Merge code

configure/proto/server_configure.proto

@@ -49,6 +49,8 @@ message ModelToolkitConf { repeated EngineDesc engines = 1; };
 message ResourceConf {
   required string model_toolkit_path = 1;
   required string model_toolkit_file = 2;
+  optional string cube_config_path = 3;
+  optional string cube_config_file = 4;
 };
 
 // DAG node depency info

demo-client/CMakeLists.txt

@@ -57,6 +57,10 @@ add_executable(text_classification_press
 target_link_libraries(text_classification_press -Wl,--whole-archive sdk-cpp -Wl,--no-whole-archive -lpthread -lcrypto -lm -lrt -lssl -ldl
         -lz)
 
+add_executable(ctr_prediction
+        ${CMAKE_CURRENT_LIST_DIR}/src/ctr_prediction.cpp)
+target_link_libraries(ctr_prediction -Wl,--whole-archive sdk-cpp
+        -Wl,--no-whole-archive -lpthread -lcrypto -lm -lrt -lssl -ldl -lz)
 # install
 install(TARGETS ximage
         RUNTIME DESTINATION
@@ -104,3 +108,11 @@ install(DIRECTORY ${CMAKE_CURRENT_LIST_DIR}/conf DESTINATION
         ${PADDLE_SERVING_INSTALL_DIR}/demo/client/text_classification/)
 install(DIRECTORY ${CMAKE_CURRENT_LIST_DIR}/data/text_classification DESTINATION
         ${PADDLE_SERVING_INSTALL_DIR}/demo/client/text_classification/data)
+
+install(TARGETS ctr_prediction
+        RUNTIME DESTINATION
+        ${PADDLE_SERVING_INSTALL_DIR}/demo/client/ctr_prediction/bin)
+install(DIRECTORY ${CMAKE_CURRENT_LIST_DIR}/conf DESTINATION
+        ${PADDLE_SERVING_INSTALL_DIR}/demo/client/ctr_prediction/)
+install(DIRECTORY ${CMAKE_CURRENT_LIST_DIR}/data/ctr_prediction DESTINATION
+        ${PADDLE_SERVING_INSTALL_DIR}/demo/client/ctr_prediction/data)

demo-client/conf/predictors.prototxt

@@ -124,3 +124,18 @@ predictors {
     }
   }
 }
+
+predictors {
+  name: "ctr_prediction_service"
+  service_name: "baidu.paddle_serving.predictor.ctr_prediction.CTRPredictionService"
+  endpoint_router: "WeightedRandomRender"
+  weighted_random_render_conf {
+    variant_weight_list: "50"
+  }
+  variants {
+    tag: "var1"
+    naming_conf {
+        cluster: "list://127.0.0.1:8010"
+    }
+  }
+}

