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mindspore/mindspore/ccsrc/device/ascend/kernel_select_ascend.cc

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/**
* Copyright 2019 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 "device/ascend/kernel_select_ascend.h"
#include <string>
#include <vector>
#include <memory>
#include <utility>
#include <map>
#include "kernel/oplib/oplib.h"
#include "kernel/kernel_query.h"
#include "session/anf_runtime_algorithm.h"
#include "kernel/kernel_build_info.h"
#include "utils/context/ms_context.h"
#include "operator/ops.h"
#include "debug/anf_ir_dump.h"
namespace mindspore {
namespace device {
namespace ascend {
namespace {
const float kWegihtBaseScore = 1;
const float kFeatureMapBaseScore = 10;
enum MatchCountPriority : int {
MATCH_COUNT_PRIORITY_BEGIN = 0,
MATCH_DTYPE_COUNT = MATCH_COUNT_PRIORITY_BEGIN,
MATCH_FORMAT_COUNT,
MATCH_SPECIAL_FORMAT_COUNT,
MATCH_OUTPUT_DTYPE_COUNT,
MATCH_COUNT_PRIORITY_END
};
const size_t kMaxCount = 0xffffffff;
const int kUnSupportMixedDataTypeIndex = -1;
bool MatchInferOutputDataType(const CNodePtr &cnode, const kernel::KernelBuildInfo &kernel_build_info) {
MS_EXCEPTION_IF_NULL(cnode);
// Check input data type
for (size_t input_index = 0; input_index < kernel_build_info.GetInputNum(); ++input_index) {
TypeId input_origin_type = AnfAlgo::GetPrevNodeOutputInferDataType(cnode, input_index);
if (kernel_build_info.GetInputDeviceType(input_index) != input_origin_type) {
return false;
}
}
// Check output data type
for (size_t output_index = 0; output_index < kernel_build_info.GetOutputNum(); ++output_index) {
if (kernel_build_info.GetOutputDeviceType(output_index) != AnfAlgo::GetOutputInferDataType(cnode, output_index)) {
return false;
}
}
return true;
}
string GetPriorityMatchFormat(const CNodePtr &cnode) {
string priority_matched_format = kOpFormat_NC1HWC0;
bool is_init = false;
bool need_change_nd = false;
for (size_t index = 0; index < AnfAlgo::GetInputTensorNum(cnode); ++index) {
auto pre_output_format = AnfAlgo::GetPrevNodeOutputFormat(cnode, index);
if (AnfAlgo::IsFeatureMapInput(cnode, index) &&
kNeedTransFormatSet.find(pre_output_format) != kNeedTransFormatSet.end()) {
priority_matched_format = !is_init ? priority_matched_format : pre_output_format;
is_init = true;
}
// feature map has two or more special format;
if (priority_matched_format != pre_output_format && pre_output_format != kOpFormat_DEFAULT) {
priority_matched_format = kOpFormat_DEFAULT;
}
auto input_shape_size = AnfAlgo::GetPrevNodeOutputInferShape(cnode, index).size();
need_change_nd = (need_change_nd || (input_shape_size != 4 && input_shape_size > 1));
}
if (need_change_nd) {
priority_matched_format = kOpFormat_DEFAULT;
}
return priority_matched_format;
}
/**
* compare two vector by priority, select a better vector, like compare two num, first compare highest num location,
* if equal then next num location
* example:[3,1,1,1] > [2,2,2,2] > [2,2,1,2] > [2,1,1,3]
*/
