We provide several pruning algorithms that support fine-grained weight pruning and structural filter pruning. Fine-grained Pruning generally results in unstructured models, which need specialized hardware or software to speed up the sparse network. Filter Pruning achieves acceleration by removing the entire filter. Some pruning algorithms use one-shot method that prune weights at once based on an importance metric. Other pruning algorithms control the pruning schedule that prune weights during optimization, including some automatic pruning algorithms.
Fine-grained Pruning
Filter Pruning
- Slim Pruner
- FPGM Pruner
- L1Filter Pruner
- L2Filter Pruner
- Activation APoZ Rank Filter Pruner
- Activation Mean Rank Filter Pruner
- Taylor FO On Weight Pruner
Pruning Schedule
- AGP Pruner
- NetAdapt Pruner
- SimulatedAnnealing Pruner
- AutoCompress Pruner
- AMC Pruner
- Sensitivity Pruner
Others
This is one basic one-shot pruner: you can set a target sparsity level (expressed as a fraction, 0.6 means we will prune 60% of the weight parameters).
We first sort the weights in the specified layer by their absolute values. And then mask to zero the smallest magnitude weights until the desired sparsity level is reached.
PyTorch code
from nni.algorithms.compression.pytorch.pruning import LevelPruner
config_list = [{ 'sparsity': 0.8, 'op_types': ['default'] }]
pruner = LevelPruner(model, config_list)
pruner.compress()PyTorch
.. autoclass:: nni.algorithms.compression.pytorch.pruning.LevelPruner
TensorFlow
.. autoclass:: nni.algorithms.compression.tensorflow.pruning.LevelPruner
This is an one-shot pruner, which adds sparsity regularization on the scaling factors of batch normalization (BN) layers during training to identify unimportant channels. The channels with small scaling factor values will be pruned. For more details, please refer to 'Learning Efficient Convolutional Networks through Network Slimming'.
PyTorch code
from nni.algorithms.compression.pytorch.pruning import SlimPruner
config_list = [{ 'sparsity': 0.8, 'op_types': ['BatchNorm2d'] }]
pruner = SlimPruner(model, config_list)
pruner.compress()PyTorch
.. autoclass:: nni.algorithms.compression.pytorch.pruning.SlimPruner
We implemented one of the experiments in Learning Efficient Convolutional Networks through Network Slimming, we pruned 70% channels in the VGGNet for CIFAR-10 in the paper, in which 88.5% parameters are pruned. Our experiments results are as follows:
| Model | Error(paper/ours) | Parameters | Pruned |
|---|---|---|---|
| VGGNet | 6.34/6.69 | 20.04M | |
| Pruned-VGGNet | 6.20/6.34 | 2.03M | 88.5% |
The experiments code can be found at :githublink:`examples/model_compress/pruning/basic_pruners_torch.py <examples/model_compress/pruning/basic_pruners_torch.py>`
python basic_pruners_torch.py --pruner slim --model vgg19 --sparsity 0.7 --speed-upThis is an one-shot pruner, which prunes filters with the smallest geometric median. FPGM chooses the filters with the most replaceable contribution. For more details, please refer to Filter Pruning via Geometric Median for Deep Convolutional Neural Networks Acceleration.
We also provide a dependency-aware mode for this pruner to get better speedup from the pruning. Please reference dependency-aware for more details.
PyTorch code
from nni.algorithms.compression.pytorch.pruning import FPGMPruner
config_list = [{
'sparsity': 0.5,
'op_types': ['Conv2d']
}]
pruner = FPGMPruner(model, config_list)
pruner.compress()PyTorch
.. autoclass:: nni.algorithms.compression.pytorch.pruning.FPGMPruner
This is an one-shot pruner, which prunes the filters in the convolution layers.
For more details, please refer to PRUNING FILTERS FOR EFFICIENT CONVNETS.
In addition, we also provide a dependency-aware mode for the L1FilterPruner. For more details about the dependency-aware mode, please reference dependency-aware mode.
