142 lines
5.1 KiB
Python
142 lines
5.1 KiB
Python
# Copyright 2024 Bytedance Ltd. and/or its affiliates
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""
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Preprocess the nq dataset to parquet format
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"""
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import re
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import os
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import json
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import datasets
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from verl.utils.hdfs_io import copy, makedirs
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import argparse
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def make_prefix(dp, template_type):
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question = dp['question']
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context = dp['context']
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# NOTE: also need to change reward_score/countdown.py
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if template_type == 'base':
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"""This works for any base model"""
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prefix = f"""Answer the given question with some potentially useful context. \
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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. \
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You should first have a reasoning process in mind and then provides the answer. \
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Show your reasoning in <think> </think> tags and return the final answer in <answer> </answer> tags, for example <answer> Beijing </answer>. \
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Question: {question} Context: {context} \n"""
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else:
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raise NotImplementedError
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return prefix
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def format_reference(retrieval_result):
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format_reference = ''
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for idx, doc_item in enumerate(retrieval_result):
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content = doc_item['contents']
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title = content.split("\n")[0]
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text = "\n".join(content.split("\n")[1:])
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format_reference += f"Doc {idx+1}(Title: {title}) {text}\n"
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return format_reference
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if __name__ == '__main__':
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parser = argparse.ArgumentParser()
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parser.add_argument('--local_dir', default='./data/nq_rag')
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parser.add_argument('--hdfs_dir', default=None)
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parser.add_argument('--template_type', type=str, default='base')
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parser.add_argument('--topk', type=int, default=3)
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parser.add_argument('--corpus_path', type=str, default='/home/peterjin/mnt/data/retrieval-corpus/wiki-18.jsonl')
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parser.add_argument('--train_retrieval_cache', type=str, default='/home/peterjin/rag_retrieval_cache/nq/e5_train_retrieval_cache_2048.json')
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parser.add_argument('--test_retrieval_cache', type=str, default='/home/peterjin/rag_retrieval_cache/nq/e5_test_retrieval_cache_10000.json')
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args = parser.parse_args()
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data_source = 'nq'
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dataset = datasets.load_dataset('RUC-NLPIR/FlashRAG_datasets', 'nq')
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train_dataset = dataset['train']
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test_dataset = dataset['test']
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# read retrieval cache
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print('reading retrieval cache...')
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retrieval_cache = json.load(open(args.train_retrieval_cache))
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# test_retrieval_cache = json.load(open(args.test_retrieval_cache))
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retrieval_cache.update(json.load(open(args.test_retrieval_cache)))
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# read corpus
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print('reading corpus...')
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corpus = {}
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with open(args.corpus_path) as f:
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readin = f.readlines()
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for line in readin:
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tmp = json.loads(line)
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corpus[tmp['id']] = tmp
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# add a column for the retrieval context
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def add_context(example):
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example['context'] = format_reference([corpus[docs["id"]] for docs in retrieval_cache[example['question']][:args.topk]])
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return example
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train_dataset = train_dataset.map(function=add_context)
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test_dataset = test_dataset.map(function=add_context)
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# add a row to each data item that represents a unique id
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def make_map_fn(split):
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def process_fn(example, idx):
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example['question'] = example['question'].strip()
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if example['question'][-1] != '?':
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example['question'] += '?'
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question = make_prefix(example, template_type=args.template_type)
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solution = {
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"target": example['golden_answers'],
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}
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data = {
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"data_source": data_source,
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"prompt": [{
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"role": "user",
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"content": question,
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}],
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"ability": "fact-reasoning",
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"reward_model": {
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"style": "rule",
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"ground_truth": solution
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},
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"extra_info": {
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'split': split,
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'index': idx,
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}
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}
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return data
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return process_fn
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train_dataset = train_dataset.map(function=make_map_fn('train'), with_indices=True)
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test_dataset = test_dataset.map(function=make_map_fn('test'), with_indices=True)
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local_dir = args.local_dir
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hdfs_dir = args.hdfs_dir
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train_dataset.to_parquet(os.path.join(local_dir, 'train.parquet'))
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test_dataset.to_parquet(os.path.join(local_dir, 'test.parquet'))
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if hdfs_dir is not None:
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makedirs(hdfs_dir)
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copy(src=local_dir, dst=hdfs_dir)
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