add simple client demo with brpc

core/general-client/CMakeLists.txt

@@ -3,3 +3,24 @@ add_subdirectory(pybind11)
 pybind11_add_module(serving_client src/general_model.cpp src/pybind_general_model.cpp)
 target_link_libraries(serving_client PRIVATE -Wl,--whole-archive utils sdk-cpp pybind python -Wl,--no-whole-archive -lpthread -lcrypto -lm -lrt -lssl -ldl -lz -Wl,-rpath,'$ORIGIN'/lib)
 endif()
+
+if(CLIENT)
+FILE(GLOB client_srcs include/*.h src/client.cpp src/brpc_client.cpp)
+add_library(client ${client_srcs})
+add_dependencies(client utils sdk-cpp)
+target_link_libraries(client utils sdk-cpp)
+endif()
+
+if(CLIENT)
+include_directories(SYSTEM  ${CMAKE_CURRENT_LIST_DIR}/../../)
+add_executable(simple_client example/simple_client.cpp)
+
+add_dependencies(simple_client utils sdk-cpp client)
+
+target_link_libraries(simple_client -Wl,--whole-archive
+        -Wl,--no-whole-archive -lpthread -lcrypto -lm -lrt -lssl -ldl -lz -Wl,-rpath,'$ORIGIN'/lib)
+
+target_link_libraries(simple_client utils)
+target_link_libraries(simple_client sdk-cpp)
+target_link_libraries(simple_client client)
+endif()
\ No newline at end of file

core/general-client/example/simple_client.cpp

+// Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include <fstream>
+#include <vector>
+
+#include "core/general-client/include/brpc_client.h"
+
+using namespace std;  // NOLINT
+
+using baidu::paddle_serving::client::ServingClient;
+using baidu::paddle_serving::client::ServingBrpcClient;
+using baidu::paddle_serving::client::PredictorInputs;
+using baidu::paddle_serving::client::PredictorOutputs;
+
+DEFINE_string(server_port, "127.0.0.1:9292", "");
+DEFINE_string(client_conf, "serving_client_conf.prototxt", "");
+DEFINE_string(test_type, "brpc", "");
+
+int main(int argc, char* argv[]) {
+
+  google::ParseCommandLineFlags(&argc, &argv, true);
+  std::string url = FLAGS_server_port;
+  std::string conf = FLAGS_client_conf;
+  std::string test_type = FLAGS_test_type;
+  LOG(INFO) << "url = " << url << ";"
+            << "client_conf = " << conf << ";"
+            << "type = " << test_type;
+  std::unique_ptr<ServingClient> client;
+  if (test_type == "brpc") {
+    client.reset(new ServingBrpcClient());
+  } else {
+    client.reset(new ServingBrpcClient());
+  }
+  std::vector<std::string> confs;
+  confs.push_back(conf);
+  if (client->init(confs, url) != 0) {
+    LOG(ERROR) << "Failed to init client!";
+    return 0;
+  }
+
+  std::vector<float> float_feed = {0.0137f, -0.1136f, 0.2553f, -0.0692f,
+            0.0582f, -0.0727f, -0.1583f, -0.0584f,
+            0.6283f, 0.4919f, 0.1856f, 0.0795f, -0.0332f};
+  std::vector<int> float_shape = {1, 13};
+  std::string feed_name = "x";
+  std::vector<std::string> fetch_name = {"price"};
+  std::vector<int> lod;
+
+  PredictorInputs input;
+  PredictorOutputs output;
+
+  input.add_float_data(float_feed, feed_name, float_shape, lod);
+
+  if (client->predict(input, output, fetch_name, 0) != 0) {
+    LOG(ERROR) << "Failed to predict!";
+  }
+  else {
+    LOG(INFO) << output.print();
+  }
+  
+  return 0;
+}

core/general-client/include/brpc_client.h

+// Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+#pragma once
+
+#include "core/general-client/include/client.h"
+#include "core/sdk-cpp/include/predictor_sdk.h"
+using baidu::paddle_serving::sdk_cpp::Predictor;
+using baidu::paddle_serving::sdk_cpp::PredictorApi;
+
+namespace baidu {
+namespace paddle_serving {
+namespace client {
+
+class ServingBrpcClient : public ServingClient {
+ public:
+
+  ServingBrpcClient() {};
+
+  ~ServingBrpcClient() {};
+
+  virtual int connect(const std::string server_port);
+
+  int predict(const PredictorInputs& inputs,
+              PredictorOutputs& outputs,