bool PriorityChooseItem(const std::vector<int> &cur_item, std::vector<int> *best_item) {
MS_EXCEPTION_IF_NULL(best_item);
if (cur_item.size() != best_item->size()) {
MS_LOG(ERROR) << "item size should be same!";
return false;
}
// Update the best_item by comparing the cur_item and best_item
for (size_t i = 0; i < cur_item.size(); i++) {
if (cur_item[i] > best_item->at(i)) {
*best_item = cur_item;
return true;
} else if (cur_item[i] == best_item->at(i)) {
continue;
} else {
return false;
}
}
return false;
}
void UpdateCurMatchCounts(const kernel::KernelBuildInfo &kernel_build_info, const std::shared_ptr<CNode> &kernel_node,
std::vector<int> *const cur_kernelinfo_match_counts) {
MS_EXCEPTION_IF_NULL(kernel_node);
MS_EXCEPTION_IF_NULL(cur_kernelinfo_match_counts);
if (cur_kernelinfo_match_counts->size() < MATCH_COUNT_PRIORITY_END) {
MS_LOG(EXCEPTION) << "Out of range cur_kernelinfo_match_counts " << MATCH_COUNT_PRIORITY_END;
}
auto pri_match_format = GetPriorityMatchFormat(kernel_node);
for (size_t input_index = 0; input_index < AnfAlgo::GetInputTensorNum(kernel_node); ++input_index) {
auto base_score = AnfAlgo::IsFeatureMapInput(kernel_node, input_index) ? kFeatureMapBaseScore : kWegihtBaseScore;
if (kernel_build_info.GetInputFormat(input_index) == AnfAlgo::GetPrevNodeOutputFormat(kernel_node, input_index)) {
(*cur_kernelinfo_match_counts)[MATCH_FORMAT_COUNT] += base_score;
}
if (kernel_build_info.GetInputDeviceType(input_index) ==
AnfAlgo::GetPrevNodeOutputDeviceDataType(kernel_node, input_index)) {
(*cur_kernelinfo_match_counts)[MATCH_DTYPE_COUNT] += base_score;
}
if (kernel_build_info.GetInputFormat(input_index) == pri_match_format) {
(*cur_kernelinfo_match_counts)[MATCH_SPECIAL_FORMAT_COUNT] += base_score;
}
}
for (size_t output_index = 0; output_index < AnfAlgo::GetOutputTensorNum(kernel_node); ++output_index) {
// cal count of same output dtype between abstract and kernel info
if (kernel_build_info.GetOutputDeviceType(output_index) ==
AnfAlgo::GetOutputInferDataType(kernel_node, output_index)) {
(*cur_kernelinfo_match_counts)[MATCH_OUTPUT_DTYPE_COUNT] += 1;
}
}
}
void SetTensorDeviceInfo(const kernel::KernelBuildInfo &selected_kernel_info, const CNodePtr &kernel_node) {
MS_EXCEPTION_IF_NULL(kernel_node);
for (size_t input_index = 0; input_index < AnfAlgo::GetInputTensorNum(kernel_node); ++input_index) {
auto input_kernel_node = AnfAlgo::GetInputNode(kernel_node, input_index);
MS_EXCEPTION_IF_NULL(input_kernel_node);
auto input_with_index = AnfAlgo::VisitKernel(input_kernel_node, 0);
MS_EXCEPTION_IF_NULL(input_with_index.first);
auto real_input_node = input_with_index.first;
if (real_input_node->isa<CNode>()) {
continue;
}
if (real_input_node->isa<Parameter>() && !AnfAlgo::IsParameterWeight(real_input_node->cast<ParameterPtr>())) {
continue;
}
std::shared_ptr<kernel::KernelBuildInfo::KernelBuildInfoBuilder> builder =
std::make_shared<kernel::KernelBuildInfo::KernelBuildInfoBuilder>();
// we set special device info of a input tensor.
