Pre Merge pull request !2123 from yanghaoran/release

inc/external/ge/ge_api_types.h

@@ -70,6 +70,8 @@ const char_t *const OPTION_EXEC_DYNAMIC_INPUT = "ge.exec.dynamicInput";
 const char_t *const OPTION_EXEC_DYNAMIC_EXECUTE_MODE = "ge.exec.dynamicGraphExecuteMode";
 const char_t *const OPTION_EXEC_DATA_INPUTS_SHAPE_RANGE = "ge.exec.dataInputsShapeRange";
 const char_t *const OPTION_EXEC_ENABLE_COPY_OUTPUT_ADDR = "ge.exec.enableCopyOutputAddr";
+const char_t *const OPTION_EXEC_GRAPH_EXEC_TIMEOUT = "ge.exec.graphExecTimeout";
+const char_t *const OPTION_EXEC_MODEL_EXEC_TIMEOUT = "ge.exec.modelExecTimeout";
 
 // Option key: memory init
 const char_t *const GRAPH_MEMORY_MAX_SIZE = "ge.graphMemoryMaxSize";

inc/external/hccl/hccl.h

@@ -145,7 +145,7 @@ extern HcclResult HcclGetRankId(HcclComm comm, uint32_t *rank);
 extern HcclResult HcclBarrier(HcclComm comm, aclrtStream stream);
 
 /**
- * @brief AllGather operator.
+ * @brief Send operator.
  *
  * @param sendBuff A pointer identifying the input data address of the operator.
  * @param count An integer(u64) identifying the number of the send data.
@@ -158,7 +158,7 @@ extern HcclResult HcclBarrier(HcclComm comm, aclrtStream stream);
 extern HcclResult HcclSend(void *sendBuf, uint64_t count, HcclDataType dataType, uint32_t destRank, HcclComm comm,
                            aclrtStream stream);
 /**
- * @brief AllGather operator.
+ * @brief Recv operator.
  *
  * @param recvBuff A pointer identifying the output data address of the operator.
  * @param count An integer(u64) identifying the number of the receive data.
@@ -171,6 +171,30 @@ extern HcclResult HcclSend(void *sendBuf, uint64_t count, HcclDataType dataType,
 extern HcclResult HcclRecv(void *recvBuf, uint64_t count, HcclDataType dataType, uint32_t srcRank, HcclComm comm,
                            aclrtStream stream);
 
+/**
+ * @brief AlltoAllV operator.
+ *
+ * @param sendBuff A pointer identifying the input data address of the operator.
+ * @param sendCounts Integer array, where entry i specifies the number of elements to send to rank i.
+ * @param sdispls Integer array, where entry i specifies the displacement (offset from sendbuf, in units of sendtype)
+ * from which to send data to rank i.
+ * @param sendType Datatype of send buffer elements, must be one of the following types: int8, int32, int64, uint64,
+ * float16, float32.
+ * @param recvBuf A pointer identifying the output data address of the operator.
+ * @param recvCounts Integer array, where entry j specifies the number of elements to receive from rank j.
+ * @param rdispls Integer array, where entry j specifies the displacement (offset from recvbuf, in units of recvtype) to
+ * which data from rank j should be written.
+ * @param recvType Datatype of receive buffer elements, must be one of the following types: int8, int32, int64, uint64,
+ * float16, float32.
+ * @param comm A pointer identifying the communication resource based on.
+ * @param stream A pointer identifying the stream information.
+ * @return HcclResult
+ */
+
+extern HcclResult HcclAlltoAllV(const void *sendBuf, const void *sendCounts, const void *sdispls, HcclDataType sendType,
+                                const void *recvBuf, const void *recvCounts, const void *rdispls, HcclDataType recvType,
+                                HcclComm comm, aclrtStream stream);
+
 /**
  * @brief Destroy HCCL comm
  *

inc/framework/common/debug/log.h

@@ -88,7 +88,7 @@
     if ((expr) != ge::GRAPH_SUCCESS) {                      \
       REPORT_CALL_ERROR("E19999", "Operator graph failed"); \
       GELOGE(ge::FAILED, __VA_ARGS__);                      \
-      return (FAILED);                                      \
+      return (ge::FAILED);                                  \
     }                                                       \
   } while (false)
 

inc/framework/common/ge_types.h

@@ -309,6 +309,7 @@ struct Options {
   int32_t physical_device_id;
   std::string profiling_mode;
   std::string profiling_options;
+  int32_t graphExecTimeout;
 };
 
 // Profiling info of task

inc/framework/common/op_types.h

@@ -45,14 +45,6 @@ class GE_FUNC_VISIBILITY OpTypeContainer {
  private:
   std::set<std::string> op_type_list_;
 };
-
-class GE_FUNC_VISIBILITY OpTypeRegistrar {
- public:
-  explicit OpTypeRegistrar(const std::string &op_type) noexcept {
-    OpTypeContainer::Instance()->Register(op_type);
-  }
-  ~OpTypeRegistrar() {}
-};
 }  // namespace ge
 
 #define REGISTER_OPTYPE_DECLARE(var_name, str_name) \
@@ -60,7 +52,7 @@ class GE_FUNC_VISIBILITY OpTypeRegistrar {
 
 #define REGISTER_OPTYPE_DEFINE(var_name, str_name) \
   const char_t *var_name = str_name;               \
-  const ge::OpTypeRegistrar g_##var_name##_reg(str_name);
+  const bool g_##var_name##_reg = (static_cast<void>(OpTypeContainer::Instance()->Register(str_name)), true);
 
 #define IS_OPTYPE_EXISTING(str_name) (ge::OpTypeContainer::Instance()->IsExisting(str_name))
 #endif  // INC_FRAMEWORK_COMMON_OP_TYPES_H_

inc/framework/common/profiling_definitions.h

@@ -164,9 +164,8 @@ class ProfilingContext {
 
   int64_t RegisterString(const std::string &str);
   int64_t RegisterStringHash(const uint64_t hash_id, const std::string &str);
-  void UpdateElementHashId(const MsprofReporterCallback reporter_callback);
-  static Status QueryHashId(const MsprofReporterCallback reporter_callback, const std::string &src_str,
-                            uint64_t &hash_id);
+  void UpdateElementHashId();
+  static Status QueryHashId(const std::string &src_str, uint64_t &hash_id);
   size_t GetRegisterStringNum() const {
     return strings_to_index_.size();
   }

inc/framework/common/runtime_tensor_desc.h

@@ -0,0 +1,38 @@
+/**
+ * Copyright (c) Huawei Technologies Co., Ltd. 2021-2022. All rights reserved.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#ifndef INC_FRAMEWORK_COMMON_RUNTIME_TENSOR_DESC_H_
+#define INC_FRAMEWORK_COMMON_RUNTIME_TENSOR_DESC_H_
+
+#include <cstdint>
+
+namespace ge {
+constexpr int64_t kMaxDimSize = 32;
+
+#pragma pack(push, 1)
+struct RuntimeTensorDesc {
+  uint64_t data_addr;
+  int64_t data_offset_size;
+  int64_t dtype;
+  int64_t shape[kMaxDimSize + 1];           // shape:Dim_Num|DIM0|DIM1|...|DIM31
+  int64_t original_shape[kMaxDimSize + 1];  // original_shape:Dim_Num|DIM0|DIM1|...|DIM31
+  int64_t format;
+  int64_t sub_format;
+  uint8_t reserved[456];  // padding to 1024 bytes
+};
+#pragma pack(pop)
+}  // namespace ge
+
+#endif  // INC_FRAMEWORK_COMMON_RUNTIME_TENSOR_DESC_H_

inc/framework/common/types.h

@@ -88,6 +88,7 @@ REGISTER_OPTYPE_DECLARE(DROPOUTDOMASKV3, "DropOutDoMaskV3");
 REGISTER_OPTYPE_DECLARE(DROPOUTDOMASKV3D, "DropOutDoMaskV3D");
 REGISTER_OPTYPE_DECLARE(SOFTMAXV2WITHDROPOUTDOMASKV3D, "SoftmaxV2WithDropOutDoMaskV3D");
 REGISTER_OPTYPE_DECLARE(DROPOUTGENMASK, "DropOutGenMask");
+REGISTER_OPTYPE_DECLARE(AXPYWITHSOFTMAXANDDROPOUTDOMASK, "AxpyWithSoftmaxAndDropOutDoMask");
 REGISTER_OPTYPE_DECLARE(CONCAT, "Concat");
 REGISTER_OPTYPE_DECLARE(ROIPOOLING, "ROIPooling");
 REGISTER_OPTYPE_DECLARE(PROPOSAL, "Proposal");

inc/framework/common/util.h

@@ -118,13 +118,13 @@
   } while (false)
 
