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