move general infer and general reader into general-server

make general_model_config unified on client side and server side
make general_model_config accessible with Python API

core/CMakeLists.txt

@@ -25,4 +25,5 @@ endif()
 
 if (NOT CLIENT_ONLY)
 add_subdirectory(predictor)
+add_subdirectory(general-server)
 endif()

core/configure/CMakeLists.txt

@@ -29,6 +29,10 @@ FILE(GLOB inc ${CMAKE_CURRENT_BINARY_DIR}/*.pb.h)
 install(FILES ${inc}
         DESTINATION ${PADDLE_SERVING_INSTALL_DIR}/include/configure)
 
+py_proto_compile(general_model_config_py_proto SRCS proto/general_model_config.proto)
+add_custom_target(general_model_config_py_proto_init ALL COMMAND ${CMAKE_COMMAND} -E touch __init__.py)
+add_dependencies(general_model_config_py_proto general_model_config_py_proto_init)
+
 if (CLIENT_ONLY)
 py_proto_compile(sdk_configure_py_proto SRCS proto/sdk_configure.proto)
 add_custom_target(sdk_configure_py_proto_init ALL COMMAND ${CMAKE_COMMAND} -E touch __init__.py)
@@ -38,6 +42,13 @@ add_custom_command(TARGET sdk_configure_py_proto POST_BUILD
 		COMMAND cp *.py ${PADDLE_SERVING_BINARY_DIR}/python/paddle_serving_client/proto
 		COMMENT "Copy generated python proto into directory paddle_serving_client/proto."
 		WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR})
+
+add_custom_command(TARGET general_model_config_py_proto POST_BUILD
+                COMMAND ${CMAKE_COMMAND} -E make_directory ${PADDLE_SERVING_BINARY_DIR}/python/paddle_serving_client/proto
+                COMMAND cp *.py ${PADDLE_SERVING_BINARY_DIR}/python/paddle_serving_client/proto
+                COMMENT "Copy generated general_model_config proto file into directory paddle_serving_client/proto."
+                WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR})
+
 endif()
 
 if (NOT CLIENT_ONLY)
@@ -49,4 +60,12 @@ add_custom_command(TARGET server_config_py_proto POST_BUILD
 		COMMAND cp *.py ${PADDLE_SERVING_BINARY_DIR}/python/paddle_serving_server/proto
 		COMMENT "Copy generated python proto into directory paddle_serving_server/proto."
 		WORKING_DIRECTORY ${CMAKE_CURRENT_BINRARY_DIR})
+
+add_custom_command(TARGET general_model_config_py_proto POST_BUILD
+		COMMAND ${CMAKE_COMMAND} -E make_directory ${PADDLE_SERVING_BINARY_DIR}/python/paddle_serving_server/proto
+		COMMAND cp *.py ${PADDLE_SERVING_BINARY_DIR}/python/paddle_serving_server/proto
+		COMMENT "Copy generated general_model_config proto file into directory paddle_serving_server/proto."
+		WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR})
+
 endif()
+

core/configure/include/configure_parser.h

@@ -19,13 +19,17 @@
 namespace baidu {
 namespace paddle_serving {
 namespace configure {
-int read_proto_conf(const std::string &conf_path,
-                    const std::string &conf_file,
-                    google::protobuf::Message *conf);
 
-int write_proto_conf(google::protobuf::Message *message,
-                     const std::string &output_path,
-                     const std::string &output_file);
+  int read_proto_conf(const std::string &conf_full_path,
+                      google::protobuf::Message *conf);
+  
+  int read_proto_conf(const std::string &conf_path,
+                      const std::string &conf_file,
+                      google::protobuf::Message *conf);
+  
+  int write_proto_conf(google::protobuf::Message *message,
+                       const std::string &output_path,
+                       const std::string &output_file);
 