PyTorch code
from nni.algorithms.compression.pytorch.pruning import L1FilterPruner
config_list = [{ 'sparsity': 0.8, 'op_types': ['Conv2d'] }]
pruner = L1FilterPruner(model, config_list)
pruner.compress()PyTorch
.. autoclass:: nni.algorithms.compression.pytorch.pruning.L1FilterPruner
We implemented one of the experiments in PRUNING FILTERS FOR EFFICIENT CONVNETS with L1FilterPruner, we pruned VGG-16 for CIFAR-10 to VGG-16-pruned-A in the paper, in which 64% parameters are pruned. Our experiments results are as follows:
| Model | Error(paper/ours) | Parameters | Pruned |
|---|---|---|---|
| VGG-16 | 6.75/6.49 | 1.5x10^7 | |
| VGG-16-pruned-A | 6.60/6.47 | 5.4x10^6 | 64.0% |
The experiments code can be found at :githublink:`examples/model_compress/pruning/basic_pruners_torch.py <examples/model_compress/pruning/basic_pruners_torch.py>`
python basic_pruners_torch.py --pruner l1filter --model vgg16 --speed-upThis is a structured pruning algorithm that prunes the filters with the smallest L2 norm of the weights. It is implemented as a one-shot pruner.
We also provide a dependency-aware mode for this pruner to get better speedup from the pruning. Please reference dependency-aware for more details.
PyTorch code
from nni.algorithms.compression.pytorch.pruning import L2FilterPruner
config_list = [{ 'sparsity': 0.8, 'op_types': ['Conv2d'] }]
pruner = L2FilterPruner(model, config_list)
pruner.compress()PyTorch
.. autoclass:: nni.algorithms.compression.pytorch.pruning.L2FilterPruner
ActivationAPoZRankFilter Pruner is a pruner which prunes the filters with the smallest importance criterion APoZ calculated from the output activations of convolution layers to achieve a preset level of network sparsity. The pruning criterion APoZ is explained in the paper Network Trimming: A Data-Driven Neuron Pruning Approach towards Efficient Deep Architectures.
The APoZ is defined as:
APoZ_{c}^{(i)} = APoZ\left(O_{c}^{(i)}\right)=\frac{\sum_{k}^{N} \sum_{j}^{M} f\left(O_{c, j}^{(i)}(k)=0\right)}{N \times M}
We also provide a dependency-aware mode for this pruner to get better speedup from the pruning. Please reference dependency-aware for more details.
PyTorch code
from nni.algorithms.compression.pytorch.pruning import ActivationAPoZRankFilterPruner
config_list = [{
'sparsity': 0.5,
'op_types': ['Conv2d']
}]
pruner = ActivationAPoZRankFilterPruner(model, config_list, statistics_batch_num=1)
pruner.compress()Note: ActivationAPoZRankFilterPruner is used to prune convolutional layers within deep neural networks, therefore the op_types field supports only convolutional layers.
You can view :githublink:`example <examples/model_compress/pruning/basic_pruners_torch.py>` for more information.
PyTorch
.. autoclass:: nni.algorithms.compression.pytorch.pruning.ActivationAPoZRankFilterPruner
ActivationMeanRankFilterPruner is a pruner which prunes the filters with the smallest importance criterion mean activation calculated from the output activations of convolution layers to achieve a preset level of network sparsity. The pruning criterion mean activation is explained in section 2.2 of the paper Pruning Convolutional Neural Networks for Resource Efficient Inference. Other pruning criteria mentioned in this paper will be supported in future release.
We also provide a dependency-aware mode for this pruner to get better speedup from the pruning. Please reference dependency-aware for more details.
PyTorch code
from nni.algorithms.compression.pytorch.pruning import ActivationMeanRankFilterPruner
config_list = [{
'sparsity': 0.5,
'op_types': ['Conv2d']
}]
pruner = ActivationMeanRankFilterPruner(model, config_list, statistics_batch_num=1)
pruner.compress()Note: ActivationMeanRankFilterPruner is used to prune convolutional layers within deep neural networks, therefore the op_types field supports only convolutional layers.
You can view :githublink:`example <examples/model_compress/pruning/basic_pruners_torch.py>` for more information.
