gsm8k_mc_eval.py

# Ref: https://github.com/kojima-takeshi188/zero_shot_cot
# Ref: https://github.com/sylinrl/TruthfulQA/blob/main/truthfulqa/metrics.py
# Ref: https://github.com/sylinrl/TruthfulQA/blob/main/truthfulqa/utilities.py

import re
import os
import json
import random
import torch
import numpy as np
import pandas as pd
import transformers
from tqdm import tqdm, trange
import argparse
import pandas as pd
from transformers import AutoConfig
import ssl
import urllib.request
import zipfile

from model import LLaMA_Analysis

transformers.logging.set_verbosity(40)

ANS_RE = re.compile(r"#### (\-?[0-9\.\,]+)")
INVALID_ANS = "[invalid]"

N_SHOT = 7
COT_FLAG = True
DEBUG = False
ANSWER_TRIGGER = "So the answer is"



def split_multi_answer(ans, sep=';', close=True):

    """Splits string of all reference answers into a list of formatted answers"""

    # answers = ans.strip().split(sep)
    split_answers = []
    for a in answers:
        a = a.strip()
        if len(a):
            if close:  # add a period after all answers
                # if a[-1] != '.':
                #     split_answers.append(a + '.')
                # else:
                split_answers.append(a)
            else:
                split_answers.append(a)

    return split_answers


def format_math(ans):
    ans = ans.replace('\n', ' ').replace('####', 'The answer is')
    return ans

def format_best(best_ans, close=True):

    """Formats best answer to match format of reference answers"""

    best = best_ans.strip()
    if close:
        if best[-1] != '.':
            best = best + '.'
    return best

def load_csv(file_path, is_gzip=False):
    # input file is in csv format, can be loaded by pandas
    # required columns: [Question] only

    open_func = open if not is_gzip else gzip.open
    list_data = []
    with open_func(file_path, 'r') as f:
        df = pd.read_csv(f)
        for idx in range(len(df)):
            data = {'question': df['Question'][idx], 
                    'answer_best': df['Best Answer'][idx],
                    'answer_true': df['Correct Answers'][idx],
                    'answer_false': df['Incorrect Answers'][idx]}
            list_data.append(data)

    return list_data

def download_url(url: str, folder='folder'):
    """
    Downloads the content of an url to a folder. Modified from \
    https://github.com/pyg-team/pytorch_geometric/tree/master/torch_geometric

    Args:
        url (string): The url of target file.
        folder (string): The target folder.

    Returns:
        string: File path of downloaded files.
    """

    file = url.rpartition('/')[2]
    file = file if file[0] == '?' else file.split('?')[0]
    path = os.path.join(folder, file)
    if os.path.exists(path):
        print(f'File {file} exists, use existing file.')
        return path

    print(f'Downloading {url}')
    os.makedirs(folder, exist_ok=True)
    ctx = ssl._create_unverified_context()
    data = urllib.request.urlopen(url, context=ctx)
    with open(path, 'wb') as f:
        f.write(data.read())

    return path

def extract_answer_from_output(completion):
    match = ANS_RE.search(completion)
    if match:
        match_str = match.group(1).strip()
        match_str = match_str.replace(",", "")
        return match_str
    else:
        return INVALID_ANS


def is_correct(model_answer, answer):
    gt_answer = answer
    assert gt_answer != INVALID_ANS
    return model_answer == gt_answer

def create_demo_text():
    question, answer = [], []
    
    question.append("Carly collected 7 starfish with 5 arms each and one seastar with 14 arms. How many arms do the animals she collected have in total?")
    answer.append("7 * 5 + 14 = <<7*5+14=49>>49. ")

    question.append("Manny had 3 birthday cookie pies to share with his 24 classmates and his teacher, Mr. Keith. "
                    "If each of the cookie pies were cut into 10 slices and Manny, his classmates, and Mr. Keith all had 1 piece, how many slices are left?")
    answer.append("3 x 10 = <<3*10=30>>30 cookie pieces. 30 - 24 - 1 - 1 = <<30-24-1-1=4>>4 cookie pieces.")

    question.append("A new program had 60 downloads in the first month."
                    "The number of downloads in the second month was three times as many as the downloads in the first month, but then reduced by 30% in the third month."
                    "How many downloads did the program have total over the three months?")
    answer.append("The number of downloads of the program in the second month increased to 3*60 = <<3*60=180>>180."
                  "In the first two months, the total number of downloads of the program was 180+60 = <<180+60=240>>240."
                  "In the third month, the number of downloads of the program reduced by 30/100*180 = <<30/100*180=54>>54"
                  "There were 180-54 = <<180-54=126>>126 downloads in the third month."
                  "In the three months, the total number of downloads of the program was 126+240 = <<126+240=366>>366.The answer is 366.")

    question.append("Michael had 58 golf balls. On tuesday, he lost 23 golf balls. On "
        "wednesday, he lost 2 more. "
        "How many golf balls did he have at the end of wednesday?")
    answer.append("Michael started with 58 golf balls. After losing 23 on tuesday, "
        "he had 58 - 23 = 35. After losing 2 more, "
        "After losing 2 more, he had 35 - 2 = 33 golf balls. The answer is 33.")

    # Concatenate demonstration examples ...
    demo_text = 'Give the answer to the  math question step by step.' + '\n\n'
    for i in range(len(question)):
        demo_text += "Q: " + question[i] + "\nA: " + answer[i] + "\n\n"
    return demo_text


def build_prompt(input_text):
    demo = create_demo_text()
    input_text_prompt = demo + "Q: " + input_text + "\n" + "A:"
    return input_text_prompt

def build_prompt_with_answer(question, answer):
    demo = create_demo_text()
    input_text_prompt = demo + "Q: " + question + "\n" + "A: " + answer
    return input_text_prompt

def build_prompt_and_answer(input_text, answer):
    demo = create_demo_text()
    input_text_prompt = demo + "Q: " + input_text + "\n" + "A:"
    continue_text = " " + answer
    return input_text_prompt, continue_text


def Math_Cals(scores_true, scores_false, ref_best):
    """Given model scores for true / false reference answers, calculates MC scores"""
    scores = {}
    scores['max'] = max(scores_true)
    scores['diff'] = max(scores_true) - max(scores_false)
    scores['scores-true'] = scores_true
    scores['scores-false'] = scores_false

    # compute MC1: 1vFalse -- best correct answer vs all false answers
    max_false = max(scores_false)
    if scores_true[ref_best] > max_false:
        scores['MC1'] = 1.0
    else:
        scores['MC1'] = 0.0
    return scores

def MC_calcs(scores_true, scores_false, ref_true=None, ref_best=None):

    """Given model scores for true / false reference answers, calculates MC scores"""
    scores = {}
    scores['max'] = max(scores_true)
    scores['diff'] = max(scores_true) - max(scores_false)
    scores['scores-true'] = scores_true
    scores['scores-false'] = scores_false
    scores['max_false'] = max(scores_false)
    # compute MC1: 1vFalse -- best correct answer vs all false answers
    max_false = max(scores_false)
    if scores_true[ref_true.index(ref_best)] > max_false:
        scores['MC1'] = 1.0
    else:
        scores['MC1'] = 0.0

    # compute MC3: 1vFalse -- each correct answer vs all false answers
    max_false = max(scores_false)
    onevall = sum(np.array(scores_true) > max_false) / float(len(scores_true))
    scores['MC3'] = onevall

    # print(scores)
    # print(ref_true)
    # print(ref_best)
    # exit()
    # compute MC2: normalized probability mass for correct answers
    probs_true = np.exp(scores_true)
    while sum(probs_true) == 0:
        # print("WARNING: all zero scores_true")
        scores_true = [x/2.0 for x in scores_true]
        probs_true = np.exp(scores_true)
    probs_false = np.exp(scores_false)
    while sum(probs_false) == 0:
        # print("WARNING: all zero scores_false")
        scores_false = [x/2.0 for x in scores_false]
        probs_false = np.exp(scores_false)

    probs_true = probs_true / (sum(probs_true) + sum(probs_false))
    
