LLM_Evaluator/evaluators/chatglm.py

import os
import re
from tqdm import tqdm
import torch
from transformers import AutoTokenizer, AutoModel, AutoConfig
from transformers.generation.logits_process import LogitsProcessor
from transformers.generation.utils import LogitsProcessorList
from evaluators.evaluator import Evaluator
from evaluators.chatglm_mixin import ChatGLMMixin


class ChatGLM_Evaluator(Evaluator, ChatGLMMixin):
    def __init__(self, choices, k, model_name, device='cpu', finetune=None, finetune_method=None):
        super(ChatGLM_Evaluator, self).__init__(choices, model_name, k)
        self.finetune_method = finetune_method
        self.finetune_name = finetune
        self.tokenizer = AutoTokenizer.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True, mirror="tuna")
        if finetune_method == "ptuning":
            CHECKPOINT_PATH = "ptuning/glm1/" + finetune
            config = AutoConfig.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True, pre_seq_len=128)
            self.model = AutoModel.from_pretrained("THUDM/chatglm-6b", config=config, trust_remote_code=True)
            prefix_state_dict = torch.load(os.path.join(CHECKPOINT_PATH, "pytorch_model.bin"))
            new_prefix_state_dict = {}
            for k, v in prefix_state_dict.items():
                if k.startswith("transformer.prefix_encoder."):
                    new_prefix_state_dict[k[len("transformer.prefix_encoder."):]] = v
            self.model.transformer.prefix_encoder.load_state_dict(new_prefix_state_dict)
            self.model = self.model.half().to(device)
            self.model.transformer.prefix_encoder.float()
            print("Model loaded! use GLM + " + finetune)
        else:
            self.model = AutoModel.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True, mirror="tuna",
                                                   resume_download=True).half().to(device)
            print("Model loaded! (GLM original)")
        # self.model = self.model.eval()