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 <!--- under the License. --><div class="section" id="creating-custom-operators-with-numpy">
 <span id="creating-custom-operators-with-numpy"></span><h1>Creating custom operators with numpy<a class="headerlink" href="#creating-custom-operators-with-numpy" title="Permalink to this headline">¶</a></h1>
 <p>In this tutorial, we will learn how to build custom operators with numpy in python. We will go through two examples:</p>
 <ul class="simple">
 <li>Custom operator without any <code class="docutils literal"><span class="pre">Parameter</span></code>s</li>
 <li>Custom operator with <code class="docutils literal"><span class="pre">Parameter</span></code>s</li>
 </ul>
 <p>Custom operator in python is easy to develop and good for prototyping, but may hurt performance. If you find it to be a bottleneck, please consider moving to a C++ based implementation in the backend.</p>
 <div class="highlight-python"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="kn">as</span> <span class="nn">np</span>
 <span class="kn">import</span> <span class="nn">mxnet</span> <span class="kn">as</span> <span class="nn">mx</span>
 <span class="kn">from</span> <span class="nn">mxnet</span> <span class="kn">import</span> <span class="n">gluon</span><span class="p">,</span> <span class="n">autograd</span>
 <span class="kn">import</span> <span class="nn">os</span>
 </pre></div>
 </div>
 <div class="section" id="parameter-less-operators">
 <span id="parameter-less-operators"></span><h2>Parameter-less operators<a class="headerlink" href="#parameter-less-operators" title="Permalink to this headline">¶</a></h2>
 <p>This operator implements the standard sigmoid activation function. This is only for illustration purposes, in real life you would use the built-in operator <code class="docutils literal"><span class="pre">mx.nd.relu</span></code>.</p>
 <div class="section" id="forward-backward-implementation">
 <span id="forward-backward-implementation"></span><h3>Forward &amp; backward implementation<a class="headerlink" href="#forward-backward-implementation" title="Permalink to this headline">¶</a></h3>
 <p>First we implement the forward and backward computation by sub-classing <code class="docutils literal"><span class="pre">mx.operator.CustomOp</span></code>:</p>
 <div class="highlight-python"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">Sigmoid</span><span class="p">(</span><span class="n">mx</span><span class="o">.</span><span class="n">operator</span><span class="o">.</span><span class="n">CustomOp</span><span class="p">):</span>
     <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">is_train</span><span class="p">,</span> <span class="n">req</span><span class="p">,</span> <span class="n">in_data</span><span class="p">,</span> <span class="n">out_data</span><span class="p">,</span> <span class="n">aux</span><span class="p">):</span>
         <span class="sd">"""Implements forward computation.</span>

 <span class="sd">        is_train : bool, whether forwarding for training or testing.</span>
 <span class="sd">        req : list of {'null', 'write', 'inplace', 'add'}, how to assign to out_data. 'null' means skip assignment, etc.</span>
 <span class="sd">        in_data : list of NDArray, input data.</span>
 <span class="sd">        out_data : list of NDArray, pre-allocated output buffers.</span>
 <span class="sd">        aux : list of NDArray, mutable auxiliary states. Usually not used.</span>
 <span class="sd">        """</span>
         <span class="n">x</span> <span class="o">=</span> <span class="n">in_data</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span>
         <span class="n">y</span> <span class="o">=</span> <span class="mf">1.0</span> <span class="o">/</span> <span class="p">(</span><span class="mf">1.0</span> <span class="o">+</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="n">x</span><span class="p">))</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">assign</span><span class="p">(</span><span class="n">out_data</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">req</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">y</span><span class="p">))</span>

     <span class="k">def</span> <span class="nf">backward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">req</span><span class="p">,</span> <span class="n">out_grad</span><span class="p">,</span> <span class="n">in_data</span><span class="p">,</span> <span class="n">out_data</span><span class="p">,</span> <span class="n">in_grad</span><span class="p">,</span> <span class="n">aux</span><span class="p">):</span>
         <span class="sd">"""Implements backward computation</span>

 <span class="sd">        req : list of {'null', 'write', 'inplace', 'add'}, how to assign to in_grad</span>
 <span class="sd">        out_grad : list of NDArray, gradient w.r.t. output data.</span>
 <span class="sd">        in_grad : list of NDArray, gradient w.r.t. input data. This is the output buffer.</span>
 <span class="sd">        """</span>
         <span class="n">y</span> <span class="o">=</span> <span class="n">out_data</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span>
         <span class="n">dy</span> <span class="o">=</span> <span class="n">out_grad</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span>
         <span class="n">dx</span> <span class="o">=</span> <span class="n">dy</span><span class="o">*</span><span class="p">(</span><span class="mf">1.0</span> <span class="o">-</span> <span class="n">y</span><span class="p">)</span><span class="o">*</span><span class="n">y</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">assign</span><span class="p">(</span><span class="n">in_grad</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">req</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">dx</span><span class="p">))</span>
 </pre></div>
 </div>
 </div>
 <div class="section" id="register-custom-operator">
 <span id="register-custom-operator"></span><h3>Register custom operator<a class="headerlink" href="#register-custom-operator" title="Permalink to this headline">¶</a></h3>
 <p>Then we need to register the custom op and describe it’s properties like input and output shapes so that mxnet can recognize it. This is done by sub-classing <code class="docutils literal"><span class="pre">mx.operator.CustomOpProp</span></code>:</p>
 <div class="highlight-python"><div class="highlight"><pre><span></span><span class="nd">@mx.operator.register</span><span class="p">(</span><span class="s2">"sigmoid"</span><span class="p">)</span>  <span class="c1"># register with name "sigmoid"</span>
 <span class="k">class</span> <span class="nc">SigmoidProp</span><span class="p">(</span><span class="n">mx</span><span class="o">.</span><span class="n">operator</span><span class="o">.</span><span class="n">CustomOpProp</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="nb">super</span><span class="p">(</span><span class="n">SigmoidProp</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="bp">True</span><span class="p">)</span>

     <span class="k">def</span> <span class="nf">list_arguments</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
         <span class="c1">#  this can be omitted if you only have 1 input.</span>
         <span class="k">return</span> <span class="p">[</span><span class="s1">'data'</span><span class="p">]</span>

     <span class="k">def</span> <span class="nf">list_outputs</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
         <span class="c1">#  this can be omitted if you only have 1 output.</span>
         <span class="k">return</span> <span class="p">[</span><span class="s1">'output'</span><span class="p">]</span>

     <span class="k">def</span> <span class="nf">infer_shape</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">in_shapes</span><span class="p">):</span>
         <span class="sd">"""Calculate output shapes from input shapes. This can be</span>
 <span class="sd">        omited if all your inputs and outputs have the same shape.</span>

 <span class="sd">        in_shapes : list of shape. Shape is described by a tuple of int.</span>
 <span class="sd">        """</span>
         <span class="n">data_shape</span> <span class="o">=</span> <span class="n">in_shapes</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
         <span class="n">output_shape</span> <span class="o">=</span> <span class="n">data_shape</span>
         <span class="c1"># return 3 lists representing inputs shapes, outputs shapes, and aux data shapes.</span>
         <span class="k">return</span> <span class="p">(</span><span class="n">data_shape</span><span class="p">,),</span> <span class="p">(</span><span class="n">output_shape</span><span class="p">,),</span> <span class="p">()</span>

