[bug] Prevent to from being ignored

sentence_transformers/SentenceTransformer.py

@@ -122,7 +122,7 @@ def __init__(self, model_name_or_path: Optional[str] = None,
             device = get_device_name()
             logger.info("Use pytorch device_name: {}".format(device))
 
-        self._target_device = torch.device(device)
+        self.to(device)
 
     def encode(self, sentences: Union[str, List[str]],
                batch_size: int = 32,
@@ -167,7 +167,7 @@ def encode(self, sentences: Union[str, List[str]],
             input_was_string = True
 
         if device is None:
-            device = self._target_device
+            device = self.device
 
         self.to(device)
 
@@ -658,7 +658,7 @@ def fit(self,
             from torch.cuda.amp import autocast
             scaler = torch.cuda.amp.GradScaler()
 
-        self.to(self._target_device)
+        self.to(self.device)
 
         dataloaders = [dataloader for dataloader, _ in train_objectives]
 
@@ -668,7 +668,7 @@ def fit(self,
 
         loss_models = [loss for _, loss in train_objectives]
         for loss_model in loss_models:
-            loss_model.to(self._target_device)
+            loss_model.to(self.device)
 
         self.best_score = -9999999
 
@@ -724,8 +724,8 @@ def fit(self,
                         data = next(data_iterator)
 
                     features, labels = data
-                    labels = labels.to(self._target_device)
-                    features = list(map(lambda batch: batch_to_device(batch, self._target_device), features))
+                    labels = labels.to(self.device)
+                    features = list(map(lambda batch: batch_to_device(batch, self.device), features))
 
                     if use_amp:
                         with autocast():
@@ -949,3 +949,14 @@ def max_seq_length(self, value):
         Property to set the maximal input sequence length for the model. Longer inputs will be truncated.
         """
         self._first_module().max_seq_length = value
+
+    @property
+    def _target_device(self) -> torch.device:
+        logger.warning(
+            "`SentenceTransformer._target_device` has been removed, please use `SentenceTransformer.device` instead.",
+        )
+        return self.device
+
+    @_target_device.setter
+    def _target_device(self, device: Optional[Union[int, str, torch.device]] = None) -> None:
+        self.to(device)

tests/test_sentence_transformer.py

@@ -2,6 +2,7 @@
 Tests general behaviour of the SentenceTransformer class
 """
 
+
 from pathlib import Path
 import tempfile
 
@@ -45,3 +46,21 @@ def test_load_with_safetensors(self):
             torch.equal(safetensors_model.encode(sentences, convert_to_tensor=True), pytorch_model.encode(sentences, convert_to_tensor=True)),
             msg="Ensure that Safetensors and PyTorch loaded models result in identical embeddings",
         )
+
+    @unittest.skipUnless(torch.cuda.is_available(), reason="CUDA must be available to test moving devices effectively.")
+    def test_to(self):
+        model = SentenceTransformer("sentence-transformers-testing/stsb-bert-tiny-safetensors", device="cpu")
+
+        test_device = torch.device("cuda")
+        self.assertEqual(model.device.type, "cpu")
+        self.assertEqual(test_device.type, "cuda")
+
+        model.to(test_device)
+        self.assertEqual(model.device.type, "cuda", msg="The model device should have updated")
+
+        model.encode("Test sentence")
+        self.assertEqual(model.device.type, "cuda", msg="Encoding shouldn't change the device")
+
+        self.assertEqual(model._target_device, model.device, msg="Prevent backwards compatibility failure for _target_device")
+        model._target_device = "cpu"
+        self.assertEqual(model.device.type, "cpu", msg="Ensure that setting `_target_device` doesn't crash.")