Merge branch 'develop' into low-precision-doc

CMakeLists.txt

@@ -60,6 +60,13 @@ option(WITH_TRT             "Compile Paddle Serving with TRT"
 option(PADDLE_ON_INFERENCE  "Compile for encryption"                             ON)
 option(WITH_OPENCV	    "Compile Paddle Serving with OPENCV"                    OFF)
 
+if(NOT DEFINED VERSION_TAG)
+  set(VERSION_TAG "0.0.0")
+endif()
+if (WITH_PYTHON)
+  message(STATUS "Compile Version Tag for wheel: ${VERSION_TAG}")
+endif()
+
 if (WITH_OPENCV)
     SET(OPENCV_DIR "" CACHE PATH "Location of libraries")
     if(NOT DEFINED OPENCV_DIR)

core/general-server/op/general_reader_op.cpp

@@ -47,17 +47,14 @@ int conf_check(const Request *req,
 
   for (int i = 0; i < var_num; ++i) {
     const Tensor &tensor = req->insts(0).tensor_array(i);
-    if (model_config->_feed_type[i] !=
-        tensor.elem_type()) {
+    if (model_config->_feed_type[i] != tensor.elem_type()) {
       LOG(ERROR) << "feed type not match.";
       return -1;
     }
-    if (model_config->_feed_shape[i].size() ==
-        tensor.shape_size()) {
+    if (model_config->_feed_shape[i].size() == tensor.shape_size()) {
       for (int j = 0; j < model_config->_feed_shape[i].size(); ++j) {
         tensor.shape(j);
-        if (model_config->_feed_shape[i][j] !=
-            tensor.shape(j)) {
+        if (model_config->_feed_shape[i][j] != tensor.shape(j)) {
           LOG(ERROR) << "feed shape not match.";
           return -1;
         }
@@ -73,6 +70,11 @@ int conf_check(const Request *req,
 int GeneralReaderOp::inference() {
   // read request from client
   const Request *req = dynamic_cast<const Request *>(get_request_message());
+  if (!req) {
+    LOG(ERROR) << "Failed get request message";
+    return -1;
+  }
+
   uint64_t log_id = req->log_id();
   int input_var_num = 0;
   std::vector<int64_t> elem_type;
@@ -80,14 +82,18 @@ int GeneralReaderOp::inference() {
   std::vector<int64_t> databuf_size;
 
   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";
+    LOG(ERROR) << "(logid=" << log_id << ") Failed get GeneralBlob";
+    return -1;
   }
 
+  TensorVector *out = &(res->tensor_vector);
+  if (!out) {
+    LOG(ERROR) << "(logid=" << log_id << ") Failed get tensor_vector of res";
+    return -1;
+  }
+
+  res->SetLogId(log_id);
   Timer timeline;
   int64_t start = timeline.TimeStampUS();
   int var_num = req->insts(0).tensor_array_size();
@@ -99,7 +105,7 @@ int GeneralReaderOp::inference() {
       baidu::paddle_serving::predictor::Resource::instance();
 
   VLOG(2) << "(logid=" << log_id << ") get resource pointer done.";
-  //get the first InferOP's model_config as ReaderOp's model_config by default.
+  // get the first InferOP's model_config as ReaderOp's model_config by default.
   std::shared_ptr<PaddleGeneralModelConfig> model_config =
       resource.get_general_model_config().front();
 
@@ -140,10 +146,11 @@ int GeneralReaderOp::inference() {
       lod_tensor.dtype = paddle::PaddleDType::INT32;
       data_len = tensor.int_data_size();
     } else if (elem_type[i] == P_STRING) {
-      //use paddle::PaddleDType::UINT8 as for String.
+      // use paddle::PaddleDType::UINT8 as for String.
       elem_size[i] = sizeof(uint8_t);
       lod_tensor.dtype = paddle::PaddleDType::UINT8;
-      //this is for vector<String>, cause the databuf_size != vector<String>.size()*sizeof(char);
+      // this is for vector<String>, cause the databuf_size !=
+      // vector<String>.size()*sizeof(char);
       for (int idx = 0; idx < tensor.data_size(); idx++) {
         data_len += tensor.data()[idx].length();
       }
@@ -157,34 +164,32 @@ int GeneralReaderOp::inference() {
       for (int k = 0; k < tensor.lod_size(); ++k) {
         lod_tensor.lod[0].push_back(tensor.lod(k));
       }
+      VLOG(2) << "(logid=" << log_id << ") var[" << i
+              << "] has lod_tensor and len=" << out->at(i).lod[0].back();
     }
 
     for (int k = 0; k < tensor.shape_size(); ++k) {
       int dim = tensor.shape(k);
-      VLOG(2) << "(logid=" << log_id << ") shape for var[" << i
-              << "]: " << dim;
+      VLOG(2) << "(logid=" << log_id << ") shape for var[" << i << "]: " << dim;
       lod_tensor.shape.push_back(dim);
     }
     lod_tensor.name = model_config->_feed_name[i];
     out->push_back(lod_tensor);
 
-    
     VLOG(2) << "(logid=" << log_id << ") tensor size for var[" << i
             << "]: " << data_len;
     databuf_size[i] = data_len * elem_size[i];
     out->at(i).data.Resize(data_len * elem_size[i]);
-    VLOG(2) << "(logid=" << log_id << ") var[" << i
-            << "] is lod_tensor and len=" << out->at(i).lod[0].back();
-    
+
     if (elem_type[i] == P_INT64) {
       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 " << tensor.int64_data(0);
       if (!dst_ptr) {
         LOG(ERROR) << "dst_ptr is nullptr";
-            return -1;
+        return -1;
       }
-      memcpy(dst_ptr, tensor.int64_data().data(),databuf_size[i]);
+      memcpy(dst_ptr, tensor.int64_data().data(), databuf_size[i]);
       /*
       int elem_num = tensor.int64_data_size();
       for (int k = 0; k < elem_num; ++k) {
@@ -197,9 +202,9 @@ int GeneralReaderOp::inference() {
               << "] is " << tensor.float_data(0);
       if (!dst_ptr) {
         LOG(ERROR) << "dst_ptr is nullptr";
-            return -1;
+        return -1;
       }
-      memcpy(dst_ptr, tensor.float_data().data(),databuf_size[i]);
+      memcpy(dst_ptr, tensor.float_data().data(), databuf_size[i]);
       /*int elem_num = tensor.float_data_size();
       for (int k = 0; k < elem_num; ++k) {
         dst_ptr[k] = tensor.float_data(k);
@@ -210,9 +215,9 @@ int GeneralReaderOp::inference() {
               << "] is " << tensor.int_data(0);
       if (!dst_ptr) {
         LOG(ERROR) << "dst_ptr is nullptr";
-            return -1;
+        return -1;
       }
-      memcpy(dst_ptr, tensor.int_data().data(),databuf_size[i]);
+      memcpy(dst_ptr, tensor.int_data().data(), databuf_size[i]);
       /*
       int elem_num = tensor.int_data_size();
       for (int k = 0; k < elem_num; ++k) {
@@ -225,7 +230,7 @@ int GeneralReaderOp::inference() {
               << "] is " << tensor.data(0);
       if (!dst_ptr) {
         LOG(ERROR) << "dst_ptr is nullptr";
-            return -1;
+        return -1;
       }
       int elem_num = tensor.data_size();
       for (int k = 0; k < elem_num; ++k) {

