msn dist_kv

core/general-server/op/general_dist_kv_infer_op.cpp

@@ -101,6 +101,7 @@ int GeneralDistKVInferOp::inference() {
               keys.begin() + key_idx);
     key_idx += dataptr_size_pairs[i].second;
   }
+
   rec::mcube::CubeAPI *cube = rec::mcube::CubeAPI::instance();
   std::vector<std::string> table_names = cube->get_table_names();
   if (table_names.size() == 0) {
@@ -109,9 +110,6 @@ int GeneralDistKVInferOp::inference() {
   }
   int ret = cube->seek(table_names[0], keys, &values);
   VLOG(2) << "(logid=" << log_id << ") cube seek status: " << ret;
-  if (values.size() != keys.size() || values[0].buff.size() == 0) {
-    LOG(ERROR) << "cube value return null";
-  }
   //size_t EMBEDDING_SIZE = values[0].buff.size() / sizeof(float);
   size_t EMBEDDING_SIZE = 9;
   TensorVector sparse_out;
@@ -125,6 +123,8 @@ int GeneralDistKVInferOp::inference() {
   baidu::paddle_serving::predictor::Resource &resource =
       baidu::paddle_serving::predictor::Resource::instance();
   std::shared_ptr<PaddleGeneralModelConfig> model_config = resource.get_general_model_config().front();
+  int cube_key_found = 0;
+  int cube_key_miss = 0; 
   for (size_t i = 0; i < in->size(); ++i) {
     if (in->at(i).dtype != paddle::PaddleDType::INT64) {
       dense_out[dense_idx] = in->at(i);
@@ -149,13 +149,26 @@ int GeneralDistKVInferOp::inference() {
       float *data_ptr = dst_ptr + x * EMBEDDING_SIZE;
       if (values[cube_val_idx].buff.size() == 0) {
          memset(data_ptr, (float)0.0, sizeof(float) * EMBEDDING_SIZE);
+         VLOG(3) << "(logid=" << log_id << ") cube key not found: " << keys[cube_val_idx];
+         ++cube_key_miss;
+	 ++cube_val_idx;
          continue;
       }
+      //VLOG(3) << "(logid=" << log_id << ") cube key found: " << keys[cube_val_idx];
       memcpy(data_ptr, values[cube_val_idx].buff.data()+10, values[cube_val_idx].buff.size()-10);
-      cube_val_idx++;
+      //VLOG(3) <<  keys[cube_val_idx] << ":" << data_ptr[0] << ", " << data_ptr[1] << ", " <<data_ptr[2] << ", " <<data_ptr[3] << ", " <<data_ptr[4] << ", " <<data_ptr[5] << ", " <<data_ptr[6] << ", " <<data_ptr[7] << ", " <<data_ptr[8];
+      ++cube_key_found;
+      ++cube_val_idx;
     }
     ++sparse_idx;
   }
+  bool cube_fail = (cube_key_found == 0);
+  if (cube_fail) {
+    LOG(WARNING) << "(logid=" << log_id << ") cube seek fail";
+    //CopyBlobInfo(input_blob, output_blob);
+    //return 0;
+  }
+  VLOG(2) << "(logid=" << log_id << ") cube key found: " << cube_key_found << " , cube key miss: " << cube_key_miss;
   VLOG(2) << "(logid=" << log_id << ") sparse tensor load success.";
   TensorVector infer_in;
   infer_in.insert(infer_in.end(), dense_out.begin(), dense_out.end());
@@ -172,7 +185,10 @@ int GeneralDistKVInferOp::inference() {
     return -1;
   }
   int64_t end = timeline.TimeStampUS();
-
+  if (cube_fail) { 
+     float *out_ptr = static_cast<float*>(out->at(0).data.data());
+     out_ptr[0] = 0.0;
+  }
   CopyBlobInfo(input_blob, output_blob);
   AddBlobInfo(output_blob, start);
   AddBlobInfo(output_blob, end);