# This code is modified from C-Eval Project: https://github.com/SJTU-LIT/ceval
import os
import re
from tqdm import tqdm
import random
import numpy as np
import torch
from transformers import AutoModelForCausalLM, LlamaTokenizer
from transformers import GenerationConfig
from evaluator import Evaluator
DEFAULT_SYSTEM_PROMPT = """You are a helpful assistant. 你是一个乐于助人的助手。"""
class Llama_Evaluator(Evaluator):
def __init__(self, choices, k, model_path, device, temperature=0.2, verbose=False):
super(Llama_Evaluator, self).__init__(choices, model_path, k)
load_type = torch.float16
self.model_path = model_path
self.device = device
self.verbose = verbose
self.tokenizer = LlamaTokenizer.from_pretrained(model_path, legacy=True)
self.model = AutoModelForCausalLM.from_pretrained(
model_path,
load_in_8bit=False,
torch_dtype=load_type,
low_cpu_mem_usage=True,
device_map='auto',
trust_remote_code=True)
self.generation_config = GenerationConfig(
temperature=temperature,
top_k=40,
top_p=0.9,
do_sample=True,
num_beams=1,
repetition_penalty=1.1,
max_new_tokens=20
)
self.sA_id = self.tokenizer.encode("A", add_special_tokens=False)[0]
self.sB_id = self.tokenizer.encode("B", add_special_tokens=False)[0]
self.sC_id = self.tokenizer.encode("C", add_special_tokens=False)[0]
self.sD_id = self.tokenizer.encode("D", add_special_tokens=False)[0]
self.A_id = self.tokenizer.encode("：A")[-1]
self.B_id = self.tokenizer.encode("：B")[-1]
self.C_id = self.tokenizer.encode("：C")[-1]
self.D_id = self.tokenizer.encode("：D")[-1]
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 = {}
if constrained_decoding is True:
self.generation_config.output_scores = True
self.generation_config.return_dict_in_generate = True
self.generation_config.max_new_tokens = 1
self.generation_config.top_p = 1.0
self.generation_config.top_k = 0
correct_num = 0
if save_result_dir:
result = []
score = []
if few_shot:
if with_prompt:
history = self.generate_alpaca2_few_shot_prompt(subject_name, dev_df, cot=cot)
else:
history = self.generate_llama2_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 = question
if with_prompt:
prompt_template = (
"[INST] <>\n"
"{system_prompt}\n"
"<>\n\n"
"{instruction} [/INST]"
)
instruction = prompt_template.format_map({'instruction': instruction,'system_prompt':DEFAULT_SYSTEM_PROMPT})
instruction = history + instruction
inputs = self.tokenizer(instruction, return_tensors="pt")
generation_output = self.model.generate(
input_ids = inputs["input_ids"].to(self.device),
attention_mask = inputs['attention_mask'].to(self.device),
eos_token_id=self.tokenizer.eos_token_id,
pad_token_id=self.tokenizer.pad_token_id,
generation_config = self.generation_config
)
batch_size, length = inputs.input_ids.shape
if constrained_decoding is True:
logits = generation_output.scores[0][0]
logits = logits.float().cpu().detach()
choices1_logits = logits[[self.sA_id,self.sB_id,self.sC_id,self.sD_id]]
choices2_logits = logits[[self.A_id,self.B_id,self.C_id,self.D_id]]
choicesAll_logits = (choices1_logits + choices2_logits).numpy()
assert not (np.any(np.isinf(choicesAll_logits)) or np.any(np.isnan(choicesAll_logits)))
ans = {0: "A", 1: "B", 2: "C", 3: "D"}[np.argmax(choicesAll_logits)]
response = self.tokenizer.decode([logits.argmax(-1).item()])
else:
response = self.tokenizer.decode(generation_output[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
if self.verbose is True:
print(f"\n======={str(row_index)}=======")
print(f"question: {question}\n")
print(f"response: {response}\n")
print(f"extracted answer: {ans}")
print(f"ground truth: {answers[row_index]} \n")
if save_result_dir:
result.append(response)
score.append(correct)
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：'
return example
def generate_llama2_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 generate_alpaca2_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"
prompt_template = (
"[INST] <>\n"
"{system_prompt}\n"
"<>\n\n"
"{instruction} [/INST]好的，我会结合{subject}相关知识回答"
)
prompt = prompt_template.format_map({'instruction':prompt,'system_prompt':DEFAULT_SYSTEM_PROMPT,'subject':subject})
k = self.k
if self.k == -1:
k = dev_df.shape[0]
for i in range(k):
line = dev_df.iloc[i, :]
q=line['question']
for choice in self.choices:
q += f'\n{choice}. {line[f"{choice}"]}'
a = line['answer']
prompt += "[INST] "+q+"\n答案：[/INST]"+a+"\n"
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