     <span class="k">def</span> <span class="nf">create_operator</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">ctx</span><span class="p">,</span> <span class="n">in_shapes</span><span class="p">,</span> <span class="n">in_dtypes</span><span class="p">):</span>
         <span class="c1">#  create and return the CustomOp class.</span>
         <span class="k">return</span> <span class="n">Sigmoid</span><span class="p">()</span>
 </pre></div>
 </div>
 </div>
 <div class="section" id="example-usage">
 <span id="example-usage"></span><h3>Example Usage<a class="headerlink" href="#example-usage" title="Permalink to this headline">¶</a></h3>
 <p>We can now use this operator by calling <code class="docutils literal"><span class="pre">mx.nd.Custom</span></code>:</p>
 <div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">])</span>
 <span class="c1"># attach gradient buffer to x for autograd</span>
 <span class="n">x</span><span class="o">.</span><span class="n">attach_grad</span><span class="p">()</span>
 <span class="c1"># forward in a record() section to save computation graph for backward</span>
 <span class="c1"># see autograd tutorial to learn more.</span>
 <span class="k">with</span> <span class="n">autograd</span><span class="o">.</span><span class="n">record</span><span class="p">():</span>
     <span class="n">y</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">Custom</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">op_type</span><span class="o">=</span><span class="s1">'sigmoid'</span><span class="p">)</span>
 <span class="k">print</span><span class="p">(</span><span class="n">y</span><span class="p">)</span>
 </pre></div>
 </div>
 <div class="highlight-python"><div class="highlight"><pre><span></span><span class="c1"># call backward computation</span>
 <span class="n">y</span><span class="o">.</span><span class="n">backward</span><span class="p">()</span>
 <span class="c1"># gradient is now saved to the grad buffer we attached previously</span>
 <span class="k">print</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">grad</span><span class="p">)</span>
 </pre></div>
 </div>
 </div>
 </div>
 <div class="section" id="parametrized-operator">
 <span id="parametrized-operator"></span><h2>Parametrized Operator<a class="headerlink" href="#parametrized-operator" title="Permalink to this headline">¶</a></h2>
 <p>In the second use case we implement an operator with learnable weights. We implement the dense (or fully connected) layer that has one input, one output, and two learnable parameters: weight and bias.</p>
 <p>The dense operator performs a dot product between data and weight, then add bias to it.</p>
 <div class="section" id="forward-backward-implementation">
 <span id="id1"></span><h3>Forward &amp; backward implementation<a class="headerlink" href="#forward-backward-implementation" title="Permalink to this headline">¶</a></h3>
 <div class="highlight-python"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">Dense</span><span class="p">(</span><span class="n">mx</span><span class="o">.</span><span class="n">operator</span><span class="o">.</span><span class="n">CustomOp</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">bias</span><span class="p">):</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">_bias</span> <span class="o">=</span> <span class="n">bias</span>

     <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">is_train</span><span class="p">,</span> <span class="n">req</span><span class="p">,</span> <span class="n">in_data</span><span class="p">,</span> <span class="n">out_data</span><span class="p">,</span> <span class="n">aux</span><span class="p">):</span>
         <span class="n">x</span> <span class="o">=</span> <span class="n">in_data</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span>
         <span class="n">weight</span> <span class="o">=</span> <span class="n">in_data</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span>
         <span class="n">y</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">weight</span><span class="o">.</span><span class="n">T</span><span class="p">)</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">_bias</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">assign</span><span class="p">(</span><span class="n">out_data</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">req</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">y</span><span class="p">))</span>

     <span class="k">def</span> <span class="nf">backward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">req</span><span class="p">,</span> <span class="n">out_grad</span><span class="p">,</span> <span class="n">in_data</span><span class="p">,</span> <span class="n">out_data</span><span class="p">,</span> <span class="n">in_grad</span><span class="p">,</span> <span class="n">aux</span><span class="p">):</span>
         <span class="n">x</span> <span class="o">=</span> <span class="n">in_data</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span>
         <span class="n">dy</span> <span class="o">=</span> <span class="n">out_grad</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span>
         <span class="n">dx</span> <span class="o">=</span> <span class="n">dy</span><span class="o">.</span><span class="n">T</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">assign</span><span class="p">(</span><span class="n">in_grad</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">req</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">dx</span><span class="p">))</span>
 </pre></div>
 </div>
 </div>
 <div class="section" id="registration">
 <span id="registration"></span><h3>Registration<a class="headerlink" href="#registration" title="Permalink to this headline">¶</a></h3>
 <div class="highlight-python"><div class="highlight"><pre><span></span><span class="nd">@mx.operator.register</span><span class="p">(</span><span class="s2">"dense"</span><span class="p">)</span>  <span class="c1"># register with name "sigmoid"</span>
 <span class="k">class</span> <span class="nc">DenseProp</span><span class="p">(</span><span class="n">mx</span><span class="o">.</span><span class="n">operator</span><span class="o">.</span><span class="n">CustomOpProp</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">bias</span><span class="p">):</span>
         <span class="nb">super</span><span class="p">(</span><span class="n">DenseProp</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="bp">True</span><span class="p">)</span>
         <span class="c1"># we use constant bias here to illustrate how to pass arguments</span>
         <span class="c1"># to operators. All arguments are in string format so you need</span>
         <span class="c1"># to convert them back to the type you want.</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">_bias</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="n">bias</span><span class="p">)</span>

     <span class="k">def</span> <span class="nf">list_arguments</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
         <span class="k">return</span> <span class="p">[</span><span class="s1">'data'</span><span class="p">,</span> <span class="s1">'weight'</span><span class="p">]</span>

     <span class="k">def</span> <span class="nf">list_outputs</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
         <span class="c1">#  this can be omitted if you only have 1 output.</span>
         <span class="k">return</span> <span class="p">[</span><span class="s1">'output'</span><span class="p">]</span>

     <span class="k">def</span> <span class="nf">infer_shape</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">in_shapes</span><span class="p">):</span>
         <span class="n">data_shape</span> <span class="o">=</span> <span class="n">in_shapes</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
         <span class="n">weight_shape</span> <span class="o">=</span> <span class="n">in_shapes</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
         <span class="n">output_shape</span> <span class="o">=</span> <span class="p">(</span><span class="n">data_shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">weight_shape</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
         <span class="c1"># return 3 lists representing inputs shapes, outputs shapes, and aux data shapes.</span>
         <span class="k">return</span> <span class="p">(</span><span class="n">data_shape</span><span class="p">,</span> <span class="n">weight_shape</span><span class="p">),</span> <span class="p">(</span><span class="n">output_shape</span><span class="p">,),</span> <span class="p">()</span>

     <span class="k">def</span> <span class="nf">create_operator</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">ctx</span><span class="p">,</span> <span class="n">in_shapes</span><span class="p">,</span> <span class="n">in_dtypes</span><span class="p">):</span>
         <span class="c1">#  create and return the CustomOp class.</span>
         <span class="k">return</span> <span class="n">Dense</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_bias</span><span class="p">)</span>
 </pre></div>
 </div>
 </div>
 <div class="section" id="use-customop-together-with-block">
 <span id="use-customop-together-with-block"></span><h3>Use CustomOp together with Block<a class="headerlink" href="#use-customop-together-with-block" title="Permalink to this headline">¶</a></h3>
 <p>Parameterized CustomOp are usually used together with Blocks, which holds the parameter.</p>
 <div class="highlight-python"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">DenseBlock</span><span class="p">(</span><span class="n">mx</span><span class="o">.</span><span class="n">gluon</span><span class="o">.</span><span class="n">Block</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">in_channels</span><span class="p">,</span> <span class="n">channels</span><span class="p">,</span> <span class="n">bias</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
         <span class="nb">super</span><span class="p">(</span><span class="n">DenseBlock</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">_bias</span> <span class="o">=</span> <span class="n">bias</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">weight</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">params</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">'weight'</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="n">channels</span><span class="p">,</span> <span class="n">in_channels</span><span class="p">))</span>

     <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
         <span class="n">ctx</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">context</span>
         <span class="k">return</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">Custom</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">weight</span><span class="o">.</span><span class="n">data</span><span class="p">(</span><span class="n">ctx</span><span class="p">),</span> <span class="n">bias</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_bias</span><span class="p">,</span> <span class="n">op_type</span><span class="o">=</span><span class="s1">'dense'</span><span class="p">)</span>
 </pre></div>
 </div>
 </div>
 <div class="section" id="example-usage">
 <span id="id2"></span><h3>Example usage<a class="headerlink" href="#example-usage" title="Permalink to this headline">¶</a></h3>
 <div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">dense</span> <span class="o">=</span> <span class="n">DenseBlock</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">)</span>
 <span class="n">dense</span><span class="o">.</span><span class="n">initialize</span><span class="p">()</span>
 <span class="n">x</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">uniform</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span>
 <span class="n">y</span> <span class="o">=</span> <span class="n">dense</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
 <span class="k">print</span><span class="p">(</span><span class="n">y</span><span class="p">)</span>
 </pre></div>
 </div>
 </div>
 </div>
 <div class="section" id="using-custom-operators-with-fork">
 <span id="using-custom-operators-with-fork"></span><h2>Using custom operators with fork<a class="headerlink" href="#using-custom-operators-with-fork" title="Permalink to this headline">¶</a></h2>
 <p>In Linux systems, the default method in multiprocessing to create process is by using fork. If there are unfinished async custom operations when forking, the program will be blocked because of python GIL. Always use sync calls like <code class="docutils literal"><span class="pre">wait_to_read</span></code> or <code class="docutils literal"><span class="pre">waitall</span></code> before calling fork.</p>
 <div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">])</span>
 <span class="n">y</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">Custom</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">op_type</span><span class="o">=</span><span class="s1">'sigmoid'</span><span class="p">)</span>
 <span class="c1"># unfinished async sigmoid operation will cause blocking</span>
 <span class="n">os</span><span class="o">.</span><span class="n">fork</span><span class="p">()</span>
 </pre></div>
 </div>
 <p>Correctly handling this will make mxnet depend upon libpython, so the workaround now is to ensure that all custom operations are executed before forking process.</p>
 <div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">])</span>
 <span class="n">y</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">Custom</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">op_type</span><span class="o">=</span><span class="s1">'sigmoid'</span><span class="p">)</span>
 <span class="c1"># force execution by reading y</span>
 <span class="nb">print</span><span class="p">(</span><span class="n">y</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">())</span>
 <span class="n">os</span><span class="o">.</span><span class="n">fork</span><span class="p">()</span>
 </pre></div>
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 <h3><a href="../../index.html">Table Of Contents</a></h3>
 <ul>
 <li><a class="reference internal" href="#">Creating custom operators with numpy</a><ul>
 <li><a class="reference internal" href="#parameter-less-operators">Parameter-less operators</a><ul>
 <li><a class="reference internal" href="#forward-backward-implementation">Forward &amp; backward implementation</a></li>
 <li><a class="reference internal" href="#register-custom-operator">Register custom operator</a></li>
 <li><a class="reference internal" href="#example-usage">Example Usage</a></li>
 </ul>
 </li>
 <li><a class="reference internal" href="#parametrized-operator">Parametrized Operator</a><ul>
 <li><a class="reference internal" href="#forward-backward-implementation">Forward &amp; backward implementation</a></li>
 <li><a class="reference internal" href="#registration">Registration</a></li>
 <li><a class="reference internal" href="#use-customop-together-with-block">Use CustomOp together with Block</a></li>
 <li><a class="reference internal" href="#example-usage">Example usage</a></li>
 </ul>
 </li>
 <li><a class="reference internal" href="#using-custom-operators-with-fork">Using custom operators with fork</a></li>
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