 // Check if the parameter is null. If yes, return PARAM_INVALID and record the error
-#define GE_CHECK_NOTNULL(val)                                                   \
-  do {                                                                          \
-    if ((val) == nullptr) {                                                     \
-      REPORT_INNER_ERROR("E19999", "Param:%s is nullptr, check invalid", #val); \
-      GELOGE(ge::FAILED, "[Check][Param:%s]null is invalid.", #val);            \
-      return ge::PARAM_INVALID;                                                 \
-    }                                                                           \
+#define GE_CHECK_NOTNULL(val, ...)                                                          \
+  do {                                                                                      \
+    if ((val) == nullptr) {                                                                 \
+      REPORT_INNER_ERROR("E19999", "Param:" #val " is nullptr, check invalid" __VA_ARGS__); \
+      GELOGE(ge::FAILED, "[Check][Param:" #val "]null is invalid" __VA_ARGS__);             \
+      return ge::PARAM_INVALID;                                                             \
+    }                                                                                       \
   } while (false)
 
 // Check if the parameter is null. If yes, just return and record the error

inc/framework/omg/parser/model_parser.h

@@ -52,7 +52,7 @@ class GE_FUNC_VISIBILITY ModelParser {
    * @return SUCCESS
    * @return Others failed
    */
-  virtual domi::Status Parse(const char *file, ge::Graph &graph) = 0;
+  virtual Status Parse(const char *file, ge::Graph &graph) = 0;
 
   /**
    * @ingroup domi_omg
@@ -64,7 +64,7 @@ class GE_FUNC_VISIBILITY ModelParser {
    * @return FAILED
    * @author
    */
-  virtual domi::Status ParseFromMemory(const char *data, uint32_t size, ge::ComputeGraphPtr &graph) = 0;
+  virtual Status ParseFromMemory(const char *data, uint32_t size, ge::ComputeGraphPtr &graph) = 0;
 
   /**
    * @ingroup domi_omg
@@ -76,7 +76,7 @@ class GE_FUNC_VISIBILITY ModelParser {
    * @return FAILED
    * @author
    */
-  virtual domi::Status ParseFromMemory(const char *data, uint32_t size, ge::Graph &graph) = 0;
+  virtual Status ParseFromMemory(const char *data, uint32_t size, ge::Graph &graph) = 0;
 
   /**
    * @ingroup domi_omg
@@ -86,7 +86,7 @@ class GE_FUNC_VISIBILITY ModelParser {
    * @return SUCCESS
    * @return Others failed
    */
-  virtual domi::Status ParseProto(const google::protobuf::Message *proto, ge::ComputeGraphPtr &graph) = 0;
+  virtual Status ParseProto(const google::protobuf::Message *proto, ge::ComputeGraphPtr &graph) = 0;
 
   /**
    * @ingroup domi_omg
@@ -97,8 +97,8 @@ class GE_FUNC_VISIBILITY ModelParser {
    * @return SUCCESS
    * @return Others failed
    */
-  virtual domi::Status ParseProtoWithSubgraph(const google::protobuf::Message *proto, GetGraphCallback callback,
-                                              ge::ComputeGraphPtr &graph) = 0;
+  virtual Status ParseProtoWithSubgraph(const google::protobuf::Message *proto, GetGraphCallback callback,
+                                        ge::ComputeGraphPtr &graph) = 0;
   /**
    * @ingroup domi_omg
    * @brief Convert model files to JSON format
@@ -107,10 +107,10 @@ class GE_FUNC_VISIBILITY ModelParser {
    * @return SUCCESS
    * @return Others failed
    */
-  virtual domi::Status ToJson(const char *model_file, const char *json_file) {
+  virtual Status ToJson(const char *model_file, const char *json_file) {
     (void)model_file;
     (void)json_file;
-    return domi::SUCCESS;
+    return SUCCESS;
   }
 
   /*
@@ -121,7 +121,7 @@ class GE_FUNC_VISIBILITY ModelParser {
    */
   virtual ge::DataType ConvertToGeDataType(const uint32_t type) = 0;
 
-  virtual domi::Status ParseAllGraph(const google::protobuf::Message *root_proto, ge::ComputeGraphPtr &root_graph) = 0;
+  virtual Status ParseAllGraph(const google::protobuf::Message *root_proto, ge::ComputeGraphPtr &root_graph) = 0;
 
   /**
    * @ingroup domi_omg
@@ -131,7 +131,7 @@ class GE_FUNC_VISIBILITY ModelParser {
    * @return SUCCESS
    * @return Others failed
    */
-  virtual domi::Status ParseProto(const std::string &serialized_proto, ge::ComputeGraphPtr &graph) {
+  virtual Status ParseProto(const std::string &serialized_proto, ge::ComputeGraphPtr &graph) {
     (void)serialized_proto;
     (void)graph;
     return UNSUPPORTED;
@@ -146,13 +146,24 @@ class GE_FUNC_VISIBILITY ModelParser {
    * @return SUCCESS
    * @return Others failed
    */
-  virtual domi::Status ParseProtoWithSubgraph(const std::string &serialized_proto, GetGraphCallbackV2 callback,
-                                              ge::ComputeGraphPtr &graph) {
+  virtual Status ParseProtoWithSubgraph(const std::string &serialized_proto, GetGraphCallbackV2 callback,
+                                        ge::ComputeGraphPtr &graph) {
     (void)serialized_proto;
     (void)callback;
     (void)graph;
     return UNSUPPORTED;
   }
+
+  virtual bool HasError() {
+    return false;
+  }
+
+  virtual Status Save(const std::string &file) {
+    (void)file;
+    return SUCCESS;
+  }
+
+  virtual void Clear(){};
 };
 }  // namespace domi
 

inc/framework/omg/parser/parser_factory.h

@@ -23,6 +23,7 @@
 #include <string>
 #include "framework/omg/omg_inner_types.h"
 #include "framework/omg/parser/parser_types.h"
+#include "external/register/register.h"
 
 namespace domi {
 class WeightsParser;
@@ -131,6 +132,12 @@ class GE_FUNC_VISIBILITY WeightsParserRegisterar {
     return std::shared_ptr<WeightsParser>(ptr);                      \
   }                                                                  \
   WeightsParserRegisterar g_##type##_Weights_Parser_Creator(type, Creator_##type##_Weights_Parser)
-};  // namespace domi
+
+class GE_FUNC_VISIBILITY OpRegTbeParserFactory {
+ public:
+  static OpRegTbeParserFactory *Instance();
+  void Finalize(const domi::OpRegistrationData &reg_data);
+};
+}  // namespace domi
 
 #endif  // INC_FRAMEWORK_OMG_PARSER_PARSER_FACTORY_H_

inc/framework/omg/parser/weights_parser.h

@@ -67,6 +67,17 @@ class GE_FUNC_VISIBILITY WeightsParser {
    * @author
    */
   virtual Status ParseFromMemory(const char *input, uint32_t lengt, ge::ComputeGraphPtr &graph) = 0;
+
+  virtual bool HasError() {
+    return false;
+  }
+
+  virtual Status Save(const std::string &file) {
+    (void)file;
+    return SUCCESS;
+  }
+
+  virtual void Clear() {}
 };
 }  // namespace domi
 

inc/framework/pne/process_node_engine.h

@@ -27,6 +27,15 @@
 #include "framework/pne/pne_model.h"
 
 namespace ge {
+class ProcessNodeEngineImpl {
+ public:
+  virtual Status OptimizeGraph(const std::vector<GeTensor> &inputs, ComputeGraphPtr &compute_graph) = 0;
+
+  virtual Status BuildGraph(ComputeGraphPtr &compute_graph, PneModelPtr &model) = 0;
+};
+
+using ProcessNodeEngineImplPtr = std::shared_ptr<ProcessNodeEngineImpl>;
+
 class ProcessNodeEngine {
  public:
   ProcessNodeEngine() = default;
@@ -45,8 +54,11 @@ class ProcessNodeEngine {
 
   virtual const std::string &GetEngineName(const ge::NodePtr &node_ptr = nullptr) const = 0;
 
