# Torch-Pruning **Repository Path**: intbingbing/Torch-Pruning ## Basic Information - **Project Name**: Torch-Pruning - **Description**: No description available - **Primary Language**: Unknown - **License**: MIT - **Default Branch**: master - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 0 - **Forks**: 0 - **Created**: 2024-01-29 - **Last Updated**: 2024-01-29 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README

Towards Any Structural Pruning

Test Status Tested PyTorch Versions License Downloads Latest Version Open In Colab arXiv

[[Documentation & Tutorials](https://github.com/VainF/Torch-Pruning/wiki)] [[FAQ](https://github.com/VainF/Torch-Pruning/wiki/Frequently-Asked-Questions)] 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)](https://github.com/horseee/LLM-Pruner), [Segment Anything Model (SAM)](https://github.com/czg1225/SlimSAM), [Diffusion Models](https://github.com/VainF/Diff-Pruning), [Yolov7](examples/yolov7/), [yolov8](examples/yolov8/), [Vision Transformers](examples/transformers/), [Swin Transformers](examples/transformers#swin-transformers-from-hf-transformers), [BERT](examples/transformers#bert-from-hf-transformers), FasterRCNN, SSD, ResNe(X)t, ConvNext, DenseNet, ConvNext, RegNet, DeepLab, etc. Different from [torch.nn.utils.prune](https://pytorch.org/tutorials/intermediate/pruning_tutorial.html) that zeroizes parameters through masking, Torch-Pruning deploys an algorithm called **[DepGraph](https://openaccess.thecvf.com/content/CVPR2023/html/Fang_DepGraph_Towards_Any_Structural_Pruning_CVPR_2023_paper.html)** to remove parameters physically. * **[Examples](examples)**: Pruning off-the-shelf models from Timm, Huggingface Transformers, Torchvision, Yolo, etc. * **[Benchmark](benchmarks)**: Reproduce the our results in the DepGraph paper. For more technical details, please refer to our CVPR'23 paper: > [**DepGraph: Towards Any Structural Pruning**](https://openaccess.thecvf.com/content/CVPR2023/html/Fang_DepGraph_Towards_Any_Structural_Pruning_CVPR_2023_paper.html) > *[Gongfan Fang](https://fangggf.github.io/), [Xinyin Ma](https://horseee.github.io/), [Mingli Song](https://person.zju.edu.cn/en/msong), [Michael Bi Mi](https://dblp.org/pid/317/0937.html), [Xinchao Wang](https://sites.google.com/site/sitexinchaowang/)* > *[Learning and Vision Lab](http://lv-nus.org/), National University of Singapore* ### Update: - [x] 2023.12.19 :rocket: [Introducing **DeepCache: Accelerating Diffusion Models for Free**](https://github.com/horseee/DeepCache) - [x] 2023.12.19 :rocket: [**SlimSAM: 0.1% Data Makes Segment Anything Slim**](https://github.com/czg1225/SlimSAM) - [x] 2023.09.06 [Pruning & Finetuning Examples for Vision Transformers, Swin Transformers, Bert](examples/transformers/) - [x] 2023.07.19 Support LLaMA, LLaMA-2, Vicuna, Baichuan, Bloom in [LLM-Pruner](https://github.com/horseee/LLM-Pruner) - [x] 2023.05.20 [**LLM-Pruner: On the Structural Pruning of Large Language Models**](https://github.com/horseee/LLM-Pruner) [*[arXiv]*](https://arxiv.org/abs/2305.11627) - [x] 2023.05.19 [Structural Pruning for Diffusion Models](https://github.com/VainF/Diff-Pruning) [*[arXiv]*](https://arxiv.org/abs/2305.10924) - [x] 2023.04.15 [Pruning and Post-training for YOLOv7 / YOLOv8](benchmarks/examples) ### **Features:** - [x] High-level Pruners: [MetaPruner](torch_pruning/pruner/algorithms/metapruner.py), [MagnitudePruner](https://arxiv.org/abs/1608.08710), [BNScalePruner](https://arxiv.org/abs/1708.06519), [GroupNormPruner](https://arxiv.org/abs/2301.12900), [GrowingRegPruner](https://arxiv.org/abs/2012.09243), RandomPruner, etc. A paper list is available on our [wiki page](https://github.com/VainF/Torch-Pruning/wiki/0.-Paper-List). - [x] Dependency Graph for automatic structural pruning - [x] [Low-level pruning functions](torch_pruning/pruner/function.py) - [x] Supported Importance Criteria: L-p Norm, Taylor, Random, BNScaling, etc. - [x] Supported modules: Linear, (Transposed) Conv, Normalization, PReLU, Embedding, MultiheadAttention, nn.Parameters, [customized modules](tests/test_customized_layer.py) and nested/composed modules. - [x] Supported operators: split, concatenation, skip connection, flatten, reshape, view, all element-wise ops, etc. - [x] [Benchmarks](benchmarks), [Tutorials](https://github.com/VainF/Torch-Pruning/wiki) and [Examples](examples) ### **Contact Us:** Please do not hesitate to open an [issue](https://github.com/VainF/Torch-Pruning/issues) if you encounter any problems with the library or the paper. Or Join our Discord or WeChat group for a chat: * Discord: [link](https://discord.gg/Pvd6hbYXRs) * WeChat Group (Group size exceeded 400): [QR Code](https://github.com/VainF/Torch-Pruning/assets/18592211/35d66130-eb03-4dcb-ad75-8df784460ad3) ## Table of Contents - [Installation](#installation) - [Quickstart](#quickstart) - [How It Works](#how-it-works) - [A Minimal Example of DepGraph](#a-minimal-example-of-depgraph) - [High-level Pruners](#high-level-pruners) - [Global Pruning](#global-pruning) - [Sparse Training](#sparse-training) - [Interactive Pruning](#interactive-pruning) - [Soft Pruning](#soft-pruning) - [Group-level Pruning](#group-level-pruning) - [Modify Module Attributes or Forward Function](#modify-module-attributes-or-forward-function) - [Save & Load](#save-and-load) - [Low-level Pruning Functions](#low-level-pruning-functions) - [Customized Layers](#customized-layers) - [Benchmarks](#benchmarks) - [Our Results on {ResNet-56 / CIFAR-10 / 2.00x}](#our-results-on-resnet-56--cifar-10--200x) - [Latency](#latency) - [Series of Works](#series-of-works) - [Citation](#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. ```bash pip install torch-pruning ``` or ```bash 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](https://github.com/VainF/Torch-Pruning/wiki) ### 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``. ```python 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](https://github.com/VainF/Torch-Pruning/wiki/3.-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. ```python 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](https://github.com/VainF/Torch-Pruning/blob/master/examples/notebook/1%20-%20Customize%20Your%20Own%20Pruners.ipynb), which shows how to implement a [slimming](https://arxiv.org/abs/1708.06519) pruner from scratch. Additionally, a more practical example is available in [benchmarks/main.py](benchmarks/main.py). ```python 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](https://github.com/VainF/Torch-Pruning/blob/dd59921365d72acb2857d3d74f75c03e477060fb/torch_pruning/pruner/algorithms/batchnorm_scale_pruner.py#L45) and [GroupNormPruner](https://github.com/VainF/Torch-Pruning/blob/dd59921365d72acb2857d3d74f75c03e477060fb/torch_pruning/pruner/algorithms/group_norm_pruner.py#L53) 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``. ```python 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. ```python 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](https://github.com/VainF/Torch-Pruning/blob/c9cea192a31f64e5ea26c095a70e2e93acf0be77/tests/test_soft_pruning.py#L39) #### 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``](https://github.com/huggingface/pytorch-image-models/blob/054c763fcaa7d241564439ae05fbe919ed85e614/timm/models/metaformer.py#L107) of timm, we have: ```python 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'. ```python 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``. ```python # 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](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: ```python 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](torch_pruning/pruner/function.py) are available: ```python '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](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](https://github.com/VainF/Torch-Pruning/blob/master/tests/test_customized_layer.py). ### Benchmarks #### Our results on {ResNet-56 / CIFAR-10 / 2.00x} | Method | Base (%) | Pruned (%) | $\Delta$ Acc (%) | Speed Up | |:-- |:--: |:--: |:--: |:--: | | NIPS [[1]](#1) | - | - |-0.03 | 1.76x | | Geometric [[2]](#2) | 93.59 | 93.26 | -0.33 | 1.70x | | Polar [[3]](#3) | 93.80 | 93.83 | +0.03 |1.88x | | CP [[4]](#4) | 92.80 | 91.80 | -1.00 |2.00x | | AMC [[5]](#5) | 92.80 | 91.90 | -0.90 |2.00x | | HRank [[6]](#6) | 93.26 | 92.17 | -0.09 |2.00x | | SFP [[7]](#7) | 93.59 | 93.36 | +0.23 |2.11x | | ResRep [[8]](#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](benchmarks) for more details. ### Series of Works > **DepGraph: Towards Any Structural Pruning** [[Project]](https://github.com/VainF/Torch-Pruning) [[Paper]](https://openaccess.thecvf.com/content/CVPR2023/html/Fang_DepGraph_Towards_Any_Structural_Pruning_CVPR_2023_paper.html) > *Gongfan Fang, Xinyin Ma, Mingli Song, Michael Bi Mi, Xinchao Wang* > CVPR 2023 > **LLM-Pruner: On the Structural Pruning of Large Language Models** [[Project]](https://github.com/horseee/LLM-Pruner) [[arXiv]](https://arxiv.org/abs/2305.11627) > *Xinyin Ma, Gongfan Fang, Xinchao Wang* > NeurIPS 2023 > **Structural Pruning for Diffusion Models** [[Project]](https://github.com/VainF/Diff-Pruning) [[arxiv]](https://arxiv.org/abs/2305.10924) > *Gongfan Fang, Xinyin Ma, Xinchao Wang* > NeurIPS 2023 > **DeepCache: Accelerating Diffusion Models for Free** [[Project]](https://github.com/horseee/DeepCache) [[Arxiv]](https://arxiv.org/abs/2312.00858) > *Xinyin Ma, Gongfan Fang, and Xinchao Wang* > Preprint 2023 > **0.1% Data Makes Segment Anything Slim** [[Project]](https://github.com/czg1225/SlimSAM) [[Arxiv]](https://arxiv.org/abs/2312.05284) > *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} } ```