[BugFix] Allow expanding TensorDictPrimer transforms shape with paren…

…t batch size (#2521)

test/test_transforms.py

@@ -159,6 +159,7 @@
 from torchrl.envs.transforms.vip import _VIPNet, VIPRewardTransform
 from torchrl.envs.utils import check_env_specs, step_mdp
 from torchrl.modules import GRUModule, LSTMModule, MLP, ProbabilisticActor, TanhNormal
+from torchrl.modules.utils import get_primers_from_module
 
 IS_WIN = platform == "win32"
 if IS_WIN:
@@ -7163,6 +7164,33 @@ def test_dict_default_value(self):
             rollout_td.get(("next", "mykey2")) == torch.tensor(1, dtype=torch.int64)
         ).all
 
+    def test_spec_shape_inplace_correction(self):
+        hidden_size = input_size = num_layers = 2
+        model = GRUModule(
+            input_size, hidden_size, num_layers, in_key="observation", out_key="action"
+        )
+        env = TransformedEnv(
+            SerialEnv(2, lambda: GymEnv("Pendulum-v1")),
+        )
+        # These primers do not have the leading batch dimension
+        # since model is agnostic to batch dimension that will be used.
+        primers = get_primers_from_module(model)
+        for primer in primers.primers:
+            assert primers.primers.get(primer).shape == torch.Size(
+                [num_layers, hidden_size]
+            )
+        env.append_transform(primers)
+
+        # Reset should add the batch dimension to the primers
+        # since the parent exists and is batch_locked.
+        td = env.reset()
+
+        for primer in primers.primers:
+            assert primers.primers.get(primer).shape == torch.Size(
+                [2, num_layers, hidden_size]
+            )
+            assert td.get(primer).shape == torch.Size([2, num_layers, hidden_size])
+
 
 class TestTimeMaxPool(TransformBase):
     @pytest.mark.parametrize("T", [2, 4])

torchrl/envs/transforms/transforms.py

@@ -4596,10 +4596,11 @@ class TensorDictPrimer(Transform):
             The corresponding value has to be a TensorSpec instance indicating
             what the value must be.
 
-    When used in a TransfomedEnv, the spec shapes must match the envs shape if
-    the parent env is batch-locked (:obj:`env.batch_locked=True`).
-    If the env is not batch-locked (e.g. model-based envs), it is assumed that the batch is
-    given by the input tensordict instead.
+    When used in a `TransformedEnv`, the spec shapes must match the environment's shape if
+    the parent environment is batch-locked (`env.batch_locked=True`). If the spec shapes and
+    parent shapes do not match, the spec shapes are modified in-place to match the leading
+    dimensions of the parent's batch size. This adjustment is made for cases where the parent
+    batch size dimension is not known during instantiation.
 
     Examples:
         >>> from torchrl.envs.libs.gym import GymEnv
@@ -4639,6 +4640,40 @@ class TensorDictPrimer(Transform):
         tensor([[1., 1., 1.],
                 [1., 1., 1.]])
 
+    Examples:
+        >>> from torchrl.envs.libs.gym import GymEnv
+        >>> from torchrl.envs import SerialEnv, TransformedEnv
+        >>> from torchrl.modules.utils import get_primers_from_module
+        >>> from torchrl.modules import GRUModule
+        >>> base_env = SerialEnv(2, lambda: GymEnv("Pendulum-v1"))
+        >>> env = TransformedEnv(base_env)
+        >>> model = GRUModule(input_size=2, hidden_size=2, in_key="observation", out_key="action")
+        >>> primers = get_primers_from_module(model)
+        >>> print(primers) # Primers shape is independent of the env batch size
+        TensorDictPrimer(primers=Composite(
+            recurrent_state: UnboundedContinuous(
+                shape=torch.Size([1, 2]),
+                space=ContinuousBox(
+                    low=Tensor(shape=torch.Size([1, 2]), device=cpu, dtype=torch.float32, contiguous=True),
+                    high=Tensor(shape=torch.Size([1, 2]), device=cpu, dtype=torch.float32, contiguous=True)),
+                device=cpu,
+                dtype=torch.float32,
+                domain=continuous),
+            device=None,
+            shape=torch.Size([])), default_value={'recurrent_state': 0.0}, random=None)
+        >>> env.append_transform(primers)
+        >>> print(env.reset()) # The primers are automatically expanded to match the env batch size
+        TensorDict(
+            fields={
+                done: Tensor(shape=torch.Size([2, 1]), device=cpu, dtype=torch.bool, is_shared=False),
+                observation: Tensor(shape=torch.Size([2, 3]), device=cpu, dtype=torch.float32, is_shared=False),
+                recurrent_state: Tensor(shape=torch.Size([2, 1, 2]), device=cpu, dtype=torch.float32, is_shared=False),
+                terminated: Tensor(shape=torch.Size([2, 1]), device=cpu, dtype=torch.bool, is_shared=False),
+                truncated: Tensor(shape=torch.Size([2, 1]), device=cpu, dtype=torch.bool, is_shared=False)},
+            batch_size=torch.Size([2]),
+            device=None,
+            is_shared=False)
+
     .. note:: Some TorchRL modules rely on specific keys being present in the environment TensorDicts,
         like :class:`~torchrl.modules.models.LSTM` or :class:`~torchrl.modules.models.GRU`.
         To facilitate this process, the method :func:`~torchrl.modules.utils.get_primers_from_module`
@@ -4764,7 +4799,7 @@ def transform_observation_spec(self, observation_spec: Composite) -> Composite:
                 # We try to set the primer shape to the observation spec shape
                 self.primers.shape = observation_spec.shape
             except ValueError:
-                # If we fail, we expnad them to that shape
+                # If we fail, we expand them to that shape
                 self.primers = self._expand_shape(self.primers)
         device = observation_spec.device
         observation_spec.update(self.primers.clone().to(device))
@@ -4831,12 +4866,17 @@ def _reset(
     ) -> TensorDictBase:
         """Sets the default values in the input tensordict.
 
-        If the parent is batch-locked, we assume that the specs have the appropriate leading
+        If the parent is batch-locked, we make sure the specs have the appropriate leading
         shape. We allow for execution when the parent is missing, in which case the
         spec shape is assumed to match the tensordict's.
-
         """
         _reset = _get_reset(self.reset_key, tensordict)
+        if (
+            self.parent
+            and self.parent.batch_locked
+            and self.primers.shape[: len(self.parent.shape)] != self.parent.batch_size
+        ):
+            self.primers = self._expand_shape(self.primers)
         if _reset.any():
             for key, spec in self.primers.items(True, True):
                 if self.random: