diff --git a/test/test_vmap.py b/test/test_vmap.py
index 3870783f2a..1b12f9b22c 100644
--- a/test/test_vmap.py
+++ b/test/test_vmap.py
@@ -3565,6 +3565,31 @@ def _assert_all_slices_unique(self, tensor):
         slices_equal.diagonal().zero_()
         self.assertEqual(slices_equal, torch.zeros_like(slices_equal))
 
+    def _reset_random(self, generator, orig_state, use_generator, seed):
+        return generator.set_state(orig_state) if use_generator else torch.manual_seed(seed)
+
+    def _assert_throws_in_error_mode(self, fn, args, in_dims):
+        with self.assertRaisesRegex(RuntimeError, r"called random operation while in randomness error mode"):
+            vmap(fn, in_dims=in_dims, randomness="error")(*args)
+
+    def _assert_throws_in_same_mode_batched(self, fn, args, in_dims):
+        with self.assertRaisesRegex(RuntimeError,
+                                    r"Vmap does not currently support same randomness with a batched tensor input"):
+            vmap(fn, in_dims=in_dims, randomness="same")(*args)
+
+    def _in_dims(self, *batched_strings):
+
+        def get_in_dim(batched_string):
+            if batched_string == "first":
+                return 0
+            if batched_string == "last":
+                return -1
+            assert batched_string == "none"
+            return None
+
+        batched_strings = batched_strings + ("first",)  # for the always batched as first dim dummy argument
+        return tuple(get_in_dim(batched_string) for batched_string in batched_strings)
+
     @parametrize('randomness', ['error', 'same', 'different'])
     @parametrize('batched_input', [True, False])
     def test_dropout(self, device, randomness, batched_input):