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. We have done a benchmark experiment, 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, and has a description information proto of LoDTensorDesc. 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. 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, and has a description information proto of LoDTensorDesc. 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. 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.
-[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() |
+| ... | ... | ... |
IfElse Operator","Design Doc: InferVarType","Design Doc: Model Format","Paddle\u591a\u8bed\u8a00\u63a5\u53e3\u5b9e\u73b0","C-API \u6a21\u578b\u63a8\u65ad\u5b9e\u73b0\u6587\u6863","RNNOp design","Optimizer Design","Design Doc: The C++ Class Parameters","Design Doc: PaddlePaddle Programs","Prune","Design Doc: Python API","Python Data Reader Design Doc","Design Doc: Distributed Training Architecture","Design Doc: Operation Graph Based Parameter Server","Design Doc: Session","Design Doc: Refactorization Overview","Design Doc: Gradient Operators Registration","Regularization in PaddlePaddle","PaddlePaddle\u53d1\u884c\u89c4\u8303","Design of Scope in Paddle","Design Doc: Selected Rows","Interaction between C++ and Python","Design for TensorArray","Background","Simple Linear Regression","Installing from Sources","PaddlePaddle in Docker Containers","Install and Build","GET STARTED","RNN Models","RNN Configuration","Build PaddlePaddle from Source Code and Run Unit Test","Contribute Code","Write New Layers","How to write a new operator","How to use Eigen in Paddle","HOW TO","Tune GPU Performance","PaddlePaddle Distributed Training","Argument Outline","Detail Description","Set Command-line Parameters","Use Case","Distributed PaddlePaddle Training on AWS with Kubernetes","Paddle On Kubernetes","<no title>","<no title>","PaddlePaddle Documentation","Cluster bootstrapping tool survey","<no title>","Chinese Word Embedding Model Tutorial","Generative Adversarial Networks (GAN)","Model Zoo - ImageNet","Quick 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\ No newline at end of file
diff --git a/develop/doc_cn/_sources/design/model_format.md.txt b/develop/doc_cn/_sources/design/model_format.md.txt
index a1c086775acbee6d05ebf69d7de9c7c3ac2cd36e..e29129fddf775939c9f7a8b49d850d523e6e5a45 100644
--- a/develop/doc_cn/_sources/design/model_format.md.txt
+++ b/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
diff --git a/develop/doc_cn/design/model_format.html b/develop/doc_cn/design/model_format.html
index 9c15a0a3d7789a45a54935bc9fa1121ebafc1aa4..a50782d700a7e974d32ffab0210ddc06fd0b0d82 100644
--- a/develop/doc_cn/design/model_format.html
+++ b/develop/doc_cn/design/model_format.html
@@ -206,21 +206,18 @@
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. We have done a benchmark experiment, 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, and has a description information proto of LoDTensorDesc. 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. 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, and has a description information proto of LoDTensorDesc. 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. 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.
-[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() |
+| ... | ... | ... |
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