yukewang96 / qgtc_ppopp22

Artifact for PPoPP22 QGTC: Accelerating Quantized GNN via GPU Tensor Core.

QGTC: Accelerating Quantized GNN via GPU Tensor Core

• Cite this project and paper.

@inproceedings{QGTC,
  title={QGTC: Accelerating Quantized GNN via GPU Tensor Core},
  author={Yuke Wang and Boyuan Feng and Yufei Ding},
  booktitle={ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming. (PPoPP'22)},
  year={2022}
}

Clone this project.

---

git clone [email protected]:YukeWang96/PPoPP22_QGTC.git

Dependencies.

---

• Python 3.7+.
• PyTorch 1.5.0+.
• Deep Graph Library.

Environment Setup.

---

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

• (i) Pull docker image:

docker pull happy233/qgtc:updated
docker run -it --rm --gpus all -v $PWD/:/qgtc happy233/qgtc:updated /bin/bash

• (ii) Build docker from scratch:

cd Docker/
./build.sh
./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

Install QGTC. Go to QGTC_module/, then run

TORCH_CUDA_ARCH_LIST="8.6" python setup.py  clean --all install 

Running Experiments

---

Download datasets

./download_dataset.sh

Figure 7. Speedup comparison.

---

• (a) Cluster GCN. You can change the bitwidth=4 at 0_7_eval_QGTC_cluster_GCN.py to [1,2,4,8] for evaluation. default is 2 bit.

./0_7a_eval_QGTC_cluster_GCN.py
./1_7a_eval_DGL_cluster_GCN.py

Check the results in QGTC_cluster_GCN_*bit.csv and DGL_cluster_GCN.csv. You will expect the result likes this.

dataset	Epoch (ms)
artist	263.646
soc-BlogCatalog	209.495
ppi	189.016
ogbn-arxiv	208.616

• (b) Batched GIN. You can change the bitwidth=4 at 0_7b_eval_QGTC_batched_GIN.py to [1,2,4,8] for evaluation. default is 2 bit.

./0_7b_eval_QGTC_batched_GIN.py
./1_7b_eval_DGL_batched_GIN.py

Check the results in QGTC_batched_GIN_*bit.csv and DGL_batched_GIN.csv.

Figure 8: Additional studies.

---

• (a) Comparison with the cuBLASgemmEX (int8) on Tensor Core.

./3_8a_QGTC_GEMM_INT8.py
cd cuBLASGemmEX/
./compile.sh
./bench_cuBLAS_INT8.py

running ./2_7c_cuBLAS_INT8.py you will get the result like this

======== 1 bit ==================
X1_height 1024, X1_width: 1024, X2_width: 16, TFLOPs: 5.847
X1_height 2048, X1_width: 2048, X2_width: 16, TFLOPs: 16.605
X1_height 4096, X1_width: 4096, X2_width: 16, TFLOPs: 40.627
X1_height 1024, X1_width: 1024, X2_width: 32, TFLOPs: 11.724
X1_height 2048, X1_width: 2048, X2_width: 32, TFLOPs: 32.666
X1_height 4096, X1_width: 4096, X2_width: 32, TFLOPs: 35.032
X1_height 1024, X1_width: 1024, X2_width: 64, TFLOPs: 23.219
X1_height 2048, X1_width: 2048, X2_width: 64, TFLOPs: 37.438
X1_height 4096, X1_width: 4096, X2_width: 64, TFLOPs: 46.768
======== 2 bit ==================
X1_height 1024, X1_width: 1024, X2_width: 16, TFLOPs: 3.934
X1_height 2048, X1_width: 2048, X2_width: 16, TFLOPs: 10.086
X1_height 4096, X1_width: 4096, X2_width: 16, TFLOPs: 20.764
X1_height 1024, X1_width: 1024, X2_width: 32, TFLOPs: 7.864
X1_height 2048, X1_width: 2048, X2_width: 32, TFLOPs: 19.762
X1_height 4096, X1_width: 4096, X2_width: 32, TFLOPs: 20.951
X1_height 1024, X1_width: 1024, X2_width: 64, TFLOPs: 15.429
X1_height 2048, X1_width: 2048, X2_width: 64, TFLOPs: 25.055
X1_height 4096, X1_width: 4096, X2_width: 64, TFLOPs: 26.818
======== 4 bit ==================
X1_height 1024, X1_width: 1024, X2_width: 16, TFLOPs: 2.488
X1_height 2048, X1_width: 2048, X2_width: 16, TFLOPs: 6.561
X1_height 4096, X1_width: 4096, X2_width: 16, TFLOPs: 12.409
X1_height 1024, X1_width: 1024, X2_width: 32, TFLOPs: 4.456
X1_height 2048, X1_width: 2048, X2_width: 32, TFLOPs: 12.807
X1_height 4096, X1_width: 4096, X2_width: 32, TFLOPs: 13.929
X1_height 1024, X1_width: 1024, X2_width: 64, TFLOPs: 10.683
X1_height 2048, X1_width: 2048, X2_width: 64, TFLOPs: 12.328
X1_height 4096, X1_width: 4096, X2_width: 64, TFLOPs: 14.196
======== 8 bit ==================
X1_height 1024, X1_width: 1024, X2_width: 16, TFLOPs: 1.541
X1_height 2048, X1_width: 2048, X2_width: 16, TFLOPs: 3.483
X1_height 4096, X1_width: 4096, X2_width: 16, TFLOPs: 6.763
X1_height 1024, X1_width: 1024, X2_width: 32, TFLOPs: 3.074
X1_height 2048, X1_width: 2048, X2_width: 32, TFLOPs: 6.816
X1_height 4096, X1_width: 4096, X2_width: 32, TFLOPs: 7.366
X1_height 1024, X1_width: 1024, X2_width: 64, TFLOPs: 5.046
X1_height 2048, X1_width: 2048, X2_width: 64, TFLOPs: 6.165
X1_height 4096, X1_width: 4096, X2_width: 64, TFLOPs: 7.324

