TC-GNN with Pytorch integration

Last update: Dec 01, 2022

Overview

TC-GNN (Running Sparse GNN on Dense Tensor Core on Ampere GPU)

Cite this project and paper.

@inproceedings{TC-GNN,
  title={TC-GNN: Accelerating Sparse Graph Neural Network Computation Via Dense Tensor Core on GPUs},
  author={Yuke Wang and Boyuan Feng and Yufei Ding},
  booktitle={Arxiv},
  year={2022}
}

Clone this project.

git clone [email protected]:YukeWang96/TCGNN-Pytorch.git

OS & Compiler:

Ubuntu 16.04+

gcc >= 7.5

cmake >= 3.14

CUDA >= 11.0 and nvcc >= 11.0

Files and Directories.

config.py: the configuration file for the shape of a TC block.
bench.py: the benchmark file for invoking main_tcgnn.py for various datasets and models.
main_tcgnn.py: the main entry for running TC-GNN.
count_TC_blocks.py: counting the total number of TC blocks without sparse-graph translation.
proc_prof.py: get the detailed GPU kernel metrics from the ncu csv output.
TCGNN_conv/: the directory for core TC-GNN implementations, including TCGNN_kernel.cu and TCGNN.cpp.

Environment Setup.

[Method-1] Install via Docker (Recommended).

Go to Docker/
Run ./build.sh
Run ./launch.sh

[Method-2] Install via Conda.

Install conda on system Toturial.
Create a conda environment:

conda create -n env_name python=3.6

Install Pytorch:

conda install pytorch torchvision torchaudio cudatoolkit=11.1 -c pytorch -c conda-forge

or using pip [Note that make sure the pip you use is the pip from current conda environment. You can check this by which pip]

pip install torch==1.8.0+cu111 torchvision==0.9.0+cu111 torchaudio==0.8.0 -f https://download.pytorch.org/whl/torch_stable.html

Install Deep Graph Library (DGL).

conda install -c dglteam dgl-cuda11.0
pip install torch requests tqdm

Install Pytorch-Geometric (PyG).

pip install torch-scatter -f https://pytorch-geometric.com/whl/torch-1.8.0+cu111.html
pip install torch-sparse -f https://pytorch-geometric.com/whl/torch-1.8.0+cu111.html
pip install torch-cluster -f https://pytorch-geometric.com/whl/torch-1.8.0+cu111.html
pip install torch-spline-conv -f https://pytorch-geometric.com/whl/torch-1.8.0+cu111.html
pip install torch-geometric

Install `TC-GNN`.

Go to TCGNN_conv/, then run

./build.sh

to install the TCGNN_conv modules with Pytorch binding. Note that this step is required for both Docker and Conda setup.

Download graph datasets.

Get the preprocessed datasets in .npy at here, then run

tar -zxvf tcgnn-ae-graphs.tar.gz

Running PyG baseline.

Go to pyg_baseline/ directory;

Pass the --model parameter in pyg_main.py with gcn and gin to profile the example GCN and GIN model, respectively;

./0_bench.py| tee run_pyg.log to run the script and the report 10 epoch runtime for all evaluated datasets.

./1_log2csv.py to convert the run_pyg.log to run_pyg.csv for ease of analysis.

Running DGL baseline.

Go to dgl_baseline/ directory

Pass the --model parameter in dgl_main.py with gcn and gin to profile the example GCN and GIN model, respectively;

./0_bench.py| tee run_dgl.log to run the script and the report 10 epoch runtime for all evaluated datasets.

./1_log2csv.py to convert the run_dgl.log to run_dgl.csv for ease of visualization.

Running TC-GNN.

Under the current project directory

./0_bench.py| tee run_TCGNN.log to run the script and the report 10 epoch runtime for all evaluated datasets.

./1_log2csv.py to convert the run_TCGNN.log to run_TCGNN.csv for ease of analysis.

Comments

Any docs about this project?

Hi I came across this project and found the implementation is quite interesting. Is there any docs/paper that detail this project? Or you have any plan to release these kinds of information in the future?

Thanks

opened by mmmeee1111 1

Releases(v0.2)

v0.2(May 11, 2021)
Add the docker support for environment setup.

Update the path and download link of datasets.

Source code(tar.gz)
Source code(zip)
v0.1(Apr 16, 2021)

Source code(tar.gz)
Source code(zip)

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TC-GNN with Pytorch integration

Related tags

Overview

TC-GNN (Running Sparse GNN on Dense Tensor Core on Ampere GPU)

Files and Directories.

Environment Setup.

[Method-1] Install via Docker (Recommended).

[Method-2] Install via Conda.

Install TC-GNN.

Download graph datasets.

Running PyG baseline.

Running DGL baseline.

Running TC-GNN.

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Comments

Any docs about this project?

Releases(v0.2)

v0.2(May 11, 2021)

v0.1(Apr 16, 2021)

Owner

YUKE WANG

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Install `TC-GNN`.