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
new_op_en.html 58.2 KB
Newer Older
T
Deploy to GitHub Pages: 1ff47b284c565d030b198705d5f18b4bd4ce53e5  
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 


<!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>How to write a new operator &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="../index_en.html">HOW TO</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../api/index_en.html">API</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../mobile/index_en.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_en.html">GET STARTED</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../getstarted/build_and_install/index_en.html">Install and Build</a><ul>
<li class="toctree-l3"><a class="reference internal" href="../../getstarted/build_and_install/pip_install_en.html">Install Using pip</a></li>
<li class="toctree-l3"><a class="reference internal" href="../../getstarted/build_and_install/docker_install_en.html">Run in Docker Containers</a></li>
<li class="toctree-l3"><a class="reference internal" href="build_en.html">Build using Docker</a></li>
<li class="toctree-l3"><a class="reference internal" href="../../getstarted/build_and_install/build_from_source_en.html">Build from Sources</a></li>
</ul>
</li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="../index_en.html">HOW TO</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../usage/cmd_parameter/index_en.html">Set Command-line Parameters</a><ul>
<li class="toctree-l3"><a class="reference internal" href="../usage/cmd_parameter/use_case_en.html">Use Case</a></li>
<li class="toctree-l3"><a class="reference internal" href="../usage/cmd_parameter/arguments_en.html">Argument Outline</a></li>
<li class="toctree-l3"><a class="reference internal" href="../usage/cmd_parameter/detail_introduction_en.html">Detail Description</a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="../usage/cluster/cluster_train_en.html">PaddlePaddle Distributed Training</a></li>
<li class="toctree-l2"><a class="reference internal" href="../usage/k8s/k8s_en.html">Paddle On Kubernetes</a></li>
<li class="toctree-l2"><a class="reference internal" href="../usage/k8s/k8s_aws_en.html">Distributed PaddlePaddle Training on AWS with Kubernetes</a></li>
<li class="toctree-l2"><a class="reference internal" href="new_layer_en.html">Write New Layers</a></li>
<li class="toctree-l2"><a class="reference internal" href="contribute_to_paddle_en.html">Contribute Code</a></li>
<li class="toctree-l2"><a class="reference internal" href="write_docs_en.html">Contribute Documentation</a></li>
<li class="toctree-l2"><a class="reference internal" href="../deep_model/rnn/index_en.html">RNN Models</a><ul>
<li class="toctree-l3"><a class="reference internal" href="../deep_model/rnn/rnn_config_en.html">RNN Configuration</a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="../optimization/gpu_profiling_en.html">Tune GPU Performance</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="../../api/index_en.html">API</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../api/v2/model_configs.html">Model Configuration</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">Data Reader Interface and DataSets</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">Training and Inference</a></li>
<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>
</ul>
</li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="../../mobile/index_en.html">MOBILE</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../mobile/cross_compiling_for_android_en.html">Build PaddlePaddle for Android</a></li>
<li class="toctree-l2"><a class="reference internal" href="../../mobile/cross_compiling_for_raspberry_en.html">Build PaddlePaddle for Raspberry Pi</a></li>
</ul>
</li>
</ul>

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

      

 







<div role="navigation" aria-label="breadcrumbs navigation">
  <ul class="wy-breadcrumbs">
      
    <li>How to write a new operator</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="how-to-write-a-new-operator">
<span id="how-to-write-a-new-operator"></span><h1>How to write a new operator<a class="headerlink" href="#how-to-write-a-new-operator" title="Permalink to this headline"></a></h1>
<ul class="simple">
<li><a class="reference external" href="#background">Background</a></li>
<li><a class="reference external" href="#implementing-c++-types">Implementing C++ Types</a><ul>
<li><a class="reference external" href="#defining-protoMaker">Defining ProtoMaker</a></li>
<li><a class="reference external" href="#defining-operator">Defining Operator</a></li>
<li><a class="reference external" href="#registering-operator">Registering Operator</a></li>
<li><a class="reference external" href="#compilation">Compilation</a></li>
</ul>
</li>
<li><a class="reference external" href="#python-binding">Python Binding</a></li>
<li><a class="reference external" href="#unit-tests">Unit Tests</a><ul>
<li><a class="reference external" href="#testing-forward-operators">Testing Forward Operators</a></li>
<li><a class="reference external" href="#testing-backward-operators">Testing Backward Operators</a></li>
<li><a class="reference external" href="#compiling-and-running">Compiling and Running</a></li>
</ul>
</li>
<li><a class="reference external" href="#remarks">Remarks</a></li>
</ul>
<div class="section" id="background">
<span id="background"></span><h2>Background<a class="headerlink" href="#background" title="Permalink to this headline"></a></h2>
<p>Here are the base types needed. For details, please refer to the design docs.</p>
<ul class="simple">
<li><code class="docutils literal"><span class="pre">framework::OperatorBase</span></code>: Operator (Op)base class.</li>
<li><code class="docutils literal"><span class="pre">framework::OpKernel</span></code>: Base class for Op computation.</li>
<li><code class="docutils literal"><span class="pre">framework::OperatorWithKernel</span></code>: Inherited from OperatorBase, describing an operator with computation.</li>
<li><code class="docutils literal"><span class="pre">class</span> <span class="pre">OpProtoAndCheckerMaker</span></code>: Describes an Operator&#8217;s input, output, attributes and description, mainly used to interface with Python API.</li>
</ul>
<p>An operator can be differentiated by whether in has kernel methods. An operator with kernel inherits from <code class="docutils literal"><span class="pre">OperatorWithKernel</span></code> while the ones without inherit from <code class="docutils literal"><span class="pre">OperatorBase</span></code>. This tutorial focuses on implementing operators with kernels. In short, an operator includes the following information:</p>
<p>Information           | Where is it defined
&#8212;&#8212;&#8212;&#8212;&#8211;  | :&#8212;&#8212;&#8212;&#8212;&#8212;&#8212;&#8212;-
OpProtoMake definition  | <code class="docutils literal"><span class="pre">.cc</span></code>files, Backward Op does not need an OpProtoMake interface.
Op definition           | <code class="docutils literal"><span class="pre">.cc</span></code> files
Kernel implementation       | The kernel methods shared between CPU and GPU are defined in <code class="docutils literal"><span class="pre">.h</span></code> files. CPU-specific kernels live in <code class="docutils literal"><span class="pre">.cc</span></code> files, while GPU-specific kernels are implemented in <code class="docutils literal"><span class="pre">.cu</span></code>files.
Registering the Op           | Ops are registered in <code class="docutils literal"><span class="pre">.cc</span></code> files; For Kernel registration, <code class="docutils literal"><span class="pre">.cc</span></code> files contain the CPU implementation, while <code class="docutils literal"><span class="pre">.cu</span></code> files contain the GPU implementation.</p>
<p>New Operator implementations are added to the list <a class="reference external" href="https://github.com/PaddlePaddle/Paddle/tree/develop/paddle/operators">paddle/operators</a>, with file names in the format <code class="docutils literal"><span class="pre">*_op.h</span></code> (if applicable), <code class="docutils literal"><span class="pre">*_op.cc</span></code>, <code class="docutils literal"><span class="pre">*_op.cu</span></code> (if applicable).** The system will use the naming scheme to automatically build operators and their corresponding Python extensions. **</p>
<p>Let&#8217;s take matrix multiplication operator, <a class="reference external" href="https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/operators/mul_op.cc">MulOp</a>, as an example to introduce the writing of an Operator with Kernel.</p>
</div>
<div class="section" id="implementing-c-types">
<span id="implementing-c-types"></span><h2>Implementing C++ Types<a class="headerlink" href="#implementing-c-types" title="Permalink to this headline"></a></h2>
<div class="section" id="defining-class-protomaker">
<span id="defining-class-protomaker"></span><h3>1. Defining Class ProtoMaker<a class="headerlink" href="#defining-class-protomaker" title="Permalink to this headline"></a></h3>
<p>Matrix Multiplication can be written as $Out = X * Y$, meaning that the operation consists of two inputs and pne output.</p>
<p>First, define <code class="docutils literal"><span class="pre">ProtoMaker</span></code> to describe the Operator&#8217;s input, output, and additional comments:</p>
<div class="highlight-cpp"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">MulOpMaker</span> <span class="o">:</span> <span class="k">public</span> <span class="n">framework</span><span class="o">::</span><span class="n">OpProtoAndCheckerMaker</span> <span class="p">{</span>
 <span class="k">public</span><span class="o">:</span>
  <span class="n">MulOpMaker</span><span class="p">(</span><span class="n">framework</span><span class="o">::</span><span class="n">OpProto</span> <span class="o">*</span><span class="n">proto</span><span class="p">,</span> <span class="n">framework</span><span class="o">::</span><span class="n">OpAttrChecker</span> <span class="o">*</span><span class="n">op_checker</span><span class="p">)</span>
      <span class="o">:</span> <span class="n">OpProtoAndCheckerMaker</span><span class="p">(</span><span class="n">proto</span><span class="p">,</span> <span class="n">op_checker</span><span class="p">)</span> <span class="p">{</span>
    <span class="n">AddInput</span><span class="p">(</span><span class="s">&quot;X&quot;</span><span class="p">,</span> <span class="s">&quot;(Tensor), 2D tensor of size (M x K)&quot;</span><span class="p">);</span>
    <span class="n">AddInput</span><span class="p">(</span><span class="s">&quot;Y&quot;</span><span class="p">,</span> <span class="s">&quot;(Tensor), 2D tensor of size (K x N)&quot;</span><span class="p">);</span>
    <span class="n">AddOutput</span><span class="p">(</span><span class="s">&quot;Out&quot;</span><span class="p">,</span> <span class="s">&quot;(Tensor), 2D tensor of size (M x N)&quot;</span><span class="p">);</span>
    <span class="n">AddComment</span><span class="p">(</span><span class="sa">R</span><span class="s">&quot;</span><span class="dl">DOC(</span><span class="s"></span>
<span class="s">Two Element Mul Operator.</span>
<span class="s">The equation is: Out = X * Y</span>
<span class="dl">)DOC</span><span class="s">&quot;</span><span class="p">);</span>
  <span class="p">}</span>
<span class="p">};</span>
</pre></div>
</div>
<p><a class="reference external" href="https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/operators/mul_op.cc#L43"><code class="docutils literal"><span class="pre">MulOpMaker</span></code></a>is inherited from<code class="docutils literal"><span class="pre">framework::OpProtoAndCheckerMaker</span></code>, consisting of 2 variables in the constructor：</p>
<ul class="simple">
<li><code class="docutils literal"><span class="pre">framework::OpProto</span></code> stores Operator input and variable attribute, used for generating Python API interfaces.</li>
<li><code class="docutils literal"><span class="pre">framework::OpAttrChecker</span></code> is used to validate variable attributes.</li>
</ul>
<p>The constructor utilizes <code class="docutils literal"><span class="pre">AddInput</span></code>, <code class="docutils literal"><span class="pre">AddOutput</span></code>, and <code class="docutils literal"><span class="pre">AddComment</span></code>, so that the corresponding information will be added to <code class="docutils literal"><span class="pre">OpProto</span></code>.</p>
<p>The code above adds two inputs <code class="docutils literal"><span class="pre">X</span></code> and <code class="docutils literal"><span class="pre">Y</span></code> to <code class="docutils literal"><span class="pre">MulOp</span></code>, an output <code class="docutils literal"><span class="pre">Out</span></code>, and their corresponding descriptions, in accordance to Paddle&#8217;s <a class="reference external" href="https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/operators/name_convention.md">naming convention</a>.</p>
<p>An additional example <a class="reference external" href="https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/operators/scale_op.cc#L37"><code class="docutils literal"><span class="pre">ScaleOp</span></code></a> is implemented as follows:</p>
<div class="highlight-cpp"><div class="highlight"><pre><span></span><span class="k">template</span> <span class="o">&lt;</span><span class="k">typename</span> <span class="n">AttrType</span><span class="o">&gt;</span>
<span class="k">class</span> <span class="nc">ScaleOpMaker</span> <span class="o">:</span> <span class="k">public</span> <span class="n">framework</span><span class="o">::</span><span class="n">OpProtoAndCheckerMaker</span> <span class="p">{</span>
 <span class="k">public</span><span class="o">:</span>
  <span class="n">ScaleOpMaker</span><span class="p">(</span><span class="n">framework</span><span class="o">::</span><span class="n">OpProto</span> <span class="o">*</span><span class="n">proto</span><span class="p">,</span> <span class="n">framework</span><span class="o">::</span><span class="n">OpAttrChecker</span> <span class="o">*</span><span class="n">op_checker</span><span class="p">)</span>
      <span class="o">:</span> <span class="n">OpProtoAndCheckerMaker</span><span class="p">(</span><span class="n">proto</span><span class="p">,</span> <span class="n">op_checker</span><span class="p">)</span> <span class="p">{</span>
    <span class="n">AddInput</span><span class="p">(</span><span class="s">&quot;X&quot;</span><span class="p">,</span> <span class="s">&quot;The input tensor of scale operator.&quot;</span><span class="p">).</span><span class="n">NotInGradient</span><span class="p">();</span>
    <span class="n">AddOutput</span><span class="p">(</span><span class="s">&quot;Out&quot;</span><span class="p">,</span> <span class="s">&quot;The output tensor of scale operator.&quot;</span><span class="p">).</span><span class="n">NotInGradient</span><span class="p">();</span>
    <span class="n">AddComment</span><span class="p">(</span><span class="sa">R</span><span class="s">&quot;</span><span class="dl">DOC(</span><span class="s">Scale operator</span>
<span class="s">The equation is: Out = scale*X</span>
<span class="dl">)DOC</span><span class="s">&quot;</span><span class="p">);</span>
    <span class="n">AddAttr</span><span class="o">&lt;</span><span class="n">AttrType</span><span class="o">&gt;</span><span class="p">(</span><span class="s">&quot;scale&quot;</span><span class="p">,</span> <span class="s">&quot;scale of scale operator.&quot;</span><span class="p">).</span><span class="n">SetDefault</span><span class="p">(</span><span class="mf">1.0</span><span class="p">);</span>
  <span class="p">}</span>
<span class="p">};</span>
</pre></div>
</div>
<p>There are two changes in this example:</p>
<ul class="simple">
<li><code class="docutils literal"><span class="pre">AddInput(&quot;X&quot;,&quot;...&quot;).NotInGradient()</span></code> expresses that input <code class="docutils literal"><span class="pre">X</span></code> is not involved in <code class="docutils literal"><span class="pre">ScaleOp</span></code>&#8216;s corresponding computation. If an input to an operator is not participating in back-propagation, please explicitly set <code class="docutils literal"><span class="pre">.NotInGradient()</span></code>.</li>
<li><code class="docutils literal"><span class="pre">AddAttr&lt;AttrType&gt;(&quot;scale&quot;,</span> <span class="pre">&quot;...&quot;).SetDefault(1.0);</span></code>  adds <code class="docutils literal"><span class="pre">scale</span></code>constant as an attribute, and sets the default value to 1.0.</li>
</ul>
</div>
<div class="section" id="defining-operator">
<span id="defining-operator"></span><h3>2. Defining Operator<a class="headerlink" href="#defining-operator" title="Permalink to this headline"></a></h3>
<p>The following code defines the interface for MulOp:</p>
<div class="highlight-cpp"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">MulOp</span> <span class="o">:</span> <span class="k">public</span> <span class="n">framework</span><span class="o">::</span><span class="n">OperatorWithKernel</span> <span class="p">{</span>
 <span class="k">public</span><span class="o">:</span>
  <span class="k">using</span> <span class="n">framework</span><span class="o">::</span><span class="n">OperatorWithKernel</span><span class="o">::</span><span class="n">OperatorWithKernel</span><span class="p">;</span>

 <span class="k">protected</span><span class="o">:</span>
  <span class="kt">void</span> <span class="n">InferShape</span><span class="p">(</span><span class="k">const</span> <span class="n">framework</span><span class="o">::</span><span class="n">InferShapeContext</span> <span class="o">&amp;</span><span class="n">ctx</span><span class="p">)</span> <span class="k">const</span> <span class="k">override</span> <span class="p">{</span>
    <span class="k">auto</span> <span class="n">dim0</span> <span class="o">=</span> <span class="n">ctx</span><span class="p">.</span><span class="n">Input</span><span class="o">&lt;</span><span class="n">Tensor</span><span class="o">&gt;</span><span class="p">(</span><span class="s">&quot;X&quot;</span><span class="p">)</span><span class="o">-&gt;</span><span class="n">dims</span><span class="p">();</span>
    <span class="k">auto</span> <span class="n">dim1</span> <span class="o">=</span> <span class="n">ctx</span><span class="p">.</span><span class="n">Input</span><span class="o">&lt;</span><span class="n">Tensor</span><span class="o">&gt;</span><span class="p">(</span><span class="s">&quot;Y&quot;</span><span class="p">)</span><span class="o">-&gt;</span><span class="n">dims</span><span class="p">();</span>
    <span class="n">PADDLE_ENFORCE_EQ</span><span class="p">(</span><span class="n">dim0</span><span class="p">.</span><span class="n">size</span><span class="p">(),</span> <span class="mi">2</span><span class="p">,</span>
                      <span class="s">&quot;input X(%s) should be a tensor with 2 dims, a matrix&quot;</span><span class="p">,</span>
                      <span class="n">ctx</span><span class="p">.</span><span class="n">op_</span><span class="p">.</span><span class="n">Input</span><span class="p">(</span><span class="s">&quot;X&quot;</span><span class="p">));</span>
    <span class="n">PADDLE_ENFORCE_EQ</span><span class="p">(</span><span class="n">dim1</span><span class="p">.</span><span class="n">size</span><span class="p">(),</span> <span class="mi">2</span><span class="p">,</span>
                      <span class="s">&quot;input Y(%s) should be a tensor with 2 dims, a matrix&quot;</span><span class="p">,</span>
                      <span class="n">ctx</span><span class="p">.</span><span class="n">op_</span><span class="p">.</span><span class="n">Input</span><span class="p">(</span><span class="s">&quot;Y&quot;</span><span class="p">));</span>
    <span class="n">PADDLE_ENFORCE_EQ</span><span class="p">(</span>
        <span class="n">dim0</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">dim1</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span>
        <span class="s">&quot;First matrix&#39;s width must be equal with second matrix&#39;s height.&quot;</span><span class="p">);</span>
    <span class="n">ctx</span><span class="p">.</span><span class="n">Output</span><span class="o">&lt;</span><span class="n">Tensor</span><span class="o">&gt;</span><span class="p">(</span><span class="s">&quot;Out&quot;</span><span class="p">)</span><span class="o">-&gt;</span><span class="n">Resize</span><span class="p">({</span><span class="n">dim0</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">dim1</span><span class="p">[</span><span class="mi">1</span><span class="p">]});</span>
  <span class="p">}</span>
<span class="p">};</span>
</pre></div>
</div>
<p><a class="reference external" href="https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/operators/mul_op.cc#L22"><code class="docutils literal"><span class="pre">MulOp</span></code></a> is inherited from <code class="docutils literal"><span class="pre">OperatorWithKernel</span></code>. Its <code class="docutils literal"><span class="pre">public</span></code> member</p>
<div class="highlight-cpp"><div class="highlight"><pre><span></span><span class="k">using</span> <span class="n">framework</span><span class="o">::</span><span class="n">OperatorWithKernel</span><span class="o">::</span><span class="n">OperatorWithKernel</span><span class="p">;</span>
</pre></div>
</div>
<p>expresses an operator constructor using base class <code class="docutils literal"><span class="pre">OperatorWithKernel</span></code>, alternatively written as</p>
<div class="highlight-cpp"><div class="highlight"><pre><span></span><span class="n">MulOp</span><span class="p">(</span><span class="k">const</span> <span class="n">std</span><span class="o">::</span><span class="n">string</span> <span class="o">&amp;</span><span class="n">type</span><span class="p">,</span> <span class="k">const</span> <span class="n">framework</span><span class="o">::</span><span class="n">VariableNameMap</span> <span class="o">&amp;</span><span class="n">inputs</span><span class="p">,</span>
      <span class="k">const</span> <span class="n">framework</span><span class="o">::</span><span class="n">VariableNameMap</span> <span class="o">&amp;</span><span class="n">outputs</span><span class="p">,</span>
      <span class="k">const</span> <span class="n">framework</span><span class="o">::</span><span class="n">AttributeMap</span> <span class="o">&amp;</span><span class="n">attrs</span><span class="p">)</span>
  <span class="o">:</span> <span class="n">OperatorWithKernel</span><span class="p">(</span><span class="n">type</span><span class="p">,</span> <span class="n">inputs</span><span class="p">,</span> <span class="n">outputs</span><span class="p">,</span> <span class="n">attrs</span><span class="p">)</span> <span class="p">{}</span>
</pre></div>
</div>
<p><code class="docutils literal"><span class="pre">InferShape</span></code> interface needs to be re-written.<code class="docutils literal"><span class="pre">InferShape</span></code> is a constant method and cannot modify Op&#8217;s member variables, its constant member <code class="docutils literal"><span class="pre">const</span> <span class="pre">framework::InferShapeContext</span> <span class="pre">&amp;ctx</span></code> can be used to extract input, output, and attributes. It functions to</p>
<ul class="simple">
<li>1). validate and error out early: it checks input data dimensions and types.</li>
<li>2). configures the tensor shape in the output.</li>
</ul>
<p>Usually <code class="docutils literal"><span class="pre">OpProtoMaker</span></code> and <code class="docutils literal"><span class="pre">Op</span></code>&#8216;s type definitions are written in <code class="docutils literal"><span class="pre">.cc</span></code> files, which also include the registration methods introduced later.</p>
</div>
<div class="section" id="defining-opkernel">
<span id="defining-opkernel"></span><h3>3. Defining OpKernel<a class="headerlink" href="#defining-opkernel" title="Permalink to this headline"></a></h3>
<p><code class="docutils literal"><span class="pre">MulKernel</span></code> inherits <code class="docutils literal"><span class="pre">framework::OpKernel</span></code>, which includes the following templates:</p>
<ul class="simple">
<li><code class="docutils literal"><span class="pre">typename</span> <span class="pre">Place</span></code> denotes device type. When different devices, namely the CPU and the GPU, share the same kernel, this template needs to be added. If they don&#8217;t share kernels, this must not be added. An example of a non-sharing kernel is <a class="reference external" href="https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/operators/cross_entropy_op.h#L43"><code class="docutils literal"><span class="pre">OnehotCrossEntropyOpKernel</span></code></a>.</li>
<li><code class="docutils literal"><span class="pre">typename</span> <span class="pre">T</span></code> denotes data type, such as <code class="docutils literal"><span class="pre">float</span></code> or <code class="docutils literal"><span class="pre">double</span></code>.</li>
</ul>
<p><code class="docutils literal"><span class="pre">MulKernel</span></code> types need to rewrite the interface for <code class="docutils literal"><span class="pre">Compute</span></code>.</p>
<ul class="simple">
<li><code class="docutils literal"><span class="pre">Compute</span></code> takes one input variable <code class="docutils literal"><span class="pre">const</span> <span class="pre">framework::ExecutionContext&amp;</span> <span class="pre">context</span></code>.</li>
<li>Compared with <code class="docutils literal"><span class="pre">InferShapeContext</span></code>, <code class="docutils literal"><span class="pre">ExecutionContext</span></code> includes device types, and can similarly extract input, output, and attribute variables.</li>
<li><code class="docutils literal"><span class="pre">Compute</span></code> implements the computation logics of an <code class="docutils literal"><span class="pre">OpKernel</span></code>.</li>
</ul>
<p><code class="docutils literal"><span class="pre">MulKernel</span></code>&#8216;s implementation of <code class="docutils literal"><span class="pre">Compute</span></code> is as follows:</p>
<div class="highlight-cpp"><div class="highlight"><pre><span></span><span class="k">template</span> <span class="o">&lt;</span><span class="k">typename</span> <span class="n">Place</span><span class="p">,</span> <span class="k">typename</span> <span class="n">T</span><span class="o">&gt;</span>
<span class="k">class</span> <span class="nc">MulKernel</span> <span class="o">:</span> <span class="k">public</span> <span class="n">framework</span><span class="o">::</span><span class="n">OpKernel</span> <span class="p">{</span>
<span class="k">public</span><span class="o">:</span>
<span class="kt">void</span> <span class="n">Compute</span><span class="p">(</span><span class="k">const</span> <span class="n">framework</span><span class="o">::</span><span class="n">ExecutionContext</span><span class="o">&amp;</span> <span class="n">context</span><span class="p">)</span> <span class="k">const</span> <span class="k">override</span> <span class="p">{</span>
  <span class="k">auto</span><span class="o">*</span> <span class="n">X</span> <span class="o">=</span> <span class="n">context</span><span class="p">.</span><span class="n">Input</span><span class="o">&lt;</span><span class="n">Tensor</span><span class="o">&gt;</span><span class="p">(</span><span class="s">&quot;X&quot;</span><span class="p">);</span>
  <span class="k">auto</span><span class="o">*</span> <span class="n">Y</span> <span class="o">=</span> <span class="n">context</span><span class="p">.</span><span class="n">Input</span><span class="o">&lt;</span><span class="n">Tensor</span><span class="o">&gt;</span><span class="p">(</span><span class="s">&quot;Y&quot;</span><span class="p">);</span>
  <span class="k">auto</span><span class="o">*</span> <span class="n">Z</span> <span class="o">=</span> <span class="n">context</span><span class="p">.</span><span class="n">Output</span><span class="o">&lt;</span><span class="n">Tensor</span><span class="o">&gt;</span><span class="p">(</span><span class="s">&quot;Out&quot;</span><span class="p">);</span>
  <span class="n">Z</span><span class="o">-&gt;</span><span class="n">mutable_data</span><span class="o">&lt;</span><span class="n">T</span><span class="o">&gt;</span><span class="p">(</span><span class="n">context</span><span class="p">.</span><span class="n">GetPlace</span><span class="p">());</span>
  <span class="k">auto</span><span class="o">*</span> <span class="n">device_context</span> <span class="o">=</span>
      <span class="k">const_cast</span><span class="o">&lt;</span><span class="n">platform</span><span class="o">::</span><span class="n">DeviceContext</span><span class="o">*&gt;</span><span class="p">(</span><span class="n">context</span><span class="p">.</span><span class="n">device_context_</span><span class="p">);</span>
  <span class="n">math</span><span class="o">::</span><span class="n">matmul</span><span class="o">&lt;</span><span class="n">Place</span><span class="p">,</span> <span class="n">T</span><span class="o">&gt;</span><span class="p">(</span><span class="o">*</span><span class="n">X</span><span class="p">,</span> <span class="nb">false</span><span class="p">,</span> <span class="o">*</span><span class="n">Y</span><span class="p">,</span> <span class="nb">false</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">Z</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="n">device_context</span><span class="p">);</span>
<span class="p">}</span>
<span class="p">};</span>
</pre></div>
</div>
<p>Note that <strong>different devices (CPU, GPU)share an Op definition; whether or not they share the same <code class="docutils literal"><span class="pre">OpKernel</span></code> depends on whether <code class="docutils literal"><span class="pre">Compute</span></code> calls functions that support both devices.</strong></p>
<p><code class="docutils literal"><span class="pre">MulOp</span></code>&#8216;s CPU and GPU share the same <code class="docutils literal"><span class="pre">Kernel</span></code>. A non-sharing  <code class="docutils literal"><span class="pre">OpKernel</span></code> example can be seen in <a class="reference external" href="https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/operators/cross_entropy_op.h#L43"><code class="docutils literal"><span class="pre">OnehotCrossEntropyOpKernel</span></code></a>.</p>
<p>To ease the writing of <code class="docutils literal"><span class="pre">OpKernel</span></code> compute, and for reusing code cross-device, <a class="reference external" href="https://bitbucket.org/eigen/eigen/src/default/unsupported/Eigen/CXX11/src/Tensor/README.md?fileviewer=file-view-default"><code class="docutils literal"><span class="pre">Eigen-unsupported</span> <span class="pre">Tensor</span></code></a> module is used to implement <code class="docutils literal"><span class="pre">Compute</span></code> interface. To learn about how the Eigen library is used in PaddlePaddle, please see <a class="reference external" href="https://github.com/PaddlePaddle/Paddle/blob/develop/doc/howto/dev/use_eigen_cn.md">usage document</a>.</p>
<p>This concludes the forward implementation of an operator. Next its operation and kernel need to be registered in a <code class="docutils literal"><span class="pre">.cc</span></code> file.</p>
<p>The definition of its corresponding backward operator, if applicable, is similar to that of an forward operator. <strong>Note that a backward operator does not include a <code class="docutils literal"><span class="pre">ProtoMaker</span></code></strong>.</p>
</div>
<div class="section" id="registering-operator">
<span id="registering-operator"></span><h3>4. Registering Operator<a class="headerlink" href="#registering-operator" title="Permalink to this headline"></a></h3>
<ul>
<li><p class="first">In <code class="docutils literal"><span class="pre">.cc</span></code> files, register forward and backward operator classes and the CPU kernel.</p>
<div class="highlight-cpp"><div class="highlight"><pre><span></span><span class="k">namespace</span> <span class="n">ops</span> <span class="o">=</span> <span class="n">paddle</span><span class="o">::</span><span class="n">operators</span><span class="p">;</span>
<span class="n">REGISTER_OP</span><span class="p">(</span><span class="n">mul</span><span class="p">,</span> <span class="n">ops</span><span class="o">::</span><span class="n">MulOp</span><span class="p">,</span> <span class="n">ops</span><span class="o">::</span><span class="n">MulOpMaker</span><span class="p">,</span> <span class="n">mul_grad</span><span class="p">,</span> <span class="n">ops</span><span class="o">::</span><span class="n">MulOpGrad</span><span class="p">);</span>
<span class="n">REGISTER_OP_CPU_KERNEL</span><span class="p">(</span><span class="n">mul</span><span class="p">,</span> <span class="n">ops</span><span class="o">::</span><span class="n">MulKernel</span><span class="o">&lt;</span><span class="n">paddle</span><span class="o">::</span><span class="n">platform</span><span class="o">::</span><span class="n">CPUPlace</span><span class="p">,</span> <span class="kt">float</span><span class="o">&gt;</span><span class="p">);</span>
<span class="n">REGISTER_OP_CPU_KERNEL</span><span class="p">(</span><span class="n">mul_grad</span><span class="p">,</span>
              <span class="n">ops</span><span class="o">::</span><span class="n">MulGradKernel</span><span class="o">&lt;</span><span class="n">paddle</span><span class="o">::</span><span class="n">platform</span><span class="o">::</span><span class="n">CPUPlace</span><span class="p">,</span> <span class="kt">float</span><span class="o">&gt;</span><span class="p">);</span>
</pre></div>
</div>
<p>In that code block,</p>
<ul class="simple">
<li><code class="docutils literal"><span class="pre">REGISTER_OP</span></code> registers the <code class="docutils literal"><span class="pre">ops::MulOp</span></code> class, type named <code class="docutils literal"><span class="pre">mul</span></code>, its type <code class="docutils literal"><span class="pre">ProtoMaker</span></code> is <code class="docutils literal"><span class="pre">ops::MulOpMaker</span></code>, registering <code class="docutils literal"><span class="pre">ops::MulOpGrad</span></code> as <code class="docutils literal"><span class="pre">mul_grad</span></code>.</li>
<li><code class="docutils literal"><span class="pre">REGISTER_OP_WITHOUT_GRADIENT</span></code> registers an operator without gradient.</li>
<li><code class="docutils literal"><span class="pre">REGISTER_OP_CPU_KERNEL</span></code> registers <code class="docutils literal"><span class="pre">ops::MulKernel</span></code> class and specialized template types <code class="docutils literal"><span class="pre">paddle::platform::CPUPlace</span></code> and <code class="docutils literal"><span class="pre">float</span></code>, which also registers <code class="docutils literal"><span class="pre">ops::MulGradKernel</span></code>.</li>
</ul>
</li>
</ul>
<ul>
<li><p class="first">Registering GPU Kernel in <code class="docutils literal"><span class="pre">.cu</span></code> files</p>
<ul class="simple">
<li>Note that if GPU Kernel is implemented using the <code class="docutils literal"><span class="pre">Eigen</span> <span class="pre">unsupported</span></code> module, then on top of <code class="docutils literal"><span class="pre">.cu</span></code>, a macro definition <code class="docutils literal"><span class="pre">#define</span> <span class="pre">EIGEN_USE_GPU</span></code> is needed, such as</li>
</ul>
<div class="highlight-cpp"><div class="highlight"><pre><span></span><span class="c1">// if use Eigen unsupported module before include head files</span>
<span class="cp">#define EIGEN_USE_GPU</span>

<span class="k">namespace</span> <span class="n">ops</span> <span class="o">=</span> <span class="n">paddle</span><span class="o">::</span><span class="n">operators</span><span class="p">;</span>
<span class="n">REGISTER_OP_GPU_KERNEL</span><span class="p">(</span><span class="n">mul</span><span class="p">,</span> <span class="n">ops</span><span class="o">::</span><span class="n">MulKernel</span><span class="o">&lt;</span><span class="n">paddle</span><span class="o">::</span><span class="n">platform</span><span class="o">::</span><span class="n">GPUPlace</span><span class="p">,</span> <span class="kt">float</span><span class="o">&gt;</span><span class="p">);</span>
<span class="n">REGISTER_OP_GPU_KERNEL</span><span class="p">(</span><span class="n">mul_grad</span><span class="p">,</span>
                       <span class="n">ops</span><span class="o">::</span><span class="n">MulGradKernel</span><span class="o">&lt;</span><span class="n">paddle</span><span class="o">::</span><span class="n">platform</span><span class="o">::</span><span class="n">GPUPlace</span><span class="p">,</span> <span class="kt">float</span><span class="o">&gt;</span><span class="p">);</span>
</pre></div>
</div>
</li>
</ul>
</div>
<div class="section" id="compilation">
<span id="compilation"></span><h3>5. Compilation<a class="headerlink" href="#compilation" title="Permalink to this headline"></a></h3>
<p>Run the following commands to compile.</p>
<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">make</span> <span class="n">mul_op</span>
</pre></div>
</div>
</div>
</div>
<div class="section" id="python-binding">
<span id="python-binding"></span><h2>Python Binding<a class="headerlink" href="#python-binding" title="Permalink to this headline"></a></h2>
<p>The system will automatically bind to Python and link it to a generated library.</p>
</div>
<div class="section" id="unit-tests">
<span id="unit-tests"></span><h2>Unit Tests<a class="headerlink" href="#unit-tests" title="Permalink to this headline"></a></h2>
<p>Unit tests for an operator include</p>
<ol class="simple">
<li>comparing a forward operator&#8217;s implementations on different devices,</li>
<li>comparing a backward operator&#8217;s implementation on different devices, and</li>
<li>a scaling test for the backward operator.</li>
</ol>
<p>Here, we introduce the <a class="reference external" href="https://github.com/PaddlePaddle/Paddle/blob/develop/python/paddle/v2/framework/tests/test_mul_op.py">unit tests for <code class="docutils literal"><span class="pre">MulOp</span></code></a>.</p>
<div class="section" id="testing-forward-operators">
<span id="testing-forward-operators"></span><h3>Testing Forward Operators<a class="headerlink" href="#testing-forward-operators" title="Permalink to this headline"></a></h3>
<p>A forward operator unit test inherits <code class="docutils literal"><span class="pre">unittest.TestCase</span></code> and defines metaclass <code class="docutils literal"><span class="pre">__metaclass__</span> <span class="pre">=</span> <span class="pre">OpTestMeta</span></code>. More concrete tests are performed in <code class="docutils literal"><span class="pre">OpTestMeta</span></code>. Testing a forward operator requires the following:</p>
<ol class="simple">
<li>Defining input, output and relevant attributes in <code class="docutils literal"><span class="pre">setUp</span></code> method.</li>
<li>Generating random input data.</li>
<li>Implementing the same computation logic in a Python script:</li>
</ol>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">unittest</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="kn">as</span> <span class="nn">np</span>
<span class="kn">from</span> <span class="nn">gradient_checker</span> <span class="kn">import</span> <span class="n">GradientChecker</span><span class="p">,</span> <span class="n">create_op</span>
<span class="kn">from</span> <span class="nn">op_test_util</span> <span class="kn">import</span> <span class="n">OpTestMeta</span>

<span class="k">class</span> <span class="nc">TestMulOp</span><span class="p">(</span><span class="n">unittest</span><span class="o">.</span><span class="n">TestCase</span><span class="p">):</span>
    <span class="vm">__metaclass__</span> <span class="o">=</span> <span class="n">OpTestMeta</span>

    <span class="k">def</span> <span class="nf">setUp</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">type</span> <span class="o">=</span> <span class="s2">&quot;mul&quot;</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">inputs</span> <span class="o">=</span> <span class="p">{</span>
            <span class="s1">&#39;X&#39;</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">random</span><span class="p">((</span><span class="mi">32</span><span class="p">,</span> <span class="mi">84</span><span class="p">))</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s2">&quot;float32&quot;</span><span class="p">),</span>
            <span class="s1">&#39;Y&#39;</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">random</span><span class="p">((</span><span class="mi">84</span><span class="p">,</span> <span class="mi">100</span><span class="p">))</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s2">&quot;float32&quot;</span><span class="p">)</span>
        <span class="p">}</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">outputs</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;Out&#39;</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">inputs</span><span class="p">[</span><span class="s1">&#39;X&#39;</span><span class="p">],</span> <span class="bp">self</span><span class="o">.</span><span class="n">inputs</span><span class="p">[</span><span class="s1">&#39;Y&#39;</span><span class="p">])}</span>
</pre></div>
</div>
<p>Get its output, and compare it with the forward operator&#8217;s own output.</p>
<p>The code above first loads required packages. In addition, we have</p>
<ul class="simple">
<li><code class="docutils literal"><span class="pre">self.type</span> <span class="pre">=</span> <span class="pre">&quot;mul&quot;</span></code> defines the type that is identical to what the operator&#8217;s registered type.</li>
<li><code class="docutils literal"><span class="pre">self.inputs</span></code> defines input, with type <code class="docutils literal"><span class="pre">numpy.array</span></code> and initializes it.</li>
<li><code class="docutils literal"><span class="pre">self.outputs</span></code> defines output and completes the same operator computation in the Python script, and returns its result from the Python script.</li>
</ul>
</div>
<div class="section" id="testing-backward-operators">
<span id="testing-backward-operators"></span><h3>Testing Backward Operators<a class="headerlink" href="#testing-backward-operators" title="Permalink to this headline"></a></h3>
<p>A backward operator unit test inherits <code class="docutils literal"><span class="pre">GradientChecker</span></code>, which inherits <code class="docutils literal"><span class="pre">unittest.TestCase</span></code>. As a result, <strong>a backward operator unit test needs to be have the prefix <code class="docutils literal"><span class="pre">test_</span></code></strong>.</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">TestMulGradOp</span><span class="p">(</span><span class="n">GradientChecker</span><span class="p">):</span>
    <span class="k">def</span> <span class="nf">setUp</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">op</span> <span class="o">=</span> <span class="n">create_op</span><span class="p">(</span><span class="s2">&quot;mul&quot;</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">inputs</span> <span class="o">=</span> <span class="p">{</span>
            <span class="s1">&#39;X&#39;</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">random</span><span class="p">((</span><span class="mi">32</span><span class="p">,</span> <span class="mi">84</span><span class="p">))</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s2">&quot;float32&quot;</span><span class="p">),</span>
