serving http init

cmake/external_libs/libevent.cmake

@@ -0,0 +1,11 @@
+mindspore_add_pkg(libevent
+        VER 2.1.12
+        LIBS event event_pthreads
+        URL https://github.com/libevent/libevent/releases/download/release-2.1.12-stable/libevent-2.1.12-stable.tar.gz
+        MD5 b5333f021f880fe76490d8a799cd79f4
+        CMAKE_OPTION -DCMAKE_BUILD_TYPE:STRING=Release -DBUILD_TESTING=OFF)
+
+include_directories(${libevent_INC})
+
+add_library(mindspore::event ALIAS libevent::event)
+add_library(mindspore::event_pthreads ALIAS libevent::event_pthreads)

cmake/mind_expression.cmake

@@ -22,6 +22,8 @@ if (ENABLE_DEBUGGER OR ENABLE_SERVING OR ENABLE_TESTCASES)
     include(${CMAKE_SOURCE_DIR}/cmake/external_libs/zlib.cmake)
     # build gRPC
     include(${CMAKE_SOURCE_DIR}/cmake/external_libs/grpc.cmake)
+    # build event
+    include(${CMAKE_SOURCE_DIR}/cmake/external_libs/libevent.cmake)
 endif()
 
 include(${CMAKE_SOURCE_DIR}/cmake/external_libs/pybind11.cmake)

cmake/package.cmake

@@ -163,6 +163,13 @@ if (ENABLE_GPU)
     )
 endif ()
 
+if (ENABLE_SERVING OR ENABLE_TESTCASES)
+    file(GLOB_RECURSE LIBEVENT_LIB_LIST
+            ${libevent_LIBPATH}/libevent*
+            ${libevent_LIBPATH}/libevent_pthreads*
+    )
+endif ()
+
 if (NOT ENABLE_GE)
     if (ENABLE_D)
         if (DEFINED ENV{ASCEND_CUSTOM_PATH})
@@ -191,6 +198,7 @@ if (NOT ENABLE_GE)
                 ${CMAKE_SOURCE_DIR}/graphengine/third_party/prebuild/${CMAKE_HOST_SYSTEM_PROCESSOR}/libslog.so
                 ${CMAKE_SOURCE_DIR}/graphengine/third_party/prebuild/${CMAKE_HOST_SYSTEM_PROCESSOR}/liberror_manager.so
                 ${CMAKE_SOURCE_DIR}/build/graphengine/libc_sec.so
+                ${LIBEVENT_LIB_LIST}
             DESTINATION ${INSTALL_LIB_DIR}
             COMPONENT mindspore
         )
@@ -273,4 +281,10 @@ if (ENABLE_SERVING)
         DESTINATION ${INSTALL_LIB_DIR}
         COMPONENT mindspore
     )
+
+    install(
+            FILES ${LIBEVENT_LIB_LIST}
+            DESTINATION ${INSTALL_LIB_DIR}
+            COMPONENT mindspore
+    )
 endif ()

include/infer_log.h

@@ -23,6 +23,7 @@
 #include <sstream>
 #include <memory>
 #include <iostream>
+#include <chrono>
 
 #ifndef ENABLE_ACL
 #include "mindspore/core/utils/log_adapter.h"
@@ -101,7 +102,18 @@ class LogWriter {
 
 #endif  // ENABLE_ACL
 
+#define MSI_TIME_STAMP_START(name) auto time_start_##name = std::chrono::steady_clock::now();
+#define MSI_TIME_STAMP_END(name)                                                                             \
+  {                                                                                                          \
+    auto time_end_##name = std::chrono::steady_clock::now();                                                 \
+    auto time_cost = std::chrono::duration<double, std::milli>(time_end_##name - time_start_##name).count(); \
+    MSI_LOG_INFO << #name " Time Cost # " << time_cost << " ms ---------------------";                       \
+  }
+
 #define INFER_STATUS(code) inference::Status(code) < inference::LogStream()
+#define ERROR_INFER_STATUS(status, type, msg) \
+  MSI_LOG_ERROR << msg;                       \
+  status = inference::Status(type, msg)
 
 }  // namespace mindspore::inference
 

include/inference.h

@@ -74,6 +74,10 @@ class MS_API InferSession {
                               const RequestBase & /*request*/, ReplyBase & /*reply*/) {
     return FAILED;
   }
+  virtual Status GetModelInputsInfo(uint32_t graph_id, std::vector<inference::InferTensor> *tensor_list) const {
+    Status status(SUCCESS);
+    return status;
+  }
   static std::shared_ptr<InferSession> CreateSession(const std::string &device, uint32_t device_id);
 };
 

mindspore/ccsrc/backend/session/ascend_inference_session.cc

@@ -212,5 +212,31 @@ std::string AscendInferenceSession::InputsInfo(const std::vector<ParameterPtr> &
   return graph + "   " + actual;
 }
 
+void AscendInferenceSession::GetModelInputsInfo(uint32_t graph_id, std::vector<tensor::TensorPtr> *inputs) const {
+  MS_LOG(INFO) << "Start get model inputs, graph id : " << graph_id;
+  auto kernel_graph = GetGraph(graph_id);
+  MS_EXCEPTION_IF_NULL(kernel_graph);
+  auto kernel_graph_inputs = kernel_graph->inputs();
+  vector<ParameterPtr> paras;
+  // find parameters of graph inputs
+  for (size_t i = 0; i < kernel_graph_inputs.size(); ++i) {
+    if (!kernel_graph_inputs[i]->isa<Parameter>()) {
+      MS_LOG(ERROR) << "Kernel graph inputs have anfnode which is not Parameter.";
+      continue;
+    }
+    auto parameter = kernel_graph_inputs[i]->cast<ParameterPtr>();
+    if (!AnfAlgo::IsParameterWeight(parameter)) {
+      vector<int> input_shape;
+      auto parameter_shape = AnfAlgo::GetOutputDeviceShape(parameter, 0);
+      (void)std::transform(parameter_shape.begin(), parameter_shape.end(), std::back_inserter(input_shape),
+                           [](const size_t dim) { return static_cast<int>(dim); });
+      auto kernel_build_info = AnfAlgo::GetSelectKernelBuildInfo(parameter);
+      auto data_type = kernel_build_info->GetOutputDeviceType(0);
+      auto ms_tensor = std::make_shared<tensor::Tensor>(data_type, input_shape);
+      inputs->push_back(ms_tensor);
+    }
+  }
+}
+
 }  // namespace session
 }  // namespace mindspore

mindspore/ccsrc/backend/session/ascend_inference_session.h

@@ -45,6 +45,7 @@ class AscendInferenceSession : public AscendSession {
   template <typename T>
   std::string PrintInputShape(std::vector<T> shape) const;
   std::string InputsInfo(const std::vector<ParameterPtr> &paras, const std::vector<tensor::TensorPtr> &inputs) const;
+  void GetModelInputsInfo(uint32_t graph_id, std::vector<tensor::TensorPtr> *inputs) const override;
 };
 MS_REG_SESSION(kDavinciInferenceDevice, AscendInferenceSession);
 }  // namespace session

mindspore/ccsrc/backend/session/infer_session.cc

@@ -220,7 +220,7 @@ Status MSInferSession::ExecuteModel(uint32_t model_id, const RequestBase &reques
   for (const auto &tensor : outputs) {
     auto out_tensor = reply.add();
     if (out_tensor == nullptr) {
-      MS_LOG(ERROR) << "Execute Model " << model_id << " Failed， add output tensor failed";
+      MS_LOG(ERROR) << "Execute Model " << model_id << " Failed add output tensor failed";
       return FAILED;
     }
     MSTensor2ServingTensor(tensor, *out_tensor);
@@ -374,4 +374,18 @@ Status MSInferSession::CheckModelInputs(uint32_t graph_id, const std::vector<ten
   return SUCCESS;
 }
 
+Status MSInferSession::GetModelInputsInfo(uint32_t model_id, std::vector<inference::InferTensor> *tensor_list) const {
+  vector<tensor::TensorPtr> inputs;
+  session_impl_->GetModelInputsInfo(model_id, &inputs);
+  if (inputs.size() == 0) {
+    MS_LOG(ERROR) << "The model inputs is NULL";
+    return FAILED;
+  }
+  for (const auto &tensor : inputs) {
+    InferTensor infer_tensor = InferTensor();
+    MSTensor2ServingTensor(tensor, infer_tensor);
+    tensor_list->push_back(infer_tensor);
+  }
+  return SUCCESS;
+}
 }  // namespace mindspore::inference

mindspore/ccsrc/backend/session/infer_session.h

@@ -43,6 +43,7 @@ class MSInferSession : public InferSession {
   Status LoadModelFromFile(const std::string &file_name, uint32_t &model_id) override;
   Status UnloadModel(uint32_t model_id) override;
   Status ExecuteModel(uint32_t model_id, const RequestBase &inputs, ReplyBase &outputs) override;
+  Status GetModelInputsInfo(uint32_t graph_id, std::vector<inference::InferTensor> *tensor_list) const override;
 
  private:
   std::shared_ptr<session::SessionBasic> session_impl_ = nullptr;

mindspore/ccsrc/backend/session/session_basic.h

@@ -97,6 +97,7 @@ class SessionBasic {
                                 std::string *error_msg) const {
     return true;
   }
+  virtual void GetModelInputsInfo(uint32_t graph_id, std::vector<tensor::TensorPtr> *inputs) const {}
 
 #ifdef ENABLE_DEBUGGER
   // set debugger

serving/CMakeLists.txt

@@ -93,7 +93,10 @@ if (ENABLE_ACL)
 endif ()
 
 include_directories(${CMAKE_BINARY_DIR})
+
 add_executable(ms_serving ${SERVING_SRC})
+#libevent
+target_link_libraries(ms_serving mindspore::event mindspore::event_pthreads)
 
 target_link_libraries(ms_serving ${_REFLECTION} ${_GRPC_GRPCPP} ${_PROTOBUF_LIBPROTOBUF} pthread)
 if (ENABLE_D)

