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