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update webUI, fix #179

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Former-commit-id: f9074fed5e22585679661588befcf266a79009f2
This commit is contained in:
hiyouga
2023-07-18 15:35:17 +08:00
parent fd8c2d4aac
commit a864a7b395
9 changed files with 247 additions and 154 deletions
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25
src/llmtuner/webui/components/eval.py
View File

@@ -10,8 +10,8 @@ from llmtuner.webui.utils import can_preview, get_preview
def create_eval_tab(top_elems: Dict[str, Component], runner: Runner) -> Dict[str, Component]:
with gr.Row():
dataset_dir = gr.Textbox(value=DEFAULT_DATA_DIR, interactive=True, scale=2)
dataset = gr.Dropdown(multiselect=True, interactive=True, scale=4)
dataset_dir = gr.Textbox(value=DEFAULT_DATA_DIR, scale=2)
dataset = gr.Dropdown(multiselect=True, scale=4)
preview_btn = gr.Button(interactive=False, scale=1)
preview_box, preview_count, preview_samples, close_btn = create_preview_box()
@@ -21,9 +21,8 @@ def create_eval_tab(top_elems: Dict[str, Component], runner: Runner) -> Dict[str
preview_btn.click(get_preview, [dataset_dir, dataset], [preview_count, preview_samples, preview_box])
with gr.Row():
max_samples = gr.Textbox(value="100000", interactive=True)
batch_size = gr.Slider(value=8, minimum=1, maximum=128, step=1, interactive=True)
quantization_bit = gr.Dropdown([8, 4])
max_samples = gr.Textbox(value="100000")
batch_size = gr.Slider(value=8, minimum=1, maximum=128, step=1)
predict = gr.Checkbox(value=True)
with gr.Row():
@@ -35,9 +34,18 @@ def create_eval_tab(top_elems: Dict[str, Component], runner: Runner) -> Dict[str
start_btn.click(
runner.run_eval,
[
top_elems["lang"], top_elems["model_name"], top_elems["checkpoints"],
top_elems["finetuning_type"], top_elems["template"],
dataset, dataset_dir, max_samples, batch_size, quantization_bit, predict
top_elems["lang"],
top_elems["model_name"],
top_elems["checkpoints"],
top_elems["finetuning_type"],
top_elems["quantization_bit"],
top_elems["template"],
top_elems["source_prefix"],
dataset_dir,
dataset,
max_samples,
batch_size,
predict
],
[output_box]
)
@@ -52,7 +60,6 @@ def create_eval_tab(top_elems: Dict[str, Component], runner: Runner) -> Dict[str
close_btn=close_btn,
max_samples=max_samples,
batch_size=batch_size,
quantization_bit=quantization_bit,
predict=predict,
start_btn=start_btn,
stop_btn=stop_btn,

12
src/llmtuner/webui/components/infer.py
View File

@@ -11,7 +11,6 @@ def create_infer_tab(top_elems: Dict[str, Component]) -> Dict[str, Component]:
with gr.Row():
load_btn = gr.Button()
unload_btn = gr.Button()
quantization_bit = gr.Dropdown([8, 4])
info_box = gr.Markdown()
@@ -21,9 +20,13 @@ def create_infer_tab(top_elems: Dict[str, Component]) -> Dict[str, Component]:
load_btn.click(
chat_model.load_model,
[
top_elems["lang"], top_elems["model_name"], top_elems["checkpoints"],
top_elems["finetuning_type"], top_elems["template"],
quantization_bit
top_elems["lang"],
top_elems["model_name"],
top_elems["checkpoints"],
top_elems["finetuning_type"],
top_elems["quantization_bit"],
top_elems["template"],
top_elems["source_prefix"]
],
[info_box]
).then(
@@ -39,7 +42,6 @@ def create_infer_tab(top_elems: Dict[str, Component]) -> Dict[str, Component]:
)
return dict(
quantization_bit=quantization_bit,
info_box=info_box,
load_btn=load_btn,
unload_btn=unload_btn,

