A pytorch pruning toolkit for structured neural network pruning and layer dependency maintaining.
A pytorch toolkit for structured neural network pruning and layer dependency maintaining
This tool will automatically detect and handle layer dependencies (channel consistency) during pruning. It is able to handle various network architectures such as DenseNet, ResNet, and Inception. See examples/test_models.py for more supported models.
This package will run your model with fake inputs and collect forward information just like
torch.jit. Then a dependency graph is established to describe the computational graph. When a pruning function (e.g. torchpruning.pruneconv ) is applied on certain layer through
DependencyGraph.get_pruning_plan, this package will traverse the whole graph to fix inconsistent modules such as BN. The pruning index will be automatically mapped to correct position if there is
torch.splitor
torch.catin your model.
Tip: please remember to save the whole model object (weights+architecture) rather than model weights only:
# save a pruned model # torch.save(model.state_dict(), 'model.pth') # weights only torch.save(model, 'model.pth') # obj (arch) + weightsload a pruned model
model = torch.load('model.pth') # no load_state_dict
| Dependency | Visualization | Example |
| :------------------: | :------------: | :-----: |
| Conv-Conv | | AlexNet |
| Conv-FC (Global Pooling or Flatten) |
| ResNet, VGG |
| Skip Connection | | ResNet
| Concatenation |
| DenseNet, ASPP |
| Split |
| torch.chunk |
Known Issues:
groups=
in_channels=
out_channels.
torch.autograd.Function.
pip install torch_pruning
import torch from torchvision.models import resnet18 import torch_pruning as tpmodel = resnet18(pretrained=True)
1. setup strategy (L1 Norm)
strategy = tp.strategy.L1Strategy() # or tp.strategy.RandomStrategy()
2. build layer dependency for resnet18
DG = tp.DependencyGraph() DG.build_dependency(model, example_inputs=torch.randn(1,3,224,224))
3. get a pruning plan from the dependency graph.
pruning_idxs = strategy(model.conv1.weight, amount=0.4) # or manually selected pruning_idxs=[2, 6, 9] pruning_plan = DG.get_pruning_plan( model.conv1, tp.prune_conv, idxs=pruning_idxs ) print(pruning_plan)
4. execute this plan (prune the model)
pruning_plan.exec()
Pruning the resnet.conv1 will affect several layers. Let's inspect the pruning plan (with pruning_idxs=[2, 6, 9]):
------------- [ prune_conv on conv1 (Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False))>, Index=[2, 6, 9], NumPruned=441] [ prune_batchnorm on bn1 (BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True))>, Index=[2, 6, 9], NumPruned=6] [ _prune_elementwise_op on _ElementWiseOp()>, Index=[2, 6, 9], NumPruned=0] [ _prune_elementwise_op on _ElementWiseOp()>, Index=[2, 6, 9], NumPruned=0] [ prune_related_conv on layer1.0.conv1 (Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False))>, Index=[2, 6, 9], NumPruned=1728] [ _prune_elementwise_op on _ElementWiseOp()>, Index=[2, 6, 9], NumPruned=0] [ prune_batchnorm on layer1.0.bn2 (BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True))>, Index=[2, 6, 9], NumPruned=6] [ prune_conv on layer1.0.conv2 (Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False))>, Index=[2, 6, 9], NumPruned=1728] [ _prune_elementwise_op on _ElementWiseOp()>, Index=[2, 6, 9], NumPruned=0] [ prune_related_conv on layer1.1.conv1 (Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False))>, Index=[2, 6, 9], NumPruned=1728] [ _prune_elementwise_op on _ElementWiseOp()>, Index=[2, 6, 9], NumPruned=0] [ prune_batchnorm on layer1.1.bn2 (BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True))>, Index=[2, 6, 9], NumPruned=6] [ prune_conv on layer1.1.conv2 (Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False))>, Index=[2, 6, 9], NumPruned=1728] [ _prune_elementwise_op on _ElementWiseOp()>, Index=[2, 6, 9], NumPruned=0] [ prune_related_conv on layer2.0.conv1 (Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False))>, Index=[2, 6, 9], NumPruned=3456] [ prune_related_conv on layer2.0.downsample.0 (Conv2d(64, 128, kernel_size=(1, 1), stride=(2, 2), bias=False))>, Index=[2, 6, 9], NumPruned=384] 11211 parameters will be pruned -------------
We have to manually handle the broken dependencies without DependencyGraph.
tp.prune_conv( model.conv1, idxs=[2,6,9] )fix the broken dependencies manually
tp.prune_batchnorm( model.bn1, idxs=[2,6,9] ) tp.prune_related_conv( model.layer2[0].conv1, idxs=[2,6,9] ) ...
Please refer to 'examples/customize_layer.py' for pruning customized layers with this package. A detailed tutorial is on the way!
During structured pruning, we need to maintain the channel consistency between different layers.
the layer dependency becomes much more complicated when the model contains skip connections or concatenations.
See paper Pruning Filters for Efficient ConvNets for more details.
cd examples python prune_resnet18_cifar10.py --mode train # 11.1M, Acc=0.9248
python prune_resnet18_cifar10.py --mode prune --round 1 --total_epochs 30 --step_size 20 # 4.5M, Acc=0.9229 python prune_resnet18_cifar10.py --mode prune --round 2 --total_epochs 30 --step_size 20 # 1.9M, Acc=0.9207 python prune_resnet18_cifar10.py --mode prune --round 3 --total_epochs 30 --step_size 20 # 0.8M, Acc=0.9176 python prune_resnet18_cifar10.py --mode prune --round 4 --total_epochs 30 --step_size 20 # 0.4M, Acc=0.9102 python prune_resnet18_cifar10.py --mode prune --round 5 --total_epochs 30 --step_size 20 # 0.2M, Acc=0.9011 ...