 }  // namespace configure
 }  // namespace paddle_serving

core/configure/proto/general_model_config.proto

@@ -16,14 +16,16 @@ syntax = "proto2";
 package baidu.paddle_serving.configure;
 
 message FeedVar {
-  required string name = 1;
-  required bool is_lod_tensor = 2;
-  required int32 feed_type = 3;
-  repeated int32 shape = 4;
+  optional string name = 1;
+  optional string alias_name = 2;
+  optional bool is_lod_tensor = 3 [ default = false ];
+  optional int32 feed_type = 4 [ default = 0 ];
+  repeated int32 shape = 5;
 }
 message FetchVar {
-  required string name = 1;
-  repeated int32 shape = 2;
+  optional string name = 1;
+  optional string alias_name = 2;
+  repeated int32 shape = 3;
 }
 message GeneralModelConfig {
   repeated FeedVar feed_var = 1;

core/configure/src/configure_parser.cpp

@@ -31,6 +31,24 @@ namespace baidu {
 namespace paddle_serving {
 namespace configure {
 
+int read_proto_conf(const std::string &conf_file_full_path,
+                    google::protobuf::Message *conf) {
+  int fd = open(conf_file_full_path.c_str(), O_RDONLY);
+  if (fd == -1) {
+    LOG(WARNING) << "File not found: " << conf_file_full_path.c_str();
+    return -1;
+  }
+
+  google::protobuf::io::FileInputStream input(fd);
+  bool success = google::protobuf::TextFormat::Parse(&input, conf);
+  close(fd);
+  if (!success) {
+    return -1;
+  }
+
+  return 0;
+}
+
 int read_proto_conf(const std::string &conf_path,
                     const std::string &conf_file,
                     google::protobuf::Message *conf) {

core/general-client/include/general_model.h

@@ -45,7 +45,7 @@ class PredictorClient {
   PredictorClient() {}
   ~PredictorClient() {}
 
-  void init(const std::string& client_conf);
+  int init(const std::string& client_conf);
 
   void set_predictor_conf(const std::string& conf_path,
                           const std::string& conf_file);

core/general-client/src/general_model.cpp

@@ -27,45 +27,42 @@ using baidu::paddle_serving::predictor::general_model::FetchInst;
 namespace baidu {
 namespace paddle_serving {
 namespace general_model {
+using configure::GeneralModelConfig;
 
-void PredictorClient::init(const std::string &conf_file) {
-  _conf_file = conf_file;
-  std::ifstream fin(conf_file);
-  if (!fin) {
-    LOG(ERROR) << "Your inference conf file can not be found";
-    exit(-1);
-  }
-  _feed_name_to_idx.clear();
-  _fetch_name_to_idx.clear();
-  _shape.clear();
-  int feed_var_num = 0;
-  int fetch_var_num = 0;
-  fin >> feed_var_num >> fetch_var_num;
-  std::string name;
-  std::string fetch_var_name;
-  int shape_num = 0;
-  int dim = 0;
-  int type_value = 0;
-  for (int i = 0; i < feed_var_num; ++i) {
-    fin >> name;
-    _feed_name_to_idx[name] = i;
-    fin >> shape_num;
-    std::vector<int> tmp_feed_shape;
-    for (int j = 0; j < shape_num; ++j) {
-      fin >> dim;
-      tmp_feed_shape.push_back(dim);
+int PredictorClient::init(const std::string &conf_file) {
+  try {
+    GeneralModelConfig model_config;
+    if (configure::read_proto_conf(conf_file.c_str(),
+                                   &model_config) != 0) {
+      LOG(ERROR) << "Failed to load general model config"
+                 << ", file path: " << conf_file;
+      return -1;
+    }
+    _feed_name_to_idx.clear();
+    _fetch_name_to_idx.clear();
+    _shape.clear();
+    int feed_var_num = model_config.feed_var_size();
+    int fetch_var_num = model_config.fetch_var_size();
+    for (int i = 0; i < feed_var_num; ++i) {
+      _feed_name_to_idx[model_config.feed_var(i).alias_name()] = i;
+      std::vector<int> tmp_feed_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));
+      }
+      _type.push_back(model_config.feed_var(i).feed_type());
+      _shape.push_back(tmp_feed_shape);
     }
-    fin >> type_value;
-    _type.push_back(type_value);
-    _shape.push_back(tmp_feed_shape);
-  }
 
