!11915 Change TensorAdd to Add, merge from r1.1 to master

From: @liangzhibo
Reviewed-by: @ginfung,@zh_qh
Signed-off-by: @zh_qh

mindspore/_extends/graph_kernel/expanders/bias_add.py

@@ -36,24 +36,24 @@ def expand_biasadd(expand_info):
                 'ExpandDims', [input_y], attrs={'axis': 1})
             input_y_expand = graph_builder.emit(
                 'ExpandDims', [input_y_expand], attrs={'axis': 2})
-            result = graph_builder.emit('TensorAdd', [input_x, input_y_expand])
+            result = graph_builder.emit('Add', [input_x, input_y_expand])
         elif input_x.data_format == "DefaultFormat":
             if len(input_x.shape) == 2:
-                result = graph_builder.emit('TensorAdd', [input_x, input_y])
+                result = graph_builder.emit('Add', [input_x, input_y])
             elif len(input_x.shape) == 3:
                 input_y_expand = graph_builder.emit(
                     'ExpandDims', [input_y], attrs={'axis': 1})
                 result = graph_builder.emit(
-                    'TensorAdd', [input_x, input_y_expand])
+                    'Add', [input_x, input_y_expand])
             else:
                 input_y_expand = graph_builder.emit(
                     'ExpandDims', [input_y], attrs={'axis': 1})
                 input_y_expand = graph_builder.emit(
                     'ExpandDims', [input_y_expand], attrs={'axis': 2})
                 result = graph_builder.emit(
-                    'TensorAdd', [input_x, input_y_expand])
+                    'Add', [input_x, input_y_expand])
         else:
-            result = graph_builder.emit('TensorAdd', [input_x, input_y])
+            result = graph_builder.emit('Add', [input_x, input_y])
 
         # set graph output.
         graph_scope.set_output(result)

mindspore/_extends/graph_kernel/expanders/fused_adam.py

@@ -49,13 +49,13 @@ def expand_fusedadam(expand_info):
         # compute result
         beta_1_mul_m = graph_builder.emit('Mul', [beta_1, m])
         one_sub_beta_1_mul_grad = graph_builder.emit('Mul', [one_sub_beta_1, gradient])
-        next_m = graph_builder.emit('TensorAdd', [beta_1_mul_m, one_sub_beta_1_mul_grad])
+        next_m = graph_builder.emit('Add', [beta_1_mul_m, one_sub_beta_1_mul_grad])
         beta_2_mul_v = graph_builder.emit('Mul', [beta_2, v])
         grad_square = graph_builder.emit('Mul', [gradient, gradient])
         one_sub_beta_2_mul_grad_square = graph_builder.emit('Mul', [one_sub_beta_2, grad_square])
-        next_v = graph_builder.emit('TensorAdd', [beta_2_mul_v, one_sub_beta_2_mul_grad_square])
+        next_v = graph_builder.emit('Add', [beta_2_mul_v, one_sub_beta_2_mul_grad_square])
         sqrt_next_v = graph_builder.emit('Sqrt', [next_v])
-        sqrt_next_v_add_eps = graph_builder.emit('TensorAdd', [sqrt_next_v, eps])
+        sqrt_next_v_add_eps = graph_builder.emit('Add', [sqrt_next_v, eps])
         update = graph_builder.emit('RealDiv', [next_m, sqrt_next_v_add_eps])
         update_with_lr = graph_builder.emit('Mul', [lr, update])
         next_para = graph_builder.emit('Sub', [param, update_with_lr])

mindspore/_extends/graph_kernel/expanders/fused_adam_weight_decay.py

@@ -52,16 +52,16 @@ def expand_fusedadamweightdecay(expand_info):
         # compute result
         beta_1_mul_m = graph_builder.emit('Mul', [beta_1, m])
         one_sub_beta_1_mul_grad = graph_builder.emit('Mul', [one_sub_beta_1, gradient])
-        next_m = graph_builder.emit('TensorAdd', [beta_1_mul_m, one_sub_beta_1_mul_grad])
+        next_m = graph_builder.emit('Add', [beta_1_mul_m, one_sub_beta_1_mul_grad])
         beta_2_mul_v = graph_builder.emit('Mul', [beta_2, v])
         grad_square = graph_builder.emit('Mul', [gradient, gradient])
         one_sub_beta_2_mul_grad_square = graph_builder.emit('Mul', [one_sub_beta_2, grad_square])
-        next_v = graph_builder.emit('TensorAdd', [beta_2_mul_v, one_sub_beta_2_mul_grad_square])
+        next_v = graph_builder.emit('Add', [beta_2_mul_v, one_sub_beta_2_mul_grad_square])
         sqrt_next_v = graph_builder.emit('Sqrt', [next_v])
-        sqrt_next_v_add_eps = graph_builder.emit('TensorAdd', [sqrt_next_v, eps])
+        sqrt_next_v_add_eps = graph_builder.emit('Add', [sqrt_next_v, eps])
         update = graph_builder.emit('RealDiv', [next_m, sqrt_next_v_add_eps])
         param_with_weight_decay = graph_builder.emit('Mul', [weight_decay, param])
-        update = graph_builder.emit('TensorAdd', [update, param_with_weight_decay])
+        update = graph_builder.emit('Add', [update, param_with_weight_decay])
         update_with_lr = graph_builder.emit('Mul', [lr, update])
         next_para = graph_builder.emit('Sub', [param, update_with_lr])
 

mindspore/_extends/graph_kernel/expanders/gelu.py

@@ -42,7 +42,7 @@ def expand_gelu(expand_info):
         pow_0 = graph_builder.emit('Mul', [mul_0, input_x])
         const_csvalue = graph_builder.value(pow_0.dtype, CSVALUE, input_desc['format'])
         mul_1 = graph_builder.emit('Mul', [pow_0, const_csvalue])
-        tanh_res = graph_builder.emit('TensorAdd', [input_x, mul_1])
+        tanh_res = graph_builder.emit('Add', [input_x, mul_1])
         const_csvalue_sqrt_two_div_pi = graph_builder.value(
             tanh_res.dtype, CSVALUE_SQRT_TWO_DIV_PI, input_desc['format'])
         y = graph_builder.emit('Mul', [tanh_res, const_csvalue_sqrt_two_div_pi])
@@ -51,7 +51,7 @@ def expand_gelu(expand_info):
         tanh_y = graph_builder.emit('Tanh', [y])
         const_one = graph_builder.value(tanh_y.dtype, ONE, input_desc['format'])
         const_half = graph_builder.value(tanh_y.dtype, HALF, input_desc['format'])
-        tanh_y_add_one = graph_builder.emit('TensorAdd', [tanh_y, const_one])
+        tanh_y_add_one = graph_builder.emit('Add', [tanh_y, const_one])
         mul_x = graph_builder.emit('Mul', [input_x, tanh_y_add_one])
         result = graph_builder.emit('Mul', [const_half, mul_x])
 

mindspore/_extends/graph_kernel/expanders/gelu_grad.py

@@ -55,18 +55,18 @@ def expand_gelugrad(expand_info):
         # cal mul_right
         mul_double = graph_builder.emit('Mul', [input_x, input_x])
         mul_double_mul_tri = graph_builder.emit('Mul', [const_csvalue_tri, mul_double])
-        mul_add_one = graph_builder.emit('TensorAdd', [const_one, mul_double_mul_tri])
+        mul_add_one = graph_builder.emit('Add', [const_one, mul_double_mul_tri])
         mul_right = graph_builder.emit('Mul', [const_csvalue_sqrt_two_div_pi, mul_add_one])
 
         # cal tanh_para
         mul_triple = graph_builder.emit('Mul', [input_x, mul_double])
         mul_triple_mul_csvalue = graph_builder.emit('Mul', [const_csvalue, mul_triple])
-        mul_add_x = graph_builder.emit('TensorAdd', [input_x, mul_triple_mul_csvalue])
+        mul_add_x = graph_builder.emit('Add', [input_x, mul_triple_mul_csvalue])
         tanh_para = graph_builder.emit('Mul', [const_csvalue_sqrt_two_div_pi, mul_add_x])
 
         # cal 0.5 * (1.0 + tanh(tahn_para))
         tanh_res = graph_builder.emit('Tanh', [tanh_para])
-        tanh_res_add_one = graph_builder.emit('TensorAdd', [const_one, tanh_res])
+        tanh_res_add_one = graph_builder.emit('Add', [const_one, tanh_res])
         half_mul_tanh_res_add_one = graph_builder.emit('Mul', [const_half, tanh_res_add_one])
 
         # cal 0.5 * x * (1.0 - tanh(tanh_para) * tanh(tanh_para)) * mul_right
@@ -77,7 +77,7 @@ def expand_gelugrad(expand_info):
         mul_final = graph_builder.emit('Mul', [mul_tmp, mul_right])
 
         # cal result
-        result_tmp = graph_builder.emit('TensorAdd', [half_mul_tanh_res_add_one, mul_final])
+        result_tmp = graph_builder.emit('Add', [half_mul_tanh_res_add_one, mul_final])
         result = graph_builder.emit('Mul', [input_dy, result_tmp])
 
         # set graph output.

mindspore/_extends/graph_kernel/expanders/layernorm.py

@@ -68,13 +68,13 @@ def expand_layernorm(expand_info):
         # Calculate normalize
         normalize_sub = graph_builder.emit('Sub', [input_x, mean])
         epsilon_v = graph_builder.value(input_x.dtype, epsilon, input_x.data_format)
-        normalize_add = graph_builder.emit('TensorAdd', [variance, epsilon_v])
+        normalize_add = graph_builder.emit('Add', [variance, epsilon_v])
         normlize_rsqrt = graph_builder.emit('Rsqrt', [normalize_add])
         normalize_mul = graph_builder.emit('Mul', [normalize_sub, normlize_rsqrt])
 
         # Calculate scale and translate
         scale_mul = graph_builder.emit('Mul', [input_gamma, normalize_mul])
-        res = graph_builder.emit('TensorAdd', [scale_mul, input_beta])
+        res = graph_builder.emit('Add', [scale_mul, input_beta])
 
         # set graph output.
         graph_scope.set_output(res, mean, variance)

mindspore/_extends/graph_kernel/expanders/layernorm_grad.py

@@ -66,7 +66,7 @@ def expand_layernormgrad(expand_info):
         mean_cof = graph_builder.value(x.dtype, (1.0 / reduce_size), x.data_format)
 
         # cal dg db
-        var_eps = graph_builder.emit('TensorAdd', [variance, eps])
+        var_eps = graph_builder.emit('Add', [variance, eps])
         sqrt_var_eps = graph_builder.emit('Sqrt', [var_eps])
         rsqrt_var_eps = graph_builder.emit('RealDiv', [const_one, sqrt_var_eps])
         x_sub_mean = graph_builder.emit('Sub', [x, mean])
@@ -100,10 +100,10 @@ def expand_layernormgrad(expand_info):
         neg_rsqrt_var_eps_mul_sum_2 = graph_builder.emit('Mul', [neg_rsqrt_var_eps, sum_2])
         sum_1_mul_sum_3 = graph_builder.emit('Mul', [sum_1, sum_3])
         mean_cof_mul_sum_1_mul_sum_3 = graph_builder.emit('Mul', [mean_cof, sum_1_mul_sum_3])
-        add_tmp = graph_builder.emit('TensorAdd', [neg_rsqrt_var_eps_mul_sum_2, mean_cof_mul_sum_1_mul_sum_3])
+        add_tmp = graph_builder.emit('Add', [neg_rsqrt_var_eps_mul_sum_2, mean_cof_mul_sum_1_mul_sum_3])
         dx_3 = graph_builder.emit('Mul', [add_tmp, mean_cof])
-        dx_tmp = graph_builder.emit('TensorAdd', [dx_1, dx_2])
-        dx = graph_builder.emit('TensorAdd', [dx_tmp, dx_3])
+        dx_tmp = graph_builder.emit('Add', [dx_1, dx_2])
+        dx = graph_builder.emit('Add', [dx_tmp, dx_3])
 
         # set graph output.
         graph_scope.set_output(dx, dg, db)

mindspore/_extends/graph_kernel/model/model.py

@@ -131,7 +131,7 @@ class PrimLib:
         ]
 
     primtives = {
-        'TensorAdd': Prim(ELEMWISE),
+        'Add': Prim(ELEMWISE),
         'Abs': Prim(ELEMWISE),
         'Neg': Prim(ELEMWISE),
         'Mul': Prim(ELEMWISE),

mindspore/ccsrc/backend/kernel_compiler/cpu/arithmetic_cpu_kernel.cc

@@ -238,7 +238,7 @@ void ArithmeticCPUKernel::LessEqual(const T *input1, const T *input2, bool *out,
 void ArithmeticCPUKernel::InitKernel(const CNodePtr &kernel_node) {
   MS_EXCEPTION_IF_NULL(kernel_node);
   std::string kernel_name = AnfAlgo::GetCNodeName(kernel_node);
-  if (kernel_name == prim::kPrimTensorAdd->name()) {
+  if (kernel_name == prim::kPrimAdd->name()) {
     operate_type_ = ADD;
   } else if (kernel_name == prim::kPrimSub->name()) {
     operate_type_ = SUB;

mindspore/ccsrc/backend/kernel_compiler/cpu/mkldnn/tensoradd_cpu_kernel.h

@@ -37,8 +37,7 @@ class TensorAddCPUKernel : public MKLCPUKernel {
 };
 
 MS_REG_CPU_KERNEL(
-  TensorAdd,
-  KernelAttr().AddInputAttr(kNumberTypeFloat32).AddInputAttr(kNumberTypeFloat32).AddOutputAttr(kNumberTypeFloat32),
+  Add, KernelAttr().AddInputAttr(kNumberTypeFloat32).AddInputAttr(kNumberTypeFloat32).AddOutputAttr(kNumberTypeFloat32),
   TensorAddCPUKernel);
 }  // namespace kernel
 }  // namespace mindspore

mindspore/ccsrc/backend/kernel_compiler/gpu/math/broadcast_gpu_kernel.cc

@@ -51,8 +51,7 @@ MS_REG_GPU_KERNEL_ONE(
   Sub, KernelAttr().AddInputAttr(kNumberTypeFloat32).AddInputAttr(kNumberTypeFloat32).AddOutputAttr(kNumberTypeFloat32),
   BroadcastOpGpuKernel, float)
 MS_REG_GPU_KERNEL_ONE(
-  TensorAdd,
-  KernelAttr().AddInputAttr(kNumberTypeFloat32).AddInputAttr(kNumberTypeFloat32).AddOutputAttr(kNumberTypeFloat32),
+  Add, KernelAttr().AddInputAttr(kNumberTypeFloat32).AddInputAttr(kNumberTypeFloat32).AddOutputAttr(kNumberTypeFloat32),
   BroadcastOpGpuKernel, float)
 MS_REG_GPU_KERNEL_ONE(
   FloorDiv,
@@ -103,8 +102,7 @@ MS_REG_GPU_KERNEL_ONE(
   Sub, KernelAttr().AddInputAttr(kNumberTypeFloat16).AddInputAttr(kNumberTypeFloat16).AddOutputAttr(kNumberTypeFloat16),
   BroadcastOpGpuKernel, half)
 MS_REG_GPU_KERNEL_ONE(
-  TensorAdd,
-  KernelAttr().AddInputAttr(kNumberTypeFloat16).AddInputAttr(kNumberTypeFloat16).AddOutputAttr(kNumberTypeFloat16),
+  Add, KernelAttr().AddInputAttr(kNumberTypeFloat16).AddInputAttr(kNumberTypeFloat16).AddOutputAttr(kNumberTypeFloat16),
   BroadcastOpGpuKernel, half)
 MS_REG_GPU_KERNEL_ONE(
   FloorDiv,
@@ -133,7 +131,7 @@ MS_REG_GPU_KERNEL_ONE(
   Equal, KernelAttr().AddInputAttr(kNumberTypeInt32).AddInputAttr(kNumberTypeInt32).AddOutputAttr(kNumberTypeBool),
   BroadcastOpGpuKernel, int)
 MS_REG_GPU_KERNEL_ONE(
-  TensorAdd, KernelAttr().AddInputAttr(kNumberTypeInt32).AddInputAttr(kNumberTypeInt32).AddOutputAttr(kNumberTypeInt32),
+  Add, KernelAttr().AddInputAttr(kNumberTypeInt32).AddInputAttr(kNumberTypeInt32).AddOutputAttr(kNumberTypeInt32),
   BroadcastOpGpuKernel, int)
 MS_REG_GPU_KERNEL_ONE(
   Minimum, KernelAttr().AddInputAttr(kNumberTypeInt32).AddInputAttr(kNumberTypeInt32).AddOutputAttr(kNumberTypeInt32),
@@ -171,7 +169,7 @@ MS_REG_GPU_KERNEL_ONE(
   Equal, KernelAttr().AddInputAttr(kNumberTypeInt64).AddInputAttr(kNumberTypeInt64).AddOutputAttr(kNumberTypeBool),
   BroadcastOpGpuKernel, int64_t)
 MS_REG_GPU_KERNEL_ONE(
-  TensorAdd, KernelAttr().AddInputAttr(kNumberTypeInt64).AddInputAttr(kNumberTypeInt64).AddOutputAttr(kNumberTypeInt64),
+  Add, KernelAttr().AddInputAttr(kNumberTypeInt64).AddInputAttr(kNumberTypeInt64).AddOutputAttr(kNumberTypeInt64),
   BroadcastOpGpuKernel, int64_t)
 MS_REG_GPU_KERNEL_ONE(
   Minimum, KernelAttr().AddInputAttr(kNumberTypeInt64).AddInputAttr(kNumberTypeInt64).AddOutputAttr(kNumberTypeInt64),

mindspore/ccsrc/backend/kernel_compiler/gpu/math/broadcast_gpu_kernel.h

@@ -145,7 +145,7 @@ class BroadcastOpGpuKernel : public GpuKernel {
     static std::map<std::string, BroadcastOpType> kBroadcastArithmetricTypeMap = {
       {"Maximum", BROADCAST_TYPE_MAXIMUM}, {"Minimum", BROADCAST_TYPE_MINIMUM},   {"Pow", BROADCAST_TYPE_POWER},
       {"RealDiv", BROADCAST_TYPE_REALDIV}, {"Mul", BROADCAST_TYPE_MUL},           {"Sub", BROADCAST_TYPE_SUB},
-      {"TensorAdd", BROADCAST_TYPE_ADD},   {"FloorDiv", BROADCAST_TYPE_FLOORDIV}, {"AbsGrad", BROADCAST_TYPE_ABSGRAD},
+      {"Add", BROADCAST_TYPE_ADD},         {"FloorDiv", BROADCAST_TYPE_FLOORDIV}, {"AbsGrad", BROADCAST_TYPE_ABSGRAD},
       {"Div", BROADCAST_TYPE_DIV},         {"DivNoNan", BROADCAST_TYPE_DIVNONAN},
     };
 

mindspore/ccsrc/backend/kernel_compiler/tbe/tbe_kernel_build.cc

@@ -1063,7 +1063,7 @@ size_t TbeKernelBuild::GetOptionalInput(const mindspore::CNodePtr &cnode, bool i
 
 std::string TbeKernelBuild::GetRealOpType(const std::string &origin_type) {
   static std::map<std::string, std::string> buffer_fussion_op_map = {
-    {parallel::DEPTHWISE_CONV2D_NATIVE, parallel::DEPTHWISE_CONV2D}, {parallel::TENSOR_ADD, parallel::ADD}};
+    {parallel::DEPTHWISE_CONV2D_NATIVE, parallel::DEPTHWISE_CONV2D}};
   string result = origin_type;
   auto iter = buffer_fussion_op_map.find(origin_type);
   if (iter != buffer_fussion_op_map.end()) {

mindspore/ccsrc/backend/optimizer/ascend/buffer_fusion/bnupdate_eltwise_eltwise_fusion_pass.cc

@@ -99,7 +99,7 @@ void BnupdateEltwiseEltwiseFusionPass::MatchSingleFusionPattern(const session::K
         AnfAlgo::GetFusionType(cnode) == kernel::FusionType::ELEMWISE && CheckEltwiseInputAndOutputSize(cnode)) {
       auto eltwise_input = cnode->input(1);
       MS_EXCEPTION_IF_NULL(eltwise_input);
-      if (eltwise_input->isa<CNode>() && AnfAlgo::CheckPrimitiveType(eltwise_input, prim::kPrimTensorAdd)) {
+      if (eltwise_input->isa<CNode>() && AnfAlgo::CheckPrimitiveType(eltwise_input, prim::kPrimAdd)) {
         MatchBnupdateAddRelu(cnode, eltwise_input, kernel_graph, candidate_fusion);
       }
     }

mindspore/ccsrc/backend/optimizer/ascend/ir_fusion/adam_apply_one_fusion.cc

@@ -28,7 +28,7 @@ const BaseRef AdamApplyOneFusion::DefinePattern() const {
   VectorRef mul1 = VectorRef({prim::kPrimMul, mul_x_input_vars_[1], input_vars_[0]});
   VectorRef mul0 = VectorRef({prim::kPrimMul, mul_x_input_vars_[0], input_vars_[2]});
   VectorRef add0 = VectorRef({add0_var_, mul0, mul1});
-  VectorRef true_div0 = VectorRef({prim_real_div, add0, VectorRef({prim::kPrimTensorAdd, sqrt0, add2_y_})});
+  VectorRef true_div0 = VectorRef({prim_real_div, add0, VectorRef({prim::kPrimAdd, sqrt0, add2_y_})});
   return VectorRef({prim::kPrimSub, input_vars_[3], VectorRef({prim::kPrimMul, input_vars_[4], true_div0})});
 }
 
@@ -41,7 +41,7 @@ const BaseRef AdamApplyOneCond1Fusion::DefinePattern() const {
   VectorRef mul1 = VectorRef({prim::kPrimMul, mul_x_input_vars_[1], input_vars_[0]});
   VectorRef mul0 = VectorRef({prim::kPrimMul, mul_x_input_vars_[0], input_vars_[2]});
   VectorRef add0 = VectorRef({add0_var_, mul0, mul1});
-  VectorRef true_div0 = VectorRef({prim_real_div, add0, VectorRef({prim::kPrimTensorAdd, add2_y_, sqrt0})});
+  VectorRef true_div0 = VectorRef({prim_real_div, add0, VectorRef({prim::kPrimAdd, add2_y_, sqrt0})});
   return VectorRef({prim::kPrimSub, input_vars_[3], VectorRef({prim::kPrimMul, input_vars_[4], true_div0})});
 }
 
@@ -54,7 +54,7 @@ const BaseRef AdamApplyOneCond2Fusion::DefinePattern() const {
   VectorRef mul1 = VectorRef({prim::kPrimMul, mul_x_input_vars_[1], input_vars_[0]});
   VectorRef mul0 = VectorRef({prim::kPrimMul, mul_x_input_vars_[0], input_vars_[2]});
   VectorRef add0 = VectorRef({add0_var_, mul0, mul1});
-  VectorRef true_div0 = VectorRef({prim_real_div, add0, VectorRef({prim::kPrimTensorAdd, sqrt0, add2_y_})});
+  VectorRef true_div0 = VectorRef({prim_real_div, add0, VectorRef({prim::kPrimAdd, sqrt0, add2_y_})});
   return VectorRef({prim::kPrimSub, input_vars_[3], VectorRef({prim::kPrimMul, true_div0, input_vars_[4]})});
 }
 
@@ -67,7 +67,7 @@ const BaseRef AdamApplyOneCond3Fusion::DefinePattern() const {
   VectorRef mul1 = VectorRef({prim::kPrimMul, mul_x_input_vars_[1], input_vars_[0]});
   VectorRef mul0 = VectorRef({prim::kPrimMul, mul_x_input_vars_[0], input_vars_[2]});
   VectorRef add0 = VectorRef({add0_var_, mul0, mul1});
-  VectorRef true_div0 = VectorRef({prim_real_div, add0, VectorRef({prim::kPrimTensorAdd, sqrt0, add2_y_})});
+  VectorRef true_div0 = VectorRef({prim_real_div, add0, VectorRef({prim::kPrimAdd, sqrt0, add2_y_})});
   return VectorRef({prim::kPrimSub, input_vars_[3], VectorRef({prim::kPrimMul, true_div0, input_vars_[4]})});
 }
 
@@ -80,7 +80,7 @@ const BaseRef AdamApplyOneCond4Fusion::DefinePattern() const {
   VectorRef mul1 = VectorRef({prim::kPrimMul, mul_x_input_vars_[1], input_vars_[0]});
   VectorRef mul0 = VectorRef({prim::kPrimMul, mul_x_input_vars_[0], input_vars_[2]});
   VectorRef add0 = VectorRef({add0_var_, mul0, mul1});
-  VectorRef true_div0 = VectorRef({prim_real_div, add0, VectorRef({prim::kPrimTensorAdd, add2_y_, sqrt0})});
+  VectorRef true_div0 = VectorRef({prim_real_div, add0, VectorRef({prim::kPrimAdd, add2_y_, sqrt0})});
   return VectorRef({prim::kPrimSub, input_vars_[3], VectorRef({prim::kPrimMul, true_div0, input_vars_[4]})});
 }
 
@@ -94,7 +94,7 @@ const BaseRef AdamApplyOneAssignFusion::DefinePattern() const {
   VectorRef mul1 = VectorRef({prim::kPrimMul, mul_x_input_vars_[1], input_vars_[0]});
   VectorRef mul0 = VectorRef({prim::kPrimMul, mul_x_input_vars_[0], input_vars_[2]});
   VectorRef add0 = VectorRef({add0_var_, mul0, mul1});
-  VectorRef true_div0 = VectorRef({prim_real_div, add0, VectorRef({prim::kPrimTensorAdd, sqrt0, add2_y_})});
+  VectorRef true_div0 = VectorRef({prim_real_div, add0, VectorRef({prim::kPrimAdd, sqrt0, add2_y_})});
   VectorRef sub0 = VectorRef({sub0_var_, input_vars_[3], VectorRef({prim::kPrimMul, input_vars_[4], true_div0})});
   VectorRef assign0 = VectorRef({prim::kPrimAssign, input_vars_[3], sub0});
   VectorRef depend0 = VectorRef({prim::kPrimDepend, sub0, assign0});
@@ -114,7 +114,7 @@ const BaseRef AdamApplyOneAssignCond1Fusion::DefinePattern() const {
   VectorRef mul1 = VectorRef({prim::kPrimMul, mul_x_input_vars_[1], input_vars_[0]});
   VectorRef mul0 = VectorRef({prim::kPrimMul, mul_x_input_vars_[0], input_vars_[2]});
   VectorRef add0 = VectorRef({add0_var_, mul0, mul1});
-  VectorRef true_div0 = VectorRef({prim_real_div, add0, VectorRef({prim::kPrimTensorAdd, add2_y_, sqrt0})});
+  VectorRef true_div0 = VectorRef({prim_real_div, add0, VectorRef({prim::kPrimAdd, add2_y_, sqrt0})});
   VectorRef sub0 = VectorRef({sub0_var_, input_vars_[3], VectorRef({prim::kPrimMul, input_vars_[4], true_div0})});
   VectorRef assign0 = VectorRef({prim::kPrimAssign, input_vars_[3], sub0});
   VectorRef depend0 = VectorRef({prim::kPrimDepend, sub0, assign0});
@@ -134,7 +134,7 @@ const BaseRef AdamApplyOneAssignCond2Fusion::DefinePattern() const {
   VectorRef mul1 = VectorRef({prim::kPrimMul, mul_x_input_vars_[1], input_vars_[0]});
   VectorRef mul0 = VectorRef({prim::kPrimMul, mul_x_input_vars_[0], input_vars_[2]});
   VectorRef add0 = VectorRef({add0_var_, mul0, mul1});
-  VectorRef true_div0 = VectorRef({prim_real_div, add0, VectorRef({prim::kPrimTensorAdd, sqrt0, add2_y_})});
+  VectorRef true_div0 = VectorRef({prim_real_div, add0, VectorRef({prim::kPrimAdd, sqrt0, add2_y_})});
   VectorRef sub0 = VectorRef({sub0_var_, input_vars_[3], VectorRef({prim::kPrimMul, true_div0, input_vars_[4]})});
   VectorRef assign0 = VectorRef({prim::kPrimAssign, input_vars_[3], sub0});
   VectorRef depend0 = VectorRef({prim::kPrimDepend, sub0, assign0});
@@ -154,7 +154,7 @@ const BaseRef AdamApplyOneAssignCond3Fusion::DefinePattern() const {
   VectorRef mul1 = VectorRef({prim::kPrimMul, mul_x_input_vars_[1], input_vars_[0]});
   VectorRef mul0 = VectorRef({prim::kPrimMul, mul_x_input_vars_[0], input_vars_[2]});
   VectorRef add0 = VectorRef({add0_var_, mul0, mul1});
-  VectorRef true_div0 = VectorRef({prim_real_div, add0, VectorRef({prim::kPrimTensorAdd, sqrt0, add2_y_})});
+  VectorRef true_div0 = VectorRef({prim_real_div, add0, VectorRef({prim::kPrimAdd, sqrt0, add2_y_})});
   VectorRef sub0 = VectorRef({sub0_var_, input_vars_[3], VectorRef({prim::kPrimMul, true_div0, input_vars_[4]})});
   VectorRef assign0 = VectorRef({prim::kPrimAssign, input_vars_[3], sub0});
   VectorRef depend0 = VectorRef({prim::kPrimDepend, sub0, assign0});
@@ -174,7 +174,7 @@ const BaseRef AdamApplyOneAssignCond4Fusion::DefinePattern() const {
   VectorRef mul1 = VectorRef({prim::kPrimMul, mul_x_input_vars_[1], input_vars_[0]});
   VectorRef mul0 = VectorRef({prim::kPrimMul, mul_x_input_vars_[0], input_vars_[2]});
   VectorRef add0 = VectorRef({add0_var_, mul0, mul1});
-  VectorRef true_div0 = VectorRef({prim_real_div, add0, VectorRef({prim::kPrimTensorAdd, add2_y_, sqrt0})});
+  VectorRef true_div0 = VectorRef({prim_real_div, add0, VectorRef({prim::kPrimAdd, add2_y_, sqrt0})});
   VectorRef sub0 = VectorRef({sub0_var_, input_vars_[3], VectorRef({prim::kPrimMul, true_div0, input_vars_[4]})});
   VectorRef assign0 = VectorRef({prim::kPrimAssign, input_vars_[3], sub0});
   VectorRef depend0 = VectorRef({prim::kPrimDepend, sub0, assign0});

mindspore/ccsrc/backend/optimizer/ascend/ir_fusion/adam_apply_one_fusion.h

@@ -38,8 +38,8 @@ class AdamApplyOneFusion : public PatternProcessPass {
       mul_x_input_vars_.push_back(std::make_shared<Var>());
     }
     add2_y_ = std::make_shared<Var>();
-    add0_var_ = std::make_shared<Var>(std::make_shared<Primitive>(prim::kPrimTensorAdd->name()));
-    add1_var_ = std::make_shared<Var>(std::make_shared<Primitive>(prim::kPrimTensorAdd->name()));
+    add0_var_ = std::make_shared<Var>(std::make_shared<Primitive>(prim::kPrimAdd->name()));
+    add1_var_ = std::make_shared<Var>(std::make_shared<Primitive>(prim::kPrimAdd->name()));
     sub0_var_ = std::make_shared<Var>(std::make_shared<Primitive>(prim::kPrimSub->name()));
   }
 

mindspore/ccsrc/backend/optimizer/ascend/ir_fusion/adam_apply_one_with_decay_rule.cc

@@ -59,10 +59,10 @@ const BaseRef AdamApplyOneWithDecayRuleCond1::DefinePattern() const {
   VectorRef mul3({prim::kPrimMul, mul3_x_, square0});
   VectorRef add1({add1_var_, mul2, mul3});
   VectorRef sqrt0({sqrt, add1});
-  VectorRef add2({prim::kPrimTensorAdd, add2_y_, sqrt0});
+  VectorRef add2({prim::kPrimAdd, add2_y_, sqrt0});
   VectorRef mul4({prim::kPrimMul, mul4_x_, input3_});
   VectorRef real_div0({real_div, add0, add2});
-  VectorRef add3({prim::kPrimTensorAdd, mul4, real_div0});
+  VectorRef add3({prim::kPrimAdd, mul4, real_div0});
   VectorRef mul5({prim::kPrimMul, input4_, add3});
   VectorRef sub0({prim::kPrimSub, input3_, mul5});
   return sub0;
@@ -79,10 +79,10 @@ const BaseRef AdamApplyOneWithDecayRuleCond2::DefinePattern() const {
   VectorRef mul3({prim::kPrimMul, mul3_x_, square0});
   VectorRef add1({add1_var_, mul2, mul3});
   VectorRef sqrt0({sqrt, add1});
-  VectorRef add2({prim::kPrimTensorAdd, sqrt0, add2_y_});
+  VectorRef add2({prim::kPrimAdd, sqrt0, add2_y_});
   VectorRef mul4({prim::kPrimMul, input3_, mul4_x_});
   VectorRef real_div0({real_div, add0, add2});
-  VectorRef add3({prim::kPrimTensorAdd, mul4, real_div0});
+  VectorRef add3({prim::kPrimAdd, mul4, real_div0});
   VectorRef mul5({prim::kPrimMul, add3, input4_});
   VectorRef sub0({prim::kPrimSub, input3_, mul5});
   return sub0;
@@ -99,10 +99,10 @@ const BaseRef AdamApplyOneWithDecayRuleCond3::DefinePattern() const {
   VectorRef mul3({prim::kPrimMul, square0, mul3_x_});
   VectorRef add1({add1_var_, mul2, mul3});
   VectorRef sqrt0({sqrt, add1});
-  VectorRef add2({prim::kPrimTensorAdd, sqrt0, add2_y_});
+  VectorRef add2({prim::kPrimAdd, sqrt0, add2_y_});
   VectorRef mul4({prim::kPrimMul, mul4_x_, input3_});
   VectorRef real_div0({real_div, add0, add2});
-  VectorRef add3({prim::kPrimTensorAdd, mul4, real_div0});
+  VectorRef add3({prim::kPrimAdd, mul4, real_div0});
   VectorRef mul5({prim::kPrimMul, add3, input4_});
   VectorRef sub0({prim::kPrimSub, input3_, mul5});
   return sub0;
@@ -119,10 +119,10 @@ const BaseRef AdamApplyOneWithDecayRuleCond4::DefinePattern() const {
   VectorRef mul3({prim::kPrimMul, mul3_x_, square0});
   VectorRef add1({add1_var_, mul2, mul3});
   VectorRef sqrt0({sqrt, add1});
-  VectorRef add2({prim::kPrimTensorAdd, add2_y_, sqrt0});
+  VectorRef add2({prim::kPrimAdd, add2_y_, sqrt0});
   VectorRef mul4({prim::kPrimMul, mul4_x_, input3_});
   VectorRef real_div0({real_div, add0, add2});
-  VectorRef add3({prim::kPrimTensorAdd, mul4, real_div0});
+  VectorRef add3({prim::kPrimAdd, mul4, real_div0});
   VectorRef mul5({prim::kPrimMul, add3, input4_});
   VectorRef sub0({prim::kPrimSub, input3_, mul5});
   return sub0;
@@ -139,10 +139,10 @@ const BaseRef AdamApplyOneWithDecayRuleCond5::DefinePattern() const {
   VectorRef mul3({prim::kPrimMul, mul3_x_, square0});
   VectorRef add1({add1_var_, mul2, mul3});
   VectorRef sqrt0({sqrt, add1});
-  VectorRef add2({prim::kPrimTensorAdd, sqrt0, add2_y_});
+  VectorRef add2({prim::kPrimAdd, sqrt0, add2_y_});
   VectorRef mul4({prim::kPrimMul, mul4_x_, input3_});
   VectorRef real_div0({real_div, add0, add2});
-  VectorRef add3({prim::kPrimTensorAdd, mul4, real_div0});
+  VectorRef add3({prim::kPrimAdd, mul4, real_div0});
   VectorRef mul5({prim::kPrimMul, add3, input4_});
   VectorRef sub0({prim::kPrimSub, input3_, mul5});
   return sub0;
@@ -159,10 +159,10 @@ const BaseRef AdamApplyOneWithDecayAssignRuleCond1::DefinePattern() const {
   VectorRef mul3({prim::kPrimMul, mul3_x_, square0});
   VectorRef add1({add1_var_, mul2, mul3});
   VectorRef sqrt0({sqrt, add1});
-  VectorRef add2({prim::kPrimTensorAdd, add2_y_, sqrt0});
+  VectorRef add2({prim::kPrimAdd, add2_y_, sqrt0});
   VectorRef mul4({prim::kPrimMul, mul4_x_, input3_});
   VectorRef real_div0({real_div, add0, add2});
-  VectorRef add3({prim::kPrimTensorAdd, mul4, real_div0});
+  VectorRef add3({prim::kPrimAdd, mul4, real_div0});
   VectorRef mul5({prim::kPrimMul, input4_, add3});
   VectorRef sub0({sub0_var_, input3_, mul5});
   VectorRef assign0 = VectorRef({prim::kPrimAssign, input3_, sub0});
@@ -184,10 +184,10 @@ const BaseRef AdamApplyOneWithDecayAssignRuleCond2::DefinePattern() const {
   VectorRef mul3({prim::kPrimMul, mul3_x_, square0});
   VectorRef add1({add1_var_, mul2, mul3});
   VectorRef sqrt0({sqrt, add1});
-  VectorRef add2({prim::kPrimTensorAdd, sqrt0, add2_y_});
+  VectorRef add2({prim::kPrimAdd, sqrt0, add2_y_});
   VectorRef mul4({prim::kPrimMul, input3_, mul4_x_});
   VectorRef real_div0({real_div, add0, add2});
-  VectorRef add3({prim::kPrimTensorAdd, mul4, real_div0});
+  VectorRef add3({prim::kPrimAdd, mul4, real_div0});
   VectorRef mul5({prim::kPrimMul, add3, input4_});
   VectorRef sub0({sub0_var_, input3_, mul5});
   VectorRef assign0 = VectorRef({prim::kPrimAssign, input3_, sub0});
@@ -209,10 +209,10 @@ const BaseRef AdamApplyOneWithDecayAssignRuleCond3::DefinePattern() const {
   VectorRef mul3({prim::kPrimMul, square0, mul3_x_});
   VectorRef add1({add1_var_, mul2, mul3});
   VectorRef sqrt0({sqrt, add1});
-  VectorRef add2({prim::kPrimTensorAdd, sqrt0, add2_y_});
+  VectorRef add2({prim::kPrimAdd, sqrt0, add2_y_});
   VectorRef mul4({prim::kPrimMul, mul4_x_, input3_});
   VectorRef real_div0({real_div, add0, add2});
-  VectorRef add3({prim::kPrimTensorAdd, mul4, real_div0});
+  VectorRef add3({prim::kPrimAdd, mul4, real_div0});
   VectorRef mul5({prim::kPrimMul, add3, input4_});
   VectorRef sub0({sub0_var_, input3_, mul5});
   VectorRef assign0 = VectorRef({prim::kPrimAssign, input3_, sub0});
@@ -234,10 +234,10 @@ const BaseRef AdamApplyOneWithDecayAssignRuleCond4::DefinePattern() const {
   VectorRef mul3({prim::kPrimMul, mul3_x_, square0});
   VectorRef add1({add1_var_, mul2, mul3});
   VectorRef sqrt0({sqrt, add1});
-  VectorRef add2({prim::kPrimTensorAdd, add2_y_, sqrt0});
+  VectorRef add2({prim::kPrimAdd, add2_y_, sqrt0});
   VectorRef mul4({prim::kPrimMul, mul4_x_, input3_});
   VectorRef real_div0({real_div, add0, add2});
-  VectorRef add3({prim::kPrimTensorAdd, mul4, real_div0});
+  VectorRef add3({prim::kPrimAdd, mul4, real_div0});
   VectorRef mul5({prim::kPrimMul, add3, input4_});
   VectorRef sub0({sub0_var_, input3_, mul5});
   VectorRef assign0 = VectorRef({prim::kPrimAssign, input3_, sub0});
@@ -259,10 +259,10 @@ const BaseRef AdamApplyOneWithDecayAssignRuleCond5::DefinePattern() const {
   VectorRef mul3({prim::kPrimMul, mul3_x_, square0});
   VectorRef add1({add1_var_, mul2, mul3});
   VectorRef sqrt0({sqrt, add1});
-  VectorRef add2({prim::kPrimTensorAdd, sqrt0, add2_y_});
+  VectorRef add2({prim::kPrimAdd, sqrt0, add2_y_});
   VectorRef mul4({prim::kPrimMul, mul4_x_, input3_});
   VectorRef real_div0({real_div, add0, add2});
-  VectorRef add3({prim::kPrimTensorAdd, mul4, real_div0});
+  VectorRef add3({prim::kPrimAdd, mul4, real_div0});
   VectorRef mul5({prim::kPrimMul, add3, input4_});
   VectorRef sub0({sub0_var_, input3_, mul5});
   VectorRef assign0 = VectorRef({prim::kPrimAssign, input3_, sub0});

mindspore/ccsrc/backend/optimizer/ascend/ir_fusion/adam_apply_one_with_decay_rule.h

@@ -38,8 +38,8 @@ class AdamApplyOneWithDecayRule : public PatternProcessPass {
     mul3_x_ = std::make_shared<Var>();
     mul4_x_ = std::make_shared<Var>();
     add2_y_ = std::make_shared<Var>();
-    add0_var_ = std::make_shared<Var>(std::make_shared<Primitive>(prim::kPrimTensorAdd->name()));
-    add1_var_ = std::make_shared<Var>(std::make_shared<Primitive>(prim::kPrimTensorAdd->name()));
+    add0_var_ = std::make_shared<Var>(std::make_shared<Primitive>(prim::kPrimAdd->name()));
+    add1_var_ = std::make_shared<Var>(std::make_shared<Primitive>(prim::kPrimAdd->name()));
     sub0_var_ = std::make_shared<Var>(std::make_shared<Primitive>(prim::kPrimSub->name()));
   }
   ~AdamApplyOneWithDecayRule() override = default;

mindspore/ccsrc/backend/optimizer/ascend/ir_fusion/lamb_next_mv_rule.cc

@@ -130,11 +130,11 @@ const BaseRef LambNextMVRuleCond1::DefinePattern() const {
   auto real_div0 = VectorRef({real_div0_var_, add0, input5_});
   auto real_div1 = VectorRef({real_div1_var_, add1, input2_});
 
-  auto add2 = VectorRef({prim::kPrimTensorAdd, add2_y_, real_div1});
+  auto add2 = VectorRef({prim::kPrimAdd, add2_y_, real_div1});
   auto sqrt0 = VectorRef({prim_rsqrt, add2});
   auto real_div2 = VectorRef({real_div2_var_, sqrt0, real_div0});
 
-  return VectorRef({prim::kPrimTensorAdd, mul4, real_div2});
+  return VectorRef({prim::kPrimAdd, mul4, real_div2});
 }
 
 BaseRef LambNextMVRuleCond1::DefineAnotherPattern() const {
@@ -147,7 +147,7 @@ BaseRef LambNextMVRuleCond1::DefineAnotherPattern() const {
   VectorRef real_div1 = VectorRef({real_div1_var_, Ys});
 
   VectorRef sqrt1 = VectorRef({prim_sqrt, real_div1});
-  VectorRef add4 = VectorRef({prim::kPrimTensorAdd, add2_y_, sqrt1});
+  VectorRef add4 = VectorRef({prim::kPrimAdd, add2_y_, sqrt1});
   VectorRef real_div4 = VectorRef({prim_real_div, real_div0, add4});
   return real_div4;
 }
@@ -166,11 +166,11 @@ const BaseRef LambNextMVRuleCond2::DefinePattern() const {
   auto real_div0 = VectorRef({real_div0_var_, add0, input5_});
   auto real_div1 = VectorRef({real_div1_var_, add1, input2_});
 
-  auto add2 = VectorRef({prim::kPrimTensorAdd, add2_y_, real_div1});
+  auto add2 = VectorRef({prim::kPrimAdd, add2_y_, real_div1});
   auto sqrt0 = VectorRef({prim_rsqrt, add2});
   auto real_div2 = VectorRef({real_div2_var_, sqrt0, real_div0});
 
-  return VectorRef({prim::kPrimTensorAdd, mul4, real_div2});
+  return VectorRef({prim::kPrimAdd, mul4, real_div2});
 }
 
 BaseRef LambNextMVRuleCond2::DefineAnotherPattern() const {
@@ -183,7 +183,7 @@ BaseRef LambNextMVRuleCond2::DefineAnotherPattern() const {
   VectorRef real_div1 = VectorRef({real_div1_var_, Ys});
 
   VectorRef sqrt1 = VectorRef({prim_sqrt, real_div1});
-  VectorRef add4 = VectorRef({prim::kPrimTensorAdd, sqrt1, add2_y_});
+  VectorRef add4 = VectorRef({prim::kPrimAdd, sqrt1, add2_y_});
   VectorRef real_div4 = VectorRef({prim_real_div, real_div0, add4});
   return real_div4;
 }
@@ -202,11 +202,11 @@ const BaseRef LambNextMVRuleCond3::DefinePattern() const {
   auto real_div0 = VectorRef({real_div0_var_, add0, input5_});
   auto real_div1 = VectorRef({real_div1_var_, add1, input2_});
 
-  auto add2 = VectorRef({prim::kPrimTensorAdd, real_div1, add2_y_});
+  auto add2 = VectorRef({prim::kPrimAdd, real_div1, add2_y_});
   auto sqrt0 = VectorRef({prim_rsqrt, add2});
   auto real_div2 = VectorRef({real_div2_var_, sqrt0, real_div0});
 
-  return VectorRef({prim::kPrimTensorAdd, mul4, real_div2});
+  return VectorRef({prim::kPrimAdd, mul4, real_div2});
 }
 
 BaseRef LambNextMVRuleCond3::DefineAnotherPattern() const {
@@ -219,7 +219,7 @@ BaseRef LambNextMVRuleCond3::DefineAnotherPattern() const {
   VectorRef real_div1 = VectorRef({real_div1_var_, Ys});
 
