Skip to content

P
Paddle

PaddlePaddle / Paddle
大约 1 年 前同步成功

通知 2298
Star 20931
Fork 5422
P
Paddle
切换分支/标签
查找文件 普通视图历史永久链接Permalink
parallel_do.html 27.2 KB
Newer Older
T
Deploy to GitHub Pages: 118d950e74be36e92c39ea77b0d8cc8474dfe9c4  
Travis CI 已提交
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 


<!DOCTYPE html>
<!--[if IE 8]><html class="no-js lt-ie9" lang="en" > <![endif]-->
<!--[if gt IE 8]><!--> <html class="no-js" lang="en" > <!--<![endif]-->
<head>
  <meta charset="utf-8">
  
  <meta name="viewport" content="width=device-width, initial-scale=1.0">
  
  <title>Design Doc: Parallel_Do in PaddlePaddle &mdash; PaddlePaddle  documentation</title>
  

  
  

  

  
  
    

  

  
  
    <link rel="stylesheet" href="../_static/css/theme.css" type="text/css" />
  

  
  
        <link rel="index" title="Index"
              href="../genindex.html"/>
        <link rel="search" title="Search" href="../search.html"/>
    <link rel="top" title="PaddlePaddle  documentation" href="../index.html"/> 

  <link rel="stylesheet" href="https://cdn.jsdelivr.net/perfect-scrollbar/0.6.14/css/perfect-scrollbar.min.css" type="text/css" />
  <link rel="stylesheet" href="../_static/css/override.css" type="text/css" />
  <script>
  var _hmt = _hmt || [];
  (function() {
    var hm = document.createElement("script");
    hm.src = "//hm.baidu.com/hm.js?b9a314ab40d04d805655aab1deee08ba";
    var s = document.getElementsByTagName("script")[0]; 
    s.parentNode.insertBefore(hm, s);
  })();
  </script>

  

  
  <script src="../_static/js/modernizr.min.js"></script>

</head>

<body class="wy-body-for-nav" role="document">

  
  <header class="site-header">
    <div class="site-logo">
      <a href="/"><img src="../_static/images/PP_w.png"></a>
    </div>
    <div class="site-nav-links">
      <div class="site-menu">
        <a class="fork-on-github" href="https://github.com/PaddlePaddle/Paddle" target="_blank"><i class="fa fa-github"></i>Fork me on Github</a>
        <div class="language-switcher dropdown">
          <a type="button" data-toggle="dropdown">
            <span>English</span>
            <i class="fa fa-angle-up"></i>
            <i class="fa fa-angle-down"></i>
          </a>
          <ul class="dropdown-menu">
            <li><a href="/doc_cn">中文</a></li>
            <li><a href="/doc">English</a></li>
          </ul>
        </div>
        <ul class="site-page-links">
          <li><a href="/">Home</a></li>
        </ul>
      </div>
      <div class="doc-module">
        
        <ul>
<li class="toctree-l1"><a class="reference internal" href="../getstarted/index_en.html">GET STARTED</a></li>
<li class="toctree-l1"><a class="reference internal" href="../build_and_install/index_en.html">Install and Build</a></li>
<li class="toctree-l1"><a class="reference internal" href="../howto/index_en.html">HOW TO</a></li>
<li class="toctree-l1"><a class="reference internal" href="../dev/index_en.html">Development</a></li>
T
Deploy to GitHub Pages: 9344e4e3006057b733116cdcfa57dd18bccd1ed9  
Travis CI 已提交
88 
<li class="toctree-l1"><a class="reference internal" href="../faq/index_en.html">FAQ</a></li>
T
Deploy to GitHub Pages: 118d950e74be36e92c39ea77b0d8cc8474dfe9c4  
Travis CI 已提交
89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 
</ul>

        
<div role="search">
  <form id="rtd-search-form" class="wy-form" action="../search.html" method="get">
    <input type="text" name="q" placeholder="Search docs" />
    <input type="hidden" name="check_keywords" value="yes" />
    <input type="hidden" name="area" value="default" />
  </form>
</div>        
      </div>
    </div>
  </header>
  
