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  <div class="section" id="paddlepaddle-design-doc">
<span id="paddlepaddle-design-doc"></span><h1>PaddlePaddle Design Doc<a class="headerlink" href="#paddlepaddle-design-doc" title="永久链接至标题"></a></h1>
<div class="section" id="ingredients">
<span id="ingredients"></span><h2>Ingredients<a class="headerlink" href="#ingredients" title="永久链接至标题"></a></h2>
<p>As our design principle is starting from the essence: how could we
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allow users to express and solve their problems as neural networks.
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Some essential concepts that our API have to provide include:</p>
<ol class="simple">
<li>A <em>topology</em> is an expression of <em>layers</em>.</li>
<li>A layer could be any kind of computation, including <em>cost</em>.</li>
<li>Some layers have parameters, some don&#8217;t. Most costs don&#8217;t have
parameters.</li>
<li>In some topologies, layers share parameters.  For
example,
<a class="reference external" href="https://github.com/PaddlePaddle/Paddle/issues/1311#issuecomment-279121850">the network for training a ranking model</a>.</li>
<li>At programming time, users specify topologies and possible sharing
of parameters.  PaddlePaddle can figure out and create parameters
required (and possibly shared) by one or more topologies.</li>
</ol>
</div>
<div class="section" id="starting-from-examples">
<span id="starting-from-examples"></span><h2>Starting from Examples<a class="headerlink" href="#starting-from-examples" title="永久链接至标题"></a></h2>
<p>As a summarization
of
<a class="reference external" href="https://github.com/PaddlePaddle/Paddle/issues/1315">our disucssion</a>,
let us present two examples here:</p>
<div class="section" id="example-1-sharing-parameters-between-layers">
<span id="example-1-sharing-parameters-between-layers"></span><h3>Example 1. Sharing Parameters between Layers<a class="headerlink" href="#example-1-sharing-parameters-between-layers" title="永久链接至标题"></a></h3>
<p>We use
the
<a class="reference external" href="https://github.com/PaddlePaddle/Paddle/issues/1311#issuecomment-279121850">3-branch ranking</a> model
in this example.  For your convenience, I copy-a-paste the model&#8217;s
topology as follows:</p>
<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">A</span> <span class="o">-&gt;</span> <span class="n">f</span> <span class="o">-</span>\
<span class="n">Q</span> <span class="o">-&gt;</span> <span class="n">f</span> <span class="o">--&gt;</span> <span class="n">cost</span>
<span class="n">B</span> <span class="o">-&gt;</span> <span class="n">f</span> <span class="o">-/</span>
</pre></div>
</div>
<p>The following program trains the topology including the cost, and then
use the sub-network in the trained topology in inference:</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">f</span><span class="p">(</span><span class="ow">in</span><span class="p">):</span>
    <span class="n">e</span> <span class="o">=</span> <span class="n">paddle</span><span class="o">.</span><span class="n">layer</span><span class="o">.</span><span class="n">embedding</span><span class="p">(</span><span class="ow">in</span><span class="p">,</span> <span class="n">parameter_name</span><span class="o">=</span><span class="s2">&quot;embedding&quot;</span><span class="p">)</span>
    <span class="n">o</span> <span class="o">=</span> <span class="n">paddle</span><span class="o">.</span><span class="n">layer</span><span class="o">.</span><span class="n">softmax</span><span class="p">(</span><span class="n">e</span><span class="p">,</span> <span class="n">parameter_name</span><span class="o">=</span><span class="s2">&quot;semantic&quot;</span><span class="p">)</span>
    <span class="k">return</span> <span class="n">o</span>

<span class="c1"># Create 3 topologies (subnets), they share parameters because all</span>
<span class="c1"># correspoinding layers have the same parameter names.</span>
<span class="n">fA</span> <span class="o">=</span> <span class="n">f</span><span class="p">(</span><span class="n">paddle</span><span class="o">.</span><span class="n">layer</span><span class="o">.</span><span class="n">data</span><span class="p">(</span><span class="n">input_name</span><span class="o">=</span><span class="s2">&quot;A&quot;</span><span class="p">))</span>
<span class="n">fB</span> <span class="o">=</span> <span class="n">f</span><span class="p">(</span><span class="n">paddle</span><span class="o">.</span><span class="n">layer</span><span class="o">.</span><span class="n">data</span><span class="p">(</span><span class="n">input_name</span><span class="o">=</span><span class="s2">&quot;B&quot;</span><span class="p">))</span>
<span class="n">fQ</span> <span class="o">=</span> <span class="n">f</span><span class="p">(</span><span class="n">paddle</span><span class="o">.</span><span class="n">layer</span><span class="o">.</span><span class="n">data</span><span class="p">(</span><span class="n">input_name</span><span class="o">=</span><span class="s2">&quot;Q&quot;</span><span class="p">))</span>

<span class="n">topology</span> <span class="o">=</span> <span class="n">paddle</span><span class="o">.</span><span class="n">layer</span><span class="o">.</span><span class="n">less_than</span><span class="p">(</span>
               <span class="n">paddle</span><span class="o">.</span><span class="n">layer</span><span class="o">.</span><span class="n">cross_entropy</span><span class="p">(</span><span class="n">fA</span><span class="p">,</span> <span class="n">fQ</span><span class="p">),</span>
               <span class="n">paddle</span><span class="o">.</span><span class="n">layer</span><span class="o">.</span><span class="n">corss_entropy</span><span class="p">(</span><span class="n">fB</span><span class="p">,</span> <span class="n">fQ</span><span class="p">))</span>

<span class="c1"># Derive parameters required in topology and create them in model.</span>
<span class="n">parameters</span> <span class="o">=</span> <span class="n">paddle</span><span class="o">.</span><span class="n">parameters</span><span class="o">.</span><span class="n">create</span><span class="p">(</span><span class="n">topology</span><span class="p">)</span>

<span class="c1"># Estimate parameters used in topology from data.</span>
<span class="n">paddle</span><span class="o">.</span><span class="n">train</span><span class="p">(</span><span class="n">topology</span><span class="p">,</span> <span class="n">parameters</span><span class="p">,</span> <span class="n">reader</span><span class="o">=</span><span class="n">read_ranking_model_data</span><span class="p">)</span>

<span class="c1"># Inference using fA (or fB or fC, as they share their parameters).</span>
<span class="p">[</span><span class="n">testA</span><span class="p">,</span> <span class="n">testB</span><span class="p">,</span> <span class="n">testQ</span><span class="p">]</span> <span class="o">=</span> <span class="n">read_ranking_model_data</span><span class="p">()</span>
<span class="k">print</span> <span class="s2">&quot;The sematic-vector of testA: &quot;</span><span class="p">,</span> <span class="n">paddle</span><span class="o">.</span><span class="n">infer</span><span class="p">(</span><span class="n">fA</span><span class="p">,</span> <span class="n">parameters</span><span class="p">,</span> <span class="n">testA</span><span class="p">)</span>
</pre></div>
</div>
</div>
<div class="section" id="example-2-sharing-parameters-between-models">
<span id="example-2-sharing-parameters-between-models"></span><h3>Example 2. Sharing Parameters between &#8220;Models&#8221;<a class="headerlink" href="#example-2-sharing-parameters-between-models" title="永久链接至标题"></a></h3>
<p>We use <a class="reference external" href="https://github.com/PaddlePaddle/book/tree/develop/gan">GAN</a> in
this example.  In the following example program, <code class="docutils literal"><span class="pre">d0</span></code> and <code class="docutils literal"><span class="pre">d1</span></code>
correspond to the two networks in the following figure:</p>
<p><img src="https://github.com/wangyang59/book/raw/00036f4b0da5225041a6824587c1a01cf20159b1/gan/image/gan_ig.png" width=400 /></p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">G</span><span class="p">(</span><span class="ow">in</span><span class="p">):</span>
    <span class="c1"># over-simplified example as G has only one layers:</span>
    <span class="k">return</span> <span class="n">paddle</span><span class="o">.</span><span class="n">layer</span><span class="o">.</span><span class="n">fc</span><span class="p">(</span><span class="ow">in</span><span class="p">,</span> <span class="n">parameter_name</span><span class="o">=</span><span class="s2">&quot;G&quot;</span><span class="p">)</span>

<span class="k">def</span> <span class="nf">D</span><span class="p">(</span><span class="ow">in</span><span class="p">);</span>
    <span class="c1"># again, over-simplified:</span>
    <span class="k">return</span> <span class="n">paddle</span><span class="o">.</span><span class="n">layer</span><span class="o">.</span><span class="n">fc</span><span class="p">(</span><span class="ow">in</span><span class="p">,</span> <span class="n">parameter_name</span><span class="o">=</span><span class="s2">&quot;D&quot;</span><span class="p">)</span>

<span class="c1"># Construct the first topology, which contains both D and G.</span>
<span class="c1"># By learning this topology, we update parameters of G.</span>
<span class="n">d0</span> <span class="o">=</span> <span class="n">paddle</span><span class="o">.</span><span class="n">layer</span><span class="o">.</span><span class="n">should_be_false</span><span class="p">(</span><span class="n">D</span><span class="p">(</span><span class="n">G</span><span class="p">(</span><span class="n">paddle</span><span class="o">.</span><span class="n">layer</span><span class="o">.</span><span class="n">data</span><span class="p">())))</span>

<span class="c1"># Construct a second topology d1, which contains only D. By</span>
<span class="c1"># training this topology, we update parameters of D.  Note</span>
<span class="c1"># that d1 share parameters with d0.</span>
<span class="n">d1</span> <span class="o">=</span> <span class="n">paddle</span><span class="o">.</span><span class="n">layer</span><span class="o">.</span><span class="n">should_be_true</span><span class="p">(</span><span class="n">D</span><span class="p">(</span><span class="n">paddle</span><span class="o">.</span><span class="n">layer</span><span class="o">.</span><span class="n">data</span><span class="p">()))</span>

<span class="c1"># Create parameters from a list of multiple topologies (models) for</span>
<span class="c1"># the chance to share parameters between these topologies.</span>
<span class="n">parameters</span> <span class="o">=</span> <span class="n">paddle</span><span class="o">.</span><span class="n">parameters</span><span class="o">.</span><span class="n">create</span><span class="p">([</span><span class="n">d0</span><span class="p">,</span> <span class="n">d1</span><span class="p">])</span>

<span class="c1"># Iterative training of GAN.</span>
<span class="k">for</span> <span class="o">...</span><span class="p">:</span>
    <span class="n">train</span><span class="p">(</span><span class="n">d0</span><span class="p">,</span> <span class="n">parameters</span><span class="p">,</span> <span class="n">reader</span><span class="o">=</span><span class="n">read_from_rng</span><span class="p">,</span> <span class="n">immutable_parameters</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;D&quot;</span><span class="p">})</span>
    <span class="n">train</span><span class="p">(</span><span class="n">d1</span><span class="p">,</span> <span class="n">parameters</span><span class="p">,</span> <span class="n">reader</span><span class="o">=</span><span class="n">read_from_realistic_images</span><span class="p">)</span>

