remove unused code

src/utils/ppo.py

@@ -214,51 +214,6 @@ class PPOPeftTrainer(PPOTrainer, PeftTrainer):
             return response[:, inputs["input_ids"].size(1):]
         return response
 
-    @PPODecorators.empty_cuda_cache()
-    def batched_forward_pass(
-        self,
-        model: AutoModelForCausalLMWithValueHead,
-        queries: torch.Tensor,
-        responses: torch.Tensor,
-        model_inputs: dict,
-    ):
-        r"""
-        Calculates model outputs in multiple batches.
-
-        Subclass and override to inject custom behavior.
-        """
-        bs = len(model_inputs["input_ids"])
-        fbs = self.config.mini_batch_size
-        all_logprobs = []
-        all_logits = []
-        all_masks = []
-        all_values = []
-
-        for i in range(int(bs / fbs)):
-            input_kwargs = {k: v[i * fbs : (i + 1) * fbs] for k, v in model_inputs.items()}
-            input_ids: torch.Tensor = input_kwargs["input_ids"] # left-padded sequences
-            logits, _, values = model(**input_kwargs)
-            logprobs = logprobs_from_logits(logits[:, :-1, :], input_ids[:, 1:])
-
-            masks = torch.zeros_like(input_ids)
-            for j in range(fbs):
-                start = (input_ids[j] == self.tokenizer.bos_token_id).nonzero()[0].item()
-                masks[j][start:] = 1
-                if len(masks[j][start:]) < 2:
-                    raise ValueError("Responses are too short. Make sure they are at least 4 tokens long.")
-
-            all_logits.append(logits)
-            all_values.append(values)
-            all_logprobs.append(logprobs)
-            all_masks.append(masks)
-
-        return (
-            torch.cat(all_logprobs),
-            torch.cat(all_logits)[:, :-1],
-            torch.cat(all_values)[:, :-1],
-            torch.cat(all_masks)[:, :-1],
-        )
-
     def save_model(self, output_dir: Optional[str] = None) -> None:
         r"""
         Saves model checkpoint.