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--- a/develop/doc/_sources/design/operator_kernel_type.md.txt
+++ b/develop/doc/_sources/design/operator_kernel_type.md.txt
+# Design Doc: The Keys of Operator Kernel Type
+## Problem
+An operator can have different kernel implementations, and each operator will have a map to store the related kernels. Fluid uses `OpKernelType` as a key to identify a unique Kernel. Before an operator runs, an certain kernel must be chosen by a key of `OpKernelType`. Currently, `OpKernelType` is defined as follows:
+
+```cpp
+struct OpKernelType {
+  platform::Place place_;
+  proto::DataType data_type_;
+};
+```
+For more details, please refer to [codes](https://github.com/PaddlePaddle/Paddle/blob/2d5ec16bc8a09fb8e0f62c89b116b0cd1d333907/paddle/framework/operator.h#L348-L374) in github.
+
+It contains two keys, `Place` and `DataType`. And these two keys will be hashed to a unique key to represent a certain type of kernel. However, these two keys are not enough. We need a more complete representation of `OpKernelType`. 
+
+We often implement a kernel of an operator with some computing library in certain device(place). Please remind that computing library and device are not one-to-one corresponding. A device can have a lot of computing libraries and a computing library can also support several devices. 
+
+For example, Eigen library can support Nvidia GPU/AMD GPU/CPU. And MKLDNN library can support Intel CPU/Intel FPGA. Both `Place` and `Library` should be a key of `OpKernelType`.
+
+It's obvious that different DataTypes, like fp64/fp32/int8 will have different kernels. But the data layout of a Tensor will also lead to different implementation. Please refer to the batch norm operator [kernels](https://github.com/PaddlePaddle/Paddle/blob/a948fac4d0ad7e0412d373b8aabeb711c2899563/paddle/operators/batch_norm_op.cc#L180-L209). Data Layout should also be taken into consideration.
+
+## Solution
+
+There are four keys to determine a kernel type of an operator: `Place`/`Library`/`DataType`/`Layout`.
+
+```cpp
+struct OpKernelType {
+  platform::Place place_;
+  platform::Library library_;
+  proto::DataType data_type_;
+  framework::Layout layout_;
+};
+```
+
+Following is the details:
+
+### Place
+
+`Place` is defined as follows:
+
+```cpp
+typedef boost::variant<CUDAPlace, ROCmPlace, FPGAPlace, CPUPlace> Place;
+```
+
+`Place` is to represent the device memory where data is locating.
+
+
+### Library
+
+One operator kernel is usually implemented based on one library. `Library` is defined as a enum variable:
+
+```cpp
+enum Library { Plain, MKLDNN, CUDNN };
+```
+
+We use `Plain` enumerator to represent default library. Since most operators in Fluid are implemented based on `Eigen` library, we take `Eigen` library as the `Plain` enumerator.
+A library usually has a corresponding `DeviceContext` which contains some handles needed by computation. Fluid now have two default DeviceContexts in CPU and CUDA, `CPUDeviceContext` and `CUDADeviceContext`. `CPUDeviceContext` contains a Eigen library handle and `CDUADeviceContext` contains a Eigen library handle and cuBLAS handle.
+
+If we want to support new Library, a new enumerator need to be added to `Library` and a new corresponding `LibraryDeviceContext` will be created.
+
+
+### DataType
+
+
+`DataType` is defined in [framework.proto](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/framework/framework.proto). Currently, int32/int64/fp32/fp64 are supported.
+
+### Layout
+
+Actually, a Tensor is a view of a block of memory. Besides a pointer to the memory, we also have to get some other descriptions of this block of memory, such as shape(ddim), stride, and layout.
+
+Different layout leads to different implementation of operator kernel. There are mainly 4 principles we have to follow to support layout in our fluid framework.
+
+- We take layout as a data member of Tensor. Layout is actually a enum variable. If fluid is built with MKLDNN, then, the memory format in MKLDNN will be added into this enum variable too.
+
+- Users have to set layout for input data. And some operators like fill_constant/random, also have to set layout of generating data. Of course, we can have some default layout, like NCHW.
+
+- The inference of Layout is at run-time, not compile-time.
