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!11284 [MSLITE][DEVELOP] add npu op instance_norm 

From: @yangruoqi713
Reviewed-by: @zhang_xue_tong,@zhanghaibo5
Signed-off-by: @zhang_xue_tong
This commit is contained in:
mindspore-ci-bot 2021-01-22 14:08:35 +08:00 committed by Gitee
parent 794ab0dfcf f28795fc3a
commit e54cafaa76
9 changed files with 158 additions and 14 deletions
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11
mindspore/lite/src/runtime/agent/npu/optimizer/npu_fusion_pass.cc
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@ -226,7 +226,16 @@ int NPUFusionPass::FormatFusion(kernel::LiteKernel *kernel) {
}
RemoveAndFreeKernel(trans_kernel);
}
pre_kernel->set_out_kernels(pre_insert_kernels);
auto pre_out_kernels = pre_kernel->out_kernels();
size_t index = 0;
for (; index < pre_out_kernels.size(); index++) {
if (pre_out_kernels[index] == kernel) {
pre_out_kernels.erase(pre_out_kernels.begin() + index);
break;
}
}
pre_out_kernels.insert(pre_out_kernels.begin() + index, pre_insert_kernels.begin(), pre_insert_kernels.end());
pre_kernel->set_out_kernels(pre_out_kernels);
RemoveAndFreeKernel(kernel);
return RET_OK;
}

8
mindspore/lite/src/runtime/kernel/arm/fp32/instance_norm_fp32.cc
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@ -62,10 +62,10 @@ int InstanceNormRun(void *cdata, int task_id) {
}
int InstanceNormCPUKernel::Run() {
src_data_ = reinterpret_cast<float *>(in_tensors_.at(0)->MutableData());
gamma_data_ = reinterpret_cast<float *>(in_tensors_.at(1)->MutableData());
beta_data_ = reinterpret_cast<float *>(in_tensors_.at(2)->MutableData());
dst_data_ = reinterpret_cast<float *>(out_tensors_.at(0)->MutableData());
src_data_ = reinterpret_cast<float *>(in_tensors_.at(0)->data_c());
gamma_data_ = reinterpret_cast<float *>(in_tensors_.at(1)->data_c());
beta_data_ = reinterpret_cast<float *>(in_tensors_.at(2)->data_c());
dst_data_ = reinterpret_cast<float *>(out_tensors_.at(0)->data_c());
auto ret = ParallelLaunch(this->context_->thread_pool_, InstanceNormRun, this, op_parameter_->thread_num_);
if (ret != RET_OK) {
MS_LOG(ERROR) << "InstanceNormRun error error_code[" << ret << "]";

9
mindspore/lite/src/runtime/kernel/arm/fp32/layer_norm_fp32.cc
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@ -83,10 +83,11 @@ int LayerNormRun(void *cdata, int task_id) {
}
int LayerNormCPUKernel::Run() {
src_data_ = reinterpret_cast<float *>(in_tensors_.at(0)->MutableData());
gamma_data_ = reinterpret_cast<float *>(in_tensors_.at(1)->MutableData());
beta_data_ = reinterpret_cast<float *>(in_tensors_.at(2)->MutableData());
dst_data_ = reinterpret_cast<float *>(out_tensors_.at(0)->MutableData());
src_data_ = reinterpret_cast<float *>(in_tensors_.at(0)->data_c());
gamma_data_ = reinterpret_cast<float *>(in_tensors_.at(1)->data_c());
beta_data_ = reinterpret_cast<float *>(in_tensors_.at(2)->data_c());
dst_data_ = reinterpret_cast<float *>(out_tensors_.at(0)->data_c());
auto ret = ParallelLaunch(this->context_->thread_pool_, LayerNormRun, this, op_parameter_->thread_num_);
if (ret != RET_OK) {
MS_LOG(ERROR) << "LayerNormRun error error_code[" << ret << "]";

4
mindspore/lite/src/runtime/kernel/arm/int8/layer_norm_int8.cc
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@ -123,8 +123,8 @@ int LayerNormInt8Run(void *cdata, int task_id) {
}
int LayerNormInt8CPUKernel::Run() {
src_ptr_ = reinterpret_cast<int8_t *>(in_tensors_.at(0)->MutableData());
dst_ptr_ = reinterpret_cast<int8_t *>(out_tensors_.at(0)->MutableData());
src_ptr_ = reinterpret_cast<int8_t *>(in_tensors_.at(0)->data_c());
dst_ptr_ = reinterpret_cast<int8_t *>(out_tensors_.at(0)->data_c());
auto ret = ParallelLaunch(this->context_->thread_pool_, LayerNormInt8Run, this, op_parameter_->thread_num_);
if (ret != RET_OK) {

