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Dense

Dense is a simple and efficient toolkit for training and running dense retrievers with deep language models. The toolkit has a modularized design for easy research; a set of command line tools are also provided for fast development and testing. A set of easy-to-use interfaces to Huggingface🤗's state-of-the-art pre-trained transformers ensures Dense's superior performance.

Dense is currently under initial development stage. We will be actively adding new features and API changes may happen.

Features

Command line interface for dense retriever training/encoding and dense index search.
Flexible and extendable Pytorch retriever models.
Highly efficient Trainer, a subclass of Huggingface Trainer, that naively support training performance features like mixed precision and distributed data parallel.
Fast and memory-efficient train/inference data access based on memory mapping with Apache Arrow through Huggingface datasets.

Installation

First install the dependencies. The current code base has been testes with,

pytorch==1.8.0  
faiss-cpu==1.6.5  
transformers==4.2.0  
datasets==1.1.3

Then clone this repo and run pip.

git https://github.com/luyug/Dense
cd Dense
pip install .

Or typically for research, install as editable,

pip install --editable .

Data Format

Training: Each line of the the Train file is a training instance,

{'query': TEXT_TYPE, 'positives': List[TEXT_TYPE], 'negatives': List[TEXT_TYPE]}
...

Inference/Encoding: Each line of the the encoding file is a piece of text to be encoded,

{text_id: "xxx", 'text': TEXT_TYPE}
...

Here TEXT_TYPE can be either raw string or pre-tokenized ids, i.e. List[int]. Using the latter can help lower data processing latency during training to reduce/eliminate GPU wait. Note: the current code requires text_id of passages/contexts to be convertible to integer, e.g. integers or string of integers.

Training (Simple)

To train a simple dense retriever, call the dense.driver.train module,

python -m dense.driver.train \  
  --output_dir $OUTDIR \  
  --model_name_or_path bert-base-uncased \  
  --do_train \  
  --save_steps 20000 \  
  --train_dir $TRAIN_DIR \
  --fp16 \  
  --per_device_train_batch_size 8 \  
  --learning_rate 5e-6 \  
  --num_train_epochs 2 \  
  --dataloader_num_workers 2

Here we picked bert-base-uncased BERT weight from Huggingface Hub and turned on AMP with --fp16 to speed up training. Several command flags are provided in addition to configure the learned model, e.g. --add_pooler which adds an linear projection. A full list command line arguments can be found in dense.arguments.

Training (Research)

Check out the run.py in examples directory for a fully configurable train/test loop. Typically you will do,

from dense.modeling import DenseModel
from dense.trainer import DenseTrainer as Trainer

...
model = DenseModel.build(
        model_args,
        data_args,
        training_args,
        config=config,
        cache_dir=model_args.cache_dir,
    )
trainer = Trainer(
        model=model,
        args=training_args,
        train_dataset=train_dataset,
        data_collator=collator,
    )
...
trainer.train()

Encoding

To encode, call the dense.driver.encode module. For large corpus, split the corpus into shards to parallelize.

for s in shard1 shar2 shard3
do
python -m dense.driver.encode \  
  --output_dir=$OUTDIR \  
  --tokenizer_name $TOK \  
  --config_name $CONFIG \  
  --model_name_or_path $MODEL_DIR \  
  --fp16 \  
  --per_device_eval_batch_size 128 \  
  --encode_in_path $CORPUS_DIR/$s.json \  
  --encoded_save_path $ENCODE_DIR/$s.pt
done

Index Search

Call the dense.faiss_retriever module,

python -m dense.faiss_retriever \  
--query_reps $ENCODE_QRY_DIR/qry.pt \  
--passage_reps $ENCODE_DIR/'*.pt' \  
--depth $DEPTH \
--batch_size -1 \
--save_text \
--save_ranking_to rank.tsv

Encoded corpus or corpus shards are loaded based on glob pattern matching of argument --passage_reps. Argument --batch_size controls number of queries passed to the FAISS index each search call and -1 will pass all queries in one call. Larger batches typically run faster (due to better memory access patterns and hardware utilization.) Setting flag --save_text will save the ranking to a tsv file with each line being qid pid score.

Alternatively paralleize search over the shards,

for s in shard1 shar2 shard3
do
python -m dense.faiss_retriever \  
--query_reps $ENCODE_QRY_DIR/qry.pt \  
--passage_reps $ENCODE_DIR/$s.pt \  
--depth $DEPTH \  
--save_ranking_to $INTERMEDIATE_DIR/$s
done

Then combine the results using the reducer module,

python -m dense.faiss_retriever.reducer \  
--score_dir $INTERMEDIATE_DIR \  
--query $ENCODE_QRY_DIR/qry.pt \  
--save_ranking_to rank.txt