update webui

Former-commit-id: d0842f682897cb227cda9e9747f42a7281970463

examples/lora_single_gpu/orpo.sh

@@ -4,7 +4,7 @@ CUDA_VISIBLE_DEVICES=0 python ../../src/train_bash.py \
     --stage orpo \
     --do_train \
     --model_name_or_path meta-llama/Llama-2-7b-hf \
-    --dataset comparison_gpt4_en \
+    --dataset orca_rlhf \
     --dataset_dir ../../data \
     --template default \
     --finetuning_type lora \

src/llmtuner/webui/components/train.py

@@ -21,10 +21,10 @@ def create_train_tab(engine: "Engine") -> Dict[str, "Component"]:
 
     with gr.Row():
         training_stage = gr.Dropdown(
-            choices=list(TRAINING_STAGES.keys()), value=list(TRAINING_STAGES.keys())[0], scale=2
+            choices=list(TRAINING_STAGES.keys()), value=list(TRAINING_STAGES.keys())[0], scale=1
         )
-        dataset_dir = gr.Textbox(value=DEFAULT_DATA_DIR, scale=2)
+        dataset_dir = gr.Textbox(value=DEFAULT_DATA_DIR, scale=1)
-        dataset = gr.Dropdown(multiselect=True, scale=4)
+        dataset = gr.Dropdown(multiselect=True, scale=2, allow_custom_value=True)
         preview_elems = create_preview_box(dataset_dir, dataset)
 
     input_elems.update({training_stage, dataset_dir, dataset})
@@ -75,11 +75,17 @@ def create_train_tab(engine: "Engine") -> Dict[str, "Component"]:
             optim = gr.Textbox(value="adamw_torch")
 
         with gr.Row():
-            resize_vocab = gr.Checkbox()
+            with gr.Column():
-            packing = gr.Checkbox()
+                resize_vocab = gr.Checkbox()
-            upcast_layernorm = gr.Checkbox()
+                packing = gr.Checkbox()
-            use_llama_pro = gr.Checkbox()
+
-            shift_attn = gr.Checkbox()
+            with gr.Column():
+                upcast_layernorm = gr.Checkbox()
+                use_llama_pro = gr.Checkbox()
+
+            with gr.Column():
+                shift_attn = gr.Checkbox()
+                report_to = gr.Checkbox()
 
     input_elems.update(
         {
@@ -93,6 +99,7 @@ def create_train_tab(engine: "Engine") -> Dict[str, "Component"]:
             upcast_layernorm,
             use_llama_pro,
             shift_attn,
+            report_to,
         }
     )
     elem_dict.update(
@@ -108,6 +115,7 @@ def create_train_tab(engine: "Engine") -> Dict[str, "Component"]:
             upcast_layernorm=upcast_layernorm,
             use_llama_pro=use_llama_pro,
             shift_attn=shift_attn,
+            report_to=report_to,
         )
     )
 

src/llmtuner/webui/locales.py

@@ -536,6 +536,20 @@ LOCALES = {
             "info": "使用 LongLoRA 提出的 shift short attention。",
         },
     },
+    "report_to": {
+        "en": {
+            "label": "Enable external logger",
+            "info": "Use TensorBoard or wandb to log experiment.",
+        },
+        "ru": {
+            "label": "Включить внешний регистратор",
+            "info": "Использовать TensorBoard или wandb для ведения журнала экспериментов.",
+        },
+        "zh": {
+            "label": "启用外部记录面板",
+            "info": "使用 TensorBoard 或 wandb 记录实验。",
+        },
+    },
     "freeze_tab": {
         "en": {
             "label": "Freeze tuning configurations",

src/llmtuner/webui/runner.py

@@ -80,20 +80,18 @@ class Runner:
         if not from_preview and not is_torch_cuda_available():
             gr.Warning(ALERTS["warn_no_cuda"][lang])
 
-        self.aborted = False
         self.logger_handler.reset()
         self.trainer_callback = LogCallback(self)
         return ""
 
     def _finalize(self, lang: str, finish_info: str) -> str:
+        finish_info = ALERTS["info_aborted"][lang] if self.aborted else finish_info
         self.thread = None
-        self.running_data = None
+        self.aborted = False
         self.running = False
+        self.running_data = None
         torch_gc()
-        if self.aborted:
+        return finish_info
-            return ALERTS["info_aborted"][lang]
-        else:
-            return finish_info
 
     def _parse_train_args(self, data: Dict["Component", Any]) -> Dict[str, Any]:
         get = lambda elem_id: data[self.manager.get_elem_by_id(elem_id)]
@@ -141,6 +139,7 @@ class Runner:
             upcast_layernorm=get("train.upcast_layernorm"),
             use_llama_pro=get("train.use_llama_pro"),
             shift_attn=get("train.shift_attn"),
+            report_to="all" if get("train.report_to") else "none",
             use_galore=get("train.use_galore"),
             output_dir=get_save_dir(get("top.model_name"), get("top.finetuning_type"), get("train.output_dir")),
             fp16=(get("train.compute_type") == "fp16"),