Disable exported_program.__call__ (#1954)

Summary:
Pull Request resolved: #1954

X-link: pytorch/pytorch#119466

`ExportedProgram` is an artifact produced by torch.export, containing the graph that is exported, along with other attributes about the original program such as the graph signature, state dict, and constants. One slightly confusing thing that users run into is that they treat the `ExportedProgram` as a `torch.nn.Module`, since the object is callable. However, as we do not plan to support all features that `torch.nn.Module`s have, like hooks, we want to create a distinction between it and the `ExportedProgram` by removing the `__call__` method. Instead users can create a proper `torch.nn.Module` through `exported_program.module()` and use that as a callable.

Reviewed By: zhxchen17

Differential Revision: D53075378

fbshipit-source-id: e17671b655b6f410fd5e70971c246f36558324ff

Author: angelayi

exir/backend/test/test_backends_lifted.py

@@ -626,7 +626,7 @@ def forward(self, x_raw, h, c):
             ),
         )
 
-        new_res = program_with_delegates.exported_program()(*inputs)
+        new_res = program_with_delegates.exported_program().module()(*inputs)
         for t1, t2 in zip(new_res, orig_res, strict=True):
             self.assertTrue(torch.allclose(t1, t2, atol=1e-03, rtol=1e-03))
 
@@ -745,7 +745,7 @@ def forward(self, x_raw, h, c):
             HTAPartitionerOnePatternDemo()
         )
 
-        new_res = traced_with_delegate.exported_program()(*inputs)
+        new_res = traced_with_delegate.exported_program().module()(*inputs)
         for t1, t2 in zip(new_res, orig_res, strict=True):
             self.assertTrue(torch.allclose(t1, t2, atol=1e-03, rtol=1e-03))
 
@@ -768,7 +768,7 @@ def forward(self, x_raw, h, c):
         #     config=exir.ExecutorchBackendConfig(extract_delegate_segments=extract_delegate_segments),
         # )
 
-        new_res = program_with_delegates.exported_program()(*inputs)
+        new_res = program_with_delegates.exported_program().module()(*inputs)
         for t1, t2 in zip(new_res, orig_res, strict=True):
             self.assertTrue(torch.allclose(t1, t2, atol=1e-03, rtol=1e-03))
 
@@ -1029,7 +1029,7 @@ def f(x, y):
         partitioned = orig
         partitioned = partitioned.to_backend(AddMulPartitionerDemo())
 
-        new_res = partitioned.exported_program()(*inputs)
+        new_res = partitioned.exported_program().module()(*inputs)
         self.assertTrue(torch.allclose(orig_res, new_res[0]))
 
         toplevel_lowered = get_lowered_submodules(
@@ -1102,7 +1102,7 @@ def f(xs, y):
             map_fn_lowered[0][1].original_module.graph_module.code
         )
 
-        new_res = partitioned.exported_program()(*inputs)
+        new_res = partitioned.exported_program().module()(*inputs)
 
         self.assertTrue(torch.allclose(orig_res, new_res[0]))
 
@@ -1153,7 +1153,7 @@ def f(xs, pred1, pred2, y):
         partitioned = orig
         partitioned = partitioned.to_backend(AddMulPartitionerDemo())
 
-        new_res = partitioned.exported_program()(*inputs)
+        new_res = partitioned.exported_program().module()(*inputs)
         self.assertTrue(torch.allclose(orig_res, new_res[0]))
 
         toplevel_lowered = get_lowered_submodules(
@@ -1224,7 +1224,7 @@ def forward(self, x: List[torch.Tensor]):
                 return self.lowered(x)
 
         gm = to_edge(export(ComposedM(), inputs))
-        gm.exported_program()(*inputs)
+        gm.exported_program().module()(*inputs)
 
     def test_dict_input(self):
         def f(x: Dict[str, torch.Tensor]):
@@ -1246,4 +1246,4 @@ def forward(self, x: List[torch.Tensor]):
                 return self.lowered(x)
 
         gm = to_edge(export(ComposedM(), inputs))
-        gm.exported_program()(*inputs)
+        gm.exported_program().module()(*inputs)

exir/tests/test_verification.py

@@ -136,7 +136,7 @@ def forward(self, x: torch.Tensor) -> torch.Tensor:
         exec_prog = to_edge(export(model2, (inputs,))).to_executorch()
 
         exported_prog = exec_prog.exported_program()
-        res = exported_prog(inputs)[0]  # noqa
+        res = exported_prog.module()(inputs)[0]  # noqa
         # Verifiers are run internally in to_edge, export, and to_executorch.
         # If we make it this far then no errors were thrown in verification