forked from huawei/mindspore2022
212 lines
9.0 KiB
C++
212 lines
9.0 KiB
C++
/**
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* Copyright 2019 Huawei Technologies Co., Ltd
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*
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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#include "device/gpu/kernel_info_setter.h"
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#include <string>
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#include <memory>
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#include "kernel/kernel.h"
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#include "utils/utils.h"
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#include "kernel/gpu/gpu_kernel_factory.h"
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#include "kernel/kernel_build_info.h"
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#include "session/anf_runtime_algorithm.h"
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#include "kernel/common_utils.h"
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#include "common/utils.h"
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#include "kernel/oplib/oplib.h"
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#include "kernel/oplib/opinfo.h"
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namespace mindspore {
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namespace device {
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namespace gpu {
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using AnfAlgo = mindspore::session::AnfRuntimeAlgorithm;
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using mindspore::kernel::KernelBuildInfo;
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namespace {
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bool CheckKernelInfo(const std::shared_ptr<KernelBuildInfo> &alternative_kernel_info,
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const std::shared_ptr<KernelBuildInfo> &selected_kernel_info) {
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MS_EXCEPTION_IF_NULL(selected_kernel_info);
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MS_EXCEPTION_IF_NULL(alternative_kernel_info);
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size_t selected_input_num = selected_kernel_info->GetInputNum();
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size_t alternative_input_num = alternative_kernel_info->GetInputNum();
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if (selected_input_num != alternative_input_num) {
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return false;
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}
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for (size_t i = 0; i < selected_input_num; i++) {
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if (selected_kernel_info->GetInputFormat(i) != alternative_kernel_info->GetInputFormat(i)) {
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return false;
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}
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if (selected_kernel_info->GetInputDeviceType(i) != alternative_kernel_info->GetInputDeviceType(i)) {
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return false;
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}
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}
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size_t selected_output_num = selected_kernel_info->GetOutputNum();
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size_t alternative_output_num = alternative_kernel_info->GetOutputNum();
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if (selected_output_num != alternative_output_num) {
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return false;
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}
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for (size_t i = 0; i < selected_output_num; i++) {
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if (selected_kernel_info->GetOutputFormat(i) != alternative_kernel_info->GetOutputFormat(i)) {
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return false;
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}
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if (selected_kernel_info->GetOutputDeviceType(i) != alternative_kernel_info->GetOutputDeviceType(i)) {
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return false;
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}
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}
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return true;
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}
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std::string SupportedTypeList(const CNodePtr &kernel_node) {
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std::string supported_type_lists =
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kernel::GpuKernelFactory::GetInstance().SupportedTypeList(AnfAlgo::GetCNodeName(kernel_node));
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if (!supported_type_lists.empty()) {
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return supported_type_lists;
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}
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std::vector<std::shared_ptr<KernelBuildInfo>> kernel_info_list;
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std::string op_name = AnfAlgo::GetCNodeName(kernel_node);
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auto op_info_ptr = mindspore::kernel::OpLib::FindOp(op_name, kernel::OpImplyType::kAKG);
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if (op_info_ptr == nullptr) {
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MS_LOG(EXCEPTION) << "Unsupported op [" << op_name << "]";
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}
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(void)ParseMetadata(kernel_node, op_info_ptr, kernel::Processor::CUDA, &kernel_info_list);
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for (size_t i = 0; i < kernel_info_list.size(); i++) {
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auto supported_akg_type = kernel_info_list[i]->GetAllInputDeviceTypes();
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auto supported_akg_type_out = kernel_info_list[i]->GetAllOutputDeviceTypes();
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std::string supported_akg_type_list = "in[";
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for (auto type : supported_akg_type) {
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supported_akg_type_list = supported_akg_type_list + mindspore::kernel::TypeId2String(type);
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}
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supported_type_lists = supported_type_lists + supported_akg_type_list + "], out[";
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for (auto type : supported_akg_type_out) {
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supported_akg_type_list = supported_akg_type_list + mindspore::kernel::TypeId2String(type);
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}
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supported_type_lists += "]; ";
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}
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return supported_type_lists;
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}
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bool SelectAkgKernel(const CNodePtr &kernel_node, const std::shared_ptr<KernelBuildInfo> &selected_kernel_info) {
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MS_EXCEPTION_IF_NULL(kernel_node);
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MS_EXCEPTION_IF_NULL(selected_kernel_info);
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std::vector<std::shared_ptr<KernelBuildInfo>> kernel_info_list;
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std::string op_name = AnfAlgo::GetCNodeName(kernel_node);
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auto op_info_ptr = mindspore::kernel::OpLib::FindOp(op_name, kernel::OpImplyType::kAKG);
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if (op_info_ptr == nullptr) {
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MS_LOG(ERROR) << "Not find op[" << op_name << "] in akg";
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return false;
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}
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if (!ParseMetadata(kernel_node, op_info_ptr, kernel::Processor::CUDA, &kernel_info_list)) {
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MS_LOG(EXCEPTION) << "Parsed metadata of op[" << op_name << "] failed.";
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}
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if (kernel_info_list.empty()) {
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MS_LOG(EXCEPTION) << "Akg dose not has metadata of op[" << op_name << "].";
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}
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bool match = std::any_of(kernel_info_list.begin(), kernel_info_list.end(),
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[&](const std::shared_ptr<KernelBuildInfo> &alternative_kernel_info) {
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return CheckKernelInfo(alternative_kernel_info, selected_kernel_info);
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});
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if (!match) {
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MS_LOG(ERROR) << "Not find op[" << op_name << "] in akg";
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return false;
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}
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return true;
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}
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void SetTensorDeviceInfo(const kernel::KernelBuildInfo &selected_kernel_info, const CNodePtr &kernel_node) {
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MS_EXCEPTION_IF_NULL(kernel_node);
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for (size_t input_index = 0; input_index < AnfAlgo::GetInputTensorNum(kernel_node); ++input_index) {
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auto input_kernel_node = kernel_node->input(input_index + 1);
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MS_EXCEPTION_IF_NULL(input_kernel_node);
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if (!input_kernel_node->isa<Parameter>()) {
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continue;
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}
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std::shared_ptr<kernel::KernelBuildInfo::KernelBuildInfoBuilder> builder =
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std::make_shared<kernel::KernelBuildInfo::KernelBuildInfoBuilder>();
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auto param = input_kernel_node->cast<ParameterPtr>();
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MS_EXCEPTION_IF_NULL(param);
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if (!AnfAlgo::IsParameterWeight(param)) {
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std::vector<std::string> output_format = {kOpFormat_DEFAULT};
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builder->SetOutputsFormat(output_format);
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std::vector<TypeId> output_type = {AnfAlgo::GetOutputInferDataType(input_kernel_node, 0)};
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builder->SetOutputsDeviceType(output_type);
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AnfAlgo::SetSelectKernelBuildInfo(builder->Build(), input_kernel_node.get());
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continue;
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}
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if ((AnfAlgo::GetOutputDeviceDataType(input_kernel_node, 0) == kTypeUnknown) ||
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(AnfAlgo::GetCNodeName(kernel_node) == "ApplyMomentum")) {
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std::vector<std::string> output_format = {selected_kernel_info.GetInputFormat(input_index)};
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builder->SetOutputsFormat(output_format);
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std::vector<TypeId> output_type = {selected_kernel_info.GetInputDeviceType(input_index)};
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builder->SetOutputsDeviceType(output_type);
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AnfAlgo::SetSelectKernelBuildInfo(builder->Build(), input_kernel_node.get());
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}
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}
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}
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} // namespace
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void SetKernelInfo(const CNodePtr &kernel_node) {
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std::vector<std::string> inputs_format;
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std::vector<TypeId> inputs_type;
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std::shared_ptr<KernelBuildInfo::KernelBuildInfoBuilder> builder =
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std::make_shared<KernelBuildInfo::KernelBuildInfoBuilder>();
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for (size_t input_index = 0; input_index < AnfAlgo::GetInputTensorNum(kernel_node); ++input_index) {
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inputs_format.emplace_back(kOpFormat_DEFAULT);
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inputs_type.push_back(AnfAlgo::GetPrevNodeOutputInferDataType(kernel_node, input_index));
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}
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builder->SetInputsFormat(inputs_format);
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builder->SetInputsDeviceType(inputs_type);
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std::vector<std::string> outputs_format;
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std::vector<TypeId> outputs_type;
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for (size_t output_index = 0; output_index < AnfAlgo::GetOutputTensorNum(kernel_node); ++output_index) {
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outputs_format.emplace_back(kOpFormat_DEFAULT);
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outputs_type.push_back(AnfAlgo::GetOutputInferDataType(kernel_node, output_index));
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}
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builder->SetOutputsFormat(outputs_format);
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builder->SetOutputsDeviceType(outputs_type);
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bool result =
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kernel::GpuKernelFactory::GetInstance().SearchRegistered(AnfAlgo::GetCNodeName(kernel_node), builder->Build());
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KernelType kernel_type = UNKNOWN_KERNEL_TYPE;
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if (!result) {
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result = SelectAkgKernel(kernel_node, builder->Build());
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kernel_type = AKG_KERNEL;
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}
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if (!result) {
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auto kernel_name = AnfAlgo::GetCNodeName(kernel_node);
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std::string build_type = "in [";
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std::for_each(std::begin(inputs_type), std::end(inputs_type),
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[&build_type](auto i) { build_type += mindspore::kernel::TypeId2String(i) + " "; });
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build_type += "] out [";
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std::for_each(std::begin(outputs_type), std::end(outputs_type),
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[&build_type](auto i) { build_type += mindspore::kernel::TypeId2String(i) + " "; });
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build_type += "]";
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auto supported_type_lists = SupportedTypeList(kernel_node);
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MS_EXCEPTION(TypeError) << "Select GPU kernel op[" << kernel_name
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<< "] fail! Incompatible data type!\nThe supported data types are " << supported_type_lists
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<< ", but get " << build_type;
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}
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builder->SetKernelType(kernel_type);
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builder->SetProcessor(kernel::Processor::CUDA);
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AnfAlgo::SetSelectKernelBuildInfo(builder->Build(), kernel_node.get());
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SetTensorDeviceInfo(*(builder->Build()), kernel_node);
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}
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} // namespace gpu
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} // namespace device
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} // namespace mindspore
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