[Bugbash] promote pruning v2 (#4733)

docs/source/tutorials/pruning_quick_start_mnist.py.md5

-8be66184a81594952338aa3da270833f
\ No newline at end of file
+ea8cdb78aeea1a44e96e8903cfeedc41
\ No newline at end of file

docs/source/tutorials/pruning_quick_start_mnist.rst

@@ -102,9 +102,9 @@ If you are familiar with defining a model and training in pytorch, you can skip
 
  .. code-block:: none
 
-    Average test loss: 0.5266, Accuracy: 8345/10000 (83%)
-    Average test loss: 0.2713, Accuracy: 9209/10000 (92%)
-    Average test loss: 0.1919, Accuracy: 9356/10000 (94%)
+    Average test loss: 0.5822, Accuracy: 8311/10000 (83%)
+    Average test loss: 0.2795, Accuracy: 9154/10000 (92%)
+    Average test loss: 0.2036, Accuracy: 9345/10000 (93%)
 
 
 
@@ -151,7 +151,7 @@ Pruners usually require `model` and `config_list` as input arguments.
 .. code-block:: default
 
 
-    from nni.algorithms.compression.v2.pytorch.pruning import L1NormPruner
+    from nni.compression.pytorch.pruning import L1NormPruner
     pruner = L1NormPruner(model, config_list)
 
     # show the wrapped model structure, `PrunerModuleWrapper` have wrapped the layers that configured in the config_list.
@@ -308,7 +308,7 @@ Because speedup will replace the masked big layers with dense small ones.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 1 minutes  24.976 seconds)
+   **Total running time of the script:** ( 1 minutes  38.500 seconds)
 
 
 .. _sphx_glr_download_tutorials_pruning_quick_start_mnist.py:

docs/source/tutorials/pruning_quick_start_mnist_zh.rst

+.. 870da81751be2f5f1d74fbe9381f9acb
+
+    .. note::
+        :class: sphx-glr-download-link-note
+
+        Click :ref:`here <sphx_glr_download_tutorials_pruning_quick_start_mnist.py>`
+        to download the full example code
+
+.. rst-class:: sphx-glr-example-title
+
+.. _sphx_glr_tutorials_pruning_quick_start_mnist.py:
+
+
+模型剪枝入门
+============
+
+模型剪枝是一种通过减小模型权重规模或中间状态规模来减小模型大小和计算量的技术。
+修剪 DNN 模型有三种常见做法：
+
+#. 训练一个模型 -> 对模型进行剪枝 -> 对剪枝后模型进行微调
+#. 在模型训练过程中进行剪枝 -> 对剪枝后模型进行微调
+#. 对模型进行剪枝 -> 从头训练剪枝后模型
+
+NNI 主要通过在剪枝阶段进行工作来支持上述所有剪枝过程。
+通过本教程可以快速了解如何在常见实践中使用 NNI 修剪模型。
+
+.. GENERATED FROM PYTHON SOURCE LINES 17-22
+
+准备工作
+--------
+
+在本教程中，我们使用一个简单的模型在 MNIST 数据集上进行了预训练。
+如果你熟悉在 pytorch 中定义模型和训练模型，可以直接跳到 `模型剪枝`_。
+
+.. GENERATED FROM PYTHON SOURCE LINES 22-35
+
+.. code-block:: default
+
+
+    import torch
+    import torch.nn.functional as F
+    from torch.optim import SGD
+
+    from scripts.compression_mnist_model import TorchModel, trainer, evaluator, device
+
+    # define the model
+    model = TorchModel().to(device)
+
+    # show the model structure, note that pruner will wrap the model layer.
+    print(model)
+
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ Out:
+
+ .. code-block:: none
+
+    TorchModel(
+      (conv1): Conv2d(1, 6, kernel_size=(5, 5), stride=(1, 1))
+      (conv2): Conv2d(6, 16, kernel_size=(5, 5), stride=(1, 1))
+      (fc1): Linear(in_features=256, out_features=120, bias=True)
+      (fc2): Linear(in_features=120, out_features=84, bias=True)
+      (fc3): Linear(in_features=84, out_features=10, bias=True)
+    )
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 36-47
+
+.. code-block:: default
+
+
+    # define the optimizer and criterion for pre-training
+
+    optimizer = SGD(model.parameters(), 1e-2)
+    criterion = F.nll_loss
+
+    # pre-train and evaluate the model on MNIST dataset
+    for epoch in range(3):
+        trainer(model, optimizer, criterion)
+        evaluator(model)
+
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ Out:
+
+ .. code-block:: none
+
+    Average test loss: 0.5266, Accuracy: 8345/10000 (83%)
+    Average test loss: 0.2713, Accuracy: 9209/10000 (92%)
+    Average test loss: 0.1919, Accuracy: 9356/10000 (94%)
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 48-58
+
+模型剪枝
+--------
+
+使用 L1NormPruner 对模型进行剪枝并生成掩码。
+通常情况下，pruner 需要原始模型和一个 ``config_list`` 作为输入参数。
+具体关于如何写 ``config_list`` 请参考 :doc:`compression config specification <../compression/compression_config_list>`。
+
+以下 `config_list` 表示 pruner 将修剪类型为 `Linear` 或 `Conv2d` 的所有层除了名为 `fc3` 的层，因为 `fc3` 被设置为 `exclude`。
+每层的最终稀疏率是 50%。而名为 `fc3` 的层将不会被修剪。
+
+.. GENERATED FROM PYTHON SOURCE LINES 58-67
+
+.. code-block:: default
+
+
+    config_list = [{
+        'sparsity_per_layer': 0.5,
+        'op_types': ['Linear', 'Conv2d']
+    }, {
+        'exclude': True,
+        'op_names': ['fc3']
+    }]
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 68-69
+
+Pruners usually require `model` and `config_list` as input arguments.
+
+.. GENERATED FROM PYTHON SOURCE LINES 69-76
+
+.. code-block:: default
+
+
+    from nni.compression.pytorch.pruning import L1NormPruner
+    pruner = L1NormPruner(model, config_list)
+
+    # show the wrapped model structure, `PrunerModuleWrapper` have wrapped the layers that configured in the config_list.
+    print(model)
+
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ Out:
+
+ .. code-block:: none
+
+    TorchModel(
+      (conv1): PrunerModuleWrapper(
+        (module): Conv2d(1, 6, kernel_size=(5, 5), stride=(1, 1))
+      )
+      (conv2): PrunerModuleWrapper(
+        (module): Conv2d(6, 16, kernel_size=(5, 5), stride=(1, 1))
+      )
+      (fc1): PrunerModuleWrapper(
+        (module): Linear(in_features=256, out_features=120, bias=True)
+      )
+      (fc2): PrunerModuleWrapper(