demo-client/src/ctr_prediction.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 <sys/stat.h>
+#include <sys/types.h>
+#include <unistd.h>
+#include <cstdlib>
+#include <fstream>
+#include <sstream>
+#include <string>
+#include <thread>  // NOLINT
+#include "sdk-cpp/ctr_prediction.pb.h"
+#include "sdk-cpp/include/common.h"
+#include "sdk-cpp/include/predictor_sdk.h"
+using baidu::paddle_serving::sdk_cpp::Predictor;
+using baidu::paddle_serving::sdk_cpp::PredictorApi;
+using baidu::paddle_serving::predictor::ctr_prediction::Request;
+using baidu::paddle_serving::predictor::ctr_prediction::Response;
+using baidu::paddle_serving::predictor::ctr_prediction::CTRReqInstance;
+using baidu::paddle_serving::predictor::ctr_prediction::CTRResInstance;
+
+int batch_size = 1;
+int sparse_num = 26;
+int dense_num = 13;
+int thread_num = 1;
+int hash_dim = 1000001;
+std::vector<float> cont_min = {0, -3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0};
+std::vector<float> cont_diff = {
+    20, 603, 100, 50, 64000, 500, 100, 50, 500, 10, 10, 10, 50};
+char* data_filename = "./data/ctr_prediction/data.txt";
+std::atomic<int> g_concurrency(0);
+std::vector<std::vector<int>> response_time;
+
+std::vector<std::string> split(const std::string& str,
+                               const std::string& pattern) {
+  std::vector<std::string> res;
+  if (str == "") return res;
+  std::string strs = str + pattern;
+  size_t pos = strs.find(pattern);
+  while (pos != strs.npos) {
+    std::string temp = strs.substr(0, pos);
+    res.push_back(temp);
+    strs = strs.substr(pos + 1, strs.size());
+    pos = strs.find(pattern);
+  }
+  return res;
+}
+
+int64_t hash(std::string str) {
+  int64_t len;
+  unsigned char* p;
+  int64_t x;
+
+  len = str.size();
+  p = (unsigned char*)str.c_str();
+  x = *p << 7;
+  while (--len >= 0) {
+    x = (1000003 * x) ^ *p++;
+  }
+  x ^= str.size();
+  if (x == -1) {
+    x = -2;
+  }
+  return x;
+}
+
+int create_req(Request* req,
+               const std::vector<std::string>& data_list,
+               int data_index,
+               int batch_size) {
+  for (int i = 0; i < batch_size; ++i) {
+    CTRReqInstance* ins = req->add_instances();
+    if (!ins) {
+      LOG(ERROR) << "Failed create req instance";
+      return -1;
+    }
+    // add data
+    std::vector<std::string> feature_list =
+        split(data_list[data_index + i], "\t");
+    for (int fi = 0; fi < dense_num; fi++) {
+      if (feature_list[fi] == "") {
+        ins->add_dense_ids(0.0);
+      } else {
+        float dense_id = std::stof(feature_list[fi]);
+        dense_id = (dense_id - cont_min[fi]) / cont_diff[fi];
+        ins->add_dense_ids(dense_id);
+      }
+    }
+    for (int fi = dense_num; fi < (dense_num + sparse_num); fi++) {
+      int64_t sparse_id =
+          hash(std::to_string(fi) + feature_list[fi]) % hash_dim;
+      if (sparse_id < 0) {
+        // diff between c++ and python
+        sparse_id += hash_dim;
+      }
+      ins->add_sparse_ids(sparse_id);
+    }
+  }
+  return 0;
+}
+void print_res(const Request& req,
+               const Response& res,
+               std::string route_tag,
+               uint64_t mid_ms,
+               uint64_t elapse_ms) {
+  if (res.err_code() != 0) {
+    LOG(ERROR) << "Get result fail :" << res.err_msg();
+    return;
+  }
+  for (uint32_t i = 0; i < res.predictions_size(); ++i) {
+    const CTRResInstance& res_ins = res.predictions(i);
+    std::ostringstream oss;
+    oss << res_ins.prob0() << " ";
+    LOG(INFO) << "Receive result " << oss.str();
+  }
+  LOG(INFO) << "Succ call predictor[ctr_prediction_service], the tag is: "
+            << route_tag << ", mid_ms: " << mid_ms
+            << ", elapse_ms: " << elapse_ms;
+}
+
+void thread_worker(PredictorApi* api,
+                   int thread_id,
+                   int batch_size,
+                   int server_concurrency,
+                   const std::vector<std::string>& data_list) {
+  // init
+  Request req;
+  Response res;
+  api->thrd_initialize();
+  std::string line;
+  int turns = 0;
+  while (turns < 1000) {
+    ///
+    timeval start;
+    gettimeofday(&start, NULL);
+    api->thrd_clear();
+    Predictor* predictor = api->fetch_predictor("ctr_prediction_service");
+    if (!predictor) {
+      LOG(ERROR) << "Failed fetch predictor: ctr_prediction_service";
+      return;
+    }
+    req.Clear();
+    res.Clear();
+    timeval mid;
+    gettimeofday(&mid, NULL);
+    uint64_t mid_ms = (mid.tv_sec * 1000 + mid.tv_usec / 1000) -
+                      (start.tv_sec * 1000 + start.tv_usec / 1000);
+    // wait for other thread
+    while (g_concurrency.load() >= server_concurrency) {
+    }
+    g_concurrency++;
+    LOG(INFO) << "Current concurrency " << g_concurrency.load();
+    int data_index = turns * batch_size;
+    if (create_req(&req, data_list, data_index, batch_size) != 0) {