  <div class="main-content-wrap">

    
    <nav class="doc-menu-vertical" role="navigation">
        
          
          <ul>
<li class="toctree-l1"><a class="reference internal" href="../getstarted/index_en.html">GET STARTED</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../getstarted/quickstart_en.html">Quick Start</a></li>
T
Deploy to GitHub Pages: 9344e4e3006057b733116cdcfa57dd18bccd1ed9  
Travis CI 已提交
112 
<li class="toctree-l2"><a class="reference internal" href="../getstarted/concepts/use_concepts_en.html">Basic Concept</a></li>
T
Deploy to GitHub Pages: 118d950e74be36e92c39ea77b0d8cc8474dfe9c4  
Travis CI 已提交
113 114 115 
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="../build_and_install/index_en.html">Install and Build</a><ul>
T
Deploy to GitHub Pages: 9344e4e3006057b733116cdcfa57dd18bccd1ed9  
Travis CI 已提交
116 
<li class="toctree-l2"><a class="reference internal" href="../build_and_install/pip_install_en.html">Install using pip</a></li>
T
Deploy to GitHub Pages: 118d950e74be36e92c39ea77b0d8cc8474dfe9c4  
Travis CI 已提交
117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 
<li class="toctree-l2"><a class="reference internal" href="../build_and_install/docker_install_en.html">Run in Docker Containers</a></li>
<li class="toctree-l2"><a class="reference internal" href="../build_and_install/build_from_source_en.html">Build from Sources</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="../howto/index_en.html">HOW TO</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../howto/cmd_parameter/index_en.html">Set Command-line Parameters</a><ul>
<li class="toctree-l3"><a class="reference internal" href="../howto/cmd_parameter/use_case_en.html">Use Case</a></li>
<li class="toctree-l3"><a class="reference internal" href="../howto/cmd_parameter/arguments_en.html">Argument Outline</a></li>
<li class="toctree-l3"><a class="reference internal" href="../howto/cmd_parameter/detail_introduction_en.html">Detail Description</a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="../howto/cluster/index_en.html">Distributed Training</a><ul>
<li class="toctree-l3"><a class="reference internal" href="../howto/cluster/preparations_en.html">Preparations</a></li>
<li class="toctree-l3"><a class="reference internal" href="../howto/cluster/cmd_argument_en.html">Command-line arguments</a></li>
<li class="toctree-l3"><a class="reference internal" href="../howto/cluster/multi_cluster/index_en.html">Use different clusters</a><ul>
T
Deploy to GitHub Pages: 9344e4e3006057b733116cdcfa57dd18bccd1ed9  
Travis CI 已提交
132 133 134 135 
<li class="toctree-l4"><a class="reference internal" href="../howto/cluster/multi_cluster/fabric_en.html">Fabric</a></li>
<li class="toctree-l4"><a class="reference internal" href="../howto/cluster/multi_cluster/openmpi_en.html">OpenMPI</a></li>
<li class="toctree-l4"><a class="reference internal" href="../howto/cluster/multi_cluster/k8s_en.html">Kubernetes</a></li>
<li class="toctree-l4"><a class="reference internal" href="../howto/cluster/multi_cluster/k8s_aws_en.html">Kubernetes on AWS</a></li>
T
Deploy to GitHub Pages: 118d950e74be36e92c39ea77b0d8cc8474dfe9c4  
Travis CI 已提交
136 137 138 139 140 141 
</ul>
</li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="../howto/rnn/index_en.html">RNN Models</a><ul>
<li class="toctree-l3"><a class="reference internal" href="../howto/rnn/rnn_config_en.html">RNN Configuration</a></li>
T
Deploy to GitHub Pages: 9344e4e3006057b733116cdcfa57dd18bccd1ed9  
Travis CI 已提交
142 143 144 
<li class="toctree-l3"><a class="reference internal" href="../howto/rnn/recurrent_group_en.html">Recurrent Group Tutorial</a></li>
<li class="toctree-l3"><a class="reference internal" href="../howto/rnn/hierarchical_layer_en.html">Layers supporting hierarchical sequence as input</a></li>
<li class="toctree-l3"><a class="reference internal" href="../howto/rnn/hrnn_rnn_api_compare_en.html">API comparision between RNN and hierarchical RNN</a></li>
T
Deploy to GitHub Pages: 118d950e74be36e92c39ea77b0d8cc8474dfe9c4  
Travis CI 已提交
145 146 147 148 149 150 151 152 153 154 
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="../howto/optimization/gpu_profiling_en.html">Tune GPU Performance</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="../dev/index_en.html">Development</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../dev/contribute_to_paddle_en.html">Contribute Code</a></li>
<li class="toctree-l2"><a class="reference internal" href="../dev/write_docs_en.html">Contribute Documentation</a></li>
</ul>
</li>
T
Deploy to GitHub Pages: 9344e4e3006057b733116cdcfa57dd18bccd1ed9  
Travis CI 已提交
155 156 157 158 159 160 161 162 
<li class="toctree-l1"><a class="reference internal" href="../faq/index_en.html">FAQ</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../faq/build_and_install/index_en.html">Install, Build and Unit test</a></li>
<li class="toctree-l2"><a class="reference internal" href="../faq/model/index_en.html">Model Configuration</a></li>
<li class="toctree-l2"><a class="reference internal" href="../faq/parameter/index_en.html">Parameter Setting</a></li>
<li class="toctree-l2"><a class="reference internal" href="../faq/local/index_en.html">Local Training and Prediction</a></li>
<li class="toctree-l2"><a class="reference internal" href="../faq/cluster/index_en.html">Cluster Training and Prediction</a></li>
</ul>
</li>
T
Deploy to GitHub Pages: 118d950e74be36e92c39ea77b0d8cc8474dfe9c4  
Travis CI 已提交
163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 
</ul>

