Dense Passage Retriever

Dense Passage Retriever (DPR) - is a set of tools and models for open-domain Q&A task. It is based on this research work and provides state-of-the-art results for multiple Q&A datasets.

Features

Dense retriever model is based on bi-encoder architecture.
Extractive Q&A reader&ranker joint model inspired by this paper.
Related data pre- and post- processing tools.
Dense retriever component for inference time logic is based on FAISS index.

Installation

Installation from the source. Python's virtual or Conda environments are recommended.

git clone git@github.com:facebookresearch/DPR.git
cd DPR
pip install .

DPR is tested on Python 3.6+ and PyTorch 1.2.0+. DPR relies on third-party libraries for encoder code implementations. It currently supports Huggingface BERT, Pytext BERT and Fairseq RoBERTa encoder models. Due to generality of the tokenization process, DPR uses Huggingface tokenizers as of now. So Huggingface is the only required dependency, Pytext & Fairseq are optional. Install them separately if you want to use those encoders.

Resources & Data formats

First, you need to prepare data for either retriever or reader training. Each of the DPR components has its own input/output data formats. You can see format descriptions below. DPR provides NQ & Trivia preprocessed datasets (and model checkpoints) to be downloaded from the cloud using our data/download_data.py tool. One needs to specify the resource name to be downloaded. Run 'python data/download_data.py' to see all options.

python data/download_data.py \
	--resource {key from download_data.py's RESOURCES_MAP}  \
	[optional --output_dir {your location}]

The resource name matching is prefix-based. So if you need to download all data resources, just use --resource data.

Retriever input data format

The data format of the Retriever training data is JSON. It contains pools of 2 types of negative passages per question, as well as positive passages and some additional information.

[
  {
	"question": "....",
	"answers": ["...", "...", "..."],
	"positive_ctxs": [{
		"title": "...",
		"text": "...."
	}],
	"negative_ctxs": ["..."],
	"hard_negative_ctxs": ["..."]
  },
  ...
]

Elements' structure for negative_ctxs & hard_negative_ctxs is exactly the same as for positive_ctxs. The preprocessed data available for downloading also contains some extra attributes which may be useful for model modifications (like bm25 scores per passage). Still, they are not currently in use by DPR.

You can download prepared NQ dataset used in the paper by using 'data.retriever.nq' key prefix. Only dev & train subsets are available in this format. We also provide question & answers only CSV data files for all train/dev/test splits. Those are used for the model evaluation since our NQ preprocessing step looses a part of original samples set. Use 'data.retriever.qas.*' resource keys to get respective sets for evaluation.

python data/download_data.py
	--resource data.retriever
	[optional --output_dir {your location}]

Retriever training

Retriever training quality depends on its effective batch size. The one reported in the paper used 8 x 32GB GPUs. In order to start training on one machine:

python train_dense_encoder.py
	--encoder_model_type {hf_bert | pytext_bert | fairseq_roberta}
	--pretrained_model_cfg {bert-base-uncased| roberta-base}
	--train_file {train files glob expression}
	--dev_file {dev files glob expression}
	--output_dir {dir to save checkpoints}

Notes:

If you use pytext_bert or fairseq_roberta, you need to download pre-trained weights and specify --pretrained_file parameter. Specify the dir location of the downloaded files for 'pretrained.fairseq.roberta-base' resource prefix for RoBERTa model or the file path for pytext BERT (resource name 'pretrained.pytext.bert-base.model').
Validation and checkpoint saving happens according to --eval_per_epoch parameter value.
There is no stop condition besides a specified amount of epochs to train.
Every evaluation saves a model checkpoint.
The best checkpoint is logged in the train process output.
Regular NLL classification loss validation for bi-encoder training can be replaced with average rank evaluation. It aggregates passage and question vectors from the input data passages pools, does large similarity matrix calculation for those representations and then averages the rank of the gold passage for each question. We found this metric more correlating with the final retrieval performance vs nll classification loss. Note however that this average rank validation works differently in DistributedDataParallel vs DataParallel PyTorch modes. See val_av_rank_* set of parameters to enable this mode and modify its settings.

