Fix/ptl1.6.0

darts/models/forecasting/pl_forecasting_module.py

@@ -17,6 +17,10 @@
 
 logger = get_logger(__name__)
 
+# Check whether we are running pytorch-lightning >= 1.6.0 or not:
+tokens = pl.__version__.split(".")
+pl_160_or_above = int(tokens[0]) >= 1 and int(tokens[1]) >= 6
+
 
 class PLForecastingModule(pl.LightningModule, ABC):
     @abstractmethod
@@ -324,10 +328,12 @@ def on_load_checkpoint(self, checkpoint: Dict[str, Any]) -> None:
 
     @property
     def epochs_trained(self):
-        # trained epochs are only 0 when global step and current epoch are 0, else current epoch + 1
         current_epoch = self.current_epoch
-        if self.current_epoch or self.global_step:
+
+        # For PTL < 1.6.0 we have to adjust:
+        if not pl_160_or_above and (self.current_epoch or self.global_step):
             current_epoch += 1
+
         return current_epoch
 
 

darts/models/forecasting/torch_forecasting_model.py

@@ -1299,16 +1299,25 @@ def save_model(self, path: str) -> None:
         path
             Path under which to save the model at its current state.
         """
+        # TODO: the parameters are saved twice currently, once with complete
+        # object, and once with PTL checkpointing.
 
         raise_if_not(
             path.endswith(".pth.tar"),
             "The given path should end with '.pth.tar'.",
             logger,
         )
 
+        # We save the whole object to keep track of everything
         with open(path, "wb") as f_out:
             torch.save(self, f_out)
 
+        # In addition, we need to use PTL save_checkpoint() to properly save the trainer and model
+        if self.trainer is not None:
+            base_path = path[:-8]
+            path_ptl_ckpt = base_path + "_ptl-ckpt.pth.tar"
+            self.trainer.save_checkpoint(path_ptl_ckpt)
+
     @staticmethod
     def load_model(path: str) -> "TorchForecastingModel":
         """loads a model from a given file path. The file name should end with '.pth.tar'
@@ -1337,6 +1346,14 @@ def load_model(path: str) -> "TorchForecastingModel":
 
         with open(path, "rb") as fin:
             model = torch.load(fin)
+
+        # If a PTL checkpoint was saved, we also need to load it:
+        base_path = path[:-8]
+        path_ptl_ckpt = base_path + "_ptl-ckpt.pth.tar"
+        if os.path.exists(path_ptl_ckpt):
+            model.model = model.model.__class__.load_from_checkpoint(path_ptl_ckpt)
+            model.trainer = model.model.trainer
+
         return model
 
     @staticmethod

darts/tests/models/forecasting/test_torch_forecasting_model.py

@@ -123,14 +123,22 @@ def test_manual_save_and_load(self):
             checkpoint_path_manual = os.path.join(model_dir, manual_name)
             os.mkdir(checkpoint_path_manual)
 
-            # save manually saved model
             checkpoint_file_name = "checkpoint_0.pth.tar"
             model_path_manual = os.path.join(
                 checkpoint_path_manual, checkpoint_file_name
             )
+            checkpoint_file_name_cpkt = "checkpoint_0_ptl-ckpt.pth.tar"
+            model_path_manual_ckpt = os.path.join(
+                checkpoint_path_manual, checkpoint_file_name_cpkt
+            )
+
+            # save manually saved model
             model_manual_save.save_model(model_path_manual)
             self.assertTrue(os.path.exists(model_path_manual))
 
+            # check that the PTL checkpoint path is also there
+            self.assertTrue(os.path.exists(model_path_manual_ckpt))
+
             # load manual save model and compare with automatic model results
             model_manual_save = RNNModel.load_model(model_path_manual)
             self.assertEqual(