-  for (int i = 0; i < fetch_var_num; ++i) {
-    fin >> name;
-    fin >> fetch_var_name;
-    _fetch_name_to_idx[name] = i;
-    _fetch_name_to_var_name[name] = fetch_var_name;
+    for (int i = 0; i < fetch_var_num; ++i) {
+      _fetch_name_to_idx[model_config.fetch_var(i).alias_name()] = i;
+      _fetch_name_to_var_name[model_config.fetch_var(i).alias_name()] =
+          model_config.fetch_var(i).name();
+    }
+  } catch (std::exception& e) {
+    LOG(ERROR) << "Failed load general model config" << e.what();
+    return -1;
   }
+  return 0;
 }
 
 void PredictorClient::set_predictor_conf(const std::string &conf_path,

core/general-client/src/pybind_general_model.cpp

@@ -33,7 +33,7 @@ PYBIND11_MODULE(serving_client, m) {
       .def(py::init())
       .def("init",
            [](PredictorClient &self, const std::string &conf) {
-             self.init(conf);
+             return self.init(conf);
            })
       .def("set_predictor_conf",
            [](PredictorClient &self,

core/general-server/CMakeLists.txt

+include_directories(SYSTEM  ${CMAKE_CURRENT_LIST_DIR}/../kvdb/include)
+include(op/CMakeLists.txt)
+include(proto/CMakeLists.txt)
+add_executable(serving ${serving_srcs})
+add_dependencies(serving pdcodegen fluid_cpu_engine pdserving paddle_fluid
+        opencv_imgcodecs cube-api)
+if (WITH_GPU)
+    add_dependencies(serving fluid_gpu_engine)
+endif()
+target_include_directories(serving PUBLIC
+        ${CMAKE_CURRENT_BINARY_DIR}/../../core/predictor
+        )
+
+if(WITH_GPU)
+    target_link_libraries(serving -Wl,--whole-archive fluid_gpu_engine
+            -Wl,--no-whole-archive)
+endif()
+
+target_link_libraries(serving -Wl,--whole-archive fluid_cpu_engine
+        -Wl,--no-whole-archive)
+
+target_link_libraries(serving paddle_fluid ${paddle_depend_libs})
+
+target_link_libraries(serving pdserving)
+target_link_libraries(serving cube-api)
+
+target_link_libraries(serving kvdb rocksdb)
+
+if(WITH_GPU)
+    target_link_libraries(serving ${CUDA_LIBRARIES})
+endif()
+
+if(WITH_MKL)
+    target_link_libraries(serving -liomp5 -lmklml_intel -lmkldnn -lpthread -lcrypto -lm -lrt -lssl -ldl -lz -lbz2)
+else()
+    target_link_libraries(serving openblas -lpthread -lcrypto -lm -lrt -lssl -ldl -lz -lbz2)
+endif()
+
+install(TARGETS serving
+        RUNTIME DESTINATION
+        ${PADDLE_SERVING_INSTALL_DIR}/demo/serving/bin)
+install(DIRECTORY ${CMAKE_CURRENT_LIST_DIR}/conf DESTINATION
+        ${PADDLE_SERVING_INSTALL_DIR}/demo/serving/)
+
+FILE(GLOB inc ${CMAKE_CURRENT_BINARY_DIR}/*.pb.h)
+install(FILES ${inc}
+        DESTINATION ${PADDLE_SERVING_INSTALL_DIR}/include/serving)
+
+if (${WITH_MKL})
+install(FILES
+        ${CMAKE_BINARY_DIR}/third_party/install/Paddle/third_party/install/mklml/lib/libmklml_intel.so
+        ${CMAKE_BINARY_DIR}/third_party/install/Paddle/third_party/install/mklml/lib/libiomp5.so
+        ${CMAKE_BINARY_DIR}/third_party/install/Paddle/third_party/install/mkldnn/lib/libmkldnn.so.0
+        DESTINATION
+        ${PADDLE_SERVING_INSTALL_DIR}/demo/serving/bin)
+endif()

core/general-server/op/CMakeLists.txt

+FILE(GLOB op_srcs ${CMAKE_CURRENT_LIST_DIR}/*.cpp)
+LIST(APPEND serving_srcs ${op_srcs})

