diff --git a/exir/backend/test/demos/rpc/test_rpc.py b/exir/backend/test/demos/rpc/test_rpc.py
index 0c0e72862fd..63feb954fee 100644
--- a/exir/backend/test/demos/rpc/test_rpc.py
+++ b/exir/backend/test/demos/rpc/test_rpc.py
@@ -8,6 +8,7 @@
 
 import torch
 from executorch import exir
+from executorch.exir import to_edge
 from executorch.exir.backend.backend_api import to_backend
 from executorch.exir.backend.test.demos.rpc.executor_backend_partitioner import (
     ExecutorBackendPartitioner,
@@ -20,6 +21,7 @@
 from executorch.extension.pybindings.portable_lib import (  # @manual
     _load_for_executorch_from_buffer,
 )
+from torch.export import export
 from torch.utils._pytree import tree_flatten
 
 """
@@ -101,16 +103,15 @@ def test_delegate_whole_program(self):
 
         simple_net = self.get_a_simple_net()
         simple_net_input = simple_net.get_example_inputs()
-        exported_program = exir.capture(
-            simple_net, simple_net_input, exir.CaptureConfig()
-        ).to_edge(
-            exir.EdgeCompileConfig(
+        exported_program = to_edge(
+            export(simple_net, simple_net_input),
+            compile_config=exir.EdgeCompileConfig(
                 _check_ir_validity=False,
-            )
+            ),
         )
         # delegate the whole graph to the client executor
         lowered_module = to_backend(
-            ExecutorBackend.__name__, exported_program.exported_program, []
+            ExecutorBackend.__name__, exported_program.exported_program(), []
         )
 
         class CompositeModule(torch.nn.Module):
@@ -123,11 +124,7 @@ def forward(self, *args):
 
         composite_model = CompositeModule()
 
-        exec_prog = (
-            exir.capture(composite_model, simple_net_input, exir.CaptureConfig())
-            .to_edge()
-            .to_executorch()
-        )
+        exec_prog = to_edge(export(composite_model, simple_net_input)).to_executorch()
 
         executorch_module = _load_for_executorch_from_buffer(exec_prog.buffer)
 
@@ -162,18 +159,14 @@ def forward(self, a, x, b):
         model = Model()
         inputs = (torch.ones(2, 2), torch.ones(2, 2), torch.ones(2, 2))
 
-        exported_program = exir.capture(model, inputs, exir.CaptureConfig()).to_edge()
+        exported_program = to_edge(export(model, inputs))
 
         # First lower to demo backend
-        demo_backend_lowered = exported_program
-        demo_backend_lowered.exported_program = to_backend(
-            exported_program.exported_program, AddMulPartitionerDemo()
-        )
+        demo_backend_lowered = exported_program.to_backend(AddMulPartitionerDemo())
 
         # Then lower to executor backend
-        executor_backend_lowered = demo_backend_lowered
-        executor_backend_lowered.exported_program = to_backend(
-            demo_backend_lowered.exported_program, ExecutorBackendPartitioner()
+        executor_backend_lowered = demo_backend_lowered.to_backend(
+            ExecutorBackendPartitioner()
         )
 
         prog_buffer = executor_backend_lowered.to_executorch()