diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/arithmetic_self_cpu_kernel.cc b/mindspore/ccsrc/plugin/device/cpu/kernel/arithmetic_self_cpu_kernel.cc
index dcc6261896a..310dffa5427 100644
--- a/mindspore/ccsrc/plugin/device/cpu/kernel/arithmetic_self_cpu_kernel.cc
+++ b/mindspore/ccsrc/plugin/device/cpu/kernel/arithmetic_self_cpu_kernel.cc
@@ -233,6 +233,16 @@ void Cosh(ArithmeticSelfCpuKernelMod *content, const T *in, T *out, size_t size)
   ParallelLaunchAutoSearch(task, size, content, &content->parallel_search_info_);
 }
 
+template <typename T>
+void ComplexAsinh(ArithmeticSelfCpuKernelMod *content, const T *in, T *out, size_t size) {
+  auto task = [&in, &out](size_t start, size_t end) {
+    for (size_t i = start; i < end; i++) {
+      out[i] = static_cast<T>(asinh(in[i]));
+    }
+  };
+  ParallelLaunchAutoSearch(task, size, content, &content->parallel_search_info_);
+}
+
 template <typename T>
 void Asinh(ArithmeticSelfCpuKernelMod *content, const T *in, T *out, size_t size) {
   auto task = [&in, &out](size_t start, size_t end) {
@@ -391,6 +401,7 @@ void ArithmeticSelfCpuKernelMod::LaunchKernelComplex(const std::vector<AddressPt
                                   std::function<void(ArithmeticSelfCpuKernelMod *, const T *, T *, size_t)>>
     arithmeticSelfFuncMap{{prim::kPrimSquare->name(), Square<T>},
                           {prim::kPrimAcosh->name(), ComplexAcosh<T>},
+                          {prim::kPrimAsinh->name(), ComplexAsinh<T>},
                           {prim::kPrimNeg->name(), Neg<T>}};
   const auto func_pair = arithmeticSelfFuncMap.find(kernel_name_);
   if (arithmeticSelfFuncMap.find(kernel_name_) == arithmeticSelfFuncMap.end()) {
diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/arithmetic_self_cpu_kernel.h b/mindspore/ccsrc/plugin/device/cpu/kernel/arithmetic_self_cpu_kernel.h
index 7881cd45062..2e304e1304f 100644
--- a/mindspore/ccsrc/plugin/device/cpu/kernel/arithmetic_self_cpu_kernel.h
+++ b/mindspore/ccsrc/plugin/device/cpu/kernel/arithmetic_self_cpu_kernel.h
@@ -21,15 +21,14 @@
 #include <memory>
 #include <set>
 #include <vector>
-
-using complex64 = std::complex<float>;
-using complex128 = std::complex<double>;
-
 #include "plugin/device/cpu/kernel/cpu_kernel.h"
 #include "plugin/device/cpu/kernel/cpu_kernel_factory.h"
 
 namespace mindspore {
 namespace kernel {
+using complex64 = std::complex<float>;
+using complex128 = std::complex<double>;
+
 class ArithmeticSelfCpuKernelMod : public NativeCpuKernelMod {
  public:
   ArithmeticSelfCpuKernelMod() = default;
@@ -164,6 +163,10 @@ MS_REG_CPU_KERNEL(Acosh, KernelAttr().AddInputAttr(kNumberTypeComplex64).AddOutp
                   ArithmeticSelfCpuKernelMod);
 MS_REG_CPU_KERNEL(Acosh, KernelAttr().AddInputAttr(kNumberTypeComplex128).AddOutputAttr(kNumberTypeComplex128),
                   ArithmeticSelfCpuKernelMod);
+MS_REG_CPU_KERNEL(Asinh, KernelAttr().AddInputAttr(kNumberTypeComplex64).AddOutputAttr(kNumberTypeComplex64),
+                  ArithmeticSelfCpuKernelMod);
+MS_REG_CPU_KERNEL(Asinh, KernelAttr().AddInputAttr(kNumberTypeComplex128).AddOutputAttr(kNumberTypeComplex128),
+                  ArithmeticSelfCpuKernelMod);
 MS_REG_CPU_KERNEL(Acosh, KernelAttr().AddInputAttr(kNumberTypeFloat32).AddOutputAttr(kNumberTypeFloat32),
                   ArithmeticSelfCpuKernelMod);
 MS_REG_CPU_KERNEL(Acosh, KernelAttr().AddInputAttr(kNumberTypeFloat64).AddOutputAttr(kNumberTypeFloat64),
@@ -211,10 +214,6 @@ MS_REG_CPU_KERNEL_T(Identity, KernelAttr().AddInputAttr(kNumberTypeFloat16).AddO
                     IdentityCpuKernelMod, float16);
 MS_REG_CPU_KERNEL_T(Identity, KernelAttr().AddInputAttr(kNumberTypeBool).AddOutputAttr(kNumberTypeBool),
                     IdentityCpuKernelMod, bool);
-MS_REG_CPU_KERNEL_T(Identity, KernelAttr().AddInputAttr(kNumberTypeComplex64).AddOutputAttr(kNumberTypeComplex64),
-                    IdentityCpuKernelMod, complex64);
-MS_REG_CPU_KERNEL_T(Identity, KernelAttr().AddInputAttr(kNumberTypeComplex128).AddOutputAttr(kNumberTypeComplex128),
-                    IdentityCpuKernelMod, complex128);
 }  // namespace kernel
 }  // namespace mindspore
 
diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/eltwise_grad_cpu_kernel.cc b/mindspore/ccsrc/plugin/device/cpu/kernel/eltwise_grad_cpu_kernel.cc
index 0a30ab66050..6970d23d191 100644
--- a/mindspore/ccsrc/plugin/device/cpu/kernel/eltwise_grad_cpu_kernel.cc
+++ b/mindspore/ccsrc/plugin/device/cpu/kernel/eltwise_grad_cpu_kernel.cc
@@ -190,6 +190,20 @@ void EltWiseGradCpuKernelMod<T>::AsinhGrad(const T *input1, const T *input2, T *
   }
 }
 
