# Compressor

We are glad to announce the alpha release for model compression toolkit on top of NNI, it's still in the experiment phase which might evolve based on usage feedback. We'd like to invite you to use, feedback and even contribute.

NNI provides an easy-to-use toolkit to help user design and use compression algorithms. It supports Tensorflow and PyTorch with unified interface. For users to compress their models, they only need to add several lines in their code. There are some popular model compression algorithms built-in in NNI. Users could further use NNI's auto tuning power to find the best compressed model, which is detailed in [Auto Model Compression](./AutoCompression.md). On the other hand, users could easily customize their new compression algorithms using NNI's interface, refer to the tutorial [here](#customize-new-compression-algorithms).

## Supported algorithms

We have provided two naive compression algorithms and three popular ones for users, including two pruning algorithms and three quantization algorithms:

|Name|Brief Introduction of Algorithm|
|---|---|
| [Level Pruner](./Pruner.md#level-pruner) | Pruning the specified ratio on each weight based on absolute values of weights |
| [AGP Pruner](./Pruner.md#agp-pruner) | Automated gradual pruning (To prune, or not to prune: exploring the efficacy of pruning for model compression) [Reference Paper](https://arxiv.org/abs/1710.01878)|
| [L1Filter Pruner](./Pruner.md#l1filter-pruner) | Pruning least important filters in convolution layers(PRUNING FILTERS FOR EFFICIENT CONVNETS)[Reference Paper](https://arxiv.org/abs/1608.08710) |
| [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) |
| [Lottery Ticket Pruner](./Pruner.md#agp-pruner) | The pruning process used by "The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks". It prunes a model iteratively. [Reference Paper](https://arxiv.org/abs/1803.03635)|
| [FPGM Pruner](./Pruner.md#fpgm-pruner) | Filter Pruning via Geometric Median for Deep Convolutional Neural Networks Acceleration [Reference Paper](https://arxiv.org/pdf/1811.00250.pdf)|
| [Naive Quantizer](./Quantizer.md#naive-quantizer) |  Quantize weights to default 8 bits |
| [QAT Quantizer](./Quantizer.md#qat-quantizer) | Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference. [Reference Paper](http://openaccess.thecvf.com/content_cvpr_2018/papers/Jacob_Quantization_and_Training_CVPR_2018_paper.pdf)|
| [DoReFa Quantizer](./Quantizer.md#dorefa-quantizer) | DoReFa-Net: Training Low Bitwidth Convolutional Neural Networks with Low Bitwidth Gradients. [Reference Paper](https://arxiv.org/abs/1606.06160)|

## Usage of built-in compression algorithms

We use a simple example to show how to modify your trial code in order to apply the compression algorithms. Let's say you want to prune all weight to 80% sparsity with Level Pruner, you can add the following three lines into your code before training your model ([here](https://github.com/microsoft/nni/tree/master/examples/model_compress) is complete code).

Tensorflow code

```python
from nni.compression.tensorflow import LevelPruner
config_list = [{ 'sparsity': 0.8, 'op_types': ['default'] }]
pruner = LevelPruner(tf.get_default_graph(), config_list)
pruner.compress()
```

PyTorch code

```python
from nni.compression.torch import LevelPruner
config_list = [{ 'sparsity': 0.8, 'op_types': ['default'] }]
pruner = LevelPruner(model, config_list)
pruner.compress()
```

You can use other compression algorithms in the package of `nni.compression`. The algorithms are implemented in both PyTorch and Tensorflow, under `nni.compression.torch` and `nni.compression.tensorflow` respectively. You can refer to [Pruner](./Pruner.md) and [Quantizer](./Quantizer.md) for detail description of supported algorithms.

The function call `pruner.compress()` modifies user defined model (in Tensorflow the model can be obtained with `tf.get_default_graph()`, while in PyTorch the model is the defined model class), and the model is modified with masks inserted. Then when you run the model, the masks take effect. The masks can be adjusted at runtime by the algorithms.

When instantiate a compression algorithm, there is `config_list` passed in. We describe how to write this config below.

### User configuration for a compression algorithm

When compressing a model, users may want to specify the ratio for sparsity, to specify different ratios for different types of operations, to exclude certain types of operations, or to compress only a certain types of operations. For users to express these kinds of requirements, we define a configuration specification. It can be seen as a python `list` object, where each element is a `dict` object. In each `dict`, there are some keys commonly supported by NNI compression:

* __op_types__: This is to specify what types of operations to be compressed. 'default' means following the algorithm's default setting.
* __op_names__: This is to specify by name what operations to be compressed. If this field is omitted, operations will not be filtered by it.
* __exclude__: Default is False. If this field is True, it means the operations with specified types and names will be excluded from the compression.

There are also other keys in the `dict`, but they are specific for every compression algorithm. For example, some , some.

The `dict`s in the `list` are applied one by one, that is, the configurations in latter `dict` will overwrite the configurations in former ones on the operations that are within the scope of both of them. 

A simple example of configuration is shown below:

```python
[
    {
        'sparsity': 0.8,
        'op_types': ['default']
    },
    {
        'sparsity': 0.6,
        'op_names': ['op_name1', 'op_name2']
    },
    {
        'exclude': True,
        'op_names': ['op_name3']
    }
]
```

It means following the algorithm's default setting for compressed operations with sparsity 0.8, but for `op_name1` and `op_name2` use sparsity 0.6, and please do not compress `op_name3`.