+      return;
+    }
+    timeval start_run;
+    gettimeofday(&start_run, NULL);
+    if (predictor->inference(&req, &res) != 0) {
+      LOG(ERROR) << "failed call predictor with req:" << req.ShortDebugString();
+      return;
+    }
+    timeval end;
+    gettimeofday(&end, NULL);
+    uint64_t elapse_ms = (end.tv_sec * 1000 + end.tv_usec / 1000) -
+                         (start_run.tv_sec * 1000 + start_run.tv_usec / 1000);
+    response_time[thread_id].push_back(elapse_ms);
+    print_res(req, res, predictor->tag(), mid_ms, elapse_ms);
+    g_concurrency--;
+    LOG(INFO) << "Done. Current concurrency " << g_concurrency.load();
+    turns++;
+  }
+  //
+  api->thrd_finalize();
+}
+void calc_time(int server_concurrency, int batch_size) {
+  std::vector<int> time_list;
+  for (auto a : response_time) {
+    time_list.insert(time_list.end(), a.begin(), a.end());
+  }
+  LOG(INFO) << "Total request : " << (time_list.size());
+  LOG(INFO) << "Batch size : " << batch_size;
+  LOG(INFO) << "Max concurrency : " << server_concurrency;
+  float total_time = 0;
+  float max_time = 0;
+  float min_time = 1000000;
+  for (int i = 0; i < time_list.size(); ++i) {
+    total_time += time_list[i];
+    if (time_list[i] > max_time) max_time = time_list[i];
+    if (time_list[i] < min_time) min_time = time_list[i];
+  }
+  float mean_time = total_time / (time_list.size());
+  float var_time;
+  for (int i = 0; i < time_list.size(); ++i) {
+    var_time += (time_list[i] - mean_time) * (time_list[i] - mean_time);
+  }
+  var_time = var_time / time_list.size();
+  LOG(INFO) << "Total time : " << total_time / server_concurrency
+            << " Variance : " << var_time << " Max time : " << max_time
+            << " Min time : " << min_time;
+  float qps = 0.0;
+  if (total_time > 0)
+    qps = (time_list.size() * 1000) / (total_time / server_concurrency);
+  LOG(INFO) << "QPS: " << qps << "/s";
+  LOG(INFO) << "Latency statistics: ";
+  sort(time_list.begin(), time_list.end());
+  int percent_pos_50 = time_list.size() * 0.5;
+  int percent_pos_80 = time_list.size() * 0.8;
+  int percent_pos_90 = time_list.size() * 0.9;
+  int percent_pos_99 = time_list.size() * 0.99;
+  int percent_pos_999 = time_list.size() * 0.999;
+  if (time_list.size() != 0) {
+    LOG(INFO) << "Mean time : " << mean_time;
+    LOG(INFO) << "50 percent ms: " << time_list[percent_pos_50];
+    LOG(INFO) << "80 percent ms: " << time_list[percent_pos_80];
+    LOG(INFO) << "90 percent ms: " << time_list[percent_pos_90];
+    LOG(INFO) << "99 percent ms: " << time_list[percent_pos_99];
+    LOG(INFO) << "99.9 percent ms: " << time_list[percent_pos_999];
+  } else {
+    LOG(INFO) << "N/A";
+  }
+}
+int main(int argc, char** argv) {
+  // initialize
+  PredictorApi api;
+  response_time.resize(thread_num);
+  int server_concurrency = thread_num;
+// log set
+#ifdef BCLOUD
+  logging::LoggingSettings settings;
+  settings.logging_dest = logging::LOG_TO_FILE;
+  std::string log_filename(argv[0]);
+  log_filename = log_filename.substr(log_filename.find_last_of('/') + 1);
+  settings.log_file = (std::string("./log/") + log_filename + ".log").c_str();
+  settings.delete_old = logging::DELETE_OLD_LOG_FILE;
+  logging::InitLogging(settings);
+  logging::ComlogSinkOptions cso;
+  cso.process_name = log_filename;
+  cso.enable_wf_device = true;
+  logging::ComlogSink::GetInstance()->Setup(&cso);
+#else
+  struct stat st_buf;
+  int ret = 0;
+  if ((ret = stat("./log", &st_buf)) != 0) {
+    mkdir("./log", 0777);
+    ret = stat("./log", &st_buf);
+    if (ret != 0) {
+      LOG(WARNING) << "Log path ./log not exist, and create fail";
+      return -1;
+    }
+  }
+  FLAGS_log_dir = "./log";
+  google::InitGoogleLogging(strdup(argv[0]));
+  FLAGS_logbufsecs = 0;
+  FLAGS_logbuflevel = -1;
+#endif
+  // predictor conf
+  if (api.create("./conf", "predictors.prototxt") != 0) {
+    LOG(ERROR) << "Failed create predictors api!";
+    return -1;
+  }
+  // read data
+  std::ifstream data_file(data_filename);
+  if (!data_file) {
+    std::cout << "read file error \n" << std::endl;
+    return -1;
+  }
+  std::vector<std::string> data_list;
+  std::string line;
+  while (getline(data_file, line)) {
+    data_list.push_back(line);
+  }
+  // create threads
+  std::vector<std::thread*> thread_pool;
+  for (int i = 0; i < server_concurrency; ++i) {
+    thread_pool.push_back(new std::thread(thread_worker,
+                                          &api,
+                                          i,
+                                          batch_size,
+                                          server_concurrency,
+                                          std::ref(data_list)));
+  }
+  for (int i = 0; i < server_concurrency; ++i) {
+    thread_pool[i]->join();
+    delete thread_pool[i];
+  }
+  calc_time(server_concurrency, batch_size);
+  api.destroy();
+  return 0;
+}

demo-serving/conf/cube.conf

+[{
+    "dict_name": "dict",
+    "shard": 2,
+    "dup": 1,
+    "timeout": 200,
+    "retry": 3,
+    "backup_request": 100,
+    "type": "ipport_list",
+    "load_balancer": "rr",
+    "nodes": [{
+        "ipport_list": "list://xxx.xxx.xxx.xxx:8000"
+    },{ 
+        "ipport_list": "list://xxx.xxx.xxx.xxx:8000"
+    }]  
+}]

demo-serving/conf/gflags.conf

 --enable_model_toolkit
+--enable_cube=false

demo-serving/conf/resource.prototxt

 model_toolkit_path: "./conf/"
 model_toolkit_file: "model_toolkit.prototxt"
+cube_config_file: "./conf/cube.conf"

demo-serving/op/ctr_prediction_op.cpp

@@ -102,37 +102,28 @@ int CTRPredictionOp::inference() {
     }
   }
 
-#if 1
   rec::mcube::CubeAPI *cube = rec::mcube::CubeAPI::instance();
   predictor::KVManager &kv_manager = predictor::KVManager::instance();
   const predictor::KVInfo *kvinfo =
       kv_manager.get_kv_info(CTR_PREDICTION_MODEL_NAME);
-  std::string table_name;
-  if (kvinfo->sparse_param_service_type != configure::EngineDesc::NONE) {
-    std::string table_name = kvinfo->sparse_param_service_table_name;
-  }
+  if (kvinfo != NULL) {
+    std::string table_name;
+    if (kvinfo->sparse_param_service_type != configure::EngineDesc::NONE) {
+      std::string table_name = kvinfo->sparse_param_service_table_name;
+    }
 
-  if (kvinfo->sparse_param_service_type == configure::EngineDesc::LOCAL) {
-    // Query local KV service
-  } else if (kvinfo->sparse_param_service_type ==
-             configure::EngineDesc::REMOTE) {
-    int ret = cube->seek(table_name, keys, &values);
-    if (ret != 0) {
-      fill_response_with_message(res, -1, "Query cube for embeddings error");
-      LOG(ERROR) << "Query cube for embeddings error";
-      return -1;
+    if (kvinfo->sparse_param_service_type == configure::EngineDesc::LOCAL) {
+      // Query local KV service
+    } else if (kvinfo->sparse_param_service_type ==
+               configure::EngineDesc::REMOTE) {
+      int ret = cube->seek(table_name, keys, &values);
+      if (ret != 0) {
+        fill_response_with_message(res, -1, "Query cube for embeddings error");
+        LOG(ERROR) << "Query cube for embeddings error";
+        return -1;
+      }
     }
   }
-#else
-  float buff[CTR_PREDICTION_EMBEDDING_SIZE] = {
-      0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.09, 0.00};
-  for (int i = 0; i < keys.size(); ++i) {
-    CubeValue value;
-    value.error = 0;
-    value.buff = std::string(reinterpret_cast<char *>(buff), sizeof(buff));
-    values.push_back(value);
-  }
-#endif
 
   // Sparse embeddings
   for (int i = 0; i < CTR_PREDICTION_SPARSE_SLOTS; ++i) {

predictor/common/constant.cpp

@@ -40,6 +40,7 @@ DEFINE_int32(
 DEFINE_int32(reload_interval_s, 10, "");
 DEFINE_bool(enable_model_toolkit, false, "enable model toolkit");
 DEFINE_string(enable_protocol_list, "baidu_std", "set protocol list");
+DEFINE_bool(enable_cube, false, "enable cube");
 
 const char* START_OP_NAME = "startup_op";
 }  // namespace predictor

predictor/common/constant.h

@@ -39,6 +39,9 @@ DECLARE_int32(num_threads);
 DECLARE_int32(reload_interval_s);
 DECLARE_bool(enable_model_toolkit);
 DECLARE_string(enable_protocol_list);
+DECLARE_bool(enable_cube);
+DECLARE_string(cube_config_path);
+DECLARE_string(cube_config_file);
 
