Merge pull request #1500 from ShiningZhang/dev_doc

add C++Serving/Inference_Protocols_CN.md

doc/C++Serving/Inference_Protocols_CN.md

+# Inference Protocols
+
+C++ Serving基于BRPC进行服务构建，支持BRPC、GRPC、RESTful请求。请求数据为protobuf格式，详见`core/general-server/proto/general_model_service.proto`。本文介绍构建请求以及解析结果的方法。
+
+## Tensor
+
+Tensor可以装载多种类型的数据，是Request和Response的基础单元。Tensor的具体定义如下：
+
+```protobuf
+message Tensor {
+  // VarType: INT64
+  repeated int64 int64_data = 1;
+
+  // VarType: FP32
+  repeated float float_data = 2;
+
+  // VarType: INT32
+  repeated int32 int_data = 3;
+
+  // VarType: FP64
+  repeated double float64_data = 4;
+
+  // VarType: UINT32
+  repeated uint32 uint32_data = 5;
+
+  // VarType: BOOL
+  repeated bool bool_data = 6;
+
+  // (No support)VarType: COMPLEX64, 2x represents the real part, 2x+1
+  // represents the imaginary part
+  repeated float complex64_data = 7;
+
+  // (No support)VarType: COMPLEX128, 2x represents the real part, 2x+1
+  // represents the imaginary part
+  repeated double complex128_data = 8;
+
+  // VarType: STRING
+  repeated string data = 9;
+
+  // Element types:
+  //   0 => INT64
+  //   1 => FP32
+  //   2 => INT32
+  //   3 => FP64
+  //   4 => INT16
+  //   5 => FP16
+  //   6 => BF16
+  //   7 => UINT8
+  //   8 => INT8
+  //   9 => BOOL
+  //  10 => COMPLEX64
+  //  11 => COMPLEX128
+  //  20 => STRING
+  int32 elem_type = 10;
+
+  // Shape of the tensor, including batch dimensions.
+  repeated int32 shape = 11;
+
+  // Level of data(LOD), support variable length data, only for fetch tensor
+  // currently.
+  repeated int32 lod = 12;
+
+  // Correspond to the variable 'name' in the model description prototxt.
+  string name = 13;
+
+  // Correspond to the variable 'alias_name' in the model description prototxt.
+  string alias_name = 14; // get from the Model prototxt
+
+  // VarType: FP16, INT16, INT8, BF16, UINT8
+  bytes tensor_content = 15;
+};
+```
+
+- elem_type：数据类型，当前支持FLOAT32, INT64, INT32, UINT8, INT8, FLOAT16
+
+|elem_type|类型|
+|---------|----|
+|0|INT64|
+|1|FLOAT32|
+|2|INT32|
+|3|FP64|
+|4|INT16|
+|5|FP16|
+|6|BF16|
+|7|UINT8|
+|8|INT8|
+
+- shape：数据维度
+- lod：lod信息，LoD(Level-of-Detail) Tensor是Paddle的高级特性，是对Tensor的一种扩充，用于支持更自由的数据输入。详见[LOD](../LOD_CN.md)
+- name/alias_name: 名称及别名，与模型配置对应
+
+### 构建FLOAT32数据Tensor
+
+```C
+// 原始数据
+std::vector<float> float_data;
+Tensor *tensor = new Tensor;
+// 设置维度，可以设置多维
+for (uint32_t j = 0; j < float_shape.size(); ++j) {
+  tensor->add_shape(float_shape[j]);
+}
+// 设置LOD信息
+for (uint32_t j = 0; j < float_lod.size(); ++j) {
+  tensor->add_lod(float_lod[j]);
+}
+// 设置类型、名称及别名
+tensor->set_elem_type(1);
+tensor->set_name(name);
+tensor->set_alias_name(alias_name);
+// 拷贝数据
+int total_number = float_data.size();
+tensor->mutable_float_data()->Resize(total_number, 0);
+memcpy(tensor->mutable_float_data()->mutable_data(), float_datadata(), total_number * sizeof(float));
+```
+
+### 构建INT8数据Tensor
+
+```C
+// 原始数据
+std::string string_data;
+Tensor *tensor = new Tensor;
+for (uint32_t j = 0; j < string_shape.size(); ++j) {
+  tensor->add_shape(string_shape[j]);
+}
+for (uint32_t j = 0; j < string_lod.size(); ++j) {
+  tensor->add_lod(string_lod[j]);
+}
+tensor->set_elem_type(8);
+tensor->set_name(name);
+tensor->set_alias_name(alias_name);
+tensor->set_tensor_content(string_data);
+```
+
+## Request
+
+Request为客户端需要发送的请求数据，其以Tensor为基础数据单元，并包含了额外的请求信息。定义如下：
+
+```protobuf
+message Request {
+  repeated Tensor tensor = 1;
+  repeated string fetch_var_names = 2;
+  bool profile_server = 3;
+  uint64 log_id = 4;
+};
+```
+
+- fetch_vat_names: 需要获取的输出数据名称，在GeneralResponseOP会根据该列表进行过滤.请参考模型文件serving_client_conf.prototxt中的`fetch_var`字段下的`alias_name`。
+- profile_server: 调试参数，打开时会输出性能信息
+- log_id: 请求ID
+
+### 构建Request
+
+当使用BRPC或GRPC进行请求时，使用protobuf形式数据，构建方式如下：
+
+```C
+Request req;
+req.set_log_id(log_id);
+for (auto &name : fetch_name) {
+  req.add_fetch_var_names(name);
+}
+// 添加Tensor
+Tensor *tensor = req.add_tensor();
+...
+```
+
+当使用RESTful请求时，可以使用JSON形式数据，具体格式如下：
+
+```JSON
+{"tensor":[{"float_data":[0.0137,-0.1136,0.2553,-0.0692,0.0582,-0.0727,-0.1583,-0.0584,0.6283,0.4919,0.1856,0.0795,-0.0332],"elem_type":1,"name":"x","alias_name":"x","shape":[1,13]}],"fetch_var_names":["price"],"log_id":0}
+```
+
+## Response
+
+Response为服务端返回给客户端的结果，包含了Tensor数据、错误码、错误信息等。定义如下：
+
+```protobuf
+message Response {
+  repeated ModelOutput outputs = 1;
+  repeated int64 profile_time = 2;
+  // Error code
+  int32 err_no = 3;
+
+  // Error messages
+  string err_msg = 4;
+};
+
+message ModelOutput {
+  repeated Tensor tensor = 1;
+  string engine_name = 2;
+}
+```
+
+- profile_time：当设置request->set_profile_server(true)时，会返回性能信息
+- err_no：错误码，详见`core/predictor/common/constant.h`
+- err_msg：错误信息，详见`core/predictor/common/constant.h`
+- engine_name：输出节点名称
+
+|err_no|err_msg|
+|---------|----|
+|0|OK|
+|-5000|"Paddle Serving Framework Internal Error."|
+|-5001|"Paddle Serving Memory Alloc Error."|
+|-5002|"Paddle Serving Array Overflow Error."|
+|-5100|"Paddle Serving Op Inference Error."|
+
+### 读取Response数据
+
+```C
+uint32_t model_num = res.outputs_size();
+for (uint32_t m_idx = 0; m_idx < model_num; ++m_idx) {
+  std::string engine_name = output.engine_name();
+  int idx = 0;
+  // 读取tensor维度
+  int shape_size = output.tensor(idx).shape_size();
+  for (int i = 0; i < shape_size; ++i) {
+    shape[i] = output.tensor(idx).shape(i);
+  }
+  // 读取LOD信息
+  int lod_size = output.tensor(idx).lod_size();
+  if (lod_size > 0) {
+    lod.resize(lod_size);
+    for (int i = 0; i < lod_size; ++i) {
+      lod[i] = output.tensor(idx).lod(i);
+    }
+  }
+  // 读取float数据
+  int size = output.tensor(idx).float_data_size();
+  float_data = std::vector<float>(
+      output.tensor(idx).float_data().begin(),
+      output.tensor(idx).float_data().begin() + size);
+  // 读取int8数据
+  string_data = output.tensor(idx).tensor_content();
+}
+```
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