bool is_ref = false;
auto op_info = mindspore::kernel::OpLib::FindOp(AnfAlgo::GetCNodeName(kernel_node), kernel::kTBE);
if (op_info != nullptr) {
is_ref = op_info->is_ref();
}
MS_EXCEPTION_IF_NULL(MsContext::GetInstance());
if (MsContext::GetInstance()->execution_mode() == kPynativeMode &&
AnfAlgo::GetOutputDeviceDataType(real_input_node, 0) != kTypeUnknown) {
continue;
}
if (AnfAlgo::GetOutputDeviceDataType(real_input_node, 0) == kTypeUnknown || is_ref) {
std::vector<std::string> output_format = {selected_kernel_info.GetInputFormat(input_index)};
builder->SetOutputsFormat(output_format);
std::vector<TypeId> output_type = {selected_kernel_info.GetInputDeviceType(input_index)};
builder->SetOutputsDeviceType(output_type);
AnfAlgo::SetSelectKernelBuildInfo(builder->Build(), real_input_node.get());
}
}
}
void AddSupportMixedPrecisionDataTypeIndex(TypeId data_type, std::vector<int> *support_index) {
MS_EXCEPTION_IF_NULL(support_index);
int index = kUnSupportMixedDataTypeIndex;
switch (data_type) {
case kNumberTypeFloat16:
index = 0;
break;
case kNumberTypeFloat32:
case kNumberTypeFloat:
index = 1;
break;
default:
break;
}
support_index->push_back(index);
}
void AddKernelInputSupportDataType(const kernel::KernelBuildInfo &kernel_build_info, size_t input_index,
std::vector<int> *support_datatype_index, std::vector<TypeId> *support_datatype) {
MS_EXCEPTION_IF_NULL(support_datatype);
auto data_type = kernel_build_info.GetInputDeviceType(input_index);
support_datatype->push_back(data_type);
AddSupportMixedPrecisionDataTypeIndex(data_type, support_datatype_index);
}
void AddKernelOutputSupportDataType(const kernel::KernelBuildInfo &kernel_build_info, size_t output_index,
std::vector<int> *support_datatype_index, std::vector<TypeId> *support_datatype) {
MS_EXCEPTION_IF_NULL(support_datatype);
auto data_type = kernel_build_info.GetOutputDeviceType(output_index);
support_datatype->push_back(data_type);
AddSupportMixedPrecisionDataTypeIndex(data_type, support_datatype_index);
}
void AddNodeInputDataType(const CNodePtr &kernel_node, size_t input_index,
std::vector<int> *node_mix_precision_datatype_index,
std::vector<TypeId> *node_mix_precision_datatype) {
AnfNodePtr cur_input = AnfAlgo::GetInputNode(kernel_node, input_index);
MS_EXCEPTION_IF_NULL(cur_input);
TypeId input_origin_type = AnfAlgo::GetPrevNodeOutputInferDataType(kernel_node, input_index);
AddSupportMixedPrecisionDataTypeIndex(input_origin_type, node_mix_precision_datatype_index);
node_mix_precision_datatype->push_back(input_origin_type);
}
void AddNodeOutputDataType(const CNodePtr &kernel_node, size_t output_index,
std::vector<int> *node_mix_precision_datatype_index,
std::vector<TypeId> *node_mix_precision_datatype) {
auto output_origin_type = AnfAlgo::GetOutputInferDataType(kernel_node, output_index);
AddSupportMixedPrecisionDataTypeIndex(output_origin_type, node_mix_precision_datatype_index);
node_mix_precision_datatype->push_back(output_origin_type);
}
void CheckDataTypeInputs(const std::vector<int> &node_mix_precision_datatype_index,
const std::vector<TypeId> &node_mix_precision_datatype,
const std::map<size_t, std::vector<TypeId>> &kernel_support_datatypes,
std::map<size_t, std::vector<int>> *kernel_match_datatype_idx) {
if (node_mix_precision_datatype_index.size() != node_mix_precision_datatype.size()) {
MS_LOG(EXCEPTION) << "node datatype index size " << node_mix_precision_datatype_index.size() << " != datatype size "
<< node_mix_precision_datatype.size();
}
MS_EXCEPTION_IF_NULL(kernel_match_datatype_idx);
if (kernel_support_datatypes.size() != kernel_match_datatype_idx->size()) {
MS_LOG(EXCEPTION) << "kernel datatype index size " << kernel_match_datatype_idx->size() << " != datatype size "