PyTorch
.. autoclass:: nni.algorithms.compression.pytorch.pruning.ActivationMeanRankFilterPruner
TaylorFOWeightFilter Pruner is a pruner which prunes convolutional layers based on estimated importance calculated from the first order taylor expansion on weights to achieve a preset level of network sparsity. The estimated importance of filters is defined as the paper Importance Estimation for Neural Network Pruning. Other pruning criteria mentioned in this paper will be supported in future release.
\widehat{\mathcal{I}}_{\mathcal{S}}^{(1)}(\mathbf{W}) \triangleq \sum_{s \in \mathcal{S}} \mathcal{I}_{s}^{(1)}(\mathbf{W})=\sum_{s \in \mathcal{S}}\left(g_{s} w_{s}\right)^{2}
We also provide a dependency-aware mode for this pruner to get better speedup from the pruning. Please reference dependency-aware for more details.
PyTorch code
from nni.algorithms.compression.pytorch.pruning import TaylorFOWeightFilterPruner
config_list = [{
'sparsity': 0.5,
'op_types': ['Conv2d']
}]
pruner = TaylorFOWeightFilterPruner(model, config_list, statistics_batch_num=1)
pruner.compress()PyTorch
.. autoclass:: nni.algorithms.compression.pytorch.pruning.TaylorFOWeightFilterPruner
This is an iterative pruner, which the sparsity is increased from an initial sparsity value si (usually 0) to a final sparsity value sf over a span of n pruning steps, starting at training step t_{0} and with pruning frequency \Delta t:
s_{t}=s_{f}+\left(s_{i}-s_{f}\right)\left(1-\frac{t-t_{0}}{n \Delta t}\right)^{3} \text { for } t \in\left\{t_{0}, t_{0}+\Delta t, \ldots, t_{0} + n \Delta t\right\}
For more details please refer to To prune, or not to prune: exploring the efficacy of pruning for model compression.
You can prune all weights from 0% to 80% sparsity in 10 epoch with the code below.
PyTorch code
from nni.algorithms.compression.pytorch.pruning import AGPPruner
config_list = [{
'initial_sparsity': 0,
'final_sparsity': 0.8,
'start_epoch': 0,
'end_epoch': 10,
'frequency': 1,
'op_types': ['default']
}]
# load a pretrained model or train a model before using a pruner
# model = MyModel()
# model.load_state_dict(torch.load('mycheckpoint.pth'))
# AGP pruner prunes model while fine tuning the model by adding a hook on
# optimizer.step(), so an optimizer is required to prune the model.
optimizer = torch.optim.SGD(model.parameters(), lr=0.001, momentum=0.9, weight_decay=1e-4)
pruner = AGPPruner(model, config_list, optimizer, pruning_algorithm='level')
pruner.compress()AGP pruner uses LevelPruner algorithms to prune the weight by default, however you can set pruning_algorithm parameter to other values to use other pruning algorithms:
level: LevelPrunerslim: SlimPrunerl1: L1FilterPrunerl2: L2FilterPrunerfpgm: FPGMPrunertaylorfo: TaylorFOWeightFilterPrunerapoz: ActivationAPoZRankFilterPrunermean_activation: ActivationMeanRankFilterPruner
You should add code below to update epoch number when you finish one epoch in your training code.
PyTorch code
pruner.update_epoch(epoch)PyTorch
.. autoclass:: nni.algorithms.compression.pytorch.pruning.AGPPruner
NetAdapt allows a user to automatically simplify a pretrained network to meet the resource budget. Given the overall sparsity, NetAdapt will automatically generate the sparsities distribution among different layers by iterative pruning.
For more details, please refer to NetAdapt: Platform-Aware Neural Network Adaptation for Mobile Applications.
PyTorch code
from nni.algorithms.compression.pytorch.pruning import NetAdaptPruner
config_list = [{
'sparsity': 0.5,
'op_types': ['Conv2d']
}]
pruner = NetAdaptPruner(model, config_list, short_term_fine_tuner=short_term_fine_tuner, evaluator=evaluator,base_algo='l1', experiment_data_dir='./')
pruner.compress()You can view :githublink:`example <examples/model_compress/pruning/auto_pruners_torch.py>` for more information.
PyTorch
.. autoclass:: nni.algorithms.compression.pytorch.pruning.NetAdaptPruner
We implement a guided heuristic search method, Simulated Annealing (SA) algorithm, with enhancement on guided search based on prior experience. The enhanced SA technique is based on the observation that a DNN layer with more number of weights often has a higher degree of model compression with less impact on overall accuracy.
