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update webUI, fix #179

This commit is contained in:
hiyouga 2023-07-18 15:35:17 +08:00
parent b9fe83fb75
commit 12d8a8633f
9 changed files with 247 additions and 154 deletions
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2
src/llmtuner/tuner/ppo/trainer.py
View File

@ -108,7 +108,7 @@ class PPOPeftTrainer(PPOTrainer, PeftTrainer):
replace_model(unwrapped_model, target="reward")
with torch.no_grad():
_, _, values = self.model(**self.prepare_model_inputs(queries, responses))
rewards = [reward for reward in values[-1].to(torch.float32)] # use float32 type
rewards = [reward for reward in values[:, -1].to(torch.float32)] # use float32 type
replace_model(unwrapped_model, target="default")
# Run PPO step

17
src/llmtuner/webui/chat.py
View File

@ -17,8 +17,14 @@ class WebChatModel(ChatModel):
self.generating_args = GeneratingArguments()
def load_model(
self, lang: str, model_name: str, checkpoints: list,
finetuning_type: str, template: str, quantization_bit: str
self,
lang: str,
model_name: str,
checkpoints: List[str],
finetuning_type: str,
quantization_bit: str,
template: str,
source_prefix: str
):
if self.model is not None:
yield ALERTS["err_exists"][lang]
@ -43,10 +49,11 @@ class WebChatModel(ChatModel):
yield ALERTS["info_loading"][lang]
args = dict(
model_name_or_path=model_name_or_path,
finetuning_type=finetuning_type,
prompt_template=template,
checkpoint_dir=checkpoint_dir,
quantization_bit=int(quantization_bit) if quantization_bit else None
finetuning_type=finetuning_type,
quantization_bit=int(quantization_bit) if quantization_bit else None,
prompt_template=template,
source_prefix=source_prefix
)
super().__init__(*get_infer_args(args))

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
)

174
src/llmtuner/webui/locales.py
View File

@ -25,6 +25,14 @@ LOCALES = {
"info": "本地模型的文件路径或 Hugging Face 的模型标识符。"
}
},
"finetuning_type": {
"en": {
"label": "Finetuning method"
},
"zh": {
"label": "微调方法"
}
},
"checkpoints": {
"en": {
"label": "Checkpoints"
@ -33,14 +41,6 @@ LOCALES = {
"label": "模型断点"
}
},
"template": {
"en": {
"label": "Prompt template"
},
"zh": {
"label": "提示模板"
}
},
"refresh_btn": {
"en": {
"value": "Refresh checkpoints"
@ -49,6 +49,36 @@ LOCALES = {
"value": "刷新断点"
}
},
"quantization_bit": {
"en": {
"label": "Quantization bit (optional)",
"info": "Enable 4/8-bit model quantization."
},
"zh": {
"label": "量化等级（非必填）",
"info": "启用 4/8 比特模型量化。"
}
},
"template": {
"en": {
"label": "Prompt template",
"info": "The template used in constructing prompts."
},
"zh": {
"label": "提示模板",
"info": "构建提示词时使用的模板"
