!11526 unifiled lite & cloud api

From: @zhoufeng54
Reviewed-by: 
Signed-off-by:

cmake/package.cmake

@@ -65,7 +65,7 @@ install(
 
 install(
     TARGETS mindspore_shared_lib
-    LIBRARY DESTINATION ${INSTALL_LIB_DIR}
+    DESTINATION ${INSTALL_LIB_DIR}
     COMPONENT mindspore
 )
 
@@ -327,7 +327,7 @@ install(
           ${CMAKE_SOURCE_DIR}/mindspore/ccsrc/minddata/dataset/include/transforms.h
           ${CMAKE_SOURCE_DIR}/mindspore/ccsrc/minddata/dataset/include/vision.h
           ${CMAKE_SOURCE_DIR}/mindspore/ccsrc/minddata/dataset/include/vision_lite.h
-          ${CMAKE_SOURCE_DIR}/mindspore/ccsrc/minddata/dataset/include/minddata_eager.h
+          ${CMAKE_SOURCE_DIR}/mindspore/ccsrc/minddata/dataset/include/execute.h
     DESTINATION ${INSTALL_BASE_DIR}/include/minddata/dataset/include
     COMPONENT mindspore
 )

cmake/package_lite.cmake

@@ -109,6 +109,8 @@ if(PLATFORM_ARM64)
             COMPONENT ${RUNTIME_COMPONENT_NAME})
     install(FILES ${TOP_DIR}/mindspore/core/ir/dtype/type_id.h DESTINATION ${RUNTIME_INC_DIR}/ir/dtype
             COMPONENT ${RUNTIME_COMPONENT_NAME})
+    install(DIRECTORY ${TOP_DIR}/include/api/ DESTINATION ${RUNTIME_INC_DIR}/api
+            COMPONENT ${RUNTIME_COMPONENT_NAME} FILES_MATCHING PATTERN "*.h" PATTERN "ascend* ops*" EXCLUDE)
     if(ENABLE_TOOLS)
         install(TARGETS benchmark RUNTIME DESTINATION ${RUNTIME_PKG_NAME}/benchmark COMPONENT ${RUNTIME_COMPONENT_NAME})
     endif()
@@ -128,6 +130,8 @@ elseif(PLATFORM_ARM32)
             COMPONENT ${RUNTIME_COMPONENT_NAME})
     install(FILES ${TOP_DIR}/mindspore/core/ir/dtype/type_id.h DESTINATION ${RUNTIME_INC_DIR}/ir/dtype
             COMPONENT ${RUNTIME_COMPONENT_NAME})
+    install(DIRECTORY ${TOP_DIR}/include/api/ DESTINATION ${RUNTIME_INC_DIR}/api
+            COMPONENT ${RUNTIME_COMPONENT_NAME} FILES_MATCHING PATTERN "*.h" PATTERN "ascend*" EXCLUDE)
     if(ENABLE_TOOLS)
         install(TARGETS benchmark RUNTIME DESTINATION ${RUNTIME_PKG_NAME}/benchmark COMPONENT ${RUNTIME_COMPONENT_NAME})
     endif()
@@ -162,6 +166,8 @@ elseif(WIN32)
         endif()
         install(FILES ${TOP_DIR}/mindspore/core/ir/dtype/type_id.h DESTINATION ${RUNTIME_INC_DIR}/ir/dtype
                 COMPONENT ${RUNTIME_COMPONENT_NAME})
+        install(DIRECTORY ${TOP_DIR}/include/api/ DESTINATION ${RUNTIME_INC_DIR}/api
+                COMPONENT ${RUNTIME_COMPONENT_NAME} FILES_MATCHING PATTERN "*.h" PATTERN "ascend*" EXCLUDE)
         set(WIN_LIB_DIR_RUN_X86 ${RUNTIME_PKG_NAME}/benchmark)
         install(FILES ${TOP_DIR}/build/mindspore/src/libmindspore-lite.a DESTINATION ${WIN_LIB_DIR_RUN_X86}
                 COMPONENT ${RUNTIME_COMPONENT_NAME})
@@ -182,6 +188,8 @@ else()
     endif()
     install(FILES ${TOP_DIR}/mindspore/core/ir/dtype/type_id.h DESTINATION ${RUNTIME_INC_DIR}/ir/dtype
             COMPONENT ${RUNTIME_COMPONENT_NAME})
+    install(DIRECTORY ${TOP_DIR}/include/api/ DESTINATION ${RUNTIME_INC_DIR}/api
+            COMPONENT ${RUNTIME_COMPONENT_NAME} FILES_MATCHING PATTERN "*.h" PATTERN "ascend*" EXCLUDE)
     install(FILES ${TOP_DIR}/mindspore/lite/build/src/libmindspore-lite.so DESTINATION ${RUNTIME_LIB_DIR}
             COMPONENT ${RUNTIME_COMPONENT_NAME})
     install(FILES ${TOP_DIR}/mindspore/lite/build/src/libmindspore-lite.a DESTINATION ${RUNTIME_LIB_DIR}

include/api/cell.h

@@ -24,7 +24,6 @@
 #include "include/api/graph.h"
 
 namespace mindspore {
-namespace api {
 class InputAndOutput;
 using Input = InputAndOutput;
 using Output = InputAndOutput;
@@ -35,7 +34,7 @@ class MS_API CellBase {
   virtual ~CellBase() = default;
   virtual std::vector<Output> Construct(const std::vector<Input> &inputs) { return {}; }
   virtual std::shared_ptr<CellBase> Clone() const = 0;
-  virtual Status Run(const std::vector<Buffer> &inputs, std::vector<Buffer> *outputs) { return SUCCESS; }
+  virtual Status Run(const std::vector<MSTensor> &inputs, std::vector<MSTensor> *outputs) { return kSuccess; }
   std::vector<Output> operator()(const std::vector<Input> &inputs) const;
 };
 
@@ -57,16 +56,16 @@ class MS_API ParameterCell final : public Cell<ParameterCell> {
   ParameterCell(ParameterCell &&);
   ParameterCell &operator=(ParameterCell &&);
 
-  explicit ParameterCell(const Tensor &);
-  ParameterCell &operator=(const Tensor &);
+  explicit ParameterCell(const MSTensor &);
+  ParameterCell &operator=(const MSTensor &);
 
-  explicit ParameterCell(Tensor &&);
-  ParameterCell &operator=(Tensor &&);
+  explicit ParameterCell(MSTensor &&);
+  ParameterCell &operator=(MSTensor &&);
 
-  Tensor GetTensor() const { return tensor_; }
+  MSTensor GetTensor() const { return tensor_; }
 
  private:
-  Tensor tensor_;
+  MSTensor tensor_;
 };
 
 class MS_API OpCellBase : public CellBase {
@@ -99,11 +98,9 @@ class MS_API GraphCell final : public Cell<GraphCell> {
   explicit GraphCell(const std::shared_ptr<Graph> &);
 
   const std::shared_ptr<Graph> &GetGraph() const { return graph_; }
-  Status Run(const std::vector<Buffer> &inputs, std::vector<Buffer> *outputs) override;
-  Status GetInputsInfo(std::vector<std::string> *names, std::vector<std::vector<int64_t>> *shapes,
-                       std::vector<DataType> *data_types, std::vector<size_t> *mem_sizes) const;
-  Status GetOutputsInfo(std::vector<std::string> *names, std::vector<std::vector<int64_t>> *shapes,
-                        std::vector<DataType> *data_types, std::vector<size_t> *mem_sizes) const;
+  Status Run(const std::vector<MSTensor> &inputs, std::vector<MSTensor> *outputs) override;
+  std::vector<MSTensor> GetInputs();
+  std::vector<MSTensor> GetOutputs();
 
  private:
   friend class ModelImpl;
@@ -119,8 +116,8 @@ class MS_API InputAndOutput {
   ~InputAndOutput() = default;
 
   // no explicit
-  InputAndOutput(const Tensor &);  // NOLINT(runtime/explicit)
-  InputAndOutput(Tensor &&);       // NOLINT(runtime/explicit)
+  InputAndOutput(const MSTensor &);  // NOLINT(runtime/explicit)
+  InputAndOutput(MSTensor &&);       // NOLINT(runtime/explicit)
 
   InputAndOutput(const std::shared_ptr<CellBase> &, const std::vector<InputAndOutput> &, int32_t index);
 
@@ -132,6 +129,5 @@ class MS_API InputAndOutput {
   std::vector<InputAndOutput> prev_;
   int32_t index_;
 };
-}  // namespace api
 }  // namespace mindspore
 #endif  // MINDSPORE_INCLUDE_API_CELL_H

include/api/context.h

@@ -16,26 +16,49 @@
 #ifndef MINDSPORE_INCLUDE_API_CONTEXT_H
 #define MINDSPORE_INCLUDE_API_CONTEXT_H
 
+#include <map>
+#include <any>
 #include <string>
 #include <memory>
 #include "include/api/types.h"
 
 namespace mindspore {
-namespace api {
-class MS_API Context {
- public:
-  static Context &Instance();
-  const std::string &GetDeviceTarget() const;
-  Context &SetDeviceTarget(const std::string &device_target);
-  uint32_t GetDeviceID() const;
-  Context &SetDeviceID(uint32_t device_id);
-
- private:
-  Context();
-  ~Context();
-  class ContextImpl;
-  std::shared_ptr<ContextImpl> impl_;
+constexpr auto kDeviceTypeAscend310 = "Ascend310";
+constexpr auto kDeviceTypeAscend910 = "Ascend910";
+
+struct MS_API Context {
+  virtual ~Context() = default;
+  std::map<std::string, std::any> params;
+};
+
+struct MS_API GlobalContext : public Context {
+  static std::shared_ptr<Context> GetGlobalContext();
+
+  static void SetGlobalDeviceTarget(const std::string &device_target);
+  static std::string GetGlobalDeviceTarget();
+
+  static void SetGlobalDeviceID(const uint32_t &device_id);
+  static uint32_t GetGlobalDeviceID();
+};
+
+struct MS_API ModelContext : public Context {
+  static void SetInsertOpConfigPath(const std::shared_ptr<Context> &context, const std::string &cfg_path);
+  static std::string GetInsertOpConfigPath(const std::shared_ptr<Context> &context);
+
+  static void SetInputFormat(const std::shared_ptr<Context> &context, const std::string &format);
+  static std::string GetInputFormat(const std::shared_ptr<Context> &context);
+
+  static void SetInputShape(const std::shared_ptr<Context> &context, const std::string &shape);
+  static std::string GetInputShape(const std::shared_ptr<Context> &context);
+
+  static void SetOutputType(const std::shared_ptr<Context> &context, enum DataType output_type);
+  static enum DataType GetOutputType(const std::shared_ptr<Context> &context);
+
+  static void SetPrecisionMode(const std::shared_ptr<Context> &context, const std::string &precision_mode);
+  static std::string GetPrecisionMode(const std::shared_ptr<Context> &context);
+
+  static void SetOpSelectImplMode(const std::shared_ptr<Context> &context, const std::string &op_select_impl_mode);
+  static std::string GetOpSelectImplMode(const std::shared_ptr<Context> &context);
 };
-}  // namespace api
 }  // namespace mindspore
 #endif  // MINDSPORE_INCLUDE_API_CONTEXT_H

include/api/data_type.h

@@ -0,0 +1,43 @@
+/**
+ * Copyright 2021 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_API_DATA_TYPE_H_
+#define MINDSPORE_INCLUDE_API_DATA_TYPE_H_
+
+namespace mindspore {
+enum class DataType : int {
+  kTypeUnknown = 0,
+  kObjectTypeString = 12,
+  kObjectTypeList = 13,
+  kObjectTypeTuple = 14,
+  kObjectTypeTensorType = 17,
+  kNumberTypeBool = 30,
+  kNumberTypeInt8 = 32,
+  kNumberTypeInt16 = 33,
+  kNumberTypeInt32 = 34,
+  kNumberTypeInt64 = 35,
+  kNumberTypeUInt8 = 37,
+  kNumberTypeUInt16 = 38,
+  kNumberTypeUInt32 = 39,
+  kNumberTypeUInt64 = 40,
+  kNumberTypeFloat16 = 42,
+  kNumberTypeFloat32 = 43,
+  kNumberTypeFloat64 = 44,
+  kNumberTypeEnd = 46,
+  // add new enum here
+  kInvalidType = INT32_MAX,
+};
+}  // namespace mindspore
+#endif  // MINDSPORE_INCLUDE_API_DATA_TYPE_H_

include/api/graph.h

@@ -16,6 +16,7 @@
 #ifndef MINDSPORE_INCLUDE_API_GRAPH_H
 #define MINDSPORE_INCLUDE_API_GRAPH_H
 
+#include <cstddef>
 #include <string>
 #include <vector>
 #include <map>
@@ -24,21 +25,21 @@
 #include "include/api/types.h"
 
 namespace mindspore {
-namespace api {
 class MS_API Graph {
  public:
   class GraphData;
   explicit Graph(const std::shared_ptr<GraphData> &graph_data);
   explicit Graph(std::shared_ptr<GraphData> &&graph_data);
+  explicit Graph(std::nullptr_t);
   ~Graph();
 
   enum ModelType ModelType() const;
+  bool operator==(std::nullptr_t) const;
 
  private:
   friend class GraphCell;
   friend class ModelImpl;
   std::shared_ptr<GraphData> graph_data_;
 };
-}  // namespace api
 }  // namespace mindspore
 #endif  // MINDSPORE_INCLUDE_API_GRAPH_H

include/api/lite_context.h

@@ -0,0 +1,77 @@
+/**
+ * Copyright 2021 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_API_LITE_CONTEXT_H
+#define MINDSPORE_INCLUDE_API_LITE_CONTEXT_H
+
+#include <string>
+#include <memory>
+#include <map>
+#include <any>
+#include "include/api/types.h"
+
+namespace mindspore {
+namespace lite {
+/// \brief CpuBindMode defined for holding bind cpu strategy argument.
+typedef enum : uint32_t {
+  NO_BIND = 0,    /**< no bind */
+  HIGHER_CPU = 1, /**< bind higher cpu first */
+  MID_CPU = 2     /**< bind middle cpu first */
+} CpuBindMode;
+
+class Allocator;
+}  // namespace lite
+
+struct MS_API Context {
+ public:
+  static void Clear(const std::shared_ptr<Context> &contxet);
+
+  static void SetAsDefault(const std::shared_ptr<Context> &contxet);
+
+  static void SetVendorName(const std::shared_ptr<Context> &contxet, const std::string &name);
+  static std::string GetVendorName(const std::shared_ptr<Context> &contxet);
+
+  static void SetThreadNum(const std::shared_ptr<Context> &contxet, int num);
+  static int GetThreadNum(const std::shared_ptr<Context> &contxet);
+
+  static void SetAllocator(const std::shared_ptr<Context> &contxet, std::shared_ptr<lite::Allocator> alloc);
+  static std::shared_ptr<lite::Allocator> GetAllocator(const std::shared_ptr<Context> &contxet);
+
+  static void ConfigCPU(const std::shared_ptr<Context> &contxet, bool config);
+  static bool IfCPUEnabled(const std::shared_ptr<Context> &contxet);
+
+  static void ConfigCPUFp16(const std::shared_ptr<Context> &contxet, bool config);
+  static bool IfCPUFp16Enabled(const std::shared_ptr<Context> &contxet);
+
+  static void SetCPUBindMode(const std::shared_ptr<Context> &contxet, lite::CpuBindMode mode);
+  static lite::CpuBindMode GetCPUBindMode(const std::shared_ptr<Context> &contxet);
+
+  static void ConfigGPU(const std::shared_ptr<Context> &contxet, bool config);
+  static bool IfGPUEnabled(const std::shared_ptr<Context> &contxet);
+
+  static void ConfigGPUFp16(const std::shared_ptr<Context> &contxet, bool config);
+  static bool IfGPUFp16Enabled(const std::shared_ptr<Context> &contxet);
+
+  static void ConfigNPU(const std::shared_ptr<Context> &contxet, bool config);
+  static bool IfNPUEnabled(const std::shared_ptr<Context> &contxet);
+
+  static void SetNPUFrequency(const std::shared_ptr<Context> &contxet, int freq);
+  static int GetNPUFrequency(const std::shared_ptr<Context> &contxet);
+
+ private:
+  std::map<std::string, std::any> context_;
+};
+}  // namespace mindspore
+#endif  // MINDSPORE_INCLUDE_API_LITE_CONTEXT_H

include/api/model.h

@@ -20,41 +20,36 @@
 #include <vector>
 #include <map>
 #include <memory>
+#include <utility>
 #include "include/api/status.h"
 #include "include/api/types.h"
 #include "include/api/graph.h"
 #include "include/api/cell.h"
 
 namespace mindspore {
-namespace api {
 class ModelImpl;
-// todo: minddata c++ interface
-class DataSet {};
+struct Context;
 
 class MS_API Model {
  public:
-  explicit Model(const std::vector<Output> &network);
-  explicit Model(const GraphCell &graph);
+  explicit Model(const std::vector<Output> &network, const std::shared_ptr<Context> &model_context = nullptr);
+  explicit Model(const GraphCell &graph, const std::shared_ptr<Context> &model_context = nullptr);
   ~Model();
   Model(const Model &) = delete;
   void operator=(const Model &) = delete;
 
-  Status Build(const std::map<std::string, std::string> &options);
+  Status Build();
+  Status Resize(const std::vector<MSTensor> &inputs, const std::vector<std::vector<int64_t>> &dims);
 
-  Status Train(const DataSet &dataset, bool data_sink, std::map<std::string, Buffer> *outputs);
-  Status Eval(const DataSet &dataset, bool data_sink, std::map<std::string, Buffer> *outputs);
-  Status Predict(const std::vector<Buffer> &inputs, std::vector<Buffer> *outputs);
+  Status Predict(const std::vector<MSTensor> &inputs, std::vector<MSTensor> *outputs);
 
-  Status GetInputsInfo(std::vector<std::string> *names, std::vector<std::vector<int64_t>> *shapes,
-                       std::vector<DataType> *data_types, std::vector<size_t> *mem_sizes) const;
-  Status GetOutputsInfo(std::vector<std::string> *names, std::vector<std::vector<int64_t>> *shapes,
-                        std::vector<DataType> *data_types, std::vector<size_t> *mem_sizes) const;
+  std::vector<MSTensor> GetInputs();
+  std::vector<MSTensor> GetOutputs();
 
   static bool CheckModelSupport(const std::string &device_type, ModelType model_type);
 
  private:
   std::shared_ptr<ModelImpl> impl_;
 };
-}  // namespace api
 }  // namespace mindspore
 #endif  // MINDSPORE_INCLUDE_API_MODEL_H

include/api/ops/ops.h

@@ -25,7 +25,6 @@
 #include "include/api/cell.h"
 
 namespace mindspore {
-namespace api {
 struct MS_API Conv2D : public OpCell<Conv2D> {
   Conv2D() : OpCell("Conv2D") {}
   ~Conv2D() override = default;
@@ -45,6 +44,5 @@ struct MS_API Conv2D : public OpCell<Conv2D> {
   std::vector<int> dilation = {1, 1, 1, 1};
   int group = 1;
 };
-}  // namespace api
 }  // namespace mindspore
 #endif  // MINDSPORE_INCLUDE_API_OPS_OPS_H

include/api/serialization.h

@@ -26,15 +26,14 @@
 #include "include/api/graph.h"
 
 namespace mindspore {
-namespace api {
 class MS_API Serialization {
  public:
+  static Graph LoadModel(const void *model_data, size_t data_size, ModelType model_type);
   static Graph LoadModel(const std::string &file, ModelType model_type);
   static Status LoadCheckPoint(const std::string &ckpt_file, std::map<std::string, Buffer> *parameters);
   static Status SetParameters(const std::map<std::string, Buffer> &parameters, Model *model);
   static Status ExportModel(const Model &model, ModelType model_type, Buffer *model_data);
   static Status ExportModel(const Model &model, ModelType model_type, const std::string &model_file);
 };
-}  // namespace api
 }  // namespace mindspore
 #endif  // MINDSPORE_INCLUDE_API_SERIALIZATION_H

include/api/status.h

@@ -17,37 +17,129 @@
 #define MINDSPORE_INCLUDE_API_STATUS_H
 
 #include <string>
+#include <ostream>
+#include <climits>
 
 namespace mindspore {
-namespace api {
-enum StatusCode {
-  SUCCESS = 0,
-  FAILED,
-  INVALID_INPUTS,
-  // insert new status code here
-  UNKNOWN = 0xFFFFFFFF
+enum CompCode : uint32_t {
+  kCore = 0x00000000u,
+  kMD = 0x10000000u,
+  kME = 0x20000000u,
+  kMC = 0x30000000u,
+  kLite = 0xF0000000u,
+};
+
+enum StatusCode : uint32_t {
+  kSuccess = 0,
+  // Core
+  kCoreFailed = kCore | 0x1,
+
+  // MD
+  kMDOutOfMemory = kMD | 1,
+  kMDShapeMisMatch = kMD | 2,
+  kMDInterrupted = kMD | 3,
+  kMDNoSpace = kMD | 4,
+  kMDPyFuncException = kMD | 5,
+  kMDDuplicateKey = kMD | 6,
+  kMDPythonInterpreterFailure = kMD | 7,
+  kMDTDTPushFailure = kMD | 8,
+  kMDFileNotExist = kMD | 9,
+  kMDProfilingError = kMD | 10,
+  kMDBoundingBoxOutOfBounds = kMD | 11,
+  kMDBoundingBoxInvalidShape = kMD | 12,
+  kMDSyntaxError = kMD | 13,
+  kMDTimeOut = kMD | 14,
+  kMDBuddySpaceFull = kMD | 15,
+  kMDNetWorkError = kMD | 16,
+  kMDNotImplementedYet = kMD | 17,
+  // Make this error code the last one. Add new error code above it.
+  kMDUnexpectedError = kMD | 127,
+
+  // ME
+  kMEFailed = kME | 0x1,
+  kMEInvalidInput = kME | 0x2,
+
+  // MC
+  kMCFailed = kMC | 0x1,
+  kMCDeviceError = kMC | 0x2,
+  kMCInvalidInput = kMC | 0x3,
+  kMCInvalidArgs = kMC | 0x4,
+
+  // Lite  // Common error code, range: [-1, -100）
+  kLiteError = kLite | (0x0FFFFFFF & -1),           /**< Common error code. */
+  kLiteNullptr = kLite | (0x0FFFFFFF & -2),         /**< NULL pointer returned.*/
+  kLiteParamInvalid = kLite | (0x0FFFFFFF & -3),    /**< Invalid parameter.*/
+  kLiteNoChange = kLite | (0x0FFFFFFF & -4),        /**< No change. */
+  kLiteSuccessExit = kLite | (0x0FFFFFFF & -5),     /**< No error but exit. */
+  kLiteMemoryFailed = kLite | (0x0FFFFFFF & -6),    /**< Fail to create memory. */
+  kLiteNotSupport = kLite | (0x0FFFFFFF & -7),      /**< Fail to support. */
+  kLiteThreadPoolError = kLite | (0x0FFFFFFF & -8), /**< Error occur in thread pool. */
+
+  // Executor error code, range: [-100,-200)
+  kLiteOutOfTensorRange = kLite | (0x0FFFFFFF & -100), /**< Failed to check range. */
+  kLiteInputTensorError = kLite | (0x0FFFFFFF & -101), /**< Failed to check input tensor. */
+  kLiteReentrantError = kLite | (0x0FFFFFFF & -102),   /**< Exist executor running. */
+
+  // Graph error code, range: [-200,-300)
+  kLiteGraphFileError = kLite | (0x0FFFFFFF & -200), /**< Failed to verify graph file. */
+
+  // Node error code, range: [-300,-400)
+  kLiteNotFindOp = kLite | (0x0FFFFFFF & -300),        /**< Failed to find operator. */
+  kLiteInvalidOpName = kLite | (0x0FFFFFFF & -301),    /**< Invalid operator name. */
+  kLiteInvalidOpAttr = kLite | (0x0FFFFFFF & -302),    /**< Invalid operator attr. */
+  kLiteOpExecuteFailure = kLite | (0x0FFFFFFF & -303), /**< Failed to execution operator. */
+
+  // Tensor error code, range: [-400,-500)
+  kLiteFormatError = kLite | (0x0FFFFFFF & -400), /**< Failed to checking tensor format. */
+
+  // InferShape error code, range: [-500,-600)
+  kLiteInferError = kLite | (0x0FFFFFFF & -500),   /**< Failed to infer shape. */
+  kLiteInferInvalid = kLite | (0x0FFFFFFF & -501), /**< Invalid infer shape before runtime. */
+
+  // User input param error code, range: [-600, 700)
+  kLiteInputParamInvalid = kLite | (0x0FFFFFFF & -600), /**< Invalid input param by user. */
 };
 
 class Status {
  public:
-  Status() : status_code_(FAILED) {}
-  Status(enum StatusCode status_code, const std::string &status_msg = "")    // NOLINT(runtime/explicit)
-    : status_code_(status_code), status_msg_(status_msg) {}
+  Status() : status_code_(kSuccess), line_of_code_(-1) {}
+  Status(enum StatusCode status_code, const std::string &status_msg = "")  // NOLINT(runtime/explicit)
+      : status_code_(status_code), status_msg_(status_msg), line_of_code_(-1) {}
+  Status(const StatusCode code, int line_of_code, const char *file_name, const std::string &extra = "");
+
   ~Status() = default;
 
-  bool IsSuccess() const { return status_code_ == SUCCESS; }
   enum StatusCode StatusCode() const { return status_code_; }
-  std::string StatusMessage() const { return status_msg_; }
+  const std::string &ToString() const { return status_msg_; }
+
+  int GetLineOfCode() const { return line_of_code_; }
+  const std::string &GetErrDescription() const { return status_msg_; }
+  const std::string &SetErrDescription(const std::string &err_description);
+
+  friend std::ostream &operator<<(std::ostream &os, const Status &s);
+
   bool operator==(const Status &other) const { return status_code_ == other.status_code_; }
   bool operator==(enum StatusCode other_code) const { return status_code_ == other_code; }
   bool operator!=(const Status &other) const { return status_code_ != other.status_code_; }
   bool operator!=(enum StatusCode other_code) const { return status_code_ != other_code; }
-  operator bool() const = delete;
+
+  explicit operator bool() const { return (status_code_ == kSuccess); }
+  explicit operator int() const { return static_cast<int>(status_code_); }
+
+  static Status OK() { return Status(StatusCode::kSuccess); }
+
+  bool IsOk() const { return (StatusCode() == StatusCode::kSuccess); }
+
+  bool IsError() const { return !IsOk(); }
+
+  static std::string CodeAsString(enum StatusCode c);
 
  private:
   enum StatusCode status_code_;
   std::string status_msg_;
+  int line_of_code_;
+  std::string file_name_;
+  std::string err_description_;
 };
-}  // namespace api
 }  // namespace mindspore
 #endif  // MINDSPORE_INCLUDE_API_STATUS_H

include/api/types.h

@@ -16,15 +16,20 @@
 #ifndef MINDSPORE_INCLUDE_API_TYPES_H
 #define MINDSPORE_INCLUDE_API_TYPES_H
 
+#include <cstddef>
 #include <string>
 #include <vector>
 #include <memory>
+#include "include/api/data_type.h"
 
+#ifdef _WIN32
+#define MS_API __declspec(dllexport)
+#else
 #define MS_API __attribute__((visibility("default")))
+#endif
 
 namespace mindspore {
-namespace api {
-enum ModelType {
+enum ModelType : uint32_t {
   kMindIR = 0,
   kAIR = 1,
   kOM = 2,
@@ -33,52 +38,38 @@ enum ModelType {
   kUnknownType = 0xFFFFFFFF
 };
 
-enum DataType {
-  kMsUnknown = 0,
-  kMsBool = 1,
-  kMsInt8 = 2,
-  kMsInt16 = 3,
-  kMsInt32 = 4,
-  kMsInt64 = 5,
-  kMsUint8 = 6,
-  kMsUint16 = 7,
-  kMsUint32 = 8,
-  kMsUint64 = 9,
-  kMsFloat16 = 10,
-  kMsFloat32 = 11,
-  kMsFloat64 = 12,
-  // insert new data type here
-  kInvalidDataType = 0xFFFFFFFF
-};
-
-class MS_API Tensor {
+class MS_API MSTensor {
  public:
-  Tensor();
-  Tensor(const std::string &name, DataType type, const std::vector<int64_t> &shape, const void *data, size_t data_len);
-  ~Tensor();
+  class Impl;
 
-  const std::string &Name() const;
-  void SetName(const std::string &name);
+  static MSTensor CreateTensor(const std::string &name, DataType type, const std::vector<int64_t> &shape,
+                               const void *data, size_t data_len) noexcept;
+  static MSTensor CreateRefTensor(const std::string &name, DataType type, const std::vector<int64_t> &shape,
+                                  const void *data, size_t data_len) noexcept;
 
-  api::DataType DataType() const;
-  void SetDataType(api::DataType type);
+  MSTensor();
+  explicit MSTensor(const std::shared_ptr<Impl> &impl);
+  MSTensor(const std::string &name, DataType type, const std::vector<int64_t> &shape, const void *data,
+           size_t data_len);
+  ~MSTensor();
 
+  const std::string &Name() const;
+  enum DataType DataType() const;
   const std::vector<int64_t> &Shape() const;
-  void SetShape(const std::vector<int64_t> &shape);
+  int64_t ElementNum() const;
 
-  const void *Data() const;
+  std::shared_ptr<const void> Data() const;
   void *MutableData();
   size_t DataSize() const;
 
-  bool ResizeData(size_t data_len);
-  bool SetData(const void *data, size_t data_len);
+  bool IsDevice() const;
 
-  int64_t ElementNum() const;
-  static int GetTypeSize(api::DataType type);
-  Tensor Clone() const;
+  MSTensor Clone() const;
+  bool operator==(std::nullptr_t) const;
 
  private:
-  class Impl;
+  friend class ModelImpl;
+  explicit MSTensor(std::nullptr_t);
   std::shared_ptr<Impl> impl_;
 };
 
@@ -101,21 +92,5 @@ class MS_API Buffer {
   class Impl;
   std::shared_ptr<Impl> impl_;
 };
-
-extern MS_API const char *kDeviceTypeAscend310;
-extern MS_API const char *kDeviceTypeAscend910;
-extern MS_API const char *kDeviceTypeGpu;
-
-constexpr auto kModelOptionDumpCfgPath = "mindspore.option.dump_config_file_path";
-constexpr auto kModelOptionInsertOpCfgPath = "mindspore.option.insert_op_config_file_path";  // aipp config file
-constexpr auto kModelOptionInputFormat = "mindspore.option.input_format";                    // nchw or nhwc
-// Mandatory while dynamic batch: e.g. "input_op_name1: n1,c2,h3,w4;input_op_name2: n4,c3,h2,w1"
-constexpr auto kModelOptionInputShape = "mindspore.option.input_shape";
-constexpr auto kModelOptionOutputType = "mindspore.option.output_type";  // "FP32", "UINT8" or "FP16", default as "FP32"
-constexpr auto kModelOptionPrecisionMode = "mindspore.option.precision_mode";
-// "force_fp16", "allow_fp32_to_fp16", "must_keep_origin_dtype" or "allow_mix_precision", default as "force_fp16"
-constexpr auto kModelOptionOpSelectImplMode = "mindspore.option.op_select_impl_mode";
-// "high_precision" or "high_performance", default as "high_performance"
-}  // namespace api
 }  // namespace mindspore
 #endif  // MINDSPORE_INCLUDE_API_TYPES_H

mindspore/ccsrc/cxx_api/CMakeLists.txt

@@ -23,7 +23,7 @@ if(ENABLE_D)
 endif()
 
 if(ENABLE_GPU)
-    file(GLOB_RECURSE API_MS_INFER_SRC ${CMAKE_CURRENT_SOURCE_DIR} "python_utils.cc" "model/ms/*.cc" "graph/gpu/*.cc")
+    file(GLOB_RECURSE API_MS_INFER_SRC ${CMAKE_CURRENT_SOURCE_DIR} "model/ms/*.cc" "graph/gpu/*.cc")
 endif()
 
 set(MSLIB_SRC ${CMAKE_CURRENT_SOURCE_DIR}/types.cc
@@ -45,8 +45,13 @@ if(CMAKE_SYSTEM_NAME MATCHES "Darwin")
     target_link_libraries(mindspore_shared_lib PRIVATE ${PYTHON_LIBRARIES} ${SECUREC_LIBRARY}
             -Wl,-force_load mindspore -Wl,-noall_load proto_input mindspore_gvar mindspore::protobuf)
 else()
-    target_link_libraries(mindspore_shared_lib PRIVATE ${PYTHON_LIBRARIES} ${SECUREC_LIBRARY}
+    if(ENABLE_D OR ENABLE_ACL)
+        target_link_libraries(mindspore_shared_lib PRIVATE ${PYTHON_LIBRARIES} ${SECUREC_LIBRARY}
             -Wl,--whole-archive mindspore -Wl,--no-whole-archive proto_input mindspore_gvar mindspore::protobuf)
+    else()
+        target_link_libraries(mindspore_shared_lib PRIVATE ${PYTHON_LIBRARIES} ${SECUREC_LIBRARY}
+          mindspore proto_input mindspore_gvar mindspore::protobuf)
+    endif()
 endif()
 
 if(ENABLE_CPU)

mindspore/ccsrc/cxx_api/cell.cc

@@ -18,7 +18,7 @@
 #include "cxx_api/factory.h"
 #include "cxx_api/graph/graph_impl.h"
 
-namespace mindspore::api {
+namespace mindspore {
 std::vector<Output> CellBase::operator()(const std::vector<Input> &inputs) const { return Clone()->Construct(inputs); }
 
 ParameterCell::ParameterCell(const ParameterCell &cell) : tensor_(cell.tensor_.Clone()) {}
@@ -40,23 +40,23 @@ ParameterCell &ParameterCell::operator=(ParameterCell &&cell) {
   return *this;
 }
 
-ParameterCell::ParameterCell(const Tensor &tensor) : tensor_(tensor.Clone()) {}
+ParameterCell::ParameterCell(const MSTensor &tensor) : tensor_(tensor.Clone()) {}
 
-ParameterCell &ParameterCell::operator=(const Tensor &tensor) {
+ParameterCell &ParameterCell::operator=(const MSTensor &tensor) {
   tensor_ = tensor.Clone();
   return *this;
 }
 
-ParameterCell::ParameterCell(Tensor &&tensor) : tensor_(tensor) {}
+ParameterCell::ParameterCell(MSTensor &&tensor) : tensor_(tensor) {}
 
-ParameterCell &ParameterCell::operator=(Tensor &&tensor) {
+ParameterCell &ParameterCell::operator=(MSTensor &&tensor) {
   tensor_ = tensor;
   return *this;
 }
 
 GraphCell::GraphCell(const Graph &graph)
     : graph_(std::make_shared<Graph>(graph)),
-      executor_(Factory<GraphCell::GraphImpl>::Instance().Create(Context::Instance().GetDeviceTarget())) {
+      executor_(Factory<GraphCell::GraphImpl>::Instance().Create(GlobalContext::GetGlobalDeviceTarget())) {
   MS_EXCEPTION_IF_NULL(graph_);
   MS_EXCEPTION_IF_NULL(executor_);
   executor_->SetGraph(graph_);
@@ -64,7 +64,7 @@ GraphCell::GraphCell(const Graph &graph)
 
 GraphCell::GraphCell(const std::shared_ptr<Graph> &graph)
     : graph_(graph),
-      executor_(Factory<GraphCell::GraphImpl>::Instance().Create(Context::Instance().GetDeviceTarget())) {
+      executor_(Factory<GraphCell::GraphImpl>::Instance().Create(GlobalContext::GetGlobalDeviceTarget())) {
   MS_EXCEPTION_IF_NULL(graph_);
   MS_EXCEPTION_IF_NULL(executor_);
   executor_->SetGraph(graph_);
@@ -72,13 +72,13 @@ GraphCell::GraphCell(const std::shared_ptr<Graph> &graph)
 
 GraphCell::GraphCell(Graph &&graph)
     : graph_(std::make_shared<Graph>(graph)),
-      executor_(Factory<GraphCell::GraphImpl>::Instance().Create(Context::Instance().GetDeviceTarget())) {
+      executor_(Factory<GraphCell::GraphImpl>::Instance().Create(GlobalContext::GetGlobalDeviceTarget())) {
   MS_EXCEPTION_IF_NULL(graph_);
   MS_EXCEPTION_IF_NULL(executor_);
   executor_->SetGraph(graph_);
 }
 
-Status GraphCell::Run(const std::vector<Buffer> &inputs, std::vector<Buffer> *outputs) {
+Status GraphCell::Run(const std::vector<MSTensor> &inputs, std::vector<MSTensor> *outputs) {
   MS_EXCEPTION_IF_NULL(executor_);
   return executor_->Run(inputs, outputs);
 }
@@ -88,25 +88,24 @@ Status GraphCell::Load() {
   return executor_->Load();
 }
 
-Status GraphCell::GetInputsInfo(std::vector<std::string> *names, std::vector<std::vector<int64_t>> *shapes,
-                                std::vector<DataType> *data_types, std::vector<size_t> *mem_sizes) const {
+std::vector<MSTensor> GraphCell::GetInputs() {
   MS_EXCEPTION_IF_NULL(executor_);
-  return executor_->GetInputsInfo(names, shapes, data_types, mem_sizes);
+  return executor_->GetInputs();
 }
 
-Status GraphCell::GetOutputsInfo(std::vector<std::string> *names, std::vector<std::vector<int64_t>> *shapes,
-                                 std::vector<DataType> *data_types, std::vector<size_t> *mem_sizes) const {
+std::vector<MSTensor> GraphCell::GetOutputs() {
   MS_EXCEPTION_IF_NULL(executor_);
-  return executor_->GetOutputsInfo(names, shapes, data_types, mem_sizes);
+  return executor_->GetOutputs();
 }
 
 InputAndOutput::InputAndOutput() : cell_(nullptr), prev_(), index_(-1) {}
 
-InputAndOutput::InputAndOutput(const Tensor &tensor)
+InputAndOutput::InputAndOutput(const MSTensor &tensor)
     : cell_(std::make_shared<ParameterCell>(tensor.Clone())), prev_(), index_(-1) {}
-InputAndOutput::InputAndOutput(Tensor &&tensor) : cell_(std::make_shared<ParameterCell>(tensor)), prev_(), index_(-1) {}
+InputAndOutput::InputAndOutput(MSTensor &&tensor)
+    : cell_(std::make_shared<ParameterCell>(tensor)), prev_(), index_(-1) {}
 
 InputAndOutput::InputAndOutput(const std::shared_ptr<CellBase> &cell, const std::vector<InputAndOutput> &prev,
                                int32_t index)
     : cell_(cell), prev_(prev), index_(index) {}
-}  // namespace mindspore::api
+}  // namespace mindspore

mindspore/ccsrc/cxx_api/context.cc

@@ -16,49 +16,119 @@
 #include "include/api/context.h"
 #include "utils/log_adapter.h"
 
-namespace mindspore::api {
-class Context::ContextImpl {
- public:
-  ContextImpl() : device_target_("NotSet"), device_id_(0) {}
-  ~ContextImpl() = default;
-  const std::string &GetDeviceTarget() const { return device_target_; }
-  void SetDeviceTarget(std::string_view device_target) { device_target_ = device_target; }
-  uint32_t GetDeviceID() const { return device_id_; }
-  void SetDeviceID(uint32_t device_id) { device_id_ = device_id; }
+constexpr auto kGlobalContextDeviceTarget = "mindspore.ascend.globalcontext.device_target";
+constexpr auto kGlobalContextDeviceID = "mindspore.ascend.globalcontext.device_id";
+constexpr auto kModelOptionInsertOpCfgPath = "mindspore.option.insert_op_config_file_path";  // aipp config file
+constexpr auto kModelOptionInputFormat = "mindspore.option.input_format";                    // nchw or nhwc
+constexpr auto kModelOptionInputShape = "mindspore.option.input_shape";
+// Mandatory while dynamic batch: e.g. "input_op_name1: n1,c2,h3,w4;input_op_name2: n4,c3,h2,w1"
+constexpr auto kModelOptionOutputType = "mindspore.option.output_type";  // "FP32", "UINT8" or "FP16", default as "FP32"
+constexpr auto kModelOptionPrecisionMode = "mindspore.option.precision_mode";
+// "force_fp16", "allow_fp32_to_fp16", "must_keep_origin_dtype" or "allow_mix_precision", default as "force_fp16"
+constexpr auto kModelOptionOpSelectImplMode = "mindspore.option.op_select_impl_mode";
 
- private:
-  std::string device_target_;
-  uint32_t device_id_;
-};
+namespace mindspore {
+template <class T>
+static T GetValue(const std::shared_ptr<Context> &context, const std::string &key) {
+  auto iter = context->params.find(key);
+  if (iter == context->params.end()) {
+    return T();
+  }
+  const std::any &value = iter->second;
+  if (value.type() != typeid(T)) {
+    return T();
+  }
 
-Context &Context::Instance() {
-  static Context context;
-  return context;
+  return std::any_cast<T>(value);
 }
 
-const std::string &Context::GetDeviceTarget() const {
-  MS_EXCEPTION_IF_NULL(impl_);
-  return impl_->GetDeviceTarget();
+std::shared_ptr<Context> GlobalContext::GetGlobalContext() {
+  static std::shared_ptr<Context> g_context = std::make_shared<Context>();
+  return g_context;
 }
 
-Context &Context::SetDeviceTarget(const std::string &device_target) {
-  MS_EXCEPTION_IF_NULL(impl_);
-  impl_->SetDeviceTarget(device_target);
-  return *this;
+void GlobalContext::SetGlobalDeviceTarget(const std::string &device_target) {
+  auto global_context = GetGlobalContext();
+  MS_EXCEPTION_IF_NULL(global_context);
+  global_context->params[kGlobalContextDeviceTarget] = device_target;
 }
 
-uint32_t Context::GetDeviceID() const {
-  MS_EXCEPTION_IF_NULL(impl_);
-  return impl_->GetDeviceID();
+std::string GlobalContext::GetGlobalDeviceTarget() {
+  auto global_context = GetGlobalContext();
+  MS_EXCEPTION_IF_NULL(global_context);
+  return GetValue<std::string>(global_context, kGlobalContextDeviceTarget);
 }
 
-Context &Context::SetDeviceID(uint32_t device_id) {
-  MS_EXCEPTION_IF_NULL(impl_);
-  impl_->SetDeviceID(device_id);
-  return *this;
+void GlobalContext::SetGlobalDeviceID(const uint32_t &device_id) {
+  auto global_context = GetGlobalContext();
+  MS_EXCEPTION_IF_NULL(global_context);
+  global_context->params[kGlobalContextDeviceID] = device_id;
 }
 
-Context::Context() : impl_(std::make_shared<Context::ContextImpl>()) { MS_EXCEPTION_IF_NULL(impl_); }
+uint32_t GlobalContext::GetGlobalDeviceID() {
+  auto global_context = GetGlobalContext();
+  MS_EXCEPTION_IF_NULL(global_context);
+  return GetValue<uint32_t>(global_context, kGlobalContextDeviceID);
+}
+
+void ModelContext::SetInsertOpConfigPath(const std::shared_ptr<Context> &context, const std::string &cfg_path) {
+  MS_EXCEPTION_IF_NULL(context);
+  context->params[kModelOptionInsertOpCfgPath] = cfg_path;
+}
+
+std::string ModelContext::GetInsertOpConfigPath(const std::shared_ptr<Context> &context) {
+  MS_EXCEPTION_IF_NULL(context);
+  return GetValue<std::string>(context, kModelOptionInsertOpCfgPath);
+}
+
+void ModelContext::SetInputFormat(const std::shared_ptr<Context> &context, const std::string &format) {
+  MS_EXCEPTION_IF_NULL(context);
+  context->params[kModelOptionInputFormat] = format;
+}
+
+std::string ModelContext::GetInputFormat(const std::shared_ptr<Context> &context) {
+  MS_EXCEPTION_IF_NULL(context);
+  return GetValue<std::string>(context, kModelOptionInputFormat);
+}
+
+void ModelContext::SetInputShape(const std::shared_ptr<Context> &context, const std::string &shape) {
+  MS_EXCEPTION_IF_NULL(context);
+  context->params[kModelOptionInputShape] = shape;
+}
+
+std::string ModelContext::GetInputShape(const std::shared_ptr<Context> &context) {
+  MS_EXCEPTION_IF_NULL(context);
+  return GetValue<std::string>(context, kModelOptionInputShape);
+}
+
+void ModelContext::SetOutputType(const std::shared_ptr<Context> &context, enum DataType output_type) {
+  MS_EXCEPTION_IF_NULL(context);
+  context->params[kModelOptionOutputType] = output_type;
+}
+
+enum DataType ModelContext::GetOutputType(const std::shared_ptr<Context> &context) {
+  MS_EXCEPTION_IF_NULL(context);
+  return GetValue<enum DataType>(context, kModelOptionOutputType);
+}
+
+void ModelContext::SetPrecisionMode(const std::shared_ptr<Context> &context, const std::string &precision_mode) {
+  MS_EXCEPTION_IF_NULL(context);
+  context->params[kModelOptionPrecisionMode] = precision_mode;
+}
+
+std::string ModelContext::GetPrecisionMode(const std::shared_ptr<Context> &context) {
+  MS_EXCEPTION_IF_NULL(context);
+  return GetValue<std::string>(context, kModelOptionPrecisionMode);
+}
 
-Context::~Context() {}
-}  // namespace mindspore::api
+void ModelContext::SetOpSelectImplMode(const std::shared_ptr<Context> &context,
+                                       const std::string &op_select_impl_mode) {
+  MS_EXCEPTION_IF_NULL(context);
+  context->params[kModelOptionOpSelectImplMode] = op_select_impl_mode;
+}
+
+std::string ModelContext::GetOpSelectImplMode(const std::shared_ptr<Context> &context) {
+  MS_EXCEPTION_IF_NULL(context);
+  return GetValue<std::string>(context, kModelOptionOpSelectImplMode);
+}
+}  // namespace mindspore

mindspore/ccsrc/cxx_api/factory.h

@@ -23,7 +23,7 @@
 #include <utility>
 #include "utils/utils.h"
 
-namespace mindspore::api {
+namespace mindspore {
 template <class T>
 class Factory {
   using U = std::function<std::shared_ptr<T>()>;
@@ -79,5 +79,5 @@ class Registrar {
 #define API_FACTORY_REG(BASE_CLASS, DEVICE_NAME, DERIVE_CLASS)                             \
   static const Registrar<BASE_CLASS> g_api_##DERIVE_CLASS##_registrar_##DEVICE_NAME##_reg( \
     #DEVICE_NAME, []() { return std::make_shared<DERIVE_CLASS>(); });
-}  // namespace mindspore::api
+}  // namespace mindspore
 #endif  // MINDSPORE_CCSRC_CXX_API_FACTORY_H

mindspore/ccsrc/cxx_api/graph/acl/acl_env_guard.cc

@@ -17,8 +17,8 @@
 #include "utils/log_adapter.h"
 #include "acl/acl.h"
 
-namespace mindspore::api {
-std::weak_ptr<AclEnvGuard> AclEnvGuard::global_acl_env_;
+namespace mindspore {
+std::shared_ptr<AclEnvGuard> AclEnvGuard::global_acl_env_;
 std::mutex AclEnvGuard::global_acl_env_mutex_;
 
 AclEnvGuard::AclEnvGuard(std::string_view cfg_file) {
@@ -42,7 +42,7 @@ std::shared_ptr<AclEnvGuard> AclEnvGuard::GetAclEnv(std::string_view cfg_file) {
   std::shared_ptr<AclEnvGuard> acl_env;
 
   std::lock_guard<std::mutex> lock(global_acl_env_mutex_);
-  acl_env = global_acl_env_.lock();
+  acl_env = global_acl_env_;
   if (acl_env != nullptr) {
     MS_LOG(INFO) << "Acl has been initialized, skip.";
   } else {
@@ -57,4 +57,4 @@ std::shared_ptr<AclEnvGuard> AclEnvGuard::GetAclEnv(std::string_view cfg_file) {
   }
   return acl_env;
 }
-}  // namespace mindspore::api
+}  // namespace mindspore

mindspore/ccsrc/cxx_api/graph/acl/acl_env_guard.h

@@ -20,7 +20,7 @@
 #include <mutex>
 #include "acl/acl_base.h"
 
-namespace mindspore::api {
+namespace mindspore {
 class __attribute__((visibility("default"))) AclEnvGuard {
  public:
   explicit AclEnvGuard(std::string_view cfg_file);
@@ -29,10 +29,10 @@ class __attribute__((visibility("default"))) AclEnvGuard {
   static std::shared_ptr<AclEnvGuard> GetAclEnv(std::string_view cfg_file);
 
  private:
-  static std::weak_ptr<AclEnvGuard> global_acl_env_;
+  static std::shared_ptr<AclEnvGuard> global_acl_env_;
   static std::mutex global_acl_env_mutex_;
 
   aclError errno_;
 };
-}  // namespace mindspore::api
+}  // namespace mindspore
 #endif  // MINDSPORE_CCSRC_CXX_API_GRAPH_ACL_ACL_ENV_GUARD_H

mindspore/ccsrc/cxx_api/graph/acl/acl_graph_impl.cc

@@ -16,53 +16,50 @@
 #include "cxx_api/graph/acl/acl_graph_impl.h"
 #include "include/api/context.h"
 #include "cxx_api/model/acl/model_converter.h"
-#include "cxx_api/python_utils.h"
 #include "utils/log_adapter.h"
 
-namespace mindspore::api {
+namespace mindspore {
 API_FACTORY_REG(GraphCell::GraphImpl, Ascend310, AclGraphImpl);
 
 AclGraphImpl::AclGraphImpl()
     : init_flag_(false),
       load_flag_(false),
       device_type_("AscendCL"),
-      device_id_(Context::Instance().GetDeviceID()),
+      device_id_(GlobalContext::GetGlobalDeviceID()),
       context_(nullptr),
       acl_env_(nullptr) {}
 
 AclGraphImpl::~AclGraphImpl() { (void)FinalizeEnv(); }
 
-Status AclGraphImpl::Run(const std::vector<Buffer> &inputs, std::vector<Buffer> *outputs) {
+Status AclGraphImpl::Run(const std::vector<MSTensor> &inputs, std::vector<MSTensor> *outputs) {
   MS_EXCEPTION_IF_NULL(outputs);
   Status ret = Load();
-  if (ret != SUCCESS) {
+  if (ret != kSuccess) {
     MS_LOG(ERROR) << "Prepare model resource failed.";
-    return FAILED;
+    return ret;
   }
 
   return model_process_.PredictFromHost(inputs, outputs);
 }
 
-Status AclGraphImpl::GetInputsInfo(std::vector<std::string> *names, std::vector<std::vector<int64_t>> *shapes,
-                                   std::vector<DataType> *data_types, std::vector<size_t> *mem_sizes) {
+std::vector<MSTensor> AclGraphImpl::GetInputs() {
   Status ret = Load();
-  if (ret != SUCCESS) {
+  if (ret != kSuccess) {
     MS_LOG(ERROR) << "Prepare model resource failed.";
-    return FAILED;
+    return {};
   }
 
-  return model_process_.GetInputsInfo(names, shapes, data_types, mem_sizes);
+  return model_process_.GetInputs();
 }
 
-Status AclGraphImpl::GetOutputsInfo(std::vector<std::string> *names, std::vector<std::vector<int64_t>> *shapes,
-                                    std::vector<DataType> *data_types, std::vector<size_t> *mem_sizes) {
+std::vector<MSTensor> AclGraphImpl::GetOutputs() {
   Status ret = Load();
-  if (ret != SUCCESS) {
+  if (ret != kSuccess) {
     MS_LOG(ERROR) << "Prepare model resource failed.";
-    return FAILED;
+    return {};
   }
 
-  return model_process_.GetOutputsInfo(names, shapes, data_types, mem_sizes);
+  return model_process_.GetOutputs();
 }
 
 Status AclGraphImpl::LoadAclModel(Buffer om_data) {
@@ -72,44 +69,44 @@ Status AclGraphImpl::LoadAclModel(Buffer om_data) {
   auto acl_ret = aclmdlLoadFromMem(om_data.Data(), om_data.DataSize(), &acl_model_id);
   if (acl_ret != ACL_ERROR_NONE) {
     MS_LOG(ERROR) << "Call aclmdlLoadFromMem failed.";
-    return FAILED;
+    return kMCDeviceError;
   }
 
   // acl init model resource
   model_process_.set_model_id(acl_model_id);
   Status ret = model_process_.PreInitModelResource();
-  if (ret != SUCCESS) {
+  if (ret != kSuccess) {
     (void)aclmdlUnload(acl_model_id);
     MS_LOG(ERROR) << "Pre init model resource failed.";
-    return FAILED;
+    return ret;
   }
 
   MS_LOG(INFO) << "Load acl model success.";
-  return SUCCESS;
+  return kSuccess;
 }
 
 Status AclGraphImpl::InitEnv() {
   if (init_flag_) {
-    return SUCCESS;
+    return kSuccess;
   }
 
   acl_env_ = AclEnvGuard::GetAclEnv("");
   if (acl_env_ == nullptr) {
     MS_LOG(ERROR) << "Acl init failed.";
-    return FAILED;
+    return kMCDeviceError;
   }
 
   aclError ret = aclrtSetDevice(device_id_);
   if (ret != ACL_ERROR_NONE) {
     MS_LOG(ERROR) << "Acl open device " << device_id_ << " failed";
-    return FAILED;
+    return kMCDeviceError;
   }
   MS_LOG(INFO) << "Open device " << device_id_ << " success";
 
   ret = aclrtCreateContext(&context_, device_id_);
   if (ret != ACL_ERROR_NONE) {
     MS_LOG(ERROR) << "Acl create context failed";
-    return FAILED;
+    return kMCDeviceError;
   }
   MS_LOG(INFO) << "Create context success";
 
@@ -117,7 +114,7 @@ Status AclGraphImpl::InitEnv() {
   ret = aclrtGetRunMode(&run_mode);
   if (ret != ACL_ERROR_NONE) {
     MS_LOG(ERROR) << "Acl get run mode failed";
-    return FAILED;
+    return kMCDeviceError;
   }
   bool is_device = (run_mode == ACL_DEVICE);
   model_process_.SetIsDevice(is_device);
@@ -125,24 +122,24 @@ Status AclGraphImpl::InitEnv() {
 
   MS_LOG(INFO) << "Init acl success, device id " << device_id_;
   init_flag_ = true;
-  return SUCCESS;
+  return kSuccess;
 }
 
 Status AclGraphImpl::FinalizeEnv() {
   if (!init_flag_) {
-    return SUCCESS;
+    return kSuccess;
   }
 
   aclError rt_ret = aclrtSetCurrentContext(context_);
   if (rt_ret != ACL_ERROR_NONE) {
     MS_LOG(ERROR) << "Set the ascend device context failed";
-    return FAILED;
+    return kMCDeviceError;
   }
 
   Status ret = model_process_.UnLoad();
-  if (ret != SUCCESS) {
+  if (ret != kSuccess) {
     MS_LOG(ERROR) << "Unload model inner failed.";
-    return FAILED;
+    return ret;
   }
 
   if (context_ != nullptr) {
@@ -161,16 +158,16 @@ Status AclGraphImpl::FinalizeEnv() {
   MS_LOG(INFO) << "End to reset device " << device_id_;
 
   init_flag_ = false;
-  return SUCCESS;
+  return kSuccess;
 }
 
 Status AclGraphImpl::Load() {
   // check graph type
   if (graph_->ModelType() != ModelType::kOM) {
     Status ret = ConvertToOM();
-    if (ret != SUCCESS) {
+    if (ret != kSuccess) {
       MS_LOG(ERROR) << "Load Failed.";
-      return FAILED;
+      return ret;
     }
   }
 
@@ -180,15 +177,15 @@ Status AclGraphImpl::Load() {
 
   // init
   Status ret = InitEnv();
-  if (ret != SUCCESS) {
+  if (ret != kSuccess) {
     MS_LOG(ERROR) << "InitEnv failed.";
-    return FAILED;
+    return ret;
   }
 
   // load model
   if (!load_flag_) {
     ret = LoadAclModel(om_data);
-    if (ret != SUCCESS) {
+    if (ret != kSuccess) {
       MS_LOG(ERROR) << "Load acl model failed.";
       return ret;
     }
@@ -198,24 +195,24 @@ Status AclGraphImpl::Load() {
   aclError rt_ret = aclrtSetCurrentContext(context_);
   if (rt_ret != ACL_ERROR_NONE) {
     MS_LOG(ERROR) << "Set the ascend device context failed";
-    return FAILED;
+    return kMCDeviceError;
   }
 
-  return SUCCESS;
+  return kSuccess;
 }
 
 Status AclGraphImpl::ConvertToOM() {
   MS_LOG(INFO) << "Start convert to om model.";
   if (graph_ == nullptr) {
     MS_LOG(ERROR) << "Invalid graph_ is null.";
-    return FAILED;
+    return kMCFailed;
   }
 
   auto &graph_data = GraphImpl::MutableGraphData();
   MS_EXCEPTION_IF_NULL(graph_data);
   if (graph_->ModelType() == ModelType::kOM) {
     MS_LOG(INFO) << "This model has been built, skip.";
-    return SUCCESS;
+    return kSuccess;
   } else if (graph_->ModelType() == ModelType::kMindIR) {
     auto func_graph = graph_data->GetFuncGraph();
     MS_EXCEPTION_IF_NULL(func_graph);
@@ -223,13 +220,13 @@ Status AclGraphImpl::ConvertToOM() {
     Buffer om_data = model_converter.LoadMindIR(func_graph);
     if (om_data.Data() == nullptr || om_data.DataSize() == 0) {
       MS_LOG(ERROR) << "Convert MindIR to OM failed.";
-      return FAILED;
+      return kMCFailed;
     }
     graph_data = std::make_shared<Graph::GraphData>(om_data, ModelType::kOM);
     MS_LOG(INFO) << "Convert MindIR to OM success.";
-    return SUCCESS;
+    return kSuccess;
   }
   MS_LOG(ERROR) << "Unsupported ModelType " << graph_->ModelType();
-  return FAILED;
+  return kMCFailed;
 }
-}  // namespace mindspore::api
+}  // namespace mindspore

mindspore/ccsrc/cxx_api/graph/acl/acl_graph_impl.h

@@ -27,18 +27,16 @@
 #include "cxx_api/graph/graph_impl.h"
 #include "cxx_api/factory.h"
 
-namespace mindspore::api {
+namespace mindspore {
 class AclGraphImpl : public GraphCell::GraphImpl {
  public:
   AclGraphImpl();
   ~AclGraphImpl() override;
 
-  Status Run(const std::vector<Buffer> &inputs, std::vector<Buffer> *outputs) override;
+  Status Run(const std::vector<MSTensor> &inputs, std::vector<MSTensor> *outputs) override;
   Status Load() override;
-  Status GetInputsInfo(std::vector<std::string> *names, std::vector<std::vector<int64_t>> *shapes,
-                       std::vector<DataType> *data_types, std::vector<size_t> *mem_sizes) override;
-  Status GetOutputsInfo(std::vector<std::string> *names, std::vector<std::vector<int64_t>> *shapes,
-                        std::vector<DataType> *data_types, std::vector<size_t> *mem_sizes) override;
+  std::vector<MSTensor> GetInputs() override;
+  std::vector<MSTensor> GetOutputs() override;
 
  private:
   Status ConvertToOM();
@@ -56,5 +54,5 @@ class AclGraphImpl : public GraphCell::GraphImpl {
 
   ModelProcess model_process_;
 };
-}  // namespace mindspore::api
+}  // namespace mindspore
 #endif  // MINDSPORE_CCSRC_CXX_API_GRAPH_ACL_ACL_GRAPH_IMPL_H

mindspore/ccsrc/cxx_api/graph/acl/model_process.cc

@@ -20,17 +20,19 @@
 #include <map>
 #include "utils/utils.h"
 
-namespace mindspore::api {
+namespace mindspore {
 static DataType TransToApiType(aclDataType data_type) {
-  static const std::map<aclDataType, api::DataType> data_type_map = {
-    {ACL_FLOAT16, api::kMsFloat16}, {ACL_FLOAT, api::kMsFloat32}, {ACL_DOUBLE, api::kMsFloat64},
-    {ACL_INT8, api::kMsInt8},       {ACL_INT16, api::kMsInt16},   {ACL_INT32, api::kMsInt32},
-    {ACL_INT64, api::kMsInt64},     {ACL_UINT8, api::kMsUint8},   {ACL_UINT16, api::kMsUint16},
-    {ACL_UINT32, api::kMsUint32},   {ACL_UINT64, api::kMsUint64}, {ACL_BOOL, api::kMsBool},
+  static const std::map<aclDataType, enum DataType> data_type_map = {
+    {ACL_FLOAT16, DataType::kNumberTypeFloat16}, {ACL_FLOAT, DataType::kNumberTypeFloat32},
+    {ACL_DOUBLE, DataType::kNumberTypeFloat64},  {ACL_INT8, DataType::kNumberTypeInt8},
+    {ACL_INT16, DataType::kNumberTypeInt16},     {ACL_INT32, DataType::kNumberTypeInt32},
+    {ACL_INT64, DataType::kNumberTypeInt64},     {ACL_UINT8, DataType::kNumberTypeUInt8},
+    {ACL_UINT16, DataType::kNumberTypeUInt16},   {ACL_UINT32, DataType::kNumberTypeUInt32},
+    {ACL_UINT64, DataType::kNumberTypeUInt64},   {ACL_BOOL, DataType::kNumberTypeBool},
   };
   auto it = data_type_map.find(data_type);
   if (it == data_type_map.end()) {
-    return api::kInvalidDataType;
+    return DataType::kTypeUnknown;
   } else {
     return it->second;
   }
@@ -51,7 +53,7 @@ inline static void PushbackIfNotNull(U *vec, T &&item) {
 }
 
 static void ConstructTensorDesc(const std::vector<AclTensorInfo> &acl_tensor_list, std::vector<std::string> *names,
-                                std::vector<std::vector<int64_t>> *shapes, std::vector<DataType> *data_types,
+                                std::vector<std::vector<int64_t>> *shapes, std::vector<enum DataType> *data_types,
                                 std::vector<size_t> *mem_sizes) {
   ClearIfNotNull(names);
   ClearIfNotNull(shapes);
@@ -66,41 +68,69 @@ static void ConstructTensorDesc(const std::vector<AclTensorInfo> &acl_tensor_lis
   }
 }
 
+static std::string ShapeToString(const std::vector<int64_t> &shape) {
+  std::string result = "[";
+  for (size_t i = 0; i < shape.size(); ++i) {
+    result += std::to_string(shape[i]);
+    if (i + 1 < shape.size()) {
+      result += ", ";
+    }
+  }
+  result += "]";
+  return result;
+}
+
+Status ModelProcess::ConstructTensors(const std::vector<AclTensorInfo> &acl_tensor_list,
+                                      std::vector<MSTensor> *tensor_list) {
+  MS_EXCEPTION_IF_NULL(tensor_list);
+  std::vector<std::string> names;
+  std::vector<std::vector<int64_t>> shapes;
+  std::vector<enum DataType> data_types;
+  std::vector<size_t> mem_sizes;
+
+  ConstructTensorDesc(acl_tensor_list, &names, &shapes, &data_types, &mem_sizes);
+  tensor_list->clear();
+  if (names.size() != acl_tensor_list.size() || shapes.size() != acl_tensor_list.size() ||
+      data_types.size() != acl_tensor_list.size() || mem_sizes.size() != acl_tensor_list.size()) {
+    MS_LOG(ERROR) << "Inner error, size do not match: names size " << names.size() << " shapes size " << shapes.size()
+                  << " data types size " << data_types.size() << " mem sizes size " << mem_sizes.size()
+                  << " acl_tensor_list size " << acl_tensor_list.size();
+    return kMCFailed;
+  }
+
+  aclrtMemcpyKind kind = is_run_on_device_ ? ACL_MEMCPY_HOST_TO_HOST : ACL_MEMCPY_DEVICE_TO_HOST;
+  for (size_t i = 0; i < acl_tensor_list.size(); ++i) {
+    tensor_list->emplace_back(names[i], data_types[i], shapes[i], nullptr, mem_sizes[i]);
+    auto ret = aclrtMemcpy((*tensor_list)[i].MutableData(), (*tensor_list)[i].DataSize(),
+                           acl_tensor_list[i].device_data, acl_tensor_list[i].buffer_size, kind);
+    if (ret != ACL_ERROR_NONE) {
+      MS_LOG(ERROR) << "Memcpy input " << i << " from " << (is_run_on_device_ ? "host" : "device")
+                    << " to host failed, memory size " << acl_tensor_list[i].buffer_size;
+      return kMCFailed;
+    }
+  }
+
+  return kSuccess;
+}
+
 Status ModelProcess::PreInitModelResource() {
   model_desc_ = aclmdlCreateDesc();
   aclError acl_ret = aclmdlGetDesc(model_desc_, model_id_);
   if (acl_ret != ACL_ERROR_NONE) {
     MS_LOG(ERROR) << "Read model desc failed";
-    return FAILED;
+    return kMCDeviceError;
   }
   Status ret = InitInputsBuffer();
-  if (ret != SUCCESS) {
+  if (ret != kSuccess) {
     MS_LOG(ERROR) << "Create input buffer failed";
-    return FAILED;
+    return ret;
   }
   ret = InitOutputsBuffer();
-  if (ret != SUCCESS) {
+  if (ret != kSuccess) {
     MS_LOG(ERROR) << "Create output buffer failed";
-    return FAILED;
+    return ret;
   }
-  return SUCCESS;
-}
-
-Status ModelProcess::LoadModelFromFile(const std::string &file_name, uint32_t *model_id) {
-  MS_EXCEPTION_IF_NULL(model_id);
-  aclError acl_ret = aclmdlLoadFromFile(file_name.c_str(), model_id);
-  if (acl_ret != ACL_ERROR_NONE) {
-    MS_LOG(ERROR) << "Read model file failed, file name is " << file_name;
-    return FAILED;
-  }
-  MS_LOG(INFO) << "Load model success " << file_name;
-  model_id_ = *model_id;
-  if (PreInitModelResource() != SUCCESS) {
-    aclmdlUnload(model_id_);
-    MS_LOG(ERROR) << "Pre init model resource failed, file name is " << file_name;
-    return FAILED;
-  }
-  return SUCCESS;
+  return kSuccess;
 }
 
 Status ModelProcess::InitInputsBuffer() {
@@ -113,8 +143,8 @@ Status ModelProcess::InitInputsBuffer() {
     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) {
-        MS_LOG(ERROR) << "Malloc device input buffer faild , input size " << buffer_size;
-        return FAILED;
+        MS_LOG(ERROR) << "Malloc device input buffer failed , input size " << buffer_size;
+        return kMCDeviceError;
       }
     }
 
@@ -125,7 +155,7 @@ Status ModelProcess::InitInputsBuffer() {
       if (!is_run_on_device_) {
         aclrtFree(data_mem_buffer);
       }
-      return FAILED;
+      return kMCDeviceError;
     }
     aclDataType data_type = aclmdlGetInputDataType(model_desc_, i);
     std::vector<int64_t> shape(dims.dims, dims.dims + dims.dimCount);
@@ -137,7 +167,7 @@ Status ModelProcess::InitInputsBuffer() {
     input_infos_.emplace_back(AclTensorInfo{data_mem_buffer, buffer_size, data_type, shape, input_name});
   }
   MS_LOG(INFO) << "Create model inputs success";
-  return SUCCESS;
+  return kSuccess;
 }
 
 Status ModelProcess::CreateDataBuffer(void **data_mem_buffer, size_t buffer_size, aclmdlDataset *dataset) {
@@ -154,14 +184,14 @@ Status ModelProcess::CreateDataBuffer(void **data_mem_buffer, size_t buffer_size
   if (!is_run_on_device_) {
     ret = aclrtMalloc(data_mem_buffer, buffer_size, ACL_MEM_MALLOC_NORMAL_ONLY);
     if (ret != ACL_ERROR_NONE) {
-      MS_LOG(ERROR) << "Malloc device buffer faild , buffer size " << buffer_size;
-      return FAILED;
+      MS_LOG(ERROR) << "Malloc device buffer failed , buffer size " << buffer_size;
+      return kMCDeviceError;
     }
   } else {
     ret = aclrtMallocHost(data_mem_buffer, buffer_size);
     if (ret != ACL_ERROR_NONE) {
-      MS_LOG(ERROR) << "Malloc device buffer faild , buffer size " << buffer_size;
-      return FAILED;
+      MS_LOG(ERROR) << "Malloc device buffer failed , buffer size " << buffer_size;
+      return kMCDeviceError;
     }
   }
 
@@ -169,16 +199,16 @@ Status ModelProcess::CreateDataBuffer(void **data_mem_buffer, size_t buffer_size
   if (data_buffer == nullptr) {
     MS_LOG(ERROR) << "Create Data Buffer failed";
     free_data_buffer(*data_mem_buffer);
-    return FAILED;
+    return kMCDeviceError;
   }
   ret = aclmdlAddDatasetBuffer(dataset, data_buffer);
   if (ret != ACL_ERROR_NONE) {
     MS_LOG(ERROR) << "add data buffer failed";
     free_data_buffer(*data_mem_buffer);
     aclDestroyDataBuffer(data_buffer);
-    return FAILED;
+    return kMCDeviceError;
   }
-  return SUCCESS;
+  return kSuccess;
 }
 
 Status ModelProcess::InitOutputsBuffer() {
@@ -186,7 +216,7 @@ Status ModelProcess::InitOutputsBuffer() {
   outputs_ = aclmdlCreateDataset();
   if (outputs_ == nullptr) {
     MS_LOG(ERROR) << "Create input dataset failed";
-    return FAILED;
+    return kMCDeviceError;
   }
   size_t output_size = aclmdlGetNumOutputs(model_desc_);
   MS_LOG(INFO) << "output_size = " << output_size;
@@ -194,9 +224,9 @@ Status ModelProcess::InitOutputsBuffer() {
     auto buffer_size = aclmdlGetOutputSizeByIndex(model_desc_, i);
 
     void *data_mem_buffer = nullptr;
-    if (CreateDataBuffer(&data_mem_buffer, buffer_size, outputs_) != SUCCESS) {
+    if (CreateDataBuffer(&data_mem_buffer, buffer_size, outputs_) != kSuccess) {
       MS_LOG(ERROR) << "add output data buffer failed, buffer size " << buffer_size;
-      return FAILED;
+      return kMCDeviceError;
     }
     aclmdlIODims dims;
     ret = aclmdlGetOutputDims(model_desc_, i, &dims);
@@ -207,7 +237,7 @@ Status ModelProcess::InitOutputsBuffer() {
       } else {
         aclrtFreeHost(data_mem_buffer);
       }
-      return FAILED;
+      return kMCDeviceError;
     }
     aclDataType data_type = aclmdlGetOutputDataType(model_desc_, i);
     std::vector<int64_t> shape(dims.dims, dims.dims + dims.dimCount);
@@ -219,7 +249,7 @@ Status ModelProcess::InitOutputsBuffer() {
     output_infos_.emplace_back(AclTensorInfo{data_mem_buffer, buffer_size, data_type, shape, output_name});
   }
   MS_LOG(INFO) << "Create model output success";
-  return SUCCESS;
+  return kSuccess;
 }
 
 void ModelProcess::DestroyInputsDataset() {
@@ -273,50 +303,60 @@ Status ModelProcess::UnLoad() {
   auto ret = aclmdlUnload(model_id_);
   if (ret != ACL_ERROR_NONE) {
     MS_LOG(ERROR) << "Unload model failed";
-    return FAILED;
+    return kMCDeviceError;
   }
   if (model_desc_ != nullptr) {
     ret = aclmdlDestroyDesc(model_desc_);
     if (ret != ACL_ERROR_NONE) {
       MS_LOG(ERROR) << "Unload model failed";
-      return FAILED;
+      return kMCDeviceError;
     }
     model_desc_ = nullptr;
   }
   DestroyInputsBuffer();
   DestroyOutputsBuffer();
   MS_LOG(INFO) << "End unload model " << model_id_;
-  return SUCCESS;
+  return kSuccess;
 }
 
-Status ModelProcess::CheckAndInitInput(const std::vector<Buffer> &inputs) {
+Status ModelProcess::CheckAndInitInput(const std::vector<MSTensor> &inputs) {
   aclError ret;
   inputs_ = aclmdlCreateDataset();
   // check inputs
   if (inputs.size() != input_infos_.size()) {
-    MS_LOG(ERROR) << "inputs count not match, required count " << input_infos_.size() << ", given count "
+    MS_LOG(ERROR) << "Inputs count not match, required count " << input_infos_.size() << ", given count "
                   << inputs.size();
-    return INVALID_INPUTS;
+    return kMCInvalidInput;
   }
   for (size_t i = 0; i < input_infos_.size(); ++i) {
+    if (inputs[i].Shape() != input_infos_[i].dims) {
+      MS_LOG(INFO) << "Note: input " << i << " shape not match, required " << ShapeToString(input_infos_[i].dims)
+                   << ", given " << ShapeToString(inputs[i].Shape());
+    }
+
+    if (inputs[i].DataType() != TransToApiType(input_infos_[i].data_type)) {
+      MS_LOG(INFO) << "Note: input " << i << " data type not match, required "
+                   << TransToApiType(input_infos_[i].data_type) << ", given " << inputs[i].DataType();
+    }
+
     if (inputs[i].DataSize() != input_infos_[i].buffer_size) {
-      MS_LOG(ERROR) << "input " << i << " data size not match, required size " << input_infos_[i].buffer_size
+      MS_LOG(ERROR) << "Input " << i << " data size not match, required size " << input_infos_[i].buffer_size
                     << ", given count " << inputs[i].DataSize();
-      return INVALID_INPUTS;
+      return kMCInvalidInput;
     }
   }
   // copy inputs
   for (size_t i = 0; i < input_infos_.size(); ++i) {
     const auto &info = input_infos_[i];
-    const auto &input = inputs[i];
-    const void *data = input.Data();
+    auto input = inputs[i];
+    const void *data = input.MutableData();
 
     void *input_buffer = nullptr;
     if (!is_run_on_device_) {
       ret = aclrtMemcpy(info.device_data, info.buffer_size, data, input.DataSize(), ACL_MEMCPY_HOST_TO_DEVICE);
       if (ret != ACL_ERROR_NONE) {
         MS_LOG(ERROR) << "Acl memcpy input " << i << " data to device failed, buffer size " << input.DataSize();
-        return FAILED;
+        return kMCDeviceError;
       }
       input_buffer = info.device_data;
     } else {
@@ -325,23 +365,23 @@ Status ModelProcess::CheckAndInitInput(const std::vector<Buffer> &inputs) {
     auto data_buffer = aclCreateDataBuffer(input_buffer, info.buffer_size);
     if (data_buffer == nullptr) {
       MS_LOG(ERROR) << "Create Data Buffer failed";
-      return FAILED;
+      return kMCDeviceError;
     }
     ret = aclmdlAddDatasetBuffer(inputs_, data_buffer);
     if (ret != ACL_ERROR_NONE) {
       MS_LOG(ERROR) << "add data buffer failed";
       aclDestroyDataBuffer(data_buffer);
-      return FAILED;
+      return kMCDeviceError;
     }
   }
-  return SUCCESS;
+  return kSuccess;
 }
 
-Status ModelProcess::PredictFromHost(const std::vector<Buffer> &inputs, std::vector<Buffer> *outputs) {
+Status ModelProcess::PredictFromHost(const std::vector<MSTensor> &inputs, std::vector<MSTensor> *outputs) {
   MS_EXCEPTION_IF_NULL(outputs);
   aclError acl_ret;
   Status ret = CheckAndInitInput(inputs);
-  if (ret != SUCCESS) {
+  if (ret != kSuccess) {
     MS_LOG(ERROR) << "check or init input failed";
     DestroyInputsDataset();
     return ret;  // forward status error
@@ -361,50 +401,48 @@ Status ModelProcess::PredictFromHost(const std::vector<Buffer> &inputs, std::vec
   DestroyInputsDataset();
   if (acl_ret != ACL_ERROR_NONE) {
     MS_LOG(ERROR) << "Execute Model Failed";
-    return FAILED;
+    return kMCDeviceError;
   }
   ret = BuildOutputs(outputs);
-  if (ret != SUCCESS) {
-    MS_LOG(ERROR) << "Build outputs faield";
-    return FAILED;
+  if (ret != kSuccess) {
+    MS_LOG(ERROR) << "Build outputs failed";
+    return ret;
   }
-  MS_LOG(INFO) << "excute model success";
-  return SUCCESS;
+  MS_LOG(INFO) << "Execute model success";
+  return kSuccess;
 }
 
-Status ModelProcess::BuildOutputs(std::vector<Buffer> *outputs) {
+Status ModelProcess::BuildOutputs(std::vector<MSTensor> *outputs) {
   MS_EXCEPTION_IF_NULL(outputs);
-  aclError ret;
   // copy outputs
   outputs->clear();
-  aclrtMemcpyKind kind = is_run_on_device_ ? ACL_MEMCPY_HOST_TO_HOST : ACL_MEMCPY_DEVICE_TO_HOST;
-  for (size_t i = 0; i < output_infos_.size(); ++i) {
-    const auto &info = output_infos_[i];
-    outputs->emplace_back(Buffer());
-    auto output = outputs->rbegin();
-    if (!output->ResizeData(info.buffer_size)) {
-      MS_LOG(ERROR) << "new output data buffer failed, data size " << info.buffer_size;
-      return FAILED;
-    }
-    ret = aclrtMemcpy(output->MutableData(), output->DataSize(), info.device_data, info.buffer_size, kind);
-    if (ret != ACL_ERROR_NONE) {
-      MS_LOG(ERROR) << "Memcpy output " << i << " from " << (is_run_on_device_ ? "host" : "device")
-                    << " to host failed, memory size " << info.buffer_size;
-      return FAILED;
-    }
+  auto inner_outputs = GetOutputs();
+  if (inner_outputs.size() != output_infos_.size()) {
+    MS_LOG(ERROR) << "Invalid inner outputs size " << inner_outputs.size() << " do not match device output infos size "
+                  << output_infos_.size();
+    return kMCFailed;
   }
-  return SUCCESS;
+  (*outputs) = inner_outputs;
+  return kSuccess;
 }
 
-Status ModelProcess::GetInputsInfo(std::vector<std::string> *names, std::vector<std::vector<int64_t>> *shapes,
-                                   std::vector<DataType> *data_types, std::vector<size_t> *mem_sizes) const {
-  ConstructTensorDesc(input_infos_, names, shapes, data_types, mem_sizes);
-  return SUCCESS;
+std::vector<MSTensor> ModelProcess::GetInputs() {
+  Status ret = ConstructTensors(input_infos_, &input_tensors_);
+  if (ret != kSuccess) {
+    MS_LOG(ERROR) << "ConstructTensors failed.";
+    input_tensors_.clear();
+  }
+
+  return input_tensors_;
 }
 
-Status ModelProcess::GetOutputsInfo(std::vector<std::string> *names, std::vector<std::vector<int64_t>> *shapes,
-                                    std::vector<DataType> *data_types, std::vector<size_t> *mem_sizes) const {
-  ConstructTensorDesc(output_infos_, names, shapes, data_types, mem_sizes);
-  return SUCCESS;
+std::vector<MSTensor> ModelProcess::GetOutputs() {
+  Status ret = ConstructTensors(output_infos_, &output_tensors_);
+  if (ret != kSuccess) {
+    MS_LOG(ERROR) << "ConstructTensors failed.";
+    output_tensors_.clear();
+  }
+
+  return output_tensors_;
 }
-}  // namespace mindspore::api
+}  // namespace mindspore

mindspore/ccsrc/cxx_api/graph/acl/model_process.h

@@ -25,7 +25,7 @@
 #include "include/api/status.h"
 #include "include/api/types.h"
 
-namespace mindspore::api {
+namespace mindspore {
 struct AclTensorInfo {
   void *device_data;
   size_t buffer_size;
@@ -45,14 +45,12 @@ class ModelProcess {
         input_infos_(),
         output_infos_() {}
   ~ModelProcess() {}
-  Status LoadModelFromFile(const std::string &file_name, uint32_t *model_id);
+
   Status UnLoad();
-  Status PredictFromHost(const std::vector<Buffer> &inputs, std::vector<Buffer> *outputs);
+  Status PredictFromHost(const std::vector<MSTensor> &inputs, std::vector<MSTensor> *outputs);
   Status PreInitModelResource();
-  Status GetInputsInfo(std::vector<std::string> *names, std::vector<std::vector<int64_t>> *shapes,
-                       std::vector<DataType> *data_types, std::vector<size_t> *mem_sizes) const;
-  Status GetOutputsInfo(std::vector<std::string> *names, std::vector<std::vector<int64_t>> *shapes,
-                        std::vector<DataType> *data_types, std::vector<size_t> *mem_sizes) const;
+  std::vector<MSTensor> GetInputs();
+  std::vector<MSTensor> GetOutputs();
 
   // override this method to avoid request/reply data copy
   void SetIsDevice(bool is_device) { is_run_on_device_ = is_device; }
@@ -62,8 +60,9 @@ class ModelProcess {
 
  private:
   Status CreateDataBuffer(void **data_mem_buffer, size_t buffer_size, aclmdlDataset *dataset);
-  Status CheckAndInitInput(const std::vector<Buffer> &inputs);
-  Status BuildOutputs(std::vector<Buffer> *outputs);
+  Status CheckAndInitInput(const std::vector<MSTensor> &inputs);
+  Status ConstructTensors(const std::vector<AclTensorInfo> &acl_tensor_list, std::vector<MSTensor> *tensor_list);
+  Status BuildOutputs(std::vector<MSTensor> *outputs);
   Status InitInputsBuffer();
   Status InitOutputsBuffer();
 
@@ -80,7 +79,9 @@ class ModelProcess {
   aclmdlDataset *outputs_;
   std::vector<AclTensorInfo> input_infos_;
   std::vector<AclTensorInfo> output_infos_;
+  std::vector<MSTensor> input_tensors_;
+  std::vector<MSTensor> output_tensors_;
 };
-}  // namespace mindspore::api
+}  // namespace mindspore
 
 #endif  // MINDSPORE_CCSRC_CXXAPI_GRAPH_ACL_MODEL_PROCESS_H

mindspore/ccsrc/cxx_api/graph/ascend/ascend_graph_impl.cc

@@ -25,91 +25,51 @@
 #include "backend/session/executor_manager.h"
 #include "runtime/device/kernel_runtime_manager.h"
 
-namespace mindspore::api {
+namespace mindspore {
 API_FACTORY_REG(GraphCell::GraphImpl, Ascend910, AscendGraphImpl);
 
 AscendGraphImpl::AscendGraphImpl()
     : session_impl_(nullptr),
       graph_id_(0),
       device_type_("Ascend"),
-      device_id_(Context::Instance().GetDeviceID()),
+      device_id_(GlobalContext::GetGlobalDeviceID()),
       context_(nullptr),
-      inputs_(),
-      outputs_(),
+      inputs_info_(),
+      outputs_info_(),
       input_names_(),
       output_names_(),
-      init_flag_(false),
       load_flag_(false) {}
 
-AscendGraphImpl::~AscendGraphImpl() { (void)FinalizeEnv(); }
+AscendGraphImpl::~AscendGraphImpl() {}
 
 Status AscendGraphImpl::InitEnv() {
-  if (init_flag_) {
-    return SUCCESS;
-  }
-  RegAllOp();
-  auto ms_context = MsContext::GetInstance();
-  if (ms_context == nullptr) {
-    MS_LOG(ERROR) << "Get Context failed!";
-    return FAILED;
-  }
-
-  ms_context->set_param<int>(MS_CTX_EXECUTION_MODE, kGraphMode);
-  ms_context->set_param<uint32_t>(MS_CTX_DEVICE_ID, device_id_);
-  ms_context->set_param<std::string>(MS_CTX_DEVICE_TARGET, kAscendDevice);
-  if (!context::OpenTsd(ms_context)) {
-    MS_LOG(ERROR) << "Session init OpenTsd failed!";
-    return FAILED;
+  MS_LOG(INFO) << "Start to init env.";
+  env_guard_ = MsEnvGuard::GetEnv(device_id_);
+  if (env_guard_ == nullptr) {
+    MS_LOG(ERROR) << "Env init failed.";
+    return kMCDeviceError;
   }
 
   session_impl_ = session::SessionFactory::Get().Create(kDavinciInferenceDevice);
   if (session_impl_ == nullptr) {
     MS_LOG(ERROR) << "Session create failed!, please make sure target device:" << kDavinciInferenceDevice
                   << " is available.";
-    return FAILED;
+    return kMCFailed;
   }
   session_impl_->Init(device_id_);
 
-  init_flag_ = true;
-  return SUCCESS;
-}
-
-Status AscendGraphImpl::FinalizeEnv() {
-  if (!init_flag_) {
-    return SUCCESS;
-  }
-
-  MS_LOG_INFO << "Start finalize env";
-  session::ExecutorManager::Instance().Clear();
-  device::KernelRuntimeManager::Instance().ClearRuntimeResource();
-
-  auto ms_context = MsContext::GetInstance();
-  if (ms_context == nullptr) {
-    MS_LOG(ERROR) << "Get Context failed!";
-    return FAILED;
-  }
-
-  {
-    PythonEnvGuard guard;
-    if (!context::CloseTsd(ms_context)) {
-      MS_LOG(ERROR) << "CloseTsd failed!";
-      return FAILED;
-    }
-  }
-
-  init_flag_ = false;
-  MS_LOG(INFO) << "End finalize env";
-  return SUCCESS;
+  MS_LOG(INFO) << "InitEnv success.";
+  return kSuccess;
 }
 
 Status AscendGraphImpl::CompileGraph(const std::shared_ptr<FuncGraph> &funcGraphPtr) {
   MS_ASSERT(session_impl_ != nullptr);
   try {
     graph_id_ = session_impl_->CompileGraph(NOT_NULL(funcGraphPtr));
-    return SUCCESS;
+    return kSuccess;
   } catch (std::exception &e) {
     MS_LOG(ERROR) << "CompileGraph failed: " << e.what();
-    return FAILED;
+    return kMCFailed;
   }
 }
 
@@ -128,104 +88,104 @@ Status AscendGraphImpl::CheckModelInputs(const std::vector<tensor::TensorPtr> &i
   MS_ASSERT(session_impl_ != nullptr);
   std::string error_msg;
   if (!session_impl_->CheckModelInputs(graph_id_, inputs, &error_msg)) {
-    return Status(INVALID_INPUTS, error_msg);
+    return Status(kMCInvalidInput, error_msg);
   }
-  return SUCCESS;
+  return kSuccess;
 }
 
-Status AscendGraphImpl::ExecuteModel(const std::vector<Buffer> &request, std::vector<Buffer> *reply) {
+Status AscendGraphImpl::ExecuteModel(const std::vector<MSTensor> &request, std::vector<MSTensor> *reply) {
   MS_EXCEPTION_IF_NULL(reply);
   if (context_ == nullptr) {
     MS_LOG(ERROR) << "rtCtx is nullptr";
-    return FAILED;
+    return kMCDeviceError;
   }
   rtError_t rt_ret = rtCtxSetCurrent(context_);
   if (rt_ret != RT_ERROR_NONE) {
     MS_LOG(ERROR) << "Set Ascend rtCtx failed";
-    return FAILED;
+    return kMCDeviceError;
   }
 
   vector<tensor::TensorPtr> inputs;
   for (size_t i = 0; i < request.size(); i++) {
-    auto &item = request[i];
-    auto input = inputs_[i];
+    auto item = request[i];
+    auto input = inputs_info_[i];
     if (input->Size() != item.DataSize()) {
       MS_LOG(ERROR) << "Input " << i << " data size " << item.DataSize() << " not match model input data size "
                     << input->Size();
-      return FAILED;
+      return kMCInvalidInput;
     }
-    auto ret = memcpy_s(input->data_c(), input->Size(), item.Data(), item.DataSize());
-    if (ret != SUCCESS) {
-      MS_LOG(ERROR) << "Tensor copy failed";
-      return FAILED;
+    auto ret = memcpy_s(input->data_c(), input->Size(), item.MutableData(), item.DataSize());
+    if (ret != kSuccess) {
+      MS_LOG(ERROR) << "MSTensor copy failed";
+      return kMCFailed;
     }
     inputs.push_back(input);
   }
-  vector<tensor::TensorPtr> outputs = RunGraph(inputs);
+  last_inputs_ = inputs;
+  std::vector<tensor::TensorPtr> outputs = RunGraph(inputs);
   if (outputs.empty()) {
     MS_LOG(ERROR) << "Execute Model Failed";
-    return FAILED;
+    return kMCFailed;
   }
+  last_outputs_ = outputs;
   reply->clear();
-  std::transform(outputs.begin(), outputs.end(), std::back_inserter(*reply),
-                 [](const tensor::TensorPtr &tensor) { return Buffer(tensor->data_c(), tensor->Size()); });
-  return SUCCESS;
+  *reply = GetOutputs();
+  return kSuccess;
 }
 
-Status AscendGraphImpl::GetInputsInfo(std::vector<std::string> *names, std::vector<std::vector<int64_t>> *shapes,
-                                      std::vector<DataType> *data_types, std::vector<size_t> *mem_sizes) {
+std::vector<MSTensor> AscendGraphImpl::GetInputs() {
   if (!load_flag_) {
     Status ret = Load();
-    if (ret != SUCCESS) {
+    if (ret != kSuccess) {
       MS_LOG(ERROR) << "PrepareModel failed.";
-      return ret;
+      return {};
     }
   }
 
-  GraphUtils::ClearIfNotNull(names);
-  GraphUtils::ClearIfNotNull(shapes);
-  GraphUtils::ClearIfNotNull(data_types);
-  GraphUtils::ClearIfNotNull(mem_sizes);
-  for (size_t i = 0; i < inputs_.size(); i++) {
-    auto &tensor = inputs_[i];
-    GraphUtils::PushbackIfNotNull(names, input_names_[i]);
-    GraphUtils::PushbackIfNotNull(shapes, tensor->shape());
-    GraphUtils::PushbackIfNotNull(data_types, GraphUtils::TransTypeId2InferDataType(tensor->data_type()));
-    GraphUtils::PushbackIfNotNull(mem_sizes, tensor->Size());
+  std::vector<MSTensor> result(inputs_info_.size());
+  for (size_t i = 0; i < inputs_info_.size(); ++i) {
+    auto &tensor = inputs_info_[i];
+    void *data = nullptr;
+    size_t data_size = tensor->Size();
+    if (i < last_inputs_.size()) {
+      data = last_inputs_[i]->data_c();
+      data_size = last_inputs_[i]->Size();
+    }
+    result[i] =
+      MSTensor(input_names_[i], static_cast<enum DataType>(tensor->data_type()), tensor->shape(), data, data_size);
   }
-  return SUCCESS;
+  return result;
 }
 
-Status AscendGraphImpl::GetOutputsInfo(std::vector<std::string> *names, std::vector<std::vector<int64_t>> *shapes,
-                                       std::vector<DataType> *data_types, std::vector<size_t> *mem_sizes) {
+std::vector<MSTensor> AscendGraphImpl::GetOutputs() {
   if (!load_flag_) {
     Status ret = Load();
-    if (ret != SUCCESS) {
+    if (ret != kSuccess) {
       MS_LOG(ERROR) << "PrepareModel failed.";
-      return ret;
+      return {};
     }
   }
 
-  GraphUtils::ClearIfNotNull(names);
-  GraphUtils::ClearIfNotNull(shapes);
-  GraphUtils::ClearIfNotNull(data_types);
-  GraphUtils::ClearIfNotNull(mem_sizes);
-  for (size_t i = 0; i < outputs_.size(); i++) {
-    auto &tensor = outputs_[i];
-    GraphUtils::PushbackIfNotNull(names, output_names_[i]);
-    GraphUtils::PushbackIfNotNull(shapes, tensor->shape());
-    GraphUtils::PushbackIfNotNull(data_types, GraphUtils::TransTypeId2InferDataType(tensor->data_type()));
-    GraphUtils::PushbackIfNotNull(mem_sizes, tensor->Size());
+  std::vector<MSTensor> result(outputs_info_.size());
+  for (size_t i = 0; i < outputs_info_.size(); ++i) {
+    auto &tensor = outputs_info_[i];
+    void *data = nullptr;
+    size_t data_size = tensor->Size();
+    if (i < last_outputs_.size()) {
+      data = last_outputs_[i]->data_c();
+      data_size = last_outputs_[i]->Size();
+    }
+    result[i] =
+      MSTensor(output_names_[i], static_cast<enum DataType>(tensor->data_type()), tensor->shape(), data, data_size);
   }
-
-  return SUCCESS;
+  return result;
 }
 
 Status AscendGraphImpl::Load() {
   // check graph type
   if (graph_->ModelType() != ModelType::kMindIR) {
     MS_LOG(ERROR) << "Unsupported model type " << graph_->ModelType();
-    return INVALID_INPUTS;
+    return kMCInvalidInput;
   }
 
   const auto &graph_data = GraphImpl::MutableGraphData();
@@ -234,34 +194,34 @@ Status AscendGraphImpl::Load() {
 
   // init
   Status ret = InitEnv();
-  if (ret != SUCCESS) {
+  if (ret != kSuccess) {
     MS_LOG(ERROR) << "InitEnv failed.";
-    return FAILED;
+    return ret;
   }
 
   // load model
   if (!load_flag_) {
     ret = CompileGraph(func_graph);
-    if (ret != SUCCESS) {
+    if (ret != kSuccess) {
       MS_LOG(ERROR) << "Compile graph model failed";
-      return FAILED;
+      return ret;
     }
-    session_impl_->GetModelInputsInfo(graph_id_, &inputs_, &input_names_);
-    session_impl_->GetModelOutputsInfo(graph_id_, &outputs_, &output_names_);
-    if (inputs_.empty() || inputs_.size() != input_names_.size()) {
+    session_impl_->GetModelInputsInfo(graph_id_, &inputs_info_, &input_names_);
+    session_impl_->GetModelOutputsInfo(graph_id_, &outputs_info_, &output_names_);
+    if (inputs_info_.empty() || inputs_info_.size() != input_names_.size()) {
       MS_LOG_ERROR << "Get model inputs info failed";
-      return FAILED;
+      return kMCInvalidInput;
     }
-    if (outputs_.empty() || outputs_.size() != output_names_.size()) {
+    if (outputs_info_.empty() || outputs_info_.size() != output_names_.size()) {
       MS_LOG_ERROR << "Get model outputs info failed";
-      return FAILED;
+      return kMCInvalidInput;
     }
 
     // save 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 FAILED;
+      return kMCDeviceError;
     }
 
     MS_LOG(INFO) << "Load model success";
@@ -271,44 +231,112 @@ Status AscendGraphImpl::Load() {
   rtError_t rt_ret = rtCtxSetCurrent(context_);
   if (rt_ret != RT_ERROR_NONE) {
     MS_LOG(ERROR) << "Set the ascend device context failed";
-    return FAILED;
+    return kMCDeviceError;
   }
 
-  return SUCCESS;
+  return kSuccess;
 }
 
-Status AscendGraphImpl::Run(const std::vector<Buffer> &inputs, std::vector<Buffer> *outputs) {
+Status AscendGraphImpl::Run(const std::vector<MSTensor> &inputs, std::vector<MSTensor> *outputs) {
   MS_EXCEPTION_IF_NULL(outputs);
   if (!load_flag_) {
     Status ret = Load();
-    if (ret != SUCCESS) {
+    if (ret != kSuccess) {
       MS_LOG(ERROR) << "PrepareModel failed.";
       return ret;
     }
   }
 
-  if (inputs.size() != inputs_.size()) {
-    MS_LOG(ERROR) << "inputs count not match, required count " << inputs_.size() << ", given count " << inputs.size();
-    return INVALID_INPUTS;
+  if (inputs.size() != inputs_info_.size()) {
+    MS_LOG(ERROR) << "inputs count not match, required count " << inputs_info_.size() << ", given count "
+                  << inputs.size();
+    return kMCInvalidInput;
   }
 
-  for (size_t i = 0; i < inputs_.size(); ++i) {
-    if (inputs[i].DataSize() != inputs_[i]->Size()) {
-      MS_LOG(ERROR) << "input " << i << " data size not match, required size " << inputs_[i]->Size() << ", given count "
-                    << inputs[i].DataSize();
-      return INVALID_INPUTS;
+  for (size_t i = 0; i < inputs_info_.size(); ++i) {
+    if (inputs[i].DataSize() != inputs_info_[i]->Size()) {
+      MS_LOG(ERROR) << "input " << i << " data size not match, required size " << inputs_info_[i]->Size()
+                    << ", given count " << inputs[i].DataSize();
+      return kMCInvalidInput;
     }
   }
-  if (ExecuteModel(inputs, outputs) != SUCCESS) {
+
+  Status ret = ExecuteModel(inputs, outputs);
+  if (ret != kSuccess) {
     MS_LOG(ERROR) << "Execute Model Failed";
-    return FAILED;
+    return ret;
   }
-  if (outputs_.size() != outputs->size()) {
+  if (outputs_info_.size() != outputs->size()) {
     MS_LOG(ERROR) << "Predict output size " << outputs->size() << " not match output size got from model info "
-                  << outputs_.size();
-    return FAILED;
+                  << outputs_info_.size();
+    return kMCFailed;
+  }
+
+  return kSuccess;
+}
+
+AscendGraphImpl::MsEnvGuard::MsEnvGuard(uint32_t device_id) {
+  MS_LOG(INFO) << "Start to init env.";
+  device_id_ = device_id;
+  RegAllOp();
+  auto ms_context = MsContext::GetInstance();
+  if (ms_context == nullptr) {
+    MS_LOG(ERROR) << "Get Context failed!";
+    errno_ = kMCFailed;
+    return;
+  }
+
+  ms_context->set_param<int>(MS_CTX_EXECUTION_MODE, kGraphMode);
+  ms_context->set_param<uint32_t>(MS_CTX_DEVICE_ID, device_id_);
+  ms_context->set_param<std::string>(MS_CTX_DEVICE_TARGET, kAscendDevice);
+  auto ret = rtSetDevice(device_id_);
+  if (ret != RT_ERROR_NONE) {
+    MS_LOG(EXCEPTION) << "Device " << device_id_ << " call rtSetDevice failed, ret[" << static_cast<int>(ret) << "]";
+  }
+
+  MS_LOG(INFO) << "InitEnv success.";
+  errno_ = kSuccess;
+}
+
+AscendGraphImpl::MsEnvGuard::~MsEnvGuard() {
+  MS_LOG(INFO) << "Start finalize env";
+  session::ExecutorManager::Instance().Clear();
+  device::KernelRuntimeManager::Instance().ClearRuntimeResource();
+
+  auto ms_context = MsContext::GetInstance();
+  if (ms_context == nullptr) {
+    MS_LOG(ERROR) << "Get Context failed!";
+    errno_ = kMCFailed;
+    return;
   }
 
-  return SUCCESS;
+  auto ret = rtDeviceReset(device_id_);
+  if (ret != RT_ERROR_NONE) {
+    MS_LOG(EXCEPTION) << "Device " << device_id_ << " call rtDeviceReset failed, ret[" << static_cast<int>(ret) << "]";
+  }
+
+  errno_ = kSuccess;
+  MS_LOG(INFO) << "End finalize env";
+}
+
+std::shared_ptr<AscendGraphImpl::MsEnvGuard> AscendGraphImpl::MsEnvGuard::GetEnv(uint32_t device_id) {
+  std::shared_ptr<MsEnvGuard> acl_env;
+  std::lock_guard<std::mutex> lock(global_ms_env_mutex_);
+  acl_env = global_ms_env_.lock();
+  if (acl_env != nullptr) {
+    MS_LOG(INFO) << "Env has been initialized, skip.";
+  } else {
+    acl_env = std::make_shared<MsEnvGuard>(device_id);
+    if (acl_env->GetErrno() != kSuccess) {
+      MS_LOG(ERROR) << "Execute aclInit Failed";
+      return nullptr;
+    }
+    global_ms_env_ = acl_env;
+    MS_LOG(INFO) << "Env init success";
+  }
+  return acl_env;
 }
-}  // namespace mindspore::api
+
+std::weak_ptr<AscendGraphImpl::MsEnvGuard> AscendGraphImpl::MsEnvGuard::global_ms_env_;
+std::mutex AscendGraphImpl::MsEnvGuard::global_ms_env_mutex_;
+}  // namespace mindspore

mindspore/ccsrc/cxx_api/graph/ascend/ascend_graph_impl.h

@@ -28,40 +28,56 @@
 #include "ir/anf.h"
 #include "cxx_api/model/model_impl.h"
 #include "runtime/context.h"
-#include "cxx_api/graph/graph_utils.h"
 
-namespace mindspore::api {
+namespace mindspore {
 class AscendGraphImpl : public GraphCell::GraphImpl {
  public:
   AscendGraphImpl();
   ~AscendGraphImpl() override;
 
-  Status Run(const std::vector<Buffer> &inputs, std::vector<Buffer> *outputs) override;
+  Status Run(const std::vector<MSTensor> &inputs, std::vector<MSTensor> *outputs) override;
   Status Load() override;
-  Status GetInputsInfo(std::vector<std::string> *names, std::vector<std::vector<int64_t>> *shapes,
-                       std::vector<DataType> *data_types, std::vector<size_t> *mem_sizes) override;
-  Status GetOutputsInfo(std::vector<std::string> *names, std::vector<std::vector<int64_t>> *shapes,
-                        std::vector<DataType> *data_types, std::vector<size_t> *mem_sizes) override;
+  std::vector<MSTensor> GetInputs() override;
+  std::vector<MSTensor> GetOutputs() override;
 
  private:
+  class MsEnvGuard;
+
   Status InitEnv();
-  Status FinalizeEnv();
   Status CompileGraph(const std::shared_ptr<FuncGraph> &funcGraphPtr);
   Status CheckModelInputs(const std::vector<tensor::TensorPtr> &inputs) const;
   std::vector<tensor::TensorPtr> RunGraph(const std::vector<tensor::TensorPtr> &inputs);
-  Status ExecuteModel(const std::vector<Buffer> &inputs, std::vector<Buffer> *outputs);
+  Status ExecuteModel(const std::vector<MSTensor> &inputs, std::vector<MSTensor> *outputs);
 
   std::shared_ptr<session::SessionBasic> session_impl_;
   uint32_t graph_id_;
   std::string device_type_;
   uint32_t device_id_;
   rtContext_t context_;
-  std::vector<tensor::TensorPtr> inputs_;
-  std::vector<tensor::TensorPtr> outputs_;
+  std::vector<tensor::TensorPtr> inputs_info_;
+  std::vector<tensor::TensorPtr> outputs_info_;
+  std::vector<tensor::TensorPtr> last_inputs_;
+  std::vector<tensor::TensorPtr> last_outputs_;
   std::vector<std::string> input_names_;
   std::vector<std::string> output_names_;
-  bool init_flag_;
   bool load_flag_;
+
+  std::shared_ptr<MsEnvGuard> env_guard_;
+};
+
+class AscendGraphImpl::MsEnvGuard {
+ public:
+  explicit MsEnvGuard(uint32_t device_id);
+  ~MsEnvGuard();
+  Status GetErrno() const { return errno_; }
+  static std::shared_ptr<MsEnvGuard> GetEnv(uint32_t device_id);
+
+ private:
+  static std::weak_ptr<MsEnvGuard> global_ms_env_;
+  static std::mutex global_ms_env_mutex_;
+
+  Status errno_;
+  uint32_t device_id_;
 };
-}  // namespace mindspore::api
+}  // namespace mindspore
 #endif  // MINDSPORE_CCSRC_CXX_API_GRAPH_MS_ASCEND_GRAPH_IMPL_H

mindspore/ccsrc/cxx_api/graph/gpu/gpu_graph_impl.cc

@@ -23,15 +23,15 @@
 #include "backend/session/executor_manager.h"
 #include "runtime/device/kernel_runtime_manager.h"
 
-namespace mindspore::api {
+namespace mindspore {
 API_FACTORY_REG(GraphCell::GraphImpl, GPU, GPUGraphImpl);
 
 GPUGraphImpl::GPUGraphImpl()
     : session_impl_(nullptr),
       graph_id_(0),
-      device_id_(Context::Instance().GetDeviceID()),
-      inputs_(),
-      outputs_(),
+      device_id_(GlobalContext::GetGlobalDeviceID()),
+      inputs_info_(),
+      outputs_info_(),
       input_names_(),
       output_names_(),
       init_flag_(false),
@@ -40,13 +40,13 @@ GPUGraphImpl::GPUGraphImpl()
 Status GPUGraphImpl::InitEnv() {
   if (init_flag_) {
     MS_LOG(WARNING) << "Initialized again, return success.";
-    return SUCCESS;
+    return kSuccess;
   }
 
   auto ms_context = MsContext::GetInstance();
   if (ms_context == nullptr) {
     MS_LOG(ERROR) << "Get Context failed!";
-    return FAILED;
+    return kMCFailed;
   }
   ms_context->set_param<int>(MS_CTX_EXECUTION_MODE, kGraphMode);
   ms_context->set_param<uint32_t>(MS_CTX_DEVICE_ID, device_id_);
@@ -57,18 +57,18 @@ Status GPUGraphImpl::InitEnv() {
   if (session_impl_ == nullptr) {
     MS_LOG(ERROR) << "Session create failed!, please make sure target device:" << kGpuInferenceDevice
                   << " is available.";
-    return FAILED;
+    return kMCFailed;
   }
 
   session_impl_->Init(device_id_);
   init_flag_ = true;
-  return SUCCESS;
+  return kSuccess;
 }
 
 Status GPUGraphImpl::FinalizeEnv() {
   if (!init_flag_) {
     MS_LOG(WARNING) << "Never initialize before, return success";
-    return SUCCESS;
+    return kSuccess;
   }
 
   MS_LOG_INFO << "Start finalize env";
@@ -77,14 +77,14 @@ Status GPUGraphImpl::FinalizeEnv() {
 
   init_flag_ = false;
   MS_LOG(INFO) << "End finalize env";
-  return SUCCESS;
+  return kSuccess;
 }
 
 Status GPUGraphImpl::Load() {
   // check graph type
   if (graph_->ModelType() != ModelType::kMindIR) {
     MS_LOG(ERROR) << "Unsupported model type " << graph_->ModelType();
-    return INVALID_INPUTS;
+    return kMCInvalidInput;
   }
 
   const auto &graph_data = GraphImpl::MutableGraphData();
@@ -93,38 +93,38 @@ Status GPUGraphImpl::Load() {
 
   // init
   Status ret = InitEnv();
-  if (ret != SUCCESS) {
+  if (ret != kSuccess) {
     MS_LOG(ERROR) << "InitEnv failed.";
-    return FAILED;
+    return kMCDeviceError;
   }
 
   ret = CompileGraph(func_graph);
-  if (ret != SUCCESS) {
+  if (ret != kSuccess) {
     MS_LOG(ERROR) << "Compile graph model failed";
-    return FAILED;
+    return kMCFailed;
   }
-  session_impl_->GetModelInputsInfo(graph_id_, &inputs_, &input_names_);
-  session_impl_->GetModelOutputsInfo(graph_id_, &outputs_, &output_names_);
-  if (inputs_.empty() || inputs_.size() != input_names_.size()) {
+  session_impl_->GetModelInputsInfo(graph_id_, &inputs_info_, &input_names_);
+  session_impl_->GetModelOutputsInfo(graph_id_, &outputs_info_, &output_names_);
+  if (inputs_info_.empty() || inputs_info_.size() != input_names_.size()) {
     MS_LOG_ERROR << "Get model inputs info failed";
-    return FAILED;
+    return kMCInvalidInput;
   }
-  if (outputs_.empty() || outputs_.size() != output_names_.size()) {
+  if (outputs_info_.empty() || outputs_info_.size() != output_names_.size()) {
     MS_LOG_ERROR << "Get model outputs info failed";
-    return FAILED;
+    return kMCInvalidInput;
   }
   load_flag_ = true;
-  return SUCCESS;
+  return kSuccess;
 }
 
 Status GPUGraphImpl::CompileGraph(const std::shared_ptr<FuncGraph> &funcGraphPtr) {
   MS_ASSERT(session_impl_ != nullptr);
   try {
     graph_id_ = session_impl_->CompileGraph(NOT_NULL(funcGraphPtr));
-    return SUCCESS;
+    return kSuccess;
   } catch (std::exception &e) {
     MS_LOG(ERROR) << "CompileGraph failed: " << e.what();
-    return FAILED;
+    return kMCFailed;
   }
 }
 
@@ -139,118 +139,118 @@ std::vector<tensor::TensorPtr> GPUGraphImpl::RunGraph(const std::vector<tensor::
   }
 }
 
-Status GPUGraphImpl::ExecuteModel(const std::vector<Buffer> &request, std::vector<Buffer> *reply) {
+Status GPUGraphImpl::ExecuteModel(const std::vector<MSTensor> &request, std::vector<MSTensor> *reply) {
   MS_EXCEPTION_IF_NULL(reply);
 
   vector<tensor::TensorPtr> inputs;
   for (size_t i = 0; i < request.size(); i++) {
     auto &item = request[i];
-    auto input = inputs_[i];
+    auto input = inputs_info_[i];
     if (input->Size() != item.DataSize()) {
       MS_LOG(ERROR) << "Input " << i << " data size " << item.DataSize() << " not match model input data size "
                     << input->Size();
-      return FAILED;
+      return kMCInvalidInput;
     }
-    auto ret = memcpy_s(input->data_c(), input->Size(), item.Data(), item.DataSize());
-    if (ret != SUCCESS) {
+    auto ret = memcpy_s(input->data_c(), input->Size(), item.Data().get(), item.DataSize());
+    if (ret != kSuccess) {
       MS_LOG(ERROR) << "Tensor copy failed";
-      return FAILED;
+      return kMCFailed;
     }
     inputs.push_back(input);
   }
-  vector<tensor::TensorPtr> outputs = RunGraph(inputs);
+  last_inputs_ = inputs;
+  std::vector<tensor::TensorPtr> outputs = RunGraph(inputs);
   if (outputs.empty()) {
     MS_LOG(ERROR) << "Execute Model Failed";
-    return FAILED;
+    return kMCFailed;
   }
+  last_outputs_ = outputs;
   reply->clear();
-  std::transform(outputs.begin(), outputs.end(), std::back_inserter(*reply),
-                 [](const tensor::TensorPtr &tensor) { return Buffer(tensor->data_c(), tensor->Size()); });
-  return SUCCESS;
+  *reply = GetOutputs();
+  return kSuccess;
 }
 
-Status GPUGraphImpl::Run(const std::vector<Buffer> &inputs, std::vector<Buffer> *outputs) {
+Status GPUGraphImpl::Run(const std::vector<MSTensor> &inputs, std::vector<MSTensor> *outputs) {
   MS_EXCEPTION_IF_NULL(outputs);
   if (!load_flag_) {
     Status ret = Load();
-    if (ret != SUCCESS) {
+    if (ret != kSuccess) {
       MS_LOG(ERROR) << "PrepareModel failed.";
       return ret;
     }
   }
 
-  if (inputs.size() != inputs_.size()) {
-    MS_LOG(ERROR) << "inputs count not match, required count " << inputs_.size() << ", given count " << inputs.size();
-    return INVALID_INPUTS;
+  if (inputs.size() != inputs_info_.size()) {
+    MS_LOG(ERROR) << "inputs count not match, required count " << inputs_info_.size() << ", given count "
+                  << inputs.size();
+    return kMCInvalidInput;
   }
 
-  for (size_t i = 0; i < inputs_.size(); ++i) {
-    if (inputs[i].DataSize() != inputs_[i]->Size()) {
-      MS_LOG(ERROR) << "input " << i << " data size not match, required size " << inputs_[i]->Size() << ", given count "
-                    << inputs[i].DataSize();
-      return INVALID_INPUTS;
+  for (size_t i = 0; i < inputs_info_.size(); ++i) {
+    if (inputs[i].DataSize() != inputs_info_[i]->Size()) {
+      MS_LOG(ERROR) << "input " << i << " data size not match, required size " << inputs_info_[i]->Size()
+                    << ", given count " << inputs[i].DataSize();
+      return kMCInvalidInput;
     }
   }
-  if (ExecuteModel(inputs, outputs) != SUCCESS) {
+  if (ExecuteModel(inputs, outputs) != kSuccess) {
     MS_LOG(ERROR) << "Execute Model Failed";
-    return FAILED;
+    return kMCFailed;
   }
-  if (outputs_.size() != outputs->size()) {
+  if (outputs_info_.size() != outputs->size()) {
     MS_LOG(ERROR) << "Predict output size " << outputs->size() << " not match output size got from model info "
-                  << outputs_.size();
-    return FAILED;
+                  << outputs_info_.size();
+    return kMCFailed;
   }
 
-  return SUCCESS;
+  return kSuccess;
 }
 
-Status GPUGraphImpl::GetInputsInfo(std::vector<std::string> *names, std::vector<std::vector<int64_t>> *shapes,
-                                   std::vector<DataType> *data_types, std::vector<size_t> *mem_sizes) {
+std::vector<MSTensor> GPUGraphImpl::GetInputs() {
   if (!load_flag_) {
     Status ret = Load();
-    if (ret != SUCCESS) {
+    if (ret != kSuccess) {
       MS_LOG(ERROR) << "PrepareModel failed.";
-      return ret;
+      return {};
     }
   }
 
-  GraphUtils::ClearIfNotNull(names);
-  GraphUtils::ClearIfNotNull(shapes);
-  GraphUtils::ClearIfNotNull(data_types);
-  GraphUtils::ClearIfNotNull(mem_sizes);
-  for (size_t i = 0; i < inputs_.size(); i++) {
-    auto &tensor = inputs_[i];
-    GraphUtils::PushbackIfNotNull(names, input_names_[i]);
-    GraphUtils::PushbackIfNotNull(shapes, tensor->shape());
-    GraphUtils::PushbackIfNotNull(data_types, GraphUtils::TransTypeId2InferDataType(tensor->data_type()));
-    GraphUtils::PushbackIfNotNull(mem_sizes, tensor->Size());
+  std::vector<MSTensor> result(inputs_info_.size());
+  for (size_t i = 0; i < inputs_info_.size(); ++i) {
+    auto &tensor = inputs_info_[i];
+    void *data = nullptr;
+    size_t data_size = tensor->Size();
+    if (i < last_inputs_.size()) {
+      data = last_inputs_[i]->data_c();
+      data_size = last_inputs_[i]->Size();
+    }
+    result[i] =
+      MSTensor(input_names_[i], static_cast<enum DataType>(tensor->data_type()), tensor->shape(), data, data_size);
   }
-  return SUCCESS;
+  return result;
 }
 
-Status GPUGraphImpl::GetOutputsInfo(std::vector<std::string> *names, std::vector<std::vector<int64_t>> *shapes,
-                                    std::vector<DataType> *data_types, std::vector<size_t> *mem_sizes) {
+std::vector<MSTensor> GPUGraphImpl::GetOutputs() {
   if (!load_flag_) {
     Status ret = Load();
-    if (ret != SUCCESS) {
+    if (ret != kSuccess) {
       MS_LOG(ERROR) << "PrepareModel failed.";
-      return ret;
+      return {};
     }
   }
 
