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csrc/spspmm.cpp deleted 100644 → 0
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#ifdef WITH_PYTHON
#include <Python.h>
#endif
#include <torch/script.h>
#include "cpu/spspmm_cpu.h"
#ifdef WITH_HIP
#include "hip/spspmm_hip.h"
#endif
#ifdef _WIN32
#ifdef WITH_PYTHON
#ifdef WITH_HIP
PyMODINIT_FUNC PyInit__spspmm_cuda(void) { return NULL; }
#else
PyMODINIT_FUNC PyInit__spspmm_cpu(void) { return NULL; }
#endif
#endif
#endif
SPARSE_API std::tuple<torch::Tensor, torch::Tensor, torch::optional<torch::Tensor>>
spspmm_sum(torch::Tensor rowptrA, torch::Tensor colA,
torch::optional<torch::Tensor> optional_valueA,
torch::Tensor rowptrB, torch::Tensor colB,
torch::optional<torch::Tensor> optional_valueB, int64_t K) {
if (rowptrA.device().is_cuda()) {
#ifdef WITH_HIP
return spspmm_cuda(rowptrA, colA, optional_valueA, rowptrB, colB,
optional_valueB, K, "sum");
#else
AT_ERROR("Not compiled with CUDA support");
#endif
} else {
return spspmm_cpu(rowptrA, colA, optional_valueA, rowptrB, colB,
optional_valueB, K, "sum");
}
}
static auto registry =
torch::RegisterOperators().op("torch_sparse::spspmm_sum", &spspmm_sum);
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Metadata-Version: 2.1
Name: torch-sparse
Version: 0.6.13
Summary: PyTorch Extension Library of Optimized Autograd Sparse Matrix Operations
Home-page: https://github.com/rusty1s/pytorch_sparse
Download-URL: https://github.com/rusty1s/pytorch_sparse/archive/0.6.13.tar.gz
Author: Matthias Fey
Author-email: matthias.fey@tu-dortmund.de
Keywords: pytorch,sparse,sparse-matrices,autograd
Classifier: Development Status :: 5 - Production/Stable
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3 :: Only
Requires-Python: >=3.7
Description-Content-Type: text/markdown
Provides-Extra: test
License-File: LICENSE
[pypi-image]: https://badge.fury.io/py/torch-sparse.svg
[pypi-url]: https://pypi.python.org/pypi/torch-sparse
[testing-image]: https://github.com/rusty1s/pytorch_sparse/actions/workflows/testing.yml/badge.svg
[testing-url]: https://github.com/rusty1s/pytorch_sparse/actions/workflows/testing.yml
[linting-image]: https://github.com/rusty1s/pytorch_sparse/actions/workflows/linting.yml/badge.svg
[linting-url]: https://github.com/rusty1s/pytorch_sparse/actions/workflows/linting.yml
[coverage-image]: https://codecov.io/gh/rusty1s/pytorch_sparse/branch/master/graph/badge.svg
[coverage-url]: https://codecov.io/github/rusty1s/pytorch_sparse?branch=master
# PyTorch Sparse
[![PyPI Version][pypi-image]][pypi-url]
[![Testing Status][testing-image]][testing-url]
[![Linting Status][linting-image]][linting-url]
[![Code Coverage][coverage-image]][coverage-url]
--------------------------------------------------------------------------------
This package consists of a small extension library of optimized sparse matrix operations with autograd support.
This package currently consists of the following methods:
* **[Coalesce](#coalesce)**
* **[Transpose](#transpose)**
* **[Sparse Dense Matrix Multiplication](#sparse-dense-matrix-multiplication)**
* **[Sparse Sparse Matrix Multiplication](#sparse-sparse-matrix-multiplication)**
All included operations work on varying data types and are implemented both for CPU and GPU.
To avoid the hazzle of creating [`torch.sparse_coo_tensor`](https://pytorch.org/docs/stable/torch.html?highlight=sparse_coo_tensor#torch.sparse_coo_tensor), this package defines operations on sparse tensors by simply passing `index` and `value` tensors as arguments ([with same shapes as defined in PyTorch](https://pytorch.org/docs/stable/sparse.html)).
Note that only `value` comes with autograd support, as `index` is discrete and therefore not differentiable.
## Installation
### Anaconda
**Update:** You can now install `pytorch-sparse` via [Anaconda](https://anaconda.org/pyg/pytorch-sparse) 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-sparse -c pyg
```
### Binaries
We alternatively provide pip wheels for all major OS/PyTorch/CUDA combinations, see [here](https://data.pyg.org/whl).
#### PyTorch 1.11
To install the binaries for PyTorch 1.11.0, simply run
```
pip install torch-scatter torch-sparse -f https://data.pyg.org/whl/torch-1.11.0+${CUDA}.html
```
where `${CUDA}` should be replaced by either `cpu`, `cu102`, `cu113`, or `cu115` depending on your PyTorch installation.
| | `cpu` | `cu102` | `cu113` | `cu115` |
|-------------|-------|---------|---------|---------|
| **Linux** | ✅ | ✅ | ✅ | ✅ |
| **Windows** | ✅ | | ✅ | ✅ |
| **macOS** | ✅ | | | |
#### PyTorch 1.10
To install the binaries for PyTorch 1.10.0, PyTorch 1.10.1 and PyTorch 1.10.2, simply run
```
pip install torch-scatter torch-sparse -f https://data.pyg.org/whl/torch-1.10.0+${CUDA}.html
```
where `${CUDA}` should be replaced by either `cpu`, `cu102`, `cu111`, or `cu113` depending on your PyTorch installation.
| | `cpu` | `cu102` | `cu111` | `cu113` |
|-------------|-------|---------|---------|---------|
| **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 and PyTorch 1.9.0 (following the same procedure).
For older versions, you might need to explicitly specify the latest supported version number 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 1.7.0 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__)"
>>> 1.7.0
$ echo $PATH
>>> /usr/local/cuda/bin:...
$ echo $CPATH
>>> /usr/local/cuda/include:...
```
If you want to additionally build `torch-sparse` with METIS support, *e.g.* for partioning, please download and install the [METIS library](http://glaros.dtc.umn.edu/gkhome/metis/metis/download) by following the instructions in the `Install.txt` file.
Note that METIS needs to be installed with 64 bit `IDXTYPEWIDTH` by changing `include/metis.h`.
Afterwards, set the environment variable `WITH_METIS=1`.
Then run:
```
pip install torch-scatter torch-sparse
```
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"
```
## Functions
### Coalesce
```
torch_sparse.coalesce(index, value, m, n, op="add") -> (torch.LongTensor, torch.Tensor)
```
Row-wise sorts `index` and removes duplicate entries.
Duplicate entries are removed by scattering them together.
For scattering, any operation of [`torch_scatter`](https://github.com/rusty1s/pytorch_scatter) can be used.
#### Parameters
* **index** *(LongTensor)* - The index tensor of sparse matrix.
* **value** *(Tensor)* - The value tensor of sparse matrix.
* **m** *(int)* - The first dimension of sparse matrix.
* **n** *(int)* - The second dimension of sparse matrix.
* **op** *(string, optional)* - The scatter operation to use. (default: `"add"`)
#### Returns
* **index** *(LongTensor)* - The coalesced index tensor of sparse matrix.
* **value** *(Tensor)* - The coalesced value tensor of sparse matrix.
#### Example
```python
import torch
from torch_sparse import coalesce
index = torch.tensor([[1, 0, 1, 0, 2, 1],
[0, 1, 1, 1, 0, 0]])
value = torch.Tensor([[1, 2], [2, 3], [3, 4], [4, 5], [5, 6], [6, 7]])
index, value = coalesce(index, value, m=3, n=2)
```
```
print(index)
tensor([[0, 1, 1, 2],
[1, 0, 1, 0]])
print(value)
tensor([[6.0, 8.0],
[7.0, 9.0],
[3.0, 4.0],
[5.0, 6.0]])
```
### Transpose
```
torch_sparse.transpose(index, value, m, n) -> (torch.LongTensor, torch.Tensor)
```
Transposes dimensions 0 and 1 of a sparse matrix.
#### Parameters
* **index** *(LongTensor)* - The index tensor of sparse matrix.
* **value** *(Tensor)* - The value tensor of sparse matrix.
* **m** *(int)* - The first dimension of sparse matrix.
* **n** *(int)* - The second dimension of sparse matrix.
* **coalesced** *(bool, optional)* - If set to `False`, will not coalesce the output. (default: `True`)
#### Returns
* **index** *(LongTensor)* - The transposed index tensor of sparse matrix.
* **value** *(Tensor)* - The transposed value tensor of sparse matrix.
#### Example
```python
import torch
from torch_sparse import transpose
index = torch.tensor([[1, 0, 1, 0, 2, 1],
[0, 1, 1, 1, 0, 0]])
value = torch.Tensor([[1, 2], [2, 3], [3, 4], [4, 5], [5, 6], [6, 7]])
index, value = transpose(index, value, 3, 2)
```
```
print(index)
tensor([[0, 0, 1, 1],
[1, 2, 0, 1]])
print(value)
tensor([[7.0, 9.0],
[5.0, 6.0],
[6.0, 8.0],
[3.0, 4.0]])
```
### Sparse Dense Matrix Multiplication
```
torch_sparse.spmm(index, value, m, n, matrix) -> torch.Tensor
```
Matrix product of a sparse matrix with a dense matrix.
#### Parameters
* **index** *(LongTensor)* - The index tensor of sparse matrix.
* **value** *(Tensor)* - The value tensor of sparse matrix.
* **m** *(int)* - The first dimension of sparse matrix.
* **n** *(int)* - The second dimension of sparse matrix.
* **matrix** *(Tensor)* - The dense matrix.
#### Returns
* **out** *(Tensor)* - The dense output matrix.
#### Example
```python
import torch
from torch_sparse import spmm
index = torch.tensor([[0, 0, 1, 2, 2],
[0, 2, 1, 0, 1]])
value = torch.Tensor([1, 2, 4, 1, 3])
matrix = torch.Tensor([[1, 4], [2, 5], [3, 6]])
out = spmm(index, value, 3, 3, matrix)
```
```
print(out)
tensor([[7.0, 16.0],
[8.0, 20.0],
[7.0, 19.0]])
```
### Sparse Sparse Matrix Multiplication
```
torch_sparse.spspmm(indexA, valueA, indexB, valueB, m, k, n) -> (torch.LongTensor, torch.Tensor)
```
Matrix product of two sparse tensors.
Both input sparse matrices need to be **coalesced** (use the `coalesced` attribute to force).
#### Parameters
* **indexA** *(LongTensor)* - The index tensor of first sparse matrix.
* **valueA** *(Tensor)* - The value tensor of first sparse matrix.
* **indexB** *(LongTensor)* - The index tensor of second sparse matrix.
* **valueB** *(Tensor)* - The value tensor of second sparse matrix.
* **m** *(int)* - The first dimension of first sparse matrix.
* **k** *(int)* - The second dimension of first sparse matrix and first dimension of second sparse matrix.
* **n** *(int)* - The second dimension of second sparse matrix.
* **coalesced** *(bool, optional)*: If set to `True`, will coalesce both input sparse matrices. (default: `False`)
#### Returns
* **index** *(LongTensor)* - The output index tensor of sparse matrix.
* **value** *(Tensor)* - The output value tensor of sparse matrix.
#### Example
```python
import torch
from torch_sparse import spspmm
indexA = torch.tensor([[0, 0, 1, 2, 2], [1, 2, 0, 0, 1]])
valueA = torch.Tensor([1, 2, 3, 4, 5])
indexB = torch.tensor([[0, 2], [1, 0]])
valueB = torch.Tensor([2, 4])
indexC, valueC = spspmm(indexA, valueA, indexB, valueB, 3, 3, 2)
```
```
print(indexC)
tensor([[0, 1, 2],
[0, 1, 1]])
print(valueC)
tensor([8.0, 6.0, 8.0])
```
## C++ API
`torch-sparse` also offers a C++ API that contains C++ equivalent of python models.