+
+- Every operator have to implement different kernels for different layouts. Let's take MKLDNN as an example, if we want to implement a MKLDNN convolution operator, we have to realize all the kernels for different layout, list at [here](http://01org.github.io/mkl-dnn/structmkldnn_1_1memory.html). And we will have a special macro to do registering kernels for MKLDNN operators.
+
+`Layout` is also defined as a enum variable:
+
+```cpp
+enum Layout {
+  kNCHW,
+  kNHWC,
+#ifdef PADDLE_WITH_MKLDNN
+  knChw8c
+  ...
+#endif
+};
+```
--- a/develop/doc/design/operator_kernel_type.html
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+  <div class="section" id="design-doc-the-keys-of-operator-kernel-type">
+<span id="design-doc-the-keys-of-operator-kernel-type"></span><h1>Design Doc: The Keys of Operator Kernel Type<a class="headerlink" href="#design-doc-the-keys-of-operator-kernel-type" title="Permalink to this headline">¶</a></h1>
+<div class="section" id="problem">
+<span id="problem"></span><h2>Problem<a class="headerlink" href="#problem" title="Permalink to this headline">¶</a></h2>
+<p>An operator can have different kernel implementations, and each operator will have a map to store the related kernels. Fluid uses <code class="docutils literal"><span class="pre">OpKernelType</span></code> as a key to identify a unique Kernel. Before an operator runs, an certain kernel must be chosen by a key of <code class="docutils literal"><span class="pre">OpKernelType</span></code>. Currently, <code class="docutils literal"><span class="pre">OpKernelType</span></code> is defined as follows:</p>
+<div class="highlight-cpp"><div class="highlight"><pre><span></span><span class="k">struct</span> <span class="n">OpKernelType</span> <span class="p">{</span>
+  <span class="n">platform</span><span class="o">::</span><span class="n">Place</span> <span class="n">place_</span><span class="p">;</span>
+  <span class="n">proto</span><span class="o">::</span><span class="n">DataType</span> <span class="n">data_type_</span><span class="p">;</span>
+<span class="p">};</span>
+</pre></div>
+</div>
+<p>For more details, please refer to <a class="reference external" href="https://github.com/PaddlePaddle/Paddle/blob/2d5ec16bc8a09fb8e0f62c89b116b0cd1d333907/paddle/framework/operator.h#L348-L374">codes</a> in github.</p>
+<p>It contains two keys, <code class="docutils literal"><span class="pre">Place</span></code> and <code class="docutils literal"><span class="pre">DataType</span></code>. And these two keys will be hashed to a unique key to represent a certain type of kernel. However, these two keys are not enough. We need a more complete representation of <code class="docutils literal"><span class="pre">OpKernelType</span></code>.</p>
+<p>We often implement a kernel of an operator with some computing library in certain device(place). Please remind that computing library and device are not one-to-one corresponding. A device can have a lot of computing libraries and a computing library can also support several devices.</p>
+<p>For example, Eigen library can support Nvidia GPU/AMD GPU/CPU. And MKLDNN library can support Intel CPU/Intel FPGA. Both <code class="docutils literal"><span class="pre">Place</span></code> and <code class="docutils literal"><span class="pre">Library</span></code> should be a key of <code class="docutils literal"><span class="pre">OpKernelType</span></code>.</p>
