merge master

README.md

@@ -192,6 +192,7 @@ Within the following table, we summarized the current NNI capabilities, we are g
       <ul>
         <li><a href="docs/en_US/Tuner/CustomizeTuner.md">CustomizeTuner</a></li>
         <li><a href="docs/en_US/Assessor/CustomizeAssessor.md">CustomizeAssessor</a></li>
+        <li><a href="docs/en_US/Tutorial/InstallCustomizedAlgos.md">Install Customized Algorithms as Builtin Tuners/Assessors/Advisors</a></li>
       </ul>
       </td>
         <td style="border-top:#FF0000 solid 0px;">

azure-pipelines.yml

@@ -8,8 +8,8 @@ jobs:
 
   steps:
   - script: |
+      set -e
       python3 -m pip install --upgrade pip setuptools --user
-      python3 -m pip install pylint==2.3.1 astroid==2.2.5 --user
       python3 -m pip install coverage --user
       echo "##vso[task.setvariable variable=PATH]${HOME}/.local/bin:${PATH}"
     displayName: 'Install python tools'
@@ -25,29 +25,16 @@ jobs:
       yarn eslint
     displayName: 'Run eslint'
   - script: |
+      set -e
       python3 -m pip install torch==1.5.0+cpu torchvision==0.6.0+cpu -f https://download.pytorch.org/whl/torch_stable.html --user
-      python3 -m pip install tensorflow==1.15.2 --user
-      python3 -m pip install keras==2.1.6 --user
+      python3 -m pip install tensorflow==2.2.0 --user
+      python3 -m pip install keras==2.4.2 --user
       python3 -m pip install gym onnx --user
       python3 -m pip install sphinx==1.8.3 sphinx-argparse==0.2.5 sphinx-markdown-tables==0.0.9 sphinx-rtd-theme==0.4.2 sphinxcontrib-websupport==1.1.0 recommonmark==0.5.0 --user
       sudo apt-get install swig -y
       nnictl package install --name=SMAC
       nnictl package install --name=BOHB
     displayName: 'Install dependencies'
-  - script: |
-      set -e
-      python3 -m pylint --rcfile pylintrc nni_annotation
-      python3 -m pylint --rcfile pylintrc nni_cmd
-      python3 -m pylint --rcfile pylintrc nni_gpu_tool
-      python3 -m pylint --rcfile pylintrc nni_trial_tool
-      python3 -m pylint --rcfile pylintrc nni
-      python3 -m pylint --rcfile pylintrc nnicli
-    displayName: 'Run pylint'
-  - script: |
-      python3 -m pip install flake8 --user
-      EXCLUDES=./src/nni_manager/,./src/webui,./tools/nni_annotation/testcase/,./examples/trials/mnist-nas/*/mnist*.py,./examples/trials/nas_cifar10/src/cifar10/general_child.py
-      python3 -m flake8 . --count --exclude=$EXCLUDES --select=E9,F63,F72,F82 --show-source --statistics
-    displayName: 'Run flake8 tests to find Python syntax errors and undefined names'
   - script: |
       cd test
       source scripts/unittest.sh
@@ -61,20 +48,23 @@ jobs:
       sphinx-build -M html . _build -W
     displayName: 'Sphinx Documentation Build check'
 
-- job: 'ubuntu_1604_python35_legacy_torch'
+- job: 'ubuntu_1604_python35_legacy_torch_tf'
   pool:
     vmImage: 'Ubuntu 16.04'
 
