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 git@github.com: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