+              std::vector<std::string>& fetch_name,
+              const uint64_t log_id);
+
+ private:
+  std::string gen_desc(const std::string server_port);
+
+ private:
+  PredictorApi _api;
+  Predictor* _predictor;
+};
+
+}  // namespace client
+}  // namespace paddle_serving
+}  // namespace baidu
\ No newline at end of file

core/general-client/include/client.h

+// Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+#pragma once
+
+#include <string>
+#include <vector>
+#include <map>
+#include <sstream>
+
+namespace baidu {
+namespace paddle_serving {
+namespace client {
+
+class PredictorInputs;
+class PredictorOutputs;
+
+class ServingClient {
+ public:
+  ServingClient() {};
+
+  virtual ~ServingClient() = default;
+
+  int init(const std::vector<std::string>& client_conf,
+           const std::string server_port);
+
+  int load_client_config(const std::vector<std::string>& client_conf);
+
+  virtual int connect(const std::string server_port) = 0;
+
+  virtual int predict(const PredictorInputs& inputs,
+                      PredictorOutputs& outputs,
+                      std::vector<std::string>& fetch_name,
+                      const uint64_t log_id) = 0;
+
+ protected:
+  std::map<std::string, int> _feed_name_to_idx;
+  std::vector<std::string> _feed_name;
+  std::map<std::string, int> _fetch_name_to_idx;
+  std::map<std::string, std::string> _fetch_name_to_var_name;
+  std::map<std::string, int> _fetch_name_to_type;
+  std::vector<std::vector<int>> _shape;
+  std::vector<int> _type;
+  std::vector<int64_t> _last_request_ts;
+};
+
+class PredictorData {
+ public:
+  PredictorData() {};
+  virtual ~PredictorData() {};
+
+  virtual void add_float_data(const std::vector<float>& data,
+                              const std::string& name,
+                              const std::vector<int>& shape,
+                              const std::vector<int>& lod);
+
+  virtual void add_int64_data(const std::vector<int64_t>& data,
+                              const std::string& name,
+                              const std::vector<int>& shape,
+                              const std::vector<int>& lod);
+
+  virtual void add_int32_data(const std::vector<int32_t>& data,
+                              const std::string& name,
+                              const std::vector<int>& shape,
+                              const std::vector<int>& lod);
+
+  virtual void add_string_data(const std::string& data,
+                               const std::string& name,
+                               const std::vector<int>& shape,
+                               const std::vector<int>& lod);
+
+  virtual const std::map<std::string, std::vector<float>>& float_data_map() const {
+    return _float_data_map;
+  };
+
+  virtual std::map<std::string, std::vector<float>>* mutable_float_data_map() {
+    return &_float_data_map;
+  };
+
+  virtual const std::map<std::string, std::vector<int64_t>>& int64_data_map() const {
+    return _int64_data_map;
+  };
+
+  virtual std::map<std::string, std::vector<int64_t>>* mutable_int64_data_map() {
+    return &_int64_data_map;
+  };
+
+  virtual const std::map<std::string, std::vector<int32_t>>& int_data_map() const {
+    return _int32_data_map;
+  };
+
+  virtual std::map<std::string, std::vector<int32_t>>* mutable_int_data_map() {
+    return &_int32_data_map;
+  };
+
+  virtual const std::map<std::string, std::string>& string_data_map() const {
+    return _string_data_map;
+  };
+
+  virtual std::map<std::string, std::string>* mutable_string_data_map() {
+    return &_string_data_map;
+  };
+
+  virtual const std::map<std::string, std::vector<int>>& shape_map() const {
+    return _shape_map;
+  };