+  virtual void SetImpl(ProcessNodeEngineImplPtr impl) = 0;
+
  protected:
   std::string engine_id_;
+  ProcessNodeEngineImplPtr impl_ = nullptr;
 };
 
 using ProcessNodeEnginePtr = std::shared_ptr<ProcessNodeEngine>;

inc/framework/runtime/gert_api.h

@@ -0,0 +1,27 @@
+/**
+ * Copyright (c) Huawei Technologies Co., Ltd. 2022. All rights reserved.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#ifndef AIR_CXX_INC_FRAMEWORK_RUNTIME_GERT_API_H_
+#define AIR_CXX_INC_FRAMEWORK_RUNTIME_GERT_API_H_
+#include "model_v2_executor.h"
+#include "common/ge_types.h"
+
+namespace gert {
+std::unique_ptr<ModelV2Executor> LoadExecutorFromFile(const char *file_path, ge::graphStatus &error_code);
+std::unique_ptr<ModelV2Executor> LoadExecutorFromModelData(const ge::ModelData &model_data,
+                                                           ge::graphStatus &error_code);
+}  // namespace gert
+#endif  // AIR_CXX_INC_FRAMEWORK_RUNTIME_GERT_API_H_

inc/framework/runtime/model_desc.h

@@ -0,0 +1,94 @@
+/**
+ * Copyright (c) Huawei Technologies Co., Ltd. 2022. All rights reserved.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#ifndef AIR_CXX_INC_FRAMEWORK_RUNTIME_MODEL_DESC_H_
+#define AIR_CXX_INC_FRAMEWORK_RUNTIME_MODEL_DESC_H_
+#include "common/ge_types.h"
+#include "exe_graph/runtime/shape.h"
+#include "exe_graph/runtime/continuous_vector.h"
+#include "exe_graph/runtime/storage_format.h"
+#include "exe_graph/runtime/storage_shape.h"
+
+namespace gert {
+class ShapeRange {
+ public:
+  const Shape &GetMin() const;
+  const Shape &GetMax() const;
+  Shape &MutableMin();
+  Shape &MutableMax();
+
+ private:
+  Shape min_;
+  Shape max_;
+};
+
+class ModelIoDesc {
+ public:
+  const char *GetName() const;
+  int32_t GetDataType() const;
+  ge::Format GetStorageFormat() const;
+  ge::Format GetOriginFormat() const;
+  int64_t GetSize() const;
+  const Shape &GetStorageShape() const;
+  const Shape &GetOriginShape() const;
+  const ShapeRange &GetOriginShapeRange() const;
+  const ShapeRange &GetStorageShapeRange() const;
+
+  void SetName(const char *name);
+  void SetDataType(int32_t data_type);
+  void SetStorageFormat(ge::Format format);
+  void SetOriginFormat(ge::Format format);
+  Shape &MutableStorageShape();
+  Shape &MutableOriginShape();
+  ShapeRange &MutableOriginShapeRange();
+  ShapeRange &MutableStorageShapeRange();
+
+ private:
+  const char *name_;
+  int32_t data_type_;
+  StorageFormat format_;
+  StorageShape shape_;
+  ShapeRange storage_shape_range_;
+  ShapeRange origin_shape_range_;
+};
+
+class ModelDesc {
+ public:
+  static size_t CalcSize(size_t input_num, size_t output_num);
+  const ModelIoDesc *GetInputDesc(size_t index) const;
+  const ModelIoDesc *GetAllInputsDesc(size_t &input_num) const;
+
+  const ModelIoDesc *GetOutputDesc(size_t index) const;
+  const ModelIoDesc *GetAllOutputsDesc(size_t &output_num) const;
+
+  ModelIoDesc *MutableInputDesc(size_t index);
+  ModelIoDesc *MutableOutputDesc(size_t index);
+  ModelIoDesc *AllMutableIoDesc(size_t &input_num, size_t &output_num);
+  void SetInputNum(size_t input_num);
+  void SetOutputNum(size_t output_num);
+
+  ge::graphStatus GetDynamicBatchInfo(std::vector<std::vector<int64_t>> &batch_info, int32_t &dynamic_type) const;
+  ge::graphStatus GetUserDesignateShapeOrder(std::vector<std::string> &user_designate_shape_order) const;
+  ge::graphStatus GetModelAttrs(std::vector<std::string> &attrs) const;
+
+ private:
+  size_t input_num_;
+  size_t output_num_;
+  ContinuousVector model_io_descs_;
+};
+}  // namespace gert
+
+#endif  // AIR_CXX_INC_FRAMEWORK_RUNTIME_MODEL_DESC_H_

inc/framework/runtime/model_v2_executor.h

@@ -0,0 +1,142 @@
+/**
+ * Copyright (c) Huawei Technologies Co., Ltd. 2022. All rights reserved.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#ifndef AIR_CXX_RUNTIME_V2_CORE_MODEL_V_2_EXECUTOR_H_
+#define AIR_CXX_RUNTIME_V2_CORE_MODEL_V_2_EXECUTOR_H_
+#include <memory>
+#include "graph/compute_graph.h"
+#include "graph/ge_error_codes.h"
+#include "model_desc.h"
+#include "runtime/stream.h"
+#include "exe_graph/runtime/tensor.h"
+
+namespace gert {
+enum SubExeGraphType { kInitExeGraph, kMainExeGraph, kDeInitExeGraph, kSubExeGraphTypeEnd };
+static constexpr char *kSubExeGraphTypeStrs[kSubExeGraphTypeEnd] = {(char *)"Init", (char *)"Main", (char *)"DeInit"};
+inline const char *GetSubExeGraphTypeStr(SubExeGraphType type) {
+  return kSubExeGraphTypeStrs[type];
+}
+
+class ResourceGuard {
+ public:
+  void *ResetExecutionData(std::unique_ptr<uint8_t[]> execution_data);
+  void ResetAnyValue(std::unique_ptr<uint8_t[]> any_values, size_t count);
+  void PushNode(void *node);
+  void PushWatcher(void *watcher);
+  void *ResetNodesArray(std::unique_ptr<uint8_t[]> nodes_array);
+  void *ResetStartNodesArray(std::unique_ptr<uint8_t[]> start_nodes_array);
+  void *ResetNodesIndgreeArray(std::unique_ptr<uint8_t[]> nodes_indgree_array);
+  void *ResetNodesWaitIndgreeArray(std::unique_ptr<uint8_t[]> nodes_indgree_array);
+  void *ResetInputsArray(std::unique_ptr<uint8_t[]> inputs_array);
+  void *ResetOutputsArray(std::unique_ptr<uint8_t[]> outputs_array);
+  void *ResetWatchersArray(std::unique_ptr<uint8_t[]> watchers_array);
+  void *ResetReadyQueue(void *ready_queue);
+  void *ResetBuffer(std::unique_ptr<uint8_t[]> buffer);
+  void *ResetComputeNodeInfo(std::unique_ptr<uint8_t[]> compute_node_info);
+  void *ResetKernelExtendInfo(std::unique_ptr<uint8_t[]> kernel_extend_info);
+  void *ResetModelDesc(std::unique_ptr<uint8_t[]> model_desc);
+
+  ~ResourceGuard();
+
+ private:
+  std::unique_ptr<uint8_t[]> execution_data_holder_;
+  size_t any_values_num_;
+  std::unique_ptr<uint8_t[]> any_values_guard_;
+
+  std::vector<std::unique_ptr<void, decltype(&free)>> nodes_guarder_;
+  std::vector<std::unique_ptr<void, decltype(&free)>> watchers_guarder_;
+  std::unique_ptr<uint8_t[]> continuous_buffer_guarder_;
+  std::unique_ptr<uint8_t[]> buffer_guarder_;
+  std::unique_ptr<uint8_t[]> compute_node_info_guarder_;
+  std::unique_ptr<uint8_t[]> kernel_extend_info_guarder_;
+  std::unique_ptr<uint8_t[]> model_desc_guarder_;
+
+  std::unique_ptr<uint8_t[]> nodes_array_guarder_;
+  std::unique_ptr<uint8_t[]> start_nodes_array_guarder_;
+  std::unique_ptr<uint8_t[]> nodes_indgree_array_guarder_;
+  std::unique_ptr<uint8_t[]> nodes_wait_indgree_array_guarder_;
+  std::unique_ptr<uint8_t[]> inputs_array_guarder_;
+  std::unique_ptr<uint8_t[]> outputs_array_guarder_;
+  std::unique_ptr<uint8_t[]> watchers_array_guarder_;
+  std::unique_ptr<void, decltype(&free)> ready_queue_guarder_{nullptr, nullptr};
+};
+
+struct ModelExecuteArg {
+  rtStream_t stream;
+};
+static_assert(std::is_standard_layout<ModelExecuteArg>::value, "The class ModelExecuteArg must be a POD");
+
+class ExeGraphExecutor {
+ public:
+  // todo unload时释放anyvalue资源
+  ge::graphStatus Load() {
+    return ge::GRAPH_SUCCESS;
+  }
+  ge::graphStatus UnLoad() {
+    return ge::GRAPH_SUCCESS;
+  }
+
+  /**
+   * 设置图执行的输入/输出，需要注意的是，使用者需要自己保证inputs/outputs刷新完全！！！
+   */
+  ge::graphStatus SpecifyInputs(void **inputs, size_t start, size_t num);
+  ge::graphStatus SpecifyOutputs(void **outputs, size_t num);
+  ge::graphStatus Execute();
+
+  const void *GetExecutionData() const {
+    return execution_data_;
+  }
+
+  ResourceGuard &GetResourceGuard();
+  void *SetExecutionData(std::unique_ptr<uint8_t[]> execution_data);
+
+ private:
+  friend class ModelV2ExecutorTestHelper;
+
+  void *execution_data_;
+  ResourceGuard resource_guard_;
+};
+class ModelV2Executor {
+ public:
+  static std::unique_ptr<ModelV2Executor> Create(const ge::ComputeGraphPtr &root_graph);
+
+  ge::graphStatus Load();
+  ge::graphStatus Execute(const ModelExecuteArg &arg, Tensor **inputs, size_t input_num, Tensor **outputs,
+                          size_t output_num);
+  ge::graphStatus ExecuteSync(Tensor **inputs, size_t input_num, Tensor **outputs, size_t output_num);
+  ge::graphStatus UnLoad();
+
+  const ModelDesc &GetModelDesc() const;
+  void SetModelDesc(ModelDesc *model_desc);
+  ModelV2Executor(const ModelV2Executor &) = delete;
+  ModelV2Executor(ModelV2Executor &&) = delete;
+  ModelV2Executor &operator=(const ModelV2Executor &) = delete;
+  ModelV2Executor &operator=(ModelV2Executor &&) = delete;
+
+ private:
+  friend class ModelV2ExecutorBuilder;
+  friend class ModelV2ExecutorTestHelper;
+  ModelV2Executor() = default;
+
+ private:
+  std::array<ExeGraphExecutor, kSubExeGraphTypeEnd> graphs_;
+  ResourceGuard resource_guard_;
+  ModelDesc *model_desc_ = nullptr;
+  rtStream_t default_stream_ = nullptr;
+};
+}  // namespace gert
+
+#endif  // AIR_CXX_RUNTIME_V2_CORE_MODEL_V_2_EXECUTOR_H_

