Scalable training for dense retrieval models.

Related tags

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
DCA - Official Python implementation of Delaunay Component Analysis algorithm

Delaunay Component Analysis (DCA) Official Python implementation of the Delaunay

Petra Poklukar 9 Sep 06, 2022
Source code and data from the RecSys 2020 article "Carousel Personalization in Music Streaming Apps with Contextual Bandits" by W. Bendada, G. Salha and T. Bontempelli

Carousel Personalization in Music Streaming Apps with Contextual Bandits - RecSys 2020 This repository provides Python code and data to reproduce expe

Deezer 48 Jan 02, 2023
Bayesian optimization in PyTorch

BoTorch is a library for Bayesian Optimization built on PyTorch. BoTorch is currently in beta and under active development! Why BoTorch ? BoTorch Prov

2.5k Dec 31, 2022
Diverse graph algorithms implemented using JGraphT library.

# 1. Installing Maven & Pandas First, please install Java (JDK11) and Python 3 if they are not already. Next, make sure that Maven (for importing J

See Woo Lee 3 Dec 17, 2022
Neural Geometric Level of Detail: Real-time Rendering with Implicit 3D Shapes (CVPR 2021 Oral)

Neural Geometric Level of Detail: Real-time Rendering with Implicit 3D Surfaces Official code release for NGLOD. For technical details, please refer t

659 Dec 27, 2022
TRACER: Extreme Attention Guided Salient Object Tracing Network implementation in PyTorch

TRACER: Extreme Attention Guided Salient Object Tracing Network This paper was accepted at AAAI 2022 SA poster session. Datasets All datasets are avai

Karel 118 Dec 29, 2022
Art Project "Schrödinger's Game of Life"

Repo of the project "Team Creative Quantum AI: Schrödinger's Game of Life" Installation new conda env: conda create --name qcml python=3.8 conda activ

ℍ◮ℕℕ◭ℍ ℝ∈ᛔ∈ℝ 2 Sep 15, 2022
Open source annotation tool for machine learning practitioners.

doccano doccano is an open source text annotation tool for humans. It provides annotation features for text classification, sequence labeling and sequ

7.1k Jan 01, 2023
This toolkit provides codes to download and pre-process the SLUE datasets, train the baseline models, and evaluate SLUE tasks.

slue-toolkit We introduce Spoken Language Understanding Evaluation (SLUE) benchmark. This toolkit provides codes to download and pre-process the SLUE

ASAPP Research 39 Sep 21, 2022
DA2Lite is an automated model compression toolkit for PyTorch.

DA2Lite (Deep Architecture to Lite) is a toolkit to compress and accelerate deep network models. ⭐ Star us on GitHub — it helps!! Frameworks & Librari

Sinhan Kang 7 Mar 22, 2022
Python package provinding tools for artistic interactive applications using AI

Documentation redrawing Python package provinding tools for artistic interactive applications using AI Created by ReDrawing Campinas team for the Open

ReDrawing Campinas 1 Sep 30, 2021
Code base for "On-the-Fly Test-time Adaptation for Medical Image Segmentation"

On-the-Fly Adaptation Official Pytorch Code base for On-the-Fly Test-time Adaptation for Medical Image Segmentation Paper Introduction One major probl

Jeya Maria Jose 17 Nov 10, 2022
Realistic lighting in ursina!

Ursina Lighting Realistic lighting in ursina! If you want to have realistic lighting in ursina, import the UrsinaLighting.py in your project and use t

17 Jul 07, 2022
Deep Residual Learning for Image Recognition

Deep Residual Learning for Image Recognition This is a Torch implementation of "Deep Residual Learning for Image Recognition",Kaiming He, Xiangyu Zhan

Kimmy 561 Dec 01, 2022
Unofficial implementation of HiFi-GAN+ from the paper "Bandwidth Extension is All You Need" by Su, et al.

HiFi-GAN+ This project is an unoffical implementation of the HiFi-GAN+ model for audio bandwidth extension, from the paper Bandwidth Extension is All

Brent M. Spell 134 Dec 30, 2022
The official PyTorch implementation of Curriculum by Smoothing (NeurIPS 2020, Spotlight).

Curriculum by Smoothing (NeurIPS 2020) The official PyTorch implementation of Curriculum by Smoothing (NeurIPS 2020, Spotlight). For any questions reg

PAIR Lab 36 Nov 23, 2022
Adversarial Robustness Comparison of Vision Transformer and MLP-Mixer to CNNs

Adversarial Robustness Comparison of Vision Transformer and MLP-Mixer to CNNs ArXiv Abstract Convolutional Neural Networks (CNNs) have become the de f

Philipp Benz 12 Oct 24, 2022
Image Segmentation Evaluation

Image Segmentation Evaluation Martin Keršner, [email protected] Evaluation

Martin Kersner 273 Oct 28, 2022
Easy and comprehensive assessment of predictive power, with support for neuroimaging features

Documentation: https://raamana.github.io/neuropredict/ News As of v0.6, neuropredict now supports regression applications i.e. predicting continuous t

Pradeep Reddy Raamana 93 Nov 29, 2022
[ICCV 2021 Oral] SnowflakeNet: Point Cloud Completion by Snowflake Point Deconvolution with Skip-Transformer

This repository contains the source code for the paper SnowflakeNet: Point Cloud Completion by Snowflake Point Deconvolution with Skip-Transformer (ICCV 2021 Oral). The project page is here.

AllenXiang 65 Dec 26, 2022