+
+  virtual std::map<std::string, std::vector<int>>* mutable_shape_map() {
+    return &_shape_map;
+  };
+
+  virtual const std::map<std::string, std::vector<int>>& lod_map() const {
+    return _lod_map;
+  };
+
+  virtual std::map<std::string, std::vector<int>>* mutable_lod_map() {
+    return &_lod_map;
+  };
+
+  virtual std::string print() {
+    std::string res;
+    res.append(map2string<std::string, float>(_float_data_map));
+    res.append(map2string<std::string, int64_t>(_int64_data_map));
+    res.append(map2string<std::string, int32_t>(_int32_data_map));
+    res.append(map2string<std::string, std::string>(_string_data_map));
+    return res;
+  }
+
+ private:
+  template<typename T1, typename T2>
+  std::string map2string(const std::map<T1, std::vector<T2>>& map) {
+    std::ostringstream oss;
+    oss.str("");
+    oss.precision(6);
+	  oss.setf(std::ios::fixed);
+    std::string key_seg = ":";
+    std::string val_seg = ",";
+    std::string end_seg = "\n";
+    typename std::map<T1, std::vector<T2>>::const_iterator it = map.begin();
+    typename std::map<T1, std::vector<T2>>::const_iterator itEnd = map.end();
+    for (; it != itEnd; it++) {
+      oss << "{";
+      oss << it->first << key_seg;
+      const std::vector<T2>& v = it->second;
+      for (size_t i = 0; i < v.size(); ++i) {
+        if (i != v.size() - 1) {
+          oss << v[i] << val_seg;
+        }
+        else {
+          oss << v[i];
+        }
+      }
+      oss << "}";
+    }
+    return oss.str();
+  };
+
+  template<typename T1, typename T2>
+  std::string map2string(const std::map<T1, T2>& map) {
+    std::ostringstream oss;
+    oss.str("");
+    std::string key_seg = ":";
+    std::string val_seg = ",";
+    std::string end_seg = "\n";
+    typename std::map<T1, T2>::const_iterator it = map.begin();
+    typename std::map<T1, T2>::const_iterator itEnd = map.end();
+    for (; it != itEnd; it++) {
+      oss << "{";
+      oss << it->first << key_seg << it->second;
+      oss << "}";
+    }
+    return oss.str();
+  };
+
+ protected:
+  std::map<std::string, std::vector<float>> _float_data_map;
+  std::map<std::string, std::vector<int64_t>> _int64_data_map;
+  std::map<std::string, std::vector<int32_t>> _int32_data_map;
+  std::map<std::string, std::string> _string_data_map;
+  std::map<std::string, std::vector<int>> _shape_map;
+  std::map<std::string, std::vector<int>> _lod_map;
+};
+
+class PredictorInputs : public PredictorData {
+ public:
+  PredictorInputs() {};
+  virtual ~PredictorInputs() {};
+};
+
+class PredictorOutputs {
+ public:
+  struct PredictorOutput {
+    std::string engine_name;
+    PredictorData data;
+  };
+
+  PredictorOutputs() {};
+  virtual ~PredictorOutputs() {};
+
+  virtual std::vector<PredictorOutputs::PredictorOutput>& datas() {
+    return _datas;
+  };
+
+  virtual std::vector<PredictorOutputs::PredictorOutput>* mutable_datas() {
+    return &_datas;
+  };
+
+  virtual void add_data(PredictorOutputs::PredictorOutput&& data) {
+    _datas.emplace_back(data);
+  };
+
+  virtual std::string print() {
+    std::string res = "";
+    for (size_t i = 0; i < _datas.size(); ++i) {
+      res.append(_datas[i].engine_name);
+      res.append(":");
+      res.append(_datas[i].data.print());
+      res.append("\n");
+    }
+    return res;
+  }
+
+ protected:
+  std::vector<PredictorOutputs::PredictorOutput> _datas;
+};
+
+}  // namespace client
+}  // namespace paddle_serving
+}  // namespace baidu
\ No newline at end of file

core/general-client/src/brpc_client.cpp

+// Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "core/general-client/include/brpc_client.h"
+#include "core/sdk-cpp/include/common.h"
+#include "core/util/include/timer.h"
