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AuHEHHPI9ufDIHtHLeL9d$Mt2IfDHEHPADE HD$Lx MtLdHD$L`HXI9A@mHI9tdHkHtH=uDmDD"cAEuHEHPDm DDcE AuHEHHPI9u@HD$HXHtHJdHD$(dH3%(uqH8[]A\A]A^A_HEHAPDm H=t'ADcEwHt$'HԂeDcDNbCT@AWAVAUATUSH8H_@dH%(HD$(1HאH|$HkHH;-ˏHrHL$HA(Lq L9]MHL)HHHH!HIII)IK.HD$IHtIHMII9umHI9ttHkHtH=uD}D0aAEuHEHPDE DDD$aDD$E AuHEHHPI9ufDIHtHLbL9d$Mt2IfDHEHPADE HD$Lp MtLaHD$L`HXI9A@mHI9tdHkHtH=uDmDD"`AEuHEHPDm DD`E AuHEHHPI9u@HD$HXHtHJaH|$@aHD$(dH3%(uoH8[]A\A]A^A_ÐHEHAPDm H=t'ADcEoHt$'H]DcDF_CQf.fAWAVAUATUSH8H_@dH%(HD$(1HǍH|$HkHH;-HrHL$HA(Lq L9]MHL)HHHH!HIII)IK.HD$IHtIHMII9umHI9ttHkHtH=uD}D ^AEuHEHPDE DDD$]DD$E AuHEHHPI9ufDIHtH<_L9d$Mt2IfDHEHPADE HD$Lp MtL^HD$L`HXI9A@mHI9tdHkHtH=uDmDD]AEuHEHPDm DD\E AuHEHHPI9u@HD$HXHtH:^H|$0^HD$(dH3%(uoH8[]A\A]A^A_ÐHEHAPDm H=t'ADcEoHt$'H|]DcD6\CNf.fAWAVAUATUSH8H_@dH%(HD$(1HH|$HkHH;-HrHL$HA(Lq L9]MHL)HHHH!HIII)IK.HD$IHtIHMII9umHI9ttHkHtH=uD}D[AEuHEHPDE DDD$ZDD$E AuHEHHPI9ufDIHtH,\L9d$Mt2IfDHEHPADE HD$Lp MtL[HD$L`HXI9A@mHI9tdHkHtH=uDmDDZAEuHEHPDm DDYE AuHEHHPI9u@HD$HXHtH*[H|$ [HD$(dH3%(uoH8[]A\A]A^A_ÐHEHAPDm H=؊t'ADcEoHt$'Hy]DcD&YCKf.fAWAVAUATIUSHH(HLHT$HLRHHLIRMHD$HT$KD-II9LHT$>CH]IJ0LL}HD$H_RHT$HLt$MtoAoBAFL9tHHLHL$CHL$LLMt RM9HtHLLHD$>HL$IHtH~YHD$LmLuHEH([]A\A]A^A_fDHIHH9HuHL$HD$E1fUHAWAVAULPATSIHH0I}hdH%(HE1L8lxHHn1EHEEHEHLsHEHHCHEHELPHH(IFHPHDžXI~LeML{ 1IGL`I|HC(HH QHHH@HPIGH L`H`IFH(LPHPHDžXH^H8HHhH^HkZIVH(HLPHPHDžXHH HHNO free blockE19999(!free_blocks_.empty())Assert %s failedblock_space_manager.cc(block_size_ != 0UL)(!(token_blocks.empty()))vector::reserve((block) != nullptr)-,ge.exec.atomicCleanPolicyge.exec.staticMemoryPolicyge.cmChiefIpge.cmChiefPortge.cmChiefWorkerDevicege.cmWorkerIpge.cmWorkerSizege.optionNameMapstream_sync_timeoutevent_sync_timeoutge.distributed_cluster_buildge.offline_model_relationge.cluster_configge.offline_hccl_compilege.streamNumge.headStreamge.perfLevelge.encryptModege.ekFilege.certFilege.hwKeyFilege.privateKeyFilege.frameworkTypege.calibrationConfFilege.insertOpFilege.outputNodeNamege.compressFlagge.exec.precision_modege.exec.precision_mode_v2ge.exec.tuneDeviceIdsge.exec.single_opge.trainFlagge.runFlagge.enabledLocalFmkopge.TBE_plugin_pathge.streamMaxParallelNumac_parallel_enablege.outputDatatypege.opSelectImplmodege.optypelistForImplmodege.hcomParallelge.fusionTensorSizege.inputShapege.dynamicNodeTypege.autoTuneModege.socVersionge.virtual_typege.engineTypege.exec.exclude_enginesge.aicoreNumge.l1Fusionge.bufferOptimizege.enableSmallChannelge.enableCompressWeightge.enableSparseMatrixWeightge.fusionSwitchFilege.compressionOptimizeConfge.customizeDtypesop_debug_configge.saveOriginalModelge.originalModelFilege.outputMaxSizege.INPUT_NODES_SET_FP16ge.opDebugLevelge.mdl_bank_pathge.display_model_infoge.op_bank_pathge.op_bank_updatege.hcomMultiModege.performance_modege.jit_compilege.exec.modify_mixlistge.exec.op_precision_modege.exec.allow_hf32ge.exec.opWaitTimeoutge.exec.opExecuteTimeoutge.externalWeightge.deterministicge.quant_dumpableinput_formatinput_shapeinput_shape_rangeop_name_mapge.dynamicBatchSizege.dynamicImageSizege.dynamicDimsge.exec.disableReuseMemorylogge.debugDirge.op_compiler_cache_dirge.op_compiler_cache_modege.build_inner_modelge.enableGraphParallelge.graphParallelOptionPathis_input_adjust_hw_layoutis_output_adjust_hw_layoutenable_scope_fusion_passesinput_data_namescompress_weight_confge.enableSingleStreamsuccessFailed to allocate memory!Parameter invalid!Failed to call CCE API!Failed to call runtime API!Internal errorsFailed to call libc_sec API!Failed to call tee API!End of sequence!Path is invalid!Failed to find any function!Parameter unsupported!Out of memory!GEInitialize Failed.GEFinalize Failed.Session constructor Failed.Session destructor Failed.Session AddGraph Failed.Session RemoveGraph Failed.Session RunGraph Failed.GE is already initialized.Failed to initialize session.Graph ID not exist.Graph ID already exist.Graph is running.Failed to initialize graph.graph ID not exist.Graph malloc failed.Graph FREE failed.Graph param is NULL.Run graph node is null.Graph preprocess failed.Graph fusion failed.Calibration failed.OME DataInput failed.Execute graph failed.Duplicate engine.Empty sub graph info.Call SetCondition first.Prepare failed.OMG SerializeModelDef failed.OMG SaveModel failed.PreRun failed.Graph subGraph id is invalid.Graph RunGraph failed.Graph get graph node failed.Graph do not init.input graph is nullaccessing an empty partitionassign engine failedParse out node failed.Empty string name.Empty variable-tensor table.Variable already exist.Variable does not exist.No output desc info.Graph is not compiled.Graph SaveModel failed.Failed to run variable pass.Failed to initialize engine.Engine finalize failed.OpsKernelInfo not exist.OpsKernelInfoStore not exist.Failed to CalcOpRunningParam.Failed to GenerateTask.Failed to OptimizeFusedGraph.Engine is not registered.device id is invalidmalloc memory failedmodel has not been loadedmodel excute failedcce create handle failedcce set stream failedcall runtime lable set failedcall runtime free failedcall runtime mem async faileddestory stream faileddestory label faileddestory model faileddestory handle faileddestory rt event failedModel file path is invalid.Model id is invalid.Queue id of model is invalid.Failed to read model file.GE AIPP is not exist.Failed to allocate P2P memorykv cache not exit!repeat request!request already complete!parameter is invalid!llm engine finalized!cluster num exceed limit!device out of memory!Prefix has already existed.Prefix does not exist.Sequence length exceed limit.No free block.Block is out of memory.Block size can't set zero[%s:%d]%lu %s: ErrorNo: %u(%s) %sAssert %s failed[%s:%d]%lu %s: ErrorNo: %u(%s) %s%s[%s:%d]%lu %s:The req[%lu] block table is emptyBlock size is 0, please check option[BlockSpaceManager][AppendSlot]The req[%lu] block table is emptyThe req[%lu] sequence length[%lu] error, old length[%lu]((seq_len_new == seq_len_old + 1))[%s:%d]%lu %s: ErrorNo: %u(%s) %sFree block is not enough. req id:%luParent req[%lu] has not allocate, please allocate first(parent_block_table != block_tables_.cend())Double allocate, child req[%lu] is already allocated(child_block_table == block_tables_.cend())Double allocate, req[%lu] is already allocated(exist_table == block_tables_.cend())[%s:%d]%lu %s:The actual_len[%lu] is bigger than max_prompt_len[%lu]ge.exec.memoryOptimizationPolicyge.shape_generalized_build_modeFailed to find any valid so file!Failed to invoke any function!Session AddGraph Failed converting protobuf GraphProto.Session RunGraph Failed converting protobuf TensorProto.GE is not yet initialized or is finalized.Multiple initializations are not supported.Finalize is not allowed before initialization.Multiple finalizations are not supported.Multiple profiling initializations are not supported.Profing initializations have not been done.Profiling command mode which is preferred is running, the api mode will not work.Session already running,not support parallel run.Can not find session with specific session id.Session manager has not been initialized.graph already running,not support parallel run.Graph FREE failed, not malloc buffer.Get computeGraph by graphNode failed.Graph which insert dynamic op failed.Graph partition success, but subGraph num is 0.Graph subGraph engine name is repeated.OME GetInputOutputDescInfo failed.Prepare Graph infershape failedRunGraph input compute graph is NULL.Graph SyncExecuteModel failed.Parse dynamic node config file failedSave model with multiple sub graphtopological sorting an partition failedunsupported feature in partitionadd placeholder end node failedOMG parse dynamic node config file failed.OMG Save Weights to Model failed.Client session options is invalid.Number of output descinfo and tensor mismatch.Graph Save Model fileNamePrefix is invalid.Failed allocating memory for model file header.Failed remove graph in node seacher.Failed add graph in node seacher.Failed set graph finish rebuild in node searcher.The node of the graph to be deleted.The node of the graph no changed.The optimize operation is suspended.Repeat operation is not allowed.Memory type HBM is necessary when engine is in deviceFailed to initialize OpsKernelInfoStore.Failed to initialize GraphOptimizer.Failed to OptimizeOriginalGraph.There is no valid so about OpsKernelInfoStore or GraphOptimizer.Failed to get graphOptimizer by name.Failed to get graphOptimizer by priority.Failed to load ge graphOptimizer.set device failed, device not readycall runtime create rtModel failedcall runtime create stream failedcall runtime bind stream to model failedcall runtime create lable failedcall runtime model load complete failedcall runtime get task id failedcall runtime kernel launch failedcall runtime kernel launchex failedcall runtime kernel fusion start failedcall runtime kernel fusion end failedcall runtime lable goto failedcall runtime lable switch failedcall runtime mem alloc managed failedcall runtime mem free managed failedcall runtime sync stream failedcall runtime model excute failedcall runtime alloc host memory failedcall runtime free host memory failedcall runtime alloc device memory failedcall runtime free device memory failedcall runtime flush cache failedunbind rtstream from rtmodel failedcall cce transfer tensor descriptor failedcall cce transfer filter descriptor failedcall cce update kernel args failedcall rutime create event failedcall rutime event record failedcall rutime stream wait event failedcall hccl hcom broadcast failedcall hccl hcom all gather failedcall hccl hcom all reduce failedcall hccl hcom reduce scatter failedGE Executor is not yet initialized.Key file path of model is invalid.Data size of model is invalid.Partition number of model is invalid.Model does not support encryption.The model is loaded repeatedly.Failed to load model partition.Failed to load weight partition.Failed to load task partition.Failed to load kernel partition.Failed to allocate feature map memory.Failed to allocate weight memory.Failed to allocate variable memory.GE Dynamic AIPP is not support to query temporarily.Graph manager initialize failed.Graph manager add graph failed.Graph manager build graph failed.Graph manager finalize failed.Graph manager save model failed.request wait to be processed timeout!decoder cluster is no link with prompt!decoder cluster is already linked with prompt cluster!decoder cluster link with prompt cluster failed!decoder cluster unlink with prompt cluster failed!decoder cluster notify prompt cluster do unlink failed!link is current processing, try again later!BlockAllocator make shared fail((block_allocator_) != nullptr)((block_allocator_->Initialize()) == ge::SUCCESS)[%s:%d]%lu %s: ErrorNo: %u(%s) %s[BlockAllocator][Free]The block ptr is null[%s:%d]%lu %s: ErrorNo: %u(%s) %s[BlockAllocator][Free]Double free, the block is already freevector::_M_fill_insertbasic_string::_S_construct null not validForkAppendSlotCanAppendSlotInitAllocateInitFreeAllocateSt11_Mutex_baseILN9__gnu_cxx12_Lock_policyE2EESt16_Sp_counted_baseILN9__gnu_cxx12_Lock_policyE2EESt19_Sp_make_shared_tagSt23_Sp_counted_ptr_inplaceIN3llm10TokenBlockESaIS1_ELN9__gnu_cxx12_Lock_policyE2EESt15_Sp_counted_ptrIPN3llm14BlockAllocatorELN9__gnu_cxx12_Lock_policyE2EEcache_engine.cccache_idge.DDK_versionge.feFlagop_debug_optionge.op_dependency_in_omCaffeMindSporeTensorFlowAndroid_NNOnnxAI_COREMIX_AICMIX_AIVAI_CPUDSAWRITE_BACKTASK_TYPE_INVALIDFFTS_PLUSVectorEngineops_kernel_info_hcclDNN_VM_RTS_OP_STOREDNN_VM_HOST_CPU_OP_STOREDNN_VM_GE_LOCAL_OP_STOREaicpu_ascend_kernelaicpu_tf_kernelAIcoreEnginedvpp_ops_kernelDSAEngineDynamicAtomicAddrClean_ge_attr_lowering_funcffts_ai_core_lower_funcffts_graph_lower_funcffts_static_graph_lower_funcffts_mix_l2_lower_func_ge_attr_calculate_funcffts_mix_l2_calc_funcinput_tensor_indexsoutput_tensor_indexsstaticdynamic_atomicBalanceModeMemoryPrioritySPMDge.evaluateGraphResourceModege.resourceConfigPathge.recomputege.topoSortingModege.graphSliceModeNPUHOST_CPUUDFPS[cache_id:%ld][Deallocate] failed, cache_id not found[%s:%d]%lu %s: ErrorNo: %u(%s) %s[cache_id:%ld][Deallocate] failed, cache_id not found[%s:%d]%lu %s: ErrorNo: %u(%s) %s[cache_id:%ld][Deallocate] failed[%s:%d]%lu %s:[cache_id:%ld][Deallocate] success[%s:%d]%lu %s: ErrorNo: %u(%s) %s[RemoveCacheKey] failed, cache_key = (%lu, %lu)[%s:%d]%lu %s:[Remove][Index] success, cache_key = (%lu, %lu)[cache_id:%ld][Pull] failed, cache_id not found[%s:%d]%lu %s: ErrorNo: %u(%s) %s[cache_id:%ld][Pull] failed, cache_id not found[cache_id:%ld][Pull] failed, dst_batch_index (%d) out of range [0, %u)[%s:%d]%lu %s: ErrorNo: %u(%s) %s[cache_id:%ld][Pull] failed, dst_batch_index (%d) out of range [0, %u)[%s:%d]%lu %s: ErrorNo: %u(%s) %s[cache_id:%ld][Pull] failed[%s:%d]%lu %s:[Pull][Cache] success, cache_id = %ld[Copy][%ld->%ld] failed, src_cache_id not found[%s:%d]%lu %s: ErrorNo: %u(%s) %s[Copy][%ld->%ld] failed, src_cache_id not found[Copy][%ld->%ld] failed, dst_cache_id not found[%s:%d]%lu %s: ErrorNo: %u(%s) %s[Copy][%ld->%ld] failed, dst_cache_id not found[Copy][%ld->%ld] failed, src_batch_index (%d) out of range [0, %u)[%s:%d]%lu %s: ErrorNo: %u(%s) %s[Copy][%ld->%ld] failed, src_batch_index (%d) out of range [0, %u)[Copy][%ld->%ld] failed, dst_batch_index (%d) out of range [0, %u)[%s:%d]%lu %s: ErrorNo: %u(%s) %s[Copy][%ld->%ld] failed, dst_batch_index (%d) out of range [0, %u)[Copy][%ld->%ld] failed, num_tensors mismatches, src_tensor_num = %u, dst_tensor_num = %u[%s:%d]%lu %s: ErrorNo: %u(%s) %s[Copy][%ld->%ld] failed, num_tensors mismatches, src_tensor_num = %u, dst_tensor_num = %u[%s:%d]%lu %s: ErrorNo: %u(%s) %s[%s:%d]%lu %s:[Copy][%ld->%ld] success, dst_batch_index = %ldcache not exist, cache_id = %ld[%s:%d]%lu %s: ErrorNo: %u(%s) %scache not exist, cache_id = %ld[cache_id:%ld][GetTensor] failed, tensor_index (%d) out of range [0, %u)[%s:%d]%lu %s: ErrorNo: %u(%s) %s[cache_id:%ld][GetTensor] failed, tensor_index (%d) out of range [0, %u)[%s:%d]%lu %s: ErrorNo: %u(%s) %sparam[%s][%ld] is less than[%s][%ld]Invalid shape: %s, dim_num (%zu) must be in range [0, 33)[%s:%d]%lu %s: ErrorNo: %u(%s) %sInvalid shape: %s, dim_num (%zu) must be in range [0, 33)[%s:%d]%lu %s: ErrorNo: %u(%s) %s[cache_id:%ld][Allocate] failed[%s:%d]%lu %s:[cache_id:%ld][Allocate] success, num_tensors = %u, shape = %sge.experiment.quant_bias_optimize, GetCacheTensorsCopyCachePullCacheRemoveCacheKeyDeallocateAllocateflow_graph_manager.ccx.f >= y.fdo not get the %s option%s is not a int64 value.init batch size failed(count > 0)(pipeline_stage_num_ > 1)ge.exec.rankId[%s:%d]%lu %s:option %s = %uinvalid output indices: %s_index__sub_graph_.omDSAGenBitMaskDSARandomNormalDSARandomTruncatedNormalDSARandomUniformcannot use push_back() with loc != nullptrcomm_groupgroup_namegroup_rank_listInvalid HcomClusterConfig: %srank_table%s must be array.%s is invalid.[%s:%d]%lu %s:om path is:%sflow_model size should be %zu(om_list.size() == 1U)parser nn io info failedmap::at./invoke_decoder_func_.jsonm_type != value_t::object || m_value.object != nullptrm_type != value_t::array || m_value.array != nullptrm_type != value_t::string || m_value.string != nullptrm_type != value_t::binary || m_value.binary != nullptr[%s:%d]%lu %s:Kv cache shape size:%lddo not get the %s or %s option[%s:%d]%lu %s: ErrorNo: %u(%s) %sdo not get the %s or %s option[%s:%d]%lu %s: ErrorNo: %u(%s) %sdo not get the %s option[%s:%d]%lu %s: ErrorNo: %u(%s) %s%s is not a int64 value.[%s:%d]%lu %s:Model input_num:%u, output_num:%u, kv cache num:%zu[DecoderManager] InitBatchDimIndex failed.[%s:%d]%lu %s: ErrorNo: %u(%s) %s[DecoderManager] InitBatchDimIndex failed.[%s:%d]%lu %s: ErrorNo: %u(%s) %sinit batch size failed[%s:%d]%lu %s:%s not set or empty, single stage model[%s:%d]%lu %s:option %s was set, value = %soption %s is invalid, item_value (%s) should be number, option value = %s[%s:%d]%lu %s: ErrorNo: %u(%s) %soption %s is invalid, item_value (%s) should be number, option value = %soption %s is invalid, item_value(%s) should > 0, option value = %soption %s is invalid, add overflow, value = %s((!ge::AddOverflow(total_count, count, total_count)))option %s is invalid, should be at least 2 items, value = %soption %s is invalid, sum of items (%ld) should be equal to %zu, value = %s(static_cast(total_count) == inputs_refdata_dtypes_.size())[%s:%d]%lu %s:pipeline stage num = %zu, kv num each stage = %s[%s:%d]%lu %s: ErrorNo: %u(%s) %sFailed to parse rank id: %srank_id (%zu) out of range [0, %zu)[%s:%d]%lu %s: ErrorNo: %u(%s) %srank_id (%zu) out of range [0, %zu)[%s:%d]%lu %s:rank_id = %zu, will run in SPMD mode, logical_device_id = %s[%s:%d]%lu %s:cache_engine_mode = %d, %s = [%s], device_indices = %spostprocess input number (%u) < inference output num (%u)[%s:%d]%lu %s: ErrorNo: %u(%s) %spostprocess input number (%u) < inference output num (%u)option of Llm Engine initialize must contain %s.[%s:%d]%lu %s: ErrorNo: %u(%s) %soption of Llm Engine initialize must contain %s.Invalid option value, option = %s, value = [%s], which is not a int64 value.[%s:%d]%lu %s: ErrorNo: %u(%s) %sInvalid option value, option = %s, value = [%s], which is not a int64 value.[%s:%d]%lu %s:Option %s = [%s]invalid type_and_index: [%s], should be ${model_type}:${index}, output mapping: [%s][%s:%d]%lu %s: ErrorNo: %u(%s) %sinvalid type_and_index: [%s], should be ${model_type}:${index}, output mapping: [%s]invalid output mapping: %s, only support postprocess model[%s:%d]%lu %s: ErrorNo: %u(%s) %sinvalid output mapping: %s, only support postprocess model[%s:%d]%lu %s: ErrorNo: %u(%s) %sinvalid output indices: %spp out index (%ld) out of range [0, %u), output mapping: %s[%s:%d]%lu %s: ErrorNo: %u(%s) %spp out index (%ld) out of range [0, %u), output mapping: %sOption %s contains duplicated indices %ld, output mapping: %s[%s:%d]%lu %s: ErrorNo: %u(%s) %sOption %s contains duplicated indices %ld, output mapping: %s[%s:%d]%lu %s:Option %s not set, will fetch all outputs[%s:%d]%lu %s:out_num:%ld, postprocess_model_output_indices = %sparser post process model inputs info failed[%s:%d]%lu %s: ErrorNo: %u(%s) %sparser post process model inputs info failedparser post process model outputs info failed[%s:%d]%lu %s: ErrorNo: %u(%s) %sparser post process model outputs info failedparser inference model io info failed[%s:%d]%lu %s: ErrorNo: %u(%s) %sparser inference model io info failed[%s:%d]%lu %s: ErrorNo: %u(%s) %sparse pipeline stage option failedparser post process model io info failed[%s:%d]%lu %s: ErrorNo: %u(%s) %sparser post process model io info failed[%s:%d]%lu %s:Udf func:%s already added[%s:%d]%lu %s:Begin to create model execute input flow data, prefix = %s, index:%zuoptions of llm engine initialize must contain %s.[%s:%d]%lu %s: ErrorNo: %u(%s) %soptions of llm engine initialize must contain %s.[%s:%d]%lu %s:postprocess om path is %s.Option %s contains empty element: %s[%s:%d]%lu %s: ErrorNo: %u(%s) %sOption %s contains empty element: %s[%s:%d]%lu %s: ErrorNo: %u(%s) %sload flow model from model buff data failed.[%s:%d]%lu %s: ErrorNo: %u(%s) %sload flow model from om failed.[%s:%d]%lu %s:%u flow_model_om_path:%s, om_name:%s.[%s:%d]%lu %s: ErrorNo: %u(%s) %sSaveToOmModel failed, flow_model:%s.[%s:%d]%lu %s: ErrorNo: %u(%s) %sin/out num between functions and invoke_json setting is different! fun_in_num:%u, fun_out_num:%u, input_num:%u, output_num:%u[%s:%d]%lu %s:json data already write to %s success.[%s:%d]%lu %s: ErrorNo: %u(%s) %scan not open file:%s to write json data.[%s:%d]%lu %s:Option %s was set, value = %s[%s:%d]%lu %s:group name = [%s], rank_id_list = %s[%s:%d]%lu %s: ErrorNo: %u(%s) %sInvalid HcomClusterConfig: %s[%s:%d]%lu %s:set rank table = %s[%s:%d]%lu %s:SetHcomClusterDescByOption success[%s:%d]%lu %s: ErrorNo: %u(%s) %sInvalid json string: %s, exception:%scannot use emplace_back() with [%s:%d]%lu %s: ErrorNo: %u(%s) %scan not open %s to write json data.[%s:%d]%lu %s:[GetDataFlowDeployInfoPath] write json data to file:%s[%s:%d]%lu %s: ErrorNo: %u(%s) %swrite json data to %s failed.[%s:%d]%lu %s: ErrorNo: %u(%s) %s[GetDataFlowDeployInfoPath] failed, flow_graph: %s.[%s:%d]%lu %s: ErrorNo: %u(%s) %s[AddForFlowGraph][AddGraph] failed, flow_graph: %s.[%s:%d]%lu %s: ErrorNo: %u(%s) %s[PromptManager] BuildForFlowGraph failed.get LLM_OPTION_CLUSTER_INFO failed, options must contain key:%s.[%s:%d]%lu %s: ErrorNo: %u(%s) %sget LLM_OPTION_CLUSTER_INFO failed, options must contain key:%s.[%s:%d]%lu %s: ErrorNo: %u(%s) %s%s must be array.logical_device_id must be string[%s:%d]%lu %s: ErrorNo: %u(%s) %slogical_device_id must be string[%s:%d]%lu %s: ErrorNo: %u(%s) %s%s is invalid.logical_device_ids num:%zu not match listen_ips_info num:%zu[%s:%d]%lu %s: ErrorNo: %u(%s) %slogical_device_ids num:%zu not match listen_ips_info num:%zu[%s:%d]%lu %s:logical device id = %s, rank id = %u.[%s:%d]%lu %s: ErrorNo: %u(%s) %sFailed to create hcom cluster desc[%s:%d]%lu %s: ErrorNo: %u(%s) %sFailed to parse device indicesmodel num:%zu not equal deployed device num:%zu(om_paths.size() == deploy_info_.GetDeployedDeviceNum())model num:%zu not om path size:%zu(model_buffer_datas.size() == om_paths.size())(model_buffer_datas.size() == device_indices.size())[%s:%d][EVENT]%lu %s:load flow_model om from %s[%s:%d]%lu %s:load flow_model om from %s%s contain empty element, please check.[%s:%d]%lu %s: ErrorNo: %u(%s) %s%s contain empty element, please check.[%s:%d]%lu %s: ErrorNo: %u(%s) %sparser om to flow model failed[%s:%d]%lu %s: ErrorNo: %u(%s) %sparser model buffer data to om failed.[%s:%d]%lu %s: ErrorNo: %u(%s) %sFailed to parse post process model options[%s:%d]%lu %s:Postprocess model is not enabledparser cluster deployed info failed[%s:%d]%lu %s: ErrorNo: %u(%s) %sparser cluster deployed info failed[%s:%d]%lu %s: ErrorNo: %u(%s) %sparser nn io info faileddeploy_device_num(%zu) cannot be evenly divided by pipeline_stage_num(%zu)[%s:%d]%lu %s: ErrorNo: %u(%s) %sdeploy_device_num(%zu) cannot be evenly divided by pipeline_stage_num(%zu)[%s:%d]%lu %s:Add node:%s to device:%s[%s:%d]%lu %s:link node:[%s:%u] to node:[%s:%u]Param:flow_graph_ is nullptr, check invalid[%s:%d]%lu %s: ErrorNo: %u(%s) %s[Check][Param:flow_graph_]null is invalidcreate func pp compile config failed[%s:%d]%lu %s: ErrorNo: %u(%s) %screate func pp compile config failed(flow_nodes.emplace(flow_node_def.name, flow_node).second)[%s:%d]%lu %s:Add node:%s to device_index=%zu, logical_device_id=%s[json.exception.] Failed to convert [%s] to number[%s:%d]%lu %s: ErrorNo: %u(%s) %sFailed to convert [%s] to numberm_object != nullptrvector::_M_default_append at line , column parse_errorparse errortype_errorinvalid_iteratorm_it.object_iterator != m_object->m_value.object->end()m_it.array_iterator != m_object->m_value.array->end()cannot get valuecannot compare iterators of different containersout_of_rangeother_errorTD8p88`n_chars < number_buffer.size() - 1true literalfalse literalnull literalstring literalnumber literal'[''{'']''}'':'','unknown tokenend of input'[', '{', or a literalsyntax error while parsing - ; last read: 'unexpected ; expected d$tP 0pobjectarraystringbooleanbinarydiscardednumbercannot use at() with key '' not foundhxhXXXH8(t$444DTdtit != m_value.object->end()cannot use operator[] with a string argument with " $$$type must be string, but is FF0123456789ABCDEFindex < 400\u%04x\u%04xinvalid UTF-8 byte at index : 