serving/core/http_process.cc

@@ -0,0 +1,423 @@
+/**
+ * Copyright 2020 Huawei Technologies Co., Ltd
+ *
+ * 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 <map>
+#include <vector>
+#include <string>
+#include <nlohmann/json.hpp>
+#include "serving/ms_service.pb.h"
+#include "util/status.h"
+#include "core/session.h"
+#include "core/http_process.h"
+
+using ms_serving::MSService;
+using ms_serving::PredictReply;
+using ms_serving::PredictRequest;
+using nlohmann::json;
+
+namespace mindspore {
+namespace serving {
+
+const int BUF_MAX = 0x1FFFFF;
+static constexpr char HTTP_DATA[] = "data";
+static constexpr char HTTP_TENSOR[] = "tensor";
+enum HTTP_TYPE { TYPE_DATA = 0, TYPE_TENSOR };
+enum HTTP_DATA_TYPE { HTTP_DATA_NONE, HTTP_DATA_INT, HTTP_DATA_FLOAT };
+static const std::map<HTTP_DATA_TYPE, ms_serving::DataType> http_to_infer_map{
+  {HTTP_DATA_NONE, ms_serving::MS_UNKNOWN},
+  {HTTP_DATA_INT, ms_serving::MS_INT32},
+  {HTTP_DATA_FLOAT, ms_serving::MS_FLOAT32}};
+
+Status GetPostMessage(struct evhttp_request *req, std::string *buf) {
+  Status status(SUCCESS);
+  size_t post_size = evbuffer_get_length(req->input_buffer);
+  if (post_size == 0) {
+    ERROR_INFER_STATUS(status, INVALID_INPUTS, "http message invalid");
+    return status;
+  } else {
+    size_t copy_len = post_size > BUF_MAX ? BUF_MAX : post_size;
+    buf->resize(copy_len);
+    memcpy(buf->data(), evbuffer_pullup(req->input_buffer, -1), copy_len);
+    return status;
+  }
+}
+Status CheckRequestValid(struct evhttp_request *http_request) {
+  Status status(SUCCESS);
+  switch (evhttp_request_get_command(http_request)) {
+    case EVHTTP_REQ_POST:
+      return status;
+    default:
+      ERROR_INFER_STATUS(status, INVALID_INPUTS, "http message only support POST right now");
+      return status;
+  }
+}
+
+void ErrorMessage(struct evhttp_request *req, Status status) {
+  json error_json = {{"error_message", status.StatusMessage()}};
+  std::string out_error_str = error_json.dump();
+  struct evbuffer *retbuff = evbuffer_new();
+  evbuffer_add(retbuff, out_error_str.data(), out_error_str.size());
+  evhttp_send_reply(req, HTTP_OK, "Client", retbuff);
+  evbuffer_free(retbuff);
+}
+
+Status CheckMessageValid(const json &message_info, HTTP_TYPE *type) {
+  Status status(SUCCESS);
+  int count = 0;
+  if (message_info.find(HTTP_DATA) != message_info.end()) {
+    *type = TYPE_DATA;
+    count++;
+  }
+  if (message_info.find(HTTP_TENSOR) != message_info.end()) {
+    *type = TYPE_TENSOR;
+    count++;
+  }
+  if (count != 1) {
+    ERROR_INFER_STATUS(status, INVALID_INPUTS, "http message must have only one type of (data, tensor, text)");
+    return status;
+  }
+  return status;
+}
+
+Status GetDataFromJson(const json &json_data, std::string *data, HTTP_DATA_TYPE *type) {
+  Status status(SUCCESS);
+  if (json_data.is_number_integer()) {
+    if (*type == HTTP_DATA_NONE) {
+      *type = HTTP_DATA_INT;
+    } else if (*type != HTTP_DATA_INT) {
+      ERROR_INFER_STATUS(status, INVALID_INPUTS, "the input data type should be consistent");
+      return status;
+    }
+    auto s_data = json_data.get<int32_t>();
+    data->append(reinterpret_cast<char *>(&s_data), sizeof(int32_t));
+    MSI_LOG(INFO) << "data size " << data->size();
+  } else if (json_data.is_number_float()) {
+    if (*type == HTTP_DATA_NONE) {
+      *type = HTTP_DATA_FLOAT;
+    } else if (*type != HTTP_DATA_FLOAT) {
+      ERROR_INFER_STATUS(status, INVALID_INPUTS, "the input data type should be consistent");
+      return status;
+    }
+    auto s_data = json_data.get<float>();
+    data->append(reinterpret_cast<char *>(&s_data), sizeof(float));
+    MSI_LOG(INFO) << "data size " << data->size();
+  } else {
+    ERROR_INFER_STATUS(status, INVALID_INPUTS, "the input data type should be int or float");
+    return status;
+  }
+  return SUCCESS;
+}
+
+Status RecusiveGetTensor(const json &json_data, size_t depth, std::vector<int> *shape, std::string *data,
+                         HTTP_DATA_TYPE *type) {
+  Status status(SUCCESS);
+  if (depth >= 10) {
+    ERROR_INFER_STATUS(status, INVALID_INPUTS, "the tensor shape dims is larger than 10");
+    return status;
+  }
+  if (!json_data.is_array()) {
+    ERROR_INFER_STATUS(status, INVALID_INPUTS, "the tensor is constructed illegally");
+    return status;
+  }
+  int cur_dim = json_data.size();
+  if (shape->size() <= depth) {
+    shape->push_back(cur_dim);
+  } else if ((*shape)[depth] != cur_dim) {
+    return INFER_STATUS(INVALID_INPUTS) << "the tensor shape is constructed illegally";
+  }
+  if (json_data.at(0).is_array()) {
+    for (const auto &item : json_data) {
+      status = RecusiveGetTensor(item, depth + 1, shape, data, type);
+      if (status != SUCCESS) {
+        return status;
+      }
+    }
+  } else {
+    // last dim, read the data
+    for (auto item : json_data) {
+      status = GetDataFromJson(item, data, type);
+      if (status != SUCCESS) {
+        return status;
+      }
+    }
+  }
+  return status;
+}
+
+Status TransDataToPredictRequest(const json &message_info, PredictRequest *request) {
+  Status status = SUCCESS;
+  auto tensors = message_info.find(HTTP_DATA);
+  if (tensors == message_info.end()) {
+    ERROR_INFER_STATUS(status, INVALID_INPUTS, "http message do not have data type");
+    return status;
+  }
+
+  if (tensors->size() == 0) {
+    ERROR_INFER_STATUS(status, INVALID_INPUTS, "the input tensor list is null");
+    return status;
+  }
+  for (const auto &tensor : *tensors) {
+    std::string msg_data;
+    HTTP_DATA_TYPE type{HTTP_DATA_NONE};
+    if (!tensor.is_array()) {
+      ERROR_INFER_STATUS(status, INVALID_INPUTS, "the tensor is constructed illegally");
+      return status;
+    }
+    if (tensor.size() == 0) {
+      ERROR_INFER_STATUS(status, INVALID_INPUTS, "the input tensor is null");
+      return status;
+    }
+    for (const auto &tensor_data : tensor) {
+      status = GetDataFromJson(tensor_data, &msg_data, &type);
+      if (status != SUCCESS) {
+        return status;
+      }
+    }
+    auto iter = http_to_infer_map.find(type);
+    if (iter == http_to_infer_map.end()) {
+      ERROR_INFER_STATUS(status, INVALID_INPUTS, "the input type is not supported right now");
+      return status;
+    }
+
+    auto infer_tensor = request->add_data();
+    infer_tensor->set_tensor_type(iter->second);
+    infer_tensor->set_data(msg_data.data(), msg_data.size());
+  }
+  // get model required shape
+  std::vector<inference::InferTensor> tensor_list;
+  status = Session::Instance().GetModelInputsInfo(tensor_list);
+  if (status != SUCCESS) {
+    ERROR_INFER_STATUS(status, FAILED, "get model inputs info failed");
+    return status;
+  }
+  if (request->data_size() != static_cast<int64_t>(tensor_list.size())) {
+    ERROR_INFER_STATUS(status, INVALID_INPUTS, "the inputs number is not equal to model required");
+    return status;
+  }
+  for (int i = 0; i < request->data_size(); i++) {
+    for (size_t j = 0;  j < tensor_list[i].shape().size(); ++j) {
+      request->mutable_data(i)->mutable_tensor_shape()->add_dims(tensor_list[i].shape()[i]);
+    }
+  }
+  return SUCCESS;
+}
+
+Status TransTensorToPredictRequest(const json &message_info, PredictRequest *request) {
+  Status status(SUCCESS);
+  auto tensors = message_info.find(HTTP_TENSOR);
+  if (tensors == message_info.end()) {
+    ERROR_INFER_STATUS(status, INVALID_INPUTS, "http message do not have tensor type");
+    return status;
+  }
+
+  for (const auto &tensor : *tensors) {
+    std::vector<int> shape;
+    std::string msg_data;
+    HTTP_DATA_TYPE type{HTTP_DATA_NONE};
+    RecusiveGetTensor(tensor, 0, &shape, &msg_data, &type);
+    MSI_LOG(INFO) << shape << ", data = " << msg_data.size();
+    auto iter = http_to_infer_map.find(type);
+    if (iter == http_to_infer_map.end()) {
+      ERROR_INFER_STATUS(status, INVALID_INPUTS, "the input type is not supported right now");
+      return status;
+    }
+    auto infer_tensor = request->add_data();
+    infer_tensor->set_tensor_type(iter->second);
+    infer_tensor->set_data(msg_data.data(), msg_data.size());
+    for (const auto dim : shape) {
+      infer_tensor->mutable_tensor_shape()->add_dims(dim);
+    }
+  }
+  return status;
+}
+
+Status TransHTTPMsgToPredictRequest(struct evhttp_request *http_request, PredictRequest *request, HTTP_TYPE *type) {
+  Status status = CheckRequestValid(http_request);
+  if (status != SUCCESS) {
+    return status;
+  }
+  std::string post_message;
+  status = GetPostMessage(http_request, &post_message);
+  if (status != SUCCESS) {
+    return status;
+  }
+
+  json message_info;
+  try {
+    message_info = nlohmann::json::parse(post_message);
+  } catch (nlohmann::json::exception &e) {
+    std::string json_exception = e.what();
+    std::string error_message = "Illegal JSON format." + json_exception;
+    ERROR_INFER_STATUS(status, INVALID_INPUTS, error_message);
+    return status;
+  }
+
+  status = CheckMessageValid(message_info, type);
+  if (status != SUCCESS) {
+    return status;
+  }
+  switch (*type) {
+    case TYPE_DATA:
+      status = TransDataToPredictRequest(message_info, request);
+      break;
+    case TYPE_TENSOR:
+      status = TransTensorToPredictRequest(message_info, request);
+      break;
+    default:
+      ERROR_INFER_STATUS(status, INVALID_INPUTS, "http message must have only one type of (data, tensor)");
+      return status;
+  }
+  return status;
+}
+
+Status GetJsonFromTensor(const ms_serving::Tensor &tensor, int len, int *pos, json *out_json) {
+  Status status(SUCCESS);
+  switch (tensor.tensor_type()) {
+    case ms_serving::MS_INT32: {
+      std::vector<int> result_tensor;
+      for (int j = 0; j < len; j++) {
+        int val;
+        memcpy(&val, reinterpret_cast<const int *>(tensor.data().data()) + *pos + j, sizeof(int));
+        result_tensor.push_back(val);
+      }
+      *out_json = result_tensor;
+      *pos += len;
+      break;
+    }
+    case ms_serving::MS_FLOAT32: {
+      std::vector<float> result_tensor;
+      for (int j = 0; j < len; j++) {
+        float val;
+        memcpy(&val, reinterpret_cast<const float *>(tensor.data().data()) + *pos + j, sizeof(float));
+        result_tensor.push_back(val);
+      }
+      *out_json = result_tensor;
+      *pos += len;
+      break;
+    }
+    default:
+      MSI_LOG(ERROR) << "the result type is not supported in restful api, type is " << tensor.tensor_type();
+      ERROR_INFER_STATUS(status, FAILED, "reply have unsupported type");
+  }
+  return status;
+}
+
+Status TransPredictReplyToData(const PredictReply &reply, json *out_json) {
+  Status status(SUCCESS);
+  for (int i = 0; i < reply.result_size(); i++) {
+    json tensor_json;
+    int num = 1;
+    for (auto j = 0; j < reply.result(i).tensor_shape().dims_size(); j++) {
+      num *= reply.result(i).tensor_shape().dims(j);
+    }
+    int pos = 0;
+    status = GetJsonFromTensor(reply.result(i), num, &pos, &tensor_json);
+    if (status != SUCCESS) {
+      return status;
+    }
+    (*out_json)["data"].push_back(tensor_json);
+  }
+  return status;
+}
+
+Status RecusiveGetJson(const ms_serving::Tensor &tensor, int depth, int *pos, json *out_json) {
+  Status status(SUCCESS);
+  if (depth >= 10) {
+    ERROR_INFER_STATUS(status, FAILED, "result tensor shape dims is larger than 10");
+    return status;
+  }
+  if (depth == tensor.tensor_shape().dims_size() - 1) {
+    status = GetJsonFromTensor(tensor, tensor.tensor_shape().dims(depth), pos, out_json);
+    if (status != SUCCESS) {
+      return status;
+    }
+  } else {
+    for (int i = 0; i < tensor.tensor_shape().dims(depth); i++) {
+      json tensor_json;
+      status = RecusiveGetJson(tensor, depth + 1, pos, &tensor_json);
+      if (status != SUCCESS) {
+        return status;
+      }
+      out_json->push_back(tensor_json);
+    }
+  }
+  return status;
+}
+
+Status TransPredictReplyToTensor(const PredictReply &reply, json *out_json) {
+  Status status(SUCCESS);
+  for (int i = 0; i < reply.result_size(); i++) {
+    json tensor_json;
+    int pos = 0;
+    status = RecusiveGetJson(reply.result(i), 0, &pos, &tensor_json);
+    if (status != SUCCESS) {
+      return status;
+    }
+    (*out_json)["tensor"].push_back(tensor_json);
+  }
+  return status;
+}
+
+Status TransPredictReplyToHTTPMsg(const PredictReply &reply, const HTTP_TYPE &type, struct evbuffer *buf) {
+  Status status(SUCCESS);
+  json out_json;
+  switch (type) {
+    case TYPE_DATA:
+      status = TransPredictReplyToData(reply, &out_json);
+      break;
+    case TYPE_TENSOR:
+      status = TransPredictReplyToTensor(reply, &out_json);
+      break;
+    default:
+      ERROR_INFER_STATUS(status, FAILED, "http message must have only one type of (data, tensor)");
+      return status;
+  }
+
+  std::string out_str = out_json.dump();
+  evbuffer_add(buf, out_str.data(), out_str.size());
+  return status;
+}
+
+void http_handler_msg(struct evhttp_request *req, void *arg) {
+  std::cout << "in handle" << std::endl;
+  PredictRequest request;
+  PredictReply reply;
+  HTTP_TYPE type;
+  auto status = TransHTTPMsgToPredictRequest(req, &request, &type);
+  if (status != SUCCESS) {
+    ErrorMessage(req, status);
+    MSI_LOG(ERROR) << "restful trans to request failed";
+    return;
+  }
+  MSI_TIME_STAMP_START(Predict)
+  status = Session::Instance().Predict(request, reply);
+  if (status != SUCCESS) {
+    ErrorMessage(req, status);
+    MSI_LOG(ERROR) << "restful predict failed";
+  }
+  MSI_TIME_STAMP_END(Predict)
+  struct evbuffer *retbuff = evbuffer_new();
+  status = TransPredictReplyToHTTPMsg(reply, type, retbuff);
+  if (status != SUCCESS) {
+    ErrorMessage(req, status);
+    MSI_LOG(ERROR) << "restful trans to reply failed";
+    return;
+  }
+  evhttp_send_reply(req, HTTP_OK, "Client", retbuff);
+  evbuffer_free(retbuff);
+}
+
+}  // namespace serving
+}  // namespace mindspore