 // STATIC Variables
 extern const char* START_OP_NAME;

predictor/framework/resource.cpp

@@ -22,7 +22,7 @@ namespace paddle_serving {
 namespace predictor {
 
 using configure::ResourceConf;
-
+using rec::mcube::CubeAPI;
 // __thread bool p_thread_initialized = false;
 
 static void dynamic_resource_deleter(void* d) {
@@ -97,6 +97,44 @@ int Resource::initialize(const std::string& path, const std::string& file) {
   return 0;
 }
 
+int Resource::cube_initialize(const std::string& path,
+                              const std::string& file) {
+  // cube
+  if (!FLAGS_enable_cube) {
+    return 0;
+  }
+
+  ResourceConf resource_conf;
+  if (configure::read_proto_conf(path, file, &resource_conf) != 0) {
+    LOG(ERROR) << "Failed initialize resource from: " << path << "/" << file;
+    return -1;
+  }
+
+  int err = 0;
+  std::string cube_config_path = resource_conf.cube_config_path();
+  if (err != 0) {
+    LOG(ERROR) << "reade cube_config_path failed, path[" << path << "], file["
+               << cube_config_path << "]";
+    return -1;
+  }
+  std::string cube_config_file = resource_conf.cube_config_file();
+  if (err != 0) {
+    LOG(ERROR) << "reade cube_config_file failed, path[" << path << "], file["
+               << cube_config_file << "]";
+    return -1;
+  }
+  err = CubeAPI::instance()->init(cube_config_file.c_str());
+  if (err != 0) {
+    LOG(ERROR) << "failed initialize cube, config: " << cube_config_path << "/"
+               << cube_config_file << " error code : " << err;
+    return -1;
+  }
+
+  LOG(INFO) << "Successfully initialize cube";
+
+  return 0;
+}
+
 int Resource::thread_initialize() {
   // mempool
   if (MempoolWrapper::instance().thread_initialize() != 0) {
@@ -198,7 +236,10 @@ int Resource::finalize() {
     LOG(ERROR) << "Failed proc finalize infer manager";
     return -1;
   }
-
+  if (CubeAPI::instance()->destroy() != 0) {
+    LOG(ERROR) << "Destory cube api failed ";
+    return -1;
+  }
   THREAD_KEY_DELETE(_tls_bspec_key);
 
   return 0;

predictor/framework/resource.h

@@ -13,7 +13,9 @@
 // limitations under the License.
 
 #pragma once
+#include <memory>
 #include <string>
+#include "cube/cube-api/include/cube_api.h"
 #include "kvdb/paddle_rocksdb.h"
 #include "predictor/common/inner_common.h"
 #include "predictor/framework/memory.h"
@@ -45,7 +47,7 @@ class Resource {
   }
 
   int initialize(const std::string& path, const std::string& file);
-
+  int cube_initialize(const std::string& path, const std::string& file);
   int thread_initialize();
 
   int thread_clear();

predictor/src/pdserving.cpp

@@ -209,6 +209,14 @@ int main(int argc, char** argv) {
   }
   LOG(INFO) << "Succ call pthread worker start function";
 
+  if (Resource::instance().cube_initialize(FLAGS_resource_path,
+                                           FLAGS_resource_file) != 0) {
+    LOG(ERROR) << "Failed initialize cube, conf: " << FLAGS_resource_path << "/"
+               << FLAGS_resource_file;
+    return -1;
+  }
+  LOG(INFO) << "Succ initialize cube";
+
   FLAGS_logtostderr = false;
 
   if (ServerManager::instance().start_and_wait() != 0) {

sdk-cpp/proto/ctr_prediction.proto

+// Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+syntax = "proto2";
+import "pds_option.proto";
+import "builtin_format.proto";
+package baidu.paddle_serving.predictor.ctr_prediction;
+
+option cc_generic_services = true;
+
+message CTRReqInstance {
+  repeated int64 sparse_ids = 1;
+  repeated float dense_ids = 2;
+};
+
+message Request { repeated CTRReqInstance instances = 1; };
+
+message CTRResInstance {
+  required float prob0 = 1;
+  required float prob1 = 2;
+};
+
+message Response {
+  repeated CTRResInstance predictions = 1;
+  required int64 err_code = 2;
+  optional string err_msg = 3;
+};
+
+service CTRPredictionService {
+  rpc inference(Request) returns (Response);
+  rpc debug(Request) returns (Response);
+  option (pds.options).generate_stub = true;
+};