Deploy to GitHub Pages: 7869a05f

develop/doc/_sources/howto/dev/new_op_en.md.txt

@@ -4,7 +4,8 @@
  - [Implementing C++ Types](#implementing-c-types)
    - [Defining ProtoMaker](#defining-protomaker)
    - [Defining Operator](#defining-operator)
-   - [Registering Operator](#registering-operator)
+   - [Defining OpKernel](#defining-opkernel)
+   - [Registering Operator and OpKernel](#registering-operator-and-opkernel)
    - [Compilation](#compilation)
  - [Python Binding](#python-binding)
  - [Unit Tests](#unit-tests)
@@ -16,12 +17,13 @@
 
 Here are the base types needed. For details, please refer to the design docs.
 
-- `framework::OperatorBase`: Operator (Op)base class.
-- `framework::OpKernel`: Base class for Op computation.
-- `framework::OperatorWithKernel`: Inherited from OperatorBase, describing an operator with computation.
 - `class OpProtoAndCheckerMaker`: Describes an Operator's input, output, attributes and description, mainly used to interface with Python API.
+- `framework::OperatorBase`: Operator (Op)base class.
+- `framework::OpKernel`: Base class for Op computation kernel.
+- `framework::OperatorWithKernel`: Inherited from OperatorBase, describing an operator with computation kernels.
+
 
-An operator can be differentiated by whether in has kernel methods. An operator with kernel inherits from `OperatorWithKernel` while the ones without inherit from `OperatorBase`. This tutorial focuses on implementing operators with kernels. In short, an operator includes the following information:
+Operators can be categorized into two groups: operator with kernel(s) and operator without kernel(s). An operator with kernel(s) inherits from `OperatorWithKernel` while the one without kernel(s) inherits from `OperatorBase`. This tutorial focuses on implementing operators with kernels. In short, an operator includes the following information:
 
 
  Information           | Where is it defined
@@ -32,7 +34,7 @@ Kernel implementation       | The kernel methods shared between CPU and CUDA are
 Registering the Op           | Ops are registered in `.cc` files; For Kernel registration, `.cc` files contain the CPU implementation, while `.cu` files contain the CUDA implementation.
 
 
-New Operator implementations are added to the list [paddle/operators](https://github.com/PaddlePaddle/Paddle/tree/develop/paddle/operators), with file names in the format `*_op.h` (if applicable), `*_op.cc`, `*_op.cu` (if applicable).** The system will use the naming scheme to automatically build operators and their corresponding Python extensions. **
+New Operator implementations are added to the list [paddle/operators](https://github.com/PaddlePaddle/Paddle/tree/develop/paddle/operators), with file names in the format `*_op.h` (if applicable), `*_op.cc`, `*_op.cu` (if applicable).** The system will use the naming scheme to automatically build operators and their corresponding Python extensions.**
 
 
 Let's take matrix multiplication operator, [MulOp](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/operators/mul_op.cc), as an example to introduce the writing of an Operator with Kernel.
@@ -156,7 +158,8 @@ Usually `OpProtoMaker` and `Op`'s type definitions are written in `.cc` files, w
 - `typename T` denotes data type, such as `float` or `double`.
 
 `MulKernel` types need to rewrite the interface for `Compute`.
-- `Compute` takes one input variable `const framework::ExecutionContext& context`.
+
+- `Compute` takes one input parameter: `const framework::ExecutionContext& context`.
 - Compared with `InferShapeContext`, `ExecutionContext` includes device types, and can similarly extract input, output, and attribute variables.
 - `Compute` implements the computation logics of an `OpKernel`.
 
@@ -177,7 +180,7 @@ Usually `OpProtoMaker` and `Op`'s type definitions are written in `.cc` files, w
   };
   ```
 
-Note that **different devices (CPU, CUDA)share an Op definition; whether or not they share the same `OpKernel` depends on whether `Compute` calls functions that support both devices.**
+Note that **different devices (CPU, CUDA)share one Op definition; whether or not they share the same `OpKernel` depends on whether `Compute` calls functions can support both devices.**
 
 `MulOp`'s CPU and CUDA share the same `Kernel`. A non-sharing  `OpKernel` example can be seen in [`OnehotCrossEntropyOpKernel`](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/operators/cross_entropy_op.h#L43).
 
@@ -188,13 +191,14 @@ This concludes the forward implementation of an operator. Next its operation and
 
 The definition of its corresponding backward operator, if applicable, is similar to that of an forward operator. **Note that a backward operator does not include a `ProtoMaker`**.
 
-### Registering Operator
+### Registering Operator and OpKernel
 
 - In `.cc` files, register forward and backward operator classes and the CPU kernel.
 
     ```cpp
     namespace ops = paddle::operators;
     REGISTER_OP(mul, ops::MulOp, ops::MulOpMaker, mul_grad, ops::MulOpGrad);
+
     REGISTER_OP_CPU_KERNEL(mul, ops::MulKernel<paddle::platform::CPUDeviceContext, float>);
     REGISTER_OP_CPU_KERNEL(mul_grad,
                   ops::MulGradKernel<paddle::platform::CPUDeviceContext, float>);
@@ -204,6 +208,7 @@ The definition of its corresponding backward operator, if applicable, is similar
 
     - `REGISTER_OP` registers the `ops::MulOp` class, type named `mul`, its type `ProtoMaker` is `ops::MulOpMaker`, registering `ops::MulOpGrad` as `mul_grad`.
     - `REGISTER_OP_WITHOUT_GRADIENT` registers an operator without gradient.
+
     - `REGISTER_OP_CPU_KERNEL` registers `ops::MulKernel` class and specialized template types `paddle::platform::CPUPlace` and `float`, which also registers `ops::MulGradKernel`.
 
 
@@ -225,6 +230,7 @@ The definition of its corresponding backward operator, if applicable, is similar
 Run the following commands to compile.
 
 ```
+# maybe you need to rerun cmake
 make mul_op
 ```
 

develop/doc/howto/dev/new_op_en.html

@@ -216,7 +216,8 @@
 <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="#defining-opkernel">Defining OpKernel</a></li>
+<li><a class="reference external" href="#registering-operator-and-opkernel">Registering Operator and OpKernel</a></li>
 <li><a class="reference external" href="#compilation">Compilation</a></li>
 </ul>
 </li>
@@ -233,19 +234,19 @@
 <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>
+<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 kernel.</li>
+<li><code class="docutils literal"><span class="pre">framework::OperatorWithKernel</span></code>: Inherited from OperatorBase, describing an operator with computation kernels.</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>Operators can be categorized into two groups: operator with kernel(s) and operator without kernel(s). An operator with kernel(s) inherits from <code class="docutils literal"><span class="pre">OperatorWithKernel</span></code> while the one without kernel(s) inherits 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 CUDA 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 CUDA-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 CUDA 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>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">
@@ -350,7 +351,7 @@ Registering the Op           | Ops are registered in <code class="docutils liter
 </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><code class="docutils literal"><span class="pre">Compute</span></code> takes one input parameter: <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>
@@ -369,18 +370,19 @@ Registering the Op           | Ops are registered in <code class="docutils liter
 <span class="p">};</span>
 </pre></div>
 </div>
-<p>Note that <strong>different devices (CPU, CUDA)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>Note that <strong>different devices (CPU, CUDA)share one 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 can support both devices.</strong></p>
 <p><code class="docutils literal"><span class="pre">MulOp</span></code>&#8216;s CPU and CUDA 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>Registering Operator<a class="headerlink" href="#registering-operator" title="Permalink to this headline">¶</a></h3>
+<div class="section" id="registering-operator-and-opkernel">
+<span id="registering-operator-and-opkernel"></span><h3>Registering Operator and OpKernel<a class="headerlink" href="#registering-operator-and-opkernel" 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">CPUDeviceContext</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">CPUDeviceContext</span><span class="p">,</span> <span class="kt">float</span><span class="o">&gt;</span><span class="p">);</span>
@@ -414,7 +416,8 @@ Registering the Op           | Ops are registered in <code class="docutils liter
 <div class="section" id="compilation">
 <span id="compilation"></span><h3>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>
+<div class="highlight-default"><div class="highlight"><pre><span></span><span class="c1"># maybe you need to rerun cmake</span>
+<span class="n">make</span> <span class="n">mul_op</span>
 </pre></div>
 </div>
 </div>