getting rid to forward call linking

src/peft/mapping.py

@@ -21,7 +21,7 @@ from .peft_model import (
     PeftModelForTokenClassification,
 )
 from .tuners import LoraConfig, PrefixTuningConfig, PromptEncoderConfig, PromptTuningConfig
-from .utils import PromptLearningConfig
+from .utils import PeftType, PromptLearningConfig
 
 
 MODEL_TYPE_TO_PEFT_MODEL_MAPPING = {
@@ -135,8 +135,11 @@ def get_peft_model(model, peft_config):
 
     model_config = model.config.to_dict()
     peft_config.base_model_name_or_path = model.__dict__.get("name_or_path", None)
-    if not isinstance(peft_config, PromptLearningConfig):
+    if peft_config.task_type not in MODEL_TYPE_TO_PEFT_MODEL_MAPPING.keys():
         peft_config = _prepare_lora_config(peft_config, model_config)
         return PeftModel(model, peft_config)
-    peft_config = _prepare_prompt_learning_config(peft_config, model_config)
+    if not isinstance(peft_config, PromptLearningConfig):
+        peft_config = _prepare_lora_config(peft_config, model_config)
+    else:
+        peft_config = _prepare_prompt_learning_config(peft_config, model_config)
     return MODEL_TYPE_TO_PEFT_MODEL_MAPPING[peft_config.task_type](model, peft_config)

src/peft/peft_model.py

@@ -76,7 +76,6 @@ class PeftModel(PushToHubMixin, torch.nn.Module):
         if getattr(self.peft_config, "modules_to_save", None) is not None:
             self.modules_to_save = self.peft_config.modules_to_save
             _set_trainable(self)
-        self.forward = self.base_model.forward
         self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
     def save_pretrained(self, save_directory, **kwargs):
@@ -246,6 +245,8 @@ class PeftModel(PushToHubMixin, torch.nn.Module):
 
     def __getattr__(self, name: str):
         """Forward missing attributes to the wrapped module."""
+        if name == "forward":
+            return getattr(self.base_model, name)
         try:
             return super().__getattr__(name)  # defer to nn.Module's logic
         except AttributeError:

src/peft/tuners/lora.py

@@ -115,7 +115,6 @@ class LoraModel(torch.nn.Module):
         self.model = model
         self._find_and_replace()
         mark_only_lora_as_trainable(self.model, self.peft_config.bias)
-        self.forward = self.model.forward
 
     def _find_and_replace(self):
         kwargs = {
@@ -174,6 +173,8 @@ class LoraModel(torch.nn.Module):
 
     def __getattr__(self, name: str):
         """Forward missing attributes to the wrapped module."""
+        if name == "forward":
+            return getattr(self.model, name)
         try:
             return super().__getattr__(name)  # defer to nn.Module's logic
         except AttributeError: