[Feature] EnvBase.auto_specs_

test/mocking_classes.py

@@ -1038,11 +1038,13 @@ def _step(
         tensordict: TensorDictBase,
     ) -> TensorDictBase:
         action = tensordict.get(self.action_key)
+        try:
+            device = self.full_action_spec[self.action_key].device
+        except KeyError:
+            device = self.device
         self.count += action.to(
             dtype=torch.int,
-            device=self.full_action_spec[self.action_key].device
-            if self.device is None
-            else self.device,
+            device=device if self.device is None else self.device,
         )
         tensordict = TensorDict(
             source={
@@ -1275,8 +1277,10 @@ def __init__(
             max_steps = torch.tensor(5)
         if start_val is None:
             start_val = torch.zeros((), dtype=torch.int32)
-        if not max_steps.shape == self.batch_size:
-            raise RuntimeError("batch_size and max_steps shape must match.")
+        if max_steps.shape != self.batch_size:
+            raise RuntimeError(
+                f"batch_size and max_steps shape must match. Got self.batch_size={self.batch_size} and max_steps.shape={max_steps.shape}."
+            )
 
         self.max_steps = max_steps
 

test/test_env.py

@@ -3526,6 +3526,34 @@ def test_single_env_spec():
     assert env.input_spec.is_in(env.input_spec_unbatched.zeros(env.shape))
 
 
+def test_auto_spec():
+    env = CountingEnv()
+    td = env.reset()
+
+    policy = lambda td, action_spec=env.full_action_spec.clone(): td.update(
+        action_spec.rand()
+    )
+
+    env.full_observation_spec = Composite(
+        shape=env.full_observation_spec.shape, device=env.full_observation_spec.device
+    )
+    env.full_action_spec = Composite(
+        shape=env.full_action_spec.shape, device=env.full_action_spec.device
+    )
+    env.full_reward_spec = Composite(
+        shape=env.full_reward_spec.shape, device=env.full_reward_spec.device
+    )
+    env.full_done_spec = Composite(
+        shape=env.full_done_spec.shape, device=env.full_done_spec.device
+    )
+    env.full_state_spec = Composite(
+        shape=env.full_state_spec.shape, device=env.full_state_spec.device
+    )
+    env._action_keys = ["action"]
+    env.auto_specs_(policy, tensordict=td.copy())
+    env.check_env_specs(tensordict=td.copy())
+
+
 if __name__ == "__main__":
     args, unknown = argparse.ArgumentParser().parse_known_args()
     pytest.main([__file__, "--capture", "no", "--exitfirst"] + unknown)

test/test_specs.py

@@ -412,6 +412,15 @@ def test_getitem(self, shape, is_complete, device, dtype):
         with pytest.raises(KeyError):
             _ = ts["UNK"]
 
+    def test_setitem_newshape(self, shape, is_complete, device, dtype):
+        ts = self._composite_spec(shape, is_complete, device, dtype)
+        new_spec = ts.clone()
+        new_spec.shape = torch.Size(())
+        new_spec.clear_device_()
+        ts["new_spec"] = new_spec
+        assert ts["new_spec"].shape == ts.shape
+        assert ts["new_spec"].device == ts.device
+
     def test_setitem_forbidden_keys(self, shape, is_complete, device, dtype):
         ts = self._composite_spec(shape, is_complete, device, dtype)
         for key in {"shape", "device", "dtype", "space"}:

torchrl/data/tensor_specs.py

@@ -4372,11 +4372,20 @@ def set(self, name, spec):
         if spec is not None:
             shape = spec.shape
             if shape[: self.ndim] != self.shape:
-                raise ValueError(
-                    "The shape of the spec and the Composite mismatch: the first "
-                    f"{self.ndim} dimensions should match but got spec.shape={spec.shape} and "
-                    f"Composite.shape={self.shape}."
-                )
+                if (
+                    isinstance(spec, Composite)
+                    and spec.ndim < self.ndim
+                    and self.shape[: spec.ndim] == spec.shape
+                ):
+                    # Try to set the composite shape
+                    spec = spec.clone()
+                    spec.shape = self.shape
+                else:
+                    raise ValueError(
+                        "The shape of the spec and the Composite mismatch: the first "
+                        f"{self.ndim} dimensions should match but got spec.shape={spec.shape} and "
+                        f"Composite.shape={self.shape}."
+                    )
         self._specs[name] = spec
 
     def __init__(
@@ -4448,6 +4457,8 @@ def clear_device_(self):
         """Clears the device of the Composite."""
         self._device = None
         for spec in self._specs.values():
+            if spec is None:
+                continue
             spec.clear_device_()
         return self
 
@@ -4530,6 +4541,10 @@ def __setitem__(self, key, value):
             and value.device != self.device
         ):
             if isinstance(value, Composite) and value.device is None:
+                # We make a clone not to mess up the spec that was provided.
+                # in set() we do the same for shape - these two ops should be grouped.
+                # we don't care about the overhead of cloning twice though because in theory
+                # we don't set specs often.
                 value = value.clone().to(self.device)
             else:
                 raise RuntimeError(