Framework overview of model compression
=======================================

.. contents::

Below picture shows the components overview of model compression framework.


.. image:: ../../img/compressor_framework.jpg
   :target: ../../img/compressor_framework.jpg
   :alt: 


There are 3 major components/classes in NNI model compression framework: ``Compressor``\ , ``Pruner`` and ``Quantizer``. Let's look at them in detail one by one:

Compressor
----------

Compressor is the base class for pruner and quntizer, it provides a unified interface for pruner and quantizer for end users, so that pruner and quantizer can be used in the same way. For example, to use a pruner:

.. code-block:: python

   from nni.algorithms.compression.pytorch.pruning import LevelPruner

   # load a pretrained model or train a model before using a pruner

   configure_list = [{
       'sparsity': 0.7,
       'op_types': ['Conv2d', 'Linear'],
   }]

   pruner = LevelPruner(model, configure_list)
   model = pruner.compress()

   # model is ready for pruning, now start finetune the model,
   # the model will be pruned during training automatically

To use a quantizer:

.. code-block:: python

   from nni.algorithms.compression.pytorch.pruning import DoReFaQuantizer

   configure_list = [{
       'quant_types': ['weight'],
       'quant_bits': {
           'weight': 8,
       },
       'op_types':['Conv2d', 'Linear']
   }]
   optimizer = torch.optim.SGD(model.parameters(), lr=0.001, momentum=0.9, weight_decay=1e-4)
   quantizer = DoReFaQuantizer(model, configure_list, optimizer)
   quantizer.compress()

View :githublink:`example code <examples/model_compress>` for more information.

``Compressor`` class provides some utility methods for subclass and users:

Set wrapper attribute
^^^^^^^^^^^^^^^^^^^^^

Sometimes ``calc_mask`` must save some state data, therefore users can use ``set_wrappers_attribute`` API to register attribute just like how buffers are registered in PyTorch modules. These buffers will be registered to ``module wrapper``. Users can access these buffers through ``module wrapper``.
In above example, we use ``set_wrappers_attribute`` to set a buffer ``if_calculated`` which is used as flag indicating if the mask of a layer is already calculated.

Collect data during forward
^^^^^^^^^^^^^^^^^^^^^^^^^^^

Sometimes users want to collect some data during the modules' forward method, for example, the mean value of the activation. This can be done by adding a customized collector to module.

.. code-block:: python

   class MyMasker(WeightMasker):
       def __init__(self, model, pruner):
           super().__init__(model, pruner)
           # Set attribute `collected_activation` for all wrappers to store
           # activations for each layer
           self.pruner.set_wrappers_attribute("collected_activation", [])
           self.activation = torch.nn.functional.relu

           def collector(wrapper, input_, output):
               # The collected activation can be accessed via each wrapper's collected_activation
               # attribute
               wrapper.collected_activation.append(self.activation(output.detach().cpu()))

           self.pruner.hook_id = self.pruner.add_activation_collector(collector)

The collector function will be called each time the forward method runs.

Users can also remove this collector like this:

.. code-block:: python

   # Save the collector identifier
   collector_id = self.pruner.add_activation_collector(collector)

   # When the collector is not used any more, it can be remove using
   # the saved collector identifier
   self.pruner.remove_activation_collector(collector_id)

----

Pruner
------

A pruner receives ``model`` , ``config_list`` as arguments. 
Some pruners like ``TaylorFOWeightFilter Pruner`` prune the model per the ``config_list`` during training loop by adding a hook on ``optimizer.step()``.

Pruner class is a subclass of Compressor, so it contains everything in the Compressor class and some additional components only for pruning, it contains:

Weight masker
^^^^^^^^^^^^^

A ``weight masker`` is the implementation of pruning algorithms, it can prune a specified layer wrapped by ``module wrapper`` with specified sparsity.

Pruning module wrapper
^^^^^^^^^^^^^^^^^^^^^^

A ``pruning module wrapper`` is a module containing:


#. the origin module
#. some buffers used by ``calc_mask``
#. a new forward method that applies masks before running the original forward method.

the reasons to use ``module wrapper``\ :


#. some buffers are needed by ``calc_mask`` to calculate masks and these buffers should be registered in ``module wrapper`` so that the original modules are not contaminated.
#. a new ``forward`` method is needed to apply masks to weight before calling the real ``forward`` method.

Pruning hook
^^^^^^^^^^^^

A pruning hook is installed on a pruner when the pruner is constructed, it is used to call pruner's calc_mask method at ``optimizer.step()`` is invoked.

----

Quantizer
---------

Quantizer class is also a subclass of ``Compressor``\ , it is used to compress models by reducing the number of bits required to represent weights or activations, which can reduce the computations and the inference time. It contains:

Quantization module wrapper
^^^^^^^^^^^^^^^^^^^^^^^^^^^

Each module/layer of the model to be quantized is wrapped by a quantization module wrapper, it provides a new ``forward`` method to quantize the original module's weight, input and output.

Quantization hook
^^^^^^^^^^^^^^^^^

A quantization hook is installed on a quntizer when it is constructed, it is call at ``optimizer.step()``.

Quantization methods
^^^^^^^^^^^^^^^^^^^^

``Quantizer`` class provides following methods for subclass to implement quantization algorithms:

.. code-block:: python

   class Quantizer(Compressor):
       """
       Base quantizer for pytorch quantizer
       """
       def quantize_weight(self, weight, wrapper, **kwargs):
           """
           quantize should overload this method to quantize weight.
           This method is effectively hooked to :meth:`forward` of the model.
           Parameters
           ----------
           weight : Tensor
               weight that needs to be quantized
           wrapper : QuantizerModuleWrapper
               the wrapper for origin module
           """
           raise NotImplementedError('Quantizer must overload quantize_weight()')

       def quantize_output(self, output, wrapper, **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
           wrapper : QuantizerModuleWrapper
               the wrapper for origin module
           """
           raise NotImplementedError('Quantizer must overload quantize_output()')

       def quantize_input(self, *inputs, wrapper, **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
           wrapper : QuantizerModuleWrapper
               the wrapper for origin module
           """
           raise NotImplementedError('Quantizer must overload quantize_input()')

----

Multi-GPU support
-----------------

On multi-GPU training, buffers and parameters are copied to multiple GPU every time the ``forward`` method runs on multiple GPU. If buffers and parameters are updated in the ``forward`` method, an ``in-place`` update is needed to ensure the update is effective.
Since ``calc_mask`` is called in the ``optimizer.step`` method, which happens after the ``forward`` method and happens only on one GPU, it supports multi-GPU naturally.
