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develop/doc/_sources/design/model_format.md.txt

@@ -12,24 +12,22 @@ 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 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, 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, 
-
-|HeaderLength|ContentLength|**LoDTensorDesc**|**TensorValue**|
+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`, 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, 
 
 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] 
-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              ..
-...
-```
+|field name  | type | description |
+| --- | --- | --- |
+| version | uint32_t | Version of saved file. Always 0 now. |
+| tensor desc length | uint32_t | TensorDesc(Protobuf message) length in bytes. |
+| tensor desc | void* | TensorDesc protobuf binary message |
+| tensor data | void* | Tensor's data in binary format. The length of `tensor_data` is decided by `TensorDesc.dims()` and `TensorDesc.data_type()` |
+| lod_level | uint64_t | Level of LoD |
+| length of lod[0] | uint64_t | [Optional] length of lod[0] in bytes. |
+| data of lod[0] | uint64_t*  | [Optional] lod[0].data() |
+| ... | ... | ... |
+
+
 
 ## Summary
 

develop/doc/design/model_format.html

@@ -192,21 +192,18 @@
 <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 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 <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 have done a <a class="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 <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 <a class="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 <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>. 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>|HeaderLength|ContentLength|<strong>LoDTensorDesc</strong>|<strong>TensorValue</strong>|</p>
+<p>As a result, we design a particular format for tensor serialization. By default, an 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 <a class="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 <code class="docutils literal"><span class="pre">dims</span></code>, 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>. 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>The table below shows a tensor&#8217;s byte view in detail. Note that all the signed values are written in the little-endian format.</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>
+<p>|field name  | type | description |
+| &#8212; | &#8212; | &#8212; |
+| version | uint32_t | Version of saved file. Always 0 now. |
+| tensor desc length | uint32_t | TensorDesc(Protobuf message) length in bytes. |
+| tensor desc | void* | TensorDesc protobuf binary message |
+| tensor data | void* | Tensor&#8217;s data in binary format. The length of <code class="docutils literal"><span class="pre">tensor_data</span></code> is decided by <code class="docutils literal"><span class="pre">TensorDesc.dims()</span></code> and <code class="docutils literal"><span class="pre">TensorDesc.data_type()</span></code> |
+| lod_level | uint64_t | Level of LoD |
+| length of lod[0] | uint64_t | [Optional] length of lod[0] in bytes. |
+| data of lod[0] | uint64_t*  | [Optional] lod[0].data() |
+| ... | ... | ... |</p>
 </div>
 <div class="section" id="summary">
 <span id="summary"></span><h2>Summary<a class="headerlink" href="#summary" title="Permalink to this headline">¶</a></h2>

develop/doc_cn/_sources/design/model_format.md.txt

@@ -12,24 +12,22 @@ 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 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, 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, 
-
-|HeaderLength|ContentLength|**LoDTensorDesc**|**TensorValue**|
+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`, 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, 
 
 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] 
-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              ..
-...
-```
+|field name  | type | description |
+| --- | --- | --- |
+| version | uint32_t | Version of saved file. Always 0 now. |
+| tensor desc length | uint32_t | TensorDesc(Protobuf message) length in bytes. |
+| tensor desc | void* | TensorDesc protobuf binary message |
+| tensor data | void* | Tensor's data in binary format. The length of `tensor_data` is decided by `TensorDesc.dims()` and `TensorDesc.data_type()` |
+| lod_level | uint64_t | Level of LoD |
+| length of lod[0] | uint64_t | [Optional] length of lod[0] in bytes. |
+| data of lod[0] | uint64_t*  | [Optional] lod[0].data() |
+| ... | ... | ... |
+
+
 
 ## Summary
 

develop/doc_cn/design/model_format.html

@@ -206,21 +206,18 @@
 <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 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 <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 have done a <a class="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 <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 <a class="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 <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>. 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>|HeaderLength|ContentLength|<strong>LoDTensorDesc</strong>|<strong>TensorValue</strong>|</p>
+<p>As a result, we design a particular format for tensor serialization. By default, an 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 <a class="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 <code class="docutils literal"><span class="pre">dims</span></code>, 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>. 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>The table below shows a tensor&#8217;s byte view in detail. Note that all the signed values are written in the little-endian format.</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>
+<p>|field name  | type | description |
+| &#8212; | &#8212; | &#8212; |
+| version | uint32_t | Version of saved file. Always 0 now. |
+| tensor desc length | uint32_t | TensorDesc(Protobuf message) length in bytes. |
+| tensor desc | void* | TensorDesc protobuf binary message |
+| tensor data | void* | Tensor&#8217;s data in binary format. The length of <code class="docutils literal"><span class="pre">tensor_data</span></code> is decided by <code class="docutils literal"><span class="pre">TensorDesc.dims()</span></code> and <code class="docutils literal"><span class="pre">TensorDesc.data_type()</span></code> |
+| lod_level | uint64_t | Level of LoD |
+| length of lod[0] | uint64_t | [Optional] length of lod[0] in bytes. |
+| data of lod[0] | uint64_t*  | [Optional] lod[0].data() |
+| ... | ... | ... |</p>
 </div>
 <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>