### Other APIs

Some compression algorithms use epochs to control the progress of compression (e.g. [AGP](./Pruner.md#agp-pruner)), and some algorithms need to do something after every minibatch. Therefore, we provide another two APIs for users to invoke. One is `update_epoch`, you can use it as follows:

Tensorflow code

```python
pruner.update_epoch(epoch, sess)
```

PyTorch code

```python
pruner.update_epoch(epoch)
```

The other is `step`, it can be called with `pruner.step()` after each minibatch. Note that not all algorithms need these two APIs, for those that do not need them, calling them is allowed but has no effect.

You can easily export the compressed model using the following API if you are pruning your model, ```state_dict``` of the sparse model weights will be stored in ```model.pth```, which can be loaded by ```torch.load('model.pth')```

```
pruner.export_model(model_path='model.pth')
```

```mask_dict ``` and pruned model in ```onnx``` format(```input_shape``` need to be specified) can also be exported like this:

```python
pruner.export_model(model_path='model.pth', mask_path='mask.pth', onnx_path='model.onnx', input_shape=[1, 1, 28, 28])
```

## Customize new compression algorithms

To simplify writing a new compression algorithm, we design programming interfaces which are simple but flexible enough. There are interfaces for pruner and quantizer respectively.

### Pruning algorithm

If you want to write a new pruning algorithm, you can write a class that inherits `nni.compression.tensorflow.Pruner` or `nni.compression.torch.Pruner` depending on which framework you use. Then, override the member functions with the logic of your algorithm.

```python
# This is writing a pruner in tensorflow.
# For writing a pruner in PyTorch, you can simply replace
# nni.compression.tensorflow.Pruner with
# nni.compression.torch.Pruner
class YourPruner(nni.compression.tensorflow.Pruner):
    def __init__(self, model, config_list):
        """
        Suggest you to use the NNI defined spec for config
        """
        super().__init__(model, config_list)

    def calc_mask(self, layer, config):
        """
        Pruners should overload this method to provide mask for weight tensors.
        The mask must have the same shape and type comparing to the weight.
        It will be applied with ``mul()`` operation on the weight.
        This method is effectively hooked to ``forward()`` method of the model.

        Parameters
        ----------
        layer: LayerInfo
            calculate mask for ``layer``'s weight
        config: dict
            the configuration for generating the mask
        """
        return your_mask

    # note for pytorch version, there is no sess in input arguments
    def update_epoch(self, epoch_num, sess):
        pass

    # note for pytorch version, there is no sess in input arguments
    def step(self, sess):
        """
        Can do some processing based on the model or weights binded
        in the func bind_model
        """
        pass
```

For the simplest algorithm, you only need to override ``calc_mask``. It receives the to-be-compressed layers one by one along with their compression configuration. You generate the mask for this weight in this function and return. Then NNI applies the mask for you.

Some algorithms generate mask based on training progress, i.e., epoch number. We provide `update_epoch` for the pruner to be aware of the training progress. It should be called at the beginning of each epoch.

Some algorithms may want global information for generating masks, for example, all weights of the model (for statistic information). Your can use `self.bound_model` in the Pruner class for accessing weights. If you also need optimizer's information (for example in Pytorch), you could override `__init__` to receive more arguments such as model's optimizer. Then `step` can process or update the information according to the algorithm. You can refer to [source code of built-in algorithms](https://github.com/microsoft/nni/tree/master/src/sdk/pynni/nni/compressors) for example implementations.

### Quantization algorithm

The interface for customizing quantization algorithm is similar to that of pruning algorithms. The only difference is that `calc_mask` is replaced with `quantize_weight`. `quantize_weight` directly returns the quantized weights rather than mask, because for quantization the quantized weights cannot be obtained by applying mask.

```python
# This is writing a Quantizer in tensorflow.
# For writing a Quantizer in PyTorch, you can simply replace
# nni.compression.tensorflow.Quantizer with
# nni.compression.torch.Quantizer
class YourQuantizer(nni.compression.tensorflow.Quantizer):
    def __init__(self, model, config_list):
        """
        Suggest you to use the NNI defined spec for config
        """
        super().__init__(model, config_list)

    def quantize_weight(self, weight, config, **kwargs):
        """
        quantize should overload this method to quantize weight tensors.
        This method is effectively hooked to :meth:`forward` of the model.

        Parameters
        ----------
        weight : Tensor
            weight that needs to be quantized
        config : dict
            the configuration for weight quantization
        """

        # Put your code to generate `new_weight` here

        return new_weight
    
    def quantize_output(self, output, config, **kwargs):
        """
        quantize should overload this method to quantize output.
        This method is effectively hooked to `:meth:`forward` of the model.

        Parameters
        ----------
        output : Tensor
            output that needs to be quantized
        config : dict
            the configuration for output quantization
        """

        # Put your code to generate `new_output` here

        return new_output

    def quantize_input(self, *inputs, config, **kwargs):
        """
        quantize should overload this method to quantize input.
        This method is effectively hooked to :meth:`forward` of the model.

        Parameters
        ----------
        inputs : Tensor
            inputs that needs to be quantized
        config : dict
            the configuration for inputs quantization
        """

        # Put your code to generate `new_input` here

        return new_input

    # note for pytorch version, there is no sess in input arguments
    def update_epoch(self, epoch_num, sess):
        pass

    # note for pytorch version, there is no sess in input arguments
    def step(self, sess):
        """
        Can do some processing based on the model or weights binded
        in the func bind_model
        """
        pass
```

### Usage of user customized compression algorithm

__[TODO]__ ...