metadef

@@ -1 +1 @@
-Subproject commit f3e9df35da67ff00a22a09ec5b369bbc4bac9e74
+Subproject commit 002617852e22767bd864db3c01595630e23f5496

third_party/fwkacllib/inc/hccl/base.h

@@ -211,6 +211,8 @@ typedef struct {
 
 #define HCCL_REQUEST_NULL   NULL
 
+#define HCCL_TAG_ANY (1 << 30)
+
 #ifdef __cplusplus
 }
 #endif // __cplusplus

third_party/fwkacllib/inc/ops/all_ops.h

@@ -42,6 +42,7 @@
 #include "list_ops.h"
 #include "logging_ops.h"
 #include "lookup_ops.h"
+#include "map_ops.h"
 #include "math_ops.h"
 #include "matrix_calculation_ops.h"
 #include "nn_batch_norm_ops.h"
@@ -79,4 +80,5 @@
 #include "warp_perspective_ops.h"
 #include "vector_search.h"
 #include "deep_md.h"
+#include "encoding_ops.h"
 #endif  // OPS_BUILT_IN_OP_PROTO_INC_ALL_OPS_H_

third_party/fwkacllib/inc/ops/array_ops.h

@@ -1550,6 +1550,39 @@ REG_OP(EnsureShape)
                             DT_FLOAT,DT_DOUBLE, DT_COMPLEX64, DT_COMPLEX128}))
     .REQUIRED_ATTR(shape, ListInt)
     .OP_END_FACTORY_REG(EnsureShape)
+
+/**
+* @brief Finds the first unique element from every consecutive group of equivalent elements.
+
+* @par Inputs:
+* x: A ND tensor.
+
+* @par Attributes:
+* @li return_idx: An optional bool. Whether to also return the indices. The default value is False
+* @li return_count: An optional bool. Whether to also return the counts for each element. The default is False.
+* @li axis: An optional int. Which one axis to apply unique. The default is 1000, which means None.
+
+* @par Outputs:
+* @li y: "x" in the unique output "y".
+* @li idx: The index of each value of "x".
+* @li count: The counts of each value of "y".
+
+* @attention Constraints:
+* UniqueConsecutive runs on the Ascend AI CPU, which delivers poor performance.
+
+* @par Third-party framework compatibility
+* Compatible with the PyTorch operator UniqueConsecutive.
+*/
+
+REG_OP(UniqueConsecutive)
+    .INPUT(x, TensorType::BasicType())
+    .OUTPUT(y, TensorType::BasicType())
+    .OUTPUT(idx, TensorType::IndexNumberType())
+    .OUTPUT(count, TensorType::IndexNumberType())
+    .ATTR(return_idx, Bool, false)
+    .ATTR(return_counts, Bool, false)
+    .ATTR(axis, Int, 1000)
+    .OP_END_FACTORY_REG(UniqueConsecutive)
 }  // namespace ge
 
 #endif  // OPS_BUILT_IN_OP_PROTO_INC_ARRAY_OPS_H_

third_party/fwkacllib/inc/ops/case_condition_ops.h

@@ -1,53 +0,0 @@
-/**
- * Copyright 2019 Huawei Technologies Co., Ltd
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/*!
- * \file case_condition_ops.h
- * \brief
- */
-#ifndef OPS_BUILT_IN_OP_PROTO_INC_CASE_CONDITION_OPS_H_
-#define OPS_BUILT_IN_OP_PROTO_INC_CASE_CONDITION_OPS_H_
-
-#include "graph/operator_reg.h"
-
-namespace ge {
-/**
-*@brief x[0] is i, x[1] is j and x[2] is k when algorithm is LU,
-y = 0 when i >= k && j < k,
-y = 1 when i == k && j == k,
-y = 2 when i > k && j == k,
-y = 3 when i == k && j > k,
-y = 4 when i > k && j > k,
-default y = 5
-use for lu decomposition
-*@par Inputs:
-*x: A Tensor of type int32/int64/uint64. \n
-
-*@par Attributes:
-*algorithm: A string, only support LU now
-*@par Outputs:
-*y: A Tensor of type int32
-*/
-REG_OP(CaseCondition)
-    .INPUT(x, TensorType({DT_INT32, DT_INT64, DT_UINT64}))
-    .OUTPUT(y, TensorType({DT_INT32}))
-    .ATTR(algorithm, String, "LU")
-    .OP_END_FACTORY_REG(CaseCondition)
-
-} // namespace ge
-
-
-#endif  // OPS_BUILT_IN_OP_PROTO_INC_CASE_CONDITION_OPS_H_

third_party/fwkacllib/inc/ops/coordinates_1d_to_2d_ops.h

@@ -1,48 +0,0 @@
-/**
- * Copyright 2019 Huawei Technologies Co., Ltd
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/*!
- * \file coordinates_1d_to_2d_ops.h
- * \brief
- */
-#ifndef OPS_BUILT_IN_OP_PROTO_INC_COORDINATES_1D_TO_2D_OPS_H_
-#define OPS_BUILT_IN_OP_PROTO_INC_COORDINATES_1D_TO_2D_OPS_H_
-
-#include "graph/operator_reg.h"
-
-namespace ge {
-/**
-*@brief Convert one-dimensional coordinates to two-dimensional coordinates.
-*@par Inputs:
-*@li x: A Tensor of type int32/int64/uint64. One-dimensional coordinates.
-*@li shape: A Tensor of type int32/int64/uint64. 4D tensor [N,C,H,W].
-*@par Outputs:
-*@li row: row of two-dimensional
-*@li col: col of two-dimensional
-*@li n: col number of two-dimensional
-*/
-REG_OP(Coordinates1DTo2D)
-    .INPUT(x, TensorType({DT_INT32, DT_INT64, DT_UINT64}))
-    .INPUT(shape, TensorType({DT_INT32, DT_INT64, DT_UINT64}))
-    .OUTPUT(row, TensorType({DT_INT32, DT_INT64, DT_UINT64}))
-    .OUTPUT(col, TensorType({DT_INT32, DT_INT64, DT_UINT64}))
-    .OUTPUT(n, TensorType({DT_INT32, DT_INT64, DT_UINT64}))
-    .OP_END_FACTORY_REG(Coordinates1DTo2D)
-
-} // namespace ge
-
-
-#endif  // OPS_BUILT_IN_OP_PROTO_INC_COORDINATES_1D_TO_2D_OPS_H_

third_party/fwkacllib/inc/ops/elewise_calculation_ops.h

@@ -1620,8 +1620,8 @@ REG_OP(Greater)
 * Compatible with the TensorFlow operator zeros_like.
 */
 REG_OP(ZerosLike)
-    .INPUT(x, TensorType::BasicType())
-    .OUTPUT(y, TensorType::BasicType())
+    .INPUT(x, TensorType({BasicType(), DT_VARIANT}))
+    .OUTPUT(y, TensorType({BasicType(), DT_VARIANT}))
     .OP_END_FACTORY_REG(ZerosLike)
 
