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    <li>Design Doc: Concurrent Programming with Fluid</li>
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  <div class="section" id="design-doc-concurrent-programming-with-fluid">
<span id="design-doc-concurrent-programming-with-fluid"></span><h1>Design Doc: Concurrent Programming with Fluid<a class="headerlink" href="#design-doc-concurrent-programming-with-fluid" title="永久链接至标题"></a></h1>
<p>With PaddlePaddle Fluid, users describe a program other than a model.  The program is a <a class="reference external" href="https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/framework/framework.proto"><code class="docutils literal"><span class="pre">ProgramDesc</span></code></a> protobuf message. TensorFlow/MxNet/Caffe2 applications generate protobuf messages too, but their protobuf messages represent the model, a graph of operators, but not the program that trains/uses the model.</p>
<p>Many know that when we program TensorFlow, we can specify the device on which each operator runs.  This allows us to create a concurrent/parallel AI application.   An interesting questions is <strong>how does a <code class="docutils literal"><span class="pre">ProgramDesc</span></code> represents a concurrent program?</strong></p>
<p>The answer relies on the fact that a <code class="docutils literal"><span class="pre">ProgramDesc</span></code> is similar to an abstract syntax tree (AST) that describes a program.  So users just program a concurrent program that they do with any concurrent programming language, e.g., <a class="reference external" href="https://golang.org">Go</a>.</p>
<div class="section" id="an-analogy">
<span id="an-analogy"></span><h2>An Analogy<a class="headerlink" href="#an-analogy" title="永久链接至标题"></a></h2>
<p>The following table compares concepts in Fluid and Go</p>
<p>| Go | Fluid |
|&#8212;-|&#8212;&#8212;-|
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|user-defined functions | <a class="reference external" href="https://github.com/PaddlePaddle/Paddle/tree/develop/python/paddle/fluid">layers</a> |
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| control-flow and built-in functions | <a class="reference external" href="https://github.com/PaddlePaddle/Paddle/tree/develop/paddle/operators">intrinsics/operators</a> |
| goroutines, channels | <a class="reference external" href="https://github.com/PaddlePaddle/Paddle/tree/develop/paddle/framework/thread_pool.h">class ThreadPool</a> |
| runtime | <a class="reference external" href="https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/framework/executor.h">class Executor</a> |</p>
</div>
<div class="section" id="an-example-concurrent-program">
<span id="an-example-concurrent-program"></span><h2>An Example Concurrent Program<a class="headerlink" href="#an-example-concurrent-program" title="永久链接至标题"></a></h2>
<p>To review all above concepts in an example, let us take a simple program and writes its distributed version.</p>
<p>Suppose that we want to parallelize a naive Fluid program (written in Go and calling Fluid&#8217;s Go binding) that multiplies two tensors.</p>
<div class="highlight-go"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="s">&quot;fluid&quot;</span>

<span class="kd">func</span> <span class="nx">paddlepaddle</span><span class="p">()</span> <span class="p">{</span>
  <span class="nx">X</span> <span class="p">=</span> <span class="nx">fluid</span><span class="p">.</span><span class="nx">read</span><span class="p">(</span><span class="o">...</span><span class="p">)</span>
  <span class="nx">W</span> <span class="p">=</span> <span class="nx">fluid</span><span class="p">.</span><span class="nx">Tensor</span><span class="p">(</span><span class="o">...</span><span class="p">)</span>
  <span class="nx">Y</span> <span class="p">=</span> <span class="nx">fluid</span><span class="p">.</span><span class="nx">mult</span><span class="p">(</span><span class="nx">X</span><span class="p">,</span> <span class="nx">W</span><span class="p">)</span>
<span class="p">}</span>
</pre></div>
</div>
<p>Please be aware that the Fluid&#8217;s Go binding provides the default <code class="docutils literal"><span class="pre">main</span></code> function, which calls the <code class="docutils literal"><span class="pre">paddlepaddle</span></code> function, which, in this case, is defined in above program and creates the following <code class="docutils literal"><span class="pre">ProgramDesc</span></code> message.</p>
