[tests] feat: add AoT compilation tests (#12203)

* feat: add a test for aot.

* up

Author: sayakpaul

tests/models/test_modeling_common.py

@@ -2059,6 +2059,7 @@ def test_torch_compile_recompilation_and_graph_break(self):
         init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()
 
         model = self.model_class(**init_dict).to(torch_device)
+        model.eval()
         model = torch.compile(model, fullgraph=True)
 
         with (
@@ -2076,6 +2077,7 @@ def test_torch_compile_repeated_blocks(self):
         init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()
 
         model = self.model_class(**init_dict).to(torch_device)
+        model.eval()
         model.compile_repeated_blocks(fullgraph=True)
 
         recompile_limit = 1
@@ -2098,7 +2100,6 @@ def test_compile_with_group_offloading(self):
 
         init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()
         model = self.model_class(**init_dict)
-
         model.eval()
         # TODO: Can test for other group offloading kwargs later if needed.
         group_offload_kwargs = {
@@ -2111,25 +2112,46 @@ def test_compile_with_group_offloading(self):
         }
         model.enable_group_offload(**group_offload_kwargs)
         model.compile()
+
         with torch.no_grad():
             _ = model(**inputs_dict)
             _ = model(**inputs_dict)
 
-    @require_torch_version_greater("2.7.1")
     def test_compile_on_different_shapes(self):
         if self.different_shapes_for_compilation is None:
             pytest.skip(f"Skipping as `different_shapes_for_compilation` is not set for {self.__class__.__name__}.")
         torch.fx.experimental._config.use_duck_shape = False
 
         init_dict, _ = self.prepare_init_args_and_inputs_for_common()
         model = self.model_class(**init_dict).to(torch_device)
+        model.eval()
         model = torch.compile(model, fullgraph=True, dynamic=True)
 
         for height, width in self.different_shapes_for_compilation:
             with torch._dynamo.config.patch(error_on_recompile=True), torch.no_grad():
                 inputs_dict = self.prepare_dummy_input(height=height, width=width)
                 _ = model(**inputs_dict)
 
+    def test_compile_works_with_aot(self):
+        from torch._inductor.package import load_package
+
+        init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()
+
+        model = self.model_class(**init_dict).to(torch_device)
+        exported_model = torch.export.export(model, args=(), kwargs=inputs_dict)
+
+        with tempfile.TemporaryDirectory() as tmpdir:
+            package_path = os.path.join(tmpdir, f"{self.model_class.__name__}.pt2")
+            _ = torch._inductor.aoti_compile_and_package(exported_model, package_path=package_path)
+            assert os.path.exists(package_path)
+            loaded_binary = load_package(package_path, run_single_threaded=True)
+
+        model.forward = loaded_binary
+
+        with torch.no_grad():
+            _ = model(**inputs_dict)
+            _ = model(**inputs_dict)
+
 
 @slow
 @require_torch_2