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develop/doc/_sources/design/simple_op_design.md.txt

+## Interaction between C++ and Python
+
+Users employ API in Python to describe their own network, however, the network construction actually happens in C++. so Protobuf is introduced to send the message between Python and C++. 
+
+The Interaction between Python and C++ can be simplified as two steps:
+
+1. C++ tells Python how many Ops there are, and what parameter do users need to offer to initialize a new Op. Python then builds API for each Op at compile time.
+
+2. Users invoke APIs built by Python and provide necessary parameters. These parameters will be sent to C++ fo finish Op construction task.
+
+### Message form C++ to Python
+
+We define a Protobuf message class `OpProto` to hold message needed in the first step. What should an `OpProto` contain? This question is equivalent to “What message do we need to offer, to build a Python API which is legal and user oriented and can use to describe a whole Op.”
+
+Following message are necessary:
+
+1. Op's name, and its simple comment.
+2. Input and output variable number; each variable's name, type, and comment.
+3. Op's attributes; each attribute includes name, type, comment, **default value** and **value range**.
+
+So `OpProto` can be defined as follows:
+
+```proto
+enum AttrType {
+	INT = 1;
+	FLOAT = 2;
+	STRING = 3;
+	INTS = 4;
+	FLOATS = 5;
+	STRINGS = 6;
+};
+
+message AttrValue {
+	AttrType type = 1;
+	optional int iv = 2;
+	optional float fv = 3;
+	optional string sv = 4;
+	repeated int ivs = 5;
+	repeated float fvs = 6;
+	repeated string svs = 7;
+};
+
+message AttrProto {
+	required string name = 1;
+	required string comment = 2;
+	required AttrType type = 3;
+};
+
+message VarProto {
+	required string name = 1;
+	required string comment = 2;
+};
+
+message OpProto {
+	repeated VarProto inputs = 1;
+	repeated VarProto outputs = 2;
+	repeated AttrProto attrs = 3;
+	required string type = 4;
+	required string comment = 5;
+};
+```
+
+To generate Python code automatically:
+
+```python 
+def create_python_ops_creatation_functions():
+	op_protos = paddle.framework.OpRegistry.get_all_op_proto()
+	for type_name in op_protos:
+		op_proto = op_protos[type_name]
+		def __impl__(**kwargs):  # User must use key word args in Paddle API
+			inputs = [kwargs.get(ipt.name, "") for ipt in op_proto.inputs]
+			outputs = [kwargs.get(opt.name, "") for opt in op_proto.outputs]
+			attrs = [cast_to_op_attr(attr, kwargs.get(attr.name, None)) for attr in op_proto.attrs]
+			opdesc = （input, outputs, type_name, attrs）
+			return paddle.framework.OpRegistry.CreateOp(opdesc)
+		__impl__.__doc__ = create_doc_string(op_proto)
+		globals()[type_name] = __impl__
+
+create_python_ops_creatation_functions()
+```
+
+### Message from Python to C++
+
+To hold message needed in the above second step, we define Protobuf message class `OpDesc`. It is used to hold user-specified parameters in Op describing.
+
+```proto
+message OpDesc {
+	required string type = 1;	
+	repeated string inputs = 2;
+	repeated string outputs = 3;
+	map<string, AttrValue> attrs = 4;
+};
+```
+
+## OpProto Register
+
+Every Op has its own `OpProto`. For using convenience, we need to register them and record all their messages. For each `Op` class, we define a corresponding `OpMaker` class, in whose constructor we implement the `OpProto`'s building process. `OpMaker`'s constructor will be invoked by another function `OpRegistry::RegisterOp()`.
+
+```cpp
+class OpProtoMaker {
+public:
+	OpProtoMaker(OpProto* proto): proto_(proto) {}
+protected:
+	OpProto* proto_;
+	void AddInput(const std::string& name, const std::string& desc) {...}
+	void AddAttr(const std::string& name, const std::string& desc, TypeId type) {...}
+	void AddComment(const std::string& comment) { ... }
+};
+
+class OpRegistry {
+public:
+	using OpCreator = std::function<OperatorBase* (OpDesc& desc)>;
+	
+	template <typename OpType, typename OpMaker>
+	static void RegisterOp(const std::string& name) {
+		gCreators_[name] = [](const OpDesc& desc) {
+			return new OpType(desc);
+		};
+		OpProto& opProto = gProtos_[name];
+		OpMaker()(&opProto);
+	}
+
+	static map<string, OpCreator> gCreators_;
+	static map<string, OpProto> gProtos_;
+};
+
+template <typename OpType, typename OpMaker>
+class OpRegister {
+  public:
+    OpRegister(std::string type) {
+        OpRegistry::RegisterOp<OpType, OpMaker>(type);
+    }
+};
+
+#define REGISTER_OP(op_class, op_maker_class, type_name)         \
+    class op_class##Register {                                   \
+      private:                                                   \
+        const static OpRegister<#op_class, #op_maker_class> reg; \
+    };                                                           \
+    const Register op_class##Register::reg(#type_name);
+    
+class CosineOp {
+// ...
