!3219 serving add acl support, extract common inference interface

Merge pull request !3219 from 徐永飞/master

CMakeLists.txt

@@ -51,6 +51,8 @@ include_directories(${CMAKE_CURRENT_SOURCE_DIR})
 include_directories(${CMAKE_CURRENT_SOURCE_DIR}/third_party/flatbuffers/include)
 include_directories(${CMAKE_CURRENT_SOURCE_DIR}/third_party/flatbuffers/include/flatbuffers)
 
+if (NOT ENABLE_ACL)
+
 include(${CMAKE_SOURCE_DIR}/cmake/dependency_utils.cmake)
 find_package(Python3 3.7 COMPONENTS Interpreter Development)
 if(Python3_FOUND)
@@ -100,8 +102,12 @@ if (ENABLE_TESTCASES)
     add_subdirectory(tests)
 endif()
 
+endif() # NOT ENABLE_ACL
+
 if (ENABLE_SERVING)
     add_subdirectory(serving)
 endif()
 
+if (NOT ENABLE_ACL)
 include(cmake/package.cmake)
+endif() # NOT ENABLE_ACL

build.sh

@@ -25,7 +25,7 @@ usage()
   echo "Usage:"
   echo "bash build.sh [-d] [-r] [-v] [-c on|off] [-t on|off] [-g on|off] [-h] [-b ge] [-m infer|train] \\"
   echo "              [-a on|off] [-Q on|off] [-p on|off] [-i] [-L] [-R] [-D on|off] [-j[n]] [-e gpu|d|cpu] \\"
-  echo "              [-P on|off] [-z [on|off]] [-M on|off] [-V 9.2|10.1] [-I] [-K] [-B on|off] [-E] [-l on|off]"
+  echo "              [-P on|off] [-z [on|off]] [-M on|off] [-V 9.2|10.1] [-I] [-K] [-B on|off] [-w on|off] [-E] [-l on|off]"
   echo ""
   echo "Options:"
   echo "    -d Debug mode"
@@ -54,6 +54,7 @@ usage()
   echo "    -I Compile predict, default off"
   echo "    -K Compile with AKG, default on"
   echo "    -s Enable serving module, default off"
+  echo "    -w Enable acl module, default off"
   echo "    -B Enable debugger, default off"
   echo "    -E Enable IBVERBS for parameter server, default off"
   echo "    -l Compile with python dependency, default on"
@@ -97,12 +98,13 @@ checkopts()
   PREDICT_PLATFORM=""
   ENABLE_AKG="on"
   ENABLE_SERVING="off"
+  ENABLE_ACL="off"
   ENABLE_DEBUGGER="off"
   ENABLE_IBVERBS="off"
   ENABLE_PYTHON="on"
 
   # Process the options
-  while getopts 'drvj:c:t:hsb:a:g:p:ie:m:l:I:LRP:Q:D:zM:V:K:sB:E' opt
+  while getopts 'drvj:c:t:hsb:a:g:p:ie:m:l:I:LRP:Q:D:zM:V:K:swB:E' opt
   do
     OPTARG=$(echo ${OPTARG} | tr '[A-Z]' '[a-z]')
     case "${opt}" in
@@ -256,6 +258,10 @@ checkopts()
         ENABLE_SERVING="on"
         echo "enable serving"
         ;;
+      w)
+        ENABLE_ACL="on"
+        echo "enable acl"
+        ;;
       B)
         check_on_off $OPTARG B
         ENABLE_DEBUGGER="on"
@@ -348,6 +354,9 @@ build_mindspore()
     if [[ "X$ENABLE_SERVING" = "Xon" ]]; then
         CMAKE_ARGS="${CMAKE_ARGS} -DENABLE_SERVING=ON"
     fi
+    if [[ "X$ENABLE_ACL" = "Xon" ]]; then
+        CMAKE_ARGS="${CMAKE_ARGS} -DENABLE_ACL=ON"
+    fi
     if [[ "X$ENABLE_DEBUGGER" = "Xon" ]]; then
         CMAKE_ARGS="${CMAKE_ARGS} -DENABLE_DEBUGGER=ON"
     fi
@@ -362,7 +371,11 @@ build_mindspore()
     if [[ -n "$VERBOSE" ]]; then
       CMAKE_VERBOSE="--verbose"
     fi
+    if [[ "X$ENABLE_ACL" = "Xon" ]]; then
+    cmake --build . ${CMAKE_VERBOSE} -j$THREAD_NUM
+    else
     cmake --build . --target package ${CMAKE_VERBOSE} -j$THREAD_NUM
+    fi
     echo "success to build mindspore project!"
 }
 

include/infer_log.h

+/**
+ * Copyright 2019 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_INFERENCE_LOG_H_
+#define MINDSPORE_INFERENCE_LOG_H_
+
+#include <stdarg.h>
+#include <stdint.h>
+#include <string>
+#include <sstream>
+#include <memory>
+#include <iostream>
+
+#ifndef ENABLE_ACL
+#include "mindspore/ccsrc/utils/log_adapter.h"
+namespace mindspore::inference {
+#define MSI_LOG(level) MS_LOG(level)
+
+#define MSI_LOG_DEBUG MSI_LOG(DEBUG)
+#define MSI_LOG_INFO MSI_LOG(INFO)
+#define MSI_LOG_WARNING MSI_LOG(WARNING)
+#define MSI_LOG_ERROR MSI_LOG(ERROR)
+
+#define MSI_ASSERT(item) MS_ASSERT(item)
+}  // namespace mindspore::inference
+
+#else  // ENABLE_ACL
+#include "acl/acl.h"
+namespace mindspore::inference {
+
+class LogStream {
+ public:
+  LogStream() { sstream_ = std::make_shared<std::stringstream>(); }
+  ~LogStream() = default;
+
+  template <typename T>
+  LogStream &operator<<(const T &val) noexcept {
+    (*sstream_) << val;
+    return *this;
+  }
+
+  LogStream &operator<<(std::ostream &func(std::ostream &os)) noexcept {
+    (*sstream_) << func;
+    return *this;
+  }
+
+  friend class LogWriter;
+
+ private:
+  std::shared_ptr<std::stringstream> sstream_;
+};
+
+template <class T, typename std::enable_if<std::is_enum<T>::value, int>::type = 0>
+constexpr std::ostream &operator<<(std::ostream &stream, const T &value) {
+  return stream << static_cast<typename std::underlying_type<T>::type>(value);
+}
+
+class LogWriter {
+ public:
+  LogWriter(const char *file, int line, const char *func, aclLogLevel log_level)
+      : file_(file), line_(line), func_(func), log_level_(log_level) {}
+  ~LogWriter() = default;
+
+  void operator<(const LogStream &stream) const noexcept __attribute__((visibility("default"))) {
+    std::ostringstream msg;
+    msg << stream.sstream_->rdbuf();
+    OutputLog(msg);
+  }
+
+ private:
+  void OutputLog(const std::ostringstream &msg) const { aclAppLog(log_level_, func_, file_, line_, msg.str().c_str()); }
+
+  const char *file_;
+  int line_;
+  const char *func_;
+  aclLogLevel log_level_;
+};
+
+#define MSILOG_IF(level) inference::LogWriter(__FILE__, __LINE__, __FUNCTION__, ACL_##level) < inference::LogStream()
+
+#define MSI_LOG(level) MSI_LOG_##level
+
+#define MSI_LOG_DEBUG MSILOG_IF(DEBUG)
+#define MSI_LOG_INFO MSILOG_IF(INFO)
+#define MSI_LOG_WARNING MSILOG_IF(WARNING)
+#define MSI_LOG_ERROR MSILOG_IF(ERROR)
+
+#define MSI_ASSERT(item)
+
+}  // namespace mindspore::inference
+
+#endif  // ENABLE_ACL
+
+#endif  // MINDSPORE_INFERENCE_LOG_H_

include/infer_tensor.h

+/**
+ * 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_INCLUDE_INFER_TENSOR_H_
+#define MINDSPORE_INCLUDE_INFER_TENSOR_H_
+
+#include <utility>
+#include <vector>
+#include <memory>
+#include <numeric>
+#include <map>
+#include <functional>
+
+#include "securec/include/securec.h"
+#include "include/infer_log.h"
+
+namespace mindspore {
+#define MS_API __attribute__((visibility("default")))
+namespace inference {
+
+enum DataType {
+  kMSI_Unknown = 0,
+  kMSI_Bool = 1,
+  kMSI_Int8 = 2,
+  kMSI_Int16 = 3,
+  kMSI_Int32 = 4,
+  kMSI_Int64 = 5,
+  kMSI_Uint8 = 6,
+  kMSI_Uint16 = 7,
+  kMSI_Uint32 = 8,
+  kMSI_Uint64 = 9,
+  kMSI_Float16 = 10,
+  kMSI_Float32 = 11,
+  kMSI_Float64 = 12,
+};
+
+class InferTensorBase {
+ public:
+  InferTensorBase() = default;
+  virtual ~InferTensorBase() = default;
+
+  virtual DataType data_type() const = 0;
+  virtual void set_data_type(DataType type) = 0;
+  virtual std::vector<int64_t> shape() const = 0;
+  virtual void set_shape(const std::vector<int64_t> &shape) = 0;
+  virtual const void *data() const = 0;
+  virtual size_t data_size() const = 0;
+  virtual bool resize_data(size_t data_len) = 0;
+  virtual void *mutable_data() = 0;
+
+  bool set_data(const void *data, size_t data_len) {
+    resize_data(data_len);
+    if (mutable_data() == nullptr) {
+      MSI_LOG_ERROR << "set data failed, data len " << data_len;
+      return false;
+    }
+    if (data_size() != data_len) {
+      MSI_LOG_ERROR << "set data failed, tensor current data size " << data_size() << " not match data len "
+                    << data_len;
+      return false;
+    }
+    if (data_len == 0) {
+      return true;
+    }
+    memcpy_s(mutable_data(), data_size(), data, data_len);
+    return true;
+  }
+
+  int64_t ElementNum() const {
+    std::vector<int64_t> shapex = shape();
+    return std::accumulate(shapex.begin(), shapex.end(), 1LL, std::multiplies<int64_t>());
+  }
+
+  int GetTypeSize(DataType type) const {
+    const std::map<DataType, size_t> type_size_map{
+      {kMSI_Bool, sizeof(bool)},       {kMSI_Float64, sizeof(double)},   {kMSI_Int8, sizeof(int8_t)},
+      {kMSI_Uint8, sizeof(uint8_t)},   {kMSI_Int16, sizeof(int16_t)},    {kMSI_Uint16, sizeof(uint16_t)},
+      {kMSI_Int32, sizeof(int32_t)},   {kMSI_Uint32, sizeof(uint32_t)},  {kMSI_Int64, sizeof(int64_t)},
+      {kMSI_Uint64, sizeof(uint64_t)}, {kMSI_Float16, sizeof(uint16_t)}, {kMSI_Float32, sizeof(float)},
+    };
+    auto it = type_size_map.find(type);
+    if (it != type_size_map.end()) {
+      return it->second;
+    }
+    return 0;
+  }
+};
+
+class InferTensor : public InferTensorBase {
+ public:
+  DataType type_;
+  std::vector<int64_t> shape_;
+  std::vector<uint8_t> data_;
+
+ public:
+  InferTensor() = default;
+  InferTensor(DataType type, std::vector<int64_t> shape, const void *data, size_t data_len) {
+    set_data_type(type);
+    set_shape(shape);
+    set_data(data, data_len);
+  }
+
+  void set_data_type(DataType type) override { type_ = type; }
+  DataType data_type() const override { return type_; }
+
+  void set_shape(const std::vector<int64_t> &shape) override { shape_ = shape; }
+  std::vector<int64_t> shape() const override { return shape_; }
+
+  const void *data() const override { return data_.data(); }
+  size_t data_size() const override { return data_.size(); }
+
+  bool resize_data(size_t data_len) override {
+    data_.resize(data_len);
+    return true;
+  }
+  void *mutable_data() override { return data_.data(); }
+};
+
+class RequestBase {
+ public:
+  virtual size_t size() const = 0;
+  virtual const InferTensorBase *operator[](size_t index) const = 0;
+};
+
+class ReplyBase {
+ public:
+  virtual size_t size() const = 0;
+  virtual InferTensorBase *operator[](size_t index) = 0;
+  virtual const InferTensorBase *operator[](size_t index) const = 0;
+  virtual InferTensorBase *add() = 0;
+  virtual void clear() = 0;
+};
+
+class VectorInferTensorWrapReply : public ReplyBase {
+ public:
+  explicit VectorInferTensorWrapReply(std::vector<InferTensor> &tensor_list) : tensor_list_(tensor_list) {}
+
+  size_t size() const { return tensor_list_.size(); }
+  InferTensorBase *operator[](size_t index) {
+    if (index >= tensor_list_.size()) {
+      MSI_LOG_ERROR << "visit invalid index " << index << " total size " << tensor_list_.size();
+      return nullptr;
+    }
+    return &(tensor_list_[index]);
+  }
+  const InferTensorBase *operator[](size_t index) const {
+    if (index >= tensor_list_.size()) {
+      MSI_LOG_ERROR << "visit invalid index " << index << " total size " << tensor_list_.size();
+      return nullptr;
+    }
+    return &(tensor_list_[index]);
+  }
+  InferTensorBase *add() {
+    tensor_list_.push_back(InferTensor());
+    return &(tensor_list_.back());
+  }
+  void clear() { tensor_list_.clear(); }
+  std::vector<InferTensor> &tensor_list_;
+};
+
+class VectorInferTensorWrapRequest : public RequestBase {
+ public:
+  explicit VectorInferTensorWrapRequest(const std::vector<InferTensor> &tensor_list) : tensor_list_(tensor_list) {}
+
+  size_t size() const { return tensor_list_.size(); }
+  const InferTensorBase *operator[](size_t index) const {
+    if (index >= tensor_list_.size()) {
+      MSI_LOG_ERROR << "visit invalid index " << index << " total size " << tensor_list_.size();
+      return nullptr;
+    }
+    return &(tensor_list_[index]);
+  }
+  const std::vector<InferTensor> &tensor_list_;
+};
+
+}  // namespace inference
+}  // namespace mindspore
+#endif  // MINDSPORE_INCLUDE_INFER_TENSOR_H_

include/inference.h

@@ -20,28 +20,32 @@
 #include <memory>
 #include <vector>
 #include <string>
-#include "include/ms_tensor.h"
+#include "include/infer_tensor.h"
 
 namespace mindspore {
-class FuncGraph;
 namespace inference {
-using VectorForMSTensorPtr = std::vector<std::shared_ptr<inference::MSTensor>>;
-class MS_API MSSession {
- public:
-  MSSession() = default;
-
-  static std::shared_ptr<MSSession> CreateSession(const std::string &device, uint32_t device_id);
-
-  virtual uint32_t CompileGraph(std::shared_ptr<FuncGraph> funcGraphPtr) = 0;
 
