!11719 [MSLITE][DEVELOP] fix bug of npu op: Rsqrt, Scale and Arithmetic

From: @yangruoqi713
Reviewed-by: @zhang_xue_tong,@zhanghaibo5
Signed-off-by: @zhang_xue_tong

mindspore/lite/src/runtime/kernel/npu/arithmetic_npu.cc

@@ -45,13 +45,10 @@ using mindspore::schema::PrimitiveType_Sub;
 namespace mindspore::kernel {
 int ArithmeticNPUKernel::IsSupport(const std::vector<lite::Tensor *> &inputs,
                                    const std::vector<lite::Tensor *> &outputs, OpParameter *opParameter) {
-  if (primitive_->Type() == PrimitiveType_Mul || primitive_->Type() == PrimitiveType_Div ||
-      primitive_->Type() == PrimitiveType_Add || primitive_->Type() == PrimitiveType_Sub) {
-    if (inputs[0]->shape() != inputs[1]->shape()) {
-      MS_LOG(WARNING) << name_ << " for the two inputs, the corresponding dimensions must have the same value."
-                      << " shape 1 is:" << inputs[0]->shape() << " shape 2 is:" << inputs[1]->shape();
-      return RET_ERROR;
-    }
+  if (inputs[0]->shape() != inputs[1]->shape()) {
+    MS_LOG(WARNING) << name_ << " for the two inputs, the corresponding dimensions must have the same value."
+                    << " shape 1 is:" << inputs[0]->shape() << " shape 2 is:" << inputs[1]->shape();
+    return RET_ERROR;
   }
   return RET_OK;
 }
@@ -68,6 +65,26 @@ ge::Operator *CreateOperator(const std::vector<ge::Operator *> &npu_inputs, cons
   return op;
 }
 
+int ArithmeticNPUKernel::SetActivation() {
+  if (activation_type_ != ActivationType_NO_ACTIVATION) {
+    act_ = new (std::nothrow) hiai::op::Activation(name_ + "_act");
+    if (act_ == nullptr) {
+      MS_LOG(ERROR) << "New activation npu operator for op " << name_ << " failed.";
+      return RET_ERROR;
+    }
+    act_->set_input_x(*op_);
+    if (activation_type_ == ActivationType_RELU) {
+      act_->set_attr_mode(1);
+    } else if (activation_type_ == ActivationType_RELU6) {
+      act_->set_attr_mode(14);
+    } else {
+      MS_LOG(ERROR) << "Unsupported activation type for op " << name_;
+      return RET_ERROR;
+    }
+  }
+  return RET_OK;
+}
+
 int ArithmeticNPUKernel::SetNPUInputs(const std::vector<lite::Tensor *> &inputs,
                                       const std::vector<lite::Tensor *> &outputs,
                                       const std::vector<ge::Operator *> &npu_inputs) {
@@ -100,6 +117,9 @@ int ArithmeticNPUKernel::SetNPUInputs(const std::vector<lite::Tensor *> &inputs,
     case PrimitiveType_Maximum:
       op = CreateOperator<hiai::op::Maximum>(npu_inputs, name_);
       break;
+    case PrimitiveType_Minimum:
+      op = CreateOperator<hiai::op::Minimum>(npu_inputs, name_);
+      break;
     case PrimitiveType_SquaredDifference:
       op = CreateOperator<hiai::op::SquaredDifference>(npu_inputs, name_);
       break;
@@ -132,21 +152,10 @@ int ArithmeticNPUKernel::SetNPUInputs(const std::vector<lite::Tensor *> &inputs,
   }
   op_ = op;
 
-  if (activation_type_ != ActivationType_NO_ACTIVATION) {
-    act_ = new (std::nothrow) hiai::op::Activation(name_ + "_act");
-    if (act_ == nullptr) {
-      MS_LOG(ERROR) << "New activation npu operator for op " << name_ << " failed.";
-      return RET_ERROR;
-    }
-    act_->set_input_x(*op_);
-    if (activation_type_ == ActivationType_RELU) {
-      act_->set_attr_mode(1);
-    } else if (activation_type_ == ActivationType_RELU6) {
-      act_->set_attr_mode(14);
-    } else {
-      MS_LOG(ERROR) << "Unsupport activation type for op " << name_;
-      return RET_ERROR;
-    }
+  auto ret = SetActivation();
+  if (ret != RET_OK) {
+    MS_LOG(ERROR) << "Arithmetic npu op set activation failed.";
+    return RET_ERROR;
   }
   return RET_OK;
 }

