[Feature] Migrate to tensordict.nn.TensorDictModule

README.md

@@ -36,7 +36,7 @@ algorithms. For instance, here's how to code a rollout in TorchRL:
   ```diff
   - obs, done = env.reset()
   + tensordict = env.reset()
-  policy = TensorDictModule(
+  policy = SafeModule(
       model,
       in_keys=["observation_pixels", "observation_vector"],
       out_keys=["action"],
@@ -106,14 +106,14 @@ Here's another example of an off-policy training loop in TorchRL (assuming that
 
 Check our TorchRL-specific [TensorDict tutorial](tutorials/tensordict.ipynb) for more information.
 
-The associated [`TensorDictModule` class](torchrl/modules/tensordict_module/common.py) which is [functorch](https://github.com/pytorch/functorch)-compatible!
-    
+The associated [`SafeModule` class](torchrl/modules/tensordict_module/common.py) which is [functorch](https://github.com/pytorch/functorch)-compatible!
+
   <details>
     <summary>Code</summary>
 
   ```diff
   transformer_model = nn.Transformer(nhead=16, num_encoder_layers=12)
-  + td_module = TensorDictModule(transformer_model, in_keys=["src", "tgt"], out_keys=["out"])
+  + td_module = SafeModule(transformer_model, in_keys=["src", "tgt"], out_keys=["out"])
   src = torch.rand((10, 32, 512))
   tgt = torch.rand((20, 32, 512))
   + tensordict = TensorDict({"src": src, "tgt": tgt}, batch_size=[20, 32])
@@ -122,19 +122,19 @@ The associated [`TensorDictModule` class](torchrl/modules/tensordict_module/comm
   + out = tensordict["out"]
   ```
 
-  The `TensorDictSequential` class allows to branch sequences of `nn.Module` instances in a highly modular way.
+  The `SafeSequential` class allows to branch sequences of `nn.Module` instances in a highly modular way.
   For instance, here is an implementation of a transformer using the encoder and decoder blocks:
   ```python
   encoder_module = TransformerEncoder(...)
-  encoder = TensorDictModule(encoder_module, in_keys=["src", "src_mask"], out_keys=["memory"])
+  encoder = SafeModule(encoder_module, in_keys=["src", "src_mask"], out_keys=["memory"])
   decoder_module = TransformerDecoder(...)
-  decoder = TensorDictModule(decoder_module, in_keys=["tgt", "memory"], out_keys=["output"])
-  transformer = TensorDictSequential(encoder, decoder)
+  decoder = SafeModule(decoder_module, in_keys=["tgt", "memory"], out_keys=["output"])
+  transformer = SafeSequential(encoder, decoder)
   assert transformer.in_keys == ["src", "src_mask", "tgt"]
   assert transformer.out_keys == ["memory", "output"]
   ```
 
-  `TensorDictSequential` allows to isolate subgraphs by querying a set of desired input / output keys:
+  `SafeSequential` allows to isolate subgraphs by querying a set of desired input / output keys:
   ```python
   transformer.select_subsequence(out_keys=["memory"])  # returns the encoder
   transformer.select_subsequence(in_keys=["tgt", "memory"])  # returns the decoder
@@ -261,9 +261,9 @@ The associated [`TensorDictModule` class](torchrl/modules/tensordict_module/comm
         kernel_sizes=[8, 4, 3],
         strides=[4, 2, 1],
     )
-    # Wrap it in a TensorDictModule, indicating what key to read in and where to
+    # Wrap it in a SafeModule, indicating what key to read in and where to
     # write out the output
-    common_module = TensorDictModule(
+    common_module = SafeModule(
         common_module,
         in_keys=["pixels"],
         out_keys=["hidden"],
@@ -277,10 +277,10 @@ The associated [`TensorDictModule` class](torchrl/modules/tensordict_module/comm
             activation=nn.ELU,
         )
     )
-    # Wrap the nn.Module in a ProbabilisticTensorDictModule, indicating how
+    # Wrap the nn.Module in a SafeProbabilisticModule, indicating how
     # to build the torch.distribution.Distribution object and what to do with it
-    policy_module = ProbabilisticTensorDictModule(  # stochastic policy
-        TensorDictModule(
+    policy_module = SafeProbabilisticModule(  # stochastic policy
+        SafeModule(
             policy_module,
             in_keys=["hidden"],
             out_keys=["loc", "scale"],
@@ -409,7 +409,7 @@ pip3 install torchrl
 This should work on linux and MacOs (not M1). For Windows and M1/M2 machines, one
 should install the library locally (see below).
 
