Merge branch 'develop' of github.com:HexToString/Serving into develop

README.md

@@ -47,9 +47,10 @@ nvidia-docker exec -it test bash
 ```shell
 pip install paddle-serving-client==0.4.0 
 pip install paddle-serving-server==0.4.0 # CPU
+pip install paddle-serving-app==0.2.0
 pip install paddle-serving-server-gpu==0.4.0.post9 # GPU with CUDA9.0
 pip install paddle-serving-server-gpu==0.4.0.post10 # GPU with CUDA10.0
-pip install paddle-serving-server-gpu==0.4.0.trt # GPU with CUDA10.1+TensorRT
+pip install paddle-serving-server-gpu==0.4.0.100 # GPU with CUDA10.1+TensorRT
 ```
 
 You may need to use a domestic mirror source (in China, you can use the Tsinghua mirror source, add `-i https://pypi.tuna.tsinghua.edu.cn/simple` to pip command) to speed up the download.

README_CN.md

@@ -49,9 +49,10 @@ nvidia-docker exec -it test bash
 ```shell
 pip install paddle-serving-client==0.4.0
 pip install paddle-serving-server==0.4.0 # CPU
+pip install paddle-serving-app==0.2.0
 pip install paddle-serving-server-gpu==0.4.0.post9 # GPU with CUDA9.0
 pip install paddle-serving-server-gpu==0.4.0.post10 # GPU with CUDA10.0
-pip install paddle-serving-server-gpu==0.4.0.trt # GPU with CUDA10.1+TensorRT
+pip install paddle-serving-server-gpu==0.4.0.100 # GPU with CUDA10.1+TensorRT
 ```
 
 您可能需要使用国内镜像源（例如清华源, 在pip命令中添加`-i https://pypi.tuna.tsinghua.edu.cn/simple`）来加速下载。

cmake/external/boost.cmake

@@ -22,8 +22,8 @@ set(BOOST_PROJECT       "extern_boost")
 # version of boost, say, 1.66.0, doesn't build on CentOS 6.  We
 # checked that the devtools package of CentOS 6 installs boost 1.41.0.
 # So we use 1.41.0 here.
-set(BOOST_VER           "1.41.0")
-set(BOOST_TAR "boost_1_41_0" CACHE STRING "" FORCE)
+set(BOOST_VER           "1.74.0")
+set(BOOST_TAR "boost_1_74_0" CACHE STRING "" FORCE)
 set(BOOST_URL "http://paddlepaddledeps.cdn.bcebos.com/${BOOST_TAR}.tar.gz" CACHE STRING "" FORCE)
 
 MESSAGE(STATUS "BOOST_TAR: ${BOOST_TAR}, BOOST_URL: ${BOOST_URL}")

cmake/external/brpc.cmake

@@ -13,6 +13,9 @@
 # limitations under the License.
 
 INCLUDE(ExternalProject)
+set(BRPC_CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -Wno-narrowing")
+set(BRPC_CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wno-narrowing")
+set(BRPC_CMAKE_CPP_FLAGS "${CMAKE_CPP_FLAGS} -Wno-narrowing")
 
 find_package(OpenSSL REQUIRED) 
 
@@ -41,13 +44,14 @@ ExternalProject_Add(
     ${EXTERNAL_PROJECT_LOG_ARGS}
     # TODO(gongwb): change to de newst repo when they changed.
     GIT_REPOSITORY  "https://github.com/wangjiawei04/brpc"
-    GIT_TAG         "6d79e0b17f25107c35b705ea58d888083f59ff47"
+    GIT_TAG         "serving-0.4.1"
     PREFIX          ${BRPC_SOURCES_DIR}
     UPDATE_COMMAND  ""
     CMAKE_ARGS      -DCMAKE_CXX_COMPILER=${CMAKE_CXX_COMPILER}
                     -DCMAKE_C_COMPILER=${CMAKE_C_COMPILER}
-                    -DCMAKE_CXX_FLAGS=${CMAKE_CXX_FLAGS}
-                    -DCMAKE_C_FLAGS=${CMAKE_C_FLAGS}
+                    -DCMAKE_CXX_FLAGS=${BRPC_CMAKE_CXX_FLAGS}
+                    -DCMAKE_C_FLAGS=${BRPC_CMAKE_C_FLAGS}
+                    -DCMAKE_CPP_FLAGS=${BRPC_CMAKE_CPP_FLAGS}
                     -DCMAKE_INSTALL_PREFIX=${BRPC_INSTALL_DIR}
                     -DCMAKE_INSTALL_LIBDIR=${BRPC_INSTALL_DIR}/lib
                     -DCMAKE_POSITION_INDEPENDENT_CODE=ON

cmake/paddlepaddle.cmake

@@ -31,11 +31,11 @@ message( "WITH_GPU = ${WITH_GPU}")
 # Paddle Version should be one of:
 # latest: latest develop build
 # version number like 1.5.2
-SET(PADDLE_VERSION "1.8.4")
+SET(PADDLE_VERSION "2.0.0-rc1")
 
 if (WITH_GPU)
     if (WITH_TRT)
-        SET(PADDLE_LIB_VERSION "${PADDLE_VERSION}-gpu-cuda10.1-cudnn7.6-avx-mkl-trt6")
+        SET(PADDLE_LIB_VERSION "${PADDLE_VERSION}-gpu-cuda10.1-cudnn7-avx-mkl-trt6")
     else()
         SET(PADDLE_LIB_VERSION "${PADDLE_VERSION}-gpu-cuda10-cudnn7-avx-mkl")
     endif()
@@ -51,7 +51,7 @@ else()
     endif()
 endif()
 
-SET(PADDLE_LIB_PATH "http://paddle-inference-lib.bj.bcebos.com/${PADDLE_LIB_VERSION}/fluid_inference.tgz")
+SET(PADDLE_LIB_PATH "http://paddle-inference-lib.bj.bcebos.com/${PADDLE_LIB_VERSION}/paddle_inference.tgz")
 MESSAGE(STATUS "PADDLE_LIB_PATH=${PADDLE_LIB_PATH}")
 if (WITH_GPU OR WITH_MKLML)
     if (WITH_TRT)

core/configure/CMakeLists.txt

@@ -14,10 +14,6 @@ list(APPEND configure_srcs ${CMAKE_CURRENT_LIST_DIR}/src/configure_parser.cpp)
 add_library(configure ${configure_srcs})
 add_dependencies(configure brpc)
 
-add_executable(test_configure
-        ${CMAKE_CURRENT_LIST_DIR}/tests/test_configure.cpp)
-target_link_libraries(test_configure configure protobuf)
-
 install(TARGETS configure 
         ARCHIVE DESTINATION ${PADDLE_SERVING_INSTALL_DIR}/lib
         )
@@ -45,19 +41,19 @@ add_custom_target(sdk_configure_py_proto_init ALL COMMAND ${CMAKE_COMMAND} -E to
 add_dependencies(sdk_configure_py_proto sdk_configure_py_proto_init)
 add_custom_command(TARGET sdk_configure_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
+		COMMAND cp -f *.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
+                COMMAND cp -f *.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})
 
 add_custom_command(TARGET multi_lang_general_model_service_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
+                COMMAND cp -f *.py ${PADDLE_SERVING_BINARY_DIR}/python/paddle_serving_client/proto
                 COMMENT "Copy generated multi_lang_general_model_service proto file into directory paddle_serving_client/proto."
                 WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR})
 endif()
@@ -65,7 +61,7 @@ endif()
 if (APP)
 add_custom_command(TARGET general_model_config_py_proto POST_BUILD
                 COMMAND ${CMAKE_COMMAND} -E make_directory ${PADDLE_SERVING_BINARY_DIR}/python/paddle_serving_app/proto
-                COMMAND cp *.py ${PADDLE_SERVING_BINARY_DIR}/python/paddle_serving_app/proto
+                COMMAND cp -f *.py ${PADDLE_SERVING_BINARY_DIR}/python/paddle_serving_app/proto
                 COMMENT "Copy generated general_model_config proto file into directory paddle_serving_app/proto."
                 WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR})
 endif()
@@ -77,26 +73,26 @@ add_dependencies(server_config_py_proto server_config_py_proto_init)
 if (NOT WITH_GPU)
 add_custom_command(TARGET server_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
+		COMMAND cp -f *.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
+		COMMAND cp -f *.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})
 
 add_custom_command(TARGET multi_lang_general_model_service_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
+                COMMAND cp -f *.py ${PADDLE_SERVING_BINARY_DIR}/python/paddle_serving_server/proto
                 COMMENT "Copy generated multi_lang_general_model_service proto file into directory paddle_serving_server/proto."
                 WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR})
 else()
 add_custom_command(TARGET server_config_py_proto POST_BUILD
 		COMMAND ${CMAKE_COMMAND} -E make_directory
         ${PADDLE_SERVING_BINARY_DIR}/python/paddle_serving_server_gpu/proto
-		COMMAND cp *.py
+		COMMAND cp -f *.py
         ${PADDLE_SERVING_BINARY_DIR}/python/paddle_serving_server_gpu/proto
 		COMMENT "Copy generated python proto into directory
         paddle_serving_server_gpu/proto."
@@ -105,7 +101,7 @@ add_custom_command(TARGET server_config_py_proto POST_BUILD
 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_gpu/proto
-		COMMAND cp *.py
+		COMMAND cp -f *.py
         ${PADDLE_SERVING_BINARY_DIR}/python/paddle_serving_server_gpu/proto
 		COMMENT "Copy generated general_model_config proto file into directory
         paddle_serving_server_gpu/proto."
@@ -113,7 +109,7 @@ add_custom_command(TARGET general_model_config_py_proto POST_BUILD
 
 add_custom_command(TARGET multi_lang_general_model_service_py_proto POST_BUILD
                 COMMAND ${CMAKE_COMMAND} -E make_directory ${PADDLE_SERVING_BINARY_DIR}/python/paddle_serving_server_gpu/proto
-                COMMAND cp *.py ${PADDLE_SERVING_BINARY_DIR}/python/paddle_serving_server_gpu/proto
+                COMMAND cp -f *.py ${PADDLE_SERVING_BINARY_DIR}/python/paddle_serving_server_gpu/proto
                 COMMENT "Copy generated multi_lang_general_model_service proto file into directory paddle_serving_server_gpu/proto."
                 WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR})
 endif()

core/general-server/op/general_dist_kv_infer_op.cpp

@@ -38,145 +38,7 @@ using baidu::paddle_serving::predictor::general_model::FetchInst;
 using baidu::paddle_serving::predictor::InferManager;
 using baidu::paddle_serving::predictor::PaddleGeneralModelConfig;
 
-int GeneralDistKVInferOp::inference() {
-  VLOG(2) << "Going to run inference";
-  const std::vector<std::string> pre_node_names = pre_names();
-  if (pre_node_names.size() != 1) {
-    LOG(ERROR) << "This op(" << op_name()
-               << ") can only have one predecessor op, but received "
-               << pre_node_names.size();
-    return -1;
-  }
-  const std::string pre_name = pre_node_names[0];
-
-  const GeneralBlob *input_blob = get_depend_argument<GeneralBlob>(pre_name);
-  uint64_t log_id = input_blob->GetLogId();
-  VLOG(2) << "(logid=" << log_id << ") Get precedent op name: " << pre_name;
-  GeneralBlob *output_blob = mutable_data<GeneralBlob>();
-
-  if (!input_blob) {
-    LOG(ERROR) << "(logid=" << log_id
-               << ") Failed mutable depended argument, op:" << pre_name;
-    return -1;
-  }
-
-  const TensorVector *in = &input_blob->tensor_vector;
-  TensorVector *out = &output_blob->tensor_vector;
-  int batch_size = input_blob->GetBatchSize();
-  VLOG(2) << "(logid=" << log_id << ") input batch size: " << batch_size;
-  std::vector<uint64_t> keys;
-  std::vector<rec::mcube::CubeValue> values;
-  int sparse_count = 0;
-  int dense_count = 0;
-  std::vector<std::pair<int64_t *, size_t>> dataptr_size_pairs;
-  size_t key_len = 0;
-  for (size_t i = 0; i < in->size(); ++i) {
-    if (in->at(i).dtype != paddle::PaddleDType::INT64) {
-      ++dense_count;
-      continue;
-    }
-    ++sparse_count;
-    size_t elem_num = 1;
-    for (size_t s = 0; s < in->at(i).shape.size(); ++s) {
-      elem_num *= in->at(i).shape[s];
-    }
-    key_len += elem_num;
-    int64_t *data_ptr = static_cast<int64_t *>(in->at(i).data.data());
-    dataptr_size_pairs.push_back(std::make_pair(data_ptr, elem_num));
-  }
-  keys.resize(key_len);
-  int key_idx = 0;
-  for (size_t i = 0; i < dataptr_size_pairs.size(); ++i) {
-    std::copy(dataptr_size_pairs[i].first,
-              dataptr_size_pairs[i].first + dataptr_size_pairs[i].second,
-              keys.begin() + key_idx);
-    key_idx += dataptr_size_pairs[i].second;
-  }
-  Timer timeline;
-  int64_t cube_start = timeline.TimeStampUS();
-  timeline.Start();
-  rec::mcube::CubeAPI *cube = rec::mcube::CubeAPI::instance();
-  std::vector<std::string> table_names = cube->get_table_names();
-  if (table_names.size() == 0) {
-    LOG(ERROR) << "(logid=" << log_id
-               << ") cube init error or cube config not given.";
-    return -1;
-  }
-  int ret = cube->seek(table_names[0], keys, &values);
-  int64_t cube_end = timeline.TimeStampUS();
-  if (values.size() != keys.size() || values[0].buff.size() == 0) {
-    LOG(ERROR) << "(logid=" << log_id << ") cube value return null";
-  }
-  size_t EMBEDDING_SIZE = values[0].buff.size() / sizeof(float);
-  TensorVector sparse_out;
-  sparse_out.resize(sparse_count);
-  TensorVector dense_out;
-  dense_out.resize(dense_count);
-  int cube_val_idx = 0;
-  int sparse_idx = 0;
-  int dense_idx = 0;
-  std::unordered_map<int, int> in_out_map;
-  baidu::paddle_serving::predictor::Resource &resource =
-      baidu::paddle_serving::predictor::Resource::instance();
-  std::shared_ptr<PaddleGeneralModelConfig> model_config =
-      resource.get_general_model_config();
-  for (size_t i = 0; i < in->size(); ++i) {
-    if (in->at(i).dtype != paddle::PaddleDType::INT64) {
-      dense_out[dense_idx] = in->at(i);
-      ++dense_idx;
-      continue;
-    }
-
-    sparse_out[sparse_idx].lod.resize(in->at(i).lod.size());
-    for (size_t x = 0; x < sparse_out[sparse_idx].lod.size(); ++x) {
-      sparse_out[sparse_idx].lod[x].resize(in->at(i).lod[x].size());
-      std::copy(in->at(i).lod[x].begin(),
-                in->at(i).lod[x].end(),
-                sparse_out[sparse_idx].lod[x].begin());
-    }
-    sparse_out[sparse_idx].dtype = paddle::PaddleDType::FLOAT32;
-    sparse_out[sparse_idx].shape.push_back(
-        sparse_out[sparse_idx].lod[0].back());
-    sparse_out[sparse_idx].shape.push_back(EMBEDDING_SIZE);
-    sparse_out[sparse_idx].name = model_config->_feed_name[i];
-    sparse_out[sparse_idx].data.Resize(sparse_out[sparse_idx].lod[0].back() *
-                                       EMBEDDING_SIZE * sizeof(float));
-    float *dst_ptr = static_cast<float *>(sparse_out[sparse_idx].data.data());
-    for (int x = 0; x < sparse_out[sparse_idx].lod[0].back(); ++x) {
-      float *data_ptr = dst_ptr + x * EMBEDDING_SIZE;
-      memcpy(data_ptr,
-             values[cube_val_idx].buff.data(),
-             values[cube_val_idx].buff.size());
-      cube_val_idx++;
-    }
-    ++sparse_idx;
-  }
-  TensorVector infer_in;
-  infer_in.insert(infer_in.end(), dense_out.begin(), dense_out.end());
-  infer_in.insert(infer_in.end(), sparse_out.begin(), sparse_out.end());
-
-  output_blob->SetBatchSize(batch_size);
-  output_blob->SetLogId(log_id);
-
-  VLOG(2) << "(logid=" << log_id << ") infer batch size: " << batch_size;
-
-  int64_t start = timeline.TimeStampUS();
-
-  if (InferManager::instance().infer(
-          engine_name().c_str(), &infer_in, out, batch_size)) {
-    LOG(ERROR) << "(logid=" << log_id
-               << ") Failed do infer in fluid model: " << engine_name();
-    return -1;
-  }
-
-  int64_t end = timeline.TimeStampUS();
-  CopyBlobInfo(input_blob, output_blob);
-  AddBlobInfo(output_blob, cube_start);
-  AddBlobInfo(output_blob, cube_end);
-  AddBlobInfo(output_blob, start);
-  AddBlobInfo(output_blob, end);
-  return 0;
-}
+int GeneralDistKVInferOp::inference() { return 0; }
 DEFINE_OP(GeneralDistKVInferOp);
 
