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DepthShrinker: A New Compression Paradigm Towards Boosting Real-Hardware Efficiency of Compact Neural Networks

Yonggan Fu, Haichuan Yang, Jiayi Yuan, Meng Li, Cheng Wan, Raghuraman Krishnamoorthi, Vikas Chandra, Yingyan Lin

Accepted at ICML 2022 [Paper Link].

DepthShrinker: Overview

• To tackle the dilemma between the low hardware utilization of existing efficient DNNs and the continuously increasing degree of computing parallelism of modern computing platforms, we propose a framework dubbed DepthShrinker, which develops hardware-friendly compact networks via shrinking the basic building blocks of existing efficient DNNs that feature irregular computation patterns into dense ones with much improved hardware utilization and thus real-hardware efficiency.

• Key idea: Shrink consecutive operations, between which the non-linear functions are properly removed, into one single dense operation.

DepthShrinker: Framework

• To identify which activation functions to remove and restore the accuracy after the removal, we implement DepthShrinker as a three-stage framework:

DepthShrinker: Experimental Results

• DepthShrinker delivers compact DNNs that outperform both SOTA channel-/layer-wise pruning techniques, e.g., a 3.06% higher accuracy and 1.53× throughput on a Tesla V100 GPU over SOTA channel-wise pruning method MetaPruning.

Code Usage

Configurations for training/evaluation are set in config/{config-file}.yaml and some arguments can also be overrided via argparse in the main.py. For example, DS.SEARCH in the config file serves as a flag to switch between the two stages of identifying redundant activation functions and finetuning the network after the removel.

Evaluate Pretrained Models of DepthShrinker

• To evaluate the accuracy of a pretrained model on ImageNet:

python main.py --cfg configs/{config-file}.yaml \
        --data_path {path-to-imagenet} \
        --batch-size 128 \
        --pretrain {path-to-pretrained-ckpt} \
        --eval \
        --dp 

• To measure the throughput of a model after shrinking:

python main.py --cfg configs/{config-file}.yaml \
        --data_path {path-to-imagenet} \
        --batch-size 128 \
        --pretrain {path-to-pretrained-ckpt} \
        --throughput \
        --dp 

Our pretrained models (at the last epoch) on MobileNetV2-1.4 are available here.

Train with DepthShrinker from Scratch

• Stage 1: To search for redundant activaton functions, set DS.SEARCH=True and the target remaining ratio of activation functions via DS.L0_SPARSITY in the config file configs/{config-file}.yaml (optionally start from a pretrained model via setting DS.PRETRAINED). Next start the search process:

python main.py --cfg configs/{config-file}.yaml \
        --data_path {path-to-imagenet} \
        --batch-size 256

• Stage 2: To finetune the resulting network in the stage 1, set DS.SEARCH=False, DS.ACT_FROM_SEARCH=True, and config DS.SEARCH_CKPT as the path to the checkpoint from the stage 1 (optionally enable self-distillation via setting DS.DISTILL), then run the above training command again.

Citation

@article{fu2022depthshrinker,
  title={DepthShrinker: A New Compression Paradigm Towards Boosting Real-Hardware Efficiency of Compact Neural Networks},
  author={Fu, Yonggan and Yang, Haichuan and Yuan, Jiayi and Li, Meng and Wan, Cheng and Krishnamoorthi, Raghuraman and Chandra, Vikas and Lin, Yingyan},
  journal={arXiv preprint arXiv:2206.00843},
  year={2022}
}

License

The majority of DepthShrinker is licensed under CC-BY-NC, however portions of the project are available under separate license terms: pytorch-image-models (Timm) is licensed under the Apache 2.0 license; Swin-Transformer is licensed under the MIT license.

Contributing

We actively welcome your pull requests! Please see CONTRIBUTING and CODE_OF_CONDUCT for more info.