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
fluid.html 29.0 KB
Newer Older
T
Deploy to GitHub Pages: 16a61004da780fb1025ea7d537e425403d6ce465  
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 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 


<!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: PaddlePaddle Fluid &mdash; PaddlePaddle  文档</title>
  

  
  

  

  
  
    

  

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

  
  
        <link rel="index" title="索引"
              href="../genindex.html"/>
        <link rel="search" title="搜索" href="../search.html"/>
    <link rel="top" title="PaddlePaddle  文档" 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_cn.html">新手入门</a></li>
<li class="toctree-l1"><a class="reference internal" href="../howto/index_cn.html">进阶指南</a></li>
<li class="toctree-l1"><a class="reference internal" href="../api/index_cn.html">API</a></li>
<li class="toctree-l1"><a class="reference internal" href="../faq/index_cn.html">FAQ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../mobile/index_cn.html">MOBILE</a></li>
</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_cn.html">新手入门</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../getstarted/build_and_install/index_cn.html">安装与编译</a><ul>
<li class="toctree-l3"><a class="reference internal" href="../getstarted/build_and_install/pip_install_cn.html">使用pip安装</a></li>
<li class="toctree-l3"><a class="reference internal" href="../getstarted/build_and_install/docker_install_cn.html">使用Docker安装运行</a></li>
<li class="toctree-l3"><a class="reference internal" href="../howto/dev/build_cn.html">用Docker编译和测试PaddlePaddle</a></li>
<li class="toctree-l3"><a class="reference internal" href="../getstarted/build_and_install/build_from_source_cn.html">从源码编译</a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="../getstarted/concepts/use_concepts_cn.html">基本使用概念</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="../howto/index_cn.html">进阶指南</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../howto/usage/cmd_parameter/index_cn.html">设置命令行参数</a><ul>
<li class="toctree-l3"><a class="reference internal" href="../howto/usage/cmd_parameter/use_case_cn.html">使用案例</a></li>
<li class="toctree-l3"><a class="reference internal" href="../howto/usage/cmd_parameter/arguments_cn.html">参数概述</a></li>
<li class="toctree-l3"><a class="reference internal" href="../howto/usage/cmd_parameter/detail_introduction_cn.html">细节描述</a></li>
</ul>
</li>
T
Deploy to GitHub Pages: 33e074dab85d23bccdb1876df6490a7a5514e395  
Travis CI 已提交
128 129 130 131 132 133 
<li class="toctree-l2"><a class="reference internal" href="../howto/usage/cluster/cluster_train_cn.html">分布式训练</a><ul>
<li class="toctree-l3"><a class="reference internal" href="../howto/usage/cluster/fabric_cn.html">fabric集群</a></li>
<li class="toctree-l3"><a class="reference internal" href="../howto/usage/cluster/openmpi_cn.html">openmpi集群</a></li>
<li class="toctree-l3"><a class="reference internal" href="../howto/usage/cluster/k8s_cn.html">kubernetes单机</a></li>
<li class="toctree-l3"><a class="reference internal" href="../howto/usage/cluster/k8s_distributed_cn.html">kubernetes distributed分布式</a></li>
<li class="toctree-l3"><a class="reference internal" href="../howto/usage/cluster/k8s_aws_cn.html">AWS上运行kubernetes集群训练</a></li>
T
Deploy to GitHub Pages: 16fd9f18e304043a1035ad237884a93be354804c  
Travis CI 已提交
134 135 
</ul>
</li>
T
Deploy to GitHub Pages: 03c858a9bee98dbbcbedd7a6be886402a2e97609  
Travis CI 已提交
136 137 138 139 140 141 
<li class="toctree-l2"><a class="reference internal" href="../howto/usage/capi/index_cn.html">PaddlePaddle C-API</a><ul>
<li class="toctree-l3"><a class="reference internal" href="../howto/usage/capi/compile_paddle_lib_cn.html">编译 PaddlePaddle 预测库</a></li>
<li class="toctree-l3"><a class="reference internal" href="../howto/usage/capi/organization_of_the_inputs_cn.html">输入/输出数据组织</a></li>
<li class="toctree-l3"><a class="reference internal" href="../howto/usage/capi/workflow_of_capi_cn.html">C-API 使用流程</a></li>
</ul>
</li>
T
Deploy to GitHub Pages: 16a61004da780fb1025ea7d537e425403d6ce465  
Travis CI 已提交
142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 
<li class="toctree-l2"><a class="reference internal" href="../howto/dev/contribute_to_paddle_cn.html">如何贡献代码</a></li>
<li class="toctree-l2"><a class="reference internal" href="../howto/dev/write_docs_cn.html">如何贡献/修改文档</a></li>
<li class="toctree-l2"><a class="reference internal" href="../howto/deep_model/rnn/index_cn.html">RNN相关模型</a><ul>
<li class="toctree-l3"><a class="reference internal" href="../howto/deep_model/rnn/rnn_config_cn.html">RNN配置</a></li>
<li class="toctree-l3"><a class="reference internal" href="../howto/deep_model/rnn/recurrent_group_cn.html">Recurrent Group教程</a></li>
<li class="toctree-l3"><a class="reference internal" href="../howto/deep_model/rnn/hierarchical_layer_cn.html">支持双层序列作为输入的Layer</a></li>
<li class="toctree-l3"><a class="reference internal" href="../howto/deep_model/rnn/hrnn_rnn_api_compare_cn.html">单双层RNN API对比介绍</a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="../howto/optimization/gpu_profiling_cn.html">GPU性能分析与调优</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="../api/index_cn.html">API</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../api/v2/model_configs.html">模型配置</a><ul>
<li class="toctree-l3"><a class="reference internal" href="../api/v2/config/activation.html">Activation</a></li>
<li class="toctree-l3"><a class="reference internal" href="../api/v2/config/layer.html">Layers</a></li>
<li class="toctree-l3"><a class="reference internal" href="../api/v2/config/evaluators.html">Evaluators</a></li>
<li class="toctree-l3"><a class="reference internal" href="../api/v2/config/optimizer.html">Optimizer</a></li>
<li class="toctree-l3"><a class="reference internal" href="../api/v2/config/pooling.html">Pooling</a></li>
<li class="toctree-l3"><a class="reference internal" href="../api/v2/config/networks.html">Networks</a></li>
<li class="toctree-l3"><a class="reference internal" href="../api/v2/config/attr.html">Parameter Attribute</a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="../api/v2/data.html">数据访问</a><ul>
<li class="toctree-l3"><a class="reference internal" href="../api/v2/data/data_reader.html">Data Reader Interface</a></li>
<li class="toctree-l3"><a class="reference internal" href="../api/v2/data/image.html">Image Interface</a></li>
<li class="toctree-l3"><a class="reference internal" href="../api/v2/data/dataset.html">Dataset</a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="../api/v2/run_logic.html">训练与应用</a></li>
T
Deploy to GitHub Pages: 5ac72d95014e453c13a389248d2d2361a45a3402  
Travis CI 已提交
172 173 174 175 176 177 178 179 180 181 182 
<li class="toctree-l2"><a class="reference internal" href="../api/v2/fluid.html">Fluid</a><ul>
<li class="toctree-l3"><a class="reference internal" href="../api/v2/fluid/layers.html">Layers</a></li>
<li class="toctree-l3"><a class="reference internal" href="../api/v2/fluid/data_feeder.html">DataFeeder</a></li>
<li class="toctree-l3"><a class="reference internal" href="../api/v2/fluid/executor.html">Executor</a></li>
<li class="toctree-l3"><a class="reference internal" href="../api/v2/fluid/initializer.html">Initializer</a></li>
<li class="toctree-l3"><a class="reference internal" href="../api/v2/fluid/evaluator.html">Evaluator</a></li>
<li class="toctree-l3"><a class="reference internal" href="../api/v2/fluid/nets.html">Nets</a></li>
<li class="toctree-l3"><a class="reference internal" href="../api/v2/fluid/optimizer.html">Optimizer</a></li>
<li class="toctree-l3"><a class="reference internal" href="../api/v2/fluid/param_attr.html">ParamAttr</a></li>
<li class="toctree-l3"><a class="reference internal" href="../api/v2/fluid/profiler.html">Profiler</a></li>
<li class="toctree-l3"><a class="reference internal" href="../api/v2/fluid/regularizer.html">Regularizer</a></li>
T
Deploy to GitHub Pages: b4d1811c9a25011e7815d62b7ebcbafb935082bd  
Travis CI 已提交
183 
<li class="toctree-l3"><a class="reference internal" href="../api/v2/fluid/io.html">IO</a></li>
T
Deploy to GitHub Pages: 5ac72d95014e453c13a389248d2d2361a45a3402  
Travis CI 已提交
184 185 
</ul>
</li>
T
Deploy to GitHub Pages: 16a61004da780fb1025ea7d537e425403d6ce465  
Travis CI 已提交
186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="../faq/index_cn.html">FAQ</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../faq/build_and_install/index_cn.html">编译安装与单元测试</a></li>
<li class="toctree-l2"><a class="reference internal" href="../faq/model/index_cn.html">模型配置</a></li>
<li class="toctree-l2"><a class="reference internal" href="../faq/parameter/index_cn.html">参数设置</a></li>
<li class="toctree-l2"><a class="reference internal" href="../faq/local/index_cn.html">本地训练与预测</a></li>
<li class="toctree-l2"><a class="reference internal" href="../faq/cluster/index_cn.html">集群训练与预测</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="../mobile/index_cn.html">MOBILE</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../mobile/cross_compiling_for_android_cn.html">Android平台编译指南</a></li>
<li class="toctree-l2"><a class="reference internal" href="../mobile/cross_compiling_for_ios_cn.html">iOS平台编译指南</a></li>
<li class="toctree-l2"><a class="reference internal" href="../mobile/cross_compiling_for_raspberry_cn.html">Raspberry Pi平台编译指南</a></li>
</ul>
</li>
</ul>

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

      

 







<div role="navigation" aria-label="breadcrumbs navigation">
  <ul class="wy-breadcrumbs">
      
    <li>Design Doc: PaddlePaddle Fluid</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-paddlepaddle-fluid">
<span id="design-doc-paddlepaddle-fluid"></span><h1>Design Doc: PaddlePaddle Fluid<a class="headerlink" href="#design-doc-paddlepaddle-fluid" title="永久链接至标题"></a></h1>
<div class="section" id="why-fluid">
<span id="why-fluid"></span><h2>Why Fluid<a class="headerlink" href="#why-fluid" title="永久链接至标题"></a></h2>
T
Deploy to GitHub Pages: c5afc3fe7cf1f1f93399cb9d6c92ed6f86a5fc74  
Travis CI 已提交
235 236 
<p>When Baidu developed PaddlePaddle in 2013, the only well-known open source deep learning system at the time was Caffe.  However, when PaddlePaddle was open-sourced in 2016, many other choices were available. There was a challenge &#8211; what is the need for open sourcing yet another deep learning framework?</p>
<p>Fluid is the answer.  Fluid is similar to PyTorch and TensorFlow Eager Execution, which describes the &#8220;process&#8221; of training or inference using the concept of a model.  In fact in PyTorch, TensorFlow Eager Execution and Fluid, there is no  concept of a model at all. The details are covered in the sections below. Fluid is currently more extreme in the above mentioned idea than PyTorch and Eager Execution, and we are trying to push Fluid towards the directions of a compiler and a new programming language for deep learning.</p>
T
Deploy to GitHub Pages: 16a61004da780fb1025ea7d537e425403d6ce465  
Travis CI 已提交
237 238 239 
</div>
<div class="section" id="the-evolution-of-deep-learning-systems">
<span id="the-evolution-of-deep-learning-systems"></span><h2>The Evolution of Deep Learning Systems<a class="headerlink" href="#the-evolution-of-deep-learning-systems" title="永久链接至标题"></a></h2>
T
Deploy to GitHub Pages: c5afc3fe7cf1f1f93399cb9d6c92ed6f86a5fc74  
Travis CI 已提交
240 241 
<p>Deep learning infrastructure is one of the fastest evolving technologies. Within four years, there have already been three generations of technologies invented.</p>
<p>| Existed since | model as sequence of layers | model as graph of operators | No model |
T
Deploy to GitHub Pages: 16a61004da780fb1025ea7d537e425403d6ce465  
Travis CI 已提交
242 243 244 245 
|&#8211;|&#8211;|&#8211;|&#8211;|
| 2013 | Caffe, Theano, Torch, PaddlePaddle | | |
| 2015 | | TensorFlow, MxNet, Caffe2, ONNX, n-graph | |
| 2016 | | | PyTorch, TensorFlow Eager Execution, PaddlePaddle Fluid |</p>
T
Deploy to GitHub Pages: c5afc3fe7cf1f1f93399cb9d6c92ed6f86a5fc74  
Travis CI 已提交
246 
<p>From the above table, we see that the deep learning technology is evolving towards getting rid of the concept of a model.  To understand the reasons behind this direction, a comparison of the <em>programming paradigms</em> or the ways to program deep learning applications using these systems, would be helpful. The following section goes over these.</p>
T
Deploy to GitHub Pages: 16a61004da780fb1025ea7d537e425403d6ce465  
Travis CI 已提交
247 248 249 
</div>
<div class="section" id="deep-learning-programming-paradigms">
<span id="deep-learning-programming-paradigms"></span><h2>Deep Learning Programming Paradigms<a class="headerlink" href="#deep-learning-programming-paradigms" title="永久链接至标题"></a></h2>
T
Deploy to GitHub Pages: c5afc3fe7cf1f1f93399cb9d6c92ed6f86a5fc74  
Travis CI 已提交
250 
<p>With the systems listed as the first or second generation, e.g., Caffe or TensorFlow, an AI application training program looks like the following:</p>
T
Deploy to GitHub Pages: 16a61004da780fb1025ea7d537e425403d6ce465  
Travis CI 已提交
251 252 253 254 255 256 257 258 259 260 
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">x</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="s2">&quot;image&quot;</span><span class="p">)</span>
<span class="n">l</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="s2">&quot;label&quot;</span><span class="p">)</span>
<span class="n">f</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="n">x</span><span class="p">,</span> <span class="n">W</span><span class="p">)</span>
<span class="n">s</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">f</span><span class="p">)</span>
<span class="n">c</span> <span class="o">=</span> <span class="n">layer</span><span class="o">.</span><span class="n">mse</span><span class="p">(</span><span class="n">l</span><span class="p">,</span> <span class="n">s</span><span class="p">)</span>

<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">xrange</span><span class="p">(</span><span class="mi">1000</span><span class="p">):</span> <span class="c1"># train for 1000 iterations</span>
    <span class="n">m</span> <span class="o">=</span> <span class="n">read_minibatch</span><span class="p">()</span>
    <span class="n">forward</span><span class="p">({</span><span class="nb">input</span><span class="o">=</span><span class="n">x</span><span class="p">,</span> <span class="n">data</span><span class="o">=</span><span class="n">m</span><span class="p">},</span> <span class="n">minimize</span><span class="o">=</span><span class="n">c</span><span class="p">)</span>
    <span class="n">backward</span><span class="p">(</span><span class="o">...</span><span class="p">)</span>
T
Deploy to GitHub Pages: c5afc3fe7cf1f1f93399cb9d6c92ed6f86a5fc74  
Travis CI 已提交
261
T
Deploy to GitHub Pages: 16a61004da780fb1025ea7d537e425403d6ce465  
Travis CI 已提交
262 263 264 265 266 
<span class="k">print</span> <span class="n">W</span> <span class="c1"># print the trained model parameters.</span>
</pre></div>
</div>
<p>The above program includes two parts:</p>
<ol class="simple">
T
Deploy to GitHub Pages: c5afc3fe7cf1f1f93399cb9d6c92ed6f86a5fc74  
Travis CI 已提交
267 268 
<li>The first part describes the model, and</li>
<li>The second part describes the training process (or inference process) for the model.</li>
T
Deploy to GitHub Pages: 16a61004da780fb1025ea7d537e425403d6ce465  
Travis CI 已提交
269 
</ol>
T
Deploy to GitHub Pages: c5afc3fe7cf1f1f93399cb9d6c92ed6f86a5fc74  
Travis CI 已提交
270 271 
<p>This paradigm has a well-known problem that limits the productivity of programmers. If the programmer made a mistake in configuring the model, the error messages wouldn&#8217;t show up until the second part is executed and <code class="docutils literal"><span class="pre">forward</span></code> and <code class="docutils literal"><span class="pre">backward</span></code> propagations are performed. This makes it difficult for the programmer to debug and locate a mistake that is located blocks away from the actual error prompt.</p>
<p>This problem of being hard to debug and re-iterate fast on a program is the primary reason that programmers, in general,  prefer PyTorch over the older systems.  Using PyTorch, we would write the above program as following:</p>
T
Deploy to GitHub Pages: 16a61004da780fb1025ea7d537e425403d6ce465  
Travis CI 已提交
272 273 274 275 276 277 278 279 280 281 
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">W</span> <span class="o">=</span> <span class="n">tensor</span><span class="p">(</span><span class="o">...</span><span class="p">)</span>

<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">xrange</span><span class="p">(</span><span class="mi">1000</span><span class="p">):</span> <span class="c1"># train for 1000 iterations</span>
    <span class="n">m</span> <span class="o">=</span> <span class="n">read_minibatch</span><span class="p">()</span>
    <span class="n">x</span> <span class="o">=</span> <span class="n">m</span><span class="p">[</span><span class="s2">&quot;image&quot;</span><span class="p">]</span>
    <span class="n">l</span> <span class="o">=</span> <span class="n">m</span><span class="p">[</span><span class="s2">&quot;label&quot;</span><span class="p">]</span>
    <span class="n">f</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="n">x</span><span class="p">,</span> <span class="n">W</span><span class="p">)</span>
    <span class="n">s</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">f</span><span class="p">)</span>
    <span class="n">c</span> <span class="o">=</span> <span class="n">layer</span><span class="o">.</span><span class="n">mse</span><span class="p">(</span><span class="n">l</span><span class="p">,</span> <span class="n">s</span><span class="p">)</span>
    <span class="n">backward</span><span class="p">()</span>
T
Deploy to GitHub Pages: c5afc3fe7cf1f1f93399cb9d6c92ed6f86a5fc74  
Travis CI 已提交
282
T
Deploy to GitHub Pages: 16a61004da780fb1025ea7d537e425403d6ce465  
Travis CI 已提交
283 284 285 
<span class="k">print</span> <span class="n">W</span> <span class="c1"># print the trained model parameters.</span>
</pre></div>
</div>
T
Deploy to GitHub Pages: c5afc3fe7cf1f1f93399cb9d6c92ed6f86a5fc74  
Travis CI 已提交
286 
<p>We can see that the main difference is the moving the model configuration part (the first step) into the training loop.  This change would allow the mistakes in model configuration to be reported where they actually appear in the programming block.  This change also represents the model better, or its forward pass, by keeping the configuration process in the training loop.</p>
T
Deploy to GitHub Pages: 16a61004da780fb1025ea7d537e425403d6ce465  
Travis CI 已提交
287 288 289 
</div>
<div class="section" id="describe-arbitrary-models-for-the-future">
<span id="describe-arbitrary-models-for-the-future"></span><h2>Describe Arbitrary Models for the Future<a class="headerlink" href="#describe-arbitrary-models-for-the-future" title="永久链接至标题"></a></h2>
T
Deploy to GitHub Pages: c5afc3fe7cf1f1f93399cb9d6c92ed6f86a5fc74  
Travis CI 已提交
290 291 
<p>Describing the process instead of the model also brings Fluid, the flexibility to define different non-standard models that haven&#8217;t been invented yet.</p>
<p>As we write out the program for the process, we can write an RNN as a loop, instead of an RNN as a layer or as an operator.  A PyTorch example would look like the following:</p>
T
Deploy to GitHub Pages: 16a61004da780fb1025ea7d537e425403d6ce465  
Travis CI 已提交
292 293 294 295 296 297 298 
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">xrange</span><span class="p">(</span><span class="mi">1000</span><span class="p">):</span>
    <span class="n">m</span> <span class="o">=</span> <span class="n">read_minibatch</span><span class="p">()</span>
    <span class="n">x</span> <span class="o">=</span> <span class="n">m</span><span class="p">[</span><span class="s2">&quot;sentence&quot;</span><span class="p">]</span>
    <span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="nb">xrange</span> <span class="n">x</span><span class="o">.</span><span class="n">len</span><span class="p">():</span>
        <span class="n">h</span><span class="p">[</span><span class="n">t</span><span class="p">]</span> <span class="o">=</span> <span class="n">the_step</span><span class="p">(</span><span class="n">x</span><span class="p">[</span><span class="n">t</span><span class="p">])</span>
</pre></div>
</div>
T
Deploy to GitHub Pages: c5afc3fe7cf1f1f93399cb9d6c92ed6f86a5fc74  
Travis CI 已提交
299 
<p>With Fluid, the training loop and the RNN in the above program are not really Python loops, but just a &#8220;loop structure&#8221; provided by Fluid and implemented in C++ as the following:</p>
T
Deploy to GitHub Pages: 16a61004da780fb1025ea7d537e425403d6ce465  
Travis CI 已提交
300 301 302 303 304 305 306 307 308 
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">train_loop</span> <span class="o">=</span> <span class="n">layers</span><span class="o">.</span><span class="n">While</span><span class="p">(</span><span class="n">cond</span><span class="p">)</span>
<span class="k">with</span> <span class="n">train_loop</span><span class="o">.</span><span class="n">block</span><span class="p">():</span>
  <span class="n">m</span> <span class="o">=</span> <span class="n">read_minibatch</span><span class="p">()</span>
  <span class="n">x</span> <span class="o">=</span> <span class="n">m</span><span class="p">[</span><span class="s2">&quot;sentence&quot;</span><span class="p">]</span>
  <span class="n">rnn</span> <span class="o">=</span> <span class="n">layers</span><span class="o">.</span><span class="n">While</span><span class="p">(</span><span class="o">...</span><span class="p">)</span>
  <span class="k">with</span> <span class="n">rnn</span><span class="o">.</span><span class="n">block</span><span class="p">():</span>
    <span class="n">h</span><span class="p">[</span><span class="n">t</span><span class="p">]</span> <span class="o">=</span> <span class="n">the_step</span><span class="p">(</span><span class="nb">input</span><span class="p">[</span><span class="n">t</span><span class="p">])</span>
</pre></div>
</div>
T
Deploy to GitHub Pages: c5afc3fe7cf1f1f93399cb9d6c92ed6f86a5fc74  
Travis CI 已提交
309 310 
<p>An actual Fluid example is described  <a class="reference external" href="https://github.com/PaddlePaddle/Paddle/blob/a91efdde6910ce92a78e3aa7157412c4c88d9ee8/python/paddle/v2/fluid/tests/test_while_op.py#L36-L44">here</a>.</p>
<p>From the example, the Fluid programs look very similar to their PyTorch equivalent programs, except that Fluid&#8217;s loop structure, wrapped with Python&#8217;s <code class="docutils literal"><span class="pre">with</span></code> statement, could run much faster than just a Python loop.</p>
T
Deploy to GitHub Pages: 16a61004da780fb1025ea7d537e425403d6ce465  
Travis CI 已提交
311 312 313 314 
<p>We have more examples of the <a class="reference external" href="https://github.com/PaddlePaddle/Paddle/blob/develop/doc/design/if_else_op.md"><code class="docutils literal"><span class="pre">if-then-else</span></code></a> structure of Fluid.</p>
</div>
<div class="section" id="turing-completeness">
<span id="turing-completeness"></span><h2>Turing Completeness<a class="headerlink" href="#turing-completeness" title="永久链接至标题"></a></h2>
T
Deploy to GitHub Pages: c5afc3fe7cf1f1f93399cb9d6c92ed6f86a5fc74  
Travis CI 已提交
315 
<p>In computability theory, a system of data-manipulation rules, such as a programming language, is said to be Turing complete if it can be used to simulate any Turing machine.  For a programming language, if it provides if-then-else and loop, it is Turing complete.  From the above examples, Fluid seems to be Turing complete; however, it is noteworthy to notice that there  is a slight difference between the <code class="docutils literal"><span class="pre">if-then-else</span></code> of Fluid and that of a programming language. The difference being that the former runs both of its branches and splits the input mini-batch into two &#8211; one for the True condition and another for the False condition. This hasn&#8217;t been researched in depth if this is equivalent to the <code class="docutils literal"><span class="pre">if-then-else</span></code> in programming languages that makes them Turing-complete.  Based on a conversation with <a class="reference external" href="https://research.google.com/pubs/104812.html">Yuang Yu</a>, it seems to be the case but this needs to be looked into in-depth.</p>
T
Deploy to GitHub Pages: 16a61004da780fb1025ea7d537e425403d6ce465  
Travis CI 已提交
316 317 318 
</div>
<div class="section" id="the-execution-of-a-fluid-program">
<span id="the-execution-of-a-fluid-program"></span><h2>The Execution of a Fluid Program<a class="headerlink" href="#the-execution-of-a-fluid-program" title="永久链接至标题"></a></h2>
T
Deploy to GitHub Pages: c5afc3fe7cf1f1f93399cb9d6c92ed6f86a5fc74  
Travis CI 已提交
319 320 
<p>There are two ways to execute a Fluid program.  When a program is executed, it creates a protobuf message <a class="reference external" href="https://github.com/PaddlePaddle/Paddle/blob/a91efdde6910ce92a78e3aa7157412c4c88d9ee8/paddle/framework/framework.proto#L145"><code class="docutils literal"><span class="pre">ProgramDesc</span></code></a> that describes the process and is conceptually like an <a class="reference external" href="https://en.wikipedia.org/wiki/Abstract_syntax_tree">abstract syntax tree</a>.</p>
<p>There is a C++ class <a class="reference external" href="https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/framework/executor.h"><code class="docutils literal"><span class="pre">Executor</span></code></a>, which runs a <code class="docutils literal"><span class="pre">ProgramDesc</span></code>, similar to how an interpreter runs a Python program.</p>
T
Deploy to GitHub Pages: cd775a130a318d7cd00178ae7cfad5127e33d4be  
Travis CI 已提交
321 
<p>Fluid is moving towards the direction of a compiler, which is explain in <a class="reference internal" href="fluid_compiler.html"><span class="doc">fluid</span></a>.</p>
T
Deploy to GitHub Pages: 16a61004da780fb1025ea7d537e425403d6ce465  
Travis CI 已提交
322 
</div>
T
Deploy to GitHub Pages: c5afc3fe7cf1f1f93399cb9d6c92ed6f86a5fc74  
Travis CI 已提交
323 324 325 326 
<div class="section" id="backward-compatibility-of-fluid">
<span id="backward-compatibility-of-fluid"></span><h2>Backward Compatibility of Fluid<a class="headerlink" href="#backward-compatibility-of-fluid" title="永久链接至标题"></a></h2>
<p>Given all the advantages from the removal of the concept of a <em>model</em>, hardware manufacturers might still prefer the existence of the concept of a model, so it would be easier for them to support multiple frameworks all at once and could run a trained model during inference.  For example, Nervana, a startup company acquired by Intel, has been working on an XPU that reads the models in the format known as <a class="reference external" href="https://github.com/NervanaSystems/ngraph">n-graph</a>.  Similarly, <a class="reference external" href="https://www.movidius.com/">Movidius</a> is producing a mobile deep learning chip that reads and runs graphs of operators.  The well-known <a class="reference external" href="https://github.com/onnx/onnx">ONNX</a> is also a file format of graphs of operators.</p>
<p>For Fluid, we can write a converter that extracts the parts in the <code class="docutils literal"><span class="pre">ProgramDesc</span></code> protobuf message, converts them into a graph of operators, and exports the graph into the ONNX or n-graph format.</p>
T
Deploy to GitHub Pages: 16a61004da780fb1025ea7d537e425403d6ce465  
Travis CI 已提交
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 
</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="../_static/translations.js"></script>
      <script type="text/javascript" src="https://cdn.bootcss.com/mathjax/2.7.0/MathJax.js"></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>