Fix

optimum/habana/transformers/trainer.py

@@ -1584,8 +1584,8 @@ def training_step(self, model: torch.nn.Module, inputs: Dict[str, Union[torch.Te
             `torch.Tensor`: The tensor with training loss on this batch.
         """
         model.train()
-        if hasattr(self.optimizer, "train") and callable(self.optimizer.train):
-            self.optimizer.train()
+        # if hasattr(self.optimizer, "train") and callable(self.optimizer.train):
+        #     self.optimizer.train()
 
         inputs = self._prepare_inputs(inputs)
 
@@ -1824,8 +1824,8 @@ def evaluation_loop(
                 self.deepspeed = self.model_wrapped
 
         model.eval()
-        if hasattr(self.optimizer, "eval") and callable(self.optimizer.eval):
-            self.optimizer.eval()
+        # if hasattr(self.optimizer, "eval") and callable(self.optimizer.eval):
+        #     self.optimizer.eval()
 
         # Do not use HPU graphs if the training is ongoing because it detaches gradients
         if args.use_hpu_graphs_for_inference and not self.is_in_train:
@@ -2233,8 +2233,8 @@ def prediction_loop(
             if self.is_deepspeed_enabled:
                 self.deepspeed = self.model_wrapped
         model.eval()
-        if hasattr(self.optimizer, "eval") and callable(self.optimizer.eval):
-            self.optimizer.eval()
+        # if hasattr(self.optimizer, "eval") and callable(self.optimizer.eval):
+        #     self.optimizer.eval()
 
         # Do not use HPU graphs if the training is ongoing because it detaches gradients
         if args.use_hpu_graphs_for_inference and not self.is_in_train: