nl_tasks/inference/MATH_infer.py

import argparse
import json
import pdb
import jsonlines

import util
from vllm import LLM, SamplingParams
import sys
MAX_INT = sys.maxsize
INVALID_ANS = "[invalid]"
MAX_TOKEN = 1408

import random
import numpy as np
import torch
import os

invalid_outputs = []
def remove_boxed(s):
    left = "\\boxed{"
    try:
        assert s[:len(left)] == left
        assert s[-1] == "}"
        return s[len(left):-1]
    except:
        return None

def process_results(doc, completion, answer):
    split_ans = completion.split('The answer is: ')
    if len(split_ans) > 1:
        ans = split_ans[-1]
        extract_ans_temp = ans.split('.\n')[0]
        extract_ans_temp = extract_ans_temp.strip()
        if len(extract_ans_temp)>0 and extract_ans_temp[-1] == '.':
            extract_ans = extract_ans_temp[0:-1]
        else:
            extract_ans = extract_ans_temp
        extract_ans = extract_ans.strip()
        if util.is_equiv(extract_ans, answer):
            return True
        else:
            return False
    else:
        temp = {'question': doc, 'output': completion, 'answer': answer}
        invalid_outputs.append(temp)
        return False
def batch_data(data_list, batch_size=1):
    n = len(data_list) // batch_size
    batch_data = []
    for i in range(n-1):
        start = i * batch_size
        end = (i+1)*batch_size
        batch_data.append(data_list[start:end])

    last_start = (n-1) * batch_size
    last_end = MAX_INT
    batch_data.append(data_list[last_start:last_end])
    return batch_data

def test_hendrycks_math(model, data_path, start=0, end=MAX_INT, batch_size=1, tensor_parallel_size=1):
    hendrycks_math_ins = []
    hendrycks_math_answers = []
    problem_prompt = (
        "Below is an instruction that describes a task. "
        "Write a response that appropriately completes the request.\n\n"
        "### Instruction:\n{instruction}\n\n### Response: Let's think step by step."
    )
    print('promt =====', problem_prompt)
    with open(data_path, "r+", encoding="utf8") as f:
        for idx, item in enumerate(jsonlines.Reader(f)):
            temp_instr = problem_prompt.format(instruction=item["instruction"])
            hendrycks_math_ins.append(temp_instr)
            solution = item['output']
            temp_ans = remove_boxed(util.last_boxed_only_string(solution))
            hendrycks_math_answers.append(temp_ans)


    print('total length ===', len(hendrycks_math_ins))
    hendrycks_math_ins = hendrycks_math_ins[start:end]
    hendrycks_math_answers = hendrycks_math_answers[start:end]
    print('lenght ====', len(hendrycks_math_ins))
    # batch_hendrycks_math_ins = batch_data(hendrycks_math_ins, batch_size=batch_size)

    stop_tokens = ["Instruction:", "Instruction", "Response:", "Response"]
    sampling_params = SamplingParams(temperature=0, top_p=1, max_tokens=MAX_TOKEN, stop=stop_tokens)
    print('sampleing =====', sampling_params)
    llm = LLM(model=model,tensor_parallel_size=tensor_parallel_size, gpu_memory_utilization=0.95)

    outputs = llm.generate(hendrycks_math_ins, sampling_params)
    res_completions = [output.outputs[0].text for output in outputs]

    results = []
    for idx, (prompt, completion, prompt_answer) in enumerate(zip(hendrycks_math_ins, res_completions, hendrycks_math_answers)):
        res = process_results(prompt, completion, prompt_answer)
        results.append(res)

    acc = sum(results) / len(results)
    print('len invalid outputs ====', len(invalid_outputs), ', invalid_outputs===', len(invalid_outputs))
    # print('start===', start, ', end====',end)
    print('length====', len(results), ', acc====', acc*100)

    current_path = args.model
    parent_dir = os.path.dirname(current_path.rstrip('/'))
    output_filename = os.path.join(parent_dir, 'output.txt')
    # output_filename = args.model + 'output.txt'
    with open(output_filename, "a", encoding="utf-8") as f:
        print(f'\n MATH math MAX TOKEN = {MAX_TOKEN}, length==== {len(results)}, math acc %====, {acc*100}', file=f)

def parse_args():
    parser = argparse.ArgumentParser()
    parser.add_argument("--model", type=str, default=0)  # model path
    parser.add_argument("--data_file", type=str, default='data/MATH_test.jsonl')  # data path
    parser.add_argument("--start", type=int, default=0) #start index
    parser.add_argument("--end", type=int, default=MAX_INT)  # end index
    parser.add_argument("--batch_size", type=int, default=50)  # batch_size
    parser.add_argument("--tensor_parallel_size", type=int, default=1)  # tensor_parallel_size
    return parser.parse_args()

def set_deterministic_seed(seed=42):
    random.seed(seed)
    np.random.seed(seed)
    torch.manual_seed(seed)
    torch.cuda.manual_seed_all(seed)
    # torch.backends.cudnn.deterministic = True
    # torch.backends.cudnn.benchmark = False
    
if __name__ == "__main__":
    args = parse_args()
    set_deterministic_seed()
    test_hendrycks_math(model=args.model, data_path=args.data_file, start=args.start, end=args.end, batch_size=args.batch_size, tensor_parallel_size=args.tensor_parallel_size)
    print('math ends', args.model)