Automatic Model Architecture Search for Reading Comprehension
=============================================================

This example shows us how to use Genetic Algorithm to find good model architectures for Reading Comprehension.

1. Search Space
---------------

Since attention and RNN have been proven effective in Reading Comprehension, we conclude the search space as follow:


#. IDENTITY (Effectively means keep training).
#. INSERT-RNN-LAYER (Inserts a LSTM. Comparing the performance of GRU and LSTM in our experiment, we decided to use LSTM here.)
#. REMOVE-RNN-LAYER
#. INSERT-ATTENTION-LAYER(Inserts an attention layer.)
#. REMOVE-ATTENTION-LAYER
#. ADD-SKIP (Identity between random layers).
#. REMOVE-SKIP (Removes random skip).


.. image:: ../../../examples/trials/ga_squad/ga_squad.png
   :target: ../../../examples/trials/ga_squad/ga_squad.png
   :alt: 


New version
^^^^^^^^^^^

Also we have another version which time cost is less and performance is better. We will release soon.

2. How to run this example in local?
------------------------------------

2.1 Use downloading script to download data
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

Execute the following command to download needed files
using the downloading script:

.. code-block:: bash

   chmod +x ./download.sh
   ./download.sh

Or Download manually


#. download ``dev-v1.1.json`` and ``train-v1.1.json`` `here <https://rajpurkar.github.io/SQuAD-explorer/>`__

.. code-block:: bash

   wget https://rajpurkar.github.io/SQuAD-explorer/dataset/train-v1.1.json
   wget https://rajpurkar.github.io/SQuAD-explorer/dataset/dev-v1.1.json


#. download ``glove.840B.300d.txt`` `here <https://nlp.stanford.edu/projects/glove/>`__

.. code-block:: bash

   wget http://nlp.stanford.edu/data/glove.840B.300d.zip
   unzip glove.840B.300d.zip

2.2 Update configuration
^^^^^^^^^^^^^^^^^^^^^^^^

Modify ``nni/examples/trials/ga_squad/config.yml``\ , here is the default configuration:

.. code-block:: yaml

   experimentName: ga-squad example
   trialCommand: python3 trial.py
   trialCodeDirectory: ~/nni/examples/trials/ga_squad

   trialGpuNumber: 0
   trialConcurrency: 1
   maxTrialNumber: 10
   maxExperimentDuration: 1h

   searchSpace: {}  # hard-coded in tuner
   tuner:
     className: customer_tuner.CustomerTuner
     codeDirectory: ~/nni/examples/tuners/ga_customer_tuner
     classArgs:
       optimize_mode: maximize

   trainingService:
     platform: local

In the **trial** part, if you want to use GPU to perform the architecture search, change ``trialGpuNum`` from ``0`` to ``1``. You need to increase the ``maxTrialNumber`` and ``maxExperimentDuration``\ , according to how long you want to wait for the search result.

2.3 submit this job
^^^^^^^^^^^^^^^^^^^

.. code-block:: bash

   nnictl create --config ~/nni/examples/trials/ga_squad/config.yml

3. Technical details about the trial
------------------------------------

3.1 How does it works
^^^^^^^^^^^^^^^^^^^^^

The evolution-algorithm based architecture for question answering has two different parts just like any other examples: the trial and the tuner.

3.2 The trial
^^^^^^^^^^^^^

The trial has a lot of different files, functions and classes. Here we will only give most of those files a brief introduction:


* ``attention.py`` contains an implementation for attention mechanism in Tensorflow.
* ``data.py`` contains functions for data preprocessing.
* ``evaluate.py`` contains the evaluation script.
* ``graph.py`` contains the definition of the computation graph.
* ``rnn.py`` contains an implementation for GRU in Tensorflow.
* ``train_model.py`` is a wrapper for the whole question answering model.

Among those files, ``trial.py`` and ``graph_to_tf.py`` are special.