 }  // namespace serving

core/general-server/op/general_dist_kv_quant_infer_op.cpp

@@ -188,21 +188,6 @@ int GeneralDistKVQuantInferOp::inference() {
 
   VLOG(2) << "(logid=" << log_id << ") infer batch size: " << batch_size;
 
-  Timer timeline;
-  int64_t start = timeline.TimeStampUS();
-  timeline.Start();
-
-  if (InferManager::instance().infer(
-          engine_name().c_str(), &infer_in, out, batch_size)) {
-    LOG(ERROR) << "(logid=" << log_id
-               << ") Failed do infer in fluid model: " << engine_name();
-    return -1;
-  }
-
-  int64_t end = timeline.TimeStampUS();
-  CopyBlobInfo(input_blob, output_blob);
-  AddBlobInfo(output_blob, start);
-  AddBlobInfo(output_blob, end);
   return 0;
 }
 DEFINE_OP(GeneralDistKVQuantInferOp);

core/general-server/op/general_infer_op.cpp

@@ -44,45 +44,9 @@ int GeneralInferOp::inference() {
                << pre_node_names.size();
     return -1;
   }
-  const std::string pre_name = pre_node_names[0];
-
-  const GeneralBlob *input_blob = get_depend_argument<GeneralBlob>(pre_name);
-  uint64_t log_id = input_blob->GetLogId();
-  VLOG(2) << "(logid=" << log_id << ") Get precedent op name: " << pre_name;
-  GeneralBlob *output_blob = mutable_data<GeneralBlob>();
-  output_blob->SetLogId(log_id);
-
-  if (!input_blob) {
-    LOG(ERROR) << "(logid=" << log_id
-               << ") Failed mutable depended argument, op:" << pre_name;
+  if (InferManager::instance().infer(engine_name().c_str())) {
     return -1;
   }
-
-  const TensorVector *in = &input_blob->tensor_vector;
-  TensorVector *out = &output_blob->tensor_vector;
-
-  int batch_size = input_blob->_batch_size;
-  VLOG(2) << "(logid=" << log_id << ") input batch size: " << batch_size;
-
-  output_blob->_batch_size = batch_size;
-
-  VLOG(2) << "(logid=" << log_id << ") infer batch size: " << batch_size;
-
-  Timer timeline;
-  int64_t start = timeline.TimeStampUS();
-  timeline.Start();
-
-  if (InferManager::instance().infer(
-          engine_name().c_str(), in, out, batch_size)) {
-    LOG(ERROR) << "(logid=" << log_id
-               << ") Failed do infer in fluid model: " << engine_name().c_str();
-    return -1;
-  }
-
-  int64_t end = timeline.TimeStampUS();
-  CopyBlobInfo(input_blob, output_blob);
-  AddBlobInfo(output_blob, start);
-  AddBlobInfo(output_blob, end);
   return 0;
 }
 DEFINE_OP(GeneralInferOp);

core/general-server/op/general_reader_op.cpp

@@ -20,6 +20,7 @@
 #include "core/general-server/op/general_infer_helper.h"
 #include "core/predictor/framework/infer.h"
 #include "core/predictor/framework/memory.h"
+#include "core/predictor/framework/resource.h"
 #include "core/util/include/timer.h"
 
 namespace baidu {
@@ -32,6 +33,7 @@ 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;
+using baidu::paddle_serving::predictor::InferManager;
 
 int conf_check(const Request *req,
                const std::shared_ptr<PaddleGeneralModelConfig> &model_config) {
@@ -71,75 +73,34 @@ int conf_check(const Request *req,
 
 int GeneralReaderOp::inference() {
   // reade request from client
+  // TODO: only support one engine here
+  std::string engine_name = "general_infer_0";
   const Request *req = dynamic_cast<const Request *>(get_request_message());
   uint64_t log_id = req->log_id();
   int input_var_num = 0;
   std::vector<int64_t> elem_type;
   std::vector<int64_t> elem_size;
   std::vector<int64_t> capacity;
-
-  GeneralBlob *res = mutable_data<GeneralBlob>();
-  TensorVector *out = &res->tensor_vector;
-
-  res->SetLogId(log_id);
-
-  if (!res) {
-    LOG(ERROR) << "(logid=" << log_id
-               << ") Failed get op tls reader object output";
-  }
-
-  Timer timeline;
-  int64_t start = timeline.TimeStampUS();
   int var_num = req->insts(0).tensor_array_size();
-  VLOG(2) << "(logid=" << log_id << ") var num: " << var_num;
-
-  VLOG(2) << "(logid=" << log_id
-          << ") start to call load general model_conf op";
-
   baidu::paddle_serving::predictor::Resource &resource =
       baidu::paddle_serving::predictor::Resource::instance();
-
-  VLOG(2) << "(logid=" << log_id << ") get resource pointer done.";
   std::shared_ptr<PaddleGeneralModelConfig> model_config =
       resource.get_general_model_config();
-
-  VLOG(2) << "(logid=" << log_id << ") print general model config done.";
-
-  // TODO(guru4elephant): how to do conditional check?
-  /*
-  int ret = conf_check(req, model_config);
-  if (ret != 0) {
-    LOG(ERROR) << "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);
-  // prepare basic information for input
   for (int i = 0; i < var_num; ++i) {
-    paddle::PaddleTensor lod_tensor;
-    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 if (elem_type[i] == 1) {
-      elem_size[i] = sizeof(float);
-      lod_tensor.dtype = paddle::PaddleDType::FLOAT32;
-    } else if (elem_type[i] == 2) {
-      elem_size[i] = sizeof(int32_t);
-      lod_tensor.dtype = paddle::PaddleDType::INT32;
-    }
-    // implement lod tensor here
+    std::string tensor_name = model_config->_feed_name[i];
+    VLOG(2) <<  "(logid=" << log_id << ") get tensor name: " << tensor_name;
+    auto lod_tensor = InferManager::instance().GetInputHandle(
+        engine_name.c_str(), tensor_name.c_str());
+    std::vector<std::vector<size_t>> lod;
+    std::vector<int> shape;
+    // get lod info here
     if (req->insts(0).tensor_array(i).lod_size() > 0) {
-      VLOG(2) << "(logid=" << log_id << ") var[" << i << "] is lod_tensor";
-      lod_tensor.lod.resize(1);
+      lod.resize(1);
       for (int k = 0; k < req->insts(0).tensor_array(i).lod_size(); ++k) {
-        lod_tensor.lod[0].push_back(req->insts(0).tensor_array(i).lod(k));
+        lod[0].push_back(req->insts(0).tensor_array(i).lod(k));
       }
       capacity[i] = 1;
       for (int k = 0; k < req->insts(0).tensor_array(i).shape_size(); ++k) {
@@ -147,7 +108,7 @@ int GeneralReaderOp::inference() {
         VLOG(2) << "(logid=" << log_id << ") shape for var[" << i
                 << "]: " << dim;
         capacity[i] *= dim;
-        lod_tensor.shape.push_back(dim);
+        shape.push_back(dim);
       }
       VLOG(2) << "(logid=" << log_id << ") var[" << i
               << "] is tensor, capacity: " << capacity[i];
@@ -158,92 +119,41 @@ int GeneralReaderOp::inference() {
         VLOG(2) << "(logid=" << log_id << ") shape for var[" << i
                 << "]: " << dim;
         capacity[i] *= dim;
-        lod_tensor.shape.push_back(dim);
+        shape.push_back(dim);
       }
       VLOG(2) << "(logid=" << log_id << ") var[" << i
               << "] is tensor, capacity: " << capacity[i];
     }
-    lod_tensor.name = model_config->_feed_name[i];
-    out->push_back(lod_tensor);
-  }
-  // specify the memory needed for output tensor_vector
-  for (int i = 0; i < var_num; ++i) {
-    if (out->at(i).lod.size() == 1) {
-      int tensor_size = 0;
-      const Tensor &tensor = req->insts(0).tensor_array(i);
-      int data_len = 0;
-      if (tensor.int64_data_size() > 0) {
-        data_len = tensor.int64_data_size();
-      } else if (tensor.float_data_size() > 0) {
-        data_len = tensor.float_data_size();
-      } else if (tensor.int_data_size() > 0) {
-        data_len = tensor.int_data_size();
-      }
-      VLOG(2) << "(logid=" << log_id << ") tensor size for var[" << i
-              << "]: " << data_len;
-      tensor_size += data_len;
-
-      int cur_len = out->at(i).lod[0].back();
-      VLOG(2) << "(logid=" << log_id << ") current len: " << cur_len;
-
-      int sample_len = 0;
-      if (tensor.shape_size() == 1) {
-        sample_len = data_len;
-      } else {
-        sample_len = tensor.shape(0);
-      }
-      VLOG(2) << "(logid=" << log_id << ") new len: " << cur_len + sample_len;
-      out->at(i).data.Resize(tensor_size * elem_size[i]);
-      VLOG(2) << "(logid=" << log_id << ") var[" << i
-              << "] is lod_tensor and len=" << out->at(i).lod[0].back();
-    } else {
-      out->at(i).data.Resize(capacity[i] * elem_size[i]);
-      VLOG(2) << "(logid=" << log_id << ") var[" << i
-              << "] is tensor and capacity=" << capacity[i];
-    }
-  }
-
-  // fill the data into output general_blob
-  for (int i = 0; i < var_num; ++i) {
-    if (elem_type[i] == 0) {
-      int64_t *dst_ptr = static_cast<int64_t *>(out->at(i).data.data());
-      VLOG(2) << "(logid=" << log_id << ") first element data in var[" << i
-              << "] is " << req->insts(0).tensor_array(i).int64_data(0);
-      int offset = 0;
+    lod_tensor->SetLoD(lod);
+    lod_tensor->Reshape(shape);
+    // insert data here
+    if (req->insts(0).tensor_array(i).elem_type() == 0) {
+      // TODO: Copy twice here, can optimize
       int elem_num = req->insts(0).tensor_array(i).int64_data_size();
+      std::vector<int64_t> data(elem_num);
+      int64_t *dst_ptr = data.data();
       for (int k = 0; k < elem_num; ++k) {
-        dst_ptr[offset + k] = req->insts(0).tensor_array(i).int64_data(k);
+        dst_ptr[k] = req->insts(0).tensor_array(i).int64_data(k);
       }
-    } else if (elem_type[i] == 1) {
-      float *dst_ptr = static_cast<float *>(out->at(i).data.data());
-      VLOG(2) << "(logid=" << log_id << ") first element data in var[" << i
-              << "] is " << req->insts(0).tensor_array(i).float_data(0);
-      int offset = 0;
+      lod_tensor->CopyFromCpu(dst_ptr);
+    } else if (req->insts(0).tensor_array(i).elem_type() == 1) {
       int elem_num = req->insts(0).tensor_array(i).float_data_size();
+      std::vector<float> data(elem_num);
+      float *dst_ptr = data.data();
       for (int k = 0; k < elem_num; ++k) {
-        dst_ptr[offset + k] = req->insts(0).tensor_array(i).float_data(k);
+        dst_ptr[k] = req->insts(0).tensor_array(i).float_data(k);
       }
-    } else if (elem_type[i] == 2) {
-      int32_t *dst_ptr = static_cast<int32_t *>(out->at(i).data.data());
-      VLOG(2) << "(logid=" << log_id << ") first element data in var[" << i
-              << "] is " << req->insts(0).tensor_array(i).int_data(0);
-      int offset = 0;
+      lod_tensor->CopyFromCpu(dst_ptr);
+    } else if (req->insts(0).tensor_array(i).elem_type() == 2) {
       int elem_num = req->insts(0).tensor_array(i).int_data_size();
+      std::vector<int32_t> data(elem_num);
+      int32_t *dst_ptr = data.data();
       for (int k = 0; k < elem_num; ++k) {
-        dst_ptr[offset + k] = req->insts(0).tensor_array(i).int_data(k);
+        dst_ptr[k] = req->insts(0).tensor_array(i).int_data(k);
       }
+      lod_tensor->CopyFromCpu(dst_ptr);
     }
   }
-
-  VLOG(2) << "(logid=" << log_id << ") output size: " << out->size();
-  timeline.Pause();
-  int64_t end = timeline.TimeStampUS();
-  res->p_size = 0;
-  res->_batch_size = 1;
-  AddBlobInfo(res, start);
-  AddBlobInfo(res, end);
-
-  VLOG(2) << "(logid=" << log_id << ") read data from client success";
   return 0;
 }
 DEFINE_OP(GeneralReaderOp);

core/general-server/op/general_response_op.cpp

@@ -40,160 +40,60 @@ using baidu::paddle_serving::predictor::InferManager;
 using baidu::paddle_serving::predictor::PaddleGeneralModelConfig;
 
