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

• Torsten Scholak
• Raymond Li
• Dzmitry Bahdanau
• Harm de Vries

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]},
}