   steps:
   - script: |
+      set -e
       python3 -m pip install --upgrade pip setuptools --user
+      python3 -m pip install pylint==2.3.1 astroid==2.2.5 --user
       python3 -m pip install coverage --user
       echo "##vso[task.setvariable variable=PATH]${HOME}/.local/bin:${PATH}"
     displayName: 'Install python tools'
   - script: |
       source install.sh
     displayName: 'Install nni toolkit via source code'
   - script: |
+      set -e
       python3 -m pip install torch==1.3.1+cpu torchvision==0.4.2+cpu -f https://download.pytorch.org/whl/torch_stable.html --user
       python3 -m pip install tensorflow==1.15.2 --user
       python3 -m pip install keras==2.1.6 --user
@@ -83,6 +73,21 @@ jobs:
       nnictl package install --name=SMAC
       nnictl package install --name=BOHB
     displayName: 'Install dependencies'
+  - script: |
+      set -e
+      python3 -m pylint --rcfile pylintrc nni_annotation
+      python3 -m pylint --rcfile pylintrc nni_cmd
+      python3 -m pylint --rcfile pylintrc nni_gpu_tool
+      python3 -m pylint --rcfile pylintrc nni_trial_tool
+      python3 -m pylint --rcfile pylintrc nni
+      python3 -m pylint --rcfile pylintrc nnicli
+    displayName: 'Run pylint'
+  - script: |
+      set -e
+      python3 -m pip install flake8 --user
+      EXCLUDES=./src/nni_manager/,./src/webui,./tools/nni_annotation/testcase/,./examples/trials/mnist-nas/*/mnist*.py,./examples/trials/nas_cifar10/src/cifar10/general_child.py
+      python3 -m flake8 . --count --exclude=$EXCLUDES --select=E9,F63,F72,F82 --show-source --statistics
+    displayName: 'Run flake8 tests to find Python syntax errors and undefined names'
   - script: |
       cd test
       source scripts/unittest.sh
@@ -98,18 +103,21 @@ jobs:
     vmImage: 'macOS-10.15'
 
   steps:
-  - script: python3 -m pip install --upgrade pip setuptools
+  - script: |
+      python3 -m pip install --upgrade pip setuptools
+      echo "##vso[task.setvariable variable=PATH]${HOME}/Library/Python/3.7/bin:${PATH}"
     displayName: 'Install python tools'
   - script: |
+      echo "network-timeout 600000" >> ${HOME}/.yarnrc
       source install.sh
-      echo "##vso[task.setvariable variable=PATH]${HOME}/Library/Python/3.7/bin:${PATH}"
     displayName: 'Install nni toolkit via source code'
   - script: |
+      set -e
       # pytorch Mac binary does not support CUDA, default is cpu version
       python3 -m pip install torchvision==0.6.0 torch==1.5.0 --user
       python3 -m pip install tensorflow==1.15.2 --user
       brew install swig@3
-      rm /usr/local/bin/swig
+      rm -f /usr/local/bin/swig
       ln -s /usr/local/opt/swig\@3/bin/swig /usr/local/bin/swig
       nnictl package install --name=SMAC
     displayName: 'Install dependencies'

deployment/pypi/setup.py

@@ -58,11 +58,13 @@
         'PythonWebHDFS',
         'hyperopt==0.1.2',
         'json_tricks',
+        'netifaces',
         'numpy',
         'scipy',
         'coverage',
         'colorama',
-        'scikit-learn>=0.20,<0.22'
+        'scikit-learn>=0.20,<0.22',
+        'pkginfo'
     ],
     classifiers = [
         'Programming Language :: Python :: 3',

docs/en_US/Compressor/CompressionReference.md

@@ -18,6 +18,16 @@
 ..  autoclass:: nni.compression.torch.utils.shape_dependency.ChannelDependency
     :members:
 
-..  autoclass:: nni.compression.torch.utils.mask_conflict.MaskConflict
+..  autoclass:: nni.compression.torch.utils.shape_dependency.GroupDependency
     :members:
+
+..  autoclass:: nni.compression.torch.utils.mask_conflict.CatMaskPadding
+    :members:
+
+..  autoclass:: nni.compression.torch.utils.mask_conflict.GroupMaskConflict
+    :members:
+
+..  autoclass:: nni.compression.torch.utils.mask_conflict.ChannelMaskConflict
+    :members:
+
 ```

docs/en_US/Compressor/CompressionUtils.md

@@ -116,8 +116,6 @@ Set 12,layer4.1.conv1
 When the masks of different layers in a model have conflict (for example, assigning different sparsities for the layers that have channel dependency), we can fix the mask conflict by MaskConflict. Specifically, the MaskConflict loads the masks exported by the pruners(L1FilterPruner, etc), and check if there is mask conflict, if so, MaskConflict sets the conflicting masks to the same value.
 
