241 lines
10 KiB
Python
241 lines
10 KiB
Python
import zhipuai
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from server.model_workers.base import ApiModelWorker
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from fastchat import conversation as conv
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import sys
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import json
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from typing import List, Literal,Dict
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class ChatGLMWorker(ApiModelWorker):
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BASE_URL = "https://open.bigmodel.cn/api/paas/v3/model-api"
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SUPPORT_MODELS = ["chatglm_pro", "chatglm_std", "chatglm_lite"]
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def __init__(
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self,
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*,
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model_names: List[str] = ["chatglm-api"],
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version: Literal["chatglm_pro", "chatglm_std", "chatglm_lite"] = "chatglm_std",
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controller_addr: str,
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worker_addr: str,
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**kwargs,
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):
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kwargs.update(model_names=model_names, controller_addr=controller_addr, worker_addr=worker_addr)
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kwargs.setdefault("context_len", 32768)
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super().__init__(**kwargs)
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self.version = version
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self.zhipuai = zhipuai
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from server.utils import get_model_worker_config
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self.zhipuai.api_key = get_model_worker_config("chatglm-api").get("api_key")
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# 这里的是chatglm api的模板，其它API的conv_template需要定制
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self.conv = conv.Conversation(
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name="chatglm-api",
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system_message="你是一个聪明、对人类有帮助的人工智能，你可以对人类提出的问题给出有用、详细、礼貌的回答。",
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messages=[],
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roles=["Human", "Assistant"],
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sep="\n### ",
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stop_str="###",
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)
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def generate_stream_gate(self, params):
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# TODO: 支持stream参数，维护request_id，传过来的prompt也有问题
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super().generate_stream_gate(params)
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model=self.version
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# if isinstance(params["prompt"], str):
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# prompt=self.prompt_collator(content_user=params["prompt"],
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# role_user="user") #[{"role": "user", "content": params["prompt"]}]
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# else:
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# prompt = params["prompt"]
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prompt = params["prompt"]
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print(prompt)
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temperature=params.get("temperature")
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top_p=params.get("top_p")
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stream = params.get("stream")
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if stream:
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return self.create_stream(model=model,
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message=prompt,
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top_p=top_p,
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temperature=temperature)
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else:
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return self.create_oneshot(model=model,
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message=prompt,
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top_p=top_p,
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temperature=temperature)
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# response = zhipuai.model_api.sse_invoke(
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# model=self.version,
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# prompt=[{"role": "user", "content": params["prompt"]}],
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# temperature=params.get("temperature"),
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# top_p=params.get("top_p"),
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# incremental=False,
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# )
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# for e in response.events():
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# if e.event == "add":
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# yield json.dumps({"error_code": 0, "text": e.data}, ensure_ascii=False).encode() + b"\0"
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# # TODO: 更健壮的消息处理
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# # elif e.event == "finish":
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# # ...
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def get_embeddings(self, params):
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# TODO: 支持embeddings
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print("embedding")
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print(params)
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def create_oneshot(self,
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message: List[Dict[str,str]]=[{"role":"user","content":"你好，你可以做什么"}],
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model:str = "chatglm_pro",
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top_p:float=0.7,
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temperature:float=0.9,
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**kwargs
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):
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response = self.zhipuai.model_api.invoke(
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model = model,
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prompt = message,
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top_p = top_p,
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temperature = temperature
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)
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if response["code"] == 200:
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result = response["data"]["choices"][-1]["content"]
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return json.dumps({"error_code": 0, "text": result}, ensure_ascii=False).encode() + b"\0"
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else:
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#TODO 确认openai的error code
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print(f"error occurred, error code:{response['code']},error msg:{response['msg']}")
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return json.dumps({"error_code": response['code'],
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"text": f"error occurred, error code:{response['code']},error msg:{response['msg']}"
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},
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ensure_ascii=False).encode() + b"\0"
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def create_stream(self,
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message: List[Dict[str,str]]=[{"role":"user","content":"你好，你可以做什么"}],
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model:str = "chatglm_pro",
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top_p:float=0.7,
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temperature:float=0.9,
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**kwargs
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):
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response = self.zhipuai.model_api.sse_invoke(
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model = model,
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prompt = message,
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top_p = top_p,
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temperature = temperature,
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incremental = True
