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  <div class="section" id="background">
<span id="background"></span><h1>Background<a class="headerlink" href="#background" title="永久链接至标题"></a></h1>
<p>PaddlePaddle divides the description of neural network computation graph into two stages: compile time and runtime.</p>
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<p>PaddlePaddle use proto message to describe compile time graph because</p>
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191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 
<ol class="simple">
<li>Computation graph should be able to be saved to a file.</li>
<li>In distributed training, the graph will be serialized and send to multiple workers.</li>
</ol>
<p>The computation graph is constructed by Data Node and Operation Node. The concept to represent them is in the table below.</p>
<p>| |compile time|runtime|
|&#8212;|&#8212;|&#8212;|
|Data|VarDesc(proto)|Variable(cpp)|
|Operation|OpDesc(proto)|Operator(cpp)|</p>
</div>
<div class="section" id="definition-of-vardesc">
<span id="definition-of-vardesc"></span><h1>Definition of VarDesc<a class="headerlink" href="#definition-of-vardesc" title="永久链接至标题"></a></h1>
<p>A VarDesc should have a name and value, in PaddlePaddle, the value will always be a tensor. Since we use LoDTensor most of the time. We add a LoDTesnorDesc to represent it.</p>
<div class="highlight-proto"><div class="highlight"><pre><span></span><span class="kd">message</span> <span class="nc">VarDesc</span> <span class="p">{</span>
  <span class="k">required</span> <span class="kt">string</span> <span class="na">name</span> <span class="o">=</span> <span class="mi">1</span><span class="p">;</span>
  <span class="k">optional</span> <span class="n">LoDTensorDesc</span> <span class="na">lod_tensor</span> <span class="o">=</span> <span class="mi">2</span><span class="p">;</span>
<span class="p">}</span>
</pre></div>
</div>
</div>
<div class="section" id="definition-of-lodtensordesc">
<span id="definition-of-lodtensordesc"></span><h1>Definition of LodTensorDesc<a class="headerlink" href="#definition-of-lodtensordesc" title="永久链接至标题"></a></h1>
<div class="highlight-proto"><div class="highlight"><pre><span></span><span class="kd">enum</span> <span class="n">DataType</span> <span class="p">{</span>
  <span class="na">BOOL</span> <span class="o">=</span> <span class="mi">0</span><span class="p">;</span>
  <span class="na">INT16</span> <span class="o">=</span> <span class="mi">1</span><span class="p">;</span>
  <span class="na">INT32</span> <span class="o">=</span> <span class="mi">2</span><span class="p">;</span>
  <span class="na">INT64</span> <span class="o">=</span> <span class="mi">3</span><span class="p">;</span>
  <span class="na">FP16</span> <span class="o">=</span> <span class="mi">4</span><span class="p">;</span>
  <span class="na">FP32</span> <span class="o">=</span> <span class="mi">5</span><span class="p">;</span>
  <span class="na">FP64</span> <span class="o">=</span> <span class="mi">6</span><span class="p">;</span>
<span class="p">}</span>

<span class="kd">message</span> <span class="nc">LoDTensorDesc</span> <span class="p">{</span>
  <span class="k">required</span> <span class="n">DataType</span> <span class="na">data_type</span> <span class="o">=</span> <span class="mi">1</span><span class="p">;</span>
  <span class="k">repeated</span> <span class="kt">int32</span> <span class="na">dims</span> <span class="o">=</span> <span class="mi">2</span><span class="p">;</span> <span class="c1">// [UNK, 640, 480] is saved as [-1, 640, 480]</span>
  <span class="k">optional</span> <span class="kt">int32</span> <span class="na">lod_level</span> <span class="o">=</span> <span class="mi">3</span> <span class="p">[</span><span class="k">default</span><span class="o">=</span><span class="mi">0</span><span class="p">];</span>
<span class="p">}</span>
</pre></div>
</div>
</div>
<div class="section" id="definition-of-variable-in-python">
<span id="definition-of-variable-in-python"></span><h1>Definition of Variable in Python<a class="headerlink" href="#definition-of-variable-in-python" title="永久链接至标题"></a></h1>
<p>In Python API, layer will take Variable as Input, and return Variable as Output. There should be a class <code class="docutils literal"><span class="pre">Variable</span></code> in python to help create and manage Variable.</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">image</span> <span class="o">=</span> <span class="n">Variable</span><span class="p">(</span><span class="n">dims</span><span class="o">=</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">640</span><span class="p">,</span> <span class="mi">480</span><span class="p">])</span>
<span class="c1"># fc1 and fc2 are both Variable</span>
<span class="n">fc1</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="nb">input</span><span class="o">=</span><span class="n">image</span><span class="p">,</span> <span class="n">output_size</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>
<span class="n">fc2</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="nb">input</span><span class="o">=</span><span class="n">fc1</span><span class="p">,</span> <span class="n">output_size</span><span class="o">=</span><span class="mi">20</span><span class="p">)</span>
</pre></div>
</div>
<div class="section" id="what-should-class-variable-have">
<span id="what-should-class-variable-have"></span><h2>what should class <code class="docutils literal"><span class="pre">Variable</span></code> Have<a class="headerlink" href="#what-should-class-variable-have" title="永久链接至标题"></a></h2>
<ol class="simple">
<li><code class="docutils literal"><span class="pre">name</span></code>.a name of string type is used to mark the value of the Variable.</li>
<li><code class="docutils literal"><span class="pre">initializer</span></code>. Since our Tensor does not have value. we will always use some Operator to fullfill it when run. So we should have a initialize method to help add the init operator.</li>
<li><code class="docutils literal"><span class="pre">operator</span></code>. Variable should record which operator produce itself. The reaon is:</li>
</ol>
<ul class="simple">
<li>we use pd.eval(targets=[var1, var2]) to run the related ops to get the value of var1 and var2. var.op is used to trace the dependency of the current variable.</li>
</ul>
<p>In PaddlePaddle, we use Block to describe Computation Graph, so in the code we will use Block but not Graph.</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">VarDesc</span>
<span class="kn">import</span> <span class="nn">LoDTensorDesc</span>
<span class="kn">import</span> <span class="nn">framework</span>

<span class="k">def</span> <span class="nf">AddInitialOperator</span><span class="p">(</span><span class="n">variable</span><span class="p">,</span> <span class="n">initializer</span><span class="p">):</span>
    <span class="c1"># add an initialize Operator to block to init this Variable</span>

<span class="k">class</span> <span class="nc">Variable</span><span class="p">(</span><span class="nb">object</span><span class="p">):</span>
   <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">name</span><span class="p">,</span> <span class="n">dims</span><span class="p">,</span> <span class="nb">type</span><span class="p">,</span> <span class="n">initializer</span><span class="p">):</span>
      <span class="bp">self</span><span class="o">.</span><span class="n">_block</span> <span class="o">=</span> <span class="n">get_default_block</span><span class="p">()</span>
      <span class="bp">self</span><span class="o">.</span><span class="n">_name</span> <span class="o">=</span> <span class="n">name</span>
      <span class="bp">self</span><span class="o">.</span><span class="n">op</span> <span class="o">=</span> <span class="bp">None</span>

      <span class="n">tensor_desc</span> <span class="o">=</span> <span class="n">LoDTensorDesc</span><span class="p">(</span><span class="n">data_type</span><span class="o">=</span><span class="nb">type</span><span class="p">,</span> <span class="n">dims</span><span class="o">=</span><span class="n">dims</span><span class="p">)</span>
      <span class="n">_var_desc</span> <span class="o">=</span> <span class="n">VarDesc</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="n">name</span><span class="p">,</span> <span class="n">lod_tensor</span><span class="o">=</span><span class="n">tensor_desc</span><span class="p">)</span>
      <span class="bp">self</span><span class="o">.</span><span class="n">_var</span> <span class="o">=</span> <span class="n">framework</span><span class="o">.</span><span class="n">CreateVar</span><span class="p">(</span><span class="n">_var_desc</span><span class="p">)</span>
      <span class="bp">self</span><span class="o">.</span><span class="n">_block</span><span class="o">.</span><span class="n">add_var</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span>

      <span class="c1"># add initial op according to initializer</span>
      <span class="k">if</span> <span class="n">initializer</span> <span class="ow">is</span> <span class="ow">not</span> <span class="bp">None</span><span class="p">:</span>
          <span class="n">AddInitialOperator</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">initializer</span><span class="p">)</span>

   <span class="k">def</span> <span class="nf">dims</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
      <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_var</span><span class="o">.</span><span class="n">dims</span><span class="p">()</span>

   <span class="k">def</span> <span class="nf">data_type</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
       <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_var</span><span class="o">.</span><span class="n">data_type</span><span class="p">()</span>

   <span class="k">def</span> <span class="nf">to_proto</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
       <span class="k">pass</span>
</pre></div>
</div>
<p>Then we can use this Variable to create a fc layer in Python.</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">paddle</span> <span class="kn">as</span> <span class="nn">pd</span>

<span class="k">def</span> <span class="nf">flatten_size</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">num_flatten_dims</span><span class="p">):</span>
  <span class="n">prod</span> <span class="o">=</span> <span class="mi">1</span> <span class="c1"># of last num_flatten_dims</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="n">num_flatten_dims</span><span class="p">):</span>
    <span class="n">prod</span> <span class="o">=</span> <span class="n">prod</span> <span class="o">*</span> <span class="n">X</span><span class="o">.</span><span class="n">dims</span><span class="p">[</span><span class="o">-</span><span class="n">i</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
  <span class="k">return</span> <span class="n">prod</span>

<span class="k">def</span> <span class="nf">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">output_size</span><span class="p">,</span> <span class="n">num_flatten_dims</span><span class="p">):</span>
  <span class="n">W</span> <span class="o">=</span> <span class="n">Variable</span><span class="p">(</span><span class="n">pd</span><span class="o">.</span><span class="n">random_uniform</span><span class="p">(),</span> <span class="nb">type</span><span class="o">=</span><span class="n">FP32</span><span class="p">,</span> <span class="n">dims</span><span class="o">=</span><span class="p">[</span><span class="n">flatten_size</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">num_flatten_dims</span><span class="p">),</span> <span class="n">output_size</span><span class="p">])</span>
  <span class="n">b</span> <span class="o">=</span> <span class="n">Variable</span><span class="p">(</span><span class="n">pd</span><span class="o">.</span><span class="n">random_uniform</span><span class="p">(),</span> <span class="nb">type</span><span class="o">=</span><span class="n">FP32</span><span class="p">,</span> <span class="n">dims</span><span class="o">=</span><span class="p">[</span><span class="n">output_size</span><span class="p">])</span>
  <span class="n">out</span> <span class="o">=</span> <span class="n">Variable</span><span class="p">(</span><span class="nb">type</span><span class="o">=</span><span class="n">FP32</span><span class="p">)</span>
  <span class="n">y</span> <span class="o">=</span> <span class="n">operator</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">b</span><span class="p">,</span> <span class="n">output</span><span class="o">=</span><span class="n">out</span><span class="p">)</span> <span class="c1"># fc will put fc op input into out</span>
  <span class="n">pd</span><span class="o">.</span><span class="n">InferShape</span><span class="p">(</span><span class="n">y</span><span class="p">)</span>
  <span class="k">return</span> <span class="n">out</span>

<span class="n">x</span> <span class="o">=</span> <span class="n">Variable</span><span class="p">(</span><span class="n">dims</span><span class="o">=</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">640</span><span class="p">,</span> <span class="mi">480</span><span class="p">])</span>
<span class="n">y</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">output_size</span><span class="o">=</span><span class="mi">100</span><span class="p">)</span>
<span class="n">z</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">y</span><span class="p">,</span> <span class="n">output_size</span><span class="o">=</span><span class="mi">200</span><span class="p">)</span>

<span class="n">paddle</span><span class="o">.</span><span class="n">eval</span><span class="p">(</span><span class="n">targets</span><span class="o">=</span><span class="p">[</span><span class="n">z</span><span class="p">],</span> <span class="o">...</span><span class="p">)</span>
<span class="k">print</span><span class="p">(</span><span class="n">z</span><span class="p">)</span>
</pre></div>
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