FIX Use model argument consistently (#1198) (#1205)

Some methods were using model and self.model interchangeably. This was
fine, as they were referring to the same object, but is also confusing.
Now model is used consistently.

src/peft/tuners/adaption_prompt/model.py

@@ -57,7 +57,7 @@ def __init__(self, model, configs: Dict, adapter_name: str):
         self._enabled = True
         self.forward = self.model.forward
         self.add_adapter(adapter_name, configs[adapter_name])
-        self._mark_only_adaption_prompts_as_trainable()
+        self._mark_only_adaption_prompts_as_trainable(self.model)
 
     def add_adapter(self, adapter_name: str, config: AdaptionPromptConfig) -> None:
         """Add an adapter with the given name and config."""
@@ -146,9 +146,9 @@ def _remove_adapted_attentions(self, adapter_name: str) -> None:
             setattr(par, config.target_modules, attn.model)
         self._cached_adapters[adapter_name] = adapted_attentions
 
-    def _mark_only_adaption_prompts_as_trainable(self) -> None:
+    def _mark_only_adaption_prompts_as_trainable(self, model: nn.Module) -> None:
         """Freeze all parameters of the model except the adaption prompts."""
-        for n, p in self.model.named_parameters():
+        for n, p in model.named_parameters():
             if not is_adaption_prompt_trainable(n):
                 p.requires_grad = False
 

src/peft/tuners/ia3/model.py

@@ -21,6 +21,7 @@
 from typing import List, Optional
 
 import torch
+from torch import nn
 from transformers.pytorch_utils import Conv1D
 
 from peft.import_utils import is_bnb_4bit_available, is_bnb_available
@@ -147,8 +148,8 @@ def _create_new_module(ia3_config, adapter_name, target, **kwargs):
     def _check_target_module_exists(ia3_config, key):
         return check_target_module_exists(ia3_config, key)
 
-    def _mark_only_adapters_as_trainable(self) -> None:
-        for n, p in self.model.named_parameters():
+    def _mark_only_adapters_as_trainable(self, model: nn.Module) -> None:
+        for n, p in model.named_parameters():
             if self.prefix not in n:
                 p.requires_grad = False
 

src/peft/tuners/lora/model.py

@@ -26,6 +26,7 @@
 from typing import List, Optional
 
 import torch
+from torch import nn
 from tqdm import tqdm
 from transformers.pytorch_utils import Conv1D
 
@@ -221,8 +222,8 @@ def _replace_module(self, parent, child_name, new_module, child):
                 weight = child.qweight if hasattr(child, "qweight") else child.weight
                 module.to(weight.device)
 
-    def _mark_only_adapters_as_trainable(self) -> None:
-        for n, p in self.model.named_parameters():
+    def _mark_only_adapters_as_trainable(self, model: nn.Module) -> None:
+        for n, p in model.named_parameters():
             if self.prefix not in n:
                 p.requires_grad = False
 
@@ -232,11 +233,11 @@ def _mark_only_adapters_as_trainable(self) -> None:
                 continue
 
             if bias == "all":
-                for n, p in self.model.named_parameters():
+                for n, p in model.named_parameters():
                     if "bias" in n:
                         p.requires_grad = True
             elif bias == "lora_only":
-                for m in self.model.modules():
+                for m in model.modules():
                     if isinstance(m, LoraLayer) and hasattr(m, "bias") and m.bias is not None:
                         m.bias.requires_grad = True
             else:

src/peft/tuners/lycoris_utils.py

@@ -272,8 +272,8 @@ def _create_new_module(cls, config: LycorisConfig, adapter_name: str, target: nn
 
         return new_module
 
-    def _mark_only_adapters_as_trainable(self) -> None:
-        for n, p in self.model.named_parameters():
+    def _mark_only_adapters_as_trainable(self, model: nn.Module) -> None:
+        for n, p in model.named_parameters():
             if self.prefix not in n:
                 p.requires_grad = False
 

src/peft/tuners/mixed/model.py

@@ -131,8 +131,8 @@ def _replace_module(self, parent, child_name, new_module, child) -> None:
             if "ranknum" in name:
                 module.to(child.weight.device)
 
-    def _mark_only_adapters_as_trainable(self) -> None:
-        for n, p in self.model.named_parameters():
+    def _mark_only_adapters_as_trainable(self, model: nn.Module) -> None:
+        for n, p in model.named_parameters():
             if not any(prefix in n for prefix in PREFIXES):
                 p.requires_grad = False
 
@@ -142,12 +142,12 @@ def _mark_only_adapters_as_trainable(self) -> None:
                 continue
 
             if bias == "all":
-                for n, p in self.model.named_parameters():
+                for n, p in model.named_parameters():
                     if "bias" in n:
                         p.requires_grad = True
             elif bias == "lora_only":
                 # TODO: check if this is needed for other supported types
-                for m in self.model.modules():
+                for m in model.modules():
                     if isinstance(m, Layers) and hasattr(m, "bias") and m.bias is not None:
                         m.bias.requires_grad = True
             else:

src/peft/tuners/tuners_utils.py

@@ -167,7 +167,7 @@ def _create_and_replace(
         ...
 
     @abstractmethod
-    def _mark_only_adapters_as_trainable(self):
+    def _mark_only_adapters_as_trainable(self, model: nn.Module):
         r"""
         A helper method to mark only the adapter layers as trainable (i.e. module.requires_grad = False) This needs to
         be overriden for all tuner classes to match the correct key names.
@@ -252,10 +252,10 @@ def inject_adapter(self, model: nn.Module, adapter_name: str):
                 f"Please check the target modules and try again."
             )
 
-        self._mark_only_adapters_as_trainable()
+        self._mark_only_adapters_as_trainable(model)
 
         if self.peft_config[adapter_name].inference_mode:
-            for n, p in self.model.named_parameters():
+            for n, p in model.named_parameters():
                 if adapter_name in n:
                     p.requires_grad = False