core/predictor/framework/infer.cpp

+// Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "core/predictor/framework/infer.h"
+
+namespace baidu {
+namespace paddle_serving {
+namespace predictor {
+
+int ReloadableInferEngine::proc_initialize_impl(
+    const configure::EngineDesc& conf, bool version) {
+  _reload_tag_file = conf.reloadable_meta();
+  _reload_type = conf.reloadable_type();
+  _model_dir = conf.model_dir();
+  _infer_thread_num = conf.runtime_thread_num();
+  _infer_batch_size = conf.batch_infer_size();
+  _infer_batch_align = conf.enable_batch_align();
+
+  _conf = conf;
+
+  if (!check_need_reload() || load(conf) != 0) {
+    LOG(ERROR) << "Failed load model_data_path" << _model_dir;
+    return -1;
+  }
+
+  if (parse_version_info(conf, version) != 0) {
+    LOG(ERROR) << "Failed parse version info";
+    return -1;
+  }
+
+  LOG(WARNING) << "Succ load model:" << _model_dir;
+  return 0;
+}
+
+int ReloadableInferEngine::proc_initialize(const configure::EngineDesc& conf,
+                                           bool version) {
+  if (proc_initialize_impl(conf, version) != 0) {
+    LOG(ERROR) << "Failed proc initialize impl";
+    return -1;
+  }
+
+  // init bsf framework
+  if (_infer_thread_num <= 0) {
+    return 0;
+  }
+
+  // init bsf framework
+  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::task_infer_impl, 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 ReloadableInferEngine::infer(const void* in,
+                                 void* out,
+                                 uint32_t batch_size) {
+  if (_infer_thread_num <= 0) {
+    return infer_impl(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 ReloadableInferEngine::thrd_initialize() {
+  if (_infer_thread_num > 0) {
+    return 0;
+  }
+  return thrd_initialize_impl();
+}
+
+int ReloadableInferEngine::thrd_clear() {
+  if (_infer_thread_num > 0) {
+    return 0;
+  }
+  return thrd_clear_impl();
+}
+
+int ReloadableInferEngine::proc_finalize() {
+  if (proc_finalize_impl() != 0) {
+    LOG(ERROR) << "Failed proc finalize impl";
+    return -1;
+  }
+
+  if (_infer_thread_num > 0) {
+    im::bsf::TaskExecutor<TaskT>::instance()->stop();
+  }
+  return 0;
+}
+
+int ReloadableInferEngine::reload() {
+  if (check_need_reload()) {
+    LOG(WARNING) << "begin reload model[" << _model_dir << "].";
+    return load(_conf);
+  }
+  return 0;
+}
+
+int ReloadableInferEngine::parse_version_info(
+    const configure::EngineDesc& config, bool version) {
+  _version = uint64_t(-1);
+  return 0;
+}
+
+bool ReloadableInferEngine::check_need_reload() {
+  if (_reload_type == "timestamp_ne") {
+    return check_timestamp_ne();
+  } else if (_reload_type == "timestamp_gt") {
+    return check_timestamp_gt();
+  } else if (_reload_type == "md5sum") {
+    return check_md5sum();
+  } else if (_reload_type == "revision") {
+    return check_revision();
+  } else if (_reload_type == "none") {
+    return false;
+  }
+
+  LOG(ERROR) << "Not support reload type: " << _reload_type;
+  return false;
+}
+
+bool ReloadableInferEngine::check_timestamp_ne() {
+  struct stat st;
+  if (stat(_reload_tag_file.c_str(), &st) != 0) {
+    LOG(ERROR) << "Failed stat config file:" << _reload_tag_file;
+    return false;
+  }
+
+  if ((st.st_mode & S_IFREG) && st.st_mtime != _last_record.timestamp) {
+    _last_record.timestamp = st.st_mtime;
+    return true;
+  }
+
+  return false;
+}
+
+bool ReloadableInferEngine::check_timestamp_gt() {
+  struct stat st;
+  if (stat(_reload_tag_file.c_str(), &st) != 0) {
+    LOG(ERROR) << "Failed stat config file:" << _reload_tag_file;
+    return false;
+  }
+
+  if ((st.st_mode & S_IFREG) && st.st_mtime > _last_record.timestamp) {
+    _last_record.timestamp = st.st_mtime;
+    return true;
+  }
+
+  return false;
+}
+
+int VersionedInferEngine::proc_initialize(const configure::EngineDesc& conf) {
+  if (proc_initialize(conf, false) != 0) {
+    LOG(ERROR) << "Failed proc intialize engine: " << conf.name().c_str();
+    return -1;
+  }
+
+  LOG(WARNING) << "Succ proc initialize engine: " << conf.name().c_str();
+  return 0;
+}
+
+int VersionedInferEngine::proc_initialize(const configure::EngineDesc& conf,
+                                          bool version) {
+  std::string engine_type = conf.type();
+  InferEngine* engine =
+      StaticInferFactory::instance().generate_object(engine_type);
+  if (!engine) {
+    LOG(ERROR) << "Failed generate engine with type:" << engine_type;
+    return -1;
+  }
+#ifndef BCLOUD
+  VLOG(2) << "FLAGS_logtostderr " << FLAGS_logtostderr;
+  int tmp = FLAGS_logtostderr;
+  if (engine->proc_initialize(conf, version) != 0) {
+    LOG(ERROR) << "Failed initialize engine, type:" << engine_type;
+    return -1;
+  }
+  VLOG(2) << "FLAGS_logtostderr " << FLAGS_logtostderr;
+  FLAGS_logtostderr = tmp;
+#else
+  if (engine->proc_initialize(conf, version) != 0) {
+    LOG(ERROR) << "Failed initialize engine, type:" << engine_type;
+    return -1;
+  }
+#endif
+  auto r = _versions.insert(std::make_pair(engine->version(), engine));
+  if (!r.second) {
+    LOG(ERROR) << "Failed insert item: " << engine->version()
+               << ", type: " << engine_type;
+    return -1;
+  }
+  LOG(WARNING) << "Succ proc initialize version engine: " << engine->version();
+  return 0;
+}
+
+int VersionedInferEngine::proc_finalize() {
+  for (auto iter = _versions.begin(); iter != _versions.end(); ++iter) {
+    if (iter->second->proc_finalize() != 0) {
+      LOG(ERROR) << "Failed proc finalize version engine: " << iter->first;
+    }
+    LOG(WARNING) << "Succ proc finalize version engine: " << iter->first;
+  }
+  return 0;
+}
+
+int VersionedInferEngine::thrd_initialize() {
+  for (auto iter = _versions.begin(); iter != _versions.end(); ++iter) {
+    if (iter->second->thrd_initialize() != 0) {
+      LOG(ERROR) << "Failed thrd initialize version engine: " << iter->first;
+      return -1;
+    }
+    LOG(WARNING) << "Succ thrd initialize version engine: " << iter->first;
+  }
+  return 0;
+}
+
+int VersionedInferEngine::thrd_clear() {
+  for (auto iter = _versions.begin(); iter != _versions.end(); ++iter) {
+    if (iter->second->thrd_clear() != 0) {
+      LOG(ERROR) << "Failed thrd clear version engine: " << iter->first;
+      return -1;
+    }
+  }
+  return 0;
+}
+
+int VersionedInferEngine::thrd_finalize() {
+  for (auto iter = _versions.begin(); iter != _versions.end(); ++iter) {
+    if (iter->second->thrd_finalize() != 0) {
+      LOG(ERROR) << "Failed thrd finalize version engine: " << iter->first;
+      return -1;
+    }
+    LOG(WARNING) << "Succ thrd finalize version engine: " << iter->first;
+  }
+  return 0;
+}
+
+int VersionedInferEngine::reload() {
+  for (auto iter = _versions.begin(); iter != _versions.end(); ++iter) {
+    if (iter->second->reload() != 0) {
+      LOG(ERROR) << "Failed reload version engine: " << iter->first;
+      return -1;
+    }
+    LOG(WARNING) << "Succ reload version engine: " << iter->first;
+  }
+  return 0;
+}
+
+uint64_t VersionedInferEngine::version() const {
+  InferEngine* engine = default_engine();
+  if (engine) {
+    return engine->version();
+  } else {
+    return uint64_t(-1);
+  }
+}
+
+// inference interface
+InferEngine* VersionedInferEngine::default_engine() const {
+  if (_versions.size() != 1) {
+    LOG(ERROR) << "Ambiguous default engine version:" << _versions.size();
+    return NULL;
+  }
+
+  return _versions.begin()->second;
+}
+
+int VersionedInferEngine::infer(const void* in,
+                                void* out,
+                                uint32_t batch_size) {
+  InferEngine* engine = default_engine();
+  if (!engine) {
+    LOG(WARNING) << "fail to get default engine";
+    return -1;
+  }
+  return engine->infer(in, out, batch_size);
+}
+
+// versioned inference interface
+int VersionedInferEngine::infer(const void* in,
+                                void* out,
+                                uint32_t batch_size,
+                                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);
+}
+
+template <typename T>
+T* VersionedInferEngine::get_core() {
+  InferEngine* engine = default_engine();
+  if (!engine) {
+    LOG(WARNING) << "fail to get core";
+    return NULL;
+  }
+  auto db_engine = dynamic_cast<DBReloadableInferEngine<T>*>(engine);
+  if (db_engine) {
+    return db_engine->get_core();
+  }
+  LOG(WARNING) << "fail to get core";
+  return NULL;
+}
+
+template <typename T>
+T* VersionedInferEngine::get_core(uint64_t version) {
+  auto iter = _versions.find(version);
+  if (iter == _versions.end()) {
+    LOG(ERROR) << "Not found version engine: " << version;
+    return NULL;
+  }
+
+  auto db_engine = dynamic_cast<DBReloadableInferEngine<T>*>(iter->second);
+  if (db_engine) {
+    return db_engine->get_core();
+  }
+  LOG(WARNING) << "fail to get core for " << version;
+  return NULL;
+}
+
+int VersionedInferEngine::proc_initialize_impl(
+    const configure::EngineDesc& conf, bool) {
+  return -1;
+}
+
+int VersionedInferEngine::thrd_initialize_impl() { return -1; }
+int VersionedInferEngine::thrd_finalize_impl() { return -1; }
+int VersionedInferEngine::thrd_clear_impl() { return -1; }
+int VersionedInferEngine::proc_finalize_impl() { return -1; }
+int VersionedInferEngine::infer_impl(const void* in,
+                                     void* out,
+                                     uint32_t batch_size) {
+  return -1;
+}
+int VersionedInferEngine::task_infer_impl(const BatchTensor& in,
+                                          BatchTensor& out) {  // NOLINT
+  return -1;
+}
+
+int InferManager::proc_initialize(const char* path, const char* file) {
+  ModelToolkitConf model_toolkit_conf;
+  if (configure::read_proto_conf(path, file, &model_toolkit_conf) != 0) {
+    LOG(ERROR) << "failed load infer config, path: " << path << "/" << file;
+    return -1;
+  }
+  size_t engine_num = model_toolkit_conf.engines_size();
+  for (size_t ei = 0; ei < engine_num; ++ei) {
+    LOG(INFO) << "model_toolkit_conf.engines(" << ei
+              << ").name: " << model_toolkit_conf.engines(ei).name();
+    std::string engine_name = model_toolkit_conf.engines(ei).name();
+    VersionedInferEngine* engine = new (std::nothrow) VersionedInferEngine();
+    if (!engine) {
+      LOG(ERROR) << "Failed generate versioned engine: " << engine_name;
+      return -1;
+    }
+    if (engine->proc_initialize(model_toolkit_conf.engines(ei)) != 0) {
+      LOG(ERROR) << "Failed initialize version engine, name:" << engine_name;
+      return -1;
+    }
+    auto r = _map.insert(std::make_pair(engine_name, engine));
+    if (!r.second) {
+      LOG(ERROR) << "Failed insert item: " << engine_name;
+      return -1;
+    }
+    LOG(WARNING) << "Succ proc initialize engine: " << engine_name;
+  }
+  return 0;
+}
+
+int InferManager::thrd_initialize() {
+  for (auto it = _map.begin(); it != _map.end(); ++it) {
+    if (it->second->thrd_initialize() != 0) {
+      LOG(ERROR) << "Failed thrd initialize engine, name: " << it->first;
+      return -1;
+    }
+    LOG(WARNING) << "Succ thrd initialize engine, name: " << it->first;
+  }
+  return 0;
+}
+
+int InferManager::thrd_clear() {
+  for (auto it = _map.begin(); it != _map.end(); ++it) {
+    if (it->second->thrd_clear() != 0) {
+      LOG(ERROR) << "Failed thrd clear engine, name: " << it->first;
+      return -1;
+    }
+  }
+  return 0;
+}
+
+int InferManager::reload() {
+  for (auto it = _map.begin(); it != _map.end(); ++it) {
+    if (it->second->reload() != 0) {
+      LOG(ERROR) << "Failed reload engine, name: " << it->first;
+      return -1;
+    }
+  }
+  return 0;
+}
+
+int InferManager::thrd_finalize() {
+  for (auto it = _map.begin(); it != _map.end(); ++it) {
+    if (it->second->thrd_finalize() != 0) {
+      LOG(ERROR) << "Failed thrd finalize engine, name: " << it->first;
+      return -1;
+    }
+    LOG(WARNING) << "Succ thrd finalize engine, name: " << it->first;
+  }
+  return 0;
+}
+
+int InferManager::proc_finalize() {
+  for (auto it = _map.begin(); it != _map.end(); ++it) {
+    if (it->second->proc_finalize() != 0) {
+      LOG(ERROR) << "Failed proc finalize engine, name: " << it->first;
+      return -1;
+    }
+    LOG(WARNING) << "Succ proc finalize engine, name: " << it->first;
+  }
+  _map.clear();
+  return 0;
+}
+
+// Inference interface
+int InferManager::infer(const char* model_name,
+                        const void* in,
+                        void* out,
+                        uint32_t batch_size) {
+  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);
+}
+
+template <typename T>
+T* InferManager::get_core(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 NULL;
+  }
+  auto infer_engine =
+      dynamic_cast<DBReloadableInferEngine<T>*>(it->second->default_engine());
+  if (infer_engine) {
+    return infer_engine->get_core();
+  }
+  LOG(WARNING) << "fail to get core for " << model_name;
+  return NULL;
+}
+
+// Versioned inference interface
+int InferManager::infer(const char* model_name,
+                        const void* in,
+                        void* out,
+                        uint32_t batch_size,
+                        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);
+}
+
+template <typename T>
+T* InferManager::get_core(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 NULL;
+  }
+  return it->second->get_core<T>(version);
+}
+
+int InferManager::query_version(const std::string& model, uint64_t& version) {
+  auto it = _map.find(model);
+  if (it == _map.end()) {
+    LOG(WARNING) << "Cannot find engine in map, model name:" << model;
+    return -1;
+  }
+  auto infer_engine = it->second->default_engine();
+  if (!infer_engine) {
+    LOG(WARNING) << "Cannot get default engine for model:" << model;
+    return -1;
+  }
+  version = infer_engine->version();
+  LOG(INFO) << "Succ get version: " << version << " for model: " << model;
+  return 0;
+}
+
+}  // namespace predictor
+}  // namespace paddle_serving
+}  // namespace baidu