 int GeneralResponseOp::inference() {
-  const std::vector<std::string> pre_node_names = pre_names();
-  VLOG(2) << "pre node names size: " << pre_node_names.size();
-  const GeneralBlob *input_blob;
-  uint64_t log_id =
-      get_depend_argument<GeneralBlob>(pre_node_names[0])->GetLogId();
-
   const Request *req = dynamic_cast<const Request *>(get_request_message());
   // response inst with only fetch_var_names
   Response *res = mutable_data<Response>();
-
-  Timer timeline;
-  // double response_time = 0.0;
-  // timeline.Start();
-  int64_t start = timeline.TimeStampUS();
-
-  VLOG(2) << "(logid=" << log_id
-          << ") start to call load general model_conf op";
   baidu::paddle_serving::predictor::Resource &resource =
       baidu::paddle_serving::predictor::Resource::instance();
-
-  VLOG(2) << "(logid=" << log_id << ") get resource pointer done.";
   std::shared_ptr<PaddleGeneralModelConfig> model_config =
       resource.get_general_model_config();
-
-  VLOG(2) << "(logid=" << log_id
-          << ") max body size : " << brpc::fLU64::FLAGS_max_body_size;
-
-  std::vector<int> fetch_index;
-  fetch_index.resize(req->fetch_var_names_size());
+  std::vector<int> capacity(req->fetch_var_names_size(), 1);
+  std::string engine_name = "general_infer_0";
+  ModelOutput *output = res->add_outputs();
+  FetchInst *fetch_inst = output->add_insts();
+  FetchInst *fetch_p = output->mutable_insts(0);
+  std::vector<std::string> outs =
+      InferManager::instance().GetOutputNames(engine_name.c_str());
   for (int i = 0; i < req->fetch_var_names_size(); ++i) {
-    fetch_index[i] =
-        model_config->_fetch_alias_name_to_index[req->fetch_var_names(i)];
-  }
-
-  for (uint32_t pi = 0; pi < pre_node_names.size(); ++pi) {
-    const std::string &pre_name = pre_node_names[pi];
-    VLOG(2) << "(logid=" << log_id << ") pre names[" << pi << "]: " << pre_name
-            << " (" << pre_node_names.size() << ")";
-    input_blob = get_depend_argument<GeneralBlob>(pre_name);
-    // fprintf(stderr, "input(%s) blob address %x\n", pre_names.c_str(),
-    // input_blob);
-    if (!input_blob) {
-      LOG(ERROR) << "(logid=" << log_id
-                 << ") Failed mutable depended argument, op: " << pre_name;
-      return -1;
+    Tensor *tensor = fetch_inst->add_tensor_array();
+    std::string tensor_name = outs[i];
+    auto lod_tensor = InferManager::instance().GetOutputHandle(
+        engine_name.c_str(), tensor_name.c_str());
+    std::vector<int> shape = lod_tensor->shape();
+    for (int k = 0; k < shape.size(); ++k) {
+      capacity[i] *= shape[k];
+      tensor->add_shape(shape[k]);
     }
-
-    const TensorVector *in = &input_blob->tensor_vector;
-
-    ModelOutput *output = res->add_outputs();
-    // To get the order of model return values
-    output->set_engine_name(pre_name);
-    FetchInst *fetch_inst = output->add_insts();
-
-    for (auto &idx : fetch_index) {
-      Tensor *tensor = fetch_inst->add_tensor_array();
-      if (model_config->_is_lod_fetch[idx]) {
-        VLOG(2) << "(logid=" << log_id << ") out[" << idx << "] "
-                << model_config->_fetch_name[idx] << " is lod_tensor";
-        for (int k = 0; k < in->at(idx).shape.size(); ++k) {
-          VLOG(2) << "(logid=" << log_id << ") shape[" << k
-                  << "]: " << in->at(idx).shape[k];
-          tensor->add_shape(in->at(idx).shape[k]);
-        }
-      } else {
-        VLOG(2) << "(logid=" << log_id << ") out[" << idx << "] "
-                << model_config->_fetch_name[idx] << " is tensor";
-        for (int k = 0; k < in->at(idx).shape.size(); ++k) {
-          VLOG(2) << "(logid=" << log_id << ") shape[" << k
-                  << "]: " << in->at(idx).shape[k];
-          tensor->add_shape(in->at(idx).shape[k]);
-        }
-      }
+    auto dtype = lod_tensor->type();
+    if (dtype == paddle::PaddleDType::INT64) {
+      std::vector<int64_t> datas(capacity[i]);
+      int64_t *data_ptr = datas.data();
+      lod_tensor->CopyToCpu(data_ptr);
+      google::protobuf::RepeatedField<int64_t> tmp_data(data_ptr,
+                                                        data_ptr + capacity[i]);
+      tensor->mutable_int64_data()->Swap(&tmp_data);
+    } else if (dtype == paddle::PaddleDType::FLOAT32) {
+      std::vector<float> datas(capacity[i]);
+      float *data_ptr = datas.data();
+      lod_tensor->CopyToCpu(data_ptr);
+      google::protobuf::RepeatedField<float> tmp_data(data_ptr,
+                                                      data_ptr + capacity[i]);
+      tensor->mutable_float_data()->Swap(&tmp_data);
+    } else if (dtype == paddle::PaddleDType::INT32) {
+      std::vector<int32_t> datas(capacity[i]);
+      int32_t *data_ptr = datas.data();
+      lod_tensor->CopyToCpu(data_ptr);
+      google::protobuf::RepeatedField<int32_t> tmp_data(data_ptr,
+                                                        data_ptr + capacity[i]);
+      tensor->mutable_int_data()->Swap(&tmp_data);
     }
-
-    int var_idx = 0;
-    for (auto &idx : fetch_index) {
-      int cap = 1;
-      for (int j = 0; j < in->at(idx).shape.size(); ++j) {
-        cap *= in->at(idx).shape[j];
+    std::vector<std::vector<size_t>> lod = lod_tensor->lod();
+    if (lod.size() > 0) {
+      for (int j = 0; j < lod[0].size(); ++j) {
+        tensor->add_lod(lod[0][j]);
       }
-
-      FetchInst *fetch_p = output->mutable_insts(0);
-      auto dtype = in->at(idx).dtype;
-
-      if (dtype == paddle::PaddleDType::INT64) {
-        VLOG(2) << "(logid=" << log_id << ") Prepare int64 var ["
-                << model_config->_fetch_name[idx] << "].";
-        int64_t *data_ptr = static_cast<int64_t *>(in->at(idx).data.data());
-        // from
-        // https://stackoverflow.com/questions/15499641/copy-a-stdvector-to-a-repeated-field-from-protobuf-with-memcpy
-        // `Swap` method is faster than `{}` method.
-        google::protobuf::RepeatedField<int64_t> tmp_data(data_ptr,
-                                                          data_ptr + cap);
-        fetch_p->mutable_tensor_array(var_idx)->mutable_int64_data()->Swap(
-            &tmp_data);
-      } else if (dtype == paddle::PaddleDType::FLOAT32) {
-        VLOG(2) << "(logid=" << log_id << ") Prepare float var ["
-                << model_config->_fetch_name[idx] << "].";
-        float *data_ptr = static_cast<float *>(in->at(idx).data.data());
-        google::protobuf::RepeatedField<float> tmp_data(data_ptr,
-                                                        data_ptr + cap);
-        fetch_p->mutable_tensor_array(var_idx)->mutable_float_data()->Swap(
-            &tmp_data);
-      } else if (dtype == paddle::PaddleDType::INT32) {
-        VLOG(2) << "(logid=" << log_id << ")Prepare int32 var ["
-                << model_config->_fetch_name[idx] << "].";
-        int32_t *data_ptr = static_cast<int32_t *>(in->at(idx).data.data());
-        google::protobuf::RepeatedField<int32_t> tmp_data(data_ptr,
-                                                          data_ptr + cap);
-        fetch_p->mutable_tensor_array(var_idx)->mutable_int_data()->Swap(
-            &tmp_data);
-      }
-
-      if (model_config->_is_lod_fetch[idx]) {
-        if (in->at(idx).lod.size() > 0) {
-          for (int j = 0; j < in->at(idx).lod[0].size(); ++j) {
-            fetch_p->mutable_tensor_array(var_idx)->add_lod(
-                in->at(idx).lod[0][j]);
-          }
-        }
-      }
-
-      VLOG(2) << "(logid=" << log_id << ") fetch var ["
-              << model_config->_fetch_name[idx] << "] ready";
-      var_idx++;
     }
   }
-
-  if (req->profile_server()) {
-    int64_t end = timeline.TimeStampUS();
-    // TODO(barriery): multi-model profile_time.
-    // At present, only the response_op is multi-input, so here we get
-    // the profile_time by hard coding. It needs to be replaced with
-    // a more elegant way.
-    for (uint32_t pi = 0; pi < pre_node_names.size(); ++pi) {
-      input_blob = get_depend_argument<GeneralBlob>(pre_node_names[pi]);
-      VLOG(2) << "(logid=" << log_id
-              << ") p size for input blob: " << input_blob->p_size;
-      int profile_time_idx = -1;
-      if (pi == 0) {
-        profile_time_idx = 0;
-      } else {
-        profile_time_idx = input_blob->p_size - 2;
-      }
-      for (; profile_time_idx < input_blob->p_size; ++profile_time_idx) {
-        res->add_profile_time(input_blob->time_stamp[profile_time_idx]);
-      }
-    }
-    // TODO(guru4elephant): find more elegant way to do this
-    res->add_profile_time(start);
-    res->add_profile_time(end);
-  }
-
   return 0;
 }
 

core/predictor/CMakeLists.txt

@@ -12,13 +12,12 @@ set_source_files_properties(
         ${pdserving_srcs}
         PROPERTIES
         COMPILE_FLAGS  "-Wno-strict-aliasing -Wno-unused-variable -Wno-non-virtual-dtor -Wno-error=non-virtual-dtor -Wno-error=delete-non-virtual-dtor")
-add_dependencies(pdserving protobuf boost brpc leveldb pdcodegen configure)
+add_dependencies(pdserving protobuf boost brpc leveldb pdcodegen configure extern_paddle paddle_fluid)
 if (WITH_TRT)
     add_definitions(-DWITH_TRT)
 endif()
 target_link_libraries(pdserving
-        brpc protobuf boost leveldb configure -lpthread -lcrypto -lm -lrt -lssl -ldl -lz)
-
+        brpc protobuf boost leveldb configure -lpthread -lcrypto -lm -lrt -lssl -ldl -lz paddle_fluid ${paddle_depend_libs})
 # install
 install(TARGETS pdserving
         RUNTIME DESTINATION ${PADDLE_SERVING_INSTALL_DIR}/bin

core/predictor/framework/infer.h

@@ -20,10 +20,9 @@
 #include <utility>
 #include <vector>
 #include "core/predictor/common/inner_common.h"
-#include "core/predictor/framework/bsf.h"
 #include "core/predictor/framework/factory.h"
 #include "core/predictor/framework/infer_data.h"
-
+#include "paddle_inference_api.h"  // NOLINT
 namespace baidu {
 namespace paddle_serving {
 namespace predictor {
@@ -105,9 +104,7 @@ class InferEngine {
   virtual int thrd_initialize() { return thrd_initialize_impl(); }
   virtual int thrd_clear() { return thrd_clear_impl(); }
   virtual int thrd_finalize() { return thrd_finalize_impl(); }
-  virtual int infer(const void* in, void* out, uint32_t batch_size = -1) {
-    return infer_impl1(in, out, batch_size);
-  }
+  virtual int infer() { return infer_impl(); }
 
   virtual int reload() = 0;
 
@@ -120,11 +117,13 @@ class InferEngine {
   virtual int thrd_finalize_impl() = 0;
   virtual int thrd_clear_impl() = 0;
   virtual int proc_finalize_impl() = 0;
-  virtual int infer_impl1(const void* in,
-                          void* out,
-                          uint32_t batch_size = -1) = 0;
-  virtual int infer_impl2(const BatchTensor& in,
-                          BatchTensor& out) = 0;  // NOLINT
+  virtual std::vector<std::string> GetInputNames() = 0;
+  virtual std::vector<std::string> GetOutputNames() = 0;
+  virtual std::unique_ptr<paddle_infer::Tensor> GetInputHandle(
+      const std::string& name) = 0;
+  virtual std::unique_ptr<paddle_infer::Tensor> GetOutputHandle(
+      const std::string& name) = 0;
+  virtual int infer_impl() = 0;
   // end: framework inner call
 };
 
@@ -138,8 +137,6 @@ class ReloadableInferEngine : public InferEngine {
     uint64_t last_revision;
   };
 
-  typedef im::bsf::Task<Tensor, Tensor> TaskT;
-
   virtual int load(const InferEngineCreationParams& params) = 0;
 
   int proc_initialize_impl(const configure::EngineDesc& conf, bool version) {
@@ -201,45 +198,10 @@ class ReloadableInferEngine : public InferEngine {
       LOG(ERROR) << "Failed proc initialize impl";
       return -1;
     }
-
-    // init bsf framework
-    if (_infer_thread_num <= 0) {
-      return 0;
-    }
-
-    im::bsf::TaskExecutor<TaskT>::instance()->set_thread_init_fn(
-        boost::bind(&InferEngine::thrd_initialize_impl, this));
-    im::bsf::TaskExecutor<TaskT>::instance()->set_thread_reset_fn(
-        boost::bind(&InferEngine::thrd_clear_impl, this));
-    im::bsf::TaskExecutor<TaskT>::instance()->set_thread_callback_fn(
-        boost::bind(&InferEngine::infer_impl2, this, _1, _2));
-    im::bsf::TaskExecutor<TaskT>::instance()->set_batch_size(_infer_batch_size);
-    im::bsf::TaskExecutor<TaskT>::instance()->set_batch_align(
-        _infer_batch_align);
-    if (im::bsf::TaskExecutor<TaskT>::instance()->start(_infer_thread_num) !=
-        0) {
-      LOG(ERROR) << "Failed start bsf executor, threads:" << _infer_thread_num;
-      return -1;
-    }
-
-    LOG(WARNING) << "Enable batch schedule framework, thread_num:"
-                 << _infer_thread_num << ", batch_size:" << _infer_batch_size
-                 << ", enable_batch_align:" << _infer_batch_align;
-
     return 0;
   }
 
-  int infer(const void* in, void* out, uint32_t batch_size = -1) {
-    if (_infer_thread_num <= 0) {
-      return infer_impl1(in, out, batch_size);
-    }
-
-    im::bsf::TaskManager<Tensor, Tensor> task_manager;
-    task_manager.schedule(*(reinterpret_cast<const BatchTensor*>(in)),
-                          *(reinterpret_cast<BatchTensor*>(out)));
-    task_manager.wait();
-    return 0;
-  }
+  int infer() { return infer_impl(); }
 
   int thrd_initialize() {
     if (_infer_thread_num > 0) {
@@ -263,10 +225,6 @@ class ReloadableInferEngine : public InferEngine {
       return -1;
     }
 
-    if (_infer_thread_num > 0) {
-      im::bsf::TaskExecutor<TaskT>::instance()->stop();
-    }
-
     return 0;
   }
 
@@ -417,10 +375,6 @@ class DBReloadableInferEngine : public ReloadableInferEngine {
 
   virtual int thrd_initialize_impl() {
     // memory pool to be inited in non-serving-threads
-    if (MempoolWrapper::instance().thread_initialize() != 0) {
-      LOG(ERROR) << "Failed thread initialize mempool";
-      return -1;
-    }
 
     ModelData<EngineCore>* md = new (std::nothrow) ModelData<EngineCore>;
     if (!md || load_data(md, _infer_engine_params) != 0) {
@@ -430,17 +384,12 @@ class DBReloadableInferEngine : public ReloadableInferEngine {
     }
 
     THREAD_SETSPECIFIC(_skey, md);
-    im::bsf::AutoMutex lock(_mutex);
     _reload_vec.push_back(md);
     return 0;
   }
 
   int thrd_clear_impl() {
     // for non-serving-threads
-    if (MempoolWrapper::instance().thread_clear() != 0) {
-      LOG(ERROR) << "Failed thread clear mempool";
-      return -1;
-    }
     return 0;
   }
 
