sync-from-trunk-to-blue-zone-1009

ge/client/module.mk

@@ -70,9 +70,10 @@ LOCAL_SHARED_LIBRARIES := \
     libregister \
     libge_compiler \
     libge_common \
-    libmsprof
-
+    libmsprof \
+    stub/libascend_hal
 
+LOCAL_STATIC_LIBRARIES := libmsprofiler
 
 LOCAL_LDFLAGS := -lrt -ldl
 
@@ -107,6 +108,7 @@ LOCAL_SHARED_LIBRARIES := \
     libge_common \
     libmsprof
 
+LOCAL_STATIC_LIBRARIES := libmsprofiler
 
 LOCAL_LDFLAGS := -lrt -ldl
 LOCAL_CFLAGS += \

ge/common/profiling/profiling_manager.cc

@@ -58,8 +58,6 @@ FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY ge::Status ProfilingManager::In
 
   GELOGI("ProfilingManager::Init  job_id:%s", job_id_.c_str());
 
-
-
   Status ret;
   if (!recv_profiling_config_.empty()) {
     GELOGI("Profiling json config from acl:%s", recv_profiling_config_.c_str());

ge/executor/module.mk

@@ -92,6 +92,7 @@ local_ge_executor_shared_library :=        \
     libregister                            \
     libmsprof                              \
     liberror_manager                       \
+    libascend_hal
 
 local_ge_executor_ldflags := -lrt -ldl     \
 

ge/graph/build/memory/graph_mem_assigner.cc

@@ -907,7 +907,6 @@ Status GraphMemoryAssigner::ReAssignAtomicMemory(bool is_loop_graph) {
     int64_t atomic_mem_start = static_cast<int64_t>(mem_iter->second.mem_offset_);
     GELOGD("Begin to reAssign atomic memory, atomic address memory start = %ld", atomic_mem_start);
 
-    for (auto &atomic_node : iter.second) {
       vector<int64_t> mem_offset_end;
       status = AssignAtomicOutputAndWorkspaceMemory(atomic_node, mem_offset_end);
       if (status != SUCCESS) {

ge/graph/build/memory/graph_mem_assigner.h

@@ -137,6 +137,9 @@ class GraphMemoryAssigner {
 
   ge::Status ReAssignAtomicMemory(bool is_loop_graph);
   
+  ge::Status FilterAtomicNodesForMemoryAssign(std::map<NodePtr, vector<NodePtr>> &normal_atomic_nodes_map,
+                                              std::vector<NodePtr> &connecting_output_atomic_nodes);
+
   ge::Status FilterAtomicNodesForMemoryAssign(std::map<NodePtr, vector<NodePtr>> &normal_atomic_nodes_map,
                                               std::vector<NodePtr> &connecting_output_atomic_nodes);
 

ge/graph/load/new_model_manager/data_dumper.cc

@@ -728,11 +728,7 @@ Status DataDumper::BuildTaskInfo(aicpu::dump::OpMappingInfo &op_mapping_info) {
     }
     if (dump_properties_.GetDumpMode() == kDumpInput) {
       if (op_iter.is_task) {
-        Status ret = DumpInput(op_iter, task);
-        if (ret != SUCCESS) {
-          GELOGE(ret, "Dump input failed");
-          return ret;
-        }
+        GE_CHK_STATUS_RET(DumpInput(op_iter, task), "Dump input failed");
       }
       op_mapping_info.mutable_task()->Add(std::move(task));
       continue;

ge/graph/load/new_model_manager/model_manager.cc

@@ -236,7 +236,6 @@ ModelManager::~ModelManager() {
   std::lock_guard<std::mutex> lock(map_mutex_);
   model_map_.clear();
   model_aicpu_kernel_.clear();
-  cust_aicpu_so_.clear();
 
   GE_IF_BOOL_EXEC(device_count > 0, GE_CHK_RT(rtDeviceReset(0)));
 }
@@ -400,6 +399,7 @@ Status ModelManager::Unload(uint32_t model_id) {
   }
   std::lock_guard<std::mutex> lock(exeception_infos_mutex_);
   exception_infos_.clear();
+  cust_aicpu_so_.clear();
   return SUCCESS;
 }
 

ge/graph/preprocess/multi_batch_copy_graph.cc

@@ -40,7 +40,6 @@
 #include "inc/pass_manager.h"
 #include "graph/common/local_context.h"
 
-using std::map;
 using std::set;
 using std::string;
 using std::vector;
@@ -264,24 +263,27 @@ Status MultiBatchGraphCopyer::Init() {
 }
 
 Status MultiBatchGraphCopyer::LabelStatus() {
-  map<string, vector<NodePtr>> frame_enters;
-  InitStatus(frame_enters);
-
+  for (const auto &data : origin_data_nodes_) {
+    auto data_shape = NodeUtils::GetOutputDesc(*data, kDataOutIndex).GetShape();
+    if (!IsAllDimsPositive(data_shape.GetDims())) {
+      origin_nodes_status_[data.get()] = kNodeInBatchBranch;
+    }
+  }
   bool changed = true;
   // If anyone of in node is kNodeInBatchBranch, it is also kNodeInBatchBranch
   while (changed) {
     changed = false;
     for (const auto &node : origin_all_nodes_) {
+      auto iter = origin_nodes_status_.find(node.get());
+      if (iter != origin_nodes_status_.end()) {
+        continue;
+      }
       for (auto &in_node : node->GetInAllNodes()) {
         bool is_in_batch = origin_nodes_status_.find(in_node.get()) != origin_nodes_status_.end() &&
                            origin_nodes_status_[in_node.get()] == kNodeInBatchBranch;
         if (is_in_batch) {
-          if (origin_nodes_status_.find(node.get()) == origin_nodes_status_.end() ||
-              origin_nodes_status_[node.get()] != kNodeInBatchBranch) {
-            origin_nodes_status_[node.get()] = kNodeInBatchBranch;
-            ResetEnterStatus(frame_enters, node);
-            changed = true;
-          }
+          origin_nodes_status_[node.get()] = kNodeInBatchBranch;
+          changed = true;
           break;
         }
       }

ge/graph/preprocess/multi_batch_copy_graph.h

@@ -69,8 +69,6 @@ class MultiBatchGraphCopyer {
 
   // label status for origin_all_nodes_
   Status LabelStatus();
-  void InitStatus(std::map<string, vector<NodePtr>> &frame_enters);
-  void ResetEnterStatus(std::map<string, vector<NodePtr>> &frame_enters, const NodePtr &node);
   // add nodes functions
   Status CreateNewNodes();
 

inc/framework/common/string_util.h

@@ -61,8 +61,10 @@ class StringUtils {
   ///  @param [in] delim  separator
   ///  @return string array after segmentation
   ///
+  /*lint -e1077*/
   static std::vector<std::string> Split(const std::string &str, char delim) {
     std::vector<std::string> elems;
+    /*lint +e1077*/
 
     if (str.empty()) {
       elems.emplace_back("");

third_party/fwkacllib/inc/ops/aipp.h

@@ -25,16 +25,21 @@
 
 namespace ge {
 /**
-*@brief Performs AI pre-processing (AIPP) on images including color space conversion (CSC),
-image normalization (by subtracting the mean value or multiplying a factor), image cropping
-(by specifying the crop start and cropping the image to the size required by the neural network), and much more. \n
+*@brief Performs AI pre-processing (AIPP) on images including color space 
+conversion (CSC),
+image normalization (by subtracting the mean value or multiplying a factor), 
+image cropping
+(by specifying the crop start and cropping the image to the size required by 
+the neural network), and much more. \n
 
 *@par Inputs:
-*@li images: An NCHW or NHWC tensor of type uint8, specifying the input to the data layer.
+*@li images: An NCHW or NHWC tensor of type uint8, specifying the input to the 
+data layer.
 *@li params: Dynamic AIPP configuration parameters of type uint8. \n
 
 *@par Attributes:
-*aipp_config_path: A required string, specifying the path of the AIPP configuration file. \n
+*aipp_config_path: A required string, specifying the path of the AIPP 
+configuration file. \n
 
 *@par Outputs:
 *features: The AIPP-processed output tensor of type float16 or uint8.

third_party/fwkacllib/inc/ops/elewise_calculation_ops.h

@@ -28,9 +28,10 @@ namespace ge {
 
 *@par Inputs:
 *Dynamic inputs, including:
-* @li x: A list of Tensor objects, each with same shape and type. The supported types are:
+* @li x: A list of Tensor objects, each with same shape and type. The supported 
+types are:
 *   float16, float32, double, int32, uint8, int16, int8, complex64, int64,
-*   qint8, quint8, qint32, uint16, complex128, uint32, uint64. It's a dynamic input. \n
+*   qint8, quint8, qint32, uint16, complex128, uint32, uint64. \n
 
 *@par Outputs:
 *y: A Tensor. Has the same shape and type as the elements of "x". \n
@@ -121,7 +122,8 @@ REG_OP(MinimumGrad)
 
 *@par Inputs:
 *One input:
-*x:A Tensor. Must be one of the following types: bool, float16, float, int8, int32, uint32, uint8,
+*x:A Tensor. Must be one of the following types: bool, float16, float, int8, 
+int32, uint32, uint8,
    int64, uint64, int16, uint16, double, complex64, complex128, qint8, quint8, qint16, quint16, qint32. \n
 
 *@par Attributes:
@@ -385,7 +387,8 @@ REG_OP(Sign)
 
 *@par Inputs:
 *Two inputs, including: \n
-*@li x1: A Tensor. Must be one of the following types: float16, float32, float64, int32, int64, complex64,complex128
+*@li x1: A Tensor. Must be one of the following types: float16, float32,
+ float64, int32, int64, complex64,complex128
 *@li x2: A Tensor. Has the same type as "x1". \n
 
 *@par Outputs:
@@ -484,12 +487,16 @@ REG_OP(Equal)
 
 *@par Inputs:
 *One input:\n
-*x: A Tensor. Must be one of the following types: float16, float32, double, complex64, complex128. \n
+*x: A Tensor. Must be one of the following types: float16, float32, double, 
+complex64, complex128. \n
 
 *@par Attributes:
-*@li base: An optional attribute of type float32, specifying the base gamma. Defaults to "-1.0".
-*@li scale: An optional attribute of type float32, specifying the scale alpha. Defaults to "1.0".
-*@li shift: An optional attribute of type float32, specifying the shift beta. Defaults to "0.0". \n
+*@li base: An optional attribute of type float32, specifying the base gamma. 
+Defaults to "-1.0".
+*@li scale: An optional attribute of type float32, specifying the scale alpha. 
+Defaults to "1.0".
+*@li shift: An optional attribute of type float32, specifying the shift beta. 
+Defaults to "0.0". \n
 
