simplify attempt

composer/models/huggingface.py

@@ -26,9 +26,9 @@
 
 try:
     from peft import PeftModel, get_peft_model
-    _peft_installed = True
+    peft_installed = True
 except:
-    _peft_installed = False
+    peft_installed = False
 
 if TYPE_CHECKING:
     import transformers
@@ -56,7 +56,9 @@ class HuggingFaceModel(ComposerModel):
         eval_metrics (list[Metric], optional): list of torchmetrics to compute on the eval_dataloader, or be accessible to :class:`Evaluator`s. Default: ``None``.
         shift_labels (bool, optional): If True, the batch's labels will be shifted before being used to calculate metrics. This should be set to true for CausalLM models and false otherwise. If not specified, `shift_labels` will be set automatically based on the model class name. Default: ``None``.
         allow_embedding_resizing (bool, optional): If True, the model's embeddings will be automatically resized when they are smaller than the tokenizer vocab size. Default: ``False``.
-
+        peft_config (PeftConfig, optional): Optional PEFT config to apply to the model. If provided, the model will be converted to a PEFT model. Only LoRA is currently supported.
+        peft_filter_state_dict_trainable (bool, optional): If True _and_ PEFT is active, the state dict will only contain the PEFT weights, not the frozen base model weights.
+        
         .. note:: To ensure correct behavior, set `shift_labels` manually if using a custom model (i.e., if `model` is not
         an instance of a registered 🤗 Transformers class).
     .. warning:: This wrapper is designed to work with 🤗 datasets that define a `labels` column.
@@ -95,16 +97,12 @@ def __init__(self,
         super().__init__()
         self.model = model
         self.config: PretrainedConfig = model.config
-        self.model_forward_args = inspect.getfullargspec(self.model.forward).args
 
-        if _peft_installed and self.model_forward_args == ['self']:
+        self.model_forward_args = inspect.signature(self.model.forward).parameters.keys()
+        if peft_installed:
             from peft import PeftModel
             if isinstance(self.model, PeftModel):
-                self.model_forward_args = inspect.getfullargspec(self.model.base_model.model.forward).args
-
-        # inspect.getfullargspec HuggingFace quantized model could not return args correctly
-        if not self.model_forward_args:
-            self.model_forward_args = inspect.signature(self.model.forward).parameters.keys()
+                self.model_forward_args = inspect.signature(self.model.base_model.model.forward).parameters.keys()
 
         if not self.model_forward_args:
             raise ValueError('Could not determine the forward arguments of the model. Please open a GitHub issue.')
@@ -113,7 +111,7 @@ def __init__(self,
 
         self.peft_filter_state_dict_trainable = peft_filter_state_dict_trainable
         if peft_config is not None:
-            if not _peft_installed:
+            if not peft_installed:
                 raise MissingConditionalImportError(extra_deps_group='peft',
                                                     conda_package='peft',
                                                     conda_channel='conda-forge')
@@ -193,7 +191,7 @@ def __init__(self,
                 log.info(f'PEFT model created. {self.model}')
 
         self.model_is_peft = False
-        if _peft_installed:
+        if peft_installed:
             from peft import PeftModel
             self.using_peft = isinstance(self.model, PeftModel)
 
@@ -576,7 +574,7 @@ def get_metadata(self):
             }
 
             # Also save PEFT config if the model is a peft model
-            if _peft_installed:
+            if peft_installed:
                 from peft import PeftModel
                 if isinstance(self.model, PeftModel):
                     active_adapter = self.model.active_adapter
@@ -678,7 +676,7 @@ def _is_registered_causal_lm(model: Union[transformers.PreTrainedModel, 'PeftMod
                                             conda_package='transformers',
                                             conda_channel='conda-forge') from e
 
-    if _peft_installed and isinstance(model, PeftModel):
+    if peft_installed and isinstance(model, PeftModel):
         model_to_check = model.base_model.model
     else:
         model_to_check = model