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refactor webui

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Former-commit-id: 813ecd8e51949c21ab6fbaa51cc2b1a84ee07952
This commit is contained in:
hiyouga
2023-10-15 03:06:21 +08:00
parent 4b1473502f
commit 6a61b4b638
14 changed files with 440 additions and 501 deletions
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148
src/llmtuner/webui/components/train.py
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@@ -9,10 +9,13 @@ 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
from llmtuner.webui.engine import Engine
def create_train_tab(top_elems: Dict[str, "Component"], runner: "Runner") -> Dict[str, "Component"]:
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
@@ -21,11 +24,17 @@ def create_train_tab(top_elems: Dict[str, "Component"], runner: "Runner") -> Dic
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])
input_elems.update({training_stage, dataset_dir, dataset})
elem_dict.update(dict(
training_stage=training_stage, dataset_dir=dataset_dir, dataset=dataset, data_preview_btn=data_preview_btn
))
preview_box, preview_count, preview_samples, close_btn = create_preview_box()
data_preview_btn.click(
get_preview,
[dataset_dir, dataset],
@@ -33,6 +42,10 @@ def create_train_tab(top_elems: Dict[str, "Component"], runner: "Runner") -> Dic
queue=False
)
elem_dict.update(dict(
preview_count=preview_count, preview_samples=preview_samples, close_btn=close_btn
))
with gr.Row():
cutoff_len = gr.Slider(value=1024, minimum=4, maximum=8192, step=1)
learning_rate = gr.Textbox(value="5e-5")
@@ -40,6 +53,12 @@ def create_train_tab(top_elems: Dict[str, "Component"], runner: "Runner") -> Dic
max_samples = gr.Textbox(value="100000")
compute_type = gr.Radio(choices=["fp16", "bf16"], 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)
@@ -49,12 +68,23 @@ def create_train_tab(top_elems: Dict[str, "Component"], runner: "Runner") -> Dic
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="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)
input_elems.update({logging_steps, save_steps, warmup_steps})
elem_dict.update(dict(
advanced_tab=advanced_tab, logging_steps=logging_steps, save_steps=save_steps, warmup_steps=warmup_steps
))
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)
@@ -62,6 +92,15 @@ def create_train_tab(top_elems: Dict[str, "Component"], runner: "Runner") -> Dic
lora_target = gr.Textbox(scale=2)
resume_lora_training = gr.Checkbox(value=True, scale=1)
input_elems.update({lora_rank, lora_dropout, lora_target, resume_lora_training})
elem_dict.update(dict(
lora_tab=lora_tab,
lora_rank=lora_rank,
lora_dropout=lora_dropout,
lora_target=lora_target,
resume_lora_training=resume_lora_training,
))
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)
@@ -70,11 +109,14 @@ def create_train_tab(top_elems: Dict[str, "Component"], runner: "Runner") -> Dic
refresh_btn.click(
list_checkpoint,
[top_elems["model_name"], top_elems["finetuning_type"]],
[engine.manager.get_elem("top.model_name"), engine.manager.get_elem("top.finetuning_type")],
[reward_model],
queue=False
)
input_elems.update({dpo_beta, reward_model})
elem_dict.update(dict(rlhf_tab=rlhf_tab, dpo_beta=dpo_beta, reward_model=reward_model, refresh_btn=refresh_btn))
with gr.Row():
cmd_preview_btn = gr.Button()
start_btn = gr.Button()
@@ -94,90 +136,22 @@ def create_train_tab(top_elems: Dict[str, "Component"], runner: "Runner") -> Dic
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"],
top_elems["flash_attn"],
top_elems["shift_attn"],
top_elems["rope_scaling"],
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,
lora_rank,
lora_dropout,
lora_target,
resume_lora_training,
dpo_beta,
reward_model,
output_dir
]
input_elems.add(output_dir)
output_elems = [output_box, process_bar]
elem_dict.update(dict(
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
))
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)
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)
process_bar.change(
gen_plot, [top_elems["model_name"], top_elems["finetuning_type"], output_dir], loss_viewer, queue=False
gen_plot,
[engine.manager.get_elem("top.model_name"), engine.manager.get_elem("top.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,
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
)
return elem_dict