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https://github.com/rusty1s/pytorch_scatter

PyTorch Extension Library of Optimized Scatter Operations
https://github.com/rusty1s/pytorch_scatter

gather pytorch scatter segment

Last synced: 4 days ago
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PyTorch Extension Library of Optimized Scatter Operations

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# 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

```