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import os
import warnings
from typing import List, Dict
import functools
from tqdm import tqdm
from multiprocessing import Pool
import faiss
import torch
import numpy as np
from transformers import AutoConfig, AutoTokenizer, AutoModel
import argparse
import datasets
def load_corpus(corpus_path: str):
corpus = datasets.load_dataset(
'json',
data_files=corpus_path,
split="train",
num_proc=4)
return corpus
def read_jsonl(file_path):
data = []
with open(file_path, "r") as f:
readin = f.readlines()
for line in readin:
data.append(json.loads(line))
return data
def load_docs(corpus, doc_idxs):
results = [corpus[int(idx)] for idx in doc_idxs]
return results
def load_model(
model_path: str,
use_fp16: bool = False
):
model_config = AutoConfig.from_pretrained(model_path, trust_remote_code=True)
model = AutoModel.from_pretrained(model_path, trust_remote_code=True)
model.eval()
model.cuda()
if use_fp16:
model = model.half()
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=True, trust_remote_code=True)
return model, tokenizer
def pooling(
pooler_output,
last_hidden_state,
attention_mask = None,
pooling_method = "mean"
):
if pooling_method == "mean":
last_hidden = last_hidden_state.masked_fill(~attention_mask[..., None].bool(), 0.0)
return last_hidden.sum(dim=1) / attention_mask.sum(dim=1)[..., None]
elif pooling_method == "cls":
return last_hidden_state[:, 0]
elif pooling_method == "pooler":
return pooler_output
else:
raise NotImplementedError("Pooling method not implemented!")
class Encoder:
def __init__(self, model_name, model_path, pooling_method, max_length, use_fp16):
self.model_name = model_name
self.model_path = model_path
self.pooling_method = pooling_method
self.max_length = max_length
self.use_fp16 = use_fp16
self.model, self.tokenizer = load_model(model_path=model_path,
use_fp16=use_fp16)
@torch.no_grad()
def encode(self, query_list: List[str], is_query=True) -> np.ndarray:
# processing query for different encoders
if isinstance(query_list, str):
query_list = [query_list]
if "e5" in self.model_name.lower():
if is_query:
query_list = [f"query: {query}" for query in query_list]
else:
query_list = [f"passage: {query}" for query in query_list]
if "bge" in self.model_name.lower():
if is_query:
query_list = [f"Represent this sentence for searching relevant passages: {query}" for query in query_list]
inputs = self.tokenizer(query_list,
max_length=self.max_length,
padding=True,
truncation=True,
return_tensors="pt"
)
inputs = {k: v.cuda() for k, v in inputs.items()}
if "T5" in type(self.model).__name__:
# T5-based retrieval model
decoder_input_ids = torch.zeros(
(inputs['input_ids'].shape[0], 1), dtype=torch.long
).to(inputs['input_ids'].device)
output = self.model(
**inputs, decoder_input_ids=decoder_input_ids, return_dict=True
)
query_emb = output.last_hidden_state[:, 0, :]
else:
output = self.model(**inputs, return_dict=True)
query_emb = pooling(output.pooler_output,
output.last_hidden_state,
inputs['attention_mask'],
self.pooling_method)
if "dpr" not in self.model_name.lower():
query_emb = torch.nn.functional.normalize(query_emb, dim=-1)
query_emb = query_emb.detach().cpu().numpy()
query_emb = query_emb.astype(np.float32, order="C")
return query_emb
class BaseRetriever:
"""Base object for all retrievers."""
def __init__(self, config):
self.config = config
self.retrieval_method = config.retrieval_method
self.topk = config.retrieval_topk
self.index_path = config.index_path
self.corpus_path = config.corpus_path
# self.cache_save_path = os.path.join(config.save_dir, 'retrieval_cache.json')
def _search(self, query: str, num: int, return_score:bool) -> List[Dict[str, str]]:
r"""Retrieve topk relevant documents in corpus.
Return:
list: contains information related to the document, including:
contents: used for building index
title: (if provided)
text: (if provided)
"""
pass
def _batch_search(self, query_list, num, return_score):
pass
def search(self, *args, **kwargs):
return self._search(*args, **kwargs)
def batch_search(self, *args, **kwargs):
return self._batch_search(*args, **kwargs)
class BM25Retriever(BaseRetriever):
r"""BM25 retriever based on pre-built pyserini index."""
def __init__(self, config):
super().__init__(config)
from pyserini.search.lucene import LuceneSearcher
self.searcher = LuceneSearcher(self.index_path)
self.contain_doc = self._check_contain_doc()
if not self.contain_doc:
self.corpus = load_corpus(self.corpus_path)
self.max_process_num = 8
def _check_contain_doc(self):
r"""Check if the index contains document content
"""
return self.searcher.doc(0).raw() is not None
def _search(self, query: str, num: int = None, return_score = False) -> List[Dict[str, str]]:
if num is None:
num = self.topk
hits = self.searcher.search(query, num)
if len(hits) < 1:
if return_score:
return [],[]
else:
return []
scores = [hit.score for hit in hits]
if len(hits) < num:
warnings.warn('Not enough documents retrieved!')