serving/core/http_process.h

@@ -0,0 +1,29 @@
+/**
+ * Copyright 2020 Huawei Technologies Co., Ltd
+ *
+ * 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.
+ */
+#ifndef MINDSPORE_SERVING_HTTP_PROCESS_H
+#define MINDSPORE_SERVING_HTTP_PROCESS_H
+
+#include <evhttp.h>
+#include <event.h>
+#include <event2/http.h>
+#include <event2/http_struct.h>
+
+namespace mindspore {
+namespace serving {
+void http_handler_msg(struct evhttp_request *req, void *arg);
+}  // namespace serving
+}  // namespace mindspore
+#endif  // MINDSPORE_SERVER_H

serving/core/server.cc

@@ -14,23 +14,25 @@
  * limitations under the License.
  */
 #include "core/server.h"
+#include <evhttp.h>
+#include <event.h>
+#include <event2/thread.h>
 #include <grpcpp/grpcpp.h>
 #include <grpcpp/health_check_service_interface.h>
 #include <grpcpp/ext/proto_server_reflection_plugin.h>
+#include <future>
+#include <memory>
 #include <string>
-#include <map>
 #include <vector>
 #include <utility>
-#include <memory>
-#include <future>
-#include <chrono>
-
 #include "include/infer_log.h"
 #include "serving/ms_service.grpc.pb.h"
 #include "core/util/option_parser.h"
 #include "core/version_control/version_controller.h"
-#include "core/util/file_system_operation.h"
+#include "core/session.h"
 #include "core/serving_tensor.h"
+#include "core/http_process.h"
+
 
 using ms_serving::MSService;
 using ms_serving::PredictReply;
@@ -39,93 +41,6 @@ using ms_serving::PredictRequest;
 namespace mindspore {
 namespace serving {
 
