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# 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
};
```
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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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# 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
};
```
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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>
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