Background

PaddlePaddle divides the description of neural network computation graph into two stages: compile time and runtime.

PaddlePaddle use proto message to describe compile time graph because

1. Computation graph should be able to be saved to a file.
2. In distributed training, the graph will be serialized and send to multiple workers.

The computation graph is constructed by Data Node and Operation Node. The concept to represent them is in the table below.

| |compile time|runtime| |—|—|—| |Data|VarDesc(proto)|Variable(cpp)| |Operation|OpDesc(proto)|Operator(cpp)|

Definition of VarDesc

A VarDesc should have a name, and value. The are two kinds of variable type in compile time, they are LoDTensor and SelectedRows.

message VarDesc {
  required string name = 1;
  enum VarType {
    LOD_TENSOR = 0;
    SELECTED_ROWS = 1;
  }
  required VarType type = 2;
  optional LoDTensorDesc lod_desc = 3;
  optional TensorDesc selected_rows_desc = 4;
  optional bool persistable = 5 [ default = false ];
}

Definition of TensorDesc

enum DataType {
  BOOL = 0;
  INT16 = 1;
  INT32 = 2;
  INT64 = 3;
  FP16 = 4;
  FP32 = 5;
  FP64 = 6;
}

message TensorDesc {
  required DataType data_type = 1;
  repeated int64 dims = 2; // [UNK, 640, 480] is saved as [-1, 640, 480]
}

A TensorDesc describes SelectedRows and LoDTensor. For details of SelectedRows, please reference SelectedRows.

Definition of LodTensorDesc

message LoDTensorDesc {
  required TensorDesc tensor = 1;
  optional int lod_level = 2;
}

A LoDTensorDesc contains a tensor and a lod_level.

Definition of Variable in Python

For Variable in Python, please reference Python API.