- Randomly initialize a pruning rate distribution (sparsities).
- While current_temperature < stop_temperature:
- generate a perturbation to current distribution
- Perform fast evaluation on the perturbated distribution
- accept the perturbation according to the performance and probability, if not accepted, return to step 1
- cool down, current_temperature <- current_temperature * cool_down_rate
For more details, please refer to AutoCompress: An Automatic DNN Structured Pruning Framework for Ultra-High Compression Rates.
PyTorch code
from nni.algorithms.compression.pytorch.pruning import SimulatedAnnealingPruner
config_list = [{
'sparsity': 0.5,
'op_types': ['Conv2d']
}]
pruner = SimulatedAnnealingPruner(model, config_list, evaluator=evaluator, base_algo='l1', cool_down_rate=0.9, experiment_data_dir='./')
pruner.compress()You can view :githublink:`example <examples/model_compress/pruning/auto_pruners_torch.py>` for more information.
PyTorch
.. autoclass:: nni.algorithms.compression.pytorch.pruning.SimulatedAnnealingPruner
For each round, AutoCompressPruner prune the model for the same sparsity to achive the overall sparsity:
1. Generate sparsities distribution using SimulatedAnnealingPruner
2. Perform ADMM-based structured pruning to generate pruning result for the next round.
Here we use `speedup` to perform real pruning.For more details, please refer to AutoCompress: An Automatic DNN Structured Pruning Framework for Ultra-High Compression Rates.
PyTorch code
from nni.algorithms.compression.pytorch.pruning import AutoCompressPruner
config_list = [{
'sparsity': 0.5,
'op_types': ['Conv2d']
}]
pruner = AutoCompressPruner(
model, config_list, trainer=trainer, evaluator=evaluator,
dummy_input=dummy_input, num_iterations=3, optimize_mode='maximize', base_algo='l1',
cool_down_rate=0.9, admm_num_iterations=30, admm_training_epochs=5, experiment_data_dir='./')
pruner.compress()You can view :githublink:`example <examples/model_compress/pruning/auto_pruners_torch.py>` for more information.
PyTorch
.. autoclass:: nni.algorithms.compression.pytorch.pruning.AutoCompressPruner
AMC pruner leverages reinforcement learning to provide the model compression policy. This learning-based compression policy outperforms conventional rule-based compression policy by having higher compression ratio, better preserving the accuracy and freeing human labor.
For more details, please refer to AMC: AutoML for Model Compression and Acceleration on Mobile Devices.
PyTorch code
from nni.algorithms.compression.pytorch.pruning import AMCPruner
config_list = [{
'op_types': ['Conv2d', 'Linear']
}]
pruner = AMCPruner(model, config_list, evaluator, val_loader, flops_ratio=0.5)
pruner.compress()You can view :githublink:`example <examples/model_compress/pruning/amc/>` for more information.
PyTorch
.. autoclass:: nni.algorithms.compression.pytorch.pruning.AMCPruner
We implemented one of the experiments in AMC: AutoML for Model Compression and Acceleration on Mobile Devices, we pruned MobileNet to 50% FLOPS for ImageNet in the paper. Our experiments results are as follows:
| Model | Top 1 acc.(paper/ours) | Top 5 acc. (paper/ours) | FLOPS |
|---|---|---|---|
| MobileNet | 70.5% / 69.9% | 89.3% / 89.1% | 50% |
The experiments code can be found at :githublink:`examples/model_compress/pruning/ <examples/model_compress/pruning/amc/>`
Alternating Direction Method of Multipliers (ADMM) is a mathematical optimization technique, by decomposing the original nonconvex problem into two subproblems that can be solved iteratively. In weight pruning problem, these two subproblems are solved via 1) gradient descent algorithm and 2) Euclidean projection respectively.
During the process of solving these two subproblems, the weights of the original model will be changed. An one-shot pruner will then be applied to prune the model according to the config list given.
This solution framework applies both to non-structured and different variations of structured pruning schemes.
