torchsort 0.0.3

Differentiable sorting and ranking in PyTorch

Torchsort

NOTE: The C++ isotonic regression solver is currently only implemented on CPU, so CUDA tensors will be copied over to CPU to perform the operations. I am currently working on the CUDA kernel implementation, which should be done soon.

A fast and differentiable sorting and ranking in PyTorch.

Pure PyTorch implementation of Fast Differentiable Sorting and Ranking (Blondel et al.). Much of the code is copied from the original Numpy implementation at google-research/fast-soft-sort, with the isotonic regression solver rewritten as a PyTorch C++ Extension.

Install

pip install torchsort

Usage

torchsort exposes two functions: soft_rank and soft_sort, each with parameters regularization ("l2" or "kl") and regularization_strength (a scalar value). Each will rank/sort the last dimension of a 2-d tensor, with an accuracy dependant upon the regularization strength:

import torch
import torchsort

x = torch.tensor([[8, 0, 5, 3, 2, 1, 6, 7, 9]])

torchsort.soft_sort(x, regularization_strength=1.0)
# tensor([[0.5556, 1.5556, 2.5556, 3.5556, 4.5556, 5.5556, 6.5556, 7.5556, 8.5556]])
torchsort.soft_sort(x, regularization_strength=0.1)
# tensor([[-0., 1., 2., 3., 5., 6., 7., 8., 9.]])

torchsort.soft_rank(x)
# tensor([[8., 1., 5., 4., 3., 2., 6., 7., 9.]])

Both operations are fully differentiable, on CPU or GPU:

x = torch.tensor([[8., 0., 5., 3., 2., 1., 6., 7., 9.]], requires_grad=True).cuda()
y = torchsort.soft_sort(x)

torch.autograd.grad(y[0, 0], x)
# (tensor([[0.1111, 0.1111, 0.1111, 0.1111, 0.1111, 0.1111, 0.1111, 0.1111, 0.1111]],
#         device='cuda:0'),)

Reference

Please site the original paper:

@inproceedings{blondel2020fast,
  title={Fast differentiable sorting and ranking},
  author={Blondel, Mathieu and Teboul, Olivier and Berthet, Quentin and Djolonga, Josip},
  booktitle={International Conference on Machine Learning},
  pages={950--959},
  year={2020},
  organization={PMLR}
}