core/general-server/op/general_infer_op.cpp

+// Copyright (c) 2020 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 "examples/demo-serving/op/general_infer_op.h"
+#include <algorithm>
+#include <iostream>
+#include <memory>
+#include <sstream>
+#include "core/predictor/framework/infer.h"
+#include "core/predictor/framework/memory.h"
+#include "core/predictor/framework/resource.h"
+#include "examples/demo-serving/op/general_reader_op.h"
+
+namespace baidu {
+namespace paddle_serving {
+namespace serving {
+
+using baidu::paddle_serving::predictor::MempoolWrapper;
+using baidu::paddle_serving::predictor::general_model::Tensor;
+using baidu::paddle_serving::predictor::general_model::Response;
+using baidu::paddle_serving::predictor::general_model::FetchInst;
+using baidu::paddle_serving::predictor::InferManager;
+
+int GeneralInferOp::inference() {
+  const GeneralReaderOutput *reader_out =
+      get_depend_argument<GeneralReaderOutput>("general_reader_op");
+  if (!reader_out) {
+    LOG(ERROR) << "Failed mutable depended argument, op:"
+               << "general_reader_op";
+    return -1;
+  }
+
+  int reader_status = reader_out->reader_status;
+  if (reader_status != 0) {
+    LOG(ERROR) << "Read request wrong.";
+    return -1;
+  }
+
+  const TensorVector *in = &reader_out->tensor_vector;
+  TensorVector *out = butil::get_object<TensorVector>();
+  int batch_size = (*in)[0].shape[0];
+  // infer
+  if (InferManager::instance().infer(GENERAL_MODEL_NAME, in, out, batch_size)) {
+    LOG(ERROR) << "Failed do infer in fluid model: " << GENERAL_MODEL_NAME;
+    return -1;
+  }
+
+  Response *res = mutable_data<Response>();
+
+  for (int i = 0; i < batch_size; ++i) {
+    FetchInst *fetch_inst = res->add_insts();
+    for (int j = 0; j < out->size(); ++j) {
+      Tensor *tensor = fetch_inst->add_tensor_array();
+      tensor->set_elem_type(1);
+      if (out->at(j).lod.size() == 1) {
+        tensor->add_shape(-1);
+      } else {
+        for (int k = 1; k < out->at(j).shape.size(); ++k) {
+          tensor->add_shape(out->at(j).shape[k]);
+        }
+      }
+    }
+  }
+
+  for (int i = 0; i < out->size(); ++i) {
+    float *data_ptr = static_cast<float *>(out->at(i).data.data());
+    int cap = 1;
+    for (int j = 1; j < out->at(i).shape.size(); ++j) {
+      cap *= out->at(i).shape[j];
+    }
+    if (out->at(i).lod.size() == 1) {
+      for (int j = 0; j < batch_size; ++j) {
+        for (int k = out->at(i).lod[0][j]; k < out->at(i).lod[0][j + 1]; k++) {
+          res->mutable_insts(j)->mutable_tensor_array(i)->add_data(
+              reinterpret_cast<char *>(&(data_ptr[k])), sizeof(float));
+        }
+      }
+    } else {
+      for (int j = 0; j < batch_size; ++j) {
+        for (int k = j * cap; k < (j + 1) * cap; ++k) {
+          res->mutable_insts(j)->mutable_tensor_array(i)->add_data(
+              reinterpret_cast<char *>(&(data_ptr[k])), sizeof(float));
+        }
+      }
+    }
+  }
+  /*
+      for (size_t i = 0; i < in->size(); ++i) {
+        (*in)[i].shape.clear();
+      }
+      in->clear();
+      butil::return_object<TensorVector>(in);
+
+      for (size_t i = 0; i < out->size(); ++i) {
+        (*out)[i].shape.clear();
+      }
+      out->clear();
+      butil::return_object<TensorVector>(out);
+    }
+  */
+  return 0;
+}
+DEFINE_OP(GeneralInferOp);
+
+}  // namespace serving
+}  // namespace paddle_serving
+}  // namespace baidu