        
    </nav>
    
    <section class="doc-content-wrap">

      

 







<div role="navigation" aria-label="breadcrumbs navigation">
  <ul class="wy-breadcrumbs">
      
    <li>Design Doc: Parallel_Do in PaddlePaddle</li>
  </ul>
</div>
      
      <div class="wy-nav-content" id="doc-content">
        <div class="rst-content">
          <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
           <div itemprop="articleBody">
            
  <div class="section" id="design-doc-parallel-do-in-paddlepaddle">
<span id="design-doc-parallel-do-in-paddlepaddle"></span><h1>Design Doc: Parallel_Do in PaddlePaddle<a class="headerlink" href="#design-doc-parallel-do-in-paddlepaddle" title="Permalink to this headline"></a></h1>
<p>In PaddlePaddle, we use parallel_do primitive to represent multithread data parallel processing.</p>
<div class="section" id="design-overview">
<span id="design-overview"></span><h2>Design overview<a class="headerlink" href="#design-overview" title="Permalink to this headline"></a></h2>
<p>The definition of a parallel_do op looks like the following</p>
<div class="highlight-c++"><div class="highlight"><pre><span></span><span class="n">AddInput</span><span class="p">(</span><span class="n">kInputs</span><span class="p">,</span> <span class="s">&quot;Inputs needed to be split onto different devices&quot;</span><span class="p">).</span><span class="n">AsDuplicable</span><span class="p">();</span>
<span class="n">AddInput</span><span class="p">(</span><span class="n">kParameters</span><span class="p">,</span> <span class="s">&quot;Parameters are duplicated over different devices&quot;</span><span class="p">)</span>
    <span class="p">.</span><span class="n">AsDuplicable</span><span class="p">();</span>
<span class="n">AddInput</span><span class="p">(</span><span class="n">kPlaces</span><span class="p">,</span> <span class="s">&quot;Devices used for parallel processing&quot;</span><span class="p">);</span>
<span class="n">AddOutput</span><span class="p">(</span><span class="n">kOutputs</span><span class="p">,</span> <span class="s">&quot;Outputs needed to be merged from different devices&quot;</span><span class="p">).</span><span class="n">AsDuplicable</span><span class="p">();</span>
<span class="n">AddOutput</span><span class="p">(</span><span class="n">kParallelScopes</span><span class="p">,</span>
          <span class="s">&quot;Scopes for all local variables in forward pass. One scope for each device&quot;</span><span class="p">);</span>
<span class="n">AddAttr</span><span class="o">&lt;</span><span class="n">framework</span><span class="o">::</span><span class="n">BlockDesc</span> <span class="o">*&gt;</span><span class="p">(</span><span class="n">kParallelBlock</span><span class="p">,</span>
                                <span class="s">&quot;List of operaters to be executed in parallel&quot;</span><span class="p">);</span>
</pre></div>
</div>
<p>A vanilla implementation of parallel_do can be shown as the following (<code class="docutils literal"><span class="pre">|</span></code> means single thread and
<code class="docutils literal"><span class="pre">||||</span></code> means multiple threads)</p>
<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">In</span> <span class="n">the</span> <span class="n">forward</span> <span class="k">pass</span>
  <span class="o">|</span>      <span class="n">Split</span> <span class="nb">input</span> <span class="n">onto</span> <span class="n">different</span> <span class="n">devices</span>
T
