torch-pruning 1.3.7

Towards Any Structural Pruning

Towards Any Structural Pruning

[Documentation & Tutorials] [FAQ]

Torch-Pruning (TP) is a library for structural pruning with 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, Yolov7, yolov8, Vision Transformers, Swin Transformers, BERT, FasterRCNN, SSD, ResNe(X)t, ConvNext, DenseNet, ConvNext, 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.
• Benchmark: 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:

• 2023.12.19 :rocket: Introducing DeepCache: Accelerating Diffusion Models for Free
• 2023.12.19 :rocket: SlimSAM: 0.1% Data Makes Segment Anything Slim
• 2023.09.06 Pruning & Finetuning Examples for Vision Transformers, Swin Transformers, Bert
• 2023.07.19 Support LLaMA, LLaMA-2, Vicuna, Baichuan, Bloom in LLM-Pruner
• 2023.05.20 LLM-Pruner: On the Structural Pruning of Large Language Models [arXiv]
• 2023.05.19 Structural Pruning for Diffusion Models [arXiv]
• 2023.04.15 Pruning and Post-training for YOLOv7 / YOLOv8

Features:

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

Contact Us:

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

• Discord: link
• WeChat Group (Group size exceeded 400): QR Code

Table of Contents

• Installation
• Quickstart
  • How It Works
  • A Minimal Example of DepGraph
  • High-level Pruners
    • Global Pruning
    • Sparse Training
    • Interactive Pruning
    • Soft Pruning
    • Group-level Pruning
    • Modify Module Attributes or Forward Function
  • Save & Load
  • Low-level Pruning Functions
  • Customized Layers
  • Benchmarks
    • 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, it is highly recommended to use PyTorch 2.0.

pip install torch-pruning 

or

git clone https://github.com/VainF/Torch-Pruning.git

Quickstart

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

How It Works

In structural pruning, a "Group" is defined as the minimal removable unit within deep networks. Most groups are composed of multiple layers that are interdependent and need to be pruned together in order to maintain the integrity of the resulting structures. However, deep networks often have complex dependencies among their layers, making structural pruning a challenging task. This work addresses this challenge by introducing an automated mechanism called "DepGraph." DepGraph allows for seamless parameter grouping and facilitates pruning in various types of deep networks.

A Minimal Example of DepGraph

Please ensure that your model is set up to enable AutoGrad without torch.no_grad or .requires_grad=False.

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

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

# 1. Build dependency graph for resnet18
DG = tp.DependencyGraph().build_dependency(model, example_inputs=torch.randn(1,3,224,224))

# 2. Group coupled layers for model.conv1
group = DG.get_pruning_group( model.conv1, tp.prune_conv_out_channels, idxs=[2, 6, 9] )

# 3. Prune grouped layers altogether
if DG.check_pruning_group(group): # avoid full pruning, i.e., channels=0.
    group.prune()
    
# 4. Save & Load
model.zero_grad() # clear gradients
torch.save(model, 'model.pth') # We can not use .state_dict as the model structure is changed.
model = torch.load('model.pth') # load the pruned model

The above example demonstrates the basic pruning pipeline with DepGraph. The target layer resnet.conv1 is coupled with multiple layers, necessitating their simultaneous removal during structural pruning. To observe the cascading effect of pruning operations, we can print the groups and observe how one pruning operation can "trigger" others. In the subsequent outputs, "A => B" indicates that pruning operation "A" triggers pruning operation "B." The group[0] refers to the pruning root in DG.get_pruning_group. For more details about grouping, please refer to Wiki - DepGraph & Group.

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

How to scan all groups (Advanced):

We can use DG.get_all_groups(ignored_layers, root_module_types) to scan and prune all groups sequentially. Each group will begin with a layer that matches one type in the root_module_types parameter. Note that DG.get_all_groups is only responsible for grouping and does not have any knowledge or understanding of which parameters should be pruned. Therefore, it is necessary to specify the pruning idxs using group.prune(idxs=idxs). This feature is useful when you want to implement your own pruning algorithms.

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, a pruner will scan all prunable groups, estimate the importance, prune the entire model, and fine-tune it using your own training code. For detailed information on this process, please refer to this tutorial, which shows how to implement a slimming pruner from scratch. Additionally, a more practical example is available in benchmarks/main.py.

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.GroupTaylorImportance() # or GroupNormImportance(p=2), 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,
)

# 3. Prune & finetune the model
base_macs, base_nparams = tp.utils.count_ops_and_params(model, example_inputs)
if isinstance(imp, tp.importance.GroupTaylorImportance):
    # Taylor expansion requires gradients for importance estimation
    loss = model(example_inputs).sum() # A dummy loss, please replace this line with your loss function and data!
    loss.backward() # before pruner.step()

pruner.step()
macs, nparams = tp.utils.count_ops_and_params(model, example_inputs)
# finetune the pruned model here
# finetune(model)
# ...

Global Pruning

With the option of global pruning (global_pruning=True), adaptive sparsity will be allocated to different layers based on their global rank of importance. While this strategy can offer performance advantages, it also carries the potential of overly pruning specific layers, resulting in a substantial decline in overall performance. If you're not very familiar with pruning, it's recommended to begin with global_pruning=False.

Sparse Training

Some pruners like BNScalePruner and GroupNormPruner support sparse training. This can be easily achieved by inserting pruner.update_regularizer() before training, and pruner.regularize(model) between loss.backward() and optimizer.step(). The pruner will accumulate the regularization gradients to .grad.

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 when you want to have control over 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" criteria 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 module attributes or forward function

In some implementation, model forward might rely on some static attributes. For example in convformer_s18 of timm, we have:

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

where the forward function relies on self.shape during forwarding. But, the true self.shape changed after pruning, which should be manually updated.

Save and Load

Method 1:

The following script saves the whole model object (structure+weights) as a 'model.pth'.

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

Method 2 (Experimental Features):

Re-create pruned models from unpruned ones using tp.state_dict and tp.load_state_dict.

# save the pruned state_dict, which includes both pruned parameters and modified attributes
state_dict = tp.state_dict(pruned_model) # the pruned model, e.g., a resnet-18-half
torch.save(state_dict, 'pruned.pth')

# create a new model, e.g. resnet18
new_model = resnet18().eval()

# load the pruned state_dict into the unpruned model.
loaded_state_dict = torch.load('pruned.pth', map_location='cpu')
tp.load_state_dict(new_model, state_dict=loaded_state_dict)

Refer to tests/test_serialization.py for an ViT example. In this example, we will prune the model and modify some attributes like model.hidden_dims.

Low-level Pruning Functions

Although it is possible to manually prune your model using low-level functions, this approach can be cumbersome and time-consuming due to the need for meticulous management of dependencies. Therefore, we strongly recommend utilizing the high-level pruners mentioned earlier to streamline and simplify the pruning process. These pruners provide a more convenient and efficient way to perform pruning on your models. 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. A more simple example is available at tests/test_customized_layer.py.

Benchmarks

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

Method	Base (%)	Pruned (%)	$\Delta$ Acc (%)	Speed Up
NIPS [1]	-	-	-0.03	1.76x
Geometric [2]	93.59	93.26	-0.33	1.70x
Polar [3]	93.80	93.83	+0.03	1.88x
CP [4]	92.80	91.80	-1.00	2.00x
AMC [5]	92.80	91.90	-0.90	2.00x
HRank [6]	93.26	92.17	-0.09	2.00x
SFP [7]	93.59	93.36	+0.23	2.11x
ResRep [8]	93.71	93.71	+0.00	2.12x
Ours-L1	93.53	92.93	-0.60	2.12x
Ours-BN	93.53	93.29	-0.24	2.12x
Ours-Group	93.53	93.77	+0.38	2.13x

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

Please refer to benchmarks for more details.

Series of Works

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

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
Preprint 2023

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}
}