For more details, please refer to A Systematic DNN Weight Pruning Framework using Alternating Direction Method of Multipliers.
PyTorch code
from nni.algorithms.compression.pytorch.pruning import ADMMPruner
config_list = [{
'sparsity': 0.8,
'op_types': ['Conv2d'],
'op_names': ['conv1']
}, {
'sparsity': 0.92,
'op_types': ['Conv2d'],
'op_names': ['conv2']
}]
pruner = ADMMPruner(model, config_list, trainer=trainer, num_iterations=30, epochs=5)
pruner.compress()You can view :githublink:`example <examples/model_compress/pruning/auto_pruners_torch.py>` for more information.
PyTorch
.. autoclass:: nni.algorithms.compression.pytorch.pruning.ADMMPruner
The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks, authors Jonathan Frankle and Michael Carbin,provides comprehensive measurement and analysis, and articulate the lottery ticket hypothesis: dense, randomly-initialized, feed-forward networks contain subnetworks (winning tickets) that -- when trained in isolation -- reach test accuracy comparable to the original network in a similar number of iterations.
In this paper, the authors use the following process to prune a model, called iterative prunning:
- Randomly initialize a neural network f(x;theta_0) (where theta0 follows D{theta}).
- Train the network for j iterations, arriving at parameters theta_j.
- Prune p% of the parameters in theta_j, creating a mask m.
- Reset the remaining parameters to their values in theta_0, creating the winning ticket f(x;m*theta_0).
- Repeat step 2, 3, and 4.
If the configured final sparsity is P (e.g., 0.8) and there are n times iterative pruning, each iterative pruning prunes 1-(1-P)^(1/n) of the weights that survive the previous round.
PyTorch code
from nni.algorithms.compression.pytorch.pruning import LotteryTicketPruner
config_list = [{
'prune_iterations': 5,
'sparsity': 0.8,
'op_types': ['default']
}]
pruner = LotteryTicketPruner(model, config_list, optimizer)
pruner.compress()
for _ in pruner.get_prune_iterations():
pruner.prune_iteration_start()
for epoch in range(epoch_num):
...The above configuration means that there are 5 times of iterative pruning. As the 5 times iterative pruning are executed in the same run, LotteryTicketPruner needs model and optimizer (Note that should add ``lr_scheduler`` if used) to reset their states every time a new prune iteration starts. Please use get_prune_iterations to get the pruning iterations, and invoke prune_iteration_start at the beginning of each iteration. epoch_num is better to be large enough for model convergence, because the hypothesis is that the performance (accuracy) got in latter rounds with high sparsity could be comparable with that got in the first round.
PyTorch
.. autoclass:: nni.algorithms.compression.pytorch.pruning.LotteryTicketPruner
We try to reproduce the experiment result of the fully connected network on MNIST using the same configuration as in the paper. The code can be referred :githublink:`here <examples/model_compress/pruning/lottery_torch_mnist_fc.py>`. In this experiment, we prune 10 times, for each pruning we train the pruned model for 50 epochs.
The above figure shows the result of the fully connected network. round0-sparsity-0.0 is the performance without pruning. Consistent with the paper, pruning around 80% also obtain similar performance compared to non-pruning, and converges a little faster. If pruning too much, e.g., larger than 94%, the accuracy becomes lower and convergence becomes a little slower. A little different from the paper, the trend of the data in the paper is relatively more clear.
For each round, SensitivityPruner prunes the model based on the sensitivity to the accuracy of each layer until meeting the final configured sparsity of the whole model:
1. Analyze the sensitivity of each layer in the current state of the model.
2. Prune each layer according to the sensitivity.For more details, please refer to Learning both Weights and Connections for Efficient Neural Networks.
PyTorch code
from nni.algorithms.compression.pytorch.pruning import SensitivityPruner
config_list = [{
'sparsity': 0.5,
'op_types': ['Conv2d']
}]
pruner = SensitivityPruner(model, config_list, finetuner=fine_tuner, evaluator=evaluator)
# eval_args and finetune_args are the parameters passed to the evaluator and finetuner respectively
pruner.compress(eval_args=[model], finetune_args=[model])PyTorch
.. autoclass:: nni.algorithms.compression.pytorch.pruning.SensitivityPruner