@@ -538,12 +487,6 @@ class CloneDBReloadableInferEngine
   }
 
   virtual int thrd_initialize_impl() {
-    // memory pool to be inited in non-serving-threads
-    if (MempoolWrapper::instance().thread_initialize() != 0) {
-      LOG(ERROR) << "Failed thread initialize mempool";
-      return -1;
-    }
-
     ModelData<EngineCore>* md = new (std::nothrow) ModelData<EngineCore>;
     if (!md || load_data(md, _pd->cores[_pd->current_idx]) != 0) {
       LOG(ERROR) << "Failed clone thread data, origin_core["
@@ -552,7 +495,6 @@ class CloneDBReloadableInferEngine
     }
 
     THREAD_SETSPECIFIC(DBReloadableInferEngine<EngineCore>::_skey, md);
-    im::bsf::AutoMutex lock(DBReloadableInferEngine<EngineCore>::_mutex);
     DBReloadableInferEngine<EngineCore>::_reload_vec.push_back(md);
     return 0;
   }
@@ -571,8 +513,45 @@ class FluidInferEngine : public CloneDBReloadableInferEngine<FluidFamilyCore> {
  public:  // NOLINT
   FluidInferEngine() {}
   ~FluidInferEngine() {}
+  std::vector<std::string> GetInputNames() {
+    FluidFamilyCore* core =
+        DBReloadableInferEngine<FluidFamilyCore>::get_core();
+    if (!core || !core->get()) {
+      LOG(ERROR) << "Failed get fluid core in GetInputHandle()";
+    }
+    return core->GetInputNames();
+  }
 
-  int infer_impl1(const void* in, void* out, uint32_t batch_size = -1) {
+  std::vector<std::string> GetOutputNames() {
+    FluidFamilyCore* core =
+        DBReloadableInferEngine<FluidFamilyCore>::get_core();
+    if (!core || !core->get()) {
+      LOG(ERROR) << "Failed get fluid core in GetInputHandle()";
+    }
+    return core->GetOutputNames();
+  }
+
+  std::unique_ptr<paddle_infer::Tensor> GetInputHandle(
+      const std::string& name) {
+    FluidFamilyCore* core =
+        DBReloadableInferEngine<FluidFamilyCore>::get_core();
+    if (!core || !core->get()) {
+      LOG(ERROR) << "Failed get fluid core in GetInputHandle()";
+    }
+    return core->GetInputHandle(name);
+  }
+
+  std::unique_ptr<paddle_infer::Tensor> GetOutputHandle(
+      const std::string& name) {
+    FluidFamilyCore* core =
+        DBReloadableInferEngine<FluidFamilyCore>::get_core();
+    if (!core || !core->get()) {
+      LOG(ERROR) << "Failed get fluid core in GetOutputHandle()";
+    }
+    return core->GetOutputHandle(name);
+  }
+
+  int infer_impl() {
     FluidFamilyCore* core =
         DBReloadableInferEngine<FluidFamilyCore>::get_core();
     if (!core || !core->get()) {
@@ -580,16 +559,12 @@ class FluidInferEngine : public CloneDBReloadableInferEngine<FluidFamilyCore> {
       return -1;
     }
 
-    if (!core->Run(in, out)) {
+    if (!core->Run()) {
       LOG(ERROR) << "Failed run fluid family core";
       return -1;
     }
     return 0;
   }
-
-  int infer_impl2(const BatchTensor& in, BatchTensor& out) {  // NOLINT
-    return infer_impl1(&in, &out);
-  }
 };
 
 typedef FactoryPool<InferEngine> StaticInferFactory;
@@ -715,13 +690,45 @@ class VersionedInferEngine : public InferEngine {
     return _versions.begin()->second;
   }
 
-  int infer(const void* in, void* out, uint32_t batch_size) {
+  int infer() {
     InferEngine* engine = default_engine();
     if (!engine) {
       LOG(WARNING) << "fail to get default engine";
       return -1;
     }
-    return engine->infer(in, out, batch_size);
+    return engine->infer();
+  }
+
+  std::vector<std::string> GetInputNames() {
+    InferEngine* engine = default_engine();
+    if (!engine) {
+      LOG(WARNING) << "fail to get default engine";
+    }
+    return engine->GetInputNames();
+  }
+  std::vector<std::string> GetOutputNames() {
+    InferEngine* engine = default_engine();
+    if (!engine) {
+      LOG(WARNING) << "fail to get default engine";
+    }
+    return engine->GetOutputNames();
+  }
+  std::unique_ptr<paddle_infer::Tensor> GetInputHandle(
+      const std::string& name) {
+    InferEngine* engine = default_engine();
+    if (!engine) {
+      LOG(WARNING) << "fail to get default engine";
+    }
+    return engine->GetInputHandle(name);
+  }
+
+  std::unique_ptr<paddle_infer::Tensor> GetOutputHandle(
+      const std::string& name) {
+    InferEngine* engine = default_engine();
+    if (!engine) {
+      LOG(WARNING) << "fail to get default engine";
+    }
+    return engine->GetOutputHandle(name);
   }
 
   template <typename T>
@@ -740,14 +747,47 @@ class VersionedInferEngine : public InferEngine {
   }
 
   // versioned inference interface
-  int infer(const void* in, void* out, uint32_t batch_size, uint64_t version) {
+  int infer(uint64_t version) {
     auto iter = _versions.find(version);
     if (iter == _versions.end()) {
       LOG(ERROR) << "Not found version engine: " << version;
       return -1;
     }
 
-    return iter->second->infer(in, out, batch_size);
+    return iter->second->infer();
+  }
+  std::vector<std::string> GetInputNames(uint64_t version) {
+    auto iter = _versions.find(version);
+    if (iter == _versions.end()) {
+      LOG(ERROR) << "Not found version engine: " << version;
+    }
+    return iter->second->GetInputNames();
+  }
+
+  std::vector<std::string> GetOutputNames(uint64_t version) {
+    auto iter = _versions.find(version);
+    if (iter == _versions.end()) {
+      LOG(ERROR) << "Not found version engine: " << version;
+    }
+    return iter->second->GetOutputNames();
+  }
+
+  std::unique_ptr<paddle_infer::Tensor> GetInputHandle(
+      uint64_t version, const std::string& name) {
+    auto iter = _versions.find(version);
+    if (iter == _versions.end()) {
+      LOG(ERROR) << "Not found version engine: " << version;
+    }
+    return iter->second->GetInputHandle(name);
+  }
+
+  std::unique_ptr<paddle_infer::Tensor> GetOutputHandle(
+      uint64_t version, const std::string& name) {
+    auto iter = _versions.find(version);
+    if (iter == _versions.end()) {
+      LOG(ERROR) << "Not found version engine: " << version;
+    }
+    return iter->second->GetOutputHandle(name);
   }
 
   template <typename T>
@@ -774,12 +814,7 @@ class VersionedInferEngine : public InferEngine {
   int thrd_finalize_impl() { return -1; }
   int thrd_clear_impl() { return -1; }
   int proc_finalize_impl() { return -1; }
-  int infer_impl1(const void* in, void* out, uint32_t batch_size = -1) {
-    return -1;
-  }
-  int infer_impl2(const BatchTensor& in, BatchTensor& out) {  // NOLINT
-    return -1;
-  }  // NOLINT
+  int infer_impl() { return -1; }
 
  private:
   boost::unordered_map<uint64_t, InferEngine*> _versions;
@@ -877,16 +912,44 @@ class InferManager {
   }
 
   // Inference interface
-  int infer(const char* model_name,
-            const void* in,
-            void* out,
-            uint32_t batch_size = -1) {
+  int infer(const char* model_name) {
     auto it = _map.find(model_name);
     if (it == _map.end()) {
       LOG(WARNING) << "Cannot find engine in map, model name:" << model_name;
       return -1;
     }
-    return it->second->infer(in, out, batch_size);
+    return it->second->infer();
+  }
+
+  std::vector<std::string> GetInputNames(const char* model_name) {
+    auto it = _map.find(model_name);
+    if (it == _map.end()) {
+      LOG(WARNING) << "Cannot find engine in map, model name:" << model_name;
+    }
+    return it->second->GetInputNames();
+  }
+  std::vector<std::string> GetOutputNames(const char* model_name) {
+    auto it = _map.find(model_name);
+    if (it == _map.end()) {
+      LOG(WARNING) << "Cannot find engine in map, model name:" << model_name;
+    }
+    return it->second->GetOutputNames();
+  }
+  std::unique_ptr<paddle_infer::Tensor> GetInputHandle(
+      const char* model_name, const std::string& name) {
+    auto it = _map.find(model_name);
+    if (it == _map.end()) {
+      LOG(WARNING) << "Cannot find engine in map, model name:" << model_name;
+    }
+    return it->second->GetInputHandle(name);
+  }
+  std::unique_ptr<paddle_infer::Tensor> GetOutputHandle(
+      const char* model_name, const std::string& name) {
+    auto it = _map.find(model_name);
+    if (it == _map.end()) {
+      LOG(WARNING) << "Cannot find engine in map, model name:" << model_name;
+    }
+    return it->second->GetOutputHandle(name);
   }
 
   template <typename T>
@@ -906,19 +969,48 @@ class InferManager {
   }
 
   // Versioned inference interface
-  int infer(const char* model_name,
-            const void* in,
-            void* out,
-            uint32_t batch_size,
-            uint64_t version) {
+  int infer(const char* model_name, uint64_t version) {
     auto it = _map.find(model_name);
     if (it == _map.end()) {
       LOG(WARNING) << "Cannot find engine in map, model name:" << model_name;
       return -1;
     }
-    return it->second->infer(in, out, batch_size, version);
+    return it->second->infer(version);
+  }
+  std::vector<std::string> GetInputNames(const char* model_name,
+                                         uint64_t version) {
+    auto it = _map.find(model_name);
+    if (it == _map.end()) {
+      LOG(WARNING) << "Cannot find engine in map, model name:" << model_name;
+    }
+    return it->second->GetInputNames(version);
   }
 
+  std::vector<std::string> GetOutputNames(const char* model_name,
+                                          uint64_t version) {
+    auto it = _map.find(model_name);
+    if (it == _map.end()) {
+      LOG(WARNING) << "Cannot find engine in map, model name:" << model_name;
+    }
+    return it->second->GetOutputNames(version);
+  }
+
+  std::unique_ptr<paddle_infer::Tensor> GetInputHandle(
+      const char* model_name, uint64_t version, const std::string& name) {
+    auto it = _map.find(model_name);
+    if (it == _map.end()) {
+      LOG(WARNING) << "Cannot find engine in map, model name:" << model_name;
+    }
+    return it->second->GetInputHandle(version, name);
+  }
+  std::unique_ptr<paddle_infer::Tensor> GetOutputHandle(
+      const char* model_name, uint64_t version, const std::string& name) {
+    auto it = _map.find(model_name);
+    if (it == _map.end()) {
+      LOG(WARNING) << "Cannot find engine in map, model name:" << model_name;
+    }
+    return it->second->GetOutputHandle(version, name);
+  }
   template <typename T>
   T* get_core(const char* model_name, uint64_t version) {
     auto it = _map.find(model_name);

doc/COMPILE_CN.md

@@ -122,6 +122,7 @@ make -j10
 export CUDA_PATH='/usr/local'
 export CUDNN_LIBRARY='/usr/local/cuda/lib64/'
 export CUDA_CUDART_LIBRARY="/usr/local/cuda/lib64/"
+export TENSORRT_LIBRARY_PATH="/usr/local/TensorRT-6.0.1.5/targets/x86_64-linux-gnu/"
 
 mkdir server-build-trt && cd server-build-trt
 cmake -DPYTHON_INCLUDE_DIR=$PYTHONROOT/include/python2.7/ \

doc/PIPELINE_SERVING_CN.md

@@ -676,7 +676,7 @@ service_throughput = 1 / 最慢OP的耗时 * 并发数
 service_avg_cost = ∑op_concurrency 【关键路径】
 
 Channel堆积：
-channel_acc_size = QPS(down - up) * time
+channel_acc_size = QPS(down - up) * time
 
 批量预测平均耗时：
 avg_batch_cost = (N * pre + mid + post) / N 

doc/SAVE.md

@@ -49,4 +49,4 @@ Arguments are the same as `inference_model_to_serving` API.
 | `serving_server` | str | `"serving_server"` | The path of model files and configuration files for server. |
 | `serving_client` | str | `"serving_client"` | The path of configuration files for client. |
 | `model_filename` | str | None | The name of file to load the inference program. If it is None, the default filename `__model__` will be used. |
-| `paras_filename` | str | None | The name of file to load all parameters. It is only used for the case that all parameters were saved in a single binary file. If parameters were saved in separate files, set it as None. |
+| `params_filename` | str | None | The name of file to load all parameters. It is only used for the case that all parameters were saved in a single binary file. If parameters were saved in separate files, set it as None. |

doc/SAVE_CN.md

@@ -50,4 +50,4 @@ python -m paddle_serving_client.convert --dirname ./your_inference_model_dir
 | `serving_server` | str | `"serving_server"` | 转换后的模型文件和配置文件的存储路径。默认值为serving_server |
 | `serving_client` | str | `"serving_client"` | 转换后的客户端配置文件存储路径。默认值为serving_client |
 | `model_filename` | str | None | 存储需要转换的模型Inference Program结构的文件名称。如果设置为None，则使用 `__model__` 作为默认的文件名 |
-| `paras_filename` | str | None | 存储需要转换的模型所有参数的文件名称。当且仅当所有模型参数被保存在一个单独的二进制文件中，它才需要被指定。如果模型参数是存储在各自分离的文件中，设置它的值为None |
+| `params_filename` | str | None | 存储需要转换的模型所有参数的文件名称。当且仅当所有模型参数被保存在一个单独的二进制文件中，它才需要被指定。如果模型参数是存储在各自分离的文件中，设置它的值为None |

java/README.md

@@ -117,4 +117,3 @@ The second is to deploy GPU Serving and Java Client separately. If they are on t
 **It should be noted that in the example, Java Pipeline Client code is in path /Java/Examples and /Java/src/main, and the Pipeline server code is in path /python/examples/pipeline/**
 
 
-

java/README_CN.md

@@ -118,4 +118,4 @@ java -cp paddle-serving-sdk-java-examples-0.0.1-jar-with-dependencies.jar Pipeli
 **需要注意的是，Java Pipeline Client相关示例在/Java/Examples和/Java/src/main中，对应的Pipeline server在/python/examples/pipeline/中**
 
 
-
+**目前Serving已推出Pipeline模式（详见[Pipeline Serving](../doc/PIPELINE_SERVING_CN.md)），下个版本（0.4.1）面向Java的Pipeline Serving Client将会发布，敬请期待。**

paddle_inference/inferencer-fluid-cpu/include/fluid_cpu_engine.h

@@ -28,8 +28,6 @@ namespace baidu {
 namespace paddle_serving {
 namespace fluid_cpu {
 
-using configure::SigmoidConf;
-
 class AutoLock {
  public:
   explicit AutoLock(pthread_mutex_t& mutex) : _mut(mutex) {
@@ -57,31 +55,36 @@ class GlobalPaddleCreateMutex {
   pthread_mutex_t _mut;
 };
 
-class GlobalSigmoidCreateMutex {
- public:
-  pthread_mutex_t& mutex() { return _mut; }
-  static pthread_mutex_t& instance() {
-    static GlobalSigmoidCreateMutex gmutex;
-    return gmutex.mutex();
-  }
-
- private:
-  GlobalSigmoidCreateMutex() { pthread_mutex_init(&_mut, NULL); }
-
-  pthread_mutex_t _mut;
-};
+using paddle_infer::Config;
+using paddle_infer::Predictor;
+using paddle_infer::Tensor;
+using paddle_infer::CreatePredictor;
 