+<p>It&#8217;s obvious that different DataTypes, like fp64/fp32/int8 will have different kernels. But the data layout of a Tensor will also lead to different implementation. Please refer to the batch norm operator <a class="reference external" href="https://github.com/PaddlePaddle/Paddle/blob/a948fac4d0ad7e0412d373b8aabeb711c2899563/paddle/operators/batch_norm_op.cc#L180-L209">kernels</a>. Data Layout should also be taken into consideration.</p>
+</div>
+<div class="section" id="solution">
+<span id="solution"></span><h2>Solution<a class="headerlink" href="#solution" title="Permalink to this headline">¶</a></h2>
+<p>There are four keys to determine a kernel type of an operator: <code class="docutils literal"><span class="pre">Place</span></code>/<code class="docutils literal"><span class="pre">Library</span></code>/<code class="docutils literal"><span class="pre">DataType</span></code>/<code class="docutils literal"><span class="pre">Layout</span></code>.</p>
+<div class="highlight-cpp"><div class="highlight"><pre><span></span><span class="k">struct</span> <span class="n">OpKernelType</span> <span class="p">{</span>
+  <span class="n">platform</span><span class="o">::</span><span class="n">Place</span> <span class="n">place_</span><span class="p">;</span>
+  <span class="n">platform</span><span class="o">::</span><span class="n">Library</span> <span class="n">library_</span><span class="p">;</span>
+  <span class="n">proto</span><span class="o">::</span><span class="n">DataType</span> <span class="n">data_type_</span><span class="p">;</span>
+  <span class="n">framework</span><span class="o">::</span><span class="n">Layout</span> <span class="n">layout_</span><span class="p">;</span>
+<span class="p">};</span>
+</pre></div>
+</div>
+<p>Following is the details:</p>
+<div class="section" id="place">
+<span id="place"></span><h3>Place<a class="headerlink" href="#place" title="Permalink to this headline">¶</a></h3>
+<p><code class="docutils literal"><span class="pre">Place</span></code> is defined as follows:</p>
+<div class="highlight-cpp"><div class="highlight"><pre><span></span><span class="k">typedef</span> <span class="n">boost</span><span class="o">::</span><span class="n">variant</span><span class="o">&lt;</span><span class="n">CUDAPlace</span><span class="p">,</span> <span class="n">ROCmPlace</span><span class="p">,</span> <span class="n">FPGAPlace</span><span class="p">,</span> <span class="n">CPUPlace</span><span class="o">&gt;</span> <span class="n">Place</span><span class="p">;</span>
+</pre></div>
+</div>
+<p><code class="docutils literal"><span class="pre">Place</span></code> is to represent the device memory where data is locating.</p>
+</div>
+<div class="section" id="library">
+<span id="library"></span><h3>Library<a class="headerlink" href="#library" title="Permalink to this headline">¶</a></h3>
+<p>One operator kernel is usually implemented based on one library. <code class="docutils literal"><span class="pre">Library</span></code> is defined as a enum variable:</p>
+<div class="highlight-cpp"><div class="highlight"><pre><span></span><span class="k">enum</span> <span class="n">Library</span> <span class="p">{</span> <span class="n">Plain</span><span class="p">,</span> <span class="n">MKLDNN</span><span class="p">,</span> <span class="n">CUDNN</span> <span class="p">};</span>
+</pre></div>
+</div>
+<p>We use <code class="docutils literal"><span class="pre">Plain</span></code> enumerator to represent default library. Since most operators in Fluid are implemented based on <code class="docutils literal"><span class="pre">Eigen</span></code> library, we take <code class="docutils literal"><span class="pre">Eigen</span></code> library as the <code class="docutils literal"><span class="pre">Plain</span></code> enumerator.
+A library usually has a corresponding <code class="docutils literal"><span class="pre">DeviceContext</span></code> which contains some handles needed by computation. Fluid now have two default DeviceContexts in CPU and CUDA, <code class="docutils literal"><span class="pre">CPUDeviceContext</span></code> and <code class="docutils literal"><span class="pre">CUDADeviceContext</span></code>. <code class="docutils literal"><span class="pre">CPUDeviceContext</span></code> contains a Eigen library handle and <code class="docutils literal"><span class="pre">CDUADeviceContext</span></code> contains a Eigen library handle and cuBLAS handle.</p>
+<p>If we want to support new Library, a new enumerator need to be added to <code class="docutils literal"><span class="pre">Library</span></code> and a new corresponding <code class="docutils literal"><span class="pre">LibraryDeviceContext</span></code> will be created.</p>
+</div>
+<div class="section" id="datatype">
+<span id="datatype"></span><h3>DataType<a class="headerlink" href="#datatype" title="Permalink to this headline">¶</a></h3>
+<p><code class="docutils literal"><span class="pre">DataType</span></code> is defined in <a class="reference external" href="https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/framework/framework.proto">framework.proto</a>. Currently, int32/int64/fp32/fp64 are supported.</p>
+</div>
+<div class="section" id="layout">
+<span id="layout"></span><h3>Layout<a class="headerlink" href="#layout" title="Permalink to this headline">¶</a></h3>
+<p>Actually, a Tensor is a view of a block of memory. Besides a pointer to the memory, we also have to get some other descriptions of this block of memory, such as shape(ddim), stride, and layout.</p>
+<p>Different layout leads to different implementation of operator kernel. There are mainly 4 principles we have to follow to support layout in our fluid framework.</p>
+<ul class="simple">
+<li>We take layout as a data member of Tensor. Layout is actually a enum variable. If fluid is built with MKLDNN, then, the memory format in MKLDNN will be added into this enum variable too.</li>
+<li>Users have to set layout for input data. And some operators like fill_constant/random, also have to set layout of generating data. Of course, we can have some default layout, like NCHW.</li>
+<li>The inference of Layout is at run-time, not compile-time.</li>
+<li>Every operator have to implement different kernels for different layouts. Let&#8217;s take MKLDNN as an example, if we want to implement a MKLDNN convolution operator, we have to realize all the kernels for different layout, list at <a class="reference external" href="http://01org.github.io/mkl-dnn/structmkldnn_1_1memory.html">here</a>. And we will have a special macro to do registering kernels for MKLDNN operators.</li>
+</ul>
+<p><code class="docutils literal"><span class="pre">Layout</span></code> is also defined as a enum variable:</p>
+<div class="highlight-cpp"><div class="highlight"><pre><span></span><span class="k">enum</span> <span class="n">Layout</span> <span class="p">{</span>
+  <span class="n">kNCHW</span><span class="p">,</span>
+  <span class="n">kNHWC</span><span class="p">,</span>
+<span class="cp">#ifdef PADDLE_WITH_MKLDNN</span>
+  <span class="n">knChw8c</span>
+  <span class="p">...</span>
+<span class="cp">#endif</span>
+<span class="p">};</span>
+</pre></div>
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--- a/develop/doc/searchindex.js
+++ b/develop/doc/searchindex.js
--- a/develop/doc_cn/_sources/design/operator_kernel_type.md.txt
+++ b/develop/doc_cn/_sources/design/operator_kernel_type.md.txt
+# Design Doc: The Keys of Operator Kernel Type
+## Problem
+An operator can have different kernel implementations, and each operator will have a map to store the related kernels. Fluid uses `OpKernelType` as a key to identify a unique Kernel. Before an operator runs, an certain kernel must be chosen by a key of `OpKernelType`. Currently, `OpKernelType` is defined as follows:
+
+```cpp
+struct OpKernelType {
+  platform::Place place_;
+  proto::DataType data_type_;
+};
+```
+For more details, please refer to [codes](https://github.com/PaddlePaddle/Paddle/blob/2d5ec16bc8a09fb8e0f62c89b116b0cd1d333907/paddle/framework/operator.h#L348-L374) in github.
+
+It contains two keys, `Place` and `DataType`. And these two keys will be hashed to a unique key to represent a certain type of kernel. However, these two keys are not enough. We need a more complete representation of `OpKernelType`. 
+
+We often implement a kernel of an operator with some computing library in certain device(place). Please remind that computing library and device are not one-to-one corresponding. A device can have a lot of computing libraries and a computing library can also support several devices. 
+
+For example, Eigen library can support Nvidia GPU/AMD GPU/CPU. And MKLDNN library can support Intel CPU/Intel FPGA. Both `Place` and `Library` should be a key of `OpKernelType`.
+
+It's obvious that different DataTypes, like fp64/fp32/int8 will have different kernels. But the data layout of a Tensor will also lead to different implementation. Please refer to the batch norm operator [kernels](https://github.com/PaddlePaddle/Paddle/blob/a948fac4d0ad7e0412d373b8aabeb711c2899563/paddle/operators/batch_norm_op.cc#L180-L209). Data Layout should also be taken into consideration.
+
+## Solution
+
+There are four keys to determine a kernel type of an operator: `Place`/`Library`/`DataType`/`Layout`.