core/general-server/op/general_infer_op.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 <vector>
+#ifdef BCLOUD
+#ifdef WITH_GPU
+#include "paddle/paddle_inference_api.h"
+#else
+#include "paddle/fluid/inference/api/paddle_inference_api.h"
+#endif
+#else
+#include "paddle_inference_api.h"  // NOLINT
+#endif
+#include "examples/demo-serving/general_model_service.pb.h"
+
+namespace baidu {
+namespace paddle_serving {
+namespace serving {
+
+static const char* GENERAL_MODEL_NAME = "general_model";
+
+class GeneralInferOp
+    : public baidu::paddle_serving::predictor::OpWithChannel<
+          baidu::paddle_serving::predictor::general_model::Response> {
+ public:
+  typedef std::vector<paddle::PaddleTensor> TensorVector;
+
+  DECLARE_OP(GeneralInferOp);
+
+  int inference();
+};
+
+}  // namespace serving
+}  // namespace paddle_serving
+}  // namespace baidu

core/general-server/op/general_reader_op.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 "examples/demo-serving/op/general_reader_op.h"
+#include <algorithm>
+#include <iostream>
+#include <memory>
+#include <sstream>
+#include "core/predictor/framework/infer.h"
+#include "core/predictor/framework/memory.h"
+
+namespace baidu {
+namespace paddle_serving {
+namespace serving {
+
+using baidu::paddle_serving::predictor::MempoolWrapper;
+using baidu::paddle_serving::predictor::general_model::Tensor;
+using baidu::paddle_serving::predictor::general_model::Request;
+using baidu::paddle_serving::predictor::general_model::FeedInst;
+using baidu::paddle_serving::predictor::PaddleGeneralModelConfig;
+
+int conf_check(const Request *req,
+               const std::shared_ptr<PaddleGeneralModelConfig> &model_config) {
+  int var_num = req->insts(0).tensor_array_size();
+  if (var_num != model_config->_feed_type.size()) {
+    VLOG(2) << "var num: " << var_num;
+    VLOG(2) << "model config var num: " << model_config->_feed_type.size();
+    LOG(ERROR) << "feed var number not match.";
+    return -1;
+  }
+  for (int i = 0; i < var_num; ++i) {
+    if (model_config->_feed_type[i] !=
+        req->insts(0).tensor_array(i).elem_type()) {
+      LOG(ERROR) << "feed type not match.";
+      return -1;
+    }
+    if (model_config->_feed_shape[i].size() ==
+        req->insts(0).tensor_array(i).shape_size()) {
+      for (int j = 0; j < model_config->_feed_shape[i].size(); ++j) {
+        req->insts(0).tensor_array(i).shape(j);
+        if (model_config->_feed_shape[i][j] !=
+            req->insts(0).tensor_array(i).shape(j)) {
+          LOG(ERROR) << "feed shape not match.";
+          return -1;
+        }
+      }
+    } else {
+      LOG(ERROR) << "feed shape not match.";
+      return -1;
+    }
+  }
+  return 0;
+}
+
+int GeneralReaderOp::inference() {
+  // reade request from client
+  const Request *req = dynamic_cast<const Request *>(get_request_message());
+
+  int batch_size = req->insts_size();
+  int input_var_num = 0;
+
+  std::vector<int64_t> elem_type;
+  std::vector<int64_t> elem_size;
+  std::vector<int64_t> capacity;
+
+  GeneralReaderOutput *res = mutable_data<GeneralReaderOutput>();
+  TensorVector *in = &res->tensor_vector;
+
+  if (!res) {
+    LOG(ERROR) << "Failed get op tls reader object output";
+  }
+  if (batch_size <= 0) {
+    res->reader_status = -1;
+    return 0;
+  }
+
+  int var_num = req->insts(0).tensor_array_size();
+  VLOG(2) << "var num: " << var_num;
+  // read config
+
+  LOG(INFO) << "start to call load general model_conf op";
+  baidu::paddle_serving::predictor::Resource &resource =
+      baidu::paddle_serving::predictor::Resource::instance();
+
+  LOG(INFO) << "get resource pointer done.";
+  std::shared_ptr<PaddleGeneralModelConfig> model_config =
+      resource.get_general_model_config();
+
+  LOG(INFO) << "print general model config done.";
+
+  // check
+  res->reader_status = conf_check(req, model_config);
+  if (res->reader_status != 0) {
