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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 pathlib import Path
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from typing import Union, Tuple
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import typer
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from peft import AutoPeftModelForCausalLM, PeftModelForCausalLM
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from transformers import (
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AutoModelForCausalLM,
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PreTrainedModel,
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PreTrainedTokenizer,
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PreTrainedTokenizerFast,
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)
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from peft import PeftModel, PeftConfig
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CUDA_VISIBLE_DEVICES = 0
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ModelType = Union[PreTrainedModel, PeftModelForCausalLM]
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TokenizerType = Union[PreTrainedTokenizer, PreTrainedTokenizerFast]
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def _resolve_path(path: Union[str, Path]) -> Path:
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return Path(path).expanduser().resolve()
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def load_model_and_tokenizer(model_dir: Union[str, Path]) -> Tuple[ModelType, TokenizerType]:
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model_dir = _resolve_path(model_dir)
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if (model_dir / 'adapter_config.json').exists():
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config = PeftConfig.from_pretrained(model_dir)
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base_model = AutoModel.from_pretrained("THUDM/chatglm3-6b", trust_remote_code=True, mirror="tuna",
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resume_download=True)
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# base_model = AutoModelForCausalLM.from_pretrained(config.base_model_name_or_path,trust_remote_code=True,
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# device_map='auto')
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model = PeftModel.from_pretrained(base_model, model_dir)
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tokenizer_dir = model.peft_config['default'].base_model_name_or_path
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else:
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model = AutoModelForCausalLM.from_pretrained(
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model_dir, trust_remote_code=True, device_map='auto'
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)
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tokenizer_dir = model_dir
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tokenizer = AutoTokenizer.from_pretrained(
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tokenizer_dir, trust_remote_code=True
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)
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return model, tokenizer
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class InvalidScoreLogitsProcessor(LogitsProcessor):
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def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> torch.FloatTensor:
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if torch.isnan(scores).any() or torch.isinf(scores).any():
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scores.zero_()
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scores[..., 5] = 5e4
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return scores
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class ChatGLM_Evaluator(Evaluator):
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def __init__(self, choices, k, model_name, device, finetune=None, finetune_method=None):
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super(ChatGLM_Evaluator, self).__init__(choices, model_name, k)
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# try adding 'mirror="tuna"' and 'resume_download=True' if facing the 'read timed out' problem
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# or directly clone the model
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self.tokenizer = AutoTokenizer.from_pretrained("THUDM/chatglm3-6b", trust_remote_code=True, mirror="tuna")
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if finetune_method == "qlora":
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model_dir = 'qlora/glm3/' + finetune
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self.model, self.tokenizer = load_model_and_tokenizer(model_dir)
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self.model = self.model.half().to(device)
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print("Model loaded! use GLM3 " + finetune)
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else:
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self.model = AutoModel.from_pretrained("THUDM/chatglm3-6b", trust_remote_code=True, mirror="tuna",
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resume_download=True).half().to(device)
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print("Model loaded! (GLM3)")
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# prompt = '以下是中国关于car_knowledge_in_train考试的单项选择题，请选出其中的正确答案。\n\n比亚迪的刀片电池采用哪种电池技术？\
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# nA. 镍氢电池\nB. 锂离子电池\nC. 磷酸铁锂电池\nD. 液态电池\n答案：'
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# response, history = self.model.chat(self.tokenizer, prompt, max_length=128)
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# print(history)
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# current_length = 0
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# response = ""
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# for resp, _ in self.model.stream_chat(self.tokenizer, prompt, max_length=300,
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# do_sample=False):
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# print(resp[current_length:], end="", flush=True)
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# current_length = len(resp)
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# response = resp
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# print('')
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self.model = self.model.eval()
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def eval_subject(self, subject_name, test_df, dev_df=None, few_shot=False, cot=False, save_result_dir=None):
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correct_num = 0
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result = []
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score = []
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answer_list = []
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if few_shot:
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history = self.generate_few_shot_prompt(subject_name, dev_df, cot=cot)
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else:
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# _ , history = self.model.chat(self.tokenizer, "接下来会提供给你一些选择题，请选出正确的答案。", do_sample=False)
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history = [{'role': 'user',
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'content': '接下来会提供给你一些选择题，请选出正确的答案，给出正确的选项即可。'},
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{'role': 'assistant',
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'content': '好的，我会尽力解答。'}]
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answers = list(test_df['answer'])
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for row_index, row in tqdm(test_df.iterrows(), total=len(test_df)):
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question = self.format_example(row, include_answer=False, cot=cot)
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if few_shot:
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history_temp = history.copy()
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response, _ = self.model.chat(self.tokenizer, question, max_length=300,
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do_sample=False, history=history_temp)
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response = response.strip()
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# For ChatGLM, we use answer extraction in answer-only mode too.