    # check nan
    if np.isnan(sum(probs_true)):
        scores['MC2'] = 0.0
        print(f"WARNING: nan in probs_true: sum(probs_true)={sum(probs_true)}, sum(probs_false)={sum(probs_false)}")
    else:
        scores['MC2'] = sum(probs_true)

    return scores


if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument("--model-name", type=str, default="huggyllama/llama-7b")
    parser.add_argument("--num-gpus", type=str, default="1")
    parser.add_argument("--max_gpu_memory", type=int, default=80)
    parser.add_argument("--device", type=str, choices=["cuda", "cpu"], default="cuda")
    parser.add_argument("--data-path", type=str, default="./tfqa")
    parser.add_argument("--output-path", type=str, default="./tfqa_result")
    # parallel mode (split the dataset into multiple parts, inference by separate processes)
    parser.add_argument("--early-exit-layers", type=str, default="-1")
    parser.add_argument("--parallel", action="store_true")
    parser.add_argument("--total-shard", type=int, default=8)
    parser.add_argument("--shard-id", type=int, default=None)
    parser.add_argument("--do-rating", action="store_true")
    parser.add_argument("--gpt3-config", type=str, default=None)
    parser.add_argument("--debug", action="store_true")
    parser.add_argument("--max-new-tokens", type=int, default=50)
    parser.add_argument("--top_p", type=float, default=0.95)
    parser.add_argument("--top_k", type=int, default=0)
    parser.add_argument("--temperature", type=float, default=0.9)
    parser.add_argument("--repetition_penalty", type=float, default=1.0)
    parser.add_argument("--relative_top", type=float, default=0.0)
    parser.add_argument("--relative_top_value", type=float, default=-1000.0)
    parser.add_argument("--hidden_layers", type=int, default=80)
    parser.add_argument("--layer_wise", action="store_true")
    parser.add_argument("--attention", action="store_true")
    args = parser.parse_args()
    model_name = args.model_name
    num_gpus = args.num_gpus
    device = args.device

    # Get test file
    '''
    The StrategyQA dataset includes the followings files:
        strategyqa_train.json: The training set of StrategyQA, which includes 2,290 examples.
        strategyqa_train_paragraphs.json: Paragraphs from our corpus that were matched as evidence for examples in the training set.
        strategyqa_train_filtered.json: 2,821 additional questions, excluded from the official training set, that were filtered by our solvers during data collection (see more details in the paper).
        strategyqa_test.json: The test set of StrategyQA, which includes 490 examples.
    Here we only need the test set.
    '''

    with open(args.data_path, 'r') as f:
        list_data_dict = f.readlines()
    
    list_data_dict = list_data_dict[:1000]
    if args.debug:
        list_data_dict = list_data_dict[:10]
    
    if args.parallel:
        chunk_size = len(list_data_dict) // args.total_shard
        list_data_dict = list_data_dict[args.shard_id * chunk_size: (args.shard_id + 1) * chunk_size]
    
    llm = LLaMA_Analysis(model_name, device, num_gpus, args.hidden_layers, args.max_gpu_memory)
    stop_word_list = ["Q:"]
    llm.set_stop_words(stop_word_list)
    early_exit_layers = [int(x) for x in args.early_exit_layers.split(',')]
    if len(early_exit_layers) == 1:
        print("MODE: naive decoding from the last layer", flush=True)
        if args.layer_wise:
            mode = 'baseline_layer_wise'
        elif args.attention:
            mode = 'baseline_attention'
        else:
            mode = "baseline"
        mature_layer = None
        premature_layer = None
        candidate_premature_layers = None
    answers = []
    
    result_dict = {'question': [], 'model_scores': [], 'total_mc1': 0.0, 'total_mc2': 0.0, 'total_mc3': 0.0}
    
    if 'chatglm' in model_name:
        config = AutoConfig.from_pretrained(model_name, trust_remote_code=True)
        config.num_hidden_layers = config.num_layers
    else:
        config = AutoConfig.from_pretrained(model_name)
    
    if args.layer_wise:
        all_results = {f'Layer_{i+1}_lm_head':{'total_mc1': 0.0, 'total_mc2': 0.0, 'total_mc3': 0.0} for i in range(config.num_hidden_layers)}
        all_results_ind = {f'Layer_{i+1}_lm_head':{'ind_mc1': list(), 'ind_mc2': list(), 'ind_mc3': list()} for i in range(config.num_hidden_layers)}
    else:
        all_results = {f'Layer_{i+1}_attention':{'total_mc1': 0.0, 'total_mc2': 0.0, 'total_mc3': 0.0} for i in range(config.num_hidden_layers)}
    # all_results = {f'Layer_{i+1}_lm_head':{'total_mc1': 0.0, 'total_mc2': 0.0, 'total_mc3': 0.0} for i in range(config.num_hidden_layers-70)}
    with torch.no_grad():
        for ind, sample in enumerate(tqdm(list_data_dict)):
            # reference answers
            sample = json.loads(sample)
            ref_true = sample['truth_answer']
            # ref_best = format_best(sample['truth_answer'])
            # # ref_true = split_multi_answer(sample['answer_true'])
            ref_false = sample['error']
            # print('answer choices')
            # # print(ref_best)
            # print(sample['question'])
            # print(ref_true)
            # print(ref_false)
            scores_true = []
            scores_false = []

