Merge pull request #511 from hiyouga/feature-autoTemplate

add template match and stage in webui

Former-commit-id: 413752ecba845cddaff5fb48db7d3d24b960eec1

src/llmtuner/webui/components/top.py

@@ -4,7 +4,7 @@ import gradio as gr
 
 from llmtuner.extras.constants import METHODS, SUPPORTED_MODELS
 from llmtuner.extras.template import templates
-from llmtuner.webui.common import list_checkpoint, get_model_path, save_config
+from llmtuner.webui.common import list_checkpoint, get_model_path, save_config, get_template
 from llmtuner.webui.utils import can_quantize
 
 if TYPE_CHECKING:
@@ -39,6 +39,7 @@ def create_top() -> Dict[str, "Component"]:
     ) # do not save config since the below line will save
 
     model_path.change(save_config, [lang, model_name, model_path])
+    model_path.change(get_template, [model_name], [template])
 
     finetuning_type.change(
         list_checkpoint, [model_name, finetuning_type], [checkpoints]

src/llmtuner/webui/components/train.py

@@ -6,6 +6,7 @@ import gradio as gr
 from llmtuner.webui.common import list_checkpoint, list_dataset, DEFAULT_DATA_DIR
 from llmtuner.webui.components.data import create_preview_box
 from llmtuner.webui.utils import can_preview, get_preview, gen_plot
+from llmtuner.extras.constants import STAGES
 
 if TYPE_CHECKING:
     from gradio.components import Component
@@ -14,6 +15,9 @@ if TYPE_CHECKING:
 
 def create_train_tab(top_elems: Dict[str, "Component"], runner: "Runner") -> Dict[str, "Component"]:
     with gr.Row():
+        stage = gr.Dropdown(choices=STAGES,
+                            value="Supervised Finetuning", scale=2)
+
         dataset_dir = gr.Textbox(value=DEFAULT_DATA_DIR, scale=2)
         dataset = gr.Dropdown(multiselect=True, scale=4)
         data_preview_btn = gr.Button(interactive=False, scale=1)
@@ -62,7 +66,6 @@ def create_train_tab(top_elems: Dict[str, "Component"], runner: "Runner") -> Dic
 
     with gr.Accordion(label="RLHF config", open=False) as rlhf_tab:
         with gr.Row():
-            rlhf_method = gr.Dropdown(choices=["None", "Reward Modeling", "PPO", "DPO"], value="None", scale=1)
             dpo_beta = gr.Slider(value=0.1, minimum=0, maximum=1, step=0.01, scale=2)
             reward_model = gr.Dropdown(scale=2)
             refresh_btn = gr.Button(scale=1)
@@ -101,6 +104,7 @@ def create_train_tab(top_elems: Dict[str, "Component"], runner: "Runner") -> Dic
         top_elems["quantization_bit"],
         top_elems["template"],
         top_elems["source_prefix"],
+        stage,
         dataset_dir,
         dataset,
         max_source_length,
@@ -122,7 +126,6 @@ def create_train_tab(top_elems: Dict[str, "Component"], runner: "Runner") -> Dic
         lora_dropout,
         lora_target,
         resume_lora_training,
-        rlhf_method,
         dpo_beta,
         reward_model,
         output_dir
@@ -142,6 +145,7 @@ def create_train_tab(top_elems: Dict[str, "Component"], runner: "Runner") -> Dic
     )
 
     return dict(
+        stage=stage,
         dataset_dir=dataset_dir,
         dataset=dataset,
         data_preview_btn=data_preview_btn,
@@ -170,7 +174,6 @@ def create_train_tab(top_elems: Dict[str, "Component"], runner: "Runner") -> Dic
         lora_target=lora_target,
         resume_lora_training=resume_lora_training,
         rlhf_tab=rlhf_tab,
-        rlhf_method=rlhf_method,
         dpo_beta=dpo_beta,
         reward_model=reward_model,
         refresh_btn=refresh_btn,