-The **nightly build** can be installed via 
+The **nightly build** can be installed via
 ```
 pip install torchrl-nightly
 ```

docs/source/reference/envs.rst

@@ -50,7 +50,7 @@ With these, the following methods are implemented:
   having reproducible results.
 - :obj:`env.rollout(max_steps, policy)`: executes a rollout in the environment for
   a maximum number of steps :obj:`max_steps` and using a policy :obj:`policy`.
-  The policy should be coded using a :obj:`TensorDictModule` (or any other
+  The policy should be coded using a :obj:`SafeModule` (or any other
   :obj:`TensorDict`-compatible module).
 
 

docs/source/reference/modules.rst

@@ -11,10 +11,9 @@ TensorDict modules
     :toctree: generated/
     :template: rl_template_noinherit.rst
 
-    TensorDictModule
-    ProbabilisticTensorDictModule
-    TensorDictSequential
-    TensorDictModuleWrapper
+    SafeModule
+    SafeProbabilisticModule
+    SafeSequential
     Actor
     ProbabilisticActor
     ValueOperator

test/smoke_test.py

@@ -6,5 +6,5 @@ def test_imports():
     )  # noqa: F401
     from torchrl.envs import Transform, TransformedEnv  # noqa: F401
     from torchrl.envs.gym_like import GymLikeEnv  # noqa: F401
-    from torchrl.modules import TensorDictModule  # noqa: F401
+    from torchrl.modules import SafeModule  # noqa: F401
     from torchrl.objectives.common import LossModule  # noqa: F401

test/test_collector.py

@@ -35,12 +35,7 @@
 from torchrl.envs import EnvCreator, ParallelEnv, SerialEnv
 from torchrl.envs.libs.gym import _has_gym, GymEnv
 from torchrl.envs.transforms import TransformedEnv, VecNorm
-from torchrl.modules import (
-    Actor,
-    LSTMNet,
-    OrnsteinUhlenbeckProcessWrapper,
-    TensorDictModule,
-)
+from torchrl.modules import Actor, LSTMNet, OrnsteinUhlenbeckProcessWrapper, SafeModule
 
 # torch.set_default_dtype(torch.double)
 
@@ -754,7 +749,7 @@ def create_env():
         return ContinuousActionVecMockEnv()
 
     n_actions = ContinuousActionVecMockEnv().action_spec.shape[-1]
-    policy = TensorDictModule(
+    policy = SafeModule(
         torch.nn.LazyLinear(n_actions), in_keys=["observation"], out_keys=["action"]
     )
     policy(create_env().reset())
@@ -898,7 +893,7 @@ def test_collector_output_keys(collector_class, init_random_frames, explicit_spe
             next=CompositeSpec(hidden1=hidden_spec, hidden2=hidden_spec),
         )
 
-    policy = TensorDictModule(**policy_kwargs)
+    policy = SafeModule(**policy_kwargs)
 
     env_maker = lambda: GymEnv(PENDULUM_VERSIONED)
 
@@ -985,12 +980,12 @@ def test_auto_wrap_modules(self, collector_class, multiple_outputs, env_maker):
 
         if collector_class is not SyncDataCollector:
             assert all(
-                isinstance(p, TensorDictModule) for p in collector._policy_dict.values()
+                isinstance(p, SafeModule) for p in collector._policy_dict.values()
             )
             assert all(p.out_keys == out_keys for p in collector._policy_dict.values())
             assert all(p.module is policy for p in collector._policy_dict.values())
         else:
-            assert isinstance(collector.policy, TensorDictModule)
+            assert isinstance(collector.policy, SafeModule)
             assert collector.policy.out_keys == out_keys
             assert collector.policy.module is policy
 

test/test_cost.py

@@ -6,7 +6,7 @@
 import argparse
 from copy import deepcopy
 
-from torchrl.modules.functional_modules import FunctionalModuleWithBuffers
+from tensordict.nn.functional_modules import FunctionalModuleWithBuffers
 