running ./bench_cuBLAS_INT8.py, you will get the result like this

M: 1024, K: 1024, N: 16, TFLOPS: 0.55
M: 2048, K: 2048, N: 16, TFLOPS: 2.58
M: 4096, K: 4096, N: 16, TFLOPS: 3.60
M: 1024, K: 1024, N: 32, TFLOPS: 3.89
M: 2048, K: 2048, N: 32, TFLOPS: 5.49
M: 4096, K: 4096, N: 32, TFLOPS: 6.49
M: 1024, K: 1024, N: 64, TFLOPS: 4.38
M: 2048, K: 2048, N: 64, TFLOPS: 6.30
M: 4096, K: 4096, N: 64, TFLOPS: 6.65

• (b) Zero-tile jumping efficiency.

./3_8_zero_tile_jumping.py

check the results in zerotile_jumping.csv

• (c) Adjacencymatrix size impact.

./3_9_adjmatrix_size.py

you will get the result like this

X1_height 1024, X1_width: 1024, X2_width: 16, TFLOPs: 5.831
X1_height 2048, X1_width: 2048, X2_width: 16, TFLOPs: 16.323
X1_height 4096, X1_width: 4096, X2_width: 16, TFLOPs: 34.425
X1_height 1024, X1_width: 1024, X2_width: 32, TFLOPs: 11.717
X1_height 2048, X1_width: 2048, X2_width: 32, TFLOPs: 32.027
X1_height 4096, X1_width: 4096, X2_width: 32, TFLOPs: 40.175
X1_height 1024, X1_width: 1024, X2_width: 64, TFLOPs: 23.158
X1_height 2048, X1_width: 2048, X2_width: 64, TFLOPs: 37.444
X1_height 4096, X1_width: 4096, X2_width: 64, TFLOPs: 46.759
X1_height 1024, X1_width: 1024, X2_width: 128, TFLOPs: 28.417
X1_height 2048, X1_width: 2048, X2_width: 128, TFLOPs: 40.646
X1_height 4096, X1_width: 4096, X2_width: 128, TFLOPs: 52.517
X1_height 1024, X1_width: 1024, X2_width: 256, TFLOPs: 32.089
X1_height 2048, X1_width: 2048, X2_width: 256, TFLOPs: 44.151
X1_height 4096, X1_width: 4096, X2_width: 256, TFLOPs: 59.508
X1_height 1024, X1_width: 1024, X2_width: 512, TFLOPs: 41.743
X1_height 2048, X1_width: 2048, X2_width: 512, TFLOPs: 49.687
X1_height 4096, X1_width: 4096, X2_width: 512, TFLOPs: 64.172
X1_height 1024, X1_width: 1024, X2_width: 1024, TFLOPs: 37.954
X1_height 2048, X1_width: 2048, X2_width: 1024, TFLOPs: 52.970
X1_height 4096, X1_width: 4096, X2_width: 1024, TFLOPs: 66.490

qgtc_ppopp22's Issues

Cuda error

Hello Yuke,

I tried using the public code and following the instructions, but I have encountered an error when running the example, "CUDA error at Quantize_val: No image kernel is available for excution on the device". I have attempted to build the environment both in docker and conda, and have tried using other possible CUDA versions, but the error persists. Would it be possible for you to provide some insight into what may be causing the error? Additionally, if you could provide any advice on how I can use your method or provide a runnable library, it would be greatly appreciated.

Thanks,
Shuang