Deep neural networks (DNNs) have achieved great success in many tasks like embedded development and scenarios that needs rapid feedbacks.
.. toctree::
:hidden:
:maxdepth: 2
Pruning <pruning>
Quantization <quantization>
Config Specification <compression_config_list>
Advanced Usage <advanced_usage>
.. attention::
NNI's model pruning framework has been upgraded to a more powerful version (named pruning v2 before nni v2.6).
The old version (`named pruning before nni v2.6 <https://nni.readthedocs.io/en/v2.6/Compression/pruning.html>`_) will be out of maintenance. If for some reason you have to use the old pruning,
v2.6 is the last nni version to support old pruning version.
.. Using rubric to prevent the section heading to be include into toc
.. rubric:: Overview
Deep neural networks (DNNs) have achieved great success in many tasks like computer vision, nature launguage processing, speech processing.
However, typical neural networks are both computationally expensive and energy-intensive,
However, typical neural networks are both computationally expensive and energy-intensive,
which can be difficult to be deployed on devices with low computation resources or with strict latency requirements.
which can be difficult to be deployed on devices with low computation resources or with strict latency requirements.
Therefore, a natural thought is to perform model compression to reduce model size and accelerate model training/inference without losing performance significantly.
Therefore, a natural thought is to perform model compression to reduce model size and accelerate model training/inference without losing performance significantly.
Model compression techniques can be divided into two categories: pruning and quantization.
Model compression techniques can be divided into two categories: pruning and quantization.
The pruning methods explore the redundancy in the model weights and try to remove/prune the redundant and uncritical weights.
The pruning methods explore the redundancy in the model weights and try to remove/prune the redundant and uncritical weights.
Quantization refers to compressing models by reducing the number of bits required to represent weights or activations functions.
Quantization refers to compress models by reducing the number of bits required to represent weights or activations.
We further elaborate on the two methods, pruning and quantization, in the following chapters. Besides, the figure below visualizes the difference between these two methods.
We further elaborate on the two methods, pruning and quantization, in the following chapters. Besides, the figure below visualizes the difference between these two methods.
.. image:: ../../img/prune_quant.jpg
.. image:: ../../img/prune_quant.jpg
:target: ../../img/prune_quant.jpg
:target: ../../img/prune_quant.jpg
:scale: 40%
:scale: 40%
:alt:
:alt:
NNI provides an easy-to-use toolkit to help users design and use model pruning and quantization algorithms.
NNI provides an easy-to-use toolkit to help users design and use model pruning and quantization algorithms.
For users to compress their models, they only need to add several lines in their code.
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.
There are some popular model compression algorithms built-in in NNI.
...
@@ -33,8 +49,7 @@ There are several core features supported by NNI model compression:
...
@@ -33,8 +49,7 @@ There are several core features supported by NNI model compression:
* Concise interface for users to customize their own compression algorithms.
* Concise interface for users to customize their own compression algorithms.
Compression Pipeline
.. rubric:: Compression Pipeline
--------------------
.. image:: ../../img/compression_flow.jpg
.. image:: ../../img/compression_flow.jpg
:target: ../../img/compression_flow.jpg
:target: ../../img/compression_flow.jpg
...
@@ -49,13 +64,7 @@ If users want to apply both, a sequential mode is recommended as common practise
...
@@ -49,13 +64,7 @@ If users want to apply both, a sequential mode is recommended as common practise
The interface and APIs are unified for both PyTorch and TensorFlow. Currently only PyTorch version has been supported, and TensorFlow version will be supported in future.
The interface and APIs are unified for both PyTorch and TensorFlow. Currently only PyTorch version has been supported, and TensorFlow version will be supported in future.
Supported Algorithms
.. rubric:: Supported Pruning Algorithms
--------------------
The supported model compression algorithms include pruning algorithms and quantization algorithms.
Pruning Algorithms
^^^^^^^^^^^^^^^^^^
Pruning algorithms compress the original network by removing redundant weights or channels of layers, which can reduce model complexity and mitigate the over-fitting issue.