 *@par Outputs:
 *y: A Tensor of the same type as "x". \n
@@ -510,7 +517,8 @@ REG_OP(Exp)
 
 *@par Inputs:
 *One input:
-*x: A Tensor. Must be one of the following types: float16, float32, double, complex64, complex128. \n
+*x: A Tensor. Must be one of the following types: float16, float32, double, 
+complex64, complex128. \n
 
 *@par Outputs:
 *y: A Tensor of the same type as "x". \n
@@ -527,7 +535,9 @@ REG_OP(Expm1)
 *@brief: Computes the reciprocal of "x". \n
 
 *@par Inputs:\n
-*x: A Tensor. Must be one of the following types: float16, float32, int32, int64, double, complex64, complex128. \n
+*x: A Tensor. Must be one of the following types: float16, float32,
+int32, int64, double,
+complex64, complex128. \n
 
 *@par Outputs:
 *y: A Tensor. Has the same type as "x". \n
@@ -749,7 +759,8 @@ REG_OP(Xlogy)
 
 *@par Inputs:
 *One input: \n
-*x: A Tensor. Must be one of the following types: float16, float32, float64, int32, int64, complex64, complex128
+*x: A Tensor. Must be one of the following types: float16, float32, float64,
+int32, int64, complex64, complex128
 
 *@par Outputs:
 *y: A Tensor. Has the same type as "x". \n
@@ -790,7 +801,8 @@ REG_OP(Rsqrt)
 
 *
 *@par Inputs:
-* x: A tensor. Must be one of the following types: float16, float32, float64, int32, int64, complex64, complex128.
+* x: A tensor. Must be one of the following types: float16, float32, float64, 
+int32, int64, complex64, complex128.
 *
 *@par Outputs:
 * y: A tensor. Has the same type as "x".
@@ -811,7 +823,8 @@ REG_OP(Asin)
 
 *
 *@par Inputs:
-*@li y: A tensor of type float16, float32, float64, int32, int64, complex64, complex128.
+*@li y: A tensor of type float16, float32, float64, 
+int32, int64, complex64, complex128.
 *@li dy: A tensor of the same type as "y".
 *
 *@attention Constraints:
@@ -838,7 +851,8 @@ REG_OP(AsinGrad)
 
 *
 *@par Inputs:
-* x: A tensor. Must be one of the following types: float16, float32, float64, int32, int64, complex64, complex128.
+* x: A tensor. Must be one of the following types: float16, float32, float64,
+int32, int64, complex64, complex128.
 *
 *@par Outputs:
 * y: A tensor. Has the same type as "x".
@@ -883,7 +897,8 @@ REG_OP(AcosGrad)
 
 *
 *@par Inputs:
-* x: A tensor. Must be one of the following types: float16, float32, float64, complex64, complex128.
+* x: A tensor. Must be one of the following types: float16, float32, float64,
+ complex64, complex128.
 *
 *@attention Constraints:
 * x Given an input tensor, the function computes inverse hyperbolic cosine of every element.\n
@@ -1160,7 +1175,8 @@ REG_OP(FusedMulAdd)
 
 *
 *@par Inputs:
-*@li x1: A tensor. Must be one of the following types: float16, float32, float64, uint8, int8, int16, int32, int64, complex64, complex128.
+*@li x1: A tensor. Must be one of the following types: float16, float32, float64,
+uint8, int8, int16, int32, int64, complex64, complex128.
 *@li x2: A tensor of the same type as "x1".
 *
 *@attention Constraints:
@@ -1189,7 +1205,8 @@ REG_OP(AddV2)
 *@brief Updates "ref" by adding "value" to it. \n
 
 *@par Inputs:
-*@li ref: A Tensor. Must be one of the following types: float16, float32, int8, int16, int32, int64, uint8, uint16, uint32, uint64.
+*@li ref: A Tensor. Must be one of the following types: float16, float32, int8,
+int16, int32, int64, uint8, uint16, uint32, uint64.
 *@li value: A Tensor of the same type as "ref". \n
 
 *@par Attributes:
@@ -1218,12 +1235,14 @@ REG_OP(AssignAdd)
 *@brief Updates "ref" by assigning "value" to it. \n
 
 *@par Inputs:
-*@li ref: A Tensor. Must be one of the following types: float16, float32, int8, int16, int32, int64, uint8, uint16, uint32, uint64.
+*@li ref: A Tensor. Must be one of the following types: float16, float32, int8, int16, 
+int32, int64, uint8, uint16, uint32, uint64.
 *@li value: A Tensor of the same type as "ref". \n
 
 *@par Attributes:
 *@li validate_shape: An optional bool. Defaults to "true".
-                     If "true", the operation will validate that the shape of "value" matches the shape of the Tensor being assigned to.
+                     If "true", the operation will validate that the shape of "value"
+                     matches the shape of the Tensor being assigned to.
 *                    If "false", "ref" will take on the shape of "value".
 *                    This attribute is reserved.
 *@li use_locking: An optional bool. Defaults to True.
@@ -1252,7 +1271,8 @@ REG_OP(Assign)
 
 *
 *@par Inputs:
-*@li var: A tensor. Must be one of the following types: float32, float64, int32, uint8, int16, int8, complex64, int64, qint8, quint8, qint32, uint16, complex128, uint32, uint64
+*@li var: A tensor. Must be one of the following types: float32, float64,
+int32, uint8, int16, int8, complex64, int64, qint8, quint8, qint32, uint16, complex128, uint32, uint64
 *@li value: A tensor of the same type as "var".
 *
 *@par Attributes:
@@ -1644,7 +1664,9 @@ REG_OP(Atan2)
 
 *
 *@par Inputs:
-*@li x1: A tensor. Must be one of the following types: float32, float64, int32, uint8, int16, int8, complex64, int64, qint8, quint8, qint32, uint16, complex128, float16, uint32, uint64
+*@li x1: A tensor. Must be one of the following types: float32, float64, int32,
+ uint8, int16, int8, complex64, int64, qint8, quint8, qint32, uint16, complex128,
+float16, uint32, uint64
 *@li x2: A tensor of the same type as "x1".
 *
 *@par Attributes:
@@ -1666,16 +1688,18 @@ REG_OP(ApproximateEqual)
 
 /**
 *@brief Returns the element-wise sum of a list of tensors.\n
-* AccumulateNV2 performs the same operation as AddN, but does not wait for all of its inputs
-to be ready before beginning to sum.\n This can save memory if inputs are ready at different times,
-since minimum temporary storage is proportional to the output size rather than the inputs size.
- Returns a Tensor of same shape and type as the elements of inputs. \n
+* AccumulateNV2 performs the same operation as AddN, but does not wait for all 
+of its inputs to be ready before beginning to sum.\n This can save memory if 
+inputs are ready at different times, \n since minimum temporary storage is 
+proportional to the output size rather than the inputs size.\n Returns a Tensor 
+of same shape and type as the elements of inputs. \n
 
 *
 *@par Inputs:
 *Dynamic inputs, including:
-* x: A tensor. Must be one of the following types: float32, float64, int32, uint8, int16, int8, complex64, int64,
-qint8, quint8, qint32, uint16, complex128, float16, uint32, uint64. It's a dynamic input. \n
+* x: A tensor. Must be one of the following types: float32, float64, int32, 
+uint8, int16, int8, complex64, int64, \n qint8, quint8, qint32, uint16, 
+complex128, float16, uint32, uint64.
 *
 *@par Outputs:
 * y: A tensor. Has the same type as "x".
@@ -1731,7 +1755,8 @@ REG_OP(FakeQuantWithMinMaxArgs)
 
 *@par Inputs:
 *Two inputs, including: \n
-*@li gradients: A Tensor of type float32. Backpropagated gradients above the FakeQuantWithMinMaxArgs operation.
+*@li gradients: A Tensor of type float32. Backpropagated gradients 
+above the FakeQuantWithMinMaxArgs operation.
 *@li x: A Tensor of type float32. Has the same type and format as "gradients".\n
 * This is the input Tensor of the FakeQuantWithMinMaxArgs operator.\n
 
@@ -2210,9 +2235,13 @@ REG_OP(BiasAdd)
 
 *@par Inputs:
 *Two inputs, including:
-*@li x: A Tensor. Must be one of the following types: float32, float64, int32, uint8, int16, int8, complex64, int64, qint8, quint8, qint32, bfloat16, uint16, complex128, float16, uint32, uint64.
+*@li x: A Tensor. Must be one of the following types: float32, float64, int32, 
+uint8, int16, int8, complex64, int64, qint8, quint8, qint32, bfloat16, uint16, 
+complex128, float16, uint32, uint64.
 *format is ND.
-*@li dimension: A Tensor. Must be one of the following types: int32, int64. Must be in the range [-rank(input x), rank(input x)]. Describes which dimension of the input Tensor to reduce across.
+*@li dimension: A Tensor. Must be one of the following types: int32, int64. 
+Must be in the range [-rank(input x), rank(input x)]. Describes which dimension 
+of the input Tensor to reduce across.
 * The format is ND.
 *@par Attributes:
 *dtype: The output type, either "int32" or "int64". Defaults to "int64". \n
@@ -2286,6 +2315,7 @@ REG_OP(ArgMaxV2)
     .ATTR(dtype, Type, DT_INT64)
     .OP_END_FACTORY_REG(ArgMaxV2)
 
+
 /**
 *@brief Returns the index with the largest value across axes of a tensor. \n
 
@@ -2298,15 +2328,16 @@ REG_OP(ArgMaxV2)
 *@li dtype: The output type, either "int32" or "int64". Defaults to "int64". \n
 
 *@par Outputs:
-*y: A multi-dimensional Tensor of type int32, specifying the index with the largest value. The dimension is one less than that of "x". \n
+*y: A multi-dimensional Tensor of type int32, specifying the index with the 
+largest value. The dimension is one less than that of "x". \n
 
 *@attention Constraints:
 *@li x: If there are multiple maximum values, the index of the first maximum value is used.
-*@li The value range of "dimension" is [-dims, dims - 1]. "dims" is the dimension length of "x". \n
+*@li The value range of "dimension" is [-dims, dims - 1]. "dims" is the 
+dimension length of "x". \n
 
 *@par Third-party framework compatibility
 * Compatible with TensorFlow operator ArgMax.
-*
 * @par Restrictions:
 *Warning: THIS FUNCTION IS EXPERIMENTAL.  Please do not use.
 */
@@ -2929,9 +2960,13 @@ REG_OP(FusedMulAddN)
 *@li bias: An ND tensor of type float16 or float32. \n
 
 *@par Attributes:
-*@li axis: An optional int32 used to compute the shape of bias input from the online bottoms. Defaults to "1".
-*@li num_axes: An optional int32 used to compute the shape of bias input from a Caffe model trained offline. Defaults to "1".
-*@li bias_from_blob: An optional bool. If "true", bias is input from a Caffe model trained offline. If "false", bias is input from online bottoms. Defaults to "true". \n
+*@li axis: An optional int32 used to compute the shape of bias input from the 
+online bottoms. Defaults to "1".
+*@li num_axes: An optional int32 used to compute the shape of bias input from a 
+Caffe model trained offline. Defaults to "1".
+*@li bias_from_blob: An optional bool. If "true", bias is input from a Caffe 
+model trained offline. If "false", bias is input from online bottoms. Defaults 
+to "true". \n
 
 *@par Outputs:
 *y: An ND tensor of type float16 or float32. \n
@@ -2939,13 +2974,25 @@ REG_OP(FusedMulAddN)
 *@attention Constraints:\n
 * Assume that the shape length of "x" is "n" and that of "bias" is "m".
 *@li "axis" is within the range [-n, n-1]. num_axes >= -1.
-*@li If "bias_from_blob = true", "num_axes = -1", and "axis >= 0", the ith axis of "bias" and the (i+"axis")th axis of "x" must have the same size (0 <= i < n-axis).\n
-* If "axis < 0", the ith axis of "bias" and the (i+n+"axis")th axis of "x" must have the same size (0 <= i < -axis).
-*@li If "bias_from_blob = true" and "num_axes = 0", "bias" is a scalar with shape length 1 and dimension size 1.
-*@li If "bias_from_blob = true", "num_axes > 0, and "axis >= 0", "axis + num_axes" must be less than or equal to "n" and the ith axis of "bias" and the (i+"axis")th axis of "x" must have the same size (0 <= i < num_axes).\n
-* If "axis < 0", "n + axis + num_axes" must be less than or equal to "n" and the ith axis of "bias" and the (i+n+"axis")th axis of "x" must have the same size (0 <= i < num_axes).
-*@li If "bias_from_blob = false", "bias" is not a scalar, and "axis >= 0","axis + m" must be less than or equal to "n" and the ith axis of "bias" and the (i+"axis")th axis of "x" must have the same size (0 <= i < m).\n
-* If "axis < 0", "n + axis + m" must be less than or equal to "n" and the ith axis of "bias" and the (i+n+"axis")th axis of "x" must have the same size (0 <= i < m).
+*@li If "bias_from_blob = true", "num_axes = -1", and "axis >= 0", the ith axis 
+of "bias" and the (i+"axis")th axis of "x" must have the same size (0 <= i < 
+n-axis).\n
+* If "axis < 0", the ith axis of "bias" and the (i+n+"axis")th axis of "x" must 
+have the same size (0 <= i < -axis).
+*@li If "bias_from_blob = true" and "num_axes = 0", "bias" is a scalar with 
+shape length 1 and dimension size 1.
+*@li If "bias_from_blob = true", "num_axes > 0, and "axis >= 0", "axis + 
+num_axes" must be less than or equal to "n" and the ith axis of "bias" and the 
+(i+"axis")th axis of "x" must have the same size (0 <= i < num_axes).\n
+* If "axis < 0", "n + axis + num_axes" must be less than or equal to "n" and 
+the ith axis of "bias" and the (i+n+"axis")th axis of "x" must have the same 
+size (0 <= i < num_axes).
+*@li If "bias_from_blob = false", "bias" is not a scalar, and "axis >= 0","axis 
++ m" must be less than or equal to "n" and the ith axis of "bias" and the (i
++"axis")th axis of "x" must have the same size (0 <= i < m).\n
+* If "axis < 0", "n + axis + m" must be less than or equal to "n" and the ith 
+axis of "bias" and the (i+n+"axis")th axis of "x" must have the same size (0 <= 
+i < m).
 *@par Third-party framework compatibility
 * Compatible with the Caffe operator Bias.
 */
@@ -3023,10 +3070,12 @@ REG_OP(FusedMulAddNL2loss)
 *@li x: A Tensor with any format. Must be one of the following types: float16, float32. \n
 
 *@par Attributes:
-*@li threshold: A required float32. Defaults to "0.0". "x" is compared with "threshold", outputs "1" for inputs above threshold; "0" otherwise. \n
+*@li threshold: A required float32. Defaults to "0.0". "x" is compared with 
+"threshold", outputs "1" for inputs above threshold; "0" otherwise. \n
 
 *@par Outputs:
-*@li y: A Tensor with any format. Has the same type as the input. Must be one of the following types: float16, float32.
+*@li y: A Tensor with any format. Has the same type as the input. Must be one 
+of the following types: float16, float32.
 *@par Third-party framework compatibility
 * Compatible with the Caffe operator Threshold.
 */
@@ -3044,11 +3093,16 @@ REG_OP(FusedMulAddNL2loss)
 *@li x: A tensor. Must be one of the following types: float16, float32. \n
 
 *@par Attributes:
-*@li axis: An optional int. Specify the axis to be cut at the input tensor. If this parameter is not provided, find the topk for each batch. Defaults to 10000
-*@li out_max_val: An optional bool. Whether to output the maximum value. If it is True, the maximum value and index are output, otherwise only the index is output.
+*@li axis: An optional int. Specify the axis to be cut at the input tensor. If 
+this parameter is not provided, find the topk for each batch. Defaults to 10000
+*@li out_max_val: An optional bool. Whether to output the maximum value. If it 
+is True, the maximum value and index are output, otherwise only the index is 
+output.
 * Defaults to False
-*@li topk: An optional int. It means the number of top tok in each axis (the value is greater than or equal to 1), and the value range must be in [1,x.shape(axis)].
-* Defaults to 1
+*@li topk: An optional int. It means the number of top tok in each axis (the 
+value is greater than or equal to 1), and the value range must be in [1,x.shape
+(axis)].
+* Defaults to 1 \n
 
 *@par Outputs:
 *@li indices: A tensor of type float16, float32, int32. The index of the maximum value of the output.
@@ -3168,7 +3222,8 @@ REG_OP(Axpy)
     .OP_END_FACTORY_REG(Axpy)
 
 /**
-*@brief Creates a criterion that measures the loss given input tensors x1 x2 and a Tensor label y with values 1 or -1. \n
+*@brief Creates a criterion that measures the loss given input tensors x1 x2 
+and a Tensor label y with values 1 or -1. \n
 
 *@par Inputs:
 *@li x1: A ND Tensor with one of the following types: int8, uint8, int32, float16, float32.

third_party/fwkacllib/inc/ops/functional_ops.h

@@ -36,7 +36,7 @@ namespace ge {
  *          if "cond" is a numerical scalar, non-zero means True and zero means False;
  *          if "cond" is a string scalar, non-empty means True and empty means False;
  *          if "cond" is not a scalar, non-empty means True and empty means False.
- *@li input: The input tensors . It's a dynamic input. \n
+ *@li input: The input tensors . \n
 
  *@par Graphs:
  *@li then_branch: A subgraph takes 'input' and returns a list of tensors,
@@ -69,7 +69,7 @@ REG_OP(_If)
  *          if "cond" is a numerical scalar, non-zero means True and zero means False;
  *          if "cond" is a string scalar, non-empty means True and empty means False;
  *          if "cond" is not a scalar, non-empty means True and empty means False.
- *@li input: The input tensors . It's a dynamic input. \n
+ *@li input: The input tensors . \n
 
  *@par Graphs:
  *@li then_branch: A subgraph takes 'input' and returns a list of tensors,
@@ -102,7 +102,7 @@ REG_OP(StatelessIf)
  *          if "cond" is a numerical scalar, non-zero means True and zero means False;
  *          if "cond" is a string scalar, non-empty means True and empty means False;
  *          if "cond" is not a scalar, non-empty means True and empty means False.
- *@li input: The input tensors . It's a dynamic input. \n
+ *@li input: The input tensors . \n
 
  *@par Graphs:
  *@li then_branch: A subgraph takes 'input' and returns a list of tensors,
@@ -129,7 +129,7 @@ REG_OP(If)
 
  *@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
+ *@li input: The input tensors, which will be passed to the subgraph . \n
 
  *@par Graphs:
  *branches: A list of subgraphs, each of which takes 'input' and returns a list of tensors,
@@ -152,7 +152,7 @@ REG_OP(Case)
  *@brief Cyclic execute the "body" subgraph until the return tensor of "cond" subgraph means False . \n
 
  *@par Inputs:
- *input: The input tensors . It's a dynamic input. \n
+ *input: The input tensors . \n
 
  *@par Graphs:
  *@li cond: A subgraph takes 'input' and returns a tensor.
@@ -183,7 +183,7 @@ REG_OP(_While)
  *@brief Cyclic execute the "body" subgraph until the return tensor of "cond" subgraph means False . \n
 
  *@par Inputs:
- *input: The input tensors . It's a dynamic input. \n
+ *input: The input tensors . \n
 
  *@par Graphs:
  *@li cond: A subgraph takes 'input' and returns a tensor.
@@ -215,7 +215,7 @@ REG_OP(While)
  *@brief Cyclic execute the "body" subgraph until the return tensor of "cond" subgraph means False . \n
 
  *@par Inputs:
- *input: The input tensors . It's a dynamic input. \n
+ *input: The input tensors . \n
 
  *@par Graphs:
  *@li cond: A subgraph takes 'input' and returns a tensor.
@@ -250,7 +250,7 @@ REG_OP(StatelessWhile)
  *@li start: A int32 scalar. The lower bound.
  *@li limit: A int32 scalar. The upper bound.
  *@li delta: A int32 scalar. The step size.
- *@li input: The input tensors, which will be passed to "body" . It's a dynamic input. \n
+ *@li input: The input tensors, which will be passed to "body" . \n
 
  *@par Graphs:
  *body: A subgraph takes 'input' and returns a another list of tensors . \n
@@ -274,7 +274,7 @@ REG_OP(For)
  *@brief Pass the input tensors to the subgraph "f" and return the output tensors . \n
 
  *@par Inputs:
- *args: The input tensors, which will be passed to "f" . It's a dynamic input. \n
+ *args: The input tensors, which will be passed to "f" . \n
 
  *@par Graphs:
  *f: A subgraph takes 'args' and returns a another list of tensors . \n
@@ -303,7 +303,7 @@ REG_OP(PartitionedCall)
  *@brief Pass the input tensors to the subgraph "f" and return the output tensors . \n
 
  *@par Inputs:
- *args: The input tensors, which will be passed to "f" . It's a dynamic input. \n
+ *args: The input tensors, which will be passed to "f" . \n
 