+
+```cpp
+struct OpKernelType {
+  platform::Place place_;
+  platform::Library library_;
+  proto::DataType data_type_;
+  framework::Layout layout_;
+};
+```
+
+Following is the details:
+
+### Place
+
+`Place` is defined as follows:
+
+```cpp
+typedef boost::variant<CUDAPlace, ROCmPlace, FPGAPlace, CPUPlace> Place;
+```
+
+`Place` is to represent the device memory where data is locating.
+
+
+### Library
+
+One operator kernel is usually implemented based on one library. `Library` is defined as a enum variable:
+
+```cpp
+enum Library { Plain, MKLDNN, CUDNN };
+```
+
+We use `Plain` enumerator to represent default library. Since most operators in Fluid are implemented based on `Eigen` library, we take `Eigen` library as the `Plain` enumerator.
+A library usually has a corresponding `DeviceContext` which contains some handles needed by computation. Fluid now have two default DeviceContexts in CPU and CUDA, `CPUDeviceContext` and `CUDADeviceContext`. `CPUDeviceContext` contains a Eigen library handle and `CDUADeviceContext` contains a Eigen library handle and cuBLAS handle.
+
+If we want to support new Library, a new enumerator need to be added to `Library` and a new corresponding `LibraryDeviceContext` will be created.
+
+
+### DataType
+
+
+`DataType` is defined in [framework.proto](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/framework/framework.proto). Currently, int32/int64/fp32/fp64 are supported.
+
+### Layout
+
+Actually, a Tensor is a view of a block of memory. Besides a pointer to the memory, we also have to get some other descriptions of this block of memory, such as shape(ddim), stride, and layout.
+
+Different layout leads to different implementation of operator kernel. There are mainly 4 principles we have to follow to support layout in our fluid framework.
+
+- We take layout as a data member of Tensor. Layout is actually a enum variable. If fluid is built with MKLDNN, then, the memory format in MKLDNN will be added into this enum variable too.
+
+- Users have to set layout for input data. And some operators like fill_constant/random, also have to set layout of generating data. Of course, we can have some default layout, like NCHW.
+
+- The inference of Layout is at run-time, not compile-time.
+
+- Every operator have to implement different kernels for different layouts. Let's take MKLDNN as an example, if we want to implement a MKLDNN convolution operator, we have to realize all the kernels for different layout, list at [here](http://01org.github.io/mkl-dnn/structmkldnn_1_1memory.html). And we will have a special macro to do registering kernels for MKLDNN operators.
+
+`Layout` is also defined as a enum variable:
+
+```cpp
+enum Layout {
+  kNCHW,
+  kNHWC,
+#ifdef PADDLE_WITH_MKLDNN
+  knChw8c
+  ...
+#endif
+};
+```
--- a/develop/doc_cn/design/operator_kernel_type.html
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+  <div class="section" id="design-doc-the-keys-of-operator-kernel-type">
+<span id="design-doc-the-keys-of-operator-kernel-type"></span><h1>Design Doc: The Keys of Operator Kernel Type<a class="headerlink" href="#design-doc-the-keys-of-operator-kernel-type" title="永久链接至标题">¶</a></h1>
+<div class="section" id="problem">
+<span id="problem"></span><h2>Problem<a class="headerlink" href="#problem" title="永久链接至标题">¶</a></h2>
+<p>An operator can have different kernel implementations, and each operator will have a map to store the related kernels. Fluid uses <code class="docutils literal"><span class="pre">OpKernelType</span></code> as a key to identify a unique Kernel. Before an operator runs, an certain kernel must be chosen by a key of <code class="docutils literal"><span class="pre">OpKernelType</span></code>. Currently, <code class="docutils literal"><span class="pre">OpKernelType</span></code> is defined as follows:</p>
+<div class="highlight-cpp"><div class="highlight"><pre><span></span><span class="k">struct</span> <span class="n">OpKernelType</span> <span class="p">{</span>
+  <span class="n">platform</span><span class="o">::</span><span class="n">Place</span> <span class="n">place_</span><span class="p">;</span>
+  <span class="n">proto</span><span class="o">::</span><span class="n">DataType</span> <span class="n">data_type_</span><span class="p">;</span>
+<span class="p">};</span>
+</pre></div>
+</div>
+<p>For more details, please refer to <a class="reference external" href="https://github.com/PaddlePaddle/Paddle/blob/2d5ec16bc8a09fb8e0f62c89b116b0cd1d333907/paddle/framework/operator.h#L348-L374">codes</a> in github.</p>
+<p>It contains two keys, <code class="docutils literal"><span class="pre">Place</span></code> and <code class="docutils literal"><span class="pre">DataType</span></code>. And these two keys will be hashed to a unique key to represent a certain type of kernel. However, these two keys are not enough. We need a more complete representation of <code class="docutils literal"><span class="pre">OpKernelType</span></code>.</p>
+<p>We often implement a kernel of an operator with some computing library in certain device(place). Please remind that computing library and device are not one-to-one corresponding. A device can have a lot of computing libraries and a computing library can also support several devices.</p>
+<p>For example, Eigen library can support Nvidia GPU/AMD GPU/CPU. And MKLDNN library can support Intel CPU/Intel FPGA. Both <code class="docutils literal"><span class="pre">Place</span></code> and <code class="docutils literal"><span class="pre">Library</span></code> should be a key of <code class="docutils literal"><span class="pre">OpKernelType</span></code>.</p>
+<p>It&#8217;s obvious that different DataTypes, like fp64/fp32/int8 will have different kernels. But the data layout of a Tensor will also lead to different implementation. Please refer to the batch norm operator <a class="reference external" href="https://github.com/PaddlePaddle/Paddle/blob/a948fac4d0ad7e0412d373b8aabeb711c2899563/paddle/operators/batch_norm_op.cc#L180-L209">kernels</a>. Data Layout should also be taken into consideration.</p>
+</div>
+<div class="section" id="solution">
+<span id="solution"></span><h2>Solution<a class="headerlink" href="#solution" title="永久链接至标题">¶</a></h2>
+<p>There are four keys to determine a kernel type of an operator: <code class="docutils literal"><span class="pre">Place</span></code>/<code class="docutils literal"><span class="pre">Library</span></code>/<code class="docutils literal"><span class="pre">DataType</span></code>/<code class="docutils literal"><span class="pre">Layout</span></code>.</p>
+<div class="highlight-cpp"><div class="highlight"><pre><span></span><span class="k">struct</span> <span class="n">OpKernelType</span> <span class="p">{</span>
+  <span class="n">platform</span><span class="o">::</span><span class="n">Place</span> <span class="n">place_</span><span class="p">;</span>
+  <span class="n">platform</span><span class="o">::</span><span class="n">Library</span> <span class="n">library_</span><span class="p">;</span>
+  <span class="n">proto</span><span class="o">::</span><span class="n">DataType</span> <span class="n">data_type_</span><span class="p">;</span>
+  <span class="n">framework</span><span class="o">::</span><span class="n">Layout</span> <span class="n">layout_</span><span class="p">;</span>
+<span class="p">};</span>
+</pre></div>
+</div>
+<p>Following is the details:</p>
+<div class="section" id="place">
+<span id="place"></span><h3>Place<a class="headerlink" href="#place" title="永久链接至标题">¶</a></h3>
+<p><code class="docutils literal"><span class="pre">Place</span></code> is defined as follows:</p>
+<div class="highlight-cpp"><div class="highlight"><pre><span></span><span class="k">typedef</span> <span class="n">boost</span><span class="o">::</span><span class="n">variant</span><span class="o">&lt;</span><span class="n">CUDAPlace</span><span class="p">,</span> <span class="n">ROCmPlace</span><span class="p">,</span> <span class="n">FPGAPlace</span><span class="p">,</span> <span class="n">CPUPlace</span><span class="o">&gt;</span> <span class="n">Place</span><span class="p">;</span>
+</pre></div>
+</div>
+<p><code class="docutils literal"><span class="pre">Place</span></code> is to represent the device memory where data is locating.</p>
+</div>
+<div class="section" id="library">
+<span id="library"></span><h3>Library<a class="headerlink" href="#library" title="永久链接至标题">¶</a></h3>
+<p>One operator kernel is usually implemented based on one library. <code class="docutils literal"><span class="pre">Library</span></code> is defined as a enum variable:</p>
+<div class="highlight-cpp"><div class="highlight"><pre><span></span><span class="k">enum</span> <span class="n">Library</span> <span class="p">{</span> <span class="n">Plain</span><span class="p">,</span> <span class="n">MKLDNN</span><span class="p">,</span> <span class="n">CUDNN</span> <span class="p">};</span>
+</pre></div>
+</div>
+<p>We use <code class="docutils literal"><span class="pre">Plain</span></code> enumerator to represent default library. Since most operators in Fluid are implemented based on <code class="docutils literal"><span class="pre">Eigen</span></code> library, we take <code class="docutils literal"><span class="pre">Eigen</span></code> library as the <code class="docutils literal"><span class="pre">Plain</span></code> enumerator.
+A library usually has a corresponding <code class="docutils literal"><span class="pre">DeviceContext</span></code> which contains some handles needed by computation. Fluid now have two default DeviceContexts in CPU and CUDA, <code class="docutils literal"><span class="pre">CPUDeviceContext</span></code> and <code class="docutils literal"><span class="pre">CUDADeviceContext</span></code>. <code class="docutils literal"><span class="pre">CPUDeviceContext</span></code> contains a Eigen library handle and <code class="docutils literal"><span class="pre">CDUADeviceContext</span></code> contains a Eigen library handle and cuBLAS handle.</p>
+<p>If we want to support new Library, a new enumerator need to be added to <code class="docutils literal"><span class="pre">Library</span></code> and a new corresponding <code class="docutils literal"><span class="pre">LibraryDeviceContext</span></code> will be created.</p>
+</div>
+<div class="section" id="datatype">
+<span id="datatype"></span><h3>DataType<a class="headerlink" href="#datatype" title="永久链接至标题">¶</a></h3>
+<p><code class="docutils literal"><span class="pre">DataType</span></code> is defined in <a class="reference external" href="https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/framework/framework.proto">framework.proto</a>. Currently, int32/int64/fp32/fp64 are supported.</p>
+</div>
+<div class="section" id="layout">
+<span id="layout"></span><h3>Layout<a class="headerlink" href="#layout" title="永久链接至标题">¶</a></h3>
+<p>Actually, a Tensor is a view of a block of memory. Besides a pointer to the memory, we also have to get some other descriptions of this block of memory, such as shape(ddim), stride, and layout.</p>
+<p>Different layout leads to different implementation of operator kernel. There are mainly 4 principles we have to follow to support layout in our fluid framework.</p>
+<ul class="simple">
+<li>We take layout as a data member of Tensor. Layout is actually a enum variable. If fluid is built with MKLDNN, then, the memory format in MKLDNN will be added into this enum variable too.</li>
+<li>Users have to set layout for input data. And some operators like fill_constant/random, also have to set layout of generating data. Of course, we can have some default layout, like NCHW.</li>
+<li>The inference of Layout is at run-time, not compile-time.</li>
+<li>Every operator have to implement different kernels for different layouts. Let&#8217;s take MKLDNN as an example, if we want to implement a MKLDNN convolution operator, we have to realize all the kernels for different layout, list at <a class="reference external" href="http://01org.github.io/mkl-dnn/structmkldnn_1_1memory.html">here</a>. And we will have a special macro to do registering kernels for MKLDNN operators.</li>
+</ul>
+<p><code class="docutils literal"><span class="pre">Layout</span></code> is also defined as a enum variable:</p>
+<div class="highlight-cpp"><div class="highlight"><pre><span></span><span class="k">enum</span> <span class="n">Layout</span> <span class="p">{</span>
+  <span class="n">kNCHW</span><span class="p">,</span>
+  <span class="n">kNHWC</span><span class="p">,</span>
+<span class="cp">#ifdef PADDLE_WITH_MKLDNN</span>
+  <span class="n">knChw8c</span>
+  <span class="p">...</span>
+<span class="cp">#endif</span>
+<span class="p">};</span>
+</pre></div>
+</div>
+</div>
+</div>
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