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update UI, fix #212

This commit is contained in:
hiyouga 2023-07-20 22:09:06 +08:00
parent c3fcb67486
commit 4d1641c1bf
12 changed files with 155 additions and 45 deletions
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2
src/llmtuner/chat/stream_chat.py
View File

@ -20,7 +20,7 @@ class ChatModel:
) -> None:
self.model, self.tokenizer = load_model_and_tokenizer(model_args, finetuning_args)
self.template = get_template(data_args.prompt_template)
self.source_prefix = data_args.source_prefix if data_args.source_prefix else ""
self.source_prefix = data_args.source_prefix or ""
self.generating_args = generating_args
def process_args(

2
src/llmtuner/extras/constants.py
View File

@ -36,7 +36,7 @@ SUPPORTED_MODELS = {
"InternLM-7B-Chat": "internlm/internlm-chat-7b"
}
DEFAULT_MODULE = { # will be deprecated
DEFAULT_MODULE = {
"LLaMA": "q_proj,v_proj",
"LLaMA2": "q_proj,v_proj",
"BLOOM": "query_key_value",

2
src/llmtuner/extras/template.py
View File

@ -215,7 +215,7 @@ Supports: https://huggingface.co/baichuan-inc/Baichuan-13B-Chat
register_template(
name="baichuan",
prefix="",
prompt=" <reserved_102> {query} <reserved_103> ",
prompt="<reserved_102>{query}<reserved_103>",
sep="</s>",
use_history=True
)

3
src/llmtuner/webui/chat.py
View File

@ -73,6 +73,7 @@ class WebChatModel(ChatModel):
chatbot: List[Tuple[str, str]],
query: str,
history: List[Tuple[str, str]],
prefix: str,
max_new_tokens: int,
top_p: float,
temperature: float
@ -80,7 +81,7 @@ class WebChatModel(ChatModel):
chatbot.append([query, ""])
response = ""
for new_text in self.stream_chat(
query, history, max_new_tokens=max_new_tokens, top_p=top_p, temperature=temperature
query, history, prefix, max_new_tokens=max_new_tokens, top_p=top_p, temperature=temperature
):
response += new_text
new_history = history + [(query, response)]

11
src/llmtuner/webui/components/chatbot.py
View File

@ -16,11 +16,9 @@ def create_chat_box(
with gr.Row():
with gr.Column(scale=4):
with gr.Column(scale=12):
query = gr.Textbox(show_label=False, lines=8)
with gr.Column(min_width=32, scale=1):
submit_btn = gr.Button(variant="primary")
prefix = gr.Textbox(show_label=False)
query = gr.Textbox(show_label=False, lines=8)
submit_btn = gr.Button(variant="primary")
with gr.Column(scale=1):
clear_btn = gr.Button()
@ -36,7 +34,7 @@ def create_chat_box(
submit_btn.click(
chat_model.predict,
[chatbot, query, history, max_new_tokens, top_p, temperature],
[chatbot, query, history, prefix, max_new_tokens, top_p, temperature],
[chatbot, history],
show_progress=True
).then(
@ -46,6 +44,7 @@ def create_chat_box(
clear_btn.click(lambda: ([], []), outputs=[chatbot, history], show_progress=True)
return chat_box, chatbot, history, dict(
prefix=prefix,
query=query,
submit_btn=submit_btn,
clear_btn=clear_btn,

3
src/llmtuner/webui/components/eval.py
View File

@ -31,7 +31,8 @@ def create_eval_tab(top_elems: Dict[str, Component], runner: Runner) -> Dict[str
start_btn = gr.Button()
stop_btn = gr.Button()
output_box = gr.Markdown()
with gr.Box():
output_box = gr.Markdown()
start_btn.click(
runner.run_eval,

2
src/llmtuner/webui/components/infer.py
View File

@ -12,7 +12,7 @@ def create_infer_tab(top_elems: Dict[str, Component]) -> Dict[str, Component]:
load_btn = gr.Button()
unload_btn = gr.Button()
info_box = gr.Markdown()
info_box = gr.Textbox(show_label=False, interactive=False)
chat_model = WebChatModel()
chat_box, chatbot, history, chat_elems = create_chat_box(chat_model)

