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.
A model is an output of the training process. One complete model consists of two parts, the **topology** and the **parameters**. In order to support industrial deployment, the model format must be self-complete and must 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.
As a result, In PaddlePaddle, the **topology** is represented 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 parameters and efficient serialization/deserialization of parameters.
## Implementation
The topology is saved as a plain text, in detail, a self-contain protobuf file.
The topology is saved as a plain text in a detailed 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.
The parameters are saved as a binary file. As we all know, the protobuf message has a limit of [64M size](https://developers.google.com/protocol-buffers/docs/reference/cpp/google.protobuf.io.coded_stream#CodedInputStream.SetTotalBytesLimit.details). We have done a [benchmark experiment](https://github.com/PaddlePaddle/Paddle/pull/4610), which shows that protobuf is not fit for the task.
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,
As a result, we design a particular format for tensor serialization. By default, an 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 all 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). A tensor stores values 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,
In detail, tensor's byte view as the table shows. Note that all the signed value written in little-endian.
The table below shows a tensor's byte view in detail. Note that all the signed values are written in the little-endian format.
```text
[offset] [type] [description]
...
...
@@ -33,4 +33,6 @@ In detail, tensor's byte view as the table shows. Note that all the signed valu
## Summary
We introduce the model format, the `ProgramDesc` describe the **topology**, and a bunch of particular format binary tensors describes the **parameters**.
- We introduce a model format.
- The `ProgramDesc` describe the model **topology**.
- A bunch of specified format binary tensors describe the **parameters**.
<spanid="design-doc-model-format"></span><h1>Design Doc: Model Format<aclass="headerlink"href="#design-doc-model-format"title="Permalink to this headline">¶</a></h1>
<divclass="section"id="motivation">
<spanid="motivation"></span><h2>Motivation<aclass="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 <aclass="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>
<p>A model is an output of the training process. One complete model consists of two parts, the <strong>topology</strong> and the <strong>parameters</strong>. In order to support industrial deployment, the model format must be self-complete and must not expose any training source code.</p>
<p>As a result, In PaddlePaddle, the <strong>topology</strong>is represented as a <aclass="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 parameters and efficient serialization/deserialization of parameters.</p>
</div>
<divclass="section"id="implementation">
<spanid="implementation"></span><h2>Implementation<aclass="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 <aclass="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 <aclass="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 <codeclass="docutils literal"><spanclass="pre">dims</span></code>, the <codeclass="docutils literal"><spanclass="pre">name</span></code> of the tensor, and the <codeclass="docutils literal"><spanclass="pre">LoD</span></code> information in <aclass="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>The topology is saved as a plain text in a detailed self-contain protobuf file.</p>
<p>The parameters are saved as a binary file. As we all know, the protobuf message has a limit of <aclass="reference external"href="https://developers.google.com/protocol-buffers/docs/reference/cpp/google.protobuf.io.coded_stream#CodedInputStream.SetTotalBytesLimit.details">64M size</a>. We have done a <aclass="reference external"href="https://github.com/PaddlePaddle/Paddle/pull/4610">benchmark experiment</a>, which shows that protobuf is not fit for the task.</p>
<p>As a result, we design a particular format for tensor serialization. By default, an arbitrary tensor in Paddle is a <aclass="reference external"href="https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/framework/lod_tensor.md">LoDTensor</a>, and has a description information proto of <aclass="reference external"href="https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/framework/framework.proto#L99">LoDTensorDesc</a>. We save the DescProto as the byte string header. It contains all the necessary information, such as the <codeclass="docutils literal"><spanclass="pre">dims</span></code>, the <codeclass="docutils literal"><spanclass="pre">name</span></code> of the tensor, and the <codeclass="docutils literal"><spanclass="pre">LoD</span></code> information in <aclass="reference external"href="https://github.com/PaddlePaddle/Paddle/blob/1c0a4c901c9fc881d120249c703b15d1c50dae7d/paddle/framework/lod_tensor.md">LoDTensor</a>. A tensor stores values 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>
0004 4 bytes integer HeaderLength, the length of LoDTensorDesc
0008 4 bytes integer ContentLength, the length of LodTensor Buffer
...
...
@@ -210,7 +210,11 @@
</div>
<divclass="section"id="summary">
<spanid="summary"></span><h2>Summary<aclass="headerlink"href="#summary"title="Permalink to this headline">¶</a></h2>
<p>We introduce the model format, the <codeclass="docutils literal"><spanclass="pre">ProgramDesc</span></code> describe the <strong>topology</strong>, and a bunch of particular format binary tensors describes the <strong>parameters</strong>.</p>
<ulclass="simple">
<li>We introduce a model format.</li>
<li>The <codeclass="docutils literal"><spanclass="pre">ProgramDesc</span></code> describe the model <strong>topology</strong>.</li>
<li>A bunch of specified format binary tensors describe the <strong>parameters</strong>.</li>
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.
A model is an output of the training process. One complete model consists of two parts, the **topology** and the **parameters**. In order to support industrial deployment, the model format must be self-complete and must 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.
As a result, In PaddlePaddle, the **topology** is represented 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 parameters and efficient serialization/deserialization of parameters.
## Implementation
The topology is saved as a plain text, in detail, a self-contain protobuf file.
The topology is saved as a plain text in a detailed 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.
The parameters are saved as a binary file. As we all know, the protobuf message has a limit of [64M size](https://developers.google.com/protocol-buffers/docs/reference/cpp/google.protobuf.io.coded_stream#CodedInputStream.SetTotalBytesLimit.details). We have done a [benchmark experiment](https://github.com/PaddlePaddle/Paddle/pull/4610), which shows that protobuf is not fit for the task.
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,
As a result, we design a particular format for tensor serialization. By default, an 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 all 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). A tensor stores values 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,
In detail, tensor's byte view as the table shows. Note that all the signed value written in little-endian.
The table below shows a tensor's byte view in detail. Note that all the signed values are written in the little-endian format.
```text
[offset] [type] [description]
...
...
@@ -33,4 +33,6 @@ In detail, tensor's byte view as the table shows. Note that all the signed valu
## Summary
We introduce the model format, the `ProgramDesc` describe the **topology**, and a bunch of particular format binary tensors describes the **parameters**.
- We introduce a model format.
- The `ProgramDesc` describe the model **topology**.
- A bunch of specified format binary tensors describe the **parameters**.
<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 <aclass="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>
<p>A model is an output of the training process. One complete model consists of two parts, the <strong>topology</strong> and the <strong>parameters</strong>. In order to support industrial deployment, the model format must be self-complete and must not expose any training source code.</p>
<p>As a result, In PaddlePaddle, the <strong>topology</strong>is represented as a <aclass="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 parameters and efficient serialization/deserialization of parameters.</p>
<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 <aclass="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 <aclass="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 <codeclass="docutils literal"><spanclass="pre">dims</span></code>, the <codeclass="docutils literal"><spanclass="pre">name</span></code> of the tensor, and the <codeclass="docutils literal"><spanclass="pre">LoD</span></code> information in <aclass="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>The topology is saved as a plain text in a detailed self-contain protobuf file.</p>
<p>The parameters are saved as a binary file. As we all know, the protobuf message has a limit of <aclass="reference external"href="https://developers.google.com/protocol-buffers/docs/reference/cpp/google.protobuf.io.coded_stream#CodedInputStream.SetTotalBytesLimit.details">64M size</a>. We have done a <aclass="reference external"href="https://github.com/PaddlePaddle/Paddle/pull/4610">benchmark experiment</a>, which shows that protobuf is not fit for the task.</p>
<p>As a result, we design a particular format for tensor serialization. By default, an arbitrary tensor in Paddle is a <aclass="reference external"href="https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/framework/lod_tensor.md">LoDTensor</a>, and has a description information proto of <aclass="reference external"href="https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/framework/framework.proto#L99">LoDTensorDesc</a>. We save the DescProto as the byte string header. It contains all the necessary information, such as the <codeclass="docutils literal"><spanclass="pre">dims</span></code>, the <codeclass="docutils literal"><spanclass="pre">name</span></code> of the tensor, and the <codeclass="docutils literal"><spanclass="pre">LoD</span></code> information in <aclass="reference external"href="https://github.com/PaddlePaddle/Paddle/blob/1c0a4c901c9fc881d120249c703b15d1c50dae7d/paddle/framework/lod_tensor.md">LoDTensor</a>. A tensor stores values 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>We introduce the model format, the <codeclass="docutils literal"><spanclass="pre">ProgramDesc</span></code> describe the <strong>topology</strong>, and a bunch of particular format binary tensors describes the <strong>parameters</strong>.</p>
<ulclass="simple">
<li>We introduce a model format.</li>
<li>The <codeclass="docutils literal"><spanclass="pre">ProgramDesc</span></code> describe the model <strong>topology</strong>.</li>
<li>A bunch of specified format binary tensors describe the <strong>parameters</strong>.</li>
