add batch predict

core/general-client/include/general_model.h

@@ -18,9 +18,9 @@
 #include <unistd.h>
 
 #include <fstream>
+#include <map>
 #include <string>
 #include <vector>
-#include <map>
 
 #include "core/sdk-cpp/builtin_format.pb.h"
 #include "core/sdk-cpp/general_model_service.pb.h"
@@ -37,46 +37,52 @@ namespace general_model {
 
 typedef std::map<std::string, std::vector<float>> FetchedMap;
 
-typedef std::map<std::string, std::vector<std::vector<float> > >
-    BatchFetchedMap;
+typedef std::map<std::string, std::vector<std::vector<float>>> BatchFetchedMap;
 
 class PredictorClient {
  public:
   PredictorClient() {}
   ~PredictorClient() {}
 
-  void init(const std::string & client_conf);
+  void init(const std::string& client_conf);
 
-  void set_predictor_conf(
-      const std::string& conf_path,
+  void set_predictor_conf(const std::string& conf_path,
                           const std::string& conf_file);
 
   int create_predictor();
 
-  std::vector<std::vector<float> > predict(
-      const std::vector<std::vector<float> > & float_feed,
-      const std::vector<std::string> & float_feed_name,
-      const std::vector<std::vector<int64_t> > & int_feed,
-      const std::vector<std::string> & int_feed_name,
-      const std::vector<std::string> & fetch_name);
-
-  std::vector<std::vector<float> > predict_with_profile(
-      const std::vector<std::vector<float> > & float_feed,
-      const std::vector<std::string> & float_feed_name,
-      const std::vector<std::vector<int64_t> > & int_feed,
-      const std::vector<std::string> & int_feed_name,
-      const std::vector<std::string> & fetch_name);
+  std::vector<std::vector<float>> predict(
+      const std::vector<std::vector<float>>& float_feed,
+      const std::vector<std::string>& float_feed_name,
+      const std::vector<std::vector<int64_t>>& int_feed,
+      const std::vector<std::string>& int_feed_name,
+      const std::vector<std::string>& fetch_name);
+
+  std::vector<std::vector<std::vector<float>>> predict_for_batch(
+      const std::vector<std::vector<std::vector<float>>>& float_feed_batch,
+      const std::vector<std::string>& float_feed_name,
+      const std::vector<std::vector<std::vector<int64_t>>>& int_feed_batch,
+      const std::vector<std::string>& int_feed_name,
+      const std::vector<std::string>& fetch_name,
+      const int64_t& batch_size);
+
+  std::vector<std::vector<float>> predict_with_profile(
+      const std::vector<std::vector<float>>& float_feed,
+      const std::vector<std::string>& float_feed_name,
+      const std::vector<std::vector<int64_t>>& int_feed,
+      const std::vector<std::string>& int_feed_name,
+      const std::vector<std::string>& fetch_name);
 
  private:
   PredictorApi _api;
-  Predictor * _predictor;
+  Predictor* _predictor;
   std::string _predictor_conf;
   std::string _predictor_path;
   std::string _conf_file;
   std::map<std::string, int> _feed_name_to_idx;
   std::map<std::string, int> _fetch_name_to_idx;
   std::map<std::string, std::string> _fetch_name_to_var_name;
-  std::vector<std::vector<int> > _shape;
+  std::vector<std::vector<int>> _shape;
   std::vector<int> _type;
 };
 

core/general-client/src/general_model.cpp

@@ -12,8 +12,8 @@
 // See the License for the specific language governing permissions and
 // limitations under the License.
 
-#include <fstream>
 #include "core/general-client/include/general_model.h"
+#include <fstream>
 #include "core/sdk-cpp/builtin_format.pb.h"
 #include "core/sdk-cpp/include/common.h"
 #include "core/sdk-cpp/include/predictor_sdk.h"
@@ -28,7 +28,7 @@ namespace baidu {
 namespace paddle_serving {
 namespace general_model {
 
-void PredictorClient::init(const std::string & conf_file) {
+void PredictorClient::init(const std::string &conf_file) {
   _conf_file = conf_file;
   std::ifstream fin(conf_file);
   if (!fin) {
@@ -68,9 +68,8 @@ void PredictorClient::init(const std::string & conf_file) {
   }
 }
 
-void PredictorClient::set_predictor_conf(
-    const std::string & conf_path,
-    const std::string & conf_file) {
+void PredictorClient::set_predictor_conf(const std::string &conf_path,
+                                         const std::string &conf_file) {
   _predictor_path = conf_path;
   _predictor_conf = conf_file;
 }
@@ -83,14 +82,13 @@ int PredictorClient::create_predictor() {
   _api.thrd_initialize();
 }
 
-std::vector<std::vector<float> > PredictorClient::predict(
-    const std::vector<std::vector<float> > & float_feed,
-    const std::vector<std::string> & float_feed_name,
-    const std::vector<std::vector<int64_t> > & int_feed,
-    const std::vector<std::string> & int_feed_name,
-    const std::vector<std::string> & fetch_name) {
-
-  std::vector<std::vector<float> > fetch_result;
+std::vector<std::vector<float>> PredictorClient::predict(
+    const std::vector<std::vector<float>> &float_feed,
+    const std::vector<std::string> &float_feed_name,
+    const std::vector<std::vector<int64_t>> &int_feed,
+    const std::vector<std::string> &int_feed_name,
+    const std::vector<std::string> &fetch_name) {
+  std::vector<std::vector<float>> fetch_result;
   if (fetch_name.size() == 0) {
     return fetch_result;
   }
@@ -100,41 +98,43 @@ std::vector<std::vector<float> > PredictorClient::predict(
   _predictor = _api.fetch_predictor("general_model");
   Request req;
   std::vector<Tensor *> tensor_vec;
-  FeedInst * inst = req.add_insts();
-  for (auto & name : float_feed_name) {
+  FeedInst *inst = req.add_insts();
+  for (auto &name : float_feed_name) {
     tensor_vec.push_back(inst->add_tensor_array());
   }
 
-  for (auto & name : int_feed_name) {
+  for (auto &name : int_feed_name) {
     tensor_vec.push_back(inst->add_tensor_array());
   }
 
   int vec_idx = 0;
-  for (auto & name : float_feed_name) {
+  for (auto &name : float_feed_name) {
     int idx = _feed_name_to_idx[name];
-    Tensor * tensor = tensor_vec[idx];
+    Tensor *tensor = tensor_vec[idx];
     for (int j = 0; j < _shape[idx].size(); ++j) {
       tensor->add_shape(_shape[idx][j]);
     }
     tensor->set_elem_type(1);
     for (int j = 0; j < float_feed[vec_idx].size(); ++j) {
-      tensor->add_data(
-          (char *)(&(float_feed[vec_idx][j])), sizeof(float));
+      tensor->add_data(const_cast<char *>(reinterpret_cast<const char *>(
+                           &(float_feed[vec_idx][j]))),
+                       sizeof(float));
     }
     vec_idx++;
   }
 