+        (module): Linear(in_features=120, out_features=84, bias=True)
+      )
+      (fc3): Linear(in_features=84, out_features=10, bias=True)
+    )
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 77-84
+
+.. code-block:: default
+
+
+    # compress the model and generate the masks
+    _, masks = pruner.compress()
+    # show the masks sparsity
+    for name, mask in masks.items():
+        print(name, ' sparsity : ', '{:.2}'.format(mask['weight'].sum() / mask['weight'].numel()))
+
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ Out:
+
+ .. code-block:: none
+
+    conv1  sparsity :  0.5
+    conv2  sparsity :  0.5
+    fc1  sparsity :  0.5
+    fc2  sparsity :  0.5
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 85-88
+
+使用 NNI 的模型加速功能和 pruner 生成好的 masks 对原始模型进行加速，注意 `ModelSpeedup` 需要 unwrapped 的模型。
+模型会在加速之后真正的在规模上变小，并且可能会达到相比于 masks 更大的稀疏率，这是因为 `ModelSpeedup` 会自动在模型中传播稀疏，
+识别由于掩码带来的冗余权重。
+
+.. GENERATED FROM PYTHON SOURCE LINES 88-97
+
+.. code-block:: default
+
+
+    # need to unwrap the model, if the model is wrapped before speedup
+    pruner._unwrap_model()
+
+    # speedup the model
+    from nni.compression.pytorch.speedup import ModelSpeedup
+
+    ModelSpeedup(model, torch.rand(3, 1, 28, 28).to(device), masks).speedup_model()
+
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ Out:
+
+ .. code-block:: none
+
+    aten::log_softmax is not Supported! Please report an issue at https://github.com/microsoft/nni. Thanks~
+    Note: .aten::log_softmax.12 does not have corresponding mask inference object
+    /home/ningshang/anaconda3/envs/nni-dev/lib/python3.8/site-packages/torch/_tensor.py:1013: UserWarning: The .grad attribute of a Tensor that is not a leaf Tensor is being accessed. Its .grad attribute won't be populated during autograd.backward(). If you indeed want the .grad field to be populated for a non-leaf Tensor, use .retain_grad() on the non-leaf Tensor. If you access the non-leaf Tensor by mistake, make sure you access the leaf Tensor instead. See github.com/pytorch/pytorch/pull/30531 for more informations. (Triggered internally at  aten/src/ATen/core/TensorBody.h:417.)
+      return self._grad
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 98-99
+
+模型在加速之后变小了。
+
+.. GENERATED FROM PYTHON SOURCE LINES 99-101
+
+.. code-block:: default
+
+    print(model)
+
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ Out:
+
+ .. code-block:: none
+
+    TorchModel(
+      (conv1): Conv2d(1, 3, kernel_size=(5, 5), stride=(1, 1))
+      (conv2): Conv2d(3, 8, kernel_size=(5, 5), stride=(1, 1))
+      (fc1): Linear(in_features=128, out_features=60, bias=True)
+      (fc2): Linear(in_features=60, out_features=42, bias=True)
+      (fc3): Linear(in_features=42, out_features=10, bias=True)
+    )
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 102-106
+
+微调压缩好的紧凑模型
+--------------------
+
+注意当前的模型已经经过了加速，如果你需要微调模型，请重新生成 optimizer。
+这是因为在加速过程中进行了层替换，原来的 optimizer 已经不适用于现在的新模型了。
+
+.. GENERATED FROM PYTHON SOURCE LINES 106-110
+
+.. code-block:: default
+
+
+    optimizer = SGD(model.parameters(), 1e-2)
+    for epoch in range(3):
+        trainer(model, optimizer, criterion)
+
+
+
+
+
+
+
+
+.. rst-class:: sphx-glr-timing
+
+   **Total running time of the script:** ( 1 minutes  24.976 seconds)
+
+
+.. _sphx_glr_download_tutorials_pruning_quick_start_mnist.py:
+
+
+.. only :: html
+
+ .. container:: sphx-glr-footer
+    :class: sphx-glr-footer-example
+
+
+
+  .. container:: sphx-glr-download sphx-glr-download-python
+
+     :download:`Download Python source code: pruning_quick_start_mnist.py <pruning_quick_start_mnist.py>`
+
+
+
+  .. container:: sphx-glr-download sphx-glr-download-jupyter
+
+     :download:`Download Jupyter notebook: pruning_quick_start_mnist.ipynb <pruning_quick_start_mnist.ipynb>`
+
+
+.. only:: html
+
+ .. rst-class:: sphx-glr-signature
+
+    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_