0xfalse\ufffd�incomplete UTF-8 string; last byte: 0xvector::_M_insert_aux!keep_stack.empty()!key_keep_stack.empty()object_elementref_stack.back()->is_array() || ref_stack.back()->is_object()iterator does not fit current valueiterator out of rangecannot use erase() with ԖȖg!ref_stack.empty()ref_stack.back()->is_object()X(88Hpstd::isfinite(value)value > 0delta >= 0x.f != 0((x.f << delta) >> delta) == x.fm_plus.e == m_minus.em_plus.e == v.ee >= -1500e <= 1500index >= 0kAlpha <= cached.e + e + 64kGamma >= cached.e + e + 64p1 > 0d <= 9len >= 1dist <= deltaten_k > 0buf[len - 1] != '0'p2 > deltastatic_cast(index) < kCachedPowers.size()p2 <= (std::numeric_limits::max)() / 10last - first >= std::numeric_limits::max_digits10len <= std::numeric_limits::max_digits10last - first >= 2 + (-kMinExp - 1) + std::numeric_limits::max_digits10last - first >= std::numeric_limits::max_digits10 + 6k > ne > -1000e < 1000{}{ [][ !val.m_value.array->empty()"bytes": [], "subtype": {"bytes":[],"subtype":null}truei != val.m_value.object->cend()std::next(i) == val.m_value.object->cend()LD |::basic_json(const BasicJsonType&) [with BasicJsonType = ascend_nlohmann::json_abi_v3_11_2::basic_json<>; typename std::enable_if<(ascend_nlohmann::json_abi_v3_11_2::detail::is_basic_json::value && (! std::is_same, BasicJsonType>::value)), int>::type = 0; ObjectType = ascend_nlohmann::json_abi_v3_11_2::ordered_map; ArrayType = std::vector; StringType = std::basic_string; BooleanType = bool; NumberIntegerType = long int; NumberUnsignedType = long unsigned int; NumberFloatType = double; AllocatorType = std::allocator; JSONSerializer = ascend_nlohmann::json_abi_v3_11_2::adl_serializer; BinaryType = std::vector]GetDataFlowDeployInfoPathBuildFlowGraphConstructFlowGraphCreateFlowDataInfoConvertInputModelsToOmPreparePostProcessFlowModelParserModelBufferDataToFlowModelParserOmToFlowModelParserOmToFlowModelAddUdfFuncInfochar* ascend_nlohmann::json_abi_v3_11_2::detail::dtoa_impl::append_exponent(char*, int)char* ascend_nlohmann::json_abi_v3_11_2::detail::dtoa_impl::format_buffer(char*, int, int, int, int)void ascend_nlohmann::json_abi_v3_11_2::detail::dtoa_impl::grisu2_round(char*, int, uint64_t, uint64_t, uint64_t, uint64_t)static ascend_nlohmann::json_abi_v3_11_2::detail::dtoa_impl::diyfp ascend_nlohmann::json_abi_v3_11_2::detail::dtoa_impl::diyfp::sub(const ascend_nlohmann::json_abi_v3_11_2::detail::dtoa_impl::diyfp&, const ascend_nlohmann::json_abi_v3_11_2::detail::dtoa_impl::diyfp&)void ascend_nlohmann::json_abi_v3_11_2::detail::dtoa_impl::grisu2_digit_gen(char*, int&, int&, ascend_nlohmann::json_abi_v3_11_2::detail::dtoa_impl::diyfp, ascend_nlohmann::json_abi_v3_11_2::detail::dtoa_impl::diyfp, ascend_nlohmann::json_abi_v3_11_2::detail::dtoa_impl::diyfp)ascend_nlohmann::json_abi_v3_11_2::detail::dtoa_impl::cached_power ascend_nlohmann::json_abi_v3_11_2::detail::dtoa_impl::get_cached_power_for_binary_exponent(int)void ascend_nlohmann::json_abi_v3_11_2::detail::dtoa_impl::grisu2(char*, int&, int&, ascend_nlohmann::json_abi_v3_11_2::detail::dtoa_impl::diyfp, ascend_nlohmann::json_abi_v3_11_2::detail::dtoa_impl::diyfp, ascend_nlohmann::json_abi_v3_11_2::detail::dtoa_impl::diyfp)static ascend_nlohmann::json_abi_v3_11_2::detail::dtoa_impl::diyfp ascend_nlohmann::json_abi_v3_11_2::detail::dtoa_impl::diyfp::normalize(ascend_nlohmann::json_abi_v3_11_2::detail::dtoa_impl::diyfp)static ascend_nlohmann::json_abi_v3_11_2::detail::dtoa_impl::diyfp ascend_nlohmann::json_abi_v3_11_2::detail::dtoa_impl::diyfp::normalize_to(const ascend_nlohmann::json_abi_v3_11_2::detail::dtoa_impl::diyfp&, int)ascend_nlohmann::json_abi_v3_11_2::detail::dtoa_impl::boundaries ascend_nlohmann::json_abi_v3_11_2::detail::dtoa_impl::compute_boundaries(FloatType) [with FloatType = double]void ascend_nlohmann::json_abi_v3_11_2::detail::dtoa_impl::grisu2(char*, int&, int&, FloatType) [with FloatType = double]char* ascend_nlohmann::json_abi_v3_11_2::detail::to_chars(char*, const char*, FloatType) [with FloatType = double]void ascend_nlohmann::json_abi_v3_11_2::detail::serializer::dump_integer(NumberType) [with NumberType = long unsigned int; typename std::enable_if<(((std::is_integral::value || std::is_same::value) || std::is_same::value) || std::is_same::value), int>::type = 0; BasicJsonType = ascend_nlohmann::json_abi_v3_11_2::basic_json]void ascend_nlohmann::json_abi_v3_11_2::detail::serializer::dump_integer(NumberType) [with NumberType = long int; typename std::enable_if<(((std::is_integral::value || std::is_same::value) || std::is_same::value) || std::is_same::value), int>::type = 0; BasicJsonType = ascend_nlohmann::json_abi_v3_11_2::basic_json]static uint8_t ascend_nlohmann::json_abi_v3_11_2::detail::serializer::decode(uint8_t&, uint32_t&, uint8_t) [with BasicJsonType = ascend_nlohmann::json_abi_v3_11_2::basic_json; uint8_t = unsigned char; uint32_t = unsigned int]void ascend_nlohmann::json_abi_v3_11_2::detail::serializer::dump_escaped(const string_t&, bool) [with BasicJsonType = ascend_nlohmann::json_abi_v3_11_2::basic_json; ascend_nlohmann::json_abi_v3_11_2::detail::serializer::string_t = std::basic_string]void ascend_nlohmann::json_abi_v3_11_2::detail::serializer::dump(const BasicJsonType&, bool, bool, unsigned int, unsigned int) [with BasicJsonType = ascend_nlohmann::json_abi_v3_11_2::basic_json]CreateFuncPpCompileConfigParserIoInfoToNumberParsePipelineStageConfigParserInferenceModelIoInfoParsePostProcessModelOutputsParsePostProcessModelInputsGetKvCacheShapeSizeParserPostProcessModelIoInfoToNumberParseDeviceIndicesascend_nlohmann::json_abi_v3_11_2::detail::iter_impl::reference ascend_nlohmann::json_abi_v3_11_2::detail::iter_impl::operator*() const [with BasicJsonType = const ascend_nlohmann::json_abi_v3_11_2::basic_json<>; ascend_nlohmann::json_abi_v3_11_2::detail::iter_impl::reference = const ascend_nlohmann::json_abi_v3_11_2::basic_json<>&]bool ascend_nlohmann::json_abi_v3_11_2::detail::json_sax_dom_parser::end_object() [with BasicJsonType = ascend_nlohmann::json_abi_v3_11_2::basic_json<>]bool ascend_nlohmann::json_abi_v3_11_2::detail::json_sax_dom_parser::key(ascend_nlohmann::json_abi_v3_11_2::detail::json_sax_dom_parser::string_t&) [with BasicJsonType = ascend_nlohmann::json_abi_v3_11_2::basic_json<>; ascend_nlohmann::json_abi_v3_11_2::detail::json_sax_dom_parser::string_t = std::basic_string]bool ascend_nlohmann::json_abi_v3_11_2::detail::json_sax_dom_parser::end_array() [with BasicJsonType = ascend_nlohmann::json_abi_v3_11_2::basic_json<>]BasicJsonType* ascend_nlohmann::json_abi_v3_11_2::detail::json_sax_dom_parser::handle_value(Value&&) [with Value = long unsigned int&; BasicJsonType = ascend_nlohmann::json_abi_v3_11_2::basic_json<>]BasicJsonType* ascend_nlohmann::json_abi_v3_11_2::detail::json_sax_dom_parser::handle_value(Value&&) [with Value = std::basic_string&; BasicJsonType = ascend_nlohmann::json_abi_v3_11_2::basic_json<>]BasicJsonType* ascend_nlohmann::json_abi_v3_11_2::detail::json_sax_dom_parser::handle_value(Value&&) [with Value = long int&; BasicJsonType = ascend_nlohmann::json_abi_v3_11_2::basic_json<>]BasicJsonType* ascend_nlohmann::json_abi_v3_11_2::detail::json_sax_dom_parser::handle_value(Value&&) [with Value = bool&; BasicJsonType = ascend_nlohmann::json_abi_v3_11_2::basic_json<>]BasicJsonType* ascend_nlohmann::json_abi_v3_11_2::detail::json_sax_dom_parser::handle_value(Value&&) [with Value = std::nullptr_t; BasicJsonType = ascend_nlohmann::json_abi_v3_11_2::basic_json<>]BasicJsonType* ascend_nlohmann::json_abi_v3_11_2::detail::json_sax_dom_parser::handle_value(Value&&) [with Value = double&; BasicJsonType = ascend_nlohmann::json_abi_v3_11_2::basic_json<>]BasicJsonType* ascend_nlohmann::json_abi_v3_11_2::detail::json_sax_dom_parser::handle_value(Value&&) [with Value = ascend_nlohmann::json_abi_v3_11_2::detail::value_t; BasicJsonType = ascend_nlohmann::json_abi_v3_11_2::basic_json<>]bool ascend_nlohmann::json_abi_v3_11_2::detail::parser::sax_parse_internal(SAX*) [with SAX = ascend_nlohmann::json_abi_v3_11_2::detail::json_sax_dom_parser >; BasicJsonType = ascend_nlohmann::json_abi_v3_11_2::basic_json<>; InputAdapterType = ascend_nlohmann::json_abi_v3_11_2::detail::iterator_input_adapter<__gnu_cxx::__normal_iterator > >]ascend_nlohmann::json_abi_v3_11_2::detail::iter_impl::pointer ascend_nlohmann::json_abi_v3_11_2::detail::iter_impl::operator->() const [with BasicJsonType = ascend_nlohmann::json_abi_v3_11_2::basic_json<>; ascend_nlohmann::json_abi_v3_11_2::detail::iter_impl::pointer = ascend_nlohmann::json_abi_v3_11_2::basic_json<>*]bool ascend_nlohmann::json_abi_v3_11_2::detail::json_sax_dom_callback_parser::end_object() [with BasicJsonType = ascend_nlohmann::json_abi_v3_11_2::basic_json<>]bool ascend_nlohmann::json_abi_v3_11_2::detail::json_sax_dom_callback_parser::end_array() [with BasicJsonType = ascend_nlohmann::json_abi_v3_11_2::basic_json<>]std::pair ascend_nlohmann::json_abi_v3_11_2::detail::json_sax_dom_callback_parser::handle_value(Value&&, bool) [with Value = long unsigned int&; BasicJsonType = ascend_nlohmann::json_abi_v3_11_2::basic_json<>]std::pair ascend_nlohmann::json_abi_v3_11_2::detail::json_sax_dom_callback_parser::handle_value(Value&&, bool) [with Value = std::basic_string&; BasicJsonType = ascend_nlohmann::json_abi_v3_11_2::basic_json<>]std::pair ascend_nlohmann::json_abi_v3_11_2::detail::json_sax_dom_callback_parser::handle_value(Value&&, bool) [with Value = long int&; BasicJsonType = ascend_nlohmann::json_abi_v3_11_2::basic_json<>]std::pair ascend_nlohmann::json_abi_v3_11_2::detail::json_sax_dom_callback_parser::handle_value(Value&&, bool) [with Value = bool&; BasicJsonType = ascend_nlohmann::json_abi_v3_11_2::basic_json<>]std::pair ascend_nlohmann::json_abi_v3_11_2::detail::json_sax_dom_callback_parser::handle_value(Value&&, bool) [with Value = std::nullptr_t; BasicJsonType = ascend_nlohmann::json_abi_v3_11_2::basic_json<>]std::pair ascend_nlohmann::json_abi_v3_11_2::detail::json_sax_dom_callback_parser::handle_value(Value&&, bool) [with Value = double&; BasicJsonType = ascend_nlohmann::json_abi_v3_11_2::basic_json<>]std::pair ascend_nlohmann::json_abi_v3_11_2::detail::json_sax_dom_callback_parser::handle_value(Value&&, bool) [with Value = ascend_nlohmann::json_abi_v3_11_2::detail::value_t; BasicJsonType = ascend_nlohmann::json_abi_v3_11_2::basic_json<>]bool ascend_nlohmann::json_abi_v3_11_2::detail::parser::sax_parse_internal(SAX*) [with SAX = ascend_nlohmann::json_abi_v3_11_2::detail::json_sax_dom_callback_parser >; BasicJsonType = ascend_nlohmann::json_abi_v3_11_2::basic_json<>; InputAdapterType = ascend_nlohmann::json_abi_v3_11_2::detail::iterator_input_adapter<__gnu_cxx::__normal_iterator > >]ascend_nlohmann::json_abi_v3_11_2::detail::lexer::token_type ascend_nlohmann::json_abi_v3_11_2::detail::lexer::scan_number() [with BasicJsonType = ascend_nlohmann::json_abi_v3_11_2::basic_json<>; InputAdapterType = ascend_nlohmann::json_abi_v3_11_2::detail::iterator_input_adapter<__gnu_cxx::__normal_iterator > >; ascend_nlohmann::json_abi_v3_11_2::detail::lexer::token_type = ascend_nlohmann::json_abi_v3_11_2::detail::lexer_base >::token_type]bool ascend_nlohmann::json_abi_v3_11_2::detail::lexer::next_byte_in_range(std::initializer_list::int_type>) [with BasicJsonType = ascend_nlohmann::json_abi_v3_11_2::basic_json<>; InputAdapterType = ascend_nlohmann::json_abi_v3_11_2::detail::iterator_input_adapter<__gnu_cxx::__normal_iterator > >; typename std::char_traits::int_type = int]int ascend_nlohmann::json_abi_v3_11_2::detail::lexer::get_codepoint() [with BasicJsonType = ascend_nlohmann::json_abi_v3_11_2::basic_json<>; InputAdapterType = ascend_nlohmann::json_abi_v3_11_2::detail::iterator_input_adapter<__gnu_cxx::__normal_iterator > >]ascend_nlohmann::json_abi_v3_11_2::detail::lexer::token_type ascend_nlohmann::json_abi_v3_11_2::detail::lexer::scan_string() [with BasicJsonType = ascend_nlohmann::json_abi_v3_11_2::basic_json<>; InputAdapterType = ascend_nlohmann::json_abi_v3_11_2::detail::iterator_input_adapter<__gnu_cxx::__normal_iterator > >; ascend_nlohmann::json_abi_v3_11_2::detail::lexer::token_type = ascend_nlohmann::json_abi_v3_11_2::detail::lexer_base >::token_type]void ascend_nlohmann::json_abi_v3_11_2::detail::lexer::unget() [with BasicJsonType = ascend_nlohmann::json_abi_v3_11_2::basic_json<>; InputAdapterType = ascend_nlohmann::json_abi_v3_11_2::detail::iterator_input_adapter<__gnu_cxx::__normal_iterator > >]static char ascend_nlohmann::json_abi_v3_11_2::detail::lexer::get_decimal_point() [with BasicJsonType = ascend_nlohmann::json_abi_v3_11_2::basic_json<>; InputAdapterType = ascend_nlohmann::json_abi_v3_11_2::detail::iterator_input_adapter<__gnu_cxx::__normal_iterator > >]void ascend_nlohmann::json_abi_v3_11_2::basic_json::assert_invariant(bool) const [with ObjectType = std::map; ArrayType = std::vector; StringType = std::basic_string; BooleanType = bool; NumberIntegerType = long int; NumberUnsignedType = long unsigned int; NumberFloatType = double; AllocatorType = std::allocator; JSONSerializer = ascend_nlohmann::json_abi_v3_11_2::adl_serializer; BinaryType = std::vector]SetHcomClusterDescByOptionascend_nlohmann::json_abi_v3_11_2::detail::iter_impl& ascend_nlohmann::json_abi_v3_11_2::detail::iter_impl::operator++() [with BasicJsonType = const ascend_nlohmann::json_abi_v3_11_2::basic_json]const value_type& ascend_nlohmann::json_abi_v3_11_2::basic_json::operator[](const typename ascend_nlohmann::json_abi_v3_11_2::basic_json::object_t::key_type&) const [with ObjectType = ascend_nlohmann::json_abi_v3_11_2::ordered_map; ArrayType = std::vector; StringType = std::basic_string; BooleanType = bool; NumberIntegerType = long int; NumberUnsignedType = long unsigned int; NumberFloatType = double; AllocatorType = std::allocator; JSONSerializer = ascend_nlohmann::json_abi_v3_11_2::adl_serializer; BinaryType = std::vector; ascend_nlohmann::json_abi_v3_11_2::basic_json::const_reference = const ascend_nlohmann::json_abi_v3_11_2::basic_json&; ascend_nlohmann::json_abi_v3_11_2::basic_json::value_type = ascend_nlohmann::json_abi_v3_11_2::basic_json; typename ascend_nlohmann::json_abi_v3_11_2::basic_json::object_t::key_type = std::basic_string]bool ascend_nlohmann::json_abi_v3_11_2::detail::iter_impl::operator==(const IterImpl&) const [with IterImpl = ascend_nlohmann::json_abi_v3_11_2::detail::iter_impl >; typename std::enable_if<(std::is_same >::value || std::is_same::value, typename std::remove_const<_Tp>::type, const ProtoType>::type> >::value), std::nullptr_t>::type = nullptr; BasicJsonType = const ascend_nlohmann::json_abi_v3_11_2::basic_json]void ascend_nlohmann::json_abi_v3_11_2::detail::iter_impl::set_end() [with BasicJsonType = const ascend_nlohmann::json_abi_v3_11_2::basic_json]ascend_nlohmann::json_abi_v3_11_2::detail::iter_impl::reference ascend_nlohmann::json_abi_v3_11_2::detail::iter_impl::operator*() const [with BasicJsonType = const ascend_nlohmann::json_abi_v3_11_2::basic_json; ascend_nlohmann::json_abi_v3_11_2::detail::iter_impl::reference = const ascend_nlohmann::json_abi_v3_11_2::basic_json&]void ascend_nlohmann::json_abi_v3_11_2::detail::iter_impl::set_begin() [with BasicJsonType = const ascend_nlohmann::json_abi_v3_11_2::basic_json]ascend_nlohmann::json_abi_v3_11_2::detail::iter_impl::iter_impl(ascend_nlohmann::json_abi_v3_11_2::detail::iter_impl::pointer) [with BasicJsonType = const ascend_nlohmann::json_abi_v3_11_2::basic_json; ascend_nlohmann::json_abi_v3_11_2::detail::iter_impl::pointer = const ascend_nlohmann::json_abi_v3_11_2::basic_json*]bool ascend_nlohmann::json_abi_v3_11_2::detail::json_sax_dom_parser::end_object() [with BasicJsonType = ascend_nlohmann::json_abi_v3_11_2::basic_json]bool ascend_nlohmann::json_abi_v3_11_2::detail::json_sax_dom_parser::key(ascend_nlohmann::json_abi_v3_11_2::detail::json_sax_dom_parser::string_t&) [with BasicJsonType = ascend_nlohmann::json_abi_v3_11_2::basic_json; ascend_nlohmann::json_abi_v3_11_2::detail::json_sax_dom_parser::string_t = std::basic_string]bool ascend_nlohmann::json_abi_v3_11_2::detail::json_sax_dom_parser::end_array() [with BasicJsonType = ascend_nlohmann::json_abi_v3_11_2::basic_json]BasicJsonType* ascend_nlohmann::json_abi_v3_11_2::detail::json_sax_dom_parser::handle_value(Value&&) [with Value = long unsigned int&; BasicJsonType = ascend_nlohmann::json_abi_v3_11_2::basic_json]BasicJsonType* ascend_nlohmann::json_abi_v3_11_2::detail::json_sax_dom_parser::handle_value(Value&&) [with Value = std::basic_string&; BasicJsonType = ascend_nlohmann::json_abi_v3_11_2::basic_json]BasicJsonType* ascend_nlohmann::json_abi_v3_11_2::detail::json_sax_dom_parser::handle_value(Value&&) [with Value = long int&; BasicJsonType = ascend_nlohmann::json_abi_v3_11_2::basic_json]BasicJsonType* ascend_nlohmann::json_abi_v3_11_2::detail::json_sax_dom_parser::handle_value(Value&&) [with Value = bool&; BasicJsonType = ascend_nlohmann::json_abi_v3_11_2::basic_json]BasicJsonType* ascend_nlohmann::json_abi_v3_11_2::detail::json_sax_dom_parser::handle_value(Value&&) [with Value = std::nullptr_t; BasicJsonType = ascend_nlohmann::json_abi_v3_11_2::basic_json]BasicJsonType* ascend_nlohmann::json_abi_v3_11_2::detail::json_sax_dom_parser::handle_value(Value&&) [with Value = double&; BasicJsonType = ascend_nlohmann::json_abi_v3_11_2::basic_json]BasicJsonType* ascend_nlohmann::json_abi_v3_11_2::detail::json_sax_dom_parser::handle_value(Value&&) [with Value = ascend_nlohmann::json_abi_v3_11_2::detail::value_t; BasicJsonType = ascend_nlohmann::json_abi_v3_11_2::basic_json]bool ascend_nlohmann::json_abi_v3_11_2::detail::parser::sax_parse_internal(SAX*) [with SAX = ascend_nlohmann::json_abi_v3_11_2::detail::json_sax_dom_parser >; BasicJsonType = ascend_nlohmann::json_abi_v3_11_2::basic_json; InputAdapterType = ascend_nlohmann::json_abi_v3_11_2::detail::iterator_input_adapter]ascend_nlohmann::json_abi_v3_11_2::detail::iter_impl::pointer ascend_nlohmann::json_abi_v3_11_2::detail::iter_impl::operator->() const [with BasicJsonType = ascend_nlohmann::json_abi_v3_11_2::basic_json; ascend_nlohmann::json_abi_v3_11_2::detail::iter_impl::pointer = ascend_nlohmann::json_abi_v3_11_2::basic_json*]bool ascend_nlohmann::json_abi_v3_11_2::detail::json_sax_dom_callback_parser::end_object() [with BasicJsonType = ascend_nlohmann::json_abi_v3_11_2::basic_json]bool ascend_nlohmann::json_abi_v3_11_2::detail::json_sax_dom_callback_parser::end_array() [with BasicJsonType = ascend_nlohmann::json_abi_v3_11_2::basic_json]std::pair ascend_nlohmann::json_abi_v3_11_2::detail::json_sax_dom_callback_parser::handle_value(Value&&, bool) [with Value = long unsigned int&; BasicJsonType = ascend_nlohmann::json_abi_v3_11_2::basic_json]std::pair ascend_nlohmann::json_abi_v3_11_2::detail::json_sax_dom_callback_parser::handle_value(Value&&, bool) [with Value = std::basic_string&; BasicJsonType = ascend_nlohmann::json_abi_v3_11_2::basic_json]std::pair ascend_nlohmann::json_abi_v3_11_2::detail::json_sax_dom_callback_parser::handle_value(Value&&, bool) [with Value = long int&; BasicJsonType = ascend_nlohmann::json_abi_v3_11_2::basic_json]std::pair ascend_nlohmann::json_abi_v3_11_2::detail::json_sax_dom_callback_parser::handle_value(Value&&, bool) [with Value = bool&; BasicJsonType = ascend_nlohmann::json_abi_v3_11_2::basic_json]std::pair ascend_nlohmann::json_abi_v3_11_2::detail::json_sax_dom_callback_parser::handle_value(Value&&, bool) [with Value = std::nullptr_t; BasicJsonType = ascend_nlohmann::json_abi_v3_11_2::basic_json]std::pair ascend_nlohmann::json_abi_v3_11_2::detail::json_sax_dom_callback_parser::handle_value(Value&&, bool) [with Value = double&; BasicJsonType = ascend_nlohmann::json_abi_v3_11_2::basic_json]std::pair ascend_nlohmann::json_abi_v3_11_2::detail::json_sax_dom_callback_parser::handle_value(Value&&, bool) [with Value = ascend_nlohmann::json_abi_v3_11_2::detail::value_t; BasicJsonType = ascend_nlohmann::json_abi_v3_11_2::basic_json]bool ascend_nlohmann::json_abi_v3_11_2::detail::parser::sax_parse_internal(SAX*) [with SAX = ascend_nlohmann::json_abi_v3_11_2::detail::json_sax_dom_callback_parser >; BasicJsonType = ascend_nlohmann::json_abi_v3_11_2::basic_json; InputAdapterType = ascend_nlohmann::json_abi_v3_11_2::detail::iterator_input_adapter]ascend_nlohmann::json_abi_v3_11_2::detail::lexer::token_type ascend_nlohmann::json_abi_v3_11_2::detail::lexer::scan_number() [with BasicJsonType = ascend_nlohmann::json_abi_v3_11_2::basic_json; InputAdapterType = ascend_nlohmann::json_abi_v3_11_2::detail::iterator_input_adapter; ascend_nlohmann::json_abi_v3_11_2::detail::lexer::token_type = ascend_nlohmann::json_abi_v3_11_2::detail::lexer_base >::token_type]bool ascend_nlohmann::json_abi_v3_11_2::detail::lexer::next_byte_in_range(std::initializer_list::int_type>) [with BasicJsonType = ascend_nlohmann::json_abi_v3_11_2::basic_json; InputAdapterType = ascend_nlohmann::json_abi_v3_11_2::detail::iterator_input_adapter; typename std::char_traits::int_type = int]int ascend_nlohmann::json_abi_v3_11_2::detail::lexer::get_codepoint() [with BasicJsonType = ascend_nlohmann::json_abi_v3_11_2::basic_json; InputAdapterType = ascend_nlohmann::json_abi_v3_11_2::detail::iterator_input_adapter]ascend_nlohmann::json_abi_v3_11_2::detail::lexer::token_type ascend_nlohmann::json_abi_v3_11_2::detail::lexer::scan_string() [with BasicJsonType = ascend_nlohmann::json_abi_v3_11_2::basic_json; InputAdapterType = ascend_nlohmann::json_abi_v3_11_2::detail::iterator_input_adapter; ascend_nlohmann::json_abi_v3_11_2::detail::lexer::token_type = ascend_nlohmann::json_abi_v3_11_2::detail::lexer_base >::token_type]void ascend_nlohmann::json_abi_v3_11_2::detail::lexer::unget() [with BasicJsonType = ascend_nlohmann::json_abi_v3_11_2::basic_json; InputAdapterType = ascend_nlohmann::json_abi_v3_11_2::detail::iterator_input_adapter]static char ascend_nlohmann::json_abi_v3_11_2::detail::lexer::get_decimal_point() [with BasicJsonType = ascend_nlohmann::json_abi_v3_11_2::basic_json; InputAdapterType = ascend_nlohmann::json_abi_v3_11_2::detail::iterator_input_adapter]void ascend_nlohmann::json_abi_v3_11_2::basic_json::assert_invariant(bool) const [with ObjectType = ascend_nlohmann::json_abi_v3_11_2::ordered_map; ArrayType = std::vector; StringType = std::basic_string; BooleanType = bool; NumberIntegerType = long int; NumberUnsignedType = long unsigned int; NumberFloatType = double; AllocatorType = std::allocator; JSONSerializer = ascend_nlohmann::json_abi_v3_11_2::adl_serializer; BinaryType = std::vector]ParserDeployInfo  _ppoutputs_indexinputs_indexfunc_namebuilt_in_flow_funcoutput_numinput_numfunc_listportiplisten_ip_infoflow_node_listbatch_deploy_infologic_device_listlogic_device_id./_dataflow_deploy_info.jsonLlmGraph_postprocess_flow_model_cluster_hcom_clusterLlmGraphBuild_ge.experiment.data_flow_deploy_info_pathllm.HcomClusterConfigllm.KvCacheCountsllm.OutputMappingllm.PostProcessOutputNumsllm.PostProcessOmCachePathllm.PostProcessInputDtypesllm.PostProcessInputShapesllm.OutputMaxSizellm.OutputNumsllm.RefInputDtypesllm.RefInputShapesllm.InputDtypesllm.InputShapesllm.ClusterInfollm.IsFlowModelllm.OmCachePathpostprocessinferencevector::_M_realloc_insertinvalid number; expected digit after '-'invalid number; expected digit after '.'invalid number; expected '+', '-', or digit after exponentinvalid number; expected digit after exponent signendptr == token_buffer.data() + token_buffer.size()!token_string.empty()current == 'u'0x0000 <= codepoint && codepoint <= 0xFFFFranges.size() == 2 || ranges.size() == 4 || ranges.size() == 6invalid string: ill-formed UTF-8 bytecurrent == '\"'invalid string: missing closing quoteinvalid string: '\u' must be followed by 4 hex digitsinvalid string: surrogate U+D800..U+DBFF must be followed by U+DC00..U+DFFF0x00 <= codepoint && codepoint <= 0x10FFFFinvalid string: surrogate U+DC00..U+DFFF must follow U+D800..U+DBFFinvalid string: forbidden character after backslashinvalid string: control character U+0000 (NUL) must be escaped to \u0000invalid string: control character U+0001 (SOH) must be escaped to \u0001invalid string: control character U+0002 (STX) must be escaped to \u0002invalid string: control character U+0003 (ETX) must be escaped to \u0003invalid string: control character U+0004 (EOT) must be escaped to \u0004invalid string: control character U+0005 (ENQ) must be escaped to \u0005invalid string: control character U+0006 (ACK) must be escaped to \u0006invalid string: control character U+0007 (BEL) must be escaped to \u0007invalid string: control character U+0008 (BS) must be escaped to \u0008 or \binvalid string: control character U+0009 (HT) must be escaped to \u0009 or \tinvalid string: control character U+000A (LF) must be escaped to \u000A or \ninvalid string: control character U+000B (VT) must be escaped to \u000Binvalid string: control character U+000C (FF) must be escaped to \u000C or \finvalid string: control character U+000D (CR) must be escaped to \u000D or \rinvalid string: control character U+000E (SO) must be escaped to \u000Einvalid string: control character U+000F (SI) must be escaped to \u000Finvalid string: control character U+0010 (DLE) must be escaped to \u0010invalid string: control character U+0011 (DC1) must be escaped to \u0011invalid string: control character U+0012 (DC2) must be escaped to \u0012invalid string: control character U+0013 (DC3) must be escaped to \u0013invalid string: control character U+0014 (DC4) must be escaped to \u0014invalid string: control character U+0015 (NAK) must be escaped to \u0015invalid string: control character U+0016 (SYN) must be escaped to \u0016invalid string: control character U+0017 (ETB) must be escaped to \u0017invalid string: control character U+0018 (CAN) must be escaped to \u0018invalid string: control character U+0019 (EM) must be escaped to \u0019invalid string: control character U+001A (SUB) must be escaped to \u001Ainvalid string: control character U+001B (ESC) must be escaped to \u001Binvalid string: control character U+001C (FS) must be escaped to \u001Cinvalid string: control character U+001D (GS) must be escaped to \u001Dinvalid string: control character U+001E (RS) must be escaped to \u001Einvalid string: control character U+001F (US) must be escaped to \u001FpX@(ȉ؇x`H0pX@(ȈЊІPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP xxxRdddddddddddd@dddddddddddddddddddddddddddddddddddddddddddd.dddddddd dddddddddddԇPx`H0ЎȌhP8 x`H0Ѝ؏؋ XXXXXXXXXXXXXXXXXXXXXXXXXXXXXX(ZllllllllllllHllllllllllllllllllllllllllllllllllllllllllll6lllll$llllllllllllll܌Xinvalid BOM; must be 0xEF 0xBB 0xBF if giveninvalid comment; missing closing '*/'invalid comment; expecting '/' or '*' after '/'invalid literal****************************************** ** ************************************************S************object keyobject separatorref_stack.back()->is_array()number overflow parsing '!states.empty()HPPИ޷޷޷޷޷޷޷޷޷޷޷޷޷޷޷޷޷޷޷޷޷޷޷޷޷޷޷޷޷޷޷޷޷޷޷޷޷޷޷޷޷޷Ƿ޷޷ǷǷǷǷǷǷǷǷǷǷԷ޷޷޷޷޷޷޷޷޷޷޷޷޷޷޷޷޷޷޷޷޷޷޷޷޷޷޷޷޷޷޷޷K޷U޷޷޷޷޷޷޷޷_޷޷޷޷޷޷޷޷޷޷޷޷޷޷޷޷޷޷A޷`PP(````(Dl p@hpB6*NNNi ll     Ltype must be number, but is of]T{K;F=4+Rccc"incompatible ReferenceType for get_ref, actual type is m_type == val.type()(p ! pp"#[#########J$ $w$n$e$$$$\$S$$$$$$$$$$$H% %u%l%c%~%~%~%Z%Q%,,,,,,,,,#,N3llm17LlmFlowModelSaverEN2ge8OperatorEN15ascend_nlohmann16json_abi_v3_11_26detail9exceptionEN15ascend_nlohmann16json_abi_v3_11_26detail11parse_errorEN15ascend_nlohmann16json_abi_v3_11_26detail16invalid_iteratorEN15ascend_nlohmann16json_abi_v3_11_26detail10type_errorEN15ascend_nlohmann16json_abi_v3_11_26detail12out_of_rangeEN15ascend_nlohmann16json_abi_v3_11_26detail11other_errorEN15ascend_nlohmann16json_abi_v3_11_26detail23output_adapter_protocolIcEEN15ascend_nlohmann16json_abi_v3_11_26detail21output_stream_adapterIcEESt15_Sp_counted_ptrIPN2ge5dflow9FlowGraphELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceIN15ascend_nlohmann16json_abi_v3_11_26detail21output_stream_adapterIcEESaIS4_ELN9__gnu_cxx12_Lock_policyE2EE   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%s: ErrorNo: %u(%s) %s[Check][Param:flow_model]null is invalidflow model is nullptr[%s:%d]%lu %s: ErrorNo: %u(%s) %srename flow model:%s file constant name failed[%s:%d]%lu %s:flow_model_om_lists[%u]:%s.[%s:%d]%lu %s: ErrorNo: %u(%s) %sGetFolderPath failed, om_path: %s.[%s:%d]%lu %s:om_path:%s prefix_path:%s[%s:%d]%lu %s: ErrorNo: %u(%s) %sload flow model from om failed, om_path: %s.Param:submodel is nullptr, check invalid[%s:%d]%lu %s: ErrorNo: %u(%s) %s[Check][Param:submodel]null is invalidflow model is not flatten, sub model[%s] has [%zu] submodels(submodel->GetSubmodels().empty())Param:submodel->GetRootGraph() is nullptr, check invalid[%s:%d]%lu %s: ErrorNo: %u(%s) %s[Check][Param:submodel->GetRootGraph()]null is invalid[%s:%d]%lu %s:get ge submodel model success, model_name=%s, graph=%s.Param:sub_flow_model is nullptr, check invalid[%s:%d]%lu %s: ErrorNo: %u(%s) %s[Check][Param:sub_flow_model]null is invalid[%s:%d]%lu %s: ErrorNo: %u(%s) %ssubmodel is nullptr, type = %s[%s:%d]%lu %s: ErrorNo: %u(%s) %ssubmodel already exist, name = %s, type = %s[%s:%d]%lu %s: ErrorNo: %u(%s) %sAddSubModel failed, model_name=%s.Param:sub_model_relation is nullptr, check invalid[%s:%d]%lu %s: ErrorNo: %u(%s) %s[Check][Param:sub_model_relation]null is invalidParam:graph is nullptr, check invalid[%s:%d]%lu %s: ErrorNo: %u(%s) %s[Check][Param:graph]null is invalid[%s:%d]%lu %s: ErrorNo: %u(%s) %sFailed to build model relation for graph[%s].Param:flow_model is nullptr, check invalid[%s:%d]%lu %s: ErrorNo: %u(%s) %s[Check][Param:flow_model]null is invalid[%s:%d]%lu %s: ErrorNo: %u(%s) %sFailed to construct sub flow model, index = %zu[%s:%d]%lu %s:flow_model_name:%s, cluster_name:%s, rank_id:%u.expect %zu models, but got %zu[%s:%d]%lu %s: ErrorNo: %u(%s) %sexpect %zu models, but got %zu[%s:%d]%lu %s:model [%s] set logical_device_id = %s[%s:%d]%lu %s: ErrorNo: %u(%s) %sFailed to merge flow models[%s:%d]%lu %s:commit run task enter.[%s:%d]%lu %s: ErrorNo: %u(%s) %sthread pool has been stopped.[%s:%d]%lu %s: ErrorNo: %u(%s) %sMake shared failed.[%s:%d]%lu %s:commit run task end[%s:%d]%lu %s: ErrorNo: %u(%s) %ssave flow model to om failed.save om's flow model to om failed[%s:%d]%lu %s: ErrorNo: %u(%s) %ssave om's flow model to om failed[%s:%d]%lu %s: ErrorNo: %u(%s) %sGetFolderPath failed, om_path: [%s]std::future_error: GetFlowModelGetFlowModelAddSubModelConstructSubFlowModelPrepareGetFlowModelSetHcomInfoToFlowModelRenameFileConstantSaveFlowModelToOmSaveFlowModelsToOmoperator()commitSaveToOm*NSt13__future_base12_Task_setterISt10unique_ptrINS_7_ResultIjEENS_12_Result_base8_DeleterEEZNS_11_Task_stateISt5_BindIFZN3llm17LlmFlowModelSaver8SaveToOmEmbRSt6vectorISsSaISsEEEUlvE_vEESaIiEFjvEE6_M_runEvEUlvE_jEE*NSt13__future_base12_Task_setterISt10unique_ptrINS_7_ResultIjEENS_12_Result_base8_DeleterEEZNS_11_Task_stateISt5_BindIFZN3llm17LlmFlowModelSaver8SaveToOmEmbRSt6vectorISsSaISsEEEUlvE_vEESaIiEFjvEE14_M_run_delayedESt8weak_ptrINS_13_State_baseV2EEEUlvE_jEE*NSt13__future_base11_Task_stateISt5_BindIFZN3llm17LlmFlowModelSaver8SaveToOmEmbRSt6vectorISsSaISsEEEUlvE_vEESaIiEFjvEEE*ZN2ge10ThreadPool6commitIRZN3llm17LlmFlowModelSaver8SaveToOmEmbRSt6vectorISsSaISsEEEUlvE_JEEESt6futureIDTclfp_spfp0_EEEOT_DpOT0_EUlvE_*St23_Sp_counted_ptr_inplaceINSt13__future_base11_Task_stateISt5_BindIFZN3llm17LlmFlowModelSaver8SaveToOmEmbRSt6vectorISsSaISsEEEUlvE_vEESaIiEFjvEEESC_LN9__gnu_cxx12_Lock_policyE2EEpostprocess_flow_model_cluster_flow_model_cluster_N2ge8PneModelEN3llm27LlmFlowModelSaverFromOmListEN3llm30LlmFlowModelSaverFromFlowModelEN3llm32LlmFlowModelSaverFromModelBufferENSt13__future_base13_State_baseV2EN2ge20ModelRelationBuilderENSt13__future_base7_ResultIjEENSt13__future_base16_Task_state_baseIFjvEEESt15_Sp_counted_ptrIPN2ge13ModelRelationELN9__gnu_cxx12_Lock_policyE2EESt15_Sp_counted_ptrIPN2ge9FlowModelELN9__gnu_cxx12_Lock_policyE2EESt15_Sp_counted_ptrIPSt13packaged_taskIFjvEELN9__gnu_cxx12_Lock_policyE2EEllm_flow_service.ccdevice_indexis_promptipportsizeof(int32_t)output_size_args__BuiltIn_cluster_unlink_func_BuiltIn_cache_allocate_func_BuiltIn_cache_get_func_BuiltIn_cache_copy_func_BuiltIn_cache_pull_funcGetCacheTensorsMultiMasterge.exec.modelDeployModege.exec.modelDeployDevicelistexpect at least 4 bytes, but got %zu, device_index = %zu[%s:%d]%lu %s: ErrorNo: %u(%s) %sexpect at least 4 bytes, but got %zu, device_index = %zu[%s:%d]%lu %s:FlowFunc returned: %d, device_index = %zu[%s:%d]%lu %s: ErrorNo: %u(%s) %sFlowFunc returned: %d, device_index = %zu[%s:%d]%lu %s:device_index = %zu, num_tensors = %dnum_tensors (%d) < 0, device_index = %zu[%s:%d]%lu %s: ErrorNo: %u(%s) %snum_tensors (%d) < 0, device_index = %zu[%s:%d]%lu %s: ErrorNo: %u(%s) %sFailed to feed input, device_index = %zuflow func index (%zu) out of range [0, %zu)[%s:%d]%lu %s: ErrorNo: %u(%s) %sflow func index (%zu) out of range [0, %zu)[%s:%d]%lu %s: ErrorNo: %u(%s) %sFailed to feed inputs[%s:%d]%lu %s: ErrorNo: %u(%s) %sFailed to fetch outputs[%s:%d]%lu %s:device_index = %zu, ip = %ld, port = %ld[%s:%d]%lu %s: ErrorNo: %u(%s) %sFailed to fetch input, device_index = %zuParam:ge_api_ is nullptr, check invalid[%s:%d]%lu %s: ErrorNo: %u(%s) %s[Check][Param:ge_api_]null is invalid[%s:%d]%lu %s: ErrorNo: %u(%s) %sexecute failed, device index = %zu[%s:%d]%lu %s: ErrorNo: %u(%s) %sFailed to execute in all devices, success/total = %zu/%zu[%s:%d]%lu %s: ErrorNo: %u(%s) %sAllocate cache return code = %u, device_index = %zu_BuiltIn_cluster_update_link_func_BuiltIn_cache_deallocate_func_BuiltIn_cache_remove_index_func_BuiltIn_cluster_kv_data_sync_func[%s:%d]%lu %s:Load flow funcs start, device_index = %zu[%s:%d]%lu %s: ErrorNo: %u(%s) %s[cache_id:%ld][Allocate] RunFlowFunc failed[Check][Result] check tensor addresses failed, expect %u, bot got %zu, device_index = %zu[%s:%d]%lu %s: ErrorNo: %u(%s) %s[Check][Result] check tensor addresses failed, expect %u, bot got %zu, device_index = %zu[%s:%d]%lu %s: ErrorNo: %u(%s) %s[cache_id:%ld][Deallocate] RunFlowFunc failed[%s:%d]%lu %s: ErrorNo: %u(%s) %s[RemoveCacheIndex] RunFlowFunc failed cache_key = (%lu, %lu)[%s:%d]%lu %s: ErrorNo: %u(%s) %s[cache_id:%ld][Pull] failed cache_key = (%lu, %lu, %lu)[%s:%d]%lu %s: ErrorNo: %u(%s) %sFailed to copy cache[%s:%d]%lu %s: ErrorNo: %u(%s) %sFailed to get cache tensor summarynum_tensors differs between devices: %s[%s:%d]%lu %s: ErrorNo: %u(%s) %snum_tensors differs between devices: %smultiple tensor is not supported yet, num_tensors = %d[%s:%d]%lu %s: ErrorNo: %u(%s) %smultiple tensor is not supported yet, num_tensors = %d[%s:%d]%lu %s: ErrorNo: %u(%s) %sFailed to get cache tensor[%s:%d]%lu %s: ErrorNo: %u(%s) %sFailed to invoke GEFinalize[%s:%d]%lu %s:LLM flow service finalized[%s:%d]%lu %s: ErrorNo: %u(%s) %sFailed to load data flow in all devices[%s:%d]%lu %s:get_cache_tensor_func_id = %zu[%s:%d]%lu %s: ErrorNo: %u(%s) %sFailed to parse deploy info[%s:%d]%lu %s: ErrorNo: %u(%s) %sparser wait time info failed[%s:%d]%lu %s:device_num in numa config = %zu, deploy to device_indices = %s[%s:%d]%lu %s: ErrorNo: %u(%s) %sFailed to initialize