v1.4.1

README.md

@@ -23,7 +23,7 @@
 
 Torch-Pruning (TP) is designed for structural pruning, facilating 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/VainF/Torch-Pruning/tree/master/examples/LLMs), [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. 
+* **General-purpose Pruning Toolkit:** TP enables structural pruning for a wide range of deep neural networks, including [Large Language Models (LLMs)](https://github.com/VainF/Torch-Pruning/tree/master/examples/LLMs), [Segment Anything Model (SAM)](https://github.com/czg1225/SlimSAM), [Diffusion Models](https://github.com/VainF/Diff-Pruning), [Vision Transformers](https://github.com/VainF/Isomorphic-Pruning), [ConvNext](https://github.com/VainF/Isomorphic-Pruning), [Yolov7](examples/yolov7/), [yolov8](examples/yolov8/),  [Swin Transformers](examples/transformers#swin-transformers-from-hf-transformers), [BERT](examples/transformers#bert-from-hf-transformers), FasterRCNN, SSD, ResNe(X)t, DenseNet, 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. 
 * [Code for reproducing paper results](reproduce): Reproduce the our results in the DepGraph paper.
 
@@ -206,15 +206,30 @@ print(f"MACs: {base_macs/1e9} G -> {macs/1e9} G, #Params: {base_nparams/1e6} M -
 MACs: 1.822177768 G -> 0.487202536 G, #Params: 11.689512 M -> 3.05588 M
 ```
 #### Global Pruning
-Global pruning perform importance ranking across all layers, which has the potential to find a 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](https://arxiv.org/abs/2407.04616) to alleviate this issue, which can be eanbled with ``isomorphic=True``. For more details, please see our offical [codebase for ViTs and CNNs](https://github.com/VainF/Isomorphic-Pruning).
+Global pruning perform importance ranking across all layers, which has the potential to find a 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](https://arxiv.org/abs/2407.04616) to alleviate this issue, which can be eanbled with ``isomorphic=True``. 
 ```python
 pruner = tp.pruner.MetaPruner(
     ...
-    ismorphic=True, # enable isomorphic pruning to improve global ranking
+    isomorphic=True, # enable isomorphic pruning to improve global ranking
     global_pruning=True, # global pruning
 )
 ```
 
+<div align="center">
+<img src="assets/isomorphic_pruning.png" width="96%">
+</div>
+
+#### 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 perfomed in this scope. This is also the core idea of [Isomorphic Pruning](https://arxiv.org/abs/2407.04616).
+```python
+pruner = tp.pruner.MetaPruner(
+    ...
+    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](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()`` 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. 

examples/timm_models/prune_timm_models.py

@@ -39,9 +39,10 @@ def main():
     num_heads = {}
 
     for m in model.modules():
-        if hasattr(m, 'head'): #isinstance(m, nn.Linear) and m.out_features == model.num_classes:
-            ignored_layers.append(model.head)
-            print("Ignore classifier layer: ", m.head)
+        #if hasattr(m, 'head'): #isinstance(m, nn.Linear) and m.out_features == model.num_classes:
+        if isinstance(m, nn.Linear) and m.out_features == model.num_classes:
+            ignored_layers.append(m)
+            print("Ignore classifier layer: ", m)
 
         # Attention layers
         if hasattr(m, 'num_heads'):

examples/transformers/readme.md

@@ -1,5 +1,8 @@
 # Transformers
 
+This example demonstrate the minimal code to prune Transformers, including Vision Transformers (ViT), Swin Transformers, and BERT. If you need a more comprehensive example for pruning and finetuning, please refer to the [codebase for Isomorphic Pruning](https://github.com/VainF/Isomorphic-Pruning), where detailed instructions and pre-pruned models are available.
+
+
 ## Pruning ViT-ImageNet-21K-ft-1K from [Timm](https://github.com/huggingface/pytorch-image-models)
 
 ### Data

tests/test_taylor_importance.py

@@ -14,16 +14,18 @@ def test_taylor():
         if isinstance(m, torch.nn.Linear) and m.out_features == 1000:
             ignored_layers.append(m) # DO NOT prune the final classifier!
 
-    iterative_steps = 5 # progressive pruning
+    iterative_steps = 1 # progressive pruning
     pruner = tp.pruner.MagnitudePruner(
         model,
         example_inputs,
         importance=imp,
         iterative_steps=iterative_steps,
-        pruning_ratio=0.5, # remove 50% channels, ResNet18 = {64, 128, 256, 512} => ResNet18_Half = {32, 64, 128, 256}
+        global_pruning=True,
+        pruning_ratio=0.1, # remove 50% channels, ResNet18 = {64, 128, 256, 512} => ResNet18_Half = {32, 64, 128, 256}
+        pruning_ratio_dict={model.layer1: 0.5, (model.layer2, model.layer3): 0.5},
         ignored_layers=ignored_layers,
     )
-
+    print(model)
     base_macs, base_nparams = tp.utils.count_ops_and_params(model, example_inputs)
     for i in range(iterative_steps):
         if isinstance(imp, tp.importance.TaylorImportance):