!31449 fix code check

Merge pull request !31449 from laiyongqiang/code_check

mindspore/ccsrc/backend/common/somas/somas_solver_core.cc

@@ -37,11 +37,11 @@ Status SomasSolverCore::MemoryAllocationSolver() {
   auto start = std::chrono::system_clock::now();
   Status retval = SUCCESS;
   size_t best = SIZE_MAX;
-  size_t best_timing = SIZE_MAX;
   if (all_) {  // loop over all heuristics
     FittingType best_branching = kBest;
     SortingType best_sorting = kGreaterSizeSmallerIndex;
     AlgorithmType best_algorithm = kManyObjects;
+    int64_t best_timing = INT64_MAX;
     uint32_t best_sol = 0;
     size_t worst = 0;
     BuildBlocks();

mindspore/ccsrc/plugin/device/ascend/hal/device/ascend_memory_adapter.cc

@@ -28,6 +28,8 @@ namespace ascend {
 constexpr float kMSMemoryRatio = 0.9375;        // 15/16
 constexpr float kReservedMemoryRatio = 0.0625;  // 1/16
 constexpr float kHalfRatio = 0.5;
+// The Ascend max available device memory is 32GB.
+constexpr float kAscendMaxDeviceMemory = 32;
 
 bool AscendMemAdapter::Initialize() {
   if (initialized_) {
@@ -158,7 +160,7 @@ uint8_t *AscendMemAdapter::MallocDynamicDevMem(size_t size, const std::string &t
 
 void AscendMemAdapter::ResetDynamicMemory() { cur_dynamic_mem_offset_ = 0; }
 
-std::string AscendMemAdapter::DevMemStatistics() {
+std::string AscendMemAdapter::DevMemStatistics() const {
   std::ostringstream oss;
   oss << "\nDevice HBM memory size: " << device_hbm_total_size_ / kMBToByte << "M";
   oss << "\nMindSpore Used memory size: " << ms_used_hbm_size_ / kMBToByte << "M";
@@ -170,7 +172,7 @@ std::string AscendMemAdapter::DevMemStatistics() {
   return oss.str();
 }
 
-std::string AscendMemAdapter::DevMemDetailInfo() {
+std::string AscendMemAdapter::DevMemDetailInfo() const {
   std::ostringstream oss;
   oss << "\nMemory Detail Info:";
   oss << "\nStatic Memory Blocks:";
@@ -192,7 +194,7 @@ size_t AscendMemAdapter::GetDeviceMemSizeFromContext() {
   MS_EXCEPTION_IF_NULL(context);
   size_t size_from_context;
   auto max_device_memory = context->get_param<float>(MS_CTX_MAX_DEVICE_MEMORY);
-  if (max_device_memory != mindspore::kDefaultMaxDeviceMemory) {
+  if (max_device_memory <= kAscendMaxDeviceMemory) {
     MS_LOG(INFO) << "context max_device_memory:" << max_device_memory;
     size_from_context = FloatToSize(max_device_memory * kGBToByte);
   } else {

mindspore/ccsrc/plugin/device/ascend/hal/device/ascend_memory_adapter.h

@@ -45,8 +45,8 @@ class AscendMemAdapter {
   [[nodiscard]] uint64_t FreeDevMemSize() const { return static_mem_offset_ - max_dynamic_mem_offset_; }
   [[nodiscard]] uint64_t MaxHbmSizeForMs() const { return max_available_ms_hbm_size_; }
   [[nodiscard]] uint64_t GetMsUsedHbmSize() const { return ms_used_hbm_size_; }
-  std::string DevMemStatistics();
-  std::string DevMemDetailInfo();
+  std::string DevMemStatistics() const;
+  std::string DevMemDetailInfo() const;
 
  private:
   struct MemoryBlock {

mindspore/ccsrc/plugin/device/ascend/kernel/hccl/hcom_all_gather.cc

@@ -19,7 +19,7 @@
 
 namespace mindspore {
 namespace kernel {
-bool HcomAllGatherKernel::Launch(const std::vector<AddressPtr> &inputs, const std::vector<AddressPtr> &,
+bool HcomAllGatherKernel::Launch(const std::vector<AddressPtr> &inputs, const std::vector<AddressPtr> &workspace,
                                  const std::vector<AddressPtr> &outputs, void *stream_ptr) {
   MS_LOG(DEBUG) << "HcomAllGather launch";
   if (inputs.empty() || outputs.empty() || hccl_data_type_list_.empty()) {

mindspore/ccsrc/plugin/device/ascend/kernel/hccl/hcom_util.cc

@@ -50,7 +50,7 @@ bool HcomUtil::GetKernelOutputShape(const AnfNodePtr &anf_node, vector<vector<si
   size_t output_num = common::AnfAlgo::GetOutputTensorNum(anf_node);
   for (size_t i = 0; i < output_num; ++i) {
     std::vector<size_t> shape_i = AnfAlgo::GetOutputDeviceShape(anf_node, i);
-    hccl_kernel_output_shape_list->emplace_back(shape_i);
+    (void)hccl_kernel_output_shape_list->emplace_back(shape_i);
   }
 