GEParam:worker_pool_ is nullptr, check invalid[%s:%d]%lu %s: ErrorNo: %u(%s) %s[Check][Param:worker_pool_]null is invalid[%s:%d]%lu %s: ErrorNo: %u(%s) %sFailed to init worker[%s:%d]%lu %s: ErrorNo: %u(%s) %slink clusters failed[%s:%d]%lu %s: ErrorNo: %u(%s) %sunlink clusters failed[%s:%d]%lu %s: ErrorNo: %u(%s) %sFailed to FunFlowFunc, device_index = %zu, logical_device_id = %s[%s:%d]%lu %s: ErrorNo: %u(%s) %sFailed to handle output tensors, device_index = %zu, logical_device_id = %s[%s:%d]%lu %s: ErrorNo: %u(%s) %sFailed to run flow func in all devicesFetchOutputsFeedInputscommitUnlinkClusterscommitLinkClustersGetCacheTensorsCopyCachePullCacheRemoveCacheIndexDeallocateConvertAllocateResultAllocateConvertToTensorSummaryConvertToStatusGetAndCheckFuturesoperator()commitLoadDataFlowSetFunctionPpInitParamcommitcommitFinalizecommitcommitInitializeRunFlowFuncLoadFlowFuncsRunSingleRunSinglecommitRunMultiRunSinglecommitRunMultiRunSinglecommitRunMulticommitRunMultiInitialize*NSt13__future_base12_Task_setterISt10unique_ptrINS_7_ResultIjEENS_12_Result_base8_DeleterEEZNS_11_Task_stateISt5_BindIFZN3llm14LlmFlowService14UnlinkClustersERKSt6vectorINS9_11ClusterInfoESaISC_EERSB_IjSaIjEEiEUlvE_vEESaIiEFjvEE6_M_runEvEUlvE_jEE*NSt13__future_base12_Task_setterISt10unique_ptrINS_7_ResultIjEENS_12_Result_base8_DeleterEEZNS_11_Task_stateISt5_BindIFZN3llm14LlmFlowService14UnlinkClustersERKSt6vectorINS9_11ClusterInfoESaISC_EERSB_IjSaIjEEiEUlvE_vEESaIiEFjvEE14_M_run_delayedESt8weak_ptrINS_13_State_baseV2EEEUlvE_jEE*NSt13__future_base12_Task_setterISt10unique_ptrINS_7_ResultIjEENS_12_Result_base8_DeleterEEZNS_11_Task_stateISt5_BindIFZN3llm14LlmFlowService12LinkClustersERKSt6vectorINS9_11ClusterInfoESaISC_EERSB_IjSaIjEEiEUlvE_vEESaIiEFjvEE6_M_runEvEUlvE_jEE*NSt13__future_base12_Task_setterISt10unique_ptrINS_7_ResultIjEENS_12_Result_base8_DeleterEEZNS_11_Task_stateISt5_BindIFZN3llm14LlmFlowService12LinkClustersERKSt6vectorINS9_11ClusterInfoESaISC_EERSB_IjSaIjEEiEUlvE_vEESaIiEFjvEE14_M_run_delayedESt8weak_ptrINS_13_State_baseV2EEEUlvE_jEE*NSt13__future_base12_Task_setterISt10unique_ptrINS_7_ResultIjEENS_12_Result_base8_DeleterEEZNS_11_Task_stateISt5_BindIFZN3llm14LlmFlowService12LoadDataFlowERKNS9_11FlowNodeDefERKSt3mapIN2ge12AscendStringESG_St4lessISG_ESaISt4pairIKSG_SG_EEEEUlvE_vEESaIiEFjvEE6_M_runEvEUlvE_jEE*NSt13__future_base12_Task_setterISt10unique_ptrINS_7_ResultIjEENS_12_Result_base8_DeleterEEZNS_11_Task_stateISt5_BindIFZN3llm14LlmFlowService12LoadDataFlowERKNS9_11FlowNodeDefERKSt3mapIN2ge12AscendStringESG_St4lessISG_ESaISt4pairIKSG_SG_EEEEUlvE_vEESaIiEFjvEE14_M_run_delayedESt8weak_ptrINS_13_State_baseV2EEEUlvE_jEE*NSt13__future_base12_Task_setterISt10unique_ptrINS_7_ResultIjEENS_12_Result_base8_DeleterEEZNS_11_Task_stateISt5_BindIFZN3llm14LlmFlowService8FinalizeEvEUlvE0_vEESaIiEFjvEE6_M_runEvEUlvE_jEE*NSt13__future_base12_Task_setterISt10unique_ptrINS_7_ResultIjEENS_12_Result_base8_DeleterEEZNS_11_Task_stateISt5_BindIFZN3llm14LlmFlowService8FinalizeEvEUlvE0_vEESaIiEFjvEE14_M_run_delayedESt8weak_ptrINS_13_State_baseV2EEEUlvE_jEE*NSt13__future_base12_Task_setterISt10unique_ptrINS_7_ResultIvEENS_12_Result_base8_DeleterEEZNS_11_Task_stateISt5_BindIFZN3llm14LlmFlowService8FinalizeEvEUlvE_vEESaIiEFvvEE6_M_runEvEUlvE_vEE*NSt13__future_base12_Task_setterISt10unique_ptrINS_7_ResultIvEENS_12_Result_base8_DeleterEEZNS_11_Task_stateISt5_BindIFZN3llm14LlmFlowService8FinalizeEvEUlvE_vEESaIiEFvvEE14_M_run_delayedESt8weak_ptrINS_13_State_baseV2EEEUlvE_vEE*NSt13__future_base12_Task_setterISt10unique_ptrINS_7_ResultIjEENS_12_Result_base8_DeleterEEZNS_11_Task_stateISt5_BindIFZN3llm14LlmFlowService10InitializeERKSt3mapIN2ge12AscendStringESD_St4lessISD_ESaISt4pairIKSD_SD_EEEEUlvE0_vEESaIiEFjvEE6_M_runEvEUlvE_jEE*NSt13__future_base12_Task_setterISt10unique_ptrINS_7_ResultIjEENS_12_Result_base8_DeleterEEZNS_11_Task_stateISt5_BindIFZN3llm14LlmFlowService10InitializeERKSt3mapIN2ge12AscendStringESD_St4lessISD_ESaISt4pairIKSD_SD_EEEEUlvE0_vEESaIiEFjvEE14_M_run_delayedESt8weak_ptrINS_13_State_baseV2EEEUlvE_jEE*NSt13__future_base12_Task_setterISt10unique_ptrINS_7_ResultIjEENS_12_Result_base8_DeleterEEZNS_11_Task_stateISt5_BindIFZN3llm14LlmFlowService10InitializeERKSt3mapIN2ge12AscendStringESD_St4lessISD_ESaISt4pairIKSD_SD_EEEEUlvE_vEESaIiEFjvEE6_M_runEvEUlvE_jEE*NSt13__future_base12_Task_setterISt10unique_ptrINS_7_ResultIjEENS_12_Result_base8_DeleterEEZNS_11_Task_stateISt5_BindIFZN3llm14LlmFlowService10InitializeERKSt3mapIN2ge12AscendStringESD_St4lessISD_ESaISt4pairIKSD_SD_EEEEUlvE_vEESaIiEFjvEE14_M_run_delayedESt8weak_ptrINS_13_State_baseV2EEEUlvE_jEE*ZZN3llm14LlmFlowService12LoadDataFlowERKNS_11FlowNodeDefERKSt3mapIN2ge12AscendStringES6_St4lessIS6_ESaISt4pairIKS6_S6_EEEENKUlvE_clEvEUlNS5_5dflow10FunctionPpEE_*NSt13__future_base11_Task_stateISt5_BindIFZN3llm14LlmFlowService10InitializeERKSt3mapIN2ge12AscendStringES6_St4lessIS6_ESaISt4pairIKS6_S6_EEEEUlvE_vEESaIiEFjvEEE*NSt13__future_base11_Task_stateISt5_BindIFZN3llm14LlmFlowService10InitializeERKSt3mapIN2ge12AscendStringES6_St4lessIS6_ESaISt4pairIKS6_S6_EEEEUlvE0_vEESaIiEFjvEEE*NSt13__future_base11_Task_stateISt5_BindIFZN3llm14LlmFlowService8FinalizeEvEUlvE_vEESaIiEFvvEEE*NSt13__future_base11_Task_stateISt5_BindIFZN3llm14LlmFlowService8FinalizeEvEUlvE0_vEESaIiEFjvEEE*NSt13__future_base11_Task_stateISt5_BindIFZN3llm14LlmFlowService12LoadDataFlowERKNS2_11FlowNodeDefERKSt3mapIN2ge12AscendStringES9_St4lessIS9_ESaISt4pairIKS9_S9_EEEEUlvE_vEESaIiEFjvEEE*NSt13__future_base11_Task_stateISt5_BindIFZN3llm14LlmFlowService12LinkClustersERKSt6vectorINS2_11ClusterInfoESaIS5_EERS4_IjSaIjEEiEUlvE_vEESaIiEFjvEEE*NSt13__future_base11_Task_stateISt5_BindIFZN3llm14LlmFlowService14UnlinkClustersERKSt6vectorINS2_11ClusterInfoESaIS5_EERS4_IjSaIjEEiEUlvE_vEESaIiEFjvEEE*ZN2ge10ThreadPool6commitIZN3llm14LlmFlowService10InitializeERKSt3mapINS_12AscendStringES5_St4lessIS5_ESaISt4pairIKS5_S5_EEEEUlvE_JEEESt6futureIDTclfp_spfp0_EEEOT_DpOT0_EUlvE_*ZN2ge10ThreadPool6commitIZN3llm14LlmFlowService10InitializeERKSt3mapINS_12AscendStringES5_St4lessIS5_ESaISt4pairIKS5_S5_EEEEUlvE0_JEEESt6futureIDTclfp_spfp0_EEEOT_DpOT0_EUlvE_*ZN2ge10ThreadPool6commitIZN3llm14LlmFlowService8FinalizeEvEUlvE_JEEESt6futureIDTclfp_spfp0_EEEOT_DpOT0_EUlvE_*ZN2ge10ThreadPool6commitIZN3llm14LlmFlowService8FinalizeEvEUlvE0_JEEESt6futureIDTclfp_spfp0_EEEOT_DpOT0_EUlvE_*ZN2ge10ThreadPool6commitIZN3llm14LlmFlowService12LoadDataFlowERKNS2_11FlowNodeDefERKSt3mapINS_12AscendStringES8_St4lessIS8_ESaISt4pairIKS8_S8_EEEEUlvE_JEEESt6futureIDTclfp_spfp0_EEEOT_DpOT0_EUlvE_*ZN2ge10ThreadPool6commitIZN3llm14LlmFlowService12LinkClustersERKSt6vectorINS2_11ClusterInfoESaIS5_EERS4_IjSaIjEEiEUlvE_JEEESt6futureIDTclfp_spfp0_EEEOT_DpOT0_EUlvE_*ZN2ge10ThreadPool6commitIZN3llm14LlmFlowService14UnlinkClustersERKSt6vectorINS2_11ClusterInfoESaIS5_EERS4_IjSaIjEEiEUlvE_JEEESt6futureIDTclfp_spfp0_EEEOT_DpOT0_EUlvE_*St23_Sp_counted_ptr_inplaceINSt13__future_base11_Task_stateISt5_BindIFZN3llm14LlmFlowService10InitializeERKSt3mapIN2ge12AscendStringES7_St4lessIS7_ESaISt4pairIKS7_S7_EEEEUlvE_vEESaIiEFjvEEESK_LN9__gnu_cxx12_Lock_policyE2EE*St23_Sp_counted_ptr_inplaceINSt13__future_base11_Task_stateISt5_BindIFZN3llm14LlmFlowService10InitializeERKSt3mapIN2ge12AscendStringES7_St4lessIS7_ESaISt4pairIKS7_S7_EEEEUlvE0_vEESaIiEFjvEEESK_LN9__gnu_cxx12_Lock_policyE2EE*St23_Sp_counted_ptr_inplaceINSt13__future_base11_Task_stateISt5_BindIFZN3llm14LlmFlowService8FinalizeEvEUlvE_vEESaIiEFvvEEES8_LN9__gnu_cxx12_Lock_policyE2EE*St23_Sp_counted_ptr_inplaceINSt13__future_base11_Task_stateISt5_BindIFZN3llm14LlmFlowService8FinalizeEvEUlvE0_vEESaIiEFjvEEES8_LN9__gnu_cxx12_Lock_policyE2EE*St23_Sp_counted_ptr_inplaceINSt13__future_base11_Task_stateISt5_BindIFZN3llm14LlmFlowService12LoadDataFlowERKNS3_11FlowNodeDefERKSt3mapIN2ge12AscendStringESA_St4lessISA_ESaISt4pairIKSA_SA_EEEEUlvE_vEESaIiEFjvEEESN_LN9__gnu_cxx12_Lock_policyE2EE*St23_Sp_counted_ptr_inplaceINSt13__future_base11_Task_stateISt5_BindIFZN3llm14LlmFlowService12LinkClustersERKSt6vectorINS3_11ClusterInfoESaIS6_EERS5_IjSaIjEEiEUlvE_vEESaIiEFjvEEESH_LN9__gnu_cxx12_Lock_policyE2EE*St23_Sp_counted_ptr_inplaceINSt13__future_base11_Task_stateISt5_BindIFZN3llm14LlmFlowService14UnlinkClustersERKSt6vectorINS3_11ClusterInfoESaIS6_EERS5_IjSaIjEEiEUlvE_vEESaIiEFjvEEESH_LN9__gnu_cxx12_Lock_policyE2EEFjmRKSt6vectorIN2ge6TensorESaIS1_EERiEFjmRKSt6vectorIN2ge6TensorESaIS1_EERS1_EFjmRKSt6vectorIN2ge6TensorESaIS1_EERN3llm19CacheAllocateResultEEFjmRKSt6vectorIN2ge6TensorESaIS1_EERjENSt13__future_base12_Task_setterISt10unique_ptrINS_7_ResultIjEENS_12_Result_base8_DeleterEEZNS_11_Task_stateISt5_BindIFZN3llm14LlmFlowService8RunMultiIN2ge6TensorEEEjmRKSt6vectorISD_SaISD_EERKSt8functionIFjmSI_RT_EERSE_ISK_SaISK_EEEUlvE_vEESaIiEFjvEE6_M_runEvEUlvE_jEENSt13__future_base12_Task_setterISt10unique_ptrINS_7_ResultIjEENS_12_Result_base8_DeleterEEZNS_11_Task_stateISt5_BindIFZN3llm14LlmFlowService8RunMultiIN2ge6TensorEEEjmRKSt6vectorISD_SaISD_EERKSt8functionIFjmSI_RT_EERSE_ISK_SaISK_EEEUlvE_vEESaIiEFjvEE14_M_run_delayedESt8weak_ptrINS_13_State_baseV2EEEUlvE_jEENSt13__future_base12_Task_setterISt10unique_ptrINS_7_ResultIjEENS_12_Result_base8_DeleterEEZNS_11_Task_stateISt5_BindIFZN3llm14LlmFlowService8RunMultiIiEEjmRKSt6vectorIN2ge6TensorESaISE_EERKSt8functionIFjmSI_RT_EERSC_ISK_SaISK_EEEUlvE_vEESaIiEFjvEE6_M_runEvEUlvE_jEENSt13__future_base12_Task_setterISt10unique_ptrINS_7_ResultIjEENS_12_Result_base8_DeleterEEZNS_11_Task_stateISt5_BindIFZN3llm14LlmFlowService8RunMultiIiEEjmRKSt6vectorIN2ge6TensorESaISE_EERKSt8functionIFjmSI_RT_EERSC_ISK_SaISK_EEEUlvE_vEESaIiEFjvEE14_M_run_delayedESt8weak_ptrINS_13_State_baseV2EEEUlvE_jEENSt13__future_base12_Task_setterISt10unique_ptrINS_7_ResultIjEENS_12_Result_base8_DeleterEEZNS_11_Task_stateISt5_BindIFZN3llm14LlmFlowService8RunMultiIjEEjmRKSt6vectorIN2ge6TensorESaISE_EERKSt8functionIFjmSI_RT_EERSC_ISK_SaISK_EEEUlvE_vEESaIiEFjvEE6_M_runEvEUlvE_jEENSt13__future_base12_Task_setterISt10unique_ptrINS_7_ResultIjEENS_12_Result_base8_DeleterEEZNS_11_Task_stateISt5_BindIFZN3llm14LlmFlowService8RunMultiIjEEjmRKSt6vectorIN2ge6TensorESaISE_EERKSt8functionIFjmSI_RT_EERSC_ISK_SaISK_EEEUlvE_vEESaIiEFjvEE14_M_run_delayedESt8weak_ptrINS_13_State_baseV2EEEUlvE_jEENSt13__future_base12_Task_setterISt10unique_ptrINS_7_ResultIjEENS_12_Result_base8_DeleterEEZNS_11_Task_stateISt5_BindIFZN3llm14LlmFlowService8RunMultiINS9_19CacheAllocateResultEEEjmRKSt6vectorIN2ge6TensorESaISF_EERKSt8functionIFjmSJ_RT_EERSD_ISL_SaISL_EEEUlvE_vEESaIiEFjvEE6_M_runEvEUlvE_jEENSt13__future_base12_Task_setterISt10unique_ptrINS_7_ResultIjEENS_12_Re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!= nullptr)llm_ge_api.cc((new_ge_api->sess_) != nullptr)Param:sess_ is nullptr, check invalid[%s:%d]%lu %s: ErrorNo: %u(%s) %s[Check][Param:sess_]null is invalidNewSessionInitializeN3llm5GeApiESt15_Sp_counted_ptrIPN2ge7SessionELN9__gnu_cxx12_Lock_policyE2EEllm_req_queue.cc[%s:%d]%lu %s:Wait failed, is_stopped = %dPopInput string is empty.(!input_string.empty())llm_utils.cc(std::isdigit(c) != 0)Output [%zu] ret is failed/\[%s:%d]%lu %s:Option: %s = %soption %s can not be negative(tmp >= 0)(iter != options.end())(%ld%s option is invalid.(!inputs_padding.empty())convert [%s] to int failed.(!dim_indexes.empty())Ascend910AAscend910BAscend910PremiumAAscend910ProAAscend910ProBAscend910B1Ascend910B2Ascend910B3Ascend910B4Ascend910B2CAscend910C1Ascend910C2Ascend910C3Ascend920AAscend310P1Ascend310P3Ascend310Ascend310B1Ascend310B2Ascend310B3Ascend310B4Ascend310M1Ascend320Ascend610Ascend610LiteAscend615Ascend710Vir01Ascend710Vir02Ascend710Vir04Ascend710Vir08BS9SX1AAHi3796CV300CSHi3796CV300ESOPTGSD3403TsnsCTsnsEAscend031Ascend035ddr_rateubblock_sizeAICPUSyncBySWTSCPUSyncBySWCounterWorkspaceSizeai_core_cntjit_compile_default_valuejit_compile_modevec_freqIntrinsic_mmadAICoreMemoryRatesAICoreSpecCPUCacheDSARandomSoCInfoSoftwareSpecVectorCoreMemoryRatesVectorCoreSpecAICoreintrinsicDtypeMapVectorCoreintrinsicDtypeMapALL_LABEL_ENDInput string contains non-numeric characters.