codefuse-chatbot/dev_opsgpt/service/model_workers/base.py

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from fastchat.conversation import Conversation
from configs import LOG_PATH
import fastchat.constants
fastchat.constants.LOGDIR = LOG_PATH
from fastchat.serve.base_model_worker import BaseModelWorker
import uuid
import json
import sys
from pydantic import BaseModel, root_validator
import fastchat
import asyncio
from dev_opsgpt.service.utils import get_model_worker_config
from typing import Dict, List, Optional
__all__ = ["ApiModelWorker", "ApiChatParams", "ApiCompletionParams", "ApiEmbeddingsParams"]
class ApiConfigParams(BaseModel):
'''
在线API配置参数未提供的值会自动从model_config.ONLINE_LLM_MODEL中读取
'''
api_base_url: Optional[str] = None
api_proxy: Optional[str] = None
api_key: Optional[str] = None
secret_key: Optional[str] = None
group_id: Optional[str] = None # for minimax
is_pro: bool = False # for minimax
APPID: Optional[str] = None # for xinghuo
APISecret: Optional[str] = None # for xinghuo
is_v2: bool = False # for xinghuo
worker_name: Optional[str] = None
class Config:
extra = "allow"
@root_validator(pre=True)
def validate_config(cls, v: Dict) -> Dict:
if config := get_model_worker_config(v.get("worker_name")):
for n in cls.__fields__:
if n in config:
v[n] = config[n]
return v
def load_config(self, worker_name: str):
self.worker_name = worker_name
if config := get_model_worker_config(worker_name):
for n in self.__fields__:
if n in config:
setattr(self, n, config[n])
return self
class ApiModelParams(ApiConfigParams):
'''
模型配置参数
'''
version: Optional[str] = None
version_url: Optional[str] = None
api_version: Optional[str] = None # for azure
deployment_name: Optional[str] = None # for azure
resource_name: Optional[str] = None # for azure
temperature: float = 0.7
max_tokens: Optional[int] = None
top_p: Optional[float] = 1.0
class ApiChatParams(ApiModelParams):
'''
chat请求参数
'''
messages: List[Dict[str, str]]
system_message: Optional[str] = None # for minimax
role_meta: Dict = {} # for minimax
class ApiCompletionParams(ApiModelParams):
prompt: str
class ApiEmbeddingsParams(ApiConfigParams):
texts: List[str]
embed_model: Optional[str] = None
to_query: bool = False # for minimax
class ApiModelWorker(BaseModelWorker):
DEFAULT_EMBED_MODEL: str = None # None means not support embedding
def __init__(
self,
model_names: List[str],
controller_addr: str = None,
worker_addr: str = None,
context_len: int = 2048,
no_register: bool = False,
**kwargs,
):
kwargs.setdefault("worker_id", uuid.uuid4().hex[:8])
kwargs.setdefault("model_path", "")
kwargs.setdefault("limit_worker_concurrency", 5)
super().__init__(model_names=model_names,
controller_addr=controller_addr,
worker_addr=worker_addr,
**kwargs)
import fastchat.serve.base_model_worker
import sys
self.logger = fastchat.serve.base_model_worker.logger
# 恢复被fastchat覆盖的标准输出
sys.stdout = sys.__stdout__
sys.stderr = sys.__stderr__
self.context_len = context_len
self.semaphore = asyncio.Semaphore(self.limit_worker_concurrency)
self.version = None
if not no_register and self.controller_addr:
self.init_heart_beat()
def count_token(self, params):
# TODO需要完善
# print("count token")
prompt = params["prompt"]
return {"count": len(str(prompt)), "error_code": 0}
def generate_stream_gate(self, params: Dict):
self.call_ct += 1
try:
prompt = params["prompt"]
if self._is_chat(prompt):
messages = self.prompt_to_messages(prompt)
messages = self.validate_messages(messages)
else: # 使用chat模仿续写功能不支持历史消息
messages = [{"role": self.user_role, "content": f"please continue writing from here: {prompt}"}]
p = ApiChatParams(
messages=messages,
temperature=params.get("temperature"),
top_p=params.get("top_p"),
max_tokens=params.get("max_new_tokens"),
version=self.version,
)
for resp in self.do_chat(p):
yield self._jsonify(resp)
except Exception as e:
yield self._jsonify({"error_code": 500, "text": f"{self.model_names[0]}请求API时发生错误{e}"})
def generate_gate(self, params):
try:
for x in self.generate_stream_gate(params):
...
return json.loads(x[:-1].decode())
except Exception as e:
return {"error_code": 500, "text": str(e)}
# 需要用户自定义的方法
def do_chat(self, params: ApiChatParams) -> Dict:
'''
执行Chat的方法默认使用模块里面的chat函数
要求返回形式{"error_code": int, "text": str}
'''
return {"error_code": 500, "text": f"{self.model_names[0]}未实现chat功能"}
# def do_completion(self, p: ApiCompletionParams) -> Dict:
# '''
# 执行Completion的方法默认使用模块里面的completion函数。
# 要求返回形式:{"error_code": int, "text": str}
# '''
# return {"error_code": 500, "text": f"{self.model_names[0]}未实现completion功能"}
def do_embeddings(self, params: ApiEmbeddingsParams) -> Dict:
'''
执行Embeddings的方法默认使用模块里面的embed_documents函数
要求返回形式{"code": int, "data": List[List[float]], "msg": str}
'''
return {"code": 500, "msg": f"{self.model_names[0]}未实现embeddings功能"}
def get_embeddings(self, params):
# fastchat对LLM做Embeddings限制很大似乎只能使用openai的。
# 在前端通过OpenAIEmbeddings发起的请求直接出错无法请求过来。
print("get_embedding")
print(params)
def make_conv_template(self, conv_template: str = None, model_path: str = None) -> Conversation:
raise NotImplementedError
def validate_messages(self, messages: List[Dict]) -> List[Dict]:
'''
有些API对mesages有特殊格式可以重写该函数替换默认的messages
之所以跟prompt_to_messages分开是因为他们应用场景不同参数不同
'''
return messages
# help methods
@property
def user_role(self):
return self.conv.roles[0]
@property
def ai_role(self):
return self.conv.roles[1]
def _jsonify(self, data: Dict) -> str:
'''
将chat函数返回的结果按照fastchat openai-api-server的格式返回
'''
return json.dumps(data, ensure_ascii=False).encode() + b"\0"
def _is_chat(self, prompt: str) -> bool:
'''
检查prompt是否由chat messages拼接而来
TODO: 存在误判的可能也许从fastchat直接传入原始messages是更好的做法
'''
key = f"{self.conv.sep}{self.user_role}:"
return key in prompt
def prompt_to_messages(self, prompt: str) -> List[Dict]:
'''
将prompt字符串拆分成messages.
'''
result = []
user_role = self.user_role
ai_role = self.ai_role
user_start = user_role + ":"
ai_start = ai_role + ":"
for msg in prompt.split(self.conv.sep)[1:-1]:
if msg.startswith(user_start):
if content := msg[len(user_start):].strip():
result.append({"role": user_role, "content": content})
elif msg.startswith(ai_start):
if content := msg[len(ai_start):].strip():
result.append({"role": ai_role, "content": content})
else:
raise RuntimeError(f"unknown role in msg: {msg}")
return result
@classmethod
def can_embedding(cls):
return cls.DEFAULT_EMBED_MODEL is not None