Add dynamo torch.Tensor.new test.

test/dynamo/test_dynamo.py

@@ -632,6 +632,31 @@ def test_all_cpu_tensor(self):
     self.assertIn('MarkStep', met.counter_names())
 
 
+class DynamoOperationsTests(test_utils.XlaTestCase):
+
+  def test_new_with_sizes(self):
+
+    # The addition operation is needed here, since the error only occurs when FakeTensorMode
+    # checks the device of the arguments of some operation. If there's no operation using the
+    # result of Tensor.new, this comparison never occurs.
+    def foo(x):
+      return x.new(*x.size()) + x
+
+    optfoo = torch.compile(backend="openxla")(foo)
+
+    t = torch.arange(9)
+    Xt = t.to(xm.xla_device())
+
+    expected = foo(t)
+    actual = optfoo(Xt).cpu()
+
+    # Here, we don't expect the actual data to be the same. Reason being that Tensor.new
+    # returns uninitialized data.
+    self.assertEqual(expected.shape, actual.shape)
+    self.assertEqual(expected.dtype, actual.dtype)
+    self.assertEqual(expected.device, actual.device)
+
+
 if __name__ == '__main__':
   test = unittest.main()
   sys.exit(0 if test.result.wasSuccessful() else 1)