follow #5115

examples/train_full/llama3_full_predict.yaml

@@ -7,7 +7,7 @@ do_predict: true
 finetuning_type: full
 
 ### dataset
-eval_dataset: alpaca_en_demo
+eval_dataset: identity,alpaca_en_demo
 template: llama3
 cutoff_len: 1024
 max_samples: 50

src/llamafactory/data/loader.py

@@ -206,8 +206,6 @@ def get_dataset(
     template = get_template_and_fix_tokenizer(tokenizer, data_args.template, data_args.tool_format)
     if data_args.train_on_prompt and template.efficient_eos:
         raise ValueError("Current template does not support `train_on_prompt`.")
-    if stage!="sft" and data_args.mask_history:
-        raise ValueError("`Train on the last turn only` is only valid for sft training.")
 
     # Load tokenized dataset
     if data_args.tokenized_path is not None:

src/llamafactory/data/processors/supervised.py

@@ -53,8 +53,11 @@ def _encode_supervised_example(
         input_ids += [image_token_id] * getattr(processor, "image_seq_length")
         labels += [IGNORE_INDEX] * getattr(processor, "image_seq_length")
 
-    encoded_pairs = template.encode_multiturn(tokenizer, messages, system, tools, mask_history)
+    encoded_pairs = template.encode_multiturn(tokenizer, messages, system, tools)
     total_length = 1 if template.efficient_eos else 0
+    if mask_history:
+        encoded_pairs = encoded_pairs[::-1]  # high priority for last turns
+
     for turn_idx, (source_ids, target_ids) in enumerate(encoded_pairs):
         if total_length >= cutoff_len:
             break
@@ -66,17 +69,20 @@ def _encode_supervised_example(
 
         if train_on_prompt:
             source_label = source_ids
-        elif turn_idx != 0 and template.efficient_eos:
+        elif template.efficient_eos:
             source_label = [tokenizer.eos_token_id] + [IGNORE_INDEX] * (source_len - 1)
         else:
             source_label = [IGNORE_INDEX] * source_len
 
-        if mask_history:
+        if mask_history and turn_idx != 0:  # train on the last turn only
-            target_label = target_ids if turn_idx==0 else [IGNORE_INDEX] * target_len
+            target_label = [IGNORE_INDEX] * target_len
+        else:
+            target_label = target_ids
+
+        if mask_history:  # reversed sequences
             input_ids = source_ids + target_ids + input_ids
             labels = source_label + target_label + labels
         else:
-            target_label = target_ids
             input_ids += source_ids + target_ids
             labels += source_label + target_label
 

src/llamafactory/data/template.py

@@ -69,16 +69,12 @@ class Template:
         messages: Sequence[Dict[str, str]],
         system: Optional[str] = None,
         tools: Optional[str] = None,
-        mask_history: bool = False,
     ) -> List[Tuple[List[int], List[int]]]:
         r"""
         Returns multiple pairs of token ids representing prompts and responses respectively.
         """
         encoded_messages = self._encode(tokenizer, messages, system, tools)
-        if not mask_history:
+        return [(encoded_messages[i], encoded_messages[i + 1]) for i in range(0, len(encoded_messages), 2)]
-            return [(encoded_messages[i], encoded_messages[i + 1]) for i in range(0, len(encoded_messages), 2)]
-        else:
-            return [(encoded_messages[i], encoded_messages[i + 1]) for i in range(len(encoded_messages)-2, -1, -2)]
 
     def extract_tool(self, content: str) -> Union[str, List[Tuple[str, str]]]:
         r"""
@@ -594,10 +590,10 @@ _register_template(
     format_separator=EmptyFormatter(slots=["\n"]),
     format_prefix=EmptyFormatter(slots=[{"bos_token"}]),
     default_system=(
-        "You are an AI programming assistant, utilizing the Deepseek Coder model, "
+        "You are an AI programming assistant, utilizing the DeepSeek Coder model, "
-        "developed by Deepseek Company, and you only answer questions related to computer science. "
+        "developed by DeepSeek Company, and you only answer questions related to computer science. "
         "For politically sensitive questions, security and privacy issues, "
-        "and other non-computer science questions, you will refuse to answer\n"
+        "and other non-computer science questions, you will refuse to answer.\n"
     ),
 )
 

src/llamafactory/hparams/data_args.py

@@ -143,4 +143,4 @@ class DataArguments:
             raise ValueError("`max_samples` is incompatible with `streaming`.")
 
