Fix saving jitted model and simplify load

docs/misc/changelog.rst

@@ -4,7 +4,7 @@ Changelog
 ==========
 
 
-Release 2.8.0a1 (WIP)
+Release 2.8.0a2 (WIP)
 --------------------------
 
 Breaking Changes:
@@ -18,6 +18,7 @@ New Features:
 
 Bug Fixes:
 ^^^^^^^^^^
+- Fixed saving and loading of Torch compiled models (using ``th.compile()``) by updating ``get_parameters()``
 
 `SB3-Contrib`_
 ^^^^^^^^^^^^^^
@@ -35,8 +36,10 @@ Deprecations:
 Others:
 ^^^^^^^
 - Updated to Python 3.10+ annotations
-- Remove some unused variables (@unexploredtest)
-- Improve type hints for distributions
+- Removed some unused variables (@unexploredtest)
+- Improved type hints for distributions
+- Simplified zip file loading by removing Python 3.6 workaround and enabling ``weights_only=True`` (PyTorch 2.x)
+- Sped up saving/loading tests
 
 Documentation:
 ^^^^^^^^^^^^^^

stable_baselines3/common/base_class.py

@@ -812,8 +812,8 @@ def get_parameters(self) -> dict[str, dict]:
         params = {}
         for name in state_dicts_names:
             attr = recursive_getattr(self, name)
-            # Retrieve state dict
-            params[name] = attr.state_dict()
+            # Retrieve state dict, and from the original model if compiled (see GH#2137)
+            params[name] = getattr(attr, "_orig_mod", attr).state_dict()
         return params
 
     def save(

stable_baselines3/common/save_util.py

@@ -439,16 +439,7 @@ def load_from_zip_file(
             pth_files = [file_name for file_name in namelist if os.path.splitext(file_name)[1] == ".pth"]
             for file_path in pth_files:
                 with archive.open(file_path, mode="r") as param_file:
-                    # File has to be seekable, but param_file is not, so load in BytesIO first
-                    # fixed in python >= 3.7
-                    file_content = io.BytesIO()
-                    file_content.write(param_file.read())
-                    # go to start of file
-                    file_content.seek(0)
-                    # Load the parameters with the right ``map_location``.
-                    # Remove ".pth" ending with splitext
-                    # Note(antonin): we cannot use weights_only=True, as it breaks with PyTorch 1.13, see GH#1911
-                    th_object = th.load(file_content, map_location=device, weights_only=False)
+                    th_object = th.load(param_file, map_location=device, weights_only=True)
                     # "tensors.pth" was renamed "pytorch_variables.pth" in v0.9.0, see PR #138
                     if file_path == "pytorch_variables.pth" or file_path == "tensors.pth":
                         # PyTorch variables (not state_dicts)

stable_baselines3/version.txt

@@ -1 +1 @@
-2.8.0a1
+2.8.0a2

tests/test_save_load.py

@@ -48,9 +48,13 @@ def test_save_load(tmp_path, model_class):
 
     env = DummyVecEnv([lambda: select_env(model_class)])
 
+    kwargs = {}
+    if model_class == PPO:
+        kwargs = {"n_steps": 64, "n_epochs": 4}
+
     # create model
-    model = model_class("MlpPolicy", env, policy_kwargs=dict(net_arch=[16]), verbose=1)
-    model.learn(total_timesteps=500)
+    model = model_class("MlpPolicy", env, policy_kwargs=dict(net_arch=[16]), verbose=1, **kwargs)
+    model.learn(total_timesteps=150)
 
     env.reset()
     observations = np.concatenate([env.step([env.action_space.sample()])[0] for _ in range(10)], axis=0)
@@ -159,10 +163,16 @@ def test_save_load(tmp_path, model_class):
         assert np.allclose(selected_actions, new_selected_actions, 1e-4)
 
         # check if learn still works
-        model.learn(total_timesteps=500)
+        model.learn(total_timesteps=150)
 
         del model
 
+    # Check that loading after compiling works, see GH#2137
+    model = model_class.load(tmp_path / "test_save.zip")
+    model.policy = th.compile(model.policy)
+    model.save(tmp_path / "test_save.zip")
+    model_class.load(tmp_path / "test_save.zip")
+
     # clear file from os
     os.remove(tmp_path / "test_save.zip")
 
