Metadata-Version: 2.1
Name: torchsort
Version: 0.1.7
Summary: Differentiable sorting and ranking in PyTorch
Home-page: https://github.com/teddykoker/torchsort
Author: Teddy Koker
License: Apache
Description: # Torchsort
        
        ![Tests](https://github.com/teddykoker/torchsort/workflows/Tests/badge.svg)
        
        Fast, differentiable sorting and ranking in PyTorch.
        
        Pure PyTorch implementation of [Fast Differentiable Sorting and
        Ranking](https://arxiv.org/abs/2002.08871) (Blondel et al.). Much of the code is
        copied from the original Numpy implementation at
        [google-research/fast-soft-sort](https://github.com/google-research/fast-soft-sort),
        with the isotonic regression solver rewritten as a PyTorch C++ and CUDA
        extension.
        
        ## Install
        
        ```bash
        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;Ampere"` 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:
        
        ```python
        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:
        
        ```python
        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'),)
        ```
        
        ## Example
        
        ### Spearman's Rank Coefficient
        
        [Spearman's rank
        coefficient](https://en.wikipedia.org/wiki/Spearman%27s_rank_correlation_coefficient)
        is a very useful metric for measuring how monotonically related two variables
        are. We can use Torchsort to create a differentiable Spearman's rank coefficient
        function so that we can optimize a model directly for this metric:
        
        ```python
        import torch
        import torchsort
        
        def spearmanr(pred, target, **kw):
            pred = torchsort.soft_rank(pred, **kw)
            target = torchsort.soft_rank(target, **kw)
            pred = pred - pred.mean()
            pred = pred / pred.norm()
            target = target - target.mean()
            target = target / target.norm()
            return (pred * target).sum()
        
        pred = torch.tensor([[1., 2., 3., 4., 5.]], requires_grad=True)
        target = torch.tensor([[5., 6., 7., 8., 7.]])
        spearman = spearmanr(pred, target)
        # tensor(0.8321)
        
        torch.autograd.grad(spearman, pred)
        # (tensor([[-5.5470e-02,  2.9802e-09,  5.5470e-02,  1.1094e-01, -1.1094e-01]]),)
        ```
        
        ## Benchmark
        
        ![Benchmark](https://github.com/teddykoker/torchsort/raw/main/extra/benchmark.png)
        
        `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.
        
        ![Benchmark](https://github.com/teddykoker/torchsort/raw/main/extra/benchmark_cuda.png)
        
        The `torchsort` CUDA kernel performs quite well with sequence lengths under
        ~2000, and scales to extremely large batch sizes. In the future the
        CUDA kernel can likely be further optimized to achieve performance closer to that of the
        built in `torch.sort`.
        
        
        ## Reference
        
        ```bibtex
        @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}
        }
        ```
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Requires-Python: >=3.7
Description-Content-Type: text/markdown
Provides-Extra: testing
