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gradio_demo.py
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gradio_demo.py
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import torch
from transformers import (
LlamaForCausalLM,
LlamaTokenizer,
StoppingCriteria,
)
import gradio as gr
import argparse
import os
from queue import Queue
from threading import Thread
import traceback
import gc
# Parse command-line arguments
parser = argparse.ArgumentParser()
parser.add_argument(
'--base_model',
default=None,
type=str,
required=True,
help='Base model path')
parser.add_argument('--lora_model', default=None, type=str,
help="If None, perform inference on the base model")
parser.add_argument(
'--tokenizer_path',
default=None,
type=str,
help='If None, lora model path or base model path will be used')
parser.add_argument(
'--gpus',
default="0",
type=str,
help='If None, cuda:0 will be used. Inference using multi-cards: --gpus=0,1,... ')
parser.add_argument('--share', default=True, help='Share gradio domain name')
parser.add_argument('--port', default=19324, type=int, help='Port of gradio demo')
parser.add_argument(
'--max_memory',
default=256,
type=int,
help='Maximum input prompt length, if exceeded model will receive prompt[-max_memory:]')
parser.add_argument(
'--load_in_8bit',
action='store_true',
help='Use 8 bit quantified model')
parser.add_argument(
'--only_cpu',
action='store_true',
help='Only use CPU for inference')
parser.add_argument(
'--alpha',
type=str,
default="1.0",
help="The scaling factor of NTK method, can be a float or 'auto'. ")
args = parser.parse_args()
if args.only_cpu is True:
args.gpus = ""
from patches import apply_attention_patch, apply_ntk_scaling_patch
apply_attention_patch(use_memory_efficient_attention=True)
apply_ntk_scaling_patch(args.alpha)
# Set CUDA devices if available
os.environ["CUDA_VISIBLE_DEVICES"] = args.gpus
# Peft library can only import after setting CUDA devices
from peft import PeftModel
# Set up the required components: model and tokenizer
def setup():
global tokenizer, model, device, share, port, max_memory
max_memory = args.max_memory
port = args.port
share = args.share
load_in_8bit = args.load_in_8bit
load_type = torch.float16
if torch.cuda.is_available():
device = torch.device(0)
else:
device = torch.device('cpu')
if args.tokenizer_path is None:
args.tokenizer_path = args.lora_model
if args.lora_model is None:
args.tokenizer_path = args.base_model
tokenizer = LlamaTokenizer.from_pretrained(args.tokenizer_path)
base_model = LlamaForCausalLM.from_pretrained(
args.base_model,
load_in_8bit=load_in_8bit,
torch_dtype=load_type,
low_cpu_mem_usage=True,
device_map='auto',
)
model_vocab_size = base_model.get_input_embeddings().weight.size(0)
tokenzier_vocab_size = len(tokenizer)
print(f"Vocab of the base model: {model_vocab_size}")
print(f"Vocab of the tokenizer: {tokenzier_vocab_size}")
if model_vocab_size != tokenzier_vocab_size:
assert tokenzier_vocab_size > model_vocab_size
print("Resize model embeddings to fit tokenizer")
base_model.resize_token_embeddings(tokenzier_vocab_size)
if args.lora_model is not None:
print("loading peft model")
model = PeftModel.from_pretrained(
base_model,
args.lora_model,
torch_dtype=load_type,
device_map='auto',
)
else:
model = base_model
if device == torch.device('cpu'):
model.float()
model.eval()
# Reset the user input
def reset_user_input():
return gr.update(value='')
# Reset the state
def reset_state():
return []
# Generate the prompt for the input of LM model
def generate_prompt(instruction):
return f"""
Below is an instruction that describes a task. Write a response that appropriately completes the request.
{instruction}
"""
# User interaction function for chat
def user(user_message, history):
return gr.update(value="", interactive=False), history + \
[[user_message, None]]
class Stream(StoppingCriteria):
def __init__(self, callback_func=None):
self.callback_func = callback_func
def __call__(self, input_ids, scores) -> bool:
if self.callback_func is not None:
self.callback_func(input_ids[0])
return False
class Iteratorize:
"""
Transforms a function that takes a callback
into a lazy iterator (generator).