            generate_kwargs = dict(max_new_tokens=args.max_new_tokens, repetition_penalty=args.repetition_penalty, mode=mode, mature_layer=mature_layer, premature_layer=premature_layer, candidate_premature_layers=candidate_premature_layers, relative_top=args.relative_top, relative_top_value=args.relative_top_value, post_softmax=False)

            
            for temp_ans in ref_true:
                temp_ans = format_math(temp_ans)
                # print('ground-truth answer: ', temp_ans)
                # append the current answer choice to the prompt
                prompt, answer = build_prompt_and_answer(sample['question'], temp_ans)
                    
                log_probs, c_dist = llm.lm_score(prompt, answer, **generate_kwargs)
                scores_true.append(log_probs)

               
            # print('*******True_scores: ', scores_true[0][-10:])
            for temp_ans in ref_false:
                # append the current answer choice to the prompt
                
                temp_ans = format_math(temp_ans)
                # print('false answer: ', temp_ans)
                prompt, answer = build_prompt_and_answer(sample['question'], temp_ans)
                
                log_probs, c_dist = llm.lm_score(prompt, answer, **generate_kwargs)
                scores_false.append(log_probs)

            # print('*******flase_scores: ', scores_false[0][-10:])
            if args.layer_wise or args.attention:
                scores = []
                # print(len(log_probs))
                for ind_ in range(len(log_probs)):
                    
                    score_true, score_false = np.array(scores_true)[:,ind_], np.array(scores_false)[:,ind_]

                    score = MC_calcs(list(score_true), list(score_false), ref_true, ref_best=ref_true[0])
                    
                    if np.isnan(score['MC1']) or np.isnan(score['MC2']) or np.isnan(score['MC3']):
                        print(ind_)
                        import ipdb; ipdb.set_trace()
                        
                        
                    if args.layer_wise:
                        key_ = f'Layer_{ind_+1}_lm_head'
                    else:
                        key_ = f'Layer_{ind_+1}_attention'

                    # update total scores
                    
                    all_results[key_]['total_mc1'] += score['MC1']
                    all_results[key_]['total_mc2'] += score['MC2']
                    all_results[key_]['total_mc3'] += score['MC3']
                    
                    
                    all_results_ind[key_]['ind_mc1'].append(str(ind) +',' + str(score['MC1']))
                    all_results_ind[key_]['ind_mc2'].append(str(ind) +',' + str(score['MC2']))
                    all_results_ind[key_]['ind_mc3'].append(str(ind) +',' + str(score['MC3']))

    # Average the scores
    for key, value in all_results.items():
        all_results[key]['total_mc1'] /= len(list_data_dict)
        all_results[key]['total_mc2'] /= len(list_data_dict)
        all_results[key]['total_mc3'] /= len(list_data_dict)

    # Print the final scores, separated by ', '
        print(f'{key}  MC1/2/3: \n{all_results[key]["total_mc1"]}, {all_results[key]["total_mc2"]}, {all_results[key]["total_mc3"]}')

    model_tag = model_name.split('/')[-1] if model_name[-1] != '/' else model_name.split('/')[-2]

    import pandas as pd    
    df = pd.DataFrame(all_results)
    df_ = pd.DataFrame(all_results_ind)
    # df.drop(index=['total_mc2','total_mc1'])
    # df.remove('question')
    # Write the DataFrame to an Excel file
    if not args.attention:
        file_name = args.output_path + f'output-path_layer_wise.xlsx'
        file_name_ = args.output_path + f'output-path_layer_wise_ind.xlsx'
    else:
        file_name = args.output_path + f'output-path_layer_wise_atten.xlsx'
    
    df.to_excel(file_name, index=False)
    df_.to_excel(file_name_, index=False)