         if self.mask_history and self.train_on_prompt:
-            raise ValueError("`Train on the last turn only` does not support `train_on_prompt`.")
+            raise ValueError("`mask_history` is incompatible with `train_on_prompt`.")

src/llamafactory/hparams/parser.py

@@ -163,11 +163,15 @@ def get_train_args(args: Optional[Dict[str, Any]] = None) -> _TRAIN_CLS:
     if finetuning_args.stage != "pt" and data_args.template is None:
         raise ValueError("Please specify which `template` to use.")
 
-    if finetuning_args.stage != "sft" and training_args.predict_with_generate:
+    if finetuning_args.stage != "sft":
-        raise ValueError("`predict_with_generate` cannot be set as True except SFT.")
+        if training_args.predict_with_generate:
+            raise ValueError("`predict_with_generate` cannot be set as True except SFT.")
 
-    if finetuning_args.stage != "sft" and data_args.neat_packing:
+        if data_args.neat_packing:
-        raise ValueError("`neat_packing` cannot be set as True except SFT.")
+            raise ValueError("`neat_packing` cannot be set as True except SFT.")
+
+        if data_args.train_on_prompt or data_args.mask_history:
+            raise ValueError("`train_on_prompt` or `mask_history` cannot be set as True except SFT.")
 
     if finetuning_args.stage == "sft" and training_args.do_predict and not training_args.predict_with_generate:
         raise ValueError("Please enable `predict_with_generate` to save model predictions.")
@@ -175,21 +179,18 @@ def get_train_args(args: Optional[Dict[str, Any]] = None) -> _TRAIN_CLS:
     if finetuning_args.stage in ["rm", "ppo"] and training_args.load_best_model_at_end:
         raise ValueError("RM and PPO stages do not support `load_best_model_at_end`.")
 
-    if finetuning_args.stage == "ppo" and not training_args.do_train:
+    if finetuning_args.stage == "ppo":
-        raise ValueError("PPO training does not support evaluation, use the SFT stage to evaluate models.")
+        if not training_args.do_train:
+            raise ValueError("PPO training does not support evaluation, use the SFT stage to evaluate models.")
 
-    if finetuning_args.stage == "ppo" and model_args.shift_attn:
+        if model_args.shift_attn:
-        raise ValueError("PPO training is incompatible with S^2-Attn.")
+            raise ValueError("PPO training is incompatible with S^2-Attn.")
 
-    if finetuning_args.stage == "ppo" and finetuning_args.reward_model_type == "lora" and model_args.use_unsloth:
+        if finetuning_args.reward_model_type == "lora" and model_args.use_unsloth:
-        raise ValueError("Unsloth does not support lora reward model.")
+            raise ValueError("Unsloth does not support lora reward model.")
 
-    if (
+        if training_args.report_to and training_args.report_to[0] not in ["wandb", "tensorboard"]:
-        finetuning_args.stage == "ppo"
+            raise ValueError("PPO only accepts wandb or tensorboard logger.")
-        and training_args.report_to
-        and training_args.report_to[0] not in ["wandb", "tensorboard"]
-    ):
-        raise ValueError("PPO only accepts wandb or tensorboard logger.")
 
     if training_args.parallel_mode == ParallelMode.NOT_DISTRIBUTED:
         raise ValueError("Please launch distributed training with `llamafactory-cli` or `torchrun`.")