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| "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY |
| KIND, either express or implied. See the License for the |
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| under the License. |
| --><div class="section" id="neural-net"> |
| <h1>Neural Net<a class="headerlink" href="#neural-net" title="Permalink to this headline">¶</a></h1> |
| <p><code class="docutils literal"><span class="pre">NeuralNet</span></code> in SINGA represents an instance of user‘s neural net model. As the |
| neural net typically consists of a set of layers, <code class="docutils literal"><span class="pre">NeuralNet</span></code> comprises |
| a set of unidirectionally connected <a class="reference external" href="layer.html">Layer</a>s. |
| This page describes how to convert an user‘s neural net into |
| the configuration of <code class="docutils literal"><span class="pre">NeuralNet</span></code>.</p> |
| <img src="../_static/images/model-category.png" align="center" width="200px"/> |
| <span><strong>Figure 1 - Categorization of popular deep learning models.</strong></span><div class="section" id="net-structure-configuration"> |
| <h2>Net structure configuration<a class="headerlink" href="#net-structure-configuration" title="Permalink to this headline">¶</a></h2> |
| <p>Users configure the <code class="docutils literal"><span class="pre">NeuralNet</span></code> by listing all layers of the neural net and |
| specifying each layer‘s source layer names. Popular deep learning models can be |
| categorized as Figure 1. The subsequent sections give details for each |
| category.</p> |
| <div class="section" id="feed-forward-models"> |
| <h3>Feed-forward models<a class="headerlink" href="#feed-forward-models" title="Permalink to this headline">¶</a></h3> |
| <div align = "left"> |
| <img src="../_static/images/mlp-net.png" align="center" width="200px"/> |
| <span><strong>Figure 2 - Net structure of a MLP model.</strong></span> |
| </div><p>Feed-forward models, e.g., CNN and MLP, can easily get configured as their layer |
| connections are undirected without circles. The |
| configuration for the MLP model shown in Figure 1 is as follows,</p> |
| <div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">net</span> <span class="p">{</span> |
| <span class="n">layer</span> <span class="p">{</span> |
| <span class="n">name</span> <span class="p">:</span> <span class="s1">'data"</span> |
| <span class="nb">type</span> <span class="p">:</span> <span class="n">kData</span> |
| <span class="p">}</span> |
| <span class="n">layer</span> <span class="p">{</span> |
| <span class="n">name</span> <span class="p">:</span> <span class="s1">'image"</span> |
| <span class="nb">type</span> <span class="p">:</span> <span class="n">kImage</span> |
| <span class="n">srclayer</span><span class="p">:</span> <span class="s1">'data'</span> |
| <span class="p">}</span> |
| <span class="n">layer</span> <span class="p">{</span> |
| <span class="n">name</span> <span class="p">:</span> <span class="s1">'label"</span> |
| <span class="nb">type</span> <span class="p">:</span> <span class="n">kLabel</span> |
| <span class="n">srclayer</span><span class="p">:</span> <span class="s1">'data'</span> |
| <span class="p">}</span> |
| <span class="n">layer</span> <span class="p">{</span> |
| <span class="n">name</span> <span class="p">:</span> <span class="s1">'hidden"</span> |
| <span class="nb">type</span> <span class="p">:</span> <span class="n">kHidden</span> |
| <span class="n">srclayer</span><span class="p">:</span> <span class="s1">'image'</span> |
| <span class="p">}</span> |
| <span class="n">layer</span> <span class="p">{</span> |
| <span class="n">name</span> <span class="p">:</span> <span class="s1">'softmax"</span> |
| <span class="nb">type</span> <span class="p">:</span> <span class="n">kSoftmaxLoss</span> |
| <span class="n">srclayer</span><span class="p">:</span> <span class="s1">'hidden'</span> |
| <span class="n">srclayer</span><span class="p">:</span> <span class="s1">'label'</span> |
| <span class="p">}</span> |
| <span class="p">}</span> |
| </pre></div> |
| </div> |
| </div> |
| <div class="section" id="energy-models"> |
