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class="externalLink" title="Apache Software Foundation"> + <span class="none"></span> + Apache Software Foundation</a> + </li> + + <li> + + <a href="http://www.comp.nus.edu.sg/~dbsystem/singa/"; class="externalLink" title="NUS Site"> + <span class="none"></span> + NUS Site</a> + </li> + </ul> + + + + <hr /> + + <div id="poweredBy"> + <div class="clear"></div> + <div class="clear"></div> + <div class="clear"></div> + <div class="clear"></div> + <a href="http://incubator.apache.org"; title="apache-incubator" class="builtBy"> + <img class="builtBy" alt="Apache Incubator" src="http://incubator.apache.org/images/egg-logo.png"; /> + </a> + </div> + </div> + </div> + + + <div id="bodyColumn" class="span10" > + + <h1>Quick Start</h1> +<hr /> +<div class="section"> +<h2><a name="SINGA_setup"></a>SINGA setup</h2> +<p>Please refer to the <a href="installation.html">installation</a> page for guidance on installing SINGA.</p> +<div class="section"> +<h3><a name="Starting_Zookeeper"></a>Starting Zookeeper</h3> +<p>SINGA uses <a class="externalLink" href="https://zookeeper.apache.org/";>zookeeper</a> to coordinate the training. Please make sure the zookeeper service is started before running SINGA.</p> +<p>If you installed the zookeeper using our thirdparty script, you can simply start it by:</p> + +<div class="source"> +<div class="source"><pre class="prettyprint">#goto top level folder +cd SINGA_ROOT +./bin/zk-service.sh start +</pre></div></div> +<p>(<tt>./bin/zk-service.sh stop</tt> stops the zookeeper).</p> +<p>Otherwise, if you launched a zookeeper by yourself but not used the default port, please edit the <tt>conf/singa.conf</tt>:</p> + +<div class="source"> +<div class="source"><pre class="prettyprint">zookeeper_host: "localhost:YOUR_PORT" +</pre></div></div></div></div> +<div class="section"> +<h2><a name="Running_in_standalone_mode"></a>Running in standalone mode</h2> +<p>Running SINGA in standalone mode is on the contrary of running it using cluster managers like <a class="externalLink" href="http://mesos.apache.org/";>Mesos</a> or <a class="externalLink" href="http://hadoop.apache.org/docs/current/hadoop-yarn/hadoop-yarn-site/YARN.html";>YARN</a>.</p> +<div class="section"> +<h3><a name="Training_on_a_single_node"></a>Training on a single node</h3> +<p>For single node training, one process will be launched to run SINGA at local host. We train the <a class="externalLink" href="http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks";>CNN model</a> over the <a class="externalLink" href="http://www.cs.toronto.edu/~kriz/cifar.html";>CIFAR-10</a> dataset as an example. The hyper-parameters are set following <a class="externalLink" href="https://code.google.com/p/cuda-convnet/";>cuda-convnet</a>. More details is available at <a href="cnn.html">CNN example</a>.</p> +<div class="section"> +<h4><a name="Preparing_data_and_job_configuration"></a>Preparing data and job configuration</h4> +<p>Download the dataset and create the data shards for training and testing.</p> + +<div class="source"> +<div class="source"><pre class="prettyprint">cd examples/cifar10/ +cp Makefile.example Makefile +make download +make create +</pre></div></div> +<p>A training dataset and a test dataset are created under <i>cifar10-train-shard</i> and <i>cifar10-test-shard</i> folder respectively. An <i>image_mean.bin</i> file is also generated, which contains the feature mean of all images.</p> +<p>Since all code used for training this CNN model is provided by SINGA as built-in implementation, there is no need to write any code. Instead, users just execute the running script (<i>../../bin/singa-run.sh</i>) by providing the job configuration file (<i>job.conf</i>). To code in SINGA, please refer to the <a href="programming-guide.html">programming guide</a>.</p></div> +<div class="section"> +<h4><a name="Training_without_parallelism"></a>Training without parallelism</h4> +<p>By default, the cluster topology has a single worker and a single server. In other words, neither the training data nor the neural net is partitioned.</p> +<p>The training is started by running:</p> + +<div class="source"> +<div class="source"><pre class="prettyprint"># goto top level folder +cd ../../ +./bin/singa-run.sh -conf examples/cifar10/job.conf +</pre></div></div> +<p>You can list the current running jobs by,</p> + +<div class="source"> +<div class="source"><pre class="prettyprint">./bin/singa-console.sh list + +JOB ID |NUM PROCS +----------|----------- +24 |1 +</pre></div></div> +<p>Jobs can be killed by,</p> + +<div class="source"> +<div class="source"><pre class="prettyprint">./bin/singa-console.sh kill JOB_ID +</pre></div></div> +<p>Logs and job information are available in <i>/tmp/singa-log</i> folder, which can be changed to other folders by setting <tt>log-dir</tt> in <i>conf/singa.conf</i>.