diff --git a/test/export/test_export.py b/test/export/test_export.py
index dcb8c5923d93b..221ea9ba075b5 100644
--- a/test/export/test_export.py
+++ b/test/export/test_export.py
@@ -1693,6 +1693,34 @@ def f():
         self.assertEqual(a.size(), torch.Size([3, 4]))
         self.assertEqual(b.size(), torch.Size([3, 4]))
 
+    def test_export_then_compile_tensor_ctor(self):
+        class M(torch.nn.Module):
+            def __init__(self,):
+                super().__init__()
+
+            def forward(self, scores, mask):
+                scores = scores.masked_fill(
+                    mask, torch.tensor(torch.finfo(scores.dtype).min)
+                )  # (bs, n_heads, q_length, k_length)
+                return scores
+
+        tensor_cpu = torch.randn(2, 4)
+        mask_cpu = torch.BoolTensor(
+            [[False,  True, False, False],
+            [False, False, False, False]]
+        )
+
+        m = M().eval()
+        # res_ref = m(tensor_cpu, mask_cpu)
+        # print("res_ref is: {}".format(res_ref), flush=True)
+
+        exported_model = capture_pre_autograd_graph(
+            m,
+            (tensor_cpu, mask_cpu),
+        )
+        optimized_model = torch.compile(exported_model)
+        optimized_model(tensor_cpu, mask_cpu)
+
 
 if __name__ == '__main__':
     run_tests()
diff --git a/torch/_subclasses/fake_tensor.py b/torch/_subclasses/fake_tensor.py
index 901396750d3f9..b36bc4c5bf8be 100644
--- a/torch/_subclasses/fake_tensor.py
+++ b/torch/_subclasses/fake_tensor.py
@@ -1465,11 +1465,14 @@ def maybe_to_constant(t):
                 return t
 
         # To constant propagate through these functions:
-        # 1, If this is a lift, the input tensor is guaranteed to be a
+        # 1, If this is a lift due to a torch.tensor call,
+        #    the input tensor is guaranteed to be a
         #    constant, so we keep a copy of the original argument along so
-        #    we can query it if we're asked to item() it at some later point
+        #    we can query it if we're asked to item() it at some later point.
+        #    (Note that you can always call a lift fn manually, so we do
+        #    have to check if there are any fake tensors!)
         # 2, Some functions that allow Python numbers to bind to Tensors, e.g, torch.div
-        if func in self.lift_fns or (
+        if (func in self.lift_fns and not flat_arg_fake_tensors) or (
             should_allow_numbers_as_tensors(func)
             and not has_symbolic_sizes
             and not flat_arg_fake_tensors
@@ -1509,11 +1512,10 @@ def maybe_to_constant(t):
         # this is generated from torch.tensor(), which does not use the
         # dispatcher, to allow wrapper subclasses to wrap the new tensor
         if func in self.lift_fns:
-            assert (
-                len(kwargs) == 0 and len(args) == 1 and type(args[0]) is torch.Tensor
-            ), f"{args} {kwargs}"
+            assert len(kwargs) == 0 and len(args) == 1, f"{args} {kwargs}"
 
-            return converter(self, args[0])
+            if type(args[0]) is torch.Tensor:
+                return converter(self, args[0])
 
         # Recompute flat_arg_fake_tensors here again in case some of the inputs
         # were real tensors and fakified in validate_and_convert_non_fake_tensors