<div class="highlight-protobuf"><div class="highlight"><pre><span></span><span class="kd">message</span> <span class="nc">ProgramDesc</span> <span class="p">{</span>
  <span class="n">block</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="n">Block</span> <span class="p">{</span>
    <span class="na">vars</span> <span class="o">=</span> <span class="p">[</span><span class="n">X</span><span class="p">,</span> <span class="n">W</span><span class="p">,</span> <span class="n">Y</span><span class="p">],</span>
    <span class="na">ops</span> <span class="o">=</span> <span class="p">[</span>
      <span class="n">read</span><span class="p">(</span><span class="na">output</span> <span class="o">=</span> <span class="n">X</span><span class="p">)</span>
      <span class="n">assign</span><span class="p">(</span><span class="na">input</span> <span class="o">=</span> <span class="o">...</span><span class="p">,</span> <span class="na">output</span> <span class="o">=</span> <span class="n">W</span><span class="p">)</span>
      <span class="n">mult</span><span class="p">(</span><span class="na">input</span> <span class="o">=</span> <span class="p">{</span><span class="n">X</span><span class="p">,</span> <span class="n">W</span><span class="p">},</span> <span class="na">output</span> <span class="o">=</span> <span class="n">Y</span><span class="p">)</span>
    <span class="p">],</span>
  <span class="p">}</span>
<span class="p">}</span>
</pre></div>
</div>
<p>Then, the default <code class="docutils literal"><span class="pre">main</span></code> function calls <code class="docutils literal"><span class="pre">fluid.run()</span></code>, which creates an instance of the <a class="reference external" href="https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/framework/executor.h"><code class="docutils literal"><span class="pre">class</span> <span class="pre">Executor</span></code></a> and calls <code class="docutils literal"><span class="pre">Executor.Run(block[0])</span></code>, where <code class="docutils literal"><span class="pre">block[0]</span></code> is the first and only block defined in above <code class="docutils literal"><span class="pre">ProgramDesc</span></code> message.</p>
<p>The default <code class="docutils literal"><span class="pre">main</span></code> function is defined as follows:</p>
<div class="highlight-go"><div class="highlight"><pre><span></span><span class="kd">func</span> <span class="nx">main</span><span class="p">()</span> <span class="p">{</span>
  <span class="nx">paddlepaddle</span><span class="p">()</span>
  <span class="nx">fluid</span><span class="p">.</span><span class="nx">run</span><span class="p">()</span>
<span class="p">}</span>
</pre></div>
</div>
</div>
<div class="section" id="the-concurrent-version">
<span id="the-concurrent-version"></span><h2>The Concurrent Version<a class="headerlink" href="#the-concurrent-version" title="永久链接至标题"></a></h2>
<p>By parallelizing the above program, we could support very big tensor X by splitting into small pieces {x_1, x_2, ...} and sent each piece to worker process/node for parallel multiplication.</p>
<p>In this case, we can write a transpiler that takes a <code class="docutils literal"><span class="pre">ProgramDesc</span></code> message that represents the above example program and outputs two <code class="docutils literal"><span class="pre">ProgramDesc</span></code> messages, one for running on the master process/node, and the other one for worker processes/nodes.</p>
<div class="section" id="the-master-program">
<span id="the-master-program"></span><h3>The Master Program<a class="headerlink" href="#the-master-program" title="永久链接至标题"></a></h3>
<p>The master program could look like the following:</p>
<div class="highlight-protobuf"><div class="highlight"><pre><span></span>message ProgramDesc {
  block[0] = Block {
    vars = [X, L, Y],
    ops = [
      read(output = X)
      kube_get_workers_addrs(output = L)
      Y = tensor_array(len(L))
      parallel_for(input = X, output = Y, 
                   attrs = {L, block_id(1)}) # referring to block 1
    ]
  }
  
  block[1] = Block {
    parent = 0,
    vars = [x, y, index],
    ops = [
      slice(input = [X, index], output = x) # index is initialized by parallel_for
      send(input = x, attrs = L[index])
      recv(outputs = y, attrs = L[index])
      assign(input = y, output = Y[index])
    ]
  }
}
</pre></div>
</div>
<p>The equivalent Fluid program (calling the Go binding) is:</p>
