Updating Var_desc.md with the updated typing system in Fluid (#8462)

* Updating Var_desc.md with the updated typing system in Fluid

* Added Channel to VarType

doc/design/var_desc.md

@@ -1,10 +1,10 @@
 ## Background
 PaddlePaddle divides the description of neural network computation into two stages: compile time and runtime. At compile time, the neural network computation is described as a `ProgramDesc` whereas at runtime an `Executor` interprets the `ProgramDesc` to compute the operations.
 
-PaddlePaddle use proto message to describe compile time program because
+PaddlePaddle uses proto message to describe compile time program because :
 
 1. The computation program description must be serializable and saved in a file.
-1. During distributed training, the sreialized program will be sent to multiple workers. It should also be possible to break the program into different components, each of which can be executed on different workers.
+1. During distributed training, the serialized program will be sent to multiple workers. It should also be possible to break the program into different components, each of which can be executed on a different worker.
 
 The computation `Program` consists of nested `Blocks`. Each `Block` will consist of data(i.e. `Variable`)  and  `Operations`. The concept to represent them is in the table below.
 
@@ -14,40 +14,52 @@ The computation `Program` consists of nested `Blocks`. Each `Block` will consist
 |Operation|OpDesc(proto)|Operator(cpp)|
 
 
-## Definition of VarDesc
+## Definition of VarType
 
-A VarDesc should have a name, and value. The are two kinds of variable type in compile time, they are `LoDTensor` and `SelectedRows`. 
+A VarDesc should have a name, type and whether or not it is persistable. The are different kinds of variable types supported in PaddlePaddle, apart from the POD_Types like: `LOD_TENSOR`, `SELECTED_ROWS`, `FEED_MINIBATCH`, `FETCH_LIST`, `STEP_SCOPES`, `LOD_RANK_TABLE`, `LOD_TENSOR_ARRAY`, `PLACE_LIST`, `READER` and `CHANNEL`. These are declared inside `VarType`. A `VarDesc` then looks as the following:
 
 ```proto
 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 ];
+  optional bool persistable = 3 [ default = false ];
 }
 ```
 
 ## Definition of TensorDesc
 
 ```proto
-enum DataType {
+message TensorDesc {
+  // Should only be PODType. Is enforced in C++
+  required Type data_type = 1;
+  repeated int64 dims = 2; // [UNK, 640, 480] is saved as [-1, 640, 480]
+}
+```
+
+The `Type` here comes from the enum defined inside of `VarType` :
+
+```proto
+enum Type {
+  // Pod Types
   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]
+  // Other types that may need additional descriptions
+  LOD_TENSOR = 7;
+  SELECTED_ROWS = 8;
+  FEED_MINIBATCH = 9;
+  FETCH_LIST = 10;
+  STEP_SCOPES = 11;
+  LOD_RANK_TABLE = 12;
+  LOD_TENSOR_ARRAY = 13;
+  PLACE_LIST = 14;
+  READER = 15;
+  CHANNEL = 16;
 }
 ```
 
@@ -58,7 +70,7 @@ A TensorDesc describes `SelectedRows` and `LoDTensor`. For details of `SelectedR
 ```proto
 message LoDTensorDesc {
   required TensorDesc tensor = 1;
-  optional int lod_level = 2;
+  optional int32 lod_level = 2 [ default = 0 ];
 }
 ```