  *@par Graphs:
  *f: A subgraph takes 'args' and returns a another list of tensors . \n

third_party/fwkacllib/inc/ops/image_ops.h

@@ -160,8 +160,10 @@ REG_OP(CropAndResize)
 *@li box_index: A Tensor of type int32. A 1-D tensor of shape [num_boxes] with int32 values in [0, batch) . \n
 
 *@par Attributes:
-*@li crop_size: list int. [crop_height, crop_width]. All cropped image patches are resized to this size.
-*@li extrapolation_value: An optional float. Defaults to 0. Value used for extrapolation, when applicable.
+*@li crop_size: list int. [crop_height, crop_width]. All cropped image patches 
+are resized to this size.
+*@li extrapolation_value: An optional float. Defaults to 0. Value used for 
+extrapolation, when applicable.
 *@li method: An optional string from: '"bilinear"'. Defaults to "bilinear" . \n
 
 *@par Outputs:
@@ -172,7 +174,6 @@ REG_OP(CropAndResize)
 
 *@par Third-party framework compatibility
 *Compatible with tensorflow CropAndResize operator.
-
 * @par Restrictions:
 * Warning: THIS FUNCTION IS DEPRECATED. Please use CropAndResize instead.
 */

third_party/fwkacllib/inc/ops/nn_batch_norm_ops.h

@@ -87,39 +87,58 @@ REG_OP(L2NormalizeGrad)
 
 *@par Inputs:
 * Five inputs, including: (NHWC, NCHW, or NC1HWC0 supported)
-*@li x: A 4D or 5D Tensor of type float16 or float32, with format NHWC or NCHW for 4D or NC1HWC0 for 5D.
-*@li scale: A Tensor of type float32. Must be 1D if input "x" is with format NHWC or NCHW. Must be 5D
+*@li x: A 4D or 5D Tensor of type float16 or float32, with format NHWC or NCHW 
+for 4D or NC1HWC0 for 5D.
+*@li scale: A Tensor of type float32. Must be 1D if input "x" is with format 
+NHWC or NCHW. Must be 5D
 if input "x" is with format NC1HWC0. Specifies the scaling factor.
 *@li offset: A Tensor of type float32. Must be 1D if input "x" is with format NHWC or NCHW. Must be 5D
 if input "x" is with format NC1HWC0. Specifies the offset.
-*@li mean: A Tensor of type float32. Must be 1D if input "x" is with format NHWC or NCHW. Must be 5D
-if input "x" is with format NC1HWC0. Specifies the mean used for inference. Must be "None" if the
+*@li mean: A Tensor of type float32. Must be 1D if input "x" is with format 
+NHWC or NCHW. Must be 5D
+if input "x" is with format NC1HWC0. Specifies the mean used for inference. 
+Must be "None" if the
 operation is used for training.
-*@li variance: A Tensor of type float32. Must be 1D if input "x" is with format NHWC or NCHW. Must be
-5D if input "x" is with format NC1HWC0. Specifies the variance used for inference. Must be "None"
+*@li variance: A Tensor of type float32. Must be 1D if input "x" is with format 
+NHWC or NCHW. Must be
+5D if input "x" is with format NC1HWC0. Specifies the variance used for 
+inference. Must be "None"
 if the operation is used for training . \n
 
 *@par Attributes:
-*@li epsilon: An optional float32, specifying the small value added to variance to avoid dividing by zero. Defaults to "0.0001".
-*@li data_format: An optional string, specifying the format of "x". Defaults to "NHWC".
-*@li is_training: An optional bool, specifying if the operation is used for training or inference. Defaults to "True" . \n
+*@li epsilon: An optional float32, specifying the small value added to variance 
+to avoid dividing by zero. Defaults to "0.0001".
+*@li data_format: An optional string, specifying the format of "x". Defaults to 
+"NHWC".
+*@li is_training: An optional bool, specifying if the operation is used for 
+training or inference. Defaults to "True" . \n
 
 *@par Outputs:
 * Five outputs, including: (NHWC, NCHW, or NC1HWC0 supported)
-*@li y: A 4D or 5D Tensor of type float16 or float32 for the normalized "x", with format NHWC or NCHW for 4D or NC1HWC0 for 5D.
-*@li batch_mean: A Tensor of type float32. Must be 1D if input "x" is with format NHWC or NCHW. Must be 5D
+*@li y: A 4D or 5D Tensor of type float16 or float32 for the normalized "x", 
+with format NHWC or NCHW for 4D or NC1HWC0 for 5D.
+*@li batch_mean: A Tensor of type float32. Must be 1D if input "x" is with 
+format NHWC or NCHW. Must be 5D
 if input "x" is with format NC1HWC0. Specifies the mean of "x".
-*@li batch_variance: A Tensor of type float32. Must be 1D if input "x" is with format NHWC or NCHW.
+*@li batch_variance: A Tensor of type float32. Must be 1D if input "x" is with 
+format NHWC or NCHW.
 Must be 5D if input "x" is with format NC1HWC0. Specifies the variance of "x".
-*@li reserve_space_1: An optional Tensor of type float32. Must be 1D if input "x" is with format NHWC or NCHW.
-Must be 5D if input "x" is with format NC1HWC0. Specifies the mean of "x" for gradient computation. Pass "None" to skip this output.
-*@li reserve_space_2: An optional Tensor of type float32. Must be 1D if input "x" is with format NHWC or NCHW.
-Must be 5D if input "x" is with format NC1HWC0. Specifies the variance of "x" for gradient computation. Pass "None" to skip this output . \n
+*@li reserve_space_1: An optional Tensor of type float32. Must be 1D if input 
+"x" is with format NHWC or NCHW.
+Must be 5D if input "x" is with format NC1HWC0. Specifies the mean of "x" for 
+gradient computation. Pass "None" to skip this output.
+*@li reserve_space_2: An optional Tensor of type float32. Must be 1D if input 
+"x" is with format NHWC or NCHW.
+Must be 5D if input "x" is with format NC1HWC0. Specifies the variance of "x" 
+for gradient computation. Pass "None" to skip this output . \n
 
 *@attention Constraints:
-*@li If the operation is used for inference and outputs "reserve_space_1" and "reserve_space_2" are available,
-then "reserve_space_1" has the same value as "mean" and "reserve_space_2" has the same value as "variance".
-*@li For Ascend 310, the result accuracy fails to reach 1‰ due to the square root instruction . \n
+*@li If the operation is used for inference and outputs "reserve_space_1" and 
+"reserve_space_2" are available,
+then "reserve_space_1" has the same value as "mean" and "reserve_space_2" has 
+the same value as "variance".
+*@li For Ascend 310, the result accuracy fails to reach 1‰ due to the square 
+root instruction . \n
 
 *@par Third-party framework compatibility
 *@li Compatible with the TensorFlow operator fused_batch_norm.
@@ -166,13 +185,17 @@ is used for training or inference. Defaults to "True" . \n
 *@li y: A 4D Tensor of type float16 or float32, for the normalized "x".
 *@li batch_mean: A 1D Tensor of type float32, for the mean of "x".
 *@li batch_variance: A 1D Tensor of type float32, for the variance of "x".
-*@li reserve_space_1: A 1D Tensor of type float32, for the mean of "x" for gradient computation.
-*@li reserve_space_2: A 1D Tensor of type float32, for the variance of "x" for gradient computation . \n
+*@li reserve_space_1: A 1D Tensor of type float32, for the mean of "x" for
+gradient computation.
+*@li reserve_space_2: A 1D Tensor of type float32, for the variance of "x" 
+for gradient computation . \n
 
 *@attention Constraints:
 *@li If the operation is used for inference, then output "reserve_space_1"
-has the same value as "mean" and output "reserve_space_2" has the same value as "variance".
-*@li For Ascend 310, the result accuracy fails to reach 1‰ due to the square root instruction . \n
+has the same value as "mean" and output "reserve_space_2" has the same value as
+"variance".
+*@li For Ascend 310, the result accuracy fails to reach 1‰ due to the square 
+root instruction . \n
 
 *@par Third-party framework compatibility
 * Compatible with the TensorFlow operator fused_batch_norm_v2.
@@ -198,23 +221,34 @@ REG_OP(BatchNormExt2)
 
 *@par Inputs:
 * Five inputs, including:
-*@li y_backprop: A 4D or 5D Tensor of type float16 or float32, with format NHWC, NCHW, or NC1HWC0, for the gradient.
-*@li x: A 4D or 5D Tensor of type float16 or float32, with format NHWC, NCHW, or NC1HWC0.
-*@li scale: A 4D or 5D Tensor of type float32, with format NHWC, NCHW, or NC1HWC0.
-*@li reserve_space_1: A 4D or 5D Tensor of type float32, with format NHWC, NCHW, or NC1HWC0. It is an output of BatchNorm.
-*@li reserve_space_2: A 4D or 5D Tensor of type float32, with format NHWC, NCHW, or NC1HWC0. It is an output of BatchNorm . \n
+*@li y_backprop: A 4D or 5D Tensor of type float16 or float32, with format 
+NHWC, NCHW, or NC1HWC0, for the gradient.
+*@li x: A 4D or 5D Tensor of type float16 or float32, with format NHWC, NCHW, 
+or NC1HWC0.
+*@li scale: A 4D or 5D Tensor of type float32, with format NHWC, NCHW, or 
+NC1HWC0.
+*@li reserve_space_1: A 4D or 5D Tensor of type float32, with format NHWC, 
+NCHW, or NC1HWC0. It is an output of BatchNorm.
+*@li reserve_space_2: A 4D or 5D Tensor of type float32, with format NHWC, 
+NCHW, or NC1HWC0. It is an output of BatchNorm . \n
 
 *@par Attributes:
-*@li epsilon: An optional float32. Defaults to "0.0001". A small float number added to the variance of "x".
+*@li epsilon: An optional float32. Defaults to "0.0001". A small float number 
+added to the variance of "x".
 *@li data_format: An optional string. Defaults to "NHWC".
 *@li is_training: An optional bool. Defaults to "true". Specifies the operation is for training (default) or inference . \n
 