37
src/llmtuner/webui/components/sft.py
View File

@ -35,12 +35,21 @@ def create_sft_tab(top_elems: Dict[str, Component], runner: Runner) -> Dict[str,
lr_scheduler_type = gr.Dropdown(
value="cosine", choices=[scheduler.value for scheduler in SchedulerType]
)
max_grad_norm = gr.Textbox(value="1.0")
dev_ratio = gr.Slider(value=0, minimum=0, maximum=1, step=0.001)
fp16 = gr.Checkbox(value=True)
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)
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)
compute_type = gr.Radio(choices=["fp16", "bf16"], value="fp16")
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, minimum=0, maximum=1, step=0.01, scale=1)
lora_target = gr.Textbox(scale=2)
with gr.Row():
start_btn = gr.Button()
@ -49,7 +58,9 @@ def create_sft_tab(top_elems: Dict[str, Component], runner: Runner) -> Dict[str,
with gr.Row():
with gr.Column(scale=4):
output_dir = gr.Textbox(interactive=True)
output_box = gr.Markdown()
with gr.Box():
output_box = gr.Markdown()
with gr.Column(scale=1):
loss_viewer = gr.Plot()
@ -74,10 +85,15 @@ def create_sft_tab(top_elems: Dict[str, Component], runner: Runner) -> Dict[str,
batch_size,
gradient_accumulation_steps,
lr_scheduler_type,
max_grad_norm,
dev_ratio,
fp16,
logging_steps,
save_steps,
warmup_steps,
compute_type,
lora_rank,
lora_dropout,
lora_target,
output_dir
],
[output_box]
@ -103,10 +119,17 @@ def create_sft_tab(top_elems: Dict[str, Component], runner: Runner) -> Dict[str,
batch_size=batch_size,
gradient_accumulation_steps=gradient_accumulation_steps,
lr_scheduler_type=lr_scheduler_type,
max_grad_norm=max_grad_norm,
dev_ratio=dev_ratio,
fp16=fp16,
advanced_tab=advanced_tab,
logging_steps=logging_steps,
save_steps=save_steps,
warmup_steps=warmup_steps,
compute_type=compute_type,
lora_tab=lora_tab,
lora_rank=lora_rank,
lora_dropout=lora_dropout,
lora_target=lora_target,
start_btn=start_btn,
stop_btn=stop_btn,
output_dir=output_dir,

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

@ -22,10 +22,11 @@ def create_top() -> Dict[str, Component]:
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)
with gr.Accordion(label="Advanced config", open=False) as advanced_tab:
with gr.Row():
quantization_bit = gr.Dropdown([8, 4], scale=1)
template = gr.Dropdown(value="default", choices=list(templates.keys()), scale=1)
source_prefix = gr.Textbox(scale=2)
model_name.change(
list_checkpoint, [model_name, finetuning_type], [checkpoints]
@ -47,9 +48,10 @@ def create_top() -> Dict[str, Component]:
model_name=model_name,
model_path=model_path,
finetuning_type=finetuning_type,
template=template,
checkpoints=checkpoints,
refresh_btn=refresh_btn,
advanced_tab=advanced_tab,
quantization_bit=quantization_bit,
template=template,
source_prefix=source_prefix
)

2
src/llmtuner/webui/interface.py
View File

@ -27,7 +27,7 @@ def create_ui() -> gr.Blocks:
with gr.Tab("Evaluate"):
eval_elems = create_eval_tab(top_elems, runner)
with gr.Tab("Inference"):
with gr.Tab("Chat"):
infer_elems = create_infer_tab(top_elems)
elem_list = [top_elems, sft_elems, eval_elems, infer_elems]