 /**

third_party/fwkacllib/inc/ops/encoding_ops.h

@@ -0,0 +1,49 @@
+/**
+ * Copyright (C) Huawei Technologies Co., Ltd 2022-2022. All rights reserved.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*!
+ * \file encoding_ops.h
+ * \brief
+ */
+#ifndef OPS_BUILT_IN_OP_PROTO_INC_ENCODING_OPS_H_
+#define OPS_BUILT_IN_OP_PROTO_INC_ENCODING_OPS_H_
+
+#include "graph/operator_reg.h"
+#include "graph/operator.h"
+
+namespace ge {
+/**
+* @brief An op to decode indices for LDPC code. \n
+
+* @par Inputs:
+* @li valid_num: an int32 tensor indicates index limit for each line.
+* @li matrix_info: an int32 2D-tensor store the block indices info of connection H matrix. \n
+
+* @par Outputs:
+* indices: an int32 2D-tensor store the concrete indices value.
+*
+* @par Restrictions:
+* Warning:THIS FUNCTION IS EXPERIMENTAL. Please do not use.
+*/
+
+REG_OP(LDPCDecode)
+    .INPUT(valid_num, TensorType({DT_INT32}))
+    .INPUT(matrix_info, TensorType({DT_INT32}))
+    .OUTPUT(indices, TensorType({DT_INT32}))
+    .OP_END_FACTORY_REG(LDPCDecode)
+}  // namespace ge
+
+#endif  // OPS_BUILT_IN_OP_PROTO_INC_ENCODING_OPS_H_

third_party/fwkacllib/inc/ops/functional_ops.h

@@ -124,6 +124,30 @@ REG_OP(If)
     .GRAPH(else_branch)
     .OP_END_FACTORY_REG(If)
 
+/**
+ *@brief Select one of the subgraphs to pass the input tensors and return the output tensors . \n
+
+ *@par Inputs:
+ *@li branch_index: A int32 scalar which determines the selected subgraph.
+ *@li input: The input tensors, which will be passed to the subgraph . It's a dynamic input. \n
+
+ *@par Graphs:
+ *branches: A list of subgraphs, each of which takes 'input' and returns a list of tensors,
+ *          whose types are the same as what every other subgraph returns . \n
+
+ *@par Outputs:
+ *output: The output tensors returned by one of branches . It's a dynamic output. \n
+
+ *@par Third-party framework compatibility
+ *@Compatible with the TensorFlow operator Case.
+ */
+REG_OP(StatelessCase)
+    .INPUT(branch_index, DT_INT32)
+    .DYNAMIC_INPUT(input, TensorType::ALL())
+    .DYNAMIC_OUTPUT(output, TensorType::ALL())
+    .DYNAMIC_GRAPH(branches)
+    .OP_END_FACTORY_REG(StatelessCase)
+
 /**
  *@brief Select one of the subgraphs to pass the input tensors and return the output tensors . \n
 

third_party/fwkacllib/inc/ops/index_to_addr_ops.h

@@ -1,63 +0,0 @@
-/**
- * Copyright 2019 Huawei Technologies Co., Ltd
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/*!
- * \file index_to_addr_ops.h
- * \brief
- */
-#ifndef OPS_BUILT_IN_OP_PROTO_INC_INDEX_TO_ADDR_OPS_H_
-#define OPS_BUILT_IN_OP_PROTO_INC_INDEX_TO_ADDR_OPS_H_
-
-#include "graph/operator_reg.h"
-
-namespace ge {
-
-/**
-*@brief get block tensor according to base addr tensor, for hccl remote read to use.
-*@par Inputs:
-*@li base_addr: A Tensor of type int64/uint64. \n
-*@li row:A Tensor of type int64/uint64. \n
-*@li col: A Tensor of type int64/uint64.
-
-*@par Outputs:
-*addr_table: list of [rank id, host addr, device addr, read size]
-
-*@par Attributes:
-*@li ori_shape: An required list int. Shape of base tensor.
-*@li block_size: An required list int. Shape of split block tensor.
-*@li ori_storage_mode: An optional string from: '"Matrix", "UT"'. Defaults to
-"Matrix". Currently only support Matrix storage
-*@li block_storage_mode: An optional string from: '"Matrix", "UT"'. Defaults to
-"Matrix". Currently only support Matrix storage
-*@li rank_id: An optional int of rank id. Defaults is 0
-*@li dtype: An optional Type of base tensor. Defaults is DT_FLOAT
-*/
-REG_OP(IndexToAddr)
-    .INPUT(base_addr, TensorType({DT_INT64, DT_UINT64}))
-    .INPUT(x, TensorType({DT_INT64, DT_UINT64}))
-    .OUTPUT(addrs_table, TensorType({DT_INT64, DT_UINT64}))
-    .REQUIRED_ATTR(ori_shape, ListInt)
-    .REQUIRED_ATTR(block_size, ListInt)
-    .ATTR(ori_storage_mode, String, "Matrix")
-    .ATTR(block_storage_mode, String, "Matrix")
-    .ATTR(rank_id, Int, 0)
-    .ATTR(dtype, Type, DT_FLOAT)
-    .OP_END_FACTORY_REG(IndexToAddr)
-
-} // namespace ge
-
-
-#endif  // OPS_BUILT_IN_OP_PROTO_INC_INDEX_TO_ADDR_OPS_H_