-  GraphUtils::ClearIfNotNull(names);
-  GraphUtils::ClearIfNotNull(shapes);
-  GraphUtils::ClearIfNotNull(data_types);
-  GraphUtils::ClearIfNotNull(mem_sizes);
-  for (size_t i = 0; i < outputs_.size(); i++) {
-    auto &tensor = outputs_[i];
-    GraphUtils::PushbackIfNotNull(names, output_names_[i]);
-    GraphUtils::PushbackIfNotNull(shapes, tensor->shape());
-    GraphUtils::PushbackIfNotNull(data_types, GraphUtils::TransTypeId2InferDataType(tensor->data_type()));
-    GraphUtils::PushbackIfNotNull(mem_sizes, tensor->Size());
+  std::vector<MSTensor> result(outputs_info_.size());
+  for (size_t i = 0; i < outputs_info_.size(); ++i) {
+    auto &tensor = outputs_info_[i];
+    void *data = nullptr;
+    size_t data_size = tensor->Size();
+    if (i < last_outputs_.size()) {
+      data = last_outputs_[i]->data_c();
+      data_size = last_outputs_[i]->Size();
+    }
+    result[i] =
+      MSTensor(output_names_[i], static_cast<enum DataType>(tensor->data_type()), tensor->shape(), data, data_size);
   }
-
-  return SUCCESS;
+  return result;
 }
-
-}  // namespace mindspore::api
+}  // namespace mindspore

mindspore/ccsrc/cxx_api/graph/gpu/gpu_graph_impl.h

@@ -25,20 +25,17 @@
 #include "backend/session/session_basic.h"
 #include "ir/anf.h"
 #include "cxx_api/model/model_impl.h"
-#include "cxx_api/graph/graph_utils.h"
 
-namespace mindspore::api {
+namespace mindspore {
 class GPUGraphImpl : public GraphCell::GraphImpl {
  public:
   GPUGraphImpl();
   ~GPUGraphImpl() override = default;
 
-  Status Run(const std::vector<Buffer> &inputs, std::vector<Buffer> *outputs) override;
+  Status Run(const std::vector<MSTensor> &inputs, std::vector<MSTensor> *outputs) override;
   Status Load() override;
-  Status GetInputsInfo(std::vector<std::string> *names, std::vector<std::vector<int64_t>> *shapes,
-                       std::vector<DataType> *data_types, std::vector<size_t> *mem_sizes) override;
-  Status GetOutputsInfo(std::vector<std::string> *names, std::vector<std::vector<int64_t>> *shapes,
-                        std::vector<DataType> *data_types, std::vector<size_t> *mem_sizes) override;
+  std::vector<MSTensor> GetInputs() override;
+  std::vector<MSTensor> GetOutputs() override;
 
  private:
   Status InitEnv();
@@ -46,14 +43,16 @@ class GPUGraphImpl : public GraphCell::GraphImpl {
   Status CompileGraph(const std::shared_ptr<FuncGraph> &funcGraphPtr);
   Status CheckModelInputs(const std::vector<tensor::TensorPtr> &inputs) const;
   std::vector<tensor::TensorPtr> RunGraph(const std::vector<tensor::TensorPtr> &inputs);
-  Status ExecuteModel(const std::vector<Buffer> &inputs, std::vector<Buffer> *outputs);
+  Status ExecuteModel(const std::vector<MSTensor> &inputs, std::vector<MSTensor> *outputs);
 
   std::shared_ptr<session::SessionBasic> session_impl_;
   uint32_t graph_id_;
   std::string device_type_;
   uint32_t device_id_;
-  std::vector<tensor::TensorPtr> inputs_;
-  std::vector<tensor::TensorPtr> outputs_;
+  std::vector<tensor::TensorPtr> inputs_info_;
+  std::vector<tensor::TensorPtr> outputs_info_;
+  std::vector<tensor::TensorPtr> last_inputs_;
+  std::vector<tensor::TensorPtr> last_outputs_;
   std::vector<std::string> input_names_;
   std::vector<std::string> output_names_;
   bool init_flag_;
@@ -63,5 +62,5 @@ class GPUGraphImpl : public GraphCell::GraphImpl {
   uint32_t batch_size_;
   uint32_t workspace_size_;
 };
-}  // namespace mindspore::api
+}  // namespace mindspore
 #endif  // MINDSPORE_CCSRC_CXX_API_GRAPH_MS_GPU_GRAPH_IMPL_H

mindspore/ccsrc/cxx_api/graph/graph.cc

@@ -17,15 +17,19 @@
 #include "cxx_api/graph/graph_data.h"
 #include "utils/log_adapter.h"
 
-namespace mindspore::api {
+namespace mindspore {
 Graph::Graph(const std::shared_ptr<GraphData> &graph_data) : graph_data_(graph_data) {}
 
 Graph::Graph(std::shared_ptr<GraphData> &&graph_data) : graph_data_(graph_data) {}
 
 Graph::~Graph() {}
 
+Graph::Graph(std::nullptr_t) : graph_data_(nullptr) {}
+
+bool Graph::operator==(std::nullptr_t) const { return graph_data_ == nullptr; }
+
 ModelType Graph::ModelType() const {
   MS_EXCEPTION_IF_NULL(graph_data_);
   return graph_data_->ModelType();
 }
-}  // namespace mindspore::api
+}  // namespace mindspore

mindspore/ccsrc/cxx_api/graph/graph_data.cc

@@ -19,7 +19,7 @@
 #include "framework/common/helper/model_helper.h"
 #endif
 
-namespace mindspore::api {
+namespace mindspore {
 Graph::GraphData::GraphData(const FuncGraphPtr &func_graph, enum ModelType model_type)
     : func_graph_(nullptr), om_data_(), model_type_(ModelType::kUnknownType) {
   if (model_type != ModelType::kMindIR) {
@@ -72,4 +72,4 @@ Buffer Graph::GraphData::GetOMData() const {
 
   return om_data_;
 }
-}  // namespace mindspore::api
+}  // namespace mindspore

mindspore/ccsrc/cxx_api/graph/graph_data.h

@@ -24,7 +24,7 @@
 #include "include/api/types.h"
 #include "ir/func_graph.h"
 
-namespace mindspore::api {
+namespace mindspore {
 class Graph::GraphData {
  public:
   GraphData();
@@ -46,5 +46,5 @@ class Graph::GraphData {
   Buffer om_data_;
   enum ModelType model_type_;
 };
-}  // namespace mindspore::api
+}  // namespace mindspore
 #endif  // MINDSPORE_CCSRC_CXX_API_GRAPH_GRAPH_DATA_H

mindspore/ccsrc/cxx_api/graph/graph_impl.h

@@ -26,7 +26,7 @@
 #include "cxx_api/graph/graph_data.h"
 #include "utils/utils.h"
 
-namespace mindspore::api {
+namespace mindspore {
 class GraphCell::GraphImpl {
  public:
   GraphImpl() = default;
@@ -35,17 +35,14 @@ class GraphCell::GraphImpl {
   std::shared_ptr<Graph::GraphData> &MutableGraphData() const { return graph_->graph_data_; }
   void SetGraph(const std::shared_ptr<Graph> &graph) { graph_ = graph; }
 
-  virtual Status Run(const std::vector<Buffer> &inputs, std::vector<Buffer> *outputs) = 0;
+  virtual Status Run(const std::vector<MSTensor> &inputs, std::vector<MSTensor> *outputs) = 0;
   virtual Status Load() = 0;
 
-  virtual Status GetInputsInfo(std::vector<std::string> *names, std::vector<std::vector<int64_t>> *shapes,
-                               std::vector<DataType> *data_types, std::vector<size_t> *mem_sizes) = 0;
-  virtual Status GetOutputsInfo(std::vector<std::string> *names, std::vector<std::vector<int64_t>> *shapes,
-                                std::vector<DataType> *data_types, std::vector<size_t> *mem_sizes) = 0;
+  virtual std::vector<MSTensor> GetInputs() = 0;
+  virtual std::vector<MSTensor> GetOutputs() = 0;
 
  protected:
   std::shared_ptr<Graph> graph_;
 };
-}  // namespace mindspore::api
-
+}  // namespace mindspore
 #endif  // MINDSPORE_CCSRC_CXX_API_GRAPH_GRAPH_IMPL_H

mindspore/ccsrc/cxx_api/graph/graph_utils.h

@@ -1,63 +0,0 @@
-/**
- * Copyright 2021 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_CXX_API_GRAPH_GRAPH_UTILS_H
-#define MINDSPORE_CCSRC_CXX_API_GRAPH_GRAPH_UTILS_H
-#include <map>
-#include <vector>
-#include "include/api/types.h"
-#include "ir/dtype/type_id.h"
-#include "utils/log_adapter.h"
-
-namespace mindspore::api {
-class GraphUtils {
- public:
-  static DataType TransTypeId2InferDataType(TypeId type_id) {
-    const std::map<TypeId, api::DataType> id2type_map{
-      {TypeId::kNumberTypeBegin, api::kMsUnknown},   {TypeId::kNumberTypeBool, api::kMsBool},
-      {TypeId::kNumberTypeFloat64, api::kMsFloat64}, {TypeId::kNumberTypeInt8, api::kMsInt8},
-      {TypeId::kNumberTypeUInt8, api::kMsUint8},     {TypeId::kNumberTypeInt16, api::kMsInt16},
-      {TypeId::kNumberTypeUInt16, api::kMsUint16},   {TypeId::kNumberTypeInt32, api::kMsInt32},
-      {TypeId::kNumberTypeUInt32, api::kMsUint32},   {TypeId::kNumberTypeInt64, api::kMsInt64},
-      {TypeId::kNumberTypeUInt64, api::kMsUint64},   {TypeId::kNumberTypeFloat16, api::kMsFloat16},
-      {TypeId::kNumberTypeFloat32, api::kMsFloat32},
-    };
-
-    auto it = id2type_map.find(type_id);
-    if (it != id2type_map.end()) {
-      return it->second;
-    }
-
-    MS_LOG(WARNING) << "Unsupported data id " << type_id;
-    return api::kMsUnknown;
-  }
-
-  template <class T>
-  inline static void ClearIfNotNull(T *vec) {
-    if (vec != nullptr) {
-      vec->clear();
-    }
-  }
-
-  template <class T, class U>
-  inline static void PushbackIfNotNull(U *vec, T &&item) {
-    if (vec != nullptr) {
-      vec->emplace_back(item);
-    }
-  }
-};
-}  // namespace mindspore::api
-
-#endif  // MINDSPORE_CCSRC_CXX_API_GRAPH_GRAPH_UTILS_H

mindspore/ccsrc/cxx_api/model/acl/acl_model.cc

@@ -16,47 +16,53 @@
 
 #include "cxx_api/model/acl/acl_model.h"
 #include <memory>
+#include "include/api/context.h"
 #include "cxx_api/factory.h"
-#include "cxx_api/python_utils.h"
 
-namespace mindspore::api {
+namespace mindspore {
 API_FACTORY_REG(ModelImpl, Ascend310, AclModel);
 
-Status AclModel::Build(const std::map<std::string, std::string> &options_map) {
+Status AclModel::Build() {
   MS_LOG(INFO) << "Start build model.";
   MS_EXCEPTION_IF_NULL(graph_);
-  std::unique_ptr<AclModelOptions> options = std::make_unique<AclModelOptions>(options_map);
-  std::string options_str = GenerateOptionsStr(options_map);
-  MS_EXCEPTION_IF_NULL(options);
-  if (graph_cell_ != nullptr && options_str == options_str_) {
+
+  if (graph_cell_ != nullptr) {
     MS_LOG(INFO) << "This model has been built, skip.";
-    return SUCCESS;
+    return kSuccess;
   }
 
   if (graph_cell_ == nullptr && graph_->ModelType() == ModelType::kOM) {
+    MS_LOG(INFO) << "Note: Load om model and all build options will be ignored.";
     graph_cell_ = std::make_shared<GraphCell>(graph_);
     MS_EXCEPTION_IF_NULL(graph_cell_);
-    if (!options_map.empty()) {
-      MS_LOG(WARNING) << "All build options will be ignored.";
-    }
-    return SUCCESS;
+    return kSuccess;
   }
 
-  auto func_graph = ModelImpl::GetFuncGraph();
-  MS_EXCEPTION_IF_NULL(func_graph);
-  model_converter_.set_options(options.get());
-  auto om_data = model_converter_.LoadMindIR(func_graph);
-  if (om_data.Data() == nullptr || om_data.DataSize() == 0) {
-    MS_LOG(ERROR) << "Load MindIR failed.";
-    return FAILED;
+  std::unique_ptr<AclModelOptions> options = std::make_unique<AclModelOptions>(model_context_);
+  MS_EXCEPTION_IF_NULL(options);
+  std::string options_key = options->GenAclOptionsKey();
+  std::shared_ptr<Graph> graph;
+  if (auto iter = dynamic_size_graph_map_.find(options_key); iter != dynamic_size_graph_map_.end()) {
+    MS_LOG(INFO) << "This options has been built, read cache.";
+    graph = iter->second;
+  } else {
+    auto func_graph = ModelImpl::GetFuncGraph();
+    MS_EXCEPTION_IF_NULL(func_graph);
+    model_converter_.set_options(options.get());
+    auto om_data = model_converter_.LoadMindIR(func_graph);
+    if (om_data.Data() == nullptr || om_data.DataSize() == 0) {
+      MS_LOG(ERROR) << "Load MindIR failed.";
+      return kMCFailed;
+    }
+    graph = std::make_shared<Graph>(std::make_shared<Graph::GraphData>(om_data, ModelType::kOM));
+    dynamic_size_graph_map_[options_key] = graph;
   }
 
-  auto graph = std::make_shared<Graph>(std::make_shared<Graph::GraphData>(om_data, ModelType::kOM));
   MS_EXCEPTION_IF_NULL(graph);
   auto graph_cell = std::make_shared<GraphCell>(graph);
   MS_EXCEPTION_IF_NULL(graph_cell);
   auto ret = ModelImpl::Load(graph_cell);
-  if (ret != SUCCESS) {
+  if (ret != kSuccess) {
     MS_LOG(ERROR) << "Load failed.";
     return ret;
   }
@@ -64,64 +70,97 @@ Status AclModel::Build(const std::map<std::string, std::string> &options_map) {
   // save result
   graph_cell_ = graph_cell;
   options_ = std::move(options);
-  options_str_ = options_str;
   MS_LOG(INFO) << "Build model success.";
-  return SUCCESS;
+  return kSuccess;
 }
 
-Status AclModel::Train(const DataSet &, std::map<std::string, Buffer> *) {
-  MS_LOG(ERROR) << "Unsupported feature.";
-  return FAILED;
-}
+Status AclModel::Resize(const std::vector<MSTensor> &inputs, const std::vector<std::vector<int64_t>> &dims) {
+  MS_LOG(INFO) << "Start to resize model.";
+  MS_EXCEPTION_IF_NULL(graph_);
+  if (graph_->ModelType() == ModelType::kOM) {
+    MS_LOG(ERROR) << "OM model is not supported to resize model.";
+    return kMCFailed;
+  }
+
+  auto origin_inputs = GetInputs();
+  if (inputs.size() != origin_inputs.size()) {
+    MS_LOG(ERROR) << "Invalid inputs size " << inputs.size() << " not match model inputs size " << origin_inputs.size();
+    return kMCInvalidInput;
+  }
+
+  if (inputs.size() != dims.size()) {
+    MS_LOG(ERROR) << "Invalid dims size " << dims.size() << " not match inputs size " << inputs.size();
+    return kMCInvalidInput;
+  }
 
-Status AclModel::Eval(const DataSet &, std::map<std::string, Buffer> *) {
-  MS_LOG(ERROR) << "Unsupported feature.";
-  return FAILED;
+  if (model_context_ == nullptr) {
+    model_context_ = std::make_shared<ModelContext>();
+  }
+
+  std::string input_shape_option;
+  for (size_t i = 0; i < inputs.size(); ++i) {
+    if (inputs[i].Name() != origin_inputs[i].Name()) {
+      MS_LOG(ERROR) << "Invalid inputs " << i << " name " << inputs[i].Name() << " not match model input name "
+                    << origin_inputs[i].Name();
+      return kMCInvalidInput;
+    }
+    input_shape_option += inputs[i].Name() + ":";
+    for (size_t j = 0; j < dims[i].size(); ++j) {
+      input_shape_option += std::to_string(dims[i][j]);
+      if (j + 1 < dims[i].size()) {
+        input_shape_option += ",";
+      }
+    }
+    if (i + 1 < inputs.size()) {
+      input_shape_option += ";";
+    }
+  }
+  MS_LOG(INFO) << "Set input size option is " << input_shape_option;
+  ModelContext::SetInputShape(model_context_, input_shape_option);
+  auto graph_cell_bak = std::move(graph_cell_);
+  auto ret = Build();
+  if (ret != kSuccess) {
+    MS_LOG(INFO) << "Resize build failed.";
+    graph_cell_ = std::move(graph_cell_bak);
+    return ret;
+  }
+  MS_LOG(INFO) << "Resize success.";
+  return kSuccess;
 }
 
-Status AclModel::Predict(const std::vector<Buffer> &inputs, std::vector<Buffer> *outputs) {
+Status AclModel::Predict(const std::vector<MSTensor> &inputs, std::vector<MSTensor> *outputs) {
   MS_EXCEPTION_IF_NULL(outputs);
   if (graph_ == nullptr) {
     MS_LOG(ERROR) << "Invalid data, graph_ is null.";
-    return FAILED;
+    return kMCFailed;
   }
 
   if (graph_cell_ == nullptr) {
     MS_LOG(WARNING) << "Model has not been built, it will be built with default options";
-    Status ret = Build({});
-    if (ret != SUCCESS) {
+    Status ret = Build();
+    if (ret != kSuccess) {
       MS_LOG(ERROR) << "Build model failed.";
-      return FAILED;
+      return ret;
     }
   }
 
   MS_EXCEPTION_IF_NULL(graph_cell_);
   Status ret = graph_cell_->Run(inputs, outputs);
-  if (ret != SUCCESS) {
+  if (ret != kSuccess) {
     MS_LOG(ERROR) << "Run graph failed.";
-    return FAILED;
+    return ret;
   }
 
-  return SUCCESS;
+  return kSuccess;
 }
 
-Status AclModel::GetInputsInfo(std::vector<std::string> *names, std::vector<std::vector<int64_t>> *shapes,
-                               std::vector<DataType> *data_types, std::vector<size_t> *mem_sizes) const {
+std::vector<MSTensor> AclModel::GetInputs() {
   MS_EXCEPTION_IF_NULL(graph_cell_);
-  return graph_cell_->GetInputsInfo(names, shapes, data_types, mem_sizes);
+  return graph_cell_->GetInputs();
 }
 
-Status AclModel::GetOutputsInfo(std::vector<std::string> *names, std::vector<std::vector<int64_t>> *shapes,
-                                std::vector<DataType> *data_types, std::vector<size_t> *mem_sizes) const {
+std::vector<MSTensor> AclModel::GetOutputs() {
   MS_EXCEPTION_IF_NULL(graph_cell_);
-  return graph_cell_->GetOutputsInfo(names, shapes, data_types, mem_sizes);
-}
-
-std::string AclModel::GenerateOptionsStr(const std::map<std::string, std::string> &options) {
-  std::string ret;
-  for (auto &[key, value] : options) {
-    ret += key + "^" + value + "^^";
-  }
-  return ret;
+  return graph_cell_->GetOutputs();
 }
-}  // namespace mindspore::api
+}  // namespace mindspore

mindspore/ccsrc/cxx_api/model/acl/acl_model.h

@@ -31,30 +31,25 @@
 #include "ir/tensor.h"
 #include "ir/anf.h"
 
-namespace mindspore::api {
+namespace mindspore {
 class AclModel : public ModelImpl {
  public:
-  AclModel() : model_converter_(), options_(nullptr), options_str_() {}
+  AclModel() : model_converter_(), options_(nullptr) {}
   ~AclModel() = default;
 
-  Status Build(const std::map<std::string, std::string> &options_map) override;
+  Status Build() override;
+  Status Resize(const std::vector<MSTensor> &inputs, const std::vector<std::vector<int64_t>> &dims) override;
 
-  Status Train(const DataSet &dataset, std::map<std::string, Buffer> *outputs) override;
-  Status Eval(const DataSet &dataset, std::map<std::string, Buffer> *outputs) override;
-  Status Predict(const std::vector<Buffer> &inputs, std::vector<Buffer> *outputs) override;
+  Status Predict(const std::vector<MSTensor> &inputs, std::vector<MSTensor> *outputs) override;
 
-  Status GetInputsInfo(std::vector<std::string> *names, std::vector<std::vector<int64_t>> *shapes,
-                       std::vector<DataType> *data_types, std::vector<size_t> *mem_sizes) const override;
-  Status GetOutputsInfo(std::vector<std::string> *names, std::vector<std::vector<int64_t>> *shapes,
-                        std::vector<DataType> *data_types, std::vector<size_t> *mem_sizes) const override;
+  std::vector<MSTensor> GetInputs() override;
+  std::vector<MSTensor> GetOutputs() override;
 
  private:
-  static std::string GenerateOptionsStr(const std::map<std::string, std::string> &options);
-
   std::shared_ptr<GraphCell> graph_cell_;
   ModelConverter model_converter_;
   std::unique_ptr<AclModelOptions> options_;
-  std::string options_str_;
+  std::map<std::string, std::shared_ptr<Graph>> dynamic_size_graph_map_;
 };
-}  // namespace mindspore::api
+}  // namespace mindspore
 #endif  // MINDSPORE_CCSRC_CXX_API_ACL_MODEL_H

mindspore/ccsrc/cxx_api/model/acl/acl_model_options.cc

@@ -18,23 +18,31 @@
 #include "utils/log_adapter.h"
 #include "external/ge/ge_api_types.h"
 
-namespace mindspore::api {
-static std::string ParseOption(const std::map<std::string, std::string> &options, const std::string &key) {
-  auto iter = options.find(key);
-  if (iter != options.end()) {
-    return iter->second;
+namespace mindspore {
+static const std::map<enum DataType, std::string> kSupportedDtypeOptionMap = {{DataType::kNumberTypeFloat16, "FP16"},
+                                                                              {DataType::kNumberTypeFloat32, "FP32"},
+                                                                              {DataType::kNumberTypeUInt8, "UINT8"}};
+
+AclModelOptions::AclModelOptions(const std::shared_ptr<Context> &context) {
+  if (context == nullptr) {
+    return;
+  }
+  insert_op_cfg_path = ModelContext::GetInsertOpConfigPath(context);
+  input_format = ModelContext::GetInputFormat(context);
+  input_shape = ModelContext::GetInputShape(context);
+
+  auto out_type = ModelContext::GetOutputType(context);
+  auto iter = kSupportedDtypeOptionMap.find(out_type);
+  if (out_type == DataType::kTypeUnknown) {
+    // do nothing
+  } else if (iter == kSupportedDtypeOptionMap.end()) {
+    MS_LOG(WARNING) << "Unsupported output type " << out_type << ", use FP32 as default.";
+  } else {
+    output_type = iter->second;
   }
-  return "";
-}
 
-AclModelOptions::AclModelOptions(const std::map<std::string, std::string> &options) {
-  // to acl
-  insert_op_cfg_path = ParseOption(options, kModelOptionInsertOpCfgPath);
-  input_format = ParseOption(options, kModelOptionInputFormat);
-  input_shape = ParseOption(options, kModelOptionInputShape);
-  output_type = ParseOption(options, kModelOptionOutputType);
-  precision_mode = ParseOption(options, kModelOptionPrecisionMode);
-  op_select_impl_mode = ParseOption(options, kModelOptionOpSelectImplMode);
+  precision_mode = ModelContext::GetPrecisionMode(context);
+  op_select_impl_mode = ModelContext::GetOpSelectImplMode(context);
 }
 
 std::tuple<std::map<std::string, std::string>, std::map<std::string, std::string>> AclModelOptions::GenAclOptions()
@@ -69,4 +77,16 @@ std::tuple<std::map<std::string, std::string>, std::map<std::string, std::string
   }
   return {init_options, build_options};
 }
-}  // namespace mindspore::api
+
+std::string AclModelOptions::GenAclOptionsKey() const {
+  auto [init_options, build_options] = GenAclOptions();
+  std::string key_str;
+  for (auto &[key, value] : init_options) {
+    key_str += key + "^" + value + "^^";
+  }
+  for (auto &[key, value] : build_options) {
+    key_str += key + "^" + value + "^^";
+  }
+  return key_str;
+}
+}  // namespace mindspore

mindspore/ccsrc/cxx_api/model/acl/acl_model_options.h

@@ -20,12 +20,13 @@
 #include <string>
 #include <map>
 #include <tuple>
+#include <memory>
 #include "include/api/types.h"
 #include "include/api/status.h"
+#include "include/api/context.h"
 
-namespace mindspore::api {
+namespace mindspore {
 struct AclModelOptions {
-  std::string output_node;  // todo: at convert.cc::BuildGraph(), no atc options
   // build options
   std::string insert_op_cfg_path;
   std::string input_format;
@@ -35,12 +36,13 @@ struct AclModelOptions {
   std::string op_select_impl_mode;
   std::string soc_version = "Ascend310";
 
-  explicit AclModelOptions(const std::map<std::string, std::string> &options);
+  explicit AclModelOptions(const std::shared_ptr<Context> &context);
   ~AclModelOptions() = default;
 
   // return tuple<init_options, build_options>
   std::tuple<std::map<std::string, std::string>, std::map<std::string, std::string>> GenAclOptions() const;
+  std::string GenAclOptionsKey() const;
 };
-}  // namespace mindspore::api
+}  // namespace mindspore
 
 #endif  // MINDSPORE_CCSRC_CXXAPI_SESSION_ACL_OPTION_PARSER_H

mindspore/ccsrc/cxx_api/model/acl/model_converter.cc

@@ -22,9 +22,8 @@
 #include "include/api/serialization.h"
 #include "graph/model.h"
 #include "cxx_api/model/model_converter_utils/multi_process.h"
-#include "cxx_api/python_utils.h"
 
-namespace mindspore::api {
+namespace mindspore {
 namespace {
 transform::TensorOrderMap GetParams(const FuncGraphPtr &anf_graph) {
   transform::TensorOrderMap res;
@@ -86,25 +85,25 @@ transform::DfGraphPtr ModelConverter::ConvertFuncGraphToAIR(const FuncGraphPtr &
     para->set_name(name);
   }
 
-  transform::DfGraphConvertor convertor(anf_graph);
+  transform::DfGraphConvertor converter(anf_graph);
   std::string net_id = "0";
   std::string init_graph = "init_subgraph." + net_id;
   std::string checkpoint_name = "save." + net_id;
 
-  convertor.set_training(false);
-  (void)convertor.ConvertAllNode().InitParam(GetParams(anf_graph)).BuildGraph();
-  (void)convertor.GenerateCheckpointGraph();
-  if (convertor.ErrCode() != 0) {
+  converter.set_training(false);
+  (void)converter.ConvertAllNode().InitParam(GetParams(anf_graph)).BuildGraph();
+  (void)converter.GenerateCheckpointGraph();
+  if (converter.ErrCode() != 0) {
     transform::DfGraphManager::GetInstance().ClearGraph();
-    MS_LOG(ERROR) << "Convert df graph failed, err:" << convertor.ErrCode();
+    MS_LOG(ERROR) << "Convert df graph failed, err:" << converter.ErrCode();
     return nullptr;
   }
-  (void)transform::DfGraphManager::GetInstance().AddGraph(anf_graph->ToString(), convertor.GetComputeGraph());
-  (void)transform::DfGraphManager::GetInstance().AddGraph(init_graph, convertor.GetInitGraph());
-  (void)transform::DfGraphManager::GetInstance().AddGraph(BROADCAST_GRAPH_NAME, convertor.GetBroadcastGraph());
+  (void)transform::DfGraphManager::GetInstance().AddGraph(anf_graph->ToString(), converter.GetComputeGraph());
+  (void)transform::DfGraphManager::GetInstance().AddGraph(init_graph, converter.GetInitGraph());
+  (void)transform::DfGraphManager::GetInstance().AddGraph(BROADCAST_GRAPH_NAME, converter.GetBroadcastGraph());
 
   transform::Status ret =
-    transform::DfGraphManager::GetInstance().AddGraph(checkpoint_name, convertor.GetSaveCheckpointGraph());
+    transform::DfGraphManager::GetInstance().AddGraph(checkpoint_name, converter.GetSaveCheckpointGraph());
   if (ret == transform::Status::SUCCESS) {
     transform::DfGraphManager::GetInstance().SetAnfGraph(checkpoint_name, anf_graph);
   }
@@ -158,7 +157,7 @@ Buffer ModelConverter::LoadMindIR(const FuncGraphPtr &func_graph) {
     auto df_graph = ConvertFuncGraphToAIR(func_graph);
     if (df_graph == nullptr) {
       MS_LOG(ERROR) << "Convert FuncGraph to AscendIR failed.";
-      return FAILED;
+      return kMCFailed;
     }
     ge::Model model;
     ge::Buffer model_data;
@@ -166,14 +165,14 @@ Buffer ModelConverter::LoadMindIR(const FuncGraphPtr &func_graph) {
     auto ge_ret = model.Save(model_data);
     if (ge_ret != ge::SUCCESS) {
       MS_LOG(ERROR) << "Save ge model to buffer failed.";
-      return FAILED;
+      return kMCFailed;
     }
 
     // send original model to child
     auto status = multi_process->SendMsg(model_data.data(), model_data.size());
-    if (!status.IsSuccess()) {
+    if (status != kSuccess) {
       MS_LOG_ERROR << "Send original model to child process failed";
-      return FAILED;
+      return status;
     }
     // receive convert model result from child
     CreateBufferCall call = [&buffer_ret](size_t msg_len) -> uint8_t * {
@@ -181,11 +180,11 @@ Buffer ModelConverter::LoadMindIR(const FuncGraphPtr &func_graph) {
       return reinterpret_cast<uint8_t *>(buffer_ret.MutableData());
     };
     status = multi_process->ReceiveMsg(call);
-    if (!status.IsSuccess()) {
+    if (status != kSuccess) {
       MS_LOG_ERROR << "Receive result model from child process failed";
-      return FAILED;
+      return status;
     }
-    return SUCCESS;
+    return kSuccess;
   };
   auto child_process = [this](MultiProcess *multi_process) -> Status {
     MS_EXCEPTION_IF_NULL(multi_process);
@@ -196,25 +195,25 @@ Buffer ModelConverter::LoadMindIR(const FuncGraphPtr &func_graph) {
       return reinterpret_cast<uint8_t *>(model.MutableData());
     };
     auto status = multi_process->ReceiveMsg(call);
-    if (!status.IsSuccess()) {
+    if (status != kSuccess) {
       MS_LOG_ERROR << "Receive original model from parent process failed";
-      return FAILED;
+      return status;
     }
     Buffer model_result = LoadAscendIRInner(model);
     if (model_result.DataSize() == 0) {
       MS_LOG_ERROR << "Convert model from MindIR to OM failed";
-      return FAILED;
+      return kMCFailed;
     }
     // send result model to parent
     status = multi_process->SendMsg(model_result.Data(), model_result.DataSize());
-    if (!status.IsSuccess()) {
+    if (status != kSuccess) {
       MS_LOG_ERROR << "Send result model to parent process failed";
-      return FAILED;
+      return status;
     }
-    return SUCCESS;
+    return kSuccess;
   };
   auto status = multi_process.MainProcess(parent_process, child_process);
-  if (!status.IsSuccess()) {
+  if (status != kSuccess) {
     MS_LOG_ERROR << "Convert MindIR model to OM model failed";
   } else {
     MS_LOG_INFO << "Convert MindIR model to OM model success";
@@ -229,9 +228,9 @@ Buffer ModelConverter::LoadAscendIR(const Buffer &model_data) {
     MS_EXCEPTION_IF_NULL(multi_process);
     // send original model to child
     auto status = multi_process->SendMsg(model_data.Data(), model_data.DataSize());
-    if (!status.IsSuccess()) {
+    if (status != kSuccess) {
       MS_LOG_ERROR << "Send original model to child process failed";
-      return FAILED;
+      return status;
     }
     // receive convert model result from child
     CreateBufferCall call = [&buffer_ret](size_t msg_len) -> uint8_t * {
@@ -239,11 +238,11 @@ Buffer ModelConverter::LoadAscendIR(const Buffer &model_data) {
       return reinterpret_cast<uint8_t *>(buffer_ret.MutableData());
     };
     status = multi_process->ReceiveMsg(call);
-    if (!status.IsSuccess()) {
+    if (status != kSuccess) {
       MS_LOG_ERROR << "Receive result model from child process failed";
-      return FAILED;
+      return status;
     }
-    return SUCCESS;
+    return kSuccess;
   };
   auto child_process = [this](MultiProcess *multi_process) -> Status {
     MS_EXCEPTION_IF_NULL(multi_process);
@@ -254,25 +253,25 @@ Buffer ModelConverter::LoadAscendIR(const Buffer &model_data) {
       return reinterpret_cast<uint8_t *>(model.MutableData());
     };
     auto status = multi_process->ReceiveMsg(call);
-    if (!status.IsSuccess()) {
+    if (status != kSuccess) {
       MS_LOG_ERROR << "Receive original model from parent process failed";
-      return FAILED;
+      return status;
     }
     Buffer model_result = LoadAscendIRInner(model);
     if (model_result.DataSize() == 0) {
       MS_LOG_ERROR << "Convert model from AIR to OM failed";
-      return FAILED;
+      return kMCFailed;
     }
     // send result model to parent
     status = multi_process->SendMsg(model_result.Data(), model_result.DataSize());
-    if (!status.IsSuccess()) {
+    if (status != kSuccess) {
       MS_LOG_ERROR << "Send result model to parent process failed";
-      return FAILED;
+      return status;
     }
-    return SUCCESS;
+    return kSuccess;
   };
   auto status = multi_process.MainProcess(parent_process, child_process);
-  if (!status.IsSuccess()) {
+  if (status != kSuccess) {
     MS_LOG_ERROR << "Convert AIR model to OM model failed";
   } else {
     MS_LOG_INFO << "Convert AIR model to OM model success";
@@ -326,4 +325,4 @@ Buffer ModelConverter::LoadAscendIRInner(const Buffer &model_data) {
   auto om_data = BuildAirModel(df_graph, init_options, build_options);
   return om_data;
 }
-}  // namespace mindspore::api
+}  // namespace mindspore

mindspore/ccsrc/cxx_api/model/acl/model_converter.h

@@ -27,7 +27,7 @@
 #include "external/ge/ge_ir_build.h"
 #include "cxx_api/model/acl/acl_model_options.h"
 
-namespace mindspore::api {
+namespace mindspore {
 class ModelConverter {
  public:
   ModelConverter() : options_(nullptr) {}
@@ -46,6 +46,5 @@ class ModelConverter {
   Buffer LoadMindIRInner(const FuncGraphPtr &func_graph);
   Buffer LoadAscendIRInner(const Buffer &model_data);
 };
-}  // namespace mindspore::api
-
+}  // namespace mindspore
 #endif  // MINDSPORE_CCSRC_CXXAPI_SESSION_ACL_MODEL_CONVERTER_H

mindspore/ccsrc/cxx_api/model/model.cc

@@ -19,49 +19,45 @@
 #include "cxx_api/factory.h"
 #include "utils/utils.h"
 
-namespace mindspore::api {
-Status Model::Build(const std::map<std::string, std::string> &options) {
+namespace mindspore {
+Status Model::Build() {
   MS_EXCEPTION_IF_NULL(impl_);
-  return impl_->Build(options);
+  return impl_->Build();
 }
 
-Status Model::Train(const DataSet &dataset, bool data_sink, std::map<std::string, Buffer> *outputs) {
+Status Model::Resize(const std::vector<MSTensor> &inputs, const std::vector<std::vector<int64_t>> &dims) {
   MS_EXCEPTION_IF_NULL(impl_);
-  return impl_->Train(dataset, outputs);
+  return impl_->Resize(inputs, dims);
 }
 
-Status Model::Eval(const DataSet &dataset, bool data_sink, std::map<std::string, Buffer> *outputs) {
-  MS_EXCEPTION_IF_NULL(impl_);
-  return impl_->Eval(dataset, outputs);
-}
-
-Status Model::Predict(const std::vector<Buffer> &inputs, std::vector<Buffer> *outputs) {
+Status Model::Predict(const std::vector<MSTensor> &inputs, std::vector<MSTensor> *outputs) {
   MS_EXCEPTION_IF_NULL(impl_);
   return impl_->Predict(inputs, outputs);
 }
 
-Status Model::GetInputsInfo(std::vector<std::string> *names, std::vector<std::vector<int64_t>> *shapes,
-                            std::vector<DataType> *data_types, std::vector<size_t> *mem_sizes) const {
+std::vector<MSTensor> Model::GetInputs() {
   MS_EXCEPTION_IF_NULL(impl_);
-  return impl_->GetInputsInfo(names, shapes, data_types, mem_sizes);
+  return impl_->GetInputs();
 }
 
-Status Model::GetOutputsInfo(std::vector<std::string> *names, std::vector<std::vector<int64_t>> *shapes,
-                             std::vector<DataType> *data_types, std::vector<size_t> *mem_sizes) const {
+std::vector<MSTensor> Model::GetOutputs() {
   MS_EXCEPTION_IF_NULL(impl_);
-  return impl_->GetOutputsInfo(names, shapes, data_types, mem_sizes);
+  return impl_->GetOutputs();
 }
 
-Model::Model(const GraphCell &graph_cell)
-    : impl_(Factory<ModelImpl>::Instance().Create(Context::Instance().GetDeviceTarget())) {
+Model::Model(const GraphCell &graph_cell, const std::shared_ptr<Context> &model_context)
+    : impl_(Factory<ModelImpl>::Instance().Create(mindspore::GlobalContext::GetGlobalDeviceTarget())) {
   if (impl_ == nullptr) {
-    MS_LOG(EXCEPTION) << "Create session type " << Context::Instance().GetDeviceTarget() << " failed";
+    MS_LOG(EXCEPTION) << "Create session type " << mindspore::GlobalContext::GetGlobalDeviceTarget() << " failed";
   }
   MS_EXCEPTION_IF_NULL(graph_cell.GetGraph());
   impl_->SetGraph(std::make_shared<Graph>(*graph_cell.GetGraph()));
+  impl_->SetContext(model_context);
 }
 