+    LOG(INFO) << "model conf of server:";
+    resource.print_general_model_config(model_config);
+    return 0;
+  }
+  // package tensor
+
+  elem_type.resize(var_num);
+  elem_size.resize(var_num);
+  capacity.resize(var_num);
+  paddle::PaddleTensor lod_tensor;
+  for (int i = 0; i < var_num; ++i) {
+    elem_type[i] = req->insts(0).tensor_array(i).elem_type();
+    VLOG(2) << "var[" << i << "] has elem type: " << elem_type[i];
+    if (elem_type[i] == 0) {  // int64
+      elem_size[i] = sizeof(int64_t);
+      lod_tensor.dtype = paddle::PaddleDType::INT64;
+    } else {
+      elem_size[i] = sizeof(float);
+      lod_tensor.dtype = paddle::PaddleDType::FLOAT32;
+    }
+
+    if (req->insts(0).tensor_array(i).shape(0) == -1) {
+      lod_tensor.lod.resize(1);
+      lod_tensor.lod[0].push_back(0);
+      VLOG(2) << "var[" << i << "] is lod_tensor";
+    } else {
+      lod_tensor.shape.push_back(batch_size);
+      capacity[i] = 1;
+      for (int k = 0; k < req->insts(0).tensor_array(i).shape_size(); ++k) {
+        int dim = req->insts(0).tensor_array(i).shape(k);
+        VLOG(2) << "shape for var[" << i << "]: " << dim;
+        capacity[i] *= dim;
+        lod_tensor.shape.push_back(dim);
+      }
+      VLOG(2) << "var[" << i << "] is tensor, capacity: " << capacity[i];
+    }
+    if (i == 0) {
+      lod_tensor.name = "words";
+    } else {
+      lod_tensor.name = "label";
+    }
+    in->push_back(lod_tensor);
+  }
+
+  for (int i = 0; i < var_num; ++i) {
+    if (in->at(i).lod.size() == 1) {
+      for (int j = 0; j < batch_size; ++j) {
+        const Tensor &tensor = req->insts(j).tensor_array(i);
+        int data_len = tensor.data_size();
+        VLOG(2) << "tensor size for var[" << i << "]: " << tensor.data_size();
+        int cur_len = in->at(i).lod[0].back();
+        VLOG(2) << "current len: " << cur_len;
+        in->at(i).lod[0].push_back(cur_len + data_len);
+        VLOG(2) << "new len: " << cur_len + data_len;
+      }
+      in->at(i).data.Resize(in->at(i).lod[0].back() * elem_size[i]);
+      in->at(i).shape = {in->at(i).lod[0].back(), 1};
+      VLOG(2) << "var[" << i
+              << "] is lod_tensor and len=" << in->at(i).lod[0].back();
+    } else {
+      in->at(i).data.Resize(batch_size * capacity[i] * elem_size[i]);
+      VLOG(2) << "var[" << i
+              << "] is tensor and capacity=" << batch_size * capacity[i];
+    }
+  }
+
+  for (int i = 0; i < var_num; ++i) {
+    if (elem_type[i] == 0) {
+      int64_t *dst_ptr = static_cast<int64_t *>(in->at(i).data.data());
+      int offset = 0;
+      for (int j = 0; j < batch_size; ++j) {
+        for (int k = 0; k < req->insts(j).tensor_array(i).data_size(); ++k) {
+          dst_ptr[offset + k] =
+              *(const int64_t *)req->insts(j).tensor_array(i).data(k).c_str();
+        }
+        if (in->at(i).lod.size() == 1) {
+          offset = in->at(i).lod[0][j + 1];
+        } else {
+          offset += capacity[i];
+        }
+      }
+    } else {
+      float *dst_ptr = static_cast<float *>(in->at(i).data.data());
+      int offset = 0;
+      for (int j = 0; j < batch_size; ++j) {
+        for (int k = 0; k < req->insts(j).tensor_array(i).data_size(); ++k) {
+          dst_ptr[offset + k] =
+              *(const float *)req->insts(j).tensor_array(i).data(k).c_str();
+        }
+        if (in->at(i).lod.size() == 1) {
+          offset = in->at(i).lod[0][j + 1];
+        } else {
+          offset += capacity[i];
+        }
+      }
+    }
+  }
+
+  VLOG(2) << "read data from client success";
+  // print request
+  std::ostringstream oss;
+  int64_t *example = reinterpret_cast<int64_t *>((*in)[0].data.data());
+  for (int i = 0; i < 10; i++) {
+    oss << *(example + i) << " ";
+  }
+  VLOG(2) << "head element of first feed var : " << oss.str();
+  //
+  return 0;
+}
+DEFINE_OP(GeneralReaderOp);
+}  // namespace serving
+}  // namespace paddle_serving
+}  // namespace baidu