third_party/fwkacllib/inc/ops/map_ops.h

@@ -0,0 +1,152 @@
+/**
+ * Copyright (c) Huawei Technologies Co., Ltd. 2022. All rights reserved.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*!
+ * \file map_ops.h
+ * \brief
+ */
+#ifndef OPS_BUILT_IN_OP_PROTO_INC_MAP_OPS_H_
+#define OPS_BUILT_IN_OP_PROTO_INC_MAP_OPS_H_
+#include "graph/operator_reg.h"
+
+namespace ge {
+/**
+* @brief Returns whether the given key exists in the map. \n
+
+* @par Inputs:
+* @li input_handle: A scalar Tensor of type variant. The original map.
+* @li key: The key to check. Supports int32, int64, string. \n
+
+* @par Outputs:
+* has_key: A scalar Tensor of type bool. Whether the key is already in the map or not. \n
+
+* @par Third-party framework compatibility.
+* Compatible with tensorflow TensorMapHasKey operator.
+*/
+REG_OP(TensorMapHasKey)
+    .INPUT(input_handle, TensorType({DT_VARIANT}))
+    .INPUT(key, TensorType({DT_INT32, DT_INT64, DT_STRING}))
+    .OUTPUT(has_key, TensorType({DT_BOOL}))
+    .OP_END_FACTORY_REG(TensorMapHasKey)
+
+/**
+* @brief Returns a tensor map with item from given key erased. \n
+
+* @par Inputs:
+* @li input_handle: A scalar Tensor of type variant. The original map.
+* @li key: The key of the value to be erased. Supports int32, int64, string. \n
+
+* @par Outputs:
+* output_handle: A scalar Tensor of type variant. The map with value from given key removed. \n
+
+* @par Third-party framework compatibility.
+* Compatible with tensorflow TensorMapErase operator.
+*/    
+REG_OP(TensorMapErase)
+    .INPUT(input_handle, TensorType({DT_VARIANT}))
+    .INPUT(key, TensorType({DT_INT32, DT_INT64, DT_STRING}))
+    .OUTPUT(output_handle, TensorType({DT_VARIANT}))
+    .OP_END_FACTORY_REG(TensorMapErase)
+
+/**
+* @brief Returns a map that is the 'input_handle'
+  with the given key-value pair inserted. \n
+
+* @par Inputs:
+* @li input_handle: The original map, Must be type: DT_VARIANT.
+* @li key: A Tensor,the key to be inserted.Must be one of
+  the following types: int32, int64, string.
+* @li value: A Tensor,the value to be inserted.Must be
+  one of BasicType types. \n
+
+* @par Outputs:
+* output_handle: The map with key and value inserted.
+  Must be type: DT_VARIANT. \n
+*/
+REG_OP(TensorMapInsert)
+    .INPUT(input_handle, TensorType({DT_VARIANT}))
+    .INPUT(key, TensorType({DT_INT32, DT_INT64, DT_STRING}))
+    .INPUT(value, BasicType)
+    .OUTPUT(output_handle, TensorType({DT_VARIANT}))
+    .OP_END_FACTORY_REG(TensorMapInsert)
+
+/**
+* @brief Returns the value from a given key in a tensor map . \n
+
+* @par Inputs:
+* @li input_handle: The input map. Must be type: DT_VARIANT.
+* @li key: A Tensor,the key to be looked up. Must be one of 
+  the following types: int32,int64,string . \n
+
+* @par Attributes:
+* value_dtype: A int. Representing the type of value . \n
+
+* @par Outputs:
+* value: A Tensor,the value found from the given key.
+*/
+REG_OP(TensorMapLookup)
+    .INPUT(input_handle, TensorType({DT_VARIANT}))
+    .INPUT(key, TensorType({DT_INT32, DT_INT64, DT_STRING}))
+    .OUTPUT(value, BasicType)
+    .REQUIRED_ATTR(value_dtype, Type)
+    .OP_END_FACTORY_REG(TensorMapLookup)
+
+/**
+* @brief return TensorMap Size. \n
+*
+* @par Inputs:
+* input_handle: A Tensor. Must be one of the following types: variant. \n
+*
+* @par Outputs:
+* size: A Tensor. Must be one of the following types: int32. \n
+*/
+REG_OP(TensorMapSize)
+    .INPUT(input_handle, TensorType({DT_VARIANT}))
+    .OUTPUT(size, TensorType({DT_INT32}))
+    .OP_END_FACTORY_REG(TensorMapSize)
+
+/**
+ * @brief Return TensorMapStackKeys \n
+ *
+ * @par Inputs:
+ * input_handle: A Tensor. Must be one of the following types: variant. \n
+ *
+ * @par Outputs:
+ * keys: A Tensor. Must be one of the following types: int32, int64, string. \n
+ * 
+ * @par Attributes:
+ * key_dtype: An required param. It is the dtype of the key.
+ */
+REG_OP(TensorMapStackKeys)
+    .INPUT(input_handle, TensorType({DT_VARIANT}))
+    .OUTPUT(keys, TensorType({DT_INT32, DT_INT64, DT_STRING}))
+    .REQUIRED_ATTR(key_dtype, Type)
+    .OP_END_FACTORY_REG(TensorMapStackKeys)
+
+/**
+* @brief Creates and returns an empty tensor map. \n
+
+* @par Outputs:
+* handle: An empty tensor map . \n
+
+* @par Third-party framework compatibility.
+* Compatible with tensorflow EmptyTensorMap operator.
+*/
+REG_OP(EmptyTensorMap)
+    .OUTPUT(handle, TensorType({DT_VARIANT}))
+    .OP_END_FACTORY_REG(EmptyTensorMap)
+}  // namespace ge
+#endif  // OPS_BUILT_IN_OP_PROTO_INC_MAP_OPS_H_

third_party/fwkacllib/inc/ops/matrix_calculation_ops.h

@@ -24,6 +24,82 @@
 #include "graph/operator_reg.h"
 
 namespace ge {
+/**
+* @brief Backprop W of AttentionLnQKV + ReduceSumD \n
+* @par Inputs:
+* Four inputs, including:
+* @li x: A Tensor. Must be one of the following types: float16.
+* @li query_dx: A Tensor. Must be one of the following types: float16.
+* @li key_dw: A Tensor. Must be one of the following types: float16.
+* @li value_dw: A Tensor. Must be one of the following types: float16.
+
+* @par Attributes:
+* @li trans_a: A optional attribute, the type is bool. Defaults to True.
+* @li trans_b: A optional attribute, the type is bool. Defaults to False. \n
+
+* @par Outputs:
+* Six outputs, including:
+* @li dw_query: A Tensor. Must be one of the following types: float16.
+* @li dw_key: A Tensor. Must be one of the following types: float16.
+* @li dw_value: A Tensor. Must be one of the following types: float16.
+* @li dbias_query: A Tensor. Must be one of the following types: float16.
+* @li dbias_key: A Tensor. Must be one of the following types: float16.
+* @li dbias_value: A Tensor. Must be one of the following types: float16. \n
+
+* @par Restrictions:
+* Warning: THIS FUNCTION IS EXPERIMENTAL. Please do not use. \n
+*/
+REG_OP(AttentionQKVGradW)
+    .INPUT(x, TensorType({DT_FLOAT16}))
+    .INPUT(query_dx, TensorType({DT_FLOAT16}))
+    .INPUT(key_dw, TensorType({DT_FLOAT16}))
+    .INPUT(value_dw, TensorType({DT_FLOAT16}))
+    .OUTPUT(dw_query, TensorType({DT_FLOAT16}))
+    .OUTPUT(dw_key, TensorType({DT_FLOAT16}))
+    .OUTPUT(dw_value, TensorType({DT_FLOAT16}))
+    .OUTPUT(dbias_query, TensorType({DT_FLOAT16}))
+    .OUTPUT(dbias_key, TensorType({DT_FLOAT16}))
+    .OUTPUT(dbias_value, TensorType({DT_FLOAT16}))
+    .ATTR(trans_a, Bool, true)
+    .ATTR(trans_b, Bool, false)
+    .OP_END_FACTORY_REG(AttentionQKVGradW)
+
+/**
+* @brief Backprop X of AttentionLnQKV + AddN \n
+* @par Inputs:
+* Seven inputs, including:
+* @li ln_dx: A Tensor. Must be one of the following types: float16.
+* @li query_dx: A Tensor. Must be one of the following types: float16.
+* @li key_dw: A Tensor. Must be one of the following types: float16.
+* @li value_dw: A Tensor. Must be one of the following types: float16.
+* @li kernel_query: A Tensor. Must be one of the following types: float16.
+* @li kernel_key: A Tensor. Must be one of the following types: float16.
+* @li kernel_value: A Tensor. Must be one of the following types: float16. \n
+
+* @par Attributes:
+* @li trans_a: A optional attribute, the type is bool. Defaults to False.
+* @li trans_b: A optional attribute, the type is bool. Defaults to True. \n
+
+* @par Outputs:
+* One outputs, including:
+* @li dx: A Tensor. Must be one of the following types: float16. \n
+
+* @par Restrictions:
+* Warning: THIS FUNCTION IS EXPERIMENTAL. Please do not use. \n
+*/
+REG_OP(AttentionQKVGradX)
+    .INPUT(ln_dx, TensorType({DT_FLOAT16}))
+    .INPUT(query_dx, TensorType({DT_FLOAT16}))
+    .INPUT(key_dw, TensorType({DT_FLOAT16}))
+    .INPUT(value_dw, TensorType({DT_FLOAT16}))
+    .INPUT(kernel_query, TensorType({DT_FLOAT16}))
+    .INPUT(kernel_key, TensorType({DT_FLOAT16}))
+    .INPUT(kernel_value, TensorType({DT_FLOAT16}))
+    .OUTPUT(dx, TensorType({DT_FLOAT16}))
+    .ATTR(trans_a, Bool, false)
+    .ATTR(trans_b, Bool, true)
+    .OP_END_FACTORY_REG(AttentionQKVGradX)
+
 /**
 * @brief
              / (MatMul -> ConfusionTransposeD).
@@ -54,6 +130,9 @@ namespace ge {
 * @li value_output: A Tensor. Must be one of the following types: float16.
 * @li mean: A Tensor. Must be one of the following types: float16.
 * @li variance: A Tensor. Must be one of the following types: float16. \n
+
+* @par Restrictions:
+* Warning: THIS FUNCTION IS EXPERIMENTAL. Please do not use. \n
 */
 REG_OP(AttentionLnQKV)
     .INPUT(x, TensorType({DT_FLOAT16}))