-#define MSI_TIME_STAMP_START(name) auto time_start_##name = std::chrono::steady_clock::now();
-#define MSI_TIME_STAMP_END(name)                                                                             \
-  {                                                                                                          \
-    auto time_end_##name = std::chrono::steady_clock::now();                                                 \
-    auto time_cost = std::chrono::duration<double, std::milli>(time_end_##name - time_start_##name).count(); \
-    MSI_LOG_INFO << #name " Time Cost # " << time_cost << " ms ---------------------";                          \
-  }
-
-Status Session::CreatDeviceSession(const std::string &device, uint32_t device_id) {
-  session_ = inference::InferSession::CreateSession(device, device_id);
-  if (session_ == nullptr) {
-    MSI_LOG(ERROR) << "Creat Session Failed";
-    return FAILED;
-  }
-  device_type_ = device;
-  return SUCCESS;
-}
-
-Session &Session::Instance() {
-  static Session instance;
-  return instance;
-}
-
-Status Session::Predict(const PredictRequest &request, PredictReply &reply) {
-  if (!model_loaded_) {
-    MSI_LOG(ERROR) << "the model has not loaded";
-    return FAILED;
-  }
-  if (session_ == nullptr) {
-    MSI_LOG(ERROR) << "the inference session has not be initialized";
-    return FAILED;
-  }
-  std::lock_guard<std::mutex> lock(mutex_);
-  MSI_LOG(INFO) << "run Predict";
-
-  if (request.images_size() > 0) {
-    ServingImagesRequest serving_images(request);
-    ServingRequest serving_request(request);
-    ServingReply serving_reply(reply);
-    Status ret = session_->ExecuteModel(graph_id_, serving_images, serving_request, serving_reply);
-    if (ret != SUCCESS) {
-      MSI_LOG(ERROR) << "execute model with images return failed";
-      return ret;
-    }
-  } else if (request.data_size() > 0) {
-    ServingRequest serving_request(request);
-    ServingReply serving_reply(reply);
-    Status ret = session_->ExecuteModel(graph_id_, serving_request, serving_reply);
-    if (ret != SUCCESS) {
-      MSI_LOG(ERROR) << "execute model with datas return failed";
-      return ret;
-    }
-  }
-
-  MSI_LOG(INFO) << "run Predict finished";
-  return SUCCESS;
-}
-
-Status Session::Warmup(const MindSporeModelPtr model) {
-  if (session_ == nullptr) {
-    MSI_LOG(ERROR) << "The CreatDeviceSession should be called, before warmup";
-    return FAILED;
-  }
-  std::lock_guard<std::mutex> lock(mutex_);
-  std::string file_name = model->GetModelPath() + '/' + model->GetModelName();
-  model_loaded_ = false;
-  MSI_TIME_STAMP_START(LoadModelFromFile)
-  auto ret = session_->LoadModelFromFile(file_name, graph_id_);
-  MSI_TIME_STAMP_END(LoadModelFromFile)
-  if (ret != SUCCESS) {
-    MSI_LOG(ERROR) << "Load graph model failed, file name is " << file_name.c_str();
-    return ret;
-  }
-  model_loaded_ = true;
-  MSI_LOG(INFO) << "Session Warmup finished";
-  return SUCCESS;
-}
-
-Status Session::Clear() {
-  if (session_ != nullptr) {
-    session_->UnloadModel(graph_id_);
-    session_->FinalizeEnv();
-    session_ = nullptr;
-  }
-  return SUCCESS;
-}
-
 namespace {
 static const uint32_t uint32max = 0x7FFFFFFF;
 std::promise<void> exit_requested;
@@ -179,6 +94,7 @@ Status Server::BuildAndStart() {
   signal(SIGINT, HandleSignal);
   signal(SIGTERM, HandleSignal);
   Status res;
+
   auto option_args = Options::Instance().GetArgs();
   std::string server_address = "0.0.0.0:" + std::to_string(option_args->grpc_port);
   std::string model_path = option_args->model_path;
@@ -198,6 +114,26 @@ Status Server::BuildAndStart() {
     ClearEnv();
     return res;
   }
+
+  // init http server
+  struct evhttp *http_server = NULL;
+  struct event_base *eb = NULL;
+  int32_t http_port = option_args->rest_api_port;
+  std::string http_addr = "0.0.0.0";
+  event_init();
+  evthread_use_pthreads();
+  eb = event_base_new();
+  http_server = evhttp_new(eb);
+  evhttp_bind_socket_with_handle(http_server, http_addr.c_str(), http_port);
+  //  http_server = evhttp_start(http_addr.c_str(), http_port);
+  if (http_server == NULL) {
+    MSI_LOG(ERROR) << "http server start failed.";
+    return res;
+  }
+  evhttp_set_timeout(http_server, 5);
+  evhttp_set_gencb(http_server, http_handler_msg, NULL);
+
+  // grpc server
   MSServiceImpl ms_service;
   grpc::EnableDefaultHealthCheckService(true);
   grpc::reflection::InitProtoReflectionServerBuilderPlugin();
@@ -214,14 +150,23 @@ Status Server::BuildAndStart() {
     ClearEnv();
     return FAILED;
   }
-  auto grpc_server_run = [&server]() { server->Wait(); };
+  auto grpc_server_run = [&server, &server_address]() {
-  std::thread serving_thread(grpc_server_run);
+    MSI_LOG(INFO) << "MS Serving grpc listening on " << server_address;
-  MSI_LOG(INFO) << "MS Serving listening on " << server_address;
+    server->Wait();
+  };
+  auto http_server_run = [&eb, &http_addr, &http_port]() {
+    MSI_LOG(INFO) << "MS Serving restful listening on " << http_addr << ":" << http_port;
+    event_base_dispatch(eb);
+  };
+  std::thread grpc_thread(grpc_server_run);
+  std::thread restful_thread(http_server_run);
   auto exit_future = exit_requested.get_future();
   exit_future.wait();
   ClearEnv();
   server->Shutdown();
-  serving_thread.join();
+  event_base_loopexit(eb, NULL);
+  grpc_thread.join();
+  restful_thread.join();
   return SUCCESS;
 }
 }  // namespace serving

serving/core/server.h

@@ -16,46 +16,10 @@
 #ifndef MINDSPORE_SERVER_H
 #define MINDSPORE_SERVER_H
 
-#include <string>
-#include <mutex>
-#include <vector>
-#include <memory>
 #include "util/status.h"
-#include "version_control/model.h"
-#include "include/inference.h"
-#include "serving/ms_service.pb.h"
-#include "serving/ms_service.grpc.pb.h"
-
 namespace mindspore {
 namespace serving {
 
-using ms_serving::PredictReply;
-using ms_serving::PredictRequest;
-using inference::Status;
-using inference::SUCCESS;
-using inference::FAILED;
-using inference::INVALID_INPUTS;
-
-class Session {
- public:
-  static Session &Instance();
-  Status CreatDeviceSession(const std::string &device, uint32_t device_id);
-  // Status Predict(const inference::MultiTensor &inputs, inference::MultiTensor &output);
-  Status Predict(const PredictRequest &request, PredictReply &reply);
-  Status Warmup(const MindSporeModelPtr model);
-  Status Clear();
-
- private:
-  Session() = default;
-  ~Session() = default;
-  int sesseion_id_{0};
-  std::shared_ptr<inference::InferSession> session_{nullptr};
-  bool model_loaded_ = false;
-  uint32_t graph_id_{0};
-  std::mutex mutex_;
-  std::string device_type_;
-};
-
 class Server {
  public:
   Server() = default;