}
},
"source_prefix": {
"en": {
"label": "Source prefix (optional)",
"info": "A sequence used as the prefix of each samples."
},
"zh": {
"label": "前缀序列（非必填）",
"info": "作为每个输入样本前缀的序列"
}
},
"dataset_dir": {
"en": {
"label": "Data dir",
@ -99,68 +129,6 @@ LOCALES = {
"value": "关闭"
}
},
"max_samples": {
"en": {
"label": "Max samples",
"info": "Maximum samples per dataset."
},
"zh": {
"label": "最大样本数",
"info": "每个数据集最多使用的样本数。"
}
},
"batch_size": {
"en": {
"label": "Batch size",
"info": "Number of samples to process per GPU."
},
"zh":{
"label": "批处理大小",
"info": "每块 GPU 上处理的样本数量。"
}
},
"quantization_bit": {
"en": {
"label": "Quantization bit",
"info": "Enable 4/8-bit model quantization."
},
"zh": {
"label": "量化",
"info": "启用 4/8 比特模型量化。"
}
},
"start_btn": {
"en": {
"value": "Start"
},
"zh": {
"value": "开始"
}
},
"stop_btn": {
"en": {
"value": "Abort"
},
"zh": {
"value": "中断"
}
},
"output_box": {
"en": {
"value": "Ready."
},
"zh": {
"value": "准备就绪。"
}
},
"finetuning_type": {
"en": {
"label": "Finetuning method"
},
"zh": {
"label": "微调方法"
}
},
"learning_rate": {
"en": {
"label": "Learning rate",
@ -181,6 +149,26 @@ LOCALES = {
"info": "需要执行的训练总轮数。"
}
},
"max_samples": {
"en": {
"label": "Max samples",
"info": "Maximum samples per dataset."
},
"zh": {
"label": "最大样本数",
"info": "每个数据集最多使用的样本数。"
}
},
"batch_size": {
"en": {
"label": "Batch size",
"info": "Number of samples to process per GPU."
},
"zh":{
"label": "批处理大小",
"info": "每块 GPU 上处理的样本数量。"
}
},
"gradient_accumulation_steps": {
"en": {
"label": "Gradient accumulation",
@ -231,6 +219,22 @@ LOCALES = {
"info": "每两次断点保存间的更新步数。"
}
},
"start_btn": {
"en": {
"value": "Start"
},
"zh": {
"value": "开始"
}
},
"stop_btn": {
"en": {
"value": "Abort"
},
"zh": {
"value": "中断"
}
},
"output_dir": {
"en": {
"label": "Checkpoint name",
@ -241,6 +245,14 @@ LOCALES = {
"info": "保存模型断点的文件夹名称。"
}
},
"output_box": {
"en": {
"value": "Ready."
},
"zh": {
"value": "准备就绪。"
}
},
"loss_viewer": {
"en": {
"label": "Loss"
@ -257,14 +269,6 @@ LOCALES = {
"label": "保存预测结果"
}
},
"info_box": {
"en": {
"value": "Model unloaded, please load a model first."
},
"zh": {
"value": "模型未加载，请先加载模型。"
}
},
"load_btn": {
"en": {
"value": "Load model"
@ -281,6 +285,14 @@ LOCALES = {
"value": "卸载模型"
}
},
"info_box": {
"en": {
"value": "Model unloaded, please load a model first."
},
"zh": {
"value": "模型未加载，请先加载模型。"
}
},
"query": {
"en": {
"placeholder": "Input..."
@ -305,12 +317,12 @@ LOCALES = {
"value": "清空历史"
}
},
"max_new_tokens": {
"max_length": {
"en": {
"label": "Maximum new tokens"
"label": "Maximum length"
},
"zh": {
"label": "最大生成长度"
"label": "最大长度"
}
},
"top_p": {