-  virtual MultiTensor RunGraph(uint32_t graph_id, const VectorForMSTensorPtr &inputs) = 0;
-
-  virtual bool CheckModelInputs(uint32_t graph_id, const VectorForMSTensorPtr &inputs) const = 0;
+class MS_API InferSession {
+ public:
+  InferSession() = default;
+  virtual ~InferSession() = default;
+  virtual bool InitEnv(const std::string &device_type, uint32_t device_id) = 0;
+  virtual bool FinalizeEnv() = 0;
+  virtual bool LoadModelFromFile(const std::string &file_name, uint32_t &model_id) = 0;
+  virtual bool UnloadModel(uint32_t model_id) = 0;
+  // override this method to avoid request/reply data copy
+  virtual bool ExecuteModel(uint32_t model_id, const RequestBase &request, ReplyBase &reply) = 0;
+
+  virtual bool ExecuteModel(uint32_t model_id, const std::vector<InferTensor> &inputs,
+                            std::vector<InferTensor> &outputs) {
+    VectorInferTensorWrapRequest request(inputs);
+    VectorInferTensorWrapReply reply(outputs);
+    return ExecuteModel(model_id, request, reply);
+  }
+
+  static std::shared_ptr<InferSession> CreateSession(const std::string &device, uint32_t device_id);
 };
 
-std::shared_ptr<FuncGraph> MS_API LoadModel(const char *model_buf, size_t size, const std::string &device);
-
-void MS_API ExitInference();
 }  // namespace inference
 }  // namespace mindspore
 #endif  // MINDSPORE_INCLUDE_MS_SESSION_H

mindspore/ccsrc/CMakeLists.txt

@@ -296,7 +296,7 @@ set(LOAD_ONNX_SRC
         ${CMAKE_CURRENT_SOURCE_DIR}/utils/load_onnx/anf_model_parser.cc
         )
 add_library(inference SHARED
-        ${CMAKE_CURRENT_SOURCE_DIR}/backend/session/session.cc
+        ${CMAKE_CURRENT_SOURCE_DIR}/backend/session/infer_session.cc
         ${LOAD_ONNX_SRC}
         )
 target_link_libraries(inference PRIVATE ${PYTHON_LIBRARIES} ${SECUREC_LIBRARY}

mindspore/ccsrc/backend/session/ascend_inference_session.cc

@@ -88,8 +88,7 @@ GraphId AscendInferenceSession::CompileGraph(NotNull<FuncGraphPtr> func_graph) {
   return graph_id;
 }
 
-bool AscendInferenceSession::CheckModelInputs(uint32_t graph_id,
-                                              const std::vector<std::shared_ptr<inference::MSTensor> > &inputs) {
+bool AscendInferenceSession::CheckModelInputs(uint32_t graph_id, const std::vector<tensor::TensorPtr> &inputs) const {
   MS_LOG(INFO) << "Start check client inputs, graph id : " << graph_id;
   auto kernel_graph = GetGraph(graph_id);
   MS_EXCEPTION_IF_NULL(kernel_graph);
@@ -119,8 +118,7 @@ bool AscendInferenceSession::CheckModelInputs(uint32_t graph_id,
   return true;
 }
 
-bool AscendInferenceSession::CompareInput(const std::shared_ptr<inference::MSTensor> &input,
-                                          const ParameterPtr &parameter) {
+bool AscendInferenceSession::CompareInput(const tensor::TensorPtr &input, const ParameterPtr &parameter) const {
   MS_EXCEPTION_IF_NULL(input);
   MS_EXCEPTION_IF_NULL(parameter);
   // compare dims
@@ -155,7 +153,7 @@ bool AscendInferenceSession::CompareInput(const std::shared_ptr<inference::MSTen
   return true;
 }
 
-std::string AscendInferenceSession::PrintInputShape(std::vector<size_t> shape) {
+std::string AscendInferenceSession::PrintInputShape(std::vector<size_t> shape) const {
   string res = "[";
   for (auto dim : shape) {
     res += " " + std::to_string(dim);

mindspore/ccsrc/backend/session/ascend_inference_session.h

@@ -39,9 +39,9 @@ class AscendInferenceSession : public AscendSession {
   void LoadInputData(const std::shared_ptr<KernelGraph> &kernel_graph,
                      const std::vector<tensor::TensorPtr> &inputs_const) const;
   GraphId CompileGraph(NotNull<FuncGraphPtr> func_graph) override;
-  bool CheckModelInputs(uint32_t graph_id, const std::vector<std::shared_ptr<inference::MSTensor>> &inputs) override;
-  bool CompareInput(const std::shared_ptr<inference::MSTensor> &input, const ParameterPtr &parameter);
-  std::string PrintInputShape(std::vector<size_t> shape);
+  bool CheckModelInputs(uint32_t graph_id, const std::vector<tensor::TensorPtr> &inputs) const override;
+  bool CompareInput(const tensor::TensorPtr &input, const ParameterPtr &parameter) const;
+  std::string PrintInputShape(std::vector<size_t> shape) const;
 };
 MS_REG_SESSION(kDavinciInferenceDevice, AscendInferenceSession);
 }  // namespace session

mindspore/ccsrc/backend/session/session.cc → mindspore/ccsrc/backend/session/infer_session.cc

@@ -14,62 +14,233 @@
  * limitations under the License.
  */
 
+#include "backend/session/infer_session.h"
 #include <memory>
 #include <algorithm>
 #include "include/inference.h"
-#include "backend/session/session.h"
 #include "utils/load_onnx/anf_converter.h"
 #include "backend/session/session_basic.h"
 #include "backend/session/session_factory.h"
 #include "utils/base_ref_utils.h"
 #include "backend/kernel_compiler/oplib/oplib.h"
+
 #ifdef ENABLE_D
 #include "utils/context/ms_context.h"
-#include "backend/session/ascend_session.h"
-#else
-#include "backend/session/cpu_session.h"
 #endif
 
+using std::string;
+using std::vector;
+
 namespace py = pybind11;
 namespace mindspore::inference {
-std::shared_ptr<FuncGraph> LoadModel(const char *model_buf, size_t size, const std::string &device) {
+
+std::shared_ptr<InferSession> InferSession::CreateSession(const std::string &device, uint32_t device_id) {
   try {
-    inference::Session::RegAllOp();
-    auto anf_graph = lite::AnfConverter::RunAnfConverter(model_buf, size);
-    return anf_graph;
-  } catch (std::exception &e) {
-    MS_LOG(ERROR) << "Inference LoadModel failed";
+    auto session = std::make_shared<MSInferSession>();
+    bool ret = session->InitEnv(device, device_id);
+    if (!ret) {
+      return nullptr;
+    }
+    return session;
+  } catch (std::bad_alloc &e) {
+    MS_LOG(ERROR) << "Inference CreatSession failed, failed to alloc memory";
+    return nullptr;
+  }
+}
+
+MSInferSession::MSInferSession() = default;
+MSInferSession::~MSInferSession() = default;
+
+std::shared_ptr<std::vector<char>> MSInferSession::ReadFile(const std::string &file) {
+  if (file.empty()) {
+    MS_LOG(ERROR) << "file is nullptr";
+    return nullptr;
+  }
+  std::string realPath = file;
+  std::ifstream ifs(realPath);
+  if (!ifs.good()) {
+    MS_LOG(ERROR) << "file: " << realPath << " is not exist";
+    return nullptr;
+  }
+
+  if (!ifs.is_open()) {
+    MS_LOG(ERROR) << "file: " << realPath << "open failed";
+    return nullptr;
+  }
+
+  ifs.seekg(0, std::ios::end);
+  size_t size = ifs.tellg();
+  std::shared_ptr<std::vector<char>> buf(new (std::nothrow) std::vector<char>(size));
+  if (buf == nullptr) {
+    MS_LOG(ERROR) << "malloc buf failed, file: " << realPath;
+    ifs.close();
+    return nullptr;
+  }
+
+  ifs.seekg(0, std::ios::beg);
+  ifs.read(buf->data(), size);
+  ifs.close();
+
+  return buf;
+}
+
+bool MSInferSession::LoadModelFromFile(const std::string &file_name, uint32_t &model_id) {
+  auto graphBuf = ReadFile(file_name);
+  if (graphBuf == nullptr) {
+    MS_LOG(ERROR) << "Read model file failed, file name is " << file_name.c_str();
+    return false;
+  }
+  auto graph = LoadModel(graphBuf->data(), graphBuf->size(), device_type_);
+  if (graph == nullptr) {
+    MS_LOG(ERROR) << "Load graph model failed, file name is " << file_name.c_str();
+    return false;
+  }
+  bool ret = CompileGraph(graph, model_id);
+  if (!ret) {
+    MS_LOG(ERROR) << "Compile graph model failed, file name is " << file_name.c_str();
+    return false;
+  }
+  MS_LOG(INFO) << "Load model from file " << file_name << " success";
+
+#ifdef ENABLE_D
+  // set d context
+  rtError_t rt_ret = rtCtxGetCurrent(&context_);
+  if (rt_ret != RT_ERROR_NONE || context_ == nullptr) {
+    MS_LOG(ERROR) << "the ascend device context is null";
+    return false;
+  }
+#endif
+
+  return true;
+}
+
+bool MSInferSession::UnloadModel(uint32_t model_id) { return true; }
+
+tensor::TensorPtr ServingTensor2MSTensor(const InferTensorBase &out_tensor) {
+  std::vector<int> shape;
+  for (auto dim : out_tensor.shape()) {
+    shape.push_back(static_cast<int>(dim));
+  }
+  TypeId data_type;
+  const std::map<inference::DataType, TypeId> type2id_map{
+    {inference::kMSI_Unknown, TypeId::kNumberTypeBegin},   {inference::kMSI_Bool, TypeId::kNumberTypeBool},
+    {inference::kMSI_Int8, TypeId::kNumberTypeInt8},       {inference::kMSI_Uint8, TypeId::kNumberTypeUInt8},
+    {inference::kMSI_Int16, TypeId::kNumberTypeInt16},     {inference::kMSI_Uint16, TypeId::kNumberTypeUInt16},
+    {inference::kMSI_Int32, TypeId::kNumberTypeInt32},     {inference::kMSI_Uint32, TypeId::kNumberTypeUInt32},
+    {inference::kMSI_Int64, TypeId::kNumberTypeInt64},     {inference::kMSI_Uint64, TypeId::kNumberTypeUInt64},
+    {inference::kMSI_Float16, TypeId::kNumberTypeFloat16}, {inference::kMSI_Float32, TypeId::kNumberTypeFloat32},
+    {inference::kMSI_Float64, TypeId::kNumberTypeFloat64},
+  };
+  auto it = type2id_map.find(out_tensor.data_type());
+  if (it == type2id_map.end()) {
+    MSI_LOG_WARNING << "undefined MSI data type " << out_tensor.data_type();
     return nullptr;
+  } else {
+    data_type = it->second;
   }
+
+  auto ms_tensor = std::make_shared<tensor::Tensor>(data_type, shape);
+  memcpy_s(ms_tensor->data_c(), ms_tensor->Size(), out_tensor.data(), out_tensor.data_size());
+  return ms_tensor;
 }
 
-void ExitInference() {
+void MSTensor2ServingTensor(tensor::TensorPtr ms_tensor, InferTensorBase &out_tensor) {
+  vector<int64_t> shape;
+  for (auto dim : ms_tensor->shape()) {
+    shape.push_back(dim);
+  }
+  out_tensor.set_shape(shape);
+
+  const std::map<TypeId, inference::DataType> id2type_map{
+    {TypeId::kNumberTypeBegin, inference::kMSI_Unknown},   {TypeId::kNumberTypeBool, inference::kMSI_Bool},
+    {TypeId::kNumberTypeFloat64, inference::kMSI_Float64}, {TypeId::kNumberTypeInt8, inference::kMSI_Int8},
+    {TypeId::kNumberTypeUInt8, inference::kMSI_Uint8},     {TypeId::kNumberTypeInt16, inference::kMSI_Int16},
+    {TypeId::kNumberTypeUInt16, inference::kMSI_Uint16},   {TypeId::kNumberTypeInt32, inference::kMSI_Int32},
+    {TypeId::kNumberTypeUInt32, inference::kMSI_Uint32},   {TypeId::kNumberTypeInt64, inference::kMSI_Int64},
+    {TypeId::kNumberTypeUInt64, inference::kMSI_Uint64},   {TypeId::kNumberTypeFloat16, inference::kMSI_Float16},
+    {TypeId::kNumberTypeFloat32, inference::kMSI_Float32},
+  };
+  auto it = id2type_map.find(ms_tensor->data_type());
+  if (it == id2type_map.end()) {
+    MSI_LOG_WARNING << "undefined MS data type " << ms_tensor->data_type();
+    out_tensor.set_data_type(inference::kMSI_Unknown);
+  } else {
+    out_tensor.set_data_type(it->second);
+  }
+  out_tensor.set_data(ms_tensor->data_c(), ms_tensor->Size());
+}
+
+bool MSInferSession::ExecuteModel(uint32_t model_id, const RequestBase &request, ReplyBase &reply) {
+#ifdef ENABLE_D
+  if (context_ == nullptr) {
+    MS_LOG(ERROR) << "rtCtx is nullptr";
+    return false;
+  }
+  rtError_t rt_ret = rtCtxSetCurrent(context_);
+  if (rt_ret != RT_ERROR_NONE) {
+    MS_LOG(ERROR) << "set Ascend rtCtx failed";
+    return false;
+  }
+#endif
+
+  vector<tensor::TensorPtr> inputs;
+  for (size_t i = 0; i < request.size(); i++) {
+    if (request[i] == nullptr) {
+      MS_LOG(ERROR) << "Execute Model " << model_id << " Failed， input tensor is null, index " << i;
+      return false;
+    }
+    auto input = ServingTensor2MSTensor(*request[i]);
+    if (input == nullptr) {
+      MS_LOG(ERROR) << "Tensor convert failed";
+      return false;
+    }
+    inputs.push_back(input);
+  }
+  if (!CheckModelInputs(model_id, inputs)) {
+    MS_LOG(ERROR) << "Check Model " << model_id << " Inputs Failed";
+    return false;
+  }
+  vector<tensor::TensorPtr> outputs = RunGraph(model_id, inputs);
+  if (outputs.empty()) {
+    MS_LOG(ERROR) << "Execute Model " << model_id << " Failed";
+    return false;
+  }
+  reply.clear();
+  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";
+      return false;
+    }
+    MSTensor2ServingTensor(tensor, *out_tensor);
+  }
+  return true;
+}
+
+bool MSInferSession::FinalizeEnv() {
   auto ms_context = MsContext::GetInstance();
   if (ms_context == nullptr) {
     MS_LOG(ERROR) << "Get Context failed!";
-    return;
+    return false;
   }
   if (!ms_context->CloseTsd()) {
     MS_LOG(ERROR) << "Inference CloseTsd failed!";
-    return;
+    return false;
   }
+  return true;
 }
 
