bert client and OP

demo-client/src/bert_service.cpp

@@ -31,7 +31,7 @@ using baidu::paddle_serving::predictor::bert_service::BertResInstance;
 using baidu::paddle_serving::predictor::bert_service::BertReqInstance;
 using baidu::paddle_serving::predictor::bert_service::Embedding_values;
 
-int batch_size = 1;
+int batch_size = 49;
 int max_seq_len = 82;
 int layer_num = 12;
 int emb_size = 768;
@@ -95,7 +95,54 @@ int create_req(Request* req,
 }
 */
 
+int create_req(Request* req,
+               const std::vector<std::string>& data_list,
+               int data_index,
+               int batch_size) {
+  // add data
+  // avoid out of boundary
+  int cur_index = data_index;
+  if (cur_index >= data_list.size()) {
+    cur_index = cur_index % data_list.size();
+  }
+
+  std::vector<std::string> feature_list = split(data_list[cur_index], ";");
+
+  std::vector<std::string> src_field = split(feature_list[0], ":");
+  std::vector<std::string> src_ids = split(src_field[1], " ");
+
+  std::vector<std::string> pos_field = split(feature_list[1], ":");
+  std::vector<std::string> pos_ids = split(pos_field[1], " ");
+
+  std::vector<std::string> sent_field = split(feature_list[2], ":");
+  std::vector<std::string> sent_ids = split(sent_field[1], " ");
+
+  std::vector<std::string> mask_field = split(feature_list[3], ":");
+  std::vector<std::string> input_mask = split(mask_field[1], " ");
+
+  std::vector<int> shape;
+  std::vector<std::string> shapes = split(src_field[0], " ");
+  for (auto x: shapes) {
+    shape.push_back(std::stoi(x));
+  }
+
+  for (int i = 0; i < batch_size && i < shape[0]; ++i) {
+    BertReqInstance* ins = req->add_instances();
+    if (!ins) {
+      LOG(ERROR) << "Failed create req instance";
+      return -1;
+    }
+    for (int fi = 0; fi < max_seq_len; fi++) {
+      ins->add_token_ids(std::stoi(src_ids[i * max_seq_len + fi]));
+      ins->add_position_ids(std::stoi(pos_ids[i * max_seq_len + fi]));
+      ins->add_sentence_type_ids(std::stoi(sent_ids[i * max_seq_len + fi]));
+      ins->add_input_masks(std::stof(input_mask[i * max_seq_len + fi]));
+    }
+  }
+  return 0;
+}
 
+#if 0
 int create_req(Request* req,
                const std::vector<std::string>& data_list,
                int data_index,
@@ -120,11 +167,11 @@ int create_req(Request* req,
     std::vector<std::string> seg_list = split(feature_list[3], " ");
     std::vector<std::string> mask_list = split(feature_list[4], " ");
     for (int fi = 0; fi < max_seq_len; fi++) {
-      if (fi < token_list.size()) {
-        ins->add_token_ids(std::stoi(token_list[fi]));
-        ins->add_sentence_type_ids(std::stoll(seg_list[fi]));
-        ins->add_position_ids(std::stoll(pos_list[fi]));
-        ins->add_input_masks(std::stof(mask_list[fi]));
+      if (fi < std::stoi(shape_list[1])) {
+        ins->add_token_ids(std::stoi(token_list[fi + (i * max_seq_len)]));
+        ins->add_sentence_type_ids(std::stoll(seg_list[fi + (i * max_seq_len)]));
+        ins->add_position_ids(std::stoll(pos_list[fi + (i * max_seq_len)]));
+        ins->add_input_masks(std::stof(mask_list[fi + (i * max_seq_len)]));
       } else {
         ins->add_token_ids(0);
         ins->add_sentence_type_ids(0);
@@ -135,6 +182,7 @@ int create_req(Request* req,
   }
   return 0;
 }
+#endif
 
 void print_res(const Request& req,
                const Response& res,
@@ -184,11 +232,17 @@ void thread_worker(PredictorApi* api,
     }
     g_concurrency++;
     LOG(INFO) << "Current concurrency " << g_concurrency.load();
+#if 0
     int data_index = turns * batch_size;
     if (create_req(&req, data_list, data_index, batch_size) != 0) {
       return;
     }
-    if (predictor->inference(&req, &res) != 0) {
+#else
+    if (create_req(&req, data_list, turns, batch_size) != 0) {
+      return;
+    }
+#endif
+   if (predictor->inference(&req, &res) != 0) {
       LOG(ERROR) << "failed call predictor with req:" << req.ShortDebugString();
       return;
     }

demo-serving/op/bert_service_op.cpp

@@ -17,6 +17,9 @@
 #include <string>
 #include "predictor/framework/infer.h"
 #include "predictor/framework/memory.h"
+#if 1
+#include <sstream>
+#endif
 namespace baidu {
 namespace paddle_serving {
 namespace serving {
@@ -28,7 +31,7 @@ using baidu::paddle_serving::predictor::bert_service::BertReqInstance;
 using baidu::paddle_serving::predictor::bert_service::Request;
 using baidu::paddle_serving::predictor::bert_service::Embedding_values;
 