   vec_idx = 0;
-  for (auto & name : int_feed_name) {
+  for (auto &name : int_feed_name) {
     int idx = _feed_name_to_idx[name];
-    Tensor * tensor = tensor_vec[idx];
+    Tensor *tensor = tensor_vec[idx];
     for (int j = 0; j < _shape[idx].size(); ++j) {
       tensor->add_shape(_shape[idx][j]);
     }
     tensor->set_elem_type(0);
     for (int j = 0; j < int_feed[vec_idx].size(); ++j) {
-      tensor->add_data(
-          (char *)(&(int_feed[vec_idx][j])), sizeof(int64_t));
+      tensor->add_data(const_cast<char *>(reinterpret_cast<const char *>(
+                           &(int_feed[vec_idx][j]))),
+                       sizeof(int64_t));
     }
     vec_idx++;
   }
@@ -147,7 +147,7 @@ std::vector<std::vector<float> > PredictorClient::predict(
     LOG(ERROR) << "failed call predictor with req: " << req.ShortDebugString();
     exit(-1);
   } else {
-    for (auto & name : fetch_name) {
+    for (auto &name : fetch_name) {
       int idx = _fetch_name_to_idx[name];
       int len = res.insts(0).tensor_array(idx).data_size();
       VLOG(3) << "fetch name: " << name;
@@ -162,8 +162,8 @@ std::vector<std::vector<float> > PredictorClient::predict(
         fetch_result[name][i] = *(const float *)
                     res.insts(0).tensor_array(idx).data(i).c_str();
         */
-        fetch_result[idx][i] = *(const float *)
-                    res.insts(0).tensor_array(idx).data(i).c_str();
+        fetch_result[idx][i] =
+            *(const float *)res.insts(0).tensor_array(idx).data(i).c_str();
       }
     }
   }
@@ -171,13 +171,105 @@ std::vector<std::vector<float> > PredictorClient::predict(
   return fetch_result;
 }
 
-std::vector<std::vector<float> > PredictorClient::predict_with_profile(
-    const std::vector<std::vector<float> > & float_feed,
-    const std::vector<std::string> & float_feed_name,
-    const std::vector<std::vector<int64_t> > & int_feed,
-    const std::vector<std::string> & int_feed_name,
-    const std::vector<std::string> & fetch_name) {
-  std::vector<std::vector<float> > res;
+std::vector<std::vector<std::vector<float>>> PredictorClient::predict_for_batch(
+    const std::vector<std::vector<std::vector<float>>> &float_feed_batch,
+    const std::vector<std::string> &float_feed_name,
+    const std::vector<std::vector<std::vector<int64_t>>> &int_feed_batch,
+    const std::vector<std::string> &int_feed_name,
+    const std::vector<std::string> &fetch_name,
+    const int64_t &batch_size) {
+  std::vector<std::vector<std::vector<float>>> fetch_result_batch;
+  if (fetch_name.size() == 0) {
+    return fetch_result_batch;
+  }
+  fetch_result_batch.resize(batch_size);
+  int fetch_name_num = fetch_name.size();
+  for (int bi = 0; bi < batch_size; bi++) {
+    fetch_result_batch[bi].resize(fetch_name_num);
+  }
+
+  _api.thrd_clear();
+  _predictor = _api.fetch_predictor("general_model");
+  Request req;
+  //
+  for (int bi = 0; bi < batch_size; bi++) {
+    std::vector<Tensor *> tensor_vec;
+    FeedInst *inst = req.add_insts();
+    std::vector<std::vector<float>> float_feed = float_feed_batch[bi];
+    std::vector<std::vector<int64_t>> int_feed = int_feed_batch[bi];
+    for (auto &name : float_feed_name) {
+      tensor_vec.push_back(inst->add_tensor_array());
+    }
+
+    for (auto &name : int_feed_name) {
+      tensor_vec.push_back(inst->add_tensor_array());
+    }
+
+    int vec_idx = 0;
+    for (auto &name : float_feed_name) {
+      int idx = _feed_name_to_idx[name];
+      Tensor *tensor = tensor_vec[idx];
+      for (int j = 0; j < _shape[idx].size(); ++j) {
+        tensor->add_shape(_shape[idx][j]);
+      }
+      tensor->set_elem_type(1);
+      for (int j = 0; j < float_feed[vec_idx].size(); ++j) {
+        tensor->add_data(const_cast<char *>(reinterpret_cast<const char *>(
+                             &(float_feed[vec_idx][j]))),
+                         sizeof(float));
+      }
+      vec_idx++;
+    }
+
+    vec_idx = 0;
+    for (auto &name : int_feed_name) {
+      int idx = _feed_name_to_idx[name];
+      Tensor *tensor = tensor_vec[idx];
+      for (int j = 0; j < _shape[idx].size(); ++j) {
+        tensor->add_shape(_shape[idx][j]);
+      }
+      tensor->set_elem_type(0);
+      for (int j = 0; j < int_feed[vec_idx].size(); ++j) {
+        tensor->add_data(const_cast<char *>(reinterpret_cast<const char *>(
+                             &(int_feed[vec_idx][j]))),
+                         sizeof(int64_t));
+      }
+      vec_idx++;
+    }
+  }
+
+  Response res;
+
+  res.Clear();
+  if (_predictor->inference(&req, &res) != 0) {
+    LOG(ERROR) << "failed call predictor with req: " << req.ShortDebugString();
+    exit(-1);
+  } else {
+    for (int bi = 0; bi < batch_size; bi++) {
+      for (auto &name : fetch_name) {
+        int idx = _fetch_name_to_idx[name];
+        int len = res.insts(0).tensor_array(idx).data_size();
+        VLOG(3) << "fetch name: " << name;
+        VLOG(3) << "tensor data size: " << len;
+        fetch_result_batch[bi][idx].resize(len);
+        for (int i = 0; i < len; ++i) {
+          fetch_result_batch[bi][idx][i] =
+              *(const float *)res.insts(0).tensor_array(idx).data(i).c_str();
+        }
+      }
+    }
+  }
+
+  return fetch_result_batch;
+}
+
+std::vector<std::vector<float>> PredictorClient::predict_with_profile(
+    const std::vector<std::vector<float>> &float_feed,
+    const std::vector<std::string> &float_feed_name,
+    const std::vector<std::vector<int64_t>> &int_feed,
+    const std::vector<std::string> &int_feed_name,
+    const std::vector<std::string> &fetch_name) {
+  std::vector<std::vector<float>> res;
   return res;
 }
 

