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SNIP-it / SNAP-it

(Un)structured Pruning via Iterative Ranking of Sensitivity Statistics

This repository is the official implementation of the paper Pruning via Iterative Ranking of Sensitivity Statistics. Currently under review. Please use this preliminary BibTex entry when referring to our work:

@article{verdenius2020pruning,
       author = {{Verdenius}, Stijn and {Stol}, Maarten and {Forr{\'e}}, Patrick},
        title = "{Pruning via Iterative Ranking of Sensitivity Statistics}",
      journal = {arXiv e-prints},
     keywords = {Computer Science - Machine Learning, Statistics - Machine Learning},
         year = 2020,
        month = jun,
          eid = {arXiv:2006.00896},
        pages = {arXiv:2006.00896},
archivePrefix = {arXiv},
       eprint = {2006.00896},
 primaryClass = {cs.LG},
}

Content

The repository implements novel pruning / compression algorithms for deep learning / neural networks. Additionally, it implements the shrinkage of actual tensors to really benefit from structured pruning without external hardware libraries. We implement:

Structured (node) pruning before training
Structured (node) pruning during training
Unstructured (weight) pruning before training
Unstructured (weight) pruning during training

Setup

Install virtualenv

pip3 install virtualenv

Create environment

virtualenv -p python3 ~/virtualenvs/SNIPIT

Activate environment

source ~/virtualenvs/SNIPIT/bin/activate

Install requirements:

pip install -r requirements.txt

If you mean to run the 'Imagenette' dataset: download that from here and unpack in /gitignored/data/, then replace CIFAR10 with IMAGENETTE below to run. Additional datasets can be added in a similar way (Imagewoof, tiny-imagenet, etc.)

Training Examples & Results

Some examples of training the models from the paper.

Structured Pruning (SNAP-it)

To run training for SNAP-it - our structured pruning before training algorithm - with a VGG16 on CIFAR10, run the following:

python3 main.py --model VGG16 --data_set CIFAR10 --prune_criterion SNAPit --pruning_limit 0.93 --epochs 80

accuracy-drop	weight sparsity	node sparsity	cumulative training FLOPS reduction
-1%	99%	93%	8 times

Unstructured Pruning (SNIP-it)

To run training for SNIP-it - our unstructured pruning algorithm - with a ResNet18 on CIFAR10, run one of the following:

## during training
python3 main.py --model ResNet18 --data_set CIFAR10 --prune_criterion SNIPitDuring --pruning_limit 0.98 --outer_layer_pruning --epochs 80 --prune_delay 4 --prune_freq 4
## before training
python3 main.py --model ResNet18 --data_set CIFAR10 --prune_criterion SNIPit --pruning_limit 0.98 --outer_layer_pruning --epochs 80

	accuracy-drop	weight sparsity
SNIP-it (during)	-0%	98%
SNIP-it (before)	-4%	98%

Adversarial Evaluation

To evaluate a model on adversarial attacks (for now only supported on unstructured), run:

python main.py --eval --model MLP5 --data_set MNIST --checkpoint_name <see_results_folder> --checkpoint_model MLP5_finished --attack CarliniWagner

Visualization

Results and saved models will be logged to the terminal, logfiles in result-folders and in tensorboard files in the /gitignored/results/ folder. To run tensorboard's interface run the following:

tensorboard --logdir ./gitignored/results/

Arguments

The regular arguments for running are the following. Additionally, there are some more found in utils/config_utils.py.

