        the operator.
+        """
+        out_repset = op_repsets.get_out_repset(0)
+        if out_repset.is_constrained():
+            return
+
+        op_node = op_repsets.op_node
+        out_respset = self.trace_node_users_to_constrain_repset(op_node, out_repset)
+
+        op_repsets.try_constrain_with_out_repset(out_respset)
+
     def constrain_op_repsets(self, op_repsets: utils.OpRepSets) -> None:
         # For most ops, constraining the argument repsets will also contrain the output
         # repset due to OpRepSets maintaining synchronization rules.
         for i in range(len(op_repsets.op_node.args)):
             if utils.is_tensor_arg_node(op_repsets.op_node.args[i]):
                 self.constrain_op_arg_repset(i, op_repsets)
 
-        # TODO(ssjia): For most ops, inputs and outputs must be synchronized, so there
-        # is no need to constrain output repsets explicitly. Currently, the exceptions
-        # (i.e. choose qparams) already define constrined repsets for the output, so
-        # there is again no need to explicitly constrain the outputs. If an operator
-        # appears later on that does not sync input and output representations, and
-        # defines ambiguous repsets for the output tensor(s), then we will need to add
-        # additional logic to this function to constrain the output repsets separately
-        # from the input repsets.
+        # However, some operators do not sync input and output representations and also
+        # define ambiguous repsets for the output tensor(s). In those cases we will need
+        # to execute additional logic to constrain the output repsets separately from
+        # the input repsets.
+        if not op_repsets.sync_primary_io_repr and op_repsets.sync_outs_repr:
+            self.constrain_op_out_repset(op_repsets)
 
     def set_op_node_tensor_reprs(
         self, graph_module: torch.fx.GraphModule, op_node: torch.fx.Node

backends/vulkan/utils.py

@@ -1106,6 +1106,25 @@ def try_constrain_with_arg_repset(
         self.assert_sync_contraints()
         return True
 
+    def try_constrain_with_out_repset(self, repset: TensorRepSet):
+        # Skip for operators that must synchronize the input and output representations
+        # or operators that have more than one output repset
+        if self.sync_primary_io_repr or len(self.outs_repset_list) > 1:
+            return False
+
+        out_current_repset = self.outs_repset_list[0]
+
+        if out_current_repset == repset:
+            return False
+
+        if not out_current_repset.any_in_common(repset):
+            return False
+
+        self.outs_repset_list[0] = out_current_repset.make_intersect(repset)
+
+        self.assert_sync_contraints()
+        return True
+
     def pick_representations(self) -> Tuple[TensorReprList, TensorReprList]:
         """
         For each tensor participating in the op, pick a representation for it among the