torch-pruning 0.2.8

A pytorch toolkit for structured neural network pruning and automatic layer dependency maintaining.

Torch-Pruning

Structural Pruning for Model Acceleration

Torch-Pruning is a general-purpose library for structural network pruning, which supports a large variaty of nerual networks like Vision Transformers, ResNet, DenseNet, RegNet, ResNext, FCN, DeepLab, VGG, etc. Please refer to tests/test_torchvision_models.py for more details about prunable models.

Pruning is a popular approach to reduce the heavy computational cost of neural networks by removing redundancies. Existing pruning methods prune networks in a case-by-case way, i.e., writing different code for different models. However, with the network designs being more and more complicated, applying traditional pruning algorithms to modern neural networks is very difficult. One of the most prominent problems in pruning comes from layer dependencies, where several layers are coupled and must be pruned simultaneously to guarantee the correctness of networks. This project provides the ability of detecting and handling layer dependencies, which allows us to prune complicated networks without too much human effort.

Features:

• Channel pruning for CNNs (e.g. ResNet, DenseNet, Deeplab) and Transformers (e.g. ViT)
• Graph Tracing and dependency fixing.
• Supported modules: Conv, Linear, BatchNorm, LayerNorm, Transposed Conv, PReLU, Embedding, MultiheadAttention, nn.Parameters and customized modules.
• Supported operations: split, concatenation, skip connection, flatten, etc.
• Pruning strategies: Random, L1, L2, etc.
• Low-level pruning functions

Updates:

02/07/2022 The latest version is under development in branch v1.0.

24/03/2022 We are drafting a paper to provide more technical details about this repo, which will be released as soon as possible, together with a new version and some practical examples for yolo and other popular networks.

How it works

Torch-Pruning will forward your model with a fake inputs and trace the computational graph just like torch.jit. A dependency graph will be established to record the relation coupling between layers. Torch-pruning will collect all affected layers according by propogating your pruning operations through the whole graph, and then return a PruningPlan for pruning. All pruning indices will be automatically transformed if there are operations like torch.split or torch.cat.

Installation

pip install torch_pruning # v0.2.7

Known Issues:

• When groups>1, only depthwise conv is supported, i.e. groups=in_channels=out_channels.
• Customized operations will be treated as element-wise op, e.g. subclass of torch.autograd.Function.

Quickstart

0. Dependenies

Dependency	Visualization	Example
Conv-Conv		AlexNet
Conv-FC (Global Pooling or Flatten)		ResNet, VGG
Skip Connection		ResNet
Concatenation		DenseNet, ASPP
Split		torch.chunk

1. A minimal example

import torch
from torchvision.models import resnet18
import torch_pruning as tp

model = resnet18(pretrained=True).eval()

# 1. setup strategy (L1 Norm)
strategy = tp.strategy.L1Strategy() # or tp.strategy.RandomStrategy()

# 2. build dependency graph 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 pruning_idxs=[2, 6, 9, ...]
pruning_plan = DG.get_pruning_plan( model.conv1, tp.prune_conv_out_channel, idxs=pruning_idxs )
print(pruning_plan)

# 4. execute this plan after checking (prune the model)
#    if the plan prunes some channels to zero, 
#    DG.check_pruning plan will return False.
if DG.check_pruning_plan(pruning_plan):
    pruning_plan.exec()

Pruning the resnet.conv1 will affect several layers. Let's inspect the pruning plan (with pruning_idxs=[2, 6, 9]). It return the pruning details and the total amount of pruned parameters. You can also customize the metrics following test_metrics.py.

--------------------------------
          Pruning Plan
--------------------------------
User pruning:
[ [DEP] ConvOutChannelPruner on conv1 (Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)) => ConvOutChannelPruner on conv1 (Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)), Index=[0, 2, 6], metric={'#params': 441}]

