From 09e2dd36d3dac10dc77c4556b269a5a7f6ffe90b Mon Sep 17 00:00:00 2001 From: Travis CI Date: Tue, 24 Oct 2017 21:17:49 +0000 Subject: [PATCH] Deploy to GitHub Pages: fd2eb55071199df6bb564ee0b30e35b3868c7371 --- .../doc/_sources/design/model_format.md.txt | 36 +++ develop/doc/design/model_format.html | 273 +++++++++++++++++ develop/doc/objects.inv | Bin 2763 -> 2782 bytes develop/doc/searchindex.js | 2 +- .../_sources/design/model_format.md.txt | 36 +++ develop/doc_cn/design/model_format.html | 288 ++++++++++++++++++ develop/doc_cn/objects.inv | Bin 3320 -> 3340 bytes develop/doc_cn/searchindex.js | 2 +- 8 files changed, 635 insertions(+), 2 deletions(-) create mode 100644 develop/doc/_sources/design/model_format.md.txt create mode 100644 develop/doc/design/model_format.html create mode 100644 develop/doc_cn/_sources/design/model_format.md.txt create mode 100644 develop/doc_cn/design/model_format.html diff --git a/develop/doc/_sources/design/model_format.md.txt b/develop/doc/_sources/design/model_format.md.txt new file mode 100644 index 00000000000..db8c36e5f5d --- /dev/null +++ b/develop/doc/_sources/design/model_format.md.txt @@ -0,0 +1,36 @@ +# 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**. diff --git a/develop/doc/design/model_format.html b/develop/doc/design/model_format.html new file mode 100644 index 00000000000..d0fcaf1e174 --- /dev/null +++ b/develop/doc/design/model_format.html @@ -0,0 +1,273 @@ + + + + + + + + + + + Design Doc: Model Format — PaddlePaddle documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ +
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+ Fork me on Github +
+ + English + + + + +
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  • Design Doc: Model Format
    • +
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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, 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. 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, 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. 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.

+
[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.

+
+
+ + +
+
+
+ + +
+ +
+

+ © Copyright 2016, PaddlePaddle developers. + +

+
+ Built with Sphinx using a theme provided by Read the Docs. + +
+ +
+
+ +
+ +
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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 new file mode 100644 index 00000000000..db8c36e5f5d --- /dev/null +++ b/develop/doc_cn/_sources/design/model_format.md.txt @@ -0,0 +1,36 @@ +# 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**. diff --git a/develop/doc_cn/design/model_format.html b/develop/doc_cn/design/model_format.html new file mode 100644 index 00000000000..eb1ae33d58d --- /dev/null +++ b/develop/doc_cn/design/model_format.html @@ -0,0 +1,288 @@ + + + + + + + + + + + Design Doc: Model Format — PaddlePaddle 文档 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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+ Fork me on Github +
+ + English + + + + +
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  • Design Doc: Model Format
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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, 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. 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, 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. 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.

+
[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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+ +
+

+ © Copyright 2016, PaddlePaddle developers. + +

+
+ Built with Sphinx using a theme provided by Read the Docs. + +
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