third_party/fwkacllib/inc/ops/nn_detect_ops.h

@@ -2276,6 +2276,46 @@ REG_OP(BalanceRois)
     .OUTPUT(balance_rois, TensorType({DT_FLOAT16, DT_FLOAT}))
     .OUTPUT(index, TensorType({DT_INT32}))
     .OP_END_FACTORY_REG(BalanceRois)
+
+/**
+* @brief First calculate the minimum closure area of the two boxes, IoU,
+* The CIoU is obtained by combining the center distance and width to height ratio and IoU. \n
+
+* @par Inputs:
+* Two inputs, including:
+* @li bboxes: Bounding boxes, a 2D Tensor of type float16 or float32 with
+* shape (4, N). "N" indicates the number of bounding boxes, and the value
+* "4" refers to [x1, y1, x2, y2] or [x, y, w, h].
+* @li gtboxes: Ground-truth boxes, a 2D Tensor of type float16 or float32
+* with shape (4, M). "M" indicates the number of ground truth boxes, and
+* the value "4" refers to [x1, y1, x2, y2] or [x, y, w, h] . \n
+
+* @par Attributes:
+* @li trans: An optional bool, true for 'xywh', false for 'xyxy'.
+* @li is_cross: An optional bool, control whether the output shape is [N, M] or [1, N]
+* @li mode: An optional string, computation mode, a character string with the value range of [iou, iof]
+* @li atan_sub_flag: An optional bool, control whether to output atan_sub. \n
+
+* @par Outputs:
+* Two outputs, including:
+* @li overlap: A 2D Tensor of type float16 or float32 with shape [N, M] or [1, N],
+* specifying the IoU or IoF ratio .
+* @li atan_sub: A 2D Tensor of type float16 or float32 with shape [N, M] or [1, N],
+* specifying the IoU or IoF ratio . \n
+
+* @attention Constraints:
+* "is_cross" only support false, "atan_sub_flag" only support true.
+*/
+REG_OP(CIoU)
+    .INPUT(bboxes, TensorType({DT_FLOAT16, DT_FLOAT}))
+    .INPUT(gtboxes, TensorType({DT_FLOAT16, DT_FLOAT}))
+    .OUTPUT(overlap, TensorType({DT_FLOAT16, DT_FLOAT}))
+    .OUTPUT(atan_sub, TensorType({DT_FLOAT16, DT_FLOAT}))
+    .ATTR(trans, Bool, false)
+    .ATTR(is_cross, Bool, true)
+    .ATTR(mode, String, "iou")
+    .ATTR(atan_sub_flag, Bool, false)
+    .OP_END_FACTORY_REG(CIoU)
 }  // namespace ge
 
 #endif  // OPS_BUILT_IN_OP_PROTO_INC_NN_DETECT_OPS_H_

third_party/fwkacllib/inc/ops/nonlinear_fuc_ops.h

@@ -458,22 +458,22 @@ REG_OP(Softsign)
     .OP_END_FACTORY_REG(Softsign)
 
 /**
- * @brief Computes softsignGrad: y_grad / (1 + abs(x)) ** 2 .
+ * @brief Computes softsignGrad: gradients / (1 + abs(features)) ** 2 .
  *
  * @par Inputs:
  * Two inputs, including:
- * @li y_grad: A Tensor.Must be one of the following types:float16, float32,
- * @li x: A Tensor of the same type and shape as "gradients".
+ * @li gradients: A Tensor.Must be one of the following types:float16, float32,
+ * @li features: A Tensor of the same type and shape as "gradients".
 
- * @par x_grad:
- * output:A Tensor. Has the same type as "y_grad".
+ * @par Outputs:
+ * output:A Tensor. Has the same type as "gradients".
  * @par Third-party framework compatibility
  * Compatible with the TensorFlow operator SoftsignGrad.
  */
 REG_OP(SoftsignGrad)
-    .INPUT(y_grad, TensorType::FloatingDataType())
-    .INPUT(x, TensorType::FloatingDataType())
-    .OUTPUT(x_grad, TensorType::FloatingDataType())
+    .INPUT(gradients, TensorType::FloatingDataType())
+    .INPUT(features, TensorType::FloatingDataType())
+    .OUTPUT(output, TensorType::FloatingDataType())
     .OP_END_FACTORY_REG(SoftsignGrad)
 
 /**
@@ -500,23 +500,23 @@ REG_OP(Selu)
     .OP_END_FACTORY_REG(Selu)
 
 /**
-* @brief Computes SeluGrad backprops: y_grad * (y + scale * alpha)
-*    if y < 0, scale * y_grad otherwise .
+* @brief Computes SeluGrad backprops: gradients * (outputs + scale * alpha)
+*    if outputs < 0, scale * gradients otherwise .
 
 * @par Inputs:
 * Two inputs, including:
-* @li y_grad: A Tensor of type RealNumberType .
-* @li y: A Tensor of type RealNumberType .
+* @li gradients: A Tensor of type RealNumberType .
+* @li outputs: A Tensor of type RealNumberType .
 * @par Outputs:
-* x_grad: A Tensor. Must have the same type as "y_grad" .
+* y: A Tensor. Must have the same type as "gradients" .
 
 * @par Third-party framework compatibility
 * Compatible with the TensorFlow operator SeluGrad.
 */
 REG_OP(SeluGrad)
-    .INPUT(y_grad, TensorType::RealNumberType())
-    .INPUT(y, TensorType::RealNumberType())
-    .OUTPUT(x_grad, TensorType::RealNumberType())
+    .INPUT(gradients, TensorType::RealNumberType())
+    .INPUT(outputs, TensorType::RealNumberType())
+    .OUTPUT(y, TensorType::RealNumberType())
     .OP_END_FACTORY_REG(SeluGrad)
 
 /**

third_party/fwkacllib/inc/ops/selection_ops.h

@@ -2612,6 +2612,34 @@ REG_OP(DynSeqOuter)
     .INPUT(seq_len2, TensorType({DT_INT32}))
     .OUTPUT(y, TensorType::BasicType())
     .OP_END_FACTORY_REG(DynSeqOuter)
+
+/**
+* @brief Returns sliced data based on max nmsed_num. \n
+
+* @par Inputs:
+* Four inputs, including:
+* @li input_nmsed_boxes: A Tensor. Must be the following types: float16.
+* @li input_nmsed_score: A Tensor. Must be the following types: float16.
+* @li input_nmsed_class: A Tensor. Must be the following types: float16.
+* @li input_nmsed_num: A Tensor. Must be the following types: int32. \n
+
+* @par Outputs:
+* output_nmsed_boxes: A Tensor. Must be the following type: float.
+* output_nmsed_score: A Tensor. Must be the following type: float.
+* output_nmsed_class: A Tensor. Must be the following type: float. \n
+
+* @par Restrictions:
+* Warning: THIS FUNCTION IS EXPERIMENTAL. Please do not use.
+*/
+REG_OP(NonMaxSuppressionBucketize)
+    .INPUT(input_nmsed_boxes, TensorType({DT_FLOAT16}))
+    .INPUT(input_nmsed_score, TensorType({DT_FLOAT16}))
+    .INPUT(input_nmsed_class, TensorType({DT_FLOAT16}))
+    .INPUT(input_nmsed_num, TensorType({DT_INT32}))
+    .OUTPUT(output_nmsed_boxes, TensorType({DT_FLOAT}))
+    .OUTPUT(output_nmsed_score, TensorType({DT_FLOAT}))
+    .OUTPUT(output_nmsed_class, TensorType({DT_FLOAT}))
+    .OP_END_FACTORY_REG(NonMaxSuppressionBucketize)
 } // namespace ge
 
 #endif  // OPS_BUILT_IN_OP_PROTO_INC_SELECTION_OPS_H_

third_party/fwkacllib/inc/ops/slice_write_ops.h

@@ -1,50 +0,0 @@
-/**
- * Copyright 2019 Huawei Technologies Co., Ltd
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/*!
- * \file slice_write_ops.h
- * \brief
- */
-#ifndef OPS_BUILT_IN_OP_PROTO_INC_SLICE_WRITE_OPS_H_
-#define OPS_BUILT_IN_OP_PROTO_INC_SLICE_WRITE_OPS_H_
-
-#include "graph/operator_reg.h"
-
-namespace ge {
-
-/**
-*@brief write tensor value to tensor x.
-*@par Inputs:
-*x: A Tensor of type float16/float/double/int32/int64. \n
-*begin:A Tensor of type int32/int64. \n
-*value: A Tensor of type float16/float/double/int32/int64.
-*@par Outputs:
-*x: same tensor with input x
-*/
-REG_OP(SliceWrite)
-    .INPUT(x, TensorType({DT_FLOAT, DT_FLOAT16, DT_DOUBLE, \
-        DT_INT32, DT_INT64}))
-    .INPUT(begin, TensorType({DT_INT32, DT_INT64}))
-    .INPUT(value, TensorType({DT_FLOAT, DT_FLOAT16, DT_DOUBLE, \
-        DT_INT32, DT_INT64}))
-    .OUTPUT(x, TensorType({DT_FLOAT, DT_FLOAT16, DT_DOUBLE, \
-        DT_INT32, DT_INT64}))
-    .OP_END_FACTORY_REG(SliceWrite)
-
-} // namespace ge
-
-
-#endif  // OPS_BUILT_IN_OP_PROTO_INC_SLICE_WRITE_OPS_H_

third_party/fwkacllib/inc/ops/sparse_ops.h

@@ -951,7 +951,7 @@ REG_OP(SerializeSparse)
         DT_UINT16, DT_INT32, DT_INT64, DT_DOUBLE, DT_FLOAT, DT_FLOAT16, \
         DT_COMPLEX64, DT_COMPLEX128, DT_RESOURCE, DT_STRING}))
     .INPUT(shape, TensorType({DT_INT64}))
-    .OUTPUT(serialized_sparse, TensorType({DT_STRING}))
+    .OUTPUT(serialized_sparse, TensorType({DT_STRING, DT_VARIANT}))
     .ATTR(out_type, Type, DT_STRING)
     .OP_END_FACTORY_REG(SerializeSparse)
 
