init

test/test_utils.py

@@ -12,7 +12,10 @@
 import _utils_internal
 import pytest
 
-from torchrl._utils import get_binary_env_var, implement_for
+import torch
+
+from _utils_internal import get_default_devices
+from torchrl._utils import _rng_decorator, get_binary_env_var, implement_for
 
 from torchrl.envs.libs.gym import gym_backend, GymWrapper, set_gym_backend
 
@@ -358,6 +361,21 @@ class MockGym:
         )  # would break with gymnasium
 
 
+@pytest.mark.parametrize("device", get_default_devices())
+def test_rng_decorator(device):
+    torch.manual_seed(10)
+    with torch.device(device):
+        s0a = torch.randn(3)
+        with _rng_decorator(0):
+            torch.randn(3)
+        s0b = torch.randn(3)
+        torch.manual_seed(10)
+        s1a = torch.randn(3)
+        s1b = torch.randn(3)
+        torch.testing.assert_close(s0a, s1a)
+        torch.testing.assert_close(s0b, s1b)
+
+
 if __name__ == "__main__":
     args, unknown = argparse.ArgumentParser().parse_known_args()
     pytest.main([__file__, "--capture", "no", "--exitfirst"] + unknown)

torchrl/_utils.py

@@ -704,3 +704,17 @@ def _replace_last(key: NestedKey, new_ending: str) -> NestedKey:
         return new_ending
     else:
         return key[:-1] + (new_ending,)
+
+
+class _rng_decorator(_DecoratorContextManager):
+    """Temporarily sets the seed and sets back the rng state when exiting."""
+
+    def __init__(self, seed):
+        self.seed = seed
+
+    def __enter__(self):
+        self._state = torch.random.get_rng_state()
+        torch.manual_seed(self.seed)
+
+    def __exit__(self, exc_type, exc_val, exc_tb):
+        torch.random.set_rng_state(self._state)

torchrl/envs/utils.py

@@ -31,7 +31,7 @@
     set_interaction_type as set_exploration_type,
 )
 from tensordict.utils import NestedKey
-from torchrl._utils import _replace_last, logger as torchrl_logger
+from torchrl._utils import _replace_last, _rng_decorator, logger as torchrl_logger
 
 from torchrl.data.tensor_specs import (
     CompositeSpec,
@@ -419,7 +419,9 @@ def _per_level_env_check(data0, data1, check_dtype):
                     )
 
 
-def check_env_specs(env, return_contiguous=True, check_dtype=True, seed=0):
+def check_env_specs(
+    env, return_contiguous=True, check_dtype=True, seed: int | None = None
+):
     """Tests an environment specs against the results of short rollout.
 
     This test function should be used as a sanity check for an env wrapped with
@@ -436,16 +438,24 @@ def check_env_specs(env, return_contiguous=True, check_dtype=True, seed=0):
             of inputs/outputs). Defaults to True.
         check_dtype (bool, optional): if False, dtype checks will be skipped.
             Defaults to True.
-        seed (int, optional): for reproducibility, a seed is set.
+        seed (int, optional): for reproducibility, a seed can be set.
+            The seed will be set in pytorch temporarily, then the RNG state will
+            be reverted to what it was before. For the env, we set the seed but since
+            setting the rng state back to what is was isn't a feature of most environment,
+            we leave it to the user to accomplish that.
+            Defaults to ``None``.
 
     Caution: this function resets the env seed. It should be used "offline" to
     check that an env is adequately constructed, but it may affect the seeding
     of an experiment and as such should be kept out of training scripts.
 
     """
     if seed is not None:
-        torch.manual_seed(seed)
-        env.set_seed(seed)
+        with _rng_decorator(seed):
+            env.set_seed(seed)
+            return check_env_specs(
+                env, return_contiguous=return_contiguous, check_dtype=check_dtype
+            )
 
     fake_tensordict = env.fake_tensordict()
     real_tensordict = env.rollout(3, return_contiguous=return_contiguous)