forked from mindspore-Ecosystem/mindspore
fix code warning
This commit is contained in:
parent
3d8c120c57
commit
37ec5c0a1f
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@ -30,7 +30,6 @@
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namespace mindspore {
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namespace mindspore {
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#define MS_API __attribute__((visibility("default")))
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#define MS_API __attribute__((visibility("default")))
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namespace inference {
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namespace inference {
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enum DataType {
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enum DataType {
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kMSI_Unknown = 0,
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kMSI_Unknown = 0,
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kMSI_Bool = 1,
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kMSI_Bool = 1,
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@ -209,7 +208,6 @@ class VectorInferTensorWrapRequest : public RequestBase {
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}
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}
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const std::vector<InferTensor> &tensor_list_;
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const std::vector<InferTensor> &tensor_list_;
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};
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};
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} // namespace inference
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} // namespace inference
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} // namespace mindspore
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} // namespace mindspore
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#endif // MINDSPORE_INCLUDE_INFER_TENSOR_H_
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#endif // MINDSPORE_INCLUDE_INFER_TENSOR_H_
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@ -25,7 +25,6 @@
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namespace mindspore {
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namespace mindspore {
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namespace inference {
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namespace inference {
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enum StatusCode { SUCCESS = 0, FAILED, INVALID_INPUTS };
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enum StatusCode { SUCCESS = 0, FAILED, INVALID_INPUTS };
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class Status {
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class Status {
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@ -57,7 +57,6 @@ CNodePtr Insert(const FuncGraphPtr &func_graph, const CNodePtr &cnode, const std
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} else {
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} else {
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new_node = kernel_graph->NewCNode(cnode);
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new_node = kernel_graph->NewCNode(cnode);
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}
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}
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} else if (op_name == kBasicLSTMCellWeightGradOpName) {
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} else if (op_name == kBasicLSTMCellWeightGradOpName) {
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std::vector<AnfNodePtr> make_tuple_inputs = {NewValueNode(prim::kPrimMakeTuple)};
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std::vector<AnfNodePtr> make_tuple_inputs = {NewValueNode(prim::kPrimMakeTuple)};
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size_t out_num = AnfAlgo::GetOutputTensorNum(cnode);
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size_t out_num = AnfAlgo::GetOutputTensorNum(cnode);
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@ -208,7 +208,6 @@ void AscendControlParser::LinkGraph(NotNull<KernelGraphPtr> kg) {
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memo.clear();
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memo.clear();
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// assign label resource
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// assign label resource
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device::ascend::AscendLabelAssign::GetInstance().AssignLabel(kg);
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device::ascend::AscendLabelAssign::GetInstance().AssignLabel(kg);
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// AttachChildGraphToReturnNode(kg, NOT_NULL(&memo));
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}
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}
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void AscendControlParser::EraseParameter(NotNull<KernelGraphPtr> root_graph,
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void AscendControlParser::EraseParameter(NotNull<KernelGraphPtr> root_graph,
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@ -68,7 +68,6 @@ void E2eDumpUtil::DumpGPUMemToFile(const std::string &file_path, const std::stri
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TensorLoader *tensor_loader = debug_services->tensor_loader();
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TensorLoader *tensor_loader = debug_services->tensor_loader();
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auto ret = tensor_loader->DumpTensorToFile(original_kernel_name, trans_flag, file_path, format, int_shapes, type,
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auto ret = tensor_loader->DumpTensorToFile(original_kernel_name, trans_flag, file_path, format, int_shapes, type,
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addr->type_id(), addr->format(), slot);
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addr->type_id(), addr->format(), slot);
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if (!ret) {
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if (!ret) {
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MS_LOG(ERROR) << "DumpTensorToFile Failed: flag:" << std::to_string(trans_flag) << ", path:" << file_path
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MS_LOG(ERROR) << "DumpTensorToFile Failed: flag:" << std::to_string(trans_flag) << ", path:" << file_path
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<< ", host_format:" << format;
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<< ", host_format:" << format;
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@ -29,7 +29,6 @@ void AscendMemoryManager::MallocDeviceMemory() {
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auto context_mem = GetDeviceMemSizeFromContext();
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auto context_mem = GetDeviceMemSizeFromContext();
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device_mem_size_ = context_mem == 0 ? kAscendDeviceMemSize : context_mem;
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device_mem_size_ = context_mem == 0 ? kAscendDeviceMemSize : context_mem;
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auto ret = rtMalloc(reinterpret_cast<void **>(&device_mem_base_), device_mem_size_, RT_MEMORY_HBM);
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auto ret = rtMalloc(reinterpret_cast<void **>(&device_mem_base_), device_mem_size_, RT_MEMORY_HBM);