else:
hits = hits[:num]
if self.contain_doc:
all_contents = [json.loads(self.searcher.doc(hit.docid).raw())['contents'] for hit in hits]
results = [{'title': content.split("\n")[0].strip("\""),
'text': "\n".join(content.split("\n")[1:]),
'contents': content} for content in all_contents]
else:
results = load_docs(self.corpus, [hit.docid for hit in hits])
if return_score:
return results, scores
else:
return results
def _batch_search(self, query_list, num: int = None, return_score = False):
# TODO: modify batch method
results = []
scores = []
for query in query_list:
item_result, item_score = self._search(query, num,True)
results.append(item_result)
scores.append(item_score)
if return_score:
return results, scores
else:
return results
def get_available_gpu_memory():
memory_info = []
for i in range(torch.cuda.device_count()):
total_memory = torch.cuda.get_device_properties(i).total_memory
allocated_memory = torch.cuda.memory_allocated(i)
free_memory = total_memory - allocated_memory
memory_info.append((i, free_memory / 1e9)) # Convert to GB
return memory_info
class DenseRetriever(BaseRetriever):
r"""Dense retriever based on pre-built faiss index."""
def __init__(self, config: dict):
super().__init__(config)
self.index = faiss.read_index(self.index_path)
if config.faiss_gpu:
co = faiss.GpuMultipleClonerOptions()
co.useFloat16 = True
co.shard = True
self.index = faiss.index_cpu_to_all_gpus(self.index, co=co)
# self.index = faiss.index_cpu_to_all_gpus(self.index)
self.corpus = load_corpus(self.corpus_path)
self.encoder = Encoder(
model_name = self.retrieval_method,
model_path = config.retrieval_model_path,
pooling_method = config.retrieval_pooling_method,
max_length = config.retrieval_query_max_length,
use_fp16 = config.retrieval_use_fp16
)
self.topk = config.retrieval_topk
self.batch_size = self.config.retrieval_batch_size
def _search(self, query: str, num: int = None, return_score = False):
if num is None:
num = self.topk
query_emb = self.encoder.encode(query)
scores, idxs = self.index.search(query_emb, k=num)
idxs = idxs[0]
scores = scores[0]
results = load_docs(self.corpus, idxs)
if return_score:
return results, scores
else:
return results
def _batch_search(self, query_list: List[str], num: int = None, return_score = False):
if isinstance(query_list, str):
query_list = [query_list]
if num is None:
num = self.topk
batch_size = self.batch_size
results = []
scores = []
for start_idx in tqdm(range(0, len(query_list), batch_size), desc='Retrieval process: '):
query_batch = query_list[start_idx:start_idx + batch_size]
# from time import time
# a = time()
batch_emb = self.encoder.encode(query_batch)
# b = time()
# print(f'################### encode time {b-a} #####################')
batch_scores, batch_idxs = self.index.search(batch_emb, k=num)
batch_scores = batch_scores.tolist()
batch_idxs = batch_idxs.tolist()
# print(f'################### search time {time()-b} #####################')
# exit()
flat_idxs = sum(batch_idxs, [])
batch_results = load_docs(self.corpus, flat_idxs)
batch_results = [batch_results[i*num : (i+1)*num] for i in range(len(batch_idxs))]
scores.extend(batch_scores)
results.extend(batch_results)
if return_score:
return results, scores
else:
return results
def get_retriever(config):
r"""Automatically select retriever class based on config's retrieval method
Args:
config (dict): configuration with 'retrieval_method' key
Returns:
Retriever: retriever instance
"""
if config.retrieval_method == "bm25":
return BM25Retriever(config)
else:
return DenseRetriever(config)
def get_dataset(config):
"""Load dataset from config."""
split_path = os.path.join(config.dataset_path, f'{config.data_split}.jsonl')
return read_jsonl(split_path)
if __name__ == '__main__':
parser = argparse.ArgumentParser(description = "Retrieval")
# Basic parameters
parser.add_argument('--retrieval_method', type=str)
parser.add_argument('--retrieval_topk', type=int, default=10)
parser.add_argument('--index_path', type=str, default=None)
parser.add_argument('--corpus_path', type=str)
parser.add_argument('--dataset_path', default=None, type=str)
parser.add_argument('--faiss_gpu', default=True, type=bool)
parser.add_argument('--data_split', default="train", type=str)
parser.add_argument('--retrieval_model_path', type=str, default=None)
parser.add_argument('--retrieval_pooling_method', default='mean', type=str)
parser.add_argument('--retrieval_query_max_length', default=256, type=str)
parser.add_argument('--retrieval_use_fp16', action='store_true', default=False)
parser.add_argument('--retrieval_batch_size', default=512, type=int)
args = parser.parse_args()
args.index_path = os.path.join(args.index_path, f'{args.retrieval_method}_Flat.index') if args.retrieval_method != 'bm25' else os.path.join(args.index_path, 'bm25')
# load dataset
all_split = get_dataset(args)
input_query = [sample['question'] for sample in all_split[:512]]
# initialize the retriever and conduct retrieval
retriever = get_retriever(args)
print('Start Retrieving ...')
results, scores = retriever.batch_search(input_query, return_score=True)
# from IPython import embed
# embed()
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