Remove max_prompt_length from experimental ORPO

trl/experimental/orpo/orpo_config.py

@@ -37,8 +37,6 @@ class ORPOConfig(TrainingArguments):
         max_length (`int` or `None`, *optional*, defaults to `1024`):
             Maximum length of the sequences (prompt + completion) in the batch. This argument is required if you want
             to use the default data collator.
-        max_prompt_length (`int` or `None`, *optional*, defaults to `512`):
-            Maximum length of the prompt. This argument is required if you want to use the default data collator.
         max_completion_length (`int`, *optional*):
             Maximum length of the completion. This argument is required if you want to use the default data collator
             and your model is an encoder-decoder.
@@ -108,13 +106,6 @@ class ORPOConfig(TrainingArguments):
         default=1024,
         metadata={"help": "Maximum length of the sequences (prompt + completion) in the batch."},
     )
-    max_prompt_length: int | None = field(
-        default=512,
-        metadata={
-            "help": "Maximum length of the prompt. This argument is required if you want to use the default data "
-            "collator and your model is an encoder-decoder."
-        },
-    )
     max_completion_length: int | None = field(
         default=None,
         metadata={

trl/experimental/orpo/orpo_trainer.py

@@ -261,14 +261,6 @@ def make_inputs_require_grad(module, input, output):
             max_length = 512
         else:
             max_length = args.max_length
-        if args.max_prompt_length is None:
-            logger.warning(
-                "`max_prompt_length` is not set in the ORPOConfig's init"
-                " it will default to `128` by default, but you should do it yourself in the future.",
-            )
-            max_prompt_length = 128
-        else:
-            max_prompt_length = args.max_prompt_length
 
         if args.max_completion_length is None and self.is_encoder_decoder:
             logger.warning(
@@ -304,7 +296,6 @@ def make_inputs_require_grad(module, input, output):
         self.max_length = max_length
         self.generate_during_eval = args.generate_during_eval
         self.padding_value = args.padding_value if args.padding_value is not None else processing_class.pad_token_id
-        self.max_prompt_length = max_prompt_length
         self.truncation_mode = args.truncation_mode
         self.processing_class = processing_class
 
@@ -492,23 +483,11 @@ def tokenize_row(self, feature, model: PreTrainedModel | nn.Module | None = None
 
             longer_response_length = max(len(chosen_tokens["input_ids"]), len(rejected_tokens["input_ids"]))
 
-            # if combined sequence is too long, truncate the prompt
-            for answer_tokens in [chosen_tokens, rejected_tokens, prompt_tokens]:
-                if len(answer_tokens["prompt_input_ids"]) + longer_response_length > self.max_length:
-                    if self.truncation_mode == "keep_start":
-                        for k in ["prompt_input_ids", "prompt_attention_mask"]:
-                            answer_tokens[k] = answer_tokens[k][: self.max_prompt_length]
-                    elif self.truncation_mode == "keep_end":
-                        for k in ["prompt_input_ids", "prompt_attention_mask"]:
-                            answer_tokens[k] = answer_tokens[k][-self.max_prompt_length :]
-                    else:
-                        raise ValueError(f"Unknown truncation mode: {self.truncation_mode}")
-
-            # if that's still too long, truncate the response
+            # if combined sequence is too long, truncate the response
             for answer_tokens in [chosen_tokens, rejected_tokens]:
                 if len(answer_tokens["prompt_input_ids"]) + longer_response_length > self.max_length:
                     for k in ["input_ids", "attention_mask"]:
-                        answer_tokens[k] = answer_tokens[k][: self.max_length - self.max_prompt_length]
+                        answer_tokens[k] = answer_tokens[k][: self.max_length - longer_response_length]
 
             # Create labels
             chosen_sequence_tokens = {
@@ -543,9 +522,7 @@ def tokenize_row(self, feature, model: PreTrainedModel | nn.Module | None = None
             rejected_tokens = self.processing_class(
                 rejected, truncation=True, max_length=self.max_completion_length, add_special_tokens=True
             )
-            prompt_tokens = self.processing_class(
-                prompt, truncation=True, max_length=self.max_prompt_length, add_special_tokens=True
-            )
+            prompt_tokens = self.processing_class(prompt, add_special_tokens=True)
 
             batch["chosen_labels"] = chosen_tokens["input_ids"]
             batch["rejected_labels"] = rejected_tokens["input_ids"]