+}
+
+struct CosineOpProtoMaker : public OpProtoMaker {
+	CosineOpProtoMaker(OpProto* proto) : OpProtoMaker(proto) {
+		AddInput("input", "input of cosine op");
+		AddAttr("scale", "scale of cosine op", float).Default(1.0).LargerThan(0.0);
+		AddType("cos");
+		AddComment("This is cos op");
+	}
+}
+
+REGISTER_OP(CosineOp, CosineOpProtoMaker, cos);
+```
+
+In `REGISTER_OP(CosineOp, CosineOpProtoMaker, cos)`, we register not only `CosineOp` but also `CosineOpProto`. As fields of `CosineOpProto`, the default value and value range of `scale` are also registered here. 
+
+## Python API
+
+Python  APIs are divided into two types, high-level API and low-level API.
+
+### High-Level API
+
+High-level API is called by users directly, so it should keep its style consistent with existing V2 APIs.
+
+Here is a sample about how a define a fc layer:
+
+```python
+hd = fc_layer(input=data, size=56, with_bias=True, activation="sigmoid");
+```
+
+`hd` is the output of `fc_layer` and it's a `variable`. It can be further sent into other layers as input.
+
+The definition of `fc_layer()`:
+
+```python
+def fc_layer(input, size, with_bias, activation):
+	attr_map = {"size":size}
+	check_attrs(attr_map)
+	w = make_variable('w')
+	if with_bias:
+		b = make_variable('b')
+	else:
+		b = None
+	fc_output = make_variable('fc_output');
+	fc_op(input, w, b, fc_output, attr_map)
+	act_output = make_variable('sigmod_output');
+	if activation == "sigmod":
+		sigmod_op(fc_output, act_output);
+	elif:
+		# ...
+	return act_output;
+``` 
+
+### Low Leval API
+
+In above sample, `fc_op` and `sigmod_op` are low-level API. They build `OpDesc` and invoke corresponding C++ code.
+
+*TODO*

develop/doc_cn/_sources/design/simple_op_design.md.txt

+## Interaction between C++ and Python
+
+Users employ API in Python to describe their own network, however, the network construction actually happens in C++. so Protobuf is introduced to send the message between Python and C++. 
+
+The Interaction between Python and C++ can be simplified as two steps:
+
+1. C++ tells Python how many Ops there are, and what parameter do users need to offer to initialize a new Op. Python then builds API for each Op at compile time.
+
+2. Users invoke APIs built by Python and provide necessary parameters. These parameters will be sent to C++ fo finish Op construction task.
+
+### Message form C++ to Python
+
+We define a Protobuf message class `OpProto` to hold message needed in the first step. What should an `OpProto` contain? This question is equivalent to “What message do we need to offer, to build a Python API which is legal and user oriented and can use to describe a whole Op.”
+
+Following message are necessary:
+
+1. Op's name, and its simple comment.
+2. Input and output variable number; each variable's name, type, and comment.
+3. Op's attributes; each attribute includes name, type, comment, **default value** and **value range**.
+
+So `OpProto` can be defined as follows:
+
+```proto
+enum AttrType {
+	INT = 1;
+	FLOAT = 2;
+	STRING = 3;
+	INTS = 4;
+	FLOATS = 5;
+	STRINGS = 6;
+};
+
+message AttrValue {
+	AttrType type = 1;
+	optional int iv = 2;
+	optional float fv = 3;
+	optional string sv = 4;
+	repeated int ivs = 5;
+	repeated float fvs = 6;
+	repeated string svs = 7;
+};
+
+message AttrProto {
+	required string name = 1;
+	required string comment = 2;
+	required AttrType type = 3;
+};
+
+message VarProto {
+	required string name = 1;
+	required string comment = 2;
+};
+
+message OpProto {
+	repeated VarProto inputs = 1;
+	repeated VarProto outputs = 2;
+	repeated AttrProto attrs = 3;
+	required string type = 4;
+	required string comment = 5;
+};
+```
+
+To generate Python code automatically:
+
+```python 
+def create_python_ops_creatation_functions():
+	op_protos = paddle.framework.OpRegistry.get_all_op_proto()
+	for type_name in op_protos:
+		op_proto = op_protos[type_name]
+		def __impl__(**kwargs):  # User must use key word args in Paddle API
+			inputs = [kwargs.get(ipt.name, "") for ipt in op_proto.inputs]
+			outputs = [kwargs.get(opt.name, "") for opt in op_proto.outputs]
+			attrs = [cast_to_op_attr(attr, kwargs.get(attr.name, None)) for attr in op_proto.attrs]
+			opdesc = （input, outputs, type_name, attrs）
+			return paddle.framework.OpRegistry.CreateOp(opdesc)
+		__impl__.__doc__ = create_doc_string(op_proto)
+		globals()[type_name] = __impl__
+
+create_python_ops_creatation_functions()
+```
+
+### Message from Python to C++
+
+To hold message needed in the above second step, we define Protobuf message class `OpDesc`. It is used to hold user-specified parameters in Op describing.