 _has_functorch = True
 try:
@@ -41,10 +41,10 @@
 from torchrl.envs.transforms import TensorDictPrimer, TransformedEnv
 from torchrl.modules import (
     DistributionalQValueActor,
-    ProbabilisticTensorDictModule,
     QValueActor,
-    TensorDictModule,
-    TensorDictSequential,
+    SafeModule,
+    SafeProbabilisticModule,
+    SafeSequential,
     WorldModelWrapper,
 )
 from torchrl.modules.distributions.continuous import NormalParamWrapper, TanhNormal
@@ -777,9 +777,7 @@ def _create_mock_actor(self, batch=2, obs_dim=3, action_dim=4, device="cpu"):
             -torch.ones(action_dim), torch.ones(action_dim), (action_dim,)
         )
         net = NormalParamWrapper(nn.Linear(obs_dim, 2 * action_dim))
-        module = TensorDictModule(
-            net, in_keys=["observation"], out_keys=["loc", "scale"]
-        )
+        module = SafeModule(net, in_keys=["observation"], out_keys=["loc", "scale"])
         actor = ProbabilisticActor(
             spec=CompositeSpec(action=action_spec, loc=None, scale=None),
             module=module,
@@ -1096,9 +1094,7 @@ def _create_mock_actor(self, batch=2, obs_dim=3, action_dim=4, device="cpu"):
             -torch.ones(action_dim), torch.ones(action_dim), (action_dim,)
         )
         net = NormalParamWrapper(nn.Linear(obs_dim, 2 * action_dim))
-        module = TensorDictModule(
-            net, in_keys=["observation"], out_keys=["loc", "scale"]
-        )
+        module = SafeModule(net, in_keys=["observation"], out_keys=["loc", "scale"])
         actor = ProbabilisticActor(
             module=module,
             distribution_class=TanhNormal,
@@ -1151,13 +1147,9 @@ def __init__(self):
             def forward(self, hidden, act):
                 return self.linear(torch.cat([hidden, act], -1))
 
-        common = TensorDictModule(
-            CommonClass(), in_keys=["observation"], out_keys=["hidden"]
-        )
+        common = SafeModule(CommonClass(), in_keys=["observation"], out_keys=["hidden"])
         actor_subnet = ProbabilisticActor(
-            TensorDictModule(
-                ActorClass(), in_keys=["hidden"], out_keys=["loc", "scale"]
-            ),
+            SafeModule(ActorClass(), in_keys=["hidden"], out_keys=["loc", "scale"]),
             dist_in_keys=["loc", "scale"],
             distribution_class=TanhNormal,
             return_log_prob=True,
@@ -1528,9 +1520,7 @@ def _create_mock_actor(self, batch=2, obs_dim=3, action_dim=4, device="cpu"):
             -torch.ones(action_dim), torch.ones(action_dim), (action_dim,)
         )
         net = NormalParamWrapper(nn.Linear(obs_dim, 2 * action_dim))
-        module = TensorDictModule(
-            net, in_keys=["observation"], out_keys=["loc", "scale"]
-        )
+        module = SafeModule(net, in_keys=["observation"], out_keys=["loc", "scale"])
         actor = ProbabilisticActor(
             module=module,
             distribution_class=TanhNormal,
@@ -1763,9 +1753,7 @@ def _create_mock_actor(self, batch=2, obs_dim=3, action_dim=4, device="cpu"):
             -torch.ones(action_dim), torch.ones(action_dim), (action_dim,)
         )
         net = NormalParamWrapper(nn.Linear(obs_dim, 2 * action_dim))
-        module = TensorDictModule(
-            net, in_keys=["observation"], out_keys=["loc", "scale"]
-        )
+        module = SafeModule(net, in_keys=["observation"], out_keys=["loc", "scale"])
         actor = ProbabilisticActor(
             module=module,
             distribution_class=TanhNormal,
@@ -1989,9 +1977,7 @@ def test_reinforce_value_net(self, advantage, gradient_mode, delay_value):
         gamma = 0.9
         value_net = ValueOperator(nn.Linear(n_obs, 1), in_keys=["observation"])
         net = NormalParamWrapper(nn.Linear(n_obs, 2 * n_act))
-        module = TensorDictModule(
-            net, in_keys=["observation"], out_keys=["loc", "scale"]
-        )
+        module = SafeModule(net, in_keys=["observation"], out_keys=["loc", "scale"])
         actor_net = ProbabilisticActor(
             module,
             distribution_class=TanhNormal,
@@ -2138,7 +2124,7 @@ def _create_world_model_model(self, rssm_hidden_dim, state_dim, mlp_num_units=20
 