Deploy to GitHub Pages: d3162339f6637114d515b4bc448fe4ae8cc81125  
Travis CI 已提交
213 
  <span class="o">|</span>      <span class="n">Copy</span> <span class="n">parameter</span> <span class="n">onto</span> <span class="n">different</span> <span class="n">devices</span>
T
Deploy to GitHub Pages: 118d950e74be36e92c39ea77b0d8cc8474dfe9c4  
Travis CI 已提交
214 215 216 217 218 219 220 221 222 223 224 225 
  <span class="o">||||</span>   <span class="n">Compute</span> <span class="n">forward</span> <span class="k">pass</span> <span class="ow">in</span> <span class="n">parallel</span>
  <span class="o">|</span>      <span class="n">Merge</span> <span class="n">output</span> <span class="kn">from</span> <span class="nn">different</span> <span class="n">devices</span>

<span class="n">In</span> <span class="n">the</span> <span class="n">backward</span> <span class="k">pass</span>
  <span class="o">|</span>      <span class="n">Split</span> <span class="n">output</span><span class="nd">@grad</span> <span class="n">onto</span> <span class="n">different</span> <span class="n">devices</span>
  <span class="o">||||</span>   <span class="n">Compute</span> <span class="n">backward</span> <span class="k">pass</span> <span class="ow">in</span> <span class="n">parallel</span>
  <span class="o">|</span>      <span class="n">accumulate</span> <span class="n">param</span><span class="nd">@grad</span> <span class="kn">from</span> <span class="nn">different</span> <span class="n">devices</span> <span class="n">to</span> <span class="n">the</span> <span class="n">first</span> <span class="n">device</span>
  <span class="o">|</span>      <span class="n">Merge</span> <span class="nb">input</span><span class="nd">@grad</span> <span class="kn">from</span> <span class="nn">different</span> <span class="n">devices</span>
  <span class="o">|</span>      <span class="n">Copy</span> <span class="n">param</span><span class="nd">@grad</span> <span class="n">to</span> <span class="n">the</span> <span class="n">place</span> <span class="n">of</span> <span class="n">parallel_do_op</span>
</pre></div>
</div>
<p>This implementation allows to write mixed device program like this</p>
T
Deploy to GitHub Pages: 2edeb639e27562569e1bc6571e43cf9f6259a498  
Travis CI 已提交
226 227 
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">W1</span> <span class="o">=</span> <span class="n">fluid</span><span class="o">.</span><span class="n">tensor</span><span class="p">(</span><span class="n">size</span><span class="o">=</span><span class="p">[</span><span class="mi">100</span><span class="p">,</span><span class="mi">20</span><span class="p">],</span> <span class="n">parameter</span><span class="o">=</span><span class="n">true</span><span class="p">)</span>
<span class="n">W2</span> <span class="o">=</span> <span class="n">fluid</span><span class="o">.</span><span class="n">tensor</span><span class="p">(</span><span class="n">size</span><span class="o">=</span><span class="p">[</span><span class="mi">20</span><span class="p">,</span><span class="mi">15</span><span class="p">],</span> <span class="n">parameter</span><span class="o">=</span><span class="n">true</span><span class="p">)</span>
T
Deploy to GitHub Pages: 118d950e74be36e92c39ea77b0d8cc8474dfe9c4  
Travis CI 已提交
228
T
Deploy to GitHub Pages: 2edeb639e27562569e1bc6571e43cf9f6259a498  
Travis CI 已提交
229 230 231 
<span class="n">data</span> <span class="o">=</span> <span class="n">layers</span><span class="o">.</span><span class="n">data</span><span class="p">()</span>

