✂️ Reintroduce truncation_mode in DPOTrainer

trl/trainer/dpo_config.py

@@ -71,14 +71,15 @@ class DPOConfig(TrainingArguments):
             Padding value to use. If `None`, the padding value of the tokenizer is used.
         label_pad_token_id (`int`, *optional*, defaults to `-100`):
             Padding value to use for labels.
-        truncation_mode (`str`, *optional*, defaults to `"keep_end"`):
-            Truncation mode to usewhen the prompt is too long, either `keep_end` or `keep_start`.
         max_prompt_length (`int` or `None`, *optional*, defaults to `512`):
             Maximum length of the prompt.
         max_completion_length (`int` or `None`, *optional*, defaults to `None`):
             Maximum length of the completion.
         max_length (`int` or `None`, *optional*, defaults to `1024`):
             Maximum length of the full sequence (prompt + completion).
+        truncation_mode (`str`, *optional*, defaults to `"keep_end"`):
+            Truncation mode to use when the sequence exceeds `max_length`. Possible values are `"keep_end"` and
+            `"keep_start"`.
         padding_free (`bool`, *optional*, defaults to `False`):
             Whether forward passes are performed without padding by flattening all sequences in the batch
             into a single continuous sequence. This approach requires associating a `position_ids` vector to track
@@ -219,13 +220,6 @@ class DPOConfig(TrainingArguments):
         default=-100,
         metadata={"help": "Padding value to use for labels."},
     )
-    truncation_mode: str = field(
-        default="keep_end",
-        metadata={
-            "help": "Truncation mode to use when the prompt is too long.",
-            "choices": ["keep_end", "keep_start"],
-        },
-    )
     max_prompt_length: Optional[int] = field(
         default=512,
         metadata={"help": "Maximum length of the prompt."},
@@ -238,6 +232,14 @@ class DPOConfig(TrainingArguments):
         default=1024,
         metadata={"help": "Maximum length of the full sequence (prompt + completion)."},
     )
+    truncation_mode: str = field(
+        default="keep_end",
+        metadata={
+            "help": "Truncation mode to use when the sequence exceeds `max_length`. Possible values are `'keep_end'` "
+            "and `'keep_start'`.",
+            "choices": ["keep_end", "keep_start"],
+        },
+    )
     padding_free: bool = field(
         default=False,
         metadata={

trl/trainer/dpo_trainer.py

@@ -388,12 +388,12 @@ def make_inputs_require_grad(module, input, output):
             if self.ref_model is not None:
                 disable_dropout_in_model(self.ref_model)
 
-        self.max_length = args.max_length
         self.generate_during_eval = args.generate_during_eval
         self.label_pad_token_id = args.label_pad_token_id
         self.max_prompt_length = args.max_prompt_length
-        self.truncation_mode = args.truncation_mode
         self.max_completion_length = args.max_completion_length
+        self.max_length = args.max_length
+        self.truncation_mode = args.truncation_mode
         self.precompute_ref_log_probs = args.precompute_ref_log_probs
         self.use_num_logits_to_keep = args.use_num_logits_to_keep
 
@@ -595,7 +595,9 @@ def tokenize_row(features, processing_class, max_prompt_length, max_completion_l
         >>> from transformers import GPT2Tokenizer
         >>> tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
         >>> features = {"prompt": "The sky is", "chosen": " blue", "rejected": " green"}
-        >>> DPOTrainer.tokenize_row(features, tokenizer, max_prompt_length=3, max_completion_length=3, add_special_tokens=False)
+        >>> DPOTrainer.tokenize_row(
+        ...     features, tokenizer, max_prompt_length=3, max_completion_length=3, add_special_tokens=False
+        ... )
         {'prompt_input_ids': [464, 6766, 318], 'chosen_input_ids': [4171, 50256], 'rejected_input_ids': [4077, 50256]}
         ```
         """
@@ -1145,10 +1147,20 @@ def concatenated_forward(self, model: nn.Module, batch: dict[str, Union[list, to
             attention_mask, input_ids, loss_mask = flush_left(attention_mask, input_ids, loss_mask)
 
             # Truncate right
-            if self.args.max_length is not None:
-                input_ids = input_ids[:, : self.args.max_length]
-                attention_mask = attention_mask[:, : self.args.max_length]
-                loss_mask = loss_mask[:, : self.args.max_length]
+            if self.max_length is not None:
+                if self.truncation_mode == "keep_end":
+                    input_ids = input_ids[:, -self.max_length :]
+                    attention_mask = attention_mask[:, -self.max_length :]
+                    loss_mask = loss_mask[:, -self.max_length :]
+                elif self.truncation_mode == "keep_start":
+                    input_ids = input_ids[:, : self.max_length]
+                    attention_mask = attention_mask[:, : self.max_length]
+                    loss_mask = loss_mask[:, : self.max_length]
+                else:
+                    raise ValueError(
+                        f"Unknown truncation mode: '{self.truncation_mode}'. Should be one of ['keep_end', "
+                        "'keep_start']."
+                    )
 
             if self.use_num_logits_to_keep:
                 # Compute num_logits_to_keep based on loss_mask pattern: