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    <li>Design Doc: Model Format</li>
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  <div class="section" id="design-doc-model-format">
<span id="design-doc-model-format"></span><h1>Design Doc: Model Format<a class="headerlink" href="#design-doc-model-format" title="永久链接至标题"></a></h1>
<div class="section" id="motivation">
<span id="motivation"></span><h2>Motivation<a class="headerlink" href="#motivation" title="永久链接至标题"></a></h2>
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<p>A model is an output of the training process. One complete model consists of two parts, the <strong>topology</strong> and the <strong>parameters</strong>. In order to support industrial deployment, the model format must be self-complete and must not expose any training source code.</p>
<p>As a result, In PaddlePaddle, the <strong>topology</strong> is represented as a  <a class="reference external" href="https://github.com/PaddlePaddle/Paddle/blob/1c0a4c901c9fc881d120249c703b15d1c50dae7d/doc/design/program.md">ProgramDesc</a>, which describes the model structure. The <strong>parameters</strong> contain all the trainable weights in the model. We must support large size parameters and efficient serialization/deserialization of parameters.</p>
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</div>
<div class="section" id="implementation">
<span id="implementation"></span><h2>Implementation<a class="headerlink" href="#implementation" title="永久链接至标题"></a></h2>
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<p>The topology is saved as a plain text in a detailed self-contain protobuf file.</p>
<p>The parameters are saved as a binary file. As we all know, the protobuf message has a limit of <a class="reference external" href="https://developers.google.com/protocol-buffers/docs/reference/cpp/google.protobuf.io.coded_stream#CodedInputStream.SetTotalBytesLimit.details">64M size</a>. We have done a <a class="reference external" href="https://github.com/PaddlePaddle/Paddle/pull/4610">benchmark experiment</a>, which shows that protobuf is not fit for the task.</p>
<p>As a result, we design a particular format for tensor serialization. By default, an arbitrary tensor in Paddle is a <a class="reference external" href="https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/framework/lod_tensor.md">LoDTensor</a>, and has a description information proto of <a class="reference external" href="https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/framework/framework.proto#L99">LoDTensorDesc</a>. We save the DescProto as the byte string header. It contains all the necessary information, such as the <code class="docutils literal"><span class="pre">dims</span></code>, the <code class="docutils literal"><span class="pre">name</span></code> of the tensor, and the <code class="docutils literal"><span class="pre">LoD</span></code> information in <a class="reference external" href="https://github.com/PaddlePaddle/Paddle/blob/1c0a4c901c9fc881d120249c703b15d1c50dae7d/paddle/framework/lod_tensor.md">LoDTensor</a>. A tensor stores values in a continuous memory buffer. For speed we dump the raw memory to disk and save it as the byte string content. So, the binary format of one tensor is,</p>
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<p>|HeaderLength|ContentLength|<strong>LoDTensorDesc</strong>|<strong>TensorValue</strong>|</p>
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<p>The table below shows a tensor&#8217;s byte view in detail. Note that all the signed values are written in the little-endian format.</p>
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<div class="highlight-text"><div class="highlight"><pre><span></span>[offset] [type]              [description] 
0004     4 bytes integer      HeaderLength, the length of LoDTensorDesc
0008     4 bytes integer      ContentLength, the length of LodTensor Buffer
0009     1 bytes char         TensorDesc
00010    1 bytes char         TensorDesc
...
00100    1 bytes char         TensorValue
00101    1 bytes char         TensorValue
00102    1 bytes char         TensorValue              ..
...
</pre></div>
</div>
</div>
<div class="section" id="summary">
<span id="summary"></span><h2>Summary<a class="headerlink" href="#summary" title="永久链接至标题"></a></h2>
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<ul class="simple">
<li>We introduce a model format.</li>
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<li>The model represented by its forward-pass computation procedure is saved in a <strong>ProgramDesc</strong> protobuf message.</li>
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<li>A bunch of specified format binary tensors describe the <strong>parameters</strong>.</li>
</ul>
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