Pruning algorithms compress the original network by removing redundant weights or channels of layers, which can reduce model complexity and mitigate the over-fitting issue.
...
@@ -99,8 +108,7 @@ Pruning algorithms compress the original network by removing redundant weights o
...
@@ -99,8 +108,7 @@ Pruning algorithms compress the original network by removing redundant weights o
- Movement Pruning: Adaptive Sparsity by Fine-Tuning `Reference Paper <https://arxiv.org/abs/2005.07683>`__
- Movement Pruning: Adaptive Sparsity by Fine-Tuning `Reference Paper <https://arxiv.org/abs/2005.07683>`__
Quantization Algorithms
.. rubric:: Supported Quantization Algorithms
^^^^^^^^^^^^^^^^^^^^^^^
Quantization algorithms compress the original network by reducing the number of bits required to represent weights or activations, which can reduce the computations and the inference time.
Quantization algorithms compress the original network by reducing the number of bits required to represent weights or activations, which can reduce the computations and the inference time.
...
@@ -124,21 +132,19 @@ Quantization algorithms compress the original network by reducing the number of
...
@@ -124,21 +132,19 @@ Quantization algorithms compress the original network by reducing the number of
- Post training quantizaiton. Collect quantization information during calibration with observers.
- Post training quantizaiton. Collect quantization information during calibration with observers.
Model Speedup
.. rubric:: Model Speedup
-------------
The final goal of model compression is to reduce inference latency and model size.
The final goal of model compression is to reduce inference latency and model size.
However, existing model compression algorithms mainly use simulation to check the performance (e.g., accuracy) of compressed model.
However, existing model compression algorithms mainly use simulation to check the performance (e.g., accuracy) of compressed model.
For example, using masks for pruning algorithms, and storing quantized values still in float32 for quantization algorithms.
For example, using masks for pruning algorithms, and storing quantized values still in float32 for quantization algorithms.
Given the output masks and quantization bits produced by those algorithms, NNI can really speed up the model. The following figure shows how NNI prunes and speeds up your models.
Given the output masks and quantization bits produced by those algorithms, NNI can really speed up the model.
The following figure shows how NNI prunes and speeds up your models.
.. image:: ../../img/pipeline_compress.jpg
.. image:: ../../img/pipeline_compress.jpg
:target: ../../img/pipeline_compress.jpg
:target: ../../img/pipeline_compress.jpg
:scale: 40%
:scale: 40%
:alt:
:alt:
The detailed tutorial of Speed Up Model with Mask can be found :doc:`here <../tutorials/pruning_speed_up>`.
The detailed tutorial of Speed Up Model with Mask can be found :doc:`here <../tutorials/pruning_speed_up>`.
The detailed tutorial of Speed Up Model with Calibration Config can be found :doc:`here <../tutorials/quantization_speed_up>`.
The detailed tutorial of Speed Up Model with Calibration Config can be found :doc:`here <../tutorials/quantization_speed_up>`.
If you want to compress your model, but don't know what compression algorithm to choose, or don't know what sparsity is suitable for your model, or just want to try more possibilities, auto compression may help you.
Users can choose different compression algorithms and define the algorithms' search space, then auto compression will launch an NNI experiment and try different compression algorithms with varying sparsity automatically.
Of course, in addition to the sparsity rate, users can also introduce other related parameters into the search space.
If you don't know what is search space or how to write search space, `this <./Tutorial/SearchSpaceSpec.rst>`__ is for your reference.
Auto compression using experience is similar to the NNI experiment in python.
The main differences are as follows:
* Use a generator to help generate search space object.
* Need to provide the model to be compressed, and the model should have already been pre-trained.
* No need to set ``trial_command``, additional need to set ``auto_compress_module`` as ``AutoCompressionExperiment`` input.
.. note::
Auto compression only supports TPE Tuner, Random Search Tuner, Anneal Tuner, Evolution Tuner right now.
Generate search space
---------------------
Due to the extensive use of nested search space, we recommend a using generator to configure search space.
