LP-SparseMAP

Differentiable sparse structured prediction in coarse factor graphs

This repo contains:

• ad3qp: an updated fork of ad3, supporting the solving of SparseMAP QPs in arbitrary factor graphs. (C++, LGPL license.)

• dysparsemap: a library that provides a dynet function using ad3qp for forward and backward pass computation for structured hidden layers. (C++, MIT license.)

• lpsmap: a python wrapper for ad3qp and some example usage scripts. (cython and python, MIT license.) This repository is a work-in-progress, with the end-goal to drastically simplify the AD3 API.

Reference

Vlad Niculae and Andre F. T. Martins. LP-SparseMAP: Differentiable Relaxed Optimization for Sparse Structured Prediction. https://arxiv.org/abs/2001.04437

lpsmap

Requirements:

• Cython
• Eigen

For examples and tests: numpy, pytest.

Installation:

export EIGEN_DIR=/path/to/eigen
python setup.py build_clib  # builds ad3 in-place
pip install .               # builds lpsmap and installs

In-place installation:

export EIGEN_DIR=/path/to/eigen
python setup.py build_clib  # builds ad3 in-place
pip install -e .            # builds lpsmap and creates a link

dysparsemap

Requires this patch to dynet in order to make dynet export cmake targets to link against. (sorry, I'm new to cmake and haven't managed to test it and make a PR yet.)

Once the patched dynet is installed, do

cd cbuild
cmake ..
make

Then you can try the dynet gradient check tests that get compiled.