[Feature] flexible return type when indexing prob sequences

tensordict/_contextlib.py

@@ -330,7 +330,11 @@ def _reverse_squeeze(self, args, kwargs, out):
 
 def _reverse_to_module(self, args, kwargs, out):
     try:
-        with out.unlock_() if not is_compiling() else contextlib.nullcontext():
+        with (
+            out.unlock_()
+            if not is_compiling() and out is not None
+            else contextlib.nullcontext()
+        ):
             return self.to_module(*args, **kwargs, swap_dest=out)
     except AttributeError:
         # This is a bit unsafe but we assume that out won't have an unlock_() if it's not a TD

tensordict/nn/probabilistic.py

@@ -990,6 +990,27 @@ def __init__(
         super().__init__(*modules, partial_tolerant=partial_tolerant)
         self.return_composite = return_composite
 
+    def __getitem__(self, index: int | slice | str) -> TensorDictModuleBase:
+        if isinstance(index, (int, str)):
+            return self.module.__getitem__(index)
+        else:
+            mods = self.module.__getitem__(index)
+            if self.return_composite and any(
+                isinstance(
+                    item,
+                    (ProbabilisticTensorDictModule, ProbabilisticTensorDictSequential),
+                )
+                for item in mods
+            ):
+                return type(self)(*mods, return_composite=self.return_composite)
+            elif isinstance(
+                mods[-1],
+                (ProbabilisticTensorDictModule, ProbabilisticTensorDictSequential),
+            ):
+                return type(self)(*mods)
+            else:
+                return TensorDictSequential(*mods)
+
     _dist_sample = ProbabilisticTensorDictModule._dist_sample
 
     @property

test/test_nn.py

@@ -2251,6 +2251,25 @@ def test_nested_keys_probabilistic_normal(self, log_prob_key):
         else:
             assert td_out[module.log_prob_key].shape == (3, 4, 1)
 
+    def test_index_prob_seq(self):
+        m0 = ProbabilisticTensorDictModule(
+            in_keys=["loc"], out_keys=["sample"], distribution_class=Normal
+        )
+        m1 = TensorDictModule(lambda x: x, in_keys=["other"], out_keys=["something"])
+        m2 = ProbabilisticTensorDictModule(
+            in_keys=["scale"], out_keys=["sample2"], distribution_class=Normal
+        )
+        seq = ProbabilisticTensorDictSequential(m0, m1, m2)
+        assert isinstance(seq[0], ProbabilisticTensorDictModule)
+        assert isinstance(seq[:2], TensorDictSequential)
+        assert not isinstance(seq[:2], ProbabilisticTensorDictSequential)
+        assert isinstance(seq[-2:], ProbabilisticTensorDictSequential)
+
+        seq = ProbabilisticTensorDictSequential(m0, m1, m2, return_composite=True)
+        assert isinstance(seq[0], ProbabilisticTensorDictModule)
+        assert isinstance(seq[:2], ProbabilisticTensorDictSequential)
+        assert isinstance(seq[-2:], ProbabilisticTensorDictSequential)
+
 
 class TestEnsembleModule:
     def test_init(self):