The following is an example. Using ``add_config()`` add subconfig, then ``dumps()`` search space dict.
.. code-block:: python
from nni.algorithms.compression.pytorch.auto_compress import AutoCompressionSearchSpaceGenerator
generator = AutoCompressionSearchSpaceGenerator()
generator.add_config('level', [
{
"sparsity": {
"_type": "uniform",
"_value": [0.01, 0.99]
},
'op_types': ['default']
}
])
generator.add_config('qat', [
{
'quant_types': ['weight', 'output'],
'quant_bits': {
'weight': 8,
'output': 8
},
'op_types': ['Conv2d', 'Linear']
}])
search_space = generator.dumps()
Now we support the following pruners and quantizers:
.. code-block:: python
PRUNER_DICT = {
'level': LevelPruner,
'slim': SlimPruner,
'l1': L1FilterPruner,
'l2': L2FilterPruner,
'fpgm': FPGMPruner,
'taylorfo': TaylorFOWeightFilterPruner,
'apoz': ActivationAPoZRankFilterPruner,
'mean_activation': ActivationMeanRankFilterPruner
}
QUANTIZER_DICT = {
'naive': NaiveQuantizer,
'qat': QAT_Quantizer,
'dorefa': DoReFaQuantizer,
'bnn': BNNQuantizer
}
Provide user model for compression
----------------------------------
Users need to inherit ``AbstractAutoCompressionModule`` and override the abstract class function.
.. code-block:: python
from nni.algorithms.compression.pytorch.auto_compress import AbstractAutoCompressionModule
class AutoCompressionModule(AbstractAutoCompressionModule):
Users need to implement at least ``model()`` and ``evaluator()``.
If you use iterative pruner, you need to additional implement ``optimizer_factory()``, ``criterion()`` and ``sparsifying_trainer()``.
If you want to finetune the model after compression, you need to implement ``optimizer_factory()``, ``criterion()``, ``post_compress_finetuning_trainer()`` and ``post_compress_finetuning_epochs()``.
The ``optimizer_factory()`` should return a factory function, the input is an iterable variable, i.e. your ``model.parameters()``, and the output is an optimizer instance.
The two kinds of ``trainer()`` should return a trainer with input ``model, optimizer, criterion, current_epoch``.
The full abstract interface refers to :githublink:`interface.py <nni/algorithms/compression/pytorch/auto_compress/interface.py>`.
An example of ``AutoCompressionModule`` implementation refers to :githublink:`auto_compress_module.py <examples/model_compress/auto_compress/torch/auto_compress_module.py>`.
Launch NNI experiment
---------------------
Similar to launch from python, the difference is no need to set ``trial_command`` and put the user-provided ``AutoCompressionModule`` as ``AutoCompressionExperiment`` input.
.. code-block:: python
from pathlib import Path
from nni.algorithms.compression.pytorch.auto_compress import AutoCompressionExperiment
from auto_compress_module import AutoCompressionModule
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 :doc:`Framework overview of model compression <legacy_framework>`
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.
# 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_masker.py>` implementations to implement your own weight masker.
Reference nni provided :githublink:`pruner <nni/algorithms/compression/pytorch/pruning/one_shot_pruner.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
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
Thereare3majorcomponents/classesinNNImodelcompressionframework:``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
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'forwardmethod,forexample,themeanvalueoftheactivation.Thiscanbedonebyaddingacustomizedcollectortomodule.
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'scalc_maskmethodat``optimizer.step()``isinvoked.
Eachmodule/layerofthemodeltobequantizediswrappedbyaquantizationmodulewrapper,itprovidesanew``forward``methodtoquantizetheoriginalmodule'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:
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.
Pruning V2 is a refactoring of the old version and provides more powerful functions.
Pruning is a common technique to compress neural network models.
Compared with the old version, the iterative pruning process is detached from the pruner and the pruner is only responsible for pruning and generating the masks once.
The pruning methods explore the redundancy in the model weights(parameters) and try to remove/prune the redundant and uncritical weights.
What's more, pruning V2 unifies the pruning process and provides a more free combination of pruning components.
