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!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
This commit is contained in:
mindspore-ci-bot 2021-01-28 14:19:49 +08:00 committed by Gitee
parent ca23f2bfd8 3dbf47b38e
commit c8fa7798f1
5 changed files with 46 additions and 26 deletions
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53
mindspore/lite/src/runtime/kernel/npu/arithmetic_npu.cc
View File

@ -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;
}

1
mindspore/lite/src/runtime/kernel/npu/arithmetic_npu.h
View File

@ -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;

3
mindspore/lite/src/runtime/kernel/npu/arithmetic_self_npu.cc
View File

@ -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;

8
mindspore/lite/src/runtime/kernel/npu/instance_norm_npu.cc
View File

@ -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;

7
mindspore/lite/src/runtime/kernel/npu/scale_npu.cc
View File

@ -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;
}