(static_cast(batch_dim_index) < input_shapes[i].size())[%s:%d]%lu %s:[InitOption]batch_size:%ldOutput[%zu] is invalid, data is empty[%s:%d]%lu %s: ErrorNo: %u(%s) %sOutput[%zu] is invalid, data is empty[%s:%d]%lu %s: ErrorNo: %u(%s) %sOutput [%zu] ret is failed[%s:%d]%lu %s: ErrorNo: %u(%s) %sFile does not exist: %sinput size error, inputs:%zu,input_shapes%zu,inputs_batch_dim_index:%zu,input_dtypes:%zu,batch_input_tensors:%zu(((inputs.size() == batch_input_tensors.size()) && (inputs.size() == input_shapes.size()) && (input_dtypes.size() == inputs_batch_dim_index.size()) && (inputs.size() == input_dtypes.size())))(batch_dim_index < static_cast(input_shape.size()))[%s:%d]%lu %s:Input:%zu is not need batching[%s:%d]%lu %s: ErrorNo: %u(%s) %sbatch index:%lu is out of range, input:%zu tensor size:%zu, model size:%zucopy input_tensor data failed.((memcpy_s(dst, copy_continuous_dim_size * element_size, input_tensor.GetData() + j * copy_continuous_dim_size * element_size, copy_continuous_dim_size * element_size)) == EOK)shape vec size[%zu] should be greater than batch_dim[%zu](shape_vec.size() > batch_dim)((tensor.SetTensorDesc(tensor_desc)) == ge::SUCCESS)InputShape contain empty element, please check.(!llm::LLMUtils::ContainsEmptyStrings(input_shape_str))(!llm::LLMUtils::ContainsEmptyStrings(input_shape_str_nums))((ge::ConvertToInt64(input_j_str, input_j)) == ge::SUCCESS)InputDtype contain empty element, please check.(!llm::LLMUtils::ContainsEmptyStrings(input_dtype_str))((ge::ConvertToInt64(input_dtype_str[i], input_i)) == ge::SUCCESS)[%s:%d]%lu %s:enable manual batching[%s:%d]%lu %s:enable auto batching[%s:%d]%lu %s: ErrorNo: %u(%s) %sInvalid batch mode: %s[%s:%d]%lu %s:get input wait time:%sinput wait time:%s is invalid, wait time value should be a positive integer.[%s:%d]%lu %s: ErrorNo: %u(%s) %sinput wait time:%s is invalid, wait time value should be a positive integer.[%s:%d]%lu %s:get sync kv wait time:%ssync kv wait time:%s is invalid, wait time value should be a positive integer.[%s:%d]%lu %s: ErrorNo: %u(%s) %ssync kv wait time:%s is invalid, wait time value should be a positive integer.[%s:%d]%lu %s:get nn execute wait time:%snn execute wait time:%s is invalid, wait time value should be a positive integer.[%s:%d]%lu %s: ErrorNo: %u(%s) %snn execute wait time:%s is invalid, wait time value should be a positive integer.[%s:%d]%lu %s:get process request wait time:%sprocess request wait time:%s is invalid, wait time value should be a positive integer.[%s:%d]%lu %s: ErrorNo: %u(%s) %sprocess request wait time:%s is invalid, wait time value should be a positive integer.convert [%s][%s] to int failed.((ge::ConvertToInt64(iter->second.GetString(), tmp)) == ge::SUCCESS)options of llm engine Initialize must contain: %s.Param:iter->second is nullptr, check invalid[%s:%d]%lu %s: ErrorNo: %u(%s) %s[Check][Param:iter->second]null is invalid[%s:%d]%lu %s:get shape info: key:[%s], value:[%s]((InputShapeSplitAnaly(input_shape_str, input_shapes)) == ge::SUCCESS)input_shape_str size != input_shapes_ size, %zu != %zu.(input_shape_str.size() == input_shapes.size())[%s:%d]%lu %s:get dtype info: key:[%s], value:[%s]((InputDtypeSplitAnaly(input_dtype_str, input_dtypes)) == ge::SUCCESS)%s size != %s size, %zu != %zu.(input_shapes.size() == input_dtypes.size())[%s:%d]%lu %s:shape:[%s] size:[%zu], dtype:[%s] size:[%zu].[%s:%d]%lu %s:get shape info: %s %s((InputShapeSplitAnaly(input_shape_str, input_shape_tmp)) == ge::SUCCESS)(input_shape_str.size() == input_shape_tmp.size())[%s:%d]%lu %s:get dtype info: %s %s((InputDtypeSplitAnaly(input_dtype_str, input_dtype_tmp)) == ge::SUCCESS)(input_shapes[i].size() == input_dtypes[i].size())[%s:%d]%lu %s:shape:%s size:%zu, dtype:%s size:%zu.Inputs batch dim index:%s is invalid((ge::ConvertToInt64(node_deployment, device_id)) == ge::SUCCESS)[%s:%d]%lu %s:[InitOption]%s has value:%s.Inputs batch input padding:%s is invalid((ge::ConvertToInt64(input_padding, input_padding_num)) == ge::SUCCESS)The option:%s, value:%s is invalid.The number of parsed option is incorrect, please check.size:%zu is not equal to inputs shape size:%zu.(batch_inputs_padding.size() == input_shapes.size())[%s:%d]%lu %s:[InitOption]llm.InputsBatchPadding size:%zu, value:%s.[%s:%d]%lu %s:%s not set or is empty%s is invalid, items size (%zu) mismatches pipeline stage num (%zu), value = [%s][%s:%d]%lu %s: ErrorNo: %u(%s) %s%s is invalid, items size (%zu) mismatches pipeline stage num (%zu), value = [%s][%s:%d]%lu %s: ErrorNo: %u(%s) %s%s is invalid, failed to convert [%s] to number, value = [%s]%s is invalid, index (%u) out of range [0, %u), option value = [%s][%s:%d]%lu %s: ErrorNo: %u(%s) %s%s is invalid, index (%u) out of range [0, %u), option value = [%s][%s:%d]%lu %s:Add pipeline stage input indices: %soptions [%s][%s] is invalid, only support 0 or 1 while .(((iter->second == kDisableFlag) || (iter->second == kEnableFlag)))((ge::ConvertToInt64(iter->second.GetString(), value)) == ge::SUCCESS)[%s:%d]%lu %s:[InitOptions] enable paged attention is %d.((parser_func(LLM_OPTION_PAGED_ATTENTION_BLOCK_SIZE, paged_attention_options.block_size)) == ge::SUCCESS)((parser_func(LLM_OPTION_PAGED_ATTENTION_BLOCKS_NUM, paged_attention_options.blocks_num)) == ge::SUCCESS)((parser_func(LLM_OPTION_PAGED_ATTENTION_MAX_SEQ_LEN, paged_attention_options.max_seq_len)) == ge::SUCCESS)((parser_func(LLM_OPTION_PAGED_ATTENTION_MAX_SEQS_NUM, paged_attention_options.max_seqs_num)) == ge::SUCCESS)[%s:%d]%lu %s:[InitOptions]paged attention options is:%s((ge::ConvertToInt64(dim_index, dim_index_num)) == ge::SUCCESS)(inputs_batch_dim_index.size() == input_shapes.size())[%s:%d]%lu %s:[InitOption]llm.InputsBatchSizeDimIndex size:%zu, value:%s.[%s:%d]%lu %s: ErrorNo: %u(%s) %scopy output_data failed.[%s:%d]%lu %s:SeparateRequestOutput success, size:%zuenable:, block_size:, blocks_num:, max_seq_len, max_seqs_num, max_prompt_lenGetFolderPathParseBatchModeUpdateInputTensoroperator()InitPagedAttentionOptionsToNumberParsePipelineInputIndicesCheckOutputsRetCodeInitInputNodeDeploymentInitMultiInputShapeAndDtypeSeparateRequestOutputUpdateBatchInputsInitBatchSizeInitBatchInputsPaddingInitBatchDimIndexIsPositiveIntegerParserWaitTimeInfoInputDtypeSplitAnalyInputShapeSplitAnalyParserInputsShapeAndDtype10llm.PagedAttentionMaxPromptLenllm.PagedAttentionMaxSeqsNumllm.PagedAttentionMaxSeqLenllm.PagedAttentionBlocksNumllm.PagedAttentionBlockSizellm.EnablePagedAttentionllm.PipelineInputIndicesllm.InputNodeDeploymentllm.batch_modellm.InputsBatchPaddingllm.ProcessRequestWaitTimellm.NnExecuteWaitTimellm.SyncKvCacheWaitTimeautomanualllm.InputWaitTimellm.InputsBatchSizeDimIndexSt15_Sp_counted_ptrIPN2ge10AlignedPtrELN9__gnu_cxx12_Lock_policyE2EEoffline_model_manager.ccexpect 1 om_path, but got %zu(om_paths.size() == 1U)[%s:%d]%lu %s: ErrorNo: %u(%s) %sFailed to invoke RunGraphDistribute[%s:%d][EVENT]%lu %s:[ExecuteModel] end, execute_time_us = %ld[%s:%d]%lu %s:[ExecuteModel] end, execute_time_us = %ld%s exist = %zu, while %s exist = %zu(exist_input_shapes == exist_input_dtypes)[%s:%d]%lu %s: ErrorNo: %u(%s) %sFailed to convert to flow model om[%s:%d]%lu %s: ErrorNo: %u(%s) %sFailed to load model[%s:%d]%lu %s:[Load][Model] success, model_id = %uExecuteModelDistributeLoadModelllm.InputDtypesllm.InputShapesinference[DecoderManager] input shape size:%zu mismatches input dtypes size:%zu(model_desc.input_shapes.size() == model_desc.input_dtypes.size())[DecoderManager] input shape size:%lu mismatches input batch dim index size:%zu(model_desc.input_shapes.size() == model_desc.inputs_batch_dim_index.size())Expect %zu inputs, but got %zu[%s:%d]%lu %s: ErrorNo: %u(%s) %sExpect %zu inputs, but got %zuinput [%zu] tensor size (%zu) is invalid, expected size = %lu[%s:%d]%lu %s: ErrorNo: %u(%s) %sinput [%zu] tensor size (%zu) is invalid, expected size = %lu[%s:%d]%lu %s:[DecoderManager] Input:%zu is not need batchingcopy input_tensor data failed. dst_max = %lu, copy_size = %lu[%s:%d]%lu %s: ErrorNo: %u(%s) %scopy input_tensor data failed. dst_max = %lu, copy_size = %lu[%s:%d]%lu %s:Request [%lu] in batch [%d] timed outprev request not finished, request id = %lu[%s:%d]%lu %s: ErrorNo: %u(%s) %sprev request not finished, request id = %lu[%s:%d]%lu %s: ErrorNo: %u(%s) %sRequest [%lu] failed to added to batch [%d][%s:%d]%lu %s:request id = %lu, batch index = %zu[%s:%d]%lu %s: ErrorNo: %u(%s) %sRequest [%lu] assemble input failed[%s:%d][EVENT]%lu %s:Request [%lu] take batch id [%d], batch index [%d][%s:%d]%lu %s:Request [%lu] take batch id [%d], batch index [%d][%s:%d]%lu %s: ErrorNo: %u(%s) %sInvalid tensor shape: %s[%s:%d]%lu %s: ErrorNo: %u(%s) %sCheck tensor size for input [%zu] failed[DecoderManager] batch dim index:%ld is out of range, dim size:%zu[%s:%d]%lu %s: ErrorNo: %u(%s) %s[DecoderManager] batch dim index:%ld is out of range, dim size:%zu[%s:%d]%lu %s:Input [%zu], shape = %s, batch_dim = %ld, copy_times = %lu, batch_stride = %lu, copy_size = %lu, single_tensor_size = %lu[%s:%d]%lu %s:Batch inputs not ready, waiting request ids = %s[%s:%d]%lu %s:request id [%ld] not existed[%s:%d][EVENT]%lu %s:request id [%ld] not existed[%s:%d][EVENT]%lu %s:Request [%ld] was complete, release batch_id [%d], batch_index [%d][%s:%d]%lu %s:Request [%ld] was complete, release batch_id [%d], batch_index [%d][%s:%d]%lu %s: ErrorNo: %u(%s) %sFailed to push to idle queue[%s:%d]%lu %s:Success to init batch input tensors, size:%zu, addr:%lu[%s:%d]%lu %s: ErrorNo: %u(%s) %sFailed to validate model desc[%s:%d]%lu %s: ErrorNo: %u(%s) %sFailed to parse copy argsInvalid tensor shape: %s[%s:%d]%lu %s:batch is emptyFailed to push to idle queuePopAllTimeoutRequestsCollectCurrentBatchCompleteRequestUpdateBatchOccupyBatchAssembleInputCheckTensorSizeParseCopyArgsInitBatchInputTensorsCheckModelDescInitializedecoder_manager.ccdecoder link clusters failedFailed to check inputs_BuiltIn_sync_kv_cache_func_BuiltIn_merge_kv_cache_func_BuiltIn_update_link_funcbuild udf flow funcs failedDecoderExecute_NnFlowData_ReqFlowData_BatchIndexFlowData_LinkInfoFlowData_invoke_postprocess_modelpp_postprocess_depend_keypostprocess_output_indicesinvoke_decoder_modelpp_devicenn_need_set_outputkv_in_data_shapekv_in_data_typecluster_idkv_sizecached_kv_max_tensor_sizebatch_numnn_input_numenable_paged_attentionsync_kv_output_indicesmerge_kv_output_indices[%s:%d]%lu %s:[DecoderManager] Finalize END.This API is not supported in auto batching mode[%s:%d]%lu %s: ErrorNo: %u(%s) %sThis API is not supported in auto batching mode[%s:%d]%lu %s: ErrorNo: %u(%s) %sdecoder link clusters faileddecoder unlink clusters failed[%s:%d]%lu %s: ErrorNo: %u(%s) %sdecoder unlink clusters failed_BuiltIn_execute_flow_model_func_BuiltIn_execute_flow_model_ssminputs tensor size != the option of input_shapes, %zu != %zu.