core/predictor/framework/infer.h

@@ -13,14 +13,14 @@
 // limitations under the License.
 
 #pragma once
+#include <pthread.h>
 #include <sys/stat.h>
 #include <sys/types.h>
 #include <unistd.h>
-#include <pthread.h>
+#include <numeric>
 #include <string>
 #include <utility>
 #include <vector>
-#include <numeric>
 #include "core/predictor/common/inner_common.h"
 #include "core/predictor/framework/bsf.h"
 #include "core/predictor/framework/factory.h"
@@ -68,7 +68,9 @@ 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_impl(in, out, batch_size); }
+  virtual int infer(const void* in, void* out, uint32_t batch_size = -1) {
+    return infer_impl(in, out, batch_size);
+  }
 
   virtual int reload() = 0;
 
@@ -82,10 +84,10 @@ class InferEngine {
   virtual int thrd_clear_impl() = 0;
   virtual int proc_finalize_impl() = 0;
   virtual int infer_impl(const void* in,
-                          void* out,
-                          uint32_t batch_size = -1) = 0;
+                         void* out,
+                         uint32_t batch_size = -1) = 0;
   virtual int task_infer_impl(const BatchTensor& in,
-                          BatchTensor& out) = 0;  // NOLINT
+                              BatchTensor& out) = 0;  // NOLINT
 
   // end: framework inner call
 };
@@ -94,194 +96,77 @@ class ReloadableInferEngine : public InferEngine {
  public:
   virtual ~ReloadableInferEngine() {}
 
-  union last_check_status {
-    time_t last_timestamp;
-    uint64_t last_md5sum;
-    uint64_t last_revision;
+  // Reloadable record
+  union ReloadableRecord {
+    time_t timestamp;
+    uint64_t md5sum;
+    uint64_t revision;
   };
 
   virtual int load(const configure::EngineDesc& conf) = 0;
   typedef im::bsf::Task<Tensor, Tensor> TaskT;
 
-  int proc_initialize_impl(const configure::EngineDesc& conf, bool version) {
-    _reload_tag_file = conf.reloadable_meta();
-    _reload_mode_tag = conf.reloadable_type();
-    _model_data_path = conf.model_dir();
-    _infer_thread_num = conf.runtime_thread_num();
-    _infer_batch_size = conf.batch_infer_size();
-    _infer_batch_align = conf.enable_batch_align();
-
-    _conf = conf;
+  int proc_initialize_impl(const configure::EngineDesc& conf, bool version);
 
-    if (!check_need_reload() || load(conf) != 0) {
-      LOG(ERROR) << "Failed load model_data_path" << _model_data_path;
-      return -1;
-    }
-
-    if (parse_version_info(conf, version) != 0) {
-      LOG(ERROR) << "Failed parse version info";
-      return -1;
-    }
-
-    LOG(WARNING) << "Succ load model_data_path" << _model_data_path;
-    return 0;
-  }
+  int proc_initialize(const configure::EngineDesc& conf, bool version);
 
-  int proc_initialize(const configure::EngineDesc& conf, bool version) {
-    if (proc_initialize_impl(conf, version) != 0) {
-      LOG(ERROR) << "Failed proc initialize impl";
-      return -1;
-    }
+  int infer(const void* in, void* out, uint32_t batch_size = -1);
 
-    // init bsf framework
-    if (_infer_thread_num <= 0) {
-      return 0;
-    }
+  int thrd_initialize();
 
-    // init bsf framework
-    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::task_infer_impl, 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;
-    }
+  int thrd_clear();
 
-    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 proc_finalize();
 
-  int infer(const void* in, void* out, uint32_t batch_size = -1) {
-    if (_infer_thread_num <= 0) {
-      return infer_impl(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 thrd_initialize() {
-    if (_infer_thread_num > 0) {
-      return 0;
-    }
-    return thrd_initialize_impl();
-  }
-
-  int thrd_clear() {
-    if (_infer_thread_num > 0) {
-      return 0;
-    }
-
-    return thrd_clear_impl();
-  }
-
-  int proc_finalize() {
-    if (proc_finalize_impl() != 0) {
-      LOG(ERROR) << "Failed proc finalize impl";
-      return -1;
-    }
-
-    if (_infer_thread_num > 0) {
-      im::bsf::TaskExecutor<TaskT>::instance()->stop();
-    }
-    return 0;
-  }
-
-  int reload() {
-    if (check_need_reload()) {
-      LOG(WARNING) << "begin reload model[" << _model_data_path << "].";
-      return load(_conf);
-    }
-    return 0;
-  }
+  int reload();
 
   uint64_t version() const { return _version; }
-  
   uint32_t thread_num() const { return _infer_thread_num; }
 
  private:
-  int parse_version_info(const configure::EngineDesc& config, bool version) {
-    _version = uint64_t(-1);
-    return 0;
-  }
-
-  bool check_need_reload() {
-    if (_reload_mode_tag == "timestamp_ne") {
-      return check_timestamp_ne();
-    } else if (_reload_mode_tag == "timestamp_gt") {
-      return check_timestamp_gt();
-    } else if (_reload_mode_tag == "md5sum") {
-      return check_md5sum();
-    } else if (_reload_mode_tag == "revision") {
-      return check_revision();
-    } else if (_reload_mode_tag == "none") {
-      return false;
-    } else {
-      LOG(ERROR) << "Not support check type: " << _reload_mode_tag;
-      return false;
-    }
-  }
+  int parse_version_info(const configure::EngineDesc& config, bool version);
 
