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
This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit https://cla.opensource.microsoft.com.
When you submit a pull request, a CLA bot will automatically determine whether you need to provide a CLA and decorate the PR appropriately (e.g., status check, comment). Simply follow the instructions provided by the bot. You will only need to do this once across all repos using our CLA.
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
This project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft trademarks or logos is subject to and must follow Microsoft's Trademark & Brand Guidelines. Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship. Any use of third-party trademarks or logos are subject to those third-party's policies.
569 Dec 21, 2022
13 Nov 27, 2022
81 Dec 08, 2022
58 May 03, 2022
1.6k Jan 06, 2023
323 Dec 29, 2022
13 Jan 03, 2022
134 Jul 10, 2022
120 Jan 04, 2023
1 Jan 09, 2023
5 Jun 09, 2022
23 Oct 19, 2022
372 Dec 26, 2022
51 Dec 10, 2022
31 Nov 19, 2022
182 Sep 06, 2021
22 Feb 24, 2022
14 Dec 19, 2022
103 Jan 05, 2023
35 Dec 27, 2022