-Model::Model(const std::vector<Output> &network) { MS_LOG(EXCEPTION) << "Unsupported feature."; }
+Model::Model(const std::vector<Output> &network, const std::shared_ptr<Context> &model_context) {
+  MS_LOG(EXCEPTION) << "Unsupported feature.";
+}
 
 Model::~Model() {}
 
@@ -69,4 +65,4 @@ bool Model::CheckModelSupport(const std::string &device_type, ModelType) {
   return Factory<ModelImpl>::Instance().CheckModelSupport(device_type);
 }
 
-}  // namespace mindspore::api
+}  // namespace mindspore

mindspore/ccsrc/cxx_api/model/model_converter_utils/multi_process.cc

@@ -24,7 +24,6 @@
 #include "cxx_api/model/model_converter_utils/shared_memory.h"
 
 namespace mindspore {
-namespace api {
 namespace {
 uint64_t kSharedMemorySize = 100ull << 20;  // 100 MB
 }
@@ -40,7 +39,7 @@ Status MultiProcess::MainProcess(ProcessFuncCall parent_process, ProcessFuncCall
   memory_size_ = kSharedMemorySize;  // 100 MB
   SharedMemory shared_memory;
   ret = shared_memory.Create(memory_size_);
-  if (!ret.IsSuccess()) {
+  if (ret != kSuccess) {
     MS_LOG_ERROR << "Create shared memory failed";
     return ret;
   }
@@ -48,10 +47,10 @@ Status MultiProcess::MainProcess(ProcessFuncCall parent_process, ProcessFuncCall
   if (pid < 0) {
     shared_memory.Destroy();
     MS_LOG_ERROR << "Fork process to convert model failed";
-    return FAILED;
+    return kMEFailed;
   }
   ret = shared_memory.Attach();
-  if (!ret.IsSuccess()) {
+  if (ret != kSuccess) {
     MS_LOG_ERROR << "Process attach shared memory failed, pid " << pid;
     return ret;
   }
@@ -87,12 +86,12 @@ Status MultiProcess::ParentProcess(ProcessFuncCall parent_process) {
   Status ret;
   try {
     ret = parent_process(this);
-    if (!ret.IsSuccess()) {
+    if (ret != kSuccess) {
       MS_LOG_ERROR << "Parent process process failed";
     }
   } catch (const std::runtime_error &ex) {
     MS_LOG_ERROR << "Catch parent process runtime error: " << ex.what();
-    ret = FAILED;
+    ret = kMEFailed;
   }
   stopped_ = true;
   send_msg_->stop = true;
@@ -108,7 +107,7 @@ void MultiProcess::ChildProcess(ProcessFuncCall child_process) {
   std::thread heartbeat_thread(MultiProcess::HeartbeatThreadFunc, this);
   try {
     auto ret = child_process(this);
-    if (!ret.IsSuccess()) {
+    if (ret != kSuccess) {
       MS_LOG_ERROR << "Child process process failed";
     }
   } catch (const std::runtime_error &ex) {
@@ -138,14 +137,14 @@ Status MultiProcess::SendMsg(const void *buffer, uint64_t msg_len) {
     }
     if (peer_stopped_) {
       if (!send_msg_->read_finish_flag) {
-        return FAILED;
+        return kMEFailed;
       }
       break;
     }
     MS_LOG_INFO << "Send end " << cur_offset << ", msg len " << sub_msg_len << ", total len " << msg_len;
   }
   MS_LOG_INFO << "End to send message to peer process, msg len " << msg_len;
-  return SUCCESS;
+  return kSuccess;
 }
 
 Status MultiProcess::ReceiveMsg(CreateBufferCall create_buffer_call) {
@@ -158,7 +157,7 @@ Status MultiProcess::ReceiveMsg(CreateBufferCall create_buffer_call) {
       usleep(1000);  // 1ms
     }
     if (peer_stopped_) {
-      return FAILED;
+      return kMEFailed;
     }
     if (msg_buffer == nullptr) {
       msg_len = receive_msg_->msg_total_len;
@@ -170,7 +169,7 @@ Status MultiProcess::ReceiveMsg(CreateBufferCall create_buffer_call) {
     receive_msg_->read_finish_flag = true;
     MS_LOG_INFO << "Receive end, current length " << cur_offset << ", total length " << msg_len << std::endl;
   } while (msg_len > cur_offset);
-  return SUCCESS;
+  return kSuccess;
 }
 
 void MultiProcess::HeartbeatThreadFunc(MultiProcess *multi_process) { multi_process->HeartbeatThreadFuncInner(); }
@@ -200,6 +199,4 @@ void MultiProcess::HeartbeatThreadFuncInner() {
     usleep(100000);  // sleep 100 ms
   }
 }
-
-}  // namespace api
 }  // namespace mindspore

mindspore/ccsrc/cxx_api/model/model_converter_utils/multi_process.h

@@ -21,7 +21,6 @@
 #include "include/api/status.h"
 
 namespace mindspore {
-namespace api {
 struct MessageFlag {
   uint64_t heartbeat = 0;
   uint64_t stop = false;
@@ -60,7 +59,5 @@ class MultiProcess {
   Status ParentProcess(ProcessFuncCall parent_process);
   void ChildProcess(ProcessFuncCall child_process);
 };
-}  // namespace api
 }  // namespace mindspore
-
 #endif  // MINDSPORE_CCSRC_CXXAPI_MULTI_PROCESS_H

mindspore/ccsrc/cxx_api/model/model_converter_utils/shared_memory.cc

@@ -20,26 +20,25 @@
 #include "mindspore/core/utils/log_adapter.h"
 
 namespace mindspore {
-namespace api {
 Status SharedMemory::Create(uint64_t memory_size) {
   auto access_mode = S_IRUSR | S_IWUSR | S_IROTH | S_IWOTH | S_IRGRP | S_IWGRP;
   shm_id_ = shmget(IPC_PRIVATE, memory_size, IPC_CREAT | IPC_EXCL | access_mode);
   if (shm_id_ == -1) {
     MS_LOG_ERROR << "Shared memory creation failed. Errno " + std::to_string(errno);
-    return FAILED;
+    return kMCFailed;
   }
   MS_LOG_INFO << "shmget success, shm id " << shm_id_;
-  return SUCCESS;
+  return kSuccess;
 }
 
 Status SharedMemory::Attach() {
   void *shmat_addr = shmat(shm_id_, nullptr, 0);
   if (shmat_addr == reinterpret_cast<void *>(-1)) {
     MS_LOG_ERROR << "Shared memory attach failed. Errno " + std::to_string(errno);
-    return FAILED;
+    return kMCFailed;
   }
   shmat_addr_ = reinterpret_cast<uint8_t *>(shmat_addr);
-  return SUCCESS;
+  return kSuccess;
 }
 
 void SharedMemory::Detach() {
@@ -63,5 +62,4 @@ void SharedMemory::Destroy() {
     MS_LOG_ERROR << errMsg;
   }
 }
-}  // namespace api
 }  // namespace mindspore

mindspore/ccsrc/cxx_api/model/model_converter_utils/shared_memory.h

@@ -20,7 +20,6 @@
 #include "include/api/status.h"
 
 namespace mindspore {
-namespace api {
 class SharedMemory {
  public:
   Status Create(uint64_t memory_size);
@@ -33,7 +32,5 @@ class SharedMemory {
   int shm_id_ = -1;
   uint8_t *shmat_addr_ = nullptr;
 };
-}  // namespace api
 }  // namespace mindspore
-
 #endif  // MINDSPORE_CCSRC_CXXAPI_SHARED_MEMORY_H

mindspore/ccsrc/cxx_api/model/model_impl.h

@@ -21,28 +21,26 @@
 #include <vector>
 #include <memory>
 #include <utility>
+#include "include/api/context.h"
 #include "include/api/model.h"
 #include "include/api/graph.h"
 #include "cxx_api/graph/graph_data.h"
 #include "utils/utils.h"
 #include "ir/func_graph.h"
 
-namespace mindspore::api {
+namespace mindspore {
 class ModelImpl {
  public:
   ModelImpl() = default;
   virtual ~ModelImpl() = default;
 
-  virtual Status Build(const std::map<std::string, std::string> &options) = 0;
+  virtual Status Build() = 0;
+  virtual Status Resize(const std::vector<MSTensor> &inputs, const std::vector<std::vector<int64_t>> &dims) = 0;
 
-  virtual Status Train(const DataSet &dataset, std::map<std::string, Buffer> *outputs) = 0;
-  virtual Status Eval(const DataSet &dataset, std::map<std::string, Buffer> *outputs) = 0;
-  virtual Status Predict(const std::vector<Buffer> &inputs, std::vector<Buffer> *outputs) = 0;
+  virtual Status Predict(const std::vector<MSTensor> &inputs, std::vector<MSTensor> *outputs) = 0;
 
-  virtual Status GetInputsInfo(std::vector<std::string> *names, std::vector<std::vector<int64_t>> *shapes,
-                               std::vector<DataType> *data_types, std::vector<size_t> *mem_sizes) const = 0;
-  virtual Status GetOutputsInfo(std::vector<std::string> *names, std::vector<std::vector<int64_t>> *shapes,
-                                std::vector<DataType> *data_types, std::vector<size_t> *mem_sizes) const = 0;
+  virtual std::vector<MSTensor> GetInputs() = 0;
+  virtual std::vector<MSTensor> GetOutputs() = 0;
 
  protected:
   Status Load(const std::shared_ptr<GraphCell> &graph_cell) {
@@ -61,11 +59,16 @@ class ModelImpl {
   }
 
   std::shared_ptr<Graph> graph_;
+  std::shared_ptr<Context> model_context_;
 
  private:
   friend class Model;
   void SetGraph(const std::shared_ptr<Graph> &graph) { graph_ = graph; }
+  void SetContext(const std::shared_ptr<Context> &model_context) {
+    if (model_context != nullptr) {
+      model_context_ = std::make_shared<Context>(*model_context);
+    }
+  }
 };
-}  // namespace mindspore::api
-
+}  // namespace mindspore
 #endif  // MINDSPORE_CCSRC_CXX_API_MODEL_MODEL_IMPL_H

mindspore/ccsrc/cxx_api/model/ms/ms_model.cc

@@ -16,18 +16,78 @@
 
 #include "cxx_api/model/ms/ms_model.h"
 #include <memory>
+#include "include/api/context.h"
 #include "utils/ms_context.h"
 #include "cxx_api/factory.h"
 
 namespace mindspore {
-namespace api {
 API_FACTORY_REG(ModelImpl, Ascend910, MsModel);
 API_FACTORY_REG(ModelImpl, GPU, MsModel);
 
-Status MsModel::Build(const std::map<std::string, std::string> &) {
+static std::string GenerateShapeKey(const std::vector<std::vector<int64_t>> &dims) {
+  std::string shape_key;
+  for (size_t i = 0; i < dims.size(); ++i) {
+    shape_key += std::to_string(i) + ":";
+    for (size_t j = 0; j < dims[i].size(); ++j) {
+      shape_key += std::to_string(dims[i][j]);
+      if (j + 1 < dims[i].size()) {
+        shape_key += ",";
+      }
+    }
+    if (i + 1 < dims.size()) {
+      shape_key += ";";
+    }
+  }
+  return shape_key;
+}
+
+std::shared_ptr<GraphCell> MsModel::GenerateGraphCell(const std::vector<std::vector<int64_t>> &dims) {
+  std::string shape_key = GenerateShapeKey(dims);
+  if (auto iter = dynamic_size_graph_map_.find(shape_key); iter != dynamic_size_graph_map_.end()) {
+    MS_LOG(INFO) << "This options has been built, read cache.";
+    return iter->second;
+  }
+
+  auto func_graph = ModelImpl::GetFuncGraph();
+  MS_EXCEPTION_IF_NULL(func_graph);
+
+  const auto &inputs = func_graph->parameters();
+  if (dims.size() != inputs.size()) {
+    MS_LOG(ERROR) << "Invalid dims size " << dims.size() << " not match model inputs size " << inputs.size();
+    return nullptr;
+  }
+  for (size_t i = 0; i < dims.size(); ++i) {
+    const auto &param = inputs[i];
+    auto shape_ptr = std::dynamic_pointer_cast<abstract::Shape>(param->Shape());
+    if (shape_ptr == nullptr) {
+      MS_LOG(ERROR) << "Inputs " << i << " is not supported to resize, debug string: " << param->DebugString();
+      return nullptr;
+    }
+    shape_ptr->shape() = dims[i];
+  }
+
+  auto graph = std::make_shared<Graph>(std::make_shared<Graph::GraphData>(func_graph, ModelType::kMindIR));
+  MS_EXCEPTION_IF_NULL(graph);
+  auto graph_cell = std::make_shared<GraphCell>(graph);
+  MS_EXCEPTION_IF_NULL(graph_cell);
+  auto ret = ModelImpl::Load(graph_cell);
+  if (ret != kSuccess) {
+    MS_LOG(ERROR) << "Load failed.";
+    return nullptr;
+  }
+  dynamic_size_graph_map_[shape_key] = graph_cell;
+  return graph_cell;
+}
+
+Status MsModel::Build() {
   MS_LOG(INFO) << "Start build model.";
   MS_EXCEPTION_IF_NULL(graph_);
 
+  if (graph_cell_ != nullptr) {
+    MS_LOG(INFO) << "This model has been built, skip.";
+    return kSuccess;
+  }
+
   auto func_graph = ModelImpl::GetFuncGraph();
   MS_EXCEPTION_IF_NULL(func_graph);
 
@@ -36,7 +96,7 @@ Status MsModel::Build(const std::map<std::string, std::string> &) {
   auto graph_cell = std::make_shared<GraphCell>(graph);
   MS_EXCEPTION_IF_NULL(graph_cell);
   auto ret = ModelImpl::Load(graph_cell);
-  if (ret != SUCCESS) {
+  if (ret != kSuccess) {
     MS_LOG(ERROR) << "Load failed.";
     return ret;
   }
@@ -44,55 +104,66 @@ Status MsModel::Build(const std::map<std::string, std::string> &) {
   // save result
   graph_cell_ = graph_cell;
   MS_LOG(INFO) << "Build model success.";
-  return SUCCESS;
+  return kSuccess;
 }
 
-Status MsModel::Train(const DataSet &, std::map<std::string, Buffer> *) {
-  MS_LOG(ERROR) << "Unsupported feature.";
-  return FAILED;
-}
+Status MsModel::Resize(const std::vector<MSTensor> &inputs, const std::vector<std::vector<int64_t>> &dims) {
+  MS_LOG(INFO) << "Start to resize model";
+  auto origin_inputs = GetInputs();
+  if (inputs.size() != origin_inputs.size()) {
+    MS_LOG(ERROR) << "Invalid inputs size " << inputs.size() << " not match model inputs size " << origin_inputs.size();
+    return kMCInvalidInput;
+  }
+
+  if (inputs.size() != dims.size()) {
+    MS_LOG(ERROR) << "Invalid dims size " << dims.size() << " not match inputs size " << inputs.size();
+    return kMCInvalidInput;
+  }
+
+  auto graph_cell = GenerateGraphCell(dims);
+  if (graph_cell == nullptr) {
+    MS_LOG(ERROR) << "GenerateGraphCell failed.";
+    return kMCFailed;
+  }
 
-Status MsModel::Eval(const DataSet &, std::map<std::string, Buffer> *) {
-  MS_LOG(ERROR) << "Unsupported feature.";
-  return FAILED;
+  MS_LOG(INFO) << "Resize model success.";
+  graph_cell_ = std::move(graph_cell);
+  return kSuccess;
 }
 
-Status MsModel::Predict(const std::vector<Buffer> &inputs, std::vector<Buffer> *outputs) {
+Status MsModel::Predict(const std::vector<MSTensor> &inputs, std::vector<MSTensor> *outputs) {
   MS_EXCEPTION_IF_NULL(outputs);
   if (graph_ == nullptr) {
     MS_LOG(ERROR) << "Invalid data, graph_ is null.";
-    return FAILED;
+    return kMCFailed;
   }
 
   if (graph_cell_ == nullptr) {
     MS_LOG(INFO) << "Model has not been built, it will be built with default options";
-    Status ret = Build({});
-    if (ret != SUCCESS) {
+    Status ret = Build();
+    if (ret != kSuccess) {
       MS_LOG(ERROR) << "Build model failed.";
-      return FAILED;
+      return ret;
     }
   }
 
   MS_EXCEPTION_IF_NULL(graph_cell_);
   Status ret = graph_cell_->Run(inputs, outputs);
-  if (ret != SUCCESS) {
+  if (ret != kSuccess) {
     MS_LOG(ERROR) << "Run graph failed.";
-    return FAILED;
+    return ret;
   }
 
-  return SUCCESS;
+  return kSuccess;
 }
 
-Status MsModel::GetInputsInfo(std::vector<std::string> *names, std::vector<std::vector<int64_t>> *shapes,
-                              std::vector<DataType> *data_types, std::vector<size_t> *mem_sizes) const {
+std::vector<MSTensor> MsModel::GetInputs() {
   MS_EXCEPTION_IF_NULL(graph_cell_);
-  return graph_cell_->GetInputsInfo(names, shapes, data_types, mem_sizes);
+  return graph_cell_->GetInputs();
 }
 
-Status MsModel::GetOutputsInfo(std::vector<std::string> *names, std::vector<std::vector<int64_t>> *shapes,
-                               std::vector<DataType> *data_types, std::vector<size_t> *mem_sizes) const {
+std::vector<MSTensor> MsModel::GetOutputs() {
   MS_EXCEPTION_IF_NULL(graph_cell_);
-  return graph_cell_->GetOutputsInfo(names, shapes, data_types, mem_sizes);
+  return graph_cell_->GetOutputs();
 }
-}  // namespace api
 }  // namespace mindspore

mindspore/ccsrc/cxx_api/model/ms/ms_model.h

@@ -33,26 +33,24 @@
 #endif
 
 namespace mindspore {
-namespace api {
 class MsModel : public ModelImpl {
  public:
   MsModel() {}
   ~MsModel() = default;
 
-  Status Build(const std::map<std::string, std::string> &options_map) override;
+  Status Build() override;
+  Status Resize(const std::vector<MSTensor> &inputs, const std::vector<std::vector<int64_t>> &dims) override;
 
-  Status Train(const DataSet &dataset, std::map<std::string, Buffer> *outputs) override;
-  Status Eval(const DataSet &dataset, std::map<std::string, Buffer> *outputs) override;
-  Status Predict(const std::vector<Buffer> &inputs, std::vector<Buffer> *outputs) override;
+  Status Predict(const std::vector<MSTensor> &inputs, std::vector<MSTensor> *outputs) override;
 
-  Status GetInputsInfo(std::vector<std::string> *names, std::vector<std::vector<int64_t>> *shapes,
-                       std::vector<DataType> *data_types, std::vector<size_t> *mem_sizes) const override;
-  Status GetOutputsInfo(std::vector<std::string> *names, std::vector<std::vector<int64_t>> *shapes,
-                        std::vector<DataType> *data_types, std::vector<size_t> *mem_sizes) const override;
+  std::vector<MSTensor> GetInputs() override;
+  std::vector<MSTensor> GetOutputs() override;
 
  private:
+  std::shared_ptr<GraphCell> GenerateGraphCell(const std::vector<std::vector<int64_t>> &dims);
+
   std::shared_ptr<GraphCell> graph_cell_;
+  std::map<std::string, std::shared_ptr<GraphCell>> dynamic_size_graph_map_;
 };
-}  // namespace api
 }  // namespace mindspore
 #endif  // MINDSPORE_CCSRC_SESSION_SESSION_BASIC_H

mindspore/ccsrc/cxx_api/ops/ops.cc

@@ -15,7 +15,7 @@
  */
 #include "include/api/ops/ops.h"
 
-namespace mindspore::api {
+namespace mindspore {
 Conv2D::Conv2D(int out_channel, const std::vector<int> &kernel_size, int mode, const std::string &pad_mode,
                const std::vector<int> &pad, const std::vector<int> &stride, const std::vector<int> &dilation, int group)
     : OpCell("Conv2D"),
@@ -35,4 +35,4 @@ Output Conv2D::operator()(const Input &input1, const Input &input2) const {
 std::vector<Output> Conv2D::Construct(const std::vector<Input> &inputs) {
   return {Output(shared_from_this(), inputs, 1)};
 }
-}  // namespace mindspore::api
+}  // namespace mindspore

mindspore/ccsrc/cxx_api/python_utils.cc

@@ -29,7 +29,7 @@ namespace py = pybind11;
 static std::mutex init_mutex;
 static bool Initialized = false;
 
-namespace mindspore::api {
+namespace mindspore {
 static void RegAllOpFromPython() {
   MsContext::GetInstance()->set_param<int>(MS_CTX_EXECUTION_MODE, kGraphMode);
   Py_Initialize();
@@ -143,4 +143,4 @@ PythonEnvGuard::~PythonEnvGuard() {
     FinalizePython();
   }
 }
-}  // namespace mindspore::api
+}  // namespace mindspore

mindspore/ccsrc/cxx_api/python_utils.h

@@ -16,7 +16,7 @@
 #ifndef MINDSPORE_CCSRC_CXXAPI_PYTHON_UTILS_H
 #define MINDSPORE_CCSRC_CXXAPI_PYTHON_UTILS_H
 
-namespace mindspore::api {
+namespace mindspore {
 void RegAllOp();
 bool PythonIsInited();
 void InitPython();
@@ -30,5 +30,5 @@ class PythonEnvGuard {
  private:
   bool origin_init_status_;
 };
-}  // namespace mindspore::api
+}  // namespace mindspore
 #endif  // MINDSPORE_CCSRC_CXXAPI_PYTHON_UTILS_H

mindspore/ccsrc/cxx_api/serialization.cc

@@ -19,7 +19,7 @@
 #include "utils/log_adapter.h"
 #include "mindspore/core/load_mindir/load_model.h"
 
-namespace mindspore::api {
+namespace mindspore {
 static Buffer ReadFile(const std::string &file) {
   Buffer buffer;
   if (file.empty()) {
@@ -68,6 +68,22 @@ static Buffer ReadFile(const std::string &file) {
   return buffer;
 }
 
+Graph Serialization::LoadModel(const void *model_data, size_t data_size, ModelType model_type) {
+  if (model_type == kMindIR) {
+    FuncGraphPtr anf_graph = nullptr;
+    try {
+      anf_graph = ConvertStreamToFuncGraph(reinterpret_cast<const char *>(model_data), data_size);
+    } catch (const std::exception &) {
+      MS_LOG(EXCEPTION) << "Load MindIR failed.";
+    }
+
+    return Graph(std::make_shared<Graph::GraphData>(anf_graph, kMindIR));
+  } else if (model_type == kOM) {
+    return Graph(std::make_shared<Graph::GraphData>(Buffer(model_data, data_size), kOM));
+  }
+  MS_LOG(EXCEPTION) << "Unsupported ModelType " << model_type;
+}
+
 Graph Serialization::LoadModel(const std::string &file, ModelType model_type) {
   Buffer data = ReadFile(file);
   if (data.Data() == nullptr) {
@@ -77,7 +93,7 @@ Graph Serialization::LoadModel(const std::string &file, ModelType model_type) {
     FuncGraphPtr anf_graph = nullptr;
     try {
       anf_graph = ConvertStreamToFuncGraph(reinterpret_cast<const char *>(data.Data()), data.DataSize());
-    } catch (std::exception &e) {
+    } catch (const std::exception &) {
       MS_LOG(EXCEPTION) << "Load MindIR failed.";
     }
 
@@ -90,21 +106,21 @@ Graph Serialization::LoadModel(const std::string &file, ModelType model_type) {
 
 Status Serialization::LoadCheckPoint(const std::string &ckpt_file, std::map<std::string, Buffer> *parameters) {
   MS_LOG(ERROR) << "Unsupported feature.";
-  return FAILED;
+  return kMEFailed;
 }
 
 Status Serialization::SetParameters(const std::map<std::string, Buffer> &parameters, Model *model) {
   MS_LOG(ERROR) << "Unsupported feature.";
-  return FAILED;
+  return kMEFailed;
 }
 
 Status Serialization::ExportModel(const Model &model, ModelType model_type, Buffer *model_data) {
   MS_LOG(ERROR) << "Unsupported feature.";
-  return FAILED;
+  return kMEFailed;
 }
 
 Status Serialization::ExportModel(const Model &model, ModelType model_type, const std::string &model_file) {
   MS_LOG(ERROR) << "Unsupported feature.";
-  return FAILED;
+  return kMEFailed;
 }
-}  // namespace mindspore::api
+}  // namespace mindspore

mindspore/ccsrc/cxx_api/types.cc

@@ -17,17 +17,20 @@
 #include <numeric>
 #include "securec/include/securec.h"
 #include "utils/utils.h"
+#include "mindspore/core/ir/api_tensor_impl.h"
 
-namespace mindspore::api {
-const char *kDeviceTypeAscend310 = "Ascend310";
-const char *kDeviceTypeAscend910 = "Ascend910";
-const char *kDeviceTypeGpu = "GPU";
-
-class DataImpl {
+namespace mindspore {
+class Buffer::Impl {
  public:
-  DataImpl() : data_() {}
-  ~DataImpl() = default;
-  DataImpl(const void *data, size_t data_len) { SetData(data, data_len); }
+  Impl() : data_() {}
+  ~Impl() = default;
+  Impl(const void *data, size_t data_len) {
+    if (data != nullptr) {
+      (void)SetData(data, data_len);
+    } else {
+      ResizeData(data_len);
+    }
+  }
 
   const void *Data() const { return data_.data(); }
   void *MutableData() { return data_.data(); }
@@ -66,132 +69,162 @@ class DataImpl {
   std::vector<uint8_t> data_;
 };
 
-class Buffer::Impl : public DataImpl {
+class TensorDefaultImpl : public MSTensor::Impl {
  public:
-  Impl() : DataImpl() {}
-  ~Impl() = default;
-  Impl(const void *data, size_t data_len) : DataImpl(data, data_len) {}
-};
+  TensorDefaultImpl() : buffer_(), name_(), type_(DataType::kTypeUnknown), shape_() {}
+  ~TensorDefaultImpl() override = default;
+  TensorDefaultImpl(const std::string &name, enum DataType type, const std::vector<int64_t> &shape, const void *data,
+                    size_t data_len)
+      : buffer_(data, data_len), name_(name), type_(type), shape_(shape) {}
+
+  const std::string &Name() const override { return name_; }
+  enum DataType DataType() const override { return type_; }
+  const std::vector<int64_t> &Shape() const override { return shape_; }
+
+  std::shared_ptr<const void> Data() const override {
+    return std::shared_ptr<const void>(buffer_.Data(), [](const void *) {});
+  }
 
-class Tensor::Impl : public DataImpl {
- public:
-  Impl() : DataImpl(), name_(), type_(DataType::kMsUnknown), shape_() {}
-  ~Impl() = default;
-  Impl(const std::string &name, api::DataType type, const std::vector<int64_t> &shape, const void *data,
-       size_t data_len)
-      : DataImpl(data, data_len), name_(name), type_(type), shape_(shape) {}
+  void *MutableData() override { return buffer_.MutableData(); }
+  size_t DataSize() const override { return buffer_.DataSize(); }
 
-  const std::string &Name() const { return name_; }
-  void SetName(const std::string &name) { name_ = name; }
+  bool IsDevice() const override { return false; }
 
-  api::DataType DataType() const { return type_; }
-  void SetDataType(api::DataType type) { type_ = type; }
+  std::shared_ptr<Impl> Clone() const override {
+    return std::make_shared<TensorDefaultImpl>(name_, type_, shape_, buffer_.Data(), buffer_.DataSize());
+  }
 
-  void SetShape(const std::vector<int64_t> &shape) { shape_ = shape; }
-  const std::vector<int64_t> &Shape() const { return shape_; }
+ private:
+  Buffer buffer_;
+  std::string name_;
+  enum DataType type_;
+  std::vector<int64_t> shape_;
+};
 
-  int64_t ElementNum() const {
-    std::vector<int64_t> shapex = Shape();
-    return std::accumulate(shapex.begin(), shapex.end(), 1LL, std::multiplies<int64_t>());
+class TensorReferenceImpl : public MSTensor::Impl {
+ public:
+  TensorReferenceImpl() : data_(nullptr), data_size_(0), name_(), type_(DataType::kTypeUnknown), shape_() {}
+  ~TensorReferenceImpl() override = default;
+  TensorReferenceImpl(const std::string &name, enum DataType type, const std::vector<int64_t> &shape, const void *data,
+                      size_t data_len)
+      : data_(data), data_size_(data_len), name_(name), type_(type), shape_(shape) {}
+
+  const std::string &Name() const override { return name_; }
+  enum DataType DataType() const override { return type_; }
+  const std::vector<int64_t> &Shape() const override { return shape_; }
+
+  std::shared_ptr<const void> Data() const override {
+    return std::shared_ptr<const void>(data_, [](const void *) {});
   }
 
-  static int GetTypeSize(api::DataType type) {
-    static const std::map<api::DataType, size_t> type_size_map = {
-      {kMsBool, sizeof(bool)},       {kMsFloat64, sizeof(double)},   {kMsInt8, sizeof(int8_t)},
-      {kMsUint8, sizeof(uint8_t)},   {kMsInt16, sizeof(int16_t)},    {kMsUint16, sizeof(uint16_t)},
-      {kMsInt32, sizeof(int32_t)},   {kMsUint32, sizeof(uint32_t)},  {kMsInt64, sizeof(int64_t)},
-      {kMsUint64, sizeof(uint64_t)}, {kMsFloat16, sizeof(uint16_t)}, {kMsFloat32, sizeof(float)},
-    };
-    auto it = type_size_map.find(type);
-    if (it != type_size_map.end()) {
-      return it->second;
-    }
+  void *MutableData() override { return const_cast<void *>(data_); }
+  size_t DataSize() const override { return data_size_; }
+
+  bool IsDevice() const override { return false; }
 
-    MS_LOG(WARNING) << "Cannot find data type " << type;
-    return 0;
+  std::shared_ptr<Impl> Clone() const override {
+    return std::make_shared<TensorReferenceImpl>(name_, type_, shape_, data_, data_size_);
   }
 
- private:
+ protected:
+  const void *data_;
+  size_t data_size_;
   std::string name_;
-  api::DataType type_;
+  enum DataType type_;
   std::vector<int64_t> shape_;
 };
 
-Tensor::Tensor() : impl_(std::make_shared<Impl>()) {}
-Tensor::Tensor(const std::string &name, api::DataType type, const std::vector<int64_t> &shape, const void *data,
-               size_t data_len)
-    : impl_(std::make_shared<Impl>(name, type, shape, data, data_len)) {}
-Tensor::~Tensor() = default;
-
-Tensor Tensor::Clone() const {
-  MS_EXCEPTION_IF_NULL(impl_);
-  Tensor ret;
-  ret.impl_ = std::make_shared<Impl>(*impl_);
-  return ret;
+MSTensor MSTensor::CreateTensor(const std::string &name, enum DataType type, const std::vector<int64_t> &shape,
+                                const void *data, size_t data_len) noexcept {
+  try {
+    std::shared_ptr<Impl> impl = std::make_shared<TensorDefaultImpl>(name, type, shape, data, data_len);
+    return MSTensor(impl);
+  } catch (const std::bad_alloc &) {
+    MS_LOG(ERROR) << "Malloc memory failed.";
+    return MSTensor(nullptr);
+  } catch (...) {
+    MS_LOG(ERROR) << "Unknown error occurred.";
+    return MSTensor(nullptr);
+  }
 }
 
-const std::string &Tensor::Name() const {
-  MS_EXCEPTION_IF_NULL(impl_);
-  return impl_->Name();
+MSTensor MSTensor::CreateRefTensor(const std::string &name, enum DataType type, const std::vector<int64_t> &shape,
+                                   const void *data, size_t data_len) noexcept {
+  try {
+    std::shared_ptr<Impl> impl = std::make_shared<TensorReferenceImpl>(name, type, shape, data, data_len);
+    return MSTensor(impl);
+  } catch (const std::bad_alloc &) {
+    MS_LOG(ERROR) << "Malloc memory failed.";
+    return MSTensor(nullptr);
+  } catch (...) {
+    MS_LOG(ERROR) << "Unknown error occurred.";
+    return MSTensor(nullptr);
+  }
 }
 
-void Tensor::SetName(const std::string &name) {
+MSTensor::MSTensor() : impl_(std::make_shared<TensorDefaultImpl>()) {}
+MSTensor::MSTensor(std::nullptr_t) : impl_(nullptr) {}
+MSTensor::MSTensor(const std::shared_ptr<Impl> &impl) : impl_(impl) { MS_EXCEPTION_IF_NULL(impl); }
+MSTensor::MSTensor(const std::string &name, enum DataType type, const std::vector<int64_t> &shape, const void *data,
+                   size_t data_len)
+    : impl_(std::make_shared<TensorDefaultImpl>(name, type, shape, data, data_len)) {}
+MSTensor::~MSTensor() = default;
+
+bool MSTensor::operator==(std::nullptr_t) const { return impl_ == nullptr; }
+
+MSTensor MSTensor::Clone() const {
   MS_EXCEPTION_IF_NULL(impl_);
-  impl_->SetName(name);
+  MSTensor ret;
+  ret.impl_ = impl_->Clone();
+  return ret;
 }
 
-DataType Tensor::DataType() const {
+const std::string &MSTensor::Name() const {
   MS_EXCEPTION_IF_NULL(impl_);
-  return impl_->DataType();
+  return impl_->Name();
 }
 
-void Tensor::SetDataType(api::DataType type) {
+enum DataType MSTensor::DataType() const {
   MS_EXCEPTION_IF_NULL(impl_);
-  impl_->SetDataType(type);
+  return impl_->DataType();
 }
 
-const std::vector<int64_t> &Tensor::Shape() const {
+const std::vector<int64_t> &MSTensor::Shape() const {
   MS_EXCEPTION_IF_NULL(impl_);
   return impl_->Shape();
 }
 
-void Tensor::SetShape(const std::vector<int64_t> &shape) {
+int64_t MSTensor::ElementNum() const {
   MS_EXCEPTION_IF_NULL(impl_);
-  impl_->SetShape(shape);
+  const auto &shape = impl_->Shape();
+  if (shape.empty()) {
+    // element number of scalar is 1
+    return 1;
+  }
+
+  return std::accumulate(shape.begin(), shape.end(), 1, std::multiplies<int64_t>());
 }
 
-const void *Tensor::Data() const {
+std::shared_ptr<const void> MSTensor::Data() const {
   MS_EXCEPTION_IF_NULL(impl_);
   return impl_->Data();
 }
 
-void *Tensor::MutableData() {
+void *MSTensor::MutableData() {
   MS_EXCEPTION_IF_NULL(impl_);
   return impl_->MutableData();
 }
 
-size_t Tensor::DataSize() const {
+size_t MSTensor::DataSize() const {
   MS_EXCEPTION_IF_NULL(impl_);
   return impl_->DataSize();
 }
 
-bool Tensor::ResizeData(size_t data_len) {
+bool MSTensor::IsDevice() const {
   MS_EXCEPTION_IF_NULL(impl_);
-  return impl_->ResizeData(data_len);
+  return impl_->IsDevice();
 }
 
-bool Tensor::SetData(const void *data, size_t data_len) {
-  MS_EXCEPTION_IF_NULL(impl_);
-  return impl_->SetData(data, data_len);
-}
-
-int64_t Tensor::ElementNum() const {
-  MS_EXCEPTION_IF_NULL(impl_);
-  return impl_->ElementNum();
-}
-
-int Tensor::GetTypeSize(api::DataType type) { return Impl::GetTypeSize(type); }
-
 Buffer::Buffer() : impl_(std::make_shared<Impl>()) {}
 Buffer::Buffer(const void *data, size_t data_len) : impl_(std::make_shared<Impl>(data, data_len)) {}
 Buffer::~Buffer() = default;
@@ -227,4 +260,4 @@ bool Buffer::SetData(const void *data, size_t data_len) {
   MS_EXCEPTION_IF_NULL(impl_);
   return impl_->SetData(data, data_len);
 }
-}  // namespace mindspore::api
+}  // namespace mindspore

mindspore/ccsrc/minddata/dataset/CMakeLists.txt

@@ -284,14 +284,7 @@ else()
 endif()
 
 add_dependencies(_c_dataengine mindspore_shared_lib)
-if(${CMAKE_SYSTEM_NAME} MATCHES "Windows")
-    set(MINDSPORE_LINK_OBJECT ${CMAKE_BINARY_DIR}/mindspore/ccsrc/cxx_api/CMakeFiles/mindspore_shared_lib.dir/objects.a)
-    target_link_libraries(_c_dataengine PRIVATE mindspore_shared_lib ${MINDSPORE_LINK_OBJECT})
-else()
-    if(ENABLE_ACL)
-        target_link_libraries(_c_dataengine PRIVATE mindspore_shared_lib)
-    endif()
-endif()
+target_link_libraries(_c_dataengine PRIVATE mindspore_shared_lib)
 
 if(USE_GLOG)
     target_link_libraries(_c_dataengine PRIVATE mindspore::glog)

mindspore/ccsrc/minddata/dataset/api/CMakeLists.txt

@@ -26,28 +26,13 @@ if(ENABLE_PYTHON)
     target_include_directories(APItoPython PRIVATE ${pybind11_INCLUDE_DIRS})
 endif()
 