 // data interface
 class FluidFamilyCore {
  public:
   virtual ~FluidFamilyCore() {}
-  virtual bool Run(const void* in_data, void* out_data) {
-    if (!_core->Run(*(std::vector<paddle::PaddleTensor>*)in_data,
-                    (std::vector<paddle::PaddleTensor>*)out_data)) {
+  virtual std::vector<std::string> GetInputNames() {
+    return _core->GetInputNames();
+  }
+
+  virtual std::unique_ptr<Tensor> GetInputHandle(const std::string& name) {
+    return _core->GetInputHandle(name);
+  }
+
+  virtual std::vector<std::string> GetOutputNames() {
+    return _core->GetOutputNames();
+  }
+
+  virtual std::unique_ptr<Tensor> GetOutputHandle(const std::string& name) {
+    return _core->GetOutputHandle(name);
+  }
+
+  virtual bool Run() {
+    if (!_core->Run()) {
       LOG(ERROR) << "Failed call Run with paddle predictor";
       return false;
     }
-
     return true;
   }
 
@@ -92,8 +95,7 @@ class FluidFamilyCore {
       LOG(ERROR) << "origin paddle Predictor is null.";
       return -1;
     }
-    paddle::PaddlePredictor* p_predictor =
-        (paddle::PaddlePredictor*)origin_core;
+    Predictor* p_predictor = (Predictor*)origin_core;
     _core = p_predictor->Clone();
     if (_core.get() == NULL) {
       LOG(ERROR) << "fail to clone paddle predictor: " << origin_core;
@@ -105,7 +107,7 @@ class FluidFamilyCore {
   virtual void* get() { return _core.get(); }
 
  protected:
-  std::unique_ptr<paddle::PaddlePredictor> _core;
+  std::shared_ptr<Predictor> _core;
 };
 
 // infer interface
@@ -119,51 +121,19 @@ class FluidCpuAnalysisCore : public FluidFamilyCore {
       return -1;
     }
 
-    paddle::AnalysisConfig analysis_config;
-    analysis_config.SetParamsFile(data_path + "/__params__");
-    analysis_config.SetProgFile(data_path + "/__model__");
-    analysis_config.DisableGpu();
-    analysis_config.SetCpuMathLibraryNumThreads(1);
+    Config config;
+    config.SetParamsFile(data_path + "/__params__");
+    config.SetProgFile(data_path + "/__model__");
+    config.DisableGpu();
+    config.SetCpuMathLibraryNumThreads(1);
 
     if (params.enable_memory_optimization()) {
-      analysis_config.EnableMemoryOptim();
+      config.EnableMemoryOptim();
     }
 
-    analysis_config.SwitchSpecifyInputNames(true);
-    AutoLock lock(GlobalPaddleCreateMutex::instance());
-    _core =
-        paddle::CreatePaddlePredictor<paddle::AnalysisConfig>(analysis_config);
-    if (NULL == _core.get()) {
-      LOG(ERROR) << "create paddle predictor failed, path: " << data_path;
-      return -1;
-    }
-
-    VLOG(2) << "create paddle predictor sucess, path: " << data_path;
-    return 0;
-  }
-};
-
-class FluidCpuNativeCore : public FluidFamilyCore {
- public:
-  int create(const predictor::InferEngineCreationParams& params) {
-    std::string data_path = params.get_path();
-    if (access(data_path.c_str(), F_OK) == -1) {
-      LOG(ERROR) << "create paddle predictor failed, path not exits: "
-                 << data_path;
-      return -1;
-    }
-
-    paddle::NativeConfig native_config;
-    native_config.param_file = data_path + "/__params__";
-    native_config.prog_file = data_path + "/__model__";
-    native_config.use_gpu = false;
-    native_config.device = 0;
-    native_config.fraction_of_gpu_memory = 0;
-
+    config.SwitchSpecifyInputNames(true);
     AutoLock lock(GlobalPaddleCreateMutex::instance());
-    _core = paddle::CreatePaddlePredictor<paddle::NativeConfig,
-                                          paddle::PaddleEngineKind::kNative>(
-        native_config);
+    _core = CreatePredictor(config);
     if (NULL == _core.get()) {
       LOG(ERROR) << "create paddle predictor failed, path: " << data_path;
       return -1;
@@ -184,54 +154,24 @@ class FluidCpuAnalysisDirCore : public FluidFamilyCore {
       return -1;
     }
 
-    paddle::AnalysisConfig analysis_config;
-    analysis_config.SetModel(data_path);
-    analysis_config.DisableGpu();
-    analysis_config.SwitchSpecifyInputNames(true);
-    analysis_config.SetCpuMathLibraryNumThreads(1);
+    Config config;
+    config.SetModel(data_path);
+    config.DisableGpu();
+    config.SwitchSpecifyInputNames(true);
+    config.SetCpuMathLibraryNumThreads(1);
 
     if (params.enable_memory_optimization()) {
-      analysis_config.EnableMemoryOptim();
+      config.EnableMemoryOptim();
     }
 
     if (params.enable_ir_optimization()) {
-      analysis_config.SwitchIrOptim(true);
+      config.SwitchIrOptim(true);
     } else {
-      analysis_config.SwitchIrOptim(false);
+      config.SwitchIrOptim(false);
     }
 
     AutoLock lock(GlobalPaddleCreateMutex::instance());
-    _core =
-        paddle::CreatePaddlePredictor<paddle::AnalysisConfig>(analysis_config);
-    if (NULL == _core.get()) {
-      LOG(ERROR) << "create paddle predictor failed, path: " << data_path;
-      return -1;
-    }
-
-    VLOG(2) << "create paddle predictor sucess, path: " << data_path;
-    return 0;
-  }
-};
-
-class FluidCpuNativeDirCore : public FluidFamilyCore {
- public:
-  int create(const predictor::InferEngineCreationParams& params) {
-    std::string data_path = params.get_path();
-    if (access(data_path.c_str(), F_OK) == -1) {
-      LOG(ERROR) << "create paddle predictor failed, path not exits: "
-                 << data_path;
-      return -1;
-    }
-
-    paddle::NativeConfig native_config;
-    native_config.model_dir = data_path;
-    native_config.use_gpu = false;
-    native_config.device = 0;
-    native_config.fraction_of_gpu_memory = 0;
-    AutoLock lock(GlobalPaddleCreateMutex::instance());
-    _core = paddle::CreatePaddlePredictor<paddle::NativeConfig,
-                                          paddle::PaddleEngineKind::kNative>(
-        native_config);
+    _core = CreatePredictor(config);
     if (NULL == _core.get()) {
       LOG(ERROR) << "create paddle predictor failed, path: " << data_path;
       return -1;
@@ -323,214 +263,6 @@ class Parameter {
   float* _params;
 };
 
-class SigmoidModel {
- public:
-  ~SigmoidModel() {}
-  int load(const char* sigmoid_w_file,
-           const char* sigmoid_b_file,
-           float exp_max,
-           float exp_min) {
-    AutoLock lock(GlobalSigmoidCreateMutex::instance());
-    if (0 != _sigmoid_w.init(2, 1, sigmoid_w_file) || 0 != _sigmoid_w.load()) {
-      LOG(ERROR) << "load params sigmoid_w failed.";
-      return -1;
-    }
-    VLOG(2) << "load sigmoid_w [" << _sigmoid_w._params[0] << "] ["
-            << _sigmoid_w._params[1] << "].";
-    if (0 != _sigmoid_b.init(2, 1, sigmoid_b_file) || 0 != _sigmoid_b.load()) {
-      LOG(ERROR) << "load params sigmoid_b failed.";
-      return -1;
-    }
-    VLOG(2) << "load sigmoid_b [" << _sigmoid_b._params[0] << "] ["
-            << _sigmoid_b._params[1] << "].";
-    _exp_max_input = exp_max;
-    _exp_min_input = exp_min;
-    return 0;
-  }
-
-  int softmax(float x, double& o) {  // NOLINT
-    float _y0 = x * _sigmoid_w._params[0] + _sigmoid_b._params[0];
-    float _y1 = x * _sigmoid_w._params[1] + _sigmoid_b._params[1];
-    _y0 = (_y0 > _exp_max_input)
-              ? _exp_max_input
-              : ((_y0 < _exp_min_input) ? _exp_min_input : _y0);
-    _y1 = (_y1 > _exp_max_input)
-              ? _exp_max_input
-              : ((_y1 < _exp_min_input) ? _exp_min_input : _y1);
-    o = 1.0f / (1.0f + exp(_y0 - _y1));
-    return 0;
-  }
-
- public:
-  Parameter _sigmoid_w;
-  Parameter _sigmoid_b;
-  float _exp_max_input;
-  float _exp_min_input;
-};
-
-class SigmoidFluidModel {
- public:
-  int softmax(float x, double& o) {  // NOLINT
-    return _sigmoid_core->softmax(x, o);
-  }  // NOLINT
-
-  std::unique_ptr<SigmoidFluidModel> Clone() {
-    std::unique_ptr<SigmoidFluidModel> clone_model;
-    clone_model.reset(new SigmoidFluidModel());
-    clone_model->_sigmoid_core = _sigmoid_core;
-    clone_model->_fluid_core = _fluid_core->Clone();
-    return std::move(clone_model);  // NOLINT
-  }
-
- public:
-  std::unique_ptr<paddle::PaddlePredictor> _fluid_core;
-  std::shared_ptr<SigmoidModel> _sigmoid_core;
-};
-
-class FluidCpuWithSigmoidCore : public FluidFamilyCore {
- public:
-  virtual ~FluidCpuWithSigmoidCore() {}
-
- public:
-  int create(const predictor::InferEngineCreationParams& params) {
-    std::string model_path = params.get_path();
-    size_t pos = model_path.find_last_of("/\\");
-    std::string conf_path = model_path.substr(0, pos);
-    std::string conf_file = model_path.substr(pos);
-    configure::SigmoidConf conf;
-    if (configure::read_proto_conf(conf_path, conf_file, &conf) != 0) {
-      LOG(ERROR) << "failed load model path: " << model_path;
-      return -1;
-    }
-
-    _core.reset(new SigmoidFluidModel);
-
-    std::string fluid_model_data_path = conf.dnn_model_path();
-    predictor::InferEngineCreationParams new_params(params);
-    new_params.set_path(fluid_model_data_path);
-    int ret = load_fluid_model(new_params);
-    if (ret < 0) {
-      LOG(ERROR) << "fail to load fluid model.";
-      return -1;
-    }
-    const char* sigmoid_w_file = conf.sigmoid_w_file().c_str();
-    const char* sigmoid_b_file = conf.sigmoid_b_file().c_str();
-    float exp_max = conf.exp_max_input();
-    float exp_min = conf.exp_min_input();
-    _core->_sigmoid_core.reset(new SigmoidModel);
-    VLOG(2) << "create sigmoid core[" << _core->_sigmoid_core.get()
-            << "], use count[" << _core->_sigmoid_core.use_count() << "].";
-    ret = _core->_sigmoid_core->load(
-        sigmoid_w_file, sigmoid_b_file, exp_max, exp_min);
-    if (ret < 0) {
-      LOG(ERROR) << "fail to load sigmoid model.";
-      return -1;
-    }
-    return 0;
-  }
-
-  virtual bool Run(const void* in_data, void* out_data) {
-    if (!_core->_fluid_core->Run(
-            *(std::vector<paddle::PaddleTensor>*)in_data,
-            (std::vector<paddle::PaddleTensor>*)out_data)) {
-      LOG(ERROR) << "Failed call Run with paddle predictor";
-      return false;
-    }
-
-    return true;
-  }
-
-  virtual int clone(SigmoidFluidModel* origin_core) {
-    if (origin_core == NULL) {
-      LOG(ERROR) << "origin paddle Predictor is null.";
-      return -1;
-    }
-    _core = origin_core->Clone();
-    if (_core.get() == NULL) {
-      LOG(ERROR) << "fail to clone paddle predictor: " << origin_core;
-      return -1;
-    }
-    VLOG(2) << "clone sigmoid core[" << _core->_sigmoid_core.get()
-            << "] use count[" << _core->_sigmoid_core.use_count() << "].";
-    return 0;
-  }
-
-  virtual SigmoidFluidModel* get() { return _core.get(); }
-
-  virtual int load_fluid_model(
-      const predictor::InferEngineCreationParams& params) = 0;
-
-  int softmax(float x, double& o) {  // NOLINT
-    return _core->_sigmoid_core->softmax(x, o);
-  }
-
- protected:
-  std::unique_ptr<SigmoidFluidModel> _core;  // NOLINT
-};
-
-class FluidCpuNativeDirWithSigmoidCore : public FluidCpuWithSigmoidCore {
- public:
-  int load_fluid_model(const predictor::InferEngineCreationParams& params) {
-    std::string data_path = params.get_path();
-    if (access(data_path.c_str(), F_OK) == -1) {
-      LOG(ERROR) << "create paddle predictor failed, path not exits: "
-                 << data_path;
-      return -1;
-    }
-
-    paddle::NativeConfig native_config;
-    native_config.model_dir = data_path;
-    native_config.use_gpu = false;
-    native_config.device = 0;
-    native_config.fraction_of_gpu_memory = 0;
-    AutoLock lock(GlobalPaddleCreateMutex::instance());
-    _core->_fluid_core =
-        paddle::CreatePaddlePredictor<paddle::NativeConfig,
-                                      paddle::PaddleEngineKind::kNative>(
-            native_config);
-    if (NULL == _core.get()) {
-      LOG(ERROR) << "create paddle predictor failed, path: " << data_path;
-      return -1;
-    }
-
-    VLOG(2) << "create paddle predictor sucess, path: " << data_path;
-    return 0;
-  }
-};
-
-class FluidCpuAnalysisDirWithSigmoidCore : public FluidCpuWithSigmoidCore {
- public:
-  int load_fluid_model(const predictor::InferEngineCreationParams& params) {
-    std::string data_path = params.get_path();
-    if (access(data_path.c_str(), F_OK) == -1) {
-      LOG(ERROR) << "create paddle predictor failed, path not exits: "
-                 << data_path;
-      return -1;
-    }
-
-    paddle::AnalysisConfig analysis_config;
-    analysis_config.SetModel(data_path);
-    analysis_config.DisableGpu();
-    analysis_config.SwitchSpecifyInputNames(true);
-    analysis_config.SetCpuMathLibraryNumThreads(1);
-
-    if (params.enable_memory_optimization()) {
-      analysis_config.EnableMemoryOptim();
-    }
-
-    AutoLock lock(GlobalPaddleCreateMutex::instance());
-    _core->_fluid_core =
-        paddle::CreatePaddlePredictor<paddle::AnalysisConfig>(analysis_config);
-    if (NULL == _core.get()) {
-      LOG(ERROR) << "create paddle predictor failed, path: " << data_path;
-      return -1;
-    }
-
-    VLOG(2) << "create paddle predictor sucess, path: " << data_path;
-    return 0;
-  }
-};
-
 }  // namespace fluid_cpu
 }  // namespace paddle_serving
 }  // namespace baidu

paddle_inference/inferencer-fluid-cpu/src/fluid_cpu_engine.cpp

@@ -30,28 +30,6 @@ REGIST_FACTORY_OBJECT_IMPL_WITH_NAME(
     ::baidu::paddle_serving::predictor::InferEngine,
     "FLUID_CPU_ANALYSIS_DIR");
 
