PEFT / Trainer: Make use of `model.active_adapters()` instead of deprecated...

PEFT / Trainer: Make use of `model.active_adapters()` instead of deprecated `model.active_adapter` whenever possible (#30738)

* Update trainer.py

* Update src/transformers/trainer.py

* Update src/transformers/trainer.py

* Update src/transformers/trainer.py

* style

* Update src/transformers/trainer.py

* Update src/transformers/trainer.py

src/transformers/trainer.py

@@ -2526,16 +2526,27 @@ class Trainer:
         # Load adapters following PR # 24096
         elif _is_peft_model(model):
             # If train a model using PEFT & LoRA, assume that adapter have been saved properly.
-            if hasattr(model, "active_adapter") and hasattr(model, "load_adapter"):
+            # TODO: in the future support only specific min PEFT versions
+            if (hasattr(model, "active_adapter") or hasattr(model, "active_adapters")) and hasattr(
+                model, "load_adapter"
+            ):
                 if os.path.exists(resume_from_checkpoint):
-                    if adapter_subdirs:
+                    # For BC for older PEFT versions
+                    if hasattr(model, "active_adapters"):
+                        active_adapters = model.active_adapters()
+                        if len(active_adapters) > 1:
+                            logger.warning("Multiple active adapters detected will only consider the first adapter")
+                        active_adapter = active_adapters[0]
+                    else:
                         active_adapter = model.active_adapter
+
+                    if adapter_subdirs:
                         for subdir_name in adapter_subdirs:
                             peft_id = os.path.join(resume_from_checkpoint, subdir_name)
                             model.load_adapter(peft_id, subdir_name, is_trainable=(subdir_name == active_adapter))
                         model.set_adapter(active_adapter)
                     else:
-                        model.load_adapter(resume_from_checkpoint, model.active_adapter, is_trainable=True)
+                        model.load_adapter(resume_from_checkpoint, active_adapter, is_trainable=True)
                 else:
                     logger.warning(
                         "The intermediate checkpoints of PEFT may not be saved correctly, "
@@ -2609,9 +2620,20 @@ class Trainer:
             else:
                 if _is_peft_model(model):
                     # If train a model using PEFT & LoRA, assume that adapter have been saved properly.
-                    if hasattr(model, "active_adapter") and hasattr(model, "load_adapter"):
+                    # TODO: in the future support only specific min PEFT versions
+                    if (hasattr(model, "active_adapter") or hasattr(model, "active_adapters")) and hasattr(
+                        model, "load_adapter"
+                    ):
+                        # For BC for older PEFT versions
+                        if hasattr(model, "active_adapters"):
+                            active_adapter = model.active_adapters()[0]
+                            if len(model.active_adapters()) > 1:
+                                logger.warning("Detected multiple active adapters, will only consider the first one")
+                        else:
+                            active_adapter = model.active_adapter
+
                         if os.path.exists(best_adapter_model_path) or os.path.exists(best_safe_adapter_model_path):
-                            model.load_adapter(self.state.best_model_checkpoint, model.active_adapter)
+                            model.load_adapter(self.state.best_model_checkpoint, active_adapter)
                             # Load_adapter has no return value present, modify it when appropriate.
                             from torch.nn.modules.module import _IncompatibleKeys