</p></div> +<div class="section"> +<h4><a name="Asynchronous_parallel_training"></a>Asynchronous parallel training</h4> + +<div class="source"> +<div class="source"><pre class="prettyprint"># job.conf +... +cluster { + nworker_groups: 2 + nworkers_per_procs: 2 + workspace: "examples/cifar10/" +} +</pre></div></div> +<p>In SINGA, <a href="architecture.html">asynchronous training</a> is enabled by launching multiple worker groups. For example, we can change the original <i>job.conf</i> to have two worker groups as shown above. By default, each worker group has one worker. Since one process is set to contain two workers. The two worker groups will run in the same process. Consequently, they run the in-memory <a href="frameworks.html">Downpour</a> training framework. Users do not need to split the dataset explicitly for each worker (group); instead, they can assign each worker (group) a random offset to the start of the dataset. The workers would run as on different data partitions.</p> + +<div class="source"> +<div class="source"><pre class="prettyprint"># job.conf +... +neuralnet { + layer { + ... + sharddata_conf { + random_skip: 5000 + } + } + ... +} +</pre></div></div> +<p>The running command is:</p> + +<div class="source"> +<div class="source"><pre class="prettyprint">./bin/singa-run.sh -conf examples/cifar10/job.conf +</pre></div></div></div> +<div class="section"> +<h4><a name="Synchronous_parallel_training"></a>Synchronous parallel training</h4> + +<div class="source"> +<div class="source"><pre class="prettyprint"># job.conf +... +cluster { + nworkers_per_group: 2 + nworkers_per_procs: 2 + workspace: "examples/cifar10/" +} +</pre></div></div> +<p>In SINGA, <a href="architecture.html">asynchronous training</a> is enabled by launching multiple workers within one worker group. For instance, we can change the original <i>job.conf</i> to have two workers in one worker group as shown above. The workers will run synchronously as they are from the same worker group. This framework is the in-memory <a href="frameworks.html">sandblaster</a>. The model is partitioned among the two workers. In specific, each layer is sliced over the two workers. The sliced layer is the same as the original layer except that it only has <tt>B/g</tt> feature instances, where <tt>B</tt> is the number of instances in a mini-batch, <tt>g</tt> is the number of workers in a group. It is also possible to partition the layer (or neural net) using <a href="neural-net.html">other schemes</a>. All other settings are the same as running without partitioning</p> + +<div class="source"> +<div class="source"><pre class="prettyprint">./bin/singa-run.sh -conf examples/cifar10/job.conf +</pre></div></div></div></div> +<div class="section"> +<h3><a name="Training_in_a_cluster"></a>Training in a cluster</h3> +<p>We can extend the above two training frameworks to a cluster by updating the cluster configuration with:</p> + +<div class="source"> +<div class="source"><pre class="prettyprint">nworker_per_procs: 1 +</pre></div></div> +<p>Every process would then create only one worker thread. Consequently, the workers would be created in different processes (i.e., nodes). The <i>hostfile</i> must be provided under <i>SINGA_ROOT/conf/</i> specifying the nodes in the cluster, e.g.,</p> + +<div class="source"> +<div class="source"><pre class="prettyprint">logbase-a01 +logbase-a02 +</pre></div></div> +<p>And the zookeeper location must be configured correctly, e.g.,</p> + +<div class="source"> +<div class="source"><pre class="prettyprint">#conf/singa.conf +zookeeper_host: "logbase-a01" +</pre></div></div> +<p>The running command is the same as for single node training:</p> + +<div class="source"> +<div class="source"><pre class="prettyprint">./bin/singa-run.sh -conf examples/cifar10/job.conf +</pre></div></div></div></div> +<div class="section"> +<h2><a name="Running_with_Mesos"></a>Running with Mesos</h2> +<p><i>working</i>…</p></div> +<div class="section"> +<h2><a name="Where_to_go_next"></a>Where to go next</h2> +<p>The <a href="programming-guide.html">programming guide</a> pages will describe how to submit a training job in SINGA.</p></div> + </div> + </div> + </div> + + <hr/> + + <footer> + <div class="container-fluid"> + <div class="row-fluid"> + +<p>Copyright © 2015 The Apache Software Foundation. All rights reserved. Apache Singa, Apache, the Apache feather logo, and the Apache Singa project logos are trademarks of The Apache Software Foundation. All other marks mentioned may be trademarks or registered trademarks of their respective owners.</p> + </div> + + + </div> + </footer> + </body> +</html>
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The auto-encoder model is trained to reduce the dimensionality of the MNIST image feature. The RBM models are trained to initialize parameters of the auto-encoder model. This example application is from <a class="externalLink" href="http://www.cs.toronto.edu/~hinton/science.pdf";>Hinton’s science paper</a>.</p> +<div class="section"> +<h2><a name="Running_instructions"></a>Running instructions</h2> +<p>Running scripts are provided in <i>SINGA_ROOT/examples/rbm</i> folder.</p> +<p>The MNIST dataset has 70,000 handwritten digit images. The <a href="data.html">data preparation</a> page has details on converting this dataset into SINGA recognizable format. Users can simply run the following commands to download and convert the dataset.</p> + +<div class="source"> +<div class="source"><pre class="prettyprint"># at SINGA_ROOT/examples/mnist/ +$ cp Makefile.example Makefile +$ make download +$ make create +</pre></div></div> +<p>The training is separated into two phases, namely pre-training and fine-tuning. The pre-training phase trains 4 RBMs in sequence,</p> + +<div class="source"> +<div class="source"><pre class="prettyprint"># at SINGA_ROOT/ +$ ./bin/singa-run.sh -conf examples/rbm/rbm1.conf +$ ./bin/singa-run.sh -conf examples/rbm/rbm2.conf +$ ./bin/singa-run.sh -conf examples/rbm/rbm3.conf +$ ./bin/singa-run.sh -conf examples/rbm/rbm4.conf +</pre></div></div> +<p>The fine-tuning phase trains the auto-encoder by,</p> + +<div class="source"> +<div class="source"><pre class="prettyprint">$ ./bin/singa-run.sh -conf examples/rbm/autoencoder.conf +</pre></div></div></div> +<div class="section"> +<h2><a name="Training_details"></a>Training details</h2> +<div class="section"> +<h3><a name="RBM1"></a>RBM1</h3> +<p><img src="../images/example-rbm1.png" align="center" width="200px" alt="" /> <span><b>Figure 1 - RBM1.</b></span></p> +<p>The neural net structure for training RBM1 is shown in Figure 1. The data layer and parser layer provides features for training RBM1. The visible layer (connected with parser layer) of RBM1 accepts the image feature (784 dimension). The hidden layer is set to have 1000 neurons (units). These two layers are configured as,</p> + +<div class="source"> +<div class="source"><pre class="prettyprint">layer{ + name: "RBMVis" + type: kRBMVis + srclayers:"mnist" + srclayers:"RBMHid" + rbm_conf{ + hdim: 1000 + } + param{ + name: "w1" + init{ + type: kGaussian + mean: 0.0 + std: 0.1 + } + } + param{ + name: "b11" + init{ + type: kConstant + value: 0.0 + } + } +} + +layer{ + name: "RBMHid" + type: kRBMHid + srclayers:"RBMVis" + rbm_conf{ + hdim: 1000 + } + param{ + name: "w1_" + share_from: "w1" + } + param{ + name: "b12" + init{ + type: kConstant + value: 0.0 + } + } +} +</pre></div></div> +<p>For RBM, the weight matrix is shared by the visible and hidden layers. For instance, <tt>w1</tt> is shared by <tt>vis</tt> and <tt>hid</tt> layers shown in Figure 1. In SINGA, we can configure the <tt>share_from</tt> field to enable <a href="param.html">parameter sharing</a> as shown above for the param <tt>w1</tt> and <tt>w1_</tt>.</p> +<p><a href="train-one-batch.html#contrastive-divergence">Contrastive Divergence</a> is configured as the algorithm for <a href="train-one-batch.html">TrainOneBatch</a>. Following Hinton’s paper, we configure the <a href="updater.html">updating protocol</a> as follows,</p> + +<div class="source"> +<div class="source"><pre class="prettyprint"># Updater Configuration +updater{ + type: kSGD + momentum: 0.2 + weight_decay: 0.0002 + learning_rate{ + base_lr: 0.1 + type: kFixed + } +} +</pre></div></div> +<p>Since the parameters of RBM0 will be used to initialize the auto-encoder, we should configure the <tt>workspace</tt> field to specify a path for the checkpoint folder. For example, if we configure it as,</p> + +<div class="source"> +<div class="source"><pre class="prettyprint">cluster { + workspace: "examples/rbm/rbm1/" +} +</pre></div></div> +<p>Then SINGA will <a href="checkpoint.html">checkpoint the parameters</a> into <i>examples/rbm/rbm1/</i>.</p></div> +<div class="section"> +<h3><a name="RBM1"></a>RBM1</h3> +<p><img src="../images/example-rbm2.png" align="center" width="200px" alt="" /> <span><b>Figure 2 - RBM2.</b></span></p> +<p>Figure 2 shows the net structure of training RBM2. The visible units of RBM2 accept the output from the Sigmoid1 layer. The Inner1 layer is a <tt>InnerProductLayer</tt> whose parameters are set to the <tt>w1</tt> and <tt>b12</tt> learned from RBM1. The neural net configuration is (with layers for data layer and parser layer omitted).</p> + +<div class="source"> +<div class="source"><pre class="prettyprint">layer{ + name: "Inner1" + type: kInnerProduct + srclayers:"mnist" + innerproduct_conf{ + num_output: 1000 + } + param{ name: "w1" } + param{ name: "b12"} +} + +layer{ + name: "Sigmoid1" + type: kSigmoid + srclayers:"Inner1" +} + +layer{ + name: "RBMVis" + type: kRBMVis + srclayers:"Sigmoid1" + srclayers:"RBMHid" + rbm_conf{ + hdim: 500 + } + param{ + name: "w2" + ... + } + param{ + name: "b21" + ... + } +} + +layer{ + name: "RBMHid" + type: kRBMHid + srclayers:"RBMVis" + rbm_conf{ + hdim: 500 + } + param{ + name: "w2_" + share_from: "w2" + } + param{ + name: "b22" + ... + } +} +</pre></div></div> +<p>To load w0 and b02 from RBM0’s checkpoint file, we configure the <tt>checkpoint_path</tt> as,</p> + +<div class="source"> +<div class="source"><pre class="prettyprint">checkpoint_path: "examples/rbm/rbm1/checkpoint/step6000-worker0" +cluster{ + workspace: "examples/rbm/rbm2" +} +</pre></div></div> +<p>The workspace is changed for checkpointing <tt>w2</tt>, <tt>b21</tt> and <tt>b22</tt> into <i>examples/rbm/rbm2/</i>.</p></div> +<div class="section"> +<h3><a name="RBM3"></a>RBM3</h3> +<p><img src="../images/example-rbm3.png" align="center" width="200px" alt="" /> <span><b>Figure 3 - RBM3.</b></span></p> +<p>Figure 3 shows the net structure of training RBM3. In this model, a layer with 250 units is added as the hidden layer of RBM3. The visible units of RBM3 accepts output from Sigmoid2 layer. Parameters of Inner1 and Innner2 are set to <tt>w1,b12,w2,b22</tt> which can be load from the checkpoint file of RBM2, i.e., “examples/rbm/rbm2/”.</p></div> +<div class="section"> +<h3><a name="RBM4"></a>RBM4</h3> +<p><img src="../images/example-rbm4.png" align="center" width="200px" alt="" /> <span><b>Figure 4 - RBM4.</b></span></p> +<p>Figure 4 shows the net structure of training RBM4. It is similar to Figure 3, but according to <a class="externalLink" href="http://www.cs.toronto.edu/~hinton/science.pdf";>Hinton’s science paper</a>, the hidden units of the top RBM (RBM4) have stochastic real-valued states drawn from a unit variance Gaussian whose mean is determined by the input from the RBM’s logistic visible units. So we add a <tt>gaussian</tt> field in the RBMHid layer to control the sampling distribution (Gaussian or Bernoulli). In addition, this RBM has a much smaller learning rate (0.001). The neural net configuration for the RBM4 and the updating protocol is (with layers for data layer and parser layer omitted),</p> + +<div class="source"> +<div class="source"><pre class="prettyprint"># Updater Configuration +updater{ + type: kSGD + momentum: 0.9 + weight_decay: 0.0002 + learning_rate{ + base_lr: 0.001 + type: kFixed + } +} + +layer{ + name: "RBMVis" + type: kRBMVis + srclayers:"Sigmoid3" + srclayers:"RBMHid" + rbm_conf{ + hdim: 30 + } + param{ + name: "w4" + ... + } + param{ + name: "b41" + ... + } +} + +layer{ + name: "RBMHid" + type: kRBMHid + srclayers:"RBMVis" + rbm_conf{ + hdim: 30 + gaussian: true + } + param{ + name: "w4_" + share_from: "w4" + } + param{ + name: "b42" + ... + } +} +</pre></div></div></div> +<div class="section"> +<h3><a name="Auto-encoder"></a>Auto-encoder</h3> +<p>In the fine-tuning stage, the 4 RBMs are “unfolded” to form encoder and decoder networks that are initialized using the parameters from the previous 4 RBMs.</p> +<p><img src="../images/example-autoencoder.png" align="center" width="500px" alt="" /> <span><b>Figure 5 - Auto-Encoders.</b></span></p> +<p>Figure 5 shows the neural net structure for training the auto-encoder. <a href="train-one-batch.html">Back propagation (kBP)</a> is configured as the algorithm for <tt>TrainOneBatch</tt>. We use the same cluster configuration as RBM models. For updater, we use <a href="updater.html#adagradupdater">AdaGrad</a> algorithm with fixed learning rate.</p> + +<div class="source"> +<div class="source"><pre class="prettyprint">### Updater Configuration +updater{ + type: kAdaGrad + learning_rate{ + base_lr: 0.01 + type: kFixed + } +} +</pre></div></div> +<p>According to <a class="externalLink" href="http://www.cs.toronto.edu/~hinton/science.pdf";>Hinton’s science paper</a>, we configure a EuclideanLoss layer to compute the reconstruction error. The neural net configuration is (with some of the middle layers omitted),</p> + +<div class="source"> +<div class="source"><pre class="prettyprint">layer{ name: "data" } +layer{ name:"mnist" } +layer{ + name: "Inner1" + param{ name: "w1" } + param{ name: "b12" } +} +layer{ name: "Sigmoid1" } +... +layer{ + name: "Inner8" + innerproduct_conf{ + num_output: 784 + transpose: true + } + param{ + name: "w8" + share_from: "w1" + } + param{ name: "b11" } +} +layer{ name: "Sigmoid8" } + +# Euclidean Loss Layer Configuration +layer{ + name: "loss" + type:kEuclideanLoss + srclayers:"Sigmoid8" + srclayers:"mnist" +} +</pre></div></div> +<p>To load pre-trained parameters from the 4 RBMs’ checkpoint file we configure <tt>checkpoint_path</tt> as</p> + +<div class="source"> +<div class="source"><pre class="prettyprint">### Checkpoint Configuration +checkpoint_path: "examples/rbm/checkpoint/rbm1/checkpoint/step6000-worker0" +checkpoint_path: "examples/rbm/checkpoint/rbm2/checkpoint/step6000-worker0" +checkpoint_path: "examples/rbm/checkpoint/rbm3/checkpoint/step6000-worker0" +checkpoint_path: "examples/rbm/checkpoint/rbm4/checkpoint/step6000-worker0" +</pre></div></div></div></div> +<div class="section"> +<h2><a name="Visualization_Results"></a>Visualization Results</h2> + +<div> +<img src="../images/rbm-weight.PNG" align="center" width="300px" alt="" /> + +<img src="../images/rbm-feature.PNG" align="center" width="300px" alt="" /> +<br /> +<span><b>Figure 6 - Bottom RBM weight matrix.