```
mkdir build
cd build
# Add -DWITH_CUDA=on support for the CUDA if needed
cmake ..
make
make install
```
## Running tests
```
pytest
```
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LICENSE
MANIFEST.in
README.md
setup.cfg
setup.py
/work/home/quyuanhao123/software/test_ocp/torch_sparse-0.6.13/csrc/convert.cpp
/work/home/quyuanhao123/software/test_ocp/torch_sparse-0.6.13/csrc/diag.cpp
/work/home/quyuanhao123/software/test_ocp/torch_sparse-0.6.13/csrc/ego_sample.cpp
/work/home/quyuanhao123/software/test_ocp/torch_sparse-0.6.13/csrc/hgt_sample.cpp
/work/home/quyuanhao123/software/test_ocp/torch_sparse-0.6.13/csrc/metis.cpp
/work/home/quyuanhao123/software/test_ocp/torch_sparse-0.6.13/csrc/neighbor_sample.cpp
/work/home/quyuanhao123/software/test_ocp/torch_sparse-0.6.13/csrc/relabel.cpp
/work/home/quyuanhao123/software/test_ocp/torch_sparse-0.6.13/csrc/rw.cpp
/work/home/quyuanhao123/software/test_ocp/torch_sparse-0.6.13/csrc/saint.cpp
/work/home/quyuanhao123/software/test_ocp/torch_sparse-0.6.13/csrc/sample.cpp
/work/home/quyuanhao123/software/test_ocp/torch_sparse-0.6.13/csrc/spmm.cpp
/work/home/quyuanhao123/software/test_ocp/torch_sparse-0.6.13/csrc/spspmm.cpp
/work/home/quyuanhao123/software/test_ocp/torch_sparse-0.6.13/csrc/version.cpp
/work/home/quyuanhao123/software/test_ocp/torch_sparse-0.6.13/csrc/cpu/convert_cpu.cpp
/work/home/quyuanhao123/software/test_ocp/torch_sparse-0.6.13/csrc/cpu/diag_cpu.cpp
/work/home/quyuanhao123/software/test_ocp/torch_sparse-0.6.13/csrc/cpu/ego_sample_cpu.cpp
/work/home/quyuanhao123/software/test_ocp/torch_sparse-0.6.13/csrc/cpu/hgt_sample_cpu.cpp
/work/home/quyuanhao123/software/test_ocp/torch_sparse-0.6.13/csrc/cpu/metis_cpu.cpp
/work/home/quyuanhao123/software/test_ocp/torch_sparse-0.6.13/csrc/cpu/neighbor_sample_cpu.cpp
/work/home/quyuanhao123/software/test_ocp/torch_sparse-0.6.13/csrc/cpu/relabel_cpu.cpp
/work/home/quyuanhao123/software/test_ocp/torch_sparse-0.6.13/csrc/cpu/rw_cpu.cpp
/work/home/quyuanhao123/software/test_ocp/torch_sparse-0.6.13/csrc/cpu/saint_cpu.cpp
/work/home/quyuanhao123/software/test_ocp/torch_sparse-0.6.13/csrc/cpu/sample_cpu.cpp
/work/home/quyuanhao123/software/test_ocp/torch_sparse-0.6.13/csrc/cpu/spmm_cpu.cpp
/work/home/quyuanhao123/software/test_ocp/torch_sparse-0.6.13/csrc/cpu/spspmm_cpu.cpp
/work/home/quyuanhao123/software/test_ocp/torch_sparse-0.6.13/csrc/hip/convert_hip_hip.hip
/work/home/quyuanhao123/software/test_ocp/torch_sparse-0.6.13/csrc/hip/diag_hip_hip.hip
/work/home/quyuanhao123/software/test_ocp/torch_sparse-0.6.13/csrc/hip/rw_hip_hip.hip
/work/home/quyuanhao123/software/test_ocp/torch_sparse-0.6.13/csrc/hip/spmm_hip_hip.hip
/work/home/quyuanhao123/software/test_ocp/torch_sparse-0.6.13/csrc/hip/spspmm_hip.hip
csrc/convert.cpp
csrc/diag.cpp
csrc/ego_sample.cpp
csrc/extensions.h
csrc/hgt_sample.cpp
csrc/metis.cpp
