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# Design Doc: Model Format
## Motivation
The model is the output of training process. One complete model consists of two parts, namely, the **topology** and the **parameters**. To support industrial deployment, we need to make the model format must be self-completed and do not expose any training source code.
As a result, In PaddlePaddle, the **topology** represents as a [ProgramDesc](https://github.com/PaddlePaddle/Paddle/blob/1c0a4c901c9fc881d120249c703b15d1c50dae7d/doc/design/program.md), which describes the model structure. The **parameters** contain all the trainable weights in the model, we must support large size parameter, and efficient serialization/deserialization.
## Implementation
The topology is saved as a plain text, in detail, a self-contain protobuf file.
The parameters are saved as a binary file. As we all know, the protobuf message has the limits of [64M size](https://developers.google.com/protocol-buffers/docs/reference/cpp/google.protobuf.io.coded_stream#CodedInputStream.SetTotalBytesLimit.details). We do a (benchmark experiment)[https://github.com/PaddlePaddle/Paddle/pull/4610], its result shows protobuf is not fit in this scene.
As a result, we design a particular format for tensor serialization. By default, arbitrary tensor in Paddle is a [LoDTensor](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/framework/lod_tensor.md), and has a description information proto of (LoDTensorDesc)[https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/framework/framework.proto#L99]. We save the DescProto as the byte string header, it contains the necessary information, such as the `dims`, the `name` of the tensor, and the `LoD` information in [LoDTensor](https://github.com/PaddlePaddle/Paddle/blob/1c0a4c901c9fc881d120249c703b15d1c50dae7d/paddle/framework/lod_tensor.md). Tensor stores value in a continuous memory buffer, for speed we dump the raw memory to disk and save it as the byte string content. So, the binary format of one tensor is,
|HeaderLength|ContentLength|**LoDTensorDesc**|**TensorValue**|
In detail, tensor's byte view as the table shows. Note that all the signed value written in little-endian.
```text
[offset] [type] [description]
0004 4 bytes integer HeaderLength, the length of LoDTensorDesc
0008 4 bytes integer ContentLength, the length of LodTensor Buffer
0009 1 bytes char TensorDesc
00010 1 bytes char TensorDesc
...
00100 1 bytes char TensorValue
00101 1 bytes char TensorValue
00102 1 bytes char TensorValue ..
...
```
## Summary
We introduce the model format, the `ProgramDesc` describe the **topology**, and a bunch of particular format binary tensors describes the **parameters**.
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<li>Design Doc: Model Format</li>
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<div class="section" id="design-doc-model-format">
<span id="design-doc-model-format"></span><h1>Design Doc: Model Format<a class="headerlink" href="#design-doc-model-format" title="Permalink to this headline"></a></h1>
<div class="section" id="motivation">
<span id="motivation"></span><h2>Motivation<a class="headerlink" href="#motivation" title="Permalink to this headline"></a></h2>
<p>The model is the output of training process. One complete model consists of two parts, namely, the <strong>topology</strong> and the <strong>parameters</strong>. To support industrial deployment, we need to make the model format must be self-completed and do not expose any training source code.</p>
<p>As a result, In PaddlePaddle, the <strong>topology</strong> represents as a <a class="reference external" href="https://github.com/PaddlePaddle/Paddle/blob/1c0a4c901c9fc881d120249c703b15d1c50dae7d/doc/design/program.md">ProgramDesc</a>, which describes the model structure. The <strong>parameters</strong> contain all the trainable weights in the model, we must support large size parameter, and efficient serialization/deserialization.</p>
</div>
<div class="section" id="implementation">
<span id="implementation"></span><h2>Implementation<a class="headerlink" href="#implementation" title="Permalink to this headline"></a></h2>
<p>The topology is saved as a plain text, in detail, a self-contain protobuf file.</p>
<p>The parameters are saved as a binary file. As we all know, the protobuf message has the limits of <a class="reference external" href="https://developers.google.com/protocol-buffers/docs/reference/cpp/google.protobuf.io.coded_stream#CodedInputStream.SetTotalBytesLimit.details">64M size</a>. We do a (benchmark experiment)[https://github.com/PaddlePaddle/Paddle/pull/4610], its result shows protobuf is not fit in this scene.</p>
<p>As a result, we design a particular format for tensor serialization. By default, arbitrary tensor in Paddle is a <a class="reference external" href="https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/framework/lod_tensor.md">LoDTensor</a>, and has a description information proto of (LoDTensorDesc)[https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/framework/framework.proto#L99]. We save the DescProto as the byte string header, it contains the necessary information, such as the <code class="docutils literal"><span class="pre">dims</span></code>, the <code class="docutils literal"><span class="pre">name</span></code> of the tensor, and the <code class="docutils literal"><span class="pre">LoD</span></code> information in <a class="reference external" href="https://github.com/PaddlePaddle/Paddle/blob/1c0a4c901c9fc881d120249c703b15d1c50dae7d/paddle/framework/lod_tensor.md">LoDTensor</a>. Tensor stores value in a continuous memory buffer, for speed we dump the raw memory to disk and save it as the byte string content. So, the binary format of one tensor is,</p>
<p>|HeaderLength|ContentLength|<strong>LoDTensorDesc</strong>|<strong>TensorValue</strong>|</p>
<p>In detail, tensor&#8217;s byte view as the table shows. Note that all the signed value written in little-endian.</p>
<div class="highlight-text"><div class="highlight"><pre><span></span>[offset] [type] [description]
0004 4 bytes integer HeaderLength, the length of LoDTensorDesc
0008 4 bytes integer ContentLength, the length of LodTensor Buffer
0009 1 bytes char TensorDesc
00010 1 bytes char TensorDesc
...
00100 1 bytes char TensorValue
00101 1 bytes char TensorValue
00102 1 bytes char TensorValue ..
...
</pre></div>
</div>
</div>
<div class="section" id="summary">
<span id="summary"></span><h2>Summary<a class="headerlink" href="#summary" title="Permalink to this headline"></a></h2>
<p>We introduce the model format, the <code class="docutils literal"><span class="pre">ProgramDesc</span></code> describe the <strong>topology</strong>, and a bunch of particular format binary tensors describes the <strong>parameters</strong>.</p>
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# Design Doc: Model Format
## Motivation
The model is the output of training process. One complete model consists of two parts, namely, the **topology** and the **parameters**. To support industrial deployment, we need to make the model format must be self-completed and do not expose any training source code.
As a result, In PaddlePaddle, the **topology** represents as a [ProgramDesc](https://github.com/PaddlePaddle/Paddle/blob/1c0a4c901c9fc881d120249c703b15d1c50dae7d/doc/design/program.md), which describes the model structure. The **parameters** contain all the trainable weights in the model, we must support large size parameter, and efficient serialization/deserialization.
## Implementation
The topology is saved as a plain text, in detail, a self-contain protobuf file.
The parameters are saved as a binary file. As we all know, the protobuf message has the limits of [64M size](https://developers.google.com/protocol-buffers/docs/reference/cpp/google.protobuf.io.coded_stream#CodedInputStream.SetTotalBytesLimit.details). We do a (benchmark experiment)[https://github.com/PaddlePaddle/Paddle/pull/4610], its result shows protobuf is not fit in this scene.
As a result, we design a particular format for tensor serialization. By default, arbitrary tensor in Paddle is a [LoDTensor](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/framework/lod_tensor.md), and has a description information proto of (LoDTensorDesc)[https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/framework/framework.proto#L99]. We save the DescProto as the byte string header, it contains the necessary information, such as the `dims`, the `name` of the tensor, and the `LoD` information in [LoDTensor](https://github.com/PaddlePaddle/Paddle/blob/1c0a4c901c9fc881d120249c703b15d1c50dae7d/paddle/framework/lod_tensor.md). Tensor stores value in a continuous memory buffer, for speed we dump the raw memory to disk and save it as the byte string content. So, the binary format of one tensor is,
|HeaderLength|ContentLength|**LoDTensorDesc**|**TensorValue**|
In detail, tensor's byte view as the table shows. Note that all the signed value written in little-endian.