</b></span> +  +  +  +  + +<span><b>Figure 7 - Top layer features.</b></span> +</div> +<p>Figure 6 visualizes sample columns of the weight matrix of RBM1, We can see the Gabor-like filters are learned. Figure 7 depicts the features extracted from the top-layer of the auto-encoder, wherein one point represents one image. Different colors represent different digits. We can see that most images are well clustered according to the ground truth.</p></div> + </div> + </div> + </div> + + <hr/> + + <footer> + <div class="container-fluid"> + <div class="row-fluid"> + +<p>Copyright © 2015 The Apache Software Foundation. All rights reserved. Apache Singa, Apache, the Apache feather logo, and the Apache Singa project logos are trademarks of The Apache Software Foundation. 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In this example, we use SINGA to train a <a class="externalLink" href="http://www.fit.vutbr.cz/research/groups/speech/publi/2010/mikolov_interspeech2010_IS100722.pdf";>RNN model</a> proposed by Tomas Mikolov for <a class="externalLink" href="https://en.wikipedia.org/wiki/Language_model";>language modeling</a>. The training objective (loss) is to minimize the <a class="externalLink" href="https://en.wikipedia.org/wiki/Perplexity";>perplexity per word</a>, which is equivalent to maximize the probability of predicting the next word given the current word in a sentence.</p> +<p>Different to the <a href="cnn.html">CNN</a>, <a href="mlp.html">MLP</a> and <a href="rbm.html">RBM</a> examples which use built-in layers(layer) and records(data), none of the layers in this example are built-in. Hence users would learn to implement their own layers and data records through this example.</p> +<div class="section"> +<h2><a name="Running_instructions"></a>Running instructions</h2> +<p>In <i>SINGA_ROOT/examples/rnnlm/</i>, scripts are provided to run the training job. First, the data is prepared by</p> + +<div class="source"> +<div class="source"><pre class="prettyprint">$ cp Makefile.example Makefile +$ make download +$ make create +</pre></div></div> +<p>Second, to compile the source code under <i>examples/rnnlm/</i>, run</p> + +<div class="source"> +<div class="source"><pre class="prettyprint">$ make rnnlm +</pre></div></div> +<p>An executable file <i>rnnlm.bin</i> will be generated.</p> +<p>Third, the training is started by passing <i>rnnlm.bin</i> and the job configuration to <i>singa-run.sh</i>,</p> + +<div class="source"> +<div class="source"><pre class="prettyprint"># at SINGA_ROOT/ +# export LD_LIBRARY_PATH=.libs:$LD_LIBRARY_PATH +$ ./bin/singa-run.sh -exec examples/rnnlm/rnnlm.bin -conf examples/rnnlm/job.conf +</pre></div></div></div> +<div class="section"> +<h2><a name="Implementations"></a>Implementations</h2> +<p><img src="../images/rnnlm.png" align="center" width="400px" alt="" /> <span><b>Figure 1 - Net structure of the RNN model.</b></span></p> +<p>The neural net structure is shown Figure 1. Word records are loaded by <tt>DataLayer</tt>. For every iteration, at most <tt>max_window</tt> word records are processed. If a sentence ending character is read, the <tt>DataLayer</tt> stops loading immediately. <tt>EmbeddingLayer</tt> looks up a word embedding matrix to extract feature vectors for words loaded by the <tt>DataLayer</tt>. These features are transformed by the <tt>HiddenLayer</tt> which propagates the features from left to right. The output feature for word at position k is influenced by words from position 0 to k-1. Finally, <tt>LossLayer</tt> computes the cross-entropy loss (see below) by predicting the next word of each word. The cross-entropy loss is computed as</p> +<p><tt>$$L(w_t)=-log P(w_{t+1}|w_t)$$</tt></p> +<p>Given <tt>$w_t$</tt> the above equation would compute over all words in the vocabulary, which is time consuming. <a class="externalLink" href="https://f25ea9ccb7d3346ce6891573d543960492b92c30.googledrive.com/host/0ByxdPXuxLPS5RFM5dVNvWVhTd0U/rnnlm-0.4b.tgz";>RNNLM Toolkit</a> accelerates the computation as</p> +<p><tt>$$P(w_{t+1}|w_t) = P(C_{w_{t+1}}|w_t) * P(w_{t+1}|C_{w_{t+1}})$$</tt></p> +<p>Words from the vocabulary are partitioned into a user-defined number of classes. The first term on the left side predicts the class of the next word, and then predicts the next word given its class. Both the number of classes and the words from one class are much smaller than the vocabulary size. The probabilities can be calculated much faster.