allow non-packing pretraining

src/llmtuner/webui/components/chatbot.py

@@ -1,4 +1,4 @@
-from typing import TYPE_CHECKING, Dict, Optional, Tuple
+from typing import TYPE_CHECKING, Dict, Tuple
 
 import gradio as gr
 
@@ -14,7 +14,7 @@ if TYPE_CHECKING:
 
 
 def create_chat_box(
-    engine: "Engine", visible: Optional[bool] = False
+    engine: "Engine", visible: bool = False
 ) -> Tuple["Block", "Component", "Component", Dict[str, "Component"]]:
     with gr.Box(visible=visible) as chat_box:
         chatbot = gr.Chatbot()

src/llmtuner/webui/components/top.py

@@ -1,4 +1,4 @@
-from typing import TYPE_CHECKING, Dict
+from typing import TYPE_CHECKING, Dict, Tuple
 
 import gradio as gr
 
@@ -12,7 +12,7 @@ if TYPE_CHECKING:
     from gradio.components import Component
 
 
-def create_top() -> Dict[str, "Component"]:
+def create_top() -> Tuple["gr.Dropdown", Dict[str, "Component"]]:
     available_models = list(SUPPORTED_MODELS.keys()) + ["Custom"]
 
     with gr.Row():
@@ -44,7 +44,7 @@ def create_top() -> Dict[str, "Component"]:
 
     refresh_btn.click(list_adapters, [model_name, finetuning_type], [adapter_path], queue=False)
 
-    return dict(
+    return lang, dict(
         lang=lang,
         model_name=model_name,
         model_path=model_path,

src/llmtuner/webui/components/train.py

@@ -4,7 +4,7 @@ import gradio as gr
 from transformers.trainer_utils import SchedulerType
 
 from ...extras.constants import TRAINING_STAGES
-from ..common import DEFAULT_DATA_DIR, list_adapters, list_dataset
+from ..common import DEFAULT_DATA_DIR, autoset_packing, list_adapters, list_dataset
 from ..components.data import create_preview_box
 from ..utils import gen_plot
 
@@ -78,7 +78,7 @@ def create_train_tab(engine: "Engine") -> Dict[str, "Component"]:
 
         with gr.Row():
             resize_vocab = gr.Checkbox()
-            sft_packing = gr.Checkbox()
+            packing = gr.Checkbox()
             upcast_layernorm = gr.Checkbox()
             use_llama_pro = gr.Checkbox()
             shift_attn = gr.Checkbox()
@@ -91,7 +91,7 @@ def create_train_tab(engine: "Engine") -> Dict[str, "Component"]:
             neftune_alpha,
             optim,
             resize_vocab,
-            sft_packing,
+            packing,
             upcast_layernorm,
             use_llama_pro,
             shift_attn,
@@ -106,7 +106,7 @@ def create_train_tab(engine: "Engine") -> Dict[str, "Component"]:
             neftune_alpha=neftune_alpha,
             optim=optim,
             resize_vocab=resize_vocab,
-            sft_packing=sft_packing,
+            packing=packing,
             upcast_layernorm=upcast_layernorm,
             use_llama_pro=use_llama_pro,
             shift_attn=shift_attn,
@@ -166,7 +166,7 @@ def create_train_tab(engine: "Engine") -> Dict[str, "Component"]:
         [engine.manager.get_elem_by_name("top.model_name"), engine.manager.get_elem_by_name("top.finetuning_type")],
         [reward_model],
         queue=False,
-    )
+    ).then(autoset_packing, [training_stage], [packing], queue=False)
 
     input_elems.update({dpo_beta, dpo_ftx, reward_model})
     elem_dict.update(dict(rlhf_tab=rlhf_tab, dpo_beta=dpo_beta, dpo_ftx=dpo_ftx, reward_model=reward_model))