fix bug in latest gradio
Former-commit-id: 44a962862b4a74e50ef5786c8d5719faaa65f63f
This commit is contained in:
hiyouga
@@ -6,7 +6,6 @@ from transformers.trainer_utils import SchedulerType
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from ...extras.constants import TRAINING_STAGES
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from ..common import DEFAULT_DATA_DIR, autoset_packing, list_adapters, list_dataset
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from ..components.data import create_preview_box
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from ..utils import gen_plot
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if TYPE_CHECKING:
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@@ -24,7 +23,7 @@ def create_train_tab(engine: "Engine") -> Dict[str, "Component"]:
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choices=list(TRAINING_STAGES.keys()), value=list(TRAINING_STAGES.keys())[0], scale=1
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)
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dataset_dir = gr.Textbox(value=DEFAULT_DATA_DIR, scale=1)
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dataset = gr.Dropdown(multiselect=True, scale=2, allow_custom_value=True)
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dataset = gr.Dropdown(multiselect=True, scale=4, allow_custom_value=True)
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preview_elems = create_preview_box(dataset_dir, dataset)
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input_elems.update({training_stage, dataset_dir, dataset})
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@@ -121,8 +120,8 @@ def create_train_tab(engine: "Engine") -> Dict[str, "Component"]:
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with gr.Accordion(open=False) as freeze_tab:
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with gr.Row():
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num_layer_trainable = gr.Slider(value=3, minimum=1, maximum=128, step=1, scale=2)
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name_module_trainable = gr.Textbox(value="all", scale=3)
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num_layer_trainable = gr.Slider(value=3, minimum=1, maximum=128, step=1)
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name_module_trainable = gr.Textbox(value="all")
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input_elems.update({num_layer_trainable, name_module_trainable})
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elem_dict.update(
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@@ -140,8 +139,10 @@ def create_train_tab(engine: "Engine") -> Dict[str, "Component"]:
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create_new_adapter = gr.Checkbox()
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with gr.Row():
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use_rslora = gr.Checkbox(scale=1)
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use_dora = gr.Checkbox(scale=1)
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with gr.Column(scale=1):
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use_rslora = gr.Checkbox()
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use_dora = gr.Checkbox()
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lora_target = gr.Textbox(scale=2)
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additional_target = gr.Textbox(scale=2)
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@@ -175,10 +176,10 @@ def create_train_tab(engine: "Engine") -> Dict[str, "Component"]:
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with gr.Accordion(open=False) as rlhf_tab:
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with gr.Row():
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dpo_beta = gr.Slider(value=0.1, minimum=0, maximum=1, step=0.01, scale=1)
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dpo_ftx = gr.Slider(value=0, minimum=0, maximum=10, step=0.01, scale=1)
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orpo_beta = gr.Slider(value=0.1, minimum=0, maximum=1, step=0.01, scale=1)
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reward_model = gr.Dropdown(multiselect=True, allow_custom_value=True, scale=2)
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dpo_beta = gr.Slider(value=0.1, minimum=0, maximum=1, step=0.01)
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dpo_ftx = gr.Slider(value=0, minimum=0, maximum=10, step=0.01)
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orpo_beta = gr.Slider(value=0.1, minimum=0, maximum=1, step=0.01)
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reward_model = gr.Dropdown(multiselect=True, allow_custom_value=True)
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input_elems.update({dpo_beta, dpo_ftx, orpo_beta, reward_model})
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elem_dict.update(
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@@ -187,11 +188,11 @@ def create_train_tab(engine: "Engine") -> Dict[str, "Component"]:
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with gr.Accordion(open=False) as galore_tab:
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with gr.Row():
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use_galore = gr.Checkbox(scale=1)
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galore_rank = gr.Slider(value=16, minimum=1, maximum=1024, step=1, scale=2)
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galore_update_interval = gr.Slider(value=200, minimum=1, maximum=1024, step=1, scale=2)
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galore_scale = gr.Slider(value=0.25, minimum=0, maximum=1, step=0.01, scale=2)
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galore_target = gr.Textbox(value="all", scale=3)
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use_galore = gr.Checkbox()
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galore_rank = gr.Slider(value=16, minimum=1, maximum=1024, step=1)
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galore_update_interval = gr.Slider(value=200, minimum=1, maximum=1024, step=1)
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galore_scale = gr.Slider(value=0.25, minimum=0, maximum=1, step=0.01)
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galore_target = gr.Textbox(value="all")
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input_elems.update({use_galore, galore_rank, galore_update_interval, galore_scale, galore_target})
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elem_dict.update(
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@@ -228,29 +229,6 @@ def create_train_tab(engine: "Engine") -> Dict[str, "Component"]:
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with gr.Column(scale=1):
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loss_viewer = gr.Plot()
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input_elems.update({output_dir, config_path})
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output_elems = [output_box, process_bar]
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cmd_preview_btn.click(engine.runner.preview_train, input_elems, output_elems, concurrency_limit=None)
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arg_save_btn.click(engine.runner.save_args, input_elems, output_elems, concurrency_limit=None)
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arg_load_btn.click(
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engine.runner.load_args,
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[engine.manager.get_elem_by_id("top.lang"), config_path],
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list(input_elems),
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concurrency_limit=None,
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)
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start_btn.click(engine.runner.run_train, input_elems, output_elems)
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stop_btn.click(engine.runner.set_abort)
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resume_btn.change(engine.runner.monitor, outputs=output_elems, concurrency_limit=None)
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dataset_dir.change(list_dataset, [dataset_dir, training_stage], [dataset], queue=False)
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training_stage.change(list_dataset, [dataset_dir, training_stage], [dataset], queue=False).then(
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list_adapters,
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[engine.manager.get_elem_by_id("top.model_name"), engine.manager.get_elem_by_id("top.finetuning_type")],
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[reward_model],
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queue=False,
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).then(autoset_packing, [training_stage], [packing], queue=False)
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elem_dict.update(
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dict(
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cmd_preview_btn=cmd_preview_btn,
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@@ -267,15 +245,27 @@ def create_train_tab(engine: "Engine") -> Dict[str, "Component"]:
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)
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)
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output_box.change(
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gen_plot,
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[
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engine.manager.get_elem_by_id("top.model_name"),
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engine.manager.get_elem_by_id("top.finetuning_type"),
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output_dir,
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],
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loss_viewer,
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queue=False,
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input_elems.update({output_dir, config_path})
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output_elems = [output_box, process_bar, loss_viewer]
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cmd_preview_btn.click(engine.runner.preview_train, input_elems, output_elems, concurrency_limit=None)
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arg_save_btn.click(engine.runner.save_args, input_elems, output_elems, concurrency_limit=None)
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arg_load_btn.click(
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engine.runner.load_args,
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[engine.manager.get_elem_by_id("top.lang"), config_path],
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list(input_elems) + [output_box],
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concurrency_limit=None,
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)
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start_btn.click(engine.runner.run_train, input_elems, output_elems)
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stop_btn.click(engine.runner.set_abort)
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resume_btn.change(engine.runner.monitor, outputs=output_elems, concurrency_limit=None)
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dataset_dir.change(list_dataset, [dataset_dir, training_stage], [dataset], queue=False)
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training_stage.change(list_dataset, [dataset_dir, training_stage], [dataset], queue=False).then(
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list_adapters,
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[engine.manager.get_elem_by_id("top.model_name"), engine.manager.get_elem_by_id("top.finetuning_type")],
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[reward_model],
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queue=False,
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).then(autoset_packing, [training_stage], [packing], queue=False)
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return elem_dict
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