   VectorRef sqrt1 = VectorRef({prim_sqrt, real_div1});
-  VectorRef add4 = VectorRef({prim::kPrimTensorAdd, sqrt1, add2_y_});
+  VectorRef add4 = VectorRef({prim::kPrimAdd, sqrt1, add2_y_});
   VectorRef real_div4 = VectorRef({prim_real_div, real_div0, add4});
   return real_div4;
 }
@@ -238,11 +238,11 @@ const BaseRef LambNextMVRuleCond4::DefinePattern() const {
   auto real_div0 = VectorRef({real_div0_var_, add0, input5_});
   auto real_div1 = VectorRef({real_div1_var_, add1, input2_});
 
-  auto add2 = VectorRef({prim::kPrimTensorAdd, real_div1, add2_y_});
+  auto add2 = VectorRef({prim::kPrimAdd, real_div1, add2_y_});
   auto sqrt0 = VectorRef({prim_rsqrt, add2});
   auto real_div2 = VectorRef({real_div2_var_, real_div0, sqrt0});
 
-  return VectorRef({prim::kPrimTensorAdd, real_div2, mul4});
+  return VectorRef({prim::kPrimAdd, real_div2, mul4});
 }
 
 BaseRef LambNextMVRuleCond4::DefineAnotherPattern() const {
@@ -255,7 +255,7 @@ BaseRef LambNextMVRuleCond4::DefineAnotherPattern() const {
   VectorRef real_div1 = VectorRef({real_div1_var_, Ys});
 
   VectorRef sqrt1 = VectorRef({prim_sqrt, real_div1});
-  VectorRef add4 = VectorRef({prim::kPrimTensorAdd, sqrt1, add2_y_});
+  VectorRef add4 = VectorRef({prim::kPrimAdd, sqrt1, add2_y_});
   VectorRef real_div4 = VectorRef({prim_real_div, real_div0, add4});
   return real_div4;
 }

mindspore/ccsrc/backend/optimizer/ascend/ir_fusion/lamb_next_mv_rule.h

@@ -49,8 +49,8 @@ class LambNextMVRule : public MultipleOutputPatternProcessPass {
     real_div0_var_ = std::make_shared<Var>(std::make_shared<Primitive>(kRealDivOpName));
     real_div1_var_ = std::make_shared<Var>(std::make_shared<Primitive>(kRealDivOpName));
     real_div2_var_ = std::make_shared<Var>(std::make_shared<Primitive>(prim::kPrimMul->name()));
-    add0_var_ = std::make_shared<Var>(std::make_shared<Primitive>(prim::kPrimTensorAdd->name()));
-    add1_var_ = std::make_shared<Var>(std::make_shared<Primitive>(prim::kPrimTensorAdd->name()));
+    add0_var_ = std::make_shared<Var>(std::make_shared<Primitive>(prim::kPrimAdd->name()));
+    add1_var_ = std::make_shared<Var>(std::make_shared<Primitive>(prim::kPrimAdd->name()));
   }
   ~LambNextMVRule() override = default;
   const BaseRef DefinePattern() const override = 0;

mindspore/ccsrc/backend/optimizer/ascend/ir_fusion/lamb_next_mv_with_decay_rule.cc

@@ -124,10 +124,10 @@ BaseRef LambNextMVWithDecayRuleCond1::DefineAnotherPattern() const {
   VectorRef real_div1 = VectorRef({real_div1_var_, Ys});
   VectorRef mul4 = VectorRef({mul4_var_, Zs});
 
-  VectorRef add2 = VectorRef({prim::kPrimTensorAdd, constant_add2_y_, real_div1});
+  VectorRef add2 = VectorRef({prim::kPrimAdd, constant_add2_y_, real_div1});
   VectorRef sqrt0 = VectorRef({prim_rsqrt, add2});
   VectorRef real_div2 = VectorRef({prim::kPrimMul, sqrt0, real_div0});
-  VectorRef add3 = VectorRef({prim::kPrimTensorAdd, mul4, real_div2});
+  VectorRef add3 = VectorRef({prim::kPrimAdd, mul4, real_div2});
   return add3;
 }
 
@@ -141,14 +141,14 @@ const BaseRef LambNextMVWithDecayRuleCond1::DefinePattern() const {
   VectorRef add1 = VectorRef({add1_var_, mul2, mul3});
   VectorRef real_div1 = VectorRef({real_div1_var_, add1, input_vars_[2]});
   VectorRef sqrt1 = VectorRef({prim_sqrt, real_div1});
-  VectorRef add4 = VectorRef({prim::kPrimTensorAdd, sqrt1, constant_add2_y_});
+  VectorRef add4 = VectorRef({prim::kPrimAdd, sqrt1, constant_add2_y_});
   VectorRef mul0 = VectorRef({prim::kPrimMul, input_vars_[4], constant_mul_input_vars_[0]});
   VectorRef mul1 = VectorRef({prim::kPrimMul, input_vars_[3], constant_mul_input_vars_[1]});
   VectorRef add0 = VectorRef({add0_var_, mul0, mul1});
   VectorRef real_div0 = VectorRef({real_div0_var_, add0, input_vars_[5]});
   VectorRef real_div4 = VectorRef({prim_deal_div, real_div0, add4});
   VectorRef mul4 = VectorRef({mul4_var_, constant_mul_input_vars_[4], input_vars_[6]});
-  VectorRef add5 = VectorRef({prim::kPrimTensorAdd, mul4, real_div4});
+  VectorRef add5 = VectorRef({prim::kPrimAdd, mul4, real_div4});
   return add5;
 }
 
@@ -165,10 +165,10 @@ BaseRef LambNextMVWithDecayRuleCond2::DefineAnotherPattern() const {
   VectorRef real_div1 = VectorRef({real_div1_var_, Ys});
   VectorRef mul4 = VectorRef({mul4_var_, Zs});
 
-  VectorRef add2 = VectorRef({prim::kPrimTensorAdd, constant_add2_y_, real_div1});
+  VectorRef add2 = VectorRef({prim::kPrimAdd, constant_add2_y_, real_div1});
   VectorRef sqrt0 = VectorRef({prim_rsqrt, add2});
   VectorRef real_div2 = VectorRef({prim::kPrimMul, sqrt0, real_div0});
-  VectorRef add3 = VectorRef({prim::kPrimTensorAdd, mul4, real_div2});
+  VectorRef add3 = VectorRef({prim::kPrimAdd, mul4, real_div2});
   return add3;
 }
 
@@ -182,14 +182,14 @@ const BaseRef LambNextMVWithDecayRuleCond2::DefinePattern() const {
   VectorRef add1 = VectorRef({add1_var_, mul2, mul3});
   VectorRef real_div1 = VectorRef({real_div1_var_, add1, input_vars_[2]});
   VectorRef sqrt1 = VectorRef({prim_sqrt, real_div1});
-  VectorRef add4 = VectorRef({prim::kPrimTensorAdd, constant_add2_y_, sqrt1});
+  VectorRef add4 = VectorRef({prim::kPrimAdd, constant_add2_y_, sqrt1});
   VectorRef mul0 = VectorRef({prim::kPrimMul, constant_mul_input_vars_[0], input_vars_[4]});
   VectorRef mul1 = VectorRef({prim::kPrimMul, constant_mul_input_vars_[1], input_vars_[3]});
   VectorRef add0 = VectorRef({add0_var_, mul0, mul1});
   VectorRef real_div0 = VectorRef({real_div0_var_, add0, input_vars_[5]});
   VectorRef real_div4 = VectorRef({prim_deal_div, real_div0, add4});
   VectorRef mul4 = VectorRef({mul4_var_, constant_mul_input_vars_[4], input_vars_[6]});
-  VectorRef add5 = VectorRef({prim::kPrimTensorAdd, mul4, real_div4});
+  VectorRef add5 = VectorRef({prim::kPrimAdd, mul4, real_div4});
   return add5;
 }
 
@@ -206,10 +206,10 @@ BaseRef LambNextMVWithDecayRuleCond3::DefineAnotherPattern() const {
   VectorRef real_div1 = VectorRef({real_div1_var_, Ys});
   VectorRef mul4 = VectorRef({mul4_var_, Zs});
 
-  VectorRef add2 = VectorRef({prim::kPrimTensorAdd, real_div1, constant_add2_y_});
+  VectorRef add2 = VectorRef({prim::kPrimAdd, real_div1, constant_add2_y_});
   VectorRef sqrt0 = VectorRef({prim_rsqrt, add2});
   VectorRef real_div2 = VectorRef({prim::kPrimMul, sqrt0, real_div0});
-  VectorRef add3 = VectorRef({prim::kPrimTensorAdd, mul4, real_div2});
+  VectorRef add3 = VectorRef({prim::kPrimAdd, mul4, real_div2});
   return add3;
 }
 
@@ -223,14 +223,14 @@ const BaseRef LambNextMVWithDecayRuleCond3::DefinePattern() const {
   VectorRef add1 = VectorRef({add1_var_, mul2, mul3});
   VectorRef real_div1 = VectorRef({real_div1_var_, add1, input_vars_[2]});
   VectorRef sqrt1 = VectorRef({prim_sqrt, real_div1});
-  VectorRef add4 = VectorRef({prim::kPrimTensorAdd, sqrt1, constant_add2_y_});
+  VectorRef add4 = VectorRef({prim::kPrimAdd, sqrt1, constant_add2_y_});
   VectorRef mul0 = VectorRef({prim::kPrimMul, input_vars_[4], constant_mul_input_vars_[0]});
   VectorRef mul1 = VectorRef({prim::kPrimMul, input_vars_[3], constant_mul_input_vars_[1]});
   VectorRef add0 = VectorRef({add0_var_, mul0, mul1});
   VectorRef real_div0 = VectorRef({real_div0_var_, add0, input_vars_[5]});
   VectorRef real_div4 = VectorRef({prim_deal_div, real_div0, add4});
   VectorRef mul4 = VectorRef({mul4_var_, input_vars_[6], constant_mul_input_vars_[4]});
-  VectorRef add5 = VectorRef({prim::kPrimTensorAdd, mul4, real_div4});
+  VectorRef add5 = VectorRef({prim::kPrimAdd, mul4, real_div4});
   return add5;
 }
 
@@ -248,10 +248,10 @@ BaseRef LambNextMVWithDecayRuleCond4::DefineAnotherPattern() const {
   VectorRef real_div1 = VectorRef({real_div1_var_, Ys});
   VectorRef mul4 = VectorRef({mul4_var_, Zs});
 
-  VectorRef add2 = VectorRef({prim::kPrimTensorAdd, real_div1, constant_add2_y_});
+  VectorRef add2 = VectorRef({prim::kPrimAdd, real_div1, constant_add2_y_});
   VectorRef sqrt0 = VectorRef({prim_rsqrt, add2});
   VectorRef real_div2 = VectorRef({prim::kPrimMul, real_div0, sqrt0});
-  VectorRef add3 = VectorRef({prim::kPrimTensorAdd, real_div2, mul4});
+  VectorRef add3 = VectorRef({prim::kPrimAdd, real_div2, mul4});
   return add3;
 }
 
@@ -265,14 +265,14 @@ const BaseRef LambNextMVWithDecayRuleCond4::DefinePattern() const {
   VectorRef add1 = VectorRef({add1_var_, mul2, mul3});
   VectorRef real_div1 = VectorRef({real_div1_var_, add1, input_vars_[2]});
   VectorRef sqrt1 = VectorRef({prim_sqrt, real_div1});
-  VectorRef add4 = VectorRef({prim::kPrimTensorAdd, sqrt1, constant_add2_y_});
+  VectorRef add4 = VectorRef({prim::kPrimAdd, sqrt1, constant_add2_y_});
   VectorRef mul0 = VectorRef({prim::kPrimMul, constant_mul_input_vars_[0], input_vars_[4]});
   VectorRef mul1 = VectorRef({prim::kPrimMul, constant_mul_input_vars_[1], input_vars_[3]});
   VectorRef add0 = VectorRef({add0_var_, mul0, mul1});
   VectorRef real_div0 = VectorRef({real_div0_var_, add0, input_vars_[5]});
   VectorRef real_div4 = VectorRef({prim_deal_div, real_div0, add4});
   VectorRef mul4 = VectorRef({mul4_var_, constant_mul_input_vars_[4], input_vars_[6]});
-  VectorRef add5 = VectorRef({prim::kPrimTensorAdd, real_div4, mul4});
+  VectorRef add5 = VectorRef({prim::kPrimAdd, real_div4, mul4});
   return add5;
 }
 }  // namespace opt

mindspore/ccsrc/backend/optimizer/ascend/ir_fusion/lamb_next_mv_with_decay_rule.h

@@ -38,8 +38,8 @@ class LambNextMVWithDecayRule : public MultipleOutputPatternProcessPass {
     mul4_var_ = std::make_shared<Var>(std::make_shared<Primitive>(prim::kPrimMul->name()));
     real_div0_var_ = std::make_shared<Var>(std::make_shared<Primitive>(kRealDivOpName));
     real_div1_var_ = std::make_shared<Var>(std::make_shared<Primitive>(kRealDivOpName));
-    add0_var_ = std::make_shared<Var>(std::make_shared<Primitive>(prim::kPrimTensorAdd->name()));
-    add1_var_ = std::make_shared<Var>(std::make_shared<Primitive>(prim::kPrimTensorAdd->name()));
+    add0_var_ = std::make_shared<Var>(std::make_shared<Primitive>(prim::kPrimAdd->name()));
+    add1_var_ = std::make_shared<Var>(std::make_shared<Primitive>(prim::kPrimAdd->name()));
   }
 
   ~LambNextMVWithDecayRule() override = default;

mindspore/ccsrc/backend/optimizer/ascend/ir_fusion/lamb_next_mv_with_decay_v1_rule.cc