<< kernel_support_datatypes.size();
}
}
bool RaiseDataTypePrecisionSelect(const std::vector<int> &node_mix_precision_datatype_index,
const std::vector<TypeId> &node_mix_precision_datatype,
const std::map<size_t, std::vector<TypeId>> &kernel_support_datatypes,
std::map<size_t, std::vector<int>> *kernel_match_datatype_idx) {
MS_EXCEPTION_IF_NULL(kernel_match_datatype_idx);
CheckDataTypeInputs(node_mix_precision_datatype_index, node_mix_precision_datatype, kernel_support_datatypes,
kernel_match_datatype_idx);
for (size_t i = 0; i < node_mix_precision_datatype_index.size(); ++i) {
if (node_mix_precision_datatype[i] == kTypeUnknown) {
continue;
}
auto iter = kernel_match_datatype_idx->begin();
while (iter != kernel_match_datatype_idx->end()) {
if (node_mix_precision_datatype_index[i] == kUnSupportMixedDataTypeIndex) {
auto find_iter = kernel_support_datatypes.find(iter->first);
if (find_iter == kernel_support_datatypes.end()) {
MS_LOG(EXCEPTION) << "kernel datatype index:%lu can not be found " << iter->first;
}
if (i >= find_iter->second.size()) {
MS_LOG(EXCEPTION) << "node index " << i << "kernel datatype size " << find_iter->second.size();
}
if (node_mix_precision_datatype[i] != find_iter->second[i]) {
iter = kernel_match_datatype_idx->erase(iter);
} else {
++iter;
}
continue;
}
auto datatype_indexes = iter->second;
if (i >= datatype_indexes.size()) {
MS_LOG(EXCEPTION) << "node datatype index: " << i << " kernel support size " << datatype_indexes.size();
}
if (datatype_indexes[i] < node_mix_precision_datatype_index[i]) {
iter = kernel_match_datatype_idx->erase(iter);
} else {
++iter;
}
}
}
return !kernel_match_datatype_idx->empty();
}
bool CanDataTypeReduce(const std::vector<int> &datatype_indexes, int check_index,
const std::vector<int> &node_mix_precision_datatype_index) {
return datatype_indexes[check_index] != kUnSupportMixedDataTypeIndex &&
datatype_indexes[check_index] <= node_mix_precision_datatype_index[check_index];
}
bool RaiseOrReduceDataTypePrecisionSelect(const std::vector<int> &node_mix_precision_datatype_index,
const std::vector<TypeId> &node_mix_precision_datatype,
const std::map<size_t, std::vector<TypeId>> &kernel_support_datatypes,
std::map<size_t, std::vector<int>> *kernel_match_datatype_idx) {
MS_EXCEPTION_IF_NULL(kernel_match_datatype_idx);
CheckDataTypeInputs(node_mix_precision_datatype_index, node_mix_precision_datatype, kernel_support_datatypes,
kernel_match_datatype_idx);
for (size_t i = 0; i < node_mix_precision_datatype_index.size(); ++i) {
if (node_mix_precision_datatype[i] == kTypeUnknown) {
continue;
}
auto iter = kernel_match_datatype_idx->begin();
while (iter != kernel_match_datatype_idx->end()) {
if (node_mix_precision_datatype_index[i] == kUnSupportMixedDataTypeIndex) {
auto find_iter = kernel_support_datatypes.find(iter->first);
if (find_iter == kernel_support_datatypes.end()) {
MS_LOG(EXCEPTION) << "kernel datatype index:%lu can not be found " << iter->first;
}
if (i >= find_iter->second.size()) {
MS_LOG(EXCEPTION) << "node index " << i << " >= kernel datatype size " << find_iter->second.size();
}
if (node_mix_precision_datatype[i] != find_iter->second[i]) {
iter = kernel_match_datatype_idx->erase(iter);
} else {
++iter;
}
continue;
}
auto datatype_indexes = iter->second;
if (i >= datatype_indexes.size()) {
MS_LOG(EXCEPTION) << "index " << i << "> kernel datatype indexes size " << datatype_indexes.size();
}
if (!CanDataTypeReduce(datatype_indexes, i, node_mix_precision_datatype_index)) {
iter = kernel_match_datatype_idx->erase(iter);
} else {
++iter;
}
}
}
return !kernel_match_datatype_idx->empty();
}