<span class="n">gpu_places</span> <span class="o">=</span> <span class="n">layers</span><span class="o">.</span><span class="n">get_place</span><span class="p">(</span><span class="n">use_gpu</span><span class="o">=</span><span class="bp">True</span><span class="p">)</span>
T
Deploy to GitHub Pages: 118d950e74be36e92c39ea77b0d8cc8474dfe9c4  
Travis CI 已提交
232 233 
<span class="c1"># parallel processing on multiple GPUs</span>
<span class="n">pd</span> <span class="o">=</span> <span class="n">ParallelDo</span><span class="p">(</span><span class="n">gpu_places</span><span class="p">)</span>
T
Deploy to GitHub Pages: 2edeb639e27562569e1bc6571e43cf9f6259a498  
Travis CI 已提交
234 235 
<span class="k">with</span> <span class="n">pd</span><span class="o">.</span><span class="n">do</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="n">data</span><span class="p">):</span>
    <span class="n">prediction</span> <span class="o">=</span> <span class="n">softmax</span><span class="p">(</span><span class="n">fc</span><span class="p">(</span><span class="n">fc</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">W1</span><span class="p">),</span> <span class="n">W2</span><span class="p">))</span>
T
Deploy to GitHub Pages: 118d950e74be36e92c39ea77b0d8cc8474dfe9c4  
Travis CI 已提交
236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 
    <span class="n">write_output</span><span class="p">(</span><span class="n">prediction</span><span class="p">)</span>
<span class="n">prediction</span> <span class="o">=</span> <span class="n">pd</span><span class="p">()</span>
<span class="n">loss</span> <span class="o">=</span> <span class="n">cross_entropy</span><span class="p">(</span><span class="n">prediction</span><span class="p">,</span> <span class="n">label</span><span class="p">)</span>
</pre></div>
</div>
<p>And the programDesc are like the following</p>
<div class="highlight-default"><div class="highlight"><pre><span></span><span class="c1"># start_program will be run by executor(CPUPlace), all w1, w2 will be allocated on CPU</span>
<span class="n">start_program</span>
<span class="p">{</span>
  <span class="nb">vars</span><span class="p">:</span> <span class="n">w1</span><span class="p">,</span> <span class="n">w2</span>
  <span class="n">ops</span><span class="p">:</span> <span class="n">init</span><span class="p">(</span><span class="n">w1</span><span class="p">),</span> <span class="n">init</span><span class="p">(</span><span class="n">w2</span><span class="p">)</span>
<span class="p">}</span>

