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import random
import tqdm
import os
import re
import sys
import torch
import numpy as np
import jsonlines
import argparse
import jsonlines
import datasets
from datasets import load_from_disk,load_dataset
from transformers import AutoModelForCausalLM, AutoTokenizer
from transformers.generation import GenerationConfig
ANS_RE = re.compile(r"#### (\-?[0-9\.\,]+)")
INVALID_ANS = "[invalid]"
def doc_to_text(doc):
return fewshot_prompt + "\nQuestion: " + doc["question"] + "\nLet's think step by step\n"
def decode(tokens_list, tokenizer, raw_text_len):
sents = []
# print(len(tokens_list))
for tokens in tokens_list:
tokens = tokens.cpu().numpy().tolist()
sent = tokenizer.tokenizer.decode(
tokens[raw_text_len:])
sent = sent.split('<|endoftext|>')[0]
sent = sent.split('\n\n\n')[0]
sent = sent.split("\n\n")[0]
sent = sent.split("Question:")[0]
sents.append(sent)
return sents
def generate_sample(model, tokenizer, input_txt):
input_ids = tokenizer.tokenizer.encode(input_txt)
raw_text_len = len(input_ids)
context_enc = torch.tensor(
[input_ids]).to(model.device)
print(f"Input text: {input_txt}\n")
outputs = model.generate(context_enc)
output_text = decode(outputs,tokenizer,raw_text_len)[0]
print(f"\nOutput text: {output_text}\n")
return output_text
def extract_answer_hf(completion):
match = ANS_RE.search(completion)
if match:
match_str = match.group(1).strip()
match_str = match_str.replace(",", "")
return eval(match_str)
else:
return INVALID_ANS
def extract_answer(completion):
try:
last_number = re.findall(r'\d+', completion)[-1]
return eval(last_number)
except:
return INVALID_ANS
def is_correct( completion, answer):
gold = extract_answer_hf(answer)
assert gold != INVALID_ANS, "No ground truth answer found in the document."
return extract_answer(completion) == gold
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='Test HF checkpoint.')
parser.add_argument("-c", "--checkpoint-path", type=str, help="Checkpoint path", default="Qwen/Qwen-7B")
parser.add_argument("-f","--sample-input-file", type=str, default=None)
parser.add_argument("-o","--sample-output-file", type=str, default="gsm8k_res.jsonl")
args = parser.parse_args()
fewshot_prompt = open("gsm8k_prompt.txt").read()
if args.sample_input_file is not None:
dataset = load_from_disk(args.sample_input_file)
else:
config = datasets.DownloadConfig(resume_download=True, max_retries=100)
dataset = load_dataset("gsm8k", 'main', download_config=config)
test = dataset["test"]
print('Loading tokenizer ...')
tokenizer = AutoTokenizer.from_pretrained(args.checkpoint_path, trust_remote_code=True)
print('Loading model ...')
model = AutoModelForCausalLM.from_pretrained(args.checkpoint_path, device_map="auto", trust_remote_code=True).eval()
model.generation_config = GenerationConfig.from_pretrained(args.checkpoint_path, trust_remote_code=True)
model.generation_config.do_sample = False
f_output = jsonlines.Writer(open(args.sample_output_file, 'w', encoding='utf-8'))
tot_length = test.num_rows
acc_res = []
for doc in test:
context = doc_to_text(doc)
completion = generate_sample(model, tokenizer, context)
answer= doc["answer"]
acc = is_correct(completion, answer)
doc["completion"]=completion
doc["acc"]=acc
f_output.write(doc)
acc_res.append(acc)
f_output.close()
print("Acc: ",np.mean(acc_res)) |