@@ -66,7 +66,7 @@ bool MatchAdd5Pattern(const AnfNodePtr &node, const AnfNodePtr &mul4, const AnfN
     return false;
   }
   auto add5 = node->cast<CNodePtr>();
-  if (AnfAlgo::GetCNodeName(add5) != prim::kPrimTensorAdd->name() || add5->inputs().size() != kAddInputNum) {
+  if (AnfAlgo::GetCNodeName(add5) != prim::kPrimAdd->name() || add5->inputs().size() != kAddInputNum) {
     return false;
   }
   auto real_div4_anf = add5->input(1);
@@ -82,7 +82,7 @@ bool MatchAdd5Pattern(const AnfNodePtr &node, const AnfNodePtr &mul4, const AnfN
     return false;
   }
   auto add4 = add4_anf->cast<CNodePtr>();
-  if (AnfAlgo::GetCNodeName(add4) != prim::kPrimTensorAdd->name() || add4->inputs().size() != kAddInputNum) {
+  if (AnfAlgo::GetCNodeName(add4) != prim::kPrimAdd->name() || add4->inputs().size() != kAddInputNum) {
     return false;
   }
   auto sqrt1_anf = add4->input(1);
@@ -140,17 +140,17 @@ const BaseRef LambNextMVWithDecayV1Rule::DefinePattern() const {
   const auto prim_real_div = std::make_shared<Primitive>(kRealDivOpName);
   VectorRef mul3({prim::kPrimMul, mul3_sub1_, input0_});
   VectorRef mul2({prim::kPrimMul, mul2_x_, input1_});
-  VectorRef add1({prim::kPrimTensorAdd, mul2, mul3});
+  VectorRef add1({prim::kPrimAdd, mul2, mul3});
   VectorRef real_div1({prim_real_div, add1, input2_});
-  VectorRef add2({prim::kPrimTensorAdd, real_div1, add2_y_});
+  VectorRef add2({prim::kPrimAdd, real_div1, add2_y_});
   VectorRef mul0({prim::kPrimMul, mul0_x_, input4_});
   VectorRef mul1({prim::kPrimMul, mul1_sub_, input3_});
   VectorRef sqrt0({prim_rsqrt, add2});
-  VectorRef add0({prim::kPrimTensorAdd, mul0, mul1});
+  VectorRef add0({prim::kPrimAdd, mul0, mul1});
   VectorRef real_div0({prim_real_div, add0, input5_});
   VectorRef real_div2({prim::kPrimMul, real_div0, sqrt0});
   VectorRef mul4({prim::kPrimMul, mul4_x_, input6_});
-  VectorRef add3({prim::kPrimTensorAdd, real_div2, mul4});
+  VectorRef add3({prim::kPrimAdd, real_div2, mul4});
   return add3;
 }
 

mindspore/ccsrc/backend/optimizer/ascend/ir_fusion/lamb_next_right_rule.cc

@@ -54,7 +54,7 @@ const BaseRef LambNextRightRule::DefinePattern() const {
   VectorRef mul3 = VectorRef({prim::kPrimMul, mul3_x_, VectorRef({prim::kPrimSquare, input0_})});
   VectorRef add1 = VectorRef({add1_var_, VectorRef({prim::kPrimMul, mul2_x_, input1_}), mul3});
   return VectorRef(
-    {prim::kPrimTensorAdd, VectorRef({prim_sqrt, VectorRef({prim::kPrimMul, add1, true_div1_recip_})}), add2_y_});
+    {prim::kPrimAdd, VectorRef({prim_sqrt, VectorRef({prim::kPrimMul, add1, true_div1_recip_})}), add2_y_});
 }
 
 const AnfNodePtr LambNextRightRule::Process(const FuncGraphPtr &func_graph, const AnfNodePtr &node,

mindspore/ccsrc/backend/optimizer/ascend/ir_fusion/lamb_next_right_rule.h

@@ -32,7 +32,7 @@ class LambNextRightRule : public PatternProcessPass {
         mul3_x_(std::make_shared<Var>()),
         true_div1_recip_(std::make_shared<Var>()),
         add2_y_(std::make_shared<Var>()),
-        add1_var_(std::make_shared<Var>(std::make_shared<Primitive>(prim::kPrimTensorAdd->name()))) {}
+        add1_var_(std::make_shared<Var>(std::make_shared<Primitive>(prim::kPrimAdd->name()))) {}
 
   ~LambNextRightRule() override = default;
   const BaseRef DefinePattern() const override;

mindspore/ccsrc/backend/optimizer/ascend/ir_fusion/mul_add_fusion.cc

@@ -58,7 +58,7 @@ bool GetMul(const FuncGraphPtr &graph, const CNodePtr &add, CNodePtr *mul, size_
 const BaseRef MulAddFusion::DefinePattern() const {
   VarPtr x = std::make_shared<Var>();
   VarPtr y = std::make_shared<Var>();
-  VectorRef pattern({prim::kPrimTensorAdd, x, y});
+  VectorRef pattern({prim::kPrimAdd, x, y});
   return pattern;
 }
 

mindspore/ccsrc/backend/optimizer/gpu/adam_fusion.cc

@@ -51,13 +51,13 @@ kernel::KernelBuildInfoPtr GenerateKernelBuildInfo(CNodePtr node) {
 }  // namespace
 
 const BaseRef AdamFusion::DefinePattern() const {
-  VectorRef next_m = VectorRef({prim::kPrimTensorAdd, VectorRef({prim::kPrimMul, beta1_, m_}),
-                                VectorRef({prim::kPrimMul, one_sub_beta1_, gradient_})});
+  VectorRef next_m = VectorRef(
+    {prim::kPrimAdd, VectorRef({prim::kPrimMul, beta1_, m_}), VectorRef({prim::kPrimMul, one_sub_beta1_, gradient_})});
   VectorRef next_v =
-    VectorRef({prim::kPrimTensorAdd, VectorRef({prim::kPrimMul, beta2_, v_}),
+    VectorRef({prim::kPrimAdd, VectorRef({prim::kPrimMul, beta2_, v_}),
                VectorRef({prim::kPrimMul, one_sub_beta2_, VectorRef({prim::kPrimSquare, gradient_})})});
-  VectorRef update = VectorRef(
-    {prim::kPrimRealDiv, next_m, VectorRef({prim::kPrimTensorAdd, eps_, VectorRef({prim::kPrimSqrt, next_v})})});
+  VectorRef update =
+    VectorRef({prim::kPrimRealDiv, next_m, VectorRef({prim::kPrimAdd, eps_, VectorRef({prim::kPrimSqrt, next_v})})});
   VectorRef update_with_lr = VectorRef({prim::kPrimMul, lr_, update});
   VectorRef next_param = VectorRef({prim::kPrimSub, param_, update_with_lr});
 

mindspore/ccsrc/backend/optimizer/gpu/adam_weight_decay_fusion.cc

@@ -51,14 +51,14 @@ kernel::KernelBuildInfoPtr GenerateKernelBuildInfo(CNodePtr node) {
 }  // namespace
 
 const BaseRef AdamWeightDecayFusion::DefinePattern() const {
-  VectorRef next_m = VectorRef({prim::kPrimTensorAdd, VectorRef({prim::kPrimMul, beta1_, m_}),
-                                VectorRef({prim::kPrimMul, one_sub_beta1_, gradient_})});
+  VectorRef next_m = VectorRef(
+    {prim::kPrimAdd, VectorRef({prim::kPrimMul, beta1_, m_}), VectorRef({prim::kPrimMul, one_sub_beta1_, gradient_})});
   VectorRef next_v =
-    VectorRef({prim::kPrimTensorAdd, VectorRef({prim::kPrimMul, beta2_, v_}),
+    VectorRef({prim::kPrimAdd, VectorRef({prim::kPrimMul, beta2_, v_}),
                VectorRef({prim::kPrimMul, one_sub_beta2_, VectorRef({prim::kPrimSquare, gradient_})})});
-  VectorRef update = VectorRef(
-    {prim::kPrimRealDiv, next_m, VectorRef({prim::kPrimTensorAdd, eps_, VectorRef({prim::kPrimSqrt, next_v})})});
-  VectorRef new_update = VectorRef({prim::kPrimTensorAdd, VectorRef({prim::kPrimMul, weight_decay_, param_}), update});
+  VectorRef update =
+    VectorRef({prim::kPrimRealDiv, next_m, VectorRef({prim::kPrimAdd, eps_, VectorRef({prim::kPrimSqrt, next_v})})});
+  VectorRef new_update = VectorRef({prim::kPrimAdd, VectorRef({prim::kPrimMul, weight_decay_, param_}), update});
 
   VectorRef update_with_lr = VectorRef({prim::kPrimMul, lr_, new_update});
   VectorRef next_param = VectorRef({prim::kPrimSub, param_, update_with_lr});

mindspore/ccsrc/backend/optimizer/gpu/add_relu_grad_v2_fusion.cc

@@ -51,7 +51,7 @@ kernel::KernelBuildInfoPtr GenerateKernelBuildInfo(CNodePtr node) {
 }  // namespace
 
 const BaseRef AddReluGradV2Fusion::DefinePattern() const {
-  VectorRef relu_grad = VectorRef({prim::kPrimReluGradV2, VectorRef({prim::kPrimTensorAdd, x1_, x2_}), mask_});
+  VectorRef relu_grad = VectorRef({prim::kPrimReluGradV2, VectorRef({prim::kPrimAdd, x1_, x2_}), mask_});
   return relu_grad;
 }
 

mindspore/ccsrc/backend/optimizer/gpu/add_relu_v2_fusion.cc

@@ -51,7 +51,7 @@ kernel::KernelBuildInfoPtr GenerateKernelBuildInfo(CNodePtr node) {
 }  // namespace
 
 const BaseRef AddReluV2Fusion::DefinePattern() const {
-  VectorRef relu = VectorRef({prim::kPrimReluV2, VectorRef({prim::kPrimTensorAdd, x1_, x2_})});
+  VectorRef relu = VectorRef({prim::kPrimReluV2, VectorRef({prim::kPrimAdd, x1_, x2_})});
   return relu;
 }
 

mindspore/ccsrc/backend/optimizer/gpu/batch_norm_add_relu_fusion.cc

@@ -30,7 +30,7 @@ namespace opt {
 const BaseRef BatchNormAddReluFusion::DefinePattern() const {
   VectorRef batch_norm_ex = VectorRef({prim::kPrimFusedBatchNormEx, x_, scale_, bias_, mean_, var_});
   VectorRef tuple_get_item = VectorRef({prim::kPrimTupleGetItem, batch_norm_ex, index_});
-  VectorRef tensor_add = VectorRef({prim::kPrimTensorAdd, tuple_get_item, z_});
+  VectorRef tensor_add = VectorRef({prim::kPrimAdd, tuple_get_item, z_});
   VectorRef relu = VectorRef({prim::kPrimRelu, tensor_add});
   return relu;
 }

mindspore/ccsrc/backend/optimizer/gpu/replace_addn_fusion.cc

@@ -42,7 +42,7 @@ const AnfNodePtr ReplaceAddNFusion::Process(const FuncGraphPtr &graph, const Anf
   MS_EXCEPTION_IF_NULL(B);
   int64_t num_input = AnfAlgo::GetNodeAttr<int64_t>(node, "n");
   if (num_input == 2) {
-    auto prim = std::make_shared<Primitive>(prim::kPrimTensorAdd->name());
+    auto prim = std::make_shared<Primitive>(prim::kPrimAdd->name());
     MS_EXCEPTION_IF_NULL(prim);
     std::vector<AnfNodePtr> inputs = {NewValueNode(prim), A, B};
     auto add_new = graph->NewCNode(inputs);

mindspore/ccsrc/backend/optimizer/graph_kernel/arithmetic_simplify.cc

@@ -47,7 +47,7 @@ AnfNodePtr NewCNodeWithInfo(const AnfNodePtrList &inputs, const AnfNodePtr &ori_
 }
 
 AnfNodePtr SimplifyAdd(const AnfNodePtr &node) {
-  if (!IsPrimitiveCNode(node, prim::kPrimTensorAdd)) {
+  if (!IsPrimitiveCNode(node, prim::kPrimAdd)) {
     return nullptr;
   }
   PatternNode<AnfNodePtr> x, y, z;
@@ -57,13 +57,13 @@ AnfNodePtr SimplifyAdd(const AnfNodePtr &node) {
   PConstant<AnfNodePtr> any_const_2(node);
 
   auto add_distri_lambda = [&node, &x, &y, &any_const]() -> AnfNodePtr {
-    auto node_tmp = NewCNodeWithInfo({NewValueNode(prim::kPrimTensorAdd), x.GetNode(node), y.GetNode(node)}, node);
+    auto node_tmp = NewCNodeWithInfo({NewValueNode(prim::kPrimAdd), x.GetNode(node), y.GetNode(node)}, node);
     auto new_cnode = NewCNodeWithInfo({NewValueNode(prim::kPrimMul), node_tmp, any_const.GetNode(node)}, node);
     return new_cnode;
   };
   auto add_union_lambda = [&node, &x, &any_const, &any_const_2]() -> AnfNodePtr {
     auto new_rhs = any_const.AddByPatternConst(any_const_2, x.GetNode(node));
-    auto new_cnode = NewCNodeWithInfo({NewValueNode(prim::kPrimTensorAdd), x.GetNode(node), new_rhs}, node);
+    auto new_cnode = NewCNodeWithInfo({NewValueNode(prim::kPrimAdd), x.GetNode(node), new_rhs}, node);
     return new_cnode;
   };
   // A + 0 = A
@@ -88,7 +88,7 @@ AnfNodePtr SimplifySub(const AnfNodePtr &node) {
   PConstant<AnfNodePtr> any_const(node);
   auto sub_toadd_lambda = [&node, &x, &any_const]() -> AnfNodePtr {
     auto new_rhs = any_const.ValueNodeWithOprations(prim::kPrimNeg);
-    auto new_cnode = NewCNodeWithInfo({NewValueNode(prim::kPrimTensorAdd), x.GetNode(node), new_rhs}, node);
+    auto new_cnode = NewCNodeWithInfo({NewValueNode(prim::kPrimAdd), x.GetNode(node), new_rhs}, node);
     return new_cnode;
   };
   // A - 0 = A
@@ -269,7 +269,7 @@ AnfNodePtr SimplifyMul(const AnfNodePtr &node) {
     return new_cnode;
   };
   auto exp_merge_lambda = [&node, &x, &y]() -> AnfNodePtr {
-    auto node_tmp = NewCNodeWithInfo({NewValueNode(prim::kPrimTensorAdd), x.GetNode(node), y.GetNode(node)}, node);
+    auto node_tmp = NewCNodeWithInfo({NewValueNode(prim::kPrimAdd), x.GetNode(node), y.GetNode(node)}, node);
     auto new_cnode = NewCNodeWithInfo({NewValueNode(prim::kPrimExp), node_tmp}, node);
     return new_cnode;
   };

mindspore/ccsrc/backend/optimizer/graph_kernel/graph_kernel_helper.cc

@@ -741,14 +741,14 @@ std::string ExtractGraphKernelName(const AnfNodePtrList &cnodes, const string &p
 std::vector<PrimitivePtr> GetFusibleOpList() {
 #if ENABLE_D
   std::vector<PrimitivePtr> fusible_basic_ops = {
-    prim::kPrimAbs,        prim::kPrimRound,      prim::kPrimNeg,     prim::kPrimExp,     prim::kPrimTensorAdd,
+    prim::kPrimAbs,        prim::kPrimRound,      prim::kPrimNeg,     prim::kPrimExp,     prim::kPrimAdd,
     prim::kPrimExpandDims, prim::kPrimMul,        prim::kPrimMinimum, prim::kPrimMaximum, prim::kPrimLog,
     prim::kPrimPow,        prim::kPrimSub,        prim::kPrimRsqrt,   prim::kPrimSqrt,    prim::kPrimAddN,
     prim::kPrimEqual,      prim::kPrimReciprocal, prim::kPrimTanh,    prim::kPrimReshape, prim::kPrimTranspose,
     prim::kPrimCast,       prim::kPrimRealDiv};
 #elif ENABLE_GPU
   std::vector<PrimitivePtr> fusible_basic_ops = {
-    prim::kPrimAbs,     prim::kPrimRound,      prim::kPrimNeg,       prim::kPrimExp,     prim::kPrimTensorAdd,
+    prim::kPrimAbs,     prim::kPrimRound,      prim::kPrimNeg,       prim::kPrimExp,     prim::kPrimAdd,
     prim::kPrimRealDiv, prim::kPrimMul,        prim::kPrimMinimum,   prim::kPrimMaximum, prim::kPrimLog,
     prim::kPrimPow,     prim::kPrimSub,        prim::kPrimRsqrt,     prim::kPrimSqrt,    prim::kPrimAddN,
     prim::kPrimEqual,   prim::kPrimReciprocal, prim::KPrimTransData, prim::kPrimSelect,  prim::kPrimGreater,

mindspore/ccsrc/frontend/optimizer/irpass.cc

@@ -52,7 +52,7 @@ namespace opt {
 namespace irpass {
 OptimizeIRPassLib::OptimizeIRPassLib() {
   arithmetic_simplify_ = MakeSubstitution(std::make_shared<ArithmeticSimplify>(), "arithmetic_simplify",
-                                          {prim::kPrimScalarAdd, prim::kPrimScalarMul, prim::kPrimTensorAdd,
+                                          {prim::kPrimScalarAdd, prim::kPrimScalarMul, prim::kPrimAdd,
                                            prim::kPrimIdentity, prim::kPrimMomentum, prim::kPrimMul, prim::kPrimPow});
   arithmetic_simplify2_ =
     MakeSubstitution(std::make_shared<ArithmeticSimplify2>(), "arithmetic_simplify2", {prim::kPrimMul});

mindspore/ccsrc/frontend/optimizer/irpass/merge_addn.h

@@ -272,7 +272,7 @@ class AddNEliminater : public AnfVisitor {
       if (tuple_inputs.size() == 3) {
         // case2: inputs size = 2, -> TensorAdd(Tensor, Tensor)
         MS_LOG(DEBUG) << "Replace AddN with two inputs with TensorAdd. " << cnode->DebugString(2);
-        ValuePtr prim_tensoradd = prim::GetPythonOps("TensorAdd", "mindspore.ops.operations");
+        ValuePtr prim_tensoradd = prim::GetPythonOps("Add", "mindspore.ops.operations");
         std::vector<AnfNodePtr> new_xs{func_graph->NewCNode({NewValueNode(prim_tensoradd)}), tuple_inputs[1],
                                        tuple_inputs[2]};
         mng->Replace(node, func_graph->NewCNode(new_xs));
@@ -299,7 +299,7 @@ class AddNEliminater : public AnfVisitor {
         ValuePtr prim_addn = prim::GetPythonOps("AddN", "mindspore.ops.operations");
         auto new_addn = func_graph->NewCNode(
           {func_graph->NewCNode({NewValueNode(prim_addn)}), func_graph->NewCNode(make_tuple_new_xs)});
-        ValuePtr prim_tensoradd = prim::GetPythonOps("TensorAdd", "mindspore.ops.operations");
+        ValuePtr prim_tensoradd = prim::GetPythonOps("Add", "mindspore.ops.operations");
         auto new_add =
           func_graph->NewCNode({func_graph->NewCNode({NewValueNode(prim_tensoradd)}), *first_valuenode, new_addn});
         (void)mng->Replace(node, new_add);

mindspore/ccsrc/frontend/parallel/auto_parallel/rec_core/rec_generate_strategy.cc

