model_format.md.txt 2.7 KB
Newer Older
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 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**.