-
-if(ENABLE_ACL)
-    add_library(cpp-API OBJECT
-            config.cc
-            datasets.cc
-            execute.cc
-            iterator.cc
-            minddata_eager.cc
-            transforms.cc
-            samplers.cc
-            text.cc
-            vision.cc
-            )
-else()
-    add_library(cpp-API OBJECT
-            config.cc
-            datasets.cc
-            execute.cc
-            iterator.cc
-            transforms.cc
-            samplers.cc
-            text.cc
-            vision.cc
-            )
-endif()
+add_library(cpp-API OBJECT
+        config.cc
+        datasets.cc
+        execute.cc
+        iterator.cc
+        transforms.cc
+        samplers.cc
+        text.cc
+        vision.cc
+        )

mindspore/ccsrc/minddata/dataset/api/de_tensor.cc

@@ -1,142 +0,0 @@
-/**
- * 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 "minddata/dataset/core/constants.h"
-#include "minddata/dataset/core/data_type.h"
-#include "minddata/dataset/include/de_tensor.h"
-#include "minddata/dataset/include/type_id.h"
-#include "mindspore/core/ir/dtype/type_id.h"
-#include "mindspore/lite/include/ms_tensor.h"
-#include "utils/hashing.h"
-#ifndef ENABLE_ANDROID
-#include "utils/log_adapter.h"
-#else
-#include "mindspore/lite/src/common/log_adapter.h"
-#endif
-
-namespace mindspore {
-namespace tensor {
-MSTensor *DETensor::CreateTensor(TypeId data_type, const std::vector<int> &shape) {
-  return new DETensor(data_type, shape);
-}
-
-MSTensor *DETensor::CreateTensor(const std::string &path) {
-  std::shared_ptr<dataset::Tensor> t;
-  (void)dataset::Tensor::CreateFromFile(path, &t);
-  return new DETensor(std::move(t));
-}
-
-MSTensor *DETensor::CreateFromMemory(TypeId data_type, const std::vector<int> &shape, void *data) {
-  std::shared_ptr<dataset::Tensor> t;
-  // prepare shape info
-  std::vector<dataset::dsize_t> t_shape;
-
-  std::transform(shape.begin(), shape.end(), std::back_inserter(t_shape),
-                 [](int s) -> dataset::dsize_t { return static_cast<dataset::dsize_t>(s); });
-
-  (void)dataset::Tensor::CreateFromMemory(dataset::TensorShape(t_shape), dataset::MSTypeToDEType(data_type),
-                                          static_cast<uint8_t *>(data), &t);
-  return new DETensor(std::move(t));
-}
-
-DETensor::DETensor(TypeId data_type, const std::vector<int> &shape) {
-  std::vector<dataset::dsize_t> t_shape;
-  t_shape.reserve(shape.size());
-  std::transform(shape.begin(), shape.end(), std::back_inserter(t_shape),
-                 [](int s) -> dataset::dsize_t { return static_cast<dataset::dsize_t>(s); });
-  dataset::Tensor::CreateEmpty(dataset::TensorShape(t_shape), dataset::MSTypeToDEType(data_type), &this->tensor_impl_);
-}
-
-DETensor::DETensor(std::shared_ptr<dataset::Tensor> tensor_ptr) { this->tensor_impl_ = std::move(tensor_ptr); }
-
-MSTensor *DETensor::ConvertToLiteTensor() {
-  // static MSTensor::CreateTensor is only for the LiteTensor
-  MSTensor *tensor = CreateTensor(this->data_type(), this->shape());
-  MS_ASSERT(tensor->Size() == this->Size());
-  memcpy_s(tensor->MutableData(), tensor->Size(), this->MutableData(), this->Size());
-  return tensor;
-}
-
-std::shared_ptr<dataset::Tensor> DETensor::tensor() const {
-  MS_ASSERT(this->tensor_impl_ != nullptr);
-  return this->tensor_impl_;
-}
-
-TypeId DETensor::data_type() const {
-  MS_ASSERT(this->tensor_impl_ != nullptr);
-  return dataset::DETypeToMSType(this->tensor_impl_->type());
-}
-
-TypeId DETensor::set_data_type(TypeId data_type) {
-  MS_ASSERT(this->tensor_impl_ != nullptr);
-  if (data_type != this->data_type()) {
-    std::shared_ptr<dataset::Tensor> temp;
-    dataset::Tensor::CreateFromMemory(this->tensor_impl_->shape(), dataset::MSTypeToDEType(data_type),
-                                      this->tensor_impl_->GetBuffer(), &temp);
-    this->tensor_impl_ = temp;
-  }
-  return data_type;
-}
-
-std::vector<int> DETensor::shape() const {
-  MS_ASSERT(this->tensor_impl_ != nullptr);
-  std::vector<dataset::dsize_t> t_shape = this->tensor_impl_->shape().AsVector();
-  std::vector<int> shape;
-  shape.reserve(t_shape.size());
-  std::transform(t_shape.begin(), t_shape.end(), std::back_inserter(shape),
-                 [](dataset::dsize_t s) -> int { return static_cast<int>(s); });
-  return shape;
-}
-
-size_t DETensor::set_shape(const std::vector<int> &shape) {
-  MS_ASSERT(this->tensor_impl_ != nullptr);
-  std::vector<dataset::dsize_t> t_shape;
-  t_shape.reserve(shape.size());
-  std::transform(shape.begin(), shape.end(), std::back_inserter(t_shape),
-                 [](int s) -> dataset::dsize_t { return static_cast<dataset::dsize_t>(s); });
-  dataset::Status rc = this->tensor_impl_->Reshape(dataset::TensorShape(t_shape));
-  return shape.size();
-}
-
-int DETensor::DimensionSize(size_t index) const {
-  MS_ASSERT(this->tensor_impl_ != nullptr);
-  int dim_size = -1;
-  auto shape = this->shape();
-  if (index < shape.size()) {
-    dim_size = shape[index];
-  } else {
-    MS_LOG(ERROR) << "Dimension index is wrong: " << index;
-  }
-  return dim_size;
-}
-
-int DETensor::ElementsNum() const {
-  MS_ASSERT(this->tensor_impl_ != nullptr);
-  return this->tensor_impl_->Size();
-}
-
-size_t DETensor::Size() const {
-  MS_ASSERT(this->tensor_impl_ != nullptr);
-  return this->tensor_impl_->SizeInBytes();
-}
-
-void *DETensor::MutableData() {
-  MS_ASSERT(this->tensor_impl_ != nullptr);
-  return this->tensor_impl_->GetMutableBuffer();
-}
-
-}  // namespace tensor
-}  // namespace mindspore

mindspore/ccsrc/minddata/dataset/api/execute.cc

@@ -14,12 +14,11 @@
  * limitations under the License.
  */
 
-#include "minddata/dataset/core/tensor_row.h"
-#ifdef ENABLE_ANDROID
-#include "minddata/dataset/include/de_tensor.h"
-#endif
 #include "minddata/dataset/include/execute.h"
+#include "minddata/dataset/core/de_tensor.h"
+#include "minddata/dataset/core/tensor_row.h"
 #include "minddata/dataset/include/tensor.h"
+#include "minddata/dataset/include/type_id.h"
 #include "minddata/dataset/kernels/tensor_op.h"
 #ifndef ENABLE_ANDROID
 #include "utils/log_adapter.h"
@@ -30,78 +29,85 @@
 namespace mindspore {
 namespace dataset {
 
-Execute::Execute(std::shared_ptr<TensorOperation> op) : op_(std::move(op)) {}
+Execute::Execute(std::shared_ptr<TensorOperation> op) { ops_.emplace_back(std::move(op)); }
 
-/// \brief Destructor
-Execute::~Execute() = default;
+Execute::Execute(std::vector<std::shared_ptr<TensorOperation>> ops) : ops_(std::move(ops)) {}
 
-#ifdef ENABLE_ANDROID
-std::shared_ptr<tensor::MSTensor> Execute::operator()(std::shared_ptr<tensor::MSTensor> input) {
-  // Build the op
-  if (op_ == nullptr) {
-    MS_LOG(ERROR) << "Input TensorOperation is not valid";
-    return nullptr;
-  }
+Status Execute::operator()(const mindspore::MSTensor &input, mindspore::MSTensor *output) {
+  // Validate input tensor
+  CHECK_FAIL_RETURN_UNEXPECTED(input.DataSize() > 0, "Input Tensor has no data");
+  CHECK_FAIL_RETURN_UNEXPECTED(!ops_.empty(), "Input TensorOperation should be provided");
 
-  std::shared_ptr<Tensor> de_input = std::dynamic_pointer_cast<tensor::DETensor>(input)->tensor();
-  if (de_input == nullptr) {
-    MS_LOG(ERROR) << "Input Tensor is not valid";
-    return nullptr;
-  }
-  std::shared_ptr<TensorOp> transform = op_->Build();
-  std::shared_ptr<Tensor> de_output;
-  Status rc = transform->Compute(de_input, &de_output);
-
-  if (rc.IsError()) {
-    // execution failed
-    MS_LOG(ERROR) << "Operation execution failed : " << rc.ToString();
-    return nullptr;
+  // Validate and build runtime ops
+  std::vector<std::shared_ptr<TensorOp>> transforms;
+  for (int32_t i = 0; i < ops_.size(); i++) {
+    CHECK_FAIL_RETURN_UNEXPECTED(ops_[i] != nullptr, "Input TensorOperation[" + std::to_string(i) + "] is null");
+    RETURN_IF_NOT_OK(ops_[i]->ValidateParams());
+    transforms.emplace_back(ops_[i]->Build());
   }
-  return std::make_shared<tensor::DETensor>(std::move(de_output));
-}
-#endif
 
-std::shared_ptr<dataset::Tensor> Execute::operator()(std::shared_ptr<dataset::Tensor> input) {
-  // Build the op
-  if (op_ == nullptr) {
-    MS_LOG(ERROR) << "Input TensorOperation is not valid";
-    return nullptr;
-  }
+  // Convert mindspore::Tensor to dataset::Tensor
+  std::shared_ptr<dataset::Tensor> de_tensor;
+  Status rc = dataset::Tensor::CreateFromMemory(dataset::TensorShape(input.Shape()),
+                                                MSTypeToDEType(static_cast<TypeId>(input.DataType())),
+                                                (const uchar *)(input.Data().get()), input.DataSize(), &de_tensor);
+  RETURN_IF_NOT_OK(rc);
 
-  if (input == nullptr) {
-    MS_LOG(ERROR) << "Input Tensor is not valid";
-    return nullptr;
-  }
-  // will add validate params once API is set
-  std::shared_ptr<TensorOp> transform = op_->Build();
-  std::shared_ptr<Tensor> de_output;
-  Status rc = transform->Compute(input, &de_output);
-
-  if (rc.IsError()) {
-    // execution failed
-    MS_LOG(ERROR) << "Operation execution failed : " << rc.ToString();
-    return nullptr;
+  // Apply transforms on tensor
+  for (auto &t : transforms) {
+    std::shared_ptr<dataset::Tensor> de_output;
+    RETURN_IF_NOT_OK(t->Compute(de_tensor, &de_output));
+
+    // For next transform
+    de_tensor = std::move(de_output);
   }
-  return de_output;
+
+  // Convert dataset::Tensor to mindspore::Tensor
+  CHECK_FAIL_RETURN_UNEXPECTED(de_tensor->HasData(), "Apply transform failed, output tensor has no data");
+  *output = mindspore::MSTensor(std::make_shared<DETensor>(de_tensor));
+  return Status::OK();
 }
 
-Status Execute::operator()(const std::vector<std::shared_ptr<Tensor>> &input_tensor_list,
-                           std::vector<std::shared_ptr<Tensor>> *output_tensor_list) {
-  CHECK_FAIL_RETURN_UNEXPECTED(op_ != nullptr, "Input TensorOperation is not valid");
+Status Execute::operator()(const std::vector<MSTensor> &input_tensor_list, std::vector<MSTensor> *output_tensor_list) {
+  // Validate input tensor
   CHECK_FAIL_RETURN_UNEXPECTED(!input_tensor_list.empty(), "Input Tensor is not valid");
+  for (auto &tensor : input_tensor_list) {
+    CHECK_FAIL_RETURN_UNEXPECTED(tensor.DataSize() > 0, "Input Tensor has no data");
+  }
+  CHECK_FAIL_RETURN_UNEXPECTED(!ops_.empty(), "Input TensorOperation should be provided");
 
-  TensorRow input, output;
-  std::copy(input_tensor_list.begin(), input_tensor_list.end(), std::back_inserter(input));
-  CHECK_FAIL_RETURN_UNEXPECTED(!input.empty(), "Input Tensor is not valid");
+  // Validate and build runtime ops
+  std::vector<std::shared_ptr<TensorOp>> transforms;
+  for (int32_t i = 0; i < ops_.size(); i++) {
+    CHECK_FAIL_RETURN_UNEXPECTED(ops_[i] != nullptr, "Input TensorOperation[" + std::to_string(i) + "] is null");
+    RETURN_IF_NOT_OK(ops_[i]->ValidateParams());
+    transforms.emplace_back(ops_[i]->Build());
+  }
+
+  TensorRow de_tensor_list;
+  for (auto &tensor : input_tensor_list) {
+    std::shared_ptr<dataset::Tensor> de_tensor;
+    Status rc = dataset::Tensor::CreateFromMemory(dataset::TensorShape(tensor.Shape()),
+                                                  MSTypeToDEType(static_cast<TypeId>(tensor.DataType())),
+                                                  (const uchar *)(tensor.Data().get()), tensor.DataSize(), &de_tensor);
+    RETURN_IF_NOT_OK(rc);
+    de_tensor_list.emplace_back(std::move(de_tensor));
+  }
 
-  std::shared_ptr<TensorOp> transform = op_->Build();
-  Status rc = transform->Compute(input, &output);
-  if (rc.IsError()) {
-    // execution failed
-    RETURN_STATUS_UNEXPECTED("Operation execution failed : " + rc.ToString());
+  // Apply transforms on tensor
+  for (auto &t : transforms) {
+    TensorRow de_output_list;
+    RETURN_IF_NOT_OK(t->Compute(de_tensor_list, &de_output_list));
+    // For next transform
+    de_tensor_list = std::move(de_output_list);
   }
 
-  std::copy(output.begin(), output.end(), std::back_inserter(*output_tensor_list));
+  for (auto &tensor : de_tensor_list) {
+    CHECK_FAIL_RETURN_UNEXPECTED(tensor->HasData(), "Apply transform failed, output tensor has no data");
+    auto ms_tensor = mindspore::MSTensor(std::make_shared<DETensor>(tensor));
+    output_tensor_list->emplace_back(ms_tensor);
+  }
+  CHECK_FAIL_RETURN_UNEXPECTED(!output_tensor_list->empty(), "Output Tensor is not valid");
   return Status::OK();
 }
 

mindspore/ccsrc/minddata/dataset/api/minddata_eager.cc

@@ -1,154 +0,0 @@
-/**
- * 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 <unistd.h>
-#include <unordered_map>
-
-#include "minddata/dataset/include/minddata_eager.h"
-#include "minddata/dataset/include/vision.h"
-#include "minddata/dataset/core/tensor.h"
-#include "minddata/dataset/kernels/tensor_op.h"
-#include "minddata/dataset/util/path.h"
-
-namespace mindspore {
-namespace api {
-
-MindDataEager::MindDataEager(std::vector<std::shared_ptr<dataset::TensorOperation>> ops) : ops_(ops) {}
-
-// Helper function to convert Type from DE to MS
-DataType ToMSType(dataset::DataType type) {
-  switch (dataset::DataType::Type(type)) {
-    case dataset::DataType::DE_BOOL:
-      return DataType::kMsBool;
-    case dataset::DataType::DE_UINT8:
-      return DataType::kMsUint8;
-    case dataset::DataType::DE_INT32:
-      return DataType::kMsInt32;
-    case dataset::DataType::DE_INT64:
-      return DataType::kMsInt64;
-    case dataset::DataType::DE_FLOAT32:
-      return DataType::kMsFloat32;
-    default:
-      return DataType::kMsUnknown;
-  }
-}
-
-// Helper function to convert Type from MS to DE
-dataset::DataType ToDEType(DataType type) {
-  switch (type) {
-    case DataType::kMsBool:
-      return dataset::DataType(dataset::DataType::DE_BOOL);
-    case DataType::kMsUint8:
-      return dataset::DataType(dataset::DataType::DE_UINT8);
-    case DataType::kMsInt32:
-      return dataset::DataType(dataset::DataType::DE_INT32);
-    case DataType::kMsInt64:
-      return dataset::DataType(dataset::DataType::DE_INT64);
-    case DataType::kMsFloat32:
-      return dataset::DataType(dataset::DataType::DE_FLOAT32);
-    default:
-      return dataset::DataType(dataset::DataType::DE_UNKNOWN);
-  }
-}
-
-Status MindDataEager::LoadImageFromDir(const std::string &image_dir, std::vector<std::shared_ptr<Tensor>> *images) {
-  // Check target directory
-  dataset::Path image_dir_(image_dir);
-  if (!image_dir_.Exists() || !image_dir_.IsDirectory()) {
-    std::string err_msg = "Target directory: " + image_dir + " does not exist or not a directory.";
-    MS_LOG(ERROR) << err_msg;
-    return Status(StatusCode::FAILED, err_msg);
-  }
-  if (access(image_dir_.toString().c_str(), R_OK) == -1) {
-    std::string err_msg = "No access to target directory: " + image_dir;
-    MS_LOG(ERROR) << err_msg;
-    return Status(StatusCode::FAILED, err_msg);
-  }
-
-  // Start reading images and constructing tensors
-  auto path_itr = dataset::Path::DirIterator::OpenDirectory(&image_dir_);
-  while (path_itr->hasNext()) {
-    dataset::Path file = path_itr->next();
-    std::shared_ptr<dataset::Tensor> image;
-    dataset::Tensor::CreateFromFile(file.toString(), &image);
-
-    std::shared_ptr<Tensor> ms_image = std::make_shared<Tensor>("image", DataType(kMsUint8), image->shape().AsVector(),
-                                                                image->GetBuffer(), image->SizeInBytes());
-    images->push_back(ms_image);
-  }
-
-  // Check if read images or not
-  if (images->empty()) {
-    std::string err_msg = "No images found in target directory: " + image_dir;
-    MS_LOG(ERROR) << err_msg;
-    return Status(StatusCode::FAILED, err_msg);
-  }
-
-  return Status(StatusCode::SUCCESS);
-}
-
-std::shared_ptr<Tensor> MindDataEager::operator()(std::shared_ptr<Tensor> input) {
-  // Validate ops
-  if (ops_.empty()) {
-    MS_LOG(ERROR) << "Input TensorOperation should be provided";
-    return nullptr;
-  }
-  for (int32_t i = 0; i < ops_.size(); i++) {
-    if (ops_[i] == nullptr) {
-      MS_LOG(ERROR) << "Input TensorOperation[" << i << "] is invalid or null";
-      return nullptr;
-    }
-  }
-  // Validate input tensor
-  if (input == nullptr) {
-    MS_LOG(ERROR) << "Input Tensor should not be null";
-    return nullptr;
-  }
-
-  // Start applying transforms in ops
-  std::shared_ptr<dataset::Tensor> de_input;
-  dataset::Tensor::CreateFromMemory(dataset::TensorShape(input->Shape()), ToDEType(input->DataType()),
-                                    (const uchar *)(input->Data()), &de_input);
-
-  for (int32_t i = 0; i < ops_.size(); i++) {
-    // Build runtime op and run
-    std::shared_ptr<dataset::Tensor> de_output;
-    std::shared_ptr<dataset::TensorOp> transform = ops_[i]->Build();
-    dataset::Status rc = transform->Compute(de_input, &de_output);
-
-    // check execution failed
-    if (rc.IsError()) {
-      MS_LOG(ERROR) << "Operation execution failed : " << rc.ToString();
-      return nullptr;
-    }
-
-    // For next transform
-    de_input = std::move(de_output);
-  }
-
-  // Convert DETensor to Tensor
-  if (!de_input->HasData()) {
-    MS_LOG(ERROR) << "Apply transform failed, output tensor has no data";
-    return nullptr;
-  }
-  std::shared_ptr<Tensor> output =
-    std::make_shared<Tensor>("transfomed", ToMSType(de_input->type()), de_input->shape().AsVector(),
-                             de_input->GetBuffer(), de_input->SizeInBytes());
-  return output;
-}
-
-}  // namespace api
-}  // namespace mindspore

mindspore/ccsrc/minddata/dataset/api/python/bindings/dataset/include/execute_binding.cc

@@ -29,25 +29,42 @@ PYBIND_REGISTER(Execute, 0, ([](const py::module *m) {
                       return execute;
                     }))
                     .def("__call__",
-                         [](Execute &self, std::shared_ptr<Tensor> in) {
-                           std::shared_ptr<Tensor> out = self(in);
-                           if (out == nullptr) {
-                             THROW_IF_ERROR([]() {
-                               RETURN_STATUS_UNEXPECTED(
-                                 "Failed to execute op in eager mode, please check ERROR log above.");
+                         [](Execute &self, const std::shared_ptr<Tensor> &de_tensor) {
+                           auto ms_tensor = mindspore::MSTensor(std::make_shared<DETensor>(de_tensor));
+                           Status rc = self(ms_tensor, &ms_tensor);
+                           if (rc.IsError()) {
+                             THROW_IF_ERROR([&rc]() {
+                               RETURN_STATUS_UNEXPECTED("Failed to execute transform op, " + rc.ToString());
                              }());
                            }
-                           return out;
+                           std::shared_ptr<dataset::Tensor> de_output_tensor;
+                           dataset::Tensor::CreateFromMemory(dataset::TensorShape(ms_tensor.Shape()),
+                                                             MSTypeToDEType(static_cast<TypeId>(ms_tensor.DataType())),
+                                                             (const uchar *)(ms_tensor.Data().get()),
+                                                             ms_tensor.DataSize(), &de_output_tensor);
+                           return de_output_tensor;
                          })
                     .def("__call__", [](Execute &self, const std::vector<std::shared_ptr<Tensor>> &input_tensor_list) {
-                      std::vector<std::shared_ptr<Tensor>> output_tensor_list;
-                      THROW_IF_ERROR(self(input_tensor_list, &output_tensor_list));
-                      if (output_tensor_list.empty()) {
-                        THROW_IF_ERROR([]() {
-                          RETURN_STATUS_UNEXPECTED("Failed to execute op in eager mode, please check ERROR log above.");
-                        }());
+                      std::vector<MSTensor> ms_input_tensor_list;
+                      std::vector<MSTensor> ms_output_tensor_list;
+                      for (auto &tensor : input_tensor_list) {
+                        auto ms_tensor = mindspore::MSTensor(std::make_shared<DETensor>(tensor));
+                        ms_input_tensor_list.emplace_back(std::move(ms_tensor));
                       }
-                      return output_tensor_list;
+                      Status rc = self(ms_input_tensor_list, &ms_output_tensor_list);
+                      if (rc.IsError()) {
+                        THROW_IF_ERROR(
+                          [&rc]() { RETURN_STATUS_UNEXPECTED("Failed to execute transform op, " + rc.ToString()); }());
+                      }
+                      std::vector<std::shared_ptr<dataset::Tensor>> de_output_tensor_list;
+                      for (auto &tensor : ms_output_tensor_list) {
+                        std::shared_ptr<dataset::Tensor> de_output_tensor;
+                        dataset::Tensor::CreateFromMemory(
+                          dataset::TensorShape(tensor.Shape()), MSTypeToDEType(static_cast<TypeId>(tensor.DataType())),
+                          (const uchar *)(tensor.Data().get()), tensor.DataSize(), &de_output_tensor);
+                        de_output_tensor_list.emplace_back(std::move(de_output_tensor));
+                      }
+                      return de_output_tensor_list;
                     });
                 }));
 }  // namespace dataset

mindspore/ccsrc/minddata/dataset/api/python/bindings/dataset/kernels/data/bindings.cc

@@ -84,7 +84,8 @@ PYBIND_REGISTER(SliceOption, 0, ([](const py::module *m) {
                       }
 
                       if (!c_slice.valid()) {
-                        THROW_IF_ERROR(Status(StatusCode::kUnexpectedError, __LINE__, __FILE__, "Wrong slice object"));
+                        THROW_IF_ERROR(
+                          Status(StatusCode::kMDUnexpectedError, __LINE__, __FILE__, "Wrong slice object"));
                       }
                       return SliceOption(c_slice);
                     }))

mindspore/ccsrc/minddata/dataset/api/python/bindings/dataset/kernels/ir/image/bindings.cc

@@ -354,7 +354,7 @@ PYBIND_REGISTER(
           for (auto handle : py_sub.cast<py::list>()) {
             py::tuple tp = handle.cast<py::tuple>();
             if (tp.is_none() || tp.size() != 2) {
-              THROW_IF_ERROR(Status(StatusCode::kUnexpectedError, "Each tuple in subpolicy should be (op, prob)."));
+              THROW_IF_ERROR(Status(StatusCode::kMDUnexpectedError, "Each tuple in subpolicy should be (op, prob)."));
             }
             std::shared_ptr<TensorOperation> t_op;
             if (py::isinstance<TensorOperation>(tp[0])) {
@@ -366,11 +366,11 @@ PYBIND_REGISTER(
                 std::make_shared<PyFuncOp>((tp[0]).cast<py::function>()));
             } else {
               THROW_IF_ERROR(
-                Status(StatusCode::kUnexpectedError, "op is neither a tensorOp, tensorOperation nor a pyfunc."));
+                Status(StatusCode::kMDUnexpectedError, "op is neither a tensorOp, tensorOperation nor a pyfunc."));
             }
             double prob = (tp[1]).cast<py::float_>();
             if (prob < 0 || prob > 1) {
-              THROW_IF_ERROR(Status(StatusCode::kUnexpectedError, "prob needs to be with [0,1]."));
+              THROW_IF_ERROR(Status(StatusCode::kMDUnexpectedError, "prob needs to be with [0,1]."));
             }
             cpp_policy.back().emplace_back(std::make_pair(t_op, prob));
           }

mindspore/ccsrc/minddata/dataset/callback/py_ds_callback.cc

@@ -51,12 +51,12 @@ Status PyDSCallback::ExecutePyfunc(py::function f, const CallbackParam &cb_param
     // Acquire Python GIL
     py::gil_scoped_acquire gil_acquire;
     if (Py_IsInitialized() == 0) {
-      return Status(StatusCode::kPythonInterpreterFailure, "Python Interpreter is finalized");
+      return Status(StatusCode::kMDPythonInterpreterFailure, "Python Interpreter is finalized");
     }
     try {
       f(cb_param);
     } catch (const py::error_already_set &e) {
-      return Status(StatusCode::kPyFuncException, e.what());
+      return Status(StatusCode::kMDPyFuncException, e.what());
     }
   }
   return Status::OK();

mindspore/ccsrc/minddata/dataset/core/CMakeLists.txt

@@ -5,6 +5,7 @@ set(DATASET_CORE_SRC_FILES
   config_manager.cc
   cv_tensor.cc
   data_type.cc
+  de_tensor.cc
   global_context.cc
   tensor.cc
   tensor_helpers.cc

mindspore/ccsrc/minddata/dataset/core/de_tensor.cc

@@ -0,0 +1,67 @@
+/**
+ * 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 "minddata/dataset/core/de_tensor.h"
+#include "minddata/dataset/core/constants.h"
+#include "minddata/dataset/core/data_type.h"
+#include "minddata/dataset/include/type_id.h"
+#include "mindspore/core/ir/dtype/type_id.h"
+#include "utils/hashing.h"
+#ifndef ENABLE_ANDROID
+#include "utils/log_adapter.h"
+#define ASSERT_NULL(ptr) MS_EXCEPTION_IF_NULL(ptr)
+#else
+#include "mindspore/lite/src/common/log_adapter.h"
+#define ASSERT_NULL(ptr) MS_ASSERT((ptr) != nullptr)
+#endif
+
+namespace mindspore {
+namespace dataset {
+
+DETensor::DETensor(std::shared_ptr<dataset::Tensor> tensor_impl)
+    : tensor_impl_(tensor_impl),
+      name_("MindDataTensor"),
+      type_(static_cast<mindspore::DataType>(DETypeToMSType(tensor_impl_->type()))),
+      shape_(tensor_impl_->shape().AsVector()) {}
+
+const std::string &DETensor::Name() const { return name_; }
+
+enum mindspore::DataType DETensor::DataType() const {
+  ASSERT_NULL(tensor_impl_);
+  return static_cast<mindspore::DataType>(DETypeToMSType(tensor_impl_->type()));
+}
+
+size_t DETensor::DataSize() const {
+  ASSERT_NULL(tensor_impl_);
+  return tensor_impl_->SizeInBytes();
+}
+
+const std::vector<int64_t> &DETensor::Shape() const { return shape_; }
+
+std::shared_ptr<const void> DETensor::Data() const {
+  return std::shared_ptr<const void>(tensor_impl_->GetBuffer(), [](const void *) {});
+}
+
+void *DETensor::MutableData() {
+  ASSERT_NULL(tensor_impl_);
+  return tensor_impl_->GetMutableBuffer();
+}
+
+bool DETensor::IsDevice() const { return false; }
+
+std::shared_ptr<mindspore::MSTensor::Impl> DETensor::Clone() const { return std::make_shared<DETensor>(tensor_impl_); }
+}  // namespace dataset
+}  // namespace mindspore

mindspore/ccsrc/minddata/dataset/core/de_tensor.h

@@ -0,0 +1,59 @@
+/**
+ * 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_MINDDATA_DATASET_CORE_DETENSOR_H_
+#define MINDSPORE_CCSRC_MINDDATA_DATASET_CORE_DETENSOR_H_
+#include <string>
+#include <vector>
+#include <memory>
+#include "include/api/types.h"
+#include "mindspore/core/ir/api_tensor_impl.h"
+#include "minddata/dataset/include/status.h"
+#include "minddata/dataset/include/tensor.h"
+
+namespace mindspore {
+namespace dataset {
+class DETensor : public mindspore::MSTensor::Impl {
+ public:
+  DETensor() = default;
+  ~DETensor() override = default;
+  explicit DETensor(std::shared_ptr<dataset::Tensor> tensor_impl);
+
+  const std::string &Name() const override;
+
+  enum mindspore::DataType DataType() const override;
+
+  size_t DataSize() const override;
+
+  const std::vector<int64_t> &Shape() const override;
+
+  std::shared_ptr<const void> Data() const override;
+
+  void *MutableData() override;
+
+  bool IsDevice() const override;
+
+  std::shared_ptr<mindspore::MSTensor::Impl> Clone() const override;
+
+ private:
+  std::shared_ptr<dataset::Tensor> tensor_impl_;
+  std::string name_;
+  enum mindspore::DataType type_;
+  std::vector<int64_t> shape_;
+};
+}  // namespace dataset
+}  // namespace mindspore
+#endif  // MINDSPORE_CCSRC_MINDDATA_DATASET_CORE_DETENSOR_H_

mindspore/ccsrc/minddata/dataset/core/tensor.h

@@ -41,23 +41,17 @@
 #include "minddata/dataset/core/data_type.h"
 #include "minddata/dataset/core/tensor_helpers.h"
 #include "minddata/dataset/core/tensor_shape.h"
+#include "minddata/dataset/core/de_tensor.h"
 #include "minddata/dataset/util/status.h"
 #include "utils/ms_utils.h"
 #ifndef ENABLE_ANDROID
 #include "proto/example.pb.h"
-#else
-#include "minddata/dataset/include/de_tensor.h"
 #endif
 
 #ifdef ENABLE_PYTHON
 namespace py = pybind11;
 #endif
 namespace mindspore {
-#ifdef ENABLE_ANDROID
-namespace tensor {
-class DETensor;
-}  // namespace tensor
-#endif
 namespace dataset {
 class Tensor;
 template <typename T>
@@ -85,7 +79,7 @@ class Tensor {
   /// \param other Tensor to be moved
   Tensor(Tensor &&other) noexcept;
 
-  /// Move assigment operator
+  /// Move assignment operator
   /// \param other Tensor to be moved
   Tensor &operator=(Tensor &&other) noexcept;
 
@@ -134,7 +128,7 @@ class Tensor {
 #ifndef ENABLE_ANDROID
   /// Create a tensor of type DE_STRING from a BytesList.
   /// \param[in] bytes_list protobuf's Bytelist
-  /// \param[in] shape shape of the outout tensor
+  /// \param[in] shape shape of the output tensor
   /// \param[out] out created Tensor
   /// \return Status Code
   static Status CreateFromByteList(const dataengine::BytesList &bytes_list, const TensorShape &shape, TensorPtr *out);
@@ -292,7 +286,7 @@ class Tensor {
       std::string err;
       err += (data_ == nullptr) ? "data_ is nullptr \t" : "";
       err += type_.IsCompatible<T>() ? "data type not compatible\t" : "";
-      return Status(StatusCode::kUnexpectedError, err);
+      return Status(StatusCode::kMDUnexpectedError, err);
     }
   }
 
@@ -343,7 +337,7 @@ class Tensor {
   void Invalidate();
 
   /// Copy input tensor into self at the location index.
-  /// Index is a vector of axises which can be incomplete:
+  /// Index is a vector of axes which can be incomplete:
   /// Ex: shape <2,3>, inserting into index {0} will replace the first row. index {1,2} will replace the last cell.
   /// \param index
   /// \param input
@@ -686,9 +680,7 @@ class Tensor {
   unsigned char *data_end_ = nullptr;
 
  private:
-#ifdef ENABLE_ANDROID
-  friend class tensor::DETensor;
-#endif
+  friend class DETensor;
 
   /// Slice numeric tensors.
   Status SliceNumeric(TensorPtr *out, const std::vector<std::vector<dsize_t>> &indices, const TensorShape &shape);

mindspore/ccsrc/minddata/dataset/engine/cache/CMakeLists.txt

@@ -73,6 +73,7 @@ if(ENABLE_CACHE)
         engine-cache-server
         _c_dataengine
         _c_mindrecord
+        mindspore
         mindspore::protobuf
         mindspore::grpc++
         mindspore_gvar
@@ -85,6 +86,7 @@ if(ENABLE_CACHE)
         engine-cache-server
         _c_dataengine
         _c_mindrecord
+        mindspore
         mindspore::protobuf
         mindspore::grpc++
         mindspore_gvar
@@ -103,6 +105,7 @@ if(ENABLE_CACHE)
 
   add_executable(cache_admin cache_admin.cc cache_admin_arg.cc)
   target_link_libraries(cache_admin _c_dataengine _c_mindrecord mindspore::protobuf ${PYTHON_LIBRARIES} pthread)
+  target_link_libraries(cache_admin mindspore mindspore_shared_lib)
 
   if(USE_GLOG)
     target_link_libraries(cache_admin mindspore::glog)

mindspore/ccsrc/minddata/dataset/engine/cache/cache_admin.cc

@@ -22,10 +22,11 @@
 #include "minddata/dataset/engine/cache/cache_common.h"
 #include "minddata/dataset/util/path.h"
 
+namespace ms = mindspore;
 namespace ds = mindspore::dataset;
 
 int main(int argc, char **argv) {
-  ds::Status rc;
+  ms::Status rc;
   ds::CacheAdminArgHandler args;
   std::stringstream arg_stream;
 

mindspore/ccsrc/minddata/dataset/engine/cache/cache_admin_arg.cc

@@ -89,7 +89,7 @@ Status CacheAdminArgHandler::AssignArg(std::string option, int32_t *out_arg, std
   ArgValue selected_arg = arg_map_[option];
   if (used_args_[selected_arg]) {
     std::string err_msg = "The " + option + " argument was given more than once.";
-    return Status(StatusCode::kSyntaxError, err_msg);
+    return Status(StatusCode::kMDSyntaxError, err_msg);
   }
 
   // Flag that this arg is used now
@@ -101,7 +101,7 @@ Status CacheAdminArgHandler::AssignArg(std::string option, int32_t *out_arg, std
   if (command_id != CommandId::kCmdUnknown) {
     if (command_id_ != CommandId::kCmdUnknown) {
       std::string err_msg = "Only one command at a time is allowed.  Invalid command: " + option;
-      return Status(StatusCode::kSyntaxError, err_msg);
+      return Status(StatusCode::kMDSyntaxError, err_msg);
     } else {
       command_id_ = command_id;
     }
@@ -113,7 +113,7 @@ Status CacheAdminArgHandler::AssignArg(std::string option, int32_t *out_arg, std
   *arg_stream >> value_as_string;
   if (value_as_string.empty()) {
     std::string err_msg = option + " option requires an argument field.  Syntax: " + option + " <field>";
-    return Status(StatusCode::kSyntaxError, err_msg);
+    return Status(StatusCode::kMDSyntaxError, err_msg);
   }
 
   // Now, attempt to convert the value into it's numeric format for output
@@ -121,7 +121,7 @@ Status CacheAdminArgHandler::AssignArg(std::string option, int32_t *out_arg, std
     *out_arg = std::stoul(value_as_string);
   } catch (const std::exception &e) {
     std::string err_msg = "Invalid numeric value: " + value_as_string;
-    return Status(StatusCode::kSyntaxError, err_msg);
+    return Status(StatusCode::kMDSyntaxError, err_msg);
   }
 
   return Status::OK();
@@ -133,7 +133,7 @@ Status CacheAdminArgHandler::AssignArg(std::string option, std::string *out_arg,
   ArgValue selected_arg = arg_map_[option];
   if (used_args_[selected_arg]) {
     std::string err_msg = "The " + option + " argument was given more than once.";
-    return Status(StatusCode::kSyntaxError, err_msg);
+    return Status(StatusCode::kMDSyntaxError, err_msg);
   }
 
   // Flag that this arg is used now
@@ -145,7 +145,7 @@ Status CacheAdminArgHandler::AssignArg(std::string option, std::string *out_arg,
   if (command_id != CommandId::kCmdUnknown) {
     if (command_id_ != CommandId::kCmdUnknown) {
       std::string err_msg = "Only one command at a time is allowed.  Invalid command: " + option;
-      return Status(StatusCode::kSyntaxError, err_msg);
+      return Status(StatusCode::kMDSyntaxError, err_msg);
     } else {
       command_id_ = command_id;
     }
@@ -158,12 +158,12 @@ Status CacheAdminArgHandler::AssignArg(std::string option, std::string *out_arg,
       *arg_stream >> *out_arg;
     } else {
       std::string err_msg = option + " option requires an argument field.  Syntax: " + option + " <field>";
-      return Status(StatusCode::kSyntaxError, err_msg);
+      return Status(StatusCode::kMDSyntaxError, err_msg);
     }
 
     if (out_arg->empty()) {
       std::string err_msg = option + " option requires an argument field.  Syntax: " + option + " <field>";
-      return Status(StatusCode::kSyntaxError, err_msg);
+      return Status(StatusCode::kMDSyntaxError, err_msg);
     }
   }
 
@@ -176,7 +176,7 @@ Status CacheAdminArgHandler::AssignArg(std::string option, float *out_arg, std::
   ArgValue selected_arg = arg_map_[option];
   if (used_args_[selected_arg]) {
     std::string err_msg = "The " + option + " argument was given more than once.";
-    return Status(StatusCode::kSyntaxError, err_msg);
+    return Status(StatusCode::kMDSyntaxError, err_msg);
   }
 
   // Flag that this arg is used now
@@ -188,7 +188,7 @@ Status CacheAdminArgHandler::AssignArg(std::string option, float *out_arg, std::
   if (command_id != CommandId::kCmdUnknown) {
     if (command_id_ != CommandId::kCmdUnknown) {
       std::string err_msg = "Only one command at a time is allowed.  Invalid command: " + option;
-      return Status(StatusCode::kSyntaxError, err_msg);
+      return Status(StatusCode::kMDSyntaxError, err_msg);
     } else {
       command_id_ = command_id;
     }
@@ -200,7 +200,7 @@ Status CacheAdminArgHandler::AssignArg(std::string option, float *out_arg, std::
   *arg_stream >> value_as_string;
   if (value_as_string.empty()) {
     std::string err_msg = option + " option requires an argument field.  Syntax: " + option + " <field>";
-    return Status(StatusCode::kSyntaxError, err_msg);
+    return Status(StatusCode::kMDSyntaxError, err_msg);
   }
 
