change self.gpt2 -> self.model

lm_eval/models/gpt2.py

@@ -39,8 +39,8 @@ class HFLM(BaseLM):
 
         # Initialize model
         if isinstance(pretrained, transformers.PreTrainedModel):
-            self.gpt2 = pretrained
-            self._device = self.gpt2.device
+            self.model = pretrained
+            self._device = self.model.device
 
             if tokenizer:
                 assert isinstance(
@@ -53,7 +53,7 @@ class HFLM(BaseLM):
                 self.tokenizer = tokenizer
             else:
                 # Get tokenizer
-                model_name = self.gpt2.name_or_path
+                model_name = self.model.name_or_path
                 self.tokenizer = transformers.AutoTokenizer.from_pretrained(
                         model_name,
                         revision=revision,
@@ -81,7 +81,7 @@ class HFLM(BaseLM):
             revision = revision + ("/" + subfolder if subfolder is not None else "")
 
             # Initialize new model and tokenizer instances
-            self.gpt2 = transformers.AutoModelForCausalLM.from_pretrained(
+            self.model = transformers.AutoModelForCausalLM.from_pretrained(
                     pretrained,
                     load_in_8bit=load_in_8bit,
                     low_cpu_mem_usage=low_cpu_mem_usage,
@@ -98,7 +98,7 @@ class HFLM(BaseLM):
         else:
             raise TypeError('Parameter pretrained should be of type str or transformers.PreTrainedModel')
 
-        self.gpt2.eval()
+        self.model.eval()
 
         self.vocab_size = self.tokenizer.vocab_size
 
@@ -134,8 +134,8 @@ class HFLM(BaseLM):
             return self._max_length
         seqlen_config_attrs = ("n_positions", "max_position_embeddings", "n_ctx")
         for attr in seqlen_config_attrs:
-            if hasattr(self.gpt2.config, attr):
-                return getattr(self.gpt2.config, attr)
+            if hasattr(self.model.config, attr):
+                return getattr(self.model.config, attr)
         if hasattr(self.tokenizer, "model_max_length"):
             if self.tokenizer.model_max_length == 1000000000000000019884624838656:
                 return self._DEFAULT_MAX_LENGTH
@@ -172,14 +172,14 @@ class HFLM(BaseLM):
         logits returned from the model
         """
         with torch.no_grad():
-            return self.gpt2(inps)[0]
+            return self.model(inps)[0]
 
     def _model_generate(self, context, max_length, eos_token_id):
         generation_kwargs = {"do_sample": False, "max_length": max_length}
         if eos_token_id is not None:
             generation_kwargs['eos_token_id'] = eos_token_id
             generation_kwargs['pad_token_id'] = eos_token_id # setting eos_token_id as pad token
-        return self.gpt2.generate(context, **generation_kwargs)
+        return self.model.generate(context, **generation_kwargs)
 
 
 # for backwards compatibility