init

torchrl/modules/tensordict_module/rnn.py

@@ -381,6 +381,8 @@ class LSTMModule(ModuleBase):
     Methods:
         set_recurrent_mode: controls whether the module should be executed in
             recurrent mode.
+        make_tensordict_primer: creates the TensorDictPrimer transforms for the environment to be aware of the
+            recurrent states of the RNN.
 
     .. note:: This module relies on specific ``recurrent_state`` keys being present in the input
         TensorDicts. To generate a :class:`~torchrl.envs.transforms.TensorDictPrimer` transform that will automatically
@@ -521,6 +523,46 @@ def __init__(
         self._recurrent_mode = False
 
     def make_tensordict_primer(self):
+        """Makes a tensordict primer for the environment.
+
+        A :class:`~torchrl.envs.TensorDictPrimer` object will ensure that the policy is aware of the supplementary
+        inputs and outputs (recurrent states) during rollout execution. That way, the data can be shared across
+        processes and dealt with properly.
+
+        Not including a ``TensorDictPrimer`` in the environment may result in poorly defined behaviours, for instance
+        in parallel settings where a step involves copying the new recurrent state from ``"next"`` to the root
+        tensordict, which the meth:`~torchrl.EnvBase.step_mdp` method will not be able to do as the recurrent states
+        are not registered within the environment specs.
+
+        Examples:
+            >>> from torchrl.collectors import SyncDataCollector
+            >>> from torchrl.envs import TransformedEnv, InitTracker
+            >>> from torchrl.envs import GymEnv
+            >>> from torchrl.modules import MLP, LSTMModule
+            >>> from torch import nn
+            >>> from tensordict.nn import TensorDictSequential as Seq, TensorDictModule as Mod
+            >>>
+            >>> env = TransformedEnv(GymEnv("Pendulum-v1"), InitTracker())
+            >>> assert env.base_env.batch_locked
+            >>> lstm_module = LSTMModule(
+            ...     input_size=env.observation_spec["observation"].shape[-1],
+            ...     hidden_size=64,
+            ...     in_keys=["observation", "rs_h", "rs_c"],
+            ...     out_keys=["intermediate", ("next", "rs_h"), ("next", "rs_c")])
+            >>> mlp = MLP(num_cells=[64], out_features=1)
+            >>> policy = Seq(lstm_module, Mod(mlp, in_keys=["intermediate"], out_keys=["action"]))
+            >>> policy(env.reset())
+            >>> env = env.append_transform(lstm_module.make_tensordict_primer())
+            >>> data_collector = SyncDataCollector(
+            ...     env,
+            ...     policy,
+            ...     frames_per_batch=10
+            ... )
+            >>> for data in data_collector:
+            ...     print(data)
+            ...     break
+
+        """
         from torchrl.envs.transforms.transforms import TensorDictPrimer
 
         def make_tuple(key):
@@ -1065,6 +1107,8 @@ class GRUModule(ModuleBase):
     Methods:
         set_recurrent_mode: controls whether the module should be executed in
             recurrent mode.
+        make_tensordict_primer: creates the TensorDictPrimer transforms for the environment to be aware of the
+            recurrent states of the RNN.
 
     .. note:: This module relies on specific ``recurrent_state`` keys being present in the input
         TensorDicts. To generate a :class:`~torchrl.envs.transforms.TensorDictPrimer` transform that will automatically
@@ -1230,6 +1274,46 @@ def __init__(
         self._recurrent_mode = False
 
     def make_tensordict_primer(self):
+        """Makes a tensordict primer for the environment.
+
+        A :class:`~torchrl.envs.TensorDictPrimer` object will ensure that the policy is aware of the supplementary
+        inputs and outputs (recurrent states) during rollout execution. That way, the data can be shared across
+        processes and dealt with properly.
+
+        Not including a ``TensorDictPrimer`` in the environment may result in poorly defined behaviours, for instance
+        in parallel settings where a step involves copying the new recurrent state from ``"next"`` to the root
+        tensordict, which the meth:`~torchrl.EnvBase.step_mdp` method will not be able to do as the recurrent states
+        are not registered within the environment specs.
+
+        Examples:
+            >>> from torchrl.collectors import SyncDataCollector
+            >>> from torchrl.envs import TransformedEnv, InitTracker
+            >>> from torchrl.envs import GymEnv
+            >>> from torchrl.modules import MLP, LSTMModule
+            >>> from torch import nn
+            >>> from tensordict.nn import TensorDictSequential as Seq, TensorDictModule as Mod
+            >>>
+            >>> env = TransformedEnv(GymEnv("Pendulum-v1"), InitTracker())
+            >>> assert env.base_env.batch_locked
+            >>> gru_module = GRUModule(
+            ...     input_size=env.observation_spec["observation"].shape[-1],
+            ...     hidden_size=64,
+            ...     in_keys=["observation", "rs"],
+            ...     out_keys=["intermediate", ("next", "rs")])
+            >>> mlp = MLP(num_cells=[64], out_features=1)
+            >>> policy = Seq(gru_module, Mod(mlp, in_keys=["intermediate"], out_keys=["action"]))
+            >>> policy(env.reset())
+            >>> env = env.append_transform(gru_module.make_tensordict_primer())
+            >>> data_collector = SyncDataCollector(
+            ...     env,
+            ...     policy,
+            ...     frames_per_batch=10
+            ... )
+            >>> for data in data_collector:
+            ...     print(data)
+            ...     break
+
+        """
         from torchrl.envs import TensorDictPrimer
 
         def make_tuple(key):