 *@par Outputs:
-*@li x_backprop: A Tensor of type float16 or float32, with format NHWC, NCHW, or NC1HWC0, for the offset of "x".
-*@li scale_backprop: A Tensor of type float32, with format NHWC, NCHW, or NC1HWC0, for the offset of "scale".
-*@li *offset_backprop: A Tensor of type float32, with format NHWC, NCHW, or NC1HWC0, for the offset of "offset".
-*@li *reserve_space_4: A Tensor of type float32, with shape NHWC, NCHW, or NC1HWC0. Pass "None" to skip this output.
-*@li *reserve_space_5: A Tensor of type float32, with shape NHWC, NCHW, or NC1HWC0. Pass "None" to skip this output . \n
+*@li x_backprop: A Tensor of type float16 or float32, with format NHWC, NCHW, 
+or NC1HWC0, for the offset of "x".
+*@li scale_backprop: A Tensor of type float32, with format NHWC, NCHW, or 
+NC1HWC0, for the offset of "scale".
+*@li *offset_backprop: A Tensor of type float32, with format NHWC, NCHW, or 
+NC1HWC0, for the offset of "offset".
+*@li *reserve_space_4: A Tensor of type float32, with shape NHWC, NCHW, or 
+NC1HWC0. Pass "None" to skip this output.
+*@li *reserve_space_5: A Tensor of type float32, with shape NHWC, NCHW, or 
+NC1HWC0. Pass "None" to skip this output . \n
 
 *@attention Constraints:
 * The preceding layer of this operator must be operator BatchNorm . \n
@@ -244,21 +278,28 @@ REG_OP(BatchNormGrad)
 
 *@par Inputs:
 * Five inputs, including:
-*@li y_backprop: A 4D Tensor of type float16 or float32, with format NHWC or NCHW, for the gradient.
+*@li y_backprop: A 4D Tensor of type float16 or float32, with format NHWC or 
+NCHW, for the gradient.
 *@li x: A 4D Tensor of type float16 or float32, with format NHWC or NCHW.
 *@li scale: A 4D Tensor of type float32, with format NHWC or NCHW.
-*@li reserve_space_1: A 4D Tensor of type float32, with format NHWC or NCHW. It is an output of BatchNormExt2.
-*@li reserve_space_2: A 4D Tensor of type float32, with format NHWC or NCHW. It is an output of BatchNormExt2 . \n
+*@li reserve_space_1: A 4D Tensor of type float32, with format NHWC or NCHW. It 
+is an output of BatchNormExt2.
+*@li reserve_space_2: A 4D Tensor of type float32, with format NHWC or NCHW. It 
+is an output of BatchNormExt2 . \n
 
 *@par Attributes:
 *@li epsilon: A required float32. A small float number added to the variance of "x".
 *@li data_format: A required string for the format.
-*@li is_training: A required bool for specifying the operation is for training (true) or inference (false) . \n
+*@li is_training: A required bool for specifying the operation is for training 
+(true) or inference (false) . \n
 
 *@par Outputs:
-*@li x_backprop: A Tensor of type float16 or float32, with format NHWC or NCHW, for the offset of "x".
-*@li scale_backprop: A Tensor of type float32, with format NHWC or NCHW, for the offset of "scale".
-*@li offset_backprop: A Tensor of type float32, with format NHWC or NCHW, for the offset of "offset".
+*@li x_backprop: A Tensor of type float16 or float32, with format NHWC or NCHW, 
+for the offset of "x".
+*@li scale_backprop: A Tensor of type float32, with format NHWC or NCHW, for 
+the offset of "scale".
+*@li offset_backprop: A Tensor of type float32, with format NHWC or NCHW, for 
+the offset of "offset".
 *@li reserve_space_3: A Tensor of type float32, with format NHWC or NCHW.
 *@li reserve_space_4: A Tensor of type float32, with format NHWC or NCHW . \n
 
@@ -290,14 +331,18 @@ REG_OP(BatchNormGradExt2)
 *@brief Performs batch normalization . \n
 
 *@par Inputs:
-*@li x: A 4D or 5D Tensor of type float16 or float32, with format NHWC or NCHW for 4D or NC1HWC0 for 5D.
-*@li mean: A Tensor of type float32 or float16. Must be 1D if input "x"  Specifies the mean used for inference.
-*@li variance: A Tensor of type float32 or float16 . Must be 1D if input "x"  Specifies the variance used for inference.
+*@li x: A 4D or 5D Tensor of type float16 or float32, with format NHWC or NCHW 
+for 4D or NC1HWC0 for 5D.
+*@li mean: A Tensor of type float32 or float16. Must be 1D if input "x"  
+Specifies the mean used for inference.
+*@li variance: A Tensor of type float32 or float16 . Must be 1D if input "x"  
+Specifies the variance used for inference.
 *@li momentum: A Tensor,represents the mean and the variance's scale factor
 *@li scale: An optional tensor of type float16 or float32, no use
 *@li offset: An optional tensor of type float16 or float32, no use
 *@par Attributes:
-*@li epsilon: An optional float32, specifying the small value added to variance to avoid dividing by zero. Defaults to "0.00001".
+*@li epsilon: An optional float32, specifying the small value added to variance 
+to avoid dividing by zero. Defaults to "0.00001".
 *@li use_global_stats: mean inference mode , only can be "True".
 *@li mode: An optional input, not use
 *@par Outputs:
@@ -315,16 +360,20 @@ REG_OP(BNInference)
     .ATTR(use_global_stats, Bool,true)
     .ATTR(mode, Int,1)
     .OP_END_FACTORY_REG(BNInference)
+
 /**
 *@brief aicpu batch normalization host  . \n
 
 *@par Inputs:
 
-*@li mean: A Tensor of type float32 or float16. Must be 1D if input "x"  Specifies the mean used for inference.
-*@li variance: A Tensor of type float32 or float16 . Must be 1D if input "x"  Specifies the variance used for inference.
+*@li mean: A Tensor of type float32 or float16. Must be 1D if input "x"  
+Specifies the mean used for inference.
+*@li variance: A Tensor of type float32 or float16 . Must be 1D if input "x"  
+Specifies the variance used for inference.
 *@li momentum: An optional float, mean and variance's Scale factor
 *@par Attributes:
-*@li epsilon: An optional float32, specifying the small value added to variance to avoid dividing by zero. Defaults to "0.00001".
+*@li epsilon: An optional float32, specifying the small value added to variance 
+to avoid dividing by zero. Defaults to "0.00001".
 *@li use_global_stats: mean inference mode , only can be "True".
 *@li mode: An optional attr, not use
 *@par Outputs:
@@ -348,14 +397,19 @@ REG_OP(BnHost)
 *@brief Performs batch normalization . \n
 
 *@par Inputs:
-*@li x: A 4D or 5D Tensor of type float16 or float32, with format NHWC or NCHW for 4D or NC1HWC0 for 5D.
-*@li mean: A Tensor of type float32 or float16. Must be 1D if input "x" Specifies the mean used for inference.
-*@li variance: A Tensor of type float32 or float16 . Must be 1D if input "x" Specifies the variance used for inference.
+*@li x: A 4D or 5D Tensor of type float16 or float32, with format NHWC or NCHW 
+for 4D or NC1HWC0 for 5D.
+*@li mean: A Tensor of type float32 or float16. Must be 1D if input "x" 
+Specifies the mean used for inference.
+*@li variance: A Tensor of type float32 or float16 . Must be 1D if input "x" 
+Specifies the variance used for inference.
 *@li scale: An optional tensor of type float16 or float32, no use
 *@li offset: An optional tensor of type float16 or float32, no use
 *@par Attributes:
-*@li momentum: An optional float32 num, represents the mean and the variance's scale factor
-*@li epsilon: An optional float32, specifying the small value added to variance to avoid dividing by zero. Defaults to "0.00001".
+*@li momentum: An optional float32 num, represents the mean and the variance's 
+scale factor
+*@li epsilon: An optional float32, specifying the small value added to variance 
+to avoid dividing by zero. Defaults to "0.00001".
 *@li use_global_stats: mean inference mode , only can be "True".
 *@li mode: An optional attr, not use
 *@par Outputs:

third_party/fwkacllib/inc/ops/nn_calculation_ops.h

@@ -310,9 +310,6 @@ REG_OP(DepthwiseConv2DBackpropInputD)
 * @par Third-party framework compatibility
 * @li Compatible with the TensorFlow operator DepthwiseConv2D.
 * @li Compatible with the Caffe operator DepthwiseConv2D.
-*
-* @par Restrictions:
-* Warning: THIS FUNCTION IS EXPERIMENTAL.  Please do not use.
 */
 REG_OP(DepthwiseConv2D)
     .INPUT(x, TensorType({DT_FLOAT16, DT_INT8}))

third_party/fwkacllib/inc/ops/nn_detect_ops.h

@@ -158,18 +158,25 @@ REG_OP(Iou)
 *@par Inputs:
 * Three inputs, including:
 *@li ydiff: A 5HD gradient input of type float32.
-*@li rois: ROI position. A 2D Tensor of float32 with shape (N, 5). "N" indicates the number of ROIs,
-the value "5" indicates the indexes of images where the ROIs are located, "x0", "x1", "y0", and "y1".
-*@li rois_n: An optional input, specifying the number of valid ROIs. This parameter is reserved . \n
+*@li rois: ROI position. A 2D Tensor of float32 with shape (N, 5). "N" 
+indicates the number of ROIs,
+the value "5" indicates the indexes of images where the ROIs are located, "x0", 
+"x1", "y0", and "y1".
+*@li rois_n: An optional input, specifying the number of valid ROIs. This 
+parameter is reserved . \n
 
 *@par Attributes:
 *@li xdiff_shape: A required list of 4 ints, obtained based on the shape of "features" of ROIAlign.
 *@li pooled_width: A required attribute of type int, specifying the W dimension.
 *@li pooled_height: A required attribute of type int, specifying the H dimension.
-*@li spatial_scale: A required attribute of type float, specifying the scaling ratio of "features" to the original image.
-*@li sample_num: An optional attribute of type int, specifying the horizontal and vertical
-sampling frequency of each output. If this attribute is set to "0", the sampling frequency is
-equal to the rounded up value of "rois", which is a floating point number. Defaults to "2" . \n
+*@li spatial_scale: A required attribute of type float, specifying the scaling 
+ratio of "features" to the original image.
+*@li sample_num: An optional attribute of type int, specifying the horizontal 
+and vertical
+sampling frequency of each output. If this attribute is set to "0", the 
+sampling frequency is
+equal to the rounded up value of "rois", which is a floating point number. 
+Defaults to "2" . \n
 
 *@par Outputs:
 *xdiff: Gradient added to input "features". Has the same 5HD shape as input "features".

third_party/fwkacllib/inc/ops/nn_norm_ops.h

@@ -924,29 +924,7 @@ REG_OP(InstanceNormV2)
     .ATTR(epsilon, Float, 0.00001)
     .OP_END_FACTORY_REG(InstanceNormV2)
 
-/**
-*@brief Performs instance normalization for inference.
-
-*@par Inputs:\n
-* Five inputs, including: (NC1HWC0 supported)
-*@li x: A Tensor of type float16 or float32.
-*@li gamma: A [N, C1, 1, 1, C0] Tensor of type float32, for the scaling gamma.
-*@li beta: A [N, C1, 1, 1, C0] Tensor of type float32, for the scaling beta.
-*@li mean: A [N, C1, 1, 1, C0] ensor of type float32, for the mean.
-*@li variance: A [N, C1, 1, 1, C0] Tensor of type float32, for the variance.
-*@li variance_sqrt: A [N, C1, 1, 1, C0] Tensor of type float32, for the variance_sqrt.
-
-*@par Outputs:\n
-*y: A Tensor of type float16 or float32 for the normalized "x".
-*batch_mean: A Tensor of type float32 for the result mean.
-*batch_ variance: A Tensor of type float32 for the result variance.
-
-*@attention Constraints:
-*For Ascend 310, the result accuracy fails to reach 1<89> due to the square root instruction.
 
