直接在pytorch 调用torch.spmm的暴力模式和message_passing的scatter机制来处理A * X的inference在A100上跑会有大的差别:比如当你的dataset从Cora-》Reddit的时候。 随着GNN的应用推广,在引入edge information以后不能再简单抽象为SpMM + GeMM的处理了。 所以在解GNN加速的时候,以往会用SpMM+GeMM的hybrid engies...
调用到backend.pytorch.sparse.py中: 由于这里调用的是C的接口,因此要去看dgl的源码了: dgl/src at master · dmlc/dgl · GitHub 这个接口对应的C代码位置在:src/array/kernel.cc 调用的是同文件下的SpMM函数。而且可以发现,目前只支持CSC和COO的格式。有意思的是CSC格式用的确实SpMMCsr函数(他俩很像,CSC...
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Magicube SpMM平均在A100 GPU上比cuSPARSE SpMM加速1.44X(最高达2.37X)。对于具有稀疏(90%)注意力图的Transformer模型的端到端推理,Magicube比VectorSparse和使用Cudnn的Pytorch(使用FP16的稠密库)获得了1.43X的加速比,且具有相当的精度。 三个库的总结 参考 ^Trevor Gale et al. "Sparse GPU Kernels for Deep ...
调用到backend.pytorch.sparse.py中: 由于这里调用的是C的接口,因此要去看dgl的源码了: dgl/src at master · dmlc/dgl · GitHub 这个接口对应的C代码位置在:src/array/kernel.cc 调用的是同文件下的SpMM函数。而且可以发现,目前只支持CSC和COO的格式。有意思的是CSC格式用的确实SpMMCsr函数(他俩很像,CSC列...
Can make spspmm support also 3D tensor (might think as batched): suppose one sparse matrix has dimensions (B, M, N), the other has dimensions (B, N, F) and the multiplication result has dimensions (B,M,F)?Owner rusty1s commented Dec 23, 2023 No that's not possible. I suggest...
New issue torch.spmm#19 Open zhuchangjiangopened this issueSep 24, 2018· 10 comments Sparse matrix multiplication … Owner jgbradley1commentedMay 28, 2019• edited InstantWindycommentedJun 2, 2019• edited Sign up for freeto join this conversation on GitHub. Already have an account?Sign in...
Prerequisites CUDA toolkit 10.1 PyTorch 1.4 GE-SpMM can be integrated to DGL. When cloning this repo, pass --recursive flag to automatically pull DGL repo. First build DGL from source. Instructions are also in this tutorial. cd $(this-repo)/dgl-custom/dgl mkdir build cd build cmake -DUS...
Source File: layers.py From graph-cnn.pytorch with MIT License 5 votes def forward(self, input, adj): support = torch.mm(input, self.weight) output = torch.spmm(adj, support) if self.bias is not None: return output + self.bias else: return output ...
Hi I'm trying to use spspmm on cuda to calculate the power of a 1086*1086 matrix with 66442 non-zero elements, and I encountered "cuda runtime error". The complete error message is as follows: THCudaCheck FAIL file=/opt/conda/conda-bld/pytorch_1556653114079/work/aten/src/THC/THC...