+template <typename T>
+void EltWiseGradCpuKernelMod<T>::ComplexAsinhGrad(const T *input1, const T *input2, T *out, size_t start,
+                                                  size_t end) const {
+  for (size_t i = start; i < end; i++) {
+    T dividend = input2[i];
+    T divisor = std::conj(cosh(input1[i]));
+    if (divisor == static_cast<T>(0)) {
+      out[i] = std::numeric_limits<T>::quiet_NaN();
+      continue;
+    }
+    out[i] = dividend / divisor;
+  }
+}
+
 template <typename T>
 void EltWiseGradCpuKernelMod<T>::AcoshGrad(const T *input1, const T *input2, T *out, size_t start, size_t end) const {
   for (size_t i = start; i < end; i++) {
@@ -291,7 +305,8 @@ void EltWiseGradCpuKernelMod<T>::InitComputeFunc() {
   if constexpr ((std::is_same_v<T, complex64>) || (std::is_same_v<T, complex128>)) {
     static const std::map<std::string,
                           std::function<void(EltWiseGradCpuKernelMod *, const T *, const T *, T *, size_t, size_t)>>
-      elt_map{{prim::kPrimAcoshGrad->name(), &EltWiseGradCpuKernelMod<T>::ComplexAcoshGrad}};
+      elt_map{{prim::kPrimAcoshGrad->name(), &EltWiseGradCpuKernelMod<T>::ComplexAcoshGrad},
+              {prim::kPrimAsinhGrad->name(), &EltWiseGradCpuKernelMod<T>::ComplexAsinhGrad}};
     if (elt_map.find(kernel_name_) == elt_map.end()) {
       MS_LOG(EXCEPTION) << "EltWiseGradCpuKernelMod does not support " << kernel_name_;
     }
diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/eltwise_grad_cpu_kernel.h b/mindspore/ccsrc/plugin/device/cpu/kernel/eltwise_grad_cpu_kernel.h
index 7881752c6bd..cfd8770429f 100644
--- a/mindspore/ccsrc/plugin/device/cpu/kernel/eltwise_grad_cpu_kernel.h
+++ b/mindspore/ccsrc/plugin/device/cpu/kernel/eltwise_grad_cpu_kernel.h
@@ -55,6 +55,7 @@ class EltWiseGradCpuKernelMod : public NativeCpuKernelMod {
   void ACosGrad(const T *input1, const T *input2, T *out, size_t start, size_t end) const;
   void AtanGrad(const T *input1, const T *input2, T *out, size_t start, size_t end) const;
   void AsinhGrad(const T *input1, const T *input2, T *out, size_t start, size_t end) const;
+  void ComplexAsinhGrad(const T *input1, const T *input2, T *out, size_t start, size_t end) const;
   void AcoshGrad(const T *input1, const T *input2, T *out, size_t start, size_t end) const;
   void ComplexAcoshGrad(const T *input1, const T *input2, T *out, size_t start, size_t end) const;
   void SoftplusGrad(const T *input1, const T *input2, T *out, size_t start, size_t end) const;
@@ -125,6 +126,22 @@ MS_REG_CPU_KERNEL_T(
   AsinhGrad,
   KernelAttr().AddInputAttr(kNumberTypeFloat32).AddInputAttr(kNumberTypeFloat32).AddOutputAttr(kNumberTypeFloat32),
   EltWiseGradCpuKernelMod, float);
+MS_REG_CPU_KERNEL_T(
+  AsinhGrad,
+  KernelAttr().AddInputAttr(kNumberTypeFloat64).AddInputAttr(kNumberTypeFloat64).AddOutputAttr(kNumberTypeFloat64),
+  EltWiseGradCpuKernelMod, double);
+MS_REG_CPU_KERNEL_T(AsinhGrad,
+                    KernelAttr()
+                      .AddInputAttr(kNumberTypeComplex64)
+                      .AddInputAttr(kNumberTypeComplex64)
+                      .AddOutputAttr(kNumberTypeComplex64),
+                    EltWiseGradCpuKernelMod, complex64);
+MS_REG_CPU_KERNEL_T(AsinhGrad,
+                    KernelAttr()
+                      .AddInputAttr(kNumberTypeComplex128)
+                      .AddInputAttr(kNumberTypeComplex128)
+                      .AddOutputAttr(kNumberTypeComplex128),
+                    EltWiseGradCpuKernelMod, complex128);
 MS_REG_CPU_KERNEL_T(
   AcoshGrad,
   KernelAttr().AddInputAttr(kNumberTypeFloat32).AddInputAttr(kNumberTypeFloat32).AddOutputAttr(kNumberTypeFloat32),
diff --git a/mindspore/core/base/core_ops.h b/mindspore/core/base/core_ops.h
index f65a35db3ce..ec44bf1406e 100644
--- a/mindspore/core/base/core_ops.h
+++ b/mindspore/core/base/core_ops.h
@@ -74,6 +74,8 @@ constexpr auto kMatrixInverse = "MatrixInverse";
 constexpr auto kMatrixDeterminant = "MatrixDeterminant";
 constexpr auto kLogMatrixDeterminant = "LogMatrixDeterminant";
 constexpr auto kCos = "Cos";
+constexpr auto kAsinh = "Asinh";
+constexpr auto kAsinhGrad = "AsinhGrad";
 constexpr auto kAbs = "Abs";
 constexpr auto kTrunc = "Trunc";
 constexpr auto kLpNorm = "LpNorm";
@@ -382,7 +384,7 @@ MS_CORE_API inline const PrimitivePtr kPrimAsin = std::make_shared<Primitive>("A
 MS_CORE_API inline const PrimitivePtr kPrimSinh = std::make_shared<Primitive>("Sinh");
 MS_CORE_API inline const PrimitivePtr kPrimCosh = std::make_shared<Primitive>("Cosh");
 MS_CORE_API inline const PrimitivePtr kPrimTanh = std::make_shared<Primitive>(kTanh);
-MS_CORE_API inline const PrimitivePtr kPrimAsinh = std::make_shared<Primitive>("Asinh");
+MS_CORE_API inline const PrimitivePtr kPrimAsinh = std::make_shared<Primitive>(kAsinh);
 MS_CORE_API inline const PrimitivePtr kPrimAcosh = std::make_shared<Primitive>(kAcosh);
 MS_CORE_API inline const PrimitivePtr kPrimAtanh = std::make_shared<Primitive>("Atanh");
 MS_CORE_API inline const PrimitivePtr kPrimApplyGradientDescent = std::make_shared<Primitive>("ApplyGradientDescent");
@@ -698,7 +700,7 @@ MS_CORE_API inline const PrimitivePtr kPrimACos = std::make_shared<Primitive>(kA
 MS_CORE_API inline const PrimitivePtr kPrimAsinGrad = std::make_shared<Primitive>("AsinGrad");
 MS_CORE_API inline const PrimitivePtr kPrimACosGrad = std::make_shared<Primitive>(kACosGrad);
 MS_CORE_API inline const PrimitivePtr kPrimAtanGrad = std::make_shared<Primitive>("AtanGrad");
-MS_CORE_API inline const PrimitivePtr kPrimAsinhGrad = std::make_shared<Primitive>("AsinhGrad");
+MS_CORE_API inline const PrimitivePtr kPrimAsinhGrad = std::make_shared<Primitive>(kAsinhGrad);
 MS_CORE_API inline const PrimitivePtr kPrimAcoshGrad = std::make_shared<Primitive>("AcoshGrad");
 MS_CORE_API inline const PrimitivePtr kPrimFloorMod = std::make_shared<Primitive>("FloorMod");
 MS_CORE_API inline const PrimitivePtr kPrimCdist = std::make_shared<Primitive>(kCdist);
diff --git a/mindspore/core/ops/asinh.cc b/mindspore/core/ops/asinh.cc
index 763fe2a8c33..0649a1dc8d0 100644
--- a/mindspore/core/ops/asinh.cc
+++ b/mindspore/core/ops/asinh.cc
@@ -14,49 +14,46 @@
  * limitations under the License.
  */
 
