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import os
from tqdm import tqdm
import json
import os
import openai
from tqdm import tqdm
import argparse
import multiprocessing
from copy import deepcopy
from functools import partial
prompt_library = {
"MCQ": "In this problem, only one option will be correct. Give a detailed solution and end the solution with the final answer.",
"MCQ(multiple)": "In this problem, multiple options can be correct. Give a detailed solution and end the solution with the final answer.",
"Integer": "In this problem, the final answer will be a non-negative integer. Give a detailed solution and end the solution with the final answer.",
"Numeric": "In this problem, the final will be a numeric value. Give the numerical answer correct upto the 2nd decimal digit. Give a detailed solution and end the solution with the final answer.",
}
few_shot_examples = json.load(open('data/few_shot_examples.json'))
def write_in_file(response_file, response_dict, question, mode, model_nickname):
if os.path.exists(response_file):
with open(response_file, 'r') as infile:
responses = json.load(infile)
else:
responses = []
found = False
for i, old_resp in enumerate(responses):
if old_resp['description'] == question['description'] and old_resp['index'] == question['index']:
responses[i][f"{model_nickname}_{mode}_response" ] = response_dict[f"{model_nickname}_{mode}_response"]
found = True
break
if not found:
responses.append(response_dict)
json.dump(sorted(responses, key=lambda elem: (elem['description'], elem['index'])), open(response_file, 'w'), indent=4)
print(f"####UPDATED {response_file}, Current size : {len(responses)}####")
def get_response(question,model, model_nickname, mode, response_file, lock):
response_dict = deepcopy(question)
prefix_prompt = prompt_library[question['type']]
suffix_prompt = ""
if mode in ['CoT', 'CoT+SC', 'CoT+Exam'] :
suffix_prompt = "Let's think step by step.\n"
ques = question["question"]
stripped_ques = ques.replace("\n\n", "\n").strip()
if mode in ['CoT+OneShot', 'CoT', 'CoT+SC', 'CoT+Exam']:
if mode == 'CoT+Exam':
if response_dict['type'] in ['MCQ', 'MCQ(multiple)']:
if response_dict['type'] == 'MCQ':
exam_prompt = "If the answer is wrong, you'll be given -1 marks. If the answer is correct, you'll be given +3 marks. If you're unsure of the answer, you can skip the question, and you'll be given 0 marks."
else:
exam_prompt = "If any of the options in the final answer is wrong, you'll be given -2 marks. If all the options are correct, you'll be given +4 marks. If some of the options are correct, you'll be given +1 for each correct option. If you're unsure of the answer, you can skip the question, and you'll be given 0 marks."
prompt = prefix_prompt + " " + exam_prompt + "\n\n" + "Problem: " + stripped_ques + "\nSolution: " + suffix_prompt
else:
print("No point doing this for Numeric/Integer questions since there is no negative marking...")
breakpoint()
else:
if mode == 'CoT+OneShot':
ex = few_shot_examples[question['subject']][question['type']]
prompt = prefix_prompt + "\n\n" + "Problem: " + ex['problem'] + "\nSolution: " + ex['solution'] + "\n\n" + "Problem: " + stripped_ques + "\nSolution: "
else:
prompt = prefix_prompt + "\n\n" + "Problem: " + stripped_ques + "\nSolution: " + suffix_prompt
else:
prompt = prefix_prompt + "\n\n" + "Problem: " + stripped_ques + suffix_prompt
prompt = prompt.strip()
response_dict[f"prompt"] = prompt
num_retries = 0
print(f'Question: {question["description"]}, Index: {question["index"]}, Model: {model_nickname}, Mode: {mode}, query begins')
while True:
try:
if model in ["text-davinci-003", "text-davinci-002", 'davinci-002']:
response = openai.Completion.create(
model=model,
prompt=prompt,
max_tokens=2048,
temperature=0 if mode in ['CoT', 'normal', 'CoT+Exam'] else 0.5,
n=1 if mode in ['CoT', 'normal', 'CoT+Exam'] else 3
)
else:
response = openai.ChatCompletion.create(
model=model,
messages=[
{"role": "system", "content": ""},
{"role": "user", "content": prompt}
],
max_tokens=2048,
temperature=0 if mode in ['CoT+OneShot', 'CoT', 'normal', 'CoT+Exam'] else 0.5,
n=1 if mode in ['CoT+OneShot', 'CoT', 'normal', 'CoT+Exam'] else 8
)
lock.acquire()
response_dict[f"{model_nickname}_{mode}_response"] = response
write_in_file(response_file, response_dict, question, mode, model_nickname)
lock.release()
break
except Exception as e:
num_retries += 1
print("Failure!", e)
return
def main():
'''
The code can restart from the already done questions in case there is a failure midpoint.
'''
args = argparse.ArgumentParser()
args.add_argument('--model', default='gpt-3.5-turbo')
args.add_argument('--data', default='data/dataset.json')
args.add_argument('--mode', default='normal')
args.add_argument('--num_procs', default=1, type=int)
args.add_argument('--max_questions', default=1, type=int)
args = args.parse_args()
openai.organization = os.getenv("OPENAI_ORG")
openai.api_key = os.getenv("OPENAI_API_KEY")
model_nickname = {
"davinci-002": "davinci-002",
"text-davinci-003": "GPT3",
"gpt-3.5-turbo": "GPT3.5",
"gpt-4-0613": "GPT4_0613",
"gpt-4-0314": "GPT4"
}
assert args.model in model_nickname.keys()
assert args.mode in ['normal', 'CoT', 'CoT+OneShot', 'CoT+Exam', 'CoT+SC']
out_file_dir = f'responses/{model_nickname[args.model]}_{args.mode}_responses'
out_file = os.path.join(out_file_dir, 'responses.json')
questions = json.load(open(args.data))
rem_ques = []
if os.path.exists(out_file):
for question in tqdm(questions[:args.max_questions]):
if os.path.exists(out_file):
with open(out_file, 'r') as infile:
responses = json.load(infile)
found = False
for i, old_resp in enumerate(responses):
if question['type'] in ['Numeric', 'Integer'] and args.mode == 'CoT+Exam':
found = True
if old_resp['description'] == question['description'] and old_resp['index'] == question['index']:
found = all([old_resp.get(
f"{model_nickname[args.model]}_{args.mode}_response", False) for model in [args.model]])
if found:
print("This question has already been done")
else:
rem_ques.append(question)
else:
os.makedirs(out_file_dir, exist_ok=True)
if args.mode == 'CoT+Exam':
rem_ques = []
for q in questions:
if q['type'] in ['MCQ', 'MCQ(multiple)']:
rem_ques.append(q)
else:
rem_ques = questions[:args.max_questions]
print(f"There are {len(rem_ques)} problems remaining")
manager = multiprocessing.Manager()
lock = manager.Lock()
pool = multiprocessing.Pool(args.num_procs)
f = partial(get_response, model=args.model, model_nickname=model_nickname[args.model], mode=args.mode, response_file=out_file, lock=lock)
pool.map(f, rem_ques)
if __name__ == '__main__':
main()
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