Machine Learning, NLP

Fast, differentiable sorting and ranking in PyTorch

Torchsort

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++ and CUDA extension.

Install

pip install torchsort

To build the CUDA extension you will need the CUDA toolchain installed. If you
want to build in an environment without a CUDA runtime (e.g. docker), you will
need to export the environment variable
TORCH_CUDA_ARCH_LIST="Pascal;Volta;Turing" before installing.

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,


 


 




 


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