[Codemod][AddExplicitStrictExportArg] caffe2/torch (#146439)

Differential Revision: D69068432

Pull Request resolved: https://github.com/pytorch/pytorch/pull/146439
Approved by: https://github.com/avikchaudhuri

test/dynamo/test_export.py

@@ -4591,7 +4591,7 @@ class ExportTestsDevice(torch._dynamo.test_case.TestCase):
         random_inputs = (torch.rand([32, 3, 32, 32]).to(device),)
         dim_x = torch.export.Dim("dim_x", min=1, max=32)
         exp_program = torch.export.export(
-            model, random_inputs, dynamic_shapes={"x": {0: dim_x}}
+            model, random_inputs, dynamic_shapes={"x": {0: dim_x}}, strict=True
         )
         output_buffer = io.BytesIO()
         # Tests if we can restore saved nn.Parameters when we load them again
@@ -4621,7 +4621,9 @@ class ExportTestsDevice(torch._dynamo.test_case.TestCase):
         batchsize = torch.export.Dim("dim0", min=3, max=1024)
         dynamic_shape_spec = {"a": [batchsize, None, None], "b": [None, None]}
 
-        torch.export.export(model, (a, b), dynamic_shapes=dynamic_shape_spec)
+        torch.export.export(
+            model, (a, b), dynamic_shapes=dynamic_shape_spec, strict=True
+        )
 
     def test_export_fast_binary_broadcast_check_unbacked(self, device):
         class MyModel(torch.nn.Module):
@@ -4634,7 +4636,7 @@ class ExportTestsDevice(torch._dynamo.test_case.TestCase):
         model = MyModel().eval().to(device)
         numel = torch.tensor(10)
         scalar = torch.randn(1)
-        torch.export.export(model, (numel, scalar))
+        torch.export.export(model, (numel, scalar), strict=True)
 
 
 common_utils.instantiate_parametrized_tests(ExportTests)

test/fx/test_fx_xform_observer.py

@@ -144,7 +144,7 @@ class TestGraphTransformObserver(TestCase):
                 return torch.neg(x)
 
         model = SimpleLinearModel()
-        gm = torch.export.export(model, (torch.rand(10),)).module()
+        gm = torch.export.export(model, (torch.rand(10),), strict=True).module()
 
         with GraphTransformObserver(gm, "test"):
             add_node = gm.graph.call_function(torch.ops.aten.add.default, (1, 1))
@@ -171,7 +171,7 @@ class TestGraphTransformObserver(TestCase):
                 return torch.neg(x)
 
         model = SimpleLinearModel()
-        gm = torch.export.export(model, (torch.rand(10),)).module()
+        gm = torch.export.export(model, (torch.rand(10),), strict=True).module()
 
         with GraphTransformObserver(gm, "test"):
             gm2 = copy.deepcopy(gm)

test/inductor/test_aot_inductor.py

@@ -2213,7 +2213,7 @@ class AOTInductorTestsTemplate:
         example_inputs = (torch.randn(10, 10, device=self.device),)
         optimized = torch._inductor.aoti_load_package(
             torch._inductor.aoti_compile_and_package(
-                torch.export.export(Model(), example_inputs)
+                torch.export.export(Model(), example_inputs, strict=True)
             )
         )
         try: