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