-std::shared_ptr<MSSession> MSSession::CreateSession(const std::string &device, uint32_t device_id) {
+std::shared_ptr<FuncGraph> MSInferSession::LoadModel(const char *model_buf, size_t size, const std::string &device) {
   try {
-    auto session = std::make_shared<inference::Session>();
-    auto ret = session->Init(device, device_id);
-    if (ret != 0) {
-      return nullptr;
-    }
-    return session;
+    auto anf_graph = lite::AnfConverter::RunAnfConverter(model_buf, size);
+    return anf_graph;
   } catch (std::exception &e) {
-    MS_LOG(ERROR) << "Inference CreatSession failed";
+    MS_LOG(ERROR) << "Inference LoadModel failed";
     return nullptr;
   }
 }
 
-void Session::RegAllOp() {
+void MSInferSession::RegAllOp() {
   static std::mutex init_mutex;
   static bool Initialized = false;
 
@@ -121,54 +292,33 @@ void Session::RegAllOp() {
   return;
 }
 
-uint32_t Session::CompileGraph(std::shared_ptr<FuncGraph> funcGraphPtr) {
+bool MSInferSession::CompileGraph(std::shared_ptr<FuncGraph> funcGraphPtr, uint32_t &model_id) {
   MS_ASSERT(session_impl_ != nullptr);
   try {
     auto graph_id = session_impl_->CompileGraph(NOT_NULL(funcGraphPtr));
     py::gil_scoped_release gil_release;
-    return graph_id;
+    model_id = graph_id;
+    return true;
   } catch (std::exception &e) {
     MS_LOG(ERROR) << "Inference CompileGraph failed";
-    return static_cast<uint32_t>(-1);
+    return false;
   }
 }
 
-MultiTensor Session::RunGraph(uint32_t graph_id, const std::vector<std::shared_ptr<inference::MSTensor>> &inputs) {
+std::vector<tensor::TensorPtr> MSInferSession::RunGraph(uint32_t graph_id,
+                                                        const std::vector<tensor::TensorPtr> &inputs) {
   try {
-    std::vector<tensor::TensorPtr> inTensors;
-    inTensors.resize(inputs.size());
-    bool has_error = false;
-    std::transform(inputs.begin(), inputs.end(), inTensors.begin(),
-                   [&has_error](const std::shared_ptr<inference::MSTensor> &tensor_ptr) -> tensor::TensorPtr {
-                     if (tensor_ptr == nullptr) {
-                       MS_LOG(WARNING) << "input MSTensor is nullptr, return nullptr";
-                       has_error = true;
-                       return nullptr;
-                     }
-                     auto tensor = static_cast<inference::Tensor *>(tensor_ptr.get());
-                     if (tensor == nullptr) {
-                       MS_LOG(ERROR) << "Can not cast input MSTensor to tensor";
-                       has_error = true;
-                       return nullptr;
-                     }
-                     return tensor->tensor();
-                   });
-    if (has_error) {
-      MS_LOG(ERROR) << "Init Tensor failed, returning empty result";
-      std::vector<std::shared_ptr<inference::MSTensor>> multiTensor;
-      return multiTensor;
-    }
     VectorRef outputs;
-    session_impl_->RunGraph(graph_id, inTensors, &outputs);
+    session_impl_->RunGraph(graph_id, inputs, &outputs);
 
     return TransformVectorRefToMultiTensor(outputs);
   } catch (std::exception &e) {
     MS_LOG(ERROR) << "Inference Rungraph failed";
-    return MultiTensor();
+    return std::vector<tensor::TensorPtr>();
   }
 }
-namespace {
-string AjustTargetName(const std::string &device) {
+
+string MSInferSession::AjustTargetName(const std::string &device) {
   if (device == kAscendDevice) {
     return std::string(kAscendDevice) + "Inference";
   } else {
@@ -176,39 +326,37 @@ string AjustTargetName(const std::string &device) {
     return "";
   }
 }
-}  // namespace
-int Session::Init(const std::string &device, uint32_t device_id) {
+
+bool MSInferSession::InitEnv(const std::string &device, uint32_t device_id) {
   RegAllOp();
   auto ms_context = MsContext::GetInstance();
   ms_context->set_execution_mode(kGraphMode);
   ms_context->set_device_id(device_id);
   auto ajust_device = AjustTargetName(device);
   if (ajust_device == "") {
-    return -1;
+    return false;
   }
   ms_context->set_device_target(device);
   session_impl_ = session::SessionFactory::Get().Create(ajust_device);
   if (session_impl_ == nullptr) {
     MS_LOG(ERROR) << "Session create failed!, please make sure target device:" << device << " is available.";
-    return -1;
+    return false;
   }
   session_impl_->Init(device_id);
   if (ms_context == nullptr) {
     MS_LOG(ERROR) << "Get Context failed!";
-    return -1;
+    return false;
   }
   if (!ms_context->OpenTsd()) {
     MS_LOG(ERROR) << "Session init OpenTsd failed!";
-    return -1;
+    return false;
   }
-  return 0;
+  return true;
 }
 
-bool Session::CheckModelInputs(uint32_t graph_id,
-                               const std::vector<std::shared_ptr<inference::MSTensor>> &inputs) const {
+bool MSInferSession::CheckModelInputs(uint32_t graph_id, const std::vector<tensor::TensorPtr> &inputs) const {
   MS_ASSERT(session_impl_ != nullptr);
   return session_impl_->CheckModelInputs(graph_id, inputs);
 }
 
-Session::Session() = default;
 }  // namespace mindspore::inference

mindspore/ccsrc/backend/session/infer_session.h

+/**
+ * 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_CCSRC_SESSION_SESSION_H
+#define MINDSPORE_CCSRC_SESSION_SESSION_H
+
+#include <vector>
+#include <string>
+#include <unordered_map>
+#include <utility>
+#include <memory>
+#include <map>
+
+#include "backend/session/session_basic.h"
+#include "ir/anf.h"
+#include "include/inference.h"
+
+#ifdef ENABLE_D
+#include "runtime/context.h"
+#endif
+
+namespace mindspore {
+namespace inference {
+class MSInferSession : public InferSession {
+ public:
+  MSInferSession();
+  ~MSInferSession();
+
+  bool InitEnv(const std::string &device_type, uint32_t device_id) override;
+  bool FinalizeEnv() override;
+  bool LoadModelFromFile(const std::string &file_name, uint32_t &model_id) override;
+  bool UnloadModel(uint32_t model_id) override;
+  bool ExecuteModel(uint32_t model_id, const RequestBase &inputs, ReplyBase &outputs) override;
+
+ private:
+  std::shared_ptr<session::SessionBasic> session_impl_ = nullptr;
+  std::vector<uint32_t> graph_id_;
+  std::string device_type_;
+  int32_t device_id_;
+#ifdef ENABLE_D
+  rtContext_t context_ = nullptr;
+#endif
+
+  std::shared_ptr<FuncGraph> LoadModel(const char *model_buf, size_t size, const std::string &device);
+  std::shared_ptr<std::vector<char>> ReadFile(const std::string &file);
+  static void RegAllOp();
+  string AjustTargetName(const std::string &device);
+  bool CompileGraph(std::shared_ptr<FuncGraph> funcGraphPtr, uint32_t &model_id);
+  bool CheckModelInputs(uint32_t graph_id, const std::vector<tensor::TensorPtr> &inputs) const;
+  std::vector<tensor::TensorPtr> RunGraph(uint32_t graph_id, const std::vector<tensor::TensorPtr> &inputs);
+};
+}  // namespace inference
+}  // namespace mindspore
+#endif  // MINDSPORE_CCSRC_SESSION_SESSION_BASIC_H

mindspore/ccsrc/backend/session/session_basic.cc

@@ -276,7 +276,7 @@ bool ExistSummaryNode(const KernelGraph *graph) {
 
 GraphId SessionBasic::graph_sum_ = 0;
 
-KernelGraphPtr SessionBasic::GetGraph(mindspore::GraphId graph_id) {
+KernelGraphPtr SessionBasic::GetGraph(mindspore::GraphId graph_id) const {
   auto it = graphs_.find(graph_id);
   if (it == graphs_.end()) {
     MS_LOG(WARNING) << "Can't find graph " << graph_id;

mindspore/ccsrc/backend/session/session_basic.h

@@ -106,9 +106,7 @@ class SessionBasic {
   virtual void GetSummaryNodes(KernelGraph *graph);
   void AssignParamKey(const KernelGraphPtr &kernel_graph);
   void InitPSParamAndOptim(const KernelGraphPtr &kernel_graph, const std::vector<tensor::TensorPtr> &inputs_const);
-  virtual bool CheckModelInputs(uint32_t graph_id, const std::vector<std::shared_ptr<inference::MSTensor>> &inputs) {
-    return true;
-  }
+  virtual bool CheckModelInputs(uint32_t graph_id, const std::vector<tensor::TensorPtr> &inputs) const { return true; }
 
 #ifdef ENABLE_DEBUGGER
   // set debugger
@@ -120,7 +118,7 @@ class SessionBasic {
 
  protected:
   // Get graph by graph id ,if not exist return null ptr
-  KernelGraphPtr GetGraph(GraphId graph_id);
+  KernelGraphPtr GetGraph(GraphId graph_id) const;
   virtual void LoadInputData(const std::shared_ptr<KernelGraph> &kernel_graph,
                              const std::vector<tensor::TensorPtr> &inputs_const) const;
   void UpdateOutputs(const std::shared_ptr<KernelGraph> &kernel_graph, VectorRef *const outputs,

mindspore/ccsrc/utils/base_ref_utils.cc

@@ -17,17 +17,17 @@
 #include <vector>
 #include <memory>
 #include "utils/base_ref_utils.h"
-#include "include/ms_tensor.h"
+#include "include/infer_tensor.h"
 #include "ir/tensor.h"
 
 namespace mindspore {
-void IterateFindTensor(std::vector<std::shared_ptr<inference::MSTensor>> *msTensors, const VectorRef &ref_list) {
+
+void IterateFindTensor(std::vector<tensor::TensorPtr> *msTensors, const VectorRef &ref_list) {
   for (size_t i = 0; i < ref_list.size(); ++i) {
     if (utils::isa<tensor::TensorPtr>(ref_list[i])) {
       auto tensor_ptr = utils::cast<std::shared_ptr<tensor::Tensor>>(ref_list[i]);
       MS_EXCEPTION_IF_NULL(tensor_ptr);
-      auto tensor = new inference::Tensor(tensor_ptr);
-      msTensors->emplace_back(std::shared_ptr<inference::MSTensor>(tensor));
+      msTensors->emplace_back(tensor_ptr);
     } else if (utils::isa<VectorRef>(ref_list[i])) {
       auto ref_iter = utils::cast<VectorRef>(ref_list[i]);
       IterateFindTensor(msTensors, ref_iter);
@@ -37,19 +37,19 @@ void IterateFindTensor(std::vector<std::shared_ptr<inference::MSTensor>> *msTens
   }
 }
 
-std::vector<std::shared_ptr<inference::MSTensor>> TransformVectorRefToMultiTensor(const VectorRef &base_ref) {
-  std::vector<std::shared_ptr<inference::MSTensor>> msTensors;
+std::vector<tensor::TensorPtr> TransformVectorRefToMultiTensor(const VectorRef &base_ref) {
+  std::vector<tensor::TensorPtr> msTensors;
   if (utils::isa<VectorRef>(base_ref)) {
     auto ref_list = utils::cast<VectorRef>(base_ref);
     IterateFindTensor(&msTensors, ref_list);
   } else if (utils::isa<tensor::Tensor>(base_ref)) {
     auto tensor_ptr = utils::cast<std::shared_ptr<tensor::Tensor>>(base_ref);
     MS_EXCEPTION_IF_NULL(tensor_ptr);
-    auto tensor = new inference::Tensor(tensor_ptr);
-    msTensors.emplace_back(std::shared_ptr<inference::MSTensor>(tensor));
+    msTensors.emplace_back(tensor_ptr);
   } else {
     MS_LOG(EXCEPTION) << "The output is not a base ref list or a tensor!";
   }
   return msTensors;
 }
+
 }  // namespace mindspore

mindspore/ccsrc/utils/base_ref_utils.h

@@ -17,11 +17,12 @@
 #include <vector>
 #include <memory>
 #include "utils/base_ref.h"
-#include "include/ms_tensor.h"
+#include "include/infer_tensor.h"
+#include "ir/tensor.h"
 