csrc/neighbor_sample.cpp
csrc/relabel.cpp
csrc/rw.cpp
csrc/saint.cpp
csrc/sample.cpp
csrc/sparse.h
csrc/spmm.cpp
csrc/spspmm.cpp
csrc/version.cpp
csrc/cpu/convert_cpu.cpp
csrc/cpu/convert_cpu.h
csrc/cpu/diag_cpu.cpp
csrc/cpu/diag_cpu.h
csrc/cpu/ego_sample_cpu.cpp
csrc/cpu/ego_sample_cpu.h
csrc/cpu/hgt_sample_cpu.cpp
csrc/cpu/hgt_sample_cpu.h
csrc/cpu/metis_cpu.cpp
csrc/cpu/metis_cpu.h
csrc/cpu/neighbor_sample_cpu.cpp
csrc/cpu/neighbor_sample_cpu.h
csrc/cpu/reducer.h
csrc/cpu/relabel_cpu.cpp
csrc/cpu/relabel_cpu.h
csrc/cpu/rw_cpu.cpp
csrc/cpu/rw_cpu.h
csrc/cpu/saint_cpu.cpp
csrc/cpu/saint_cpu.h
csrc/cpu/sample_cpu.cpp
csrc/cpu/sample_cpu.h
csrc/cpu/spmm_cpu.cpp
csrc/cpu/spmm_cpu.h
csrc/cpu/spspmm_cpu.cpp
csrc/cpu/spspmm_cpu.h
csrc/cpu/utils.h
csrc/hip/atomics.cuh
csrc/hip/convert_hip.h
csrc/hip/convert_hip.hip
csrc/hip/convert_hip_hip.hip
csrc/hip/diag_hip.h
csrc/hip/diag_hip.hip
csrc/hip/diag_hip_hip.hip
csrc/hip/reducer.cuh
csrc/hip/rw_hip.h
csrc/hip/rw_hip.hip
csrc/hip/rw_hip_hip.hip
csrc/hip/spmm_hip.h
csrc/hip/spmm_hip.hip
csrc/hip/spmm_hip_hip.hip
csrc/hip/spspmm_hip.h
csrc/hip/spspmm_hip.hip
csrc/hip/utils.cuh
torch_sparse/__init__.py
torch_sparse/add.py
torch_sparse/bandwidth.py
torch_sparse/cat.py
torch_sparse/coalesce.py
torch_sparse/convert.py
torch_sparse/diag.py
torch_sparse/eye.py
torch_sparse/index_select.py
torch_sparse/masked_select.py
torch_sparse/matmul.py
torch_sparse/metis.py
torch_sparse/mul.py
torch_sparse/narrow.py
torch_sparse/padding.py
torch_sparse/permute.py
torch_sparse/reduce.py
torch_sparse/rw.py
torch_sparse/saint.py
torch_sparse/sample.py
torch_sparse/select.py
torch_sparse/spadd.py
torch_sparse/spmm.py
torch_sparse/spspmm.py
torch_sparse/storage.py
torch_sparse/tensor.py
torch_sparse/transpose.py
torch_sparse/utils.py
torch_sparse.egg-info/PKG-INFO
torch_sparse.egg-info/SOURCES.txt
torch_sparse.egg-info/dependency_links.txt
torch_sparse.egg-info/requires.txt
torch_sparse.egg-info/top_level.txt
\ No newline at end of file
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scipy
[test]
pytest
pytest-cov
torch_sparse/padding.py deleted 100644 → 0
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from typing import Tuple, List
import torch
from torch_sparse.tensor import SparseTensor
def padded_index(src: SparseTensor, binptr: torch.Tensor
) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.
Tensor, List[int], List[int]]:
return torch.ops.torch_sparse.padded_index(src.storage.rowptr(),
src.storage.col(),
src.storage.rowcount(), binptr)
def padded_index_select(src: torch.Tensor, index: torch.Tensor,
fill_value: float = 0.) -> torch.Tensor:
fill_value = torch.tensor(fill_value, dtype=src.dtype)
return torch.ops.torch_sparse.padded_index_select(src, index, fill_value)
SparseTensor.padded_index = padded_index
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