@@ -860,7 +860,7 @@ Strategys GenerateStrategiesFromStrategy(const std::vector<std::shared_ptr<Opera
   if (ops[iter_ops]->type() == L2_NORMALIZE) {
     return PrepareL2Normalize(ops, iter_ops, basic_stra);
   }
-  if (ops[iter_ops]->type() == TENSOR_ADD || ops[iter_ops]->type() == SUB || ops[iter_ops]->type() == MUL ||
+  if (ops[iter_ops]->type() == ADD || ops[iter_ops]->type() == SUB || ops[iter_ops]->type() == MUL ||
       ops[iter_ops]->type() == DIV) {
     return CheckBroadcast(ops, iter_ops, basic_stra);
   }

mindspore/ccsrc/frontend/parallel/auto_parallel/rec_core/rec_parse_graph.h

@@ -78,7 +78,7 @@ const std::map<std::string, OperatorType> DictOpType{
   // Elm-wise OP
   {TRANSPOSE, OperatorType::kRecElmWiseOp},
   {L2_NORMALIZE, OperatorType::kRecElmWiseOp},
-  {TENSOR_ADD, OperatorType::kRecElmWiseOp},
+  {ADD, OperatorType::kRecElmWiseOp},
   {TENSOR_DOT, OperatorType::kRecElmWiseOp},
   {SUB, OperatorType::kRecElmWiseOp},
   {MUL, OperatorType::kRecElmWiseOp},

mindspore/ccsrc/frontend/parallel/dynamic_creator.h

@@ -86,7 +86,7 @@ REGISTER(LogSoftmaxInfo);
 REGISTER(ActivationInfo);
 REGISTER(SoftmaxCrossEntropyWithLogitsInfo);
 REGISTER(SubInfo);
-REGISTER(TensorAddInfo);
+REGISTER(AddInfo);
 REGISTER(BiasAddInfo);
 REGISTER(MulInfo);
 REGISTER(DivInfo);

mindspore/ccsrc/frontend/parallel/ops_info/arithmetic_info.h

@@ -60,12 +60,11 @@ class SubInfo : public ArithmeticBase {
   ~SubInfo() override = default;
 };
 
-class TensorAddInfo : public ArithmeticBase {
+class AddInfo : public ArithmeticBase {
  public:
-  TensorAddInfo(const std::string &name, const Shapes &inputs_shape, const Shapes &outputs_shape,
-                const PrimitiveAttrs &attrs)
+  AddInfo(const std::string &name, const Shapes &inputs_shape, const Shapes &outputs_shape, const PrimitiveAttrs &attrs)
       : ArithmeticBase(name, inputs_shape, outputs_shape, attrs, std::make_shared<TensorAddCost>()) {}
-  ~TensorAddInfo() override = default;
+  ~AddInfo() override = default;
 };
 
 class MulInfo : public ArithmeticBase {

mindspore/ccsrc/frontend/parallel/ops_info/onehot_info.cc

@@ -191,7 +191,7 @@ Status OneHotInfo::ComputeReplaceGraph(const CNodePtr &cnode) {
   auto equal = gen_g.PushBack({gen_g.NewOpInst(EQUAL), floor_div, CreateInt32Tensor(mod_rank_)});
   auto cast = gen_g.PushBack({gen_g.NewOpInst(CAST), equal, CreatTypeInt(32)});
   auto mul2 = gen_g.PushBack({gen_g.NewOpInst(MUL), sub1, cast});
-  auto tensor_add = gen_g.PushBack({gen_g.NewOpInst(TENSOR_ADD), mul2, CreateInt32Tensor(1)});
+  auto tensor_add = gen_g.PushBack({gen_g.NewOpInst(ADD), mul2, CreateInt32Tensor(1)});
   auto mul3 = gen_g.PushBack({gen_g.NewOpInst(MUL), cast, tensor_add});
   auto sub2 = gen_g.PushBack({gen_g.NewOpInst(SUB), mul3, CreateInt32Tensor(1)});
   Attr attr_onehot_axis = std::make_pair(AXIS, axis_value_ptr_);

mindspore/ccsrc/frontend/parallel/ops_info/ops_utils.h

@@ -200,7 +200,7 @@ constexpr char MAXPOOLV2[] = "MaxPoolV2";
 constexpr char L2_NORMALIZE[] = "L2Normalize";
 constexpr char TRANSPOSE[] = "Transpose";
 constexpr char RESHAPE[] = "Reshape";
-constexpr char TENSOR_ADD[] = "TensorAdd";
+constexpr char ADD[] = "Add";
 constexpr char BIAS_ADD[] = "BiasAdd";
 constexpr char SUB[] = "Sub";
 constexpr char MUL[] = "Mul";
@@ -315,7 +315,6 @@ constexpr char UNSORTED_SEGMENT_MIN[] = "UnsortedSegmentMin";
 constexpr char UNSORTED_SEGMENT_MAX[] = "UnsortedSegmentMax";
 constexpr char DEPTHWISE_CONV2D_NATIVE[] = "DepthwiseConv2dNative";
 constexpr char DEPTHWISE_CONV2D[] = "DepthwiseConv2D";
-constexpr char ADD[] = "Add";
 constexpr char DROPOUT[] = "Dropout";
 constexpr char KStridedSlice[] = "StridedSlice";
 constexpr char UNIQUE[] = "Unique";

mindspore/ccsrc/frontend/parallel/step_auto_parallel.cc

@@ -151,7 +151,7 @@ bool IsSplittableOperator(const std::string &op_name) {
   // clang-format off
   static const std::set<std::string> splittable_op =
     {MATMUL, TRANSPOSE, GELU, TANH, SOFTMAX, SUB, MUL, DIV, RESHAPE, GREATER, LOG_SOFTMAX, ACTIVATION, PRELU,
-     FLOORDIV, L2_NORMALIZE, TENSOR_ADD, MAXPOOL, MAXPOOLV2, VIRTUAL_DATA_SET, RELU, ONEHOT, DROPOUT_DO_MASK,
+     FLOORDIV, L2_NORMALIZE, ADD, MAXPOOL, MAXPOOLV2, VIRTUAL_DATA_SET, RELU, ONEHOT, DROPOUT_DO_MASK,
      REDUCE_MAX, REDUCE_MIN, ARGMAXWITHVALUE, ARGMINWITHVALUE, REDUCE_SUM, CONV2D, FUSE_BATCH_NORM, POOLING,
      MAX_POOL_WITH_ARGMAX, SIMPLE_MEAN, FLATTEN, BATCH_NORM, LAYER_NORM, BIAS_ADD, ASSIGN_SUB, COS, ACOS, EXP, PACK,
      LOG, REDUCE_MEAN, REAL_DIV, SIGMOID, POW, MAXIMUM, MINIMUM, EQUAL, NOT_EQUAL, LOGICALNOT, GATHERV2, SQRT, CONCAT,

mindspore/ccsrc/transform/express_ir/onnx_exporter.cc

@@ -165,7 +165,7 @@ class OpNameInfo {
 #define OPERATOR_ONNX_CONVERT_DEFINE(name, onnx_name, impl) \
   OpNameInfo GetOpOnnxConvertInfo_##name() { return impl.set_op_type(#name).set_onnx_type(#onnx_name); }
 
-OPERATOR_ONNX_CONVERT_DEFINE(TensorAdd, Add, OpNameInfo())
+OPERATOR_ONNX_CONVERT_DEFINE(Add, Add, OpNameInfo())
 OPERATOR_ONNX_CONVERT_DEFINE(Mul, Mul, OpNameInfo())
 
 OPERATOR_ONNX_CONVERT_DEFINE(ReLU, Relu, OpNameInfo())
@@ -257,7 +257,7 @@ OPERATOR_ONNX_CONVERT_DEFINE(Sub, Sub, OpNameInfo())
 #define OP_CONVERT_FUNCTION_NAME(name) GetOpOnnxConvertInfo_##name
 
 void RegisterOpConverters(const std::function<void(OpNameInfo &&)> &fn) {
-  fn(OP_CONVERT_FUNCTION_NAME(TensorAdd)());
+  fn(OP_CONVERT_FUNCTION_NAME(Add)());
   fn(OP_CONVERT_FUNCTION_NAME(Mul)());
 
   fn(OP_CONVERT_FUNCTION_NAME(ReLU)());

mindspore/ccsrc/transform/graph_ir/op_declare/elewise_calculation_ops_declare.cc

@@ -29,7 +29,7 @@ REG_ADPT_DESC(StateSetItem, prim::kPrimStateSetItem->name(), ADPT_DESC(Assign))
 INPUT_MAP(Add) = {{1, INPUT_DESC(x1)}, {2, INPUT_DESC(x2)}};
 ATTR_MAP(Add) = EMPTY_ATTR_MAP;
 OUTPUT_MAP(Add) = {{0, OUTPUT_DESC(y)}};
-REG_ADPT_DESC(Add, prim::kPrimTensorAdd->name(),
+REG_ADPT_DESC(Add, prim::kPrimAdd->name(),
               std::make_shared<OpAdapterDesc>(
                 std::make_shared<OpAdapter<Add>>(ExtraAttr({{"mode", MakeValue(static_cast<int64_t>(1))}})),
                 std::make_shared<OpAdapter<Add>>(ExtraAttr({{"mode", MakeValue(static_cast<int64_t>(1))}}))))

mindspore/ccsrc/utils/utils.h

@@ -215,7 +215,7 @@ constexpr auto kAvgPoolGradGpuOpName = "AvgPoolGradGpu";
 constexpr auto kmaxPoolGradOpName = "MaxPoolGrad";
 constexpr auto kMaxPoolWithArgmaxOpName = "MaxPoolWithArgmax";
 constexpr auto kMaxPoolGradWithArgmaxOpName = "MaxPoolGradWithArgmax";
-constexpr auto kTensorAddOpName = "TensorAdd";
+constexpr auto kTensorAddOpName = "Add";
 constexpr auto kCastOpName = "Cast";
 constexpr auto kGreaterEqualOpName = "GreaterEqual";
 constexpr auto kAbsOpName = "Abs";

mindspore/compression/export/quant_export.py

@@ -46,7 +46,7 @@ class ExportToQuantInferNetwork:
     Returns:
         Cell, Infer network.
     """
-    __quant_op_name__ = ["TensorAdd", "Sub", "Mul", "RealDiv"]
+    __quant_op_name__ = ["Add", "Sub", "Mul", "RealDiv"]
 
     def __init__(self, network, mean, std_dev, *inputs, is_mindir=False):
         network = Validator.check_isinstance('network', network, (nn.Cell,))
@@ -225,7 +225,7 @@ class ExportManualQuantNetwork(ExportToQuantInferNetwork):
         Returns:
             Cell, Infer network.
         """
-    __quant_op_name__ = ["TensorAdd", "Sub", "Mul", "RealDiv"]
+    __quant_op_name__ = ["Add", "Sub", "Mul", "RealDiv"]
 
     def __init__(self, network, mean, std_dev, *inputs, is_mindir=False):
         super(ExportManualQuantNetwork, self).__init__(network, mean, std_dev, *inputs, is_mindir)

mindspore/compression/quant/qat.py

@@ -173,7 +173,7 @@ class QuantizationAwareTraining(Quantizer):
         >>> quantizer = QuantizationAwareTraining(bn_fold=False, per_channel=[True, False], symmetric=[True, False])
         >>> net_qat = quantizer.quantize(net)
     """
-    __quant_op_name__ = ["TensorAdd", "Sub", "Mul", "RealDiv"]
+    __quant_op_name__ = ["Add", "Sub", "Mul", "RealDiv"]
 
     def __init__(self,
                  bn_fold=True,

mindspore/core/abstract/infer_functions.h

@@ -91,8 +91,8 @@ AbstractBasePtr InferImplMinOrMaxGrad(const AnalysisEnginePtr &, const Primitive
 AbstractBasePtr InferImplMul(const AnalysisEnginePtr &, const PrimitivePtr &primitive,
                              const AbstractBasePtrList &args_spec_list);
 
-AbstractBasePtr InferImplTensorAdd(const AnalysisEnginePtr &, const PrimitivePtr &primitive,
-                                   const AbstractBasePtrList &args_spec_list);
+AbstractBasePtr InferImplAdd(const AnalysisEnginePtr &, const PrimitivePtr &primitive,
+                             const AbstractBasePtrList &args_spec_list);
 
 AbstractBasePtr InferImplSquare(const AnalysisEnginePtr &, const PrimitivePtr &primitive,
                                 const AbstractBasePtrList &args_spec_list);

mindspore/core/abstract/prim_maths.cc

@@ -60,8 +60,8 @@ AbstractBasePtr InferImplSqrtGrad(const AnalysisEnginePtr &, const PrimitivePtr
   return out->Broaden();
 }
 
