torchpsort

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Fast, differentiable sorting and ranking without custom C++ or CUDA extensions

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Fast, differentiable sorting and ranking **without custom C++ or CUDA extensions**. This is a lightweight implementation of [Fast Differentiable Sorting and Ranking (Blondel et al.)](https://arxiv.org/abs/2002.08871) based on [fast-soft-sort](https://github.com/google-research/fast-soft-sort) and inspired by [torchsort](https://github.com/teddykoker/torchsort). Unlike [torchsort](https://github.com/teddykoker/torchsort), this version requires no custom C++ or CUDA extensions, making it easy to install and portable across platforms. While optimized C++/CUDA implementations may have a performance advantage for extremely large sequence lengths, this version is optimized for typical deep learning workflows. Try it here:
[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/RektPunk/torchpsort/blob/main/examples/notebook.ipynb)

## Installation

```bash
pip install torchpsort
```

> [!NOTE]
> The CPU forward implementation uses [Numba](https://numba.pydata.org/) for JIT compilation like [fast-soft-sort](https://github.com/google-research/fast-soft-sort). Numba is installed automatically as a dependency of `torchpsort`. On CUDA tensors, the forward pass uses a separate chunked GPU implementation instead.

torchpsort

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