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ans, direct_extract = self.extract_cot_answer(row, response)
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else: # zero-shot by extracting answer from distribution
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response, _ = self.model.chat(self.tokenizer, question, max_length=300,
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do_sample=False, history=history)
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response = response.strip()
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ans, direct_extract = self.extract_cot_answer(row, response)
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print(response, ans)
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# ans = self.generate_dist(self.model, self.tokenizer, question, do_sample=False, max_length=2048,
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# history=history)
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if ans == answers[row_index]:
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correct_num += 1
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correct = 1
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else:
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correct = 0
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if save_result_dir:
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# if few_shot:
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result.append(response)
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answer_list.append(ans)
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score.append(correct)
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correct_ratio = 100 * correct_num / len(answers)
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if save_result_dir:
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# if few_shot:
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test_df['model_output'] = result
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test_df['correctness'] = score
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test_df['model_answer'] = answer_list
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result_file_name = f'{subject_name}_{correct_ratio}_test.csv'
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if few_shot:
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result_file_name = f'{subject_name}_{correct_ratio}_few_shot_test.csv'
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test_df.to_csv(os.path.join(save_result_dir, result_file_name))
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return correct_ratio
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def eval_qa(self, subject_name, qa_df, save_result_dir=None):
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# history = []
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history = [{'role': 'user',
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'content': '接下来会给你一些一些汽车领域相关问题，请回答。'},
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{'role': 'assistant',
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'content': '好的，我会尽力解答。'}]
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for row_index, row in tqdm(qa_df.iterrows(), total=len(qa_df)):
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question = row['question']
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response, _ = self.model.chat(self.tokenizer, question, max_length=300, do_sample=False, history=history)
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response = response.strip()
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qa_df.loc[row_index, 'model_output'] = response
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if save_result_dir:
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result_file_name = f'{subject_name}_qa_test_result.csv'
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qa_df.to_csv(os.path.join(save_result_dir, result_file_name))
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def generate_few_shot_prompt(self, subject, dev_df, cot=False):
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message = []
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k = self.k
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if self.k == -1:
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k = dev_df.shape[0]
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message.extend(self.format_example(dev_df.iloc[0, :], cot=cot,
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add_prompt=f"以下是中国关于{subject}考试的单项选择题，请选出其中的正确答案。\n\n"))
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for i in range(1, k):
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message.extend(self.format_example(dev_df.iloc[i, :], cot=cot))
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return message
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def format_example(self, line, include_answer=True, cot=False, add_prompt=''):
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example = add_prompt + line['question']
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# print(example)
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for choice in self.choices:
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example += f'\n{choice}. {line[f"{choice}"]}'
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example += '\n答案：'
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if include_answer:
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if cot:
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ans = "让我们一步一步思考，\n" + line["explanation"] + f"\n所以答案是{line['answer']}。"
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else:
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ans = line["answer"]
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m = [{
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'role': 'user',
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'content': example
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}, {
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'role': 'assistant',
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'content': ans
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}]
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return m
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return example
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def extract_cot_answer(self, line, gen_ans):
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m = re.findall(r'所以答案是(.+?)。', gen_ans, re.M)
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if len(m) > 0 and m[-1] in self.choices:
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return m[-1], True
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answer_patterns = [
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r'([ABCD])是正确的',
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r'选项([ABCD])正确',
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r'答案为([ABCD])',
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r'答案是([ABCD])',
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r'答案([ABCD])',
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r'选择([ABCD])',
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r'答案：([ABCD])',
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r'选择答案([ABCD])'
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]
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# RE extraction
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for answer_pattern in answer_patterns:
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m = re.search(answer_pattern, gen_ans, re.M)
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if m:
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answer = m.group(1)
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return answer, False
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# only containing one choice-character
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m = re.findall(r'[ABCD]', gen_ans, re.M)
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if len(m) == 1:
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answer = m[0]
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return answer, False
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answer_word_counter = 0
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# only containing one choice-context
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for c in self.choices:
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if str(line[f'{c}']) in gen_ans:
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answer = c
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answer_word_counter += 1
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if answer_word_counter == 1:
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return answer, False
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return '-', False
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def generate_dist(self, model, tokenizer, query, history, num_beams=1, max_length=2048,
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do_sample=False, top_p=0.7, temperature=0.95, logits_processor=None, **kwargs):
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if history is None:
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history = []
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if logits_processor is None:
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logits_processor = LogitsProcessorList()
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logits_processor.append(InvalidScoreLogitsProcessor())
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gen_kwargs = {"num_beams": num_beams, "do_sample": do_sample, "top_p": top_p, "max_length": 2048,
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"temperature": temperature, "logits_processor": logits_processor, **kwargs}
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if not history:
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prompt = query
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else:
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prompt = ""
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for i, (old_query, response) in enumerate(history):
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prompt += "[Round {}]\n问：{}\n答：{}\n".format(i, old_query, response)
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prompt += "[Round {}]\n问：{}\n答：".format(len(history), query)
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inputs = tokenizer([prompt], return_tensors="pt")
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inputs = inputs.to(model.device)
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outputs = model.generate(**inputs, return_dict_in_generate=True, output_scores=True, **gen_kwargs)
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score = outputs.scores[0][0].tolist()
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choice_score = [score[167], score[333], score[251], score[416]]
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ranked_index = [index for index, value in
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sorted(list(enumerate(choice_score)), key=lambda x: x[1], reverse=True)]
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return self.choices[ranked_index[0]]
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