71 lines
2.3 KiB
Python
71 lines
2.3 KiB
Python
from abc import ABC
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from langchain.llms.base import LLM
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from typing import Optional, List
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from models.loader import LoaderCheckPoint
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from models.base import (BaseAnswer,
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AnswerResult)
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class BaichuanLLMChain(BaseAnswer, LLM, ABC):
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max_token: int = 10000
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temperature: float = 0.01
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top_p = 0.9
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checkPoint: LoaderCheckPoint = None
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# history = []
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history_len: int = 10
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def __init__(self, checkPoint: LoaderCheckPoint = None):
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super().__init__()
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self.checkPoint = checkPoint
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@property
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def _llm_type(self) -> str:
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return "BaichuanLLMChain"
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@property
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def _check_point(self) -> LoaderCheckPoint:
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return self.checkPoint
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@property
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def _history_len(self) -> int:
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return self.history_len
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def set_history_len(self, history_len: int = 10) -> None:
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self.history_len = history_len
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def _call(self, prompt: str, stop: Optional[List[str]] = None) -> str:
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print(f"__call:{prompt}")
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response, _ = self.checkPoint.model.chat(
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self.checkPoint.tokenizer,
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prompt,
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# history=[],
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# max_length=self.max_token,
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# temperature=self.temperature
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)
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print(f"response:{response}")
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print(f"+++++++++++++++++++++++++++++++++++")
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return response
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def _generate_answer(self, prompt: str,
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history: List[List[str]] = [],
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streaming: bool = False):
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messages = []
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messages.append({"role": "user", "content": prompt})
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if streaming:
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for inum, stream_resp in enumerate(self.checkPoint.model.chat(
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self.checkPoint.tokenizer,
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messages,
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stream=True
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)):
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self.checkPoint.clear_torch_cache()
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answer_result = AnswerResult()
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answer_result.llm_output = {"answer": stream_resp}
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yield answer_result
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else:
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response = self.checkPoint.model.chat(
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self.checkPoint.tokenizer,
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messages
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)
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self.checkPoint.clear_torch_cache()
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answer_result = AnswerResult()
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answer_result.llm_output = {"answer": response}
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yield answer_result |