The redundant elements are pruned from the model, their values are zeroed and we make sure they don't take part in the back-propagation process.
Task generator only cares about the pruning effect that should be achieved in each round, and uses a config list to express how to pruning in the next step.
Pruner will reset with the model and config list given by task generator then generate the masks in current step.
The following concepts can help you understand pruning in NNI.
.. Using rubric to prevent the section heading to be include into toc
.. rubric:: Pruning Target
Pruning target means where we apply the sparsity.
Most pruning methods prune the weights to reduce the model size and accelerate the inference latency.
Other pruning methods also apply sparsity on the inputs, outputs or intermediate states to accelerate the inference latency.
NNI support pruning module weights right now, and will support other pruning targets in the future.
.. rubric:: Basic Pruner
Basic pruner generates the masks for each pruning targets (weights) for a determined sparsity ratio.
It usually takes model and config as input arguments, then generate a mask for the model.
.. rubric:: Scheduled Pruner
Scheduled pruner decides how to allocate sparsity ratio to each pruning targets, it also handles the pruning speed up and finetuning logic.
From the implementation logic, the scheduled pruner is a combination of pruning scheduler, basic pruner and task generator.
Task generator only cares about the pruning effect that should be achieved in each round, and uses a config list to express how to pruning.
Basic pruner will reset with the model and config list given by task generator then generate the masks.
For a clearer structure vision, please refer to the figure below.
For a clearer structure vision, please refer to the figure below.
.. image:: ../../img/pruning_process.png
.. image:: ../../img/pruning_process.png
:target: ../../img/pruning_process.png
:target: ../../img/pruning_process.png
:scale: 80%
:align: center
:alt:
:alt:
A pruning process is usually driven by a pruning scheduler, it contains a specific pruner and a task generator.
More information about scheduled pruning process please refer to :doc:`Pruning Scheduler <pruning_scheduler>`.
.. rubric:: Granularity
Fine-grained pruning or unstructured pruning refers to pruning each individual weights separately.
Coarse-grained pruning or structured pruning is pruning entire group of weights, such as a convolutional filter.
:ref:`level-pruner` is the only fine-grained pruner in NNI, all other pruners pruning the output channels on weights.
In these pruning algorithms, the pruner will prune each layer separately. While pruning a layer,
the algorithm will quantify the importance of each filter based on some specific rules(such as l1 norm), and prune the less important output channels.
We use pruning convolutional layers as an example to explain ``dependency aware`` mode.
As :doc:`dependency analysis utils <./compression_utils>` shows, if the output channels of two convolutional layers(conv1, conv2) are added together,
then these two convolutional layers have channel dependency with each other(more details please see :doc:`Compression Utils <./compression_utils>` ).
Take the following figure as an example.
.. image:: ../../img/mask_conflict.jpg
:target: ../../img/mask_conflict.jpg
:scale: 80%
:align: center
:alt:
If we prune the first 50% of output channels (filters) for conv1, and prune the last 50% of output channels for conv2.
Although both layers have pruned 50% of the filters, the speedup module still needs to add zeros to align the output channels.
In this case, we cannot harvest the speed benefit from the model pruning.
To better gain the speed benefit of the model pruning, we add a dependency-aware mode for the ``Pruner`` that can prune the output channels.
In the dependency-aware mode, the pruner prunes the model not only based on the metric of each output channels, but also the topology of the whole network architecture.
In the dependency-aware mode (``dependency_aware`` is set ``True``), the pruner will try to prune the same output channels for the layers that have the channel dependencies with each other, as shown in the following figure.
.. image:: ../../img/dependency-aware.jpg
:target: ../../img/dependency-aware.jpg
:scale: 80%
:align: center
:alt:
Take the dependency-aware mode of :ref:`l1-norm-pruner` as an example.
Specifically, the pruner will calculate the L1 norm (for example) sum of all the layers in the dependency set for each channel.
Obviously, the number of channels that can actually be pruned of this dependency set in the end is determined by the minimum sparsity of layers in this dependency set (denoted by ``min_sparsity``).
