Fix set of model parallel in the Trainer when no GPUs are available (#25239)

src/transformers/trainer.py

@@ -378,14 +378,17 @@ class Trainer:
             devices = [device for device in set(model.hf_device_map.values()) if device not in ["cpu", "disk"]]
             if len(devices) > 1:
                 self.is_model_parallel = True
-            else:
+            elif len(devices) == 1:
                 self.is_model_parallel = self.args.device != torch.device(devices[0])
+            else:
+                self.is_model_parallel = False
 
             # warn users
-            logger.info(
-                "You have loaded a model on multiple GPUs. `is_model_parallel` attribute will be force-set"
-                " to `True` to avoid any unexpected behavior such as device placement mismatching."
-            )
+            if self.is_model_parallel:
+                logger.info(
+                    "You have loaded a model on multiple GPUs. `is_model_parallel` attribute will be force-set"
+                    " to `True` to avoid any unexpected behavior such as device placement mismatching."
+                )
 
         # At this stage the model is already loaded
         if getattr(model, "is_quantized", False):