-#include <algorithm>
-#include <map>
-#include <memory>
-#include <set>
-#include <string>
-#include <vector>
-
 #include "ops/asinh.h"
-#include "ops/op_utils.h"
-#include "utils/check_convert_utils.h"
-#include "abstract/primitive_infer_map.h"
-#include "abstract/param_validator.h"
 
 namespace mindspore {
 namespace ops {
 namespace {
+const size_t InputNum = 1;
+const int64_t MaxDim = 8;
+
 abstract::ShapePtr AsinhInferShape(const PrimitivePtr &primitive, const std::vector<AbstractBasePtr> &input_args) {
   auto prim_name = primitive->name();
   (void)CheckAndConvertUtils::CheckArgs<abstract::AbstractTensor>(prim_name, input_args, 0);
-  auto x = input_args[0]->BuildShape();
+  auto x = input_args[kInputIndex0]->BuildShape();
   MS_EXCEPTION_IF_NULL(x);
+  auto in_shape = CheckAndConvertUtils::ConvertShapePtrToShapeMap(input_args[0]->BuildShape())[kShape];
+  (void)CheckAndConvertUtils::CheckInteger("The dimension of Asinh input", SizeToLong(in_shape.size()), kLessThan,
+                                           MaxDim, prim_name);
   auto shape_element = x->cast<abstract::ShapePtr>();
   MS_EXCEPTION_IF_NULL(shape_element);
   return shape_element;
 }
+
 TypePtr AsinhInferType(const PrimitivePtr &primitive, const std::vector<AbstractBasePtr> &input_args) {
   auto prim_name = primitive->name();
-  MS_EXCEPTION_IF_NULL(input_args[0]);
-  auto x_type = input_args[0]->BuildType();
-  (void)CheckAndConvertUtils::CheckTensorTypeValid("input_x", x_type, common_valid_types, prim_name);
-  return x_type;
+  const std::set<TypePtr> valid_types = {kFloat16, kFloat32, kFloat64, kComplex64, kComplex128};
+  auto x_type = input_args[kInputIndex0]->BuildType();
+  (void)CheckAndConvertUtils::CheckTensorTypeValid("x", x_type, valid_types, prim_name);
+  return input_args[kInputIndex0]->BuildType();
 }
 }  // namespace
 
