[BugFix] Fix batch_locked check in check_env_specs + error message callable

torchrl/envs/utils.py

@@ -14,7 +14,7 @@
 import re
 import warnings
 from enum import Enum
-from typing import Any, Dict, List
+from typing import Any
 
 import torch
 
@@ -329,9 +329,9 @@ def step_mdp(
     exclude_reward: bool = True,
     exclude_done: bool = False,
     exclude_action: bool = True,
-    reward_keys: NestedKey | List[NestedKey] = "reward",
-    done_keys: NestedKey | List[NestedKey] = "done",
-    action_keys: NestedKey | List[NestedKey] = "action",
+    reward_keys: NestedKey | list[NestedKey] = "reward",
+    done_keys: NestedKey | list[NestedKey] = "done",
+    action_keys: NestedKey | list[NestedKey] = "action",
 ) -> TensorDictBase:
     """Creates a new tensordict that reflects a step in time of the input tensordict.
 
@@ -680,8 +680,8 @@ def _per_level_env_check(data0, data1, check_dtype):
 
 
 def check_env_specs(
-    env,
-    return_contiguous=True,
+    env: torchrl.envs.EnvBase,  # noqa
+    return_contiguous: bool | None = None,
     check_dtype=True,
     seed: int | None = None,
     tensordict: TensorDictBase | None = None,
@@ -700,7 +700,7 @@ def check_env_specs(
         env (EnvBase): the env for which the specs have to be checked against data.
         return_contiguous (bool, optional): if ``True``, the random rollout will be called with
             return_contiguous=True. This will fail in some cases (e.g. heterogeneous shapes
-            of inputs/outputs). Defaults to True.
+            of inputs/outputs). Defaults to ``None`` (determined by the presence of dynamic specs).
         check_dtype (bool, optional): if False, dtype checks will be skipped.
             Defaults to True.
         seed (int, optional): for reproducibility, a seed can be set.
@@ -718,6 +718,8 @@ def check_env_specs(
     of an experiment and as such should be kept out of training scripts.
 
     """
+    if return_contiguous is None:
+        return_contiguous = not env._has_dynamic_specs
     if break_when_any_done == "both":
         check_env_specs(
             env,
@@ -746,7 +748,7 @@ def check_env_specs(
             )
 
     fake_tensordict = env.fake_tensordict()
-    if not env._batch_locked and tensordict is not None:
+    if not env.batch_locked and tensordict is not None:
         shape = torch.broadcast_shapes(fake_tensordict.shape, tensordict.shape)
         fake_tensordict = fake_tensordict.expand(shape)
         tensordict = tensordict.expand(shape)
@@ -786,10 +788,13 @@ def check_env_specs(
     - List of keys present in fake but not in real: {fake_tensordict_keys-real_tensordict_keys}.
 """
         )
-    zeroing_err_msg = (
-        "zeroing the two tensordicts did not make them identical. "
-        f"Check for discrepancies:\nFake=\n{fake_tensordict}\nReal=\n{real_tensordict}"
-    )
+
+    def zeroing_err_msg():
+        return (
+            "zeroing the two tensordicts did not make them identical. "
+            f"Check for discrepancies:\nFake=\n{fake_tensordict}\nReal=\n{real_tensordict}"
+        )
+
     from torchrl.envs.common import _has_dynamic_specs
 
     if _has_dynamic_specs(env.specs):
@@ -799,7 +804,7 @@ def check_env_specs(
         ):
             fake = fake.apply(lambda x, y: x.expand_as(y), real)
             if (torch.zeros_like(real) != torch.zeros_like(fake)).any():
-                raise AssertionError(zeroing_err_msg)
+                raise AssertionError(zeroing_err_msg())
 
             # Checks shapes and eventually dtypes of keys at all nesting levels
             _per_level_env_check(fake, real, check_dtype=check_dtype)
@@ -809,7 +814,7 @@ def check_env_specs(
             torch.zeros_like(fake_tensordict_select)
             != torch.zeros_like(real_tensordict_select)
         ).any():
-            raise AssertionError(zeroing_err_msg)
+            raise AssertionError(zeroing_err_msg())
 
         # Checks shapes and eventually dtypes of keys at all nesting levels
         _per_level_env_check(
@@ -1028,14 +1033,14 @@ class MarlGroupMapType(Enum):
     ALL_IN_ONE_GROUP = 1
     ONE_GROUP_PER_AGENT = 2
 
-    def get_group_map(self, agent_names: List[str]):
+    def get_group_map(self, agent_names: list[str]):
         if self == MarlGroupMapType.ALL_IN_ONE_GROUP:
             return {"agents": agent_names}
         elif self == MarlGroupMapType.ONE_GROUP_PER_AGENT:
             return {agent_name: [agent_name] for agent_name in agent_names}
 
 
-def check_marl_grouping(group_map: Dict[str, List[str]], agent_names: List[str]):
+def check_marl_grouping(group_map: dict[str, list[str]], agent_names: list[str]):
     """Check MARL group map.
 
     Performs checks on the group map of a marl environment to assess its validity.
@@ -1379,7 +1384,7 @@ def skim_through(td, reset=reset):
 def _update_during_reset(
     tensordict_reset: TensorDictBase,
     tensordict: TensorDictBase,
-    reset_keys: List[NestedKey],
+    reset_keys: list[NestedKey],
 ):
     """Updates the input tensordict with the reset data, based on the reset keys."""
     if not reset_keys: