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from transformers import AutoModel, AutoTokenizer
import time
import os
import json
import threading
import importlib
from toolbox import update_ui, get_conf
from multiprocessing import Process, Pipe
load_message = "ChatGLMFT尚未加载，加载需要一段时间。注意，取决于`config.py`的配置，ChatGLMFT消耗大量的内存（CPU）或显存（GPU），也许会导致低配计算机卡死 ……"
def string_to_options(arguments):
import argparse
import shlex
# Create an argparse.ArgumentParser instance
parser = argparse.ArgumentParser()
# Add command-line arguments
parser.add_argument("--llm_to_learn", type=str, help="LLM model to learn", default="gpt-3.5-turbo")
parser.add_argument("--prompt_prefix", type=str, help="Prompt prefix", default='')
parser.add_argument("--system_prompt", type=str, help="System prompt", default='')
parser.add_argument("--batch", type=int, help="System prompt", default=50)
# Parse the arguments
args = parser.parse_args(shlex.split(arguments))
return args
#################################################################################
class GetGLMFTHandle(Process):
def __init__(self):
super().__init__(daemon=True)
self.parent, self.child = Pipe()
self.chatglmft_model = None
self.chatglmft_tokenizer = None
self.info = ""
self.success = True
self.check_dependency()
self.start()
self.threadLock = threading.Lock()
def check_dependency(self):
try:
import sentencepiece
self.info = "依赖检测通过"
self.success = True
except:
self.info = "缺少ChatGLMFT的依赖，如果要使用ChatGLMFT，除了基础的pip依赖以外，您还需要运行`pip install -r request_llm/requirements_chatglm.txt`安装ChatGLM的依赖。"
self.success = False
def ready(self):
return self.chatglmft_model is not None
def run(self):
# 子进程执行
# 第一次运行，加载参数
retry = 0
while True:
try:
if self.chatglmft_model is None:
from transformers import AutoConfig
import torch
# conf = 'request_llm/current_ptune_model.json'
# if not os.path.exists(conf): raise RuntimeError('找不到微调模型信息')
# with open(conf, 'r', encoding='utf8') as f:
# model_args = json.loads(f.read())
CHATGLM_PTUNING_CHECKPOINT, = get_conf('CHATGLM_PTUNING_CHECKPOINT')
assert os.path.exists(CHATGLM_PTUNING_CHECKPOINT), "找不到微调模型检查点"
conf = os.path.join(CHATGLM_PTUNING_CHECKPOINT, "config.json")
with open(conf, 'r', encoding='utf8') as f:
model_args = json.loads(f.read())
if 'model_name_or_path' not in model_args:
model_args['model_name_or_path'] = model_args['_name_or_path']
self.chatglmft_tokenizer = AutoTokenizer.from_pretrained(
model_args['model_name_or_path'], trust_remote_code=True)
config = AutoConfig.from_pretrained(
model_args['model_name_or_path'], trust_remote_code=True)
config.pre_seq_len = model_args['pre_seq_len']
config.prefix_projection = model_args['prefix_projection']
print(f"Loading prefix_encoder weight from {CHATGLM_PTUNING_CHECKPOINT}")
model = AutoModel.from_pretrained(model_args['model_name_or_path'], config=config, trust_remote_code=True)
prefix_state_dict = torch.load(os.path.join(CHATGLM_PTUNING_CHECKPOINT, "pytorch_model.bin"))
new_prefix_state_dict = {}
for k, v in prefix_state_dict.items():
if k.startswith("transformer.prefix_encoder."):
new_prefix_state_dict[k[len("transformer.prefix_encoder."):]] = v
model.transformer.prefix_encoder.load_state_dict(new_prefix_state_dict)
if model_args['quantization_bit'] is not None:
print(f"Quantized to {model_args['quantization_bit']} bit")
model = model.quantize(model_args['quantization_bit'])
model = model.cuda()
if model_args['pre_seq_len'] is not None:
# P-tuning v2
model.transformer.prefix_encoder.float()