<span class="n">main_program</span>
<span class="p">{</span>
<span class="n">block0</span> <span class="p">{</span>
T
Deploy to GitHub Pages: 2edeb639e27562569e1bc6571e43cf9f6259a498  
Travis CI 已提交
252 
  <span class="nb">vars</span><span class="p">:</span> <span class="n">data</span><span class="p">,</span> <span class="n">places</span><span class="p">,</span> <span class="n">w1</span><span class="p">,</span> <span class="n">w2</span><span class="p">,</span> <span class="n">w1_grad</span><span class="p">,</span> <span class="n">w2_grad</span><span class="p">,</span>
T
Deploy to GitHub Pages: 118d950e74be36e92c39ea77b0d8cc8474dfe9c4  
Travis CI 已提交
253 254 255 256 257 
  <span class="n">ops</span><span class="p">:</span> <span class="n">data</span><span class="p">,</span> <span class="n">get_place</span><span class="p">,</span> <span class="n">parallel_do</span><span class="p">(</span><span class="n">block1</span><span class="p">),</span>
       <span class="n">parallel_do_grad</span><span class="p">(</span><span class="n">block2</span><span class="p">),</span>
       <span class="n">sgd</span><span class="p">(</span><span class="n">w2</span><span class="p">,</span> <span class="n">w2_grad</span><span class="p">),</span>
       <span class="n">sgd</span><span class="p">(</span><span class="n">w1</span><span class="p">,</span> <span class="n">w1_grad</span><span class="p">)</span>
<span class="p">}</span>
T
Deploy to GitHub Pages: 2edeb639e27562569e1bc6571e43cf9f6259a498  
Travis CI 已提交
258 
<span class="n">block1</span> <span class="p">{</span> <span class="c1"># the forward pass</span>
T
Deploy to GitHub Pages: 118d950e74be36e92c39ea77b0d8cc8474dfe9c4  
Travis CI 已提交
259 260 261 262 
  <span class="n">parent_block</span><span class="p">:</span> <span class="mi">0</span>
  <span class="nb">vars</span><span class="p">:</span> <span class="n">data</span><span class="p">,</span> <span class="n">h1</span><span class="p">,</span> <span class="n">h2</span><span class="p">,</span> <span class="n">loss</span>
  <span class="n">ops</span><span class="p">:</span> <span class="n">fc</span><span class="p">,</span> <span class="n">fc</span><span class="p">,</span> <span class="n">softmax</span>
<span class="p">}</span>
T
Deploy to GitHub Pages: 2edeb639e27562569e1bc6571e43cf9f6259a498  
Travis CI 已提交
263 
<span class="n">block2</span> <span class="p">{</span> <span class="c1"># the backward pass</span>
T
Deploy to GitHub Pages: 118d950e74be36e92c39ea77b0d8cc8474dfe9c4  
Travis CI 已提交
264 
  <span class="n">parent_block</span><span class="p">:</span> <span class="mi">1</span>
T
Deploy to GitHub Pages: 2edeb639e27562569e1bc6571e43cf9f6259a498  
Travis CI 已提交
265 
  <span class="nb">vars</span><span class="p">:</span> <span class="n">data_grad</span><span class="p">,</span> <span class="n">h1_grad</span><span class="p">,</span> <span class="n">h2_grad</span><span class="p">,</span> <span class="n">loss_gard</span><span class="p">,</span> <span class="n">local_w1_grad</span><span class="p">,</span> <span class="n">local_w2_grad</span>
T
Deploy to GitHub Pages: 118d950e74be36e92c39ea77b0d8cc8474dfe9c4  
Travis CI 已提交
266 267 268 269 270 271 272 273 
  <span class="n">ops</span><span class="p">:</span> <span class="n">softmax_grad</span><span class="p">,</span>
       <span class="n">fc_grad</span>
       <span class="n">fc_grad</span>
<span class="p">}</span>
<span class="p">}</span>
</pre></div>
</div>
</div>
T
Deploy to GitHub Pages: d3162339f6637114d515b4bc448fe4ae8cc81125  
Travis CI 已提交
274 275 
<div class="section" id="performance-imporvement">
<span id="performance-imporvement"></span><h2>Performance Imporvement<a class="headerlink" href="#performance-imporvement" title="Permalink to this headline"></a></h2>
T
Deploy to GitHub Pages: 118d950e74be36e92c39ea77b0d8cc8474dfe9c4  
Travis CI 已提交
276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 
<p>There are serial places we can make this parallel_do faster.</p>
<div class="section" id="forward-split-input-onto-different-devices">
<span id="forward-split-input-onto-different-devices"></span><h3>forward: split input onto different devices<a class="headerlink" href="#forward-split-input-onto-different-devices" title="Permalink to this headline"></a></h3>
<p>If the input of the parallel_do is independent from any prior opeartors, we can avoid this step by
prefetching the input onto different devices in a seperate background thread. And the python code
looks like this.</p>
<div class="highlight-python"><div class="highlight"><pre><span></span>pd = ParallelDo(gpu_places)
with pd.do():
    feature = get_data_from_prefetch_queue(gpu_places)
    prediction = my_net(feature)
    write_output(activation)
</pre></div>
</div>
</div>
<div class="section" id="forward-copy-parameter-to-onto-different-devices">
<span id="forward-copy-parameter-to-onto-different-devices"></span><h3>forward: Copy parameter to onto different devices<a class="headerlink" href="#forward-copy-parameter-to-onto-different-devices" title="Permalink to this headline"></a></h3>
<p>We can avoid this step by making each device have a copy of the parameter. This requires:</p>
<ol class="simple">
<li><code class="docutils literal"><span class="pre">fluid.default_start_up_program()</span></code> to be run on all devices</li>
<li>In the backward, allreduce param&#64;grad at different devices, this requires<ol>
<li><code class="docutils literal"><span class="pre">backward.py</span></code> add <code class="docutils literal"><span class="pre">allreduce</span></code> operators at parallel_do_grad</li>
<li><code class="docutils literal"><span class="pre">allreduce</span></code> operators need to be called in async mode to achieve maximum throughput</li>
</ol>
</li>
<li>apply gradients related op(i.e. cliping, normalization, decay, sgd) on different devices in parallel</li>
</ol>
<p>By doing so, we also avoided &#8220;backward: accumulate param&#64;grad from different devices to the first device&#8221;.
And the ProgramDesc looks like the following</p>
<div class="highlight-default"><div class="highlight"><pre><span></span><span class="c1"># w1, w2 will be allocated on all GPUs</span>
<span class="n">start_program</span>
<span class="p">{</span>
<span class="n">block0</span> <span class="p">{</span>
  <span class="n">parallel_do</span><span class="p">(</span><span class="n">block1</span><span class="p">)</span>
<span class="p">}</span>
<span class="n">block1</span> <span class="p">{</span>
  <span class="n">parent_block</span><span class="p">:</span> <span class="mi">0</span>
  <span class="nb">vars</span><span class="p">:</span> <span class="n">w1</span><span class="p">,</span> <span class="n">w2</span>
  <span class="n">ops</span><span class="p">:</span> <span class="n">init</span><span class="p">(</span><span class="n">w1</span><span class="p">),</span> <span class="n">init</span><span class="p">(</span><span class="n">w2</span><span class="p">)</span>
<span class="p">}</span>
<span class="p">}</span>