   return true;

mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_json/tbe_json_utils.cc

@@ -116,7 +116,7 @@ std::vector<int64_t> TbeJsonUtils::GetOutputDeviceShapeForTbeBuild(const AnfNode
   auto format = AnfAlgo::GetOutputFormat(anf_node, real_idx);
   shape = AnfAlgo::GetOutputDeviceShapeForTbeBuild(anf_node, real_idx, format);
   if (shape.empty()) {
-    shape.emplace_back(1);
+    (void)shape.emplace_back(1);
   }
   return shape;
 }

mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc

@@ -137,13 +137,13 @@ void PrintInfo(const nlohmann::json &info, const std::string &job_name, const in
   auto message = GetJsonValue<std::string>(info, kMessage);
   if (level == 0) {
     MS_LOG(DEBUG) << "Job id:" << job_id << ", name :" << job_name << ", message:" << message;
-  } else if (level == INFO) {
+  } else if (level == static_cast<size_t>(INFO)) {
     MS_LOG(INFO) << "Job id:" << job_id << ", name :" << job_name << ", message:" << message;
-  } else if (level == WARNING) {
+  } else if (level == static_cast<size_t>(WARNING)) {
     MS_LOG(WARNING) << "Job id:" << job_id << ", name :" << job_name << ", message:" << message;
-  } else if (level == ERROR) {
+  } else if (level == static_cast<size_t>(ERROR)) {
     MS_LOG(ERROR) << "Job id:" << job_id << ", name :" << job_name << ", message:" << message;
-  } else if (level == EXCEPTION) {
+  } else if (level == static_cast<size_t>(EXCEPTION)) {
     ReportToErrorManager(message);
   }
 }
@@ -637,7 +637,7 @@ JsonNameMap TbeKernelCompileManager::GetAllSuccessFusion() {
     if (TbeUtils::SearchCache(json_name) != nullptr) {
       (void)success_fusion_ops_.emplace(scope_id, full_name);
     }
-    iter++;
+    (void)iter++;
   }
   return success_fusion_ops_;
 }

mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_select/tbe_kernel_broadcast_selecter.cc

@@ -133,7 +133,7 @@ bool TbeKernelBroadCastSelecter::IsBroadCastSupportFracZ(SupportFormat *support_
   if (HasScalarInput()) {
     for (const auto &shape : input_shapes_) {
       if (IsScalarShape(shape)) {
-        input_support_format.emplace_back(kOpFormat_DEFAULT);
+        (void)input_support_format.emplace_back(kOpFormat_DEFAULT);
       } else {
         if (!Is4DShape(shape)) {
           return false;
@@ -141,7 +141,7 @@ bool TbeKernelBroadCastSelecter::IsBroadCastSupportFracZ(SupportFormat *support_
         if (shape[kChannelN] % kAlignmented16 != 0 || shape[kChannelC] % kAlignmented16 != 0) {
           return false;
         }
-        input_support_format.emplace_back(kOpFormat_FRAC_Z);
+        (void)input_support_format.emplace_back(kOpFormat_FRAC_Z);
       }
     }
   } else {
@@ -295,8 +295,8 @@ bool TbeKernelBroadCastSelecter::IsBroadCastSupportNDC1HWC0(SupportFormat *suppo
     input_support_format.assign(input_num_, kOpFormat_NDC1HWC0);
   }
   GenOutputSupportFormat(kOpFormat_NDC1HWC0, &output_support_format);
-  support_format->input_format.emplace_back(input_support_format);
-  support_format->output_format.emplace_back(output_support_format);
+  (void)support_format->input_format.emplace_back(input_support_format);
+  (void)support_format->output_format.emplace_back(output_support_format);
   return true;
 }
 

mindspore/ccsrc/plugin/device/ascend/optimizer/buffer_fusion/stridedread_conv_stridedwrite_fusion_pass.h

@@ -29,7 +29,7 @@ namespace mindspore {
 namespace opt {
 class StridedReadConvStridedWriteFusionPass : public FusionBasePass {
  public:
-  explicit StridedReadConvStridedWriteFusionPass(FusionIdAllocatorPtr idAllocator)
+  explicit StridedReadConvStridedWriteFusionPass(const FusionIdAllocatorPtr idAllocator)
       : FusionBasePass("StridedReadConvStridedWriteFusionPass", idAllocator) {}
   ~StridedReadConvStridedWriteFusionPass() override = default;
   void MatchSingleFusionPattern(const session::KernelGraph &kernel_graph, FusedNodeRecord *candidate_fusion) override;

mindspore/ccsrc/plugin/device/ascend/optimizer/enhancer/split_inputs_for_reduce_scatter.cc