<span class="c1"># Use d1 for inference:</span>
<span class="k">print</span> <span class="s2">&quot;D thinks a batch of images are realistic &quot;</span><span class="p">,</span> <span class="n">infer</span><span class="p">(</span><span class="n">d1</span><span class="p">,</span> <span class="n">parameters</span><span class="p">,</span> <span class="n">read_mnist_images</span><span class="p">)</span>
</pre></div>
</div>
</div>
<div class="section" id="summarization">
<span id="summarization"></span><h3>Summarization<a class="headerlink" href="#summarization" title="永久链接至标题"></a></h3>
<p>Above two programs reveal some important design concerns:</p>
<ol class="simple">
<li>Users describe a topology as an expression of layers.  Every layer
has a <em>parameter name</em>.  If the users don&#8217;t specify it explicitly, it&#8217;s automatically generated as a unique name.  By
specifying the parameter name, users can specify the sharing of
parameters between layers and even between topologies.</li>
<li><code class="docutils literal"><span class="pre">paddle.parameters.create</span></code> figures out parameters required by one
or more topologies from parameter names of layers.  It creates these
parameters and returns a <code class="docutils literal"><span class="pre">ParameterSet</span></code> object, which is in essence
a map from <em>parameter names</em> to <em>parameters</em>.</li>
<li>At training and inference time, <code class="docutils literal"><span class="pre">paddle.train</span></code> and <code class="docutils literal"><span class="pre">paddle.infer</span></code>
requires both a topology and the parameter set that holds the parameters of that topology.  There are some reasons:<ol>
<li>This prevents users from forgetting to call
<code class="docutils literal"><span class="pre">paddle.parameters.create</span></code>.</li>
<li><code class="docutils literal"><span class="pre">paddle.train</span></code> needs to know which parameter set to update.</li>
<li>Users could load another (pre-trained) parameter set and use it
with a topology in <code class="docutils literal"><span class="pre">train.infer</span></code>.</li>
</ol>
</li>
<li>By specifying the <code class="docutils literal"><span class="pre">immutable_parameters</span></code> parameter of
<code class="docutils literal"><span class="pre">paddle.train</span></code>, we can forbid the update of these parameters.</li>
</ol>
</div>
</div>
<div class="section" id="reader">
<span id="reader"></span><h2>Reader<a class="headerlink" href="#reader" title="永久链接至标题"></a></h2>
<p>Not all programming frameworks allow users to define I/O functions.
An example is Google MapReduce, which can only read from text,
SSTable, and RecordIO files.  Hadoop MapReduce allows users to define
readers and writers by deriving from base classes <code class="docutils literal"><span class="pre">Reader</span></code> and
<code class="docutils literal"><span class="pre">Writer</span></code>.  The former is less flexible but also less error-prone.  We
decide to provide the flexibility to users to define their readers.</p>
<p>There are some open questions here:</p>
<ol class="simple">
<li><strong>Should a reader return a Python dictionary?</strong></li>
<li><strong>How to map multiple outputs from a reader to multiple data layers?</strong></li>
<li><strong>How to easily compose some existing readers to read more data and
feed a topology with more data layers?</strong></li>
</ol>
</div>
<div class="section" id="training">
<span id="training"></span><h2>Training<a class="headerlink" href="#training" title="永久链接至标题"></a></h2>
<p>The recommended way to training a model is to call <code class="docutils literal"><span class="pre">paddle.train</span></code>,
which simply calls <code class="docutils literal"><span class="pre">paddle.trainer.Default</span></code>, a global variable of
type <code class="docutils literal"><span class="pre">paddle.trainer.SGD</span></code>.  Equivalently, we can do</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">opt</span> <span class="o">=</span> <span class="n">paddle</span><span class="o">.</span><span class="n">trainer</span><span class="o">.</span><span class="n">SGD</span><span class="p">(</span><span class="o">...</span><span class="p">,</span> <span class="n">paddle</span><span class="o">.</span><span class="n">updater</span><span class="o">.</span><span class="n">Adam</span><span class="p">(</span><span class="o">...</span><span class="p">))</span>
<span class="n">opt</span><span class="o">.</span><span class="n">train</span><span class="p">(</span><span class="n">topology</span><span class="p">,</span> <span class="n">parameters</span><span class="p">,</span> <span class="n">reader</span><span class="o">=</span><span class="n">read</span><span class="p">,</span> <span class="o">...</span><span class="p">)</span>
</pre></div>
</div>
<div class="section" id="updater">
<span id="updater"></span><h3>Updater<a class="headerlink" href="#updater" title="永久链接至标题"></a></h3>
<p>Please be aware that a trainer can accept an updater as its data
member, where an updater is a class derived from
<code class="docutils literal"><span class="pre">paddle.trainer.Updater</span></code>.  This is to make it easier to customize
trainers, as discussed
<a class="reference external" href="https://github.com/PaddlePaddle/Paddle/issues/1319">here</a>.</p>
</div>
<div class="section" id="event-handler">
<span id="event-handler"></span><h3>Event Handler<a class="headerlink" href="#event-handler" title="永久链接至标题"></a></h3>
<p><code class="docutils literal"><span class="pre">paddle.train</span></code> and <code class="docutils literal"><span class="pre">paddle.trainer.XXX.train</span></code> take an optional
parameter <code class="docutils literal"><span class="pre">event_handler</span></code>, which should be either <code class="docutils literal"><span class="pre">None</span></code> or a function
that handle some events:</p>
<ol class="simple">
<li>BeginTraining</li>
<li>EndTraining</li>
<li>BeginIteration</li>
<li>EndIteration</li>
<li>BeginPass</li>
<li>EndPass</li>
</ol>
<p>where EndPass is sent if and only if the reader yields
<code class="docutils literal"><span class="pre">end_pass=True</span></code>.</p>
<p>An example as follows:</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">event_handler</span><span class="p">(</span><span class="n">event</span><span class="p">):</span>
    <span class="k">if</span> <span class="n">ininstance</span><span class="p">(</span><span class="n">event</span><span class="p">,</span> <span class="n">paddle</span><span class="o">.</span><span class="n">event</span><span class="o">.</span><span class="n">EndIteration</span><span class="p">):</span>
        <span class="k">print</span> <span class="n">paddle</span><span class="o">.</span><span class="n">test</span><span class="p">(</span><span class="o">...</span><span class="p">)</span>

