2023-03-31 20:09:40 +08:00
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from langchain.llms.base import LLM
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from typing import Optional, List
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from langchain.llms.utils import enforce_stop_tokens
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from transformers import AutoTokenizer, AutoModel
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2023-04-07 10:45:44 +08:00
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import torch
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DEVICE = "cuda"
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DEVICE_ID = "0"
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CUDA_DEVICE = f"{DEVICE}:{DEVICE_ID}" if DEVICE_ID else DEVICE
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def torch_gc():
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if torch.cuda.is_available():
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with torch.cuda.device(CUDA_DEVICE):
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torch.cuda.empty_cache()
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torch.cuda.ipc_collect()
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2023-03-31 20:09:40 +08:00
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2023-04-09 23:30:15 +08:00
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tokenizer = AutoTokenizer.from_pretrained(
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"/Users/liuqian/Downloads/ChatGLM-6B/chatglm_hf_model",
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# "THUDM/chatglm-6b",
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trust_remote_code=True
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)
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model = (
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AutoModel.from_pretrained(
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"/Users/liuqian/Downloads/ChatGLM-6B/chatglm_hf_model",
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# "THUDM/chatglm-6b",
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trust_remote_code=True)
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.float()
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.to("mps")
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# .half()
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# .cuda()
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)
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2023-03-31 20:09:40 +08:00
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class ChatGLM(LLM):
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max_token: int = 10000
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temperature: float = 0.1
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top_p = 0.9
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history = []
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def __init__(self):
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super().__init__()
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@property
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def _llm_type(self) -> str:
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return "ChatGLM"
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def _call(self,
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prompt: str,
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stop: Optional[List[str]] = None) -> str:
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2023-04-09 23:30:15 +08:00
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response, updated_history = model.chat(
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tokenizer,
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2023-03-31 20:09:40 +08:00
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prompt,
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history=self.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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2023-04-07 10:46:02 +08:00
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torch_gc()
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2023-03-31 20:09:40 +08:00
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print("history: ", self.history)
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if stop is not None:
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response = enforce_stop_tokens(response, stop)
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self.history = updated_history
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return response
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2023-04-09 23:23:11 +08:00
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2023-04-09 23:30:15 +08:00
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def get_num_tokens(self, text: str) -> int:
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tokenized_text = tokenizer.tokenize(text)
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return len(tokenized_text)
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if __name__ == "__main__":
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history = []
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while True:
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query = input("Input your question 请输入问题:")
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resp, history = model.chat(tokenizer,
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query,
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history=history,
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temperature=0.01,
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max_length=100000)
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print(resp)
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