</p> +<p>The perplexity per word is computed by,</p> +<p><tt>$$PPL = 10^{- avg_t log_{10} P(w_{t+1}|w_t)}$$</tt></p> +<div class="section"> +<h3><a name="Data_preparation"></a>Data preparation</h3> +<p>We use a small dataset provided by the <a class="externalLink" href="https://f25ea9ccb7d3346ce6891573d543960492b92c30.googledrive.com/host/0ByxdPXuxLPS5RFM5dVNvWVhTd0U/rnnlm-0.4b.tgz";>RNNLM Toolkit</a>. It has 10,000 training sentences, with 71350 words in total and 3720 unique words. The subsequent steps follow the instructions in <a href="data.html">Data Preparation</a> to convert the raw data into records and insert them into data stores.</p> +<div class="section"> +<h4><a name="Download_source_data"></a>Download source data</h4> + +<div class="source"> +<div class="source"><pre class="prettyprint"># in SINGA_ROOT/examples/rnnlm/ +cp Makefile.example Makefile +make download +</pre></div></div></div> +<div class="section"> +<h4><a name="Define_record_format"></a>Define record format</h4> +<p>We define the word record as follows,</p> + +<div class="source"> +<div class="source"><pre class="prettyprint"># in SINGA_ROOT/examples/rnnlm/rnnlm.proto +message WordRecord { + optional string word = 1; + optional int32 word_index = 2; + optional int32 class_index = 3; + optional int32 class_start = 4; + optional int32 class_end = 5; +} +</pre></div></div> +<p>It includes the word string and its index in the vocabulary. Words in the vocabulary are sorted based on their frequency in the training dataset. The sorted list is cut into 100 sublists such that each sublist has 1/100 total word frequency. Each sublist is called a class. Hence each word has a <tt>class_index</tt> ([0,100)). The <tt>class_start</tt> is the index of the first word in the same class as <tt>word</tt>. The <tt>class_end</tt> is the index of the first word in the next class.</p></div> +<div class="section"> +<h4><a name="Create_data_stores"></a>Create data stores</h4> +<p>We use code from RNNLM Toolkit to read words, and sort them into classes. The main function in <i>create_store.cc</i> first creates word classes based on the training dataset. Second it calls the following function to create data store for the training, validation and test dataset.</p> + +<div class="source"> +<div class="source"><pre class="prettyprint">int create_data(const char *input_file, const char *output_file); +</pre></div></div> +<p><tt>input</tt> is the path to training/validation/testing text file from the RNNLM Toolkit, <tt>output</tt> is output store file. This function starts with</p> + +<div class="source"> +<div class="source"><pre class="prettyprint">singa::io::KVFile store; +store.Open(output, signa::io::kCreate); +</pre></div></div> +<p>Then it reads the words one by one. For each word it creates a <tt>WordRecord</tt> instance, and inserts it into the store,</p> + +<div class="source"> +<div class="source"><pre class="prettyprint">int wcnt = 0; // word count +WordRecord wordRecord; +while(1) { + readWord(wordstr, fin); + if (feof(fin)) break; + ...// fill in the wordRecord; + string val; + wordRecord.SerializeToString(&val); + int length = snprintf(key, BUFFER_LEN, "%05d", wcnt++); + store.Write(string(key, length), val); +} +</pre></div></div> +<p>Compilation and running commands are provided in the <i>Makefile.example</i>. After executing</p> + +<div class="source"> +<div class="source"><pre class="prettyprint">make create +</pre></div></div> +<p><i>train_data.bin</i>, <i>test_data.bin</i> and <i>valid_data.bin</i> will be created.</p></div></div> +<div class="section"> +<h3><a name="Layer_implementation"></a>Layer implementation</h3> +<p>4 user-defined layers are implemented for this application. Following the guide for implementing <a href="layer#implementing-a-new-layer-subclass">new Layer subclasses</a>, we extend the <a href="../api/classsinga_1_1LayerProto.html">LayerProto</a> to include the configuration messages of user-defined layers as shown below (3 out of the 7 layers have specific configurations),</p> + +<div class="source"> +<div class="source"><pre class="prettyprint">import "job.proto"; // Layer message for SINGA is defined + +//For implementation of RNNLM application +extend singa.LayerProto { + optional EmbeddingProto embedding_conf = 101; + optional LossProto loss_conf = 102; + optional DataProto data_conf = 103; +} +</pre></div></div> +<p>In the subsequent sections, we describe the implementation of each layer, including its configuration message.</p> +<div class="section"> +<h4><a name="RNNLayer"></a>RNNLayer</h4> +<p>This is the base layer of all other layers for this applications. It is defined as follows,</p> + +<div class="source"> +<div class="source"><pre class="prettyprint">class RNNLayer : virtual public Layer { +public: + inline int window() { return window_; } +protected: + int window_; +}; +</pre></div></div> +<p>For this application, two iterations may process different number of words. Because sentences have different lengths. The <tt>DataLayer</tt> decides the effective window size. All other layers call its source layers to get the effective window size and resets <tt>window_</tt> in <tt>ComputeFeature</tt> function.