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ServiceNow completed its acquisition of Element AI on January 8, 2021. All references to Element AI in the materials that are part of this project should refer to ServiceNow.

DuoRAT

This repository contains the implementation of the DuoRAT model as described in the respective technical report. Using this code you can

  • train and evaluate models on the Spider dataset
  • evaluate models on other single-database text2sql datasets
  • use trained models to perform text2sql parsing for any SQLite database or a CSV file

Setup

Download data and third party submodules: git submodule update --init

Download the Spider dataset:

bash scripts/download_and_preprocess_spider.sh

This script downloads Spider, splits the examples by database, and writes a json file to data/database.

Now, create the docker image:

make build-image

Create a directory to save models, and run interactive container:

mkdir logdir
nvidia-docker run -it -u $(id -u ${USER}) --name my_duorat --rm -v $PWD/logdir:/logdir -v $PWD/data/:/app/data duorat

(please disregard the "I have not name!" warning)

Running the code

Train the model:

python scripts/train.py --config configs/duorat/duorat-finetune-bert-large.jsonnet --logdir /logdir/duorat-bert

This script will first run a preprocessing step that creates the following files in the specified --logdir:

  • target_vocab.pkl: model's target vocabulary, obtained from the training set
  • train.pkl, val.pkl: preprocessed training and validation sets (tokenization, schema-linking and converting the output SQL into a sequence of actions)

Training will further save a number of files in the (mounted) log directory /logdir/duorat-bert: the config that was used config-{date}.json, model checkpoints model_{best/last}_checkpoint, some logs log.txt, and the inference outputs output-{step}. If your gpu does not have enough memory to run the model, you can try config=configs/duorat/duorat-12G.jsonnet instead. Note that caching of the preprocessed input tensors will significantly speed up training after the second epoch. During training, inference is run on the dev set once in a while. Here's how you can run inference manually:

python scripts/infer.py --logdir /logdir/duorat-bert --output /logdir/duorat-bert/my_inference_output
python scripts/eval.py --config configs/duorat/duorat-good-no-bert.jsonnet --section val --inferred /logdir/duorat-bert/my_inference_output --output /logdir/duorat-bert/my_inference_output.eval

To look at evaluation results:

>>> import json
>>> d = json.load(open('<PATH FOR EVAL OUTPUT>')) 
>>> print(d['total_scores']['all']['exact']) # should be ~0.69

Inference on new databases

Simply run

python scripts/interactive.py --logdir /logdir/duorat-bert --db-id [your_db]

[your_db] must be either an SQLite or CSV file. Type a question and the model will convert it into a query, which will then be executed on your database.

A batch mode inference script is also available: scripts/infer_questions.py.

New transition system

This codebase makes it possible to implement and use your own transition system (given a grammar, parse SQL to a tree representation and a sequence of actions) with this model. See the readme in duorat/asdl/ (from tranX)

Evaluation on Text2SQL datasets other than SPIDER

Our code support model evaluation on other Text2SQL datasets using the data from text2sql-data. We follow the methodology proposed by Suhr et al, 2020.

To run the download and conversion code:

Step 1

(for now this works only 5 out of 8 datasets; ATIS, Scholar and Advising are still TODO)

Build and start the MySQL docker container (do it outside of the interactive my_duorat container):

bash scripts/mysql_docker_build_and_run.sh

Step 2

Download the dataset and convert the dataset of interest, e.g. for GeoQuery:

bash scripts/download_michigan.sh geo

For IMDB, Yelp and Academic this might take a while.

Edit data/michigan.libsonnet to include only the datasets that you downloaded.

Step 4

Infer and evaluate the queries for all questions:

python scripts/infer_questions.py --logdir /logdir/duorat-bert --data-config data/michigan.libsonnet --questions data/database/geo_test/examples.json --output-google /logdir/duorat-bert/inferred_geo.json
python scripts/evaluation_google.py --predictions_filepath /logdir/duorat-bert/inferred_geo.json --output_filepath /logdir/duorat-bert/output_geo.json 
    --cache_filepath data/database/geo_test/geo_cache.json  --timeout 180
python scripts/filter_results.py /logdir/duorat-bert/output_geo.json

You might want to change the timeout if your system outputs correct but slow to execute queries.

Who we are

Acknowledgements

This implementation is originally based on the seq2struct codebase. Further model development in many aspects followed the RAT-SQL paper.

How to cite

@article{scholak_duorat_2020,
title = {{DuoRAT}: {Towards} {Simpler} {Text}-to-{SQL} {Models}},
author = {Scholak, Torsten and Li, Raymond and Bahdanau, Dzmitry and de Vries, Harm and Pal, Chris},
year = {2020},
journal = {arXiv:2010.11119 [cs]},
}