-REGIST_FACTORY_OBJECT_IMPL_WITH_NAME(
-    ::baidu::paddle_serving::predictor::FluidInferEngine<
-        FluidCpuAnalysisDirWithSigmoidCore>,
-    ::baidu::paddle_serving::predictor::InferEngine,
-    "FLUID_CPU_ANALYSIS_DIR_SIGMOID");
-
-REGIST_FACTORY_OBJECT_IMPL_WITH_NAME(
-    ::baidu::paddle_serving::predictor::FluidInferEngine<FluidCpuNativeCore>,
-    ::baidu::paddle_serving::predictor::InferEngine,
-    "FLUID_CPU_NATIVE");
-
-REGIST_FACTORY_OBJECT_IMPL_WITH_NAME(
-    ::baidu::paddle_serving::predictor::FluidInferEngine<FluidCpuNativeDirCore>,
-    ::baidu::paddle_serving::predictor::InferEngine,
-    "FLUID_CPU_NATIVE_DIR");
-
-REGIST_FACTORY_OBJECT_IMPL_WITH_NAME(
-    ::baidu::paddle_serving::predictor::FluidInferEngine<
-        FluidCpuNativeDirWithSigmoidCore>,
-    ::baidu::paddle_serving::predictor::InferEngine,
-    "FLUID_CPU_NATIVE_DIR_SIGMOID");
-
 }  // namespace fluid_cpu
 }  // namespace paddle_serving
 }  // namespace baidu

paddle_inference/inferencer-fluid-gpu/include/fluid_gpu_engine.h

@@ -61,31 +61,36 @@ class GlobalPaddleCreateMutex {
   pthread_mutex_t _mut;
 };
 
-class GlobalSigmoidCreateMutex {
- public:
-  pthread_mutex_t& mutex() { return _mut; }
-  static pthread_mutex_t& instance() {
-    static GlobalSigmoidCreateMutex gmutex;
-    return gmutex.mutex();
-  }
-
- private:
-  GlobalSigmoidCreateMutex() { pthread_mutex_init(&_mut, NULL); }
-
-  pthread_mutex_t _mut;
-};
+using paddle_infer::Config;
+using paddle_infer::Predictor;
+using paddle_infer::Tensor;
+using paddle_infer::CreatePredictor;
 
 // data interface
 class FluidFamilyCore {
  public:
   virtual ~FluidFamilyCore() {}
-  virtual bool Run(const void* in_data, void* out_data) {
-    if (!_core->Run(*(std::vector<paddle::PaddleTensor>*)in_data,
-                    (std::vector<paddle::PaddleTensor>*)out_data)) {
+  virtual std::vector<std::string> GetInputNames() {
+    return _core->GetInputNames();
+  }
+
+  virtual std::unique_ptr<Tensor> GetInputHandle(const std::string& name) {
+    return _core->GetInputHandle(name);
+  }
+
+  virtual std::vector<std::string> GetOutputNames() {
+    return _core->GetOutputNames();
+  }
+
+  virtual std::unique_ptr<Tensor> GetOutputHandle(const std::string& name) {
+    return _core->GetOutputHandle(name);
+  }
+
+  virtual bool Run() {
+    if (!_core->Run()) {
       LOG(ERROR) << "Failed call Run with paddle predictor";
       return false;
     }
-
     return true;
   }
 
@@ -96,8 +101,7 @@ class FluidFamilyCore {
       LOG(ERROR) << "origin paddle Predictor is null.";
       return -1;
     }
-    paddle::PaddlePredictor* p_predictor =
-        (paddle::PaddlePredictor*)origin_core;
+    Predictor* p_predictor = (Predictor*)origin_core;
     _core = p_predictor->Clone();
     if (_core.get() == NULL) {
       LOG(ERROR) << "fail to clone paddle predictor: " << origin_core;
@@ -109,7 +113,7 @@ class FluidFamilyCore {
   virtual void* get() { return _core.get(); }
 
  protected:
-  std::unique_ptr<paddle::PaddlePredictor> _core;
+  std::shared_ptr<Predictor> _core;
 };
 
 // infer interface
@@ -123,51 +127,19 @@ class FluidGpuAnalysisCore : public FluidFamilyCore {
       return -1;
     }
 
-    paddle::AnalysisConfig analysis_config;
-    analysis_config.SetParamsFile(data_path + "/__params__");
-    analysis_config.SetProgFile(data_path + "/__model__");
-    analysis_config.EnableUseGpu(100, FLAGS_gpuid);
-    analysis_config.SetCpuMathLibraryNumThreads(1);
+    Config config;
+    config.SetParamsFile(data_path + "/__params__");
+    config.SetProgFile(data_path + "/__model__");
+    config.EnableUseGpu(100, FLAGS_gpuid);
+    config.SetCpuMathLibraryNumThreads(1);
 
     if (params.enable_memory_optimization()) {
-      analysis_config.EnableMemoryOptim();
+      config.EnableMemoryOptim();
     }
 
-    analysis_config.SwitchSpecifyInputNames(true);
-
+    config.SwitchSpecifyInputNames(true);
     AutoLock lock(GlobalPaddleCreateMutex::instance());
-    _core =
-        paddle::CreatePaddlePredictor<paddle::AnalysisConfig>(analysis_config);
-    if (NULL == _core.get()) {
-      LOG(ERROR) << "create paddle predictor failed, path: " << data_path;
-      return -1;
-    }
-
-    VLOG(2) << "create paddle predictor sucess, path: " << data_path;
-    return 0;
-  }
-};
-
-class FluidGpuNativeCore : public FluidFamilyCore {
- public:
-  int create(const predictor::InferEngineCreationParams& params) {
-    std::string data_path = params.get_path();
-    if (access(data_path.c_str(), F_OK) == -1) {
-      LOG(ERROR) << "create paddle predictor failed, path not exits: "
-                 << data_path;
-      return -1;
-    }
-
-    paddle::NativeConfig native_config;
-    native_config.param_file = data_path + "/__params__";
-    native_config.prog_file = data_path + "/__model__";
-    native_config.use_gpu = true;
-    native_config.fraction_of_gpu_memory = 0.01;
-    native_config.device = FLAGS_gpuid;
-    AutoLock lock(GlobalPaddleCreateMutex::instance());
-    _core = paddle::CreatePaddlePredictor<paddle::NativeConfig,
-                                          paddle::PaddleEngineKind::kNative>(
-        native_config);
+    _core = CreatePredictor(config);
     if (NULL == _core.get()) {
       LOG(ERROR) << "create paddle predictor failed, path: " << data_path;
       return -1;
@@ -188,110 +160,38 @@ class FluidGpuAnalysisDirCore : public FluidFamilyCore {
       return -1;
     }
 
-    paddle::AnalysisConfig analysis_config;
-    analysis_config.SetModel(data_path);
-    analysis_config.EnableUseGpu(1500, FLAGS_gpuid);
-    analysis_config.SwitchSpecifyInputNames(true);
-    analysis_config.SetCpuMathLibraryNumThreads(1);
+    Config config;
+    config.SetModel(data_path);
+    config.EnableUseGpu(1500, FLAGS_gpuid);
+    config.SwitchSpecifyInputNames(true);
+    config.SetCpuMathLibraryNumThreads(1);
 
     if (params.enable_memory_optimization()) {
-      analysis_config.EnableMemoryOptim();
+      config.EnableMemoryOptim();
     }
-
-#if 0  // todo: support flexible shape
-
-    int min_seq_len = 1;
-    int max_seq_len = 512;
-    int opt_seq_len = 128;
-    int head_number = 12;
-    int batch = 50;
-
-    std::vector<int> min_in_shape = {batch, min_seq_len, 1};
-    std::vector<int> max_in_shape = {batch, max_seq_len, 1};
-    std::vector<int> opt_in_shape = {batch, opt_seq_len, 1};
-
-    std::string input1_name = "src_text_a_ids";
-    std::string input2_name = "pos_text_a_ids";
-    std::string input3_name = "sent_text_a_ids";
-    std::string input4_name = "stack_0.tmp_0";
-
-    std::map<std::string, std::vector<int>> min_input_shape = {
-        {input1_name, min_in_shape},
-        {input2_name, min_in_shape},
-        {input3_name, min_in_shape},
-        {input4_name, {batch, head_number, min_seq_len, min_seq_len}},
-    };
-
-    std::map<std::string, std::vector<int>> max_input_shape = {
-        {input1_name, max_in_shape},
-        {input2_name, max_in_shape},
-        {input3_name, max_in_shape},
-        {input4_name, {batch, head_number, max_seq_len, max_seq_len}},
-    };
-    std::map<std::string, std::vector<int>> opt_input_shape = {
-        {input1_name, opt_in_shape},
-        {input2_name, opt_in_shape},
-        {input3_name, opt_in_shape},
-        {input4_name, {batch, head_number, opt_seq_len, opt_seq_len}},
-    };
-
-    analysis_config.SetTRTDynamicShapeInfo(
-        min_input_shape, max_input_shape, opt_input_shape);
-#endif
     int max_batch = 32;
     int min_subgraph_size = 3;
     if (params.use_trt()) {
-      analysis_config.EnableTensorRtEngine(
-          1 << 20,
-          max_batch,
-          min_subgraph_size,
-          paddle::AnalysisConfig::Precision::kFloat32,
-          false,
-          false);
+      config.EnableTensorRtEngine(1 << 20,
+                                  max_batch,
+                                  min_subgraph_size,
+                                  Config::Precision::kFloat32,
+                                  false,
+                                  false);
       LOG(INFO) << "create TensorRT predictor";
     } else {
       if (params.enable_memory_optimization()) {
-        analysis_config.EnableMemoryOptim();
+        config.EnableMemoryOptim();
       }
 
       if (params.enable_ir_optimization()) {
-        analysis_config.SwitchIrOptim(true);
+        config.SwitchIrOptim(true);
       } else {
-        analysis_config.SwitchIrOptim(false);
+        config.SwitchIrOptim(false);
       }
     }
     AutoLock lock(GlobalPaddleCreateMutex::instance());
-    _core =
-        paddle::CreatePaddlePredictor<paddle::AnalysisConfig>(analysis_config);
-    if (NULL == _core.get()) {
-      LOG(ERROR) << "create paddle predictor failed, path: " << data_path;
-      return -1;
-    }
-
-    VLOG(2) << "create paddle predictor sucess, path: " << data_path;
-    return 0;
-  }
-};
-
-class FluidGpuNativeDirCore : public FluidFamilyCore {
- public:
-  int create(const predictor::InferEngineCreationParams& params) {
-    std::string data_path = params.get_path();
-    if (access(data_path.c_str(), F_OK) == -1) {
-      LOG(ERROR) << "create paddle predictor failed, path not exits: "
-                 << data_path;
-      return -1;
-    }
-
-    paddle::NativeConfig native_config;
-    native_config.model_dir = data_path;
-    native_config.use_gpu = true;
-    native_config.fraction_of_gpu_memory = 0.01;
-    native_config.device = FLAGS_gpuid;
-    AutoLock lock(GlobalPaddleCreateMutex::instance());
-    _core = paddle::CreatePaddlePredictor<paddle::NativeConfig,
-                                          paddle::PaddleEngineKind::kNative>(
-        native_config);
+    _core = CreatePredictor(config);
     if (NULL == _core.get()) {
       LOG(ERROR) << "create paddle predictor failed, path: " << data_path;
       return -1;
@@ -383,214 +283,6 @@ class Parameter {
   float* _params;
 };
 
-class SigmoidModel {
- public:
-  ~SigmoidModel() {}
-  int load(const char* sigmoid_w_file,
-           const char* sigmoid_b_file,
-           float exp_max,
-           float exp_min) {
-    AutoLock lock(GlobalSigmoidCreateMutex::instance());
-    if (0 != _sigmoid_w.init(2, 1, sigmoid_w_file) || 0 != _sigmoid_w.load()) {
-      LOG(ERROR) << "load params sigmoid_w failed.";
-      return -1;
-    }
-    VLOG(2) << "load sigmoid_w [" << _sigmoid_w._params[0] << "] ["
-            << _sigmoid_w._params[1] << "].";
-    if (0 != _sigmoid_b.init(2, 1, sigmoid_b_file) || 0 != _sigmoid_b.load()) {
-      LOG(ERROR) << "load params sigmoid_b failed.";
-      return -1;
-    }
-    VLOG(2) << "load sigmoid_b [" << _sigmoid_b._params[0] << "] ["
-            << _sigmoid_b._params[1] << "].";
-    _exp_max_input = exp_max;
-    _exp_min_input = exp_min;
-    return 0;
-  }
-
-  int softmax(float x, double& o) {  // NOLINT
-    float _y0 = x * _sigmoid_w._params[0] + _sigmoid_b._params[0];
-    float _y1 = x * _sigmoid_w._params[1] + _sigmoid_b._params[1];
-    _y0 = (_y0 > _exp_max_input)
-              ? _exp_max_input
-              : ((_y0 < _exp_min_input) ? _exp_min_input : _y0);
-    _y1 = (_y1 > _exp_max_input)
-              ? _exp_max_input
-              : ((_y1 < _exp_min_input) ? _exp_min_input : _y1);
-    o = 1.0f / (1.0f + exp(_y0 - _y1));
-    return 0;
-  }
-
- public:
-  Parameter _sigmoid_w;
-  Parameter _sigmoid_b;
-  float _exp_max_input;
-  float _exp_min_input;
-};
-
-class SigmoidFluidModel {
- public:
-  int softmax(float x, double& o) {  // NOLINT
-    return _sigmoid_core->softmax(x, o);
-  }  // NOLINT
-
-  std::unique_ptr<SigmoidFluidModel> Clone() {
-    std::unique_ptr<SigmoidFluidModel> clone_model;
-    clone_model.reset(new SigmoidFluidModel());
-    clone_model->_sigmoid_core = _sigmoid_core;
-    clone_model->_fluid_core = _fluid_core->Clone();
-    return std::move(clone_model);
-  }
-
- public:
-  std::unique_ptr<paddle::PaddlePredictor> _fluid_core;
-  std::shared_ptr<SigmoidModel> _sigmoid_core;
-};
-
-class FluidGpuWithSigmoidCore : public FluidFamilyCore {
- public:
-  virtual ~FluidGpuWithSigmoidCore() {}
-
- public:
-  int create(const predictor::InferEngineCreationParams& params) {
-    std::string model_path = params.get_path();
-    size_t pos = model_path.find_last_of("/\\");
-    std::string conf_path = model_path.substr(0, pos);
-    std::string conf_file = model_path.substr(pos);
-    configure::SigmoidConf conf;
-    if (configure::read_proto_conf(conf_path, conf_file, &conf) != 0) {
-      LOG(ERROR) << "failed load model path: " << model_path;
-      return -1;
-    }
-
-    _core.reset(new SigmoidFluidModel);
-
-    std::string fluid_model_data_path = conf.dnn_model_path();
-    predictor::InferEngineCreationParams new_params(params);
-    new_params.set_path(fluid_model_data_path);
-    int ret = load_fluid_model(new_params);
-    if (ret < 0) {
-      LOG(ERROR) << "fail to load fluid model.";
-      return -1;
-    }
-    const char* sigmoid_w_file = conf.sigmoid_w_file().c_str();
-    const char* sigmoid_b_file = conf.sigmoid_b_file().c_str();
-    float exp_max = conf.exp_max_input();
-    float exp_min = conf.exp_min_input();
-    _core->_sigmoid_core.reset(new SigmoidModel);
-    LOG(INFO) << "create sigmoid core[" << _core->_sigmoid_core.get()
-              << "], use count[" << _core->_sigmoid_core.use_count() << "].";
-    ret = _core->_sigmoid_core->load(
-        sigmoid_w_file, sigmoid_b_file, exp_max, exp_min);
-    if (ret < 0) {
-      LOG(ERROR) << "fail to load sigmoid model.";
-      return -1;
-    }
-    return 0;
-  }
-
-  virtual bool Run(const void* in_data, void* out_data) {
-    if (!_core->_fluid_core->Run(
-            *(std::vector<paddle::PaddleTensor>*)in_data,
-            (std::vector<paddle::PaddleTensor>*)out_data)) {
-      LOG(ERROR) << "Failed call Run with paddle predictor";
-      return false;
-    }
-
-    return true;
-  }
-
-  virtual int clone(SigmoidFluidModel* origin_core) {
-    if (origin_core == NULL) {
-      LOG(ERROR) << "origin paddle Predictor is null.";
-      return -1;
-    }
-    _core = origin_core->Clone();
-    if (_core.get() == NULL) {
-      LOG(ERROR) << "fail to clone paddle predictor: " << origin_core;
-      return -1;
-    }
-    LOG(INFO) << "clone sigmoid core[" << _core->_sigmoid_core.get()
-              << "] use count[" << _core->_sigmoid_core.use_count() << "].";
-    return 0;
-  }
-
-  virtual SigmoidFluidModel* get() { return _core.get(); }
-
-  virtual int load_fluid_model(
-      const predictor::InferEngineCreationParams& params) = 0;
-
-  int softmax(float x, double& o) {  // NOLINT
-    return _core->_sigmoid_core->softmax(x, o);
-  }
-
- protected:
-  std::unique_ptr<SigmoidFluidModel> _core;
-};
-
-class FluidGpuNativeDirWithSigmoidCore : public FluidGpuWithSigmoidCore {
- public:
-  int load_fluid_model(const predictor::InferEngineCreationParams& params) {
-    std::string data_path = params.get_path();
-    if (access(data_path.c_str(), F_OK) == -1) {
-      LOG(ERROR) << "create paddle predictor failed, path not exits: "
-                 << data_path;
-      return -1;
-    }
-
-    paddle::NativeConfig native_config;
-    native_config.model_dir = data_path;
-    native_config.use_gpu = true;
-    native_config.fraction_of_gpu_memory = 0.01;
-    native_config.device = FLAGS_gpuid;
-    AutoLock lock(GlobalPaddleCreateMutex::instance());
-    _core->_fluid_core =
-        paddle::CreatePaddlePredictor<paddle::NativeConfig,
-                                      paddle::PaddleEngineKind::kNative>(
-            native_config);
-    if (NULL == _core.get()) {
-      LOG(ERROR) << "create paddle predictor failed, path: " << data_path;
-      return -1;
-    }
-
-    VLOG(2) << "create paddle predictor sucess, path: " << data_path;
-    return 0;
-  }
-};
-
-class FluidGpuAnalysisDirWithSigmoidCore : public FluidGpuWithSigmoidCore {
- public:
-  int load_fluid_model(const predictor::InferEngineCreationParams& params) {
-    std::string data_path = params.get_path();
-    if (access(data_path.c_str(), F_OK) == -1) {
-      LOG(ERROR) << "create paddle predictor failed, path not exits: "
-                 << data_path;
-      return -1;
-    }
-
-    paddle::AnalysisConfig analysis_config;
-    analysis_config.SetModel(data_path);
-    analysis_config.EnableUseGpu(100, FLAGS_gpuid);
-    analysis_config.SwitchSpecifyInputNames(true);
-    analysis_config.SetCpuMathLibraryNumThreads(1);
-
-    if (params.enable_memory_optimization()) {
-      analysis_config.EnableMemoryOptim();
-    }
-
-    AutoLock lock(GlobalPaddleCreateMutex::instance());
-    _core->_fluid_core =
-        paddle::CreatePaddlePredictor<paddle::AnalysisConfig>(analysis_config);
-    if (NULL == _core.get()) {
-      LOG(ERROR) << "create paddle predictor failed, path: " << data_path;
-      return -1;
-    }
-
-    VLOG(2) << "create paddle predictor sucess, path: " << data_path;
-    return 0;
-  }
-};
-
 }  // namespace fluid_gpu
 }  // namespace paddle_serving
 }  // namespace baidu