 ```
-from nni.compression.torch.utils.mask_conflict import MaskConflict
-mc = MaskConflict('./resnet18_mask', net, data)
-mc.fix_mask_conflict()
-mc.export('./resnet18_fixed_mask')
+from nni.compression.torch.utils.mask_conflict import fix_mask_conflict
+fixed_mask = fix_mask_conflict('./resnet18_mask', net, data)
 ```

docs/en_US/Compressor/Overview.md

@@ -26,6 +26,7 @@ Pruning algorithms compress the original network by removing redundant weights o
 | [ActivationAPoZRankFilterPruner](./Pruner.md#ActivationAPoZRankFilterPruner) | Pruning filters based on the metric APoZ (average percentage of zeros) which measures the percentage of zeros in activations of (convolutional) layers. [Reference Paper](https://arxiv.org/abs/1607.03250) |
 | [ActivationMeanRankFilterPruner](./Pruner.md#ActivationMeanRankFilterPruner) | Pruning filters based on the metric that calculates the smallest mean value of output activations |
 | [Slim Pruner](./Pruner.md#slim-pruner) | Pruning channels in convolution layers by pruning scaling factors in BN layers(Learning Efficient Convolutional Networks through Network Slimming) [Reference Paper](https://arxiv.org/abs/1708.06519) |
+| [TaylorFO Pruner](./Pruner.md#taylorfoweightfilterpruner) | Pruning filters based on the first order taylor expansion on weights(Importance Estimation for Neural Network Pruning) [Reference Paper](http://jankautz.com/publications/Importance4NNPruning_CVPR19.pdf) |
 
 
 **Quantization**

docs/en_US/NAS/ClassicNas.md

@@ -0,0 +1,33 @@
+# Classic NAS Algorithms
+
+In classic NAS algorithms, each architecture is trained as a trial and the NAS algorithm acts as a tuner. Thus, this training mode naturally fits within the NNI hyper-parameter tuning framework, where Tuner generates new architecture for the next trial and trials run in the training service.
+
+## Quick Start
+
+The following example shows how to use classic NAS algorithms. You can see it is quite similar to NNI hyper-parameter tuning.
+
+```python
+model = Net()
+
+# get the chosen architecture from tuner and apply it on model
+get_and_apply_next_architecture(model)
+train(model)  # your code for training the model
+acc = test(model)  # test the trained model
+nni.report_final_result(acc)  # report the performance of the chosen architecture
+```
+
+First, instantiate the model. Search space has been defined in this model through `LayerChoice` and `InputChoice`. After that, user should invoke `get_and_apply_next_architecture(model)` to settle down to a specific architecture. This function receives the architecture from tuner (i.e., the classic NAS algorithm) and applies the architecture to `model`. At this point, `model` becomes a specific architecture rather than a search space. Then users are free to train this model just like training a normal PyTorch model. After get the accuracy of this model, users should invoke `nni.report_final_result(acc)` to report the result to the tuner.
+
+At this point, trial code is ready. Then, we can prepare an NNI experiment, i.e., search space file and experiment config file. Different from NNI hyper-parameter tuning, search space file is automatically generated from the trial code by running the command (the detailed usage of this command can be found [here](../Tutorial/Nnictl.md)):
+
+`nnictl ss_gen --trial_command="the command for running your trial code"`
+
+A file named `nni_auto_gen_search_space.json` is generated by this command. Then put the path of the generated search space in the field `searchSpacePath` of the experiment config file. The other fields of the config file can be filled by referring [this tutorial](../Tutorial/QuickStart.md).
+
+Currently, we only support [PPO Tuner](../Tuner/BuiltinTuner.md) and [random tuner](https://github.com/microsoft/nni/tree/master/examples/tuners/random_nas_tuner) for classic NAS. More classic NAS algorithms will be supported soon.
+
+The complete examples can be found [here](https://github.com/microsoft/nni/tree/master/examples/nas/classic_nas) for PyTorch and [here](https://github.com/microsoft/nni/tree/master/examples/nas/classic_nas-tf) for TensorFlow.
+
+## Standalone mode for easy debugging
+
+We support a standalone mode for easy debugging, where you can directly run the trial command without launching an NNI experiment. This is for checking whether your trial code can correctly run. The first candidate(s) are chosen for `LayerChoice` and `InputChoice` in this standalone mode.