%global _empty_manifest_terminate_build 0
Name:		python-torch-scatter
Version:	2.1.1
Release:	1
Summary:	PyTorch Extension Library of Optimized Scatter Operations
License:	MIT License
URL:		https://github.com/rusty1s/pytorch_scatter
Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/db/ae/3dee934b7118aec8528a6832dbb3cf079e13dd442c4600cae8d29a4f9fea/torch_scatter-2.1.1.tar.gz
BuildArch:	noarch


%description
**[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 2.0
To install the binaries for PyTorch 2.0.0, simply run
```
pip install torch-scatter -f https://data.pyg.org/whl/torch-2.0.0+${CUDA}.html
```
where `${CUDA}` should be replaced by either `cpu`, `cu117`, or `cu118` depending on your PyTorch installation.
|             | `cpu` | `cu117` | `cu118` |
|-------------|-------|---------|---------|
| **Linux**   | ✅    | ✅      | ✅      |
| **Windows** | ✅    | ✅      | ✅      |
| **macOS**   | ✅    |         |         |
#### PyTorch 1.13
To install the binaries for PyTorch 1.13.0, simply run
```
pip install torch-scatter -f https://data.pyg.org/whl/torch-1.13.0+${CUDA}.html
```
where `${CUDA}` should be replaced by either `cpu`, `cu116`, or `cu117` depending on your PyTorch installation.
|             | `cpu` | `cu116` | `cu117` |
|-------------|-------|---------|---------|
| **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 and PyTorch 1.12.0/1.12.1 (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
```

%package -n python3-torch-scatter
Summary:	PyTorch Extension Library of Optimized Scatter Operations
Provides:	python-torch-scatter
BuildRequires:	python3-devel
BuildRequires:	python3-setuptools
BuildRequires:	python3-pip
%description -n python3-torch-scatter
**[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 2.0
To install the binaries for PyTorch 2.0.0, simply run
```
pip install torch-scatter -f https://data.pyg.org/whl/torch-2.0.0+${CUDA}.html
```
where `${CUDA}` should be replaced by either `cpu`, `cu117`, or `cu118` depending on your PyTorch installation.
|             | `cpu` | `cu117` | `cu118` |
|-------------|-------|---------|---------|
| **Linux**   | ✅    | ✅      | ✅      |
| **Windows** | ✅    | ✅      | ✅      |
| **macOS**   | ✅    |         |         |
#### PyTorch 1.13
To install the binaries for PyTorch 1.13.0, simply run
```
pip install torch-scatter -f https://data.pyg.org/whl/torch-1.13.0+${CUDA}.html
```
where `${CUDA}` should be replaced by either `cpu`, `cu116`, or `cu117` depending on your PyTorch installation.
|             | `cpu` | `cu116` | `cu117` |
|-------------|-------|---------|---------|
| **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 and PyTorch 1.12.0/1.12.1 (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
```

%package help
Summary:	Development documents and examples for torch-scatter
Provides:	python3-torch-scatter-doc
%description help
**[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 2.0
To install the binaries for PyTorch 2.0.0, simply run
```
pip install torch-scatter -f https://data.pyg.org/whl/torch-2.0.0+${CUDA}.html
```
where `${CUDA}` should be replaced by either `cpu`, `cu117`, or `cu118` depending on your PyTorch installation.
|             | `cpu` | `cu117` | `cu118` |
|-------------|-------|---------|---------|
| **Linux**   | ✅    | ✅      | ✅      |
| **Windows** | ✅    | ✅      | ✅      |
| **macOS**   | ✅    |         |         |
#### PyTorch 1.13
To install the binaries for PyTorch 1.13.0, simply run
```
pip install torch-scatter -f https://data.pyg.org/whl/torch-1.13.0+${CUDA}.html
```
where `${CUDA}` should be replaced by either `cpu`, `cu116`, or `cu117` depending on your PyTorch installation.
|             | `cpu` | `cu116` | `cu117` |
|-------------|-------|---------|---------|
| **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 and PyTorch 1.12.0/1.12.1 (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
```

%prep
%autosetup -n torch-scatter-2.1.1

%build
%py3_build

%install
%py3_install
install -d -m755 %{buildroot}/%{_pkgdocdir}
if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi
if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi
if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi
if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi
pushd %{buildroot}
if [ -d usr/lib ]; then
	find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/lib64 ]; then
	find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/bin ]; then
	find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/sbin ]; then
	find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst
fi
touch doclist.lst
if [ -d usr/share/man ]; then
	find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst
fi
popd
mv %{buildroot}/filelist.lst .
mv %{buildroot}/doclist.lst .

%files -n python3-torch-scatter -f filelist.lst
%dir %{python3_sitelib}/*

%files help -f doclist.lst
%{_docdir}/*

%changelog
* Tue Apr 11 2023 Python_Bot <Python_Bot@openeuler.org> - 2.1.1-1
- Package Spec generated