+#include "core/sdk-cpp/builtin_format.pb.h"
+#include "core/sdk-cpp/general_model_service.pb.h"
+DEFINE_bool(profile_client, false, "");
+DEFINE_bool(profile_server, false, "");
+#define BRPC_MAX_BODY_SIZE 512 * 1024 * 1024
+
+namespace baidu {
+namespace paddle_serving {
+namespace client {
+
+using baidu::paddle_serving::Timer;
+using baidu::paddle_serving::predictor::general_model::Request;
+using baidu::paddle_serving::predictor::general_model::Response;
+using baidu::paddle_serving::predictor::general_model::Tensor;
+enum ProtoDataType { P_INT64, P_FLOAT32, P_INT32, P_STRING };
+
+using configure::SDKConf;
+using configure::VariantConf;
+using configure::Predictor;
+using configure::VariantConf;
+
+int ServingBrpcClient::connect(const std::string server_port) {
+  brpc::fLU64::FLAGS_max_body_size = BRPC_MAX_BODY_SIZE;
+  if (_api.create(gen_desc(server_port)) != 0) {
+    LOG(ERROR) << "Predictor Creation Failed";
+    return -1;
+  }
+  // _api.thrd_initialize();
+  return 0;
+}
+
+std::string ServingBrpcClient::gen_desc(const std::string server_port) {
+  SDKConf sdk_conf;
+
+  Predictor* predictor = sdk_conf.add_predictors();
+  predictor->set_name("general_model");
+  predictor->set_service_name("baidu.paddle_serving.predictor.general_model.GeneralModelService");
+  predictor->set_endpoint_router("WeightedRandomRender");
+  predictor->mutable_weighted_random_render_conf()->set_variant_weight_list("100");
+  VariantConf* predictor_var = predictor->add_variants();
+  predictor_var->set_tag("default_tag_1");
+  std::string cluster = "list://" + server_port;
+  predictor_var->mutable_naming_conf()->set_cluster(cluster);
+
+  VariantConf* var = sdk_conf.mutable_default_variant_conf();
+  var->set_tag("default");
+  var->mutable_connection_conf()->set_connect_timeout_ms(2000);
+  var->mutable_connection_conf()->set_rpc_timeout_ms(200000);
+  var->mutable_connection_conf()->set_connect_retry_count(2);
+  var->mutable_connection_conf()->set_max_connection_per_host(100);
+  var->mutable_connection_conf()->set_hedge_request_timeout_ms(-1);
+  var->mutable_connection_conf()->set_hedge_fetch_retry_count(2);
+  var->mutable_connection_conf()->set_connection_type("pooled");
+  var->mutable_connection_conf()->set_connect_timeout_ms(2000);
+
+  var->mutable_naming_conf()->set_cluster_filter_strategy("Default");
+  var->mutable_naming_conf()->set_load_balance_strategy("la");
+
+  var->mutable_rpc_parameter()->set_compress_type(0);
+  var->mutable_rpc_parameter()->set_package_size(20);
+  var->mutable_rpc_parameter()->set_protocol("baidu_std");
+  var->mutable_rpc_parameter()->set_max_channel_per_request(3);
+
+  return sdk_conf.SerializePartialAsString();
+}
+
+static int pre_process(const PredictorInputs& inputs,
+                        const std::map<std::string, int>& feed_name_to_idx,
+                        const std::vector<std::string>& feed_name,
+                        Request& req) {
+  const std::map<std::string, std::vector<float>>& float_feed_map = inputs.float_data_map();
+  const std::map<std::string, std::vector<int64_t>>& int64_feed_map = inputs.int64_data_map();
+  const std::map<std::string, std::vector<int32_t>>& int32_feed_map = inputs.int_data_map();
+  const std::map<std::string, std::string>& string_feed_map = inputs.string_data_map();
+  const std::map<std::string, std::vector<int>>& shape_map = inputs.shape_map();
+  const std::map<std::string, std::vector<int>>& lod_map = inputs.lod_map();
+
+  VLOG(2) << "float feed name size: " << float_feed_map.size();
+  VLOG(2) << "int feed name size: " << int64_feed_map.size();
+  VLOG(2) << "string feed name size: " << string_feed_map.size();
+
+  // batch is already in Tensor.
+
+  for (std::map<std::string, std::vector<float>>::const_iterator iter = float_feed_map.begin();
+        iter != float_feed_map.end();
+        ++iter) {
+    std::string name = iter->first;
+    const std::vector<float>& float_data = iter->second;
+    const std::vector<int>& float_shape = shape_map.at(name);
+    const std::vector<int>& float_lod = lod_map.at(name);
+    std::map<std::string, int>::const_iterator feed_name_it = feed_name_to_idx.find(name);
+    if (feed_name_it == feed_name_to_idx.end()) {
+      LOG(ERROR) << "Do not find [" << name << "] in feed_map!";
+      return -1;
+    }
+    int idx = feed_name_to_idx.at(name);
+    VLOG(2) << "prepare float feed " << name << " idx " << idx;
+    int total_number = float_data.size();
+    Tensor *tensor = req.add_tensor();
+
+    VLOG(2) << "prepare float feed " << name << " shape size "
+            << float_shape.size();
+    for (uint32_t j = 0; j < float_shape.size(); ++j) {
+      tensor->add_shape(float_shape[j]);
+    }
+    for (uint32_t j = 0; j < float_lod.size(); ++j) {
+      tensor->add_lod(float_lod[j]);
+    }
+    tensor->set_elem_type(P_FLOAT32);
+
+    tensor->set_name(feed_name[idx]);
+    tensor->set_alias_name(name);
+
+    google::protobuf::RepeatedField<float> data(float_data.begin(), float_data.end());
+    tensor->mutable_float_data()->Swap(&data);
+  }
+
+  for (std::map<std::string, std::vector<int64_t>>::const_iterator iter = int64_feed_map.begin();
+        iter != int64_feed_map.end();
+        ++iter) {
+    std::string name = iter->first;
+    const std::vector<int64_t>& int64_data = iter->second;
+    const std::vector<int>& int64_shape = shape_map.at(name);
+    const std::vector<int>& int64_lod = lod_map.at(name);
+    std::map<std::string, int>::const_iterator feed_name_it = feed_name_to_idx.find(name);
+    if (feed_name_it == feed_name_to_idx.end()) {
+      LOG(ERROR) << "Do not find [" << name << "] in feed_map!";
+      return -1;
+    }
+    int idx = feed_name_to_idx.at(name);
+    Tensor *tensor = req.add_tensor();
+    int total_number = int64_data.size();
+
+    for (uint32_t j = 0; j < int64_shape.size(); ++j) {
+      tensor->add_shape(int64_shape[j]);
+    }
+    for (uint32_t j = 0; j < int64_lod.size(); ++j) {
+      tensor->add_lod(int64_lod[j]);
+    }
+    tensor->set_elem_type(P_INT64);
+    tensor->set_name(feed_name[idx]);
+    tensor->set_alias_name(name);
+
+    google::protobuf::RepeatedField<google::protobuf::int64> data(int64_data.begin(), int64_data.end());
+    tensor->mutable_int64_data()->Swap(&data);
+  }
+
+  for (std::map<std::string, std::vector<int32_t>>::const_iterator iter = int32_feed_map.begin();
+        iter != int32_feed_map.end();
+        ++iter) {
+    std::string name = iter->first;
+    const std::vector<int32_t>& int32_data = iter->second;
+    const std::vector<int>& int32_shape = shape_map.at(name);
+    const std::vector<int>& int32_lod = lod_map.at(name);
+    std::map<std::string, int>::const_iterator feed_name_it = feed_name_to_idx.find(name);
+    if (feed_name_it == feed_name_to_idx.end()) {
+      LOG(ERROR) << "Do not find [" << name << "] in feed_map!";
+      return -1;
+    }
+    int idx = feed_name_to_idx.at(name);
+    Tensor *tensor = req.add_tensor();
+
+    for (uint32_t j = 0; j < int32_shape.size(); ++j) {
+      tensor->add_shape(int32_shape[j]);
+    }
+    for (uint32_t j = 0; j < int32_lod.size(); ++j) {
+      tensor->add_lod(int32_lod[j]);
+    }
+    tensor->set_elem_type(P_INT32);
+    tensor->set_name(feed_name[idx]);
+    tensor->set_alias_name(name);
+
+    google::protobuf::RepeatedField<google::protobuf::int32> data(int32_data.begin(), int32_data.end());
+    tensor->mutable_int_data()->Swap(&data);
+  }
+
+  for (std::map<std::string, std::string>::const_iterator iter = string_feed_map.begin();
+        iter != string_feed_map.end();
+        ++iter) {
+    std::string name = iter->first;
+    const std::string& string_data = iter->second;
+    const std::vector<int>& string_shape = shape_map.at(name);
+    const std::vector<int>& string_lod = lod_map.at(name);
+    std::map<std::string, int>::const_iterator feed_name_it = feed_name_to_idx.find(name);
+    if (feed_name_it == feed_name_to_idx.end()) {
+      LOG(ERROR) << "Do not find [" << name << "] in feed_map!";
+      return -1;
+    }
+    int idx = feed_name_to_idx.at(name);
+    Tensor *tensor = req.add_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(P_STRING);
+    tensor->set_name(feed_name[idx]);
+    tensor->set_alias_name(name);
+
+    const int string_shape_size = string_shape.size();
+    // string_shape[vec_idx] = [1];cause numpy has no datatype of string.