 #ifndef MINDSPORE_CCSRC_UTILS_BASE_REF_UTILS_H
 #define MINDSPORE_CCSRC_UTILS_BASE_REF_UTILS_H
 namespace mindspore {
-std::vector<std::shared_ptr<inference::MSTensor>> TransformVectorRefToMultiTensor(const VectorRef &base_ref);
+std::vector<tensor::TensorPtr> TransformVectorRefToMultiTensor(const VectorRef &base_ref);
 }  // namespace mindspore
 #endif  // MINDSPORE_CCSRC_UTILS_BASE_REF_UTILS_H

mindspore/core/ir/lite/tensor.cc

@@ -85,68 +85,4 @@ bool Tensor::operator==(const Value &other) const {
   }
 }
 }  // namespace tensor
-
-namespace inference {
-MSTensor *MSTensor::CreateTensor(TypeId data_type, const std::vector<int> &shape) {
-  return new Tensor(data_type, shape);
-}
-
-Tensor::Tensor() { this->tensor_impl_ = std::make_shared<tensor::Tensor>(); }
-
-Tensor::Tensor(TypeId data_type, const std::vector<int> &shape) {
-  this->tensor_impl_ = std::make_shared<tensor::Tensor>(data_type, shape);
-}
-
-Tensor::Tensor(std::shared_ptr<tensor::Tensor> tensor_ptr) { this->tensor_impl_ = std::move(tensor_ptr); }
-
-TypeId Tensor::data_type() const {
-  MS_ASSERT(this->tensor_impl_ != nullptr);
-  return this->tensor_impl_->data_type();
-}
-
-TypeId Tensor::set_data_type(TypeId data_type) {
-  MS_ASSERT(this->tensor_impl_ != nullptr);
-  return this->tensor_impl_->set_data_type(data_type);
-}
-
-std::vector<int> Tensor::shape() const {
-  MS_ASSERT(this->tensor_impl_ != nullptr);
-  return this->tensor_impl_->shape();
-}
-
-size_t Tensor::set_shape(const std::vector<int> &shape) {
-  MS_ASSERT(this->tensor_impl_ != nullptr);
-  return this->tensor_impl_->set_shape(shape);
-}
-
-int Tensor::DimensionSize(size_t index) const {
-  MS_ASSERT(this->tensor_impl_ != nullptr);
-  return this->tensor_impl_->DimensionSize(index);
-}
-
-int Tensor::ElementsNum() const {
-  MS_ASSERT(this->tensor_impl_ != nullptr);
-  return this->tensor_impl_->ElementsNum();
-}
-
-std::size_t Tensor::hash() const {
-  MS_ASSERT(this->tensor_impl_ != nullptr);
-  return this->tensor_impl_->hash();
-}
-
-std::shared_ptr<tensor::Tensor> Tensor::tensor() const {
-  MS_ASSERT(this->tensor_impl_ != nullptr);
-  return this->tensor_impl_;
-}
-
-size_t Tensor::Size() const {
-  MS_ASSERT(this->tensor_impl_ != nullptr);
-  return this->tensor_impl_->Size();
-}
-
-void *Tensor::MutableData() const {
-  MS_ASSERT(this->tensor_impl_ != nullptr);
-  return this->tensor_impl_->data();
-}
-}  // namespace inference
 }  // namespace mindspore

mindspore/core/ir/lite/tensor.h

@@ -56,42 +56,6 @@ class Tensor : public MetaTensor {
 
 using TensorPtr = std::shared_ptr<Tensor>;
 }  // namespace tensor
-
-namespace inference {
-class Tensor : public MSTensor {
- public:
-  Tensor();
-
-  Tensor(TypeId data_type, const std::vector<int> &shape);
-
-  explicit Tensor(std::shared_ptr<tensor::Tensor> tensor_ptr);
-
-  ~Tensor() = default;
-
-  TypeId data_type() const override;
-
-  TypeId set_data_type(const TypeId data_type) override;
-
-  std::vector<int> shape() const override;
-
-  size_t set_shape(const std::vector<int> &shape) override;
-
-  int DimensionSize(size_t index) const override;
-
-  int ElementsNum() const override;
-
-  std::size_t hash() const override;
-
-  std::shared_ptr<tensor::Tensor> tensor() const;
-
-  size_t Size() const override;
-
-  void *MutableData() const override;
-
- protected:
-  std::shared_ptr<tensor::Tensor> tensor_impl_;
-};
-}  // namespace inference
 }  // namespace mindspore
 
 #endif  // MINDSPORE_CORE_IR_LITE_TENSOR_H_

mindspore/core/ir/tensor.cc

@@ -453,67 +453,4 @@ TypeId Tensor::set_data_type(const TypeId data_type) {
   return data_type;
 }
 }  // namespace tensor
-
-namespace inference {
-MSTensor *MSTensor::CreateTensor(TypeId data_type, const std::vector<int> &shape) {
-  return new Tensor(data_type, shape);
-}
-
-Tensor::Tensor(TypeId data_type, const std::vector<int> &shape) {
-  this->tensor_impl_ = std::make_shared<tensor::Tensor>(data_type, shape);
-}
-
-Tensor::Tensor(std::shared_ptr<tensor::Tensor> tensor_ptr) { this->tensor_impl_ = std::move(tensor_ptr); }
-
-TypeId Tensor::data_type() const {
-  MS_ASSERT(this->tensor_impl_ != nullptr);
-  return this->tensor_impl_->data_type();
-}
-
-TypeId Tensor::set_data_type(TypeId data_type) {
-  MS_ASSERT(this->tensor_impl_ != nullptr);
-  return this->tensor_impl_->set_data_type(data_type);
-}
-
-std::vector<int> Tensor::shape() const {
-  MS_ASSERT(this->tensor_impl_ != nullptr);
-  return this->tensor_impl_->shape();
-}
-
-size_t Tensor::set_shape(const std::vector<int> &shape) {
-  MS_ASSERT(this->tensor_impl_ != nullptr);
-  return this->tensor_impl_->set_shape(shape);
-}
-
-int Tensor::DimensionSize(size_t index) const {
-  MS_ASSERT(this->tensor_impl_ != nullptr);
-  return this->tensor_impl_->DimensionSize(index);
-}
-
-int Tensor::ElementsNum() const {
-  MS_ASSERT(this->tensor_impl_ != nullptr);
-  return this->tensor_impl_->ElementsNum();
-}
-
-std::size_t Tensor::hash() const {
-  MS_ASSERT(this->tensor_impl_ != nullptr);
-  return this->tensor_impl_->hash();
-}
-
-std::shared_ptr<tensor::Tensor> Tensor::tensor() const {
-  MS_ASSERT(this->tensor_impl_ != nullptr);
-  return this->tensor_impl_;
-}
-
-size_t Tensor::Size() const {
-  MS_ASSERT(this->tensor_impl_ != nullptr);
-  return this->tensor_impl_->data().nbytes();
-}
-
-void *Tensor::MutableData() const {
-  MS_ASSERT(this->tensor_impl_ != nullptr);
-  return this->tensor_impl_->data_c();
-}
-
-}  // namespace inference
 }  // namespace mindspore

mindspore/core/ir/tensor.h

@@ -25,7 +25,6 @@
 #include "Eigen/Core"
 #include "ir/device_sync.h"
 #include "ir/meta_tensor.h"
-#include "include/ms_tensor.h"
 #include "utils/log_adapter.h"
 
 using float16 = Eigen::half;
@@ -237,40 +236,6 @@ class Tensor : public MetaTensor {
 using TensorPtr = std::shared_ptr<Tensor>;
 using TensorPtrList = std::vector<std::shared_ptr<Tensor>>;
 }  // namespace tensor
-
-namespace inference {
-class Tensor : public MSTensor {
- public:
-  Tensor(TypeId data_type, const std::vector<int> &shape);
-
-  explicit Tensor(std::shared_ptr<tensor::Tensor> tensor_ptr);
-
-  ~Tensor() = default;
-
-  TypeId data_type() const override;
-
-  TypeId set_data_type(const TypeId data_type) override;
-
-  std::vector<int> shape() const override;
-
-  size_t set_shape(const std::vector<int> &shape) override;
-
-  int DimensionSize(size_t index) const override;
-
-  int ElementsNum() const override;
-
-  std::size_t hash() const override;
-
-  std::shared_ptr<tensor::Tensor> tensor() const;
-
-  size_t Size() const override;
-
-  void *MutableData() const override;
-
- protected:
-  std::shared_ptr<tensor::Tensor> tensor_impl_;
-};
-}  // namespace inference
 }  // namespace mindspore
 
 #endif  // MINDSPORE_CORE_IR_TENSOR_H_

serving/CMakeLists.txt

@@ -13,19 +13,19 @@ add_library(protobuf::libprotobuf ALIAS protobuf::protobuf)
 add_executable(protobuf::libprotoc ALIAS protobuf::protoc)
 
 set(_PROTOBUF_LIBPROTOBUF protobuf::libprotobuf)
-if(CMAKE_CROSSCOMPILING)
+if (CMAKE_CROSSCOMPILING)
     find_program(_PROTOBUF_PROTOC protoc)
-else()
+else ()
     set(_PROTOBUF_PROTOC $<TARGET_FILE:protobuf::protoc>)
-endif()
+endif ()
 
 # Find gRPC installation
 # Looks for gRPCConfig.cmake file installed by gRPC's cmake installation.
 if (EXISTS ${grpc_ROOT}/lib64)
     set(gRPC_DIR "${grpc_ROOT}/lib64/cmake/grpc")
-else()
+else ()
     set(gRPC_DIR "${grpc_ROOT}/lib/cmake/grpc")
-endif()
+endif ()
 message("serving using grpc_DIR : " ${gPRC_DIR})
 
 find_package(gRPC CONFIG REQUIRED)
@@ -34,11 +34,11 @@ message(STATUS "Using gRPC ${gRPC_VERSION}")
 set(_GRPC_GRPCPP gRPC::grpc++)
 set(_REFLECTION gRPC::grpc++_reflection)
 
-if(CMAKE_CROSSCOMPILING)
+if (CMAKE_CROSSCOMPILING)
     find_program(_GRPC_CPP_PLUGIN_EXECUTABLE grpc_cpp_plugin)
-else()
+else ()
     set(_GRPC_CPP_PLUGIN_EXECUTABLE $<TARGET_FILE:gRPC::grpc_cpp_plugin>)
-endif()
+endif ()
 
 # Proto file
 get_filename_component(hw_proto "ms_service.proto" ABSOLUTE)
@@ -67,11 +67,36 @@ file(GLOB_RECURSE CORE_SRC_LIST RELATIVE ${CMAKE_CURRENT_SOURCE_DIR}
 
 list(APPEND SERVING_SRC "main.cc" ${hw_proto_srcs} ${hw_grpc_srcs} ${CORE_SRC_LIST})
 
+option(ENABLE_ACL "enable acl" OFF)
+
+if (ENABLE_ACL)
+    if (DEFINED ENV{ASCEND_CUSTOM_PATH})
+        set(ASCEND_PATH $ENV{ASCEND_CUSTOM_PATH})
+    else ()
+        set(ASCEND_PATH /usr/local/Ascend)
+    endif ()
+    set(ACL_LIB_DIR ${ASCEND_PATH}/acllib/)
+    MESSAGE("acl lib dir " ${ACL_LIB_DIR})
+
+    include_directories(${ACL_LIB_DIR}/include/)
+    file(GLOB_RECURSE ACL_SESSION_SRC_LIST RELATIVE ${CMAKE_CURRENT_SOURCE_DIR} "acl/*.cc")
+    list(APPEND SERVING_SRC ${ACL_SESSION_SRC_LIST})
+endif ()
+
 include_directories(${CMAKE_BINARY_DIR})
 add_executable(ms_serving ${SERVING_SRC})
-target_link_libraries(ms_serving inference mindspore_gvar)
+
 target_link_libraries(ms_serving ${_REFLECTION} ${_GRPC_GRPCPP} ${_PROTOBUF_LIBPROTOBUF} pthread)
 if (ENABLE_D)
     add_compile_definitions(ENABLE_D)
     target_link_libraries(ms_serving ${RUNTIME_LIB})
-endif()
+endif ()
+
+if (ENABLE_ACL)
+    add_compile_definitions(ENABLE_ACL)
+    set(ALC_LIB_SO ${ACL_LIB_DIR}/lib64/libruntime.so ${ACL_LIB_DIR}/lib64/libascendcl.so
+            ${ACL_LIB_DIR}/lib64/libacl_retr.so ${ACL_LIB_DIR}/lib64/libacl_cblas.so)
+    target_link_libraries(ms_serving ${ALC_LIB_SO})
+else ()
+    target_link_libraries(ms_serving inference mindspore_gvar)
+endif ()

serving/acl/acl_session.cc

+/**
+ * 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 <memory>
+#include <algorithm>
+#include "serving/acl/acl_session.h"
+#include "include/infer_log.h"
+
+namespace mindspore::inference {
+
+std::shared_ptr<InferSession> InferSession::CreateSession(const std::string &device, uint32_t device_id) {
+  try {
+    auto session = std::make_shared<AclSession>();
+    auto ret = session->InitEnv(device, device_id);
+    if (!ret) {
+      return nullptr;
+    }
+    return session;
+  } catch (std::exception &e) {
+    MSI_LOG_ERROR << "Inference CreatSession failed";
+    return nullptr;
+  }
+}
+
+bool AclSession::LoadModelFromFile(const std::string &file_name, uint32_t &model_id) {
+  return model_process_.LoadModelFromFile(file_name, model_id);
+}
+
+bool AclSession::UnloadModel(uint32_t model_id) {
+  model_process_.UnLoad();
+  return true;
+}
+
+bool AclSession::ExecuteModel(uint32_t model_id, const RequestBase &request,
+                              ReplyBase &reply) {  // set d context
+  aclError rt_ret = aclrtSetCurrentContext(context_);
+  if (rt_ret != ACL_ERROR_NONE) {
+    MSI_LOG_ERROR << "set the ascend device context failed";
+    return false;
+  }
+  return model_process_.Execute(request, reply);
+}
+
+bool AclSession::InitEnv(const std::string &device_type, uint32_t device_id) {
+  device_type_ = device_type;
+  device_id_ = device_id;
+  auto ret = aclInit(nullptr);
+  if (ret != ACL_ERROR_NONE) {
+    MSI_LOG_ERROR << "Execute aclInit Failed";
+    return false;
+  }
+  MSI_LOG_INFO << "acl init success";
+
+  ret = aclrtSetDevice(device_id_);
+  if (ret != ACL_ERROR_NONE) {
+    MSI_LOG_ERROR << "acl open device " << device_id_ << " failed";
+    return false;
+  }
+  MSI_LOG_INFO << "open device " << device_id_ << " success";
+
+  ret = aclrtCreateContext(&context_, device_id_);
+  if (ret != ACL_ERROR_NONE) {
+    MSI_LOG_ERROR << "acl create context failed";
+    return false;
+  }
+  MSI_LOG_INFO << "create context success";
+
+  ret = aclrtCreateStream(&stream_);
+  if (ret != ACL_ERROR_NONE) {
+    MSI_LOG_ERROR << "acl create stream failed";
+    return false;
+  }
+  MSI_LOG_INFO << "create stream success";
+
+  aclrtRunMode run_mode;
+  ret = aclrtGetRunMode(&run_mode);
+  if (ret != ACL_ERROR_NONE) {
+    MSI_LOG_ERROR << "acl get run mode failed";
+    return false;
+  }
+  bool is_device = (run_mode == ACL_DEVICE);
+  model_process_.SetIsDevice(is_device);
+  MSI_LOG_INFO << "get run mode success is device input/output " << is_device;
+
+  MSI_LOG_INFO << "Init acl success, device id " << device_id_;
+  return true;
+}
+
+bool AclSession::FinalizeEnv() {
+  aclError ret;
+  if (stream_ != nullptr) {
+    ret = aclrtDestroyStream(stream_);
+    if (ret != ACL_ERROR_NONE) {
+      MSI_LOG_ERROR << "destroy stream failed";
+    }
+    stream_ = nullptr;
+  }
+  MSI_LOG_INFO << "end to destroy stream";
+  if (context_ != nullptr) {
+    ret = aclrtDestroyContext(context_);
+    if (ret != ACL_ERROR_NONE) {
+      MSI_LOG_ERROR << "destroy context failed";
+    }
+    context_ = nullptr;
+  }
+  MSI_LOG_INFO << "end to destroy context";
+
+  ret = aclrtResetDevice(device_id_);
+  if (ret != ACL_ERROR_NONE) {
+    MSI_LOG_ERROR << "reset devie " << device_id_ << " failed";
+  }
+  MSI_LOG_INFO << "end to reset device " << device_id_;
+
+  ret = aclFinalize();
+  if (ret != ACL_ERROR_NONE) {
+    MSI_LOG_ERROR << "finalize acl failed";
+  }
+  MSI_LOG_INFO << "end to finalize acl";
+  return true;
+}
+
+AclSession::AclSession() = default;
+}  // namespace mindspore::inference

mindspore/ccsrc/backend/session/session.h → serving/acl/acl_session.h

@@ -13,8 +13,8 @@
  * See the License for the specific language governing permissions and
  * limitations under the License.
  */
-#ifndef MINDSPORE_CCSRC_BACKEND_SESSION_SESSION_H
-#define MINDSPORE_CCSRC_BACKEND_SESSION_SESSION_H
+#ifndef MINDSPORE_SERVING_ACL_SESSION_H
+#define MINDSPORE_SERVING_ACL_SESSION_H
 
