follow #4878 fix #4684

src/llamafactory/data/processors/supervised.py

@@ -63,18 +63,19 @@ def _encode_supervised_example(
         total_length += source_len + target_len
 
         if data_args.train_on_prompt:
-            source_mask = source_ids
+            source_label = source_ids
         elif turn_idx != 0 and template.efficient_eos:
-            source_mask = [tokenizer.eos_token_id] + [IGNORE_INDEX] * (source_len - 1)
+            source_label = [tokenizer.eos_token_id] + [IGNORE_INDEX] * (source_len - 1)
         else:
-            source_mask = [IGNORE_INDEX] * source_len
+            source_label = [IGNORE_INDEX] * source_len
 
-        input_ids += source_ids + target_ids
-
-        if data_args.train_last_turn_only and turn_idx != len(encoded_pairs) - 1:
-            labels += source_mask + [IGNORE_INDEX] * len(target_ids)
+        if data_args.mask_history and turn_idx != len(encoded_pairs) - 1:
+            target_label = [IGNORE_INDEX] * target_len
         else:
-            labels += source_mask + target_ids
+            target_label = target_ids
+
+        input_ids += source_ids + target_ids
+        labels += source_label + target_label
 
     if template.efficient_eos:
         input_ids += [tokenizer.eos_token_id]

src/llamafactory/hparams/data_args.py

@@ -41,17 +41,17 @@ class DataArguments:
         default="data",
         metadata={"help": "Path to the folder containing the datasets."},
     )
-    train_last_turn_only: Optional[bool] = field(
-        default=False,
-        metadata={"help": "Whether or not to train the last turn only."},
-    )
     cutoff_len: int = field(
         default=1024,
         metadata={"help": "The cutoff length of the tokenized inputs in the dataset."},
     )
     train_on_prompt: bool = field(
         default=False,
-        metadata={"help": "Whether to disable the mask on the prompt or not."},
+        metadata={"help": "Whether or not to disable the mask on the prompt."},
+    )
+    mask_history: bool = field(
+        default=False,
+        metadata={"help": "Whether or not to mask the history and train on the last turn only."},
     )
     streaming: bool = field(
         default=False,

src/llamafactory/hparams/parser.py

@@ -162,9 +162,6 @@ def get_train_args(args: Optional[Dict[str, Any]] = None) -> _TRAIN_CLS:
     # Check arguments
     if finetuning_args.stage != "pt" and data_args.template is None:
         raise ValueError("Please specify which `template` to use.")
-
-    if finetuning_args.stage == "pt" and data_args.train_last_turn_only:
-        raise ValueError("PT stage does not support `train_last_turn_only`.")
 
     if finetuning_args.stage != "sft" and training_args.predict_with_generate:
         raise ValueError("`predict_with_generate` cannot be set as True except SFT.")

src/llamafactory/webui/components/train.py

@@ -44,11 +44,10 @@ def create_train_tab(engine: "Engine") -> Dict[str, "Component"]:
         )
         dataset_dir = gr.Textbox(value=DEFAULT_DATA_DIR, scale=1)
         dataset = gr.Dropdown(multiselect=True, allow_custom_value=True, scale=4)
-        train_last_turn_only = gr.Checkbox()
         preview_elems = create_preview_box(dataset_dir, dataset)
 
-    input_elems.update({training_stage, dataset_dir, dataset,train_last_turn_only})
-    elem_dict.update(dict(training_stage=training_stage, dataset_dir=dataset_dir, dataset=dataset,train_last_turn_only=train_last_turn_only, **preview_elems))
+    input_elems.update({training_stage, dataset_dir, dataset})
+    elem_dict.update(dict(training_stage=training_stage, dataset_dir=dataset_dir, dataset=dataset, **preview_elems))
 
     with gr.Row():
         learning_rate = gr.Textbox(value="5e-5")
@@ -99,6 +98,10 @@ def create_train_tab(engine: "Engine") -> Dict[str, "Component"]:
                 packing = gr.Checkbox()
                 neat_packing = gr.Checkbox()
 
