Dynamic Structure Pruning

Dynamic Structure Pruning for Compressing CNNs [AAAI 2023]

Jun-Hyung Park, Yeachan Kim, Junho Kim, Joon-Young Choi, and SangKeun Lee

Generate compact and efficient CNNs based on grouping and pruning that transform single-branch convolutional layers into multi-branch convolutional layers [AAAI] [arXiv]

Introduction

Dynamic Structure Pruning automatically learns intra-channel sparsity by optimizing filter groups and regularizing group channels. This allows for higher efficiency from fine-grained pruing granularities, while producing generally accelerable (i.e., regular) structures.

Requirements

• Python 3.7
• PyTorch 1.10.0
• TorchVision 0.11.0
• tqdm

How to use DSP in your code

You should first train pre-trained models to learn groups and then prune and finetune the group-learned models.

Our group-learning and pruning modules require three steps.

1. Defining a wrapper
2. Initializing
3. Processing after every update (step)

Following sections show code examples using our modules.

Differentiable Group Learning

from dsp_module import *

...

# After defining your model, optimizer, criterion, etc.
group_trainer = GroupWrapper(model, optimizer, criterion, regularization_power, total_num_iterations, num_groups, temperature)

...

# Training iteration
for epoch in range(args.epochs):
    for x, y in train_dataloader:
        # Before forward (model(x))
        group_trainer.initialize()
        out = model(x)
        ...

        # After model update (optimizer.step())
        group_trainer.after_step(x, y)

...

Group Channel Pruning

from dsp_module import *

...

# Before loading group-learned checkpoints
pruner = PruneWrapper(model, num_groups, fp_every_nth_conv)

# fp_every_nth_conv means it prunes filters of every nth convolution layers.
# In our paper, we prune filters of the final layer in each residual block.
# In CIFAR-10, fp_every_nth_conv = 2, and in ImageNet, fp_every_nth_conv = 2 (ResNet18) or 3 (ResNet50)
# If your models have irregular numbers of layers in each residual block, 
# you can specify the indices of layers by setting fp_layer_indices=[1, 3, 5, 8, 11, 14, ...]
# If you set both fp_every_nth_conv and fp_layer_indices, the latter is prioritized.

...

# Before training starts
flops, params = pruner.initialize(pruning_rate)

# Training iteration
for epoch in range(args.epochs):
    for x, y in train_dataloader:
        
        ...

        # After model update (optimizer.step())
        pruner.after_step()
...

Please refer to our CIFAR-10 pruning codes (cifar_dsp.py and cifar_finetune.py) to help your understanding of our modules.

Pruning on CIFAR-10

Pretraining

# pretrain ResNet20
python cifar_pretrain.py -l 20 [--save-dir ./cifarmodel] [--epochs 164] [--batch-size 128] [--lr 0.1] [--momentum 0.9] [--wd 1e-4]

# pretrain ResNet56
python cifar_pretrain.py -l 56 [--save-dir ./cifarmodel] [--epochs 164] [--batch-size 128] [--lr 0.1] [--momentum 0.9] [--wd 1e-4]

Differentiable Group Learning

# ResNet20 with group 4, lambda=2e-3
python cifar_dsp.py -l 20 -g 4 -r 2e-3

# ResNet20 with group 2, lambda=2e-3
python cifar_dsp.py -l 20 -g 2 -r 2e-3

# ResNet56 with group 4, lambda=5e-4
python cifar_dsp.py -l 56 -g 4 -r 5e-4

Group Channel Pruning

# ResNet20 with group 4, pruning rate=0.5
python cifar_finetune.py -l 20 -g 4 -p 0.5

# ResNet56 with group 4, pruning rate=0.5
python cifar_finetune.py -l 56 -g 4 -p 0.5

Packing Pruned Models

python pack_model.py --ckpt [pruned_model_path] --save [save_path]

We upload checkpoints that show the median accuracy of five runs

Model	ACC	P.FLOPS	P.PARAMS	CKPT
ResNet20 (g=4)	92.22	63.57	50.45	Link
ResNet20 (g=3)	92.14	62.43	49.15	Link
ResNet20 (g=2)	92.07	61.23	48.35	Link
ResNet56 (g=4)	94.25	65.11	56.30	Link
ResNet56 (g=3)	94.07	64.14	55.01	Link
ResNet56 (g=2)	93.99	63.26	56.24	Link

How to use checkpoints

import torch
cnn = torch.jit.load('[CKPT_PATH]')
# You can use TensorRT or torch.jit.optimize_for_inference to achieve further acceleration.

We slightly changed the implementation of regularization scaling to obtain better speedup.

As a result, pruned results may be different from those in the paper (usually more pruned FLOPS and fewer pruned parameters).

Pruning on ImageNet

Pruned Models

Model	Top-1 ACC	P.FLOPS	P.PARAMS	CKPT
ResNet18 (g=2)	69.55 67.95 65.81	60.08 70.05 80.00	45.70 57.25 69.92	Link Link Link
ResNet50 (g=2)	76.54 75.45 73.29	70.00 80.03 90.02	52.54 66.00 81.64	Link Link Link

TODO

• Implement model-agnostic pruner
• Release ImageNet models