+
+```proto
+message OpDesc {
+	required string type = 1;	
+	repeated string inputs = 2;
+	repeated string outputs = 3;
+	map<string, AttrValue> attrs = 4;
+};
+```
+
+## OpProto Register
+
+Every Op has its own `OpProto`. For using convenience, we need to register them and record all their messages. For each `Op` class, we define a corresponding `OpMaker` class, in whose constructor we implement the `OpProto`'s building process. `OpMaker`'s constructor will be invoked by another function `OpRegistry::RegisterOp()`.
+
+```cpp
+class OpProtoMaker {
+public:
+	OpProtoMaker(OpProto* proto): proto_(proto) {}
+protected:
+	OpProto* proto_;
+	void AddInput(const std::string& name, const std::string& desc) {...}
+	void AddAttr(const std::string& name, const std::string& desc, TypeId type) {...}
+	void AddComment(const std::string& comment) { ... }
+};
+
+class OpRegistry {
+public:
+	using OpCreator = std::function<OperatorBase* (OpDesc& desc)>;
+	
+	template <typename OpType, typename OpMaker>
+	static void RegisterOp(const std::string& name) {
+		gCreators_[name] = [](const OpDesc& desc) {
+			return new OpType(desc);
+		};
+		OpProto& opProto = gProtos_[name];
+		OpMaker()(&opProto);
+	}
+
+	static map<string, OpCreator> gCreators_;
+	static map<string, OpProto> gProtos_;
+};
+
+template <typename OpType, typename OpMaker>
+class OpRegister {
+  public:
+    OpRegister(std::string type) {
+        OpRegistry::RegisterOp<OpType, OpMaker>(type);
+    }
+};
+
+#define REGISTER_OP(op_class, op_maker_class, type_name)         \
+    class op_class##Register {                                   \
+      private:                                                   \
+        const static OpRegister<#op_class, #op_maker_class> reg; \
+    };                                                           \
+    const Register op_class##Register::reg(#type_name);
+    
+class CosineOp {
+// ...
+}
+
+struct CosineOpProtoMaker : public OpProtoMaker {
+	CosineOpProtoMaker(OpProto* proto) : OpProtoMaker(proto) {
+		AddInput("input", "input of cosine op");
+		AddAttr("scale", "scale of cosine op", float).Default(1.0).LargerThan(0.0);
+		AddType("cos");
+		AddComment("This is cos op");
+	}
+}
+
+REGISTER_OP(CosineOp, CosineOpProtoMaker, cos);
+```
+
+In `REGISTER_OP(CosineOp, CosineOpProtoMaker, cos)`, we register not only `CosineOp` but also `CosineOpProto`. As fields of `CosineOpProto`, the default value and value range of `scale` are also registered here. 
+
+## Python API
+
+Python  APIs are divided into two types, high-level API and low-level API.
+
+### High-Level API
+
+High-level API is called by users directly, so it should keep its style consistent with existing V2 APIs.
+
+Here is a sample about how a define a fc layer:
+
+```python
+hd = fc_layer(input=data, size=56, with_bias=True, activation="sigmoid");
+```
+
+`hd` is the output of `fc_layer` and it's a `variable`. It can be further sent into other layers as input.
+
+The definition of `fc_layer()`:
+
+```python
+def fc_layer(input, size, with_bias, activation):
+	attr_map = {"size":size}
+	check_attrs(attr_map)
+	w = make_variable('w')
+	if with_bias:
+		b = make_variable('b')
+	else:
+		b = None
+	fc_output = make_variable('fc_output');
+	fc_op(input, w, b, fc_output, attr_map)
+	act_output = make_variable('sigmod_output');
+	if activation == "sigmod":
+		sigmod_op(fc_output, act_output);
+	elif:
+		# ...
+	return act_output;
+``` 
+
+### Low Leval API
+
+In above sample, `fc_op` and `sigmod_op` are low-level API. They build `OpDesc` and invoke corresponding C++ code.
+
+*TODO*