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from typing import TYPE_CHECKING, Dict
import gradio as gr
from transformers.trainer_utils import SchedulerType
from ...extras.constants import TRAINING_STAGES
from ..common import DEFAULT_DATA_DIR, list_adapters, list_dataset
from ..components.data import create_preview_box
from ..utils import gen_plot
if TYPE_CHECKING:
from gradio.components import Component
from ..engine import Engine
def create_train_tab(engine: "Engine") -> Dict[str, "Component"]:
input_elems = engine.manager.get_base_elems()
elem_dict = dict()
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)
preview_elems = create_preview_box(dataset_dir, dataset)
training_stage.change(list_dataset, [dataset_dir, training_stage], [dataset], queue=False)
dataset_dir.change(list_dataset, [dataset_dir, training_stage], [dataset], queue=False)
input_elems.update({training_stage, dataset_dir, dataset})
elem_dict.update(dict(training_stage=training_stage, dataset_dir=dataset_dir, dataset=dataset, **preview_elems))
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", "fp32"], value="fp16")
input_elems.update({cutoff_len, learning_rate, num_train_epochs, max_samples, compute_type})
elem_dict.update(
dict(
cutoff_len=cutoff_len,
learning_rate=learning_rate,
num_train_epochs=num_train_epochs,
max_samples=max_samples,
compute_type=compute_type,
)
)
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)
input_elems.update({batch_size, gradient_accumulation_steps, lr_scheduler_type, max_grad_norm, val_size})
elem_dict.update(
dict(
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,
)
)
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)
warmup_steps = gr.Slider(value=0, minimum=0, maximum=5000, step=1)
neftune_alpha = gr.Slider(value=0, minimum=0, maximum=10, step=0.1)
with gr.Column():
sft_packing = gr.Checkbox(value=False)
upcast_layernorm = gr.Checkbox(value=False)
input_elems.update({logging_steps, save_steps, warmup_steps, neftune_alpha, sft_packing, upcast_layernorm})
elem_dict.update(
dict(
extra_tab=extra_tab,
logging_steps=logging_steps,
save_steps=save_steps,
warmup_steps=warmup_steps,
neftune_alpha=neftune_alpha,
sft_packing=sft_packing,
upcast_layernorm=upcast_layernorm,
)
)
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=1)
additional_target = gr.Textbox(scale=1)
create_new_adapter = gr.Checkbox(scale=1)
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,
create_new_adapter=create_new_adapter,
)
)
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=1)
dpo_ftx = gr.Slider(value=0, minimum=0, maximum=10, step=0.01, scale=1)
reward_model = gr.Dropdown(scale=2, allow_custom_value=True)
refresh_btn = gr.Button(scale=1)
refresh_btn.click(
list_adapters,
[engine.manager.get_elem_by_name("top.model_name"), engine.manager.get_elem_by_name("top.finetuning_type")],
[reward_model],
queue=False,
)
input_elems.update({dpo_beta, dpo_ftx, reward_model})
elem_dict.update(
dict(rlhf_tab=rlhf_tab, dpo_beta=dpo_beta, dpo_ftx=dpo_ftx, reward_model=reward_model, refresh_btn=refresh_btn)
)
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():
resume_btn = gr.Checkbox(visible=False, interactive=False, value=False)
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_elems.add(output_dir)
output_elems = [output_box, process_bar]
cmd_preview_btn.click(engine.runner.preview_train, input_elems, output_elems)
start_btn.click(engine.runner.run_train, input_elems, output_elems)
stop_btn.click(engine.runner.set_abort, queue=False)
resume_btn.change(engine.runner.monitor, outputs=output_elems)
elem_dict.update(
dict(
cmd_preview_btn=cmd_preview_btn,
start_btn=start_btn,
stop_btn=stop_btn,
output_dir=output_dir,
resume_btn=resume_btn,
process_bar=process_bar,
output_box=output_box,
loss_viewer=loss_viewer,
)
)
output_box.change(
gen_plot,
[
engine.manager.get_elem_by_name("top.model_name"),
engine.manager.get_elem_by_name("top.finetuning_type"),
output_dir,
],
loss_viewer,
queue=False,
)
return elem_dict
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