-  bool check_timestamp_ne() {
-    struct stat st;
-    if (stat(_reload_tag_file.c_str(), &st) != 0) {
-      LOG(ERROR) << "Failed stat config file:" << _reload_tag_file;
-      return false;
-    }
+  bool check_need_reload();
 
-    if ((st.st_mode & S_IFREG) && st.st_mtime != _last_status.last_timestamp) {
-      _last_status.last_timestamp = st.st_mtime;
-      return true;
-    }
+  bool check_timestamp_ne();
 
-    return false;
-  }
-
-  bool check_timestamp_gt() {
-    struct stat st;
-    if (stat(_reload_tag_file.c_str(), &st) != 0) {
-      LOG(ERROR) << "Failed stat config file:" << _reload_tag_file;
-      return false;
-    }
-
-    if ((st.st_mode & S_IFREG) && st.st_mtime > _last_status.last_timestamp) {
-      _last_status.last_timestamp = st.st_mtime;
-      return true;
-    }
-
-    return false;
-  }
+  bool check_timestamp_gt();
 
   bool check_md5sum() { return false; }
 
   bool check_revision() { return false; }
 
  protected:
-  std::string _model_data_path;
+  // Model directory
+  std::string _model_dir;
+
+  // The description of inference engine
   configure::EngineDesc _conf;
 
  private:
+  // Tag file of reloadable model
   std::string _reload_tag_file;
-  std::string _reload_mode_tag;
-  last_check_status _last_status;
+
+  // Type of reload, e.g. timestamp_ne, timestamp_gt, md5sum, reversion
+  std::string _reload_type;
+
+  // Record the last loading infermation
+  ReloadableRecord _last_record;
+
+  // Number of inference threads
   uint32_t _infer_thread_num;
+
+  // Size of inference batch
   uint32_t _infer_batch_size;
+
+  // Need to align batch_size in inferring
   bool _infer_batch_align;
+
+  // model version
   uint64_t _version;
 };
 
+// Lock free switching two models
 template <typename EngineCore>
 struct ModelData {
   ModelData() : current_idx(1) {
@@ -322,12 +207,10 @@ class DBReloadableInferEngine : public ReloadableInferEngine {
     }
 
     LOG(WARNING) << "Succ load engine, path: " << conf.model_dir();
-
     return 0;
   }
 
-  int load_data(ModelData<EngineCore>* md,
-                const configure::EngineDesc& conf) {
+  int load_data(ModelData<EngineCore>* md, const configure::EngineDesc& conf) {
     uint32_t next_idx = (md->current_idx + 1) % 2;
     if (md->cores[next_idx]) {
       delete md->cores[next_idx];
@@ -335,7 +218,7 @@ class DBReloadableInferEngine : public ReloadableInferEngine {
 
     md->cores[next_idx] = new (std::nothrow) EngineCore;
 
-    //params.dump();
+    // params.dump();
     if (!md->cores[next_idx] || md->cores[next_idx]->create(conf) != 0) {
       LOG(ERROR) << "Failed create model, path: " << conf.model_dir();
       return -1;
@@ -353,11 +236,11 @@ class DBReloadableInferEngine : public ReloadableInferEngine {
 
     ModelData<EngineCore>* md = new (std::nothrow) ModelData<EngineCore>;
     if (!md || load_data(md, _conf) != 0) {
-      LOG(ERROR) << "Failed create thread data from "
-                 << _conf.model_dir();
+      LOG(ERROR) << "Failed create thread data from " << _conf.model_dir();
       return -1;
     }
 
+    LOG(ERROR) << "THREAD_SETSPECIFIC _skey = md";
     THREAD_SETSPECIFIC(_skey, md);
     im::bsf::AutoMutex lock(_mutex);
     _reload_vec.push_back(md);
@@ -395,8 +278,6 @@ class DBReloadableInferEngine : public ReloadableInferEngine {
   THREAD_KEY_T _skey;
   THREAD_MUTEX_T _mutex;
   std::vector<ModelData<EngineCore>*> _reload_vec;
-
- private:
 };
 
 // 多个EngineCore共用同一份模型数据
@@ -441,7 +322,6 @@ class CloneDBReloadableInferEngine
     }
 
     LOG(WARNING) << "Succ load clone model, path[" << conf.model_dir() << "]";
-
     return 0;
   }
 
@@ -491,112 +371,129 @@ class CloneDBReloadableInferEngine
       _pd;  // 进程级EngineCore，多个线程级EngineCore共用该对象的模型数据
 };
 
