Merge branch 'develop' into xpu-example

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/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