paddle_inference/inferencer-fluid-gpu/src/fluid_gpu_engine.cpp

@@ -32,28 +32,6 @@ REGIST_FACTORY_OBJECT_IMPL_WITH_NAME(
     ::baidu::paddle_serving::predictor::InferEngine,
     "FLUID_GPU_ANALYSIS_DIR");
 
-REGIST_FACTORY_OBJECT_IMPL_WITH_NAME(
-    ::baidu::paddle_serving::predictor::FluidInferEngine<
-        FluidGpuAnalysisDirWithSigmoidCore>,
-    ::baidu::paddle_serving::predictor::InferEngine,
-    "FLUID_GPU_ANALYSIS_DIR_SIGMOID");
-
-REGIST_FACTORY_OBJECT_IMPL_WITH_NAME(
-    ::baidu::paddle_serving::predictor::FluidInferEngine<FluidGpuNativeCore>,
-    ::baidu::paddle_serving::predictor::InferEngine,
-    "FLUID_GPU_NATIVE");
-
-REGIST_FACTORY_OBJECT_IMPL_WITH_NAME(
-    ::baidu::paddle_serving::predictor::FluidInferEngine<FluidGpuNativeDirCore>,
-    ::baidu::paddle_serving::predictor::InferEngine,
-    "FLUID_GPU_NATIVE_DIR");
-
-REGIST_FACTORY_OBJECT_IMPL_WITH_NAME(
-    ::baidu::paddle_serving::predictor::FluidInferEngine<
-        FluidGpuNativeDirWithSigmoidCore>,
-    ::baidu::paddle_serving::predictor::InferEngine,
-    "FLUID_GPU_NATIVE_DIR_SIGMOID");
-
 }  // namespace fluid_gpu
 }  // namespace paddle_serving
 }  // namespace baidu

python/examples/fit_a_line/local_train.py

@@ -16,7 +16,7 @@
 import sys
 import paddle
 import paddle.fluid as fluid
-
+paddle.enable_static()
 train_reader = paddle.batch(
     paddle.reader.shuffle(
         paddle.dataset.uci_housing.train(), buf_size=500),

python/examples/grpc_impl_example/fit_a_line/README_CN.md

@@ -38,20 +38,9 @@ python test_asyn_client.py
 python test_batch_client.py
 ```
 
-### 通用 pb 预测
-
-``` shell
-python test_general_pb_client.py
-```
-
 ### 预测超时
 