+    // we pass string via vector<vector<string> >.
+    if (string_shape_size != 1) {
+      LOG(ERROR) << "string_shape_size should be 1-D, but received is : "
+                 << string_shape_size;
+      return -1;
+    }
+    switch (string_shape_size) {
+      case 1: {
+        tensor->add_data(string_data);
+        break;
+      }
+    }
+  }
+  return 0;
+}
+
+static int post_process(const Response& res,
+                        std::vector<std::string>& fetch_name,
+                        std::map<std::string, int>& fetch_name_to_type,
+                        PredictorOutputs& outputs) {
+  VLOG(2) << "get model output num";
+  uint32_t model_num = res.outputs_size();
+  VLOG(2) << "model num: " << model_num;
+  for (uint32_t m_idx = 0; m_idx < model_num; ++m_idx) {
+    VLOG(2) << "process model output index: " << m_idx;
+    auto& output = res.outputs(m_idx);
+    PredictorOutputs::PredictorOutput predictor_output;
+    predictor_output.engine_name = output.engine_name();
+    std::map<std::string, std::vector<float>>& float_data_map = *predictor_output.data.mutable_float_data_map();
+    std::map<std::string, std::vector<int64_t>>& int64_data_map = *predictor_output.data.mutable_int64_data_map();
+    std::map<std::string, std::vector<int32_t>>& int32_data_map = *predictor_output.data.mutable_int_data_map();
+    std::map<std::string, std::string>& string_data_map = *predictor_output.data.mutable_string_data_map();
+    std::map<std::string, std::vector<int>>& shape_map = *predictor_output.data.mutable_shape_map();
+    std::map<std::string, std::vector<int>>& lod_map = *predictor_output.data.mutable_lod_map();
+
+    int idx = 0;
+    for (auto &name : fetch_name) {
+      // int idx = _fetch_name_to_idx[name];
+      int shape_size = output.tensor(idx).shape_size();
+      VLOG(2) << "fetch var " << name << " index " << idx << " shape size "
+              << shape_size;
+      shape_map[name].resize(shape_size);
+      for (int i = 0; i < shape_size; ++i) {
+        shape_map[name][i] = output.tensor(idx).shape(i);
+      }
+      int lod_size = output.tensor(idx).lod_size();
+      if (lod_size > 0) {
+        lod_map[name].resize(lod_size);
+        for (int i = 0; i < lod_size; ++i) {
+          lod_map[name][i] = output.tensor(idx).lod(i);
+        }
+      }
+      idx += 1;
+    }
+    idx = 0;
+
+    for (auto &name : fetch_name) {
+      // int idx = _fetch_name_to_idx[name];
+      if (fetch_name_to_type[name] == P_INT64) {
+        VLOG(2) << "ferch var " << name << "type int64";
+        int size = output.tensor(idx).int64_data_size();
+        int64_data_map[name] = std::vector<int64_t>(
+            output.tensor(idx).int64_data().begin(),
+            output.tensor(idx).int64_data().begin() + size);
+      } else if (fetch_name_to_type[name] == P_FLOAT32) {
+        VLOG(2) << "fetch var " << name << "type float";
+        int size = output.tensor(idx).float_data_size();
+        float_data_map[name] = std::vector<float>(
+            output.tensor(idx).float_data().begin(),
+            output.tensor(idx).float_data().begin() + size);
+      } else if (fetch_name_to_type[name] == P_INT32) {
+        VLOG(2) << "fetch var " << name << "type int32";
+        int size = output.tensor(idx).int_data_size();
+        int32_data_map[name] = std::vector<int32_t>(
+            output.tensor(idx).int_data().begin(),
+            output.tensor(idx).int_data().begin() + size);
+      }
+      idx += 1;
+    }
+    outputs.add_data(std::move(predictor_output));
+  }
+  return 0;
+}
+
+int ServingBrpcClient::predict(const PredictorInputs& inputs,
+                               PredictorOutputs& outputs,