@@ -979,7 +979,7 @@ REG_OP(SerializeManySparse)
         DT_UINT16, DT_INT32, DT_INT64, DT_DOUBLE, DT_FLOAT, DT_FLOAT16, \
         DT_COMPLEX64, DT_COMPLEX128, DT_RESOURCE, DT_STRING}))
     .INPUT(shape, TensorType({DT_INT64}))
-    .OUTPUT(serialized_sparse, TensorType({DT_STRING}))
+    .OUTPUT(serialized_sparse, TensorType({DT_STRING, DT_VARIANT}))
     .ATTR(out_type, Type, DT_STRING)
     .OP_END_FACTORY_REG(SerializeManySparse)
 
@@ -1002,7 +1002,7 @@ REG_OP(SerializeManySparse)
 * Compatible with the TensorFlow operator DeserializeSparse.
 */
 REG_OP(DeserializeSparse)
-    .INPUT(serialized_sparse, TensorType({DT_STRING}))
+    .INPUT(serialized_sparse, TensorType({DT_STRING, DT_VARIANT}))
     .OUTPUT(indices, TensorType({DT_INT64}))
     .OUTPUT(values, TensorType({DT_BOOL, DT_INT8, DT_UINT8, DT_INT16, \
         DT_UINT16, DT_INT32, DT_INT64, DT_DOUBLE, DT_FLOAT, DT_FLOAT16, \

third_party/fwkacllib/inc/ops/vector_search.h

@@ -154,6 +154,98 @@ REG_OP(CalcBucketsLimitAndOffset)
     .OUTPUT(buckets_offset, TensorType({DT_INT32, DT_INT64}))
     .REQUIRED_ATTR(total_limit, Int)
     .OP_END_FACTORY_REG(CalcBucketsLimitAndOffset)
+
+/**
+*@brief get block tensor according to base addr tensor, for hccl remote read to use.
+*@par Inputs:
+*@li base_addr: A Tensor of type int64/uint64. \n
+*@li row:A Tensor of type int64/uint64. \n
+*@li col: A Tensor of type int64/uint64.
+
+*@par Outputs:
+*addr_table: list of [rank id, host addr, device addr, read size]
+
+*@par Attributes:
+*@li ori_shape: An required list int. Shape of base tensor.
+*@li block_size: An required list int. Shape of split block tensor.
+*@li ori_storage_mode: An optional string from: '"Matrix", "UT"'. Defaults to
+"Matrix". Currently only support Matrix storage
+*@li block_storage_mode: An optional string from: '"Matrix", "UT"'. Defaults to
+"Matrix". Currently only support Matrix storage
+*@li rank_id: An optional int of rank id. Defaults is 0
+*@li dtype: An optional Type of base tensor. Defaults is DT_FLOAT
+*/
+REG_OP(IndexToAddr)
+    .INPUT(base_addr, TensorType({DT_INT64, DT_UINT64}))
+    .INPUT(x, TensorType({DT_INT64, DT_UINT64}))
+    .OUTPUT(addrs_table, TensorType({DT_INT64, DT_UINT64}))
+    .REQUIRED_ATTR(ori_shape, ListInt)
+    .REQUIRED_ATTR(block_size, ListInt)
+    .ATTR(ori_storage_mode, String, "Matrix")
+    .ATTR(block_storage_mode, String, "Matrix")
+    .ATTR(rank_id, Int, 0)
+    .ATTR(dtype, Type, DT_FLOAT)
+    .OP_END_FACTORY_REG(IndexToAddr)
+
+/**
+*@brief Convert one-dimensional coordinates to two-dimensional coordinates.
+*@par Inputs:
+*@li x: A Tensor of type int32/int64/uint64. One-dimensional coordinates.
+*@li shape: A Tensor of type int32/int64/uint64. 4D tensor [N,C,H,W].
+*@par Outputs:
+*@li row: row of two-dimensional
+*@li col: col of two-dimensional
+*@li n: col number of two-dimensional
+*/
+REG_OP(Coordinates1DTo2D)
+    .INPUT(x, TensorType({DT_INT32, DT_INT64, DT_UINT64}))
+    .INPUT(shape, TensorType({DT_INT32, DT_INT64, DT_UINT64}))
+    .OUTPUT(row, TensorType({DT_INT32, DT_INT64, DT_UINT64}))
+    .OUTPUT(col, TensorType({DT_INT32, DT_INT64, DT_UINT64}))
+    .OUTPUT(n, TensorType({DT_INT32, DT_INT64, DT_UINT64}))
+    .OP_END_FACTORY_REG(Coordinates1DTo2D)
+
+/**
+*@brief x[0] is i, x[1] is j and x[2] is k when algorithm is LU,
+y = 0 when i >= k && j < k,
+y = 1 when i == k && j == k,
+y = 2 when i > k && j == k,
+y = 3 when i == k && j > k,
+y = 4 when i > k && j > k,
+default y = 5
+use for lu decomposition
+*@par Inputs:
+*x: A Tensor of type int32/int64/uint64. \n
+
+*@par Attributes:
+*algorithm: A string, only support LU now
+*@par Outputs:
+*y: A Tensor of type int32
+*/
+REG_OP(CaseCondition)
+    .INPUT(x, TensorType({DT_INT32, DT_INT64, DT_UINT64}))
+    .OUTPUT(y, TensorType({DT_INT32}))
+    .ATTR(algorithm, String, "LU")
+    .OP_END_FACTORY_REG(CaseCondition)
+
+/**
+*@brief write tensor value to tensor x.
+*@par Inputs:
+*x: A Tensor of type float16/float/double/int32/int64. \n
+*begin:A Tensor of type int32/int64. \n
+*value: A Tensor of type float16/float/double/int32/int64.
+*@par Outputs:
+*x: same tensor with input x
+*/
+REG_OP(SliceWrite)
+    .INPUT(x, TensorType({DT_FLOAT, DT_FLOAT16, DT_DOUBLE, \
+        DT_INT32, DT_INT64}))
+    .INPUT(begin, TensorType({DT_INT32, DT_INT64}))
+    .INPUT(value, TensorType({DT_FLOAT, DT_FLOAT16, DT_DOUBLE, \
+        DT_INT32, DT_INT64}))
+    .OUTPUT(x, TensorType({DT_FLOAT, DT_FLOAT16, DT_DOUBLE, \
+        DT_INT32, DT_INT64}))
+    .OP_END_FACTORY_REG(SliceWrite)
 } // namespace ge
 
 #endif  // OPS_BUILT_IN_OP_PROTO_INC_VECTOR_SEARCH_H_

third_party/fwkacllib/inc/runtime/rt_model.h

@@ -263,6 +263,18 @@ typedef struct tagrtStreamLabelGotoTask_t {
     uint8_t reserved[36];
 } rtStreamLabelGotoTask_t;
 
+typedef struct tagrtNpuGetFloatStatusTask_t {
+    uint64_t outputAddr;
+    uint64_t outputSize;
+    uint32_t checkMode;
+    uint8_t reserved[20];
+} rtNpuGetFloatStatusTask_t;
+
+typedef struct tagrtNpuClearFloatStatusTask_t {
+    uint32_t checkMode;
+    uint8_t reserved[36];
+} rtNpuClearFloatStatusTask_t;
+
 typedef struct tagTaskInfo {
     uint32_t type;
     uint32_t streamID;
@@ -288,6 +300,8 @@ typedef struct tagTaskInfo {
         rtStreamSwitchNTaskInfo_t streamSwitchNTask;
         rtStreamLabelSwitchByIndexTask_t streamLabelSwitchIndexTask;
         rtStreamLabelGotoTask_t streamLabelGotoTask;
+        rtNpuGetFloatStatusTask_t npuGetFloatStatusTask;
+        rtNpuClearFloatStatusTask_t npuClearFloatStatusTask;
         uint32_t reserved[10];
     } u;
 } rtTaskInfo_t;