-* @par Restrictions:
-* Warning: THIS FUNCTION IS DEPRECATED. Please use INInferV2 instead.
-*/
 REG_OP(INInferV2D)
     .INPUT(x, TensorType({DT_FLOAT16, DT_FLOAT}))
     .OPTIONAL_INPUT(gamma, TensorType({DT_FLOAT}))

third_party/fwkacllib/inc/ops/nn_pooling_ops.h

@@ -168,9 +168,6 @@ REG_OP(AvgPoolV2)
 
 *@par Third-party framework compatibility
 * Compatible with the TensorFlow operator AvgPool3D.
-*
-* @par Restrictions:
-*Warning: THIS FUNCTION IS EXPERIMENTAL.  Please do not use.
 */
 REG_OP(AvgPool3D)
     .INPUT(x, TensorType({DT_FLOAT16, DT_FLOAT32, DT_DOUBLE}))

third_party/fwkacllib/inc/ops/nn_training_ops.h

@@ -111,9 +111,6 @@ REG_OP(ApplyAdaMax)
 *
 *@par Third-party framework compatibility
 *Compatible with the TensorFlow operator ApplyAdaMax.
-*
-* @par Restrictions:
-* Warning: THIS FUNCTION IS DEPRECATED. Please use ApplyAdaMax instead.
 */
 REG_OP(ApplyAdaMaxD)
     .INPUT(var, TensorType::NumberType())
@@ -352,9 +349,6 @@ REG_OP(ApplyMomentum)
 * accum: A mutable tensor. Has the same type as input "accum".
 *@par Third-party framework compatibility
 *Compatible with the TensorFlow operator ApplyMomentum.
-*
-* @par Restrictions:
-* Warning: THIS FUNCTION IS DEPRECATED. Please use ApplyMomentum instead.
 */
 
 REG_OP(ApplyMomentumD)
@@ -681,9 +675,6 @@ REG_OP(ApplyPowerSign)
 *
 *@par Third-party framework compatibility
 *Compatible with the TensorFlow operator ApplyPowerSign.
-*
-* @par Restrictions:
-* Warning: THIS FUNCTION IS DEPRECATED. Please use ApplyPowerSign instead.
 */
 REG_OP(ApplyPowerSignD)
     .INPUT(var, TensorType::NumberType())
@@ -804,9 +795,6 @@ REG_OP(ApplyAddSign)
 
 *@par Third-party framework compatibility
 * Compatible with the TensorFlow operator ApplyAddSign.
-*
-* @par Restrictions:
-* Warning: THIS FUNCTION IS DEPRECATED. Please use ApplyAddSign instead.
 */
 REG_OP(ApplyAddSignD)
     .INPUT(var, TensorType::NumberType())
@@ -928,9 +916,6 @@ REG_OP(ApplyCenteredRMSProp)
 
 *@par Third-party framework compatibility
 *Compatible with the TensorFlow operator ApplyCenteredRMSPropD.
-*
-* @par Restrictions:
-* Warning: THIS FUNCTION IS DEPRECATED. Please use ApplyCenteredRMSProp instead.
 */
 REG_OP(ApplyCenteredRMSPropD)
     .INPUT(var, TensorType::NumberType())
@@ -1049,9 +1034,6 @@ REG_OP(ApplyAdagrad)
 *
 *@par Third-party framework compatibility
 *Compatible with the TensorFlow operator ApplyAdagrad.
-*
-* @par Restrictions:
-* Warning: THIS FUNCTION IS DEPRECATED. Please use ApplyAdagrad instead.
 */
 REG_OP(ApplyAdagradD)
     .INPUT(var, TensorType::NumberType())
@@ -1236,9 +1218,6 @@ REG_OP(ApplyAdagradDA)
 
 *@par Third-party framework compatibility
 *Compatible with the TensorFlow operator ApplyAdagradDA.
-*
-* @par Restrictions:
-* Warning: THIS FUNCTION IS DEPRECATED. Please use ApplyAdagradDA instead.
 */
 REG_OP(ApplyAdagradDAD)
     .INPUT(var, TensorType::NumberType())
@@ -1496,9 +1475,6 @@ REG_OP(ApplyProximalAdagrad)
 
 *@par Third-party framework compatibility
 *Compatible with the TensorFlow operator ApplyProximalAdagradD.
-*
-* @par Restrictions:
-* Warning: THIS FUNCTION IS DEPRECATED. Please use ApplyProximalAdagrad instead.
 */
 REG_OP(ApplyProximalAdagradD)
     .INPUT(var, TensorType::NumberType())
@@ -1592,9 +1568,6 @@ REG_OP(SparseApplyProximalAdagrad)
 
 *@par Third-party framework compatibility
 *Compatible with the TensorFlow operator SparseApplyProximalAdagrad.
-
-* @par Restrictions:
-* Warning: THIS FUNCTION IS DEPRECATED. Please use SparseApplyProximalAdagrad instead.
 */
 REG_OP(SparseApplyProximalAdagradD)
     .INPUT(var, TensorType::NumberType())
@@ -1681,9 +1654,6 @@ REG_OP(ApplyFtrl)
 
 *@par Third-party framework compatibility
 *Compatible with the TensorFlow operator ApplyFtrl.
-*
-* @par Restrictions:
-* Warning: THIS FUNCTION IS DEPRECATED. Please use ApplyFtrl instead.
 */
 REG_OP(ApplyFtrlD)
     .INPUT(var, TensorType::NumberType())
@@ -1775,9 +1745,6 @@ REG_OP(ApplyFtrlV2)
 
 *@par Third-party framework compatibility
 *Compatible with the TensorFlow operator ApplyFtrlV2.
-*
-* @par Restrictions:
-* Warning: THIS FUNCTION IS DEPRECATED. Please use ApplyFtrlV2 instead.
 */
 REG_OP(ApplyFtrlV2D)
     .INPUT(var, TensorType::NumberType())
@@ -1890,9 +1857,6 @@ REG_OP(ApplyAdam)
 
 *@par Third-party framework compatibility
 *Compatible with the TensorFlow operator ApplyAdam.
-*
-* @par Restrictions:
-* Warning: THIS FUNCTION IS DEPRECATED. Please use ApplyAdam instead.
 */
 REG_OP(ApplyAdamD)
     .INPUT(var, TensorType::NumberType())
@@ -1981,9 +1945,6 @@ REG_OP(ApplyAdadelta)
 
 *@par Third-party framework compatibility
 * Compatible with the TensorFlow operator ApplyAdadelta.
-
-* @par Restrictions:
-* Warning: THIS FUNCTION IS DEPRECATED. Please use ApplyAdadelta instead.
 */
 REG_OP(ApplyAdadeltaD)
     .INPUT(var, TensorType::NumberType())

third_party/fwkacllib/inc/ops/pad_ops.h

@@ -65,9 +65,6 @@ REG_OP(Fill)
 *
 *@par Outputs:
 * y: A tensor. Has the same type as "value".
-*
-* @par Restrictions:
-* Warning: THIS FUNCTION IS DEPRECATED. Please use Fill instead.
 */
 REG_OP(FillD)
     .INPUT(value, TensorType({DT_FLOAT, DT_FLOAT16, DT_INT8, DT_INT16,
@@ -125,9 +122,6 @@ REG_OP(BroadcastTo)
 *
 *@par Third-party framework compatibility
 *Compatible with the TensorFlow operator BroadcastTo.
-*
-* @par Restrictions:
-* Warning: THIS FUNCTION IS DEPRECATED. Please use BroadcastTo instead.
 */
 REG_OP(BroadcastToD)
     .INPUT(x, TensorType::BasicType())
@@ -175,9 +169,6 @@ REG_OP(Pad)
 
 *@par Third-party framework compatibility:
 * Compatible with TensorFlow operator Pad.
-*
-* @par Restrictions:
-* Warning: THIS FUNCTION IS DEPRECATED. Please use Pad instead.
 */
 REG_OP(PadD)
     .INPUT(x, TensorType({DT_FLOAT16, DT_FLOAT, DT_INT8, DT_UINT8, DT_FLOAT}))
@@ -272,9 +263,6 @@ REG_OP(PadV3D)
 *@see Diag()
 *@par Third-party framework compatibility
 * Compatible with the TensorFlow operator Diag.
-*
-* @par Restrictions:
-* Warning: THIS FUNCTION IS DEPRECATED. Please use Diag instead.
 */
 REG_OP(DiagD)
     .INPUT(x, TensorType({DT_FLOAT16, DT_FLOAT, DT_INT32}))

third_party/fwkacllib/inc/ops/ragged_conversion_ops.h

@@ -30,7 +30,7 @@ namespace ge {
 *@par Inputs:
 *Two inputs, including:
 *@li rt_nested_splits: A list of at least 1 Tensor objects with the same type
-in: int32, int64. The row_splits for the RaggedTensor. It's a dynamic input.
+in: int32, int64. The row_splits for the RaggedTensor.
 *@li rt_dense_values: A Tensor. The flat_values for the RaggedTensor
 Must be one of the following types: bool, int8, int16, uint16, int32,
 int64, double, float, float16 . \n
@@ -66,7 +66,7 @@ REG_OP(RaggedTensorToSparse)
 *@li values:A 1D tensor representing the values of the ragged tensor.
 *@li default_value:A `Tensor`. Must have the same type as `values`.
 *@li row_partition_tensors:A list of at least 1 `Tensor` objects with the same
-type in: `int64`, `int32` . It's a dynamic input.\n
+type in: `int64`, `int32` .\n
 
 *@par Attributes:
 *@li num_row_partition_tensors:Numbers of row partition tensors.

third_party/fwkacllib/inc/ops/random_ops.h

@@ -374,9 +374,6 @@ REG_OP(DropOutGenMask)
 
