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llama_evaluator.py
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# 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 LlamaForCausalLM, LlamaTokenizer
from evaluator import Evaluator
class Llama_Evaluator(Evaluator):
def __init__(self, choices, k, model_path, device, temperature=0.2):
super(Llama_Evaluator, self).__init__(choices, model_path, k)
load_type = torch.float16
self.model_path = model_path
self.device = device
self.tokenizer = LlamaTokenizer.from_pretrained(model_path)
self.model = LlamaForCausalLM.from_pretrained(
model_path,
load_in_8bit=False,
torch_dtype=load_type,
low_cpu_mem_usage=True,
device_map='auto')
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.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:
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
if with_prompt:
prompt_template = (
"Below is an instruction that describes a task. "
"Write a response that appropriately completes the request.\n\n"
"### Instruction:\n{instruction}\n\n### Response: ")
instruction = prompt_template.format_map({'instruction': instruction,'subject':subject_name})
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,
**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
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