<span class="n">main_program</span>
<span class="p">{</span>
<span class="n">block0</span> <span class="p">{</span>
  <span class="nb">vars</span><span class="p">:</span> <span class="n">data</span><span class="p">,</span> <span class="n">places</span><span class="p">,</span> <span class="n">w1</span><span class="p">,</span> <span class="n">w2</span>
  <span class="n">ops</span><span class="p">:</span> <span class="n">data</span><span class="p">,</span> <span class="n">get_place</span><span class="p">,</span> <span class="n">parallel_do</span><span class="p">(</span><span class="n">block1</span><span class="p">),</span>
       <span class="n">parallel_do_grad</span><span class="p">(</span><span class="n">block2</span><span class="p">),</span>      <span class="c1"># append_backward</span>
       <span class="n">parallel_do</span><span class="p">(</span><span class="n">block3</span><span class="p">)</span>            <span class="c1"># append_optimization</span>
       
<span class="p">}</span>
<span class="n">block1</span> <span class="p">{</span>
  <span class="n">parent_block</span><span class="p">:</span> <span class="mi">0</span>
  <span class="nb">vars</span><span class="p">:</span> <span class="n">data</span><span class="p">,</span> <span class="n">h1</span><span class="p">,</span> <span class="n">h2</span><span class="p">,</span> <span class="n">loss</span>
  <span class="n">ops</span><span class="p">:</span> <span class="n">fc</span><span class="p">,</span> <span class="n">fc</span><span class="p">,</span> <span class="n">softmax</span>
<span class="p">}</span>
<span class="n">block2</span> <span class="p">{</span>
  <span class="n">parent_block</span><span class="p">:</span> <span class="mi">1</span>
  <span class="nb">vars</span><span class="p">:</span> <span class="n">data_grad</span><span class="p">,</span> <span class="n">h1_grad</span><span class="p">,</span> <span class="n">h2_grad</span><span class="p">,</span> <span class="n">loss_gard</span><span class="p">,</span> <span class="n">w1_grad</span><span class="p">,</span> <span class="n">w2_grad</span>
  <span class="n">ops</span><span class="p">:</span> <span class="n">softmax_grad</span><span class="p">,</span>
       <span class="n">fc_grad</span><span class="p">,</span> <span class="n">allreduce</span><span class="p">(</span><span class="n">places</span><span class="p">,</span> <span class="n">scopes</span><span class="p">,</span> <span class="n">w1_grad</span><span class="p">),</span>
       <span class="n">fc_grad</span><span class="p">,</span> <span class="n">allreduce</span><span class="p">(</span><span class="n">places</span><span class="p">,</span> <span class="n">scopes</span><span class="p">,</span> <span class="n">w2_grad</span><span class="p">)</span>
<span class="p">}</span>
<span class="n">block3</span> <span class="p">{</span>
  <span class="n">parent_block</span><span class="p">:</span> <span class="mi">0</span>
  <span class="nb">vars</span><span class="p">:</span> <span class="n">lr</span>
  <span class="n">ops</span><span class="p">:</span> <span class="n">sgd</span><span class="p">(</span><span class="n">w2</span><span class="p">,</span> <span class="n">w2_grad</span><span class="p">),</span>
       <span class="n">sgd</span><span class="p">(</span><span class="n">w1</span><span class="p">,</span> <span class="n">w1_grad</span><span class="p">)</span>
<span class="p">}</span>
<span class="p">}</span>
</pre></div>
</div>
</div>
</div>
</div>


           </div>
          </div>
          <footer>
  

  <hr/>

  <div role="contentinfo">
    <p>
        &copy; Copyright 2016, PaddlePaddle developers.

    </p>
  </div>
  Built with <a href="http://sphinx-doc.org/">Sphinx</a> using a <a href="https://github.com/snide/sphinx_rtd_theme">theme</a> provided by <a href="https://readthedocs.org">Read the Docs</a>. 

</footer>

        </div>
      </div>

    </section>

  </div>
  


  

    <script type="text/javascript">
        var DOCUMENTATION_OPTIONS = {
            URL_ROOT:'../',
            VERSION:'',
            COLLAPSE_INDEX:false,
            FILE_SUFFIX:'.html',
            HAS_SOURCE:  true,
            SOURCELINK_SUFFIX: ".txt",
        };
    </script>
      <script type="text/javascript" src="../_static/jquery.js"></script>
      <script type="text/javascript" src="../_static/underscore.js"></script>
      <script type="text/javascript" src="../_static/doctools.js"></script>
      <script type="text/javascript" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.0/MathJax.js?config=TeX-AMS-MML_HTMLorMML"></script>
       
  

  
  
    <script type="text/javascript" src="../_static/js/theme.js"></script>
  
  
  <script src="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.7/js/bootstrap.min.js" integrity="sha384-Tc5IQib027qvyjSMfHjOMaLkfuWVxZxUPnCJA7l2mCWNIpG9mGCD8wGNIcPD7Txa" crossorigin="anonymous"></script>
  <script src="https://cdn.jsdelivr.net/perfect-scrollbar/0.6.14/js/perfect-scrollbar.jquery.min.js"></script>
  <script src="../_static/js/paddle_doc_init.js"></script> 

</body>
</html>