106
src/llmtuner/webui/locales.py
View File

@ -49,6 +49,14 @@ LOCALES = {
"value": "刷新断点"
}
},
"advanced_tab": {
"en": {
"label": "Advanced configurations"
},
"zh": {
"label": "高级设置"
}
},
"quantization_bit": {
"en": {
"label": "Quantization bit (optional)",
@ -71,12 +79,12 @@ LOCALES = {
},
"source_prefix": {
"en": {
"label": "Source prefix (optional)",
"info": "A sequence used as the prefix of each samples."
"label": "System prompt (optional)",
"info": "A sequence used as the default system prompt."
},
"zh": {
"label": "前缀序列（非必填）",
"info": "作为每个输入样本前缀的序列"
"label": "系统提示词（非必填）",
"info": "默认使用的系统提示词"
}
},
"dataset_dir": {
@ -209,6 +217,16 @@ LOCALES = {
"info": "采用的学习率调节器名称。"
}
},
"max_grad_norm": {
"en": {
"label": "Maximum gradient norm",
"info": "Norm for gradient clipping.."
},
"zh": {
"label": "最大梯度范数",
"info": "用于梯度裁剪的范数。"
}
},
"dev_ratio": {
"en": {
"label": "Dev ratio",
@ -219,20 +237,10 @@ LOCALES = {
"info": "验证集占全部样本的百分比。"
}
},
"fp16": {
"en": {
"label": "fp16",
"info": "Whether to use fp16 mixed precision training."
},
"zh": {
"label": "fp16",
"info": "是否启用 FP16 混合精度训练。"
}
},
"logging_steps": {
"en": {
"label": "Logging steps",
"info": "Number of update steps between two logs."
"info": "Number of steps between two logs."
},
"zh": {
"label": "日志间隔",
@ -242,13 +250,71 @@ LOCALES = {
"save_steps": {
"en": {
"label": "Save steps",
"info": "Number of updates steps between two checkpoints."
"info": "Number of steps between two checkpoints."
},
"zh": {
"label": "保存间隔",
"info": "每两次断点保存间的更新步数。"
}
},
"warmup_steps": {
"en": {
"label": "Warmup steps",
"info": "Number of steps used for warmup."
},
"zh": {
"label": "预热步数",
"info": "学习率预热采用的步数。"
}
},
"compute_type": {
"en": {
"label": "Compute type",
"info": "Whether to use fp16 or bf16 mixed precision training."
},
"zh": {
"label": "计算类型",
"info": "是否启用 FP16 或 BF16 混合精度训练。"
}
},
"lora_tab": {
"en": {
"label": "LoRA configurations"
},
"zh": {
"label": "LoRA 参数设置"
}
},
"lora_rank": {
"en": {
"label": "LoRA rank",
"info": "The rank of LoRA matrices."
},
"zh": {
"label": "LoRA 秩",
"info": "LoRA 矩阵的秩。"
}
},
"lora_dropout": {
"en": {
"label": "LoRA Dropout",
"info": "Dropout ratio of LoRA weights."
},
"zh": {
"label": "LoRA 随机丢弃",
"info": "LoRA 权重随机丢弃的概率。"
}
},
"lora_target": {
"en": {
"label": "LoRA modules (optional)",
"info": "The name(s) of target modules to apply LoRA. Use commas to separate multiple modules."
},
"zh": {
"label": "LoRA 作用层（非必填）",
"info": "应用 LoRA 的线性层名称。使用英文逗号分隔多个名称。"
}
},
"start_btn": {
"en": {
"value": "Start"
@ -323,6 +389,14 @@ LOCALES = {
"value": "模型未加载，请先加载模型。"
}
},
"prefix": {
"en": {
"placeholder": "System prompt (optional)"
},
"zh": {
"placeholder": "系统提示词（非必填）"
}
},
"query": {
"en": {
"placeholder": "Input..."

18
src/llmtuner/webui/runner.py
View File

@ -6,7 +6,7 @@ import transformers
from typing import List, Optional, Tuple
from llmtuner.extras.callbacks import LogCallback
from llmtuner.extras.constants import DEFAULT_MODULE # will be deprecated
from llmtuner.extras.constants import DEFAULT_MODULE
from llmtuner.extras.logging import LoggerHandler
from llmtuner.extras.misc import torch_gc
from llmtuner.tuner import get_train_args, run_sft
@ -77,10 +77,15 @@ class Runner:
batch_size: int,
gradient_accumulation_steps: int,
lr_scheduler_type: str,
max_grad_norm: str,
dev_ratio: float,
fp16: bool,
logging_steps: int,
save_steps: int,
warmup_steps: int,
compute_type: str,
lora_rank: int,
lora_dropout: float,
lora_target: str,
output_dir: str
):
model_name_or_path, error, logger_handler, trainer_callback = self.initialize(lang, model_name, dataset)
@ -99,7 +104,6 @@ class Runner:
model_name_or_path=model_name_or_path,
do_train=True,
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,
@ -115,9 +119,15 @@ class Runner:
per_device_train_batch_size=batch_size,
gradient_accumulation_steps=gradient_accumulation_steps,
lr_scheduler_type=lr_scheduler_type,
fp16=fp16,
max_grad_norm=float(max_grad_norm),
logging_steps=logging_steps,
save_steps=save_steps,
warmup_steps=warmup_steps,
fp16=(compute_type == "fp16"),
bf16=(compute_type == "bf16"),
lora_rank=lora_rank,
lora_dropout=lora_dropout,
lora_target=lora_target or DEFAULT_MODULE.get(model_name.split("-")[0], "q_proj,v_proj"),
output_dir=os.path.join(get_save_dir(model_name), finetuning_type, output_dir)
)