 AbstractBasePtr AsinhInfer(const abstract::AnalysisEnginePtr &, const PrimitivePtr &primitive,
                            const std::vector<AbstractBasePtr> &input_args) {
   MS_EXCEPTION_IF_NULL(primitive);
-  const int64_t input_num = 1;
-  CheckAndConvertUtils::CheckInputArgs(input_args, kEqual, input_num, primitive->name());
-  auto infer_type = AsinhInferType(primitive, input_args);
-  auto infer_shape = AsinhInferShape(primitive, input_args);
-  return abstract::MakeAbstract(infer_shape, infer_type);
+  auto prim_name = primitive->name();
+  (void)CheckAndConvertUtils::CheckInputArgs(input_args, kEqual, InputNum, prim_name);
+  auto types = AsinhInferType(primitive, input_args);
+  auto shapes = AsinhInferShape(primitive, input_args);
+  return abstract::MakeAbstract(shapes, types);
 }
+
 REGISTER_PRIMITIVE_EVAL_IMPL(Asinh, prim::kPrimAsinh, AsinhInfer, nullptr, true);
 }  // namespace ops
 }  // namespace mindspore
diff --git a/mindspore/core/ops/asinh.h b/mindspore/core/ops/asinh.h
index 4e0c3592681..a7a77ac3711 100644
--- a/mindspore/core/ops/asinh.h
+++ b/mindspore/core/ops/asinh.h
@@ -19,23 +19,34 @@
 
 #include <map>
 #include <memory>
+#include <set>
 #include <string>
 #include <vector>
-
-#include "ops/primitive_c.h"
 #include "abstract/abstract_value.h"
+#include "ops/primitive_c.h"
 #include "utils/check_convert_utils.h"
+#include "ops/op_utils.h"
 
 namespace mindspore {
 namespace ops {
 constexpr auto kNameAsinh = "Asinh";
-class Asinh : public PrimitiveC {
+/// \brief Computes arcsinh of input tensors element-wise.
+/// Refer to Python API @ref mindspore.ops.Asinh for more details.
+class MS_CORE_API Asinh : public PrimitiveC {
  public:
-  Asinh() : PrimitiveC(kNameAsinh) { InitIOName({"x"}, {"output"}); }
+  /// \brief Constructor.
+  Asinh() : PrimitiveC(kNameAsinh) { InitIOName({"x"}, {"y"}); }
+  /// \brief Destructor.
   ~Asinh() = default;
+
   MS_DECLARE_PARENT(Asinh, PrimitiveC);
   void Init() {}
 };
+
+AbstractBasePtr AsinhInfer(const abstract::AnalysisEnginePtr &, const PrimitivePtr &primitive,
+                           const std::vector<AbstractBasePtr> &input_args);
+
+using PrimAsinhPtr = std::shared_ptr<Asinh>;
 }  // namespace ops
 }  // namespace mindspore
 
diff --git a/mindspore/core/ops/grad/asinh_grad.cc b/mindspore/core/ops/grad/asinh_grad.cc
index 9f911844a9a..2beecc7d8c2 100644
--- a/mindspore/core/ops/grad/asinh_grad.cc
+++ b/mindspore/core/ops/grad/asinh_grad.cc
@@ -15,24 +15,16 @@
  */
 
 #include "ops/grad/asinh_grad.h"
-#include <algorithm>
-#include <set>
-#include "abstract/param_validator.h"
-#include "utils/check_convert_utils.h"
-#include "abstract/primitive_infer_map.h"
 
 namespace mindspore {
 namespace ops {
 namespace {
+const size_t InputNum = 2;
+
 abstract::ShapePtr AsinhGradInferShape(const PrimitivePtr &primitive, const std::vector<AbstractBasePtr> &input_args) {
-  MS_EXCEPTION_IF_NULL(primitive);
   auto prim_name = primitive->name();
-  const int64_t input_num = 2;
-  (void)CheckAndConvertUtils::CheckInteger("input number", SizeToLong(input_args.size()), kEqual, input_num, prim_name);
-  for (const auto &item : input_args) {
-    MS_EXCEPTION_IF_NULL(item);
-  }
-  auto x = input_args[0]->BuildShape();
+  (void)CheckAndConvertUtils::CheckArgs<abstract::AbstractTensor>(prim_name, input_args, 0);
+  auto x = input_args[kInputIndex0]->BuildShape();
   MS_EXCEPTION_IF_NULL(x);
   auto shape_element = x->cast<abstract::ShapePtr>();
   MS_EXCEPTION_IF_NULL(shape_element);
@@ -40,24 +32,24 @@ abstract::ShapePtr AsinhGradInferShape(const PrimitivePtr &primitive, const std:
 }
 
 TypePtr AsinhGradInferType(const PrimitivePtr &primitive, const std::vector<AbstractBasePtr> &input_args) {
-  const std::set<TypePtr> valid_types = {kFloat16, kFloat32};
-  MS_EXCEPTION_IF_NULL(primitive);
   auto prim_name = primitive->name();
-  const int64_t input_num = 2;
-  (void)CheckAndConvertUtils::CheckInteger("input number", SizeToLong(input_args.size()), kEqual, input_num, prim_name);
-  MS_EXCEPTION_IF_NULL(input_args[0]);
-  auto x_type = input_args[0]->BuildType();
-  MS_EXCEPTION_IF_NULL(x_type);
-  (void)CheckAndConvertUtils::CheckTensorTypeValid("input_x", x_type, valid_types, prim_name);
-  return x_type;
+  const std::set<TypePtr> valid_types = {kFloat16, kFloat32, kFloat64, kComplex64, kComplex128};
+  std::map<std::string, TypePtr> types;
+  (void)types.emplace("y", input_args[kInputIndex0]->BuildType());
+  (void)types.emplace("dy", input_args[kInputIndex1]->BuildType());
+  (void)CheckAndConvertUtils::CheckTensorTypeSame(types, valid_types, prim_name);
+  return input_args[kInputIndex0]->BuildType();
 }
 }  // namespace
 
 AbstractBasePtr AsinhGradInfer(const abstract::AnalysisEnginePtr &, const PrimitivePtr &primitive,
                                const std::vector<AbstractBasePtr> &input_args) {
-  auto type = AsinhGradInferType(primitive, input_args);
-  auto shape = AsinhGradInferShape(primitive, input_args);
-  return abstract::MakeAbstract(shape, type);
+  MS_EXCEPTION_IF_NULL(primitive);
+  auto prim_name = primitive->name();
+  (void)CheckAndConvertUtils::CheckInputArgs(input_args, kEqual, InputNum, prim_name);
+  auto types = AsinhGradInferType(primitive, input_args);
+  auto shapes = AsinhGradInferShape(primitive, input_args);
+  return abstract::MakeAbstract(shapes, types);
 }
 
 REGISTER_PRIMITIVE_EVAL_IMPL(AsinhGrad, prim::kPrimAsinhGrad, AsinhGradInfer, nullptr, true);
diff --git a/mindspore/core/ops/grad/asinh_grad.h b/mindspore/core/ops/grad/asinh_grad.h
index 1febce7a27e..a8b1bd10dca 100644
--- a/mindspore/core/ops/grad/asinh_grad.h
+++ b/mindspore/core/ops/grad/asinh_grad.h
@@ -16,24 +16,32 @@
 
