trying diff approaches

src/peft/mapping.py

@@ -21,7 +21,7 @@ from .peft_model import (
     PeftModelForTokenClassification,
 )
 from .tuners import LoraConfig, PrefixTuningConfig, PromptEncoderConfig, PromptTuningConfig
-from .utils import PeftType, PromptLearningConfig
+from .utils import PromptLearningConfig
 
 
 MODEL_TYPE_TO_PEFT_MODEL_MAPPING = {

src/peft/peft_model.py

@@ -245,13 +245,20 @@ 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:
             return getattr(self.base_model, name)
 
+    def forward(self, *args, **kwargs):
+        """
+        Forward pass of the model.
+        """
+        if isinstance(self.peft_config, PromptLearningConfig):
+            return self.base_model(*args, **kwargs)
+        else:
+            return self.base_model.model(*args, **kwargs)
+
 
 class PeftModelForSequenceClassification(PeftModel):
     """

src/peft/tuners/lora.py

@@ -115,6 +115,7 @@ 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 = {
@@ -173,8 +174,6 @@ 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: