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
Hard cater examples from Hopper ICLR paper

CATER-h Honglu Zhou*, Asim Kadav, Farley Lai, Alexandru Niculescu-Mizil, Martin Renqiang Min, Mubbasir Kapadia, Hans Peter Graf (*Contact: honglu.zhou

NECLA ML Group 6 May 11, 2021
Using machine learning to predict and analyze high and low reader engagement for New York Times articles posted to Facebook.

How The New York Times can increase Engagement on Facebook Using machine learning to understand characteristics of news content that garners "high" Fa

Jessica Miles 0 Sep 16, 2021
[CVPR 2020] 3D Photography using Context-aware Layered Depth Inpainting

[CVPR 2020] 3D Photography using Context-aware Layered Depth Inpainting [Paper] [Project Website] [Google Colab] We propose a method for converting a

Virginia Tech Vision and Learning Lab 6.2k Jan 01, 2023
Repo 4 basic seminar §How to make human machine readable"

WORK IN PROGRESS... Notebooks from the Seminar: Human Machine Readable WS21/22 Introduction into programming Georg Trogemann, Christian Heck, Mattis

experimental-informatics 3 May 29, 2022
Efficient 6-DoF Grasp Generation in Cluttered Scenes

Contact-GraspNet Contact-GraspNet: Efficient 6-DoF Grasp Generation in Cluttered Scenes Martin Sundermeyer, Arsalan Mousavian, Rudolph Triebel, Dieter

NVIDIA Research Projects 148 Dec 28, 2022
code for our ECCV 2020 paper "A Balanced and Uncertainty-aware Approach for Partial Domain Adaptation"

Code for our ECCV (2020) paper A Balanced and Uncertainty-aware Approach for Partial Domain Adaptation. Prerequisites: python == 3.6.8 pytorch ==1.1.0

32 Nov 27, 2022
Source code for NAACL 2021 paper "TR-BERT: Dynamic Token Reduction for Accelerating BERT Inference"

TR-BERT Source code and dataset for "TR-BERT: Dynamic Token Reduction for Accelerating BERT Inference". The code is based on huggaface's transformers.

THUNLP 37 Oct 30, 2022
Personalized Transfer of User Preferences for Cross-domain Recommendation (PTUPCDR)

Personalized Transfer of User Preferences for Cross-domain Recommendation (PTUPCDR) This is the official implementation of our paper Personalized Tran

Yongchun Zhu 81 Dec 29, 2022
Character Controllers using Motion VAEs

Character Controllers using Motion VAEs This repo is the codebase for the SIGGRAPH 2020 paper with the title above. Please find the paper and demo at

Electronic Arts 165 Jan 03, 2023
Source code for the paper "PLOME: Pre-training with Misspelled Knowledge for Chinese Spelling Correction" in ACL2021

PLOME:Pre-training with Misspelled Knowledge for Chinese Spelling Correction (ACL2021) This repository provides the code and data of the work in ACL20

197 Nov 26, 2022
Pointer-generator - Code for the ACL 2017 paper Get To The Point: Summarization with Pointer-Generator Networks

Note: this code is no longer actively maintained. However, feel free to use the Issues section to discuss the code with other users. Some users have u

Abi See 2.1k Jan 04, 2023
Keras code and weights files for popular deep learning models.

Trained image classification models for Keras THIS REPOSITORY IS DEPRECATED. USE THE MODULE keras.applications INSTEAD. Pull requests will not be revi

François Chollet 7.2k Dec 29, 2022
Open source implementation of AceNAS: Learning to Rank Ace Neural Architectures with Weak Supervision of Weight Sharing

AceNAS This repo is the experiment code of AceNAS, and is not considered as an official release. We are working on integrating AceNAS as a built-in st

Yuge Zhang 6 Sep 07, 2022
implementation for paper "ShelfNet for fast semantic segmentation"

ShelfNet-lightweight for paper (ShelfNet for fast semantic segmentation) This repo contains implementation of ShelfNet-lightweight models for real-tim

Juntang Zhuang 252 Sep 16, 2022
Deconfounding Temporal Autoencoder: Estimating Treatment Effects over Time Using Noisy Proxies

Deconfounding Temporal Autoencoder (DTA) This is a repository for the paper "Deconfounding Temporal Autoencoder: Estimating Treatment Effects over Tim

Milan Kuzmanovic 3 Feb 04, 2022
Official pytorch implementation of "DSPoint: Dual-scale Point Cloud Recognition with High-frequency Fusion"

DSPoint Official pytorch implementation of "DSPoint: Dual-scale Point Cloud Recognition with High-frequency Fusion" Coming soon, as soon as I finish a

Ziyao Zeng 14 Feb 26, 2022
Knowledge Management for Humans using Machine Learning & Tags

HyperTag HyperTag helps humans intuitively express how they think about their files using tags and machine learning.

Ravn Tech, Inc. 165 Nov 04, 2022
Pacman-AI - AI project designed by UC Berkeley. Designed reflex and minimax agents for the game Pacman.

Pacman AI Jussi Doherty CAP 4601 - Introduction to Artificial Intelligence - Fall 2020 Python version 3.0+ Source of this project This repo contains a

Jussi Doherty 1 Jan 03, 2022
Dark Finix: All in one hacking framework with almost 100 tools

Dark Finix - Hacking Framework. Dark Finix is a all in one hacking framework wit

Md. Nur habib 2 Feb 18, 2022
A fuzzing framework for SMT solvers

yinyang A fuzzing framework for SMT solvers. Given a set of seed SMT formulas, yinyang generates mutant formulas to stress-test SMT solvers. yinyang c

Project Yin-Yang for SMT Solver Testing 145 Jan 04, 2023