<div class="highlight-go"><div class="highlight"><pre><span></span><span class="kd">func</span> <span class="nx">main</span><span class="p">()</span> <span class="p">{</span>  <span class="c1">//// block 0</span>
  <span class="nx">X</span> <span class="p">=</span> <span class="nx">fluid</span><span class="p">.</span><span class="nx">read</span><span class="p">(</span><span class="o">...</span><span class="p">)</span>
  <span class="nx">L</span> <span class="p">=</span> <span class="nx">fluid</span><span class="p">.</span><span class="nx">k8s</span><span class="p">.</span><span class="nx">get_worker_addrs</span><span class="p">()</span>
  <span class="nx">Y</span> <span class="p">=</span> <span class="nx">fluid</span><span class="p">.</span><span class="nx">tensor_array</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="nx">L</span><span class="p">))</span>
  <span class="nx">fluid</span><span class="p">.</span><span class="nx">parallel_for</span><span class="p">(</span><span class="nx">X</span><span class="p">,</span> <span class="nx">L</span><span class="p">,</span> 
                     <span class="kd">func</span><span class="p">(</span><span class="nx">index</span> <span class="kt">int</span><span class="p">)</span> <span class="p">{</span>  <span class="c1">//// block 1</span>
                       <span class="nx">x</span> <span class="p">=</span> <span class="nx">X</span><span class="p">[</span><span class="nx">index</span><span class="p">]</span>
                       <span class="nx">fluid</span><span class="p">.</span><span class="nx">send</span><span class="p">(</span><span class="nx">L</span><span class="p">[</span><span class="nx">index</span><span class="p">],</span> <span class="nx">x</span><span class="p">)</span>
                       <span class="nx">y</span> <span class="p">=</span> <span class="nx">fluid</span><span class="p">.</span><span class="nx">recv</span><span class="p">(</span><span class="nx">L</span><span class="p">[</span><span class="nx">index</span><span class="p">])</span>
                       <span class="nx">Y</span><span class="p">[</span><span class="nx">index</span><span class="p">]</span> <span class="p">=</span> <span class="nx">y</span>
                     <span class="p">})</span>
<span class="p">}</span>
</pre></div>
</div>
<p>An explanation of the above program:</p>
<ul class="simple">
<li><code class="docutils literal"><span class="pre">fluid.k8s</span></code> is a package that provides access to Kubernetes API.</li>
<li><code class="docutils literal"><span class="pre">fluid.k8s.get_worker_addrs</span></code> returns the list of IP and ports of all pods of the current job except for the current one (the master pod).</li>
<li><code class="docutils literal"><span class="pre">fluid.tensor_array</span></code> creates a <a class="reference external" href="https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/framework/lod_tensor_array.h">tensor array</a>.  <code class="docutils literal"><span class="pre">fluid.parallel_for</span></code> creates a <code class="docutils literal"><span class="pre">ParallelFor</span></code> intrinsic, which, when executed,<ol>
<li>creates <code class="docutils literal"><span class="pre">len(L)</span></code> scopes, each for the concurrent running of the sub-block (block 1 in this case), and initializes a variable named &#8220;index&#8221; in the scope to an integer value in the range <code class="docutils literal"><span class="pre">[0,</span> <span class="pre">len(L)-1]</span></code>, and</li>
<li>creates <code class="docutils literal"><span class="pre">len(L)</span></code> threads by calling into the <code class="docutils literal"><span class="pre">ThreadPool</span></code> singleton, each thread<ol>
<li>creates an Executor instance, and</li>
<li>calls <code class="docutils literal"><span class="pre">Executor.Run(block)</span></code>, where <code class="docutils literal"><span class="pre">block</span></code> is block 1 as explained above.</li>
</ol>
</li>
</ol>
</li>
</ul>
<ol class="simple">
<li>Please be aware that block 1 is a sub-block of block 0, so ops in block 1 could refer to variables defined in block 0.</li>
</ol>
</div>
<div class="section" id="the-worker-program">
<span id="the-worker-program"></span><h3>The Worker Program<a class="headerlink" href="#the-worker-program" title="永久链接至标题"></a></h3>
<p>The worker program looks like</p>
<div class="highlight-go"><div class="highlight"><pre><span></span><span class="kd">func</span> <span class="nx">main</span><span class="p">()</span> <span class="p">{</span>
