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from typing import TYPE_CHECKING, Dict
from transformers.trainer_utils import SchedulerType
import gradio as gr
from llmtuner.extras.constants import TRAINING_STAGES
from llmtuner.webui.common import list_checkpoint, list_dataset, DEFAULT_DATA_DIR
from llmtuner.webui.components.data import create_preview_box
from llmtuner.webui.utils import can_preview, get_preview, gen_plot
if TYPE_CHECKING:
from gradio.components import Component
from llmtuner.webui.runner import Runner
def create_train_tab(top_elems: Dict[str, "Component"], runner: "Runner") -> Dict[str, "Component"]:
with gr.Row():
training_stage = gr.Dropdown(
choices=list(TRAINING_STAGES.keys()), value=list(TRAINING_STAGES.keys())[0], scale=2
)
dataset_dir = gr.Textbox(value=DEFAULT_DATA_DIR, scale=2)
dataset = gr.Dropdown(multiselect=True, scale=4)
data_preview_btn = gr.Button(interactive=False, scale=1)
preview_box, preview_count, preview_samples, close_btn = create_preview_box()
training_stage.change(list_dataset, [dataset_dir, training_stage], [dataset])
dataset_dir.change(list_dataset, [dataset_dir, training_stage], [dataset])
dataset.change(can_preview, [dataset_dir, dataset], [data_preview_btn])
data_preview_btn.click(
get_preview,
[dataset_dir, dataset],
[preview_count, preview_samples, preview_box],
queue=False
)
with gr.Row():
cutoff_len = gr.Slider(value=1024, minimum=4, maximum=8192, step=1)
learning_rate = gr.Textbox(value="5e-5")
num_train_epochs = gr.Textbox(value="3.0")
max_samples = gr.Textbox(value="100000")
compute_type = gr.Radio(choices=["fp16", "bf16"], value="fp16")
with gr.Row():
batch_size = gr.Slider(value=4, minimum=1, maximum=512, step=1)
gradient_accumulation_steps = gr.Slider(value=4, minimum=1, maximum=512, step=1)
lr_scheduler_type = gr.Dropdown(
choices=[scheduler.value for scheduler in SchedulerType], value="cosine"
)
max_grad_norm = gr.Textbox(value="1.0")
val_size = gr.Slider(value=0, minimum=0, maximum=1, step=0.001)
with gr.Accordion(label="Advanced config", open=False) as advanced_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)
warmup_steps = gr.Slider(value=0, minimum=0, maximum=5000, step=1)
flash_attn = gr.Checkbox(value=False)
rope_scaling = gr.Checkbox(value=False)
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, scale=1)
lora_dropout = gr.Slider(value=0.1, minimum=0, maximum=1, step=0.01, scale=1)
lora_target = gr.Textbox(scale=2)
resume_lora_training = gr.Checkbox(value=True, scale=1)
with gr.Accordion(label="RLHF config", open=False) as rlhf_tab:
with gr.Row():
dpo_beta = gr.Slider(value=0.1, minimum=0, maximum=1, step=0.01, scale=2)
reward_model = gr.Dropdown(scale=2)
refresh_btn = gr.Button(scale=1)
refresh_btn.click(
list_checkpoint,
[top_elems["model_name"], top_elems["finetuning_type"]],
[reward_model],
queue=False
)
with gr.Row():
cmd_preview_btn = gr.Button()
start_btn = gr.Button()
stop_btn = gr.Button()
with gr.Row():
with gr.Column(scale=3):
with gr.Row():
output_dir = gr.Textbox()
with gr.Row():
process_bar = gr.Slider(visible=False, interactive=False)
with gr.Box():
output_box = gr.Markdown()
with gr.Column(scale=1):
loss_viewer = gr.Plot()
input_components = [
top_elems["lang"],
top_elems["model_name"],
top_elems["checkpoints"],
top_elems["finetuning_type"],
top_elems["quantization_bit"],
top_elems["template"],
top_elems["system_prompt"],
training_stage,
dataset_dir,
dataset,
cutoff_len,
learning_rate,
num_train_epochs,
max_samples,
compute_type,
batch_size,
gradient_accumulation_steps,
lr_scheduler_type,
max_grad_norm,
val_size,
logging_steps,
save_steps,
warmup_steps,
flash_attn,
rope_scaling,
lora_rank,
lora_dropout,
lora_target,
resume_lora_training,
dpo_beta,
reward_model,
output_dir
]
output_components = [
output_box,
process_bar
]
cmd_preview_btn.click(runner.preview_train, input_components, output_components)
start_btn.click(runner.run_train, input_components, output_components)
stop_btn.click(runner.set_abort, queue=False)
process_bar.change(
gen_plot, [top_elems["model_name"], top_elems["finetuning_type"], output_dir], loss_viewer, queue=False
)
return dict(
training_stage=training_stage,
dataset_dir=dataset_dir,
dataset=dataset,
data_preview_btn=data_preview_btn,
preview_count=preview_count,
preview_samples=preview_samples,
close_btn=close_btn,
cutoff_len=cutoff_len,
learning_rate=learning_rate,
num_train_epochs=num_train_epochs,
max_samples=max_samples,
compute_type=compute_type,
batch_size=batch_size,
gradient_accumulation_steps=gradient_accumulation_steps,
lr_scheduler_type=lr_scheduler_type,
max_grad_norm=max_grad_norm,
val_size=val_size,
advanced_tab=advanced_tab,
logging_steps=logging_steps,
save_steps=save_steps,
warmup_steps=warmup_steps,
flash_attn=flash_attn,
rope_scaling=rope_scaling,
lora_tab=lora_tab,
lora_rank=lora_rank,
lora_dropout=lora_dropout,
lora_target=lora_target,
resume_lora_training=resume_lora_training,
rlhf_tab=rlhf_tab,
dpo_beta=dpo_beta,
reward_model=reward_model,
refresh_btn=refresh_btn,
cmd_preview_btn=cmd_preview_btn,
start_btn=start_btn,
stop_btn=stop_btn,
output_dir=output_dir,
output_box=output_box,
loss_viewer=loss_viewer
)
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