         # World Model and reward model
         rssm_rollout = RSSMRollout(
-            TensorDictModule(
+            SafeModule(
                 rssm_prior,
                 in_keys=["state", "belief", "action"],
                 out_keys=[
@@ -2148,7 +2134,7 @@ def _create_world_model_model(self, rssm_hidden_dim, state_dim, mlp_num_units=20
                     ("next", "belief"),
                 ],
             ),
-            TensorDictModule(
+            SafeModule(
                 rssm_posterior,
                 in_keys=[("next", "belief"), ("next", "encoded_latents")],
                 out_keys=[
@@ -2162,20 +2148,20 @@ def _create_world_model_model(self, rssm_hidden_dim, state_dim, mlp_num_units=20
             out_features=1, depth=2, num_cells=mlp_num_units, activation_class=nn.ELU
         )
         # World Model and reward model
-        world_modeler = TensorDictSequential(
-            TensorDictModule(
+        world_modeler = SafeSequential(
+            SafeModule(
                 obs_encoder,
                 in_keys=[("next", "pixels")],
                 out_keys=[("next", "encoded_latents")],
             ),
             rssm_rollout,
-            TensorDictModule(
+            SafeModule(
                 obs_decoder,
                 in_keys=[("next", "state"), ("next", "belief")],
                 out_keys=[("next", "reco_pixels")],
             ),
         )
-        reward_module = TensorDictModule(
+        reward_module = SafeModule(
             reward_module,
             in_keys=[("next", "state"), ("next", "belief")],
             out_keys=["reward"],
@@ -2209,8 +2195,8 @@ def _create_mb_env(self, rssm_hidden_dim, state_dim, mlp_num_units=200):
         reward_module = MLP(
             out_features=1, depth=2, num_cells=mlp_num_units, activation_class=nn.ELU
         )
-        transition_model = TensorDictSequential(
-            TensorDictModule(
+        transition_model = SafeSequential(
+            SafeModule(
                 rssm_prior,
                 in_keys=["state", "belief", "action"],
                 out_keys=[
@@ -2221,7 +2207,7 @@ def _create_mb_env(self, rssm_hidden_dim, state_dim, mlp_num_units=200):
                 ],
             ),
         )
-        reward_model = TensorDictModule(
+        reward_model = SafeModule(
             reward_module,
             in_keys=["state", "belief"],
             out_keys=["reward"],
@@ -2255,8 +2241,8 @@ def _create_actor_model(self, rssm_hidden_dim, state_dim, mlp_num_units=200):
             num_cells=mlp_num_units,
             activation_class=nn.ELU,
         )
-        actor_model = ProbabilisticTensorDictModule(
-            TensorDictModule(
+        actor_model = SafeProbabilisticModule(
+            SafeModule(
                 actor_module,
                 in_keys=["state", "belief"],
                 out_keys=["loc", "scale"],
@@ -2278,7 +2264,7 @@ def _create_actor_model(self, rssm_hidden_dim, state_dim, mlp_num_units=200):
         return actor_model
 
     def _create_value_model(self, rssm_hidden_dim, state_dim, mlp_num_units=200):
-        value_model = TensorDictModule(
+        value_model = SafeModule(
             MLP(
                 out_features=1,
                 depth=3,
@@ -2380,7 +2366,7 @@ def test_dreamer_env(self, device, imagination_horizon, discount_loss):
         # test reconstruction
         with pytest.raises(ValueError, match="No observation decoder provided"):
             mb_env.decode_obs(rollout)
-        mb_env.obs_decoder = TensorDictModule(
+        mb_env.obs_decoder = SafeModule(
             nn.LazyLinear(4, device=device),
             in_keys=["state"],
             out_keys=["reco_observation"],
@@ -2896,13 +2882,13 @@ def test_shared_params(dest, expected_dtype, expected_device):
     if torch.cuda.device_count() == 0 and dest == "cuda":
         pytest.skip("no cuda device available")
     module_hidden = torch.nn.Linear(4, 4)
-    td_module_hidden = TensorDictModule(
+    td_module_hidden = SafeModule(
         module=module_hidden,
         spec=None,
         in_keys=["observation"],
         out_keys=["hidden"],
     )
-    module_action = TensorDictModule(
+    module_action = SafeModule(
         NormalParamWrapper(torch.nn.Linear(4, 8)),
         in_keys=["hidden"],
         out_keys=["loc", "scale"],