 #include <vector>
 #include <string>
@@ -23,31 +23,28 @@
 #include <memory>
 #include <map>
 
-#include "backend/session/session_basic.h"
-#include "ir/anf.h"
 #include "include/inference.h"
+#include "serving/acl/model_process.h"
 
 namespace mindspore {
 namespace inference {
-class Session : public MSSession {
+class AclSession : public InferSession {
  public:
-  Session();
+  AclSession();
 
-  uint32_t CompileGraph(std::shared_ptr<FuncGraph> funcGraphPtr) override;
-
-  MultiTensor RunGraph(uint32_t graph_id, const std::vector<std::shared_ptr<inference::MSTensor>> &inputs) override;
-
-  bool CheckModelInputs(uint32_t graph_id,
-                        const std::vector<std::shared_ptr<inference::MSTensor>> &inputs) const override;
-
-  int Init(const std::string &device, uint32_t device_id);
-
-  static void RegAllOp();
+  bool InitEnv(const std::string &device_type, uint32_t device_id) override;
+  bool FinalizeEnv() override;
+  bool LoadModelFromFile(const std::string &file_name, uint32_t &model_id) override;
+  bool UnloadModel(uint32_t model_id) override;
+  bool ExecuteModel(uint32_t model_id, const RequestBase &request, ReplyBase &reply) override;
 
  private:
-  std::shared_ptr<session::SessionBasic> session_impl_ = nullptr;
-  std::vector<uint32_t> graph_id_;
+  std::string device_type_;
+  int32_t device_id_;
+  aclrtStream stream_ = nullptr;
+  aclrtContext context_ = nullptr;
+  ModelProcess model_process_;
 };
 }  // namespace inference
 }  // namespace mindspore
-#endif  // MINDSPORE_CCSRC_BACKEND_SESSION_SESSION_BASIC_H
+#endif  // MINDSPORE_SERVING_ACL_SESSION_H

serving/acl/model_process.cc

+/**
+ * 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 "serving/acl/model_process.h"
+#include <algorithm>
+#include <unordered_map>
+
+#include "include/infer_log.h"
+
+namespace mindspore {
+namespace inference {
+
+bool ModelProcess::LoadModelFromFile(const std::string &file_name, uint32_t &model_id) {
+  aclError acl_ret = aclmdlLoadFromFile(file_name.c_str(), &model_id);
+  if (acl_ret != ACL_ERROR_NONE) {
+    MSI_LOG_ERROR << "Read model file failed, file name is " << file_name;
+    return false;
+  }
+  MSI_LOG_INFO << "Load model success " << file_name;
+
+  model_desc_ = aclmdlCreateDesc();
+  acl_ret = aclmdlGetDesc(model_desc_, model_id);
+  if (acl_ret != ACL_ERROR_NONE) {
+    MSI_LOG_ERROR << "Read model desc failed";
+    return false;
+  }
+  bool ret = InitInputsBuffer();
+  if (!ret) {
+    MSI_LOG_ERROR << "Create input buffer failed";
+    return false;
+  }
+  ret = InitOutputsBuffer();
+  if (!ret) {
+    MSI_LOG_ERROR << "Create output buffer failed";
+    return false;
+  }
+  model_id_ = model_id;
+  return true;
+}
+
+bool ModelProcess::InitInputsBuffer() {
+  aclError ret;
+  inputs_ = aclmdlCreateDataset();
+  if (inputs_ == nullptr) {
+    MSI_LOG_ERROR << "Create input dataset failed";
+    return false;
+  }
+  size_t input_size = aclmdlGetNumInputs(model_desc_);
+
+  for (size_t i = 0; i < input_size; ++i) {
+    auto buffer_size = aclmdlGetInputSizeByIndex(model_desc_, i);
+    void *data_mem_buffer = nullptr;
+    if (!is_run_on_device_) {  // need to copy input/output to/from device
+      ret = aclrtMalloc(&data_mem_buffer, buffer_size, ACL_MEM_MALLOC_NORMAL_ONLY);
+      if (ret != ACL_ERROR_NONE) {
+        MSI_LOG_ERROR << "Malloc device input buffer faild , input size " << buffer_size;
+        return false;
+      }
+    }
+
+    aclmdlIODims dims;
+    ret = aclmdlGetInputDims(model_desc_, i, &dims);
+    if (ret != ACL_ERROR_NONE) {
+      MSI_LOG_ERROR << "Get input shape failed";
+      return false;
+    }
+    aclDataType dataType = aclmdlGetInputDataType(model_desc_, i);
+    std::vector<int64_t> shape(dims.dims, dims.dims + dims.dimCount);
+    input_infos_.emplace_back(AclTensorInfo{data_mem_buffer, buffer_size, dataType, shape});
+  }
+  MSI_LOG_INFO << "Create model inputs success";
+  return true;
+}
+
+bool ModelProcess::CreateDataBuffer(void *&data_mem_buffer, size_t buffer_size, aclmdlDataset *dataset) {
+  aclError ret;
+  auto free_data_buffer = [this](void *dataMemBuffer) {
+    if (!is_run_on_device_) {
+      aclrtFree(dataMemBuffer);
+    } else {
+      aclrtFreeHost(dataMemBuffer);
+    }
+  };
+  if (!is_run_on_device_) {
+    ret = aclrtMalloc(&data_mem_buffer, buffer_size, ACL_MEM_MALLOC_NORMAL_ONLY);
+    if (ret != ACL_ERROR_NONE) {
+      MSI_LOG_ERROR << "Malloc device buffer faild , buffer size " << buffer_size;
+      return false;
+    }
+  } else {
+    ret = aclrtMallocHost(&data_mem_buffer, buffer_size);
+    if (ret != ACL_ERROR_NONE) {
+      MSI_LOG_ERROR << "Malloc device buffer faild , buffer size " << buffer_size;
+      return false;
+    }
+  }
+
+  auto data_buffer = aclCreateDataBuffer(data_mem_buffer, buffer_size);
+  if (data_buffer == nullptr) {
+    MSI_LOG_ERROR << "Create Data Buffer failed";
+    free_data_buffer(data_mem_buffer);
+    return false;
+  }
+  ret = aclmdlAddDatasetBuffer(dataset, data_buffer);
+  if (ret != ACL_ERROR_NONE) {
+    MSI_LOG_ERROR << "add data buffer failed";
+    free_data_buffer(data_mem_buffer);
+    aclDestroyDataBuffer(data_buffer);
+    return false;
+  }
+  return true;
+}
+
+bool ModelProcess::InitOutputsBuffer() {
+  aclError ret;
+  outputs_ = aclmdlCreateDataset();
+  if (outputs_ == nullptr) {
+    MSI_LOG_ERROR << "Create input dataset failed";
+    return false;
+  }
+  size_t output_size = aclmdlGetNumOutputs(model_desc_);
+  for (size_t i = 0; i < output_size; ++i) {
+    auto buffer_size = aclmdlGetOutputSizeByIndex(model_desc_, i);
+
+    void *data_mem_buffer = nullptr;
+    if (CreateDataBuffer(data_mem_buffer, buffer_size, outputs_) != true) {
+      MSI_LOG_ERROR << "add output data buffer failed, buffer size " << buffer_size;
+      return false;
+    }
+    aclmdlIODims dims;
+    ret = aclmdlGetOutputDims(model_desc_, i, &dims);
+    if (ret != ACL_ERROR_NONE) {
+      MSI_LOG_ERROR << "Get input shape failed";
+      return false;
+    }
+    aclDataType dataType = aclmdlGetOutputDataType(model_desc_, i);
+    std::vector<int64_t> shape(dims.dims, dims.dims + dims.dimCount);
+    output_infos_.emplace_back(AclTensorInfo{data_mem_buffer, buffer_size, dataType, shape});
+  }
+  MSI_LOG_INFO << "Create model output success";
+  return true;
+}
+
+void ModelProcess::DestroyInputsDataset() {
+  if (inputs_ == nullptr) {
+    return;
+  }
+  for (size_t i = 0; i < aclmdlGetDatasetNumBuffers(inputs_); i++) {
+    auto dataBuffer = aclmdlGetDatasetBuffer(inputs_, i);
+    aclDestroyDataBuffer(dataBuffer);
+  }
+  aclmdlDestroyDataset(inputs_);
+  inputs_ = nullptr;
+}
+
+void ModelProcess::DestroyInputsDataMem() {
+  if (!is_run_on_device_) {
+    for (const auto &item : input_infos_) {
+      aclrtFree(item.device_data);
+    }
+  }
+  input_infos_.clear();
+}
+
+void ModelProcess::DestroyInputsBuffer() {
+  DestroyInputsDataset();
+  DestroyInputsDataMem();
+}
+
+void ModelProcess::DestroyOutputsBuffer() {
+  if (outputs_ == nullptr) {
+    return;
+  }
+  for (size_t i = 0; i < aclmdlGetDatasetNumBuffers(outputs_); i++) {
+    auto dataBuffer = aclmdlGetDatasetBuffer(outputs_, i);
+    auto data = aclGetDataBufferAddr(dataBuffer);
+    if (!is_run_on_device_) {
+      aclrtFree(data);
+    } else {
+      aclrtFreeHost(data);
+    }
+    aclDestroyDataBuffer(dataBuffer);
+  }
+  aclmdlDestroyDataset(outputs_);
+  outputs_ = nullptr;
+  output_infos_.clear();
+}
+
+void ModelProcess::UnLoad() {
+  auto ret = aclmdlUnload(model_id_);
+  if (ret != ACL_ERROR_NONE) {
+    MSI_LOG_ERROR << "Unload model failed";
+  }
+  if (model_desc_ != nullptr) {
+    aclmdlDestroyDesc(model_desc_);
+    model_desc_ = nullptr;
+  }
+  DestroyInputsBuffer();
+  DestroyOutputsBuffer();
+  MSI_LOG_INFO << "End unload model " << model_id_;
+}
+
+bool ModelProcess::CheckAndInitInput(const RequestBase &request) {
+  aclError ret;
+  inputs_ = aclmdlCreateDataset();
+  // check inputs
+  if (request.size() != input_infos_.size()) {
+    MSI_LOG_ERROR << "inputs count not match, required count " << input_infos_.size() << ", given count "
+                  << request.size();
+    return false;
+  }
+  for (size_t i = 0; i < input_infos_.size(); i++) {
+    if (request[i] == nullptr) {
+      MSI_LOG_ERROR << "input " << i << " cannot be null";
+      return false;
+    }
+    if (request[i]->data_size() != input_infos_[i].buffer_size) {
+      MSI_LOG_ERROR << "input " << i << " data size not match, required size " << input_infos_[i].buffer_size
+                    << ", given count " << request[i]->data_size();
+      return false;
+    }
+  }
+  // copy inputs
+  for (size_t i = 0; i < input_infos_.size(); i++) {
+    void *input_buffer = nullptr;
+    auto &info = input_infos_[i];
+    const void *data = request[i]->data();
+    if (!is_run_on_device_) {
+      ret = aclrtMemcpy(info.device_data, info.buffer_size, data, request[i]->data_size(), ACL_MEMCPY_HOST_TO_DEVICE);
+      if (ret != ACL_ERROR_NONE) {
+        MSI_LOG_ERROR << "memcpy input " << i << " data to device failed, buffer size " << request[i]->data_size();
+        return false;
+      }
+      input_buffer = info.device_data;
+    } else {
+      input_buffer = const_cast<void *>(data);
+    }
+    auto data_buffer = aclCreateDataBuffer(input_buffer, info.buffer_size);
+    if (data_buffer == nullptr) {
+      MSI_LOG_ERROR << "Create Data Buffer failed";
+      return false;
+    }
+    ret = aclmdlAddDatasetBuffer(inputs_, data_buffer);
+    if (ret != ACL_ERROR_NONE) {
+      MSI_LOG_ERROR << "add data buffer failed";
+      aclDestroyDataBuffer(data_buffer);
+      return false;
+    }
+  }
+  return true;
+}
+
+bool ModelProcess::BuildOutputs(ReplyBase &reply) {
+  aclError ret;
+  // copy outputs
+  reply.clear();
+
+  std::unordered_map<aclDataType, inference::DataType> dataTypeMap = {
+    {ACL_FLOAT16, inference::kMSI_Float16}, {ACL_FLOAT, inference::kMSI_Float32}, {ACL_DOUBLE, inference::kMSI_Float64},
+    {ACL_INT8, inference::kMSI_Int8},       {ACL_INT16, inference::kMSI_Int16},   {ACL_INT32, inference::kMSI_Int32},
+    {ACL_INT64, inference::kMSI_Int64},     {ACL_UINT8, inference::kMSI_Uint8},   {ACL_UINT16, inference::kMSI_Uint16},
+    {ACL_UINT32, inference::kMSI_Uint32},   {ACL_UINT64, inference::kMSI_Uint64}, {ACL_BOOL, inference::kMSI_Bool},
+  };
+  auto trans_to_serving_type = [&dataTypeMap](aclDataType data_type) {
+    auto it = dataTypeMap.find(data_type);
+    if (it == dataTypeMap.end()) {
+      return inference::kMSI_Unknown;
+    } else {
+      return it->second;
+    }
+  };
+  for (size_t i = 0; i < output_infos_.size(); i++) {
+    auto &info = output_infos_[i];
+    auto output = reply.add();
+    if (output == nullptr) {
+      MSI_LOG_ERROR << "add new output failed";
+      return false;
+    }
+    output->set_data_type(trans_to_serving_type(info.data_type));
+    output->set_shape(info.dims);
+    if (!output->resize_data(info.buffer_size)) {
+      MSI_LOG_ERROR << "new output data buffer failed, data size " << info.buffer_size;
+      return false;
+    }
+    if (!is_run_on_device_) {
+      ret = aclrtMemcpy(output->mutable_data(), output->data_size(), info.device_data, info.buffer_size,
+                        ACL_MEMCPY_DEVICE_TO_HOST);
+      if (ret != ACL_ERROR_NONE) {
+        MSI_LOG_ERROR << "Memcpy output " << i << " to host failed, memory size " << info.buffer_size;
+        return false;
+      }
+    } else {
+      ret = aclrtMemcpy(output->mutable_data(), output->data_size(), info.device_data, info.buffer_size,
+                        ACL_MEMCPY_HOST_TO_HOST);
+      if (ret != ACL_ERROR_NONE) {
+        MSI_LOG_ERROR << "Memcpy output " << i << " to host failed, memory size " << info.buffer_size;
+        return false;
+      }
+    }
+  }
+  return true;
+}
+
+bool ModelProcess::Execute(const RequestBase &request, ReplyBase &reply) {
+  aclError acl_ret;
+  if (CheckAndInitInput(request) != true) {
+    MSI_LOG_ERROR << "check or init input failed";
+    DestroyInputsDataset();
+    return false;
+  }
+  acl_ret = aclmdlExecute(model_id_, inputs_, outputs_);
+  DestroyInputsDataset();
+  if (acl_ret != ACL_ERROR_NONE) {
+    MSI_LOG_ERROR << "Execute Model Failed";
+    return false;
+  }
+  bool ret = BuildOutputs(reply);
+  if (!ret) {
+    MSI_LOG_ERROR << "Build outputs faield";
+    return false;
+  }
+  MSI_LOG_INFO << "excute model success";
+  return true;
+}
+
+}  // namespace inference
+}  // namespace mindspore