Coupled pruning:
[ [DEP] ConvOutChannelPruner on conv1 (Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)) => BatchnormPruner on bn1 (BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)), Index=[0, 2, 6], metric={'#params': 6}]
[ [DEP] BatchnormPruner on bn1 (BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)) => ElementWiseOpPruner on _ElementWiseOp(ReluBackward0), Index=[0, 2, 6], metric={}]
[ [DEP] ElementWiseOpPruner on _ElementWiseOp(ReluBackward0) => ElementWiseOpPruner on _ElementWiseOp(MaxPool2DWithIndicesBackward0), Index=[0, 2, 6], metric={}]
[ [DEP] ElementWiseOpPruner on _ElementWiseOp(MaxPool2DWithIndicesBackward0) => ElementWiseOpPruner on _ElementWiseOp(AddBackward0), Index=[0, 2, 6], metric={}]
[ [DEP] ElementWiseOpPruner on _ElementWiseOp(MaxPool2DWithIndicesBackward0) => ConvInChannelPruner on layer1.0.conv1 (Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)), Index=[0, 2, 6], metric={'#params': 1728}]
[ [DEP] ElementWiseOpPruner on _ElementWiseOp(AddBackward0) => BatchnormPruner on layer1.0.bn2 (BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)), Index=[0, 2, 6], metric={'#params': 6}]
[ [DEP] ElementWiseOpPruner on _ElementWiseOp(AddBackward0) => ElementWiseOpPruner on _ElementWiseOp(ReluBackward0), Index=[0, 2, 6], metric={}]
[ [DEP] ElementWiseOpPruner on _ElementWiseOp(ReluBackward0) => ElementWiseOpPruner on _ElementWiseOp(AddBackward0), Index=[0, 2, 6], metric={}]
[ [DEP] ElementWiseOpPruner on _ElementWiseOp(ReluBackward0) => ConvInChannelPruner on layer1.1.conv1 (Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)), Index=[0, 2, 6], metric={'#params': 1728}]
[ [DEP] ElementWiseOpPruner on _ElementWiseOp(AddBackward0) => BatchnormPruner on layer1.1.bn2 (BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)), Index=[0, 2, 6], metric={'#params': 6}]
[ [DEP] ElementWiseOpPruner on _ElementWiseOp(AddBackward0) => ElementWiseOpPruner on _ElementWiseOp(ReluBackward0), Index=[0, 2, 6], metric={}]
[ [DEP] ElementWiseOpPruner on _ElementWiseOp(ReluBackward0) => ConvInChannelPruner on layer2.0.downsample.0 (Conv2d(64, 128, kernel_size=(1, 1), stride=(2, 2), bias=False)), Index=[0, 2, 6], metric={'#params': 384}]
[ [DEP] ElementWiseOpPruner on _ElementWiseOp(ReluBackward0) => ConvInChannelPruner on layer2.0.conv1 (Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)), Index=[0, 2, 6], metric={'#params': 3456}]
[ [DEP] BatchnormPruner on layer1.1.bn2 (BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)) => ConvOutChannelPruner on layer1.1.conv2 (Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)), Index=[0, 2, 6], metric={'#params': 1728}]
[ [DEP] BatchnormPruner on layer1.0.bn2 (BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)) => ConvOutChannelPruner on layer1.0.conv2 (Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)), Index=[0, 2, 6], metric={'#params': 1728}]

Metric Sum: {'#params': 11211}
--------------------------------

Tip: please remember to save the whole model object (weights+architecture) after pruning, instead of saving the weights dict:

# save a pruned model
# torch.save(model.state_dict(), 'model.pth') # weights only
torch.save(model, 'model.pth') # obj (arch + weights), recommended.

# load a pruned model
model = torch.load('model.pth') # no load_state_dict

2. Low-level pruning functions

The following pruning functions are available:

tp.prune_conv_in_channel
tp.prune_conv_out_channel
tp.prune_group_conv
tp.prune_batchnorm 
tp.prune_linear_in_channel 
tp.prune_linear_out_channel 
tp.prune_prelu
tp.prune_layernorm 
tp.prune_embedding 
tp.prune_parameter
tp.prune_multihead_attention

You can prune your model manually without DependencyGraph:

tp.prune_conv_out_channel( model.conv1, idxs=[2,6,9] )

# fix the broken dependencies manually
tp.prune_batchnorm( model.bn1, idxs=[2,6,9] )
tp.prune_conv_in_channel( model.layer2[0].conv1, idxs=[2,6,9] )
...

3. Group Convs

We provide a tool tp.helpers.gconv2convs() to transform Group Conv to a group of vanilla convs. Please refer to test_convnext.py for more details.

4. Customized Layers

Please refer to examples/customized_layer.py.

5. Rounding channels for device-friendly network pruning

You can round the channels by passing a round_to parameter to strategy. For example, the following script will round the number of channels to 16xN (e.g., 16, 32, 48, 64).

strategy = tp.strategy.L1Strategy()
pruning_idxs = strategy(model.conv1.weight, amount=0.2, round_to=16)

Please refer to https://github.com/VainF/Torch-Pruning/issues/38 for more details.

5. Example: pruning ResNet18 on Cifar10

5.1. Scratch training

cd examples/cifar_minimal
python prune_resnet18_cifar10.py --mode train # 11.1M, Acc=0.9248

5.2. Pruning and fintuning

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
...

Layer Dependency

During structured pruning, we need to maintain the channel consistency between different layers.

A Simple Case

More Complicated Cases

the layer dependency becomes much more complicated when the model contains skip connections or concatenations.

Residual Block:

Concatenation:

See paper Pruning Filters for Efficient ConvNets for more details.