Refactor model compression examples

docs/en_US/Compression/AutoPruningUsingTuners.rst

@@ -6,116 +6,70 @@ It's convenient to implement auto model pruning with NNI compression and NNI tun
 First, model compression with NNI
 ---------------------------------
 
-You can easily compress a model with NNI compression. Take pruning for example, you can prune a pretrained model with LevelPruner like this
+You can easily compress a model with NNI compression. Take pruning for example, you can prune a pretrained model with L2FilterPruner like this
 
 .. code-block:: python
 
-   from nni.algorithms.compression.pytorch.pruning import LevelPruner
-   config_list = [{ 'sparsity': 0.8, 'op_types': ['default'] }]
-   pruner = LevelPruner(model, config_list)
+   from nni.algorithms.compression.pytorch.pruning import L2FilterPruner
+   config_list = [{ 'sparsity': 0.5, 'op_types': ['Conv2d'] }]
+   pruner = L2FilterPruner(model, config_list)
    pruner.compress()
 
-The 'default' op_type stands for the module types defined in :githublink:`default_layers.py <nni/compression/pytorch/default_layers.py>` for pytorch.
+The 'Conv2d' op_type stands for the module types defined in :githublink:`default_layers.py <nni/compression/pytorch/default_layers.py>` for pytorch.
 
-Therefore ``{ 'sparsity': 0.8, 'op_types': ['default'] }``\ means that **all layers with specified op_types will be compressed with the same 0.8 sparsity**. When ``pruner.compress()`` called, the model is compressed with masks and after that you can normally fine tune this model and **pruned weights won't be updated** which have been masked.
+Therefore ``{ 'sparsity': 0.5, 'op_types': ['Conv2d'] }``\ means that **all layers with specified op_types will be compressed with the same 0.5 sparsity**. When ``pruner.compress()`` called, the model is compressed with masks and after that you can normally fine tune this model and **pruned weights won't be updated** which have been masked.
 
 Then, make this automatic
 -------------------------
 
-The previous example manually choosed LevelPruner and pruned all layers with the same sparsity, this is obviously sub-optimal because different layers may have different redundancy. Layer sparsity should be carefully tuned to achieve least model performance degradation and this can be done with NNI tuners.
-
-The first thing we need to do is to design a search space, here we use a nested search space which contains  choosing pruning algorithm and optimizing layer sparsity.
-
-.. code-block:: json
-
-   {
-     "prune_method": {
-       "_type": "choice",
-       "_value": [
-         {
-           "_name": "agp",
-           "conv0_sparsity": {
-             "_type": "uniform",
-             "_value": [
-               0.1,
-               0.9
-             ]
-           },
-           "conv1_sparsity": {
-             "_type": "uniform",
-             "_value": [
-               0.1,
-               0.9
-             ]
-           },
-         },
-         {
-           "_name": "level",
-           "conv0_sparsity": {
-             "_type": "uniform",
-             "_value": [
-               0.1,
-               0.9
-             ]
-           },
-           "conv1_sparsity": {
-             "_type": "uniform",
-             "_value": [
-               0.01,
-               0.9
-             ]
-           },
-         }
-       ]
-     }
-   }
-
-Then we need to modify our codes for few lines
+The previous example manually chose L2FilterPruner and pruned with a specified sparsity. Different sparsity and different pruners may have different effects on different models. This process can be done with NNI tuners.
+
+Firstly, modify our codes for few lines
 
 .. code-block:: python
 
-   import nni
-   from nni.algorithms.compression.pytorch.pruning import *
-   params = nni.get_parameters()
-   conv0_sparsity = params['prune_method']['conv0_sparsity']
-   conv1_sparsity = params['prune_method']['conv1_sparsity']
-   # these raw sparsity should be scaled if you need total sparsity constrained
-   config_list_level = [{ 'sparsity': conv0_sparsity, 'op_name': 'conv0' },
-                        { 'sparsity': conv1_sparsity, 'op_name': 'conv1' }]
-   config_list_agp = [{'initial_sparsity': 0, 'final_sparsity': conv0_sparsity,
-                       'start_epoch': 0, 'end_epoch': 3,
-                       'frequency': 1,'op_name': 'conv0' },
-                      {'initial_sparsity': 0, 'final_sparsity': conv1_sparsity,
-                       'start_epoch': 0, 'end_epoch': 3,
-                       'frequency': 1,'op_name': 'conv1' },]
-   PRUNERS = {'level':LevelPruner(model, config_list_level), 'agp':AGPPruner(model, config_list_agp)}
-   pruner = PRUNERS(params['prune_method']['_name'])
-   pruner.compress()
-   ... # fine tuning
-   acc = evaluate(model) # evaluation
-   nni.report_final_results(acc)
+    import nni
+    from nni.algorithms.compression.pytorch.pruning import *
+
+    params = nni.get_parameters()
+    sparsity = params['sparsity']
+    pruner_name = params['pruner']
+    model_name = params['model']
+
+    model, pruner = get_model_pruner(model_name, pruner_name, sparsity)
+    pruner.compress()
+
+    train(model)  # your code for fine-tuning the model
+    acc = test(model)  # test the fine-tuned model
+    nni.report_final_results(acc)
 
-Last, define our task and automatically tuning pruning methods with layers sparsity
+Then, define a ``config`` file in YAML to automatically tuning model, pruning algorithm and sparsity.
 
 .. code-block:: yaml
 
-   authorName: default
-   experimentName: Auto_Compression
-   trialConcurrency: 2
-   maxExecDuration: 100h
-   maxTrialNum: 500
-   #choice: local, remote, pai
-   trainingServicePlatform: local
-   #choice: true, false
-   useAnnotation: False
-   searchSpacePath: search_space.json
-   tuner:
-     #choice: TPE, Random, Anneal...
-     builtinTunerName: TPE
-     classArgs:
-       #choice: maximize, minimize
-       optimize_mode: maximize
-   trial:
-     command: bash run_prune.sh
-     codeDir: .
-     gpuNum: 1
+    searchSpace:
+    sparsity:
+      _type: choice
+      _value: [0.25, 0.5, 0.75]
+    pruner:
+      _type: choice
+      _value: ['slim', 'l2filter', 'fpgm', 'apoz']
+    model:
+      _type: choice
+      _value: ['vgg16', 'vgg19']
+    trainingService:
+    platform: local
+    trialCodeDirectory: .
+    trialCommand: python3 basic_pruners_torch.py --nni
+    trialConcurrency: 1
+    trialGpuNumber: 0
+    tuner:
+      name: grid
+
+The full example can be found :githublink:`here <examples/model_compress/pruning/config.yml>`
+
+Finally, start the searching via
+
+.. code-block:: bash
+
+   nnictl create -c config.yml