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| <h1 class="post-title">Create New Operators</h1> |
| <h3></h3></header> |
| |
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| <h3 style="text-transform: capitalize; padding-left:10px">faq</h3> |
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| <!--- Licensed to the Apache Software Foundation (ASF) under one --> |
| |
| <!--- or more contributor license agreements. See the NOTICE file --> |
| |
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| |
| <h1 id="how-to-create-new-operators-layers">How to Create New Operators (Layers)</h1> |
| |
| <p>This tutorials walks you through the process of creating new MXNet operators (or layers). |
| We've done our best to provide high-speed operators for most common use cases. |
| However, if you're engaged in research, |
| there's a good chance you'll want to define custom layers, |
| like a novel loss function. In these cases, you have two options:</p> |
| |
| <ul> |
| <li><p>Use CustomOp to write new operators using a front-end language (e.g., Python) that run on CPUs or GPUs. |
| Depending on your implementation, this can range from very fast (if you only use operators under mx.nd) to very slow (if you copy out the data, using <code>.asnumpy()</code>).</p></li> |
| <li><p>Use C++/mshadow (CUDA). This provides the best performance, but can be difficult |
| if you're not familiar with MXNet, mshadow, or Cuda.</p></li> |
| </ul> |
| |
| <h2 id="customop">CustomOp</h2> |
| |
| <p>Implementing an operator in Python is simple. |
| As an example, let's create a softmax operator. |
| Start by subclassing <code>mxnet.operator.CustomOp</code>, |
| and then override a few methods:</p> |
| <div class="highlight"><pre><code class="language-python" data-lang="python"><span class="kn">import</span> <span class="nn">os</span> |
| <span class="kn">import</span> <span class="nn">mxnet</span> <span class="k">as</span> <span class="n">mx</span> |
| <span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="n">np</span> |
| |
| <span class="k">class</span> <span class="nc">Softmax</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="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="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="n">x</span> <span class="o">-</span> <span class="n">x</span><span class="o">.</span><span class="nb">max</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">((</span><span class="n">x</span><span class="o">.</span><span class="n">shape</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="n">y</span> <span class="o">/=</span> <span class="n">y</span><span class="o">.</span><span class="nb">sum</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">((</span><span class="n">x</span><span class="o">.</span><span class="n">shape</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="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> |
| </code></pre></div> |
| <p>We defined the computation for the forward pass of our operator. |
| The forward function takes a list of input and a list of output NDArrays. |
| For convenience, we called <code>.asnumpy()</code> on the first NDArray in input |
| and convert it to a CPU-based NumPy array. |
| This can be very slow. If you want the best performance, |
| keep data in the NDArray format and use operators under mx.nd to do the computation.</p> |
| |
| <p>At the end, we used CustomOp.assign to assign the resulting array y to out_data[0]. It handles assignment based on the value of req, which can be 'write', 'add', or 'null'.</p> |
| |
| <p>Then do the same for the backward pass:</p> |
| <div class="highlight"><pre><code class="language-python" data-lang="python"><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">l</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="o">.</span><span class="n">ravel</span><span class="p">()</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="nb">int</span><span class="p">)</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">y</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">l</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]),</span> <span class="n">l</span><span class="p">]</span> <span class="o">-=</span> <span class="mf">1.0</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">y</span><span class="p">))</span> |
| </code></pre></div> |
| <p>Softmax defines the computation of our custom operator, |
| but you still need to define its input/output format |
| by subclassing mx.operator.CustomOpProp. |
| First, register the new operator with the name 'softmax':</p> |
| <div class="highlight"><pre><code class="language-python" data-lang="python"><span class="o">@</span><span class="n">mx</span><span class="o">.</span><span class="n">operator</span><span class="o">.</span><span class="n">register</span><span class="p">(</span><span class="s">"softmax"</span><span class="p">)</span> |
| <span class="k">class</span> <span class="nc">SoftmaxProp</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> |
| </code></pre></div> |
| <p>Then, call the base constructor with <code>need_top_grad=False</code> |
| because softmax is a loss layer and you don't need gradient input from preceding layers:</p> |
| <div class="highlight"><pre><code class="language-python" data-lang="python"><span class="k">def</span> <span class="nf">__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">SoftmaxProp</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="n">__init__</span><span class="p">(</span><span class="n">need_top_grad</span><span class="o">=</span><span class="bp">False</span><span class="p">)</span> |
| </code></pre></div> |
| <p>Then declare the input and output:</p> |
| <div class="highlight"><pre><code class="language-python" data-lang="python"><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="s">'data'</span><span class="p">,</span> <span class="s">'label'</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="k">return</span> <span class="p">[</span><span class="s">'output'</span><span class="p">]</span> |
| </code></pre></div> |
| <p>Note that list_arguments declares both input and parameter. |
| We recommend ordering them as follows: <code>['input1', 'input2', ... , 'weight1', 'weight2', ...]</code></p> |
| |
| <p>Next, provide <code>infer_shape</code> to declare the shape of the output/weight |
| and check the consistency of the input shapes:</p> |
| <div class="highlight"><pre><code class="language-python" data-lang="python"><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_shape</span><span class="p">):</span> |
| <span class="n">data_shape</span> <span class="o">=</span> <span class="n">in_shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> |
| <span class="n">label_shape</span> <span class="o">=</span> <span class="p">(</span><span class="n">in_shape</span><span class="p">[</span><span class="mi">0</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">in_shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> |
| <span class="k">return</span> <span class="p">[</span><span class="n">data_shape</span><span class="p">,</span> <span class="n">label_shape</span><span class="p">],</span> <span class="p">[</span><span class="n">output_shape</span><span class="p">],</span> <span class="p">[]</span> |
| </code></pre></div> |
| <p>The first axis of an input/output tensor corresponds to different examples within the batch. |
| The label is a set of integers, one for each data entry, |
| and the output has the same shape as the input. |
| The <code>infer_shape</code> function should always return three lists in this order: |
| inputs, outputs, and auxiliary states (which we don't have here), |
| even if one of them is empty.</p> |
| |
| <p>Optionally, you can also define <code>infer_type</code> to declare the input and output data type of your operator. Supported types are <code>np.float32</code>, <code>np.float64</code>, <code>np.float16</code>, <code>np.uint8</code>, and <code>np.int32</code>.</p> |
| <div class="highlight"><pre><code class="language-python" data-lang="python"><span class="k">def</span> <span class="nf">infer_type</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">in_type</span><span class="p">):</span> |
| <span class="n">dtype</span> <span class="o">=</span> <span class="n">in_type</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> |
| <span class="k">return</span> <span class="p">[</span><span class="n">dtype</span><span class="p">,</span> <span class="n">dtype</span><span class="p">],</span> <span class="p">[</span><span class="n">dtype</span><span class="p">],</span> <span class="p">[]</span> |
| </code></pre></div> |
| <p>Finally, define a create_operator function that will be called by the back end to create an instance of softmax:</p> |
| <div class="highlight"><pre><code class="language-python" data-lang="python"><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">shapes</span><span class="p">,</span> <span class="n">dtypes</span><span class="p">):</span> |
| <span class="k">return</span> <span class="n">Softmax</span><span class="p">()</span> |
| </code></pre></div> |
| <p>To use the custom operator, create a mx.sym.Custom symbol with op_type as the registered name:</p> |
| <div class="highlight"><pre><code class="language-python" data-lang="python"><span class="n">mlp</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">symbol</span><span class="o">.</span><span class="n">Custom</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">fc3</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s">'softmax'</span><span class="p">,</span> <span class="n">op_type</span><span class="o">=</span><span class="s">'softmax'</span><span class="p">)</span> |
| </code></pre></div> |
| <p>Please see the full code for this example <a href="https://github.com/apache/mxnet/blob/v1.x/example/numpy-ops/custom_softmax.py">here</a>.</p> |
| |
| <h2 id="c">C++</h2> |
| |
| <p>With MXNet v0.9 (the NNVM refactor) or later, creating new operators has become easier. |
| Operators are now registered with NNVM. |
| The following code is an example on how to register an operator (checkout <a href="https://github.com/apache/mxnet/tree/v1.x/src/operator/tensor">src/operator/tensor</a> for more examples):</p> |
| <div class="highlight"><pre><code class="language-c++" data-lang="c++"><span class="n">NNVM_REGISTER_OP</span><span class="p">(</span><span class="n">abs</span><span class="p">)</span> |
| <span class="p">.</span><span class="n">MXNET_DESCRIBE</span><span class="p">(</span><span class="s">"Take absolute value of the src"</span><span class="p">)</span> |
| <span class="p">.</span><span class="n">set_num_inputs</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span> |
| <span class="p">.</span><span class="n">set_num_outputs</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span> |
| <span class="p">.</span><span class="n">set_attr</span><span class="o"><</span><span class="n">nnvm</span><span class="o">::</span><span class="n">FInferShape</span><span class="o">></span><span class="p">(</span><span class="s">"FInferShape"</span><span class="p">,</span> <span class="n">ElemwiseShape</span><span class="o"><</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="o">></span><span class="p">);</span> |
| </code></pre></div> |
| <p>The syntax is quite simple, we register the operator with a name, |
| then set number of inputs and outputs. |
| You can register attributes with any key (<code>FInferShape</code> for example) to any operator, |
| without having to modify a central class interface definition.</p> |
| |
| <h3 id="operator-attribute-system">Operator Attribute System</h3> |
| |
| <p>One of the biggest improvements brought by NNVM is the operator attribute system. |
| This is like traits for types in common languages like C++. |
| We can register any attribute to any operator, with the syntax</p> |
| <div class="highlight"><pre><code class="language-c++" data-lang="c++"><span class="n">NNVM_REGISTER_OP</span><span class="p">(</span><span class="n">op</span><span class="o">-</span><span class="n">name</span><span class="p">)</span> |
| <span class="p">.</span><span class="n">set_attr</span><span class="o"><</span><span class="n">AttributeType</span><span class="o">></span><span class="p">(</span><span class="s">"AttributeKey"</span><span class="p">,</span> <span class="n">CorrespondingAttributeObject</span><span class="p">);</span> |
| </code></pre></div> |
| <p>These attributes can be retrieved later for various purposes. |
| For example, <code>FInferShape</code> is used for shape inference, <code>FCompute<cpu></code> is used for carrying out actual computation on CPU.</p> |
| |
| <p>As long as all attributes registered with the same key have the same type, |
| we can register any attributes to operators. |
| The more attribute an operator provides, |
| the more information the system can use for optimization.</p> |
| |
| <h3 id="list-of-basic-attributes">List of basic attributes</h3> |
| |
| <p>In this section, we will go through the basic attributes MXNet expect for all operators. |
| You can find the definition for them in the following two files:</p> |
| |
| <ul> |
| <li><a href="https://github.com/dmlc/nnvm/blob/master/include/nnvm/op_attr_types.h">nnvm/op_attr_types.h</a></li> |
| <li><a href="https://github.com/apache/mxnet/blob/v1.x/include/mxnet/op_attr_types.h">mxnet/op_attr_types.h</a></li> |
| </ul> |
| |
| <h4 id="descriptions-optional">Descriptions (Optional)</h4> |
| |
| <p><code>.describe(comment)</code> adds a comment to the operator. Use <code>.MXNET_DESCRIBE(comment)</code> to add the current file name and line number to comment.</p> |
| |
| <h4 id="attribute-parser-optional">Attribute Parser (Optional)</h4> |
| |
| <p>Set attribute parser with <code>.set_attr_parser(PARSER)</code> where PARSER is a function with prototype <code>void(nnvm::NodeAttr* attrs)</code>. This function should parse the key-word arguments in <code>attrs->dict</code> and store the result in <code>attrs->parsed</code>.</p> |
| |
| <p>Simple arguments can be parsed like |
| <code>c++ |
| NNVM_REGISTER_OP(scalar_op) |
| .set_attr_parser( |
| [](NodeAttrs* attrs) { |
| attrs->parsed = dmlc::stod(attrs->dict["scalar"]); |
| }) |
| </code></p> |
| |
| <p>The parsed arguments can then be accessed in other attribute functions with |
| <code>c++ |
| double alpha = nnvm::get<double>(attrs.parsed); |
| </code></p> |
| |
| <p>More complex ops can use <code>dmlc::Parameters</code> and <code>ParamParser</code> (defined in operator_common.h) for parsing:</p> |
| <div class="highlight"><pre><code class="language-c++" data-lang="c++"><span class="cp">#include <dmlc/parameter.h> |
| #include <operator_common.h> |
| </span><span class="k">struct</span> <span class="n">ActivationParam</span> <span class="o">:</span> <span class="k">public</span> <span class="n">dmlc</span><span class="o">::</span><span class="n">Parameter</span><span class="o"><</span><span class="n">ActivationParam</span><span class="o">></span> <span class="p">{</span> |
| <span class="c1">// use int for enumeration</span> |
| <span class="kt">int</span> <span class="n">act_type</span><span class="p">;</span> |
| <span class="n">DMLC_DECLARE_PARAMETER</span><span class="p">(</span><span class="n">ActivationParam</span><span class="p">)</span> <span class="p">{</span> |
| <span class="n">DMLC_DECLARE_FIELD</span><span class="p">(</span><span class="n">act_type</span><span class="p">)</span> |
| <span class="p">.</span><span class="n">add_enum</span><span class="p">(</span><span class="s">"relu"</span><span class="p">,</span> <span class="n">activation</span><span class="o">::</span><span class="n">kReLU</span><span class="p">)</span> |
| <span class="p">.</span><span class="n">add_enum</span><span class="p">(</span><span class="s">"sigmoid"</span><span class="p">,</span> <span class="n">activation</span><span class="o">::</span><span class="n">kSigmoid</span><span class="p">)</span> |
| <span class="p">.</span><span class="n">add_enum</span><span class="p">(</span><span class="s">"tanh"</span><span class="p">,</span> <span class="n">activation</span><span class="o">::</span><span class="n">kTanh</span><span class="p">)</span> |
| <span class="p">.</span><span class="n">add_enum</span><span class="p">(</span><span class="s">"softrelu"</span><span class="p">,</span> <span class="n">activation</span><span class="o">::</span><span class="n">kSoftReLU</span><span class="p">)</span> |
| <span class="p">.</span><span class="n">describe</span><span class="p">(</span><span class="s">"Activation function to be applied."</span><span class="p">);</span> |
| <span class="p">}</span> |
| <span class="p">};</span> |
| <span class="n">NNVM_REGISTER_OP</span><span class="p">(</span><span class="n">Activation</span><span class="p">)</span> |
| <span class="p">.</span><span class="n">set_attr_parser</span><span class="p">(</span><span class="n">ParamParser</span><span class="o"><</span><span class="n">ActivationParam</span><span class="o">></span><span class="p">);</span> |
| <span class="c1">// access with:</span> |
| <span class="c1">// const ActivationParam& param = nnvm::get<ActivationParam>(attrs.parsed);</span> |
| </code></pre></div> |
| <h4 id="inputs-outputs">Inputs & Outputs</h4> |
| |
| <p>Number of inputs/outputs can be set with <code>.set_num_inputs(n_in)</code> and <code>.set_num_outputs(n_out)</code> |
| where n_in and n_out are integers.</p> |
| |
| <p>Alternatively, if the number of inputs/outputs is variable and depends on arguments, |
| you can set <code>n_in</code>/<code>n_out</code> to functions with prototype <code>uint32_t(const nnvm::NodeAttrs& attrs)</code> |
| that return the number of inputs/outputs based on parsed arguments.</p> |
| |
| <p>Outputs can be made invisible to other operators by registering <code>FNumVisibleOutputs</code> |
| and returning an integer smaller than <code>n_out</code>.</p> |
| |
| <p>Inputs/outputs can be named by registering <code>FListInputNames</code> and <code>FListOutputNames</code> with prototype <code>std::vector<std::string>(const NodeAttrs& attrs)</code>.</p> |
| |
| <h4 id="argument-descriptions">Argument Descriptions</h4> |
| |
| <p>Set argument descriptions with <code>.add_argument(name, type, comment)</code>. |
| This is necessary for operators to be properly called imperatively.</p> |
| |
| <p>First, add NDArray arguments <code>num_inputs</code> times with type "NDArray" |
| or one time with type "NDArray[]" for ops with variable length inputs.</p> |
| |
| <p>Then add key-word arguments with proper type (float, string, etc). |
| Operators that parse key-word arguments with <code>dmlc::Parameter</code> |
| can add argument descriptions in bulk with <code>.add_arguments(ActivationParam::__FIELDS__())</code> |
| (NDArray arguments still need to be manually added with type "NDArray").</p> |
| |
| <h4 id="finfershape-or-tisbackward-for-backward-only-ops">FInferShape or TIsBackward (for Backward Only Ops)</h4> |
| |
| <p>Normally operators need to have <code>FInferShape</code> with prototype <code>bool(const nnvm::NodeAttrs& attrs, mxnet::ShapeVector *in_attrs, mxnet::ShapeVector *out_attrs)</code>. <code>FInferShape</code> fills unknown shapes (<code>shape.ndim() == 0</code>) in in_attrs/out_attrs based on known shapes in in_attrs/out_attrs. Use <code>ElemwiseShape<n_in, n_out></code> for simple operators with uniform shapes.</p> |
| |
| <p>Operators that are only used for a backward pass can instead register <code>.set_attr<nnvm::TIsBackward>("TIsBackward", true)</code> |
| and their shapes with be copied from the corresponding forward operators.</p> |
| |
| <h4 id="finfertype">FInferType</h4> |
| |
| <p>Similar to <code>FInferShape</code>, <code>FInferType</code> fills unknown types (-1) based on known types. Use <code>ElemwiseType<n_in, n_out></code> for simple operators with uniform types. Operators that registered <code>TIsBackward</code> don't need to register this.</p> |
| |
| <h4 id="finplaceoption-optional">FInplaceOption (Optional)</h4> |
| |
| <p><code>FInplaceOption</code> with prototype <code>std::vector<std::pair<int, int> >(const NodeAttrs& attrs)</code> |
| specifies which input/output pairs can be computed in-place |
| and share memory with each other. |
| Each pair (i, j) in the returned list means |
| that the i-th input can share memory with the j-th output.</p> |
| |
| <h4 id="fgradient-optional-for-imperative-use-required-for-symbolic-use">FGradient (Optional for imperative use, required for symbolic use)</h4> |
| |
| <p>If an operator has gradient, it can be described with <code>FGradient</code> with prototype</p> |
| <div class="highlight"><pre><code class="language-c++" data-lang="c++"><span class="n">std</span><span class="o">::</span><span class="n">vector</span><span class="o"><</span><span class="n">nnvm</span><span class="o">::</span><span class="n">NodeEntry</span><span class="o">></span><span class="p">(</span><span class="k">const</span> <span class="n">nnvm</span><span class="o">::</span><span class="n">ObjectPtr</span><span class="o">&</span> <span class="n">n</span><span class="p">,</span> |
| <span class="k">const</span> <span class="n">std</span><span class="o">::</span><span class="n">vector</span><span class="o"><</span><span class="n">nnvm</span><span class="o">::</span><span class="n">NodeEntry</span><span class="o">>&</span> <span class="n">ograds</span><span class="p">)</span> |
| </code></pre></div> |
| <p>Use utility functions <code>ElemwiseGradUseIn{op_name}</code>, <code>ElemwiseGradUseOut{op_name}</code>, <code>ElemwiseGradUseNone{op_name}</code> for ops that need corresponding forward op's input, |
| output or nothing to calculating gradient.</p> |
| |
| <p>For more complicated patterns, use <code>MakeGradNode(op_name, n, heads, dict)</code> to create gradient entries, |
| where heads are input entries to the backward op, composed from ograds and n->inputs.</p> |
| |
| <p>When assembling a return vector of <code>std::vector<nnvm::NodeEntry> ret;</code> a common pattern would be to |
| either create nodes in place as in:</p> |
| <div class="highlight"><pre><code class="language-c++" data-lang="c++"><span class="n">ret</span><span class="p">.</span><span class="n">emplace_back</span><span class="p">(</span><span class="n">MakeNode</span><span class="p">(</span><span class="s">"zeros_like"</span><span class="p">,</span> <span class="n">n</span><span class="o">-></span><span class="n">attrs</span><span class="p">.</span><span class="n">name</span> <span class="o">+</span> <span class="s">"_xyz_backward"</span><span class="p">,</span> |
| <span class="p">{</span><span class="n">n</span><span class="o">-></span><span class="n">inputs</span><span class="p">[</span><span class="mi">1</span><span class="p">]},</span> <span class="nb">nullptr</span><span class="p">,</span> <span class="o">&</span><span class="n">n</span><span class="p">))</span> |
| </code></pre></div> |
| <p>Or create the node, modify and then move into NodeEntry's constructor if this node is not to be used |
| again. This avoids uneccessary copies of the shared_ptr.</p> |
| <div class="highlight"><pre><code class="language-c++" data-lang="c++"><span class="k">for</span> <span class="p">(</span><span class="kt">size_t</span> <span class="n">i</span> <span class="o">=</span> <span class="mi">0</span><span class="p">;</span> <span class="n">i</span> <span class="o"><</span> <span class="n">n</span><span class="o">-></span><span class="n">inputs</span><span class="p">.</span><span class="n">size</span><span class="p">();</span> <span class="o">++</span><span class="n">i</span><span class="p">)</span> <span class="p">{</span> |
| <span class="n">nnvm</span><span class="o">::</span><span class="n">ObjectPtr</span> <span class="n">node</span> <span class="o">=</span> <span class="n">nnvm</span><span class="o">::</span><span class="n">Node</span><span class="o">::</span><span class="n">Create</span><span class="p">();</span> |
| <span class="n">node</span><span class="o">-></span><span class="n">attrs</span><span class="p">.</span><span class="n">op</span> <span class="o">=</span> <span class="n">copy_op</span><span class="p">;</span> |
| <span class="n">node</span><span class="o">-></span><span class="n">inputs</span> <span class="o">=</span> <span class="p">{</span><span class="n">ograds</span><span class="p">[</span><span class="mi">0</span><span class="p">]};</span> |
| <span class="n">ret</span><span class="p">.</span><span class="n">emplace_back</span><span class="p">(</span><span class="n">std</span><span class="o">::</span><span class="n">move</span><span class="p">(</span><span class="n">node</span><span class="p">));</span> |
| <span class="p">}</span> |
| </code></pre></div> |
| <p>The first case uses RVO and the second in place construction.</p> |
| |
| <h4 id="fcomputexpu">FCompute<xpu></h4> |
| |
| <p>Simple operators can register FCompute<xpu> with <code>.set_attr<FCompute>("FCompute<cpu>", ...)</code> and <code>.set_attr<FCompute>("FCompute<gpu>", ...)</code> for both CPU and (optionally) GPU computation.</p> |
| |
| <p>FCompute has prototype</p> |
| <div class="highlight"><pre><code class="language-c++" data-lang="c++"><span class="kt">void</span><span class="p">(</span><span class="k">const</span> <span class="n">nnvm</span><span class="o">::</span><span class="n">NodeAttrs</span><span class="o">&</span> <span class="n">attrs</span><span class="p">,</span> |
| <span class="k">const</span> <span class="n">OpContext</span><span class="o">&</span> <span class="n">ctx</span><span class="p">,</span> |
| <span class="k">const</span> <span class="n">std</span><span class="o">::</span><span class="n">vector</span><span class="o"><</span><span class="n">TBlob</span><span class="o">>&</span> <span class="n">inputs</span><span class="p">,</span> |
| <span class="k">const</span> <span class="n">std</span><span class="o">::</span><span class="n">vector</span><span class="o"><</span><span class="n">OpReqType</span><span class="o">>&</span> <span class="n">req</span><span class="p">,</span> |
| <span class="k">const</span> <span class="n">std</span><span class="o">::</span><span class="n">vector</span><span class="o"><</span><span class="n">TBlob</span><span class="o">>&</span> <span class="n">outputs</span><span class="p">)</span> |
| </code></pre></div> |
| <p><code>req</code> has the same length as <code>outputs</code>. |
| Each entry of <code>req</code> specifies |
| how the corresponding <code>output</code> should be written to. |
| <code>OpReqType</code> is defined as:</p> |
| <div class="highlight"><pre><code class="language-c++" data-lang="c++"><span class="k">enum</span> <span class="n">OpReqType</span> <span class="p">{</span> |
| <span class="n">kNullOp</span><span class="p">,</span> |
| <span class="n">kWriteTo</span><span class="p">,</span> |
| <span class="n">kWriteInplace</span><span class="p">,</span> |
| <span class="n">kAddTo</span> |
| <span class="p">};</span> |
| </code></pre></div> |
| <p>Normally, the <code>req</code> of all <code>outputs</code> should be <code>kWriteTo</code>, |
| meaning that the provided <code>outputs</code> tensor is a <em>raw</em> memory block, |
| so the operator should write results directly into it. |
| In some cases, for example, when calculating the gradient tensor, |
| it would be great if we could accumulate the result, |
| rather than directly overwrite the tensor contents |
| so that no extra space needs to be created each time. |
| In such cases, the corresponding <code>req</code> is set to <code>kAddTo</code>, |
| indicating that a <code>+=</code> should be used.</p> |
| |
| <h3 id="example-abs-operator">Example: abs operator</h3> |
| <div class="highlight"><pre><code class="language-c++" data-lang="c++"><span class="n">NNVM_REGISTER_OP</span><span class="p">(</span><span class="n">abs</span><span class="p">)</span> |
| <span class="p">.</span><span class="n">MXNET_DESCRIBE</span><span class="p">(</span><span class="s">"Take absolute value of the src"</span><span class="p">)</span> |
| <span class="p">.</span><span class="n">set_num_inputs</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span> |
| <span class="p">.</span><span class="n">set_num_outputs</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span> |
| <span class="p">.</span><span class="n">set_attr</span><span class="o"><</span><span class="n">nnvm</span><span class="o">::</span><span class="n">FInferShape</span><span class="o">></span><span class="p">(</span><span class="s">"FInferShape"</span><span class="p">,</span> <span class="n">ElemwiseShape</span><span class="o"><</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="o">></span><span class="p">)</span> |
| <span class="p">.</span><span class="n">set_attr</span><span class="o"><</span><span class="n">nnvm</span><span class="o">::</span><span class="n">FInferType</span><span class="o">></span><span class="p">(</span><span class="s">"FInferType"</span><span class="p">,</span> <span class="n">ElemwiseType</span><span class="o"><</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="o">></span><span class="p">)</span> |
| <span class="p">.</span><span class="n">set_attr</span><span class="o"><</span><span class="n">nnvm</span><span class="o">::</span><span class="n">FInplaceOption</span><span class="o">></span><span class="p">(</span><span class="s">"FInplaceOption"</span><span class="p">,</span> |
| <span class="p">[](</span><span class="k">const</span> <span class="n">NodeAttrs</span><span class="o">&</span> <span class="n">attrs</span><span class="p">){</span> |
| <span class="k">return</span> <span class="n">std</span><span class="o">::</span><span class="n">vector</span><span class="o"><</span><span class="n">std</span><span class="o">::</span><span class="n">pair</span><span class="o"><</span><span class="kt">int</span><span class="p">,</span> <span class="kt">int</span><span class="o">></span> <span class="o">></span><span class="p">{{</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">}};</span> |
| <span class="p">})</span> |
