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# <div align="center"><strong>torch-sparse-0.6.13</strong></div>
**编译安装步骤** ## 简介
torch-sparse是PyTorch的一个扩展库,用于处理稀疏数据。它提供了一些功能强大的稀疏矩阵操作,以及能够高效执行稀疏计算的函数和工具。
- 1. 安装与dtk版本对应的pytorch torchvision whl(可在光合社区下载https://developer.hpccube.com/tool/)以及其他依赖库 ## 依赖安装
+ pytorch1.10或者pytorch1.13 (建议dtk-22.04.2、dtk-23.04与dtk-23.10)
+ python 3.7-3.10
例如:
### 1、使用源码编译方式安装
pip install https://cancon.hpccube.com:65024/directlink/4/pytorch/dtk22.10/torch-1.10.0a0+git2040069.dtk2210-cp38-cp38-manylinux2014_x86_64.whl
#### 编译环境准备
pip install https://cancon.hpccube.com:65024/directlink/4/vision/dtk22.10/torchvision-0.10.0a0+e04d001.dtk2210-cp38-cp38-manylinux2014_x86_64.whl 提供2种环境准备方式:
pip install -r requirements.txt 1. 基于光源pytorch基础镜像环境:镜像下载地址:[https://sourcefind.cn/#/image/dcu/pytorch](https://sourcefind.cn/#/image/dcu/pytorch),根据pytorch、python、dtk及系统下载对应的镜像版本。
- 2. 添加编译时的conda环境及部分库的环境变量 2. 基于现有python环境:安装pytorch,pytorch whl包下载目录:[https://cancon.hpccube.com:65024/4/main/pytorch](https://cancon.hpccube.com:65024/4/main/pytorch/dtk23.04),根据python、dtk版本,下载对应pytorch的whl包。安装命令如下:
```shell
- 2.1 激活对应的conda环境: pip install torch* (下载的torch的whl包)
pip install setuptools==59.5.0 wheel
```
source ~/miniconda3/etc/profile.d/conda.sh
#### 源码编译安装
conda activate torch1.10_py39_dtk22.10 - 代码下载
```shell
- 2.2 加载对应的module,包括dtk: git clone http://developer.hpccube.com/codes/aicomponent/torch-sparce # 根据编译需要切换分支
```
- 源码编译(进入torch-sparse目录):
module purge ```
export C_INCLUDE_PATH=/public/software/apps/DeepLearning/PyTorch_Lib/gflags-2.1.2-build/include:$C_INCLUDE_PATH
module load compiler/devtoolset/7.3.1 mpi/hpcx/gcc-7.3.1 compiler/dtk/22.10.1 export CPLUS_INCLUDE_PATH=/public/software/apps/DeepLearning/PyTorch_Lib/gflags-2.1.2-build/include:$CPLUS_INCLUDE_PATH
export C_INCLUDE_PATH=/public/software/apps/DeepLearning/PyTorch_Lib/glog-build/include:$C_INCLUDE_PATH
module list export CPLUS_INCLUDE_PATH=/public/software/apps/DeepLearning/PyTorch_Lib/glog-build/include:$CPLUS_INCLUDE_PATH
export C_INCLUDE_PATH=$ROCM_PATH/rocrand/include:$C_INCLUDE_PATH
- 2.3 加载所需的依赖库的环境变量(根据各集群实际路径调整): export CPLUS_INCLUDE_PATH=$ROCM_PATH/rocrand/include:$CPLUS_INCLUDE_PATH
export LD_LIBRARY_PATH=$ROCM_PATH/rocrand/lib:$LD_LIBRARY_PATH
export FORCE_ONLY_HIP=1
export C_INCLUDE_PATH=/public/software/apps/DeepLearning/PyTorch_Lib/gflags-2.1.2-build/include:$C_INCLUDE_PATH export CC=hipcc
export CXX=hipcc
export CPLUS_INCLUDE_PATH=/public/software/apps/DeepLearning/PyTorch_Lib/gflags-2.1.2-build/include:$CPLUS_INCLUDE_PATH
python setup.py install
export C_INCLUDE_PATH=/public/software/apps/DeepLearning/PyTorch_Lib/glog-build/include:$C_INCLUDE_PATH ```
#### 注意事项
export CPLUS_INCLUDE_PATH=/public/software/apps/DeepLearning/PyTorch_Lib/glog-build/include:$CPLUS_INCLUDE_PATH + 若使用pip install下载安装过慢,可添加pypi清华源:-i https://pypi.tuna.tsinghua.edu.cn/simple/
+ ROCM_PATH为dtk的路径,默认为/opt/dtk
export C_INCLUDE_PATH=$ROCM_PATH/rocrand/include:$C_INCLUDE_PATH
## 验证
export CPLUS_INCLUDE_PATH=$ROCM_PATH/rocrand/include:$CPLUS_INCLUDE_PATH
```python
export LD_LIBRARY_PATH=$ROCM_PATH/rocrand/lib:$LD_LIBRARY_PATH import torch
from torch_sparse import coalesce
- 2.4 修改编译器环境变量:
index = torch.tensor([[1, 0, 1, 0, 2, 1],
[0, 1, 1, 1, 0, 0]])
export FORCE_ONLY_HIP=1 value = torch.Tensor([[1, 2], [2, 3], [3, 4], [4, 5], [5, 6], [6, 7]])
export CC=hipcc index, value = coalesce(index, value, m=3, n=2)
```
export CXX=hipcc
```
- 3. 编译安装 print(index)
tensor([[0, 1, 1, 2],
[1, 0, 1, 0]])
python setup.py install print(value)
tensor([[6.0, 8.0],
[7.0, 9.0],
[3.0, 4.0],
[5.0, 6.0]])
```
## Known Issue
- 该库没有基于cpu环境修改,仅支持dcu,请在有dcu卡的环境运行。
- 如需完整使用所有pyg功能,请pip install torch-geometric
## 参考资料
- [README_ORIGIN](README_ORIGIN.md)
- [https://pypi.org/project/torch-sparse/0.6.13/](https://pypi.org/project/torch-sparse/0.6.13/)
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