-AbstractBasePtr InferImplTensorAdd(const AnalysisEnginePtr &, const PrimitivePtr &primitive,
-                                   const AbstractBasePtrList &args_spec_list) {
+AbstractBasePtr InferImplAdd(const AnalysisEnginePtr &, const PrimitivePtr &primitive,
+                             const AbstractBasePtrList &args_spec_list) {
   // Inputs: two tensors.
   const std::string op_name = primitive->name();
   CheckArgsSize(op_name, args_spec_list, 2);

mindspore/core/abstract/primitive_infer_map.cc

@@ -37,7 +37,7 @@ PrimitiveEvalImplMap &GetPrimitiveToEvalImplMap() {
     {prim::kPrimMaximumGrad, {InferImplMinOrMaxGrad, true}},
     {prim::kPrimMinimumGrad, {InferImplMinOrMaxGrad, true}},
     {prim::kPrimMul, {InferImplMul, true}},
-    {prim::kPrimTensorAdd, {InferImplTensorAdd, true}},
+    {prim::kPrimAdd, {InferImplAdd, true}},
     {prim::kPrimSquare, {InferImplSquare, true}},
     {prim::kPrimSqrt, {InferImplSqrt, true}},
     {prim::kPrimSqrtGrad, {InferImplSqrtGrad, true}},

mindspore/core/base/core_ops.h

@@ -236,7 +236,7 @@ inline const PrimitivePtr kPrimSparseTensorGetIndices = std::make_shared<Primiti
 inline const PrimitivePtr kPrimSparseTensorGetDenseShape = std::make_shared<Primitive>("SparseTensorGetDenseShape");
 
 // Maths
-inline const PrimitivePtr kPrimTensorAdd = std::make_shared<Primitive>("TensorAdd");
+inline const PrimitivePtr kPrimAdd = std::make_shared<Primitive>("Add");
 inline const PrimitivePtr kPrimMatMul = std::make_shared<Primitive>("MatMul");
 inline const PrimitivePtr kPrimBatchMatMul = std::make_shared<Primitive>("BatchMatMul");
 inline const PrimitivePtr kPrimMaximumGrad = std::make_shared<Primitive>("MaximumGrad");

mindspore/core/c_ops/add.cc

@@ -49,6 +49,6 @@ AbstractBasePtr AddInfer(const abstract::AnalysisEnginePtr &, const PrimitivePtr
   return std::make_shared<abstract::AbstractTensor>(InferType(primitive, input_args),
                                                     InferShape(primitive, input_args)->shape());
 }
-REGISTER_PRIMITIVE_EVAL_IMPL(Add, prim::kPrimTensorAdd, AddInfer);
+REGISTER_PRIMITIVE_EVAL_IMPL(Add, prim::kPrimAdd, AddInfer);
 REGISTER_PRIMITIVE_C(kNameAdd, Add);
 }  // namespace mindspore

mindspore/core/ir/pattern_matcher.h

@@ -989,7 +989,7 @@ class PConstant : public PBase<PConstant<T> > {
   }
 
 // Arithmetic operations
-BIN_OPERATION_PATTERN(operator+, prim::kPrimTensorAdd, true);
+BIN_OPERATION_PATTERN(operator+, prim::kPrimAdd, true);
 BIN_OPERATION_PATTERN(operator*, prim::kPrimMul, true);
 BIN_OPERATION_PATTERN(operator/, prim::kPrimRealDiv, false);
 BIN_OPERATION_PATTERN(operator-, prim::kPrimSub, false);

mindspore/nn/_graph_kernels/graph_kernels.py

@@ -225,7 +225,7 @@ class LambNextMV(GraphKernel):
     def __init__(self):
         super(LambNextMV, self).__init__()
         self.mul = P.Mul()
-        self.add = P.TensorAdd()
+        self.add = P.Add()
         self.div = P.RealDiv()
         self.sqrt = P.Sqrt()
         self.rsqrt = P.Rsqrt()

mindspore/nn/layer/activation.py

@@ -651,7 +651,7 @@ class LogSigmoid(Cell):
         super(LogSigmoid, self).__init__()
         self.mul = P.Mul()
         self.exp = P.Exp()
-        self.add = P.TensorAdd()
+        self.add = P.Add()
         self.rec = P.Reciprocal()
         self.log = P.Log()
 

mindspore/nn/layer/embedding.py

@@ -441,13 +441,13 @@ class MultiFieldEmbeddingLookup(EmbeddingLookup):
 
         self.mul = P.Mul()
         self.inf_mask_mul = P.Mul()
-        self.bias_add = P.TensorAdd()
-        self.inf_add = P.TensorAdd()
+        self.bias_add = P.Add()
+        self.inf_add = P.Add()
         self.merge_op = None
         self.count_op = P.UnsortedSegmentSum()
         self.abs = P.Abs()
         self.equal = P.Equal()
-        self.add = P.TensorAdd()
+        self.add = P.Add()
         self.cast = P.Cast()
         self.div_no_nan = P.DivNoNan()
         self.expand = P.ExpandDims()

mindspore/nn/layer/quant.py

@@ -99,8 +99,8 @@ class BatchNormFoldCell(Cell):
             else:
                 batch_mean = P.ZerosLike()(variance)
                 batch_std = P.OnesLike()(variance)
-                running_mean = P.TensorAdd()(mean, 0.)
-                running_std = P.Sqrt()(P.TensorAdd()(variance, self.epsilon))
+                running_mean = P.Add()(mean, 0.)
+                running_std = P.Sqrt()(P.Add()(variance, self.epsilon))
         return batch_mean, batch_std, running_mean, running_std
 
 
@@ -559,7 +559,7 @@ class Conv2dBnFoldQuantOneConv(Cell):
         return s
 
     def construct(self, x):
-        running_std = P.Sqrt()(P.TensorAdd()(self.moving_variance, self.eps))
+        running_std = P.Sqrt()(P.Add()(self.moving_variance, self.eps))
         scale_factor = self.gamma / running_std
         if self.channel_axis:
             scale_factor = self.reshape(scale_factor, (1, -1, 1, 1))
@@ -1236,7 +1236,7 @@ class TensorAddQuant(Cell):
                                                       ema=True,
                                                       ema_decay=ema_decay,
                                                       quant_dtype=quant_dtype)
-        self.add = P.TensorAdd()
+        self.add = P.Add()
 
     def construct(self, x1, x2):
         x = self.add(x1, x2)

mindspore/ops/_grad/grad_math_ops.py

@@ -155,9 +155,9 @@ def bprop_batchmatmul(self):
     return bprop
 
 
-@bprop_getters.register(P.TensorAdd)
+@bprop_getters.register(P.Add)
 def get_bprop_tensor_add(self):
-    """Grad definition for `TensorAdd` operation."""
+    """Grad definition for `Add` operation."""
 
     def bprop(x, y, out, dout):
         return binop_grad_common(x, y, dout, dout)

mindspore/ops/_op_impl/akg/ascend/add.py

@@ -13,10 +13,10 @@
 # limitations under the License.
 # ============================================================================
 
-"""TensorAdd op"""
+"""Add op"""
 from mindspore.ops.op_info_register import op_info_register, AkgAscendRegOp, DataType as DT
 
-op_info = AkgAscendRegOp("TensorAdd") \
+op_info = AkgAscendRegOp("Add") \
     .fusion_type("ELEMWISE") \
     .input(0, "x") \
     .input(1, "y") \
@@ -38,5 +38,5 @@ op_info = AkgAscendRegOp("TensorAdd") \
 
 @op_info_register(op_info)
 def _add_akg():
-    """TensorAdd Akg register"""
+    """Add Akg register"""
     return

mindspore/ops/_op_impl/tbe/tensor_add.py

@@ -13,10 +13,10 @@
 # limitations under the License.
 # ============================================================================
 
-"""TensorAdd op"""
+"""Add op"""
 from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
-tensor_add_op_info = TBERegOp("TensorAdd") \
+tensor_add_op_info = TBERegOp("Add") \
     .fusion_type("ELEMWISE") \
     .async_flag(False) \
     .binfile_name("add.so") \

mindspore/ops/_op_impl/tbe/tensor_add_ds.py

@@ -16,7 +16,7 @@
 """TensorAdd op"""
 from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
-tensor_add_op_info = TBERegOp("TensorAdd") \
+tensor_add_op_info = TBERegOp("Add") \
     .fusion_type("ELEMWISE") \
     .async_flag(False) \
     .binfile_name("add.so") \

mindspore/ops/composite/base.py

@@ -395,7 +395,7 @@ class MultitypeFuncGraph(MultitypeFuncGraph_):
         >>> from mindspore.ops import Primitive, operations as P
         >>> from mindspore import dtype as mstype
         >>>
-        >>> tensor_add = P.TensorAdd()
+        >>> tensor_add = P.Add()
         >>> add = MultitypeFuncGraph('add')
         >>> @add.register("Number", "Number")
         ... def add_scala(x, y):

mindspore/ops/functional.py

@@ -51,7 +51,7 @@ merge = P.Merge()
 geswitch = P.GeSwitch()
 addn = P.AddN()
 absolute = P.Abs()
-tensor_add = P.TensorAdd()
+tensor_add = P.Add()
 neg_tensor = P.Neg()
 tensor_lt = P.Less()
 tensor_le = P.LessEqual()

mindspore/ops/operations/__init__.py

@@ -54,7 +54,7 @@ from .math_ops import (Abs, ACos, Asin, Asinh, AddN, AccumulateNV2, AssignAdd, A
                        NPUGetFloatStatus, Pow, RealDiv, IsNan, IsInf, IsFinite, FloatStatus,
                        Reciprocal, CumSum, HistogramFixedWidth, SquaredDifference, Xdivy, Xlogy,
                        Sin, Sqrt, Rsqrt, BesselI0e, BesselI1e, TruncateDiv, TruncateMod,
-                       Square, Sub, TensorAdd, Sign, Round, SquareSumAll, Atan, Atanh, Cosh, Sinh, Eps, Tan,
+                       Square, Sub, TensorAdd, Add, Sign, Round, SquareSumAll, Atan, Atanh, Cosh, Sinh, Eps, Tan,
                        MatrixInverse)
 
 from .random_ops import (RandomChoiceWithMask, StandardNormal, Gamma, Poisson, UniformInt, UniformReal,
@@ -102,6 +102,7 @@ __all__ = [
     'Sort',
     'EditDistance',
     'CropAndResize',
+    'Add',
     'TensorAdd',
     'Argmax',
     'Argmin',

mindspore/ops/operations/control_ops.py

@@ -106,7 +106,7 @@ class GeSwitch(PrimitiveWithInfer):
         ...     def __init__(self):
         ...         super(Net, self).__init__()
         ...         self.square = ops.Square()
-        ...         self.add = ops.TensorAdd()
+        ...         self.add = ops.Add()
         ...         self.value = Tensor(np.full((1), 3), mindspore.float32)
         ...         self.switch = ops.GeSwitch()
         ...         self.merge = ops.Merge()

mindspore/ops/operations/debug_ops.py

@@ -66,7 +66,7 @@ class ScalarSummary(PrimitiveWithInfer):
         ...     def __init__(self,):
         ...         super(SummaryDemo, self).__init__()
         ...         self.summary = ops.ScalarSummary()
-        ...         self.add = ops.TensorAdd()
+        ...         self.add = ops.Add()
         ...
         ...     def construct(self, x, y):
         ...         name = "x"
@@ -149,7 +149,7 @@ class TensorSummary(PrimitiveWithInfer):
         ...     def __init__(self,):
         ...         super(SummaryDemo, self).__init__()
         ...         self.summary = ops.TensorSummary()
-        ...         self.add = ops.TensorAdd()
+        ...         self.add = ops.Add()
         ...
         ...     def construct(self, x, y):
         ...         x = self.add(x, y)
@@ -191,7 +191,7 @@ class HistogramSummary(PrimitiveWithInfer):
         ...     def __init__(self,):
         ...         super(SummaryDemo, self).__init__()
         ...         self.summary = ops.HistogramSummary()
-        ...         self.add = ops.TensorAdd()
+        ...         self.add = ops.Add()
         ...
         ...     def construct(self, x, y):
         ...         x = self.add(x, y)
@@ -409,7 +409,7 @@ class Assert(PrimitiveWithInfer):
         ...     def __init__(self):
         ...         super(AssertDemo, self).__init__()
         ...         self.assert1 = ops.Assert(summarize=10)
-        ...         self.add = ops.TensorAdd()
+        ...         self.add = ops.Add()
         ...
         ...     def construct(self, x, y):
         ...         data = self.add(x, y)

mindspore/ops/operations/math_ops.py

@@ -18,6 +18,7 @@
 import copy
 
 import numpy as np
+from mindspore import log as logger
 from ... import context
 from .. import signature as sig
 from ..._checkparam import Validator as validator
@@ -114,7 +115,7 @@ class _BitwiseBinaryOp(_MathBinaryOp):
         return _BitwiseBinaryOp._check_bitwise_op_input_type(x1_type, x2_type, self.name)
 
 
-class TensorAdd(_MathBinaryOp):
+class Add(_MathBinaryOp):
     r"""
     Adds two input tensors element-wise.
 
@@ -143,7 +144,7 @@ class TensorAdd(_MathBinaryOp):
         ``Ascend`` ``GPU`` ``CPU``
 
     Examples:
-        >>> add = ops.TensorAdd()
+        >>> add = ops.Add()
         >>> input_x = Tensor(np.array([1, 2, 3]).astype(np.float32))
         >>> input_y = Tensor(np.array([4, 5, 6]).astype(np.float32))
         >>> output = add(input_x, input_y)
@@ -160,6 +161,10 @@ class TensorAdd(_MathBinaryOp):
             return Tensor(out)
         return None
 
+def TensorAdd():
+    """Warning: This will be changed later"""
+    logger.warning("WARN_DEPRECATED: The usage of TensorAdd is deprecated. Please use Add.")
+    return Add()
 
 class AssignAdd(PrimitiveWithInfer):
     """

model_zoo/official/cv/centerface/src/mobile_v2.py

@@ -16,7 +16,7 @@
 
 import mindspore.nn as nn
 from mindspore.ops import operations as P
-from mindspore.ops.operations import TensorAdd
+from mindspore.ops.operations import Add
 
 from src.var_init import KaimingNormal
 
@@ -91,7 +91,7 @@ class InvertedResidual(nn.Cell):
         ])
 
         self.conv = nn.SequentialCell(layers)
-        self.add = TensorAdd()
+        self.add = Add()
         self.cast = P.Cast()
 
     def construct(self, x):

model_zoo/official/cv/cnnctc/src/cnn_ctc.py

@@ -198,7 +198,7 @@ class BasicBlock(nn.Cell):
         self.bn2 = ms_fused_bn(planes)
         self.relu = P.ReLU()
         self.downsample = downsample
-        self.add = P.TensorAdd()
+        self.add = P.Add()
 
     def construct(self, x):
         residual = x

model_zoo/official/cv/deeplabv3/src/nets/deeplab_v3/deeplab_v3.py

@@ -102,7 +102,7 @@ class Bottleneck(nn.Cell):
         self.relu = nn.ReLU()
         self.downsample = downsample
 
-        self.add = P.TensorAdd()
+        self.add = P.Add()
 
     def construct(self, x):
         identity = x

model_zoo/official/cv/faster_rcnn/src/FasterRcnn/resnet50.py

@@ -222,7 +222,7 @@ class ResidualBlockUsing(nn.Cell):
                 self.bn_down_sample = self.bn_down_sample.set_train()
             if not weights_update:
                 self.conv_down_sample.weight.requires_grad = False
-        self.add = P.TensorAdd()
+        self.add = P.Add()
 
     def construct(self, x):
         identity = x

model_zoo/official/cv/maskrcnn/src/maskrcnn/resnet50.py

@@ -218,7 +218,7 @@ class ResidualBlockUsing(nn.Cell):
                 self.bn_down_sample = self.bn_down_sample.set_train()
             if not weights_update:
                 self.conv_down_sample.weight.requires_grad = False
-        self.add = P.TensorAdd()
+        self.add = P.Add()
 
     def construct(self, x):
         identity = x

model_zoo/official/cv/mobilenetv2/src/mobilenetV2.py

@@ -16,7 +16,7 @@
 import numpy as np
 import mindspore.nn as nn
 from mindspore.ops import operations as P
-from mindspore.ops.operations import TensorAdd
+from mindspore.ops.operations import Add
 from mindspore import Tensor
 
