Merge pull request #24 from barrierye/pipeling-log

add log_id to proto; TODO: recompile pdcodegen

core/general-client/include/general_model.h

@@ -227,7 +227,8 @@ class PredictorClient {
       const std::vector<std::vector<int>>& int_shape,
       const std::vector<std::string>& fetch_name,
       PredictorRes& predict_res_batch,  // NOLINT
-      const int& pid);
+      const int& pid,
+      const uint64_t log_id);
 
   int numpy_predict(
       const std::vector<std::vector<py::array_t<float>>>& float_feed_batch,
@@ -238,7 +239,8 @@ class PredictorClient {
       const std::vector<std::vector<int>>& int_shape,
       const std::vector<std::string>& fetch_name,
       PredictorRes& predict_res_batch,  // NOLINT
-      const int& pid);
+      const int& pid,
+      const uint64_t log_id);
 
  private:
   PredictorApi _api;

core/general-client/src/general_model.cpp

@@ -144,7 +144,8 @@ int PredictorClient::batch_predict(
     const std::vector<std::vector<int>> &int_shape,
     const std::vector<std::string> &fetch_name,
     PredictorRes &predict_res_batch,
-    const int &pid) {
+    const int &pid,
+    const uint64_t log_id) {
   int batch_size = std::max(float_feed_batch.size(), int_feed_batch.size());
 
   predict_res_batch.clear();
@@ -162,6 +163,8 @@ int PredictorClient::batch_predict(
   VLOG(2) << "int feed name size: " << int_feed_name.size();
   VLOG(2) << "max body size : " << brpc::fLU64::FLAGS_max_body_size;
   Request req;
+  req.set_log_id(log_id);
+  VLOG(2) << "(logid=" << req.log_id() << ")";
   for (auto &name : fetch_name) {
     req.add_fetch_var_names(name);
   }
@@ -356,7 +359,8 @@ int PredictorClient::numpy_predict(
     const std::vector<std::vector<int>> &int_shape,
     const std::vector<std::string> &fetch_name,
     PredictorRes &predict_res_batch,
-    const int &pid) {
+    const int &pid,
+    const uint64_t log_id) {
   int batch_size = std::max(float_feed_batch.size(), int_feed_batch.size());
   VLOG(2) << "batch size: " << batch_size;
   predict_res_batch.clear();
@@ -374,6 +378,8 @@ int PredictorClient::numpy_predict(
   VLOG(2) << "int feed name size: " << int_feed_name.size();
   VLOG(2) << "max body size : " << brpc::fLU64::FLAGS_max_body_size;
   Request req;
+  req.set_log_id(log_id);
+  VLOG(2) << "(logid=" << req.log_id() << ")";
   for (auto &name : fetch_name) {
     req.add_fetch_var_names(name);
   }

core/general-client/src/pybind_general_model.cpp

@@ -107,7 +107,8 @@ PYBIND11_MODULE(serving_client, m) {
               const std::vector<std::vector<int>> &int_shape,
               const std::vector<std::string> &fetch_name,
               PredictorRes &predict_res_batch,
-              const int &pid) {
+              const int &pid,
+              const uint64_t log_id) {
              return self.batch_predict(float_feed_batch,
                                        float_feed_name,
                                        float_shape,
@@ -116,7 +117,8 @@ PYBIND11_MODULE(serving_client, m) {
                                        int_shape,
                                        fetch_name,
                                        predict_res_batch,
-                                       pid);
+                                       pid,
+                                       log_id);
            },
            py::call_guard<py::gil_scoped_release>())
       .def("numpy_predict",
@@ -131,7 +133,8 @@ PYBIND11_MODULE(serving_client, m) {
               const std::vector<std::vector<int>> &int_shape,
               const std::vector<std::string> &fetch_name,
               PredictorRes &predict_res_batch,
-              const int &pid) {
+              const int &pid,
+              const uint64_t log_id) {
              return self.numpy_predict(float_feed_batch,
                                        float_feed_name,
                                        float_shape,
@@ -140,7 +143,8 @@ PYBIND11_MODULE(serving_client, m) {
                                        int_shape,
                                        fetch_name,
                                        predict_res_batch,
-                                       pid);
+                                       pid,
+                                       log_id);
            },
            py::call_guard<py::gil_scoped_release>());
 }

core/general-server/proto/general_model_service.proto

@@ -37,6 +37,7 @@ message Request {
   repeated FeedInst insts = 1;
   repeated string fetch_var_names = 2;
   optional bool profile_server = 3 [ default = false ];
+  required uint64 log_id = 4 [ default = 0 ];
 };
 
 message Response {

core/pdcodegen/src/pdcodegen.cpp

@@ -280,6 +280,7 @@ class PdsCodeGenerator : public CodeGenerator {
             "  baidu::rpc::ClosureGuard done_guard(done);\n"
             "  baidu::rpc::Controller* cntl = \n"
             "        static_cast<baidu::rpc::Controller*>(cntl_base);\n"
+            "  cntl->set_log_id(request->log_id());\n"
             "  ::baidu::paddle_serving::predictor::InferService* svr = \n"
             "       "
             "::baidu::paddle_serving::predictor::InferServiceManager::instance("
@@ -317,6 +318,7 @@ class PdsCodeGenerator : public CodeGenerator {
             "  baidu::rpc::ClosureGuard done_guard(done);\n"
             "  baidu::rpc::Controller* cntl = \n"
             "        static_cast<baidu::rpc::Controller*>(cntl_base);\n"
+            "  cntl->set_log_id(request->log_id());\n"
             "  ::baidu::paddle_serving::predictor::InferService* svr = \n"
             "       "
             "::baidu::paddle_serving::predictor::InferServiceManager::instance("

core/sdk-cpp/proto/general_model_service.proto

@@ -37,6 +37,7 @@ message Request {
   repeated FeedInst insts = 1;
   repeated string fetch_var_names = 2;
   optional bool profile_server = 3 [ default = false ];
+  required uint64 log_id = 4 [ default = 0 ];
 };
 
 message Response {

python/paddle_serving_client/__init__.py

@@ -233,7 +233,7 @@ class Client(object):
             #    key))
             pass
 
-    def predict(self, feed=None, fetch=None, need_variant_tag=False):
+    def predict(self, feed=None, fetch=None, need_variant_tag=False, log_id=0):
         self.profile_.record('py_prepro_0')
 
         if feed is None or fetch is None:
@@ -319,12 +319,12 @@ class Client(object):
             res = self.client_handle_.numpy_predict(
                 float_slot_batch, float_feed_names, float_shape, int_slot_batch,
                 int_feed_names, int_shape, fetch_names, result_batch_handle,
-                self.pid)
+                self.pid, log_id)
         elif self.has_numpy_input == False:
             res = self.client_handle_.batch_predict(
                 float_slot_batch, float_feed_names, float_shape, int_slot_batch,
                 int_feed_names, int_shape, fetch_names, result_batch_handle,
-                self.pid)
+                self.pid, log_id)
         else:
             raise ValueError(
                 "Please make sure the inputs are all in list type or all in numpy.array type"