# pytorch_scatter **Repository Path**: week233/pytorch_scatter ## Basic Information - **Project Name**: pytorch_scatter - **Description**: PyTorch Extension Library of Optimized Scatter Operations - **Primary Language**: Unknown - **License**: MIT - **Default Branch**: master - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 0 - **Forks**: 0 - **Created**: 2025-04-01 - **Last Updated**: 2025-04-01 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README [pypi-image]: https://badge.fury.io/py/torch-scatter.svg [pypi-url]: https://pypi.python.org/pypi/torch-scatter [testing-image]: https://github.com/rusty1s/pytorch_scatter/actions/workflows/testing.yml/badge.svg [testing-url]: https://github.com/rusty1s/pytorch_scatter/actions/workflows/testing.yml [linting-image]: https://github.com/rusty1s/pytorch_scatter/actions/workflows/linting.yml/badge.svg [linting-url]: https://github.com/rusty1s/pytorch_scatter/actions/workflows/linting.yml [docs-image]: https://readthedocs.org/projects/pytorch-scatter/badge/?version=latest [docs-url]: https://pytorch-scatter.readthedocs.io/en/latest/?badge=latest [coverage-image]: https://codecov.io/gh/rusty1s/pytorch_scatter/branch/master/graph/badge.svg [coverage-url]: https://codecov.io/github/rusty1s/pytorch_scatter?branch=master # PyTorch Scatter [![PyPI Version][pypi-image]][pypi-url] [![Testing Status][testing-image]][testing-url] [![Linting Status][linting-image]][linting-url] [![Docs Status][docs-image]][docs-url] [![Code Coverage][coverage-image]][coverage-url]

-------------------------------------------------------------------------------- **[Documentation](https://pytorch-scatter.readthedocs.io)** This package consists of a small extension library of highly optimized sparse update (scatter and segment) operations for the use in [PyTorch](http://pytorch.org/), which are missing in the main package. Scatter and segment operations can be roughly described as reduce operations based on a given "group-index" tensor. Segment operations require the "group-index" tensor to be sorted, whereas scatter operations are not subject to these requirements. The package consists of the following operations with reduction types `"sum"|"mean"|"min"|"max"`: * [**scatter**](https://pytorch-scatter.readthedocs.io/en/latest/functions/scatter.html) based on arbitrary indices * [**segment_coo**](https://pytorch-scatter.readthedocs.io/en/latest/functions/segment_coo.html) based on sorted indices * [**segment_csr**](https://pytorch-scatter.readthedocs.io/en/latest/functions/segment_csr.html) based on compressed indices via pointers In addition, we provide the following **composite functions** which make use of `scatter_*` operations under the hood: `scatter_std`, `scatter_logsumexp`, `scatter_softmax` and `scatter_log_softmax`. All included operations are broadcastable, work on varying data types, are implemented both for CPU and GPU with corresponding backward implementations, and are fully traceable. ## Installation ### Anaconda **Update:** You can now install `pytorch-scatter` via [Anaconda](https://anaconda.org/pyg/pytorch-scatter) for all major OS/PyTorch/CUDA combinations 🤗 Given that you have [`pytorch >= 1.8.0` installed](https://pytorch.org/get-started/locally/), simply run ``` conda install pytorch-scatter -c pyg ``` ### Binaries We alternatively provide pip wheels for all major OS/PyTorch/CUDA combinations, see [here](https://data.pyg.org/whl). #### PyTorch 2.5 To install the binaries for PyTorch 2.5.0, simply run ``` pip install torch-scatter -f https://data.pyg.org/whl/torch-2.5.0+${CUDA}.html ``` where `${CUDA}` should be replaced by either `cpu`, `cu118`, `cu121`, or `cu124` depending on your PyTorch installation. | | `cpu` | `cu118` | `cu121` | `cu124` | |-------------|-------|---------|---------|---------| | **Linux** | ✅ | ✅ | ✅ | ✅ | | **Windows** | ✅ | ✅ | ✅ | ✅ | | **macOS** | ✅ | | | | #### PyTorch 2.4 To install the binaries for PyTorch 2.4.0, simply run ``` pip install torch-scatter -f https://data.pyg.org/whl/torch-2.4.0+${CUDA}.html ``` where `${CUDA}` should be replaced by either `cpu`, `cu118`, `cu121`, or `cu124` depending on your PyTorch installation. | | `cpu` | `cu118` | `cu121` | `cu124` | |-------------|-------|---------|---------|---------| | **Linux** | ✅ | ✅ | ✅ | ✅ | | **Windows** | ✅ | ✅ | ✅ | ✅ | | **macOS** | ✅ | | | | **Note:** Binaries of older versions are also provided for PyTorch 1.4.0, PyTorch 1.5.0, PyTorch 1.6.0, PyTorch 1.7.0/1.7.1, PyTorch 1.8.0/1.8.1, PyTorch 1.9.0, PyTorch 1.10.0/1.10.1/1.10.2, PyTorch 1.11.0, PyTorch 1.12.0/1.12.1, PyTorch 1.13.0/1.13.1, PyTorch 2.0.0/2.0.1, PyTorch 2.1.0/2.1.1/2.1.2, PyTorch 2.2.0/2.2.1/2.2.2, and PyTorch 2.3.0/2.3.1 (following the same procedure). For older versions, you need to explicitly specify the latest supported version number or install via `pip install --no-index` in order to prevent a manual installation from source. You can look up the latest supported version number [here](https://data.pyg.org/whl). ### From source Ensure that at least PyTorch 1.4.0 is installed and verify that `cuda/bin` and `cuda/include` are in your `$PATH` and `$CPATH` respectively, *e.g.*: ``` $ python -c "import torch; print(torch.__version__)" >>> 1.4.0 $ echo $PATH >>> /usr/local/cuda/bin:... $ echo $CPATH >>> /usr/local/cuda/include:... ``` Then run: ``` pip install torch-scatter ``` When running in a docker container without NVIDIA driver, PyTorch needs to evaluate the compute capabilities and may fail. In this case, ensure that the compute capabilities are set via `TORCH_CUDA_ARCH_LIST`, *e.g.*: ``` export TORCH_CUDA_ARCH_LIST = "6.0 6.1 7.2+PTX 7.5+PTX" ``` ## Example ```py import torch from torch_scatter import scatter_max src = torch.tensor([[2, 0, 1, 4, 3], [0, 2, 1, 3, 4]]) index = torch.tensor([[4, 5, 4, 2, 3], [0, 0, 2, 2, 1]]) out, argmax = scatter_max(src, index, dim=-1) ``` ``` print(out) tensor([[0, 0, 4, 3, 2, 0], [2, 4, 3, 0, 0, 0]]) print(argmax) tensor([[5, 5, 3, 4, 0, 1] [1, 4, 3, 5, 5, 5]]) ``` ## Running tests ``` pytest ``` ## C++ API `torch-scatter` also offers a C++ API that contains C++ equivalent of python models. For this, we need to add `TorchLib` to the `-DCMAKE_PREFIX_PATH` (*e.g.*, it may exists in `{CONDA}/lib/python{X.X}/site-packages/torch` if installed via `conda`): ``` mkdir build cd build # Add -DWITH_CUDA=on support for CUDA support cmake -DCMAKE_PREFIX_PATH="..." .. make make install ```