```text
[offset] [type] [description]
0004 4 bytes integer HeaderLength, the length of LoDTensorDesc
0008 4 bytes integer ContentLength, the length of LodTensor Buffer
0009 1 bytes char TensorDesc
00010 1 bytes char TensorDesc
...
00100 1 bytes char TensorValue
00101 1 bytes char TensorValue
00102 1 bytes char TensorValue ..
...
```
## Summary
We introduce the model format, the `ProgramDesc` describe the **topology**, and a bunch of particular format binary tensors describes the **parameters**.
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<div class="section" id="design-doc-model-format">
<span id="design-doc-model-format"></span><h1>Design Doc: Model Format<a class="headerlink" href="#design-doc-model-format" title="永久链接至标题"></a></h1>
<div class="section" id="motivation">
<span id="motivation"></span><h2>Motivation<a class="headerlink" href="#motivation" title="永久链接至标题"></a></h2>
<p>The model is the output of training process. One complete model consists of two parts, namely, the <strong>topology</strong> and the <strong>parameters</strong>. To support industrial deployment, we need to make the model format must be self-completed and do not expose any training source code.</p>
<p>As a result, In PaddlePaddle, the <strong>topology</strong> represents as a <a class="reference external" href="https://github.com/PaddlePaddle/Paddle/blob/1c0a4c901c9fc881d120249c703b15d1c50dae7d/doc/design/program.md">ProgramDesc</a>, which describes the model structure. The <strong>parameters</strong> contain all the trainable weights in the model, we must support large size parameter, and efficient serialization/deserialization.</p>
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<div class="section" id="implementation">
<span id="implementation"></span><h2>Implementation<a class="headerlink" href="#implementation" title="永久链接至标题"></a></h2>
<p>The topology is saved as a plain text, in detail, a self-contain protobuf file.</p>
<p>The parameters are saved as a binary file. As we all know, the protobuf message has the limits of <a class="reference external" href="https://developers.google.com/protocol-buffers/docs/reference/cpp/google.protobuf.io.coded_stream#CodedInputStream.SetTotalBytesLimit.details">64M size</a>. We do a (benchmark experiment)[https://github.com/PaddlePaddle/Paddle/pull/4610], its result shows protobuf is not fit in this scene.</p>
<p>As a result, we design a particular format for tensor serialization. By default, arbitrary tensor in Paddle is a <a class="reference external" href="https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/framework/lod_tensor.md">LoDTensor</a>, and has a description information proto of (LoDTensorDesc)[https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/framework/framework.proto#L99]. We save the DescProto as the byte string header, it contains the necessary information, such as the <code class="docutils literal"><span class="pre">dims</span></code>, the <code class="docutils literal"><span class="pre">name</span></code> of the tensor, and the <code class="docutils literal"><span class="pre">LoD</span></code> information in <a class="reference external" href="https://github.com/PaddlePaddle/Paddle/blob/1c0a4c901c9fc881d120249c703b15d1c50dae7d/paddle/framework/lod_tensor.md">LoDTensor</a>. Tensor stores value in a continuous memory buffer, for speed we dump the raw memory to disk and save it as the byte string content. So, the binary format of one tensor is,</p>
<p>|HeaderLength|ContentLength|<strong>LoDTensorDesc</strong>|<strong>TensorValue</strong>|</p>
<p>In detail, tensor&#8217;s byte view as the table shows. Note that all the signed value written in little-endian.</p>
<div class="highlight-text"><div class="highlight"><pre><span></span>[offset] [type] [description]
0004 4 bytes integer HeaderLength, the length of LoDTensorDesc
0008 4 bytes integer ContentLength, the length of LodTensor Buffer
0009 1 bytes char TensorDesc
00010 1 bytes char TensorDesc
...
00100 1 bytes char TensorValue
00101 1 bytes char TensorValue
00102 1 bytes char TensorValue ..
...
</pre></div>
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<div class="section" id="summary">
<span id="summary"></span><h2>Summary<a class="headerlink" href="#summary" title="永久链接至标题"></a></h2>
<p>We introduce the model format, the <code class="docutils literal"><span class="pre">ProgramDesc</span></code> describe the <strong>topology</strong>, and a bunch of particular format binary tensors describes the <strong>parameters</strong>.</p>
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