46
src/llmtuner/webui/components/sft.py
View File

@@ -12,8 +12,8 @@ from llmtuner.webui.utils import can_preview, get_preview, gen_plot
def create_sft_tab(top_elems: Dict[str, Component], runner: Runner) -> Dict[str, Component]:
with gr.Row():
dataset_dir = gr.Textbox(value=DEFAULT_DATA_DIR, interactive=True, scale=2)
dataset = gr.Dropdown(multiselect=True, interactive=True, scale=4)
dataset_dir = gr.Textbox(value=DEFAULT_DATA_DIR, scale=2)
dataset = gr.Dropdown(multiselect=True, scale=4)
preview_btn = gr.Button(interactive=False, scale=1)
preview_box, preview_count, preview_samples, close_btn = create_preview_box()
@@ -23,22 +23,21 @@ def create_sft_tab(top_elems: Dict[str, Component], runner: Runner) -> Dict[str,
preview_btn.click(get_preview, [dataset_dir, dataset], [preview_count, preview_samples, preview_box])
with gr.Row():
learning_rate = gr.Textbox(value="5e-5", interactive=True)
num_train_epochs = gr.Textbox(value="3.0", interactive=True)
max_samples = gr.Textbox(value="100000", interactive=True)
quantization_bit = gr.Dropdown([8, 4])
learning_rate = gr.Textbox(value="5e-5")
num_train_epochs = gr.Textbox(value="3.0")
max_samples = gr.Textbox(value="100000")
with gr.Row():
batch_size = gr.Slider(value=4, minimum=1, maximum=128, step=1, interactive=True)
gradient_accumulation_steps = gr.Slider(value=4, minimum=1, maximum=32, step=1, interactive=True)
batch_size = gr.Slider(value=4, minimum=1, maximum=128, step=1)
gradient_accumulation_steps = gr.Slider(value=4, minimum=1, maximum=32, step=1)
lr_scheduler_type = gr.Dropdown(
value="cosine", choices=[scheduler.value for scheduler in SchedulerType], interactive=True
value="cosine", choices=[scheduler.value for scheduler in SchedulerType]
)
fp16 = gr.Checkbox(value=True)
with gr.Row():
logging_steps = gr.Slider(value=5, minimum=5, maximum=1000, step=5, interactive=True)
save_steps = gr.Slider(value=100, minimum=10, maximum=2000, step=10, interactive=True)
logging_steps = gr.Slider(value=5, minimum=5, maximum=1000, step=5)
save_steps = gr.Slider(value=100, minimum=10, maximum=2000, step=10)
with gr.Row():
start_btn = gr.Button()
@@ -55,11 +54,25 @@ def create_sft_tab(top_elems: Dict[str, Component], runner: Runner) -> Dict[str,
start_btn.click(
runner.run_train,
[
top_elems["lang"], top_elems["model_name"], top_elems["checkpoints"],
top_elems["finetuning_type"], top_elems["template"],
dataset, dataset_dir, learning_rate, num_train_epochs, max_samples,
fp16, quantization_bit, batch_size, gradient_accumulation_steps,
lr_scheduler_type, logging_steps, save_steps, output_dir
top_elems["lang"],
top_elems["model_name"],
top_elems["checkpoints"],
top_elems["finetuning_type"],
top_elems["quantization_bit"],
top_elems["template"],
top_elems["source_prefix"],
dataset_dir,
dataset,
learning_rate,
num_train_epochs,
max_samples,
batch_size,
gradient_accumulation_steps,
lr_scheduler_type,
fp16,
logging_steps,
save_steps,
output_dir
],
[output_box]
)
@@ -79,7 +92,6 @@ def create_sft_tab(top_elems: Dict[str, Component], runner: Runner) -> Dict[str,
learning_rate=learning_rate,
num_train_epochs=num_train_epochs,
max_samples=max_samples,
quantization_bit=quantization_bit,
batch_size=batch_size,
gradient_accumulation_steps=gradient_accumulation_steps,
lr_scheduler_type=lr_scheduler_type,

25
src/llmtuner/webui/components/top.py
View File

@@ -6,29 +6,40 @@ from gradio.components import Component
from llmtuner.extras.constants import METHODS, SUPPORTED_MODELS
from llmtuner.extras.template import templates
from llmtuner.webui.common import list_checkpoint, get_model_path, save_config
from llmtuner.webui.utils import can_quantize
def create_top() -> Dict[str, Component]:
available_models = list(SUPPORTED_MODELS.keys()) + ["Custom"]
with gr.Row():
lang = gr.Dropdown(choices=["en", "zh"], value="en", interactive=True, scale=1)
lang = gr.Dropdown(choices=["en", "zh"], value="en", scale=1)
model_name = gr.Dropdown(choices=available_models, scale=3)
model_path = gr.Textbox(scale=3)
with gr.Row():
finetuning_type = gr.Dropdown(value="lora", choices=METHODS, interactive=True, scale=1)
template = gr.Dropdown(value="default", choices=list(templates.keys()), interactive=True, scale=1)
checkpoints = gr.Dropdown(multiselect=True, interactive=True, scale=4)
finetuning_type = gr.Dropdown(value="lora", choices=METHODS, scale=1)
checkpoints = gr.Dropdown(multiselect=True, scale=5)
refresh_btn = gr.Button(scale=1)
with gr.Row():
quantization_bit = gr.Dropdown([8, 4], scale=1)
template = gr.Dropdown(value="default", choices=list(templates.keys()), scale=2)
source_prefix = gr.Textbox(scale=4)
model_name.change(
list_checkpoint, [model_name, finetuning_type], [checkpoints]
).then(
get_model_path, [model_name], [model_path]
) # do not save config since the below line will save
model_path.change(save_config, [model_name, model_path])
finetuning_type.change(list_checkpoint, [model_name, finetuning_type], [checkpoints])
finetuning_type.change(
list_checkpoint, [model_name, finetuning_type], [checkpoints]
).then(
can_quantize, [finetuning_type], [quantization_bit]
)
refresh_btn.click(list_checkpoint, [model_name, finetuning_type], [checkpoints])
return dict(
@@ -38,5 +49,7 @@ def create_top() -> Dict[str, Component]:
finetuning_type=finetuning_type,
template=template,
checkpoints=checkpoints,
refresh_btn=refresh_btn
refresh_btn=refresh_btn,
quantization_bit=quantization_bit,
source_prefix=source_prefix
)