core/general-server/op/general_reader_op.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 <vector>
+#ifdef BCLOUD
+#ifdef WITH_GPU
+#include "paddle/paddle_inference_api.h"
+#else
+#include "paddle/fluid/inference/api/paddle_inference_api.h"
+#endif
+#else
+#include "paddle_inference_api.h"  // NOLINT
+#endif
+#include <string>
+#include "core/predictor/framework/resource.h"
+#include "examples/demo-serving/general_model_service.pb.h"
+#include "examples/demo-serving/load_general_model_service.pb.h"
+
+namespace baidu {
+namespace paddle_serving {
+namespace serving {
+
+struct GeneralReaderOutput {
+  std::vector<paddle::PaddleTensor> tensor_vector;
+  int reader_status = 0;
+
+  void Clear() {
+    size_t tensor_count = tensor_vector.size();
+    for (size_t ti = 0; ti < tensor_count; ++ti) {
+      tensor_vector[ti].shape.clear();
+    }
+    tensor_vector.clear();
+  }
+  std::string ShortDebugString() const { return "Not implemented!"; }
+};
+
+class GeneralReaderOp : public baidu::paddle_serving::predictor::OpWithChannel<
+                            GeneralReaderOutput> {
+ public:
+  typedef std::vector<paddle::PaddleTensor> TensorVector;
+
+  DECLARE_OP(GeneralReaderOp);
+
+  int inference();
+};
+
+}  // namespace serving
+}  // namespace paddle_serving
+}  // namespace baidu

core/general-server/proto/CMakeLists.txt

+LIST(APPEND protofiles
+	${CMAKE_CURRENT_LIST_DIR}/load_general_model_service.proto
+	${CMAKE_CURRENT_LIST_DIR}/general_model_service.proto
+)
+
+PROTOBUF_GENERATE_SERVING_CPP(TRUE PROTO_SRCS PROTO_HDRS ${protofiles})
+LIST(APPEND serving_srcs ${PROTO_SRCS})

core/general-server/proto/general_model_service.proto

+// 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.
+
+syntax = "proto2";
+import "pds_option.proto";
+import "builtin_format.proto";
+package baidu.paddle_serving.predictor.general_model;
+
+option cc_generic_services = true;
+
+message Tensor {
+  repeated bytes data = 1;
+  optional int32 elem_type = 2;
+  repeated int32 shape = 3;
+};
+
+message FeedInst {
+  repeated Tensor tensor_array = 1;
+};
+
+message FetchInst {
+  repeated Tensor tensor_array = 1;
+};
+
+message Request {
+  repeated FeedInst insts = 1;
+};
+
+message Response {
+  repeated FetchInst insts = 1;
+};
+
+service GeneralModelService {
+  rpc inference(Request) returns (Response);
+  rpc debug(Request) returns (Response);
+  option (pds.options).generate_impl = true;
+};

core/general-server/proto/load_general_model_service.proto

+// 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.
+
+syntax = "proto2";
+import "pds_option.proto";
+package baidu.paddle_serving.predictor.load_general_model_service;
+
+option cc_generic_services = true;
+
+message RequestAndResponse {
+  required int32 a = 1;
+  required float b = 2;
+};
+
+service LoadGeneralModelService {
+  rpc inference(RequestAndResponse) returns (RequestAndResponse);
+  rpc debug(RequestAndResponse) returns (RequestAndResponse);
+  option (pds.options).generate_impl = true;
+};