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)
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for event in response.events():
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if event.event == "add":
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# yield event.data
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yield json.dumps({"error_code": 0, "text": event.data}, ensure_ascii=False).encode() + b"\0"
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elif event.event == "error" or event.event == "interrupted":
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# return event.data
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yield json.dumps({"error_code": 0, "text": event.data}, ensure_ascii=False).encode() + b"\0"
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elif event.event == "finish":
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# yield event.data
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yield json.dumps({"error_code": 0, "text": event.data}, ensure_ascii=False).encode() + b"\0"
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print(event.meta)
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else:
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print("Something get wrong with ZhipuAPILoader.create_chat_completion_stream")
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print(event.data)
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yield json.dumps({"error_code": 1, "text": event.data}, ensure_ascii=False).encode() + b"\0"
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def create_chat_completion(self,
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model: str = "chatglm_pro",
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prompt:List[Dict[str,str]]=[{"role":"user","content":"你好，你可以做什么"}],
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top_p:float=0.7,
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temperature:float=0.9,
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stream:bool=False):
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if stream:
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return self.create_stream(model=model,
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message=prompt,
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top_p=top_p,
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temperature=temperature)
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else:
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return self.create_oneshot(model=model,
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message=prompt,
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top_p=top_p,
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temperature=temperature)
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async def acreate_chat_completion(self,
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prompt: List[Dict[str,str]]=[{"role":"system","content":"你是一个人工智能助手"},
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{"role":"user","content":"你好。"}],
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model:str = "chatglm_pro",
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top_p:float=0.7,
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temperature:float=0.9,
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**kwargs):
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response = await self.zhipuai.model_api.async_invoke(
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model = model,
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prompt = prompt,
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top_p = top_p,
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temperature = temperature
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)
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if response["code"] == 200:
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task_id = response['data']['task_id']
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status = "PROCESSING"
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while status != "SUCCESS":
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# await asyncio.sleep(3) #
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resp = self.zhipuai.model_api.query_async_invoke_result(task_id)
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status = resp['data']['task_status']
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return resp['data']['choices'][-1]['content']
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else:
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print(f"error occurred, error code:{response['code']},error msg:{response['msg']}")
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return
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def create_completion(self,
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prompt:str="你好",
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model:str="chatglm_pro",
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top_p:float=0.7,
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temperature:float=0.9,
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stream:bool=False,
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**kwargs):
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message = self.prompt_collator(content_user=prompt)
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if stream:
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return self.create_stream(model=model,
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message=message,
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top_p=top_p,
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temperature=temperature)
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else:
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return self.create_oneshot(model=model,
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message=message,
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top_p=top_p,
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temperature=temperature)
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#? make it a sync function?
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async def acreate_completion(self,
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prompt:str="你好",
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model:str = "chatglm_pro",
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top_p:float=0.7,
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temperature:float=0.9,
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**kwargs):
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message = self.prompt_collator(content_user=prompt)
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response = self.zhipuai.model_api.async_invoke(
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model = model,
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prompt = message,
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top_p = top_p,
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temperature = temperature
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)
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if response["code"] == 200:
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task_id = response['data']['task_id']
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status = "PROCESSING"
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while status != "SUCCESS":
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# await asyncio.sleep(3) #
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resp = self.zhipuai.model_api.query_async_invoke_result(task_id)
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status = resp['data']['task_status']
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return resp['data']['choices'][-1]['content']
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else:
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print(f"error occurred, error code:{response['code']},error msg:{response['msg']}")
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return
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if __name__ == "__main__":
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import uvicorn
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from server.utils import MakeFastAPIOffline
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from fastchat.serve.model_worker import app
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worker = ChatGLMWorker(
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controller_addr="http://127.0.0.1:20001",
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worker_addr="http://127.0.0.1:20003",
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)
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sys.modules["fastchat.serve.model_worker"].worker = worker
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MakeFastAPIOffline(app)
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uvicorn.run(app, port=20003)
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