Adapted from: https://stackoverflow.com/a/9969000
"""
def __init__(self, func, kwargs=None, callback=None):
self.mfunc = func
self.c_callback = callback
self.q = Queue()
self.sentinel = object()
self.kwargs = kwargs or {}
self.stop_now = False
def _callback(val):
if self.stop_now:
raise ValueError
self.q.put(val)
def gentask():
try:
ret = self.mfunc(callback=_callback, **self.kwargs)
except ValueError:
pass
except Exception:
traceback.print_exc()
clear_torch_cache()
self.q.put(self.sentinel)
if self.c_callback:
self.c_callback(ret)
self.thread = Thread(target=gentask)
self.thread.start()
def __iter__(self):
return self
def __next__(self):
obj = self.q.get(True, None)
if obj is self.sentinel:
raise StopIteration
else:
return obj
def __del__(self):
clear_torch_cache()
def __enter__(self):
return self
def __exit__(self, exc_type, exc_val, exc_tb):
self.stop_now = True
clear_torch_cache()
def clear_torch_cache():
gc.collect()
if torch.cuda.device_count() > 0:
torch.cuda.empty_cache()
# Perform prediction based on the user input and history
@torch.no_grad()
def predict(
history,
max_new_tokens=128,
top_p=0.75,
temperature=0.1,
top_k=40,
do_sample=True,
repetition_penalty=1.0
):
history[-1][1] = ""
if len(history) != 0:
input = "".join(["### Instruction:\n" +
i[0] +
"\n\n" +
"### Response: " +
i[1] +
("\n\n" if i[1] != "" else "") for i in history])
if len(input) > max_memory:
input = input[-max_memory:]
prompt = generate_prompt(input)
inputs = tokenizer(prompt, return_tensors="pt")
input_ids = inputs["input_ids"].to(device)
generate_params = {
'input_ids': input_ids,
'max_new_tokens': max_new_tokens,
'top_p': top_p,
'temperature': temperature,
'top_k': top_k,
'do_sample': do_sample,
'repetition_penalty': repetition_penalty,
}
def generate_with_callback(callback=None, **kwargs):
if 'stopping_criteria' in kwargs:
kwargs['stopping_criteria'].append(Stream(callback_func=callback))
else:
kwargs['stopping_criteria'] = [Stream(callback_func=callback)]
clear_torch_cache()
with torch.no_grad():
model.generate(**kwargs)
def generate_with_streaming(**kwargs):
return Iteratorize(generate_with_callback, kwargs, callback=None)
with generate_with_streaming(**generate_params) as generator:
for output in generator:
next_token_ids = output[len(input_ids[0]):]
if next_token_ids[0] == tokenizer.eos_token_id:
break
new_tokens = tokenizer.decode(
next_token_ids, skip_special_tokens=True)
if isinstance(tokenizer, LlamaTokenizer) and len(next_token_ids) > 0:
if tokenizer.convert_ids_to_tokens(int(next_token_ids[0])).startswith('▁'):
new_tokens = ' ' + new_tokens
history[-1][1] = new_tokens
yield history
if len(next_token_ids) >= max_new_tokens:
break
# Call the setup function to initialize the components
setup()
# Create the Gradio interface
with gr.Blocks() as demo:
github_banner_path = 'https://raw.githubusercontent.com/ymcui/Chinese-LLaMA-Alpaca/main/pics/banner.png'
gr.HTML(f'<p align="center"><a href="https://github.com/ymcui/Chinese-LLaMA-Alpaca"><img src={github_banner_path} width="700"/></a></p>')
gr.Markdown("> 为了促进大模型在中文NLP社区的开放研究，本项目开源了中文LLaMA模型和指令精调的Alpaca大模型。这些模型在原版LLaMA的基础上扩充了中文词表并使用了中文数据进行二次预训练，进一步提升了中文基础语义理解能力。同时，中文Alpaca模型进一步使用了中文指令数据进行精调，显著提升了模型对指令的理解和执行能力。")
chatbot = gr.Chatbot()
with gr.Row():
with gr.Column(scale=4):
with gr.Column(scale=12):
user_input = gr.Textbox(
show_label=False,
placeholder="Shift + Enter发送消息...",
lines=10).style(
container=False)
with gr.Column(min_width=32, scale=1):
submitBtn = gr.Button("Submit", variant="primary")
with gr.Column(scale=1):
emptyBtn = gr.Button("Clear History")
max_new_token = gr.Slider(
0,
4096,
value=512,
step=1.0,
label="Maximum New Token Length",
interactive=True)
top_p = gr.Slider(0, 1, value=0.9, step=0.01,
label="Top P", interactive=True)
temperature = gr.Slider(
0,
1,
value=0.5,
step=0.01,
label="Temperature",
interactive=True)
top_k = gr.Slider(1, 40, value=40, step=1,
label="Top K", interactive=True)
do_sample = gr.Checkbox(
value=True,
label="Do Sample",
info="use random sample strategy",
interactive=True)
repetition_penalty = gr.Slider(
1.0,
3.0,
value=1.1,
step=0.1,
label="Repetition Penalty",
interactive=True)
params = [user_input, chatbot]
predict_params = [
chatbot,
max_new_token,
top_p,
temperature,
top_k,
do_sample,
repetition_penalty]
submitBtn.click(
user,
params,
params,
queue=False).then(
predict,
predict_params,
chatbot).then(
lambda: gr.update(
interactive=True),
None,
[user_input],
queue=False)
user_input.submit(
user,
params,
params,
queue=False).then(
predict,
predict_params,
chatbot).then(
lambda: gr.update(
interactive=True),
None,
[user_input],
queue=False)
submitBtn.click(reset_user_input, [], [user_input])
emptyBtn.click(reset_state, outputs=[chatbot], show_progress=True)
# Launch the Gradio interface
demo.queue().launch(
share=share,
inbrowser=True,
server_name='0.0.0.0',
server_port=port)