DominoSearch
This is repository for codes and models of NeurIPS2021 paper - DominoSearch: Find layer-wise fine-grained N:M sparse schemes from dense neural networks
Instructions and other materials will be released soon.
Search:
git clone https://github.com/NM-sparsity/DominoSearch.git
cd DominoSearch/DominoSearch/search/script_resnet_ImageNet
We provide several search scripts for different sparse-ratio target, you can specify your own target and change the parameters accordingly. Note, you need to first specify your ImageNet dataset path
The searching phase could take 2-3 hours, then you will get searched schemes stored in a txt file, which will be needed as input for mixed-sparsity training.
Below is an example of output formate.
{'SparseConv0_3-64-(7, 7)': [16, 16], 'SparseConv1_64-64-(1, 1)': [16, 16], 'SparseConv2_64-64-(3, 3)': [4, 16], 'SparseConv3_64-256-(1, 1)': [8, 16], 'SparseConv4_64-256-(1, 1)': [8, 16], 'SparseConv5_256-64-(1, 1)': [8, 16], 'SparseConv6_64-64-(3, 3)': [4, 16], 'SparseConv7_64-256-(1, 1)': [8, 16], 'SparseConv8_256-64-(1, 1)': [8, 16], 'SparseConv9_64-64-(3, 3)': [4, 16], 'SparseConv10_64-256-(1, 1)': [8, 16], 'SparseConv11_256-128-(1, 1)': [8, 16], 'SparseConv12_128-128-(3, 3)': [2, 16], 'SparseConv13_128-512-(1, 1)': [8, 16], 'SparseConv14_256-512-(1, 1)': [4, 16], 'SparseConv15_512-128-(1, 1)': [8, 16], 'SparseConv16_128-128-(3, 3)': [4, 16], 'SparseConv17_128-512-(1, 1)': [8, 16], 'SparseConv18_512-128-(1, 1)': [8, 16], 'SparseConv19_128-128-(3, 3)': [4, 16], 'SparseConv20_128-512-(1, 1)': [8, 16], 'SparseConv21_512-128-(1, 1)': [8, 16], 'SparseConv22_128-128-(3, 3)': [2, 16], 'SparseConv23_128-512-(1, 1)': [8, 16], 'SparseConv24_512-256-(1, 1)': [4, 16], 'SparseConv25_256-256-(3, 3)': [2, 16], 'SparseConv26_256-1024-(1, 1)': [4, 16], 'SparseConv27_512-1024-(1, 1)': [4, 16], 'SparseConv28_1024-256-(1, 1)': [4, 16], 'SparseConv29_256-256-(3, 3)': [2, 16], 'SparseConv30_256-1024-(1, 1)': [4, 16], 'SparseConv31_1024-256-(1, 1)': [4, 16], 'SparseConv32_256-256-(3, 3)': [2, 16], 'SparseConv33_256-1024-(1, 1)': [4, 16], 'SparseConv34_1024-256-(1, 1)': [4, 16], 'SparseConv35_256-256-(3, 3)': [2, 16], 'SparseConv36_256-1024-(1, 1)': [4, 16], 'SparseConv37_1024-256-(1, 1)': [4, 16], 'SparseConv38_256-256-(3, 3)': [2, 16], 'SparseConv39_256-1024-(1, 1)': [4, 16], 'SparseConv40_1024-256-(1, 1)': [4, 16], 'SparseConv41_256-256-(3, 3)': [2, 16], 'SparseConv42_256-1024-(1, 1)': [4, 16], 'SparseConv43_1024-512-(1, 1)': [4, 16], 'SparseConv44_512-512-(3, 3)': [2, 16], 'SparseConv45_512-2048-(1, 1)': [4, 16], 'SparseConv46_1024-2048-(1, 1)': [2, 16], 'SparseConv47_2048-512-(1, 1)': [4, 16], 'SparseConv48_512-512-(3, 3)': [2, 16], 'SparseConv49_512-2048-(1, 1)': [4, 16], 'SparseConv50_2048-512-(1, 1)': [4, 16], 'SparseConv51_512-512-(3, 3)': [2, 16], 'SparseConv52_512-2048-(1, 1)': [4, 16], 'Linear0_2048-1000': [4, 16]}
Train:
After getting the layer-wise sparse schemes, we need to fine-tune with the schemes to recover the accuracy. The training code is based on NM-sparsity, where we made some changes to support flexible N:M schemes.
Below is an example of training layer-wise sparse resnet50 with 80% overall sparsity.
cd DominoSearch\DominoSearch\train\classification_sparsity_level\train_imagenet
python -m torch.distributed.launch --nproc_per_node=8 ../train_imagenet.py --config ./configs/config_resnet50.yaml --base_lr 0.01 --decay 0.0005 --epochs 120 --schemes_file ./schemes/resnet50_M16_0.80.txt --model_dir ./resnet50/resnet50_0.80_M16
Experiments
We provide the trained models of the experiments. Please check our paper for details and intepretations of the experiments.
ResNet50 experiments in section 4.1
| Model Name | TOP1 Accuracy | Trained Model | Searched schemes |
|---|---|---|---|
| resnet50 - 0.80 model size | 76.7 | google drive | google drive |
| resnet50 - 0.875 model size | 75.7 | google drive | google drive |
| resnet50 - 0.9375 model size | 73.5 | google drive | google drive |
| resnet50 - 8x FLOPs | 75.4 | google drive | google drive |
| resnet50- 16x FLOPs | 73.4 | google drive | google drive |
Ablation experiments of ResNet50 in section 5.3
| Model Name | TOP1 Accuracy | Trained Model | Train log |
|---|---|---|---|
| Ablation E3 | 76.1 | google drive | google drive |
| Ablation E4 | 76.4 | google drive | google drive |
| Ablation E6 | 76.6 | google drive | google drive |
| Ablation E7 | 75.6 | google drive | google drive |
Citation
@inproceedings{
sun2021dominosearch,
title={DominoSearch: Find layer-wise fine-grained N:M sparse schemes from dense neural networks},
author={Wei Sun and Aojun Zhou and Sander Stuijk and Rob G. J. Wijnhoven and Andrew Nelson and Hongsheng Li and Henk Corporaal},
booktitle={Thirty-Fifth Conference on Neural Information Processing Systems},
year={2021},
url={https://openreview.net/forum?id=IGrC6koW_g}
}
7 Feb 10, 2022
214 Jan 08, 2023
686 Nov 24, 2022
3.2k Dec 30, 2022
532 Dec 29, 2022
132 Dec 26, 2022
70 Dec 14, 2022
1 Feb 15, 2022
106 Jan 06, 2023
24 Dec 13, 2022
304 Dec 28, 2022
53 Jan 22, 2022
10 Nov 20, 2022
26 Sep 18, 2022
4.9k Dec 31, 2022
6 Jul 30, 2022
365 Jan 09, 2023
4.5k Jan 07, 2023
51 Dec 10, 2022
112 Dec 23, 2022