</p></div> +<div class="section"> +<h4><a name="DataLayer"></a>DataLayer</h4> +<p>DataLayer is for loading Records.</p> + +<div class="source"> +<div class="source"><pre class="prettyprint">class DataLayer : public RNNLayer, singa::InputLayer { + public: + void Setup(const LayerProto& proto, const vector<Layer*>& srclayers) override; + void ComputeFeature(int flag, const vector<Layer*>& srclayers) override; + int max_window() const { + return max_window_; + } + private: + int max_window_; + singa::io::Store* store_; +}; +</pre></div></div> +<p>The Setup function gets the user configured max window size.</p> + +<div class="source"> +<div class="source"><pre class="prettyprint">max_window_ = proto.GetExtension(input_conf).max_window(); +</pre></div></div> +<p>The <tt>ComputeFeature</tt> function loads at most max_window records. It could also stop when the sentence ending character is encountered.</p> + +<div class="source"> +<div class="source"><pre class="prettyprint">...// shift the last record to the first +window_ = max_window_; +for (int i = 1; i <= max_window_; i++) { + // load record; break if it is the ending character +} +</pre></div></div> +<p>The configuration of <tt>DataLayer</tt> is like</p> + +<div class="source"> +<div class="source"><pre class="prettyprint">name: "data" +user_type: "kData" +[data_conf] { + path: "examples/rnnlm/train_data.bin" + max_window: 10 +} +</pre></div></div></div> +<div class="section"> +<h4><a name="EmbeddingLayer"></a>EmbeddingLayer</h4> +<p>This layer gets records from <tt>DataLayer</tt>. For each record, the word index is parsed and used to get the corresponding word feature vector from the embedding matrix.</p> +<p>The class is declared as follows,</p> + +<div class="source"> +<div class="source"><pre class="prettyprint">class EmbeddingLayer : public RNNLayer { + ... + const std::vector<Param*> GetParams() const override { + std::vector<Param*> params{embed_}; + return params; + } + private: + int word_dim_, vocab_size_; + Param* embed_; +} +</pre></div></div> +<p>The <tt>embed_</tt> field is a matrix whose values are parameter to be learned. The matrix size is <tt>vocab_size_</tt> x <tt>word_dim_</tt>.</p> +<p>The Setup function reads configurations for <tt>word_dim_</tt> and <tt>vocab_size_</tt>. Then it allocates feature Blob for <tt>max_window</tt> words and setups <tt>embed_</tt>.</p> + +<div class="source"> +<div class="source"><pre class="prettyprint">int max_window = srclayers[0]->data(this).shape()[0]; +word_dim_ = proto.GetExtension(embedding_conf).word_dim(); +data_.Reshape(vector<int>{max_window, word_dim_}); +... +embed_->Setup(vector<int>{vocab_size_, word_dim_}); +</pre></div></div> +<p>The <tt>ComputeFeature</tt> function simply copies the feature vector from the <tt>embed_</tt> matrix into the feature Blob.</p> + +<div class="source"> +<div class="source"><pre class="prettyprint"># reset effective window size +window_ = datalayer->window(); +auto records = datalayer->records(); +... +for (int t = 0; t < window_; t++) { + int idx <- word index + Copy(words[t], embed[idx]); +} +</pre></div></div> +<p>The <tt>ComputeGradient</tt> function copies back the gradients to the <tt>embed_</tt> matrix.</p> +<p>The configuration for <tt>EmbeddingLayer</tt> is like,</p> + +<div class="source"> +<div class="source"><pre class="prettyprint">user_type: "kEmbedding" +[embedding_conf] { + word_dim: 15 + vocab_size: 3720 +} +srclayers: "data" +param { + name: "w1" + init { + type: kUniform + low:-0.3 + high:0.3 + } +} +</pre></div></div></div> +<div class="section"> +<h4><a name="HiddenLayer"></a>HiddenLayer</h4> +<p>This layer unrolls the recurrent connections for at most max_window times. The feature for position k is computed based on the feature from the embedding layer (position k) and the feature at position k-1 of this layer. The formula is</p> +<p><tt>$$f[k]=\sigma (f[t-1]*W+src[t])$$</tt></p> +<p>where <tt>$W$</tt> is a matrix with <tt>word_dim_</tt> x <tt>word_dim_</tt> parameters.</p> +<p>If you want to implement a recurrent neural network following our design, this layer is of vital importance for you to refer to.</p> + +<div class="source"> +<div class="source"><pre class="prettyprint">class HiddenLayer : public RNNLayer { + ... + const std::vector<Param*> GetParams() const override { + std::vector<Param*> params{weight_}; + return params; + } +private: + Param* weight_; +}; +</pre></div></div> +<p>The <tt>Setup</tt> function setups the weight matrix as</p> + +<div class="source"> +<div class="source"><pre class="prettyprint">weight_->Setup(std::vector<int>{word_dim, word_dim}); +</pre></div></div> +<p>The <tt>ComputeFeature</tt> function gets the effective window size (<tt>window_</tt>) from its source layer i.e., the embedding layer. Then it propagates the feature from position 0 to position <tt>window_</tt> -1. The detailed descriptions for this process are illustrated as follows.