 #ifndef MINDSPORE_CORE_OPS_ASINH_GRAD_H_
 #define MINDSPORE_CORE_OPS_ASINH_GRAD_H_
+
 #include <map>
-#include <vector>
-#include <string>
 #include <memory>
-#include "ops/primitive_c.h"
-#include "ops/op_utils.h"
+#include <set>
+#include <string>
+#include <vector>
 #include "abstract/abstract_value.h"
+#include "ops/primitive_c.h"
 #include "utils/check_convert_utils.h"
+#include "ops/op_utils.h"
 
 namespace mindspore {
 namespace ops {
 constexpr auto kNameAsinhGrad = "AsinhGrad";
+
 class AsinhGrad : public PrimitiveC {
  public:
-  AsinhGrad() : PrimitiveC(kNameAsinhGrad) { InitIOName({"x"}, {"output"}); }
+  AsinhGrad() : PrimitiveC(kNameAsinhGrad) { InitIOName({"y", "dy"}, {"z"}); }
   ~AsinhGrad() = default;
+
   MS_DECLARE_PARENT(AsinhGrad, PrimitiveC);
 };
+
+AbstractBasePtr AsinhGradInfer(const abstract::AnalysisEnginePtr &, const PrimitivePtr &primitive,
+                               const std::vector<AbstractBasePtr> &input_args);
+using PrimAsinhGradPtr = std::shared_ptr<AsinhGrad>;
 }  // namespace ops
 }  // namespace mindspore
 
diff --git a/mindspore/python/mindspore/ops/_op_impl/aicpu/__init__.py b/mindspore/python/mindspore/ops/_op_impl/aicpu/__init__.py
index af352b6d80b..86d27084f12 100644
--- a/mindspore/python/mindspore/ops/_op_impl/aicpu/__init__.py
+++ b/mindspore/python/mindspore/ops/_op_impl/aicpu/__init__.py
@@ -93,6 +93,8 @@ from .trans_data import _trans_data_aicpu
 from .stack_push_pop import _stack_init_aicpu
 from .stack_push_pop import _stack_push_aicpu
 from .stack_push_pop import _stack_pop_aicpu
+from .asinh import _asinh_aicpu
+from .asinh_grad import _asinh_grad_aicpu
 from .stack_push_pop import _stack_destroy_aicpu
 from .ctc_greedy_decoder import _ctc_greedy_decoder_aicpu
 from .resize_bilinear import _resize_bilinear_aicpu
diff --git a/mindspore/python/mindspore/ops/_op_impl/aicpu/asinh.py b/mindspore/python/mindspore/ops/_op_impl/aicpu/asinh.py
new file mode 100644
index 00000000000..11e8b6feaef
--- /dev/null
+++ b/mindspore/python/mindspore/ops/_op_impl/aicpu/asinh.py
@@ -0,0 +1,34 @@
+# Copyright 2021 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+
+"""Asinh op"""
+from mindspore.ops.op_info_register import op_info_register, AiCPURegOp, DataType
+
+asinh_op_info = AiCPURegOp("Asinh") \
+    .fusion_type("ELEMWISE") \
+    .input(0, "x", "required") \
+    .output(0, "y", "required") \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default) \
+    .dtype_format(DataType.F64_Default, DataType.F64_Default) \
+    .dtype_format(DataType.C64_Default, DataType.C64_Default) \
+    .dtype_format(DataType.C128_Default, DataType.C128_Default) \
+    .get_op_info()
+
+
+@op_info_register(asinh_op_info)
+def _asinh_aicpu():
+    """Asinh AiCPU register"""
+    return
diff --git a/mindspore/python/mindspore/ops/_op_impl/aicpu/asinh_grad.py b/mindspore/python/mindspore/ops/_op_impl/aicpu/asinh_grad.py
new file mode 100644
index 00000000000..b1f9989dda6
--- /dev/null
+++ b/mindspore/python/mindspore/ops/_op_impl/aicpu/asinh_grad.py
@@ -0,0 +1,35 @@
+# Copyright 2021 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+
+"""AsinhGrad op"""
+from mindspore.ops.op_info_register import op_info_register, AiCPURegOp, DataType
+
+asinh_grad_op_info = AiCPURegOp("AsinhGrad") \
+    .fusion_type("ELEMWISE") \
+    .input(0, "y", "required") \
+    .input(1, "dy", "required") \
+    .output(0, "z", "required") \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default, DataType.F16_Default) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default, DataType.F32_Default) \
+    .dtype_format(DataType.F64_Default, DataType.F64_Default, DataType.F64_Default) \
+    .dtype_format(DataType.C64_Default, DataType.C64_Default, DataType.C64_Default) \
+    .dtype_format(DataType.C128_Default, DataType.C128_Default, DataType.C128_Default) \
+    .get_op_info()
+
+
+@op_info_register(asinh_grad_op_info)
+def _asinh_grad_aicpu():
+    """AsinhGrad AiCPU register"""
+    return
diff --git a/mindspore/python/mindspore/ops/operations/_grad_ops.py b/mindspore/python/mindspore/ops/operations/_grad_ops.py
index e3a355c27ba..4db574ee31a 100644
--- a/mindspore/python/mindspore/ops/operations/_grad_ops.py
+++ b/mindspore/python/mindspore/ops/operations/_grad_ops.py
@@ -65,12 +65,13 @@ class AsinGrad(Primitive):
         """Initialize AsinGrad"""
 