docs/source/tutorials/sg_execution_times.rst

@@ -5,12 +5,10 @@
 
 Computation times
 =================
-**03:10.992** total execution time for **tutorials** files:
+**01:38.500** total execution time for **tutorials** files:
 
 +-----------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorials_quantization_quick_start_mnist.py` (``quantization_quick_start_mnist.py``) | 01:46.015 | 0.0 MB |
-+-----------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorials_pruning_quick_start_mnist.py` (``pruning_quick_start_mnist.py``)           | 01:24.976 | 0.0 MB |
+| :ref:`sphx_glr_tutorials_pruning_quick_start_mnist.py` (``pruning_quick_start_mnist.py``)           | 01:38.500 | 0.0 MB |
 +-----------------------------------------------------------------------------------------------------+-----------+--------+
 | :ref:`sphx_glr_tutorials_hello_nas.py` (``hello_nas.py``)                                           | 00:00.000 | 0.0 MB |
 +-----------------------------------------------------------------------------------------------------+-----------+--------+
@@ -22,5 +20,7 @@ Computation times
 +-----------------------------------------------------------------------------------------------------+-----------+--------+
 | :ref:`sphx_glr_tutorials_quantization_customize.py` (``quantization_customize.py``)                 | 00:00.000 | 0.0 MB |
 +-----------------------------------------------------------------------------------------------------+-----------+--------+
+| :ref:`sphx_glr_tutorials_quantization_quick_start_mnist.py` (``quantization_quick_start_mnist.py``) | 00:00.000 | 0.0 MB |
++-----------------------------------------------------------------------------------------------------+-----------+--------+
 | :ref:`sphx_glr_tutorials_quantization_speedup.py` (``quantization_speedup.py``)                     | 00:00.000 | 0.0 MB |
 +-----------------------------------------------------------------------------------------------------+-----------+--------+

examples/model_compress/end2end_compression.py

@@ -15,7 +15,7 @@ import torch.optim as optim
 from torch.optim.lr_scheduler import StepLR
 from torchvision import datasets, transforms
 
-from nni.compression.pytorch.utils.counter import count_flops_params
+from nni.compression.pytorch.utils import count_flops_params
 from nni.compression.pytorch import ModelSpeedup
 
 from nni.algorithms.compression.pytorch.pruning import L1FilterPruner

examples/model_compress/pruning/v2/activation_pruning_torch.py → examples/model_compress/pruning/activation_pruning_torch.py

@@ -16,11 +16,11 @@ from torch.optim.lr_scheduler import MultiStepLR
 
 import nni
 from nni.compression.pytorch import ModelSpeedup
-from nni.compression.pytorch.utils.counter import count_flops_params
-from nni.algorithms.compression.v2.pytorch.pruning.basic_pruner import ActivationAPoZRankPruner, ActivationMeanRankPruner
+from nni.compression.pytorch.utils import count_flops_params
+from nni.compression.pytorch.pruning import ActivationAPoZRankPruner, ActivationMeanRankPruner
 
 from pathlib import Path
-sys.path.append(str(Path(__file__).absolute().parents[2] / 'models'))
+sys.path.append(str(Path(__file__).absolute().parents[1] / 'models'))
 from cifar10.vgg import VGG
 
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

examples/model_compress/pruning/v2/admm_pruning_torch.py → examples/model_compress/pruning/admm_pruning_torch.py

@@ -16,11 +16,11 @@ from torch.optim.lr_scheduler import MultiStepLR
 
 import nni
 from nni.compression.pytorch.speedup import ModelSpeedup
-from nni.compression.pytorch.utils.counter import count_flops_params
-from nni.algorithms.compression.v2.pytorch.pruning.basic_pruner import ADMMPruner
+from nni.compression.pytorch.utils import count_flops_params
+from nni.compression.pytorch.pruning import ADMMPruner
 
 from pathlib import Path
-sys.path.append(str(Path(__file__).absolute().parents[2] / 'models'))
+sys.path.append(str(Path(__file__).absolute().parents[1] / 'models'))
 from cifar10.vgg import VGG
 
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

examples/model_compress/pruning/v2/amc_pruning_torch.py → examples/model_compress/pruning/amc_pruning_torch.py

@@ -5,11 +5,11 @@ import torch
 from torchvision import datasets, transforms
 from torch.optim.lr_scheduler import MultiStepLR
 