 *@par Third-party framework compatibility
 * Compatible with the TensorFlow operator lin_space.
-*
-* @par Restrictions:
-* Warning: THIS FUNCTION IS DEPRECATED. Please use LinSpace instead.
 */
 REG_OP(LinSpaceD)
     .INPUT(assist, TensorType({DT_FLOAT}))

third_party/fwkacllib/inc/ops/reduce_ops.h

@@ -353,9 +353,6 @@ REG_OP(ReduceSum)
 
 *@par Third-party framework compatibility
 * Compatible with the TensorFlow operator Sum.
-*
-* @par Restrictions:
-* Warning: THIS FUNCTION IS DEPRECATED. Please use ReduceSum instead.
 */
 REG_OP(ReduceSumD)
     .INPUT(x, TensorType({DT_FLOAT16, DT_FLOAT}))
@@ -381,9 +378,6 @@ REG_OP(ReduceSumD)
 
 *@par Third-party framework compatibility
 * Compatible with the TensorFlow operator ReduceAll.
-*
-* @par Restrictions:
-* Warning: THIS FUNCTION IS DEPRECATED. Please use ReduceAll instead.
 */
 REG_OP(ReduceAllD)
     .INPUT(x, TensorType({DT_BOOL}))
@@ -459,9 +453,6 @@ REG_OP(ReduceProd)
 
 *@par Third-party framework compatibility
 * Compatible with the TensorFlow operator ReduceProd.
-*
-* @par Restrictions:
-* Warning: THIS FUNCTION IS DEPRECATED. Please use ReduceProd instead.
 */
 REG_OP(ReduceProdD)
     .INPUT(x,TensorType({DT_FLOAT, DT_UINT8, DT_INT8, DT_INT32, DT_FLOAT16}))
@@ -516,9 +507,6 @@ REG_OP(ReduceMean)
 
 *@par Third-party framework compatibility:
 * Compatible with the TensorFlow operator ReduceMean.
-*
-* @par Restrictions:
-* Warning: THIS FUNCTION IS DEPRECATED. Please use ReduceMean instead.
 */
 REG_OP(ReduceMeanD)
     .INPUT(x, TensorType({DT_FLOAT16, DT_FLOAT}))
@@ -573,9 +561,6 @@ REG_OP(ReduceMax)
 
 *@par Third-party framework compatibility
 * Compatible with TensorFlow operator Max.
-*
-* @par Restrictions:
-* Warning: THIS FUNCTION IS DEPRECATED. Please use ReduceMax instead.
 */
 REG_OP(ReduceMaxD)
     .INPUT(x, TensorType({DT_FLOAT, DT_UINT8, DT_INT8,
@@ -630,9 +615,6 @@ REG_OP(ReduceMin)
 
 *@par Third-party framework compatibility
 * Compatible with the TensorFlow operator reduce_min.
-*
-* @par Restrictions:
-* Warning: THIS FUNCTION IS DEPRECATED. Please use ReduceMin instead.
 */
 REG_OP(ReduceMinD)
     .INPUT(x, TensorType({DT_FLOAT16,DT_FLOAT,DT_INT8,DT_UINT8}))
@@ -699,9 +681,6 @@ REG_OP(ReduceAny)
 *
 *@par Third-party framework compatibility
 *Compatible with the TensorFlow operator reduce_any.
-*
-* @par Restrictions:
-* Warning: THIS FUNCTION IS DEPRECATED. Please use ReduceAny instead.
 */
 REG_OP(ReduceAnyD)
     .INPUT(x, TensorType({DT_BOOL}))
@@ -787,9 +766,6 @@ REG_OP(EuclideanNorm)
 
 *@par Third-party framework compatibility
 * Compatible with the TensorFlow operator EuclideanNorm.
-*
-* @par Restrictions:
-* Warning: THIS FUNCTION IS DEPRECATED. Please use EuclideanNorm instead.
 */
 REG_OP(EuclideanNormD)
     .INPUT(x, TensorType({DT_FLOAT, DT_INT32, DT_FLOAT16}))

third_party/fwkacllib/inc/ops/rnn.h

@@ -92,6 +92,7 @@ REG_OP(DynamicLSTM)
     .OUTPUT(output_h, TensorType({DT_FLOAT32}))
     .OP_END_FACTORY_REG(DynamicLSTM)
 
+
 /**
 *@brief: DynamicRNNGrad calculation.
 *@par Inputs:
@@ -126,7 +127,7 @@ REG_OP(DynamicLSTM)
 *@li keep_prob:An float identifying the keep prob in the op. Default to 1.
 *@li cell_clip:An float identifying the cell clip in the op. Default to -1.
 *@li num_proj:An integer identifying the num projection in the op. Default to 0.
-*@li time_major:An bool identifying the time major in the op. Default to false.
+*@li time_major:An bool identifying the time major in the op. Default to true.
 *@li activation:An string identifying the type of activation function in the op. Default to "tanh". Only tanh is currently supported.
 *@li forget_bias:An float identifying the forget bias in the op. Default to 0.
 *@li is_training:An bool identifying is training in the op. Default to true.

third_party/fwkacllib/inc/ops/save_ops.h

@@ -28,7 +28,7 @@ namespace ge {
 /**
 *@brief Mark which tensors need to be saved to the ckpt file.
 *@par Inputs:
-*tensors: A list of input tensor.It's a dynamic input.
+*tensors: A list of input tensor.
 *@par Restrictions:
 *Warning: THIS FUNCTION IS EXPERIMENTAL. Please do not use.
 */

third_party/fwkacllib/inc/ops/sdca_ops.h

@@ -35,16 +35,16 @@ namespace ge {
 *rate . \n
 
 *@par Inputs:
-*@li sparse_example_indices: a list of vectors which contain example indices.It's a dynamic input.
-*@li sparse_feature_indices: a list of vectors which contain feature indices.It's a dynamic input.
-*@li sparse_feature_values: a list of vectors which contains feature value associated with each feature group.It's a dynamic input.
-*@li dense_features: a list of matrices which contains the dense feature values.It's a dynamic input.
+*@li sparse_example_indices: a list of vectors which contain example indices.
+*@li sparse_feature_indices: a list of vectors which contain feature indices.
+*@li sparse_feature_values: a list of vectors which contains feature value associated with each feature group.
+*@li dense_features: a list of matrices which contains the dense feature values.
 *@li example_weights: a vector which contains the weight associated with each example.
 *@li example_labels: a vector which contains the label/target associated with each example.
 *@li sparse_indices: a list of vectors where each value is the indices which has
-*corresponding weights in sparse_weights. This field maybe omitted for the dense approach.It's a dynamic input.
+*corresponding weights in sparse_weights. This field maybe omitted for the dense approach.
 *@li sparse_weights: a list of vectors where each value is the weight associated with a sparse feature group.
-*@li dense_weights: a list of vectors where the values are the weights associated with a dense feature group.It's a dynamic input.
+*@li dense_weights: a list of vectors where the values are the weights associated with a dense feature group.
 *@li example_state_data: a list of vectors containing the example state data.
 *@li loss_type: Type of the primal loss. Currently SdcaSolver supports logistic, squared and hinge losses.
 *@li l1: Symmetric l1 regularization strength.
@@ -61,7 +61,6 @@ namespace ge {
 *@par Third-party framework compatibility
 * Compatible with tensorflow SdcaOptimizerV2 operator.
 */
-
 REG_OP(SdcaOptimizerV2)
     .DYNAMIC_INPUT(sparse_example_indices, TensorType({DT_INT64}))
     .DYNAMIC_INPUT(sparse_feature_indices, TensorType({DT_INT64}))

third_party/fwkacllib/inc/ops/selection_ops.h

@@ -79,9 +79,6 @@ REG_OP(Range)
 
 *@see Range()
 *@since V100R001C33
-*
-* @par Restrictions:
-* Warning: THIS FUNCTION IS DEPRECATED. Please use Range instead.
 */
 REG_OP(RangeD)
     .INPUT(x, TensorType({DT_FLOAT,DT_INT32}))
@@ -226,9 +223,6 @@ REG_OP(GatherV2)
 
 *@par Third-party framework compatibility
 * Compatible with the TensorFlow operator GatherV2.
-*
-* @par Restrictions:
-* Warning: THIS FUNCTION IS DEPRECATED. Please use GatherV2 instead.
 */
 REG_OP(GatherV2D)
     .INPUT(x, TensorType({DT_FLOAT, DT_FLOAT16, DT_INT32, DT_UINT32, DT_INT8, DT_UINT8,
@@ -331,9 +325,6 @@ REG_OP(StridedSlice)
 
 *@par Third-party framework compatibility
 * Compatible with the TensorFlow operator StridedSlice.
-
-* @par Restrictions:
-* Warning: THIS FUNCTION IS DEPRECATED. Please use StridedSlice instead.
 */
 REG_OP(StridedSliceD)
     .INPUT(x, TensorType({DT_FLOAT, DT_FLOAT16, DT_INT32, DT_UINT8, DT_INT8,
@@ -389,9 +380,6 @@ REG_OP(StridedSliceD)
 
 *@par Third-party framework compatibility
 * Compatible with the TensorFlow operator StridedSliceGradD.
-
-* @par Restrictions:
-* Warning: THIS FUNCTION IS DEPRECATED. Please use StridedSliceGrad instead.
 */
 REG_OP(StridedSliceGradD)
     .INPUT(dy, TensorType::BasicType())
@@ -503,9 +491,6 @@ REG_OP(UnsortedSegmentSum)
 
 *@par Third-party framework compatibility
 * Compatible with the TensorFlow operator UnsortedSegmentSum.
-
-* @par Restrictions:
-* Warning: THIS FUNCTION IS DEPRECATED. Please use UnsortedSegmentSum instead.
 */
 REG_OP(UnsortedSegmentSumD)
     .INPUT(x, TensorType({DT_FLOAT, DT_FLOAT16, DT_INT32, DT_INT8, DT_UINT8}))
@@ -730,9 +715,6 @@ REG_OP(OneHot)
 
 *@par Third-party framework compatibility:
 * Compatible with the TensorFlow operator OneHot.
-*
-* @par Restrictions:
-* Warning: THIS FUNCTION IS DEPRECATED. Please use OneHot instead.
 */
 REG_OP(OneHotD)
     .INPUT(x, TensorType({DT_UINT8, DT_INT32}))
@@ -808,7 +790,7 @@ REG_OP(SliceD)
 * @li assist_seq: A 1D tensor of type float16.
 * with size of 2N, which "N" is the last dimension.
 * The first N numbers is indices, and the next N numbers is deviation of casting
-* int32 to float16. \n
+* float16 to int32 . \n
 
 * @par Attributes:
 * @li k: A required int that is at least 0, specifying the number of top elements
@@ -817,7 +799,7 @@ REG_OP(SliceD)
 * If true, the resulting "k" elements will be sorted by the values in descending
 * order.
 * @li dim: An optional int. Defaults to -1. For reserved use.
-* @li largest: An optional bool. Defaults to true. For reserved use. \n
+* @li largest: An optional bool. Defaults to true. For reserved use.
 