            <span class="s1">&#39;Y&#39;</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">random</span><span class="p">((</span><span class="mi">84</span><span class="p">,</span> <span class="mi">100</span><span class="p">))</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s2">&quot;float32&quot;</span><span class="p">)</span>
        <span class="p">}</span>

    <span class="k">def</span> <span class="nf">test_check_grad_normal</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
        <span class="c1"># mul op will enlarge the relative error</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">check_grad</span><span class="p">([</span><span class="s1">&#39;X&#39;</span><span class="p">,</span> <span class="s1">&#39;Y&#39;</span><span class="p">],</span> <span class="s1">&#39;Out&#39;</span><span class="p">,</span> <span class="n">max_relative_error</span><span class="o">=</span><span class="mf">0.5</span><span class="p">)</span>

    <span class="k">def</span> <span class="nf">test_check_grad_ingore_x</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">check_grad</span><span class="p">(</span>
            <span class="p">[</span><span class="s1">&#39;Y&#39;</span><span class="p">],</span> <span class="s1">&#39;Out&#39;</span><span class="p">,</span> <span class="n">max_relative_error</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">no_grad_set</span><span class="o">=</span><span class="nb">set</span><span class="p">(</span><span class="s2">&quot;X&quot;</span><span class="p">))</span>

    <span class="k">def</span> <span class="nf">test_check_grad_ingore_y</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">check_grad</span><span class="p">(</span>
            <span class="p">[</span><span class="s1">&#39;X&#39;</span><span class="p">],</span> <span class="s1">&#39;Out&#39;</span><span class="p">,</span> <span class="n">max_relative_error</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">no_grad_set</span><span class="o">=</span><span class="nb">set</span><span class="p">(</span><span class="s1">&#39;Y&#39;</span><span class="p">))</span>
</pre></div>
</div>
<p>Some key points in the code above include:</p>
<ul class="simple">
<li><code class="docutils literal"><span class="pre">create_op(&quot;mul&quot;)</span></code> creates the backward operator&#8217;s corresponding forward operator.</li>
<li><code class="docutils literal"><span class="pre">test_normal</span></code> calls <code class="docutils literal"><span class="pre">check_grad</span></code> to validate scaling tests&#8217; correctness and stability through numeric methods.<ul>
<li>The first variable <code class="docutils literal"><span class="pre">[&quot;X&quot;,</span> <span class="pre">&quot;Y&quot;]</span></code> appoints <code class="docutils literal"><span class="pre">X</span></code> and <code class="docutils literal"><span class="pre">Y</span></code> to be scale tested.</li>
<li>The second variable <code class="docutils literal"><span class="pre">&quot;Out&quot;</span></code> points to the network&#8217;s final output target <code class="docutils literal"><span class="pre">Out</span></code>.</li>
<li>The third variable <code class="docutils literal"><span class="pre">max_relative_error</span></code> points to the maximum relative tolerance error during scaling tests.</li>
</ul>
</li>
<li><code class="docutils literal"><span class="pre">test_check_grad_ingore_x</span></code> and <code class="docutils literal"><span class="pre">test_check_grad_ingore_y</span></code>branches test the cases where there is only one scaling input.</li>
</ul>
</div>
<div class="section" id="compiling-and-running">
<span id="compiling-and-running"></span><h3>Compiling and Running<a class="headerlink" href="#compiling-and-running" title="Permalink to this headline"></a></h3>
<p>Any new unit testing file of the format <code class="docutils literal"><span class="pre">test_*.py</span></code>  added to the director <code class="docutils literal"><span class="pre">python/paddle/v2/framework/tests</span></code> is automatically added to the project to compile.</p>
<p>Note that <strong>unlike the compile test for Ops, running unit tests requires compiling the entire project</strong> and requires compiling with flag <code class="docutils literal"><span class="pre">WITH_TESTING</span></code> on i.e. <code class="docutils literal"><span class="pre">cmake</span> <span class="pre">paddle_dir</span> <span class="pre">-DWITH_TESTING=ON</span></code>.</p>
<p>After successfully compiling the project, run the following command to run unit tests:</p>
<div class="highlight-bash"><div class="highlight"><pre><span></span>make <span class="nb">test</span> <span class="nv">ARGS</span><span class="o">=</span><span class="s2">&quot;-R test_mul_op -V&quot;</span>
</pre></div>
</div>
<p>Or,</p>
<div class="highlight-bash"><div class="highlight"><pre><span></span>ctest -R test_mul_op
</pre></div>
</div>
</div>
</div>
<div class="section" id="remarks">
<span id="remarks"></span><h2>Remarks<a class="headerlink" href="#remarks" title="Permalink to this headline"></a></h2>
<ul class="simple">
<li>Every <code class="docutils literal"><span class="pre">*_op.h</span></code> (if applicable), <code class="docutils literal"><span class="pre">*_op.cc</span></code>, and <code class="docutils literal"><span class="pre">*_op.cu</span></code> (if applicable) must be created for a unique Op. Compiling will fail if multiple operators are included per file.</li>
<li>The type with which an operator is registered needs to be identical to the Op&#8217;s name. Registering <code class="docutils literal"><span class="pre">REGISTER_OP(B,</span> <span class="pre">...)</span></code> in <code class="docutils literal"><span class="pre">A_op.cc</span></code> will cause unit testing failures.</li>
<li>If the operator does not implement a GPU kernel, please refrain from creating an empty <code class="docutils literal"><span class="pre">*_op.cu</span></code> file, or else unit tests will fail.</li>
<li>If multiple operators rely on some shared methods, a file NOT named <code class="docutils literal"><span class="pre">*_op.*</span></code> can be created to store them, such as <code class="docutils literal"><span class="pre">gather.h</span></code>.</li>
</ul>
</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>