 
-class AsinhGrad(PrimitiveWithInfer):
+class AsinhGrad(Primitive):
     """Performs grad of Asinh operation."""
 
     @prim_attr_register
     def __init__(self):
         """Initialize AsinhGrad"""
+        self.init_prim_io_names(inputs=['y', 'dy'], outputs=['z'])
 
 
 class ReciprocalGrad(Primitive):
diff --git a/mindspore/python/mindspore/ops/operations/math_ops.py b/mindspore/python/mindspore/ops/operations/math_ops.py
index 299db4a559d..191d8d61af9 100644
--- a/mindspore/python/mindspore/ops/operations/math_ops.py
+++ b/mindspore/python/mindspore/ops/operations/math_ops.py
@@ -3473,7 +3473,6 @@ class Asinh(Primitive):
     Inputs:
         - **x** (Tensor) - The shape of tensor is
           :math:`(N,*)` where :math:`*` means, any number of additional dimensions, its rank should be less than 8.
-          The data type should be one of the following types: float16, float32.
 
     Outputs:
         Tensor, has the same shape and type as `x`.
@@ -3489,13 +3488,13 @@ class Asinh(Primitive):
         >>> x = Tensor(np.array([-5.0, 1.5, 3.0, 100.0]), mindspore.float32)
         >>> output = asinh(x)
         >>> print(output)
-        [-2.3124385  1.1947632  1.8184465  5.298342 ]
+        [-2.3124382  1.1947632  1.8184465  5.298342 ]
     """
 
     @prim_attr_register
     def __init__(self):
         """Initialize Asinh"""
-
+        self.init_prim_io_names(inputs=['x'], outputs=['y'])
 
 class Sinh(Primitive):
     r"""
diff --git a/tests/ut/python/ops/test_ops.py b/tests/ut/python/ops/test_ops.py
index 626f3e69941..78a1b1ea7b3 100755
--- a/tests/ut/python/ops/test_ops.py
+++ b/tests/ut/python/ops/test_ops.py
@@ -1268,6 +1268,10 @@ test_case_math_ops = [
         'block': P.Asinh(),
         'desc_inputs': [[3, 4, 5]],
         'desc_bprop': [[3, 4, 5]]}),
+    ('AsinhGrad', {
+        'block': G.AsinhGrad(),
+        'desc_inputs': [[2, 3], [2, 3]],
+        'skip': ['backward']}),
     ('Tan', {
         'block': P.Tan(),
         'desc_inputs': [[2, 3]],