-from nni.algorithms.compression.v2.pytorch.pruning import AMCPruner
-from nni.compression.pytorch.utils.counter import count_flops_params
+from nni.compression.pytorch.pruning import AMCPruner
+from nni.compression.pytorch.utils import count_flops_params
 
 from pathlib import Path
-sys.path.append(str(Path(__file__).absolute().parents[2] / 'models'))
+sys.path.append(str(Path(__file__).absolute().parents[1] / 'models'))
 from cifar10.vgg import VGG
 
 

examples/model_compress/pruning/v2/auto_compress_pruner.py → examples/model_compress/pruning/auto_compress_pruner.py

@@ -5,10 +5,10 @@ import torch
 from torchvision import datasets, transforms
 
 import nni
-from nni.algorithms.compression.v2.pytorch.pruning import AutoCompressPruner
+from nni.compression.pytorch.pruning import AutoCompressPruner
 
 from pathlib import Path
-sys.path.append(str(Path(__file__).absolute().parents[2] / 'models'))
+sys.path.append(str(Path(__file__).absolute().parents[1] / 'models'))
 from cifar10.vgg import VGG
 
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

examples/model_compress/pruning/v2/fpgm_pruning_torch.py → examples/model_compress/pruning/fpgm_pruning_torch.py

@@ -15,11 +15,11 @@ from torchvision import datasets, transforms
 from torch.optim.lr_scheduler import MultiStepLR
 
 from nni.compression.pytorch import ModelSpeedup
-from nni.compression.pytorch.utils.counter import count_flops_params
-from nni.algorithms.compression.v2.pytorch.pruning.basic_pruner import FPGMPruner
+from nni.compression.pytorch.utils import count_flops_params
+from nni.compression.pytorch.pruning import FPGMPruner
 
 from pathlib import Path
-sys.path.append(str(Path(__file__).absolute().parents[2] / 'models'))
+sys.path.append(str(Path(__file__).absolute().parents[1] / 'models'))
 from cifar10.vgg import VGG
 
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

examples/model_compress/pruning/v2/iterative_pruning_torch.py → examples/model_compress/pruning/iterative_pruning_torch.py

@@ -13,14 +13,14 @@ from tqdm import tqdm
 import torch
 from torchvision import datasets, transforms
 
-from nni.algorithms.compression.v2.pytorch.pruning import (
+from nni.compression.pytorch.pruning import (
     LinearPruner,
     AGPPruner,
     LotteryTicketPruner
 )
 
 from pathlib import Path
-sys.path.append(str(Path(__file__).absolute().parents[2] / 'models'))
+sys.path.append(str(Path(__file__).absolute().parents[1] / 'models'))
 from cifar10.vgg import VGG
 
 

examples/model_compress/pruning/amc/amc_train.py → examples/model_compress/pruning/legacy/amc/amc_train.py

@@ -23,7 +23,7 @@ from data import get_dataset
 from utils import AverageMeter, accuracy, progress_bar
 
 from pathlib import Path
-sys.path.append(str(Path(__file__).absolute().parents[2] / 'models'))
+sys.path.append(str(Path(__file__).absolute().parents[3] / 'models'))
 from mobilenet import MobileNet
 from mobilenet_v2 import MobileNetV2
 

examples/model_compress/pruning/auto_pruners_torch.py → examples/model_compress/pruning/legacy/auto_pruners_torch.py

@@ -17,10 +17,10 @@ from torchvision import datasets, transforms
 from nni.algorithms.compression.pytorch.pruning import L1FilterPruner, L2FilterPruner, FPGMPruner
 from nni.algorithms.compression.pytorch.pruning import SimulatedAnnealingPruner, ADMMPruner, NetAdaptPruner, AutoCompressPruner
 from nni.compression.pytorch import ModelSpeedup
-from nni.compression.pytorch.utils.counter import count_flops_params
+from nni.compression.pytorch.utils import count_flops_params
 
 from pathlib import Path
-sys.path.append(str(Path(__file__).absolute().parents[1] / 'models'))
+sys.path.append(str(Path(__file__).absolute().parents[2] / 'models'))
 from mnist.lenet import LeNet
 from cifar10.vgg import VGG
 from cifar10.resnet import ResNet18, ResNet50