void AddNodeAndKernelDataType(const CNodePtr &kernel_node, const kernel::KernelBuildInfo &kernel_build_info,
std::vector<int> *support_indexes, std::vector<TypeId> *node_mix_precision_datatype,
std::vector<TypeId> *support_datatypes,
std::vector<int> *node_mix_precision_datatype_index) {
MS_EXCEPTION_IF_NULL(node_mix_precision_datatype);
bool add_node_datatype_flag = false;
if (node_mix_precision_datatype->size() == 0) {
add_node_datatype_flag = true;
}
for (size_t input_index = 0; input_index < kernel_build_info.GetInputNum(); ++input_index) {
AddKernelInputSupportDataType(kernel_build_info, input_index, support_indexes, support_datatypes);
if (add_node_datatype_flag) {
AddNodeInputDataType(kernel_node, input_index, node_mix_precision_datatype_index, node_mix_precision_datatype);
}
}
// Check output data type
for (size_t output_index = 0; output_index < kernel_build_info.GetOutputNum(); ++output_index) {
AddKernelOutputSupportDataType(kernel_build_info, output_index, support_indexes, support_datatypes);
if (add_node_datatype_flag) {
AddNodeOutputDataType(kernel_node, output_index, node_mix_precision_datatype_index, node_mix_precision_datatype);
}
}
}
void PrecisionReduce(const std::vector<int> &node_mix_precision_datatype_index,
const std::vector<TypeId> &node_mix_precision_datatype,
const std::map<size_t, std::vector<TypeId>> &kernel_support_datatype,
std::map<size_t, std::vector<int>> *kernel_match_datatype_idx, bool *precision_reduce) {
MS_EXCEPTION_IF_NULL(kernel_match_datatype_idx);
auto context_ptr = MsContext::GetInstance();
MS_EXCEPTION_IF_NULL(context_ptr);
MS_EXCEPTION_IF_NULL(precision_reduce);
std::map<size_t, std::vector<int>> kernel_match_datatype_idx_copy = *kernel_match_datatype_idx;
// raise precision
bool selected_ret = RaiseDataTypePrecisionSelect(node_mix_precision_datatype_index, node_mix_precision_datatype,
kernel_support_datatype, kernel_match_datatype_idx);
if (selected_ret) {
*precision_reduce = false;
return;
}
if (context_ptr->enable_reduce_precision()) {
selected_ret = RaiseOrReduceDataTypePrecisionSelect(node_mix_precision_datatype_index, node_mix_precision_datatype,
kernel_support_datatype, &kernel_match_datatype_idx_copy);
}
if (selected_ret) {
*precision_reduce = true;
*kernel_match_datatype_idx = kernel_match_datatype_idx_copy;
}
}
void PrintRaiseOrReducePrecisionSelectedInfo(const CNodePtr &cnode,
const std::shared_ptr<kernel::KernelBuildInfo> &selected_kernel_build_info,
bool precision_reduce) {
MS_EXCEPTION_IF_NULL(selected_kernel_build_info);
MS_EXCEPTION_IF_NULL(cnode);
std::ostringstream buffer;
buffer << cnode->DebugString();
if (precision_reduce) {
buffer << " reduce precision, node datatype: ";
} else {
buffer << " raise precision, node datatype: ";
}
PrintInputAndOutputInferType(buffer, cnode);
buffer << ", select kernel:" << selected_kernel_build_info->ToString();
MS_LOG(INFO) << buffer.str();
}
std::shared_ptr<kernel::KernelBuildInfo> ChooseMatchedKernelInfo(
const CNodePtr &kernel_node, const std::vector<std::shared_ptr<kernel::KernelBuildInfo>> &kernel_info_list) {
if (kernel_info_list.empty()) {
return nullptr;
}
std::vector<int> most_match_counts = {-1, -1, -1, -1};
size_t selected_index = 0;
for (size_t info_index = 0; info_index < kernel_info_list.size(); ++info_index) {
std::vector<int> cur_kernel_info_match_counts = {0, 0, 0, 0};
auto kernel_build_info = *(kernel_info_list[info_index]);
std::shared_ptr<kernel::KernelBuildInfo> kernel_info_ptr = kernel_info_list[info_index];
UpdateCurMatchCounts(*kernel_info_ptr, kernel_node, &cur_kernel_info_match_counts);
// Currently the selection policy is the match format count first, and then is datatype counts.
