Release 2.2.1: Hotfix file closing

docs/misc/changelog.rst

@@ -3,10 +3,16 @@
 Changelog
 ==========
 
-Release 2.2.0 (2023-11-16)
+Release 2.2.1 (2023-11-17)
 --------------------------
 **Support for options at reset, bug fixes and better error messages**
 
+.. note::
+
+  SB3 v2.2.0 was yanked after a breaking change was found in `GH#1751 <https://github.com/DLR-RM/stable-baselines3/issues/1751>`_.
+  Please use SB3 v2.2.1 and not v2.2.0.
+
+
 Breaking Changes:
 ^^^^^^^^^^^^^^^^^
 - Switched to ``ruff`` for sorting imports (isort is no longer needed), black and ruff version now require a minimum version
@@ -32,7 +38,9 @@ Bug Fixes:
 - Fixed success reward dtype in ``SimpleMultiObsEnv`` (@NixGD)
 - Fixed check_env for Sequence observation space (@corentinlger)
 - Prevents instantiating BitFlippingEnv with conflicting observation spaces (@kylesayrs)
-- Fixed ResourceWarning when loading and saving models (files were not closed)
+- Fixed ResourceWarning when loading and saving models (files were not closed), please note that only path are closed automatically,
+  the behavior stay the same for tempfiles (they need to be closed manually),
+  the behavior is now consistent when loading/saving replay buffer
 
 `SB3-Contrib`_
 ^^^^^^^^^^^^^^
@@ -76,6 +84,7 @@ Others:
 - Switched to PyTorch 2.1.0 in the CI (fixes type annotations)
 - Fixed ``stable_baselines3/common/policies.py`` type hints
 - Switched to ``mypy`` only for checking types
+- Added tests to check consistency when saving/loading files
 
 Documentation:
 ^^^^^^^^^^^^^^

stable_baselines3/common/save_util.py

@@ -308,28 +308,31 @@ def save_to_zip_file(
     :param pytorch_variables: Other PyTorch variables expected to contain name and value of the variable.
     :param verbose: Verbosity level: 0 for no output, 1 for info messages, 2 for debug messages
     """
-    with open_path(save_path, "w", verbose=0, suffix="zip") as save_path:
-        # data/params can be None, so do not
-        # try to serialize them blindly
+    file = open_path(save_path, "w", verbose=0, suffix="zip")
+    # data/params can be None, so do not
+    # try to serialize them blindly
+    if data is not None:
+        serialized_data = data_to_json(data)
+
+    # Create a zip-archive and write our objects there.
+    with zipfile.ZipFile(file, mode="w") as archive:
+        # Do not try to save "None" elements
         if data is not None:
-            serialized_data = data_to_json(data)
-
-        # Create a zip-archive and write our objects there.
-        with zipfile.ZipFile(save_path, mode="w") as archive:
-            # Do not try to save "None" elements
-            if data is not None:
-                archive.writestr("data", serialized_data)
-            if pytorch_variables is not None:
-                with archive.open("pytorch_variables.pth", mode="w", force_zip64=True) as pytorch_variables_file:
-                    th.save(pytorch_variables, pytorch_variables_file)
-            if params is not None:
-                for file_name, dict_ in params.items():
-                    with archive.open(file_name + ".pth", mode="w", force_zip64=True) as param_file:
-                        th.save(dict_, param_file)
-            # Save metadata: library version when file was saved
-            archive.writestr("_stable_baselines3_version", sb3.__version__)
-            # Save system info about the current python env
-            archive.writestr("system_info.txt", get_system_info(print_info=False)[1])
+            archive.writestr("data", serialized_data)
+        if pytorch_variables is not None:
+            with archive.open("pytorch_variables.pth", mode="w", force_zip64=True) as pytorch_variables_file:
+                th.save(pytorch_variables, pytorch_variables_file)
+        if params is not None:
+            for file_name, dict_ in params.items():
+                with archive.open(file_name + ".pth", mode="w", force_zip64=True) as param_file:
+                    th.save(dict_, param_file)
+        # Save metadata: library version when file was saved
+        archive.writestr("_stable_baselines3_version", sb3.__version__)
+        # Save system info about the current python env
+        archive.writestr("system_info.txt", get_system_info(print_info=False)[1])
+
+    if isinstance(save_path, (str, pathlib.Path)):
+        file.close()
 