| <h3>Energy models<a class="headerlink" href="#energy-models" title="Permalink to this headline">¶</a></h3> |
| <img src="../_static/images/rbm-rnn.png" align="center" width="500px"/> |
| <span><strong>Figure 3 - Convert connections in RBM and RNN.</strong></span><p>For energy models including RBM, DBM, |
| etc., their connections are undirected (i.e., Category B). To represent these models using |
| <code class="docutils literal"><span class="pre">NeuralNet</span></code>, users can simply replace each connection with two directed |
| connections, as shown in Figure 3a. In other words, for each pair of connected layers, their source |
| layer field should include each other‘s name. |
| The full <a class="reference external" href="rbm.html">RBM example</a> has |
| detailed neural net configuration for a RBM model, which looks like</p> |
| <div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">net</span> <span class="p">{</span> |
| <span class="n">layer</span> <span class="p">{</span> |
| <span class="n">name</span> <span class="p">:</span> <span class="s2">"vis"</span> |
| <span class="nb">type</span> <span class="p">:</span> <span class="n">kVisLayer</span> |
| <span class="n">param</span> <span class="p">{</span> |
| <span class="n">name</span> <span class="p">:</span> <span class="s2">"w1"</span> |
| <span class="p">}</span> |
| <span class="n">srclayer</span><span class="p">:</span> <span class="s2">"hid"</span> |
| <span class="p">}</span> |
| <span class="n">layer</span> <span class="p">{</span> |
| <span class="n">name</span> <span class="p">:</span> <span class="s2">"hid"</span> |
| <span class="nb">type</span> <span class="p">:</span> <span class="n">kHidLayer</span> |
| <span class="n">param</span> <span class="p">{</span> |
| <span class="n">name</span> <span class="p">:</span> <span class="s2">"w2"</span> |
| <span class="n">share_from</span><span class="p">:</span> <span class="s2">"w1"</span> |
| <span class="p">}</span> |
| <span class="n">srclayer</span><span class="p">:</span> <span class="s2">"vis"</span> |
| <span class="p">}</span> |
| <span class="p">}</span> |
| </pre></div> |
| </div> |
| </div> |
| <div class="section" id="rnn-models"> |
| <h3>RNN models<a class="headerlink" href="#rnn-models" title="Permalink to this headline">¶</a></h3> |
| <p>For recurrent neural networks (RNN), users can remove the recurrent connections |
| by unrolling the recurrent layer. For example, in Figure 3b, the original |
| layer is unrolled into a new layer with 4 internal layers. In this way, the |
| model is like a normal feed-forward model, thus can be configured similarly. |
| The <a class="reference external" href="rnn.html">RNN example</a> has a full neural net |
| configuration for a RNN model.</p> |
| </div> |
| </div> |
| <div class="section" id="configuration-for-multiple-nets"> |
| <h2>Configuration for multiple nets<a class="headerlink" href="#configuration-for-multiple-nets" title="Permalink to this headline">¶</a></h2> |
| <p>Typically, a training job includes three neural nets for |
| training, validation and test phase respectively. The three neural nets share most |
| layers except the data layer, loss layer or output layer, etc.. To avoid |
| redundant configurations for the shared layers, users can uses the <code class="docutils literal"><span class="pre">exclude</span></code> |
| filed to filter a layer in the neural net, e.g., the following layer will be |
| filtered when creating the testing <code class="docutils literal"><span class="pre">NeuralNet</span></code>.</p> |
| <div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">layer</span> <span class="p">{</span> |
| <span class="o">...</span> |
| <span class="n">exclude</span> <span class="p">:</span> <span class="n">kTest</span> <span class="c1"># filter this layer for creating test net</span> |
| <span class="p">}</span> |
| </pre></div> |
| </div> |
| </div> |
| <div class="section" id="neural-net-partitioning"> |
| <h2>Neural net partitioning<a class="headerlink" href="#neural-net-partitioning" title="Permalink to this headline">¶</a></h2> |
| <p>A neural net can be partitioned in different ways to distribute the training |
| over multiple workers.</p> |
| <div class="section" id="batch-and-feature-dimension"> |
