refactor adapter hparam

Former-commit-id: f82aece9ebd6df83a7a005cc7cbbcec07fa6e14d

src/llmtuner/webui/components/export.py

@@ -14,7 +14,7 @@ def save_model(
     lang: str,
     model_name: str,
     model_path: str,
-    checkpoints: List[str],
+    adapter_path: List[str],
     finetuning_type: str,
     template: str,
     max_shard_size: int,
@@ -25,8 +25,8 @@ def save_model(
         error = ALERTS["err_no_model"][lang]
     elif not model_path:
         error = ALERTS["err_no_path"][lang]
-    elif not checkpoints:
-        error = ALERTS["err_no_checkpoint"][lang]
+    elif not adapter_path:
+        error = ALERTS["err_no_adapter"][lang]
     elif not export_dir:
         error = ALERTS["err_no_export_dir"][lang]
 
@@ -37,7 +37,7 @@ def save_model(
 
     args = dict(
         model_name_or_path=model_path,
-        checkpoint_dir=",".join([get_save_dir(model_name, finetuning_type, ckpt) for ckpt in checkpoints]),
+        adapter_name_or_path=",".join([get_save_dir(model_name, finetuning_type, adapter) for adapter in adapter_path]),
         finetuning_type=finetuning_type,
         template=template,
         export_dir=export_dir,
@@ -63,7 +63,7 @@ def create_export_tab(engine: "Engine") -> Dict[str, "Component"]:
             engine.manager.get_elem_by_name("top.lang"),
             engine.manager.get_elem_by_name("top.model_name"),
             engine.manager.get_elem_by_name("top.model_path"),
-            engine.manager.get_elem_by_name("top.checkpoints"),
+            engine.manager.get_elem_by_name("top.adapter_path"),
             engine.manager.get_elem_by_name("top.finetuning_type"),
             engine.manager.get_elem_by_name("top.template"),
             max_shard_size,

src/llmtuner/webui/components/top.py

@@ -3,7 +3,7 @@ from typing import TYPE_CHECKING, Dict
 
 from llmtuner.data.template import templates
 from llmtuner.extras.constants import METHODS, SUPPORTED_MODELS
-from llmtuner.webui.common import get_model_path, get_template, list_checkpoint, save_config
+from llmtuner.webui.common import get_model_path, get_template, list_adapters, save_config
 from llmtuner.webui.utils import can_quantize
 
 if TYPE_CHECKING:
@@ -20,7 +20,7 @@ def create_top() -> Dict[str, "Component"]:
 
     with gr.Row():
         finetuning_type = gr.Dropdown(choices=METHODS, value="lora", scale=1)
-        checkpoints = gr.Dropdown(multiselect=True, scale=5)
+        adapter_path = gr.Dropdown(multiselect=True, scale=5)
         refresh_btn = gr.Button(scale=1)
 
     with gr.Accordion(label="Advanced config", open=False) as advanced_tab:
@@ -34,7 +34,7 @@ def create_top() -> Dict[str, "Component"]:
                 shift_attn = gr.Checkbox(value=False)
 
     model_name.change(
-        list_checkpoint, [model_name, finetuning_type], [checkpoints], queue=False
+        list_adapters, [model_name, finetuning_type], [adapter_path], queue=False
     ).then(
         get_model_path, [model_name], [model_path], queue=False
     ).then(
@@ -44,13 +44,13 @@ def create_top() -> Dict[str, "Component"]:
     model_path.change(save_config, inputs=[lang, model_name, model_path], queue=False)
 
     finetuning_type.change(
-        list_checkpoint, [model_name, finetuning_type], [checkpoints], queue=False
+        list_adapters, [model_name, finetuning_type], [adapter_path], queue=False
     ).then(
         can_quantize, [finetuning_type], [quantization_bit], queue=False
     )
 
