support lora for llama pro

src/llmtuner/webui/components/train.py

@@ -108,6 +108,18 @@ def create_train_tab(engine: "Engine") -> Dict[str, "Component"]:
         )
     )
 
+    with gr.Accordion(label="Freeze config", open=False) as freeze_tab:
+        with gr.Row():
+            num_layer_trainable = gr.Slider(value=3, minimum=1, maximum=128, step=1, scale=2)
+            name_module_trainable = gr.Textbox(scale=3)
+
+    input_elems.update({num_layer_trainable, name_module_trainable})
+    elem_dict.update(
+        dict(
+            freeze_tab=freeze_tab, num_layer_trainable=num_layer_trainable, name_module_trainable=name_module_trainable
+        )
+    )
+
     with gr.Accordion(label="LoRA config", open=False) as lora_tab:
         with gr.Row():
             lora_rank = gr.Slider(value=8, minimum=1, maximum=1024, step=1)

src/llmtuner/webui/locales.py

@@ -508,6 +508,45 @@ LOCALES = {
             "info": "仅训练块扩展后的参数。",
         },
     },
+    "freeze_tab": {
+        "en": {
+            "label": "Freeze tuning configurations",
+        },
+        "ru": {
+            "label": "конфигурации для настройки заморозки",
+        },
+        "zh": {
+            "label": "部分参数微调设置",
+        },
+    },
+    "num_layer_trainable": {
+        "en": {
+            "label": "Trainable layers",
+            "info": "The number of trainable layers.",
+        },
+        "ru": {
+            "label": "Обучаемые слои",
+            "info": "Количество обучаемых слоев.",
+        },
+        "zh": {
+            "label": "可训练层数",
+            "info": "可训练模型层的数量。",
+        },
+    },
+    "name_module_trainable": {
+        "en": {
+            "label": "Trainable modules",
+            "info": "The name of trainable modules. Use commas to separate multiple modules.",
+        },
+        "ru": {
+            "label": "Обучаемые модули",
+            "info": "Название обучаемых модулей. Используйте запятые для разделения нескольких модулей.",
+        },
+        "zh": {
+            "label": "可训练模块",
+            "info": "可训练模块的名称。使用英文逗号分隔多个名称。",
+        },
+    },
     "lora_tab": {
         "en": {
             "label": "LoRA configurations",

src/llmtuner/webui/runner.py

@@ -129,26 +129,34 @@ class Runner:
             sft_packing=get("train.sft_packing"),
             upcast_layernorm=get("train.upcast_layernorm"),
             use_llama_pro=get("train.use_llama_pro"),
-            lora_rank=get("train.lora_rank"),
-            lora_dropout=get("train.lora_dropout"),
-            lora_target=get("train.lora_target") or get_module(get("top.model_name")),
-            additional_target=get("train.additional_target") or None,
-            use_rslora=get("train.use_rslora"),
-            create_new_adapter=get("train.create_new_adapter"),
             output_dir=get_save_dir(get("top.model_name"), get("top.finetuning_type"), get("train.output_dir")),
             fp16=(get("train.compute_type") == "fp16"),
             bf16=(get("train.compute_type") == "bf16"),
         )
         args["disable_tqdm"] = True
 
-        if TRAINING_STAGES[get("train.training_stage")] in ["rm", "ppo", "dpo"]:
-            args["create_new_adapter"] = args["quantization_bit"] is None
+        if args["finetuning_type"] == "freeze":
+            args["num_layer_trainable"] = int(get("train.num_layer_trainable"))
+            args["name_module_trainable"] = get("train.name_module_trainable")
+        elif args["finetuning_type"] == "lora":
+            args["lora_rank"] = get("train.lora_rank")
+            args["lora_dropout"] = get("train.lora_dropout")
+            args["lora_target"] = get("train.lora_target") or get_module(get("top.model_name"))
+            args["additional_target"] = get("train.additional_target") or None
+            args["use_rslora"] = get("train.use_rslora")
+            if TRAINING_STAGES[get("train.training_stage")] in ["rm", "ppo", "dpo"]:
+                args["create_new_adapter"] = args["quantization_bit"] is None
+            else:
+                args["create_new_adapter"] = get("train.create_new_adapter")
+
+            if args["use_llama_pro"]:
+                args["num_layer_trainable"] = int(get("train.num_layer_trainable"))
 
         if args["stage"] == "ppo":
             args["reward_model"] = get_save_dir(
                 get("top.model_name"), get("top.finetuning_type"), get("train.reward_model")
             )
-            args["reward_model_type"] = "lora" if get("top.finetuning_type") == "lora" else "full"
+            args["reward_model_type"] = "lora" if args["finetuning_type"] == "lora" else "full"
 
         if args["stage"] == "dpo":
             args["dpo_beta"] = get("train.dpo_beta")