-template <typename PaddleInferenceCore>
+template <typename EngineCore>
 #ifdef WITH_TRT
-class FluidInferEngine : public DBReloadableInferEngine<PaddleInferenceCore> {
+class FluidInferEngine : public DBReloadableInferEngine<EngineCore> {
 #else
-class FluidInferEngine : public CloneDBReloadableInferEngine<PaddleInferenceCore> {
+class FluidInferEngine : public CloneDBReloadableInferEngine<EngineCore> {
 #endif
  public:  // NOLINT
   FluidInferEngine() {}
   ~FluidInferEngine() {}
   typedef std::vector<paddle::PaddleTensor> TensorVector;
   int infer_impl(const void* in, void* out, uint32_t batch_size = -1) {
-    //First of all, get the real core acording to the template parameter 'PaddleInferenceCore'.
-    PaddleInferenceCore* core =DBReloadableInferEngine<PaddleInferenceCore>::get_core();
+    // First of all, get the real core acording to the
+    // Template parameter <EngineCore>.
+    EngineCore* core = DBReloadableInferEngine<EngineCore>::get_core();
     if (!core || !core->get()) {
       LOG(ERROR) << "Failed get fluid core in infer_impl()";
       return -1;
     }
-    //We use the for loop to process the input data.
-    //Inside each for loop, use the in[i]->name as inputName and call 'core->GetInputHandle(inputName)' to get the pointer of InputData.
-    //Set the lod and shape information of InputData first. then copy data from cpu to the core.
-    const TensorVector* tensorVector_in_pointer = reinterpret_cast<const TensorVector*>(in);
-    for (int i=0; i < tensorVector_in_pointer->size(); ++i) {
-      auto lod_tensor_in = core->GetInputHandle((*tensorVector_in_pointer)[i].name);
+    // We use the for loop to process the input data.
+    // Inside each for loop, use the in[i]->name as inputName and call
+    // 'core->GetInputHandle(inputName)' to get the pointer of InputData.
+    // Set the lod and shape information of InputData first.
+    // Then copy data from cpu to the core.
+    const TensorVector* tensorVector_in_pointer =
+        reinterpret_cast<const TensorVector*>(in);
+    for (int i = 0; i < tensorVector_in_pointer->size(); ++i) {
+      auto lod_tensor_in =
+          core->GetInputHandle((*tensorVector_in_pointer)[i].name);
       lod_tensor_in->SetLoD((*tensorVector_in_pointer)[i].lod);
       lod_tensor_in->Reshape((*tensorVector_in_pointer)[i].shape);
       void* origin_data = (*tensorVector_in_pointer)[i].data.data();
-      //Because the core needs to determine the size of memory space according to the data type passed in.
-      //The pointer type of data must be one of float *,int64_t*,int32_t* instead void*.
+      // Because the core needs to determine the size of memory space
+      // according to the data type passed in.
+      // The pointer type of data must be one of
+      // float *,int64_t*,int32_t* instead void*.
       if ((*tensorVector_in_pointer)[i].dtype == paddle::PaddleDType::FLOAT32) {
         float* data = static_cast<float*>(origin_data);
         lod_tensor_in->CopyFromCpu(data);
-      }else if ((*tensorVector_in_pointer)[i].dtype == paddle::PaddleDType::INT64) {
+      } else if ((*tensorVector_in_pointer)[i].dtype ==
+                 paddle::PaddleDType::INT64) {
         int64_t* data = static_cast<int64_t*>(origin_data);
         lod_tensor_in->CopyFromCpu(data);
-      }else if ((*tensorVector_in_pointer)[i].dtype == paddle::PaddleDType::INT32) {
+      } else if ((*tensorVector_in_pointer)[i].dtype ==
+                 paddle::PaddleDType::INT32) {
         int32_t* data = static_cast<int32_t*>(origin_data);
         lod_tensor_in->CopyFromCpu(data);
       }
     }
-    //After the input data is passed in, call 'core->Run()' perform the prediction process.
+    // After the input data is passed in,
+    // call 'core->Run()' perform the prediction process.
     if (!core->Run()) {
-        LOG(ERROR) << "Failed run fluid family core";
-        return -1;
+      LOG(ERROR) << "Failed run fluid family core";
+      return -1;
     }
-    
-    //In order to get the results, first, call the 'core->GetOutputNames()' to get the name of output(which is a dict like {OutputName:pointer of OutputValue}).
-    //Then, use for-loop to get OutputValue by calling 'core->GetOutputHandle'.
+    // In order to get the results,
+    // first, call the 'core->GetOutputNames()' to get the name of output
+    // (which is a dict like {OutputName:pointer of OutputValue}).
+    // Then, use for-loop to get OutputValue by calling 'core->GetOutputHandle'.
     std::vector<std::string> outnames = core->GetOutputNames();
     std::vector<int> output_shape;
-    int out_num =0;
-    int dataType =0;
+    int out_num = 0;
+    int dataType = 0;
     void* databuf_data = NULL;
     char* databuf_char = NULL;
     size_t databuf_size = 0;
-    TensorVector* tensorVector_out_pointer = reinterpret_cast<TensorVector*>(out);
+    TensorVector* tensorVector_out_pointer =
+        reinterpret_cast<TensorVector*>(out);
     if (!tensorVector_out_pointer) {
       LOG(ERROR) << "tensorVector_out_pointer is nullptr,error";
       return -1;
     }
-    //Get the type and shape information of OutputData first. then copy data to cpu from the core.
-    //The pointer type of data_out must be one of float *,int64_t*,int32_t* instead void*.
-    for (int i=0; i < outnames.size(); ++i) {
+    // Get the type and shape information of OutputData first.
+    // then copy data to cpu from the core.
+    // The pointer type of data_out must be one of
+    // float *,int64_t*,int32_t* instead void*.
+    for (int i = 0; i < outnames.size(); ++i) {
       auto lod_tensor_out = core->GetOutputHandle(outnames[i]);
       output_shape = lod_tensor_out->shape();
-      out_num = std::accumulate(output_shape.begin(), output_shape.end(), 1, std::multiplies<int>());
+      out_num = std::accumulate(
+          output_shape.begin(), output_shape.end(), 1, std::multiplies<int>());
       dataType = lod_tensor_out->type();
       if (dataType == paddle::PaddleDType::FLOAT32) {
-        databuf_size = out_num*sizeof(float);
+        databuf_size = out_num * sizeof(float);
         databuf_data = MempoolWrapper::instance().malloc(databuf_size);
         if (!databuf_data) {
-            LOG(ERROR) << "Malloc failed, size: " << databuf_size;
-            return -1;
+          LOG(ERROR) << "Malloc failed, size: " << databuf_size;
+          return -1;
         }
         float* data_out = reinterpret_cast<float*>(databuf_data);
         lod_tensor_out->CopyToCpu(data_out);
         databuf_char = reinterpret_cast<char*>(data_out);
-      }else if (dataType == paddle::PaddleDType::INT64) {
-        databuf_size = out_num*sizeof(int64_t);
+      } else if (dataType == paddle::PaddleDType::INT64) {
+        databuf_size = out_num * sizeof(int64_t);
         databuf_data = MempoolWrapper::instance().malloc(databuf_size);
         if (!databuf_data) {
-            LOG(ERROR) << "Malloc failed, size: " << databuf_size;
-            return -1;
+          LOG(ERROR) << "Malloc failed, size: " << databuf_size;
+          return -1;
         }
         int64_t* data_out = reinterpret_cast<int64_t*>(databuf_data);
         lod_tensor_out->CopyToCpu(data_out);
         databuf_char = reinterpret_cast<char*>(data_out);
-      }else if (dataType == paddle::PaddleDType::INT32) {
-        databuf_size = out_num*sizeof(int32_t);
+      } else if (dataType == paddle::PaddleDType::INT32) {
+        databuf_size = out_num * sizeof(int32_t);
         databuf_data = MempoolWrapper::instance().malloc(databuf_size);
         if (!databuf_data) {
-            LOG(ERROR) << "Malloc failed, size: " << databuf_size;
-            return -1;
+          LOG(ERROR) << "Malloc failed, size: " << databuf_size;
+          return -1;
         }
         int32_t* data_out = reinterpret_cast<int32_t*>(databuf_data);
         lod_tensor_out->CopyToCpu(data_out);
         databuf_char = reinterpret_cast<char*>(data_out);
       }
-      //Because task scheduling requires OPs to use 'Channel'(which is a data structure) to transfer data between OPs.
-      //We need to copy the processed data to the 'Channel' for the next OP.
-      //In this function, it means we should copy the 'databuf_char' to the pointer 'void* out'.(which is also called ‘tensorVector_out_pointer’)
+      // Because task scheduling requires OPs to use 'Channel'
+      // (which is a data structure) to transfer data between OPs.
+      // We need to copy the processed data to the 'Channel' for the next OP.
+      // In this function, it means we should copy the 'databuf_char' to
+      // 'void* out'.(which is also called ‘tensorVector_out_pointer’)
       paddle::PaddleTensor tensor_out;
       tensor_out.name = outnames[i];
       tensor_out.dtype = paddle::PaddleDType(dataType);
       tensor_out.shape.assign(output_shape.begin(), output_shape.end());
       std::vector<std::vector<size_t>> out_lod = lod_tensor_out->lod();
-      for (int li=0; li < out_lod.size(); ++li) {
+      for (int li = 0; li < out_lod.size(); ++li) {
         std::vector<size_t> lod_element;
         lod_element.assign(out_lod[li].begin(), out_lod[li].end());
         tensor_out.lod.push_back(lod_element);
@@ -611,8 +508,6 @@ class FluidInferEngine : public CloneDBReloadableInferEngine<PaddleInferenceCore
   int task_infer_impl(const BatchTensor& in, BatchTensor& out) {  // NOLINT
     return infer_impl(&in, &out);
   }
-
-
 };
 
 typedef FactoryPool<InferEngine> StaticInferFactory;
@@ -622,185 +517,49 @@ class VersionedInferEngine : public InferEngine {
   VersionedInferEngine() { _versions.clear(); }
   ~VersionedInferEngine() {}
 
-  int proc_initialize(const configure::EngineDesc& conf) {
-    if (proc_initialize(conf, false) != 0) {
-      LOG(ERROR) << "Failed proc intialize engine: " << conf.name().c_str();
-      return -1;
-    }
+  int proc_initialize(const configure::EngineDesc& conf);
 
-    LOG(WARNING) << "Succ proc initialize engine: " << conf.name().c_str();
-    return 0;
-  }
+  int proc_initialize(const configure::EngineDesc& conf, bool version);
 
-  int proc_initialize(const configure::EngineDesc& conf, bool version) {
-    std::string engine_type = conf.type();
-    InferEngine* engine =
-        StaticInferFactory::instance().generate_object(engine_type);
-    if (!engine) {
-      LOG(ERROR) << "Failed generate engine with type:" << engine_type;
-      return -1;
-    }
-#ifndef BCLOUD
-    VLOG(2) << "FLAGS_logtostderr " << FLAGS_logtostderr;
-    int tmp = FLAGS_logtostderr;
-    if (engine->proc_initialize(conf, version) != 0) {
-      LOG(ERROR) << "Failed initialize engine, type:" << engine_type;
-      return -1;
-    }
-    VLOG(2) << "FLAGS_logtostderr " << FLAGS_logtostderr;
-    FLAGS_logtostderr = tmp;
-#else
-    if (engine->proc_initialize(conf, version) != 0) {
-      LOG(ERROR) << "Failed initialize engine, type:" << engine_type;
-      return -1;
-    }
-#endif
-    auto r = _versions.insert(std::make_pair(engine->version(), engine));
-    if (!r.second) {
-      LOG(ERROR) << "Failed insert item: " << engine->version()
-                 << ", type: " << engine_type;
-      return -1;
-    }
-    LOG(WARNING) << "Succ proc initialize version engine: "
-                 << engine->version();
-    return 0;
-  }
+  int proc_finalize();
 
-  int proc_finalize() {
-    for (auto iter = _versions.begin(); iter != _versions.end(); ++iter) {
-      if (iter->second->proc_finalize() != 0) {
-        LOG(ERROR) << "Failed proc finalize version engine: " << iter->first;
-      }
-      LOG(WARNING) << "Succ proc finalize version engine: " << iter->first;
-    }
-    return 0;
-  }
+  int thrd_initialize();
 
-  int thrd_initialize() {
-    for (auto iter = _versions.begin(); iter != _versions.end(); ++iter) {
-      if (iter->second->thrd_initialize() != 0) {
-        LOG(ERROR) << "Failed thrd initialize version engine: " << iter->first;
-        return -1;
-      }
-      LOG(WARNING) << "Succ thrd initialize version engine: " << iter->first;
-    }
-    return 0;
-  }
+  int thrd_clear();
 
-  int thrd_clear() {
-    for (auto iter = _versions.begin(); iter != _versions.end(); ++iter) {
-      if (iter->second->thrd_clear() != 0) {
-        LOG(ERROR) << "Failed thrd clear version engine: " << iter->first;
-        return -1;
-      }
-    }
-    return 0;
-  }
+  int thrd_finalize();
 
-  int thrd_finalize() {
-    for (auto iter = _versions.begin(); iter != _versions.end(); ++iter) {
-      if (iter->second->thrd_finalize() != 0) {
-        LOG(ERROR) << "Failed thrd finalize version engine: " << iter->first;
-        return -1;
-      }
-      LOG(WARNING) << "Succ thrd finalize version engine: " << iter->first;
-    }
-    return 0;
-  }
-
-  int reload() {
-    for (auto iter = _versions.begin(); iter != _versions.end(); ++iter) {
-      if (iter->second->reload() != 0) {
-        LOG(ERROR) << "Failed reload version engine: " << iter->first;
-        return -1;
-      }
-      LOG(WARNING) << "Succ reload version engine: " << iter->first;
-    }
-    return 0;
-  }
+  int reload();
 