89
mindspore/lite/src/runtime/kernel/npu/instance_norm_npu.cc Normal file
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@ -0,0 +1,89 @@
/**
* Copyright 2021 Huawei Technologies Co., Ltd
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#include "src/runtime/kernel/npu/instance_norm_npu.h"
#include <memory>
#include "src/kernel_registry.h"
#include "src/runtime/agent/npu/npu_converter_utils.h"
using mindspore::kernel::KERNEL_ARCH::kNPU;
using mindspore::lite::KernelRegistrar;
using mindspore::schema::PrimitiveType_InstanceNorm;
namespace mindspore::kernel {
int LayerNormNPUKernel::IsSupport(const std::vector<lite::Tensor *> &inputs, const std::vector<lite::Tensor *> &outputs,
OpParameter *opParameter) {
return RET_OK;
}
int LayerNormNPUKernel::SetNPUInputs(const std::vector<lite::Tensor *> &inputs,
const std::vector<lite::Tensor *> &outputs,
const std::vector<ge::Operator *> &npu_inputs) {
op_ = new (std::nothrow) hiai::op::InstanceNorm(name_);
if (op_ == nullptr) {
MS_LOG(ERROR) << "New layer norm npu operator for op " << name_ << " failed.";
return RET_ERROR;
}
op_->set_input_x(*npu_inputs[0]);
auto gamma = new (std::nothrow) hiai::op::Const(name_ + "_gamma");
if (gamma == nullptr) {
MS_LOG(ERROR) << "New gamma const failed.";
return RET_ERROR;
}
auto gamma_shape = inputs[1]->shape();
std::shared_ptr<ge::Tensor> gamma_tensor = std::shared_ptr<ge::Tensor>(new (std::nothrow) ge::Tensor());
if (gamma_tensor == nullptr) {
MS_LOG(ERROR) << "new gamma_tensor failed.";
return RET_ERROR;
}
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());
op_->set_input_gamma(*gamma);
auto beta = new (std::nothrow) hiai::op::Const(name_ + "_beta");
if (beta == nullptr) {
MS_LOG(ERROR) << "New beta const failed.";
return RET_ERROR;
}
auto beta_shape = inputs[1]->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()));
beta_tensor->SetTensorDesc(beta_tensor_desc);
beta_tensor->SetData(reinterpret_cast<const uint8_t *>(inputs.data()), inputs[1]->Size());
op_->set_input_beta(*beta);
op_->set_attr_epsilon(layer_norm_param_->epsilon_);
return RET_OK;
}
ge::Operator *mindspore::kernel::LayerNormNPUKernel::GetNPUOp() { return this->op_; }
LayerNormNPUKernel::~LayerNormNPUKernel() {
if (op_ != nullptr) {
delete op_;
op_ = nullptr;
}
}
REG_KERNEL(kNPU, kNumberTypeFloat32, PrimitiveType_InstanceNorm, NPUKernelCreator<LayerNormNPUKernel>)
} // namespace mindspore::kernel

45
mindspore/lite/src/runtime/kernel/npu/instance_norm_npu.h Normal file
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@ -0,0 +1,45 @@
/**
* Copyright 2021 Huawei Technologies Co., Ltd
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#ifndef MINDSPORE_LITE_SRC_RUNTIME_KERNEL_NPU_LAYER_NORM_NPU_H_
#define MINDSPORE_LITE_SRC_RUNTIME_KERNEL_NPU_LAYER_NORM_NPU_H_
#include <vector>
#include "nnacl/layer_norm_parameter.h"
#include "src/runtime/kernel/npu/npu_kernel.h"
#include "include/graph/op/all_ops.h"
namespace mindspore::kernel {
class LayerNormNPUKernel : public NPUKernel {
public:
LayerNormNPUKernel(OpParameter *parameter, const std::vector<lite::Tensor *> &inputs,
const std::vector<lite::Tensor *> &outputs, const lite::InnerContext *ctx,
const mindspore::lite::PrimitiveC *primitive)
: NPUKernel(parameter, inputs, outputs, ctx, primitive) {
layer_norm_param_ = reinterpret_cast<LayerNormParameter *>(parameter);
}
~LayerNormNPUKernel() override;
int IsSupport(const std::vector<lite::Tensor *> &inputs, const std::vector<lite::Tensor *> &outputs,
OpParameter *opParameter) override;
int SetNPUInputs(const std::vector<lite::Tensor *> &inputs, const std::vector<lite::Tensor *> &outputs,
const std::vector<ge::Operator *> &npu_inputs) override;
ge::Operator *GetNPUOp() override;
private:
hiai::op::InstanceNorm *op_ = nullptr;
LayerNormParameter *layer_norm_param_;
};
} // namespace mindspore::kernel
#endif // MINDSPORE_LITE_SRC_RUNTIME_KERNEL_NPU_LAYER_NORM_NPU_H_

2
mindspore/lite/src/runtime/kernel/npu/scale_npu.h
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@ -42,4 +42,4 @@ class ScaleNPUKernel : public NPUKernel {
ScaleParameter *scale_parameter_;
};
} // namespace mindspore::kernel
#endif // MINDSPORE_LITE_SRC_RUNTIME_KERNEL_NPU_Scale_NPU_H_
#endif // MINDSPORE_LITE_SRC_RUNTIME_KERNEL_NPU_SCALE_NPU_H_

2
mindspore/lite/src/runtime/kernel/npu/squeeze_npu.cc
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@ -1,5 +1,5 @@
/**
* Copyright 2020 Huawei Technologies Co., Ltd
* Copyright 2021 Huawei Technologies Co., Ltd
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.

2
mindspore/lite/src/runtime/kernel/npu/squeeze_npu.h
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@ -1,5 +1,5 @@
/**
* Copyright 2020 Huawei Technologies Co., Ltd
* Copyright 2021 Huawei Technologies Co., Ltd
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.