mirror of
https://github.com/hiyouga/LLaMA-Factory.git
synced 2025-08-02 11:42:49 +08:00
296 lines
11 KiB
Python
296 lines
11 KiB
Python
from typing import TYPE_CHECKING, Dict
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from transformers.trainer_utils import SchedulerType
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from ...extras.constants import TRAINING_STAGES
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from ...extras.packages import is_gradio_available
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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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if is_gradio_available():
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import gradio as gr
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if TYPE_CHECKING:
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from gradio.components import Component
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from ..engine import Engine
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def create_train_tab(engine: "Engine") -> Dict[str, "Component"]:
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input_elems = engine.manager.get_base_elems()
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elem_dict = dict()
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with gr.Row():
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training_stage = gr.Dropdown(
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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, allow_custom_value=True, scale=4)
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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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elem_dict.update(dict(training_stage=training_stage, dataset_dir=dataset_dir, dataset=dataset, **preview_elems))
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with gr.Row():
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learning_rate = gr.Textbox(value="5e-5")
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num_train_epochs = gr.Textbox(value="3.0")
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max_grad_norm = gr.Textbox(value="1.0")
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max_samples = gr.Textbox(value="100000")
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compute_type = gr.Dropdown(choices=["fp16", "bf16", "fp32", "pure_bf16"], value="fp16")
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input_elems.update({learning_rate, num_train_epochs, max_grad_norm, max_samples, compute_type})
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elem_dict.update(
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dict(
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learning_rate=learning_rate,
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num_train_epochs=num_train_epochs,
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max_grad_norm=max_grad_norm,
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max_samples=max_samples,
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compute_type=compute_type,
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)
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)
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with gr.Row():
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cutoff_len = gr.Slider(minimum=4, maximum=65536, value=1024, step=1)
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batch_size = gr.Slider(minimum=1, maximum=1024, value=2, step=1)
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gradient_accumulation_steps = gr.Slider(minimum=1, maximum=1024, value=8, step=1)
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val_size = gr.Slider(minimum=0, maximum=1, value=0, step=0.001)
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lr_scheduler_type = gr.Dropdown(choices=[scheduler.value for scheduler in SchedulerType], value="cosine")
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input_elems.update({cutoff_len, batch_size, gradient_accumulation_steps, val_size, lr_scheduler_type})
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elem_dict.update(
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dict(
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cutoff_len=cutoff_len,
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batch_size=batch_size,
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gradient_accumulation_steps=gradient_accumulation_steps,
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val_size=val_size,
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lr_scheduler_type=lr_scheduler_type,
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)
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)
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with gr.Accordion(open=False) as extra_tab:
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with gr.Row():
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logging_steps = gr.Slider(minimum=1, maximum=1000, value=5, step=5)
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save_steps = gr.Slider(minimum=10, maximum=5000, value=100, step=10)
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warmup_steps = gr.Slider(minimum=0, maximum=5000, value=0, step=1)
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neftune_alpha = gr.Slider(minimum=0, maximum=10, value=0, step=0.1)
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optim = gr.Textbox(value="adamw_torch")
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with gr.Row():
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with gr.Column():
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resize_vocab = gr.Checkbox()
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packing = gr.Checkbox()
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with gr.Column():
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upcast_layernorm = gr.Checkbox()
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use_llama_pro = gr.Checkbox()
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with gr.Column():
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shift_attn = gr.Checkbox()
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report_to = gr.Checkbox()
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input_elems.update(
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{
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logging_steps,
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save_steps,
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warmup_steps,
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neftune_alpha,
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optim,
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resize_vocab,
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packing,
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upcast_layernorm,
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use_llama_pro,
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shift_attn,
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report_to,
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}
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)
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elem_dict.update(
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dict(
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extra_tab=extra_tab,
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logging_steps=logging_steps,
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save_steps=save_steps,
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warmup_steps=warmup_steps,
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neftune_alpha=neftune_alpha,
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optim=optim,
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resize_vocab=resize_vocab,
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packing=packing,
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upcast_layernorm=upcast_layernorm,
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use_llama_pro=use_llama_pro,
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shift_attn=shift_attn,
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report_to=report_to,
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)
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)
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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(minimum=1, maximum=128, value=2, 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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dict(
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freeze_tab=freeze_tab, num_layer_trainable=num_layer_trainable, name_module_trainable=name_module_trainable
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)
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)
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with gr.Accordion(open=False) as lora_tab:
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with gr.Row():
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lora_rank = gr.Slider(minimum=1, maximum=1024, value=8, step=1)
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lora_alpha = gr.Slider(minimum=1, maximum=2048, value=16, step=1)
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lora_dropout = gr.Slider(minimum=0, maximum=1, value=0, step=0.01)
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loraplus_lr_ratio = gr.Slider(minimum=0, maximum=64, value=0, step=0.01)
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create_new_adapter = gr.Checkbox()
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with gr.Row():
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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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input_elems.update(
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{
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lora_rank,
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lora_alpha,
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lora_dropout,
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loraplus_lr_ratio,
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create_new_adapter,
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use_rslora,
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use_dora,
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lora_target,
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additional_target,
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}
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)
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elem_dict.update(
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dict(
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lora_tab=lora_tab,
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lora_rank=lora_rank,
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lora_alpha=lora_alpha,
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lora_dropout=lora_dropout,
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loraplus_lr_ratio=loraplus_lr_ratio,
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create_new_adapter=create_new_adapter,
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use_rslora=use_rslora,
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use_dora=use_dora,
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lora_target=lora_target,
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additional_target=additional_target,
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)
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)
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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(minimum=0, maximum=1, value=0.1, step=0.01)
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dpo_ftx = gr.Slider(minimum=0, maximum=10, value=0, step=0.01)
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orpo_beta = gr.Slider(minimum=0, maximum=1, value=0.1, 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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dict(rlhf_tab=rlhf_tab, dpo_beta=dpo_beta, dpo_ftx=dpo_ftx, orpo_beta=orpo_beta, reward_model=reward_model)
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)
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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()
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galore_rank = gr.Slider(minimum=1, maximum=1024, value=16, step=1)
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galore_update_interval = gr.Slider(minimum=1, maximum=1024, value=200, step=1)
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galore_scale = gr.Slider(minimum=0, maximum=1, value=0.25, 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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dict(
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galore_tab=galore_tab,
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use_galore=use_galore,
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galore_rank=galore_rank,
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galore_update_interval=galore_update_interval,
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galore_scale=galore_scale,
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galore_target=galore_target,
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)
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)
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with gr.Accordion(open=False) as badam_tab:
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with gr.Row():
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use_badam = gr.Checkbox()
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badam_mode = gr.Dropdown(choices=["layer", "ratio"], value="layer")
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badam_switch_mode = gr.Dropdown(choices=["ascending", "descending", "random", "fixed"], value="ascending")
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badam_switch_interval = gr.Slider(minimum=1, maximum=1024, value=50, step=1)
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badam_update_ratio = gr.Slider(minimum=0, maximum=1, value=0.05, step=0.01)
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input_elems.update({use_badam, badam_mode, badam_switch_mode, badam_switch_interval, badam_update_ratio})
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elem_dict.update(
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dict(
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badam_tab=badam_tab,
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use_badam=use_badam,
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badam_mode=badam_mode,
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badam_switch_mode=badam_switch_mode,
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badam_switch_interval=badam_switch_interval,
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badam_update_ratio=badam_update_ratio,
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)
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)
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with gr.Row():
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cmd_preview_btn = gr.Button()
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arg_save_btn = gr.Button()
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arg_load_btn = gr.Button()
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start_btn = gr.Button(variant="primary")
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stop_btn = gr.Button(variant="stop")
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with gr.Row():
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with gr.Column(scale=3):
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with gr.Row():
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output_dir = gr.Textbox()
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config_path = gr.Textbox()
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with gr.Row():
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resume_btn = gr.Checkbox(visible=False, interactive=False)
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progress_bar = gr.Slider(visible=False, interactive=False)
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with gr.Row():
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output_box = gr.Markdown()
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with gr.Column(scale=1):
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loss_viewer = gr.Plot()
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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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arg_save_btn=arg_save_btn,
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arg_load_btn=arg_load_btn,
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start_btn=start_btn,
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stop_btn=stop_btn,
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output_dir=output_dir,
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config_path=config_path,
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resume_btn=resume_btn,
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progress_bar=progress_bar,
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output_box=output_box,
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loss_viewer=loss_viewer,
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)
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)
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input_elems.update({output_dir, config_path})
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output_elems = [output_box, progress_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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