+                               std::vector<std::string>& fetch_name,
+                               const uint64_t log_id) {
+  Timer timeline;
+  int64_t preprocess_start = timeline.TimeStampUS();
+
+  _api.thrd_initialize();
+  std::string variant_tag;
+  _predictor = _api.fetch_predictor("general_model", &variant_tag);
+  if (_predictor == NULL) {
+    LOG(ERROR) << "Failed fetch predictor so predict error!";
+    return -1;
+  }
+  // predict_res_batch.set_variant_tag(variant_tag);
+  VLOG(2) << "fetch general model predictor done.";
+  VLOG(2) << "variant_tag:" << variant_tag;
+  VLOG(2) << "max body size : " << brpc::fLU64::FLAGS_max_body_size;
+  Request req;
+  req.set_log_id(log_id);
+  for (auto &name : fetch_name) {
+    req.add_fetch_var_names(name);
+  }
+
+  if (pre_process(inputs, _feed_name_to_idx, _feed_name, req) != 0) {
+    LOG(ERROR) << "Failed to preprocess req!";
+    return -1;
+  }
+
+  int64_t preprocess_end = timeline.TimeStampUS();
+  int64_t client_infer_start = timeline.TimeStampUS();
+  Response res;
+
+  int64_t client_infer_end = 0;
+  int64_t postprocess_start = 0;
+  int64_t postprocess_end = 0;
+
+  if (FLAGS_profile_client) {
+    if (FLAGS_profile_server) {
+      req.set_profile_server(true);
+    }
+  }
+
+  res.Clear();
+  if (_predictor->inference(&req, &res) != 0) {
+    LOG(ERROR) << "failed call predictor with req: " << req.ShortDebugString();
+    return -1;
+  }
+
+  client_infer_end = timeline.TimeStampUS();
+  postprocess_start = client_infer_end;
+  if (post_process(res, fetch_name, _fetch_name_to_type, outputs) != 0) {
+    LOG(ERROR) << "Failed to post_process res!";
+    return -1;
+  }
+  postprocess_end = timeline.TimeStampUS();
+
+  if (FLAGS_profile_client) {
+    std::ostringstream oss;
+    oss << "PROFILE\t"
+        << "pid:" << getpid() << "\t"
+        << "prepro_0:" << preprocess_start << " "
+        << "prepro_1:" << preprocess_end << " "
+        << "client_infer_0:" << client_infer_start << " "
+        << "client_infer_1:" << client_infer_end << " ";
+    if (FLAGS_profile_server) {
+      int op_num = res.profile_time_size() / 2;
+      for (int i = 0; i < op_num; ++i) {
+        oss << "op" << i << "_0:" << res.profile_time(i * 2) << " ";
+        oss << "op" << i << "_1:" << res.profile_time(i * 2 + 1) << " ";
+      }
+    }
+
+    oss << "postpro_0:" << postprocess_start << " ";
+    oss << "postpro_1:" << postprocess_end;
+
+    fprintf(stderr, "%s\n", oss.str().c_str());
+  }
+
+  _api.thrd_clear();
+
+  return 0;
+}
+
+}  // namespace general_model
+}  // namespace paddle_serving
+}  // namespace baidu

core/general-client/src/client.cpp

+// Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "core/general-client/include/client.h"
+#include "core/sdk-cpp/include/common.h"
+
+namespace baidu {
+namespace paddle_serving {
+namespace client {
+using configure::GeneralModelConfig;
+
+int ServingClient::init(const std::vector<std::string>& client_conf,
+           const std::string server_port) {
+  if (load_client_config(client_conf) != 0) {
+    LOG(ERROR) << "Failed to load client config";
+    return -1;
+  }
+
+  if (connect(server_port) != 0) {
+    LOG(ERROR) << "Failed to connect";
+    return -1;
+  }
+
+  return 0;
+}
+
+int ServingClient::load_client_config(const std::vector<std::string> &conf_file) {
+  try {
+    GeneralModelConfig model_config;
+    if (configure::read_proto_conf(conf_file[0].c_str(), &model_config) != 0) {
+      LOG(ERROR) << "Failed to load general model config"
+                 << ", file path: " << conf_file[0];
+      return -1;
+    }
+
+    _feed_name_to_idx.clear();
+    _fetch_name_to_idx.clear();
+    _shape.clear();
