From dc64308f6e379f538ae2838030dbf5e9ceda5612 Mon Sep 17 00:00:00 2001 From: Travis CI Date: Thu, 26 Oct 2017 03:40:03 +0000 Subject: [PATCH] Deploy to GitHub Pages: efc2464f6cff14a5f771bb7e1e6ad8a0366ff110 --- .../doc/_sources/design/model_format.md.txt | 28 +++++++++---------- develop/doc/design/model_format.html | 25 ++++++++--------- develop/doc/searchindex.js | 2 +- .../_sources/design/model_format.md.txt | 28 +++++++++---------- develop/doc_cn/design/model_format.html | 25 ++++++++--------- develop/doc_cn/searchindex.js | 2 +- 6 files changed, 50 insertions(+), 60 deletions(-) diff --git a/develop/doc/_sources/design/model_format.md.txt b/develop/doc/_sources/design/model_format.md.txt index a1c086775ac..e29129fddf7 100644 --- a/develop/doc/_sources/design/model_format.md.txt +++ b/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 diff --git a/develop/doc/design/model_format.html b/develop/doc/design/model_format.html index 6490a8d96fd..33f6994dbe8 100644 --- a/develop/doc/design/model_format.html +++ b/develop/doc/design/model_format.html @@ -192,21 +192,18 @@

Implementation

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() | +| ... | ... | ... |

Summary

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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 a1c086775ac..e29129fddf7 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 9c15a0a3d77..a50782d700a 100644 --- a/develop/doc_cn/design/model_format.html +++ b/develop/doc_cn/design/model_format.html @@ -206,21 +206,18 @@

Implementation

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() | +| ... | ... | ... |

Summary

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