 * @par Outputs:
 * @li values: A Tensor, specifying the sorted data. Has the same type as "input".
@@ -1280,9 +1262,6 @@ REG_OP(InplaceUpdate)
 
 *@par Third-party framework compatibility
 *Compatible with the TensorFlow operator InplaceUpdate.
-*
-* @par Restrictions:
-* Warning: THIS FUNCTION IS DEPRECATED. Please use InplaceUpdate instead.
 */
 REG_OP(InplaceUpdateD)
     .INPUT(x, TensorType({DT_FLOAT, DT_FLOAT16, DT_INT32}))
@@ -1335,9 +1314,6 @@ REG_OP(InplaceAdd)
 
 *@par Third-party framework compatibility
 *Compatible with the TensorFlow operator InplaceAdd.
-*
-* @par Restrictions:
-* Warning: THIS FUNCTION IS DEPRECATED. Please use InplaceAdd instead.
 */
 REG_OP(InplaceAddD)
     .INPUT(x, TensorType({DT_FLOAT, DT_FLOAT16, DT_INT32}))
@@ -1389,9 +1365,6 @@ REG_OP(InplaceSub)
 
 *@par Third-party framework compatibility
 *Compatible with the TensorFlow operator InplaceSub.
-*
-* @par Restrictions:
-* Warning: THIS FUNCTION IS DEPRECATED. Please use InplaceSub instead.
 */
 REG_OP(InplaceSubD)
     .INPUT(x, TensorType({DT_FLOAT, DT_FLOAT16, DT_INT32}))
@@ -1443,9 +1416,6 @@ REG_OP(ScatterNonAliasingAdd)
 * @par Outputs:
 * y: A Tensor of type RealNumberType . \n
 
-* @attention Constraints:
-* @li segment_ids must be non-negative tensor.
-
 * @see UnsortedSegmentSum(), UnsortedSegmentProd(),
 
 * @par Third-party framework compatibility
@@ -1473,9 +1443,6 @@ REG_OP(UnsortedSegmentMin)
 * @par Outputs:
 * y: A Tensor.Must have the same type as input "x" . \n
 
-* @attention Constraints:
-* @li segment_ids must be non-negative tensor.
-
 * @see UnsortedSegmentProdD(), UnsortedSegmentSumD(),
 *
 * @par Restrictions:
@@ -1501,9 +1468,6 @@ REG_OP(UnsortedSegmentMinD)
 * @par Outputs:
 * y: A Tensor of type RealNumberType . \n
 
-* @attention Constraints:
-* @li segment_ids must be non-negative tensor.
-
 * @see UnsortedSegmentSum(), UnsortedSegmentProd(),
 
 * @par Third-party framework compatibility
@@ -1531,9 +1495,6 @@ REG_OP(UnsortedSegmentMax)
 * @par Outputs:
 * y: A Tensor.Must have the same type as input "x" . \n
 
-* @attention Constraints:
-* @li segment_ids must be non-negative tensor.
-
 * @see UnsortedSegmentProdD(),
 *
 * @par Restrictions:
@@ -1558,9 +1519,6 @@ REG_OP(UnsortedSegmentMaxD)
 * @par Outputs:
 * y: A Tensor of type NumberType . \n
 
-* @attention Constraints:
-* @li segment_ids must be non-negative tensor.
-
 * @see UnsortedSegmentSum(), UnsortedSegmentMin(),
 
 * @par Third-party framework compatibility
@@ -1592,9 +1550,6 @@ REG_OP(UnsortedSegmentProd)
 * @li segment_ids must be non-negative tensor.
 
 * @see UnsortedSegmentMinD()
-*
-* @par Restrictions:
-* Warning: THIS FUNCTION IS DEPRECATED. Please use UnsortedSegmentProd instead.
 */
 REG_OP(UnsortedSegmentProdD)
     .INPUT(x, TensorType({DT_FLOAT, DT_FLOAT16, DT_INT32, DT_INT16}))
@@ -1910,9 +1865,6 @@ REG_OP(CumulativeLogsumexp)
 *y: A Tensor. Has the same type as "x".
 *@par Third-party framework compatibility
 * Compatible with the TensorFlow operator Cumsum.
-*
-* @par Restrictions:
-* Warning: THIS FUNCTION IS DEPRECATED. Please use CumulativeLogsumexp instead.
 */
 REG_OP(CumulativeLogsumexpD)
     .INPUT(x, TensorType({DT_DOUBLE, DT_FLOAT, DT_FLOAT16}))

third_party/fwkacllib/inc/ops/split_combination_ops.h

@@ -75,9 +75,6 @@ REG_OP(Split)
 
 *@par Third-party framework compatibility
 * Compatible with the TensorFlow operator Split.
-
-* @par Restrictions:
-* Warning: THIS FUNCTION IS DEPRECATED. Please use Split instead.
 */
 REG_OP(SplitD)
     .INPUT(x, TensorType({DT_INT8, DT_INT16, DT_INT32, DT_INT64, DT_UINT8,
@@ -144,9 +141,6 @@ Under the caffe framework, the conversion of slice_point through the cut point t
 Under the caffe framework,size_splits or axis transformat to split_dim.Only one can effect.
 *@par Third-party framework compatibility
 * Compatible with the TensorFlow operator SplitV.
-
-* @par Restrictions:
-* Warning: THIS FUNCTION IS DEPRECATED. Please use SplitV instead.
 */
 REG_OP(SplitVD)
     .INPUT(x, TensorType({DT_INT8, DT_INT16, DT_INT32, DT_INT64, DT_UINT8,
@@ -164,8 +158,7 @@ REG_OP(SplitVD)
 * Two inputs, including:
 * @li values: A list of Tensors. Must be one of the following types: int8, int16, int32,
 *     int64, uint8, uint16, uint32, uint64, float16, float32.
-*     Tensors to be concatenated. All must have size 1 in the first dimension and same shape.
-*     It's a dynamic input.
+*     Tensors to be concatenated. All must have size 1 in the first dimension and same shape. 
 * @li shape: A Tensor of the same type as "x".
 * The final shape of the result. Should be equal to the shapes of any input
 * but with the number of input values in the first dimension . \n
@@ -314,7 +307,7 @@ REG_OP(Concat)
 
 *@par Inputs:
 * x: A list of N Tensors. Must be one of the following types: int8, int16, int32,
-*     int64, uint8, uint16, uint32, uint64, float16, float32, bool . It's a dynamic input. \n
+*     int64, uint8, uint16, uint32, uint64, float16, float32, bool . \n
 
 *@par Attributes:
 *@li axis: A optional int, defaultvalue is 0.
@@ -340,7 +333,7 @@ REG_OP(Pack)
 *@par Inputs:
 *Two inputs, including:
 * @li concat_dim: A Tensor of type int32.
-* @li x: A list of 1D Tensor objects of type int32 . It's a dynamic input. \n
+* @li x: A list of 1D Tensor objects of type int32 . \n
 
 *@par Attributes:
 *N: A required int . \n
@@ -364,7 +357,7 @@ REG_OP(ConcatOffset)
 *@par Inputs:
 *Two inputs, including:
 * @li concat_dim: A Tensor of type int32.
-* @li x: A list of 1D Tensor objects of type int32 . It's a dynamic input. \n
+* @li x: A list of 1D Tensor objects of type int32 . \n
 
 *@par Attributes:
 *@li Concat_dim: A required int. Must be within the rank of input "x".

third_party/fwkacllib/inc/ops/transformation_ops.h

@@ -235,12 +235,8 @@ REG_OP(BatchToSpaceND)
 *@par Outputs:
 *y: A Tensor with format NC1HWC0. Has the same type as input "x".
 
-
 *@par Third-party framework compatibility
 * Compatible with the TensorFlow operator BatchToSpaceND.
-*
-* @par Restrictions:
-* Warning: THIS FUNCTION IS DEPRECATED. Please use BatchToSpaceND instead.
 */
 REG_OP(BatchToSpaceNDD)
     .INPUT(x, TensorType::BasicType())
@@ -287,9 +283,6 @@ REG_OP(SpaceToBatchND)
 
 *@par Third-party framework compatibility
 * Compatible with the TensorFlow operator SpaceToBatchND.
-*
-* @par Restrictions:
-* Warning: THIS FUNCTION IS DEPRECATED. Please use SpaceToBatchND instead.
 */
 REG_OP(SpaceToBatchNDD)
     .INPUT(x, TensorType::BasicType())
@@ -411,9 +404,6 @@ REG_OP(BatchToSpace)
 
 *@par Third-party framework compatibility
 * Compatible with the TensorFlow operator BatchToSpace.
-*
-* @par Restrictions:
-* Warning: THIS FUNCTION IS DEPRECATED. Please use BatchToSpace instead.
 */
 REG_OP(BatchToSpaceD)
     .INPUT(x, TensorType({DT_FLOAT16, DT_FLOAT, DT_DOUBLE, DT_INT64, DT_INT32, DT_UINT8,
@@ -467,9 +457,6 @@ REG_OP(SpaceToBatch)
 *y: A Tensor. Has the same type as input "x".
 *@par Third-party framework compatibility
 *@ Compatible with the TensorFlow operator SpaceToBatch.
-*
-* @par Restrictions:
-* Warning: THIS FUNCTION IS DEPRECATED. Please use SpaceToBatch instead.
 */
 REG_OP(SpaceToBatchD)
     .INPUT(x, TensorType::BasicType())
@@ -598,9 +585,6 @@ REG_OP(ExtractVolumePatches)
 
 *@par Outputs:
 *y: A Tensor. Has the same type as "x".
-*
-* @par Restrictions:
-* Warning: THIS FUNCTION IS DEPRECATED. Please use ConfusionTranspose instead.
 */
 REG_OP(ConfusionTransposeD)
     .INPUT(x, TensorType({DT_INT8, DT_INT16, DT_INT32, DT_INT64, DT_UINT8,

third_party/fwkacllib/inc/toolchain/adx_datadump_server.h

@@ -1,12 +1,18 @@
 /**
-* @file adx_datadump_server.h
-*
-* Copyright (c) Huawei Technologies Co., Ltd. 2020-2020. All rights reserved.
-*
-* This program is distributed in the hope that it will be useful,
-* but WITHOUT ANY WARRANTY; without even the implied warranty of
-* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
-*/
+ * Copyright 2019-2020 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 ADX_DATADUMP_SERVER_H
 #define ADX_DATADUMP_SERVER_H