</p> + +<div class="source"> +<div class="source"><pre class="prettyprint">void HiddenLayer::ComputeFeature() { + for(int t = 0; t < window_size; t++){ + if(t == 0) + Copy(data[t], src[t]); + else + data[t]=sigmoid(data[t-1]*W + src[t]); + } +} +</pre></div></div> +<p>The <tt>ComputeGradient</tt> function computes the gradient of the loss w.r.t. W and the source layer. Particularly, for each position k, since data[k] contributes to data[k+1] and the feature at position k in its destination layer (the loss layer), grad[k] should contains the gradient from two parts. The destination layer has already computed the gradient from the loss layer into grad[k]; In the <tt>ComputeGradient</tt> function, we need to add the gradient from position k+1.</p> + +<div class="source"> +<div class="source"><pre class="prettyprint">void HiddenLayer::ComputeGradient(){ + ... + for (int k = window_ - 1; k >= 0; k--) { + if (k < window_ - 1) { + grad[k] += dot(grad[k + 1], weight.T()); // add gradient from position t+1. + } + grad[k] =... // compute gL/gy[t], y[t]=data[t-1]*W+src[t] + } + gweight = dot(data.Slice(0, window_-1).T(), grad.Slice(1, window_)); + Copy(gsrc, grad); +} +</pre></div></div> +<p>After the loop, we get the gradient of the loss w.r.t y[k], which is used to compute the gradient of W and the src[k].</p></div> +<div class="section"> +<h4><a name="LossLayer"></a>LossLayer</h4> +<p>This layer computes the cross-entropy loss and the <tt>$log_{10}P(w_{t+1}|w_t)$</tt> (which could be averaged over all words by users to get the PPL value).</p> +<p>There are two configuration fields to be specified by users.</p> + +<div class="source"> +<div class="source"><pre class="prettyprint">message LossProto { + optional int32 nclass = 1; + optional int32 vocab_size = 2; +} +</pre></div></div> +<p>There are two weight matrices to be learned</p> + +<div class="source"> +<div class="source"><pre class="prettyprint">class LossLayer : public RNNLayer { + ... + private: + Param* word_weight_, *class_weight_; +} +</pre></div></div> +<p>The ComputeFeature function computes the two probabilities respectively.</p> +<p><tt>$$P(C_{w_{t+1}}|w_t) = Softmax(w_t * class\_weight_)$$</tt> <tt>$$P(w_{t+1}|C_{w_{t+1}}) = Softmax(w_t * word\_weight[class\_start:class\_end])$$</tt></p> +<p><tt>$w_t$</tt> is the feature from the hidden layer for the k-th word, its ground truth next word is <tt>$w_{t+1}$</tt>. The first equation computes the probability distribution over all classes for the next word. The second equation computes the probability distribution over the words in the ground truth class for the next word.</p> +<p>The ComputeGradient function computes the gradient of the source layer (i.e., the hidden layer) and the two weight matrices.</p></div></div> +<div class="section"> +<h3><a name="Updater_Configuration"></a>Updater Configuration</h3> +<p>We employ kFixedStep type of the learning rate change method and the configuration is as follows. We decay the learning rate once the performance does not increase on the validation dataset.</p> + +<div class="source"> +<div class="source"><pre class="prettyprint">updater{ + type: kSGD + learning_rate { + type: kFixedStep + fixedstep_conf:{ + step:0 + step:48810 + step:56945 + step:65080 + step:73215 + step_lr:0.1 + step_lr:0.05 + step_lr:0.025 + step_lr:0.0125 + step_lr:0.00625 + } + } +} +</pre></div></div></div> +<div class="section"> +<h3><a name="TrainOneBatch_Function"></a>TrainOneBatch() Function</h3> +<p>We use BP (BackPropagation) algorithm to train the RNN model here. The corresponding configuration can be seen below.</p> + +<div class="source"> +<div class="source"><pre class="prettyprint"># In job.conf file +train_one_batch { + alg: kBackPropagation +} +</pre></div></div></div> +<div class="section"> +<h3><a name="Cluster_Configuration"></a>Cluster Configuration</h3> +<p>The default cluster configuration can be used, i.e., single worker and single server in a single process.</p></div></div> + </div> + </div> + </div> + + <hr/> + + <footer> + <div class="container-fluid"> + <div class="row-fluid"> + +<p>Copyright © 2015 The Apache Software Foundation. All rights reserved. Apache Singa, Apache, the Apache feather logo, and the Apache Singa project logos are trademarks of The Apache Software Foundation. All other marks mentioned may be trademarks or registered trademarks of their respective owners.</p> + </div> + + + </div> + </footer> + </body> +</html>
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