<span class="n">paddle</span><span class="o">.</span><span class="n">train</span><span class="p">(</span><span class="n">topology</span><span class="p">,</span> <span class="n">parameters</span><span class="p">,</span> <span class="n">reader</span><span class="p">,</span> <span class="n">event_handler</span><span class="p">)</span>
</pre></div>
</div>
<p>If we are writing a PaddlePaddle program in and for iPython/Jypyter,
we can use metaplotlib in the event handler to plot a curve of
cost/error versus iterations, as shown
<a class="reference external" href="https://blog.dominodatalab.com/interactive-dashboards-in-jupyter/">here</a>.</p>
</div>
<div class="section" id="distributed-training">
<span id="distributed-training"></span><h3>Distributed Training<a class="headerlink" href="#distributed-training" title="永久链接至标题"></a></h3>
<p>If users want to do distributed training on a cluster, s/he should
call <code class="docutils literal"><span class="pre">paddle.dist_train</span></code> and provides access tokens to the cluster as
a parameter.</p>
<p>For example, if the user has a TLS certificate that allows him to
access a Kubernetes cluster, s/he should be able to call</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">paddle</span><span class="o">.</span><span class="n">dist_train</span><span class="p">(</span><span class="n">model</span><span class="p">,</span>
                  <span class="n">trainer</span><span class="o">=</span><span class="n">paddle</span><span class="o">.</span><span class="n">trainer</span><span class="o">.</span><span class="n">SGD</span><span class="p">(</span><span class="o">...</span><span class="p">,</span>
                                             <span class="n">paddle</span><span class="o">.</span><span class="n">updater</span><span class="o">.</span><span class="n">Adam</span><span class="p">(</span><span class="o">...</span><span class="p">)),</span>
                  <span class="n">reader</span><span class="o">=</span><span class="n">read</span><span class="p">,</span>
                  <span class="n">k8s_user</span><span class="o">=</span><span class="s2">&quot;yi&quot;</span><span class="p">,</span>
                  <span class="n">k8s_token</span><span class="o">=</span><span class="s2">&quot;kube_cluster_tls.pem&quot;</span><span class="p">,</span>
                  <span class="n">k8s_job</span><span class="o">=</span><span class="s2">&quot;hello&quot;</span><span class="p">,</span>
                  <span class="n">num_parameter_servers</span><span class="o">=</span><span class="mi">15</span><span class="p">)</span>
</pre></div>
</div>
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Travis CI 已提交
415 
<p>The pseudo code of <code class="docutils literal"><span class="pre">paddle.dist_train</span></code> is as follows:</p>
T
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416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">dist_train</span><span class="p">(</span><span class="n">topology</span><span class="p">,</span> <span class="n">parameters</span><span class="p">,</span> <span class="n">trainer</span><span class="p">,</span> <span class="n">reader</span><span class="p">,</span> <span class="o">...</span><span class="p">):</span>
    <span class="k">if</span> <span class="n">os</span><span class="o">.</span><span class="n">getenv</span><span class="p">(</span><span class="s2">&quot;KUBERNETES_SERVICE_HOST&quot;</span><span class="p">)</span> <span class="o">==</span> <span class="bp">None</span><span class="p">:</span>
        <span class="n">image_name</span> <span class="o">=</span> <span class="n">k8s_user</span> <span class="o">+</span> <span class="s1">&#39;/&#39;</span> <span class="o">+</span> <span class="n">k8s_job</span>
        <span class="n">docker_build</span><span class="p">(</span><span class="n">image_name</span><span class="p">)</span>
        <span class="n">docker_push</span><span class="p">()</span>
        <span class="n">kube_ctrl_start_job</span><span class="p">(</span><span class="n">image_name</span><span class="p">,</span> <span class="n">k8s_user</span><span class="p">,</span> <span class="n">k8s_token</span><span class="p">)</span>
    <span class="k">else</span><span class="p">:</span>
        <span class="n">rank</span> <span class="o">=</span> <span class="n">kube_list_containers_in_job_and_return_current_containers_rank</span><span class="p">()</span>
        <span class="k">if</span> <span class="n">rank</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
            <span class="n">master</span><span class="p">()</span>
        <span class="k">elif</span> <span class="n">rank</span> <span class="o">&lt;</span> <span class="mi">15</span><span class="p">:</span>
            <span class="n">parameter_server</span><span class="p">()</span>
        <span class="k">else</span><span class="p">:</span>
            <span class="n">trainer</span><span class="o">.</span><span class="n">train</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">reader</span><span class="o">=</span><span class="n">read</span><span class="p">)</span>
</pre></div>
</div>
<p>Please be aware that if a process is running on the Kubernetes
cluster, it will have some environment variables pre-defined.</p>
<p>If <code class="docutils literal"><span class="pre">dist_train</span></code> doesn&#8217;t see these environment variables, it knows
that it&#8217;s running on users&#8217; personal computer, and it should work as a
<em>launcher</em>.  Otherwise, it knows that it&#8217;s running on the cluster and
need to figure out its role as either the master, or a trainer, or a
parameter server.</p>
</div>
</div>
</div>


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