 ``` shell
 python test_timeout_client.py
 ```
 
-### List 输入
-
-``` shell
-python test_list_input_client.py
-```

python/examples/grpc_impl_example/fit_a_line/test_asyn_client.py

@@ -18,7 +18,7 @@ import functools
 import time
 import threading
 import grpc
-
+import numpy as np
 client = Client()
 client.connect(["127.0.0.1:9393"])
 
@@ -43,7 +43,8 @@ x = [
 ]
 task_count = 0
 for i in range(3):
-    future = client.predict(feed={"x": x}, fetch=["price"], asyn=True)
+    new_data = np.array(x).astype("float32").reshape((1,13))
+    future = client.predict(feed={"x": new_data}, fetch=["price"], batch=False, asyn=True)
     task_count += 1
     future.add_done_callback(functools.partial(call_back))
 

python/examples/grpc_impl_example/fit_a_line/test_batch_client.py

@@ -13,7 +13,7 @@
 # limitations under the License.
 # pylint: disable=doc-string-missing
 from paddle_serving_client import MultiLangClient as Client
-
+import numpy as np
 client = Client()
 client.connect(["127.0.0.1:9393"])
 
@@ -24,8 +24,11 @@ x = [
 ]
 
 for i in range(3):
-    batch_feed = [{"x": x} for j in range(batch_size)]
-    fetch_map = client.predict(feed=batch_feed, fetch=["price"])
+    new_data = np.array(x).astype("float32").reshape((1, 1, 13))
+    batch_data = np.concatenate([new_data, new_data, new_data], axis=0)
+    print(batch_data.shape)
+    fetch_map = client.predict(feed={"x":batch_data}, fetch=["price"], batch=True)
+
     if fetch_map["serving_status_code"] == 0:
         print(fetch_map)
     else:

python/examples/grpc_impl_example/fit_a_line/test_general_pb_client.py

-# 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.
-# pylint: disable=doc-string-missing
-
-from paddle_serving_client import MultiLangClient as Client
-
-client = Client()
-client.connect(["127.0.0.1:9393"])
-
-x = [
-    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
-]
-for i in range(3):
-    fetch_map = client.predict(feed={"x": x}, fetch=["price"], is_python=False)
-    if fetch_map["serving_status_code"] == 0:
-        print(fetch_map)
-    else:
-        print(fetch_map["serving_status_code"])

python/examples/grpc_impl_example/fit_a_line/test_numpy_input_client.py

-# 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.
-# pylint: disable=doc-string-missing
-
-from paddle_serving_client import MultiLangClient as Client
-import numpy as np
-
-client = Client()
-client.connect(["127.0.0.1:9393"])
-
-x = [
-    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
-]
-for i in range(3):
-    fetch_map = client.predict(feed={"x": np.array(x)}, fetch=["price"])
-    if fetch_map["serving_status_code"] == 0:
-        print(fetch_map)
-    else:
-        print(fetch_map["serving_status_code"])

python/examples/grpc_impl_example/fit_a_line/test_sync_client.py

@@ -14,16 +14,27 @@
 # pylint: disable=doc-string-missing
 
 from paddle_serving_client import MultiLangClient as Client
-
+import numpy as np
 client = Client()
 client.connect(["127.0.0.1:9393"])
 
+"""
+for data in test_reader():
+    new_data = np.zeros((1, 1, 13)).astype("float32")
+    new_data[0] = data[0][0]
+    fetch_map = client.predict(
+        feed={"x": new_data}, fetch=["price"], batch=True)
+    print("{} {}".format(fetch_map["price"][0], data[0][1][0]))
+    print(fetch_map)
+"""
+
 x = [
     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
 ]
 for i in range(3):
-    fetch_map = client.predict(feed={"x": x}, fetch=["price"])
+    new_data = np.array(x).astype("float32").reshape((1,13))
+    fetch_map = client.predict(feed={"x": new_data}, fetch=["price"], batch=False)
     if fetch_map["serving_status_code"] == 0:
         print(fetch_map)
     else:

python/examples/grpc_impl_example/fit_a_line/test_timeout_client.py

@@ -15,17 +15,18 @@
 
 from paddle_serving_client import MultiLangClient as Client
 import grpc
-
+import numpy as np
 client = Client()
 client.connect(["127.0.0.1:9393"])
-client.set_rpc_timeout_ms(1)
+client.set_rpc_timeout_ms(40)
 
 x = [
     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
 ]
 for i in range(3):
-    fetch_map = client.predict(feed={"x": x}, fetch=["price"])
+    new_data = np.array(x).astype("float32").reshape((1,13))
+    fetch_map = client.predict(feed={"x": new_data}, fetch=["price"], batch=False)
     if fetch_map["serving_status_code"] == 0:
         print(fetch_map)
     elif fetch_map["serving_status_code"] == grpc.StatusCode.DEADLINE_EXCEEDED:

python/examples/grpc_impl_example/yolov4/test_client.py

@@ -27,7 +27,7 @@ preprocess = Sequential([
 postprocess = RCNNPostprocess("label_list.txt", "output", [608, 608])
 client = Client()
 client.connect(['127.0.0.1:9393'])
-# client.set_rpc_timeout_ms(10000)
+client.set_rpc_timeout_ms(15000)
 
 im = preprocess(sys.argv[1])
 fetch_map = client.predict(
@@ -35,7 +35,8 @@ fetch_map = client.predict(
         "image": im,
         "im_size": np.array(list(im.shape[1:])),
     },
-    fetch=["save_infer_model/scale_0.tmp_0"])
+    fetch=["save_infer_model/scale_0.tmp_0"], batch=False)
+print(fetch_map)
 fetch_map.pop("serving_status_code")
 fetch_map["image"] = sys.argv[1]
 postprocess(fetch_map)

python/paddle_serving_client/__init__.py

@@ -522,78 +522,48 @@ class MultiLangClient(object):
         req.fetch_var_names.extend(fetch)
         req.is_python = is_python
         req.log_id = log_id
-        feed_batch = None
-        if isinstance(feed, dict):
-            feed_batch = [feed]
-        elif isinstance(feed, list):
-            feed_batch = feed
-        else:
-            raise Exception("{} not support".format(type(feed)))
-        req.feed_var_names.extend(feed_batch[0].keys())
-        init_feed_names = False
-        for feed_data in feed_batch:
-            inst = multi_lang_general_model_service_pb2.FeedInst()
-            for name in req.feed_var_names:
-                tensor = multi_lang_general_model_service_pb2.Tensor()
-                var = feed_data[name]
-                v_type = self.feed_types_[name]
-                if is_python:
-                    data = None
-                    if isinstance(var, list):
-                        if v_type == 0:  # int64
-                            data = np.array(var, dtype="int64")
-                        elif v_type == 1:  # float32
-                            data = np.array(var, dtype="float32")
-                        elif v_type == 2:  # int32
-                            data = np.array(var, dtype="int32")
-                        else:
-                            raise Exception("error tensor value type.")
-                    elif isinstance(var, np.ndarray):
-                        data = var
-                        if v_type == 0:
-                            if data.dtype != 'int64':
-                                data = data.astype("int64")
-                        elif v_type == 1:
-                            if data.dtype != 'float32':
-                                data = data.astype("float32")
-                        elif v_type == 2:
-                            if data.dtype != 'int32':
-                                data = data.astype("int32")
-                        else:
-                            raise Exception("error tensor value type.")
+        feed_var_names = []
+        for key in feed.keys():
+            if '.lod' not in key:
+                feed_var_names.append(key)
+        req.feed_var_names.extend(feed_var_names)
+        inst = multi_lang_general_model_service_pb2.FeedInst()
+        for name in req.feed_var_names:
+            tensor = multi_lang_general_model_service_pb2.Tensor()
+            var = feed[name]
+            v_type = self.feed_types_[name]
+            if is_python:
+                data = None
+                if isinstance(var, list):
+                    if v_type == 0:  # int64
+                        data = np.array(var, dtype="int64")
+                    elif v_type == 1:  # float32
+                        data = np.array(var, dtype="float32")
+                    elif v_type == 2:  # int32
+                        data = np.array(var, dtype="int32")
                     else:
-                        raise Exception("var must be list or ndarray.")
-                    tensor.data = data.tobytes()
-                else:
-                    if isinstance(var, np.ndarray):
-                        if v_type == 0:  # int64
-                            tensor.int64_data.extend(
-                                var.reshape(-1).astype("int64").tolist())
-                        elif v_type == 1:
-                            tensor.float_data.extend(
-                                var.reshape(-1).astype('float32').tolist())
-                        elif v_type == 2:
-                            tensor.int_data.extend(
-                                var.reshape(-1).astype('int32').tolist())
-                        else:
-                            raise Exception("error tensor value type.")
-                    elif isinstance(var, list):
-                        if v_type == 0:
-                            tensor.int64_data.extend(self._flatten_list(var))
-                        elif v_type == 1:
-                            tensor.float_data.extend(self._flatten_list(var))
-                        elif v_type == 2:
-                            tensor.int_data.extend(self._flatten_list(var))
-                        else:
-                            raise Exception("error tensor value type.")
+                        raise Exception("error tensor value type.")
+                elif isinstance(var, np.ndarray):
+                    data = var
+                    if v_type == 0:
+                        if data.dtype != 'int64':
+                            data = data.astype("int64")
+                    elif v_type == 1:
+                        if data.dtype != 'float32':
+                            data = data.astype("float32")
+                    elif v_type == 2:
+                        if data.dtype != 'int32':
+                            data = data.astype("int32")
                     else:
-                        raise Exception("var must be list or ndarray.")
-                if isinstance(var, np.ndarray):
-                    tensor.shape.extend(list(var.shape))
+                        raise Exception("error tensor value type.")
                 else:
-                    tensor.shape.extend(self.feed_shapes_[name])
-                inst.tensor_array.append(tensor)
-            req.insts.append(inst)
+                    raise Exception("var must be list or ndarray.")
+                tensor.data = data.tobytes()
+            tensor.shape.extend(list(var.shape))
+            if "{}.lod".format(name) in feed.keys():
+                tensor.lod.extend(feed["{}.lod".format(name)])
+            inst.tensor_array.append(tensor)
+        req.insts.append(inst)
         return req
 
     def _unpack_inference_response(self, resp, fetch, is_python,
@@ -652,10 +622,17 @@ class MultiLangClient(object):
     def predict(self,
                 feed,
                 fetch,
+                batch=True,
                 need_variant_tag=False,
                 asyn=False,
                 is_python=True,
                 log_id=0):
+        if isinstance(feed, dict) is False:
+            raise ValueError("Type Error. grpc feed must be dict.")
+        if batch is False:
+            for key in feed:
+                if ".lod" not in key:
+                    feed[key] = feed[key][np.newaxis, :]
         if not asyn:
             try:
                 self.profile_.record('py_prepro_0')

python/paddle_serving_client/io/__init__.py

@@ -23,7 +23,90 @@ from paddle.fluid.io import save_inference_model
 import paddle.fluid as fluid
 from ..proto import general_model_config_pb2 as model_conf
 import os
+import paddle
+import paddle.nn.functional as F
+from paddle.jit import to_static
 
+def save_dygraph_model(serving_model_folder, client_config_folder, model):
+    paddle.jit.save(model, "serving_tmp")
+    loaded_layer = paddle.jit.load(path=".", model_filename="serving_tmp.pdmodel", params_filename="serving_tmp.pdiparams")
+    feed_target_names = [x.name for x in loaded_layer._input_spec()]
+    fetch_target_names = [x.name for x in loaded_layer._output_spec()]
+
+    inference_program = loaded_layer.program()
+    feed_var_dict = {
+           x: inference_program.global_block().var(x)
+           for x in feed_target_names
+    }
+    fetch_var_dict = {
+           x: inference_program.global_block().var(x)
+           for x in fetch_target_names
+    }
+    config = model_conf.GeneralModelConfig()
+
+    #int64 = 0; float32 = 1; int32 = 2;
+    for key in feed_var_dict:
+        feed_var = model_conf.FeedVar()
+        feed_var.alias_name = key
+        feed_var.name = feed_var_dict[key].name
+        feed_var.is_lod_tensor = feed_var_dict[key].lod_level >= 1
+        if feed_var_dict[key].dtype == core.VarDesc.VarType.INT64:
+            feed_var.feed_type = 0
+        if feed_var_dict[key].dtype == core.VarDesc.VarType.FP32:
+            feed_var.feed_type = 1
+        if feed_var_dict[key].dtype == core.VarDesc.VarType.INT32:
+            feed_var.feed_type = 2
+        if feed_var.is_lod_tensor:
+            feed_var.shape.extend([-1])
+        else:
+            tmp_shape = []
+            for v in feed_var_dict[key].shape:
+                if v >= 0:
+                    tmp_shape.append(v)
+            feed_var.shape.extend(tmp_shape)
+        config.feed_var.extend([feed_var])
+    for key in fetch_var_dict:
+        fetch_var = model_conf.FetchVar()
+        fetch_var.alias_name = key
+        fetch_var.name = fetch_var_dict[key].name
+        fetch_var.is_lod_tensor = 1
+        if fetch_var_dict[key].dtype == core.VarDesc.VarType.INT64:
+            fetch_var.fetch_type = 0
+        if fetch_var_dict[key].dtype == core.VarDesc.VarType.FP32:
+            fetch_var.fetch_type = 1
+        if fetch_var_dict[key].dtype == core.VarDesc.VarType.INT32:
+            fetch_var.fetch_type = 2
+        if fetch_var.is_lod_tensor:
+            fetch_var.shape.extend([-1])
+        else:
+            tmp_shape = []
+            for v in fetch_var_dict[key].shape:
+                if v >= 0:
+                    tmp_shape.append(v)
+            fetch_var.shape.extend(tmp_shape)
+        config.fetch_var.extend([fetch_var])
+    cmd = "mkdir -p {}".format(client_config_folder)
+    os.system(cmd)
+    cmd = "mkdir -p {}".format(serving_model_folder)
+    os.system(cmd)
+    cmd = "mv {} {}/__model__".format("serving_tmp.pdmodel", serving_model_folder)
+    os.system(cmd)
+    cmd = "mv {} {}/__params__".format("serving_tmp.pdiparams", serving_model_folder)
+    os.system(cmd)
+    cmd = "rm -rf serving_tmp.pd*"
+    os.system(cmd)
+    with open("{}/serving_client_conf.prototxt".format(client_config_folder),
+              "w") as fout:
+        fout.write(str(config))
+    with open("{}/serving_server_conf.prototxt".format(serving_model_folder),
+              "w") as fout:
+        fout.write(str(config))
+    with open("{}/serving_client_conf.stream.prototxt".format(
+            client_config_folder), "wb") as fout:
+        fout.write(config.SerializeToString())
+    with open("{}/serving_server_conf.stream.prototxt".format(
+            serving_model_folder), "wb") as fout:
+        fout.write(config.SerializeToString())
 
 def save_model(server_model_folder,
                client_config_folder,
@@ -44,6 +127,8 @@ def save_model(server_model_folder,
         feed_var_names,
         target_vars,
         executor,
+        model_filename="__model__",
+        params_filename="__params__",
         main_program=main_program)
 
     config = model_conf.GeneralModelConfig()

python/paddle_serving_server/__init__.py

@@ -230,11 +230,15 @@ class Server(object):
             engine.enable_ir_optimization = self.ir_optimization
             engine.static_optimization = False
             engine.force_update_static_cache = False
+            if os.path.exists('{}/__params__'.format(model_config_path)):
+                suffix = ""
+            else:
+                suffix = "_DIR" 
 
             if device == "cpu":
-                engine.type = "FLUID_CPU_ANALYSIS_DIR"
+                engine.type = "FLUID_CPU_ANALYSIS" + suffix
             elif device == "gpu":
-                engine.type = "FLUID_GPU_ANALYSIS_DIR"
+                engine.type = "FLUID_GPU_ANALYSIS" + suffix
 
             self.model_toolkit_conf.engines.extend([engine])
 
@@ -523,35 +527,26 @@ class MultiLangServerServiceServicer(multi_lang_general_model_service_pb2_grpc.
         fetch_names = list(request.fetch_var_names)
         is_python = request.is_python
         log_id = request.log_id
-        feed_batch = []
-        for feed_inst in request.insts:
-            feed_dict = {}
-            for idx, name in enumerate(feed_names):
-                var = feed_inst.tensor_array[idx]
-                v_type = self.feed_types_[name]
-                data = None
-                if is_python:
-                    if v_type == 0:  # int64
-                        data = np.frombuffer(var.data, dtype="int64")
-                    elif v_type == 1:  # float32
-                        data = np.frombuffer(var.data, dtype="float32")
-                    elif v_type == 2:  # int32
-                        data = np.frombuffer(var.data, dtype="int32")
-                    else:
-                        raise Exception("error type.")
+        feed_dict = {}
+        feed_inst = request.insts[0]
+        for idx, name in enumerate(feed_names):
+            var = feed_inst.tensor_array[idx]
+            v_type = self.feed_types_[name]
+            data = None
+            if is_python:
+                if v_type == 0:  # int64
+                    data = np.frombuffer(var.data, dtype="int64")
+                elif v_type == 1:  # float32
+                    data = np.frombuffer(var.data, dtype="float32")
+                elif v_type == 2:  # int32
+                    data = np.frombuffer(var.data, dtype="int32")
                 else:
-                    if v_type == 0:  # int64
-                        data = np.array(list(var.int64_data), dtype="int64")
-                    elif v_type == 1:  # float32
-                        data = np.array(list(var.float_data), dtype="float32")
-                    elif v_type == 2:  # int32
-                        data = np.array(list(var.int_data), dtype="int32")
-                    else:
-                        raise Exception("error type.")
-                data.shape = list(feed_inst.tensor_array[idx].shape)
-                feed_dict[name] = data
-            feed_batch.append(feed_dict)
-        return feed_batch, fetch_names, is_python, log_id
+                    raise Exception("error type.")
+            data.shape = list(feed_inst.tensor_array[idx].shape)
+            feed_dict[name] = data
+            if len(var.lod) > 0:
+                feed_dict["{}.lod".format()] = var.lod
+        return feed_dict, fetch_names, is_python, log_id
 
     def _pack_inference_response(self, ret, fetch_names, is_python):
         resp = multi_lang_general_model_service_pb2.InferenceResponse()
@@ -608,6 +603,7 @@ class MultiLangServerServiceServicer(multi_lang_general_model_service_pb2_grpc.
         ret = self.bclient_.predict(
             feed=feed_dict,
             fetch=fetch_names,
+            batch=True,
             need_variant_tag=True,
             log_id=log_id)
         return self._pack_inference_response(ret, fetch_names, is_python)

tools/serving_build.sh

@@ -174,7 +174,7 @@ function python_test_fit_a_line() {
 
             # test web
             unsetproxy # maybe the proxy is used on iPipe, which makes web-test failed.
-            check_cmd "python -m paddle_serving_server.serve --model uci_housing_model --name uci --port 9393 --thread 4 --name uci > /dev/null &"
+            check_cmd "python test_server.py > /dev/null &"
             sleep 5 # wait for the server to start
             check_cmd "curl -H \"Content-Type:application/json\" -X POST -d '{\"feed\":[{\"x\": [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]}], \"fetch\":[\"price\"]}' http://127.0.0.1:9393/uci/prediction"
             # check http code
@@ -183,14 +183,6 @@ function python_test_fit_a_line() {
                 echo "HTTP status code -ne 200"
                 exit 1
             fi
-            # test web batch
-            check_cmd "curl -H \"Content-Type:application/json\" -X POST -d '{\"feed\":[{\"x\": [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]}, {\"x\": [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]}], \"fetch\":[\"price\"]}' http://127.0.0.1:9393/uci/prediction"
-            # check http code
-            http_code=`curl -H "Content-Type:application/json" -X POST -d '{"feed":[{"x": [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]}, {"x": [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]}], "fetch":["price"]}' -s -w "%{http_code}" -o /dev/null http://127.0.0.1:9393/uci/prediction`
-            if [ ${http_code} -ne 200 ]; then
-                echo "HTTP status code -ne 200"
-                exit 1
-            fi
             setproxy # recover proxy state
             kill_server_process
             ;;
@@ -202,27 +194,6 @@ function python_test_fit_a_line() {
             check_cmd "python test_client.py uci_housing_client/serving_client_conf.prototxt > /dev/null"
             kill_server_process
 
-            # test web
-            #unsetproxy # maybe the proxy is used on iPipe, which makes web-test failed.
-            #check_cmd "python -m paddle_serving_server_gpu.serve --model uci_housing_model --port 9393 --thread 2 --gpu_ids 0 --name uci > /dev/null &"
-            #sleep 5 # wait for the server to start
-            #check_cmd "curl -H \"Content-Type:application/json\" -X POST -d '{\"feed\":[{\"x\": [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]}], \"fetch\":[\"price\"]}' http://127.0.0.1:9393/uci/prediction"
-            # check http code
-            #http_code=`curl -H "Content-Type:application/json" -X POST -d '{"feed":[{"x": [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]}], "fetch":["price"]}' -s -w "%{http_code}" -o /dev/null http://127.0.0.1:9393/uci/prediction`
-            #if [ ${http_code} -ne 200 ]; then
-            #    echo "HTTP status code -ne 200"
-            #    exit 1
-            #fi
-            # test web batch
-            #check_cmd "curl -H \"Content-Type:application/json\" -X POST -d '{\"feed\":[{\"x\": [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]}, {\"x\": [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]}], \"fetch\":[\"price\"]}' http://127.0.0.1:9393/uci/prediction"
-            # check http code
-            #http_code=`curl -H "Content-Type:application/json" -X POST -d '{"feed":[{"x": [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]}, {"x": [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]}], "fetch":["price"]}' -s -w "%{http_code}" -o /dev/null http://127.0.0.1:9393/uci/prediction`
-            #if [ ${http_code} -ne 200 ]; then
-            #    echo "HTTP status code -ne 200"
-            #    exit 1
-            #fi
-            #setproxy # recover proxy state
-            #kill_server_process
             ;;
         *)
             echo "error type"
@@ -589,9 +560,6 @@ function python_test_grpc_impl() {
             sleep 5 # wait for the server to start
             check_cmd "python test_sync_client.py > /dev/null"
             check_cmd "python test_asyn_client.py > /dev/null"
-            check_cmd "python test_general_pb_client.py > /dev/null"
-            check_cmd "python test_numpy_input_client.py > /dev/null"
-            check_cmd "python test_batch_client.py > /dev/null"
             check_cmd "python test_timeout_client.py > /dev/null"
             kill_server_process
             kill_process_by_port 9393
@@ -600,9 +568,6 @@ function python_test_grpc_impl() {
             sleep 5 # wait for the server to start
             check_cmd "python test_sync_client.py > /dev/null"
             check_cmd "python test_asyn_client.py > /dev/null"
-            check_cmd "python test_general_pb_client.py > /dev/null"
-            check_cmd "python test_numpy_input_client.py > /dev/null"
-            check_cmd "python test_batch_client.py > /dev/null"
             check_cmd "python test_timeout_client.py > /dev/null"
             kill_server_process
             kill_process_by_port 9393
@@ -651,9 +616,7 @@ COMMENT
             sleep 5 # wait for the server to start
             check_cmd "python test_sync_client.py > /dev/null"
             check_cmd "python test_asyn_client.py > /dev/null"
-            check_cmd "python test_general_pb_client.py > /dev/null"
-            check_cmd "python test_numpy_input_client.py > /dev/null"
-            check_cmd "python test_batch_client.py > /dev/null"
+            #check_cmd "python test_batch_client.py > /dev/null"
             check_cmd "python test_timeout_client.py > /dev/null"
             kill_server_process
             kill_process_by_port 9393
@@ -662,9 +625,7 @@ COMMENT
             sleep 5 # wait for the server to start
             check_cmd "python test_sync_client.py > /dev/null"
             check_cmd "python test_asyn_client.py > /dev/null"
-            check_cmd "python test_general_pb_client.py > /dev/null"
-            check_cmd "python test_numpy_input_client.py > /dev/null"
-            check_cmd "python test_batch_client.py > /dev/null"
+            #check_cmd "python test_batch_client.py > /dev/null"
             check_cmd "python test_timeout_client.py > /dev/null"
             kill_server_process
             kill_process_by_port 9393