Move warn in BaseTuner

src/peft/mapping.py

@@ -65,7 +65,7 @@
     XLoraConfig,
 )
 from .tuners.tuners_utils import BaseTuner as _BaseTuner
-from .utils import _prepare_prompt_learning_config, EMBEDDING_LAYER_NAMES
+from .utils import _prepare_prompt_learning_config
 
 
 if TYPE_CHECKING:
@@ -130,13 +130,6 @@ def get_peft_config(config_dict: dict[str, Any]) -> PeftConfig:
 
     return PEFT_TYPE_TO_CONFIG_MAPPING[config_dict["peft_type"]](**config_dict)
 
-def warn_if_tied_embeddings_in_target_modules(model_config, peft_config):
-    if model_config.get("tie_word_embeddings"):
-        for target_module in peft_config.target_modules:
-            if target_module in EMBEDDING_LAYER_NAMES:
-                warnings.warn(
-                    f"{model_config['tie_word_embeddings']=} and a tied {target_module=} is passed to peft config. This can lead to complications, for example when merging the adapter. Are you sure you want to use the target module {target_module}?"
-                )
 
 def get_peft_model(
     model: PreTrainedModel,
@@ -174,8 +167,6 @@ def get_peft_model(
     new_name = model.__dict__.get("name_or_path", None)
     peft_config.base_model_name_or_path = new_name
 
-    warn_if_tied_embeddings_in_target_modules(model_config, peft_config)
-
     if (old_name is not None) and (old_name != new_name):
         warnings.warn(
             f"The PEFT config's `base_model_name_or_path` was renamed from '{old_name}' to '{new_name}'. "

src/peft/peft_model.py

@@ -1231,9 +1231,7 @@ def create_or_update_model_card(self, output_dir: str):
 
         card.data["library_name"] = "peft"
 
-        model_config = getattr(self, "config", None)
-        if hasattr(model_config, "to_dict"):
-            model_config = model_config.to_dict()
+        model_config = BaseTuner.get_model_config(self, default=None)
         if model_config is not None and "_name_or_path" in model_config:
             card.data["base_model"] = model_config["_name_or_path"]
 

src/peft/tuners/lora/model.py

@@ -447,6 +447,7 @@ def _check_merge_allowed(self):
 
         Currently gptq quantization and replicated layers do not support merging.
         """
+        self._warn_if_tied_embeddings_in_target_modules(self.model)
         if getattr(self.model, "quantization_method", None) == "gptq":
             raise ValueError("Cannot merge LORA layers when the model is gptq quantized")
         if self.peft_config.get("layer_replication"):

src/peft/tuners/tuners_utils.py

@@ -30,7 +30,7 @@
 from transformers.pytorch_utils import Conv1D
 
 from peft.utils import INCLUDE_LINEAR_LAYERS_SHORTHAND
-from peft.utils.constants import DUMMY_TARGET_MODULES
+from peft.utils.constants import DUMMY_TARGET_MODULES, EMBEDDING_LAYER_NAMES
 from peft.utils.peft_types import PeftType
 
 from ..config import PeftConfig
@@ -108,7 +108,6 @@ def onload_layer(layer):
             offload_state_dict(safetensors_filename, layer.base_layer._hf_hook.weights_map)
         layer.base_layer._hf_hook.post_forward(layer.base_layer, torch.tensor([]))
 
-
 class BaseTuner(nn.Module, ABC):
     r"""
     A base tuner model that provides the common methods and attributes for all tuners that are injectable into a
@@ -387,9 +386,7 @@ def inject_adapter(self, model: nn.Module, adapter_name: str, autocast_adapter_d
         _check_for_modules_to_save = getattr(peft_config, "modules_to_save", None) is not None
         _has_modules_to_save = False
 
-        model_config = getattr(model, "config", {"model_type": "custom"})
-        if hasattr(model_config, "to_dict"):
-            model_config = model_config.to_dict()
+        model_config = self.get_model_config(model)
 
         peft_config = self._prepare_adapter_config(peft_config, model_config)
 
@@ -430,6 +427,8 @@ def inject_adapter(self, model: nn.Module, adapter_name: str, autocast_adapter_d
             parent, target, target_name = _get_submodules(model, key)
             self._create_and_replace(peft_config, adapter_name, target, target_name, parent, current_key=key)
 
+        self._warn_if_tied_embeddings_in_target_modules(model=model)
+
         # Handle X-LoRA case.
         if not is_target_modules_in_base_model and hasattr(peft_config, "target_modules"):
             raise ValueError(
@@ -493,6 +492,25 @@ def _unloading_checks(self, adapter_names: Optional[list[str]]):
         )
         if is_modules_to_save_available and len(adapters_to_consider) > 1:
             raise ValueError("Cannot unload multiple adapters that specify `modules_to_save`.")
+
+    @staticmethod
+    def get_model_config(model, default={"model_type": "custom"}):
+        model_config = getattr(model, "config", default)
+        if hasattr(model_config, "to_dict"):
+            model_config = model_config.to_dict()
+        return model_config
+
+
+    def _warn_if_tied_embeddings_in_target_modules(self, model):
+            model_config = self.get_model_config(model)
+            if model_config.get("tie_word_embeddings"):
+                for target_module in self.targeted_module_names:
+                    if target_module in EMBEDDING_LAYER_NAMES:
+                        warnings.warn(
+                            f"{model_config.get('tie_word_embeddings')=} and a tied {target_module=} is passed to peft config.\n"
+                            "This can lead to complications, for example when merging the adapter.\n"
+                            f"Are you sure you want to use the target module {target_module}?"
+                        )
 
 
 class BaseTunerLayer(ABC):

src/peft/tuners/vera/model.py

@@ -107,9 +107,7 @@ def _find_dim(self, config) -> tuple[int, int]:
 
         This will be used for determining the size of the shared vera_A and vera_B matrices.
         """
-        model_config = getattr(self.model, "config", {"model_type": "custom"})
-        if hasattr(model_config, "to_dict"):
-            model_config = model_config.to_dict()
+        model_config = self.get_model_config(self.model)
 
         peft_config = self._prepare_adapter_config(config, model_config)
         peft_config = _maybe_include_all_linear_layers(peft_config, self.model)

src/peft/utils/__init__.py

@@ -52,4 +52,3 @@
     cast_mixed_precision_params,
 )
 from .save_and_load import get_peft_model_state_dict, set_peft_model_state_dict, load_peft_weights
-from .constants import EMBEDDING_LAYER_NAMES