-  uint64_t version() const {
-    InferEngine* engine = default_engine();
-    if (engine) {
-      return engine->version();
-    } else {
-      return uint64_t(-1);
-    }
-  }
+  uint64_t version() const;
 
   // inference interface
-  InferEngine* default_engine() const {
-    if (_versions.size() != 1) {
-      LOG(ERROR) << "Ambiguous default engine version:" << _versions.size();
-      return NULL;
-    }
-
-    return _versions.begin()->second;
-  }
+  InferEngine* default_engine() const;
 
-  int infer(const void* in, void* out, uint32_t batch_size) {
-    InferEngine* engine = default_engine();
-    if (!engine) {
-      LOG(WARNING) << "fail to get default engine";
-      return -1;
-    }
-    return engine->infer(in, out, batch_size);
-  }
+  int infer(const void* in, void* out, uint32_t batch_size);
 
   template <typename T>
-  T* get_core() {
-    InferEngine* engine = default_engine();
-    if (!engine) {
-      LOG(WARNING) << "fail to get core";
-      return NULL;
-    }
-    auto db_engine = dynamic_cast<DBReloadableInferEngine<T>*>(engine);
-    if (db_engine) {
-      return db_engine->get_core();
-    }
-    LOG(WARNING) << "fail to get core";
-    return NULL;
-  }
+  T* get_core();
 
   // versioned inference interface
-  int infer(const void* in, void* out, uint32_t batch_size, 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);
-  }
+  int infer(const void* in, void* out, uint32_t batch_size, uint64_t version);
 
   template <typename T>
-  T* get_core(uint64_t version) {
-    auto iter = _versions.find(version);
-    if (iter == _versions.end()) {
-      LOG(ERROR) << "Not found version engine: " << version;
-      return NULL;
-    }
+  T* get_core(uint64_t version);
 
-    auto db_engine = dynamic_cast<DBReloadableInferEngine<T>*>(iter->second);
-    if (db_engine) {
-      return db_engine->get_core();
-    }
-    LOG(WARNING) << "fail to get core for " << version;
-    return NULL;
-  }
+  int proc_initialize_impl(const configure::EngineDesc& conf, bool);
 
-  // --
-  int proc_initialize_impl(const configure::EngineDesc& conf, bool) {
-    return -1;
-  }
-  int thrd_initialize_impl() { return -1; }
-  int thrd_finalize_impl() { return -1; }
-  int thrd_clear_impl() { return -1; }
-  int proc_finalize_impl() { return -1; }
-  int infer_impl(const void* in, void* out, uint32_t batch_size = -1) { return -1; }
-  int task_infer_impl(const BatchTensor& in, BatchTensor& out) {  // NOLINT
-    return -1;
-  }  // NOLINT
+  int thrd_initialize_impl();
+
+  int thrd_finalize_impl();
+
+  int thrd_clear_impl();
+
+  int proc_finalize_impl();
+
+  int infer_impl(const void* in, void* out, uint32_t batch_size = -1);
+
+  int task_infer_impl(const BatchTensor& in, BatchTensor& out);
 
  private:
   boost::unordered_map<uint64_t, InferEngine*> _versions;
@@ -813,158 +572,38 @@ class InferManager {
     return ins;
   }
 
-  int proc_initialize(const char* path, const char* file) {
-    ModelToolkitConf model_toolkit_conf;
-    if (configure::read_proto_conf(path, file, &model_toolkit_conf) != 0) {
-      LOG(ERROR) << "failed load infer config, path: " << path << "/" << file;
-      return -1;
-    }
-    size_t engine_num = model_toolkit_conf.engines_size();
-    for (size_t ei = 0; ei < engine_num; ++ei) {
-      LOG(INFO) << "model_toolkit_conf.engines(" << ei
-                << ").name: " << model_toolkit_conf.engines(ei).name();
-      std::string engine_name = model_toolkit_conf.engines(ei).name();
-      VersionedInferEngine* engine = new (std::nothrow) VersionedInferEngine();
-      if (!engine) {
-        LOG(ERROR) << "Failed generate versioned engine: " << engine_name;
-        return -1;
-      }
-      if (engine->proc_initialize(model_toolkit_conf.engines(ei)) != 0) {
-        LOG(ERROR) << "Failed initialize version engine, name:" << engine_name;
-        return -1;
-      }
-      auto r = _map.insert(std::make_pair(engine_name, engine));
-      if (!r.second) {
-        LOG(ERROR) << "Failed insert item: " << engine_name;
-        return -1;
-      }
-      LOG(WARNING) << "Succ proc initialize engine: " << engine_name;
-    }
-    return 0;
-  }
+  int proc_initialize(const char* path, const char* file);
 
-  int thrd_initialize() {
-    for (auto it = _map.begin(); it != _map.end(); ++it) {
-      if (it->second->thrd_initialize() != 0) {
-        LOG(ERROR) << "Failed thrd initialize engine, name: " << it->first;
-        return -1;
-      }
-      LOG(WARNING) << "Succ thrd initialize engine, name: " << it->first;
-    }
-    return 0;
-  }
+  int thrd_initialize();
 
-  int thrd_clear() {
-    for (auto it = _map.begin(); it != _map.end(); ++it) {
-      if (it->second->thrd_clear() != 0) {
-        LOG(ERROR) << "Failed thrd clear engine, name: " << it->first;
-        return -1;
-      }
-    }
-    return 0;
-  }
+  int thrd_clear();
 
-  int reload() {
-    for (auto it = _map.begin(); it != _map.end(); ++it) {
-      if (it->second->reload() != 0) {
-        LOG(ERROR) << "Failed reload engine, name: " << it->first;
-        return -1;
-      }
-    }
-    return 0;
-  }
+  int reload();
 
-  int thrd_finalize() {
-    for (auto it = _map.begin(); it != _map.end(); ++it) {
-      if (it->second->thrd_finalize() != 0) {
-        LOG(ERROR) << "Failed thrd finalize engine, name: " << it->first;
-        return -1;
-      }
-      LOG(WARNING) << "Succ thrd finalize engine, name: " << it->first;
-    }
-    return 0;
-  }
+  int thrd_finalize();
 
-  int proc_finalize() {
-    for (auto it = _map.begin(); it != _map.end(); ++it) {
-      if (it->second->proc_finalize() != 0) {
-        LOG(ERROR) << "Failed proc finalize engine, name: " << it->first;
-        return -1;
-      }
-      LOG(WARNING) << "Succ proc finalize engine, name: " << it->first;
-    }
-    _map.clear();
-    return 0;
-  }
+  int proc_finalize();
 
   // Inference interface
   int infer(const char* model_name,
             const void* in,
             void* out,
-            uint32_t batch_size = -1) {
-    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);
-  }
+            uint32_t batch_size = -1);
 
   template <typename T>
-  T* get_core(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 NULL;
-    }
-    auto infer_engine =
-        dynamic_cast<DBReloadableInferEngine<T>*>(it->second->default_engine());
-    if (infer_engine) {
-      return infer_engine->get_core();
-    }
-    LOG(WARNING) << "fail to get core for " << model_name;
-    return NULL;
-  }
+  T* get_core(const char* model_name);
 
   // Versioned inference interface
-  int infer(const char* model_name, 
+  int infer(const char* model_name,
             const void* in,
             void* out,
             uint32_t batch_size,
-            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);
-  }
+            uint64_t version);
 
   template <typename T>
-  T* get_core(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 NULL;
-    }
-    return it->second->get_core<T>(version);
-  }
+  T* get_core(const char* model_name, uint64_t version);
 
-  int query_version(const std::string& model, uint64_t& version) {  // NOLINT
-    auto it = _map.find(model);
-    if (it == _map.end()) {
-      LOG(WARNING) << "Cannot find engine in map, model name:" << model;
-      return -1;
-    }
-    auto infer_engine = it->second->default_engine();
-    if (!infer_engine) {
-      LOG(WARNING) << "Cannot get default engine for model:" << model;
-      return -1;
-    }
-    version = infer_engine->version();
-    LOG(INFO) << "Succ get version: " << version << " for model: " << model;
-    return 0;
-  }
+  int query_version(const std::string& model, uint64_t& version);
 
  private:
   boost::unordered_map<std::string, VersionedInferEngine*> _map;

core/predictor/framework/resource.cpp

@@ -27,22 +27,9 @@ namespace predictor {
 using configure::ResourceConf;
 using configure::GeneralModelConfig;
 using rec::mcube::CubeAPI;
-// __thread bool p_thread_initialized = false;
 
-static void dynamic_resource_deleter(void* d) {
-#if 1
-  LOG(INFO) << "dynamic_resource_delete on " << bthread_self();
-#endif
-  delete static_cast<DynamicResource*>(d);
-}
-
-DynamicResource::DynamicResource() {}
-
-DynamicResource::~DynamicResource() {}
-
-int DynamicResource::initialize() { return 0; }
-
-std::vector<std::shared_ptr<PaddleGeneralModelConfig> > Resource::get_general_model_config() {
+std::vector<std::shared_ptr<PaddleGeneralModelConfig>>
+Resource::get_general_model_config() {
   return _configs;
 }
 