+            with gr.Column():
+                train_on_prompt = gr.Checkbox()
+                mask_history = gr.Checkbox()
+
             with gr.Column():
                 resize_vocab = gr.Checkbox()
                 use_llama_pro = gr.Checkbox()
@@ -116,6 +119,8 @@ def create_train_tab(engine: "Engine") -> Dict[str, "Component"]:
             optim,
             packing,
             neat_packing,
+            train_on_prompt,
+            mask_history,
             resize_vocab,
             use_llama_pro,
             shift_attn,
@@ -132,6 +137,8 @@ def create_train_tab(engine: "Engine") -> Dict[str, "Component"]:
             optim=optim,
             packing=packing,
             neat_packing=neat_packing,
+            train_on_prompt=train_on_prompt,
+            mask_history=mask_history,
             resize_vocab=resize_vocab,
             use_llama_pro=use_llama_pro,
             shift_attn=shift_attn,

src/llamafactory/webui/interface.py

@@ -32,7 +32,7 @@
     import gradio as gr
 
 
-def create_ui(demo_mode: bool = False) -> gr.Blocks:
+def create_ui(demo_mode: bool = False) -> "gr.Blocks":
     engine = Engine(demo_mode=demo_mode, pure_chat=False)
 
     with gr.Blocks(title="LLaMA Board", css=CSS) as demo:
@@ -67,7 +67,7 @@ def create_ui(demo_mode: bool = False) -> gr.Blocks:
     return demo
 
 
-def create_web_demo() -> gr.Blocks:
+def create_web_demo() -> "gr.Blocks":
     engine = Engine(pure_chat=True)
 
     with gr.Blocks(title="Web Demo", css=CSS) as demo:

src/llamafactory/webui/locales.py

@@ -522,32 +522,46 @@
             "info": "避免打包后的序列产生交叉注意力。",
         },
     },
-    "resize_vocab": {
+    "train_on_prompt": {
         "en": {
-            "label": "Resize token embeddings",
-            "info": "Resize the tokenizer vocab and the embedding layers.",
+            "label": "Train on prompt",
+            "info": "Disable the label mask on the prompt (only for SFT).",
         },
         "ru": {
-            "label": "Изменение размера токенных эмбеддингов",
-            "info": "Изменить размер словаря токенизатора и слоев эмбеддинга.",
+            "label": "Тренировка на подсказке",
+            "info": "Отключить маску меток на подсказке (только для SFT).",
         },
         "zh": {
-            "label": "更改词表大小",
-            "info": "更改分词器词表和嵌入层的大小。",
+            "label": "学习提示词",
+            "info": "不在提示词的部分添加掩码（仅适用于 SFT）。",
         },
     },
-    "train_last_turn_only": {
+    "mask_history": {
         "en": {
-            "label": "Train last turn only",
-            "info": "Train the model with the last turn only in multi turn.",
+            "label": "Mask history",
+            "info": "Train on the last turn only (only for SFT).",
         },
         "ru": {
-            "label": "Обучать только последний поворот",
-            "info": "Обучать модель только последним поворотом в многоповоротном диалоге.",
+            "label": "История масок",
+            "info": "Тренироваться только на последнем шаге (только для SFT).",
         },
         "zh": {
-            "label": "仅最后一轮参与训练",
-            "info": "多轮对话仅使用最后一轮计算loss。",
+            "label": "不学习历史对话",
+            "info": "仅学习最后一轮对话（仅适用于 SFT）。",
+        },
+    },
+    "resize_vocab": {
+        "en": {
+            "label": "Resize token embeddings",
+            "info": "Resize the tokenizer vocab and the embedding layers.",
+        },
+        "ru": {
+            "label": "Изменение размера токенных эмбеддингов",
+            "info": "Изменить размер словаря токенизатора и слоев эмбеддинга.",
+        },
+        "zh": {
+            "label": "更改词表大小",
+            "info": "更改分词器词表和嵌入层的大小。",
         },
     },
     "use_llama_pro": {

src/llamafactory/webui/runner.py

@@ -125,7 +125,6 @@ def _parse_train_args(self, data: Dict["Component", Any]) -> Dict[str, Any]:
             visual_inputs=get("top.visual_inputs"),
             dataset_dir=get("train.dataset_dir"),
             dataset=",".join(get("train.dataset")),
-            train_last_turn_only=get("train.train_last_turn_only"),
             cutoff_len=get("train.cutoff_len"),
             learning_rate=float(get("train.learning_rate")),
             num_train_epochs=float(get("train.num_train_epochs")),
@@ -141,6 +140,8 @@ def _parse_train_args(self, data: Dict["Component", Any]) -> Dict[str, Any]:
             optim=get("train.optim"),
             packing=get("train.packing") or get("train.neat_packing"),
             neat_packing=get("train.neat_packing"),
+            train_on_prompt=get("train.train_on_prompt"),
+            mask_history=get("train.mask_history"),
             resize_vocab=get("train.resize_vocab"),
             use_llama_pro=get("train.use_llama_pro"),
             shift_attn=get("train.shift_attn"),