| 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() |
|
|