 
 def save_to_pkl(path: Union[str, pathlib.Path, io.BufferedIOBase], obj: Any, verbose: int = 0) -> None:
@@ -344,10 +347,12 @@ def save_to_pkl(path: Union[str, pathlib.Path, io.BufferedIOBase], obj: Any, ver
     :param obj: The object to save.
     :param verbose: Verbosity level: 0 for no output, 1 for info messages, 2 for debug messages
     """
-    with open_path(path, "w", verbose=verbose, suffix="pkl") as file_handler:
-        # Use protocol>=4 to support saving replay buffers >= 4Gb
-        # See https://docs.python.org/3/library/pickle.html
-        pickle.dump(obj, file_handler, protocol=pickle.HIGHEST_PROTOCOL)
+    file = open_path(path, "w", verbose=verbose, suffix="pkl")
+    # Use protocol>=4 to support saving replay buffers >= 4Gb
+    # See https://docs.python.org/3/library/pickle.html
+    pickle.dump(obj, file, protocol=pickle.HIGHEST_PROTOCOL)
+    if isinstance(path, (str, pathlib.Path)):
+        file.close()
 
 
 def load_from_pkl(path: Union[str, pathlib.Path, io.BufferedIOBase], verbose: int = 0) -> Any:
@@ -360,8 +365,11 @@ def load_from_pkl(path: Union[str, pathlib.Path, io.BufferedIOBase], verbose: in
         path actually exists. If path is a io.BufferedIOBase the path exists.
     :param verbose: Verbosity level: 0 for no output, 1 for info messages, 2 for debug messages
     """
-    with open_path(path, "r", verbose=verbose, suffix="pkl") as file_handler:
-        return pickle.load(file_handler)
+    file = open_path(path, "r", verbose=verbose, suffix="pkl")
+    obj = pickle.load(file)
+    if isinstance(path, (str, pathlib.Path)):
+        file.close()
+    return obj
 
 
 def load_from_zip_file(
@@ -391,14 +399,14 @@ def load_from_zip_file(
     :return: Class parameters, model state_dicts (aka "params", dict of state_dict)
         and dict of pytorch variables
     """
-    load_path = open_path(load_path, "r", verbose=verbose, suffix="zip")
+    file = open_path(load_path, "r", verbose=verbose, suffix="zip")
 
     # set device to cpu if cuda is not available
     device = get_device(device=device)
 
     # Open the zip archive and load data
     try:
-        with zipfile.ZipFile(load_path) as archive:
+        with zipfile.ZipFile(file) as archive:
             namelist = archive.namelist()
             # If data or parameters is not in the
             # zip archive, assume they were stored
@@ -451,5 +459,6 @@ def load_from_zip_file(
         # load_path wasn't a zip file
         raise ValueError(f"Error: the file {load_path} wasn't a zip-file") from e
     finally:
-        load_path.close()
+        if isinstance(load_path, (str, pathlib.Path)):
+            file.close()
     return data, params, pytorch_variables

stable_baselines3/version.txt

@@ -1 +1 @@
-2.2.0
+2.2.1

tests/test_save_load.py

@@ -3,6 +3,7 @@
 import json
 import os
 import pathlib
+import tempfile
 import warnings
 import zipfile
 from collections import OrderedDict
@@ -752,7 +753,33 @@ def test_dqn_target_update_interval(tmp_path):
 # Turn warnings into errors
 @pytest.mark.filterwarnings("error")
 def test_no_resource_warning(tmp_path):
+    # Check behavior of save/load
+    # see https://github.com/DLR-RM/stable-baselines3/issues/1751
+
     # check that files are properly closed
     # Create a PPO agent and save it
     PPO("MlpPolicy", "CartPole-v1").save(tmp_path / "dqn_cartpole")
     PPO.load(tmp_path / "dqn_cartpole")
+
+    PPO("MlpPolicy", "CartPole-v1").save(str(tmp_path / "dqn_cartpole"))
+    PPO.load(str(tmp_path / "dqn_cartpole"))
+
+    # Do the same but in memory, should not close the file
+    with tempfile.TemporaryFile() as fp:
+        PPO("MlpPolicy", "CartPole-v1").save(fp)
+        PPO.load(fp)
+        assert not fp.closed
+
+    # Same but with replay buffer
+    model = SAC("MlpPolicy", "Pendulum-v1", buffer_size=200)
+    model.save_replay_buffer(tmp_path / "replay")
+    model.load_replay_buffer(tmp_path / "replay")
+
+    model.save_replay_buffer(str(tmp_path / "replay"))
+    model.load_replay_buffer(str(tmp_path / "replay"))
+
+    with tempfile.TemporaryFile() as fp:
+        model.save_replay_buffer(fp)
+        fp.seek(0)
+        model.load_replay_buffer(fp)
+        assert not fp.closed