@@ -96,8 +83,6 @@ void Resource::print_general_model_config(
   }
 }
 
-int DynamicResource::clear() { return 0; }
-
 int Resource::initialize(const std::string& path, const std::string& file) {
   ResourceConf resource_conf;
   if (configure::read_proto_conf(path, file, &resource_conf) != 0) {
@@ -150,29 +135,25 @@ int Resource::initialize(const std::string& path, const std::string& file) {
 
   if (FLAGS_enable_model_toolkit) {
     size_t model_toolkit_num = resource_conf.model_toolkit_path_size();
-    for (size_t mi=0; mi < model_toolkit_num; ++mi) {
+    for (size_t mi = 0; mi < model_toolkit_num; ++mi) {
       std::string model_toolkit_path = resource_conf.model_toolkit_path(mi);
       std::string model_toolkit_file = resource_conf.model_toolkit_file(mi);
 
       if (InferManager::instance().proc_initialize(
               model_toolkit_path.c_str(), model_toolkit_file.c_str()) != 0) {
         LOG(ERROR) << "failed proc initialize modeltoolkit, config: "
-                  << model_toolkit_path << "/" << model_toolkit_file;
+                   << model_toolkit_path << "/" << model_toolkit_file;
         return -1;
       }
 
       if (KVManager::instance().proc_initialize(
               model_toolkit_path.c_str(), model_toolkit_file.c_str()) != 0) {
         LOG(ERROR) << "Failed proc initialize kvmanager, config: "
-                  << model_toolkit_path << "/" << model_toolkit_file;
+                   << model_toolkit_path << "/" << model_toolkit_file;
       }
     }
   }
 
-  if (THREAD_KEY_CREATE(&_tls_bspec_key, dynamic_resource_deleter) != 0) {
-    LOG(ERROR) << "unable to create tls_bthread_key of thrd_data";
-    return -1;
-  }
   // init rocksDB or cube instance
   if (resource_conf.has_cube_config_file() &&
       resource_conf.has_cube_config_path()) {
@@ -225,18 +206,16 @@ int Resource::general_model_initialize(const std::string& path,
     return -1;
   }
   size_t general_model_num = resource_conf.general_model_path_size();
-  for (size_t gi=0; gi < general_model_num; ++gi) {
-
-
+  for (size_t gi = 0; gi < general_model_num; ++gi) {
     std::string general_model_path = resource_conf.general_model_path(gi);
     std::string general_model_file = resource_conf.general_model_file(gi);
 
     GeneralModelConfig model_config;
     if (configure::read_proto_conf(general_model_path.c_str(),
-                                  general_model_file.c_str(),
-                                  &model_config) != 0) {
-      LOG(ERROR) << "Failed initialize model config from: " << general_model_path
-                << "/" << general_model_file;
+                                   general_model_file.c_str(),
+                                   &model_config) != 0) {
+      LOG(ERROR) << "Failed initialize model config from: "
+                 << general_model_path << "/" << general_model_file;
       return -1;
     }
     auto _config = std::make_shared<PaddleGeneralModelConfig>();
@@ -249,7 +228,7 @@ int Resource::general_model_initialize(const std::string& path,
     _config->_is_lod_feed.resize(feed_var_num);
     _config->_capacity.resize(feed_var_num);
     _config->_feed_shape.resize(feed_var_num);
-    for (int i=0; i < feed_var_num; ++i) {
+    for (int i = 0; i < feed_var_num; ++i) {
       _config->_feed_name[i] = model_config.feed_var(i).name();
       _config->_feed_alias_name[i] = model_config.feed_var(i).alias_name();
       VLOG(2) << "feed var[" << i << "]: " << _config->_feed_name[i];
@@ -265,7 +244,7 @@ int Resource::general_model_initialize(const std::string& path,
         VLOG(2) << "var[" << i << "] is tensor";
         _config->_capacity[i] = 1;
         _config->_is_lod_feed[i] = false;
-        for (int j=0; j < model_config.feed_var(i).shape_size(); ++j) {
+        for (int j = 0; j < model_config.feed_var(i).shape_size(); ++j) {
           int32_t dim = model_config.feed_var(i).shape(j);
           VLOG(2) << "var[" << i << "].shape[" << i << "]: " << dim;
           _config->_feed_shape[i].push_back(dim);
@@ -279,7 +258,7 @@ int Resource::general_model_initialize(const std::string& path,
     _config->_fetch_name.resize(fetch_var_num);
     _config->_fetch_alias_name.resize(fetch_var_num);
     _config->_fetch_shape.resize(fetch_var_num);
-    for (int i=0; i < fetch_var_num; ++i) {
+    for (int i = 0; i < fetch_var_num; ++i) {
       _config->_fetch_name[i] = model_config.fetch_var(i).name();
       _config->_fetch_alias_name[i] = model_config.fetch_var(i).alias_name();
       _config->_fetch_name_to_index[_config->_fetch_name[i]] = i;
@@ -290,7 +269,7 @@ int Resource::general_model_initialize(const std::string& path,
         _config->_is_lod_fetch[i] = true;
       } else {
         _config->_is_lod_fetch[i] = false;
-        for (int j=0; j < model_config.fetch_var(i).shape_size(); ++j) {
+        for (int j = 0; j < model_config.fetch_var(i).shape_size(); ++j) {
           int dim = model_config.fetch_var(i).shape(j);
           _config->_fetch_shape[i].push_back(dim);
         }
@@ -316,36 +295,6 @@ int Resource::thread_initialize() {
     return -1;
   }
 
-  DynamicResource* p_dynamic_resource =
-      reinterpret_cast<DynamicResource*>(THREAD_GETSPECIFIC(_tls_bspec_key));
-  if (p_dynamic_resource == NULL) {
-    p_dynamic_resource = new (std::nothrow) DynamicResource;
-    if (p_dynamic_resource == NULL) {
-      LOG(ERROR) << "failed to create tls DynamicResource";
-      return -1;
-    }
-    if (p_dynamic_resource->initialize() != 0) {
-      LOG(ERROR) << "DynamicResource initialize failed.";
-      delete p_dynamic_resource;
-      p_dynamic_resource = NULL;
-      return -1;
-    }
-
-    if (THREAD_SETSPECIFIC(_tls_bspec_key, p_dynamic_resource) != 0) {
-      LOG(ERROR) << "unable to set tls DynamicResource";
-      delete p_dynamic_resource;
-      p_dynamic_resource = NULL;
-      return -1;
-    }
-  }
-#if 0
-    LOG(INFO) << "Successfully thread initialized dynamic resource";
-#else
-  LOG(INFO) << bthread_self()
-            << ": Successfully thread initialized dynamic resource "
-            << p_dynamic_resource;
-
-#endif
   return 0;
 }
 
@@ -362,26 +311,6 @@ int Resource::thread_clear() {
     LOG(ERROR) << "Failed thrd clear infer manager";
     return -1;
   }
-
-  DynamicResource* p_dynamic_resource =
-      reinterpret_cast<DynamicResource*>(THREAD_GETSPECIFIC(_tls_bspec_key));
-  if (p_dynamic_resource == NULL) {
-#if 0
-    LOG(ERROR) << "tls dynamic resource shouldn't be null after "
-        << "thread_initialize";
-#else
-    LOG(ERROR)
-        << bthread_self()
-        << ": tls dynamic resource shouldn't be null after thread_initialize";
-#endif
-    return -1;
-  }
-  if (p_dynamic_resource->clear() != 0) {
-    LOG(ERROR) << "Failed to invoke dynamic resource clear";
-    return -1;
-  }
-
-  // ...
   return 0;
 }
 size_t Resource::get_cube_quant_bits() { return this->cube_quant_bits; }

core/predictor/framework/resource.h

@@ -54,15 +54,6 @@ class PaddleGeneralModelConfig {
 };
 
 class BaseRdDict;
-struct DynamicResource {
-  DynamicResource();
-
-  ~DynamicResource();
-
-  int initialize();
-
-  int clear();
-};
 
 class Resource {
  public:
@@ -94,20 +85,17 @@ class Resource {
 
   int finalize();
 
-  std::vector<std::shared_ptr<PaddleGeneralModelConfig> > get_general_model_config();
+  std::vector<std::shared_ptr<PaddleGeneralModelConfig>>
+  get_general_model_config();
 
   void print_general_model_config(
       const std::shared_ptr<PaddleGeneralModelConfig>& config);
 
-  DynamicResource* get_dynamic_resource() {
-    return reinterpret_cast<DynamicResource*>(
-        THREAD_GETSPECIFIC(_tls_bspec_key));
-  }
   size_t get_cube_quant_bits();
 
  private:
   int thread_finalize() { return 0; }
-  std::vector<std::shared_ptr<PaddleGeneralModelConfig> > _configs;
+  std::vector<std::shared_ptr<PaddleGeneralModelConfig>> _configs;
   std::string cube_config_fullpath;
   int cube_quant_bits;  // 0 if no empty
 

paddle_inference/paddle/include/paddle_engine.h

@@ -57,9 +57,9 @@ PrecisionType GetPrecision(const std::string& precision_data) {
 }
 
 // Engine Base
-class PaddleEngineBase {
+class EngineCore {
  public:
-  virtual ~PaddleEngineBase() {}
+  virtual ~EngineCore() {}
   virtual std::vector<std::string> GetInputNames() {
     return _predictor->GetInputNames();
   }
@@ -107,7 +107,7 @@ class PaddleEngineBase {
 };
 