   // Now, attempt to convert the value into it's string format for output
@@ -208,7 +208,7 @@ Status CacheAdminArgHandler::AssignArg(std::string option, float *out_arg, std::
     *out_arg = std::stof(value_as_string, nullptr);
   } catch (const std::exception &e) {
     std::string err_msg = "Invalid numeric value: " + value_as_string;
-    return Status(StatusCode::kSyntaxError, err_msg);
+    return Status(StatusCode::kMDSyntaxError, err_msg);
   }
 
   return Status::OK();
@@ -224,7 +224,7 @@ Status CacheAdminArgHandler::ParseArgStream(std::stringstream *arg_stream) {
         if (hostname_ != std::string(kCfgDefaultCacheHost)) {
           std::string err_msg =
             "Invalid host interface: " + hostname_ + ". Current limitation, only 127.0.0.1 can be used.";
-          return Status(StatusCode::kSyntaxError, err_msg);
+          return Status(StatusCode::kMDSyntaxError, err_msg);
         }
         break;
       }
@@ -304,7 +304,7 @@ Status CacheAdminArgHandler::Validate() {
   if (!trailing_args_.empty()) {
     std::string err_msg = "Invalid arguments provided: " + trailing_args_;
     err_msg += "\nPlease try `cache_admin --help` for more information";
-    return Status(StatusCode::kSyntaxError, err_msg);
+    return Status(StatusCode::kMDSyntaxError, err_msg);
   }
 
   // The user must pick at least one command.  i.e. it's meaningless to just give a hostname or port but no command to
@@ -312,18 +312,18 @@ Status CacheAdminArgHandler::Validate() {
   if (command_id_ == CommandId::kCmdUnknown) {
     std::string err_msg = "No command provided";
     err_msg += "\nPlease try `cache_admin --help` for more information";
-    return Status(StatusCode::kSyntaxError, err_msg);
+    return Status(StatusCode::kMDSyntaxError, err_msg);
   }
 
   // Additional checks here
   auto max_num_workers = std::max<int32_t>(std::thread::hardware_concurrency(), 100);
   if (num_workers_ < 1 || num_workers_ > max_num_workers)
-    return Status(StatusCode::kSyntaxError,
+    return Status(StatusCode::kMDSyntaxError,
                   "Number of workers must be in range of 1 and " + std::to_string(max_num_workers) + ".");
-  if (log_level_ < 0 || log_level_ > 3) return Status(StatusCode::kSyntaxError, "Log level must be in range (0..3).");
+  if (log_level_ < 0 || log_level_ > 3) return Status(StatusCode::kMDSyntaxError, "Log level must be in range (0..3).");
   if (memory_cap_ratio_ <= 0 || memory_cap_ratio_ > 1)
-    return Status(StatusCode::kSyntaxError, "Memory cap ratio should be positive and no greater than 1");
-  if (port_ < 1025 || port_ > 65535) return Status(StatusCode::kSyntaxError, "Port must be in range (1025..65535).");
+    return Status(StatusCode::kMDSyntaxError, "Memory cap ratio should be positive and no greater than 1");
+  if (port_ < 1025 || port_ > 65535) return Status(StatusCode::kMDSyntaxError, "Port must be in range (1025..65535).");
 
   return Status::OK();
 }
@@ -467,9 +467,9 @@ Status CacheAdminArgHandler::StopServer(CommandId command_id) {
   Status rc = rq->Wait();
   if (rc.IsError()) {
     msg.RemoveResourcesOnExit();
-    if (rc.IsNetWorkError()) {
+    if (rc == StatusCode::kMDNetWorkError) {
       std::string errMsg = "Server on port " + std::to_string(port_) + " is not up or has been shutdown already.";
-      return Status(StatusCode::kNetWorkError, errMsg);
+      return Status(StatusCode::kMDNetWorkError, errMsg);
     }
     return rc;
   }
@@ -544,7 +544,7 @@ Status CacheAdminArgHandler::StartServer(CommandId command_id) {
     if (WIFEXITED(status)) {
       auto exit_status = WEXITSTATUS(status);
       if (exit_status) {
-        return Status(StatusCode::kUnexpectedError, msg);
+        return Status(StatusCode::kMDUnexpectedError, msg);
       } else {
         // Not an error, some info message goes to stdout
         std::cout << msg << std::endl;

mindspore/ccsrc/minddata/dataset/engine/cache/cache_arena.cc

@@ -75,7 +75,7 @@ Status CachedSharedMemory::AllocateSharedMemory(int32_t client_id, size_t sz, vo
   do {
     std::unique_lock<std::mutex> lock(mux_[slot]);
     rc = shm_pool_[slot]->Allocate(sz, p);
-    if (rc.IsOutofMemory()) {
+    if (rc == StatusCode::kMDOutOfMemory) {
       slot = (slot + 1) % shm_pool_.size();
     }
   } while (rc.IsError() && slot != begin_slot);

mindspore/ccsrc/minddata/dataset/engine/cache/cache_client.cc

@@ -137,7 +137,7 @@ Status CacheClient::WriteBuffer(std::unique_ptr<DataBuffer> &&in) const {
 
 Status CacheClient::AsyncWriteRow(const TensorRow &row) {
   if (async_buffer_stream_ == nullptr) {
-    return Status(StatusCode::kNotImplementedYet);
+    return Status(StatusCode::kMDNotImplementedYet);
   }
   RETURN_IF_NOT_OK(async_buffer_stream_->AsyncWrite(row));
   return Status::OK();
@@ -145,7 +145,7 @@ Status CacheClient::AsyncWriteRow(const TensorRow &row) {
 
 Status CacheClient::AsyncWriteBuffer(std::unique_ptr<DataBuffer> &&in) {
   if (async_buffer_stream_ == nullptr) {
-    return Status(StatusCode::kNotImplementedYet);
+    return Status(StatusCode::kMDNotImplementedYet);
   } else {
     Status rc;
     std::unique_ptr<TensorQTable> tensor_table = std::make_unique<TensorQTable>();
@@ -155,7 +155,7 @@ Status CacheClient::AsyncWriteBuffer(std::unique_ptr<DataBuffer> &&in) {
         TensorRow row;
         RETURN_IF_NOT_OK(in->PopRow(&row));
         rc = AsyncWriteRow(row);
-        if (rc.get_code() == StatusCode::kNotImplementedYet) {
+        if (rc.StatusCode() == StatusCode::kMDNotImplementedYet) {
           tensor_table->push_back(row);
         } else if (rc.IsError()) {
           return rc;
@@ -165,7 +165,7 @@ Status CacheClient::AsyncWriteBuffer(std::unique_ptr<DataBuffer> &&in) {
     // If not all of them can be sent async, return what's left back to the caller.
     if (!tensor_table->empty()) {
       in->set_tensor_table(std::move(tensor_table));
-      return Status(StatusCode::kNotImplementedYet);
+      return Status(StatusCode::kMDNotImplementedYet);
     }
   }
   return Status::OK();
@@ -225,7 +225,8 @@ Status CacheClient::CreateCache(uint32_t tree_crc, bool generate_id) {
     auto cache_state = static_cast<CacheServiceState>(out);
     if (cache_state == CacheServiceState::kFetchPhase ||
         (cache_state == CacheServiceState::kBuildPhase && cookie_.empty())) {
-      return Status(StatusCode::kDuplicateKey, __LINE__, __FILE__, "Not an error and we should bypass the build phase");
+      return Status(StatusCode::kMDDuplicateKey, __LINE__, __FILE__,
+                    "Not an error and we should bypass the build phase");
     }
   } else {
     cinfo_.set_crc(tree_crc);  // It's really a new cache we're creating so save our crc in the client
@@ -243,10 +244,10 @@ Status CacheClient::CreateCache(uint32_t tree_crc, bool generate_id) {
     auto rq = std::make_shared<CreateCacheRequest>(this, cinfo_, cache_mem_sz_, createFlag);
     RETURN_IF_NOT_OK(PushRequest(rq));
     Status rc = rq->Wait();
-    bool success = (rc.IsOk() || rc.get_code() == StatusCode::kDuplicateKey);
+    bool success = (rc.IsOk() || rc.StatusCode() == StatusCode::kMDDuplicateKey);
     // If we get kDuplicateKey, it just means we aren't the first one to create the cache,
     // and we will continue to parse the result.
-    if (rc.get_code() == StatusCode::kDuplicateKey) {
+    if (rc.StatusCode() == StatusCode::kMDDuplicateKey) {
       RETURN_IF_NOT_OK(rq->PostReply());
     }
     if (success) {
@@ -443,7 +444,7 @@ Status CacheClient::AsyncBufferStream::AsyncWrite(const TensorRow &row) {
   }
   // If the size is too big, tell the user to send it directly.
   if (sz > kAsyncBufferSize) {
-    return Status(StatusCode::kNotImplementedYet);
+    return Status(StatusCode::kMDNotImplementedYet);
   }
   std::unique_lock<std::mutex> lock(mux_);
   // Check error from the server side while we have the lock;

mindspore/ccsrc/minddata/dataset/engine/cache/cache_common.h

@@ -66,7 +66,7 @@ enum class CacheServiceState : int8_t {
 /// \param rc[in] Status object
 /// \param reply[in/out] pointer to pre-allocated protobuf object
 inline void Status2CacheReply(const Status &rc, CacheReply *reply) {
-  reply->set_rc(static_cast<int32_t>(rc.get_code()));
+  reply->set_rc(static_cast<int32_t>(rc.StatusCode()));
   reply->set_msg(rc.ToString());
 }
 /// \brief Generate the unix socket file we use on both client/server side given a tcp/ip port number

mindspore/ccsrc/minddata/dataset/engine/cache/cache_fbb.cc

@@ -98,7 +98,7 @@ Status SerializeTensorRowHeader(const TensorRow &row, std::shared_ptr<flatbuffer
     (*out_fbb) = std::move(fbb);
     return Status::OK();
   } catch (const std::bad_alloc &e) {
-    return Status(StatusCode::kOutOfMemory, __LINE__, __FILE__);
+    return Status(StatusCode::kMDOutOfMemory, __LINE__, __FILE__);
   }
 }
 

mindspore/ccsrc/minddata/dataset/engine/cache/cache_grpc_client.cc

@@ -95,7 +95,7 @@ Status CacheClientGreeter::HandleRequest(std::shared_ptr<BaseRequest> rq) {
     std::unique_lock<std::mutex> lck(mux_);
     auto r = req_.emplace(seqNo, std::move(tag));
     if (!r.second) {
-      return Status(StatusCode::kUnexpectedError, __LINE__, __FILE__);
+      return Status(StatusCode::kMDUnexpectedError, __LINE__, __FILE__);
     }
   }
   // Last step is to tag the request.
@@ -124,7 +124,7 @@ Status CacheClientGreeter::WorkerEntry() {
           } else {
             err_msg = rq->rc_.error_message() + ". GRPC Code " + std::to_string(error_code);
           }
-          Status remote_rc = Status(StatusCode::kNetWorkError, __LINE__, __FILE__, err_msg);
+          Status remote_rc = Status(StatusCode::kMDNetWorkError, __LINE__, __FILE__, err_msg);
           Status2CacheReply(remote_rc, &rq->base_rq_->reply_);
         }
         // Notify the waiting thread.

mindspore/ccsrc/minddata/dataset/engine/cache/cache_ipc.cc

@@ -25,7 +25,7 @@ Status PortToFtok(int port, SharedMemory::shm_key_t *out) {
   shmkey = ftok(unix_path.data(), 'a');
   if (shmkey == (key_t)-1) {
     std::string errMsg = "Unable to create a ftok token. Errno = " + std::to_string(errno);
-    return Status(errno == ENOENT ? StatusCode::kFileNotExist : StatusCode::kUnexpectedError, errMsg);
+    return Status(errno == ENOENT ? StatusCode::kMDFileNotExist : StatusCode::kMDUnexpectedError, errMsg);
   }
   *out = shmkey;
   return Status::OK();
@@ -56,7 +56,7 @@ Status SharedMessage::SendStatus(const Status &rc) {
   CacheMsgBuf msg{
     1,
   };
-  msg.body.status.err_code = static_cast<int32_t>(rc.get_code());
+  msg.body.status.err_code = static_cast<int32_t>(rc.StatusCode());
   auto err = memcpy_s(msg.body.status.err_msg, kSharedMessageSize, rc.ToString().data(), rc.ToString().size());
   CHECK_FAIL_RETURN_UNEXPECTED(err == EOK, "memcpy_s failed. err = " + std::to_string(err));
   msg.body.status.err_msg[rc.ToString().size()] = '\0';

mindspore/ccsrc/minddata/dataset/engine/cache/cache_main.cc

@@ -25,16 +25,17 @@
 #include <chrono>
 #include "minddata/dataset/engine/cache/cache_common.h"
 #include "minddata/dataset/engine/cache/cache_ipc.h"
+namespace ms = mindspore;
 namespace ds = mindspore::dataset;
 
 /// Start the server
 /// \param argv
 /// \return Status object
-ds::Status StartServer(int argc, char **argv) {
-  ds::Status rc;
+ms::Status StartServer(int argc, char **argv) {
+  ms::Status rc;
   ds::CacheServer::Builder builder;
   if (argc != 8) {
-    return ds::Status(ds::StatusCode::kSyntaxError);
+    return ms::Status(ms::StatusCode::kMDSyntaxError);
   }
 
   int32_t port = strtol(argv[3], nullptr, 10);
@@ -53,7 +54,7 @@ ds::Status StartServer(int argc, char **argv) {
   // is called. This is a standard procedure for daemonize a process on unix.
   if (chdir("/") == -1) {
     std::string errMsg = "Unable to change directory to /. Errno = " + std::to_string(errno);
-    return ds::Status(ds::StatusCode::kUnexpectedError, __LINE__, __FILE__, errMsg);
+    return ms::Status(ms::StatusCode::kMDUnexpectedError, __LINE__, __FILE__, errMsg);
   }
 
   // A message queue for communication between parent and child (if we fork).
@@ -80,13 +81,13 @@ ds::Status StartServer(int argc, char **argv) {
     // failed to fork
     if (pid < 0) {
       std::string errMsg = "Failed to fork process for cache server. Errno = " + std::to_string(errno);
-      return ds::Status(ds::StatusCode::kUnexpectedError, __LINE__, __FILE__, errMsg);
+      return ms::Status(ms::StatusCode::kMDUnexpectedError, __LINE__, __FILE__, errMsg);
     } else if (pid > 0) {
       // Parent and will be responsible for remove the queue on exit.
       msg.RemoveResourcesOnExit();
       // Sleep one second and we attach to the msg que
       std::this_thread::sleep_for(std::chrono::seconds(1));
-      ds::Status child_rc;
+      ms::Status child_rc;
       rc = msg.ReceiveStatus(&child_rc);
       if (rc.IsError()) {
         return rc;
@@ -101,7 +102,7 @@ ds::Status StartServer(int argc, char **argv) {
                    "logs (under "
                 << ds::DefaultLogDir() << ") for any issues that may happen after startup\n";
       signal(SIGCHLD, SIG_IGN);  // ignore sig child signal.
-      return ds::Status::OK();
+      return ms::Status::OK();
     } else {
       // Child process will continue from here if daemonize and parent has already exited.
       // If we are running in the foreground, none of the code in block below will be run.
@@ -110,7 +111,7 @@ ds::Status StartServer(int argc, char **argv) {
       sid = setsid();
       if (sid < 0) {
         std::string errMsg = "Failed to setsid(). Errno = " + std::to_string(errno);
-        return ds::Status(ds::StatusCode::kUnexpectedError, __LINE__, __FILE__, errMsg);
+        return ms::Status(ms::StatusCode::kMDUnexpectedError, __LINE__, __FILE__, errMsg);
       }
       close(0);
       close(1);
@@ -137,10 +138,10 @@ ds::Status StartServer(int argc, char **argv) {
 
 int main(int argc, char **argv) {
   // This executable is not to be called directly, and should be invoked by cache_admin executable.
-  ds::Status rc = StartServer(argc, argv);
+  ms::Status rc = StartServer(argc, argv);
   // Check result
   if (rc.IsError()) {
-    auto errCode = rc.get_code();
+    auto errCode = rc.StatusCode();
     auto errMsg = rc.ToString();
     std::cerr << errMsg << std::endl;
     return static_cast<int>(errCode);

mindspore/ccsrc/minddata/dataset/engine/cache/cache_numa.cc

@@ -136,7 +136,7 @@ Status NumaMemoryPool::Allocate(size_t n, void **p) {
           if (rc.IsOk()) {
             *p = ptr;
             break;
-          } else if (rc.IsOutofMemory()) {
+          } else if (rc == StatusCode::kMDOutOfMemory) {
             inx = (inx + 1) % num_slots;
           } else {
             return rc;
@@ -162,7 +162,7 @@ Status NumaMemoryPool::Allocate(size_t n, void **p) {
       if (rc.IsOk()) {
         *p = ptr;
         break;
-      } else if (rc.IsOutofMemory()) {
+      } else if (rc == StatusCode::kMDOutOfMemory) {
         // Make the next arena and continue.
         slot = (slot + 1) % num_segments;
       } else {
@@ -172,7 +172,7 @@ Status NumaMemoryPool::Allocate(size_t n, void **p) {
   }
   // Handle the case we have done one round robin search.
   if (ptr == nullptr) {
-    return Status(StatusCode::kOutOfMemory, __LINE__, __FILE__);
+    return Status(StatusCode::kMDOutOfMemory, __LINE__, __FILE__);
   }
   return rc;
 }

mindspore/ccsrc/minddata/dataset/engine/cache/cache_pool.cc

@@ -108,7 +108,7 @@ Status CachePool::Insert(CachePool::key_type key, const std::vector<ReadableSlic
       bl.ptr = nullptr;
       return rc;
     }
-  } else if (rc.IsOutofMemory()) {
+  } else if (rc == StatusCode::kMDOutOfMemory) {
     // If no memory, write to disk.
     if (sm_ != nullptr) {
       MS_LOG(DEBUG) << "Spill to disk directly ... " << bl.sz << " bytes.";
@@ -116,7 +116,7 @@ Status CachePool::Insert(CachePool::key_type key, const std::vector<ReadableSlic
     } else {
       // If asked to spill to disk instead but there is no storage set up, simply return no memory
       // instead.
-      return Status(StatusCode::kOutOfMemory, __LINE__, __FILE__, "No enough storage for cache server to cache data");
+      return Status(StatusCode::kMDOutOfMemory, __LINE__, __FILE__, "No enough storage for cache server to cache data");
     }
   } else {
     return rc;
@@ -125,7 +125,7 @@ Status CachePool::Insert(CachePool::key_type key, const std::vector<ReadableSlic
   try {
     rc = tree_->DoInsert(key, bl);
   } catch (const std::bad_alloc &e) {
-    rc = Status(StatusCode::kOutOfMemory, __LINE__, __FILE__);
+    rc = Status(StatusCode::kMDOutOfMemory, __LINE__, __FILE__);
   }
   // Duplicate key is treated as error and we will also free the memory.
   if (rc.IsError() && bl.ptr != nullptr) {

mindspore/ccsrc/minddata/dataset/engine/cache/cache_request.cc

@@ -223,7 +223,7 @@ Status CreateCacheRequest::Prepare() {
     rq_.add_buf_data(fbb.GetBufferPointer(), fbb.GetSize());
     return Status::OK();
   } catch (const std::bad_alloc &e) {
-    return Status(StatusCode::kOutOfMemory, __LINE__, __FILE__);
+    return Status(StatusCode::kMDOutOfMemory, __LINE__, __FILE__);
   }
 }
 
@@ -277,7 +277,7 @@ Status CacheSchemaRequest::SerializeCacheSchemaRequest(const std::unordered_map<
     rq_.add_buf_data(fbb.GetBufferPointer(), fbb.GetSize());
     return Status::OK();
   } catch (const std::bad_alloc &e) {
-    return Status(StatusCode::kOutOfMemory, __LINE__, __FILE__);
+    return Status(StatusCode::kMDOutOfMemory, __LINE__, __FILE__);
   }
 }
 

mindspore/ccsrc/minddata/dataset/engine/cache/cache_server.cc

@@ -169,7 +169,7 @@ Status CacheServer::GlobalMemoryCheck(uint64_t cache_mem_sz) {
     int64_t mem_consumed = stat.stat_.num_mem_cached * stat.stat_.average_cache_sz;
     max_avail -= mem_consumed;
     if (max_avail <= 0) {
-      return Status(StatusCode::kOutOfMemory, __LINE__, __FILE__, "Please destroy some sessions");
+      return Status(StatusCode::kMDOutOfMemory, __LINE__, __FILE__, "Please destroy some sessions");
     }
     ++it;
   }
@@ -179,12 +179,12 @@ Status CacheServer::GlobalMemoryCheck(uint64_t cache_mem_sz) {
   if (max_avail < avail_mem) {
     int64_t req_mem = cache_mem_sz * 1048576L;  // It is in MB unit.
     if (req_mem > max_avail) {
-      return Status(StatusCode::kOutOfMemory, __LINE__, __FILE__, "Please destroy some sessions");
+      return Status(StatusCode::kMDOutOfMemory, __LINE__, __FILE__, "Please destroy some sessions");
     } else if (req_mem == 0) {
       // This cache request is specifying unlimited memory up to the memory cap. If we have consumed more than
       // 85% of our limit, fail this request.
       if (static_cast<float>(max_avail) / static_cast<float>(avail_mem) <= 0.15) {
-        return Status(StatusCode::kOutOfMemory, __LINE__, __FILE__, "Please destroy some sessions");
+        return Status(StatusCode::kMDOutOfMemory, __LINE__, __FILE__, "Please destroy some sessions");
       }
     }
   }
@@ -249,7 +249,7 @@ Status CacheServer::CreateService(CacheRequest *rq, CacheReply *reply) {
       client_id = cs->num_clients_.fetch_add(1);
       all_caches_.emplace(connection_id, std::move(cs));
     } catch (const std::bad_alloc &e) {
-      return Status(StatusCode::kOutOfMemory);
+      return Status(StatusCode::kMDOutOfMemory);
     }
   }
 
@@ -276,7 +276,7 @@ Status CacheServer::CreateService(CacheRequest *rq, CacheReply *reply) {
   reply->set_result(fbb.GetBufferPointer(), fbb.GetSize());
   // We can return OK but we will return a duplicate key so user can act accordingly to either ignore it
   // treat it as OK.
-  return duplicate ? Status(StatusCode::kDuplicateKey) : Status::OK();
+  return duplicate ? Status(StatusCode::kMDDuplicateKey) : Status::OK();
 }
 
 Status CacheServer::DestroyCache(CacheRequest *rq) {
@@ -306,7 +306,7 @@ Status CacheServer::CacheRow(CacheRequest *rq, CacheReply *reply) {
   CacheService *cs = GetService(connection_id);
   if (cs == nullptr) {
     std::string errMsg = "Cache id " + std::to_string(connection_id) + " not found";
-    return Status(StatusCode::kUnexpectedError, __LINE__, __FILE__, errMsg);
+    return Status(StatusCode::kMDUnexpectedError, __LINE__, __FILE__, errMsg);
   } else {
     auto sz = rq->buf_data_size();
     std::vector<const void *> buffers;
@@ -326,7 +326,7 @@ Status CacheServer::CacheRow(CacheRequest *rq, CacheReply *reply) {
       RETURN_IF_NOT_OK(cs->CacheRow(buffers, &id));
       reply->set_result(std::to_string(id));
     } else {
-      return Status(StatusCode::kUnexpectedError, __LINE__, __FILE__, "Cookie mismatch");
+      return Status(StatusCode::kMDUnexpectedError, __LINE__, __FILE__, "Cookie mismatch");
     }
   }
   return Status::OK();
@@ -353,7 +353,7 @@ Status CacheServer::FastCacheRow(CacheRequest *rq, CacheReply *reply) {
   Status rc;
   if (cs == nullptr) {
     std::string errMsg = "Cache id " + std::to_string(connection_id) + " not found";
-    rc = Status(StatusCode::kUnexpectedError, __LINE__, __FILE__, errMsg);
+    rc = Status(StatusCode::kMDUnexpectedError, __LINE__, __FILE__, errMsg);
   } else {
     // Only if the cookie matches, we can accept insert into this cache that has a build phase
     if (!cs->HasBuildPhase() || cookie == cs->cookie()) {
@@ -365,11 +365,11 @@ Status CacheServer::FastCacheRow(CacheRequest *rq, CacheReply *reply) {
     } else {
       auto state = cs->GetState();
       if (state != CacheServiceState::kFetchPhase) {
-        rc = Status(StatusCode::kUnexpectedError, __LINE__, __FILE__,
+        rc = Status(StatusCode::kMDUnexpectedError, __LINE__, __FILE__,
                     "Cache service is not in fetch phase. The current phase is " +
                       std::to_string(static_cast<int8_t>(state)) + ". Client id: " + std::to_string(client_id));
       } else {
-        rc = Status(StatusCode::kUnexpectedError, __LINE__, __FILE__,
+        rc = Status(StatusCode::kMDUnexpectedError, __LINE__, __FILE__,
                     "Cookie mismatch. Client id: " + std::to_string(client_id));
       }
     }
@@ -413,7 +413,7 @@ Status CacheServer::InternalFetchRow(CacheRequest *rq) {
   Status rc;
   if (cs == nullptr) {
     std::string errMsg = "Connection " + std::to_string(connection_id) + " not found";
-    return Status(StatusCode::kUnexpectedError, __LINE__, __FILE__, errMsg);
+    return Status(StatusCode::kMDUnexpectedError, __LINE__, __FILE__, errMsg);
   }
   rc = cs->InternalFetchRow(flatbuffers::GetRoot<FetchRowMsg>(rq->buf_data(0).data()));
   // This is an internal request and is not tied to rpc. But need to post because there
@@ -494,7 +494,7 @@ Status CacheServer::BatchFetchRows(CacheRequest *rq, CacheReply *reply) {
   CacheService *cs = GetService(connection_id);
   if (cs == nullptr) {
     std::string errMsg = "Cache id " + std::to_string(connection_id) + " not found";
-    return Status(StatusCode::kUnexpectedError, __LINE__, __FILE__, errMsg);
+    return Status(StatusCode::kMDUnexpectedError, __LINE__, __FILE__, errMsg);
   } else {
     CHECK_FAIL_RETURN_UNEXPECTED(!rq->buf_data().empty(), "Missing row id");
     auto &row_id_buf = rq->buf_data(0);
@@ -551,7 +551,7 @@ Status CacheServer::BatchFetchRows(CacheRequest *rq, CacheReply *reply) {
         mem.resize(mem_sz);
         CHECK_FAIL_RETURN_UNEXPECTED(mem.capacity() >= mem_sz, "Programming error");
       } catch (const std::bad_alloc &e) {
-        return Status(StatusCode::kOutOfMemory);
+        return Status(StatusCode::kMDOutOfMemory);
       }
       WritableSlice dest(mem.data(), mem_sz);
       RETURN_IF_NOT_OK(BatchFetch(fbb, &dest));
@@ -568,7 +568,7 @@ Status CacheServer::GetStat(CacheRequest *rq, CacheReply *reply) {
   CacheService *cs = GetService(connection_id);
   if (cs == nullptr) {
     std::string errMsg = "Connection " + std::to_string(connection_id) + " not found";
-    return Status(StatusCode::kUnexpectedError, __LINE__, __FILE__, errMsg);
+    return Status(StatusCode::kMDUnexpectedError, __LINE__, __FILE__, errMsg);
   } else {
     CacheService::ServiceStat svc_stat;
     RETURN_IF_NOT_OK(cs->GetStat(&svc_stat));
@@ -595,7 +595,7 @@ Status CacheServer::CacheSchema(CacheRequest *rq) {
   CacheService *cs = GetService(connection_id);
   if (cs == nullptr) {
     std::string errMsg = "Connection " + std::to_string(connection_id) + " not found";
-    return Status(StatusCode::kUnexpectedError, __LINE__, __FILE__, errMsg);
+    return Status(StatusCode::kMDUnexpectedError, __LINE__, __FILE__, errMsg);
   } else {
     CHECK_FAIL_RETURN_UNEXPECTED(!rq->buf_data().empty(), "Missing schema information");
     auto &create_schema_buf = rq->buf_data(0);
@@ -611,7 +611,7 @@ Status CacheServer::FetchSchema(CacheRequest *rq, CacheReply *reply) {
   CacheService *cs = GetService(connection_id);
   if (cs == nullptr) {
     std::string errMsg = "Connection " + std::to_string(connection_id) + " not found";
-    return Status(StatusCode::kUnexpectedError, __LINE__, __FILE__, errMsg);
+    return Status(StatusCode::kMDUnexpectedError, __LINE__, __FILE__, errMsg);
   } else {
     // We are going to use std::string to allocate and hold the result which will be eventually
     // 'moved' to the protobuf message (which underneath is also a std::string) for the purpose
@@ -630,7 +630,7 @@ Status CacheServer::BuildPhaseDone(CacheRequest *rq) {
   CacheService *cs = GetService(connection_id);
   if (cs == nullptr) {
     std::string errMsg = "Connection " + std::to_string(connection_id) + " not found";
-    return Status(StatusCode::kUnexpectedError, __LINE__, __FILE__, errMsg);
+    return Status(StatusCode::kMDUnexpectedError, __LINE__, __FILE__, errMsg);
   } else {
     // First piece of data is the cookie
     CHECK_FAIL_RETURN_UNEXPECTED(!rq->buf_data().empty(), "Missing cookie");
@@ -639,7 +639,7 @@ Status CacheServer::BuildPhaseDone(CacheRequest *rq) {
     if (cookie == cs->cookie()) {
       RETURN_IF_NOT_OK(cs->BuildPhaseDone());
     } else {
-      return Status(StatusCode::kUnexpectedError, __LINE__, __FILE__, "Cookie mismatch");
+      return Status(StatusCode::kMDUnexpectedError, __LINE__, __FILE__, "Cookie mismatch");
     }
   }
   return Status::OK();
@@ -652,7 +652,7 @@ Status CacheServer::GetCacheMissKeys(CacheRequest *rq, CacheReply *reply) {
   CacheService *cs = GetService(connection_id);
   if (cs == nullptr) {
     std::string errMsg = "Connection " + std::to_string(connection_id) + " not found";
-    return Status(StatusCode::kUnexpectedError, __LINE__, __FILE__, errMsg);
+    return Status(StatusCode::kMDUnexpectedError, __LINE__, __FILE__, errMsg);
   } else {
     std::vector<row_id_type> gap;
     RETURN_IF_NOT_OK(cs->FindKeysMiss(&gap));
@@ -680,7 +680,7 @@ Status CacheServer::ToggleWriteMode(CacheRequest *rq) {
   CacheService *cs = GetService(connection_id);
   if (cs == nullptr) {
     std::string errMsg = "Connection " + std::to_string(connection_id) + " not found";
-    return Status(StatusCode::kUnexpectedError, __LINE__, __FILE__, errMsg);
+    return Status(StatusCode::kMDUnexpectedError, __LINE__, __FILE__, errMsg);
   } else {
     // First piece of data is the on/off flag
     CHECK_FAIL_RETURN_UNEXPECTED(!rq->buf_data().empty(), "Missing action flag");
@@ -747,7 +747,7 @@ Status CacheServer::ConnectReset(CacheRequest *rq) {
   CacheService *cs = GetService(connection_id);
   if (cs == nullptr) {
     std::string errMsg = "Connection " + std::to_string(connection_id) + " not found";
-    return Status(StatusCode::kUnexpectedError, __LINE__, __FILE__, errMsg);
+    return Status(StatusCode::kMDUnexpectedError, __LINE__, __FILE__, errMsg);
   } else {
     auto client_id = rq->client_id();
     MS_LOG(WARNING) << "Client id " << client_id << " with connection id " << connection_id << " disconnects";
@@ -836,7 +836,7 @@ Status CacheServer::ProcessRowRequest(CacheServerRequest *cache_req, bool *inter
     default:
       std::string errMsg("Internal error, request type is not row request: ");
       errMsg += std::to_string(static_cast<uint16_t>(cache_req->type_));
-      cache_req->rc_ = Status(StatusCode::kUnexpectedError, __LINE__, __FILE__, errMsg);
+      cache_req->rc_ = Status(StatusCode::kMDUnexpectedError, __LINE__, __FILE__, errMsg);
   }
   return Status::OK();
 }
@@ -860,7 +860,7 @@ Status CacheServer::ProcessSessionRequest(CacheServerRequest *cache_req) {
     default:
       std::string errMsg("Internal error, request type is not session request: ");
       errMsg += std::to_string(static_cast<uint16_t>(cache_req->type_));
-      cache_req->rc_ = Status(StatusCode::kUnexpectedError, __LINE__, __FILE__, errMsg);
+      cache_req->rc_ = Status(StatusCode::kMDUnexpectedError, __LINE__, __FILE__, errMsg);
   }
   return Status::OK();
 }
@@ -931,7 +931,7 @@ Status CacheServer::ProcessAdminRequest(CacheServerRequest *cache_req) {
     default:
       std::string errMsg("Internal error, request type is not admin request: ");
       errMsg += std::to_string(static_cast<uint16_t>(cache_req->type_));
-      cache_req->rc_ = Status(StatusCode::kUnexpectedError, __LINE__, __FILE__, errMsg);
+      cache_req->rc_ = Status(StatusCode::kMDUnexpectedError, __LINE__, __FILE__, errMsg);
   }
   return Status::OK();
 }
@@ -949,7 +949,7 @@ Status CacheServer::ProcessRequest(CacheServerRequest *cache_req) {
   } else {
     std::string errMsg("Unknown request type : ");
     errMsg += std::to_string(static_cast<uint16_t>(cache_req->type_));
-    cache_req->rc_ = Status(StatusCode::kUnexpectedError, __LINE__, __FILE__, errMsg);
+    cache_req->rc_ = Status(StatusCode::kMDUnexpectedError, __LINE__, __FILE__, errMsg);
   }
 