| <h3>Batch and feature dimension<a class="headerlink" href="#batch-and-feature-dimension" title="Permalink to this headline">¶</a></h3> |
| <img src="../_static/images/partition_fc.png" align="center" width="400px"/> |
| <span><strong>Figure 4 - Partitioning of a fully connected layer.</strong></span><p>Every layer‘s feature blob is considered a matrix whose rows are feature |
| vectors. Thus, one layer can be split on two dimensions. Partitioning on |
| dimension 0 (also called batch dimension) slices the feature matrix by rows. |
| For instance, if the mini-batch size is 256 and the layer is partitioned into 2 |
| sub-layers, each sub-layer would have 128 feature vectors in its feature blob. |
| Partitioning on this dimension has no effect on the parameters, as every |
| <a class="reference external" href="param.html">Param</a> object is replicated in the sub-layers. Partitioning on dimension |
| 1 (also called feature dimension) slices the feature matrix by columns. For |
| example, suppose the original feature vector has 50 units, after partitioning |
| into 2 sub-layers, each sub-layer would have 25 units. This partitioning may |
| result in <a class="reference external" href="param.html">Param</a> object being split, as shown in |
| Figure 4. Both the bias vector and weight matrix are |
| partitioned into two sub-layers.</p> |
| </div> |
| <div class="section" id="partitioning-configuration"> |
| <h3>Partitioning configuration<a class="headerlink" href="#partitioning-configuration" title="Permalink to this headline">¶</a></h3> |
| <p>There are 4 partitioning schemes, whose configurations are give below,</p> |
| <ol> |
| <li><p class="first">Partitioning each singe layer into sub-layers on batch dimension (see |
| below). It is enabled by configuring the partition dimension of the layer to |
| 0, e.g.,</p> |
| <div class="highlight-default"><div class="highlight"><pre><span></span> <span class="c1"># with other fields omitted</span> |
| <span class="n">layer</span> <span class="p">{</span> |
| <span class="n">partition_dim</span><span class="p">:</span> <span class="mi">0</span> |
| <span class="p">}</span> |
| </pre></div> |
| </div> |
| </li> |
| <li><p class="first">Partitioning each singe layer into sub-layers on feature dimension (see |
| below). It is enabled by configuring the partition dimension of the layer to |
| 1, e.g.,</p> |
| <div class="highlight-default"><div class="highlight"><pre><span></span> <span class="c1"># with other fields omitted</span> |
| <span class="n">layer</span> <span class="p">{</span> |
| <span class="n">partition_dim</span><span class="p">:</span> <span class="mi">1</span> |
| <span class="p">}</span> |
| </pre></div> |
| </div> |
| </li> |
| <li><p class="first">Partitioning all layers into different subsets. It is enabled by |
| configuring the location ID of a layer, e.g.,</p> |
| <div class="highlight-default"><div class="highlight"><pre><span></span> <span class="c1"># with other fields omitted</span> |
| <span class="n">layer</span> <span class="p">{</span> |
| <span class="n">location</span><span class="p">:</span> <span class="mi">1</span> |
| <span class="p">}</span> |
| <span class="n">layer</span> <span class="p">{</span> |
| <span class="n">location</span><span class="p">:</span> <span class="mi">0</span> |
| <span class="p">}</span> |
| </pre></div> |
| </div> |
| </li> |
| <li><p class="first">Hybrid partitioning of strategy 1, 2 and 3. The hybrid partitioning is |
| useful for large models. An example application is to implement the |
| <a class="reference external" href="http://arxiv.org/abs/1404.5997">idea proposed by Alex</a>. |
| Hybrid partitioning is configured like,</p> |
| <div class="highlight-default"><div class="highlight"><pre><span></span> <span class="c1"># with other fields omitted</span> |
| <span class="n">layer</span> <span class="p">{</span> |
| <span class="n">location</span><span class="p">:</span> <span class="mi">1</span> |
| <span class="p">}</span> |
| <span class="n">layer</span> <span class="p">{</span> |
| <span class="n">location</span><span class="p">:</span> <span class="mi">0</span> |
| <span class="p">}</span> |
| <span class="n">layer</span> <span class="p">{</span> |
| <span class="n">partition_dim</span><span class="p">:</span> <span class="mi">0</span> |
| <span class="n">location</span><span class="p">:</span> <span class="mi">0</span> |
| <span class="p">}</span> |
| <span class="n">layer</span> <span class="p">{</span> |
| <span class="n">partition_dim</span><span class="p">:</span> <span class="mi">1</span> |
| <span class="n">location</span><span class="p">:</span> <span class="mi">0</span> |
| <span class="p">}</span> |
| </pre></div> |
| </div> |
| </li> |
| </ol> |
| <p>Currently SINGA supports strategy-2 well. Other partitioning strategies are |
| are under test and will be released in later version.</p> |
| </div> |
| </div> |
| <div class="section" id="parameter-sharing"> |
| <h2>Parameter sharing<a class="headerlink" href="#parameter-sharing" title="Permalink to this headline">¶</a></h2> |
| <p>Parameters can be shared in two cases,</p> |
| <ul class="simple"> |
| <li>sharing parameters among layers via user configuration. For example, the |
| visible layer and hidden layer of a RBM shares the weight matrix, which is configured through |
| the <code class="docutils literal"><span class="pre">share_from</span></code> field as shown in the above RBM configuration. The |
| configurations must be the same (except name) for shared parameters.</li> |
| <li>due to neural net partitioning, some <code class="docutils literal"><span class="pre">Param</span></code> objects are replicated into |
| different workers, e.g., partitioning one layer on batch dimension. These |
| workers share parameter values. SINGA controls this kind of parameter |
| sharing automatically, users do not need to do any configuration.</li> |
| <li>the <code class="docutils literal"><span class="pre">NeuralNet</span></code> for training and testing (and validation) share most layers |
| , thus share <code class="docutils literal"><span class="pre">Param</span></code> values.</li> |
| </ul> |
| <p>If the shared <code class="docutils literal"><span class="pre">Param</span></code> instances resident in the same process (may in different |
| threads), they use the same chunk of memory space for their values. But they |
| would have different memory spaces for their gradients. In fact, their |
| gradients will be averaged by the stub or server.</p> |
| </div> |
| <div class="section" id="advanced-user-guide"> |
| <h2>Advanced user guide<a class="headerlink" href="#advanced-user-guide" title="Permalink to this headline">¶</a></h2> |
| <div class="section" id="creation"> |
| <h3>Creation<a class="headerlink" href="#creation" title="Permalink to this headline">¶</a></h3> |
| <div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">static</span> <span class="n">NeuralNet</span><span class="o">*</span> <span class="n">NeuralNet</span><span class="p">::</span><span class="n">Create</span><span class="p">(</span><span class="n">const</span> <span class="n">NetProto</span><span class="o">&</span> <span class="n">np</span><span class="p">,</span> <span class="n">Phase</span> <span class="n">phase</span><span class="p">,</span> <span class="nb">int</span> <span class="n">num</span><span class="p">);</span> |
| </pre></div> |
| </div> |
| <p>The above function creates a <code class="docutils literal"><span class="pre">NeuralNet</span></code> for a given phase, and returns a |
| pointer to the <code class="docutils literal"><span class="pre">NeuralNet</span></code> instance. The phase is in {kTrain, |
| kValidation, kTest}. <code class="docutils literal"><span class="pre">num</span></code> is used for net partitioning which indicates the |
| number of partitions. Typically, a training job includes three neural nets for |
| training, validation and test phase respectively. The three neural nets share most |
| layers except the data layer, loss layer or output layer, etc.. The <code class="docutils literal"><span class="pre">Create</span></code> |
| function takes in the full net configuration including layers for training, |
| validation and test. It removes layers for phases other than the specified |
| phase based on the <code class="docutils literal"><span class="pre">exclude</span></code> field in |
| <a class="reference external" href="layer.html">layer configuration</a>:</p> |