core/general-client/src/pybind_general_model.cpp

+// Copyright (c) 2020 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 <Python.h>
 #include <pybind11/pybind11.h>
+#include <pybind11/stl.h>
 #include <unordered_map>
 #include "core/general-client/include/general_model.h"
 
-#include <pybind11/stl.h>
-
 namespace py = pybind11;
 
 using baidu::paddle_serving::general_model::FetchedMap;
@@ -19,28 +32,47 @@ PYBIND11_MODULE(serving_client, m) {
   py::class_<PredictorClient>(m, "PredictorClient", py::buffer_protocol())
       .def(py::init())
       .def("init",
-           [](PredictorClient &self, const std::string & conf) {
+           [](PredictorClient &self, const std::string &conf) {
              self.init(conf);
            })
       .def("set_predictor_conf",
-           [](PredictorClient &self, const std::string & conf_path,
-              const std::string & conf_file) {
+           [](PredictorClient &self,
+              const std::string &conf_path,
+              const std::string &conf_file) {
              self.set_predictor_conf(conf_path, conf_file);
            })
       .def("create_predictor",
-           [](PredictorClient & self) {
-             self.create_predictor();
-           })
+           [](PredictorClient &self) { self.create_predictor(); })
       .def("predict",
            [](PredictorClient &self,
-              const std::vector<std::vector<float> > & float_feed,
-              const std::vector<std::string> & float_feed_name,
-              const std::vector<std::vector<int64_t> > & int_feed,
-              const std::vector<std::string> & int_feed_name,
-              const std::vector<std::string> & fetch_name) {
+              const std::vector<std::vector<float>> &float_feed,
+              const std::vector<std::string> &float_feed_name,
+              const std::vector<std::vector<int64_t>> &int_feed,
+              const std::vector<std::string> &int_feed_name,
+              const std::vector<std::string> &fetch_name) {
+             return self.predict(float_feed,
+                                 float_feed_name,
+                                 int_feed,
+                                 int_feed_name,
+                                 fetch_name);
+           })
 