According to the L1 norm sum of each channel, the pruner will prune the same ``min_sparsity`` channels for all the layers.
Next, the pruner will additionally prune ``sparsity`` - ``min_sparsity`` channels for each convolutional layer based on its own L1 norm of each channel.
For example, suppose the output channels of ``conv1``, ``conv2`` are added together and the configured sparsities of ``conv1`` and ``conv2`` are 0.3, 0.2 respectively.
In this case, the ``dependency-aware pruner`` will
* First, prune the same 20% of channels for `conv1` and `conv2` according to L1 norm sum of `conv1` and `conv2`.
* Second, the pruner will additionally prune 10% channels for `conv1` according to the L1 norm of each channel of `conv1`.
.. Note::
In addition, for the convolutional layers that have more than one filter group,
``dependency-aware pruner`` will also try to prune the same number of the channels for each filter group.
Overall, this pruner will prune the model according to the L1 norm of each filter and try to meet the topological constrains (channel dependency, etc) to improve the final speed gain after the speedup process.
But users can also use pruner directly like in the pruning V1.
In the dependency-aware mode, the pruner will provide a better speed gain from the model pruning.
In these pruning algorithms, the pruner will prune each layer separately. While pruning a layer,
the algorithm will quantify the importance of each filter based on some specific rules(such as l1 norm), and prune the less important output channels.
We use pruning convolutional layers as an example to explain ``dependency aware`` mode.
As :doc:`dependency analysis utils <./compression_utils>` shows, if the output channels of two convolutional layers(conv1, conv2) are added together,
then these two convolutional layers have channel dependency with each other(more details please see :doc:`Compression Utils <./compression_utils>` ).
Take the following figure as an example.
.. image:: ../../img/mask_conflict.jpg
:target: ../../img/mask_conflict.jpg
:alt:
If we prune the first 50% of output channels (filters) for conv1, and prune the last 50% of output channels for conv2.
Although both layers have pruned 50% of the filters, the speedup module still needs to add zeros to align the output channels.
In this case, we cannot harvest the speed benefit from the model pruning.
To better gain the speed benefit of the model pruning, we add a dependency-aware mode for the ``Pruner`` that can prune the output channels.
In the dependency-aware mode, the pruner prunes the model not only based on the metric of each output channels, but also the topology of the whole network architecture.
In the dependency-aware mode (``dependency_aware`` is set ``True``), the pruner will try to prune the same output channels for the layers that have the channel dependencies with each other, as shown in the following figure.
.. image:: ../../img/dependency-aware.jpg
:target: ../../img/dependency-aware.jpg
:alt:
Take the dependency-aware mode of :ref:`l1-norm-pruner` as an example.
Specifically, the pruner will calculate the L1 norm (for example) sum of all the layers in the dependency set for each channel.
Obviously, the number of channels that can actually be pruned of this dependency set in the end is determined by the minimum sparsity of layers in this dependency set (denoted by ``min_sparsity``).
According to the L1 norm sum of each channel, the pruner will prune the same ``min_sparsity`` channels for all the layers.
Next, the pruner will additionally prune ``sparsity`` - ``min_sparsity`` channels for each convolutional layer based on its own L1 norm of each channel.
For example, suppose the output channels of ``conv1``, ``conv2`` are added together and the configured sparsities of ``conv1`` and ``conv2`` are 0.3, 0.2 respectively.
In this case, the ``dependency-aware pruner`` will
* First, prune the same 20% of channels for `conv1` and `conv2` according to L1 norm sum of `conv1` and `conv2`.
* Second, the pruner will additionally prune 10% channels for `conv1` according to the L1 norm of each channel of `conv1`.
In addition, for the convolutional layers that have more than one filter group,
``dependency-aware pruner`` will also try to prune the same number of the channels for each filter group.
Overall, this pruner will prune the model according to the L1 norm of each filter and try to meet the topological constrains (channel dependency, etc) to improve the final speed gain after the speedup process.
In the dependency-aware mode, the pruner will provide a better speed gain from the model pruning.