See the section 'Best hyperparameter settings' below as e2e example for our best setups.

Generating representations for large documents set

Generating representation vectors for the static documents dataset is a highly parallelizable process which can take up to a few days if computed on a single GPU. You might want to use multiple available GPU servers by running the script on each of them independently and specifying their own shards.

python generate_dense_embeddings.py \
	--model_file {path to biencoder checkpoint} \
	--ctx_file {path to psgs_w100.tsv file} \
	--shard_id {shard_num, 0-based} --num_shards {total number of shards} \
	--out_file ${out files location + name PREFX}

Note: you can use much large batch size here compared to training mode. For example, setting --batch_size 128 for 2 GPU(16gb) server should work fine.

Retriever validation against the entire set of documents:

python dense_retriever.py \
	--model_file ${path to biencoder checkpoint} \
	--ctx_file  {path to all documents .tsv file} \
	--qa_file {path to test|dev .csv file} \
	--encoded_ctx_file "{encoded document files glob expression}" \
	--out_file {path to output json file with results}

The tool writes retrieved results for subsequent reader model training into specified out_file. It is a json with the following format:

[
    {
        "question": "...",
        "answers": ["...", "...", ... ],
        "ctxs": [
            {
                "id": "...", # passage id from database tsv file
                "title": "",
                "text": "....",
                "score": "...",  # retriever score
                "has_answer": true|false
     },
]

Results are sorted by their similarity score, from most relevant to least relevant.

By default, dense_retriever uses exhaustive search process, but you can opt in to use HNSW FAISS index by --hnsw_index flag. Note that using this index may be useless from the research point of view since their fast retrieval process comes at the cost of much longer indexing time and higher RAM usage. The similarity score provided is the dot product in the (default) case of exhaustive search and L2 distance in a modified representations space in case of HNSW index.

Optional reader model input data pre-processing.

Since the reader model uses a specific combination of positive and negative passages for each question and also needs to know the answer span location in the bpe-tokenized form, it is recommended to preprocess and serialize the output from the retriever model before starting the reader training. This saves hours at train time. If you don't run this preprocessing, the Reader training pipeline checks if the input file(s) extension is .pkl and if not, preprocesses and caches results automatically in the same folder as the original files.

python preprocess_reader_data.py \
	--retriever_results {path to a file with results from dense_retriever.py} \
	--gold_passages {path to gold passages info} \
	--do_lower_case \
	--pretrained_model_cfg {pretrained_cfg} \
	--encoder_model_type {hf_bert | pytext_bert | fairseq_roberta} \
	--out_file {path to for output files} \
	--is_train_set

Reader model training

python train_reader.py \
	--encoder_model_type {hf_bert | pytext_bert | fairseq_roberta} \
	--pretrained_model_cfg {bert-base-uncased| roberta-base} \
	--train_file "{globe expression for train files from #5 or #6 above}" \
	--dev_file "{globe expression for train files}" \
	--output_dir {path to output dir}

Notes:

if you use pytext_bert or fairseq_roberta, you need to download pre-trained weights and specify --pretrained_file parameter. Specify the dir location of the downloaded files for 'pretrained.fairseq.roberta-base' resource prefix for RoBERTa model or the file path for pytext BERT (resource name 'pretrained.pytext.bert-base.model').
Reader training pipeline does model validation every --eval_step batches
As the bi-encoder, it saves model checkpoints on every validation
Like the bi-encoder, there is no stop condition besides a specified amount of epochs to train.
Like the bi-encoder, there is no best checkpoint selection logic, so one needs to select that based on dev set validation performance which is logged in the train process output.
Our current code only calculates the Exact Match metric.

Distributed training

Use Pytorch's distributed training launcher tool:

python -m torch.distributed.launch \
	--nproc_per_node={WORLD_SIZE}  {non distributed scipt name & parameters}

Note:

all batch size related parameters are specified per gpu in distributed mode(DistributedDataParallel) and for all available gpus in DataParallel (single node - multi gpu) mode.