-const uint32_t MAX_SEQ_LEN = 64;
+const uint32_t MAX_SEQ_LEN = 82;
 const bool POOLING = true;
 const int LAYER_NUM = 12;
 const int EMB_SIZE = 768;
@@ -105,24 +108,51 @@ int BertServiceOp::inference() {
     index += MAX_SEQ_LEN;
   }
 
+#if 0
+  int64_t *src_data = static_cast<int64_t *>(src_ids.data.data());
+  std::ostringstream oss;
+  oss << "src_ids: ";
+  for (int i = 0; i < MAX_SEQ_LEN * batch_size; ++i) {
+    oss << src_data[i] << " ";
+  }
+  LOG(INFO) << oss.str();
+
+#endif
   in->push_back(src_ids);
   in->push_back(pos_ids);
   in->push_back(seg_ids);
   in->push_back(input_masks);
 
   TensorVector *out = butil::get_object<TensorVector>();
+//  TensorVector out;
+/*
   if (!out) {
     LOG(ERROR) << "Failed get tls output object";
     return -1;
   }
-
+*/
     LOG(INFO) << "batch_size : " << batch_size;
-    LOG(INFO) << "MAX_SEQ_LEN : " << (*in)[0].shape[1];
-    float* example = (float*)(*in)[3].data.data();
-    for(uint32_t i = 0; i < MAX_SEQ_LEN; i++){
-        LOG(INFO) << *(example + i);
+    for (int j = 0; j < 3; j ++) {
+        LOG(INFO) << "name : " << (*in)[j].name << " shape : " << (*in)[j].shape[0]
+            << " " <<  (*in)[j].shape[1] << " " << (*in)[j].shape[2];
+        int64_t* example = (int64_t*)(*in)[j].data.data();
+        std::ostringstream oss;
+        for(uint32_t i = MAX_SEQ_LEN * (batch_size - 1); i < MAX_SEQ_LEN * batch_size; i++){
+            oss << *(example + i);
+        }
+        LOG(INFO) << "data : " << oss.str();
     }
 
+    for (int j =3; j < 4; j++) {
+        LOG(INFO) << "name : " << (*in)[j].name << " shape : " << (*in)[j].shape[0]
+            << " " <<  (*in)[j].shape[1] << " " << (*in)[j].shape[2];
+        float* example = (float*)(*in)[j].data.data();
+        std::ostringstream oss;
+        for(uint32_t i = MAX_SEQ_LEN * (batch_size - 1); i < MAX_SEQ_LEN * batch_size; i++){
+            oss << *(example + i);
+        }
+        LOG(INFO) << "data : " << oss.str();
+    }
 
   if (predictor::InferManager::instance().infer(
           BERT_MODEL_NAME, in, out, batch_size)) {
@@ -130,6 +160,13 @@ int BertServiceOp::inference() {
     return -1;
   }
 
+/*
+  paddle::NativeConfig config;
+  config.model_dir = "./data/model/paddle/fluid/bert";
+  auto predictor = CreatePaddlePredictor(config);
+  predictor->Run(*in, &out);
+*/
+#if 0
   //  float *out_data = static_cast<float *>(out->at(0).data.data());
   LOG(INFO) << "check point";
   /*
@@ -160,6 +197,42 @@ int BertServiceOp::inference() {
     out->clear();
     butil::return_object<TensorVector>(out);
   */
+
+#else
+  float *out_data = static_cast<float *>(out->at(0).data.data());
+  std::ostringstream oss;
+  oss << "Shape: [";
+
+  for (auto x: out->at(0).shape) {
+    oss << x << " ";
+  }
+  oss << "]";
+
+  LOG(INFO) << oss.str();
+
+  // Output shape  is [batch_size x 3]
+  for (uint32_t bi = 0; bi < batch_size; bi++) {
+    BertResInstance *res_instance = res->add_instances();
+    std::ostringstream oss;
+    oss << "Sample " << bi << " [";
+    oss << out_data[bi * 3 + 0] << " "
+        << out_data[bi * 3 + 1] << " "
+        << out_data[bi * 3 + 2] << "]";
+    LOG(INFO) << oss.str();
+  }
+
+  for (size_t i = 0; i < in->size(); ++i) {
+    (*in)[i].shape.clear();
+  }
+  in->clear();
+  butil::return_object<TensorVector>(in);
+
+  for (size_t i = 0; i < out->size(); ++i) {
+    (*out)[i].shape.clear();
+  }
+  out->clear();
+  butil::return_object<TensorVector>(out);
+#endif
   return 0;
 }