| <div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">layer</span> <span class="p">{</span> |
| <span class="o">...</span> |
| <span class="n">exclude</span> <span class="p">:</span> <span class="n">kTest</span> <span class="c1"># filter this layer for creating test net</span> |
| <span class="p">}</span> |
| </pre></div> |
| </div> |
| <p>The filtered net configuration is passed to the constructor of <code class="docutils literal"><span class="pre">NeuralNet</span></code>:</p> |
| <div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">NeuralNet</span><span class="p">::</span><span class="n">NeuralNet</span><span class="p">(</span><span class="n">NetProto</span> <span class="n">netproto</span><span class="p">,</span> <span class="nb">int</span> <span class="n">npartitions</span><span class="p">);</span> |
| </pre></div> |
| </div> |
| <p>The constructor creates a graph representing the net structure firstly in</p> |
| <div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">Graph</span><span class="o">*</span> <span class="n">NeuralNet</span><span class="p">::</span><span class="n">CreateGraph</span><span class="p">(</span><span class="n">const</span> <span class="n">NetProto</span><span class="o">&</span> <span class="n">netproto</span><span class="p">,</span> <span class="nb">int</span> <span class="n">npartitions</span><span class="p">);</span> |
| </pre></div> |
| </div> |
| <p>Next, it creates a layer for each node and connects layers if their nodes are |
| connected.</p> |
| <div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">void</span> <span class="n">NeuralNet</span><span class="p">::</span><span class="n">CreateNetFromGraph</span><span class="p">(</span><span class="n">Graph</span><span class="o">*</span> <span class="n">graph</span><span class="p">,</span> <span class="nb">int</span> <span class="n">npartitions</span><span class="p">);</span> |
| </pre></div> |
| </div> |
| <p>Since the <code class="docutils literal"><span class="pre">NeuralNet</span></code> instance may be shared among multiple workers, the |
| <code class="docutils literal"><span class="pre">Create</span></code> function returns a pointer to the <code class="docutils literal"><span class="pre">NeuralNet</span></code> instance .</p> |
| </div> |
| <div class="section" id="id1"> |
| <h3>Parameter sharing<a class="headerlink" href="#id1" title="Permalink to this headline">¶</a></h3> |
| <p><code class="docutils literal"><span class="pre">Param</span></code> sharing |
| is enabled by first sharing the Param configuration (in <code class="docutils literal"><span class="pre">NeuralNet::Create</span></code>) |
| to create two similar (e.g., the same shape) Param objects, and then calling |
| (in <code class="docutils literal"><span class="pre">NeuralNet::CreateNetFromGraph</span></code>),</p> |
| <div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">void</span> <span class="n">Param</span><span class="p">::</span><span class="n">ShareFrom</span><span class="p">(</span><span class="n">const</span> <span class="n">Param</span><span class="o">&</span> <span class="n">from</span><span class="p">);</span> |
| </pre></div> |
| </div> |
| <p>It is also possible to share <code class="docutils literal"><span class="pre">Param</span></code>s of two nets, e.g., sharing parameters of |
| the training net and the test net,</p> |
| <div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">void</span> <span class="n">NeuralNet</span><span class="p">:</span><span class="n">ShareParamsFrom</span><span class="p">(</span><span class="n">NeuralNet</span><span class="o">*</span> <span class="n">other</span><span class="p">);</span> |
| </pre></div> |
| </div> |
| <p>It will call <code class="docutils literal"><span class="pre">Param::ShareFrom</span></code> for each Param object.</p> |
| </div> |
| <div class="section" id="access-functions"> |
| <h3>Access functions<a class="headerlink" href="#access-functions" title="Permalink to this headline">¶</a></h3> |
| <p><code class="docutils literal"><span class="pre">NeuralNet</span></code> provides a couple of access function to get the layers and params |
| of the net:</p> |
