Fix bug to load model to CPU when choosen by user.

lm_eval/models/huggingface.py

@@ -170,6 +170,7 @@ def __init__(
                 if device and device in device_list:
                     self._device = torch.device(device)
                     eval_logger.info(f"Using device '{device}'")
+                    print(f"Using device '{device}'")
                     if device in ("mps", "mps:0") and version.parse(
                         torch.__version__
                     ) < version.parse("2.1"):
@@ -239,14 +240,15 @@ def __init__(
             self.model.eval()
             self.model.tie_weights()
 
-        if isinstance(pretrained, str) and (gpus >= 1 or str(self.device) == "mps"):
+        if isinstance(pretrained, str) and (gpus >= 1 or str(self.device) in ["mps", "cpu"]):
             # TODO: can remove this whole snippet except in the mps case, perhaps?
-            if not (parallelize or autogptq or hasattr(self, "accelerator")):
+            if not (parallelize or autogptq):
                 # place model onto device requested manually,
                 # if not using HF Accelerate or device_map
                 # or any other option that preloads model onto device
                 try:
                     self.model.to(self.device)
+                    print(f"Model placed onto device '{self.device}'")
                 except ValueError:
                     eval_logger.debug(
                         "Failed to place model onto specified device. This may be because the model is quantized via `bitsandbytes` or `device_map` is provided. If the desired GPU is being used, this message is safe to ignore."

tests/models/test_huggingface.py

@@ -100,7 +100,7 @@ class Test_HFLM:
         -45969.47155761719,
         -7158.90625,
     ]
-    LM = HFLM(pretrained="EleutherAI/pythia-70m", device="cpu", dtype="bfloat16")
+    LM = HFLM(pretrained="EleutherAI/pythia-70m", device="cpu", dtype="float32")
 
     def test_logliklihood(self) -> None:
         res = self.LM.loglikelihood(self.MULTIPLE_CH)