self.chatglmft_model = model.eval()
break
else:
break
except Exception as e:
retry += 1
if retry > 3:
self.child.send('[Local Message] Call ChatGLMFT fail 不能正常加载ChatGLMFT的参数。')
raise RuntimeError("不能正常加载ChatGLMFT的参数！")
while True:
# 进入任务等待状态
kwargs = self.child.recv()
# 收到消息，开始请求
try:
for response, history in self.chatglmft_model.stream_chat(self.chatglmft_tokenizer, **kwargs):
self.child.send(response)
# # 中途接收可能的终止指令（如果有的话）
# if self.child.poll():
# command = self.child.recv()
# if command == '[Terminate]': break
except:
from toolbox import trimmed_format_exc
self.child.send('[Local Message] Call ChatGLMFT fail.' + '\n```\n' + trimmed_format_exc() + '\n```\n')
# 请求处理结束，开始下一个循环
self.child.send('[Finish]')
def stream_chat(self, **kwargs):
# 主进程执行
self.threadLock.acquire()
self.parent.send(kwargs)
while True:
res = self.parent.recv()
if res != '[Finish]':
yield res
else:
break
self.threadLock.release()
global glmft_handle
glmft_handle = None
#################################################################################
def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="", observe_window=[], console_slience=False):
"""
多线程方法
函数的说明请见 request_llm/bridge_all.py
"""
global glmft_handle
if glmft_handle is None:
glmft_handle = GetGLMFTHandle()
if len(observe_window) >= 1: observe_window[0] = load_message + "\n\n" + glmft_handle.info
if not glmft_handle.success:
error = glmft_handle.info
glmft_handle = None
raise RuntimeError(error)
# chatglmft 没有 sys_prompt 接口，因此把prompt加入 history
history_feedin = []
history_feedin.append(["What can I do?", sys_prompt])
for i in range(len(history)//2):
history_feedin.append([history[2*i], history[2*i+1]] )
watch_dog_patience = 5 # 看门狗 (watchdog) 的耐心, 设置5秒即可
response = ""
for response in glmft_handle.stream_chat(query=inputs, history=history_feedin, max_length=llm_kwargs['max_length'], top_p=llm_kwargs['top_p'], temperature=llm_kwargs['temperature']):
if len(observe_window) >= 1: observe_window[0] = response
if len(observe_window) >= 2:
if (time.time()-observe_window[1]) > watch_dog_patience:
raise RuntimeError("程序终止。")
return response
def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_prompt='', stream = True, additional_fn=None):
"""
单线程方法
函数的说明请见 request_llm/bridge_all.py
"""
chatbot.append((inputs, ""))
global glmft_handle
if glmft_handle is None:
glmft_handle = GetGLMFTHandle()
chatbot[-1] = (inputs, load_message + "\n\n" + glmft_handle.info)
yield from update_ui(chatbot=chatbot, history=[])
if not glmft_handle.success:
glmft_handle = None
return
if additional_fn is not None:
from core_functional import handle_core_functionality
inputs, history = handle_core_functionality(additional_fn, inputs, history, chatbot)
# 处理历史信息
history_feedin = []
history_feedin.append(["What can I do?", system_prompt] )
for i in range(len(history)//2):
history_feedin.append([history[2*i], history[2*i+1]] )
# 开始接收chatglmft的回复
response = "[Local Message]: 等待ChatGLMFT响应中 ..."
for response in glmft_handle.stream_chat(query=inputs, history=history_feedin, max_length=llm_kwargs['max_length'], top_p=llm_kwargs['top_p'], temperature=llm_kwargs['temperature']):
chatbot[-1] = (inputs, response)
yield from update_ui(chatbot=chatbot, history=history)
# 总结输出
if response == "[Local Message]: 等待ChatGLMFT响应中 ...":
response = "[Local Message]: ChatGLMFT响应异常 ..."
history.extend([inputs, response])
yield from update_ui(chatbot=chatbot, history=history)
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