serving/core/session.cc

@@ -0,0 +1,136 @@
+/**
+ * Copyright 2020 Huawei Technologies Co., Ltd
+ *
+ * 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 "core/session.h"
+#include <grpcpp/grpcpp.h>
+#include <string>
+#include <map>
+#include <vector>
+#include <utility>
+#include <memory>
+#include <chrono>
+
+#include "include/infer_log.h"
+#include "serving/ms_service.grpc.pb.h"
+#include "core/util/option_parser.h"
+#include "core/version_control/version_controller.h"
+#include "core/util/file_system_operation.h"
+#include "core/serving_tensor.h"
+
+using ms_serving::MSService;
+using ms_serving::PredictReply;
+using ms_serving::PredictRequest;
+
+namespace mindspore {
+namespace serving {
+
+Status Session::CreatDeviceSession(const std::string &device, uint32_t device_id) {
+  session_ = inference::InferSession::CreateSession(device, device_id);
+  if (session_ == nullptr) {
+    MSI_LOG(ERROR) << "Creat Session Failed";
+    return FAILED;
+  }
+  device_type_ = device;
+  return SUCCESS;
+}
+
+Session &Session::Instance() {
+  static Session instance;
+  return instance;
+}
+
+Status Session::Predict(const PredictRequest &request, PredictReply &reply) {
+  if (!model_loaded_) {
+    MSI_LOG(ERROR) << "the model has not loaded";
+    return FAILED;
+  }
+  if (session_ == nullptr) {
+    MSI_LOG(ERROR) << "the inference session has not be initialized";
+    return FAILED;
+  }
+  std::lock_guard<std::mutex> lock(mutex_);
+  MSI_LOG(INFO) << "run Predict";
+
+  if (request.images_size() > 0) {
+    ServingImagesRequest serving_images(request);
+    ServingRequest serving_request(request);
+    ServingReply serving_reply(reply);
+    Status ret = session_->ExecuteModel(graph_id_, serving_images, serving_request, serving_reply);
+    if (ret != SUCCESS) {
+      MSI_LOG(ERROR) << "execute model with images return failed";
+      return ret;
+    }
+  } else if (request.data_size() > 0) {
+    ServingRequest serving_request(request);
+    ServingReply serving_reply(reply);
+    Status ret = session_->ExecuteModel(graph_id_, serving_request, serving_reply);
+    if (ret != SUCCESS) {
+      MSI_LOG(ERROR) << "execute model with datas return failed";
+      return ret;
+    }
+  }
+
+  MSI_LOG(INFO) << "run Predict finished";
+  return SUCCESS;
+}
+
+Status Session::Warmup(const MindSporeModelPtr model) {
+  if (session_ == nullptr) {
+    MSI_LOG(ERROR) << "The CreatDeviceSession should be called, before warmup";
+    return FAILED;
+  }
+  std::lock_guard<std::mutex> lock(mutex_);
+  std::string file_name = model->GetModelPath() + '/' + model->GetModelName();
+  model_loaded_ = false;
+  MSI_TIME_STAMP_START(LoadModelFromFile)
+  auto ret = session_->LoadModelFromFile(file_name, graph_id_);
+  MSI_TIME_STAMP_END(LoadModelFromFile)
+  if (ret != SUCCESS) {
+    MSI_LOG(ERROR) << "Load graph model failed, file name is " << file_name.c_str();
+    return ret;
+  }
+  model_loaded_ = true;
+  MSI_LOG(INFO) << "Session Warmup finished";
+  return SUCCESS;
+}
+
+Status Session::Clear() {
+  if (session_ != nullptr) {
+    session_->UnloadModel(graph_id_);
+    session_->FinalizeEnv();
+    session_ = nullptr;
+  }
+  return SUCCESS;
+}
+
+Status Session::GetModelInputsInfo(std::vector<inference::InferTensor> &tensor_list) {
+  if (!model_loaded_) {
+    MSI_LOG(ERROR) << "the model has not loaded";
+    return FAILED;
+  }
+  if (session_ == nullptr) {
+    MSI_LOG(ERROR) << "the inference session has not be initialized";
+    return FAILED;
+  }
+  std::lock_guard<std::mutex> lock(mutex_);
+  Status ret = session_->GetModelInputsInfo(graph_id_, &tensor_list);
+  if (ret != SUCCESS) {
+    MSI_LOG(ERROR) << "get model inputs info failed";
+  }
+  return ret;
+}
+
+}  // namespace serving
+}  // namespace mindspore

serving/core/session.h

@@ -0,0 +1,62 @@
+/**
+ * Copyright 2020 Huawei Technologies Co., Ltd
+ *
+ * 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.
+ */
+#ifndef MINDSPORE_SERVING_SESSION_H
+#define MINDSPORE_SERVING_SESSION_H
+
+#include <string>
+#include <mutex>
+#include <vector>
+#include <memory>
+#include "util/status.h"
+#include "version_control/model.h"
+#include "include/inference.h"
+#include "serving/ms_service.pb.h"
+#include "serving/ms_service.grpc.pb.h"
+
+namespace mindspore {
+namespace serving {
+
+using inference::FAILED;
+using inference::INVALID_INPUTS;
+using inference::Status;
+using inference::SUCCESS;
+using ms_serving::PredictReply;
+using ms_serving::PredictRequest;
+
+class Session {
+ public:
+  static Session &Instance();
+  Status CreatDeviceSession(const std::string &device, uint32_t device_id);
+  // Status Predict(const inference::MultiTensor &inputs, inference::MultiTensor &output);
+  Status Predict(const PredictRequest &request, PredictReply &reply);
+  Status Warmup(const MindSporeModelPtr model);
+  Status Clear();
+  Status GetModelInputsInfo(std::vector<inference::InferTensor> &tensor_list);
+
+ private:
+  Session() = default;
+  ~Session() = default;
+  int sesseion_id_{0};
+  std::shared_ptr<inference::InferSession> session_{nullptr};
+  bool model_loaded_ = false;
+  uint32_t graph_id_{0};
+  std::mutex mutex_;
+  std::string device_type_;
+};
+
+}  // namespace serving
+}  // namespace mindspore
+#endif  // MINDSPORE_SERVER_H