-             return self.predict(float_feed, float_feed_name,
-                                 int_feed, int_feed_name, fetch_name);
+      .def("predict_for_batch",
+           [](PredictorClient &self,
+              const std::vector<std::vector<std::vector<float>>>
+                  &float_feed_batch,
+              const std::vector<std::string> &float_feed_name,
+              const std::vector<std::vector<std::vector<int64_t>>>
+                  &int_feed_batch,
+              const std::vector<std::string> &int_feed_name,
+              const std::vector<std::string> &fetch_name,
+              const int64_t &batch_size) {
+             return self.predict_for_batch(float_feed_batch,
+                                           float_feed_name,
+                                           int_feed_batch,
+                                           int_feed_name,
+                                           fetch_name,
+                                           batch_size);
            });
 }
 

python/examples/imdb/test_client_multithread.py

@@ -15,7 +15,7 @@
 from paddle_serving import Client
 import sys
 import subprocess
-from multiprocessing import Pool, Queue
+from multiprocessing import Pool
 import time
 
 

python/paddle_serving/serving_client/__init__.py

@@ -19,6 +19,7 @@ import time
 int_type = 0
 float_type = 1
 
+
 class SDKConfig(object):
     def __init__(self):
         self.sdk_desc = sdk.SDKConf()
@@ -37,7 +38,8 @@ class SDKConfig(object):
 
         variant_desc = sdk.VariantConf()
         variant_desc.tag = "var1"
-        variant_desc.naming_conf.cluster = "list://{}".format(":".join(self.endpoints))
+        variant_desc.naming_conf.cluster = "list://{}".format(":".join(
+            self.endpoints))
 
         predictor_desc.variants.extend([variant_desc])
 
@@ -114,8 +116,7 @@ class Client(object):
         predictor_file = "%s_predictor.conf" % timestamp
         with open(predictor_path + predictor_file, "w") as fout:
             fout.write(sdk_desc)
-        self.client_handle_.set_predictor_conf(
-            predictor_path, predictor_file)
+        self.client_handle_.set_predictor_conf(predictor_path, predictor_file)
         self.client_handle_.create_predictor()
 
     def get_feed_names(self):
@@ -145,9 +146,7 @@ class Client(object):
                 fetch_names.append(key)
 
         result = self.client_handle_.predict(
-            float_slot, float_feed_names,
-            int_slot, int_feed_names,
-            fetch_names)
+            float_slot, float_feed_names, int_slot, int_feed_names, fetch_names)
 
         result_map = {}
         for i, name in enumerate(fetch_names):
@@ -155,3 +154,41 @@ class Client(object):
 
         return result_map
 
+    def predict_for_batch(self, feed_batch=[], fetch=[]):
+        batch_size = len(feed_batch)
+        int_slot_batch = []
+        float_slot_batch = []
+        int_feed_names = []
+        float_feed_names = []
+        fetch_names = []
+        for feed in feed_batch:
+            int_slot = []
+            float_slot = []
+            for key in feed:
+                if key not in self.feed_names_:
+                    continue
+                if self.feed_types_[key] == int_type:
+                    int_feed_names.append(key)
+                    int_slot.append(feed[key])
+                elif self.feed_types_[key] == float_type:
+                    float_feed_names.append(key)
+                    float_slot.append(feed[key])
+            int_slot_batch.append(int_slot)
+            float_slot_batch.append(float_slot)
+
+        for key in fetch:
+            if key in self.fetch_names_:
+                fetch_names.append(key)
+
+        result_batch = self.client_handle_.predict_for_batch(
+            float_slot_batch, float_feed_names, int_slot_batch, int_feed_names,
+            fetch_names, batch_size)
+
+        result_map_batch = []
+        for result in result_batch:
+            result_map = {}
+            for i, name in enumerate(fetch_names):
+                result_map[name] = result[i]
+            result_map_batch.append(result_map)
+
+        return result_map_batch