include/ms_tensor.h → serving/acl/model_process.h

@@ -14,56 +14,61 @@
  * limitations under the License.
  */
 
-#ifndef MINDSPORE_INCLUDE_MS_TENSOR_H_
-#define MINDSPORE_INCLUDE_MS_TENSOR_H_
-
-#include <utility>
+#ifndef INC_MODEL_PROCESS_ACL
+#define INC_MODEL_PROCESS_ACL
 #include <vector>
-#include <memory>
-#include "mindspore/core/ir/dtype/type_id.h"
+#include <string>
+#include "acl/acl.h"
+#include "acl/acl_mdl.h"
+#include "acl/acl_rt.h"
+#include "serving/core/util/status.h"
+#include "include/inference.h"
 
 namespace mindspore {
-#define MS_API __attribute__((visibility("default")))
 namespace inference {
-class MS_API MSTensor {
- public:
-  MSTensor() = default;
-  // brief Create a MSTensor pointer.
-  //
-  // param data_type DataTypeId of tensor to be created.
-  // param shape Shape of tensor to be created.
-  // return MSTensor pointer.
-  static MSTensor *CreateTensor(TypeId data_type, const std::vector<int> &shape);
 
-  ~MSTensor() = default;
+struct AclTensorInfo {
+  void *device_data;
+  size_t buffer_size;
+  aclDataType data_type;
+  std::vector<int64_t> dims;
+};
 
-  virtual TypeId data_type() const = 0;
+class ModelProcess {
+ public:
+  ModelProcess() {}
+  ~ModelProcess() {}
 
-  virtual TypeId set_data_type(const TypeId data_type) = 0;
+  bool LoadModelFromFile(const std::string &file_name, uint32_t &model_id);
+  void UnLoad();
 
-  virtual std::vector<int> shape() const = 0;
+  // override this method to avoid request/reply data copy
+  bool Execute(const RequestBase &request, ReplyBase &reply);
 
-  virtual size_t set_shape(const std::vector<int> &shape) = 0;
+  void SetIsDevice(bool is_device) { is_run_on_device_ = is_device; }
 
-  virtual int DimensionSize(size_t index) const = 0;
-  // brief Get number of element in MSTensor.
-  //
-  // return Number of element in MSTensor.
-  virtual int ElementsNum() const = 0;
+ private:
+  uint32_t model_id_ = 0xffffffff;
+  bool is_run_on_device_ = false;
+  aclmdlDesc *model_desc_ = nullptr;
+  aclmdlDataset *inputs_ = nullptr;
+  aclmdlDataset *outputs_ = nullptr;
+  std::vector<AclTensorInfo> input_infos_;
+  std::vector<AclTensorInfo> output_infos_;
 
-  virtual std::size_t hash() const = 0;
-  // brief Get byte size of data in MSTensor.
-  //
-  // return Byte size of data in MSTensor.
-  virtual size_t Size() const = 0;
-  // brief Get pointer of data in MSTensor.
-  //
-  // The data pointer can be used to both write or read data in MSTensor.
-  //
-  // return A pointer points to data in MSTensor.
-  virtual void *MutableData() const = 0;
+  bool CreateDataBuffer(void *&data_mem_buffer, size_t buffer_size, aclmdlDataset *dataset);
+  bool CheckAndInitInput(const RequestBase &request);
+  bool BuildOutputs(ReplyBase &reply);
+
+  bool InitInputsBuffer();
+  bool InitOutputsBuffer();
+  void DestroyInputsDataset();
+  void DestroyInputsDataMem();
+  void DestroyInputsBuffer();
+  void DestroyOutputsBuffer();
 };
-using MultiTensor = std::vector<std::shared_ptr<inference::MSTensor>>;
+
 }  // namespace inference
 }  // namespace mindspore
-#endif  // MINDSPORE_INCLUDE_MS_TENSOR_H_
+
+#endif

serving/core/server.cc

@@ -23,14 +23,14 @@
 #include <utility>
 #include <memory>
 #include <future>
+#include <chrono>
 
-#include "mindspore/ccsrc/utils/log_adapter.h"
+#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 "mindspore/ccsrc/utils/context/ms_context.h"
 #include "core/util/file_system_operation.h"
-#include "graphengine/third_party/fwkacllib/inc/runtime/context.h"
+#include "core/serving_tensor.h"
 
 using ms_serving::MSService;
 using ms_serving::PredictReply;
@@ -38,12 +38,19 @@ using ms_serving::PredictRequest;
 
 namespace mindspore {
 namespace serving {
-using MSTensorPtr = std::shared_ptr<inference::MSTensor>;
+
+#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::MSSession::CreateSession(device, device_id);
+  session_ = inference::InferSession::CreateSession(device, device_id);
   if (session_ == nullptr) {
-    MS_LOG(ERROR) << "Creat Session Failed";
+    MSI_LOG(ERROR) << "Creat Session Failed";
     return FAILED;
   }
   device_type_ = device;
@@ -55,53 +62,56 @@ Session &Session::Instance() {
   return instance;
 }
 
-Status Session::Predict(const std::vector<MSTensorPtr> &inputs, inference::MultiTensor *outputs) {
-  if (last_graph_ == nullptr) {
-    MS_LOG(ERROR) << "the model has not loaded";
+Status Session::Predict(const PredictRequest &request, PredictReply &reply) {
+  if (!model_loaded_) {
+    MSI_LOG(ERROR) << "the model has not loaded";
     return FAILED;
   }
   if (session_ == nullptr) {
-    MS_LOG(ERROR) << "the inference session has not be initialized";
+    MSI_LOG(ERROR) << "the inference session has not be initialized";
     return FAILED;
   }
   std::lock_guard<std::mutex> lock(mutex_);
-  MS_LOG(INFO) << "run Predict";
+  MSI_LOG(INFO) << "run Predict";
 
-  if (!session_->CheckModelInputs(graph_id_, inputs)) {
-    MS_LOG(ERROR) << "Input error.";
+  ServingRequest serving_request(request);
+  ServingReply serving_reply(reply);
+
+  auto ret = session_->ExecuteModel(graph_id_, serving_request, serving_reply);
+  MSI_LOG(INFO) << "run Predict finished";
+  if (!ret) {
+    MSI_LOG(ERROR) << "execute model return failed";
     return FAILED;
   }
-
-  *outputs = session_->RunGraph(graph_id_, inputs);
-  MS_LOG(INFO) << "run Predict finished";
   return SUCCESS;
 }
 
 Status Session::Warmup(const MindSporeModelPtr model) {
   if (session_ == nullptr) {
-    MS_LOG(ERROR) << "The CreatDeviceSession should be called, before warmup";
+    MSI_LOG(ERROR) << "The CreatDeviceSession should be called, before warmup";
     return FAILED;
   }
   std::lock_guard<std::mutex> lock(mutex_);
-  size_t size = 0;
   std::string file_name = model->GetModelPath() + '/' + model->GetModelName();
-  char *graphBuf = ReadFile(file_name.c_str(), &size);
-  if (graphBuf == nullptr) {
-    MS_LOG(ERROR) << "Read model file failed, file name is " << file_name.c_str();
+  model_loaded_ = false;
+  MSI_TIME_STAMP_START(LoadModelFromFile)
+  auto ret = session_->LoadModelFromFile(file_name, graph_id_);
+  MSI_TIME_STAMP_END(LoadModelFromFile)
+  if (!ret) {
+    MSI_LOG(ERROR) << "Load graph model failed, file name is " << file_name.c_str();
     return FAILED;
   }
-  last_graph_ = inference::LoadModel(graphBuf, size, device_type_);
-  if (last_graph_ == nullptr) {
-    MS_LOG(ERROR) << "Load graph model failed, file name is " << file_name.c_str();
-    return FAILED;
-  }
-  graph_id_ = session_->CompileGraph(last_graph_);
-  MS_LOG(INFO) << "Session Warmup finished";
+  model_loaded_ = true;
+  MSI_LOG(INFO) << "Session Warmup finished";
   return SUCCESS;
 }
 
 Status Session::Clear() {
-  session_ = nullptr;
+  if (session_ != nullptr) {
+    session_->UnloadModel(graph_id_);
+    session_->FinalizeEnv();
+    session_ = nullptr;
+  }
   return SUCCESS;
 }
 
@@ -109,121 +119,30 @@ namespace {
 static const uint32_t uint32max = 0x7FFFFFFF;
 std::promise<void> exit_requested;
 
-const std::map<ms_serving::DataType, TypeId> type2id_map{
-  {ms_serving::MS_UNKNOWN, TypeId::kNumberTypeBegin},   {ms_serving::MS_BOOL, TypeId::kNumberTypeBool},
-  {ms_serving::MS_INT8, TypeId::kNumberTypeInt8},       {ms_serving::MS_UINT8, TypeId::kNumberTypeUInt8},
-  {ms_serving::MS_INT16, TypeId::kNumberTypeInt16},     {ms_serving::MS_UINT16, TypeId::kNumberTypeUInt16},
-  {ms_serving::MS_INT32, TypeId::kNumberTypeInt32},     {ms_serving::MS_UINT32, TypeId::kNumberTypeUInt32},
-  {ms_serving::MS_INT64, TypeId::kNumberTypeInt64},     {ms_serving::MS_UINT64, TypeId::kNumberTypeUInt64},
-  {ms_serving::MS_FLOAT16, TypeId::kNumberTypeFloat16}, {ms_serving::MS_FLOAT32, TypeId::kNumberTypeFloat32},
-  {ms_serving::MS_FLOAT64, TypeId::kNumberTypeFloat64},
-};
-
-const std::map<TypeId, ms_serving::DataType> id2type_map{
-  {TypeId::kNumberTypeBegin, ms_serving::MS_UNKNOWN},   {TypeId::kNumberTypeBool, ms_serving::MS_BOOL},
-  {TypeId::kNumberTypeInt8, ms_serving::MS_INT8},       {TypeId::kNumberTypeUInt8, ms_serving::MS_UINT8},
-  {TypeId::kNumberTypeInt16, ms_serving::MS_INT16},     {TypeId::kNumberTypeUInt16, ms_serving::MS_UINT16},
-  {TypeId::kNumberTypeInt32, ms_serving::MS_INT32},     {TypeId::kNumberTypeUInt32, ms_serving::MS_UINT32},
-  {TypeId::kNumberTypeInt64, ms_serving::MS_INT64},     {TypeId::kNumberTypeUInt64, ms_serving::MS_UINT64},
-  {TypeId::kNumberTypeFloat16, ms_serving::MS_FLOAT16}, {TypeId::kNumberTypeFloat32, ms_serving::MS_FLOAT32},
-  {TypeId::kNumberTypeFloat64, ms_serving::MS_FLOAT64},
-};
-const std::map<ms_serving::DataType, size_t> length_map{
-  {ms_serving::MS_UNKNOWN, 0},
-  {ms_serving::MS_BOOL, sizeof(bool)},
-  {ms_serving::MS_INT8, sizeof(int8_t)},
-  {ms_serving::MS_UINT8, sizeof(uint8_t)},
-  {ms_serving::MS_INT16, sizeof(int16_t)},
-  {ms_serving::MS_UINT16, sizeof(uint16_t)},
-  {ms_serving::MS_INT32, sizeof(int32_t)},
-  {ms_serving::MS_UINT32, sizeof(uint32_t)},
-  {ms_serving::MS_INT64, sizeof(int64_t)},
-  {ms_serving::MS_UINT64, sizeof(uint64_t)},
-  {ms_serving::MS_FLOAT16, 2},
-  {ms_serving::MS_FLOAT32, 4},
-  {ms_serving::MS_FLOAT64, 8},
-};
-MSTensorPtr ServingTensor2MSTensor(const ms_serving::Tensor &tensor) {
-  std::vector<int> shape;
-  for (auto dim : tensor.tensor_shape().dims()) {
-    shape.push_back(static_cast<int>(dim));
-  }
-  auto iter = type2id_map.find(tensor.tensor_type());
-  if (iter == type2id_map.end()) {
-    MS_LOG(ERROR) << "input tensor type is wrong, type is " << tensor.tensor_type();
-    return nullptr;
-  }
-  TypeId type = iter->second;
-  auto ms_tensor = std::shared_ptr<inference::MSTensor>(inference::MSTensor::CreateTensor(type, shape));
-  memcpy_s(ms_tensor->MutableData(), ms_tensor->Size(), tensor.data().data(), tensor.data().size());
-  return ms_tensor;
-}
-
-ms_serving::Tensor MSTensor2ServingTensor(MSTensorPtr ms_tensor) {
-  ms_serving::Tensor tensor;
-  ms_serving::TensorShape shape;
-  for (auto dim : ms_tensor->shape()) {
-    shape.add_dims(dim);
-  }
-  *tensor.mutable_tensor_shape() = shape;
-  auto iter = id2type_map.find(ms_tensor->data_type());
-  if (iter == id2type_map.end()) {
-    MS_LOG(ERROR) << "input tensor type is wrong, type is " << tensor.tensor_type();
-    return tensor;
-  }
-  tensor.set_tensor_type(iter->second);
-  tensor.set_data(ms_tensor->MutableData(), ms_tensor->Size());
-  return tensor;
-}
-
 void ClearEnv() {
   Session::Instance().Clear();
-  inference::ExitInference();
+  // inference::ExitInference();
 }
 void HandleSignal(int sig) { exit_requested.set_value(); }
 