| <span class="p">.</span><span class="n">set_attr</span><span class="o"><</span><span class="n">FCompute</span><span class="o">></span><span class="p">(</span><span class="s">"FCompute<cpu>"</span><span class="p">,</span> <span class="n">UnaryCompute</span><span class="o"><</span><span class="n">cpu</span><span class="p">,</span> <span class="n">mshadow_op</span><span class="o">::</span><span class="n">abs</span><span class="o">></span><span class="p">)</span> |
| <span class="p">.</span><span class="n">set_attr</span><span class="o"><</span><span class="n">nnvm</span><span class="o">::</span><span class="n">FGradient</span><span class="o">></span><span class="p">(</span><span class="s">"FGradient"</span><span class="p">,</span> <span class="n">ElemwiseGradUseIn</span><span class="p">{</span><span class="s">"_backward_abs"</span><span class="p">});</span> |
| <span class="p">.</span><span class="n">add_argument</span><span class="p">(</span><span class="s">"data"</span><span class="p">,</span> <span class="s">"NDArray"</span><span class="p">,</span> <span class="s">"Source input"</span><span class="p">)</span> |
| |
| <span class="n">NNVM_REGISTER_OP</span><span class="p">(</span><span class="n">_backward_abs</span><span class="p">)</span> |
| <span class="p">.</span><span class="n">set_num_inputs</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span> |
| <span class="p">.</span><span class="n">set_num_outputs</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span> |
| <span class="p">.</span><span class="n">set_attr</span><span class="o"><</span><span class="n">nnvm</span><span class="o">::</span><span class="n">FInferShape</span><span class="o">></span><span class="p">(</span><span class="s">"FInferShape"</span><span class="p">,</span> <span class="n">ElemwiseShape</span><span class="o"><</span><span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="o">></span><span class="p">)</span> |
| <span class="p">.</span><span class="n">set_attr</span><span class="o"><</span><span class="n">nnvm</span><span class="o">::</span><span class="n">FInferType</span><span class="o">></span><span class="p">(</span><span class="s">"FInferType"</span><span class="p">,</span> <span class="n">ElemwiseType</span><span class="o"><</span><span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="o">></span><span class="p">)</span> |
| <span class="p">.</span><span class="n">set_attr</span><span class="o"><</span><span class="n">nnvm</span><span class="o">::</span><span class="n">FInplaceOption</span><span class="o">></span><span class="p">(</span><span class="s">"FInplaceOption"</span><span class="p">,</span> |
| <span class="p">[](</span><span class="k">const</span> <span class="n">NodeAttrs</span><span class="o">&</span> <span class="n">attrs</span><span class="p">){</span> |
| <span class="k">return</span> <span class="n">std</span><span class="o">::</span><span class="n">vector</span><span class="o"><</span><span class="n">std</span><span class="o">::</span><span class="n">pair</span><span class="o"><</span><span class="kt">int</span><span class="p">,</span> <span class="kt">int</span><span class="o">></span> <span class="o">></span><span class="p">{{</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">},</span> <span class="p">{</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">}};</span> |
| <span class="p">})</span> |
| <span class="p">.</span><span class="n">set_attr</span><span class="o"><</span><span class="n">FCompute</span><span class="o">></span><span class="p">(</span><span class="s">"FCompute<cpu>"</span><span class="p">,</span> <span class="n">BinaryCompute</span><span class="o"><</span><span class="n">cpu</span><span class="p">,</span> <span class="n">backward_grad</span><span class="o"><</span><span class="n">mshadow_op</span><span class="o">::</span><span class="n">sign</span><span class="o">></span> <span class="o">></span><span class="p">);</span> |
| </code></pre></div> |
| <h3 id="legacy-operators">Legacy Operators</h3> |
| |
| <p>For the legacy (pre 0.9) way of defining operators with C++, please see: |
| - <a href="/versions/1.9.1/api/architecture/overview.html#operators-in-mxnet">Developer Guide - Operators</a> |
| - <a href="/versions/1.9.1/api/architecture/overview.html#simpleop-the-unified-operator-api">Developer Guide - SimpleOp</a></p> |
| |
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