if (PriorityChooseItem(cur_kernel_info_match_counts, &most_match_counts)) {
selected_index = SizeToInt(info_index);
}
}
return kernel_info_list[selected_index];
}
std::vector<std::shared_ptr<kernel::KernelBuildInfo>> GetAllMatchedFilteredKernelInfo(
const CNodePtr &cnode, const std::vector<std::shared_ptr<kernel::KernelBuildInfo>> &kernel_info_list) {
std::vector<std::shared_ptr<kernel::KernelBuildInfo>> result;
for (const auto &kernel_build_info : kernel_info_list) {
MS_EXCEPTION_IF_NULL(kernel_build_info);
if (!MatchInferOutputDataType(cnode, *kernel_build_info)) {
continue;
}
result.push_back(kernel_build_info);
}
return result;
}
std::vector<std::shared_ptr<kernel::KernelBuildInfo>> FilterRaisedOrReducePrecisionMatchedKernelInfo(
const CNodePtr &cnode, const std::vector<std::shared_ptr<kernel::KernelBuildInfo>> &kernel_info_list,
bool *precision_reduce) {
std::vector<std::shared_ptr<kernel::KernelBuildInfo>> filtered_kernel_info_list;
std::map<size_t, std::vector<int>> kernel_match_datatype_idx;
std::map<size_t, std::vector<TypeId>> kernel_support_datatype;
std::vector<int> node_mix_precision_datatype_index;
std::vector<TypeId> node_mix_precision_datatype;
for (size_t info_index = 0; info_index < kernel_info_list.size(); ++info_index) {
std::vector<int> support_indexes;
std::vector<TypeId> support_datatypes;
MS_EXCEPTION_IF_NULL(kernel_info_list[info_index]);
AddNodeAndKernelDataType(cnode, *kernel_info_list[info_index], &support_indexes, &node_mix_precision_datatype,
&support_datatypes, &node_mix_precision_datatype_index);
kernel_match_datatype_idx[info_index] = support_indexes;
kernel_support_datatype[info_index] = support_datatypes;
}
PrecisionReduce(node_mix_precision_datatype_index, node_mix_precision_datatype, kernel_support_datatype,
&kernel_match_datatype_idx, precision_reduce);
std::transform(
kernel_match_datatype_idx.begin(), kernel_match_datatype_idx.end(), std::back_inserter(filtered_kernel_info_list),
[&](const std::pair<size_t, std::vector<int>> &matched_idx) -> std::shared_ptr<kernel::KernelBuildInfo> {
return kernel_info_list[matched_idx.first];
});
return filtered_kernel_info_list;
}
} // namespace
void SelectKernelInfo(const CNodePtr &kernel_node) {
std::vector<std::shared_ptr<kernel::KernelBuildInfo>> kernel_info_list;
MS_EXCEPTION_IF_NULL(kernel_node);
bool precision_reduce = false;
std::shared_ptr<kernel::KernelBuildInfo> selected_kernel_info = nullptr;
kernel::KernelQuery(kernel_node, &kernel_info_list);
// filter kernel info matched with me infered type
auto filtered_kernel_info_list = GetAllMatchedFilteredKernelInfo(kernel_node, kernel_info_list);
if (!filtered_kernel_info_list.empty()) {
selected_kernel_info = ChooseMatchedKernelInfo(kernel_node, filtered_kernel_info_list);
} else {
// selected kernel info using raised precision or reduce precision
filtered_kernel_info_list =
FilterRaisedOrReducePrecisionMatchedKernelInfo(kernel_node, kernel_info_list, &precision_reduce);
selected_kernel_info = ChooseMatchedKernelInfo(kernel_node, filtered_kernel_info_list);
if (selected_kernel_info == nullptr) {
std::ostringstream buffer;
PrintInputAndOutputInferType(buffer, kernel_node);
MS_LOG(EXCEPTION) << "The node [" << kernel_node->DebugString()
<< "] cannot find valid kernel info, not supported the type" << buffer.str();
} else {
PrintRaiseOrReducePrecisionSelectedInfo(kernel_node, selected_kernel_info, precision_reduce);
}
}
AnfAlgo::SetSelectKernelBuildInfo(selected_kernel_info, kernel_node.get());
// Set format and data type for input tensor.
SetTensorDeviceInfo(*selected_kernel_info, kernel_node);
}
bool CheckKernelAccuracySupported(const CNodePtr &kernel_node,
const kernel::KernelBuildInfoPtr &new_kernel_build_info) {
MS_EXCEPTION_IF_NULL(kernel_node);
std::vector<std::shared_ptr<kernel::KernelBuildInfo>> kernel_info_list;
kernel::KernelQuery(kernel_node, &kernel_info_list);
auto result = std::find_if(kernel_info_list.begin(), kernel_info_list.end(),
[&new_kernel_build_info](const kernel::KernelBuildInfoPtr item) {
MS_EXCEPTION_IF_NULL(item);
return *item == *new_kernel_build_info;
});
return result != kernel_info_list.end();
}
} // namespace ascend
} // namespace device
} // namespace mindspore
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