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alpaca_chatbot.py
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alpaca_chatbot.py
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import os
import sys
import random
import fire
import gradio as gr
import torch
import transformers
from peft import PeftModel
from transformers import GenerationConfig
from transformers import LlamaForCausalLM
from transformers import LlamaTokenizer
import mdtex2html
from multi_turn_alpaca.utils.prompter import Prompter
if torch.cuda.is_available():
device = "cuda"
else:
device = "cpu"
load_8bit: bool = False
base_model: str = "decapoda-research/llama-7b-hf"
lora_weights: str = "./multi-turn-alpaca"
prompt_template: str = "multi_turn" # The prompt template to use, will default to alpaca.
prompter = Prompter(prompt_template)
tokenizer = LlamaTokenizer.from_pretrained(base_model)
model = LlamaForCausalLM.from_pretrained(
base_model,
load_in_8bit=load_8bit,
torch_dtype=torch.float16,
device_map="auto",
)
model = PeftModel.from_pretrained(
model,
lora_weights,
torch_dtype=torch.float16,
)
# unwind broken decapoda-research config
model.config.pad_token_id = tokenizer.pad_token_id = 0 # unk
model.config.bos_token_id = 1
model.config.eos_token_id = 2
if not load_8bit:
model.half() # seems to fix bugs for some users.
model.eval()
if torch.__version__ >= "2" and sys.platform != "win32":
model = torch.compile(model)
"""Override Chatbot.postprocess"""
def postprocess(self, y):
if y is None:
return []
for i, (message, response) in enumerate(y):
y[i] = (
None if message is None else mdtex2html.convert((message)),
None if response is None else mdtex2html.convert(response),
)
return y
gr.Chatbot.postprocess = postprocess
def parse_text(text):
"""copy from https://github.com/GaiZhenbiao/ChuanhuChatGPT/"""
lines = text.split("\n")
lines = [line for line in lines if line != ""]
count = 0
for i, line in enumerate(lines):
if "```" in line:
count += 1
items = line.split('`')
if count % 2 == 1:
lines[i] = f'<pre><code class="language-{items[-1]}">'
else:
lines[i] = f'<br></code></pre>'
else:
if i > 0:
if count % 2 == 1:
line = line.replace("`", "\`")
line = line.replace("<", "<")
line = line.replace(">", ">")
line = line.replace(" ", " ")
line = line.replace("*", "*")
line = line.replace("_", "_")
line = line.replace("-", "-")
line = line.replace(".", ".")
line = line.replace("!", "!")
line = line.replace("(", "(")
line = line.replace(")", ")")
line = line.replace("$", "$")
lines[i] = "<br>"+line
text = "".join(lines)
return text
def predict(input, chatbot, max_new_tokens, num_beams, do_sample, temperature, top_p, history):
current_utterance = 'user: {instruction} assistant: '.format(instruction=input)
if history:
instruction = history + ' ' + current_utterance
else:
instruction = current_utterance
prompt = prompter.generate_prompt(instruction)
print('prompt: ' + prompt)
inputs = tokenizer(prompt, return_tensors="pt")
input_ids = inputs["input_ids"].to(device)
generation_config = GenerationConfig(
max_new_tokens=max_new_tokens,
num_beams=num_beams,
do_sample=do_sample,
temperature=temperature,
top_p=top_p
)
with torch.no_grad():
generation_output = model.generate(
input_ids=input_ids,
generation_config=generation_config,
return_dict_in_generate=True,
output_scores=True,
)
s = generation_output.sequences[0]
output = tokenizer.decode(s)
response = output[len(prompt):]
chatbot.append((parse_text(input), parse_text(response)))
history = instruction + response
return chatbot, history
def reset_user_input():
return gr.update(value='')
def reset_state():
return [], ''
with gr.Blocks() as demo:
gr.HTML("""<h1 align="center">Multi-turn Alpaca (主要支持英文)</h1>""")
gr.HTML("""<h1 align="center"><a href="https://github.com/l294265421/multi-turn-alpaca">GitHub</a></h1>""")
chatbot = gr.Chatbot()
with gr.Row():
with gr.Column(scale=4):
with gr.Column(scale=12):
user_input = gr.Textbox(show_label=False, placeholder="Input...", lines=7).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_tokens = gr.Slider(0, 1024, value=128, step=1.0, label="Maximum Token Number", interactive=True)
num_beams = gr.components.Slider(
minimum=1, maximum=4, step=1, value=1, label="Beams"
)
with gr.Column(scale=1):
do_sample = gr.components.Checkbox(value=False, label='Do sample')
temperature = gr.Slider(0, 1, value=0.95, step=0.01, label="Temperature", interactive=True)
top_p = gr.Slider(0, 1, value=0.7, step=0.01, label="Top P", interactive=True)
history = gr.State('')
# input, chatbot, max_new_tokens, num_beams, do_sample, temperature, top_p, history
submitBtn.click(predict, [user_input, chatbot, max_new_tokens, num_beams, do_sample, temperature, top_p, history],
[chatbot, history],
show_progress=True)
submitBtn.click(reset_user_input, [], [user_input])
emptyBtn.click(reset_state, outputs=[chatbot, history], show_progress=True)
server_name = '0.0.0.0'
share_gradio = True
result = demo.queue().launch(server_name=server_name, share=share_gradio, prevent_thread_lock=True)
print('result:')
print(result[2], flush=True)
demo.block_thread()