| <div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">const</span> <span class="n">std</span><span class="p">::</span><span class="n">vector</span><span class="o"><</span><span class="n">Layer</span><span class="o">*>&</span> <span class="n">layers</span><span class="p">()</span> <span class="n">const</span><span class="p">;</span> |
| <span class="n">const</span> <span class="n">std</span><span class="p">::</span><span class="n">vector</span><span class="o"><</span><span class="n">Param</span><span class="o">*>&</span> <span class="n">params</span><span class="p">()</span> <span class="n">const</span> <span class="p">;</span> |
| <span class="n">Layer</span><span class="o">*</span> <span class="n">name2layer</span><span class="p">(</span><span class="n">string</span> <span class="n">name</span><span class="p">)</span> <span class="n">const</span><span class="p">;</span> |
| <span class="n">Param</span><span class="o">*</span> <span class="n">paramid2param</span><span class="p">(</span><span class="nb">int</span> <span class="nb">id</span><span class="p">)</span> <span class="n">const</span><span class="p">;</span> |
| </pre></div> |
| </div> |
| </div> |
| <div class="section" id="partitioning"> |
| <h3>Partitioning<a class="headerlink" href="#partitioning" title="Permalink to this headline">¶</a></h3> |
| <div class="section" id="implementation"> |
| <h4>Implementation<a class="headerlink" href="#implementation" title="Permalink to this headline">¶</a></h4> |
| <p>SINGA partitions the neural net in <code class="docutils literal"><span class="pre">CreateGraph</span></code> function, which creates one |
| node for each (partitioned) layer. For example, if one layer‘s partition |
| dimension is 0 or 1, then it creates <code class="docutils literal"><span class="pre">npartition</span></code> nodes for it; if the |
| partition dimension is -1, a single node is created, i.e., no partitioning. |
| Each node is assigned a partition (or location) ID. If the original layer is |
| configured with a location ID, then the ID is assigned to each newly created node. |
| These nodes are connected according to the connections of the original layers. |
| Some connection layers will be added automatically. |
| For instance, if two connected sub-layers are located at two |
| different workers, then a pair of bridge layers is inserted to transfer the |
| feature (and gradient) blob between them. When two layers are partitioned on |
| different dimensions, a concatenation layer which concatenates feature rows (or |
| columns) and a slice layer which slices feature rows (or columns) would be |
| inserted. These connection layers help making the network communication and |
| synchronization transparent to the users.</p> |
| </div> |
| <div class="section" id="dispatching-partitions-to-workers"> |
| <h4>Dispatching partitions to workers<a class="headerlink" href="#dispatching-partitions-to-workers" title="Permalink to this headline">¶</a></h4> |
| <p>Each (partitioned) layer is assigned a location ID, based on which it is dispatched to one |
| worker. Particularly, the pointer to the <code class="docutils literal"><span class="pre">NeuralNet</span></code> instance is passed |
| to every worker within the same group, but each worker only computes over the |
| layers that have the same partition (or location) ID as the worker‘s ID. When |
| every worker computes the gradients of the entire model parameters |
| (strategy-2), we refer to this process as data parallelism. When different |
| workers compute the gradients of different parameters (strategy-3 or |
| strategy-1), we call this process model parallelism. The hybrid partitioning |
| leads to hybrid parallelism where some workers compute the gradients of the |
| same subset of model parameters while other workers compute on different model |
| parameters. For example, to implement the hybrid parallelism in for the |
| <a class="reference external" href="http://arxiv.org/abs/1404.5997">DCNN model</a>, we set <code class="docutils literal"><span class="pre">partition_dim</span> <span class="pre">=</span> <span class="pre">0</span></code> for |
| lower layers and <code class="docutils literal"><span class="pre">partition_dim</span> <span class="pre">=</span> <span class="pre">1</span></code> for higher layers.</p> |
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