@@ -284,8 +294,8 @@ def test_exclude_include_saved_params(tmp_path, model_class):
 
 
 def test_save_load_pytorch_var(tmp_path):
-    model = SAC("MlpPolicy", "Pendulum-v1", seed=3, policy_kwargs=dict(net_arch=[64], n_critics=1))
-    model.learn(200)
+    model = SAC("MlpPolicy", "Pendulum-v1", learning_starts=10, seed=3, policy_kwargs=dict(net_arch=[64], n_critics=1))
+    model.learn(110)
     save_path = str(tmp_path / "sac_pendulum")
     model.save(save_path)
     env = model.get_env()
@@ -295,14 +305,14 @@ def test_save_load_pytorch_var(tmp_path):
 
     model = SAC.load(save_path, env=env)
     assert th.allclose(log_ent_coef_before, model.log_ent_coef)
-    model.learn(200)
+    model.learn(50)
     log_ent_coef_after = model.log_ent_coef
     # Check that the entropy coefficient is still optimized
     assert not th.allclose(log_ent_coef_before, log_ent_coef_after)
 
     # With a fixed entropy coef
     model = SAC("MlpPolicy", "Pendulum-v1", seed=3, ent_coef=0.01, policy_kwargs=dict(net_arch=[64], n_critics=1))
-    model.learn(200)
+    model.learn(110)
     save_path = str(tmp_path / "sac_pendulum")
     model.save(save_path)
     env = model.get_env()
@@ -313,7 +323,7 @@ def test_save_load_pytorch_var(tmp_path):
 
     model = SAC.load(save_path, env=env)
     assert th.allclose(ent_coef_before, model.ent_coef_tensor)
-    model.learn(200)
+    model.learn(50)
     ent_coef_after = model.ent_coef_tensor
     assert model.log_ent_coef is None
     # Check that the entropy coefficient is still the same
@@ -354,9 +364,9 @@ def test_save_load_replay_buffer(tmp_path, model_class):
     path = pathlib.Path(tmp_path / "logs/replay_buffer.pkl")
     path.parent.mkdir(exist_ok=True, parents=True)  # to not raise a warning
     model = model_class(
-        "MlpPolicy", select_env(model_class), buffer_size=1000, policy_kwargs=dict(net_arch=[64]), learning_starts=200
+        "MlpPolicy", select_env(model_class), buffer_size=1000, policy_kwargs=dict(net_arch=[64]), learning_starts=100
     )
-    model.learn(300)
+    model.learn(150)
     old_replay_buffer = deepcopy(model.replay_buffer)
     model.save_replay_buffer(path)
     model.replay_buffer = None
@@ -410,14 +420,14 @@ def test_warn_buffer(recwarn, model_class, optimize_memory_usage):
         learning_starts=10,
     )
 
-    model.learn(150)
+    model.learn(50)
 
-    model.learn(150, reset_num_timesteps=False)
+    model.learn(50, reset_num_timesteps=False)
 
     # Check that there is no warning
     assert len(recwarn) == 0
 
-    model.learn(150)
+    model.learn(50)
 
     if optimize_memory_usage:
         assert len(recwarn) == 1
@@ -439,6 +449,10 @@ def test_save_load_policy(tmp_path, model_class, policy_str, use_sde):
     """
     kwargs = dict(policy_kwargs=dict(net_arch=[16]))
 
+    if model_class == PPO:
+        kwargs["n_steps"] = 64
+        kwargs["n_epochs"] = 2
+
     # gSDE is only applicable for A2C, PPO and SAC
     if use_sde and model_class not in [A2C, PPO, SAC]:
         pytest.skip()
@@ -461,7 +475,7 @@ def test_save_load_policy(tmp_path, model_class, policy_str, use_sde):
 
     # create model
     model = model_class(policy_str, env, verbose=1, **kwargs)
-    model.learn(total_timesteps=300)
+    model.learn(total_timesteps=150)
 
     env.reset()
     observations = np.concatenate([env.step([env.action_space.sample()])[0] for _ in range(10)], axis=0)
@@ -556,7 +570,7 @@ def test_save_load_q_net(tmp_path, model_class, policy_str):
 
     # create model
     model = model_class(policy_str, env, verbose=1, **kwargs)
-    model.learn(total_timesteps=300)
+    model.learn(total_timesteps=150)
 
     env.reset()
     observations = np.concatenate([env.step([env.action_space.sample()])[0] for _ in range(10)], axis=0)