Update program.md (#4618)

* Update program.md
* Update
* Update

doc/design/program.md

-# Design Doc: ProgramDesc
+# Design Doc: PaddlePaddle Programs
 
-The basic structure of a PaddlePaddle program is some nested blocks, as a C++ or Java program.
+## Compile and Execution
+
+A PaddlePaddle program consists of two parts -- the first generates a `ProgramDesc` protobuf message that describes the program, and the second runs this message using a C++ class `Executor`.
 
-As described in [graph.md](./graph.md), the first five lines of the following PaddlePaddle program
+A simple example PaddlePaddle program can be found in [graph.md](./graph.md):
 
 ```python
 x = layer.data("images")
@@ -13,36 +15,112 @@ optimize(cost)
 train(cost, reader=mnist.train())
 ```
 
-generates, or compiles, a PaddelPaddle program, which is represented by the following protobuf message:
+The first five lines of the following PaddlePaddle program generates, or, compiles, the `ProgramDesc` message.  The last line runs it.
 
-```protobuf
-message ProgramDesc {
-  repeated BlockDesc blocks = 1;
+## Programs and Blocks
+
+The basic structure of a PaddlePaddle program is some nested blocks, as a C++ or Java program.
+
+- program: some nested blocks
+- [block](./block.md):
+  - some local variable definitions, and
+  - a sequence of operators
+
+The concept of block comes from usual programs.  For example, the following C++ program has three blocks:
+
+```c++
+int main() { // block 0
+  int i = 0;
+  if (i < 10) { // block 1
+    for (int j = 0; j < 10; j++) { // block 2
+    }
+  }
+  return 0;
 }
+```
+
+The following PaddlePaddle program has three blocks:
+
+```python
+import paddle as pd  // block 0
+
+x = minibatch([10, 20, 30]) # shape=[None, 1]
+y = var(1) # shape=[1], value=1
+z = minibatch([10, 20, 30]) # shape=[None, 1]
+cond = larger_than(x, 15) # [false, true, true]
 
+ie = pd.ifelse()
+with ie.true_block():  // block 1
+    d = pd.layer.add_scalar(x, y)
+    ie.output(d, pd.layer.softmax(d))
+with ie.false_block():  // block 2
+    d = pd.layer.fc(z)
+    ie.output(d, d+1)
+o1, o2 = ie(cond)
+```
+
+## `BlockDesc` and `ProgramDesc`
+
+All protobuf messages are defined in `framework.proto`.
+
+`BlockDesc` is straight-forward -- it includes local variable definitions, `vars`, and a sequence of operators, `ops`.
+
+```protobuf
 message BlockDesc {
   required int32 parent = 1;
   repeated VarDesc vars = 2;
   repeated OpDesc ops = 3;
 }
+```
+
+The parent ID indicates the parent block so that operators in a block can refer to variables defined locally and also those defined in their ancestor blocks.
+
+All hierarchical blocks in a program are flattened and stored in an array. The block ID is the index of the block in this array.
+
+```protobuf
+message ProgramDesc {
+  repeated BlockDesc blocks = 1;
+}
+```
+
+
+### Global Block
 
+The global block is the first one in the above array.
+
+## Operators that Use Blocks
+
+In the above example, the operator `IfElseOp` has two blocks -- the true branch and the false branch.
+
+The definition of `OpDesc` shows that an operator could have some attributes:
+
+```protobuf
 message OpDesc {
   AttrDesc attrs = 1;
   ...
 }
+```
 
+and an attribute could be of type block, which is, in fact, a block ID as described above:
+
+```
 message AttrDesc {
-  required AttrType type = 1;
+  required string name = 1;
 
-  // index into ProgramDesc::blocks when type==BLOCK
-  optional int32 block = 2;
+  enum AttrType {
+    INT = 1,
+    STRING = 2,
+    ...
+    BLOCK = ...
+  }
+  required AttrType type = 2;
+
+  optional int32 block = 10; // when type == BLOCK
   ...
 }
 ```
 
-When each of the first five lines runs, related Python function, e.g., `layer.fc`, calls C++ InferShape functions.  This InferShape function needs to access the properties of VarDesc's accessed by the current OpDesc. These VarDesc's might not be defined in the current block, but in some ancestor blocks.  This requires that we can trace the parent of a block.
-
-A nested block is often an attribute of an operator, most likely, an IfElseOp or a WhileOp.  In above solution, all blocks are in `ProgramDesc::blocks`, this implicitly assigns a zero-based ID to each block -- the index of the block in `ProgramDesc::blocks`.  So that `AttrDesc::block` could be an integer block ID.
+## InferShape
 
 With this design, the InferShape function should take the following parameters:
 

paddle/framework/framework.proto

@@ -115,4 +115,7 @@ message BlockDesc {
   repeated OpDesc ops = 4;
 }
 
+// Please refer to
+// https://github.com/PaddlePaddle/Paddle/blob/develop/doc/design/program.md
+// for more details.
 message ProgramDesc { repeated BlockDesc blocks = 1; }