83
src/llmtuner/webui/runner.py
View File

@ -3,7 +3,7 @@ import os
import threading
import time
import transformers
from typing import Optional, Tuple
from typing import List, Optional, Tuple
from llmtuner.extras.callbacks import LogCallback
from llmtuner.extras.constants import DEFAULT_MODULE # will be deprecated
@ -59,10 +59,26 @@ class Runner:
return finish_info if finish_info is not None else ALERTS["info_finished"][lang]
def run_train(
self, lang, model_name, checkpoints, finetuning_type, 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
self,
lang: str,
model_name: str,
checkpoints: List[str],
finetuning_type: str,
quantization_bit: str,
template: str,
source_prefix: str,
dataset_dir: str,
dataset: List[str],
learning_rate: str,
num_train_epochs: str,
max_samples: str,
batch_size: int,
gradient_accumulation_steps: int,
lr_scheduler_type: str,
fp16: bool,
logging_steps: int,
save_steps: int,
output_dir: str
):
model_name_or_path, error, logger_handler, trainer_callback = self.initialize(lang, model_name, dataset)
if error:
@ -79,24 +95,25 @@ class Runner:
args = dict(
model_name_or_path=model_name_or_path,
do_train=True,
finetuning_type=finetuning_type,
lora_target=DEFAULT_MODULE.get(model_name.split("-")[0], None) or "q_proj,v_proj",
prompt_template=template,
dataset=",".join(dataset),
dataset_dir=dataset_dir,
max_samples=int(max_samples),
output_dir=os.path.join(get_save_dir(model_name), finetuning_type, output_dir),
checkpoint_dir=checkpoint_dir,
overwrite_cache=True,
lora_target=DEFAULT_MODULE.get(model_name.split("-")[0], None) or "q_proj,v_proj",
checkpoint_dir=checkpoint_dir,
finetuning_type=finetuning_type,
quantization_bit=int(quantization_bit) if quantization_bit else None,
prompt_template=template,
source_prefix=source_prefix,
dataset_dir=dataset_dir,
dataset=",".join(dataset),
learning_rate=float(learning_rate),
num_train_epochs=float(num_train_epochs),
max_samples=int(max_samples),
per_device_train_batch_size=batch_size,
gradient_accumulation_steps=gradient_accumulation_steps,
lr_scheduler_type=lr_scheduler_type,
fp16=fp16,
logging_steps=logging_steps,
save_steps=save_steps,
learning_rate=float(learning_rate),
num_train_epochs=float(num_train_epochs),
fp16=fp16,
quantization_bit=int(quantization_bit) if quantization_bit else None
output_dir=os.path.join(get_save_dir(model_name), finetuning_type, output_dir)
)
model_args, data_args, training_args, finetuning_args, _ = get_train_args(args)
@ -120,8 +137,19 @@ class Runner:
yield self.finalize(lang)
def run_eval(
self, lang, model_name, checkpoints, finetuning_type, template,
dataset, dataset_dir, max_samples, batch_size, quantization_bit, predict
self,
lang: str,
model_name: str,
checkpoints: List[str],
finetuning_type: str,
quantization_bit: str,
template: str,
source_prefix: str,
dataset_dir: str,
dataset: List[str],
max_samples: str,
batch_size: int,
predict: bool
):
model_name_or_path, error, logger_handler, trainer_callback = self.initialize(lang, model_name, dataset)
if error:
@ -140,17 +168,18 @@ class Runner:
args = dict(
model_name_or_path=model_name_or_path,
do_eval=True,
finetuning_type=finetuning_type,
prompt_template=template,
dataset=",".join(dataset),
dataset_dir=dataset_dir,
max_samples=int(max_samples),
output_dir=output_dir,
checkpoint_dir=checkpoint_dir,
overwrite_cache=True,
predict_with_generate=True,
checkpoint_dir=checkpoint_dir,
finetuning_type=finetuning_type,
quantization_bit=int(quantization_bit) if quantization_bit else None,
prompt_template=template,
source_prefix=source_prefix,
dataset_dir=dataset_dir,
dataset=",".join(dataset),
max_samples=int(max_samples),
per_device_eval_batch_size=batch_size,
quantization_bit=int(quantization_bit) if quantization_bit else None
output_dir=output_dir
)
if predict:

17
src/llmtuner/webui/utils.py
View File

@ -3,7 +3,7 @@ import json
import gradio as gr
import matplotlib.figure
import matplotlib.pyplot as plt
from typing import Tuple
from typing import Any, Dict, Tuple
from datetime import datetime
from llmtuner.extras.ploting import smooth
@ -23,7 +23,7 @@ def get_time() -> str:
return datetime.now().strftime('%Y-%m-%d-%H-%M-%S')
def can_preview(dataset_dir: str, dataset: list) -> dict:
def can_preview(dataset_dir: str, dataset: list) -> Dict[str, Any]:
with open(os.path.join(dataset_dir, DATA_CONFIG), "r", encoding="utf-8") as f:
dataset_info = json.load(f)
if (
@ -36,7 +36,7 @@ def can_preview(dataset_dir: str, dataset: list) -> dict:
return gr.update(interactive=False)
def get_preview(dataset_dir: str, dataset: list) -> Tuple[int, list, dict]:
def get_preview(dataset_dir: str, dataset: list) -> Tuple[int, list, Dict[str, Any]]:
with open(os.path.join(dataset_dir, DATA_CONFIG), "r", encoding="utf-8") as f:
dataset_info = json.load(f)
data_file = dataset_info[dataset[0]]["file_name"]
@ -45,6 +45,13 @@ def get_preview(dataset_dir: str, dataset: list) -> Tuple[int, list, dict]:
return len(data), data[:2], gr.update(visible=True)
def can_quantize(finetuning_type: str) -> Dict[str, Any]:
if finetuning_type != "lora":
return gr.update(value="", interactive=False)
else:
return gr.update(interactive=True)
def get_eval_results(path: os.PathLike) -> str:
with open(path, "r", encoding="utf-8") as f:
result = json.dumps(json.load(f), indent=4)
@ -66,6 +73,10 @@ def gen_plot(base_model: str, finetuning_type: str, output_dir: str) -> matplotl
if log_info.get("loss", None):
steps.append(log_info["current_steps"])
losses.append(log_info["loss"])
if len(losses) == 0:
return None
ax.plot(steps, losses, alpha=0.4, label="original")
ax.plot(steps, smooth(losses), label="smoothed")
ax.legend()