     refresh_btn.click(
-        list_checkpoint, [model_name, finetuning_type], [checkpoints], queue=False
+        list_adapters, [model_name, finetuning_type], [adapter_path], queue=False
     )
 
     return dict(
@@ -58,7 +58,7 @@ def create_top() -> Dict[str, "Component"]:
         model_name=model_name,
         model_path=model_path,
         finetuning_type=finetuning_type,
-        checkpoints=checkpoints,
+        adapter_path=adapter_path,
         refresh_btn=refresh_btn,
         advanced_tab=advanced_tab,
         quantization_bit=quantization_bit,

src/llmtuner/webui/components/train.py

@@ -3,7 +3,7 @@ from typing import TYPE_CHECKING, Dict
 from transformers.trainer_utils import SchedulerType
 
 from llmtuner.extras.constants import TRAINING_STAGES
-from llmtuner.webui.common import list_checkpoint, list_dataset, DEFAULT_DATA_DIR
+from llmtuner.webui.common import list_adapters, list_dataset, DEFAULT_DATA_DIR
 from llmtuner.webui.components.data import create_preview_box
 from llmtuner.webui.utils import gen_plot
 
@@ -60,7 +60,7 @@ def create_train_tab(engine: "Engine") -> Dict[str, "Component"]:
         lr_scheduler_type=lr_scheduler_type, max_grad_norm=max_grad_norm, val_size=val_size
     ))
 
-    with gr.Accordion(label="Advanced config", open=False) as advanced_tab:
+    with gr.Accordion(label="Extra config", open=False) as extra_tab:
         with gr.Row():
             logging_steps = gr.Slider(value=5, minimum=5, maximum=1000, step=5)
             save_steps = gr.Slider(value=100, minimum=10, maximum=5000, step=10)
@@ -73,7 +73,7 @@ def create_train_tab(engine: "Engine") -> Dict[str, "Component"]:
 
     input_elems.update({logging_steps, save_steps, warmup_steps, neftune_alpha, train_on_prompt, upcast_layernorm})
     elem_dict.update(dict(
-        advanced_tab=advanced_tab, logging_steps=logging_steps, save_steps=save_steps, warmup_steps=warmup_steps,
+        extra_tab=extra_tab, logging_steps=logging_steps, save_steps=save_steps, warmup_steps=warmup_steps,
         neftune_alpha=neftune_alpha, train_on_prompt=train_on_prompt, upcast_layernorm=upcast_layernorm
     ))
 
@@ -83,12 +83,12 @@ def create_train_tab(engine: "Engine") -> Dict[str, "Component"]:
             lora_dropout = gr.Slider(value=0.1, minimum=0, maximum=1, step=0.01, scale=1)
             lora_target = gr.Textbox(scale=1)
             additional_target = gr.Textbox(scale=1)
-            resume_lora_training = gr.Checkbox(value=True, scale=1)
+            create_new_adapter = gr.Checkbox(scale=1)
 
-    input_elems.update({lora_rank, lora_dropout, lora_target, additional_target, resume_lora_training})
+    input_elems.update({lora_rank, lora_dropout, lora_target, additional_target, create_new_adapter})
     elem_dict.update(dict(
         lora_tab=lora_tab, lora_rank=lora_rank, lora_dropout=lora_dropout, lora_target=lora_target,
-        additional_target=additional_target, resume_lora_training=resume_lora_training,
+        additional_target=additional_target, create_new_adapter=create_new_adapter
     ))
 
     with gr.Accordion(label="RLHF config", open=False) as rlhf_tab:
@@ -98,7 +98,7 @@ def create_train_tab(engine: "Engine") -> Dict[str, "Component"]:
             refresh_btn = gr.Button(scale=1)
 
     refresh_btn.click(
-        list_checkpoint,
+        list_adapters,
         [engine.manager.get_elem_by_name("top.model_name"), engine.manager.get_elem_by_name("top.finetuning_type")],
         [reward_model],
         queue=False