argument	description	type
--model	The neural network architecture from `/models/networks/`	str
--data_set	The dataset from `/utils/dataloaders.py`	str
--prune_criterion	The pruning criterion from `/models/criterions/`	str
--batch_size	The batch size	int
--optimizer	The optimizer model class from [ADAM, SGD & RMSPROP]	str
--loss	The loss function from `/models/losses/`	str
--train_scheme	The training scheme from `/models/trainers/` (if applicable)	str
--test_scheme	The testing scheme from `/models/testers/` (if applicable)	str
--eval	Add to run in test mode	bool
--attack	Name of adersarial attack if that is the test_scheme	str
--device	Device [cuda or cpu]	srt
--run_name	Extra run identification for generated run folder	str
--checkpoint_name	Load from this checkpoint folder if not None	str
--checkpoint_model	Load this model from checkpoint_name	str
--outer_layer_pruning	Prunes outer layers too. Use iff pruning unstructured	bool
--enable_rewinding	Does rewinding of weights (for IMP)	bool
--rewind_epoch	Epoch to rewind to	int
--l0	Run with L0-reg layers, overrides some other options	bool
--l0_reg	L0 regularisation hyperparameter	float
--hoyer_square	Run with hoyersquare, overrides some other options	bool
--group_hoyer_square	Run with grouphoyersquare, overrides some other options	bool
--hoyer_reg	Hoyer regularisation hyperparameter	float
--learning_rate	Learning rate	float
--pruning_limit	Final sparsity endeavour for applicable pruning criterions	float
--pruning_rate	Outdated pruning_limit, still used for UnstructuredRandom	float
--snip_steps	`S` from paper algorithm box 1. Number of pruning steps	int
--epochs	How long to train for	int
--prune_delay	`Tau` from paper algorithm box 1. How long to start pruning	int
--prune_freq	`Tau` from paper algorithm box 1 again. How often to prune	int
--seed	Random seed to run with	int
--tuning	Run with train and held out validationset, omit testset	bool

Some notes:

please note that as of now, residual connections (e.g. ResNets) and structured pruning are not supported together.

please note that as of now, structured pruning and --outer_layer_pruning are not supported together.

please note that as of now, if running unstructured pruning, you should also run with --outer_layer_pruning.

Codebase Design

Codebase is built modularly so that every criterion or model that is added to its designated folder, provided its filename is equal to its classname, can be ran via string argument immediately. This way its easily extendable.
The same goes for training schemes; implemented here as classes and automatically loaded in by string reference. When you need new functionality concerning one aspect of training you can simply inheret the DefaultTrainer and then override only that function you need differently. Alternatively, you can make your own training scheme, the sky is the limit!
All entry-points go through main.py, where the required models are loaded and thereafter redirected to the right training or testing scheme.
All results show up at the path /gitignored/results/ in its own (date-stamped) folder. In here you find a copy of the codebase at the time of execution, its calling command, tensorboard output, saved models and logs.
In the file utils/autoconfig.json certain automatic configurations get set to make it easier to run different models in sequence. You can disable this with --disable_autoconfig, but it is strongly recommended against.

How to run the other baselines

## unpruned baselines
python3 main.py --model VGG16 --data_set CIFAR10 --prune_criterion EmptyCrit --epochs 80 --pruning_limit 0.0
python3 main.py --model ResNet18 --data_set CIFAR10 --prune_criterion EmptyCrit --epochs 80 --pruning_limit 0.0

## structured baselines
python3 main.py --model VGG16 --data_set CIFAR10 --prune_criterion StructuredRandom --pruning_limit 0.93 --epochs 80
python3 main.py --model VGG16 --data_set CIFAR10 --prune_criterion GateDecorators --pruning_limit 0.93 --epochs 70 --checkpoint_name <unpruned_results_folder> --checkpoint_model VGG16_finished 
python3 main.py --model VGG16 --data_set CIFAR10 --prune_criterion EfficientConvNets --pruning_limit 0.93 --epochs 80 --prune_delay 69 --prune_freq 1
python3 main.py --model VGG16 --data_set CIFAR10 --prune_criterion GroupHoyerSquare --hoyer_reg <REG> --epochs 80 --prune_delay 69 --prune_freq 1 --group_hoyer_square
python3 main.py --model VGG16 --data_set CIFAR10 --l0_reg <REG> --epochs 160 --l0

## unstructured baselines
python3 main.py --model ResNet18 --data_set CIFAR10 --prune_criterion UnstructuredRandom --pruning_rate 0.98 --pruning_limit 0.98 --outer_layer_pruning --epochs 80
python3 main.py --model ResNet18 --data_set CIFAR10 --prune_criterion <SNIP or GRASP> --pruning_limit 0.98 --outer_layer_pruning --epochs 80
python3 main.py --model ResNet18 --data_set CIFAR10 --prune_criterion HoyerSquare --hoyer_reg <REG> --outer_layer_pruning --epochs 80 --prune_delay 69 --prune_freq 1 --hoyer_square
python3 main.py --model ResNet18 --data_set CIFAR10 --prune_criterion IMP --pruning_limit 0.98 --outer_layer_pruning --epochs 80 --prune_delay 4 --prune_freq 4 --enable_rewinding --rewind_to 6

Licence

MIT Licence