Skip to content
/ Teg Public

A differentiable programming language with an integration primitive that soundly handles interactions among the derivative, integral, and discontinuities.

Notifications You must be signed in to change notification settings

ChezJrk/Teg

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

Teg

Teg is a differentiable programming language that includes an integral primitive, which allows for soundly optimizing integrals with discontinuous integrands. This is a research artifact for the paper: Systematically Differentiating Parametric Discontinuities. This repository contains the core library implementation, while the applications can be found at https://github.com/ChezJrk/teg_applications. The applications include image stylization, fitting shader parameters, trajectory optimization, and optimizing physical designs.

Installation Instructions

Teg requires Python 3.6+. To install Teg run:

git clone https://github.com/ChezJrk/Teg.git
cd Teg
pip install -e .

Illustrative Example

A minimal illustrative example is:

\frac{d}{dt} \int_{x = 0}^1 [x < t]

This is the integral of a step discontinuity that jumps from 1 to 0 at . If we set , then the result is 1, but discretizing before computing the derivative as is standard in differentiable programming languages (e.g., PyTorch and TensorFlow) results in a derivative of 0. Our language correctly models the interaction between the integral and the parametric discontinuity. In our language the implementation for this simple function is:

from teg import TegVar, Var, Teg, IfElse
from teg.derivs import FwdDeriv
from teg.eval.numpy_eval import evaluate

x, t = TegVar('x'), Var('t', 0.5)
expr = Teg(0, 1, IfElse(x < t, 1, 0), x)
deriv_expr = FwdDeriv(expr, [(t, 1)])
print(evaluate(deriv_expr))  # prints 1

Code Structure

Our implementation is in the teg folder:

  • derivs has the implementation of the source-to-source derivative including code for computing the forward and reverse derivatives in fwd_deriv.py and reverse_deriv.py respectively. Supported discontinuous functions are in the edge folder.
  • eval and include have all of the code for evaluating expressions either by interpreting locally in Python or compiling to C.
  • ir includes an intermediate representation useful in compiling code down to C.
  • lang includes the main language primitives with the base language in base.py and teg.py and the extended language (that has the Dirac delta function) is in extended.py.
  • maps and math specifies basic math libraries.
  • passes includes source-to-source compilation passes. Notably, reduce.py has the lowering code from the external language to the internal language.

We have a test folder with all of the systems tests.

Compiling to C

It is possible to compile Teg programs to C using python3 -m teg --compile --target C [[FILE NAME]].py. See teg/__main__.py for more options. To specify C compilation flags, use python3 -m teg --include-options.

Citation

@article{BangaruMichel2021DiscontinuousAutodiff,
  title = {Systematically Differentiating Parametric Discontinuities},
  author = {Bangaru, Sai and Michel, Jesse and Mu, Kevin and Bernstein, Gilbert and Li, Tzu-Mao and Ragan-Kelley, Jonathan},
  journal = {ACM Trans. Graph.},
  volume = {40},
  number = {107}, 
  pages = {107:1-107:17},
  year = {2021},
  publisher = {ACM},
}

About

A differentiable programming language with an integration primitive that soundly handles interactions among the derivative, integral, and discontinuities.

Topics

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Contributors 4

  •  
  •  
  •  
  •  

Languages