+    int feed_var_num = model_config.feed_var_size();
+    _feed_name.clear();
+    VLOG(2) << "feed var num: " << feed_var_num;
+    for (int i = 0; i < feed_var_num; ++i) {
+      _feed_name_to_idx[model_config.feed_var(i).alias_name()] = i;
+      VLOG(2) << "feed [" << i << "]"
+              << " name: " << model_config.feed_var(i).name();
+      _feed_name.push_back(model_config.feed_var(i).name());
+      VLOG(2) << "feed alias name: " << model_config.feed_var(i).alias_name()
+              << " index: " << i;
+      std::vector<int> tmp_feed_shape;
+      VLOG(2) << "feed"
+              << "[" << i << "] shape:";
+      for (int j = 0; j < model_config.feed_var(i).shape_size(); ++j) {
+        tmp_feed_shape.push_back(model_config.feed_var(i).shape(j));
+        VLOG(2) << "shape[" << j << "]: " << model_config.feed_var(i).shape(j);
+      }
+      _type.push_back(model_config.feed_var(i).feed_type());
+      VLOG(2) << "feed"
+              << "[" << i
+              << "] feed type: " << model_config.feed_var(i).feed_type();
+      _shape.push_back(tmp_feed_shape);
+    }
+
+    if (conf_file.size() > 1) {
+      model_config.Clear();
+      if (configure::read_proto_conf(conf_file[conf_file.size() - 1].c_str(),
+                                     &model_config) != 0) {
+        LOG(ERROR) << "Failed to load general model config"
+                   << ", file path: " << conf_file[conf_file.size() - 1];
+        return -1;
+      }
+    }
+    int fetch_var_num = model_config.fetch_var_size();
+    VLOG(2) << "fetch_var_num: " << fetch_var_num;
+    for (int i = 0; i < fetch_var_num; ++i) {
+      _fetch_name_to_idx[model_config.fetch_var(i).alias_name()] = i;
+      VLOG(2) << "fetch [" << i << "]"
+              << " alias name: " << model_config.fetch_var(i).alias_name();
+      _fetch_name_to_var_name[model_config.fetch_var(i).alias_name()] =
+          model_config.fetch_var(i).name();
+      _fetch_name_to_type[model_config.fetch_var(i).alias_name()] =
+          model_config.fetch_var(i).fetch_type();
+    }
+  } catch (std::exception &e) {
+    LOG(ERROR) << "Failed load general model config" << e.what();
+    return -1;
+  }
+  return 0;
+}
+
+void PredictorData::add_float_data(const std::vector<float>& data,
+                                    const std::string& name,
+                                    const std::vector<int>& shape,
+                                    const std::vector<int>& lod) {
+  _float_data_map[name] = data;
+  _shape_map[name] = shape;
+  _lod_map[name] = lod;
+}
+
+void PredictorData::add_int64_data(const std::vector<int64_t>& data,
+                                    const std::string& name,
+                                    const std::vector<int>& shape,
+                                    const std::vector<int>& lod) {
+  _int64_data_map[name] = data;
+  _shape_map[name] = shape;
+  _lod_map[name] = lod;
+}
+
+void PredictorData::add_int32_data(const std::vector<int32_t>& data,
+                                    const std::string& name,
+                                    const std::vector<int>& shape,
+                                    const std::vector<int>& lod) {
+  _int32_data_map[name] = data;
+  _shape_map[name] = shape;
+  _lod_map[name] = lod;
+}
+
+void PredictorData::add_string_data(const std::string& data,
+                                    const std::string& name,
+                                    const std::vector<int>& shape,
+                                    const std::vector<int>& lod) {
+  _string_data_map[name] = data;
+  _shape_map[name] = shape;
+  _lod_map[name] = lod;
+}
+
+}  // namespace general_model
+}  // namespace paddle_serving
+}  // namespace baidu