# Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # Adapted from https://github.com/ymcui/Chinese-LLaMA-Alpaca and https://github.com/SJTU-LIT/ceval import os import random import re import numpy as np import paddle from evaluator import Evaluator from tqdm import tqdm from paddlenlp.transformers import AutoModelForCausalLM, AutoTokenizer class ModelEvaluator(Evaluator): def __init__(self, choices, k, model_name_or_path, temperature=0.2): super().__init__(choices, model_name_or_path, k) self.model_name_or_path = model_name_or_path self.tokenizer = AutoTokenizer.from_pretrained(model_name_or_path) self.model = AutoModelForCausalLM.from_pretrained(model_name_or_path, dtype="float16") self.model.eval() self.generation_config = dict( temperature=temperature, top_k=40, top_p=0.9, do_sample=True, num_beams=1, repetition_penalty=1.1, max_new_tokens=20, ) self.A_id = self.tokenizer.encode("A", add_special_tokens=False)["input_ids"][0] self.B_id = self.tokenizer.encode("B", add_special_tokens=False)["input_ids"][0] self.C_id = self.tokenizer.encode("C", add_special_tokens=False)["input_ids"][0] self.D_id = self.tokenizer.encode("D", add_special_tokens=False)["input_ids"][0] def eval_subject( self, subject_name, test_df, dev_df=None, few_shot=False, cot=False, save_result_dir=None, with_prompt=False, constrained_decoding=False, do_test=False, ): all_answers = {} correct_num = 0 if save_result_dir: result = [] score = [] if few_shot: history = self.generate_few_shot_prompt(subject_name, dev_df, cot=cot) else: history = "" answers = ["NA"] * len(test_df) if do_test is True else list(test_df["answer"]) for row_index, row in tqdm(test_df.iterrows(), total=len(test_df)): question = self.format_example(row, include_answer=False, cot=cot, with_prompt=with_prompt) instruction = history + question inputs = self.tokenizer(instruction, return_tensors="pd") batch_size, length = inputs.input_ids.shape if constrained_decoding is True: # batch_size is 1, take the last logits as the logits for next token prediction with paddle.no_grad(): logits = self.model(**inputs)[0][0, -1, :] choices_logits = logits[[self.A_id, self.B_id, self.C_id, self.D_id]].numpy() assert not (np.any(np.isinf(choices_logits)) or np.any(np.isnan(choices_logits))) ans = {0: "A", 1: "B", 2: "C", 3: "D"}[np.argmax(choices_logits)] response = self.tokenizer.decode([logits.argmax(-1).item()]) else: generation_output = self.model.generate( **inputs, eos_token_id=self.tokenizer.eos_token_id, pad_token_id=self.tokenizer.pad_token_id, **self.generation_config, ) response = self.tokenizer.decode(generation_output[0][0, length:], skip_special_tokens=True) ans, direct_extract = self.extract_answer(row, response) if ans == answers[row_index]: correct_num += 1 correct = 1 else: correct = 0 print(f"\n=======begin {str(row_index)}=======") print("question: ", question) print("response: ", response) print("ans: ", ans) print("ground truth: ", answers[row_index], "\n") if save_result_dir: result.append(response) score.append(correct) print(f"=======end {str(row_index)}=======") all_answers[str(row_index)] = ans correct_ratio = 100 * correct_num / len(answers) if save_result_dir: test_df["model_output"] = result test_df["correctness"] = score test_df.to_csv(os.path.join(save_result_dir, f"{subject_name}_test.csv")) return correct_ratio, all_answers def format_example(self, line, include_answer=True, cot=False, with_prompt=False): example = line["question"] for choice in self.choices: example += f'\n{choice}. {line[f"{choice}"]}' if include_answer: if cot: example += "\n答案：让我们一步一步思考，\n" + line["explanation"] + f"\n所以答案是{line['answer']}。\n\n" else: example += "\n答案：" + line["answer"] + "\n\n" else: if with_prompt is False: if cot: example += "\n答案：让我们一步一步思考，\n1." else: example += "\n答案：" else: if cot: example += "\n答案是什么？让我们一步一步思考，\n1." else: example += "\n答案是什么？ " return example def generate_few_shot_prompt(self, subject, dev_df, cot=False): 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" k = self.k if self.k == -1: k = dev_df.shape[0] for i in range(k): prompt += self.format_example(dev_df.iloc[i, :], include_answer=True, cot=cot) return prompt def extract_answer(self, line, gen_ans): m = re.findall(r"所以答案是(.+?)。", gen_ans, re.M) if len(m) > 0 and m[-1] in self.choices: return m[-1], True answer_patterns = [ r"([ABCD])是正确的", r"选项([ABCD])正确", r"答案为([ABCD])", r"答案是([ABCD])", r"答案([ABCD])", r"选择([ABCD])", r"答案：([ABCD])", r"选择答案([ABCD])", ] # RE extraction for answer_pattern in answer_patterns: m = re.search(answer_pattern, gen_ans, re.M) if m: answer = m.group(1) return answer, False # only containing one choice-character m = re.findall(r"[ABCD]", gen_ans, re.M) if len(m) >= 1: answer = m[0] return answer, False # only containing one choice-context choices_dict = {} pattern = "" for c in self.choices: choices_dict[str(line[f"{c}"])] = c pattern += re.escape(str(line[f"{c}"])) + "|" pattern = pattern[:-1] m = re.findall(pattern, gen_ans, re.M) print("w/ escape:", repr(pattern), gen_ans, (len(m) >= 1)) if len(m) >= 1: answer = choices_dict[m[0]] return answer, False return random.choice("ABCD"), False