revert back to saving and loading a TFM with torch rather than pickle (#2692)

* revert back to saving and loading a TFM with torch rather than pickle

* update changelog

Author: dennisbader

CHANGELOG.md

@@ -18,6 +18,8 @@ but cannot always guarantee backwards compatibility. Changes that may **break co
 
 **Fixed**
 
+- 🔴 / 🟢 Fixed a bug which raised an error when loading torch models that were saved with Darts versions < 0.33.0. This is a breaking change and models saved with version 0.33.0 will not be loadable anymore. [#2692](https://github.com/unit8co/darts/pull/2692) by [Dennis Bader](https://github.com/dennisbader).
+
 **Dependencies**
 
 ### For developers of the library:

darts/models/forecasting/torch_forecasting_model.py

@@ -1765,7 +1765,8 @@ def save(
             path = self._default_save_path() + ".pt"
 
         # save the TorchForecastingModel (does not save the PyTorch LightningModule, and Trainer)
-        super().save(path, clean=clean)
+        with open(path, "wb") as f_out:
+            torch.save(self if not clean else self._clean(), f_out)
 
         # save the LightningModule checkpoint (weights only with `clean=True`)
         path_ptl_ckpt = path + ".ckpt"
@@ -1802,7 +1803,7 @@ def load(
                 model_loaded = RNNModel.load(path)
             ..
 
-        Example for loading an :class:`RNNModel` to GPU:
+        Example for loading an :class:`RNNModel` to GPU that was trained on CPU:
 
             .. highlight:: python
             .. code-block:: python
@@ -1812,6 +1813,16 @@ def load(
                 model_loaded = RNNModel.load(path, pl_trainer_kwargs={"accelerator": "gpu"})
             ..
 
+        Example for loading an :class:`RNNModel` to CPU that was saved on GPU:
+
+            .. highlight:: python
+            .. code-block:: python
+
+                from darts.models import RNNModel
+
+                model_loaded = RNNModel.load(path, map_location="cpu", pl_trainer_kwargs={"accelerator": "gpu"})
+            ..
+
         Parameters
         ----------
         path
@@ -1825,11 +1836,15 @@ def load(
             for more information about the supported kwargs.
         **kwargs
             Additional kwargs for PyTorch Lightning's :func:`LightningModule.load_from_checkpoint()` method,
+            such as ``map_location`` to load the model onto a different device than the one on which it was saved.
             For more information, read the `official documentation <https://pytorch-lightning.readthedocs.io/en/stable/
             common/lightning_module.html#load-from-checkpoint>`_.
         """
         # load the base TorchForecastingModel (does not contain the actual PyTorch LightningModule)
-        model: TorchForecastingModel = ForecastingModel.load(path)
+        with open(path, "rb") as fin:
+            model: TorchForecastingModel = torch.load(
+                fin, weights_only=False, map_location=kwargs.get("map_location", None)
+            )
 
         # if a checkpoint was saved, we also load the PyTorch LightningModule from checkpoint
         path_ptl_ckpt = path + ".ckpt"
@@ -1927,7 +1942,9 @@ def load_from_checkpoint(
             f"Could not find base model save file `{INIT_MODEL_NAME}` in {model_dir}.",
             logger,
         )
-        model: TorchForecastingModel = ForecastingModel.load(base_model_path)
+        model: TorchForecastingModel = torch.load(
+            base_model_path, weights_only=False, map_location=kwargs.get("map_location")
+        )
 
         # load PyTorch LightningModule from checkpoint
         # if file_name is None, find the path of the best or most recent checkpoint in savepath
@@ -2093,7 +2110,12 @@ def load_weights_from_checkpoint(
                 )
 
             # updating model attributes before self._init_model() which create new tfm ckpt
-            tfm_save: TorchForecastingModel = ForecastingModel.load(tfm_save_file_path)
+            with open(tfm_save_file_path, "rb") as tfm_save_file:
+                tfm_save: TorchForecastingModel = torch.load(
+                    tfm_save_file,
+                    weights_only=False,
+                    map_location=kwargs.get("map_location", None),
+                )
 
             # encoders are necessary for direct inference
             self.encoders, self.add_encoders = self._load_encoders(