 // Paddle Inference Engine
-class PaddleInferenceEngine : public PaddleEngineBase {
+class PaddleInferenceEngine : public EngineCore {
  public:
   int create(const configure::EngineDesc& engine_conf) {
     std::string model_path = engine_conf.model_dir();

python/examples/fit_a_line/benchmark.py

@@ -25,19 +25,24 @@ args = benchmark_args()
 
 
 def single_func(idx, resource):
+    train_reader = paddle.batch(
+        paddle.reader.shuffle(
+            paddle.dataset.uci_housing.train(), buf_size=500),
+        batch_size=1)
+    total_number = sum(1 for _ in train_reader())
+
     if args.request == "rpc":
         client = Client()
         client.load_client_config(args.model)
         client.connect([args.endpoint])
-        train_reader = paddle.batch(
-            paddle.reader.shuffle(
-                paddle.dataset.uci_housing.train(), buf_size=500),
-            batch_size=1)
         start = time.time()
         for data in train_reader():
+            #new_data = np.zeros((1, 13)).astype("float32")
+            #new_data[0] = data[0][0]
+            #fetch_map = client.predict(feed={"x": new_data}, fetch=["price"], batch=True)
             fetch_map = client.predict(feed={"x": data[0][0]}, fetch=["price"])
         end = time.time()
-        return [[end - start]]
+        return [[end - start], [total_number]]
     elif args.request == "http":
         train_reader = paddle.batch(
             paddle.reader.shuffle(
@@ -49,7 +54,7 @@ def single_func(idx, resource):
                 'http://{}/uci/prediction'.format(args.endpoint),
                 data={"x": data[0]})
         end = time.time()
-        return [[end - start]]
+        return [[end - start], [total_number]]
 
 
 multi_thread_runner = MultiThreadRunner()

python/gen_version.py

@@ -47,3 +47,5 @@ elif package_name.endswith('xpu'):
 path = "paddle_serving_" + sys.argv[1]
 commit_id = subprocess.check_output(['git', 'rev-parse', 'HEAD'])
 update_info(path + "/version.py", "commit_id", commit_id)
+
+update_info(path + "/version.py", "version_tag", "${VERSION_TAG}")

python/paddle_serving_app/__init__.py

@@ -12,3 +12,6 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 from .models import ServingModels
+from . import version
+
+__version__ = version.version_tag

python/paddle_serving_app/version.py

@@ -12,5 +12,5 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 """ Paddle Serving App version string """
-serving_app_version = "0.0.0"
+version_tag = "0.0.0"
 commit_id = ""

python/paddle_serving_client/__init__.py

@@ -17,4 +17,4 @@ from . import version
 from . import client
 from .client import *
 
-__version__ = version.serving_client_version
+__version__ = version.version_tag

python/paddle_serving_client/client.py

@@ -155,7 +155,7 @@ class Client(object):
         file_path_list = []
         for single_model_config in model_config_path_list:
             if os.path.isdir(single_model_config):
-                file_path_list.append("{}/serving_server_conf.prototxt".format(
+                file_path_list.append("{}/serving_client_conf.prototxt".format(
                     single_model_config))
             elif os.path.isfile(single_model_config):
                 file_path_list.append(single_model_config)
@@ -574,7 +574,7 @@ class MultiLangClient(object):
         file_path_list = []
         for single_model_config in model_config_path_list:
             if os.path.isdir(single_model_config):
-                file_path_list.append("{}/serving_server_conf.prototxt".format(
+                file_path_list.append("{}/serving_client_conf.prototxt".format(
                     single_model_config))
             elif os.path.isfile(single_model_config):
                 file_path_list.append(single_model_config)

python/paddle_serving_client/version.py

@@ -12,7 +12,6 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 """ Paddle Serving Client version string """
-serving_client_version = "0.0.0"
-serving_server_version = "0.0.0"
+version_tag = "0.0.0"
 module_proto_version = "0.0.0"
 commit_id = ""

python/paddle_serving_server/__init__.py

@@ -31,4 +31,4 @@ from paddle_serving_server import (
 from .dag import *
 from .server import *
 
-__version__ = version.serving_server_version
+__version__ = version.version_tag

python/paddle_serving_server/serve.py

@@ -103,7 +103,7 @@ def serve_args():
 
 
 def start_standard_model(serving_port):  # pylint: disable=doc-string-missing
-    args = parse_args()
+    args = serve_args()
     thread_num = args.thread
     model = args.model
     port = serving_port
@@ -410,6 +410,7 @@ if __name__ == "__main__":
             use_lite=args.use_lite,
             use_xpu=args.use_xpu,
             ir_optim=args.ir_optim,
+            thread_num=args.thread,
             precision=args.precision,
             use_calib=args.use_calib)
         web_service.run_rpc_service()

python/paddle_serving_server/server.py

@@ -22,7 +22,7 @@ from .proto import general_model_config_pb2 as m_config
 from .proto import multi_lang_general_model_service_pb2_grpc
 import google.protobuf.text_format
 import time
-from .version import serving_server_version, version_suffix, device_type
+from .version import version_tag, version_suffix, device_type
 from contextlib import closing
 import argparse
 
@@ -369,7 +369,7 @@ class Server(object):
         version_file = open("{}/version.py".format(self.module_path), "r")
 
         folder_name = "serving-%s-%s" % (self.get_serving_bin_name(),
-                                         serving_server_version)
+                                         version_tag)
         tar_name = "%s.tar.gz" % folder_name
         bin_url = "https://paddle-serving.bj.bcebos.com/bin/%s" % tar_name
 

python/paddle_serving_server/version.py

@@ -12,10 +12,9 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 """ Paddle Serving Server version string """
-serving_client_version = "0.0.0"
-serving_server_version = "0.0.0"
-module_proto_version = "0.0.0"
+version_tag = "0.0.0"
 version_suffix = ""
+module_proto_version = "0.0.0"
 device_type = "0"
 cuda_version = "9"
 commit_id = ""

python/paddle_serving_server/web_service.py

@@ -176,10 +176,12 @@ class WebService(object):
                        use_xpu=False,
                        ir_optim=False,
                        gpuid=0,
+                       thread_num=2,
                        mem_optim=True):
         print("This API will be deprecated later. Please do not use it")
         self.workdir = workdir
         self.port = port
+        self.thread_num = thread_num
         self.device = device
         self.gpuid = gpuid
         self.port_list = []
@@ -197,7 +199,7 @@ class WebService(object):
                     self.workdir,
                     self.port_list[0],
                     -1,
-                    thread_num=2,
+                    thread_num=self.thread_num,
                     mem_optim=mem_optim,
                     use_lite=use_lite,
                     use_xpu=use_xpu,
@@ -211,7 +213,7 @@ class WebService(object):
                         "{}_{}".format(self.workdir, i),
                         self.port_list[i],
                         gpuid,
-                        thread_num=2,
+                        thread_num=self.thread_num,
                         mem_optim=mem_optim,
                         use_lite=use_lite,
                         use_xpu=use_xpu,

python/setup.py.app.in

@@ -22,7 +22,7 @@ import os
 from setuptools import setup, Distribution, Extension
 from setuptools import find_packages
 from setuptools import setup
-from paddle_serving_app.version import serving_app_version
+from paddle_serving_app.version import version_tag
 from pkg_resources import DistributionNotFound, get_distribution
         
 def python_version():
@@ -78,7 +78,7 @@ package_dir={'paddle_serving_app':
 
 setup(
     name='paddle-serving-app',
-    version=serving_app_version.replace('-', ''),
+    version=version_tag.replace('-', ''),
     description=
     ('Paddle Serving Package for saved model with PaddlePaddle'),
     url='https://github.com/PaddlePaddle/Serving',

python/setup.py.client.in

@@ -22,7 +22,7 @@ import sys
 from setuptools import setup, Distribution, Extension
 from setuptools import find_packages
 from setuptools import setup
-from paddle_serving_client.version import serving_client_version
+from paddle_serving_client.version import version_tag
 import util
 
 py_version = sys.version_info
@@ -79,7 +79,7 @@ package_dir={'paddle_serving_client':
 
 setup(
     name='paddle-serving-client',
-    version=serving_client_version.replace('-', ''),
+    version=version_tag.replace('-', ''),
     description=
     ('Paddle Serving Package for saved model with PaddlePaddle'),
     url='https://github.com/PaddlePaddle/Serving',

python/setup.py.in

@@ -19,7 +19,7 @@ from __future__ import print_function
 from setuptools import setup, Distribution, Extension
 from setuptools import find_packages
 from setuptools import setup
-from paddle_serving.version import serving_client_version
+from paddle_serving.version import version_tag
 from grpc_tools import protoc
 import util
 
@@ -43,7 +43,7 @@ package_dir={'paddle_serving.serving_client':
 
 setup(
     name='paddle-serving-client',
-    version=serving_client_version.replace('-', ''),
+    version=version_tag.replace('-', ''),
     description=
     ('Paddle Serving Package for saved model with PaddlePaddle'),
     url='https://github.com/PaddlePaddle/Serving',

python/setup.py.server.in

@@ -19,10 +19,10 @@ from __future__ import print_function
 from setuptools import setup, Distribution, Extension
 from setuptools import find_packages
 from setuptools import setup
-from paddle_serving_server.version import serving_server_version, version_suffix
+from paddle_serving_server.version import version_tag, version_suffix
 import util
 
-package_version = serving_server_version.replace('-', '')
+package_version = version_tag.replace('-', '')
 if version_suffix != "":
     version_suffix = "post" + version_suffix
     package_version = package_version + "." + version_suffix