  <span class="nx">W</span> <span class="p">=</span> <span class="nx">Tensor</span><span class="p">(</span><span class="o">...</span><span class="p">)</span>
  <span class="nx">x</span> <span class="p">=</span> <span class="nx">fluid</span><span class="p">.</span><span class="nx">listen_and_do</span><span class="p">(</span>
        <span class="nx">fluid</span><span class="p">.</span><span class="nx">k8s</span><span class="p">.</span><span class="nx">self_addr</span><span class="p">(),</span>
        <span class="kd">func</span><span class="p">(</span><span class="nx">input</span> <span class="nx">Tensor</span><span class="p">)</span> <span class="p">{</span>
          <span class="nx">output</span> <span class="p">=</span> <span class="nx">fluid</span><span class="p">.</span><span class="nx">mult</span><span class="p">(</span><span class="nx">input</span><span class="p">,</span> <span class="nx">W</span><span class="p">)</span>
        <span class="p">})</span>
<span class="p">}</span>
</pre></div>
</div>
<p>where</p>
<ul class="simple">
<li><code class="docutils literal"><span class="pre">fluid.listen_and_do</span></code> creates a <code class="docutils literal"><span class="pre">ListenAndDo</span></code> intrinsic, which, when executed,<ol>
<li>listens on the current pod&#8217;s IP address, as returned by <code class="docutils literal"><span class="pre">fliud.k8s.self_addr()</span></code>,</li>
<li>once a connection is established,<ol>
<li>creates a scope of two parameters, &#8220;input&#8221; and &#8220;output&#8221;,</li>
<li>reads a <a class="reference external" href="https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/framework/variable.h">Fluid variable</a> and saves it into &#8220;input&#8221;,</li>
<li>creates an Executor instance and calls <code class="docutils literal"><span class="pre">Executor.Run(block)</span></code>, where the block is generated by running the lambda specified as the second parameter of <code class="docutils literal"><span class="pre">fluid.listen_and_do</span></code>.</li>
</ol>
</li>
</ol>
</li>
</ul>
</div>
</div>
<div class="section" id="summarization">
<span id="summarization"></span><h2>Summarization<a class="headerlink" href="#summarization" title="永久链接至标题"></a></h2>
<p>From the above example, we see that:</p>
<ol class="simple">
<li>Fluid enables the imperative programming paradigm by:<ol>
<li>letting users describe a program, but not a model (a sequence of layers, or a graph of operators), and</li>
<li>call the <code class="docutils literal"><span class="pre">fluid.run</span></code> function that runs the program implicitly.</li>
</ol>
</li>
<li>The program is described as a <code class="docutils literal"><span class="pre">ProgramDesc</span></code> protobuf message.</li>
<li>Function <code class="docutils literal"><span class="pre">Executor.Run</span></code> takes a block, instead of a <code class="docutils literal"><span class="pre">ProgramDesc</span></code>, as its parameter.</li>
<li><code class="docutils literal"><span class="pre">fluid.run</span></code> calls <code class="docutils literal"><span class="pre">Executor.Run</span></code> to run the first block in the <code class="docutils literal"><span class="pre">ProgramDesc</span></code> message.</li>
<li><code class="docutils literal"><span class="pre">Executor.Run</span></code>&#8216;s implementation is extremely simple &#8211; it doesn&#8217;t plan the execution nor create threads; instead, it runs on the current thread and execute intrinsics/operators&#8217; <code class="docutils literal"><span class="pre">Run</span></code> method sequentially as they appear in the <code class="docutils literal"><span class="pre">Block.ops</span></code> array.</li>
<li>Intrinsics/operators&#8217; <code class="docutils literal"><span class="pre">Run</span></code> method might create threads.  For example, the <code class="docutils literal"><span class="pre">ListenAndDo</span></code> operator creates a thread to handle each incoming request.</li>
<li>Threads are not necessarily OS thread; instead, they could be <a class="reference external" href="https://en.wikipedia.org/wiki/Green_threads">green threads</a> managed by ThreadPool.  Multiple green threads might run on the same OS thread.  An example green threads is Go&#8217;s <a class="reference external" href="https://tour.golang.org/concurrency/1">goroutines</a>.</li>
</ol>
</div>
</div>


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