diff --git a/test/test_tensordictmodules.py b/test/test_tensordictmodules.py
index f66a560f2de..ba6f3790090 100644
--- a/test/test_tensordictmodules.py
+++ b/test/test_tensordictmodules.py
@@ -1837,6 +1837,9 @@ def create_transformed_env():
             assert (data.get(("next", "recurrent_state_c")) != 0.0).all()
             assert (data.get("recurrent_state_c") != 0.0).any()
 
+    @pytest.mark.skipif(
+        not _has_functorch, reason="vmap can only be used with functorch"
+    )
     def test_lstm_vmap_complex_model(self):
         # Tests that all ops in GRU are compatible with VMAP (when build using
         # the PT backend).
@@ -1889,7 +1892,7 @@ def call(data, params):
             with params.to_module(training_model):
                 return training_model(data)
 
-        assert torch.vmap(call, (None, 0))(data, params).shape == torch.Size(
+        assert vmap(call, (None, 0))(data, params).shape == torch.Size(
             (2, 50, 11)
         )
 
@@ -2163,6 +2166,9 @@ def create_transformed_env():
             assert (data.get("recurrent_state") != 0.0).any()
             assert (data.get(("next", "recurrent_state")) != 0.0).all()
 
+    @pytest.mark.skipif(
+        not _has_functorch, reason="vmap can only be used with functorch"
+    )
     def test_gru_vmap_complex_model(self):
         # Tests that all ops in GRU are compatible with VMAP (when build using
         # the PT backend).
@@ -2215,7 +2221,7 @@ def call(data, params):
             with params.to_module(training_model):
                 return training_model(data)
 
-        assert torch.vmap(call, (None, 0))(data, params).shape == torch.Size(
+        assert vmap(call, (None, 0))(data, params).shape == torch.Size(
             (2, 50, 11)
         )