Customize New Compression Algorithm
===================================

.. contents::

In order to simplify the process of writing new compression algorithms, we have designed simple and flexible programming interface, which covers pruning and quantization. Below, we first demonstrate how to customize a new pruning algorithm and then demonstrate how to customize a new quantization algorithm.

**Important Note** To better understand how to customize new pruning/quantization algorithms, users should first understand the framework that supports various pruning algorithms in NNI. Reference `Framework overview of model compression <../Compression/Framework.rst>`__

Customize a new pruning algorithm
---------------------------------

Implementing a new pruning algorithm requires implementing a ``weight masker`` class which shoud be a subclass of ``WeightMasker``\ , and a ``pruner`` class, which should be a subclass ``Pruner``.

An implementation of ``weight masker`` may look like this:

.. code-block:: python

   class MyMasker(WeightMasker):
       def __init__(self, model, pruner):
           super().__init__(model, pruner)
           # You can do some initialization here, such as collecting some statistics data
           # if it is necessary for your algorithms to calculate the masks.

       def calc_mask(self, sparsity, wrapper, wrapper_idx=None):
           # calculate the masks based on the wrapper.weight, and sparsity, 
           # and anything else
           # mask = ...
           return {'weight_mask': mask}

You can reference nni provided :githublink:`weight masker <nni/algorithms/compression/pytorch/pruning/structured_pruning.py>` implementations to implement your own weight masker.

A basic ``pruner`` looks likes this:

.. code-block:: python

   class MyPruner(Pruner):
       def __init__(self, model, config_list, optimizer):
           super().__init__(model, config_list, optimizer)
           self.set_wrappers_attribute("if_calculated", False)
           # construct a weight masker instance
           self.masker = MyMasker(model, self)

       def calc_mask(self, wrapper, wrapper_idx=None):
           sparsity = wrapper.config['sparsity']
           if wrapper.if_calculated:
               # Already pruned, do not prune again as a one-shot pruner
               return None
           else:
               # call your masker to actually calcuate the mask for this layer
               masks = self.masker.calc_mask(sparsity=sparsity, wrapper=wrapper, wrapper_idx=wrapper_idx)
               wrapper.if_calculated = True
               return masks

Reference nni provided :githublink:`pruner <nni/algorithms/compression/pytorch/pruning/one_shot.py>` implementations to implement your own pruner class.

----

Customize a new quantization algorithm
--------------------------------------

To write a new quantization algorithm, you can write a class that inherits ``nni.compression.pytorch.Quantizer``. Then, override the member functions with the logic of your algorithm. The member function to override is ``quantize_weight``. ``quantize_weight`` directly returns the quantized weights rather than mask, because for quantization the quantized weights cannot be obtained by applying mask.

.. code-block:: python

   from nni.compression.pytorch import Quantizer

   class YourQuantizer(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

       def update_epoch(self, epoch_num):
           pass

       def step(self):
           """
           Can do some processing based on the model or weights binded
           in the func bind_model
           """
           pass

Customize backward function
^^^^^^^^^^^^^^^^^^^^^^^^^^^

Sometimes it's necessary for a quantization operation to have a customized backward function, such as `Straight-Through Estimator <https://stackoverflow.com/questions/38361314/the-concept-of-straight-through-estimator-ste>`__\ , user can customize a backward function as follow:

.. code-block:: python

   from nni.compression.pytorch.compressor import Quantizer, QuantGrad, QuantType

   class ClipGrad(QuantGrad):
       @staticmethod
       def quant_backward(tensor, grad_output, quant_type):
           """
           This method should be overrided by subclass to provide customized backward function,
           default implementation is Straight-Through Estimator
           Parameters
           ----------
           tensor : Tensor
               input of quantization operation
           grad_output : Tensor
               gradient of the output of quantization operation
           quant_type : QuantType
               the type of quantization, it can be `QuantType.QUANT_INPUT`, `QuantType.QUANT_WEIGHT`, `QuantType.QUANT_OUTPUT`,
               you can define different behavior for different types.
           Returns
           -------
           tensor
               gradient of the input of quantization operation
           """

           # for quant_output function, set grad to zero if the absolute value of tensor is larger than 1
           if quant_type == QuantType.QUANT_OUTPUT: 
               grad_output[torch.abs(tensor) > 1] = 0
           return grad_output


   class YourQuantizer(Quantizer):
       def __init__(self, model, config_list):
           super().__init__(model, config_list)
           # set your customized backward function to overwrite default backward function
           self.quant_grad = ClipGrad

If you do not customize ``QuantGrad``\ , the default backward is Straight-Through Estimator. 
*Coming Soon* ...
