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Torch-Pruning (TP) is designed for structural pruning, facilitating the following features:

• General-purpose Pruning Toolkit: TP enables structural pruning for a wide range of deep neural networks, including Large Language Models (LLMs), Segment Anything Model (SAM), Diffusion Models, Vision Transformers, ConvNext, Yolov7, yolov8, Swin Transformers, BERT, FasterRCNN, SSD, ResNe(X)t, DenseNet, RegNet, DeepLab, etc. Different from torch.nn.utils.prune that zeroizes parameters through masking, Torch-Pruning deploys an algorithm called DepGraph to remove parameters physically.
• Examples: Pruning off-the-shelf models from Timm, Huggingface Transformers, Torchvision, Yolo, etc.
• Code for reproducing paper results: Reproduce the our results in the DepGraph paper.

For more technical details, please refer to our CVPR'23 paper:

DepGraph: Towards Any Structural Pruning
Gongfan Fang, Xinyin Ma, Mingli Song, Michael Bi Mi, Xinchao Wang
Learning and Vision Lab, National University of Singapore

Update:

• 🔥 2024.11.17 We are working to add more LLMs, including Llama-3, Llama-2, Phi-3, Qwen-2.
• 🔥 2024.09.27 Check our latest work, MaskLLM (NeurIPS 24 Spotlight), for learnable semi-structured sparsity of LLMs.
• 🚀 2024.07.20 Add Isomorphic Pruning (ECCV'24). A SOTA method for Vision Transformers and Modern CNNs.

Features:

• :zap: High-level Pruners: MetaPruner, MagnitudePruner, BNScalePruner, GroupNormPruner, GrowingRegPruner, RandomPruner, etc. A paper list is available here.
• :zap: Dependency Graph for automatic structural pruning
• :zap: Low-level pruning functions
• :zap: Importance Scores: L-p Norm, Taylor, Random, BNScaling, etc.
• :zap: Supported modules: Linear, (Transposed) Conv, Normalization, PReLU, Embedding, MultiheadAttention, nn.Parameters, customized modules and nested/composed modules.
• :zap: Supported operators: split, concatenation, skip connection, flatten, reshape, view, all element-wise ops, etc.
• :zap: Examples, Tutorials and code to reproduce paper results,

Contact Us:

Please do not hesitate to open an issue if you encounter any problems with the library or the paper.
Or Join our WeChat group for a chat:

• WeChat Group Group-2 (>200/500), Group-1 (500/500, FULL).

Table of Contents

• Installation
• Quickstart
  • How It Works
  • A Minimal Example of DepGraph
  • High-level Pruners
    • Global Pruning and Isomorphic Pruning
    • Pruning Ratios
    • Sparse Training (Optional)
    • Interactive Pruning
    • Soft Pruning
    • Group-level Pruning
    • Modify Module Attributes or Forward Function
  • Save & Load
  • Low-level Pruning Functions
  • Customized Layers
  • Reproduce Paper Results
    • Our Results on {ResNet-56 / CIFAR-10 / 2.00x}
    • Latency
  • Series of Works
• Citation

Installation

Torch-Pruning is compatible with both PyTorch 1.x and 2.x versions. However, PyTorch 2.0+ is highly recommended.

pip install torch-pruning 

For editable installation:

git clone https://github.com/VainF/Torch-Pruning.git
cd Torch-Pruning && pip install -e .

Quickstart

Here we provide a quick start for Torch-Pruning. More explained details can be found in Tutorals

How It Works

Structural pruning removes a Group of parameters distributed across different layers. Parameters in each group will be coupled due the dependency between layers and thus must be removed simultaneously to maintain the structural integrity of the model. Torch-Pruning implements a mechanism called DependencyGraph to automatically identify dependencies and collect groups for pruning.

A Minimal Example of DepGraph

Tip: Please make sure that AutoGrad is enabled since TP will analyze the model structure with the Pytorch AutoGrad. This means we need to disable torch.no_grad() or something similar when building the dependency graph.

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

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

# 1. Build dependency graph for a resnet18. This requires a dummy input for forwarding
DG = tp.DependencyGraph().build_dependency(model, example_inputs=torch.randn(1,3,224,224))

# 2. Get the group for pruning model.conv1 with the specified channel idxs
group = DG.get_pruning_group( model.conv1, tp.prune_conv_out_channels, idxs=[2, 6, 9] )

# 3. Do the pruning
if DG.check_pruning_group(group): # avoid over-pruning, i.e., channels=0.
    group.prune()
    
# 4. Save & Load
model.zero_grad() # clear gradients to avoid a large file size
torch.save(model, 'model.pth') # !! no .state_dict for saving
model = torch.load('model.pth') # load the pruned model

The above example shows the basic pruning pipeline using DepGraph. The target layer model.conv1 is coupled with multiple layers, necessitating their simultaneous removal in structural pruning. We can print the group to take a look at the internal dependencies. In the subsequent outputs, "A => B" indicates that pruning operation "A" triggers pruning operation "B." The first group[0] refers to the root of pruning. For more details about grouping, please refer to Wiki - DepGraph & Group.

print(group.details()) # or print(group)

--------------------------------
          Pruning Group
--------------------------------
[0] prune_out_channels on conv1 (Conv2d(3, 61, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)) => prune_out_channels on conv1 (Conv2d(3, 61, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)), idxs (3) =[2, 6, 9]  (Pruning Root)
[1] prune_out_channels on conv1 (Conv2d(3, 61, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)) => prune_out_channels on bn1 (BatchNorm2d(61, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)), idxs (3) =[2, 6, 9] 
[2] prune_out_channels on bn1 (BatchNorm2d(61, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)) => prune_out_channels on _ElementWiseOp_20(ReluBackward0), idxs (3) =[2, 6, 9] 
[3] prune_out_channels on _ElementWiseOp_20(ReluBackward0) => prune_out_channels on _ElementWiseOp_19(MaxPool2DWithIndicesBackward0), idxs (3) =[2, 6, 9] 
[4] prune_out_channels on _ElementWiseOp_19(MaxPool2DWithIndicesBackward0) => prune_out_channels on _ElementWiseOp_18(AddBackward0), idxs (3) =[2, 6, 9] 
[5] prune_out_channels on _ElementWiseOp_19(MaxPool2DWithIndicesBackward0) => prune_in_channels on layer1.0.conv1 (Conv2d(61, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)), idxs (3) =[2, 6, 9] 
[6] prune_out_channels on _ElementWiseOp_18(AddBackward0) => prune_out_channels on layer1.0.bn2 (BatchNorm2d(61, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)), idxs (3) =[2, 6, 9] 
[7] prune_out_channels on _ElementWiseOp_18(AddBackward0) => prune_out_channels on _ElementWiseOp_17(ReluBackward0), idxs (3) =[2, 6, 9] 
[8] prune_out_channels on _ElementWiseOp_17(ReluBackward0) => prune_out_channels on _ElementWiseOp_16(AddBackward0), idxs (3) =[2, 6, 9] 
[9] prune_out_channels on _ElementWiseOp_17(ReluBackward0) => prune_in_channels on layer1.1.conv1 (Conv2d(61, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)), idxs (3) =[2, 6, 9] 
[10] prune_out_channels on _ElementWiseOp_16(AddBackward0) => prune_out_channels on layer1.1.bn2 (BatchNorm2d(61, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)), idxs (3) =[2, 6, 9] 
[11] prune_out_channels on _ElementWiseOp_16(AddBackward0) => prune_out_channels on _ElementWiseOp_15(ReluBackward0), idxs (3) =[2, 6, 9] 
[12] prune_out_channels on _ElementWiseOp_15(ReluBackward0) => prune_in_channels on layer2.0.downsample.0 (Conv2d(61, 128, kernel_size=(1, 1), stride=(2, 2), bias=False)), idxs (3) =[2, 6, 9] 
[13] prune_out_channels on _ElementWiseOp_15(ReluBackward0) => prune_in_channels on layer2.0.conv1 (Conv2d(61, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)), idxs (3) =[2, 6, 9] 
[14] prune_out_channels on layer1.1.bn2 (BatchNorm2d(61, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)) => prune_out_channels on layer1.1.conv2 (Conv2d(64, 61, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)), idxs (3) =[2, 6, 9] 
[15] prune_out_channels on layer1.0.bn2 (BatchNorm2d(61, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)) => prune_out_channels on layer1.0.conv2 (Conv2d(64, 61, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)), idxs (3) =[2, 6, 9] 
--------------------------------

How to scan all groups (Advanced):

There might be many groups in a model. We can use DG.get_all_groups(ignored_layers, root_module_types) to scan all prunable groups sequentially. Each group will begin with a layer that matches nn.Module types in the root_module_types. Note that DG.get_all_groups is only for grouping and does not know which channel/dim should be pruned.

for group in DG.get_all_groups(ignored_layers=[model.conv1], root_module_types=[nn.Conv2d, nn.Linear]):
    # Handle groups in sequential order
    idxs = [2,4,6] # your pruning indices
    group.prune(idxs=idxs)
    print(group)

High-level Pruners

With DepGraph, we developed several high-level pruners in this repository to facilitate effortless pruning. By specifying the desired channel pruning ratio, the pruner will scan all prunable groups, estimate weight importance, perform pruning, and fine-tune the remaining weights using your training code. For detailed information on this process, please refer to this tutorial, which shows how to implement a Network Slimming (ICCV 2017) pruner from scratch. Additionally, a more practical example is available in VainF/Isomorphic-Pruning.

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

model = resnet18(pretrained=True)
example_inputs = torch.randn(1, 3, 224, 224)

# 1. Importance criterion
imp = tp.importance.GroupNormImportance(p=2) # or GroupTaylorImportance(), GroupHessianImportance(), etc.

# 2. Initialize a pruner with the model and the importance criterion
ignored_layers = []
for m in model.modules():
    if isinstance(m, torch.nn.Linear) and m.out_features == 1000:
        ignored_layers.append(m) # DO NOT prune the final classifier!

pruner = tp.pruner.MetaPruner( # We can always choose MetaPruner if sparse training is not required.
    model,
    example_inputs,
    importance=imp,
    pruning_ratio=0.5, # remove 50% channels, ResNet18 = {64, 128, 256, 512} => ResNet18_Half = {32, 64, 128, 256}
    # pruning_ratio_dict = {model.conv1: 0.2, model.layer2: 0.8}, # customized pruning ratios for layers or blocks
    ignored_layers=ignored_layers,
    round_to=8, # It's recommended to round dims/channels to 4x or 8x for acceleration. Please see: https://docs.nvidia.com/deeplearning/performance/dl-performance-convolutional/index.html
)

# 3. Prune & finetune the model
base_macs, base_nparams = tp.utils.count_ops_and_params(model, example_inputs)
pruner.step()
macs, nparams = tp.utils.count_ops_and_params(model, example_inputs)
print(f"MACs: {base_macs/1e9} G -> {macs/1e9} G, #Params: {base_nparams/1e6} M -> {nparams/1e6} M")
# finetune the pruned model here
# finetune(model)
# ...

# Note: In TP, pruning ratio means channel pruning ratio.
#       Since both in & out channels will be removed by p%,
#       the corresponding parameter pruning ratio will be roughly 1-(1-p%)^2.
#       In this example, 3.06 ~= 11.69 * (1-0.5)^2 = 2.92
MACs: 1.822177768 G -> 0.487202536 G, #Params: 11.689512 M -> 3.05588 M

Global Pruning and Isomorphic Pruning

Global pruning performs importance ranking across all layers, which has the potential to find better structures. This can be easily achieved by setting global_pruning=True in the pruner. While this strategy can possibly offer performance advantages, it also carries the potential of overly pruning specific layers, resulting in a substantial decline in overall performance. We provide an alternative algorithm called Isomorphic Pruning to alleviate this issue, which can be enabled with isomorphic=True. Comprehensive examples for ViT & ConvNext pruning are available in this project.

pruner = tp.pruner.MetaPruner(
    ...
    isomorphic=True, # enable isomorphic pruning to improve global ranking
    global_pruning=True, # global pruning
)

Pruning Ratios

The default pruning ratio can be set by pruning_ratio. If you want to customize the pruning ratio for some layers or blocks, you can use pruning_ratio_dict. The key of the dict can be an nn.Module or a tuple of nn.Module. In the second case, all modules in the tuple will form a scope and share the pruning ratio. Global ranking will be performed in this scope. This is also the core idea of Isomorphic Pruning.

pruner = tp.pruner.MetaPruner(
    ...
    global_pruning=True,
    pruning_ratio=0.5, # default pruning ratio
    pruning_ratio_dict = {(model.layer1, model.layer2): 0.4, model.layer3: 0.2}, 
    # Global pruning will be performed on layer1 and layer2
)

Sparse Training (Optional)

Some pruners like BNScalePruner and GroupNormPruner support sparse training. This can be easily achieved by inserting pruner.update_regularizer() and pruner.regularize(model) in your standard training loops. The pruner will accumulate the regularization gradients to .grad. Sparse training is optional and may be expensive for pruning.

for epoch in range(epochs):
    model.train()
    pruner.update_regularizer() # <== initialize regularizer
    for i, (data, target) in enumerate(train_loader):
        data, target = data.to(device), target.to(device)
        optimizer.zero_grad()
        out = model(data)
        loss = F.cross_entropy(out, target)
        loss.backward() # after loss.backward()
        pruner.regularize(model) # <== for sparse training
        optimizer.step() # before optimizer.step()

Interactive Pruning

All high-level pruners offer support for interactive pruning. You can utilize the method pruner.step(interactive=True) to retrieve all the groups and interactively prune them by calling group.prune(). This feature is particularly useful if you want to control or monitor the pruning process.

for i in range(iterative_steps):
    for group in pruner.step(interactive=True): # Warning: groups must be handled sequentially. Do not keep them as a list.
        print(group) 
        # do whatever you like with the group 
        dep, idxs = group[0] # get the idxs
        target_module = dep.target.module # get the root module
        pruning_fn = dep.handler # get the pruning function
        group.prune()
        # group.prune(idxs=[0, 2, 6]) # It is even possible to change the pruning behaviour with the idxs parameter
    macs, nparams = tp.utils.count_ops_and_params(model, example_inputs)
    # finetune your model here
    # finetune(model)
    # ...

Soft Pruning

It is easy to implement Soft Pruning leveraging interactive=True, which zeros out parameters without removing them. An example can be found in tests/test_soft_pruning.py

Group-level Pruning

With DepGraph, it is easy to design some "group-level" importance scores to estimate the importance of a whole group rather than a single layer. This feature can be also used to sparsify coupled layers, making all the to-be-pruned parameters consistently sparse. In Torch-pruning, all pruners work at the group level. Check the following results to see how grouping improves the performance of pruning.

• Pruning a ResNet50 pre-trained on ImageNet-1K without fine-tuning.

• Pruning a Vision Transformer pre-trained on ImageNet-1K without fine-tuning.

Modify static attributes or forward functions

In some implementations, model forwarding might rely on some static attributes. For example in convformer_s18 of timm, we have self.shape which will be changed after pruning. These attributes should be updated manually since it is impossible for TP to know the purpose of these attributes.

class Scale(nn.Module):
    """
    Scale vector by element multiplications.
    """

    def __init__(self, dim, init_value=1.0, trainable=True, use_nchw=True):
        super().__init__()
        self.shape = (dim, 1, 1) if use_nchw else (dim,) # static shape, which should be updated after pruning
        self.scale = nn.Parameter(init_value * torch.ones(dim), requires_grad=trainable)

    def forward(self, x):
        return x * self.scale.view(self.shape) # => x * self.scale.view(-1, 1, 1), this works for pruning

Save and Load

The following script saves the whole model object (structure+weights) as a 'model.pth'. You can load it using the standard PyTorch API. Just remember that we save and load the whole model without .state_dict or .load_state_dict. This is because the pruned model will have a different structure after pruning from the original definition in your model.py.

model.zero_grad() # Remove gradients
torch.save(model, 'model.pth') # without .state_dict
model = torch.load('model.pth') # load the pruned model

Low-level Pruning Functions

In Torch-Pruning, we provide a series of low-level pruning functions that only prune a single layer or module. To manually prune the model.conv1 of a ResNet-18, the pruning pipeline should look like this:

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

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

The following pruning functions are available:

'prune_conv_out_channels',
'prune_conv_in_channels',
'prune_depthwise_conv_out_channels',
'prune_depthwise_conv_in_channels',
'prune_batchnorm_out_channels',
'prune_batchnorm_in_channels',
'prune_linear_out_channels',
'prune_linear_in_channels',
'prune_prelu_out_channels',
'prune_prelu_in_channels',
'prune_layernorm_out_channels',
'prune_layernorm_in_channels',
'prune_embedding_out_channels',
'prune_embedding_in_channels',
'prune_parameter_out_channels',
'prune_parameter_in_channels',
'prune_multihead_attention_out_channels',
'prune_multihead_attention_in_channels',
'prune_groupnorm_out_channels',
'prune_groupnorm_in_channels',
'prune_instancenorm_out_channels',
'prune_instancenorm_in_channels',

Customized Layers

Please refer to examples/transformers/prune_hf_swin.py, which implements a new pruner for the customized module SwinPatchMerging. Another simple example is available at tests/test_customized_layer.py.

Reproduce Paper Results

Please see reproduce.

Our results on {ResNet-56 / CIFAR-10 / 2.00x}

Latency

Latency test on ResNet-50, Batch Size=64.

