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!31549 Clean code 

Merge pull request !31549 from tanghuikang/clean_code
This commit is contained in:
i-robot 2022-03-19 03:18:04 +00:00 committed by Gitee
parent ff7df8e00f afdc255d74
commit 7a8edeea50
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GPG Key ID: 173E9B9CA92EEF8F
10 changed files with 19 additions and 19 deletions
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2
mindspore/ccsrc/backend/common/somas/somas_solver_pre.cc
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@ -54,7 +54,7 @@ Status SomasSolverPre::AddContiguousInfoInMap(const vector<vector<size_t>> &cont
for (size_t i = 0; i < aux.size() - 1; i++) {
auto index1 = aux[i];
auto index2 = aux[i + 1];
if (CheckTensors(pTensors, index1, index2) == FAILED) {
if (CheckTensors(pTensors, SizeToUint(index1), SizeToUint(index2)) == FAILED) {
return FAILED;
}
tensors[index1]->right_ = tensors[index2];

1
mindspore/ccsrc/cxx_api/model/acl/acl_vm/acl_multi_graph_session.cc
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@ -26,7 +26,6 @@
#include "cxx_api/graph/graph_data.h"
namespace mindspore::session {
void MultiGraphAclSession::Init(uint32_t device_id) { InitExecutor(kDavinciMultiGraphInferenceDevice, device_id); }
GraphId MultiGraphAclSession::CompileGraphImpl(const AnfNodePtrList &lst, const AnfNodePtrList &outputs) {

5
mindspore/ccsrc/plugin/device/ascend/hal/device/ge_runtime/task/event_record_task.cc
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@ -38,7 +38,10 @@ EventRecordTask::EventRecordTask(const ModelContext &model_context,
event_ = event_list[event_id];
}
EventRecordTask::~EventRecordTask() {}
EventRecordTask::~EventRecordTask() {
stream_ = nullptr;
event_ = nullptr;
}
void EventRecordTask::Distribute() {
MS_LOG(INFO) << "EventRecordTask Distribute start, stream: " << stream_ << ", event: " << event_

2
mindspore/ccsrc/plugin/device/ascend/hal/device/ge_runtime/task/label_goto_task.cc
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@ -51,6 +51,8 @@ LabelGotoTask::~LabelGotoTask() {
}
index_value_ = nullptr;
}
stream_ = nullptr;
rt_model_handle_ = nullptr;
}
void LabelGotoTask::Distribute() {

10
mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_select/tbe_kernel_select.cc
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@ -562,13 +562,13 @@ void TbeKernelSelect::PrintSupportedFormat(const SupportFormat &support_format)
auto output_items = support_format.output_format.at(i);
std::string print_str = "[";
for (const auto &input : input_items) {
print_str.append(input);
print_str.append(", ");
(void)print_str.append(input);
(void)print_str.append(", ");
}
print_str.append("] -->");
(void)print_str.append("] -->");
for (const auto &output : output_items) {
print_str.append(output);
print_str.append(", ");
(void)print_str.append(output);
(void)print_str.append(", ");
}
MS_LOG(INFO) << "Support format: " << print_str;
}

4
mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_utils.cc
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@ -221,10 +221,6 @@ void TbeUtils::UpdateCache(const std::string &kernel_name) {
KernelPackPtr TbeUtils::SearchCache(const std::string &kernel_name, const bool is_akg) {
// search cache.
KernelMeta *bin_map = KernelMeta::GetInstance();
if (bin_map == nullptr) {
MS_LOG(INFO) << "kernel cache is invalid.";
return nullptr;
}
return bin_map->GetKernelPack(kernel_name, is_akg);
}