mindspore/lite/src/runtime/kernel/npu/arithmetic_npu.h

@@ -39,6 +39,7 @@ class ArithmeticNPUKernel : public NPUKernel {
   ge::Operator *GetNPUOp() override;
 
  private:
+  int SetActivation();
   int activation_type_;
   ge::Operator *op_ = nullptr;
   hiai::op::Activation *act_ = nullptr;

mindspore/lite/src/runtime/kernel/npu/arithmetic_self_npu.cc

@@ -68,6 +68,9 @@ int ArithmeticSelfNPUKernel::SetNPUInputs(const std::vector<lite::Tensor *> &inp
     case PrimitiveType_Sqrt:
       op = CreateOperator<hiai::op::Sqrt>(npu_inputs[0], name_);
       break;
+    case PrimitiveType_Rsqrt:
+      op = CreateOperator<hiai::op::Rsqrt>(npu_inputs[0], name_);
+      break;
     case PrimitiveType_Sin:
       op = CreateOperator<hiai::op::Sin>(npu_inputs[0], name_);
       break;

mindspore/lite/src/runtime/kernel/npu/instance_norm_npu.cc

@@ -53,7 +53,7 @@ int InstanceNormNPUKernel::SetNPUInputs(const std::vector<lite::Tensor *> &input
   ge::TensorDesc gamma_tensor_desc(lite::ConverterToNPUShape({1, gamma_shape[0], 1, 1}), ge::FORMAT_NCHW,
                                    lite::ConverterToNPUDataType(inputs[1]->data_type()));
   gamma_tensor->SetTensorDesc(gamma_tensor_desc);
-  gamma_tensor->SetData(reinterpret_cast<const uint8_t *>(inputs.data()), inputs[1]->Size());
+  gamma_tensor->SetData(reinterpret_cast<const uint8_t *>(inputs[1]->data_c()), inputs[1]->Size());
   op_->set_input_gamma(*gamma);
 
   auto beta = new (std::nothrow) hiai::op::Const(name_ + "_beta");
@@ -61,16 +61,16 @@ int InstanceNormNPUKernel::SetNPUInputs(const std::vector<lite::Tensor *> &input
     MS_LOG(ERROR) << "New beta const failed.";
     return RET_ERROR;
   }
-  auto beta_shape = inputs[1]->shape();
+  auto beta_shape = inputs[2]->shape();
   std::shared_ptr<ge::Tensor> beta_tensor = std::shared_ptr<ge::Tensor>(new (std::nothrow) ge::Tensor());
   if (beta_tensor == nullptr) {
     MS_LOG(ERROR) << "new beta_tensor failed.";
     return RET_ERROR;
   }
   ge::TensorDesc beta_tensor_desc(lite::ConverterToNPUShape({1, beta_shape[0], 1, 1}), ge::FORMAT_NCHW,
-                                  lite::ConverterToNPUDataType(inputs[1]->data_type()));
+                                  lite::ConverterToNPUDataType(inputs[2]->data_type()));
   beta_tensor->SetTensorDesc(beta_tensor_desc);
-  beta_tensor->SetData(reinterpret_cast<const uint8_t *>(inputs.data()), inputs[1]->Size());
+  beta_tensor->SetData(reinterpret_cast<const uint8_t *>(inputs[2]->data_c()), inputs[2]->Size());
   op_->set_input_beta(*beta);
   op_->set_attr_epsilon(instance_norm_param_->epsilon_);
   return RET_OK;

mindspore/lite/src/runtime/kernel/npu/scale_npu.cc

@@ -24,6 +24,13 @@ using mindspore::schema::PrimitiveType_Scale;
 namespace mindspore::kernel {
 int ScaleNPUKernel::IsSupport(const std::vector<lite::Tensor *> &inputs, const std::vector<lite::Tensor *> &outputs,
                               OpParameter *opParameter) {
+  if (scale_parameter_->axis_ < 0) {
+    scale_parameter_->axis_ = scale_parameter_->axis_ + inputs.size();
+  }
+  if (scale_parameter_->axis_ != 1) {
+    MS_LOG(ERROR) << "Npu scale axis attr only support 1, now is " << scale_parameter_->axis_;
+    return RET_ERROR;
+  }
   return RET_OK;
 }