Fast, differentiable sorting and ranking in pure PyTorch without C++ or CUDA. This is a lightweight implementation of Fast Differentiable Sorting and Ranking (Blondel et al.) and inspired by torchsort. Unlike the torchsort, this version contains no C++ or CUDA extensions, making it easy to install and portable across platforms. While the original C++/CUDA implementation may have a performance edge for extremely large batch sizes, this pure PyTorch version is optimized to be efficient for standard deep learning workflows. Try it here:
Installation
pip install torchpsort
[!NOTE] The streaming CPU implementation is intended to run in eager mode. Because it relies on a sequential Python loop over the sequence length (
p) to achieveO(Bp)memory usage,torch.compilemay introduce substantial compilation overhead for large sequences. The GPU chunked implementation, however, remains compatible withtorch.compile.
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