serving/core/util/option_parser.cc

@@ -160,6 +160,8 @@ void Options::CreateOptions() {
   std::vector<Option> options = {
     Option("port", &args_->grpc_port,
            "[Optional] Port to listen on for gRPC API, default is 5500, range from 1 to 65535"),
+    Option("rest_api_port", &args_->rest_api_port,
+           "[Optional] Port to listen on for RESTful API, default is 5501, range from 1 to 65535"),
     Option("model_name", &args_->model_name, "[Required] model name "),
     Option("model_path", &args_->model_path, "[Required] the path of the model files"),
     Option("device_id", &args_->device_id, "[Optional] the device id, default is 0, range from 0 to 7"),
@@ -184,6 +186,14 @@ bool Options::CheckOptions() {
     std::cout << "the port should be in [1~65535]" << std::endl;
     return false;
   }
+  if (args_->rest_api_port < 1 || args_->rest_api_port > 65535) {
+    std::cout << "the rest_api_port should be in [1~65535]" << std::endl;
+    return false;
+  }
+  if (args_->rest_api_port == args_->grpc_port) {
+    std::cout << "the rest_api_port and grpc port should not be same" << std::endl;
+    return false;
+  }
   return true;
 }
 

serving/core/util/option_parser.h

@@ -24,6 +24,7 @@ namespace mindspore {
 namespace serving {
 struct Arguments {
   int32_t grpc_port = 5500;
+  int32_t rest_api_port = 5501;
   std::string grpc_socket_path;
   std::string ssl_config_file;
   int32_t poll_model_wait_seconds = 1;

serving/core/version_control/version_controller.cc

@@ -21,7 +21,7 @@
 #include <memory>
 #include "util/file_system_operation.h"
 #include "include/infer_log.h"
-#include "core/server.h"
+#include "core/session.h"
 
 namespace mindspore {
 namespace serving {

serving/example/export_model/add_model.py

@@ -30,10 +30,9 @@ class Net(nn.Cell):
     def construct(self, x_, y_):
         return self.add(x_, y_)
 
-x = np.ones(4).astype(np.float32)
-y = np.ones(4).astype(np.float32)
-
 def export_net():
+    x = np.ones([2, 2]).astype(np.float32)
+    y = np.ones([2, 2]).astype(np.float32)
     add = Net()
     output = add(Tensor(x), Tensor(y))
     export(add, Tensor(x), Tensor(y), file_name='tensor_add.mindir', file_format='MINDIR')

serving/example/python_client/ms_client.py

@@ -37,14 +37,14 @@ def run():
     request = ms_service_pb2.PredictRequest()
 
     x = request.data.add()
-    x.tensor_shape.dims.extend([4])
+    x.tensor_shape.dims.extend([2, 2])
     x.tensor_type = ms_service_pb2.MS_FLOAT32
-    x.data = (np.ones([4]).astype(np.float32)).tobytes()
+    x.data = (np.ones([2, 2]).astype(np.float32)).tobytes()
 
     y = request.data.add()
-    y.tensor_shape.dims.extend([4])
+    y.tensor_shape.dims.extend([2, 2])
     y.tensor_type = ms_service_pb2.MS_FLOAT32
-    y.data = (np.ones([4]).astype(np.float32)).tobytes()
+    y.data = (np.ones([2, 2]).astype(np.float32)).tobytes()
 
     try:
         result = stub.Predict(request)

tests/st/serving/serving.sh

@@ -61,13 +61,13 @@ start_service()
     echo "$2 faile to start."
   fi
 
-  result=`grep -E 'MS Serving listening on 0.0.0.0:5500|MS Serving listening on 0.0.0.0:5501' $2_service.log | wc -l`
+  result=`grep -E 'MS Serving grpc listening on 0.0.0.0:5500|MS Serving listening on 0.0.0.0:5501' $2_service.log | wc -l`
   count=0
   while [[ ${result} -ne 1 && ${count} -lt 150 ]]
   do
     sleep 1
     count=$(($count+1))
-    result=`grep -E 'MS Serving listening on 0.0.0.0:5500|MS Serving listening on 0.0.0.0:5501' $2_service.log | wc -l`
+    result=`grep -E 'MS Serving grpc listening on 0.0.0.0:5500|MS Serving listening on 0.0.0.0:5501' $2_service.log | wc -l`
   done
 
   if [ ${count} -eq 150 ]

tests/ut/cpp/CMakeLists.txt

@@ -184,7 +184,7 @@ if (ENABLE_GE)
 endif()
 
 if (CMAKE_SYSTEM_NAME MATCHES "Linux")
-    target_link_libraries(ut_tests PRIVATE mindspore::gtest mindspore_gvar ${PYTHON_LIBRARIES} pthread util dl)
+    target_link_libraries(ut_tests PRIVATE mindspore::gtest mindspore::event mindspore::event_pthreads mindspore_gvar ${PYTHON_LIBRARIES} pthread util dl)
     if (ENABLE_MINDDATA)
         target_link_libraries(ut_tests PRIVATE _c_dataengine _c_mindrecord)
     endif()

tests/ut/cpp/serving/CMakeLists.txt

@@ -87,4 +87,3 @@ include_directories(${CMAKE_CURRENT_BINARY_DIR})
 include_directories(${CMAKE_CURRENT_BINARY_DIR}/../)
 add_library(ut_serving_obj OBJECT ${SERVING_SRC_TEST})
 
-

tests/ut/cpp/serving/acl_session_test_common.h

@@ -19,6 +19,7 @@
 
 #include "common/common_test.h"
 #include "serving/core/server.h"
+#include "serving/core/session.h"
 #include "include/inference.h"
 #include "include/infer_tensor.h"
 #include "serving/core/serving_tensor.h"