[Iter 0]        Pruning ratio: 0.00,         MACs: 4.12 G,   Params: 25.56 M,        Latency: 45.22 ms +- 0.03 ms
[Iter 1]        Pruning ratio: 0.05,         MACs: 3.68 G,   Params: 22.97 M,        Latency: 46.53 ms +- 0.06 ms
[Iter 2]        Pruning ratio: 0.10,         MACs: 3.31 G,   Params: 20.63 M,        Latency: 43.85 ms +- 0.08 ms
[Iter 3]        Pruning ratio: 0.15,         MACs: 2.97 G,   Params: 18.36 M,        Latency: 41.22 ms +- 0.10 ms
[Iter 4]        Pruning ratio: 0.20,         MACs: 2.63 G,   Params: 16.27 M,        Latency: 39.28 ms +- 0.20 ms
[Iter 5]        Pruning ratio: 0.25,         MACs: 2.35 G,   Params: 14.39 M,        Latency: 34.60 ms +- 0.19 ms
[Iter 6]        Pruning ratio: 0.30,         MACs: 2.02 G,   Params: 12.46 M,        Latency: 33.38 ms +- 0.27 ms
[Iter 7]        Pruning ratio: 0.35,         MACs: 1.74 G,   Params: 10.75 M,        Latency: 31.46 ms +- 0.20 ms
[Iter 8]        Pruning ratio: 0.40,         MACs: 1.50 G,   Params: 9.14 M,         Latency: 29.04 ms +- 0.19 ms
[Iter 9]        Pruning ratio: 0.45,         MACs: 1.26 G,   Params: 7.68 M,         Latency: 27.47 ms +- 0.28 ms
[Iter 10]       Pruning ratio: 0.50,         MACs: 1.07 G,   Params: 6.41 M,         Latency: 20.68 ms +- 0.13 ms
[Iter 11]       Pruning ratio: 0.55,         MACs: 0.85 G,   Params: 5.14 M,         Latency: 20.48 ms +- 0.21 ms
[Iter 12]       Pruning ratio: 0.60,         MACs: 0.67 G,   Params: 4.07 M,         Latency: 18.12 ms +- 0.15 ms
[Iter 13]       Pruning ratio: 0.65,         MACs: 0.53 G,   Params: 3.10 M,         Latency: 15.19 ms +- 0.01 ms
[Iter 14]       Pruning ratio: 0.70,         MACs: 0.39 G,   Params: 2.28 M,         Latency: 13.47 ms +- 0.01 ms
[Iter 15]       Pruning ratio: 0.75,         MACs: 0.29 G,   Params: 1.61 M,         Latency: 10.07 ms +- 0.01 ms
[Iter 16]       Pruning ratio: 0.80,         MACs: 0.18 G,   Params: 1.01 M,         Latency: 8.96 ms +- 0.02 ms
[Iter 17]       Pruning ratio: 0.85,         MACs: 0.10 G,   Params: 0.57 M,         Latency: 7.03 ms +- 0.04 ms
[Iter 18]       Pruning ratio: 0.90,         MACs: 0.05 G,   Params: 0.25 M,         Latency: 5.81 ms +- 0.03 ms
[Iter 19]       Pruning ratio: 0.95,         MACs: 0.01 G,   Params: 0.06 M,         Latency: 5.70 ms +- 0.03 ms
[Iter 20]       Pruning ratio: 1.00,         MACs: 0.01 G,   Params: 0.06 M,         Latency: 5.71 ms +- 0.03 ms

Series of Works

DepGraph: Towards Any Structural Pruning [Project] [Paper]
Gongfan Fang, Xinyin Ma, Mingli Song, Michael Bi Mi, Xinchao Wang
CVPR 2023

Isomorphic Pruning for Vision Models [Project] [Arxiv]
Gongfan Fang, Xinyin Ma, Michael Bi Mi, Xinchao Wang
ECCV 2024

LLM-Pruner: On the Structural Pruning of Large Language Models [Project] [arXiv]
Xinyin Ma, Gongfan Fang, Xinchao Wang
NeurIPS 2023

Structural Pruning for Diffusion Models [Project] [arxiv]
Gongfan Fang, Xinyin Ma, Xinchao Wang
NeurIPS 2023

DeepCache: Accelerating Diffusion Models for Free [Project] [Arxiv]
Xinyin Ma, Gongfan Fang, and Xinchao Wang
CVPR 2024

SlimSAM: 0.1% Data Makes Segment Anything Slim [Project] [Arxiv]
Zigeng Chen, Gongfan Fang, Xinyin Ma, Xinchao Wang
Preprint 2023

Citation

@inproceedings{fang2023depgraph,
  title={Depgraph: Towards any structural pruning},
  author={Fang, Gongfan and Ma, Xinyin and Song, Mingli and Mi, Michael Bi and Wang, Xinchao},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={16091--16101},
  year={2023}
}