Scalable training for dense retrieval models.

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Deep Learningdpr-scale
Overview

Scalable implementation of dense retrieval.

Training on cluster

By default it trains locally:

PYTHONPATH=.:$PYTHONPATH python dpr_scale/main.py trainer.gpus=1

SLURM Training

To train the model on SLURM, run:

PYTHONPATH=.:$PYTHONPATH python dpr_scale/main.py -m trainer=slurm trainer.num_nodes=2 trainer.gpus=2

Reproduce DPR on 8 gpus

PYTHONPATH=.:$PYTHONPATH python dpr_scale/main.py -m --config-name nq.yaml  +hydra.launcher.name=dpr_stl_nq_reproduce

Generate embeddings on Wikipedia

PYTHONPATH=.:$PYTHONPATH python dpr_scale/generate_embeddings.py -m --config-name nq.yaml datamodule=generate datamodule.test_path=psgs_w100.tsv +task.ctx_embeddings_dir=<CTX_EMBEDDINGS_DIR> +task.checkpoint_path=<CHECKPOINT_PATH>

Get retrieval results

Currently this runs on 1 GPU. Use CTX_EMBEDDINGS_DIR from above.

PYTHONPATH=.:$PYTHONPATH python dpr_scale/run_retrieval.py --config-name nq.yaml trainer=gpu_1_host trainer.gpus=1 +task.output_path=<PATH_TO_OUTPUT_JSON> +task.ctx_embeddings_dir=<CTX_EMBEDDINGS_DIR> +task.checkpoint_path=<CHECKPOINT_PATH> +task.passages=psgs_w100.tsv datamodule.test_path=<PATH_TO_QUERIES_JSONL>

Generate query embeddings

Alternatively, query embedding generation and retrieval can be separated. After query embeddings are generated using the following command, the run_retrieval_fb.py or run_retrieval_multiset.py script can be used to perform retrieval.

PYTHONPATH=.:$PYTHONPATH python dpr_scale/generate_query_embeddings.py -m --config-name nq.yaml trainer.gpus=1 datamodule.test_path=<PATH_TO_QUERIES_JSONL> +task.ctx_embeddings_dir=<CTX_EMBEDDINGS_DIR> +task.checkpoint_path=<CHECKPOINT_PATH> +task.query_emb_output_path=<OUTPUT_TO_QUERY_EMB>

Get evaluation metrics for a given JSON output file

python dpr_scale/eval_dpr.py --retrieval <PATH_TO_OUTPUT_JSON> --topk 1 5 10 20 50 100

Get evaluation metrics for MSMARCO

python dpr_scale/msmarco_eval.py ~data/msmarco/qrels.dev.small.tsv PATH_TO_OUTPUT_JSON

Domain-matched Pre-training Tasks for Dense Retrieval

Paper: https://arxiv.org/abs/2107.13602

The sections below provide links to datasets and pretrained models, as well as, instructions to prepare datasets, pretrain and fine-tune them.

Q&A Datasets

PAQ

Download the dataset from here

Conversational Datasets

You can download the dataset from the respective tables.

Reddit

File Download Link
train download
dev download

ConvAI2

File Download Link
train download
dev download

DSTC7

File Download Link
train download
dev download
test download

Prepare by downloading the tar ball linked here, and using the command below.

DSTC7_DATA_ROOT=<path_of_dir_where_the_data_is_extracted>
python dpr_scale/data_prep/prep_conv_datasets.py \
    --dataset dstc7 \
    --in_file_path $DSTC7_DATA_ROOT/ubuntu_train_subtask_1_augmented.json \
    --out_file_path $DSTC7_DATA_ROOT/ubuntu_train.jsonl

Ubuntu V2

File Download Link
train download
dev download
test download

Prepare by downloading the tar ball linked here, and using the command below.

UBUNTUV2_DATA_ROOT=<path_of_dir_where_the_data_is_extracted>
python dpr_scale/data_prep/prep_conv_datasets.py \
    --dataset ubuntu2 \
    --in_file_path $UBUNTUV2_DATA_ROOT/train.csv \
    --out_file_path $UBUNTUV2_DATA_ROOT/train.jsonl

Pretraining DPR

Pretrained Checkpoints

Pretrained Model Dataset Download Link
BERT-base PAQ download
BERT-large PAQ download
BERT-base Reddit download
BERT-large Reddit download
RoBERTa-base Reddit download
RoBERTa-large Reddit download

Pretraining on PAQ dataset

DPR_ROOT=<path_of_your_repo's_root>
MODEL="bert-large-uncased"
NODES=8
BSZ=16
MAX_EPOCHS=20
LR=1e-5
TIMOUT_MINS=4320
EXP_DIR=<path_of_the_experiment_dir>
TRAIN_PATH=<path_of_the_training_data_file>
mkdir -p ${EXP_DIR}/logs
PYTHONPATH=$DPR_ROOT python ${DPR_ROOT}/dpr_scale/main.py -m \
    --config-dir ${DPR_ROOT}/dpr_scale/conf \
    --config-name nq.yaml \
    hydra.launcher.timeout_min=$TIMOUT_MINS \
    hydra.sweep.dir=${EXP_DIR} \
    trainer.num_nodes=${NODES} \
    task.optim.lr=${LR} \
    task.model.model_path=${MODEL} \
    trainer.max_epochs=${MAX_EPOCHS} \
    datamodule.train_path=$TRAIN_PATH \
    datamodule.batch_size=${BSZ} \
    datamodule.num_negative=1 \
    datamodule.num_val_negative=10 \
    datamodule.num_test_negative=50 > ${EXP_DIR}/logs/log.out 2> ${EXP_DIR}/logs/log.err &

Pretraining on Reddit dataset

# Use a batch size of 16 for BERT and RoBERTa base models.
BSZ=4
NODES=8
MAX_EPOCHS=5
WARMUP_STEPS=10000
LR=1e-5
MODEL="roberta-large"
EXP_DIR=<path_of_the_experiment_dir>
PYTHONPATH=. python dpr_scale/main.py -m \
    --config-dir ${DPR_ROOT}/dpr_scale/conf \
    --config-name reddit.yaml \
    hydra.launcher.nodes=${NODES} \
    hydra.sweep.dir=${EXP_DIR} \
    trainer.num_nodes=${NODES} \
    task.optim.lr=${LR} \
    task.model.model_path=${MODEL} \
    trainer.max_epochs=${MAX_EPOCHS} \
    task.warmup_steps=${WARMUP_STEPS} \
    datamodule.batch_size=${BSZ} > ${EXP_DIR}/logs/log.out 2> ${EXP_DIR}/logs/log.err &

Fine-tuning DPR on downstream tasks/datasets

Fine-tune the pretrained PAQ checkpoint

# You can also try 2e-5 or 5e-5. Usually these 3 learning rates work best.
LR=1e-5
# Use a batch size of 32 for BERT and RoBERTa base models.
BSZ=12
MODEL="bert-large-uncased"
MAX_EPOCHS=40
WARMUP_STEPS=1000
NODES=1
PRETRAINED_CKPT_PATH=<path_of_checkpoint_pretrained_on_reddit>
EXP_DIR=<path_of_the_experiment_dir>
PYTHONPATH=. python dpr_scale/main.py -m \
    --config-dir ${DPR_ROOT}/dpr_scale/conf \
    --config-name nq.yaml \
    hydra.launcher.name=${NAME} \
    hydra.sweep.dir=${EXP_DIR} \
    trainer.num_nodes=${NODES} \
    trainer.max_epochs=${MAX_EPOCHS} \
    datamodule.num_negative=1 \
    datamodule.num_val_negative=25 \
    datamodule.num_test_negative=50 \
    +trainer.val_check_interval=150 \
    task.warmup_steps=${WARMUP_STEPS} \
    task.optim.lr=${LR} \
    task.pretrained_checkpoint_path=$PRETRAINED_CKPT_PATH \
    task.model.model_path=${MODEL} \
    datamodule.batch_size=${BSZ} > ${EXP_DIR}/logs/log.out 2> ${EXP_DIR}/logs/log.err &

Fine-tune the pretrained Reddit checkpoint

Batch sizes that worked on Volta 32GB GPUs for respective model and datasets.

Model Dataset Batch Size
BERT/RoBERTa base ConvAI2 64
RBERT/RoBERTa base ConvAI2 16
BERT/RoBERTa base DSTC7 24
BERT/RoBERTa base DSTC7 8
BERT/RoBERTa base Ubuntu V2 64
BERT/RoBERTa large Ubuntu V2 16 
# Change the config file name to convai2.yaml or dstc7.yaml for the respective datasets.
CONFIG_FILE_NAME=ubuntuv2.yaml
# You can also try 2e-5 or 5e-5. Usually these 3 learning rates work best.
LR=1e-5
BSZ=16
NODES=1
MAX_EPOCHS=5
WARMUP_STEPS=10000
MODEL="roberta-large"
PRETRAINED_CKPT_PATH=<path_of_checkpoint_pretrained_on_reddit>
EXP_DIR=<path_of_the_experiment_dir>
PYTHONPATH=${DPR_ROOT} python ${DPR_ROOT}/dpr_scale/main.py -m \
    --config-dir=${DPR_ROOT}/dpr_scale/conf \
    --config-name=$CONFIG_FILE_NAME \
    hydra.launcher.nodes=${NODES} \
    hydra.sweep.dir=${EXP_DIR} \
    trainer.num_nodes=${NODES} \
    trainer.max_epochs=${MAX_EPOCHS} \
    +trainer.val_check_interval=150 \
    task.pretrained_checkpoint_path=$PRETRAINED_CKPT_PATH \
    task.warmup_steps=${WARMUP_STEPS} \
    task.optim.lr=${LR} \
    task.model.model_path=$MODEL \
    datamodule.batch_size=${BSZ} > ${EXP_DIR}/logs/log.out 2> ${EXP_DIR}/logs/log.err &

License

dpr-scale is CC-BY-NC 4.0 licensed as of now.

Owner
Facebook Research
Facebook Research
GitHub Repository
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