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import os
import gc
import torch
import torch.nn as nn
import argparse
import gradio as gr
from transformers import AutoTokenizer, LlamaForCausalLM
from utils import SteamGenerationMixin
class MindBot(object):
def __init__(self, model_path, tokenizer_path,if_int8=False):
# self.device = torch.device("cuda")
# device_ids = [1, 2]
if if_int8:
self.model = SteamGenerationMixin.from_pretrained(model_path, device_map='auto', load_in_8bit=True).eval()
else:
self.model = SteamGenerationMixin.from_pretrained(model_path, device_map='auto').half().eval()
self.tokenizer = AutoTokenizer.from_pretrained(tokenizer_path)
# sp_tokens = {'additional_special_tokens': ['<human>', '<bot>']}
# self.tokenizer.add_special_tokens(sp_tokens)
self.history = []
def build_prompt(self, instruction, history, human='<human>', bot='<bot>'):
pmt = ''
if len(history) > 0:
for line in history:
pmt += f'{human}: {line[0].strip()}\n{bot}: {line[1]}\n'
pmt += f'{human}: {instruction.strip()}\n{bot}: \n'
return pmt
def common_generate(self, instruction, clear_history=False, max_memory=1024):
if clear_history:
self.history = []
prompt = self.build_prompt(instruction, self.history)
input_ids = self.tokenizer(prompt, return_tensors="pt").input_ids
if input_ids.shape[1] > max_memory:
input_ids = input_ids[:, -max_memory:]
prompt_len = input_ids.shape[1]
# common method
generation_output = self.model.generate(
input_ids.cuda(),
max_new_tokens=1024,
do_sample=True,
top_p=0.85,
temperature=0.8,
repetition_penalty=1.,
eos_token_id=2,
bos_token_id=1,
pad_token_id=0
)
s = generation_output[0][prompt_len:]
output = self.tokenizer.decode(s, skip_special_tokens=True)
# output = output
output = output.replace("Belle", "IDEA")
self.history.append((instruction, output))
print('api history: ======> \n', self.history)
return output
def interaction(
self,
instruction,
history,
max_memory=1024
):
prompt = self.build_prompt(instruction, history)
input_ids = self.tokenizer(prompt, return_tensors="pt").input_ids
if input_ids.shape[1] > max_memory:
input_ids = input_ids[:, -max_memory:]
prompt_len = input_ids.shape[1]
# stream generation method
try:
tmp = history.copy()
output = ''
with torch.no_grad():
for generation_output in self.model.stream_generate(
input_ids.cuda(),
max_new_tokens=1024,
do_sample=True,
top_p=0.85,
temperature=0.8,
repetition_penalty=1.,
eos_token_id=2,
bos_token_id=1,
pad_token_id=0
):
s = generation_output[0][prompt_len:]
output = self.tokenizer.decode(s, skip_special_tokens=True)
output = output.replace('\n', '<br>')
tmp.append((instruction, output))
yield '', tmp
tmp.pop()
# gc.collect()
# torch.cuda.empty_cache()
history.append((instruction, output))
print('input -----> \n', prompt)
print('output -------> \n', output)
print('history: ======> \n', history)
except torch.cuda.OutOfMemoryError:
gc.collect()
torch.cuda.empty_cache()
self.model.empty_cache()
return "", history
def new_chat_bot(self):
with gr.Blocks(title='IDEA MindBot', css=".gradio-container {max-width: 50% !important;} .bgcolor {color: white !important; background: #FFA500 !important;}") as demo:
gr.Markdown("<center><h1>IDEA MindBot</h1></center>")
gr.Markdown("<center>本页面基于hugging face支持的设备搭建</center>")
with gr.Row():
chatbot = gr.Chatbot(label='MindBot').style(height=500)
with gr.Row():
msg = gr.Textbox(label="Input")
with gr.Row():
with gr.Column(scale=0.5):
clear = gr.Button("Clear")
with gr.Column(scale=0.5):
submit = gr.Button("Submit", elem_classes='bgcolor')
msg.submit(self.interaction, [msg, chatbot], [msg, chatbot])
clear.click(lambda: None, None, chatbot, queue=False)
submit.click(self.interaction, [msg, chatbot], [msg, chatbot])
return demo.queue(concurrency_count=5)
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument(
"--model_path",
type=str,
default="/cognitive_comp/songchao/checkpoints/global_step3200-hf"
)
args = parser.parse_args()
mind_bot = MindBot(args.model_path)
demo = mind_bot.new_chat_bot()
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