[Feature] Enable storing rollouts on a different device

test/test_env.py

@@ -827,6 +827,28 @@ def test_batch_unlocked_with_batch_size(self, device):
     #         env.observation_spec = env.observation_spec.clone()
     #         assert not env._cache
 
+    @pytest.mark.parametrize("storing_device", get_default_devices())
+    def test_storing_device(self, storing_device):
+        """Ensure rollout data tensors are moved to the requested storing_device."""
+        env = ContinuousActionVecMockEnv(device="cpu")
+
+        td = env.rollout(
+            10,
+            storing_device=torch.device(storing_device)
+            if storing_device is not None
+            else None,
+        )
+
+        expected_device = (
+            torch.device(storing_device) if storing_device is not None else env.device
+        )
+
+        assert td.device == expected_device
+
+        for _, item in td.items(True, True):
+            if isinstance(item, torch.Tensor):
+                assert item.device == expected_device
+
 
 class TestRollout:
     @pytest.mark.skipif(not _has_gym, reason="no gym")

torchrl/envs/common.py

@@ -3085,6 +3085,7 @@ def rollout(
         set_truncated: bool = False,
         out=None,
         trust_policy: bool = False,
+        storing_device: DEVICE_TYPING | None = None,
     ) -> TensorDictBase:
         """Executes a rollout in the environment.
 
@@ -3140,6 +3141,8 @@ def rollout(
             trust_policy (bool, optional): if ``True``, a non-TensorDictModule policy will be trusted to be
                 assumed to be compatible with the collector. This defaults to ``True`` for CudaGraphModules
                 and ``False`` otherwise.
+            storing_device (Device, optional): if provided, the tensordict will be stored on this device.
+                Defaults to ``None``.
 
         Returns:
             TensorDict object containing the resulting trajectory.
@@ -3372,6 +3375,9 @@ def rollout(
             "policy": policy,
             "policy_device": policy_device,
             "env_device": env_device,
+            "storing_device": None
+            if storing_device is None
+            else torch.device(storing_device),
             "callback": callback,
         }
         if break_when_any_done or break_when_all_done:
@@ -3508,6 +3514,7 @@ def _rollout_stop_early(
         policy,
         policy_device,
         env_device,
+        storing_device,
         callback,
     ):
         # Get the sync func
@@ -3531,7 +3538,10 @@ def _rollout_stop_early(
                 else:
                     tensordict.clear_device_()
             tensordict = self.step(tensordict)
-            td_append = tensordict.copy()
+            if storing_device is None or tensordict.device == storing_device:
+                td_append = tensordict.copy()
+            else:
+                td_append = tensordict.to(storing_device)
             if break_when_all_done:
                 if partial_steps is not True and not partial_steps.all():
                     # At least one step is partial
@@ -3589,6 +3599,7 @@ def _rollout_nonstop(
         policy,
         policy_device,
         env_device,
+        storing_device,
         callback,
     ):
         if auto_cast_to_device:
@@ -3614,7 +3625,10 @@ def _rollout_nonstop(
                 tensordict = self.step(tensordict_)
             else:
                 tensordict, tensordict_ = self.step_and_maybe_reset(tensordict_)
-            tensordicts.append(tensordict)
+            if storing_device is None or tensordict.device == storing_device:
+                tensordicts.append(tensordict)
+            else:
+                tensordicts.append(tensordict.to(storing_device))
             if i == max_steps - 1:
                 # we don't truncate as one could potentially continue the run
                 break