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f338eb3 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 | # Copyright 2024 Bytedance Ltd. and/or its affiliates
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Preprocess the nq dataset to parquet format
"""
import re
import os
import json
import datasets
from verl.utils.hdfs_io import copy, makedirs
import argparse
def make_prefix(dp, template_type):
question = dp['question']
context = dp['context']
# NOTE: also need to change reward_score/countdown.py
if template_type == 'base':
"""This works for any base model"""
prefix = f"""Answer the given question with some potentially useful context. \
You should analyze the question carefully, evaluate the given context (which may or may not be useful), and then generate an accurate and well-reasoned response. \
You should first have a reasoning process in mind and then provides the answer. \
Show your reasoning in <think> </think> tags and return the final answer in <answer> </answer> tags, for example <answer> Beijing </answer>. \
Question: {question} Context: {context} \n"""
else:
raise NotImplementedError
return prefix
def format_reference(retrieval_result):
format_reference = ''
for idx, doc_item in enumerate(retrieval_result):
content = doc_item['contents']
title = content.split("\n")[0]
text = "\n".join(content.split("\n")[1:])
format_reference += f"Doc {idx+1}(Title: {title}) {text}\n"
return format_reference
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--local_dir', default='./data/nq_rag')
parser.add_argument('--hdfs_dir', default=None)
parser.add_argument('--template_type', type=str, default='base')
parser.add_argument('--topk', type=int, default=3)
parser.add_argument('--corpus_path', type=str, default='/home/peterjin/mnt/data/retrieval-corpus/wiki-18.jsonl')
parser.add_argument('--train_retrieval_cache', type=str, default='/home/peterjin/rag_retrieval_cache/nq/e5_train_retrieval_cache_2048.json')
parser.add_argument('--test_retrieval_cache', type=str, default='/home/peterjin/rag_retrieval_cache/nq/e5_test_retrieval_cache_10000.json')
args = parser.parse_args()
data_source = 'nq'
dataset = datasets.load_dataset('RUC-NLPIR/FlashRAG_datasets', 'nq')
train_dataset = dataset['train']
test_dataset = dataset['test']
# read retrieval cache
print('reading retrieval cache...')
retrieval_cache = json.load(open(args.train_retrieval_cache))
# test_retrieval_cache = json.load(open(args.test_retrieval_cache))
retrieval_cache.update(json.load(open(args.test_retrieval_cache)))
# read corpus
print('reading corpus...')
corpus = {}
with open(args.corpus_path) as f:
readin = f.readlines()
for line in readin:
tmp = json.loads(line)
corpus[tmp['id']] = tmp
# add a column for the retrieval context
def add_context(example):
example['context'] = format_reference([corpus[docs["id"]] for docs in retrieval_cache[example['question']][:args.topk]])
return example
train_dataset = train_dataset.map(function=add_context)
test_dataset = test_dataset.map(function=add_context)
# add a row to each data item that represents a unique id
def make_map_fn(split):
def process_fn(example, idx):
example['question'] = example['question'].strip()
if example['question'][-1] != '?':
example['question'] += '?'
question = make_prefix(example, template_type=args.template_type)
solution = {
"target": example['golden_answers'],
}
data = {
"data_source": data_source,
"prompt": [{
"role": "user",
"content": question,
}],
"ability": "fact-reasoning",
"reward_model": {
"style": "rule",
"ground_truth": solution
},
"extra_info": {
'split': split,
'index': idx,
}
}
return data
return process_fn
train_dataset = train_dataset.map(function=make_map_fn('train'), with_indices=True)
test_dataset = test_dataset.map(function=make_map_fn('test'), with_indices=True)
local_dir = args.local_dir
hdfs_dir = args.hdfs_dir
train_dataset.to_parquet(os.path.join(local_dir, 'train.parquet'))
test_dataset.to_parquet(os.path.join(local_dir, 'test.parquet'))
if hdfs_dir is not None:
makedirs(hdfs_dir)
copy(src=local_dir, dst=hdfs_dir)
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