add logid in op

core/general-server/op/general_copy_op.cpp

@@ -45,36 +45,41 @@ int GeneralCopyOp::inference() {
   const std::string pre_name = pre_node_names[0];
 
   const GeneralBlob *input_blob = get_depend_argument<GeneralBlob>(pre_name);
-  VLOG(2) << "precedent name: " << pre_name;
+  uint64_t log_id = input_blob->GetLogId();
+
+  VLOG(2) << "(logid=" << log_id << ") precedent name: " << pre_name;
   const TensorVector *in = &input_blob->tensor_vector;
-  VLOG(2) << "input size: " << in->size();
+  VLOG(2) << "(logid=" << log_id << ") input size: " << in->size();
   int batch_size = input_blob->GetBatchSize();
   int input_var_num = 0;
 
   GeneralBlob *res = mutable_data<GeneralBlob>();
+  res->SetLogId(log_id);
   TensorVector *out = &res->tensor_vector;
 
-  VLOG(2) << "input batch size: " << batch_size;
+  VLOG(2) << "(logid=" << log_id << ") input batch size: " << batch_size;
   res->SetBatchSize(batch_size);
 
   if (!res) {
-    LOG(ERROR) << "Failed get op tls reader object output";
+    LOG(ERROR) << "(logid=" << log_id
+               << ") Failed get op tls reader object output";
   }
 
   Timer timeline;
   int64_t start = timeline.TimeStampUS();
 
-  VLOG(2) << "Going to init lod tensor";
+  VLOG(2) << "(logid=" << log_id << ") Going to init lod tensor";
   for (int i = 0; i < in->size(); ++i) {
     paddle::PaddleTensor lod_tensor;
     CopyLod(&in->at(i), &lod_tensor);
     lod_tensor.dtype = in->at(i).dtype;
     lod_tensor.name = in->at(i).name;
-    VLOG(2) << "lod tensor [" << i << "].name = " << lod_tensor.name;
+    VLOG(2) << "(logid=" << log_id << ") lod tensor [" << i
+            << "].name = " << lod_tensor.name;
     out->push_back(lod_tensor);
   }
 
-  VLOG(2) << "pack done.";
+  VLOG(2) << "(logid=" << log_id << ") pack done.";
 
   for (int i = 0; i < out->size(); ++i) {
     int64_t *src_ptr = static_cast<int64_t *>(in->at(i).data.data());
@@ -86,7 +91,7 @@ int GeneralCopyOp::inference() {
     }
   }
 
-  VLOG(2) << "output done.";
+  VLOG(2) << "(logid=" << log_id << ") output done.";
 
   timeline.Pause();
   int64_t end = timeline.TimeStampUS();
@@ -94,7 +99,7 @@ int GeneralCopyOp::inference() {
   AddBlobInfo(res, start);
   AddBlobInfo(res, end);
 
-  VLOG(2) << "read data from client success";
+  VLOG(2) << "(logid=" << log_id << ") read data from client success";
   return 0;
 }
 

core/general-server/op/general_dist_kv_infer_op.cpp

@@ -50,18 +50,20 @@ int GeneralDistKVInferOp::inference() {
   const std::string pre_name = pre_node_names[0];
 
   const GeneralBlob *input_blob = get_depend_argument<GeneralBlob>(pre_name);
-  VLOG(2) << "Get precedent op name: " << pre_name;
+  uint64_t log_id = input_blob->GetLogId();
+  VLOG(2) << "(logid=" << log_id << ") Get precedent op name: " << pre_name;
   GeneralBlob *output_blob = mutable_data<GeneralBlob>();
 
   if (!input_blob) {
-    LOG(ERROR) << "Failed mutable depended argument, op:" << pre_name;
+    LOG(ERROR) << "(logid=" << log_id
+               << ") Failed mutable depended argument, op:" << pre_name;
     return -1;
   }
 
   const TensorVector *in = &input_blob->tensor_vector;
   TensorVector *out = &output_blob->tensor_vector;
   int batch_size = input_blob->GetBatchSize();
-  VLOG(2) << "input batch size: " << batch_size;
+  VLOG(2) << "(logid=" << log_id << ") input batch size: " << batch_size;
   std::vector<uint64_t> keys;
   std::vector<rec::mcube::CubeValue> values;
   int sparse_count = 0;
@@ -96,13 +98,14 @@ int GeneralDistKVInferOp::inference() {
   rec::mcube::CubeAPI *cube = rec::mcube::CubeAPI::instance();
   std::vector<std::string> table_names = cube->get_table_names();
   if (table_names.size() == 0) {
-    LOG(ERROR) << "cube init error or cube config not given.";
+    LOG(ERROR) << "(logid=" << log_id
+               << ") cube init error or cube config not given.";
     return -1;
   }
   int ret = cube->seek(table_names[0], keys, &values);
   int64_t cube_end = timeline.TimeStampUS();
   if (values.size() != keys.size() || values[0].buff.size() == 0) {
-    LOG(ERROR) << "cube value return null";
+    LOG(ERROR) << "(logid=" << log_id << ") cube value return null";
   }
   size_t EMBEDDING_SIZE = values[0].buff.size() / sizeof(float);
   TensorVector sparse_out;
@@ -153,14 +156,16 @@ int GeneralDistKVInferOp::inference() {
   infer_in.insert(infer_in.end(), sparse_out.begin(), sparse_out.end());
 
   output_blob->SetBatchSize(batch_size);
+  output_blob->SetLogId(log_id);
 
-  VLOG(2) << "infer batch size: " << batch_size;
+  VLOG(2) << "(logid=" << log_id << ") infer batch size: " << batch_size;
 
   int64_t start = timeline.TimeStampUS();
 
   if (InferManager::instance().infer(
           engine_name().c_str(), &infer_in, out, batch_size)) {
-    LOG(ERROR) << "Failed do infer in fluid model: " << engine_name();
+    LOG(ERROR) << "(logid=" << log_id
+               << ") Failed do infer in fluid model: " << engine_name();
     return -1;
   }
 

core/general-server/op/general_dist_kv_quant_infer_op.cpp

@@ -59,10 +59,13 @@ int GeneralDistKVQuantInferOp::inference() {
     return -1;
   }
 
+  uint64_t log_id = input_blob->GetLogId();
+  output_blob->SetLogId(log_id);
+
   const TensorVector *in = &input_blob->tensor_vector;
   TensorVector *out = &output_blob->tensor_vector;
   int batch_size = input_blob->GetBatchSize();
-  VLOG(2) << "input batch size: " << batch_size;
+  VLOG(2) << "(logid=" << log_id << ") input batch size: " << batch_size;
   std::vector<uint64_t> keys;
   std::vector<rec::mcube::CubeValue> values;
   int sparse_count = 0;
@@ -94,13 +97,14 @@ int GeneralDistKVQuantInferOp::inference() {
   rec::mcube::CubeAPI *cube = rec::mcube::CubeAPI::instance();
   std::vector<std::string> table_names = cube->get_table_names();
   if (table_names.size() == 0) {
-    LOG(ERROR) << "cube init error or cube config not given.";
+    LOG(ERROR) << "(logid=" << log_id
+               << ") cube init error or cube config not given.";
     return -1;
   }
   int ret = cube->seek(table_names[0], keys, &values);
 
   if (values.size() != keys.size() || values[0].buff.size() == 0) {
-    LOG(ERROR) << "cube value return null";
+    LOG(ERROR) << "(logid=" << log_id << ") cube value return null";
   }
 
   TensorVector sparse_out;
@@ -182,7 +186,7 @@ int GeneralDistKVQuantInferOp::inference() {
 
   output_blob->SetBatchSize(batch_size);
 
-  VLOG(2) << "infer batch size: " << batch_size;
+  VLOG(2) << "(logid=" << log_id << ") infer batch size: " << batch_size;
 
   Timer timeline;
   int64_t start = timeline.TimeStampUS();
@@ -190,7 +194,8 @@ int GeneralDistKVQuantInferOp::inference() {
 
   if (InferManager::instance().infer(
           engine_name().c_str(), &infer_in, out, batch_size)) {
-    LOG(ERROR) << "Failed do infer in fluid model: " << engine_name();
+    LOG(ERROR) << "(logid=" << log_id
+               << ") Failed do infer in fluid model: " << engine_name();
     return -1;
   }
 

core/general-server/op/general_text_response_op.cpp

@@ -74,9 +74,10 @@ int GeneralTextResponseOp::inference() {
       return -1;
     }
 
+    uint64_t log_id = input_blob->GetLogId();
     const TensorVector *in = &input_blob->tensor_vector;
     int batch_size = input_blob->GetBatchSize();
-    VLOG(2) << "input batch size: " << batch_size;
+    VLOG(2) << "(logid=" << log_id << ") input batch size: " << batch_size;
 
     ModelOutput *output = res->add_outputs();
     output->set_engine_name(