 __all__ = ['MobileNetV2', 'MobileNetV2Backbone', 'MobileNetV2Head', 'mobilenet_v2']
@@ -129,7 +129,7 @@ class InvertedResidual(nn.Cell):
             nn.BatchNorm2d(oup),
         ])
         self.conv = nn.SequentialCell(layers)
-        self.add = TensorAdd()
+        self.add = Add()
         self.cast = P.Cast()
 
     def construct(self, x):

model_zoo/official/cv/mobilenetv2/src/mobilenetV2_fusion.py

@@ -120,7 +120,7 @@ class InvertedResidual(nn.Cell):
             nn.Conv2dBnAct(hidden_dim, oup, kernel_size=1, stride=1, pad_mode='pad', padding=0, group=1, has_bn=True)
         ])
         self.conv = nn.SequentialCell(layers)
-        self.add = P.TensorAdd()
+        self.add = P.Add()
 
     def construct(self, x):
         out = self.conv(x)

model_zoo/official/cv/mobilenetv2_quant/src/mobilenetV2.py

@@ -123,7 +123,7 @@ class InvertedResidual(nn.Cell):
             nn.Conv2dBnAct(hidden_dim, oup, kernel_size=1, stride=1, pad_mode='pad', padding=0, group=1, has_bn=True)
         ])
         self.conv = nn.SequentialCell(layers)
-        self.add = P.TensorAdd()
+        self.add = P.Add()
 
     def construct(self, x):
         out = self.conv(x)

model_zoo/official/cv/mobilenetv3/src/mobilenetV3.py

@@ -197,7 +197,7 @@ class ResUnit(nn.Cell):
                           padding=0, act_type=act_type, use_act=False)
         if num_in != num_out or stride != 1:
             self.use_short_cut_conv = False
-        self.add = P.TensorAdd() if self.use_short_cut_conv else None
+        self.add = P.Add() if self.use_short_cut_conv else None
 
     def construct(self, x):
         """construct"""

model_zoo/official/cv/psenet/src/ETSNET/dice_loss.py

@@ -49,7 +49,7 @@ class DiceLoss(_Loss):
         self.logical_or = P.LogicalOr()
         self.equal = P.Equal()
         self.zeros_like = P.ZerosLike()
-        self.add = P.TensorAdd()
+        self.add = P.Add()
         self.gather = P.Gather()
 
     def ohem_batch(self, scores, gt_texts, training_masks):

model_zoo/official/cv/psenet/src/ETSNET/resnet50.py

@@ -61,7 +61,7 @@ class ResidualBlock(nn.Cell):
                                           kernel_size=1, stride=stride)
             self.bn_down_sample = _bn(out_channels, momentum=momentum)
 
-        self.add = P.TensorAdd()
+        self.add = P.Add()
 
     def construct(self, x):
         identity = x

model_zoo/official/cv/resnet/src/resnet.py

@@ -152,7 +152,7 @@ class ResidualBlock(nn.Cell):
             else:
                 self.down_sample_layer = nn.SequentialCell([_conv1x1(in_channel, out_channel, stride,
                                                                      use_se=self.use_se), _bn(out_channel)])
-        self.add = P.TensorAdd()
+        self.add = P.Add()
 
     def construct(self, x):
         identity = x

model_zoo/official/cv/resnet/src/resnet_gpu_benchmark.py

@@ -119,7 +119,7 @@ class ResidualBlock(nn.Cell):
 
         if self.down_sample:
             self.down_sample_layer = nn.SequentialCell([_conv1x1(in_channel, out_channel, stride), _bn(out_channel)])
-        self.add = P.TensorAdd()
+        self.add = P.Add()
 
     def construct(self, x):
         identity = x

model_zoo/official/cv/resnet50_quant/models/resnet_quant.py

@@ -85,7 +85,7 @@ class ResidualBlock(nn.Cell):
             self.down_sample_layer = nn.Conv2dBnAct(in_channel, out_channel,
                                                     kernel_size=1, stride=stride,
                                                     pad_mode='same', padding=0, has_bn=True, activation='relu')
-        self.add = P.TensorAdd()
+        self.add = P.Add()
         self.relu = P.ReLU()
 
     def construct(self, x):

model_zoo/official/cv/resnet_thor/src/resnet_thor.py

@@ -215,7 +215,7 @@ class ResidualBlock(nn.Cell):
                                                                  frequency=frequency,
                                                                  batch_size=batch_size),
                                                         _bn(out_channel)])
-        self.add = P.TensorAdd()
+        self.add = P.Add()
 
     def construct(self, x):
         identity = x

model_zoo/official/cv/resnet_thor/src/thor_layer.py

@@ -333,7 +333,7 @@ class Dense_Thor_GPU(Cell):
         self.gather = P.Gather()
         self.freq = Tensor(frequency, mstype.int32)
         self.axis = 0
-        self.add = P.TensorAdd()
+        self.add = P.Add()
         self.sqrt = P.Sqrt()
         self.cholesky = P.CholeskyTrsm(split_dim=split_dim)
         self.vector_matmul = P.BatchMatMul(transpose_a=True)
@@ -690,7 +690,7 @@ class Dense_Thor(Cell):
         self.exp = P.Exp()
         self.dampingA = Tensor(np.identity(2048), mstype.float32)
         self.dampingG = Tensor(np.identity(1024), mstype.float32)
-        self.add = P.TensorAdd()
+        self.add = P.Add()
         self.sqrt = P.Sqrt()
         self.getG = P.InsertGradientOf(self.save_gradient)
 

model_zoo/official/cv/resnext50/src/backbone/resnet.py

@@ -16,7 +16,7 @@
 ResNet based ResNext
 """
 import mindspore.nn as nn
-from mindspore.ops.operations import TensorAdd, Split, Concat
+from mindspore.ops.operations import Add, Split, Concat
 from mindspore.ops import operations as P
 from mindspore.common.initializer import TruncatedNormal
 
@@ -105,7 +105,7 @@ class BasicBlock(nn.Cell):
             self.down_sample = down_sample
             self.down_sample_flag = True
 
-        self.add = TensorAdd()
+        self.add = Add()
 
     def construct(self, x):
         identity = x
@@ -176,7 +176,7 @@ class Bottleneck(nn.Cell):
             self.down_sample_flag = True
 
         self.cast = P.Cast()
-        self.add = TensorAdd()
+        self.add = Add()
 
     def construct(self, x):
         identity = x

model_zoo/official/cv/retinaface_resnet50/src/network.py

@@ -95,7 +95,7 @@ class ResidualBlock(nn.Cell):
         if self.down_sample:
             self.down_sample_layer = nn.SequentialCell([_conv1x1(in_channel, out_channel, stride),
                                                         _bn(out_channel)])
-        self.add = P.TensorAdd()
+        self.add = P.Add()
 
     def construct(self, x):
         identity = x

model_zoo/official/cv/shufflenetv1/src/shufflenetv1.py

@@ -68,7 +68,7 @@ class ShuffleV1Block(nn.Cell):
             outputs = oup
 
         self.relu = nn.ReLU()
-        self.add = P.TensorAdd()
+        self.add = P.Add()
         self.concat = P.Concat(1)
         self.shape = P.Shape()
         self.transpose = P.Transpose()

model_zoo/official/cv/squeezenet/src/squeezenet.py

@@ -170,7 +170,7 @@ class SqueezeNet_Residual(nn.Cell):
 
         self.relu = nn.ReLU()
         self.max_pool2d = nn.MaxPool2d(kernel_size=3, stride=2)
-        self.add = P.TensorAdd()
+        self.add = P.Add()
         self.dropout = nn.Dropout(keep_prob=0.5)
         self.mean = P.ReduceMean(keep_dims=True)
         self.flatten = nn.Flatten()

model_zoo/official/cv/ssd/src/ssd.py

@@ -133,7 +133,7 @@ class InvertedResidual(nn.Cell):
             _bn(oup),
         ])
         self.conv = nn.SequentialCell(layers)
-        self.add = P.TensorAdd()
+        self.add = P.Add()
         self.cast = P.Cast()
         self.last_relu = last_relu
         self.relu = nn.ReLU6()

model_zoo/official/cv/xception/src/Xception.py

@@ -68,7 +68,7 @@ class Block(nn.Cell):
         if strides != 1:
             rep.append(nn.MaxPool2d(3, strides, pad_mode="same"))
         self.rep = nn.SequentialCell(*rep)
-        self.add = P.TensorAdd()
+        self.add = P.Add()
 
     def construct(self, inp):
         x = self.rep(inp)

model_zoo/official/cv/yolov3_darknet53/src/darknet.py

@@ -62,7 +62,7 @@ class ResidualBlock(nn.Cell):
         out_chls = out_channels//2
         self.conv1 = conv_block(in_channels, out_chls, kernel_size=1, stride=1)
         self.conv2 = conv_block(out_chls, out_channels, kernel_size=3, stride=1)
-        self.add = P.TensorAdd()
+        self.add = P.Add()
 
     def construct(self, x):
         identity = x

model_zoo/official/cv/yolov3_darknet53_quant/src/darknet.py

@@ -59,7 +59,7 @@ class ResidualBlock(nn.Cell):
         out_chls = out_channels//2
         self.conv1 = conv_block(in_channels, out_chls, kernel_size=1, stride=1)
         self.conv2 = conv_block(out_chls, out_channels, kernel_size=3, stride=1)
-        self.add = P.TensorAdd()
+        self.add = P.Add()
 
     def construct(self, x):
         identity = x

model_zoo/official/cv/yolov3_resnet18/src/yolov3.py

@@ -107,7 +107,7 @@ class BasicBlock(nn.Cell):
         self.downsample = (in_channels != out_channels)
         if self.downsample:
             self.down_sample_layer = _conv2d(in_channels, out_channels, 1, stride=stride)
-        self.add = P.TensorAdd()
+        self.add = P.Add()
 
     def construct(self, x):
         identity = x

model_zoo/official/cv/yolov4/src/cspdarknet53.py

@@ -76,7 +76,7 @@ class ResidualBlock(nn.Cell):
         out_chls = out_channels
         self.conv1 = conv_block(in_channels, out_chls, kernel_size=1, stride=1)
         self.conv2 = conv_block(out_chls, out_channels, kernel_size=3, stride=1)
-        self.add = P.TensorAdd()
+        self.add = P.Add()
 
     def construct(self, x):
         identity = x
@@ -111,7 +111,7 @@ class CspDarkNet53(nn.Cell):
         self.outchannel = 1024
         self.detect = detect
         self.concat = P.Concat(axis=1)
-        self.add = P.TensorAdd()
+        self.add = P.Add()
 
         self.conv0 = conv_block(3, 32, kernel_size=3, stride=1)
         self.conv1 = conv_block(32, 64, kernel_size=3, stride=2)

model_zoo/official/nlp/bert/src/bert_model.py

@@ -188,7 +188,7 @@ class EmbeddingPostprocessor(nn.Cell):
             use_one_hot=False)
         self.layernorm = nn.LayerNorm((embedding_size,))
         self.position_ids = Tensor(np.arange(seq).reshape(-1, seq).astype(np.int32))
-        self.add = P.TensorAdd()
+        self.add = P.Add()
 
     def construct(self, token_type_ids, word_embeddings):
         """Postprocessors apply positional and token type embeddings to word embeddings."""
@@ -226,7 +226,7 @@ class BertOutput(nn.Cell):
                               weight_init=TruncatedNormal(initializer_range)).to_float(compute_type)
         self.dropout = nn.Dropout(1 - dropout_prob)
         self.dropout_prob = dropout_prob
-        self.add = P.TensorAdd()
+        self.add = P.Add()
         self.layernorm = nn.LayerNorm((out_channels,)).to_float(compute_type)
         self.cast = P.Cast()
 
@@ -444,7 +444,7 @@ class BertAttention(nn.Cell):
         if self.has_attention_mask:
             self.expand_dims = P.ExpandDims()
             self.sub = P.Sub()
-            self.add = P.TensorAdd()
+            self.add = P.Add()
             self.cast = P.Cast()
             self.get_dtype = P.DType()
         if do_return_2d_tensor:

model_zoo/official/nlp/bert_thor/src/bert_model.py

@@ -227,7 +227,7 @@ class EmbeddingPostprocessor(nn.Cell):
             frequency=frequency)
         self.position_ids = Tensor(np.arange(seq).reshape(-1, seq).astype(np.int32))
         self.layernorm = nn.LayerNorm((embedding_size,))
-        self.add = P.TensorAdd()
+        self.add = P.Add()
 
     def construct(self, token_type_ids, word_embeddings):
         """construct of EmbeddingPostprocessor"""
@@ -275,7 +275,7 @@ class BertOutput(nn.Cell):
                                 batch_size=batch_size).to_float(compute_type)
         self.dropout = nn.Dropout(1 - dropout_prob)
         self.dropout_prob = dropout_prob
-        self.add = P.TensorAdd()
+        self.add = P.Add()
         self.layernorm = nn.LayerNorm((out_channels,)).to_float(compute_type)
         self.cast = P.Cast()
 
@@ -522,7 +522,7 @@ class BertAttention(nn.Cell):
         if self.has_attention_mask:
             self.expand_dims = P.ExpandDims()
             self.sub = P.Sub()
-            self.add = P.TensorAdd()
+            self.add = P.Add()
             self.cast = P.Cast()
             self.get_dtype = P.DType()
         if do_return_2d_tensor:

model_zoo/official/nlp/gnmt_v2/src/gnmt_model/beam_search.py

@@ -35,7 +35,7 @@ class LengthPenalty(nn.Cell):
     def __init__(self, weight=1.0, compute_type=mstype.float32):
         super(LengthPenalty, self).__init__()
         self.weight = weight
-        self.add = P.TensorAdd()
+        self.add = P.Add()
         self.pow = P.Pow()
         self.div = P.RealDiv()
         self.five = Tensor(5.0, mstype.float32)
@@ -188,7 +188,7 @@ class BeamSearchDecoder(nn.Cell):
         self.decoder = decoder
         self.is_using_while = is_using_while
 
-        self.add = P.TensorAdd()
+        self.add = P.Add()
         self.expand = P.ExpandDims()
         self.reshape = P.Reshape()
         self.shape_flat = (-1,)

model_zoo/official/nlp/mass/src/transformer/beam_search.py

@@ -36,7 +36,7 @@ class LengthPenalty(nn.Cell):
         super(LengthPenalty, self).__init__()
         self.weight = weight
 
-        self.add = P.TensorAdd()
+        self.add = P.Add()
         self.pow = P.Pow()
         self.div = P.RealDiv()
 
@@ -178,7 +178,7 @@ class BeamSearchDecoder(nn.Cell):
 
         self.decoder = decoder
 
-        self.add = P.TensorAdd()
+        self.add = P.Add()
         self.expand = P.ExpandDims()
         self.reshape = P.Reshape()
         self.shape_flat = (-1,)

model_zoo/official/nlp/mass/src/transformer/multi_head_attention.py

@@ -138,7 +138,7 @@ class MultiHeadAttention(nn.Cell):
         if self.has_attention_mask:
             self.expand_dims = P.ExpandDims()
             self.sub = P.Sub()
-            self.add = P.TensorAdd()
+            self.add = P.Add()
             self.cast = P.Cast()
             self.get_dtype = P.DType()