-#ifdef ENABLE_D
-static rtContext_t g_ctx = nullptr;
-#endif
 }  // namespace
 
 // Service Implement
 class MSServiceImpl final : public MSService::Service {
   grpc::Status Predict(grpc::ServerContext *context, const PredictRequest *request, PredictReply *reply) override {
     std::lock_guard<std::mutex> lock(mutex_);
-#ifdef ENABLE_D
-    if (g_ctx == nullptr) {
-      MS_LOG(ERROR) << "rtCtx is nullptr";
-      return grpc::Status::CANCELLED;
-    }
-    rtError_t rt_ret = rtCtxSetCurrent(g_ctx);
-    if (rt_ret != RT_ERROR_NONE) {
-      MS_LOG(ERROR) << "set Ascend rtCtx failed";
-    }
-#endif
-    std::vector<MSTensorPtr> inputs;
-    inference::MultiTensor outputs;
-    for (int i = 0; i < request->data_size(); i++) {
-      auto input = ServingTensor2MSTensor(request->data(i));
-      if (input == nullptr) {
-        MS_LOG(ERROR) << "Tensor convert failed";
-        return grpc::Status::CANCELLED;
-      }
-      inputs.push_back(input);
-    }
-    auto res = Session::Instance().Predict(inputs, &outputs);
+    MSI_TIME_STAMP_START(Predict)
+    auto res = Session::Instance().Predict(*request, *reply);
+    MSI_TIME_STAMP_END(Predict)
     if (res != SUCCESS) {
       return grpc::Status::CANCELLED;
     }
-    for (const auto &tensor : outputs) {
-      *reply->add_result() = MSTensor2ServingTensor(tensor);
-    }
-    MS_LOG(INFO) << "Finish call service Eval";
+    MSI_LOG(INFO) << "Finish call service Eval";
     return grpc::Status::OK;
   }
 
   grpc::Status Test(grpc::ServerContext *context, const PredictRequest *request, PredictReply *reply) override {
-    MS_LOG(INFO) << "TestService call";
+    MSI_LOG(INFO) << "TestService call";
     return grpc::Status::OK;
   }
   std::mutex mutex_;
@@ -242,28 +161,17 @@ Status Server::BuildAndStart() {
   auto device_id = option_args->device_id;
   res = Session::Instance().CreatDeviceSession(device_type, device_id);
   if (res != SUCCESS) {
-    MS_LOG(ERROR) << "creat session failed";
+    MSI_LOG(ERROR) << "creat session failed";
     ClearEnv();
     return res;
   }
   VersionController version_controller(option_args->poll_model_wait_seconds, model_path, model_name);
   res = version_controller.Run();
   if (res != SUCCESS) {
-    MS_LOG(ERROR) << "load model failed";
+    MSI_LOG(ERROR) << "load model failed";
     ClearEnv();
     return res;
   }
-#ifdef ENABLE_D
-  // set d context
-  rtContext_t ctx = nullptr;
-  rtError_t rt_ret = rtCtxGetCurrent(&ctx);
-  if (rt_ret != RT_ERROR_NONE || ctx == nullptr) {
-    MS_LOG(ERROR) << "the ascend device context is null";
-    ClearEnv();
-    return FAILED;
-  }
-  g_ctx = ctx;
-#endif
   MSServiceImpl ms_service;
   grpc::EnableDefaultHealthCheckService(true);
   grpc::reflection::InitProtoReflectionServerBuilderPlugin();
@@ -276,13 +184,13 @@ Status Server::BuildAndStart() {
   serverBuilder.RegisterService(&ms_service);
   std::unique_ptr<grpc::Server> server(serverBuilder.BuildAndStart());
   if (server == nullptr) {
-    MS_LOG(ERROR) << "The serving server create failed";
+    MSI_LOG(ERROR) << "The serving server create failed";
     ClearEnv();
     return FAILED;
   }
   auto grpc_server_run = [&server]() { server->Wait(); };
   std::thread serving_thread(grpc_server_run);
-  MS_LOG(INFO) << "MS Serving listening on " << server_address;
+  MSI_LOG(INFO) << "MS Serving listening on " << server_address;
   auto exit_future = exit_requested.get_future();
   exit_future.wait();
   ClearEnv();

serving/core/server.h

@@ -23,14 +23,21 @@
 #include "util/status.h"
 #include "version_control/model.h"
 #include "include/inference.h"
-#include "mindspore/ccsrc/debug/info.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;
+
 class Session {
  public:
   static Session &Instance();
   Status CreatDeviceSession(const std::string &device, uint32_t device_id);
-  Status Predict(const std::vector<std::shared_ptr<inference::MSTensor>> &inputs, inference::MultiTensor *output);
+  // Status Predict(const inference::MultiTensor &inputs, inference::MultiTensor &output);
+  Status Predict(const PredictRequest &request, PredictReply &reply);
   Status Warmup(const MindSporeModelPtr model);
   Status Clear();
 
@@ -38,8 +45,8 @@ class Session {
   Session() = default;
   ~Session() = default;
   int sesseion_id_{0};
-  std::shared_ptr<inference::MSSession> session_{nullptr};
-  FuncGraphPtr last_graph_{nullptr};
+  std::shared_ptr<inference::InferSession> session_{nullptr};
+  bool model_loaded_ = false;
   uint32_t graph_id_{0};
   std::mutex mutex_;
   std::string device_type_;

serving/core/serving_tensor.cc

+/**
+ * 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/serving_tensor.h"
+#include <vector>
+#include <unordered_map>
+#include <string>
+#include <algorithm>
+#include "include/infer_log.h"
+
+using std::string;
+using std::unordered_map;
+using std::vector;
+
+namespace mindspore {
+namespace serving {
+
+using inference::DataType;
+using inference::InferTensorBase;
+
+const size_t kMaxShapeElementCount = INT32_MAX;
+const size_t kMaxDataBufferSize = UINT32_MAX;
+
+ServingTensor::ServingTensor(ms_serving::Tensor &other) : tensor_(other) {}
+
+ServingTensor::~ServingTensor() {}
+
+DataType ServingTensor::data_type() const {
+  const std::unordered_map<ms_serving::DataType, inference::DataType> type2id_map{
+    {ms_serving::MS_UNKNOWN, inference::kMSI_Unknown}, {ms_serving::MS_BOOL, inference::kMSI_Bool},
+    {ms_serving::MS_INT8, inference::kMSI_Int8},       {ms_serving::MS_UINT8, inference::kMSI_Uint8},
+    {ms_serving::MS_INT16, inference::kMSI_Int16},     {ms_serving::MS_UINT16, inference::kMSI_Uint16},
+    {ms_serving::MS_INT32, inference::kMSI_Int32},     {ms_serving::MS_UINT32, inference::kMSI_Uint32},
+    {ms_serving::MS_INT64, inference::kMSI_Int64},     {ms_serving::MS_UINT64, inference::kMSI_Uint64},
+    {ms_serving::MS_FLOAT16, inference::kMSI_Float16}, {ms_serving::MS_FLOAT32, inference::kMSI_Float32},
+    {ms_serving::MS_FLOAT64, inference::kMSI_Float64},
+  };
+  auto it = type2id_map.find(tensor_.tensor_type());
+  if (it == type2id_map.end()) {
+    MSI_LOG_WARNING << "failed to get data type, undefined data type " << tensor_.tensor_type();
+    return inference::kMSI_Unknown;
+  } else {
+    return it->second;
+  }
+}
+
+void ServingTensor::set_data_type(DataType data_type) {
+  const std::unordered_map<inference::DataType, ms_serving::DataType> id2type_map{
+    {inference::kMSI_Unknown, ms_serving::MS_UNKNOWN}, {inference::kMSI_Bool, ms_serving::MS_BOOL},
+    {inference::kMSI_Float64, ms_serving::MS_FLOAT64}, {inference::kMSI_Int8, ms_serving::MS_INT8},
+    {inference::kMSI_Uint8, ms_serving::MS_UINT8},     {inference::kMSI_Int16, ms_serving::MS_INT16},
+    {inference::kMSI_Uint16, ms_serving::MS_UINT16},   {inference::kMSI_Int32, ms_serving::MS_INT32},
+    {inference::kMSI_Uint32, ms_serving::MS_UINT32},   {inference::kMSI_Int64, ms_serving::MS_INT64},
+    {inference::kMSI_Uint64, ms_serving::MS_UINT64},   {inference::kMSI_Float16, ms_serving::MS_FLOAT16},
+    {inference::kMSI_Float32, ms_serving::MS_FLOAT32},
+  };
+  auto it = id2type_map.find(data_type);
+  if (it == id2type_map.end()) {
+    MSI_LOG_WARNING << "failed to set data type, undefined data type " << data_type;
+    tensor_.set_tensor_type(ms_serving::MS_UNKNOWN);
+  } else {
+    tensor_.set_tensor_type(it->second);
+  }
+}
+
+std::vector<int64_t> ServingTensor::shape() const {
+  std::vector<int64_t> result;
+  auto dims = tensor_.tensor_shape().dims();
+  std::transform(dims.begin(), dims.end(), std::back_inserter(result), [](const int64_t dim) { return dim; });
+  return result;
+}
+
+void ServingTensor::set_shape(const std::vector<int64_t> &shape) {
+  auto tensor_shape = tensor_.mutable_tensor_shape();
+  tensor_shape->Clear();
+  size_t element_count = 1;
+  for (auto dim : shape) {
+    if (dim <= 0 || element_count > kMaxShapeElementCount / dim) {
+      MSI_LOG_ERROR << "failed to set shape, invalid dim num " << dim;
+      tensor_shape->Clear();
+      return;
+    }
+    element_count *= dim;
+    tensor_shape->add_dims(dim);
+  }
+}
+
+bool ServingTensor::resize_data(size_t data_len) {
+  string *buffer = tensor_.mutable_data();
+  if (buffer == nullptr) {
+    MSI_LOG_ERROR << "invalid buffer data";
+    return false;
+  }
+  buffer->resize(data_len);
+  return true;
+}
+
+size_t ServingTensor::data_size() const { return tensor_.data().size(); }
+
+void *ServingTensor::mutable_data() { return const_cast<char *>(tensor_.mutable_data()->data()); }
+
+const void *ServingTensor::data() const { return tensor_.data().data(); }
+
+ServingRequest::ServingRequest(const ms_serving::PredictRequest &request) : request_(request) {
+  auto &data = request_.data();
+  std::transform(data.begin(), data.end(), std::back_inserter(cache_),
+                 [](const ms_serving::Tensor &item) { return ServingTensor(const_cast<ms_serving::Tensor &>(item)); });
+}
+
+size_t ServingRequest::size() const { return request_.data_size(); }
+
+const InferTensorBase *ServingRequest::operator[](size_t index) const {
+  if (index >= cache_.size()) {
+    MSI_LOG_ERROR << "visit invalid index " << index << " total size " << cache_.size();
+    return nullptr;
+  }
+  return &(cache_[index]);
+}
+
+size_t ServingReply::size() const { return cache_.size(); }
+
+InferTensorBase *ServingReply::operator[](size_t index) {
+  if (index >= cache_.size()) {
+    MSI_LOG_ERROR << "visit invalid index " << index << " total size " << cache_.size();
+    return nullptr;
+  }
+  return &(cache_[index]);
+}
+
+const InferTensorBase *ServingReply::operator[](size_t index) const {
+  if (index >= cache_.size()) {
+    MSI_LOG_ERROR << "visit invalid index " << index << " total size " << cache_.size();
+    return nullptr;
+  }
+  return &(cache_[index]);
+}
+
+InferTensorBase *ServingReply::add() {
+  auto new_item = reply_.add_result();
+  if (new_item == nullptr) {
+    MSI_LOG_ERROR << "add new item failed, current total size " << cache_.size();
+    return nullptr;
+  }
+  cache_.push_back(ServingTensor(*new_item));
+  return &(cache_.back());
+}
+
+void ServingReply::clear() { reply_.mutable_result()->Clear(); }
+
+}  // namespace serving
+}  // namespace mindspore

serving/core/serving_tensor.h

+/**
+ * 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_TENSOR_H_
+#define MINDSPORE_SERVING_TENSOR_H_
+
+#include <utility>
+#include <vector>
+#include <memory>
+#include "include/infer_tensor.h"
+#include "serving/ms_service.pb.h"
+
+namespace mindspore {
+namespace serving {
+
+class MS_API ServingTensor : public inference::InferTensorBase {
+ public:
+  // the other's lifetime must longer than this object
+  explicit ServingTensor(ms_serving::Tensor &other);
+  ~ServingTensor();
+
+  inference::DataType data_type() const override;
+  void set_data_type(inference::DataType type) override;
+  std::vector<int64_t> shape() const override;
+  void set_shape(const std::vector<int64_t> &shape) override;
+  const void *data() const override;
+  size_t data_size() const override;
+  bool resize_data(size_t data_len) override;
+  void *mutable_data() override;
+
+ private:
+  // if tensor_ is reference from other ms_serving::Tensor, the other's lifetime must
+  // longer than this object
+  ms_serving::Tensor &tensor_;
+};
+
+class ServingRequest : public inference::RequestBase {
+ public:
+  explicit ServingRequest(const ms_serving::PredictRequest &request);
+
+  size_t size() const override;
+  const inference::InferTensorBase *operator[](size_t index) const override;
+
+ private:
+  const ms_serving::PredictRequest &request_;
+  std::vector<ServingTensor> cache_;
+};
+
+class ServingReply : public inference::ReplyBase {
+ public:
+  explicit ServingReply(ms_serving::PredictReply &reply) : reply_(reply) {}
+
+  size_t size() const override;
+  inference::InferTensorBase *operator[](size_t index) override;
+  const inference::InferTensorBase *operator[](size_t index) const override;
+  inference::InferTensorBase *add() override;
+  void clear() override;
+
+ private:
+  ms_serving::PredictReply &reply_;
+  std::vector<ServingTensor> cache_;
+};
+
+}  // namespace serving
+}  // namespace mindspore
+#endif  // MINDSPORE_SERVING_TENSOR_H_

