# Automatic Model Architecture Search for Reading Comprehension This example shows us how to use Genetic Algorithm to find good model architectures for Reading Comprehension task. ## Search Space Since attention and recurrent neural network (RNN) module have been proven effective in Reading Comprehension. We conclude the search space as follow: 1. IDENTITY (Effectively means keep training). 2. INSERT-RNN-LAYER (Inserts a LSTM. Comparing the performance of GRU and LSTM in our experiment, we decided to use LSTM here.) 3. REMOVE-RNN-LAYER 4. INSERT-ATTENTION-LAYER(Inserts a attention layer.) 5. REMOVE-ATTENTION-LAYER 6. ADD-SKIP (Identity between random layers). 7. REMOVE-SKIP (Removes random skip). ![ga-squad-logo](./ga_squad.png) ## New version Also we have another version which time cost is less and performance is better. We will release soon. # How to run this example? ## Use downloading script to download data Execute the following command to download needed files using the downloading script: ``` chmod +x ./download.sh ./download.sh ``` ## Download manually 1. download "dev-v1.1.json" and "train-v1.1.json" in https://rajpurkar.github.io/SQuAD-explorer/ ``` wget https://rajpurkar.github.io/SQuAD-explorer/dataset/train-v1.1.json wget https://rajpurkar.github.io/SQuAD-explorer/dataset/dev-v1.1.json ``` 2. download "glove.840B.300d.txt" in https://nlp.stanford.edu/projects/glove/ ``` wget http://nlp.stanford.edu/data/glove.840B.300d.zip unzip glove.840B.300d.zip ``` ## Update configuration Modify `nni/examples/trials/ga_squad/config.yaml`, here is the default configuration: ``` authorName: default experimentName: example_ga_squad trialConcurrency: 1 maxExecDuration: 1h maxTrialNum: 1 #choice: local, remote trainingServicePlatform: local #choice: true, false useAnnotation: false tuner: codeDir: ~/nni/examples/tuners/ga_customer_tuner classFileName: customer_tuner.py className: CustomerTuner classArgs: optimize_mode: maximize trial: command: python3 trial.py codeDir: ~/nni/examples/trials/ga_squad gpuNum: 0 ``` In the "trial" part, if you want to use GPU to perform the architecture search, change `gpuNum` from `0` to `1`. You need to increase the `maxTrialNum` and `maxExecDuration`, according to how long you want to wait for the search result. `trialConcurrency` is the number of trials running concurrently, which is the number of GPUs you want to use, if you are setting `gpuNum` to 1. ## submit this job ``` nnictl create --config ~/nni/examples/trials/ga_squad/config.yaml ``` # Techinal details about the trial ## 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. ### 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` is special. `graph_to_tf.py` has a function named as `graph_to_network`, here is its skelton code: ``` 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. ``` topology = graph.is_topology() ``` performs topological sorting on the internal graph representation, and the code inside the loop: ``` for _, topo_i in enumerate(topology): ``` performs actually conversion that maps each layer to a part in Tensorflow computation graph. ### 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`: ``` 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: ``` 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. ## 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. ``` { "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 has 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 correspond 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.