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if (ret != RT_ERROR_NONE) {
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if (ret != RT_ERROR_NONE) {
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MS_EXCEPTION(DeviceProcessError) << "rtMalloc mem size[" << device_mem_size_ << "] fail, ret[" << ret << "]";
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MS_EXCEPTION(DeviceProcessError) << "rtMalloc mem size[" << device_mem_size_ << "] fail, ret[" << ret << "]";
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}
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}
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@ -541,7 +541,6 @@ void AscendStreamAssign::InsertStreamActiveForCommon(const NotNull<KernelGraphPt
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if (AnfAlgo::GetCNodeName(cur_cnode_ptr) == kStreamSwitchOpName) {
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if (AnfAlgo::GetCNodeName(cur_cnode_ptr) == kStreamSwitchOpName) {
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MS_LOG(INFO) << "Insert StreamActive op after FP StreamSwitch for stream parallel";
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MS_LOG(INFO) << "Insert StreamActive op after FP StreamSwitch for stream parallel";
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// UpdateStreamSwitch(graph_ptr, cur_cnode_ptr, &update_cnode_list);
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update_cnode_list.emplace_back(cur_cnode_ptr);
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update_cnode_list.emplace_back(cur_cnode_ptr);
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} else {
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} else {
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update_cnode_list.emplace_back(cur_cnode_ptr);
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update_cnode_list.emplace_back(cur_cnode_ptr);
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@ -1781,7 +1780,6 @@ void AscendStreamAssign::FindEventRelations(const NotNull<KernelGraphPtr> &graph
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MS_LOG(INFO) << "Event_id:" << AnfAlgo::GetNodeAttr<uint32_t>(item.first, kAttrEventId);
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MS_LOG(INFO) << "Event_id:" << AnfAlgo::GetNodeAttr<uint32_t>(item.first, kAttrEventId);
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}
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}
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}
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}
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} // namespace ascend
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} // namespace ascend
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} // namespace device
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} // namespace device
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} // namespace mindspore
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} // namespace mindspore
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@ -57,9 +57,6 @@ constexpr const char *kOpTypeOpDebug = "Opdebug";
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namespace mindspore {
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namespace mindspore {
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namespace device {
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namespace device {
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namespace ascend {
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namespace ascend {
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static void DumpKernelOutput(const CNodePtr &kernel, void *args, NotNull<aicpu::dump::Task *> task);
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static void DumpKernelInput(const CNodePtr &kernel, void *args, NotNull<aicpu::dump::Task *> task);
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static void RtLoadDumpData(const aicpu::dump::OpMappingInfo &dump_info, void **ptr);
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DataDumper::~DataDumper() {
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DataDumper::~DataDumper() {
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ReleaseDevMem(&dev_load_mem_);
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ReleaseDevMem(&dev_load_mem_);
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@ -328,7 +325,7 @@ void DataDumper::OpDebugUnregister() {
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}
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}
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}
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}
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void RtLoadDumpData(const aicpu::dump::OpMappingInfo &dump_info, void **ptr) {
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void DataDumper::RtLoadDumpData(const aicpu::dump::OpMappingInfo &dump_info, void **ptr) {
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std::string proto_str;
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std::string proto_str;
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size_t proto_size = dump_info.ByteSizeLong();
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size_t proto_size = dump_info.ByteSizeLong();
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bool ret = dump_info.SerializeToString(&proto_str);
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bool ret = dump_info.SerializeToString(&proto_str);
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@ -357,7 +354,7 @@ void RtLoadDumpData(const aicpu::dump::OpMappingInfo &dump_info, void **ptr) {
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}
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}
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}
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}
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void DumpKernelOutput(const CNodePtr &kernel, void *args, NotNull<aicpu::dump::Task *> task) {
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void DataDumper::DumpKernelOutput(const CNodePtr &kernel, void *args, NotNull<aicpu::dump::Task *> task) {
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if (!DumpJsonParser::GetInstance().OutputNeedDump()) {
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if (!DumpJsonParser::GetInstance().OutputNeedDump()) {
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MS_LOG(INFO) << "Skip dump output";
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MS_LOG(INFO) << "Skip dump output";
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return;
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return;
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@ -391,7 +388,7 @@ void DumpKernelOutput(const CNodePtr &kernel, void *args, NotNull<aicpu::dump::T
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}
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}
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}
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}
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void DumpKernelInput(const CNodePtr &kernel, void *args, NotNull<aicpu::dump::Task *> task) {
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void DataDumper::DumpKernelInput(const CNodePtr &kernel, void *args, NotNull<aicpu::dump::Task *> task) {
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if (!DumpJsonParser::GetInstance().InputNeedDump()) {
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if (!DumpJsonParser::GetInstance().InputNeedDump()) {
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MS_LOG(INFO) << "Skip dump input";