serving/core/util/file_system_operation.cc

@@ -25,43 +25,10 @@
 #include <ctime>
 #include <fstream>
 #include <memory>
-#include "mindspore/ccsrc/utils/log_adapter.h"
+#include "include/infer_log.h"
 
 namespace mindspore {
 namespace serving {
-char *ReadFile(const char *file, size_t *size) {
-  if (file == nullptr) {
-    MS_LOG(ERROR) << "file is nullptr";
-    return nullptr;
-  }
-  MS_ASSERT(size != nullptr);
-  std::string realPath = file;
-  std::ifstream ifs(realPath);
-  if (!ifs.good()) {
-    MS_LOG(ERROR) << "file: " << realPath << " is not exist";
-    return nullptr;
-  }
-
-  if (!ifs.is_open()) {
-    MS_LOG(ERROR) << "file: " << realPath << "open failed";
-    return nullptr;
-  }
-
-  ifs.seekg(0, std::ios::end);
-  *size = ifs.tellg();
-  std::unique_ptr<char> buf(new (std::nothrow) char[*size]);
-  if (buf == nullptr) {
-    MS_LOG(ERROR) << "malloc buf failed, file: " << realPath;
-    ifs.close();
-    return nullptr;
-  }
-
-  ifs.seekg(0, std::ios::beg);
-  ifs.read(buf.get(), *size);
-  ifs.close();
-
-  return buf.release();
-}
 
 bool DirOrFileExist(const std::string &file_path) {
   int ret = access(file_path.c_str(), 0);
@@ -74,7 +41,7 @@ std::vector<std::string> GetAllSubDirs(const std::string &dir_path) {
   std::vector<std::string> SubDirs;
 
   if ((dir = opendir(dir_path.c_str())) == NULL) {
-    MS_LOG(ERROR) << "Open " << dir_path << " error!";
+    MSI_LOG(ERROR) << "Open " << dir_path << " error!";
     return std::vector<std::string>();
   }
 

serving/core/util/option_parser.cc

@@ -19,10 +19,11 @@
 #include <cstring>
 #include <iostream>
 #include <iomanip>
-#include "mindspore/ccsrc/utils/log_adapter.h"
+#include "include/infer_log.h"
 
 namespace mindspore {
 namespace serving {
+
 bool StartWith(const std::string &str, const std::string &expected) {
   return expected.empty() ||
          (str.size() >= expected.size() && memcmp(str.data(), expected.data(), expected.size()) == 0);

serving/core/version_control/model.cc

@@ -15,18 +15,19 @@
  */
 #include "core/version_control/model.h"
 #include <string>
-#include "mindspore/ccsrc/utils/log_adapter.h"
+#include "include/infer_log.h"
 
 namespace mindspore {
 namespace serving {
+
 MindSporeModel::MindSporeModel(const std::string &model_name, const std::string &model_path,
                                const std::string &model_version, const time_t &last_update_time)
     : model_name_(model_name),
       model_path_(model_path),
       model_version_(model_version),
       last_update_time_(last_update_time) {
-  MS_LOG(INFO) << "init mindspore model, model_name = " << model_name_ << ", model_path = " << model_path_
-               << ", model_version = " << model_version_ << ", last_update_time = " << last_update_time_;
+  MSI_LOG(INFO) << "init mindspore model, model_name = " << model_name_ << ", model_path = " << model_path_
+                << ", model_version = " << model_version_ << ", last_update_time = " << last_update_time_;
 }
 }  // namespace serving
 }  // namespace mindspore

serving/core/version_control/version_controller.cc

@@ -20,11 +20,12 @@
 #include <ctime>
 #include <memory>
 #include "util/file_system_operation.h"
-#include "mindspore/ccsrc/utils/log_adapter.h"
+#include "include/infer_log.h"
 #include "core/server.h"
 
 namespace mindspore {
 namespace serving {
+
 volatile bool stop_poll = false;
 
 std::string GetVersionFromPath(const std::string &path) {
@@ -96,7 +97,7 @@ Status VersionController::Run() {
 
 Status VersionController::CreateInitModels() {
   if (!DirOrFileExist(models_path_)) {
-    MS_LOG(ERROR) << "Model Path Not Exist!" << std::endl;
+    MSI_LOG(ERROR) << "Model Path Not Exist!" << std::endl;
     return FAILED;
   }
   std::vector<std::string> SubDirs = GetAllSubDirs(models_path_);
@@ -115,7 +116,7 @@ Status VersionController::CreateInitModels() {
     }
   }
   if (valid_models_.empty()) {
-    MS_LOG(ERROR) << "There is no valid model for serving";
+    MSI_LOG(ERROR) << "There is no valid model for serving";
     return FAILED;
   }
   auto ret = Session::Instance().Warmup(valid_models_.back());

serving/cpp_example/CMakeLists.txt

@@ -8,33 +8,33 @@ add_compile_definitions(_GLIBCXX_USE_CXX11_ABI=0)
 find_package(Threads REQUIRED)
 
 # This branch assumes that gRPC and all its dependencies are already installed
-  # on this system, so they can be located by find_package().
+# on this system, so they can be located by find_package().
 
-  # Find Protobuf installation
-  # Looks for protobuf-config.cmake file installed by Protobuf's cmake installation.
-  set(protobuf_MODULE_COMPATIBLE TRUE)
-  find_package(Protobuf CONFIG REQUIRED)
-  message(STATUS "Using protobuf ${protobuf_VERSION}")
+# Find Protobuf installation
+# Looks for protobuf-config.cmake file installed by Protobuf's cmake installation.
+set(protobuf_MODULE_COMPATIBLE TRUE)
+find_package(Protobuf CONFIG REQUIRED)
+message(STATUS "Using protobuf ${protobuf_VERSION}")
 
-  set(_PROTOBUF_LIBPROTOBUF protobuf::libprotobuf)
-  set(_REFLECTION gRPC::grpc++_reflection)
-  if(CMAKE_CROSSCOMPILING)
-      find_program(_PROTOBUF_PROTOC protoc)
-  else()
-      set(_PROTOBUF_PROTOC $<TARGET_FILE:protobuf::protoc>)
-  endif()
+set(_PROTOBUF_LIBPROTOBUF protobuf::libprotobuf)
+set(_REFLECTION gRPC::grpc++_reflection)
+if (CMAKE_CROSSCOMPILING)
+    find_program(_PROTOBUF_PROTOC protoc)
+else ()
+    set(_PROTOBUF_PROTOC $<TARGET_FILE:protobuf::protoc>)
+endif ()
 
-  # Find gRPC installation
-  # Looks for gRPCConfig.cmake file installed by gRPC's cmake installation.
-  find_package(gRPC CONFIG REQUIRED)
-  message(STATUS "Using gRPC ${gRPC_VERSION}")
+# Find gRPC installation
+# Looks for gRPCConfig.cmake file installed by gRPC's cmake installation.
+find_package(gRPC CONFIG REQUIRED)
+message(STATUS "Using gRPC ${gRPC_VERSION}")
 
-  set(_GRPC_GRPCPP gRPC::grpc++)
-  if(CMAKE_CROSSCOMPILING)
-      find_program(_GRPC_CPP_PLUGIN_EXECUTABLE grpc_cpp_plugin)
-  else()
-      set(_GRPC_CPP_PLUGIN_EXECUTABLE $<TARGET_FILE:gRPC::grpc_cpp_plugin>)
-  endif()
+set(_GRPC_GRPCPP gRPC::grpc++)
+if (CMAKE_CROSSCOMPILING)
+    find_program(_GRPC_CPP_PLUGIN_EXECUTABLE grpc_cpp_plugin)
+else ()
+    set(_GRPC_CPP_PLUGIN_EXECUTABLE $<TARGET_FILE:gRPC::grpc_cpp_plugin>)
+endif ()
 
 # Proto file
 get_filename_component(hw_proto "../ms_service.proto" ABSOLUTE)
@@ -59,7 +59,7 @@ add_custom_command(
 include_directories("${CMAKE_CURRENT_BINARY_DIR}")
 
 # Targets greeter_[async_](client|server)
-foreach(_target
+foreach (_target
         ms_client ms_server)
     add_executable(${_target} "${_target}.cc"
             ${hw_proto_srcs}
@@ -68,4 +68,4 @@ foreach(_target
             ${_REFLECTION}
             ${_GRPC_GRPCPP}
             ${_PROTOBUF_LIBPROTOBUF})
-endforeach()
+endforeach ()

serving/cpp_example/ms_client.cc

@@ -211,12 +211,77 @@ PredictRequest ReadBertInput() {
   return request;
 }
 
+PredictRequest ReadLenetInput() {
+  size_t size;
+  auto buf = ReadFile("lenet_img.bin", &size);
+  if (buf == nullptr) {
+    std::cout << "read file failed" << std::endl;
+    return PredictRequest();
+  }
+  PredictRequest request;
+  auto cur = buf;
+  if (size > 0) {
+    Tensor data;
+    TensorShape shape;
+    // set type
+    data.set_tensor_type(ms_serving::MS_FLOAT32);
+
+    // set shape
+    shape.add_dims(size / sizeof(float));
+    *data.mutable_tensor_shape() = shape;
+
+    // set data
+    data.set_data(cur, size);
+    *request.add_data() = data;
+  }
+  std::cout << "get input data size " << size << std::endl;
+  return request;
+}
+
+PredictRequest ReadOtherInput(const std::string &data_file) {
+  size_t size;
+  auto buf = ReadFile(data_file.c_str(), &size);
+  if (buf == nullptr) {
+    std::cout << "read file failed" << std::endl;
+    return PredictRequest();
+  }
+  PredictRequest request;
+  auto cur = buf;
+  if (size > 0) {
+    Tensor data;
+    TensorShape shape;
+    // set type
+    data.set_tensor_type(ms_serving::MS_FLOAT32);
+
+    // set shape
+    shape.add_dims(size / sizeof(float));
+    *data.mutable_tensor_shape() = shape;
+
+    // set data
+    data.set_data(cur, size);
+    *request.add_data() = data;
+  }
+  std::cout << "get input data size " << size << std::endl;
+  return request;
+}
+
+template <class DT>
+void print_array_item(const DT *data, size_t size) {
+  for (size_t i = 0; i < size && i < 100; i++) {
+    std::cout << data[i] << '\t';
+    if ((i + 1) % 10 == 0) {
+      std::cout << std::endl;
+    }
+  }
+  std::cout << std::endl;
+}
+
 class MSClient {
  public:
   explicit MSClient(std::shared_ptr<Channel> channel) : stub_(MSService::NewStub(channel)) {}
   ~MSClient() = default;
 
-  std::string Predict(const std::string &type) {
+  std::string Predict(const std::string &type, const std::string &data_file) {
     // Data we are sending to the server.
     PredictRequest request;
     if (type == "add") {
@@ -234,6 +299,10 @@ class MSClient {
       *request.add_data() = data;
     } else if (type == "bert") {
       request = ReadBertInput();
+    } else if (type == "lenet") {
+      request = ReadLenetInput();
+    } else if (type == "other") {
+      request = ReadOtherInput(data_file);
     } else {
       std::cout << "type only support bert or add, but input is " << type << std::endl;
     }
@@ -256,6 +325,20 @@ class MSClient {
 
     // Act upon its status.
     if (status.ok()) {
+      for (size_t i = 0; i < reply.result_size(); i++) {
+        auto result = reply.result(i);
+        if (result.tensor_type() == ms_serving::DataType::MS_FLOAT32) {
+          print_array_item(reinterpret_cast<const float *>(result.data().data()), result.data().size() / sizeof(float));
+        } else if (result.tensor_type() == ms_serving::DataType::MS_INT32) {
+          print_array_item(reinterpret_cast<const int32_t *>(result.data().data()),
+                           result.data().size() / sizeof(int32_t));
+        } else if (result.tensor_type() == ms_serving::DataType::MS_UINT32) {
+          print_array_item(reinterpret_cast<const uint32_t *>(result.data().data()),
+                           result.data().size() / sizeof(uint32_t));
+        } else {
+          std::cout << "output datatype " << result.tensor_type() << std::endl;
+        }
+      }
       return "RPC OK";
     } else {
       std::cout << status.error_code() << ": " << status.error_message() << std::endl;
@@ -277,6 +360,8 @@ int main(int argc, char **argv) {
   std::string arg_target_str("--target");
   std::string type;
   std::string arg_type_str("--type");
+  std::string arg_data_str("--data");
+  std::string data = "default_data.bin";
   if (argc > 2) {
     {
       // parse target
@@ -304,19 +389,33 @@ int main(int argc, char **argv) {
         if (arg_val2[start_pos] == '=') {
           type = arg_val2.substr(start_pos + 1);
         } else {
-          std::cout << "The only correct argument syntax is --target=" << std::endl;
+          std::cout << "The only correct argument syntax is --type=" << std::endl;
           return 0;
         }
       } else {
         type = "add";
       }
     }
+    if (argc > 3) {
+      // parse type
+      std::string arg_val3 = argv[3];
+      size_t start_pos = arg_val3.find(arg_data_str);
+      if (start_pos != std::string::npos) {
+        start_pos += arg_data_str.size();
+        if (arg_val3[start_pos] == '=') {
+          data = arg_val3.substr(start_pos + 1);
+        } else {
+          std::cout << "The only correct argument syntax is --data=" << std::endl;
+          return 0;
+        }
+      }
+    }
   } else {
     target_str = "localhost:5500";
     type = "add";
   }
   MSClient client(grpc::CreateChannel(target_str, grpc::InsecureChannelCredentials()));
-  std::string reply = client.Predict(type);
+  std::string reply = client.Predict(type, data);
   std::cout << "client received: " << reply << std::endl;
 
   return 0;