Merge

README.md

@@ -1,8 +1,8 @@
 <br>
 <div align="center">
 <img src="https://user-images.githubusercontent.com/18592211/232830417-0b21a874-516e-4420-8984-4de414a35085.png" width="400px"></img>
+<h2></h2>
 <h3>Towards Any Structural Pruning<h3>
-
 <img src="assets/intro.png" width="50%">
 </div>
 
@@ -11,7 +11,7 @@
   <a href="https://pytorch.org/"><img src="https://img.shields.io/badge/PyTorch-1.8 %20%7C%201.12 %20%7C%202.0-673ab7.svg" alt="Tested PyTorch Versions"></a>
   <a href="https://opensource.org/licenses/MIT"><img src="https://img.shields.io/badge/License-MIT-4caf50.svg" alt="License"></a>
   <a href="https://pepy.tech/project/Torch-Pruning"><img src="https://static.pepy.tech/badge/Torch-Pruning?color=2196f3" alt="Downloads"></a>
-  <a href="https://github.com/VainF/Torch-Pruning/releases/latest"><img src="https://img.shields.io/badge/Latest%20Version-1.3.2-3f51b5.svg" alt="Latest Version"></a>
+  <a href="https://github.com/VainF/Torch-Pruning/releases/latest"><img src="https://img.shields.io/badge/Latest%20Version-1.3.3-3f51b5.svg" alt="Latest Version"></a>
   <a href="https://colab.research.google.com/drive/1TRvELQDNj9PwM-EERWbF3IQOyxZeDepp?usp=sharing">
   <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
 </a>
@@ -23,8 +23,8 @@
 
 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), [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. Check our [Paper List](https://github.com/VainF/Torch-Pruning/wiki/0.-Paper-List) for more details.
-* **[Examples](examples)**: Play around with off-the-shelf models from Huggingface Transformers, Timm, Torchvision, Yolo, etc.  
+* **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), [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:
@@ -52,7 +52,7 @@ For more technical details, please refer to our CVPR'23 paper:
 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/Z6r34MnE)
-  * WeChat (Group size exceeded 300): [QR Code](https://github.com/VainF/Torch-Pruning/assets/18592211/35d66130-eb03-4dcb-ad75-8df784460ad3)
+  * 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)
@@ -77,7 +77,7 @@ Or Join our Discord or WeChat group for a chat:
 
 ## Installation
 
-Torch-Pruning is compatible with both PyTorch 1.x and 2.x versions. However, it is highly recommended to use PyTorch 1.12.1 or higher.
+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 
@@ -93,7 +93,7 @@ Here we provide a quick start for Torch-Pruning. More explained details can be f
 
 ### How It Works
 
-In structural pruning, a "Group" is defined as the minimal unit that can be removed within deep networks. These 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.
+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.
 
 <div align="center">
 <img src="assets/dep.png" width="100%">
@@ -419,6 +419,14 @@ Please refer to [benchmarks](benchmarks) for more details.
 > *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
 ```

setup.py

@@ -5,7 +5,7 @@
 
 setuptools.setup(
     name="torch-pruning",
-    version="v1.3.2",
+    version="v1.3.3",
     author="Gongfan Fang",
     author_email="gongfan@u.nus.edu",
     description="Towards Any Structural Pruning",

torch_pruning/pruner/algorithms/batchnorm_scale_pruner.py

@@ -101,8 +101,8 @@ def regularize(self, model, reg=None, bias=False):
                     m.weight.grad.data.add_(reg*torch.sign(m.weight.data))
         else:
             for group in self._groups:
-                group_l2norm_sq = self._l2_imp(group)
-                if group_l2norm_sq is None:
+                group_l2norm_sq = self._l2_imp(group) + 1e-9 # + 1e-9 to avoid inf
+                if group_l2norm_sq is None or torch.any(torch.isnan(group_l2norm_sq)):  # avoid nan
                     continue
                 gamma = reg * (1 / group_l2norm_sq.sqrt())
 

torch_pruning/pruner/algorithms/group_norm_pruner.py

@@ -92,6 +92,8 @@ def regularize(self, model, alpha=2**4, bias=False):
         for i, group in enumerate(self._groups):
             ch_groups = self._get_channel_groups(group)
             imp = self.estimate_importance(group).sqrt()
+            if torch.any(torch.isnan(imp)):  # avoid nan
+                continue
             gamma = alpha**((imp.max() - imp) / (imp.max() - imp.min()))
 
             # Update Gradient

torch_pruning/pruner/algorithms/growing_reg_pruner.py

@@ -110,7 +110,7 @@ def step(self, interactive=False):
     def regularize(self, model, bias=False):
         for i, group in enumerate(self._groups):
             group_l2norm_sq = self.estimate_importance(group)
-            if group_l2norm_sq is None:
+            if group_l2norm_sq is None or torch.any(torch.isnan(group_l2norm_sq)): # avoid nan
                 continue
             gamma = self.group_reg[group]
             for k, (dep, idxs) in enumerate(group):