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MS_LOG(INFO) << "Skip dump input";
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return;
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return;
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@ -65,6 +65,9 @@ class DataDumper {
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void SetOpDebugMappingInfo(const NotNull<aicpu::dump::OpMappingInfo *> dump_info) const;
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void SetOpDebugMappingInfo(const NotNull<aicpu::dump::OpMappingInfo *> dump_info) const;
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void ConstructDumpTask(NotNull<const CNodePtr &> kernel, NotNull<aicpu::dump::Task *> dump_task) const;
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void ConstructDumpTask(NotNull<const CNodePtr &> kernel, NotNull<aicpu::dump::Task *> dump_task) const;
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void GetNeedDumpKernelList(NotNull<std::map<std::string, CNodePtr> *> kernel_map) const;
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void GetNeedDumpKernelList(NotNull<std::map<std::string, CNodePtr> *> kernel_map) const;
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static void DumpKernelOutput(const CNodePtr &kernel, void *args, NotNull<aicpu::dump::Task *> task);
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static void DumpKernelInput(const CNodePtr &kernel, void *args, NotNull<aicpu::dump::Task *> task);
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static void RtLoadDumpData(const aicpu::dump::OpMappingInfo &dump_info, void **ptr);
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std::function<void *()> model_handle_;
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std::function<void *()> model_handle_;
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uint32_t debug_task_id_;
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uint32_t debug_task_id_;
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@ -541,7 +541,6 @@ KernelSelectStatus SelectKernelInfo(const CNodePtr &kernel_node, KernelType kern
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kernel::KernelQuery(kernel_node, &kernel_info_list, kernel_type);
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kernel::KernelQuery(kernel_node, &kernel_info_list, kernel_type);
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auto select_status = SetMatchedKernelInfo(kernel_node, kernel_info_list);
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auto select_status = SetMatchedKernelInfo(kernel_node, kernel_info_list);
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// If aicore not find valid kernel info reloading aicpu kernel info list to find it
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// If aicore not find valid kernel info reloading aicpu kernel info list to find it
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if (select_status == kNoMatched) {
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if (select_status == kNoMatched) {
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MS_LOG(WARNING) << "The node [" << kernel_node->DebugString()
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MS_LOG(WARNING) << "The node [" << kernel_node->DebugString()
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<< "] cannot find valid TBE kernel info, try to get aicpu kernel info";
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<< "] cannot find valid TBE kernel info, try to get aicpu kernel info";
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@ -33,7 +33,7 @@ inline void *GetMPIAdapterHandle() {
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return handle;
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return handle;
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}
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}
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inline void *GetMPIAdapterFunc(const char *name) {
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void *GetMPIAdapterFunc(const char *name) {
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static void *handle = GetMPIAdapterHandle();
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static void *handle = GetMPIAdapterHandle();
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if (handle == nullptr) {
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if (handle == nullptr) {
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MS_LOG(EXCEPTION) << "Load lib " << name << " failed, make sure you have installed it!";
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MS_LOG(EXCEPTION) << "Load lib " << name << " failed, make sure you have installed it!";
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@ -28,7 +28,6 @@ DvppProcess::DvppProcess() {}
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DvppProcess::~DvppProcess() {}
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DvppProcess::~DvppProcess() {}
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static uint32_t ToEven(uint32_t num) { return (num + 1) / 2 * 2; }
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static uint32_t ToEven(uint32_t num) { return (num + 1) / 2 * 2; }
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static uint32_t ToOdd(uint32_t num) {
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static uint32_t ToOdd(uint32_t num) {
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if (num == 0) {
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if (num == 0) {
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return 1;
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return 1;
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@ -13,13 +13,13 @@
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# limitations under the License.
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# limitations under the License.
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# ============================================================================
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# ============================================================================
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import pytest
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import numpy as np
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import mindspore as ms
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import mindspore as ms
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from mindspore.nn import ReLU
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from mindspore.nn import ReLU
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from mindspore.nn import Cell
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from mindspore.nn import Cell
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from mindspore.common.tensor import Tensor
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from mindspore.common.tensor import Tensor
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from mindspore.ops import operations as P
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from mindspore.ops import operations as P
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import numpy as np
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import pytest
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@pytest.mark.level0
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@pytest.mark.level0
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@pytest.mark.platform_arm_ascend_training
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@pytest.mark.platform_arm_ascend_training
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