fix reserved label len

src/llmtuner/data/preprocess.py

@@ -55,7 +55,12 @@ def preprocess_supervised_dataset(
         input_ids, labels = [], []
         for turn_idx, (source_ids, target_ids) in enumerate(
             template.encode_multiturn(
-                tokenizer, messages, examples["system"][i], examples["tools"][i], data_args.cutoff_len
+                tokenizer,
+                messages,
+                examples["system"][i],
+                examples["tools"][i],
+                data_args.cutoff_len,
+                data_args.reserved_label_len,
             )
         ):
             if data_args.train_on_prompt:
@@ -143,7 +148,12 @@ def preprocess_unsupervised_dataset(
             messages = examples["prompt"][i] + [{"role": Role.ASSISTANT, "content": ""}]
 
         input_ids, labels = template.encode_oneturn(
-            tokenizer, messages, examples["system"][i], examples["tools"][i], data_args.cutoff_len
+            tokenizer,
+            messages,
+            examples["system"][i],
+            examples["tools"][i],
+            data_args.cutoff_len,
+            data_args.reserved_label_len,
         )
 
         if template.efficient_eos:
@@ -172,10 +182,20 @@ def preprocess_pairwise_dataset(
         rejected_messages = examples["prompt"][i] + [examples["response"][i][1]]
 
         prompt_ids, chosen_ids = template.encode_oneturn(
-            tokenizer, chosen_messages, examples["system"][i], examples["tools"][i], data_args.cutoff_len
+            tokenizer,
+            chosen_messages,
+            examples["system"][i],
+            examples["tools"][i],
+            data_args.cutoff_len,
+            data_args.reserved_label_len,
         )
         _, rejected_ids = template.encode_oneturn(
-            tokenizer, rejected_messages, examples["system"][i], examples["tools"][i], data_args.cutoff_len
+            tokenizer,
+            rejected_messages,
+            examples["system"][i],
+            examples["tools"][i],
+            data_args.cutoff_len,
+            data_args.reserved_label_len,
         )
 
         if template.efficient_eos:

src/llmtuner/data/template.py

@@ -37,7 +37,7 @@ class Template:
         system: Optional[str] = None,
         tools: Optional[str] = None,
         cutoff_len: Optional[int] = 1_000_000,
-        reserved_label_len: Optional[int] = 16,
+        reserved_label_len: Optional[int] = 1,
     ) -> Tuple[List[int], List[int]]:
         r"""
         Returns a single pair of token ids representing prompt and response respectively.
@@ -57,7 +57,7 @@ class Template:
         system: Optional[str] = None,
         tools: Optional[str] = None,
         cutoff_len: Optional[int] = 1_000_000,
-        reserved_label_len: Optional[int] = 16,
+        reserved_label_len: Optional[int] = 1,
     ) -> Sequence[Tuple[List[int], List[int]]]:
         r"""
         Returns multiple pairs of token ids representing prompts and responses respectively.
@@ -144,10 +144,10 @@ class Template:
                 max_len=(cutoff_len - total_length),
                 reserved_label_len=reserved_label_len,
             )
-            encoded_messages[i] = encoded_messages[i][:max_source_len]
+            source_ids = encoded_messages[i][:max_source_len]
-            encoded_messages[i + 1] = encoded_messages[i + 1][:max_target_len]
+            target_ids = encoded_messages[i + 1][:max_target_len]
-            total_length += len(encoded_messages[i]) + len(encoded_messages[i + 1])
+            total_length += len(source_ids) + len(target_ids)
-            encoded_pairs.append((encoded_messages[i], encoded_messages[i + 1]))
+            encoded_pairs.append((source_ids, target_ids))
 
         return encoded_pairs
 

src/llmtuner/hparams/data_args.py

@@ -21,10 +21,10 @@ class DataArguments:
         default="train", metadata={"help": "Which dataset split to use for training and evaluation."}
     )
     cutoff_len: Optional[int] = field(
-        default=1024, metadata={"help": "The maximum length of the model inputs after tokenization."}
+        default=1024, metadata={"help": "The cutoff length of the model inputs after tokenization."}
     )
     reserved_label_len: Optional[int] = field(
-        default=1, metadata={"help": "The maximum length reserved for label after tokenization."}
+        default=1, metadata={"help": "The minimum cutoff length reserved for label after tokenization."}
     )
     train_on_prompt: Optional[bool] = field(
         default=False, metadata={"help": "Whether to disable the mask on the prompt or not."}
@@ -57,7 +57,7 @@ class DataArguments:
     ignore_pad_token_for_loss: Optional[bool] = field(
         default=True,
         metadata={
-            "help": "Whether to ignore the tokens corresponding to padded labels in the loss computation or not."
+            "help": "Whether or not to ignore the tokens corresponding to padded labels in the loss computation."
         },
     )
     val_size: Optional[float] = field(