add error handling for moving model to device

lm_eval/models/gpt2.py

@@ -65,9 +65,12 @@ class HFLM(BaseLM):
             revision=revision,
             torch_dtype=_get_dtype(dtype),
             trust_remote_code=trust_remote_code,
-        ).to(self.device)
-        self.gpt2.eval()
-
+        ).eval()
+        if not load_in_8bit:
+            try:
+                self.gpt2.to(self.device)
+            except:
+                print("Failed to place model onto specified device. This may be because the model is quantized via `bitsandbytes`. If the desired GPU is being used, this message is safe to ignore.")
         self.tokenizer = transformers.AutoTokenizer.from_pretrained(
             pretrained if tokenizer is None else tokenizer,
             revision=revision,

lm_eval/models/huggingface.py

@@ -235,8 +235,11 @@ class HuggingFaceAutoLM(BaseLM):
             # the user specified one so we force `self._device` to be the same as
             # `lm_head`'s.
             self._device = self.model.hf_device_map["lm_head"]
-        if not use_accelerate:
+        if not use_accelerate and not (load_in_4bit or load_in_8bit):
+            try:
                 self.model.to(self._device)
+            except:
+                print("Failed to place model onto specified device. This may be because the model is quantized via `bitsandbytes`. If the desired GPU is being used, this message is safe to ignore.")
 
     def _create_auto_model(
         self,