Add test for torch.export.export

Summary:
att

Test Plan:
python test/integration/test_integration/py -k TestExport

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Tasks:

Tags:

test/integration/test_integration.py

@@ -1428,6 +1428,7 @@ class TestAOTI(unittest.TestCase):
     @parameterized.expand(
         list(itertools.product(TENSOR_SUBCLASS_APIS, COMMON_DEVICES, COMMON_DTYPES)),
     )
+    @run_supported_device_dtype
     def test_aoti(self, api, test_device, test_dtype):
         if not TORCH_VERSION_AFTER_2_4:
             self.skipTest("aoti compatibility requires 2.4+.")
@@ -1442,11 +1443,6 @@ def test_aoti(self, api, test_device, test_dtype):
         if test_dtype != torch.bfloat16:
             self.skipTest(f"{api} in {test_dtype} is not support for aoti compilation yet")
 
-        if test_device == "cuda" and not torch.cuda.is_available():
-            self.skipTest(f"Need CUDA available.")
-        if test_device == "cuda" and torch.cuda.is_available() and test_dtype == torch.bfloat16 and torch.cuda.get_device_capability() < (8, 0):
-            self.skipTest("Need CUDA and SM80+ available.")
-
         m, k, n = 32, 64, 32
 
         class test_model(nn.Module):
@@ -1479,5 +1475,53 @@ def forward(self, x):
         torch._export.aot_compile(model, example_inputs)
 
 
+class TestExport(unittest.TestCase):
+    @parameterized.expand(
+        list(itertools.product(TENSOR_SUBCLASS_APIS, COMMON_DEVICES, COMMON_DTYPES)),
+    )
+    @run_supported_device_dtype
+    def test_aoti(self, api, test_device, test_dtype):
+        if not TORCH_VERSION_AFTER_2_4:
+            self.skipTest("aoti compatibility requires 2.4+.")
+
+        logger.info(f"TestExport: {api}, {test_device}, {test_dtype}")
+
+        if test_dtype != torch.bfloat16:
+            self.skipTest(f"{api} in {test_dtype} is not support for aoti compilation yet")
+
+        m, k, n = 32, 64, 32
+
+        class test_model(nn.Module):
+            def __init__(self):
+                super().__init__()
+                self.lin1 = nn.Linear(k, n)
+                self.relu = nn.ReLU()
+                self.lin2 = nn.Linear(n, n)
+
+            def forward(self, x):
+                x = self.lin1(x)
+                x = self.relu(x)
+                x = self.lin2(x)
+                return x
+
+        x = torch.randn(m, k, dtype=test_dtype, device=test_device)
+
+        # get float reference
+        model = test_model().to(dtype=test_dtype, device=test_device).eval()
+        ref_f = model(x)
+
+        kwargs = {"dtype": test_dtype}
+        api(model, **kwargs)
+
+        # running model
+        ref = model(x)
+
+        # make sure it compiles
+        example_inputs = (x,)
+        model = torch.export.export(model, example_inputs).module()
+        after_export = model(x)
+        self.assertTrue(torch.equal(after_export, ref))
+
+
 if __name__ == "__main__":
     unittest.main()