88 lines
2.7 KiB
Python
88 lines
2.7 KiB
Python
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from configs.basic_config import LOG_PATH
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import fastchat.constants
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fastchat.constants.LOGDIR = LOG_PATH
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from fastchat.serve.base_model_worker import BaseModelWorker
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import uuid
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import json
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import sys
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from pydantic import BaseModel
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import fastchat
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import asyncio
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from typing import Dict, List
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# 恢复被fastchat覆盖的标准输出
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sys.stdout = sys.__stdout__
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sys.stderr = sys.__stderr__
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class ApiModelOutMsg(BaseModel):
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error_code: int = 0
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text: str
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class ApiModelWorker(BaseModelWorker):
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BASE_URL: str
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SUPPORT_MODELS: List
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def __init__(
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self,
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model_names: List[str],
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controller_addr: str,
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worker_addr: str,
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context_len: int = 2048,
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**kwargs,
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):
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kwargs.setdefault("worker_id", uuid.uuid4().hex[:8])
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kwargs.setdefault("model_path", "")
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kwargs.setdefault("limit_worker_concurrency", 5)
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super().__init__(model_names=model_names,
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controller_addr=controller_addr,
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worker_addr=worker_addr,
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**kwargs)
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self.context_len = context_len
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self.semaphore = asyncio.Semaphore(self.limit_worker_concurrency)
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self.init_heart_beat()
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def count_token(self, params):
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# TODO：需要完善
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# print("count token")
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prompt = params["prompt"]
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return {"count": len(str(prompt)), "error_code": 0}
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def generate_stream_gate(self, params):
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self.call_ct += 1
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def generate_gate(self, params):
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for x in self.generate_stream_gate(params):
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pass
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return json.loads(x[:-1].decode())
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def get_embeddings(self, params):
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print("embedding")
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# print(params)
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# help methods
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def get_config(self):
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from server.utils import get_model_worker_config
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return get_model_worker_config(self.model_names[0])
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def prompt_to_messages(self, prompt: str) -> List[Dict]:
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'''
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将prompt字符串拆分成messages.
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'''
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result = []
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user_role = self.conv.roles[0]
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ai_role = self.conv.roles[1]
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user_start = user_role + ":"
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ai_start = ai_role + ":"
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for msg in prompt.split(self.conv.sep)[1:-1]:
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if msg.startswith(user_start):
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if content := msg[len(user_start):].strip():
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result.append({"role": user_role, "content": content})
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elif msg.startswith(ai_start):
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if content := msg[len(ai_start):].strip():
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result.append({"role": ai_role, "content": content})
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else:
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raise RuntimeError(f"unknown role in msg: {msg}")
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return result
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