@@ -47,7 +47,8 @@ std::vector<AnfNodePtr> SplitInputsForReduceScatter::InsertSplitForInput(const F
       min_shape[0] /= static_cast<int64_t>(rank_size_t);
       max_shape[0] /= static_cast<int64_t>(rank_size_t);
       ShapeVector shape_tmp;
-      std::transform(output_node_shape.begin(), output_node_shape.end(), std::back_inserter(shape_tmp), SizeToLong);
+      (void)std::transform(output_node_shape.begin(), output_node_shape.end(), std::back_inserter(shape_tmp),
+                           SizeToLong);
       std::vector<BaseShapePtr> shapes(rank_size_t, std::make_shared<abstract::Shape>(shape_tmp, min_shape, max_shape));
       common::AnfAlgo::SetOutputTypeAndDetailShape(dtypes, shapes, split.get());
     } else {

mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fission/dynamic_rnn_grad_fission_v2.cc

@@ -290,9 +290,9 @@ AnfNodePtr DynamicRnnGradFissionV2::AddLSTMInputGradNode(const FuncGraphPtr &fun
   std::vector<TypeId> split_types;
   std::vector<int64_t> size_split;
   for (size_t i = 0; i < num_split_x; ++i) {
-    split_shapes.emplace_back(split_c_dims);
-    split_types.emplace_back(kNumberTypeFloat32);
-    size_split.emplace_back(1);
+    (void)split_shapes.emplace_back(split_c_dims);
+    (void)split_types.emplace_back(kNumberTypeFloat32);
+    (void)size_split.emplace_back(1);
   }
   // Create lstm_split_c
   auto lstm_split_c = CreateLSTMSPlitV(func_graph, origin_input7, split_shapes, split_types, size_split, num_split_x);

mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fusion/add_input_to_output.cc

@@ -51,7 +51,7 @@ void AddOutputs(const CNodePtr &cnode, const std::vector<size_t> &input_indices)
     AbstractBasePtrList origin_abstract_list = origin_abstract_tuple->elements();
     (void)std::copy(origin_abstract_list.begin(), origin_abstract_list.end(), std::back_inserter(abstract_list));
   } else {
-    abstract_list.emplace_back(origin_abstract);
+    (void)abstract_list.emplace_back(origin_abstract);
   }
 
   for (size_t i = 0; i < input_indices.size(); ++i) {

mindspore/ccsrc/plugin/device/ascend/optimizer/mindir/neighbor_exchange_v2_unify_mindir.cc

@@ -128,7 +128,6 @@ CNodePtr CreateSplitNode(const FuncGraphPtr &graph, const std::vector<AnfNodePtr
   MS_EXCEPTION_IF_NULL(num_split);
   if (split_input.empty()) {
     MS_LOG(EXCEPTION) << "The input is empty, can not create splitv node.";
-    return nullptr;
   }
   auto split_v = pass.NewCNode(split_input, graph);
   MS_EXCEPTION_IF_NULL(split_v);
@@ -623,8 +622,8 @@ CNodePtr NeighborExchangeV2UnifyMindIR::CreateMiddleConcat(
 
     ++input_num_all;
     single_shape[concat_dim] += LongToSize(last_len);
-    max_shape[concat_dim] += (is_dynamic) ? last_len : 0;
-    min_shape[concat_dim] += (is_dynamic) ? last_len : 0;
+    max_shape[concat_dim] += (is_dynamic) ? static_cast<int64_t>(last_len) : 0;
+    min_shape[concat_dim] += (is_dynamic) ? static_cast<int64_t>(last_len) : 0;
   }
 
   std::vector<TypeId> concat_output_dtype = {common::AnfAlgo::GetOutputInferDataType(all_to_all_v_outputs[0], 0)};

mindspore/ccsrc/plugin/device/ascend/optimizer/mindir/space_batch_nd_attr_update.cc

@@ -75,7 +75,7 @@ const AnfNodePtr BatchToSpaceNDAttrUpdate::Process(const FuncGraphPtr &graph, co
   }
   auto crops = common::AnfAlgo::GetNodeAttr<std::vector<std::vector<int64_t>>>(node, kAttrCrops);
   if (crops.size() == kBlockShapeDimNum) {
-    crops.emplace(crops.begin(), std::vector<int64_t>{0, 0});
+    (void)crops.emplace(crops.begin(), std::vector<int64_t>{0, 0});
     common::AnfAlgo::SetNodeAttr(kAttrCrops, MakeValue(crops), node);
   }
   return node;

mindspore/ccsrc/runtime/device/executor/dynamic_kernel.cc

@@ -122,7 +122,8 @@ void DynamicKernel::InferShapeForNopNode(AnfNodePtr *input_node) {
   nop_road.push(*input_node);
 
   auto temp_node = input_node;
-  while (true) {
+  bool loop = true;
+  while (loop) {
     auto input_node_with_idx = common::AnfAlgo::GetPrevNodeOutput(*temp_node, 0);
     auto in_node = input_node_with_idx.first;
     MS_EXCEPTION_IF_NULL(in_node);
@@ -130,7 +131,7 @@ void DynamicKernel::InferShapeForNopNode(AnfNodePtr *input_node) {
       nop_road.push(in_node);
       temp_node = &in_node;
     } else {
-      break;
+      loop = false;
     }
   }