INTENT is a novel interactive program synthesizer for TensorFlow. It enables end users to interactively decompose a complex tensor transformation task to simpler ones to be solved by a synthesizer. This codebase contains the official implementation of the tool from our paper:
INTENT: Interactive Tensor Transformation Synthesis
Zhanhui Zhou*, Man To Tang*, Qiping Pan*, Shangyin Tan, Xinyu Wang, Tianyi Zhang
Symposium on User Interface Software and Technology (UIST) 2022
This implementation is based on TF-Coder, which is a program synthesis tool that runs a combinatorial search to find TensorFlow expressions. The tensorflow-coder package is modified to support extra features for decomposition and visualization.
There is a growing interest in adopting Deep Learning (DL) given its superior performance in many domains. However, modern DL frameworks such as TensorFlow often come with a steep learning curve. In this work, we propose INTENT, an interactive system that infers user intent and generates corresponding TensorFlow code on behalf of users. INTENT helps users understand and validate the semantics of generated code by rendering individual tensor transformation steps with intermediate results and element-wise data provenance. Users can further guide INTENT by marking certain TensorFlow operators as desired or undesired, or directly manipulating the generated code. A within-subjects user study with 18 participants shows that users can finish programming tasks in TensorFlow more successfully with only half the time, compared with a variant of INTENT that has no interaction or visualization support.
Miniconda (optional) Miniconda is a light-weight open source package management system for python.
Checkout https://docs.conda.io/en/latest/miniconda.html for installation instructions.
# create an env for int and keep it activated during development
$ conda create --name intent python=3.9
$ conda activate intent
Yarn (required) Yarn is a package management system for js.
Checkout https://classic.yarnpkg.com/lang/en/docs/install/#debian-stable for installation instructions.
tensorflow-coder
$ pip install -e ./tensorflow-coder
server
$ pip install -e ./server
client
$ cd client
$ yarn
$ cd client
$ yarn build
$ cd ..
$ cd server
$ sh bin/INTENT_run.sh
Then go to http://localhost:8000 in your browser (chrome recommended).
The project was developed under the supervision of Prof. Tianyi Zhang at Purdue and Prof. Xinyu Wang at UMich. Special thanks to Yitao Huang and Lyubing Qiang for their contributions to the first version of INTENT.
If you found our paper/code useful in your research, please consider citing:
@inproceedings{zhou2022intent,
author = {Zhou, Zhanhui and Tang, Man To and Pan, Qiping and Tan, Shangyin and Wang, Xinyu and Zhang, Tianyi},
title = {INTENT: Interactive Tensor Transformation Synthesis},
booktitle = {Proceedings of the 35nd Annual ACM Symposium on User Interface Software and Technology},
year = {2022},
}