2
mindspore/ccsrc/plugin/device/ascend/optimizer/buffer_fusion/batchmatmul_fusedmuladd_fusion_pass.h
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@ -29,7 +29,7 @@ namespace mindspore {
namespace opt {
class BatchMatmulFusedMulAddFusionPass : public FusionBasePass {
public:
explicit BatchMatmulFusedMulAddFusionPass(FusionIdAllocatorPtr idAllocator)
explicit BatchMatmulFusedMulAddFusionPass(const FusionIdAllocatorPtr &idAllocator)
: FusionBasePass("BatchMatmulFusedMulAddFusionPass", idAllocator) {
PassSwitchManager::GetInstance().RegistLicPass(name(), OptPassEnum::BatchMatmulFusedMulAddFusionPass);
}

2
mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fission/bce_with_logits_loss_fission.cc
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@ -35,7 +35,7 @@ AnfNodePtr BCEWithLogitsLossFission::AddReduceNode(const FuncGraphPtr &func_grap
// Copy a new sigmoid node, shape of output is the same as input
std::vector<AnfNodePtr> new_simoid_inputs = {
NewValueNode(std::make_shared<Primitive>(prim::kPrimBCEWithLogitsLoss->name()))};
new_simoid_inputs.insert(new_simoid_inputs.end(), cnode->inputs().begin() + 1, cnode->inputs().end());
(void)new_simoid_inputs.insert(new_simoid_inputs.end(), cnode->inputs().begin() + 1, cnode->inputs().end());
CNodePtr new_cnode = NewCNode(new_simoid_inputs, func_graph);
MS_EXCEPTION_IF_NULL(new_cnode);
auto predict_input = cnode->inputs()[kIndex1];

4
mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fission/broadcastto_fission.cc
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@ -29,10 +29,10 @@ CNodePtr AddCastNode(const FuncGraphPtr &func_graph, const TypeId dst_type, cons
std::vector<AnfNodePtr> new_cast_inputs = {NewValueNode(std::make_shared<Primitive>(prim::kPrimCast->name()))};
BaseShapePtr shape;
if (fir_flag) {
new_cast_inputs.emplace_back(input_node->inputs()[kIndex1]);
(void)new_cast_inputs.emplace_back(input_node->inputs()[kIndex1]);
shape = common::AnfAlgo::GetOutputDetailShape(input_node->inputs()[kIndex1], 0);
} else {
new_cast_inputs.emplace_back(input_node);
(void)new_cast_inputs.emplace_back(input_node);
shape = common::AnfAlgo::GetOutputDetailShape(input_node, 0);
}
CNodePtr new_cast = NewCNode(new_cast_inputs, func_graph);

6
mindspore/ccsrc/plugin/device/ascend/optimizer/mindir/sparse_softmax_cross_entropy_with_logits_unify_mindir.cc
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@ -281,7 +281,7 @@ CNodePtr CreateExpandDimsPynative(const FuncGraphPtr &graph, const CNodePtr &rea
expand_dims_node->set_scope(real_div_node->scope());
std::vector<size_t> y_shape = common::AnfAlgo::GetOutputInferShape(real_div_node, 0);
y_shape.emplace_back(1);
(void)y_shape.emplace_back(1);
if (AnfUtils::IsShapeDynamic(y_shape)) {
auto min_shape = common::AnfAlgo::GetOutputMinShape(real_div_node, 0);
auto max_shape = common::AnfAlgo::GetOutputMaxShape(real_div_node, 0);
@ -552,8 +552,8 @@ const AnfNodePtr GradSparseSoftmaxCrossEntropyWithLogitsUnifyMindIR::Process(con
auto manager = graph->manager();
MS_EXCEPTION_IF_NULL(manager);
manager->Replace(sparse_softmax_node, reduce_node);
manager->Replace(mul_node, new_mul_node);
(void)manager->Replace(sparse_softmax_node, reduce_node);
(void)manager->Replace(mul_node, new_mul_node);
std::vector<AnfNodePtr> inputs = {NewValueNode(std::make_shared<Primitive>(prim::kPrimDepend->name())),
NewValueNode(MakeValue<bool>(true)), NewValueNode(MakeValue<bool>(true))};
auto new_depend = graph->NewCNode(inputs);