torchsort 0.0.7

Differentiable sorting and ranking in PyTorch

Torchsort

Fast, 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.

NOTE: I am actively working on this. The API should remain about the same; but expect more optimizations and benchmarks soon. 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.

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'),)

Benchmark

torchsort and fast_soft_sort each operate with a time complexity of O(n log n), each with some additional overhead when compared to the built-in torch.sort. With a batch size of 1 (see left), the Numba JIT'd forward pass of fast_soft_sort performs about on-par with the torchsort CPU kernel, however its backward pass still relies on some Python code, which greatly penalizes its performance.

Furthermore, the torchsort kernel supports batches, and yields much better performance than fast_soft_sort as the batch size increases.

CUDA kernel is coming soon!

Reference

@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}
}