Fix ReplaceConvolutionOptionalArgsWithConcreteArgsPass

backends/cadence/aot/replace_ops.py

@@ -498,14 +498,15 @@ def targets(self) -> list[EdgeOpOverload]:
             exir_ops.edge.cadence.conv1d.default,
             exir_ops.edge.cadence.conv2d.default,
             exir_ops.edge.cadence.conv3d.default,
-            exir_ops.edge.cadence.transposed_convolution,
+            exir_ops.edge.cadence.transposed_convolution.default,
         ]
 
     def maybe_remove_or_replace(self, node: torch.fx.Node) -> bool:
         # Check if this is a transposed convolution
         assert isinstance(node.target, EdgeOpOverload)
-        op_packet = get_edge_overload_packet(node.target)
-        is_transposed = op_packet == exir_ops.edge.cadence.transposed_convolution
+        is_transposed = (
+            node.target == exir_ops.edge.cadence.transposed_convolution.default
+        )
         num_expected_args = 9 if is_transposed else 7
         assert len(node.args) == num_expected_args
         # Check if the bias is concrete
@@ -515,13 +516,15 @@ def maybe_remove_or_replace(self, node: torch.fx.Node) -> bool:
         # The bias length is the number of out channels.
         out_shape = node.meta["val"].shape
         bias_size = out_shape[1]
-        # Create a zero bias tensor (bias is not a constant tensor,
+
+        # Create a zero bias tensor
         with node.graph.inserting_before(node):
             zero_bias = node.graph.call_function(
                 exir_ops.edge.aten.full.default,
                 args=([bias_size], 0.0),
                 kwargs={"dtype": torch.float32},
             )
+            # Create proper metadata for the zero_bias node
             zero_bias.meta = node.meta
             new_args = list(node.args)
             new_args[2] = zero_bias