``graph_to_tf.py`` has a function named as ``graph_to_network``\ , here is its skeleton code:

.. code-block:: python

   def graph_to_network(input1,
                        input2,
                        input1_lengths,
                        input2_lengths,
                        graph,
                        dropout_rate,
                        is_training,
                        num_heads=1,
                        rnn_units=256):
       topology = graph.is_topology()
       layers = dict()
       layers_sequence_lengths = dict()
       num_units = input1.get_shape().as_list()[-1]
       layers[0] = input1*tf.sqrt(tf.cast(num_units, tf.float32)) + \
           positional_encoding(input1, scale=False, zero_pad=False)
       layers[1] = input2*tf.sqrt(tf.cast(num_units, tf.float32))
       layers[0] = dropout(layers[0], dropout_rate, is_training)
       layers[1] = dropout(layers[1], dropout_rate, is_training)
       layers_sequence_lengths[0] = input1_lengths
       layers_sequence_lengths[1] = input2_lengths
       for _, topo_i in enumerate(topology):
           if topo_i == '|':
               continue
           if graph.layers[topo_i].graph_type == LayerType.input.value:
               ...
           elif graph.layers[topo_i].graph_type == LayerType.attention.value:
               ...
           # More layers to handle

As we can see, this function is actually a compiler, that converts the internal model DAG configuration (which will be introduced in the ``Model configuration format`` section) ``graph``\ , to a Tensorflow computation graph.

.. code-block:: python

   topology = graph.is_topology()

performs topological sorting on the internal graph representation, and the code inside the loop:

.. code-block:: python

   for _, topo_i in enumerate(topology):
       ...

performs actually conversion that maps each layer to a part in Tensorflow computation graph.

3.3 The tuner
^^^^^^^^^^^^^

The tuner is much more simple than the trial. They actually share the same ``graph.py``. Besides, the tuner has a ``customer_tuner.py``\ , the most important class in which is ``CustomerTuner``\ :

.. code-block:: python

   class CustomerTuner(Tuner):
       # ......

       def generate_parameters(self, parameter_id):
           """Returns a set of trial graph config, as a serializable object.
           parameter_id : int
           """
           if len(self.population) <= 0:
               logger.debug("the len of poplution lower than zero.")
               raise Exception('The population is empty')
           pos = -1
           for i in range(len(self.population)):
               if self.population[i].result == None:
                   pos = i
                   break
           if pos != -1:
               indiv = copy.deepcopy(self.population[pos])
               self.population.pop(pos)
               temp = json.loads(graph_dumps(indiv.config))
           else:
               random.shuffle(self.population)
               if self.population[0].result > self.population[1].result:
                   self.population[0] = self.population[1]
               indiv = copy.deepcopy(self.population[0])
               self.population.pop(1)
               indiv.mutation()
               graph = indiv.config
               temp =  json.loads(graph_dumps(graph))

       # ......

As we can see, the overloaded method ``generate_parameters`` implements a pretty naive mutation algorithm. The code lines:

.. code-block:: python

               if self.population[0].result > self.population[1].result:
                   self.population[0] = self.population[1]
               indiv = copy.deepcopy(self.population[0])

controls the mutation process. It will always take two random individuals in the population, only keeping and mutating the one with better result.

3.4 Model configuration format
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

Here is an example of the model configuration, which is passed from the tuner to the trial in the architecture search procedure.

.. code-block:: json

   {
       "max_layer_num": 50,
       "layers": [
           {
               "input_size": 0,
               "type": 3,
               "output_size": 1,
               "input": [],
               "size": "x",
               "output": [4, 5],
               "is_delete": false
           },
           {
               "input_size": 0,
               "type": 3,
               "output_size": 1,
               "input": [],
               "size": "y",
               "output": [4, 5],
               "is_delete": false
           },
           {
               "input_size": 1,
               "type": 4,
               "output_size": 0,
               "input": [6],
               "size": "x",
               "output": [],
               "is_delete": false
           },
           {
               "input_size": 1,
               "type": 4,
               "output_size": 0,
               "input": [5],
               "size": "y",
               "output": [],
               "is_delete": false
           },
           {"Comment": "More layers will be here for actual graphs."}
       ]
   }

Every model configuration will have a "layers" section, which is a JSON list of layer definitions. The definition of each layer is also a JSON object, where:


* ``type`` is the type of the layer. 0, 1, 2, 3, 4 corresponds to attention, self-attention, RNN, input and output layer respectively.
* ``size`` is the length of the output. "x", "y" correspond to document length / question length, respectively.
* ``input_size`` is the number of inputs the layer has.
* ``input`` is the indices of layers taken as input of this layer.
* ``output`` is the indices of layers use this layer's output as their input.
* ``is_delete`` means whether the layer is still available.