core/predictor/common/constant.cpp

@@ -38,12 +38,12 @@ DEFINE_int32(
     0,
     "Number of pthreads that server runs on, not change if this value <= 0");
 DEFINE_int32(reload_interval_s, 10, "");
-DEFINE_bool(enable_model_toolkit, false, "enable model toolkit");
+DEFINE_bool(enable_model_toolkit, true, "enable model toolkit");
 DEFINE_string(enable_protocol_list, "baidu_std", "set protocol list");
 DEFINE_bool(enable_cube, false, "enable cube");
 DEFINE_string(general_model_path, "./conf", "");
 DEFINE_string(general_model_file, "general_model.prototxt", "");
-DEFINE_bool(enable_general_model, false, "enable general model");
+DEFINE_bool(enable_general_model, true, "enable general model");
 
 const char* START_OP_NAME = "startup_op";
 }  // namespace predictor

core/predictor/framework/resource.cpp

@@ -155,8 +155,11 @@ int Resource::initialize(const std::string& path, const std::string& file) {
 // model config
 int Resource::general_model_initialize(const std::string& path,
                                        const std::string& file) {
+  VLOG(2) << "general model path: " << path;
+  VLOG(2) << "general model file: " << file;
   if (!FLAGS_enable_general_model) {
-    return 0;
+    LOG(ERROR) << "general model is not enabled";
+    return -1;
   }
   ResourceConf resource_conf;
   if (configure::read_proto_conf(path, file, &resource_conf) != 0) {
@@ -183,6 +186,8 @@ int Resource::general_model_initialize(const std::string& path,
 
   _config.reset(new PaddleGeneralModelConfig());
   int feed_var_num = model_config.feed_var_size();
+  VLOG(2) << "load general model config";
+  VLOG(2) << "feed var num: " << feed_var_num;
   _config->_feed_name.resize(feed_var_num);
   _config->_feed_type.resize(feed_var_num);
   _config->_is_lod_feed.resize(feed_var_num);
@@ -190,15 +195,23 @@ int Resource::general_model_initialize(const std::string& path,
   _config->_feed_shape.resize(feed_var_num);
   for (int i = 0; i < feed_var_num; ++i) {
     _config->_feed_name[i] = model_config.feed_var(i).name();
+    VLOG(2) << "feed var[" << i << "]: "
+            << _config->_feed_name[i];
     _config->_feed_type[i] = model_config.feed_var(i).feed_type();
+    VLOG(2) << "feed type[" << i << "]: "
+            << _config->_feed_type[i];
+
     if (model_config.feed_var(i).is_lod_tensor()) {
+      VLOG(2) << "var[" << i << "] is lod tensor";
       _config->_feed_shape[i] = {-1};
       _config->_is_lod_feed[i] = true;
     } else {
+      VLOG(2) << "var[" << i << "] is tensor";
       _config->_capacity[i] = 1;
       _config->_is_lod_feed[i] = false;
       for (int j = 0; j < model_config.feed_var(i).shape_size(); ++j) {
         int32_t dim = model_config.feed_var(i).shape(j);
+        VLOG(2) << "var[" << i << "].shape[" << i << "]: " << dim;
         _config->_feed_shape[i].push_back(dim);
         _config->_capacity[i] *= dim;
       }

core/predictor/src/pdserving.cpp

@@ -126,10 +126,6 @@ int main(int argc, char** argv) {
     return 0;
   }
 
-  if (!FLAGS_g) {
-    google::SetCommandLineOption("flagfile", "conf/gflags.conf");
-  }
-
   google::ParseCommandLineFlags(&argc, &argv, true);
 
   g_change_server_port();

core/sdk-cpp/include/common.h

@@ -55,6 +55,7 @@
 
 #include "core/configure/include/configure_parser.h"
 #include "core/configure/sdk_configure.pb.h"
+#include "core/configure/general_model_config.pb.h"
 
 #include "core/sdk-cpp/include/utils.h"