BiDR
Repo for WWW 2022 paper: Progressively Optimized Bi-Granular Document Representation for Scalable Embedding Based Retrieval.
Requirements
torch==1.7
transformers==4.6
faiss-gpu==1.6.4.post2
Data Download and Preprocess
bash download_data.sh
python preprocess.py
These commands will download and preprocess the MSMARCO Passage and Doc dataset, then the resutls will be saved to ./data.
We take the Passage dataset as the example to show the running workflow.
Conventional Workflow
Representation Learning
Train the encoder with random negative (or set --hardneg_json to provied bm25/hard negatives) :
mkdir log
dataset=passage
savename=dense_global_model
python train.py --model_name_or_path roberta-base \
--max_query_length 24 --max_doc_length 128 \
--data_dir ./data/${dataset}/preprocess \
--learning_rate 1e-4 --optimizer_str adamw \
--per_device_train_batch_size 128 \
--per_query_neg_num 1 \
--generate_batch_method random \
--loss_method multi_ce \
--savename ${savename} --save_model_path ./model \
--world_size 8 --gpu_rank 0_1_2_3_4_5_6_7 --master_port 13256 \
--num_train_epochs 30 \
--use_pq False \
|tee ./log/${savename}.log
Unsupervised Quantization
Generate dense embeddings of queries and docs:
data_type=passage
savename=dense_global_model
epoch=20
python ./inference.py \
--data_type ${data_type} \
--preprocess_dir ./data/${data_type}/preprocess/ \
--max_doc_length 256 --max_query_length 32 \
--eval_batch_size 512 \
--ckpt_path ./model/${savename}/${epoch}/ \
--output_dir evaluate/${savename}_${epoch}
Product Quantization based on Faiss and recall performance:
data_type=passage
savename=dense_global_model
epoch=20
python ./test/lightweight_ann.py \
--output_dir ./data/${data_type}/evaluate/${savename}_${epoch} \
--ckpt_path /model/${savename}/${epoch}/ \
--subvector_num 96 \
--index opq \
--topk 1000 \
--data_type ${data_type} \
--MRR_cutoff 10 \
--Recall_cutoff 5 10 30 50 100
Progressively Optimized Bi-Granular Document Representation
Sparse Representation Learning
Instead of running unsupervised quantization for the well-learned dense embeddings, the sparse embeddings are generated from contrastive learning, which optimizes the global discrimination and helps to enable high-quality answers to be covered in candidate search.
Train
We find that using Faiss OPQ to initialize the PQ module has a significant gain for MSMARCO dataset. But for the largest dataset: Ads dataset, initialization with Faiss OPQ is redundant and has no promotion.
dataset=passage
savename=sparse_global_model
python train.py --model_name_or_path ./model/dense_global_model/20 \
--max_query_length 24 --max_doc_length 128 \
--data_dir ./data/${dataset}/preprocess \
--learning_rate 1e-4 --optimizer_str adamw \
--per_device_train_batch_size 128 \
--per_query_neg_num 1 \
--generate_batch_method random \
--loss_method multi_ce \
--savename ${savename} --save_model_path ./model \
--world_size 8 --gpu_rank 0_1_2_3_4_5_6_7 --master_port 13256 \
--num_train_epochs 30 \
--use_pq True \
--init_index_path ./data/${data_type}/evaluate/dense_global_model_20/OPQ96,PQ96x8.index \
--partition 96 --centroids 256 --quantloss_weight 0.0 \
|tee ./log/${savename}.log
where the ./model/dense_global_model/20 and ./data/${data_type}/evaluate/dense_global_model_20/OPQ96,PQ96x8.index is generated by conventional workflow.
Test
data_type=passage
savename=sparse_global_model
epoch=20
python ./inference.py \
--data_type ${data_type} \
--preprocess_dir ./data/${data_type}/preprocess/ \
--max_doc_length 256 --max_query_length 32 \
--eval_batch_size 512 \
--ckpt_path ./model/${savename}/${epoch}/ \
--output_dir evaluate/${savename}_${epoch}
python ./test/lightweight_ann.py \
--output_dir ./data/${data_type}/evaluate/${savename}_${epoch} \
--subvector_num 96 \
--index opq \
--topk 1000 \
--data_type ${data_type} \
--MRR_cutoff 10 \
--Recall_cutoff 5 10 30 50 100 \
--ckpt_path ./model/${savename}/${epoch}/ \
--init_index_path ./data/${data_type}/evaluate/dense_global_model_20/OPQ96,PQ96x8.index
Dense Representation Learning
The dense embeddings are optimized based on the candidate distribution generated by sparse embeddings. We propose a novel sampling strategy called locality-centric sampling to enhance local discrimination: construct a bipartite proximity graph and conduct random walk or snow sample on it.
Train
Encode the quries in train set and generate the candidates for all train queries:
data_type=passage
savename=sparse_global_model
epoch=20
python ./inference.py \
--data_type ${data_type} \
--preprocess_dir ./data/${data_type}/preprocess/ \
--max_doc_length 256 --max_query_length 32 \
--eval_batch_size 512 \
--ckpt_path ./model/${savename}/${epoch}/ \
--output_dir evaluate/${savename}_${epoch} \
--mode train
python ./test/lightweight_ann.py \
--output_dir ./data/${data_type}/evaluate/${savename}_${epoch} \
--subvector_num 96 \
--index opq \
--topk 1000 \
--data_type ${data_type} \
--MRR_cutoff 10 \
--Recall_cutoff 5 10 30 50 100 \
--ckpt_path ./model/${savename}/${epoch}/ \
--init_index_path ./data/${data_type}/evaluate/dense_global_model_20/OPQ96,PQ96x8.index \
--mode train \
--save_hardneg_to_json
This command will save the train_hardneg.json to output_dir. Then train the dense embeddings to distinguish the ground truth from the negative in candidate:
dataset=passage
savename=dense_local_model
python train.py --model_name_or_path roberta-base \
--max_query_length 24 --max_doc_length 128 \
--data_dir ./data/${dataset}/preprocess \
--learning_rate 1e-4 --optimizer_str adamw \
--per_device_train_batch_size 128 \
--per_query_neg_num 1 \
--generate_batch_method {random_walk or snow_sample} \
--loss_method multi_ce \
--savename ${savename} --save_model_path ./model \
--world_size 8 --gpu_rank 0_1_2_3_4_5_6_7 --master_port 13256 \
--num_train_epochs 30 \
--use_pq False \
--hardneg_json ./data/${data_type}/evaluate/sparse_global_model_20/train_hardneg.json \
--mink 0 --maxk 200 \
|tee ./log/${savename}.log
Test
data_type=passage
savename=dense_local_model
epoch=10
python ./inference.py \
--data_type ${data_type} \
--preprocess_dir ./data/${data_type}/preprocess/ \
--ckpt_path ./model/${savename}/${epoch}/ \
--max_doc_length 256 --max_query_length 32 \
--eval_batch_size 512 \
--ckpt_path ./model/${savename}/${epoch}/ \
--output_dir evaluate/${savename}_${epoch}
python ./test/post_verification.py \
--data_type ${data_type} \
--output_dir evaluate/${savename}_${epoch} \
--candidate_from_ann ./data/${data_type}/evaluate/sparse_global_model_20/dev.rank_1000_score_faiss_opq.tsv \
--MRR_cutoff 10 \
--Recall_cutoff 5 10 30 50 100
Contributing
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This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact [email protected] with any additional questions or comments.
Trademarks
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