Best hyperparameter settings

e2e example with the best settings for NQ dataset.

1. Download all retriever training and validation data:

python data/download_data.py --resource data.wikipedia_split.psgs_w100
python data/download_data.py --resource data.retriever.nq
python data/download_data.py --resource data.retriever.qas.nq

2. Biencoder(Retriever) training in single set mode.

We used distributed training mode on a single 8 GPU x 32 GB server

python -m torch.distributed.launch \
	--nproc_per_node=8 train_dense_encoder.py \
	--max_grad_norm 2.0 \
	--encoder_model_type hf_bert \
	--pretrained_model_cfg bert-base-uncased \
	--seed 12345 \
	--sequence_length 256 \
	--warmup_steps 1237 \
	--batch_size 16 \
	--do_lower_case \
	--train_file "{glob expression to train files for 'data.retriever.nq' resource}" \
	--dev_file {path to downloaded data.retriever.qas.nq-dev resource} \
	--output_dir {your output dir} \
	--learning_rate 2e-05 \
	--num_train_epochs 40 \
	--dev_batch_size 16 \
	--val_av_rank_start_epoch 30

This takes about a day to complete the training for 40 epochs. It swiches to Average Rank validation on epoch 30 and it should be around 25 at the end. The best checkpoint for bi-encoder is usually the last, but it should not be so different if you take any after epoch ~ 25.

3. Generate embeddings for Wikipedia.

Just use instructions for "Generating representations for large documents set". It takes about 40 minutes to produce 21 mln passages representation vectors on 50 2 GPU servers.

4. Evaluate retrieval accuracy and generate top passage results for each of the train/dev/test datasets.

python dense_retriever.py \
	--model_file {path to checkpoint file from step 1} \
	--ctx_file {path to psgs_w100.tsv file} \
	--qa_file {path to test/dev qas file} \
	--encoded_ctx_file "{glob expression for generated files from step 3}" \
	--out_file {path for output json files} \
	--n-docs 100 \
	--validation_workers 32 \
	--batch_size 64

Adjust batch_size based on the available number of GPUs, 64 should work for 2 GPU server.

5. Reader training

We trained reader model for large datasets using a single 8 GPU x 32 GB server.

python train_reader.py \
	--seed 42 \
	--learning_rate 1e-5 \
	--eval_step 2000 \
	--do_lower_case \
	--eval_top_docs 50 \
	--encoder_model_type hf_bert \
	--pretrained_model_cfg bert-base-uncased \
	--train_file "{glob expression for train output files from step 4}" \
	--dev_file {glob expression for dev output file from step 4} \
	--warmup_steps 0 \
	--sequence_length 350 \
	--batch_size 16 \
	--passages_per_question 24 \
	--num_train_epochs 100000 \
	--dev_batch_size 72 \
	--passages_per_question_predict 50 \
	--output_dir {your save dir path}

We found that using the learning rate above works best with static schedule, so one needs to stop training manually based on evaluation performance dynamics. Our best results were achieved on 16-18 training epochs or after ~60k model updates.

We provide all input and intermediate results for e2e pipeline for NQ dataset and most of the similar resources for Trivia.

Misc.

TREC validation requires regexp based matching. We support only retriever validation in regexp mode. See --math parameter options.
WEbQ validation requires entity normalization, which is not included as of now.

Reference

If you plan to use DPR in your project, please consider citing our paper:

@misc{karpukhin2020dense,
    title={Dense Passage Retrieval for Open-Domain Question Answering},
    author={Vladimir Karpukhin and Barlas Oğuz and Sewon Min and Patrick Lewis and Ledell Wu and Sergey Edunov and Danqi Chen and Wen-tau Yih},
    year={2020},
    eprint={2004.04906},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}

License

DPR is CC-BY-NC 4.0 licensed as of now.

yizhilll/DPR