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https://github.com/MachineLearningSystem/ATC23-TCGNN-Pytorch
TC-GNN with Pytorch integration
https://github.com/MachineLearningSystem/ATC23-TCGNN-Pytorch
Last synced: 9 days ago
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TC-GNN with Pytorch integration
- Host: GitHub
- URL: https://github.com/MachineLearningSystem/ATC23-TCGNN-Pytorch
- Owner: MachineLearningSystem
- Fork: true (YukeWang96/TC-GNN_ATC23)
- Created: 2023-04-30T08:11:49.000Z (over 1 year ago)
- Default Branch: master
- Last Pushed: 2023-04-28T16:16:20.000Z (over 1 year ago)
- Last Synced: 2024-08-02T19:37:11.521Z (4 months ago)
- Homepage:
- Size: 73.4 MB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
- awesome-AI-system - TC-GNN: Accelerating Sparse Graph Neural Network Computation Via Dense Tensor Core on GPUs ATC'23
README
# TC-GNN (Running Sparse GNN on Dense Tensor Core on Ampere GPU)
+ **Cite this project and [paper](https://arxiv.org/abs/2112.02052).**
```
@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 Zheng Wang and Guyue Huang and Yufei Ding},
booktitle={USENIX Annual Technical Conference},
year={2023}
}
```
+ **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](https://www.digitalocean.com/community/tutorials/how-to-install-anaconda-on-ubuntu-18-04-quickstart)**.
+ 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)`**](https://github.com/dmlc/dgl).
```
conda install -c dglteam dgl-cuda11.0
pip install torch requests tqdm
```
+ Install [**`Pytorch-Geometric (PyG)`**](https://github.com/rusty1s/pytorch_geometric).
```
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](https://storage.googleapis.com/graph_dataset/tcgnn-ae-graphs.tar.gz),
then run
```
wget https://storage.googleapis.com/graph_dataset/tcgnn-ae-graphs.tar.gz
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.