   // Notify it is done, and move on to the next request.
@@ -1045,7 +1045,7 @@ Status CacheServer::GetFreeRequestTag(CacheServerRequest **q) {
   RETURN_UNEXPECTED_IF_NULL(q);
   auto *p = new (std::nothrow) CacheServerRequest();
   if (p == nullptr) {
-    return Status(StatusCode::kOutOfMemory, __LINE__, __FILE__);
+    return Status(StatusCode::kMDOutOfMemory, __LINE__, __FILE__);
   }
   *q = p;
   return Status::OK();
@@ -1091,7 +1091,7 @@ Status CacheServer::DestroySession(CacheRequest *rq) {
     } else {
       std::string errMsg =
         "Session id " + std::to_string(drop_session_id) + " not found in server on port " + std::to_string(port_) + ".";
-      return Status(StatusCode::kFileNotExist, errMsg);
+      return Status(StatusCode::kMDFileNotExist, errMsg);
     }
   }
 }
@@ -1148,7 +1148,7 @@ Status CacheServer::GetCacheState(CacheRequest *rq, CacheReply *reply) {
   CacheService *cs = GetService(connection_id);
   if (cs == nullptr) {
     std::string errMsg = "Connection " + std::to_string(connection_id) + " not found";
-    return Status(StatusCode::kUnexpectedError, __LINE__, __FILE__, errMsg);
+    return Status(StatusCode::kMDUnexpectedError, __LINE__, __FILE__, errMsg);
   } else {
     auto state = cs->GetState();
     reply->set_result(std::to_string(static_cast<int8_t>(state)));
@@ -1247,7 +1247,7 @@ Status CacheServer::Builder::IpcResourceCleanup() {
     std::string errMsg = "Cache server is already up and running";
     // We return a duplicate error. The main() will intercept
     // and output a proper message
-    return Status(StatusCode::kDuplicateKey, errMsg);
+    return Status(StatusCode::kMDDuplicateKey, errMsg);
   }
   return Status::OK();
 }

mindspore/ccsrc/minddata/dataset/engine/cache/cache_server.h

@@ -419,7 +419,7 @@ class CacheServer : public Service {
     Status GetRc() {
       Status rc;
       for (auto &cache_rc : rc_lists_) {
-        if (cache_rc.IsError() && !cache_rc.IsInterrupted() && rc.IsOk()) {
+        if (cache_rc.IsError() && cache_rc != StatusCode::kMDInterrupted && rc.IsOk()) {
           rc = cache_rc;
         }
       }

mindspore/ccsrc/minddata/dataset/engine/cache/cache_service.cc

@@ -42,7 +42,7 @@ Status CacheService::DoServiceStart() {
       // Return an error if we use more than recommended memory.
       std::string errMsg = "Requesting cache size " + std::to_string(cache_mem_sz_) +
                            " while available system memory " + std::to_string(avail_mem);
-      return Status(StatusCode::kOutOfMemory, __LINE__, __FILE__, errMsg);
+      return Status(StatusCode::kMDOutOfMemory, __LINE__, __FILE__, errMsg);
     }
     memory_cap_ratio = static_cast<float>(cache_mem_sz_) / avail_mem;
   }
@@ -79,7 +79,7 @@ Status CacheService::CacheRow(const std::vector<const void *> &buf, row_id_type
   if (st_ == CacheServiceState::kNoLocking) {
     // We ignore write this request once we turn off locking on the B+ tree. So we will just
     // return out of memory from now on.
-    return Status(StatusCode::kOutOfMemory);
+    return Status(StatusCode::kMDOutOfMemory);
   }
   try {
     // The first buffer is a flatbuffer which describes the rest of the buffers follow
@@ -119,16 +119,16 @@ Status CacheService::CacheRow(const std::vector<const void *> &buf, row_id_type
     }
     // Now we cache the buffer.
     Status rc = cp_->Insert(*row_id_generated, all_data);
-    if (rc == Status(StatusCode::kDuplicateKey)) {
+    if (rc == Status(StatusCode::kMDDuplicateKey)) {
       MS_LOG(DEBUG) << "Ignoring duplicate key.";
     } else {
       if (HasBuildPhase()) {
         // For cache service that has a build phase, record the error in the state
         // so other clients can be aware of the new state. There is nothing one can
         // do to resume other than to drop the cache.
-        if (rc.IsNoSpace()) {
+        if (rc == StatusCode::kMDNoSpace) {
           st_ = CacheServiceState::kNoSpace;
-        } else if (rc.IsOutofMemory()) {
+        } else if (rc == StatusCode::kMDOutOfMemory) {
           st_ = CacheServiceState::kOutOfMemory;
         }
       }
@@ -152,7 +152,7 @@ Status CacheService::FastCacheRow(const ReadableSlice &src, row_id_type *row_id_
   if (st_ == CacheServiceState::kNoLocking) {
     // We ignore write this request once we turn off locking on the B+ tree. So we will just
     // return out of memory from now on.
-    return Status(StatusCode::kOutOfMemory);
+    return Status(StatusCode::kMDOutOfMemory);
   }
   try {
     // If we don't need to generate id, we need to find it from the buffer.
@@ -172,16 +172,16 @@ Status CacheService::FastCacheRow(const ReadableSlice &src, row_id_type *row_id_
     }
     // Now we cache the buffer.
     Status rc = cp_->Insert(*row_id_generated, {src});
-    if (rc == Status(StatusCode::kDuplicateKey)) {
+    if (rc == Status(StatusCode::kMDDuplicateKey)) {
       MS_LOG(DEBUG) << "Ignoring duplicate key.";
     } else {
       if (HasBuildPhase()) {
         // For cache service that has a build phase, record the error in the state
         // so other clients can be aware of the new state. There is nothing one can
         // do to resume other than to drop the cache.
-        if (rc.IsNoSpace()) {
+        if (rc == StatusCode::kMDNoSpace) {
           st_ = CacheServiceState::kNoSpace;
-        } else if (rc.IsOutofMemory()) {
+        } else if (rc == StatusCode::kMDOutOfMemory) {
           st_ = CacheServiceState::kOutOfMemory;
         }
       }
@@ -307,7 +307,7 @@ Status CacheService::FetchSchema(std::string *out) const {
   if (!mem.empty()) {
     *out = std::move(mem);
   } else {
-    return Status(StatusCode::kFileNotExist, __LINE__, __FILE__, "No schema has been cached");
+    return Status(StatusCode::kMDFileNotExist, __LINE__, __FILE__, "No schema has been cached");
   }
   return Status::OK();
 }

mindspore/ccsrc/minddata/dataset/engine/cache/perf/cache_msg.cc

@@ -36,7 +36,7 @@ Status CachePerfMsg::Receive(int32_t qID) {
   auto err = msgrcv(qID, reinterpret_cast<void *>(&small_msg_), sizeof(small_msg_.body.msg), 0, MSG_NOERROR);
   if (err == -1) {
     if (errno == EIDRM) {
-      return Status(StatusCode::kInterrupted);
+      return Status(StatusCode::kMDInterrupted);
     } else {
       std::string errMsg = "Failed to call msgrcv. Errno = " + std::to_string(errno);
       RETURN_STATUS_UNEXPECTED(errMsg);

mindspore/ccsrc/minddata/dataset/engine/cache/perf/cache_perf.cc

@@ -33,7 +33,7 @@ int main(int argc, char **argv) {
     if (rc.IsError()) {
       std::cerr << rc.ToString() << std::endl;
     }
-    return static_cast<int>(rc.get_code());
+    return static_cast<int>(rc.StatusCode());
   }
   return 0;
 }

mindspore/ccsrc/minddata/dataset/engine/cache/perf/cache_perf_run.h

@@ -100,5 +100,7 @@ class CachePerfRun {
 };
 }  // namespace dataset
 }  // namespace mindspore
-
+// todo: waiting for the master of the codes to refactor
+#define get_code StatusCode
+#define kDuplicateKey kMDDuplicateKey
 #endif  // MINDSPORE_CCSRC_MINDDATA_DATASET_ENGINE_CACHE_PERF_RUN_H_

mindspore/ccsrc/minddata/dataset/engine/cache/perf/cache_pipeline.cc

@@ -33,12 +33,12 @@ int main(int argc, char **argv) {
     // If we hit any error, send the rc back to the parent.
     if (rc.IsError()) {
       ds::ErrorMsg proto;
-      proto.set_rc(static_cast<int32_t>(rc.get_code()));
+      proto.set_rc(static_cast<int32_t>(rc.StatusCode()));
       proto.set_msg(rc.ToString());
       ds::CachePerfMsg msg;
       (void)cachePipelineRun.SendMessage(&msg, ds::CachePerfMsg::MessageType::kError, &proto);
     }
-    return static_cast<int>(rc.get_code());
+    return static_cast<int>(rc.StatusCode());
   }
   return 0;
 }

mindspore/ccsrc/minddata/dataset/engine/cache/perf/cache_pipeline_run.h

@@ -115,5 +115,9 @@ class CachePipelineRun {
 };
 }  // namespace dataset
 }  // namespace mindspore
-
+// todo: waiting for the master of the codes to refactor
+#define get_code StatusCode
+#define kDuplicateKey kMDDuplicateKey
+#define IsOutofMemory() StatusCode() == StatusCode::kMDOutOfMemory
+#define IsNoSpace() StatusCode() == StatusCode::kMDNoSpace
 #endif  // MINDSPORE_CCSRC_MINDDATA_DATASET_ENGINE_CACHE_PIPELINE_RUN_H_

mindspore/ccsrc/minddata/dataset/engine/cache/storage_container.cc

@@ -104,7 +104,7 @@ Status StorageContainer::Write(const ReadableSlice &dest, off64_t offset) const
   if (r_sz != sz) {
     errno_t err = (r_sz == 0) ? EOF : errno;
     if (errno == ENOSPC) {
-      return Status(StatusCode::kNoSpace, __LINE__, __FILE__);
+      return Status(StatusCode::kMDNoSpace, __LINE__, __FILE__);
     } else {
       RETURN_STATUS_UNEXPECTED(strerror(err));
     }
@@ -157,7 +157,7 @@ Status StorageContainer::CreateStorageContainer(std::shared_ptr<StorageContainer
   Status rc;
   auto sc = new (std::nothrow) StorageContainer(path);
   if (sc == nullptr) {
-    return Status(StatusCode::kOutOfMemory);
+    return Status(StatusCode::kMDOutOfMemory);
   }
   rc = sc->Create();
   if (rc.IsOk()) {

mindspore/ccsrc/minddata/dataset/engine/cache/storage_manager.cc

@@ -96,9 +96,9 @@ Status StorageManager::Write(key_type *key, const std::vector<ReadableSlice> &bu
     cont = containers_.at(num_containers - 1);
     off64_t offset;
     Status rc = cont->Insert(buf, &offset);
-    if (rc.get_code() == StatusCode::kBuddySpaceFull) {
+    if (rc.StatusCode() == StatusCode::kMDBuddySpaceFull) {
       create_new_container = true;
-      // Remember how many containers we saw. In the next iteration we will do a comparision to see
+      // Remember how many containers we saw. In the next iteration we will do a comparison to see
       // if someone has already created it.
       last_num_container = num_containers;
     } else if (rc.IsOk()) {

mindspore/ccsrc/minddata/dataset/engine/data_schema.cc

@@ -140,7 +140,7 @@ Status ColDescriptor::MaterializeTensorShape(int32_t num_elements, TensorShape *
     // If we already had an unknown dimension, then we cannot have a second unknown dimension.
     // We only support the compute of a single unknown dim.
     if (requested_shape[i] == TensorShape::kDimUnknown && unknown_dim_position != TensorShape::kDimUnknown) {
-      return Status(StatusCode::kUnexpectedError, __LINE__, __FILE__,
+      return Status(StatusCode::kMDUnexpectedError, __LINE__, __FILE__,
                     "Requested shape has more than one unknown dimension!");
     }
 
@@ -312,12 +312,12 @@ Status DataSchema::ColumnLoad(nlohmann::json column_child_tree, const std::strin
   }
   // data type is mandatory field
   if (type_str.empty())
-    return Status(StatusCode::kUnexpectedError, __LINE__, __FILE__,
+    return Status(StatusCode::kMDUnexpectedError, __LINE__, __FILE__,
                   "json schema file for column " + col_name + " has invalid or missing column type.");
 
   // rank number is mandatory field
   if (rank_value <= -1)
-    return Status(StatusCode::kUnexpectedError, __LINE__, __FILE__,
+    return Status(StatusCode::kMDUnexpectedError, __LINE__, __FILE__,
                   "json schema file for column " + col_name + " must define a positive rank value.");
 
   // Create the column descriptor for this column from the data we pulled from the json file
@@ -425,7 +425,7 @@ Status DataSchema::AddColumn(const ColDescriptor &cd) {
 Status DataSchema::PreLoadExceptionCheck(const nlohmann::json &js) {
   // Check if columns node exists.  It is required for building schema from file.
   if (js.find("columns") == js.end())
-    return Status(StatusCode::kUnexpectedError, __LINE__, __FILE__,
+    return Status(StatusCode::kMDUnexpectedError, __LINE__, __FILE__,
                   "\"columns\" node is required in the schema json file.");
   return Status::OK();
 }
@@ -434,12 +434,12 @@ Status DataSchema::PreLoadExceptionCheck(const nlohmann::json &js) {
 // name to column index number.
 Status DataSchema::GetColumnNameMap(std::unordered_map<std::string, int32_t> *out_column_name_map) {
   if (out_column_name_map == nullptr) {
-    return Status(StatusCode::kUnexpectedError, __LINE__, __FILE__, "unexpected null output column name map.");
+    return Status(StatusCode::kMDUnexpectedError, __LINE__, __FILE__, "unexpected null output column name map.");
   }
 
   for (int32_t i = 0; i < col_descs_.size(); ++i) {
     if (col_descs_[i].name().empty()) {
-      return Status(StatusCode::kUnexpectedError, __LINE__, __FILE__,
+      return Status(StatusCode::kMDUnexpectedError, __LINE__, __FILE__,
                     "Constructing column name map from schema, but found empty column name.");
     }
     (*out_column_name_map)[col_descs_[i].name()] = i;

mindspore/ccsrc/minddata/dataset/engine/dataset_iterator.cc

@@ -290,7 +290,7 @@ Status ChildIterator::Drain() {
     RETURN_IF_NOT_OK(current_op_->GetNextInput(&curr_buffer_, worker_id_, child_idx_));
   }
   if (curr_buffer_->eof()) {
-    return Status(StatusCode::kUnexpectedError, __LINE__, __FILE__, "Child iterator picked up EOF in drain.");
+    return Status(StatusCode::kMDUnexpectedError, __LINE__, __FILE__, "Child iterator picked up EOF in drain.");
   }
   return Status::OK();
 }

mindspore/ccsrc/minddata/dataset/engine/datasetops/barrier_op.cc

@@ -122,7 +122,8 @@ Status BarrierOp::prepare(TensorQTable *const table) {
   clean_up_ = false;
   buffer_id_ = 0;
   if (table == nullptr) {
-    return Status(StatusCode::kUnexpectedError, __LINE__, __FILE__, "BarrierOp prepare phase requires a tensor table.");
+    return Status(StatusCode::kMDUnexpectedError, __LINE__, __FILE__,
+                  "BarrierOp prepare phase requires a tensor table.");
   }
   // fill initial row
   TensorRow new_row = {};
@@ -150,7 +151,7 @@ Status BarrierOp::prepare(TensorQTable *const table) {
 // fillBuffer always expects a new table to fill
 Status BarrierOp::fillBuffer(TensorQTable *const table) {
   if (table == nullptr) {
-    return Status(StatusCode::kUnexpectedError, __LINE__, __FILE__, "BarrierOp fillBuffer null table pointer.");
+    return Status(StatusCode::kMDUnexpectedError, __LINE__, __FILE__, "BarrierOp fillBuffer null table pointer.");
   }
   TensorRow new_row = {};
   while (table->size() < static_cast<size_t>(rows_per_buffer_)) {
@@ -172,7 +173,7 @@ Status BarrierOp::blockCond() {
   {
     py::gil_scoped_acquire gil_acquire;
     if (Py_IsInitialized() == 0) {
-      return Status(StatusCode::kPythonInterpreterFailure, "Python Interpreter is finalized");
+      return Status(StatusCode::kMDPythonInterpreterFailure, "Python Interpreter is finalized");
     }
     // we have condition name, however the flexibility is in python today
     try {
@@ -180,11 +181,11 @@ Status BarrierOp::blockCond() {
       py::object ret_py_obj = condition_function_();
       // Process the return value
       if (!py::isinstance<py::bool_>(ret_py_obj)) {
-        return Status(StatusCode::kPyFuncException,
+        return Status(StatusCode::kMDPyFuncException,
                       "Invalid parameter, condition wait function should return true/false.");
       }
     } catch (const py::error_already_set &e) {
-      return Status(StatusCode::kPyFuncException, e.what());
+      return Status(StatusCode::kMDPyFuncException, e.what());
     }
   }
   return Status::OK();

mindspore/ccsrc/minddata/dataset/engine/datasetops/batch_op.cc

@@ -61,7 +61,7 @@ Status BatchOp::Builder::SanityCheck() {
   err += builder_num_workers_ <= 0 ? "Invalid parameter, num_parallel_workers must be greater than 0, but got " +
                                        std::to_string(builder_num_workers_) + ".\n"
                                    : "";
-  return err.empty() ? Status::OK() : Status(StatusCode::kUnexpectedError, __LINE__, __FILE__, common::SafeCStr(err));
+  return err.empty() ? Status::OK() : Status(StatusCode::kMDUnexpectedError, __LINE__, __FILE__, common::SafeCStr(err));
 }
 
 #ifdef ENABLE_PYTHON
@@ -261,7 +261,7 @@ Status BatchOp::MakeBatchedBuffer(std::pair<std::unique_ptr<TensorQTable>, CBatc
 
 Status BatchOp::LaunchThreadsAndInitOp() {
   if (tree_ == nullptr) {
-    return Status(StatusCode::kUnexpectedError, __LINE__, __FILE__, "Pipeline init failed, Execution tree not set.");
+    return Status(StatusCode::kMDUnexpectedError, __LINE__, __FILE__, "Pipeline init failed, Execution tree not set.");
   }
   RETURN_IF_NOT_OK(worker_queues_.Register(tree_->AllTasks()));
   RETURN_IF_NOT_OK(
@@ -338,23 +338,23 @@ Status BatchOp::InvokeBatchSizeFunc(int32_t *batch_size, CBatchInfo info) {
     // Acquire Python GIL
     py::gil_scoped_acquire gil_acquire;
     if (Py_IsInitialized() == 0) {
-      return Status(StatusCode::kPythonInterpreterFailure, "Python Interpreter is finalized.");
+      return Status(StatusCode::kMDPythonInterpreterFailure, "Python Interpreter is finalized.");
     }
     try {
       py::object size = batch_size_func_(info);
       *batch_size = size.cast<int32_t>();
       if (*batch_size <= 0) {
-        return Status(StatusCode::kPyFuncException,
+        return Status(StatusCode::kMDPyFuncException,
                       "Invalid parameter, batch size function should return an integer greater than 0.");
       }
     } catch (const py::error_already_set &e) {
-      return Status(StatusCode::kPyFuncException, e.what());
+      return Status(StatusCode::kMDPyFuncException, e.what());
     } catch (const py::cast_error &e) {
-      return Status(StatusCode::kPyFuncException,
+      return Status(StatusCode::kMDPyFuncException,
                     "Invalid parameter, batch size function should return an integer greater than 0.");
     }
   }
-  return Status(StatusCode::kOK, "Batch size func call succeed.");
+  return Status(StatusCode::kSuccess, "Batch size func call succeed.");
 }
 
 Status BatchOp::InvokeBatchMapFunc(TensorTable *input, TensorTable *output, CBatchInfo info) {
@@ -362,7 +362,7 @@ Status BatchOp::InvokeBatchMapFunc(TensorTable *input, TensorTable *output, CBat
     // Acquire Python GIL
     py::gil_scoped_acquire gil_acquire;
     if (Py_IsInitialized() == 0) {
-      return Status(StatusCode::kPythonInterpreterFailure, "Python Interpreter is finalized.");
+      return Status(StatusCode::kMDPythonInterpreterFailure, "Python Interpreter is finalized.");
     }
     try {
       // Prepare batch map call back parameters
@@ -407,9 +407,9 @@ Status BatchOp::InvokeBatchMapFunc(TensorTable *input, TensorTable *output, CBat
         output->push_back(std::move(output_batch));
       }
     } catch (const py::error_already_set &e) {
-      return Status(StatusCode::kPyFuncException, e.what());
+      return Status(StatusCode::kMDPyFuncException, e.what());
     } catch (const py::cast_error &e) {
-      return Status(StatusCode::kPyFuncException,
+      return Status(StatusCode::kMDPyFuncException,
                     "Invalid parameter, batch map function should return a tuple of list of numpy array.");
     }
   }

mindspore/ccsrc/minddata/dataset/engine/datasetops/bucket_batch_by_length_op.cc

@@ -191,7 +191,7 @@ Status BucketBatchByLengthOp::PadAndBatchBucket(int32_t bucket_index, int32_t ba
           if (bucket_index + 1 >= bucket_boundaries_.size()) {
             std::string error_message =
               "Invalid data, requested to pad to bucket boundary, element falls in last bucket.";
-            return Status(StatusCode::kUnexpectedError, __LINE__, __FILE__, error_message);
+            return Status(StatusCode::kMDUnexpectedError, __LINE__, __FILE__, error_message);
           }
 
           pad_shape[i] = bucket_boundaries_[bucket_index + 1] - 1;

mindspore/ccsrc/minddata/dataset/engine/datasetops/build_sentence_piece_vocab_op.cc

@@ -42,7 +42,7 @@ BuildSentencePieceVocabOp::BuildSentencePieceVocabOp(std::shared_ptr<SentencePie
 
 Status BuildSentencePieceVocabOp::operator()() {
   if (tree_ == nullptr) {
-    return Status(StatusCode::kUnexpectedError, __LINE__, __FILE__, "Pipeline init failed, Execution tree not set.");
+    return Status(StatusCode::kMDUnexpectedError, __LINE__, __FILE__, "Pipeline init failed, Execution tree not set.");
   }
   RETURN_IF_NOT_OK(sentence_queue_->Register(tree_->AllTasks()));
   RETURN_IF_NOT_OK(tree_->AllTasks()->CreateAsyncTask(
@@ -84,10 +84,10 @@ Status BuildSentencePieceVocabOp::SentenceThread() {
   sentencepiece::util::Status s_status =
     sentencepiece::SentencePieceTrainer::Train(BuildParams(), sentence_iter.get(), &model_proto);
   if (!s_status.ok()) {
-    return Status(StatusCode::kUnexpectedError, __LINE__, __FILE__, s_status.message());
+    return Status(StatusCode::kMDUnexpectedError, __LINE__, __FILE__, s_status.message());
   } else {
     if (vocab_ == nullptr) {
-      return Status(StatusCode::kUnexpectedError, __LINE__, __FILE__,
+      return Status(StatusCode::kMDUnexpectedError, __LINE__, __FILE__,
                     "Invalid parameter, sentencepiece vocab not set.");
     }
     vocab_->set_model_proto(model_proto);
@@ -145,7 +145,7 @@ void BuildSentencePieceVocabOp::Next(std::string *sentence) {
 
   if (new_row[col_id_]->type().IsNumeric() || new_row[col_id_]->Rank() > 1) {
     ret_status_ =
-      Status(StatusCode::kUnexpectedError, __LINE__, __FILE__,
+      Status(StatusCode::kMDUnexpectedError, __LINE__, __FILE__,
              "Invalid data, build_sentence_piece_vocab only works on string data with rank equal to 1, got type: " +
                new_row[col_id_]->type().ToString() + "and rank: " + std::to_string(new_row[col_id_]->Rank()));
     read_done_ = true;

mindspore/ccsrc/minddata/dataset/engine/datasetops/build_vocab_op.cc

@@ -80,7 +80,7 @@ Status BuildVocabOp::WorkerEntry(int32_t worker_id) {
 Status BuildVocabOp::operator()() {
   // launch the collector thread
   if (tree_ == nullptr) {
-    return Status(StatusCode::kUnexpectedError, __LINE__, __FILE__, "Pipeline init failed, Execution tree not set.");
+    return Status(StatusCode::kMDUnexpectedError, __LINE__, __FILE__, "Pipeline init failed, Execution tree not set.");
   }
   RETURN_IF_NOT_OK(distributor_queue_->Register(tree_->AllTasks()));
   RETURN_IF_NOT_OK(collector_queue_->Register(tree_->AllTasks()));

mindspore/ccsrc/minddata/dataset/engine/datasetops/cache_base_op.cc

@@ -233,7 +233,7 @@ Status CacheBase::UpdateColumnMapFromCache() {
   // Get the schema from the server. It may not be there yet. So tolerate the error.
   if (column_name_id_map_.empty()) {
     rc = cache_client_->FetchSchema(&column_name_id_map_);
-    if (rc == Status(StatusCode::kFileNotExist)) {
+    if (rc == Status(StatusCode::kMDFileNotExist)) {
       MS_LOG(DEBUG) << "Schema not in the server yet.";
       rc = Status::OK();
     }
@@ -304,14 +304,14 @@ Status CacheBase::Prefetcher(int32_t worker_id) {
       int32_t retry_count = 0;
       do {
         rc = PrefetchRows(prefetch_keys, &cache_miss);
-        if (rc.IsNetWorkError() && retry_count < max_retries) {
+        if (rc == StatusCode::kMDNetWorkError && retry_count < max_retries) {
           // If we get some network error, we will attempt some retries
           retry_count++;
         } else if (rc.IsError()) {
           MS_LOG(WARNING) << rc.ToString();
           return rc;
         }
-      } while (rc.IsNetWorkError());
+      } while (rc == StatusCode::kMDNetWorkError);
       // In case any thread is waiting for the rows to come back and blocked on a semaphore,
       // we will put an empty row in the local cache.
       if (rc.IsError() && AllowCacheMiss()) {

mindspore/ccsrc/minddata/dataset/engine/datasetops/cache_lookup_op.cc

@@ -39,12 +39,12 @@ CacheLookupOp::Builder::Builder() : build_cache_client_(nullptr), build_sampler_
 // Check if the required parameters are set by the builder.
 Status CacheLookupOp::Builder::SanityCheck() const {
   if (build_cache_client_ == nullptr) {
-    return Status(StatusCode::kUnexpectedError, __LINE__, __FILE__,
+    return Status(StatusCode::kMDUnexpectedError, __LINE__, __FILE__,
                   "Invalid parameter, CacheLookupOp requires a CacheClient, but got nullptr.");
   }
   // Make sure the cache client has a valid session
   if (!build_cache_client_->session_id()) {
-    return Status(StatusCode::kUnexpectedError, __LINE__, __FILE__,
+    return Status(StatusCode::kMDUnexpectedError, __LINE__, __FILE__,
                   "Invalid parameter, cache client for CacheLookupOp requires a session id which is not equal to 0.");
   }
   return Status::OK();
@@ -59,7 +59,7 @@ Status CacheLookupOp::Builder::Build(std::shared_ptr<CacheLookupOp> *ptr) {
 }
 Status CacheLookupOp::operator()() {
   if (!sampler_) {
-    return Status(StatusCode::kUnexpectedError, __LINE__, __FILE__,
+    return Status(StatusCode::kMDUnexpectedError, __LINE__, __FILE__,
                   "Invalid parameter, CacheLookupOp requires a sampler before it can be executed, but got nullptr.");
   }
   RETURN_IF_NOT_OK(RegisterResources());

mindspore/ccsrc/minddata/dataset/engine/datasetops/cache_merge_op.cc

@@ -129,7 +129,7 @@ Status CacheMergeOp::CacheMissWorkerEntry(int32_t workerId) {
       Status rc;
       if ((rc = cache_client_->FlushAsyncWriteBuffer()).IsError()) {
         cache_missing_rows_ = false;
-        if (rc.IsOutofMemory() || rc.IsNoSpace()) {
+        if (rc == StatusCode::kMDOutOfMemory || rc == kMDNoSpace) {
           cache_client_->ServerRunningOutOfResources();
         } else {
           MS_LOG(INFO) << "Async row flushing not successful: " << rc.ToString();
@@ -156,7 +156,7 @@ Status CacheMergeOp::CacheMissWorkerEntry(int32_t workerId) {
             rc = rq->AsyncSendCacheRequest(cache_client_, row);
             if (rc.IsOk()) {
               RETURN_IF_NOT_OK(io_que_->EmplaceBack(row_id));
-            } else if (rc.IsOutofMemory() || rc.IsNoSpace()) {
+            } else if (rc == StatusCode::kMDOutOfMemory || rc == kMDNoSpace) {
               cache_missing_rows_ = false;
               cache_client_->ServerRunningOutOfResources();
             }
@@ -188,9 +188,9 @@ Status CacheMergeOp::Cleaner() {
     Status rc = rq->CheckCacheResult();
     if (rc.IsError()) {
       // If interrupt, time to quit.
-      if (rc.IsInterrupted()) {
+      if (rc == StatusCode::kMDInterrupted) {
         return Status::OK();
-      } else if (rc.IsOutofMemory() || rc.IsNoSpace()) {
+      } else if (rc == StatusCode::kMDOutOfMemory || rc == kMDNoSpace) {
         // The server is hitting some limit and we will turn off caching from now on.
         cache_missing_rows_ = false;
         cache_client_->ServerRunningOutOfResources();
@@ -215,7 +215,7 @@ Status CacheMergeOp::PrepareNodePostAction() {  // Run any common code from supe
   // Construct the cache
   const bool generate_ids = false;
   Status rc = cache_client_->CreateCache(cache_crc, generate_ids);
-  if (rc.get_code() == StatusCode::kDuplicateKey) {
+  if (rc.StatusCode() == StatusCode::kMDDuplicateKey) {
     // We are told the cache has been created already.
     MS_LOG(INFO) << "Cache created already";
     rc = Status::OK();
@@ -244,12 +244,12 @@ CacheMergeOp::Builder::Builder() : build_cache_client_(nullptr), build_sampler_(
 // Check if the required parameters are set by the builder.
 Status CacheMergeOp::Builder::SanityCheck() const {
   if (build_cache_client_ == nullptr) {
-    return Status(StatusCode::kUnexpectedError, __LINE__, __FILE__,
+    return Status(StatusCode::kMDUnexpectedError, __LINE__, __FILE__,
                   "Invalid parameter, CacheMergeOp requires a CacheClient, but got nullptr.");
   }
   // Make sure the cache client has a valid session
   if (!build_cache_client_->session_id()) {
-    return Status(StatusCode::kUnexpectedError, __LINE__, __FILE__,
+    return Status(StatusCode::kMDUnexpectedError, __LINE__, __FILE__,
                   "Invalid parameter, cache client for CacheMergeOp requires a session id which is not equal to 0.");
   }
   return Status::OK();
@@ -316,7 +316,7 @@ Status CacheMergeOp::TensorRowCacheRequest::AsyncSendCacheRequest(const std::sha
     // We will do a deep copy but write directly into CacheRequest protobuf or shared memory
     Status rc;
     rc = cc->AsyncWriteRow(row);
-    if (rc.get_code() == StatusCode::kNotImplementedYet) {
+    if (rc.StatusCode() == StatusCode::kMDNotImplementedYet) {
       cleaner_copy_ = std::make_shared<CacheRowRequest>(cc.get());
       rc = cleaner_copy_->SerializeCacheRowRequest(cc.get(), row);
       if (rc.IsOk()) {

mindspore/ccsrc/minddata/dataset/engine/datasetops/cache_op.cc

@@ -41,12 +41,12 @@ CacheOp::Builder::Builder() : build_cache_client_(nullptr), build_sampler_(nullp
 // Check if the required parameters are set by the builder.
 Status CacheOp::Builder::SanityCheck() const {
   if (build_cache_client_ == nullptr) {
-    return Status(StatusCode::kUnexpectedError, __LINE__, __FILE__,
+    return Status(StatusCode::kMDUnexpectedError, __LINE__, __FILE__,
                   "Invalid parameter, CacheOp requires a CacheClient, but got nullptr.");
   }
   // Make sure the cache client has a valid session
   if (!build_cache_client_->session_id()) {
-    return Status(StatusCode::kUnexpectedError, __LINE__, __FILE__,
+    return Status(StatusCode::kMDUnexpectedError, __LINE__, __FILE__,
                   "Invalid parameter, cache client for CacheOp requires a session id which is not equal to 0.");
   }
   return Status::OK();
@@ -78,7 +78,7 @@ Status CacheOp::InitCache() { return Status::OK(); }
 // This class functor will provide the master loop that drives the logic for performing the work
 Status CacheOp::operator()() {
   if (!sampler_) {
-    return Status(StatusCode::kUnexpectedError, __LINE__, __FILE__,
+    return Status(StatusCode::kMDUnexpectedError, __LINE__, __FILE__,
                   "Invalid parameter, CacheOp requires a sampler before it can be executed, but got nullptr.");
   }
   RETURN_IF_NOT_OK(RegisterResources());
@@ -113,7 +113,7 @@ Status CacheOp::CacheAllRows(int32_t worker_id) {
         Status rc;
         // Do the Async write if we attach to the shared memory.
         rc = cache_client_->AsyncWriteBuffer(std::move(db_ptr));
-        if (rc.get_code() == StatusCode::kNotImplementedYet) {
+        if (rc.StatusCode() == StatusCode::kMDNotImplementedYet) {
           RETURN_IF_NOT_OK(cache_client_->WriteBuffer(std::move(db_ptr)));
         } else if (rc.IsError()) {
           return rc;
@@ -169,9 +169,9 @@ Status CacheOp::WaitForCachingAllRows() {
         BuildPhaseDone = true;
         break;
       case CacheServiceState::kOutOfMemory:
-        return Status(StatusCode::kOutOfMemory, "Cache server is running out of memory");
+        return Status(StatusCode::kMDOutOfMemory, "Cache server is running out of memory");
       case CacheServiceState::kNoSpace:
-        return Status(StatusCode::kNoSpace, "Cache server is running of out spill storage");
+        return Status(StatusCode::kMDNoSpace, "Cache server is running of out spill storage");
       case CacheServiceState::kNone:
       case CacheServiceState::kError:
       default:
@@ -246,7 +246,7 @@ Status CacheOp::PrepareNodePostAction() {
   // Construct the cache
   const bool generate_ids = true;
   Status rc = cache_client_->CreateCache(cache_crc, generate_ids);
-  if (rc.get_code() == StatusCode::kDuplicateKey) {
+  if (rc.StatusCode() == StatusCode::kMDDuplicateKey) {
     // We are told the cache has been created already. So we skip the build phase.
     phase_ = Phase::kFetchPhase;
     rc = Status::OK();

mindspore/ccsrc/minddata/dataset/engine/datasetops/device_queue_op.cc

@@ -157,18 +157,14 @@ Status DeviceQueueOp::SendDataToAscend() {
       TensorRow currRow;
       for (int row_id = 0; row_id < current_buffer->NumRows(); row_id++) {
         RETURN_IF_NOT_OK(current_buffer->GetRow(row_id, &currRow));
-        while (stop_send_ && ascend_keep_waiting_) {
-          MS_LOG(DEBUG) << "stop_send flag is set, waiting for continue signal...";
-          std::this_thread::sleep_for(std::chrono::microseconds(100));
-        }
+        WaitContinueSignal();
         auto status = tdtInstancePtr->hostPush(currRow, true, channel_name_, isProfilingEnable, tdt_cost);
         if (status == TdtStatus::FAILED) {
           if (stop_send_) {
             MS_LOG(INFO) << "stop_send received";
             return Status::OK();
-          } else {
-            return Status(StatusCode::kTDTPushFailure, "TDT Push Failed");
           }
+          return Status(StatusCode::kMDTDTPushFailure, "TDT Push Failed");
         }
         if (create_data_info_queue_) {
           DATA_INFO data_info;
@@ -200,9 +196,8 @@ Status DeviceQueueOp::SendDataToAscend() {
         if (stop_send_) {
           MS_LOG(INFO) << "stop_send received";
           return Status::OK();
-        } else {
-          return Status(StatusCode::kTDTPushFailure, "TDT Push Failed");
         }
+        return Status(StatusCode::kMDTDTPushFailure, "TDT Push Failed");
       }
       MS_LOG(INFO) << "an epoch has already sent, now stop send data.";
       stop_send_ = true;
@@ -219,13 +214,19 @@ Status DeviceQueueOp::SendDataToAscend() {
 
   return Status::OK();
 }
+void DeviceQueueOp::WaitContinueSignal() const {
+  while (stop_send_ && ascend_keep_waiting_) {
+    MS_LOG(DEBUG) << "stop_send flag is set, waiting for continue signal...";
+    std::this_thread::sleep_for(std::chrono::microseconds(100));
+  }
+}
 
 #endif
 
 #ifdef ENABLE_TDTQUE
 Status DeviceQueueOp::GetDataInfo(DATA_INFO *data_info) {
   if (!create_data_info_queue_) {
-    return Status(StatusCode::kUnexpectedError, __LINE__, __FILE__, "DataInfo queue is not created.");
+    return Status(StatusCode::kMDUnexpectedError, __LINE__, __FILE__, "DataInfo queue is not created.");
   }
   // This place has a race condition with operator(), so the first one
   // arrive here will do the initialize work.
@@ -241,7 +242,7 @@ Status DeviceQueueOp::GetDataInfo(DATA_INFO *data_info) {
 }
 #else
 Status DeviceQueueOp::GetDataInfo(DATA_INFO *data_info) {
-  return Status(StatusCode::kUnexpectedError, __LINE__, __FILE__, "GetDataInfo is not supported yet.");
+  return Status(StatusCode::kMDUnexpectedError, __LINE__, __FILE__, "GetDataInfo is not supported yet.");
 }
 #endif
 
@@ -301,7 +302,7 @@ Status DeviceQueueOp::PushDataToGPU() {
       }
       handle = GpuBufferMgr::GetInstance().Open(0, channel_name_, data_size, release_function);
       if (handle == INVALID_HANDLE) {
-        return Status(StatusCode::kUnexpectedError, __LINE__, __FILE__, "Failed to open channel for sending data.");
+        return Status(StatusCode::kMDUnexpectedError, __LINE__, __FILE__, "Failed to open channel for sending data.");
       }
       is_open = true;
     }
@@ -309,14 +310,14 @@ Status DeviceQueueOp::PushDataToGPU() {
     // Data prefetch only when PS mode enables cache.
     if (items.size() > 0) {
       if (!ps::PsDataPrefetch::GetInstance().PrefetchData(channel_name_, items[0].data_ptr_, items[0].data_len_)) {
-        return Status(StatusCode::kTimeOut, __LINE__, __FILE__, "Failed to prefetch data.");
+        return Status(StatusCode::kMDTimeOut, __LINE__, __FILE__, "Failed to prefetch data.");
       }
     }
     while (!GpuBufferMgr::GetInstance().IsClosed() && !TaskManager::FindMe()->Interrupted()) {
       BlockQueueStatus_T ret = GpuBufferMgr::GetInstance().Push(handle, items, WAIT_TIME);
       if (ret) {
         if (ret == BlockQueueStatus_T::ERROR_INPUT) {
-          return Status(StatusCode::kUnexpectedError, __LINE__, __FILE__, "Invalid input data, please check it.");
+          return Status(StatusCode::kMDUnexpectedError, __LINE__, __FILE__, "Invalid input data, please check it.");
         } else {
           if (!stop_send_) {
             MS_LOG(DEBUG) << "Retry pushing data...";
@@ -438,13 +439,13 @@ Status DeviceQueueOp::MallocForGPUData(std::vector<device::DataItemGpu> *items,
   for (auto &sub_item : *items) {
     RETURN_IF_NOT_OK(pool_[worker_id]->Allocate(sub_item.data_len_, &sub_item.data_ptr_));
     if (sub_item.data_ptr_ == nullptr) {
-      return Status(StatusCode::kOutOfMemory, __LINE__, __FILE__, "Memory malloc failed.");
+      return Status(StatusCode::kMDOutOfMemory, __LINE__, __FILE__, "Memory malloc failed.");
     }
     const unsigned char *column_data = curr_row[i]->GetBuffer();
     if (memcpy_s(sub_item.data_ptr_, sub_item.data_len_, column_data,
                  static_cast<uint32_t>(curr_row[i++]->SizeInBytes())) != 0) {
       MS_LOG(ERROR) << "memcpy_s failed!";
-      return Status(StatusCode::kUnexpectedError, __LINE__, __FILE__, "memcpy_s failed.");
+      return Status(StatusCode::kMDUnexpectedError, __LINE__, __FILE__, "memcpy_s failed.");
     }
   }
 

mindspore/ccsrc/minddata/dataset/engine/datasetops/device_queue_op.h

@@ -190,6 +190,7 @@ class DeviceQueueOp : public PipelineOp {
 
  private:
 #ifdef ENABLE_TDTQUE
+  void WaitContinueSignal() const;
   Status SendDataToAscend();
   bool ascend_keep_waiting_;
 #endif

mindspore/ccsrc/minddata/dataset/engine/datasetops/filter_op.cc

@@ -43,7 +43,7 @@ Status FilterOp::Builder::SanityCheck() {
   err += builder_num_workers_ <= 0 ? "Invalid parameter, num_parallel_workers must be greater than 0, but got " +
                                        std::to_string(builder_num_workers_) + ".\n"
                                    : "";
-  return err.empty() ? Status::OK() : Status(StatusCode::kUnexpectedError, __LINE__, __FILE__, common::SafeCStr(err));
+  return err.empty() ? Status::OK() : Status(StatusCode::kMDUnexpectedError, __LINE__, __FILE__, common::SafeCStr(err));
 }
 
 FilterOp::Builder::Builder() {
@@ -66,7 +66,7 @@ FilterOp::FilterOp(const std::vector<std::string> &in_col_names, int32_t num_wor
 Status FilterOp::operator()() {
   // The operator class just starts off threads by calling the tree_ function.
   if (tree_ == nullptr) {
-    return Status(StatusCode::kUnexpectedError, __LINE__, __FILE__, "Pipeline init failed, Execution tree not set.");
+    return Status(StatusCode::kMDUnexpectedError, __LINE__, __FILE__, "Pipeline init failed, Execution tree not set.");
   }
   filter_queues_.Init(num_workers_, oc_queue_size_);
   RETURN_IF_NOT_OK(filter_queues_.Register(tree_->AllTasks()));
@@ -244,7 +244,7 @@ Status FilterOp::InvokePredicateFunc(const TensorRow &input, bool *out_predicate
   RETURN_IF_NOT_OK(predicate_func_->Compute(input, &output));
   RETURN_IF_NOT_OK(output.at(0)->GetItemAt(out_predicate, {}));
 
-  return Status(StatusCode::kOK, "FilterOp predicate func call succeed");
+  return Status(StatusCode::kSuccess, "FilterOp predicate func call succeed");
 }
 
 // Visitor accept method for NodePass