(inputs.size() == input_shapes_[model_id].size())(inputs_batch_dim_indexs_[model_id][i] < static_cast(input_shape.size()))[%s:%d]%lu %s:%u batch dim index:%ld[%s:%d]%lu %s: ErrorNo: %u(%s) %s%u input shape is not equal to the %s option given before. %s != %s[%s:%d]%lu %s: ErrorNo: %u(%s) %s%u input dtype is not equal to the %s option given before. %d != %d[%s:%d]%lu %s: ErrorNo: %u(%s) %sFailed to check inputs[%s:%d]%lu %s: ErrorNo: %u(%s) %sFailed to get batch id[%s:%d]%lu %s: ErrorNo: %u(%s) %sFailed to check request ids[%s:%d]%lu %s: ErrorNo: %u(%s) %sFailed to execute decoder model, batch_id = %d[%s:%d][EVENT]%lu %s:[LlmPerf] ExecuteModelAsync ended, batch_id = %d, elapsed_us = %ld[%s:%d]%lu %s:[LlmPerf] ExecuteModelAsync ended, batch_id = %d, elapsed_us = %ld[%s:%d]%lu %s: ErrorNo: %u(%s) %sFailed to fetch output, batch_id = %d[%s:%d]%lu %s: ErrorNo: %u(%s) %sFailed to fetch model outputs[%s:%d][EVENT]%lu %s:[LlmPerf] FetchOutput ended, batch_id = %d, elapsed_us = %ld[%s:%d]%lu %s:[LlmPerf] FetchOutput ended, batch_id = %d, elapsed_us = %ld[%s:%d]%lu %s:Decoder nn_inputs_indices_:%s, sync_kv_func_req_input_indices_:%s, decoder_batch_index_input_indices_:%s, update_link_input_indices_:%s[%s:%d]%lu %s:Decoder out decoder_finish_sync_kv_indices:%s, decoder_finish_merge_kv_indices:%s, update_link_finish_status_indices:%s, nn_outputs_indices:%s[%s:%d]%lu %s:batch_size:%zu, max_block_num_per_seq:%zu[%s:%d][EVENT]%lu %s:[DecoderManager] Req_id:%lu seq len is 0.[%s:%d]%lu %s:[DecoderManager] Req_id:%lu seq len is 0.[%s:%d]%lu %s: ErrorNo: %u(%s) %sReq_id:%lu append slot failed as no free block.[%s:%d]%lu %s: ErrorNo: %u(%s) %sCan not find request:%lu batch index.((LLMUtils::UpdateInputTensor( 0UL, actual_batch_size, reinterpret_cast(paged_attention_inputs_data_.block_table.data()), paged_attention_inputs_[0UL])) == ge::SUCCESS)((LLMUtils::UpdateInputTensor( 0UL, 1, reinterpret_cast(single_batch_paged_attention_inputs_data_.block_table.data()), single_batch_paged_attention_inputs_[0UL])) == ge::SUCCESS)[%s:%d]%lu %s:[DecoderManager] CreateDecoderFlowNodes begin.[%s:%d]%lu %s: ErrorNo: %u(%s) %sbuild udf flow funcs failed[%s:%d]%lu %s: ErrorNo: %u(%s) %sRequest [%lu] seq len:%lu over limit: block_num:%lu, block_size:%lu[%s:%d]%lu %s: ErrorNo: %u(%s) %sRequest [%lu] create single batch paged attention tensor failedThis API is not supported in manual batching mode[%s:%d]%lu %s: ErrorNo: %u(%s) %sThis API is not supported in manual batching mode(merged_inputs.size() == input_shapes_[model_id].size())[%s:%d]%lu %s: ErrorNo: %u(%s) %sRequest [%lu] failed to check inputs[%s:%d]%lu %s: ErrorNo: %u(%s) %s[DecoderManager] push req_id:%lu failed.[%s:%d]%lu %s:[DecoderManager][Call] RunDecoderAsync successfully, req_id:%lu.[%s:%d]%lu %s: ErrorNo: %u(%s) %sGenerate paged attention input failedReqs size %zu should be equal to batch_indices size %zu.(req_ids.size() == batch_indices.size())[%s:%d]%lu %s: ErrorNo: %u(%s) %sSsm not support pipeline partition now.[%s:%d]%lu %s:Model %zu option %s = %sOption [%s] value is invalid, value = %s, expect enable|disable[%s:%d]%lu %s: ErrorNo: %u(%s) %sOption [%s] value is invalid, value = %s, expect enable|disable[%s:%d]%lu %s:pipeline execution was disabled by option %s[%s:%d]%lu %s:pipeline execution was enabled, pipeline stage num = %zu[%s:%d]%lu %s:postprocess input indices = %s, batch_id_input_indices = %s, control_input_indices = %s[%s:%d]%lu %s:stage [%zu] need control input[%s:%d]%lu %s:pipeline stage = %zu, input indices = %s[%s:%d][EVENT]%lu %s:[DecoderManager] cluster:%lu begin initialize.[%s:%d]%lu %s:[DecoderManager] cluster:%lu begin initialize.[%s:%d]%lu %s: ErrorNo: %u(%s) %s[DecoderManager] Failed to initialize paged attention[%s:%d]%lu %s: ErrorNo: %u(%s) %sPageAttention can not use with multiple models.[%s:%d]%lu %s: ErrorNo: %u(%s) %sparse flow graph info failed[%s:%d]%lu %s: ErrorNo: %u(%s) %s[DecoderManager] parse pipeline stage execution failed[%s:%d]%lu %s: ErrorNo: %u(%s) %sconstruct flow graph failed[%s:%d]%lu %s: ErrorNo: %u(%s) %sbuild flow graph failed[%s:%d]%lu %s: ErrorNo: %u(%s) %s[DecoderManager] parse batch mode failed.[%s:%d]%lu %s: ErrorNo: %u(%s) %s[DecoderManager] Failed to initialize scheduler[%s:%d][EVENT]%lu %s:[DecoderManager] cluster:%lu end initialize.[%s:%d]%lu %s:[DecoderManager] cluster:%lu end initialize.[%s:%d][EVENT]%lu %s:[DecoderManager] cluster:%lu end initialize, cost time:%zu[%s:%d]%lu %s:[DecoderManager] cluster:%lu end initialize, cost time:%zupostprocess_external_input_numvector::_M_range_insertvector::_M_fill_insertUnlinkClustersLinkClustersRunDecoderCreateSingleBatchPagedAttentionInputInitGeneratePagedAttentionInputRunDecoderMergeKvFromCacheToBatchPullKvToCacheRunDecoderCheckInputsRunDecoderAsyncFinalizeInitializePrepareExecuteNodeInfoBuildUdfFlowFuncsPrepareIoIndicesForStagesParsePipelineExecutionPrepareIoIndices*ZN3llm14DecoderManager22PrepareExecuteNodeInfoERKSt6vectorINS_12FlowDataInfoESaIS2_EERS1_INS_12FlowNodeInfoESaIS7_EEEUlN2ge5dflow10FunctionPpEE_*ZN3llm14DecoderManager10RunDecoderERKNS_6LLMReqERKSt6vectorIN2ge6TensorESaIS6_EERS8_EUljSB_E0_*ZN3llm14DecoderManager10RunDecoderERKSt6vectorImSaImEERKS1_IN2ge6TensorESaIS7_EERS9_mEUljSC_E_disableenablellm.PipelineExecutionllm.InputDtypesllm.InputShapesK?decoder_model_executor.cc((output_buffer) != nullptr)Operator graph failedOutput [%zu] tensor size changed, cached size = %zu, current size = %zu[%s:%d]%lu %s: ErrorNo: %u(%s) %sOutput [%zu] tensor size changed, cached size = %zu, current size = %zu[%s:%d]%lu %s:DecoderModelExecutor finalized[%s:%d]%lu %s:DecoderModelExecutor initialized, execution_timeout_in_ms = %d[%s:%d]%lu %s:decoder_inputs_token_indices_ size:%zu, tensor size:%zu[%s:%d]%lu %s: ErrorNo: %u(%s) %sdecoder graph feed data failed.[%s:%d]%lu %s:Decoder model feed inputs successfully[%s:%d]%lu %s:Output tensors already prepared[%s:%d]%lu %s:PrepareOutputTensors started[%s:%d]%lu %s:PrepareOutputTensors ended successfully[%s:%d]%lu %s:output tensor desc not cached yetOutput [%zu] is invalid, batch dim = %ld, but expect %zu[%s:%d]%lu %s: ErrorNo: %u(%s) %sOutput [%zu] is invalid, batch dim = %ld, but expect %zu[%s:%d]%lu %s: ErrorNo: %u(%s) %sFailed to get tensor size, output index = %zu, shape = %s[%s:%d]%lu %s: ErrorNo: %u(%s) %sFailed to update tensor batch dim, input index = %zu, shape = %s[%s:%d]%lu %s:Output index = %zu, shape = %s, single_batch_shape = [%s], dtype = %d, size = %zu[%s:%d][EVENT]%lu %s:Callback thread exit[%s:%d]%lu %s:Callback thread exitPrepareOutputTensorsTryPrepareOutputTensorsCheckOutputTensorSizesCacheOutputTensorDescsGetModelOutputCallbackThreadExecuteModelAsyncFinalizeInitializeNSt6thread11_State_implINS_8_InvokerISt5tupleIJMN3llm20DecoderModelExecutorEFvlEPS4_mEEEEEEdecoder_scheduler.cc((defer_lock) != nullptr)[%s:%d]%lu %s:No new request[%s:%d]%lu %s: ErrorNo: %u(%s) %sRequest [%lu] Failed to merge kv to batch[%s:%d]%lu %s:Request [%lu] already completed[%s:%d]%lu %s: ErrorNo: %u(%s) %sRequest [%lu] failed to add to batch[%s:%d][EVENT]%lu %s:[LlmPerf] Collect batch ended, batch_size = %zu, wait_success = %d, elapsed_us = %ld[%s:%d]%lu %s:[LlmPerf] Collect batch ended, batch_size = %zu, wait_success = %d, elapsed_us = %ldcopy output_data failed. size = %zu[%s:%d]%lu %s: ErrorNo: %u(%s) %scopy output_data failed. size = %zu[%s:%d]%lu %s:GetOutputTensors success, batch index = %d[%s:%d]%lu %s:request id [%ld] not in batch, add to prepare queueRequest [%lu] request queue is full, enqueue timeout[%s:%d]%lu %s: ErrorNo: %u(%s) %sRequest [%lu] request queue is full, enqueue timeout[%s:%d][EVENT]%lu %s:Request [%lu] added to prepare queue[%s:%d]%lu %s:Request [%lu] added to prepare queue[%s:%d][EVENT]%lu %s:Request [%lu] added to batch[%s:%d]%lu %s:Request [%lu] added to batch[%s:%d]%lu %s:batch_id = %zu, idle_batch_size = %zu(batch_states_[batch_id].WaitIdleBatchIndex(batch_index, timeout_in_ms))[%s:%d]%lu %s:will merge to batch_id = %d, batch_index = %d[%s:%d]%lu %s:Request [%lu] Sync kv start[%s:%d]%lu %s: ErrorNo: %u(%s) %sRequest [%lu] failed to pull KVRequest [%lu] timed out after pull KV[%s:%d]%lu %s: ErrorNo: %u(%s) %sRequest [%lu] timed out after pull KVRequest [%lu] failed to get an idle batch index in [%d] ms[%s:%d]%lu %s: ErrorNo: %u(%s) %sRequest [%lu] failed to get an idle batch index in [%d] ms[%s:%d]%lu %s:Request [%lu] Sync kv ended, ret = %u[%s:%d]%lu %s: ErrorNo: %u(%s) %sFailed to prepare output tensors[%s:%d]%lu %s:Request [%lu] step [%d] process failed, error_code = %u, elapsed_us = %ld[%s:%d]%lu %s: ErrorNo: %u(%s) %sRequest [%lu] step [%d] process failed, error_code = %u, elapsed_us = %ld[%s:%d][EVENT]%lu %s:Scheduler finalize start[%s:%d]%lu %s:Scheduler finalize start[%s:%d][EVENT]%lu %s:Scheduler finalized successfully[%s:%d]%lu %s:Scheduler finalized successfully[%s:%d]%lu %s:TimeoutRequestsEvictThread started[%s:%d]%lu %s:TimeoutRequestsEvictThread ended[%s:%d][EVENT]%lu %s:SyncKV thread started[%s:%d]%lu %s:SyncKV thread started[%s:%d][EVENT]%lu %s:SyncKV thread ended[%s:%d]%lu %s:SyncKV thread ended[%s:%d][EVENT]%lu %s:[LlmPerf] CallbackOutputs ended, batch size = %zu, elapsed_us = %ld[%s:%d]%lu %s:[LlmPerf] CallbackOutputs ended, batch size = %zu, elapsed_us = %ld[%s:%d][EVENT]%lu %s:Request [%lu] step [%d] callback ended, request total elapsed_us = %ld[%s:%d]%lu %s:Request [%lu] step [%d] callback ended, request total elapsed_us = %ld[%s:%d]%lu %s:process thread started[%s:%d]%lu %s:process batch[%d][%s:%d][EVENT]%lu %s:process thread ended[%s:%d]%lu %s:process thread endedappend slot for req:%lu failed, ret=%u((ret == ge::LLM_NO_FREE_BLOCK))[%s:%d]%lu %s:req_id:%lu append slot failed, as no free block.(paged_attention_inputs_.size() == kPagedAttentionTensorNum)((LLMUtils::UpdateInputTensor( 0UL, actual_batch_size, reinterpret_cast(paged_attention_inputs_data_.block_table.data()), paged_attention_inputs_[0])) == ge::SUCCESS)[%s:%d]%lu %s: ErrorNo: %u(%s) %sConstruct non user input failed[%s:%d]%lu %s: ErrorNo: %u(%s) %sFailed to execute model[%s:%d][EVENT]%lu %s:[LlmPerf] ExecuteModelAsync ended, elapsed_us = %ld[%s:%d]%lu %s:[LlmPerf] ExecuteModelAsync ended, elapsed_us = %ld[%s:%d][EVENT]%lu %s:[LlmPerf] PrepareOutputTensor ended, elapsed_us = %ld[%s:%d]%lu %s:[LlmPerf] PrepareOutputTensor ended, elapsed_us = %ld[%s:%d]%lu %s: ErrorNo: %u(%s) %sFailed to get model outputs[%s:%d]%lu %s: ErrorNo: %u(%s) %sFailed to cache output tensor desc[%s:%d][EVENT]%lu %s:[LlmPerf] FetchOutput ended, elapsed_us = %ld[%s:%d]%lu %s:[LlmPerf] FetchOutput ended, elapsed_us = %ld[%s:%d]%lu %s:Scheduler initializedUpdatePagedAttentionInputTensorApendSlotForReqInitGetOutputTensorsCallbackOnErrorCallbackOutputsCollectCurrentBatchCollectCurrentBatchMergeKvoperator()ExecuteModelAsyncExecuteModelWaitIdleBatchIndexSyncKvTimeoutRequestEvictThreadSyncKvThreadPipelineInferenceThreadInferenceThreadDispatchRequestFinalizeInitializePP*ZN3llm16DecoderScheduler17ExecuteModelAsyncERKNS_12BatchManagerEiRSt6vectorINS_14DecoderRequestESaIS5_EEOSt11unique_lockISt5mutexEEUljRS4_IN2ge6TensorESaISE_EEE_NSt6thread11_State_implINS_8_InvokerISt5tupleIJMN3llm16DecoderSchedulerEFvvEPS4_EEEEEESt15_Sp_counted_ptrIPSt11unique_lockISt5mutexELN9__gnu_cxx12_Lock_policyE2EEkv_cache_manager.ccAll requests are invalid;[%s:%d]%lu %s: ErrorNo: %u(%s) %sReq seq len %llu is bigger than max seq len %llu, req id:%llu[%s:%d]%lu %s: ErrorNo: %u(%s) %sAll requests are invalid[%s:%d]%lu %s:Got batch id = %d by first valid request [%lu][%s:%d]%lu %s: ErrorNo: %u(%s) %sRequest [%lu] not in any batch[%s:%d]%lu %s:Request[%lu] Feed sync KV request start, input index = %ld, batch_id = %d, batch index = %d[%s:%d]%lu %s: ErrorNo: %u(%s) %sFailed to feed sync kv request, request = %lu[%s:%d][EVENT]%lu %s:[LlmPerf] Request [%lu] Feed MergeKV ended, elapsed_us = %ld[%s:%d]%lu %s:[LlmPerf] Request [%lu] Feed MergeKV ended, elapsed_us = %ld[%s:%d]%lu %s:Request [%lu] finish out indices size:%zu, index0:%u[%s:%d]%lu %s: ErrorNo: %u(%s) %sFailed to fetch sync kv response, request = %lu[%s:%d]%lu %s: ErrorNo: %u(%s) %sRequest [%lu] failed to merge KV[%s:%d][EVENT]%lu %s:[LlmPerf] Request [%lu] Fetch MergeKV ended, elapsed_us = %ld[%s:%d]%lu %s:[LlmPerf] Request [%lu] Fetch MergeKV ended, elapsed_us = %ld[%s:%d]%lu %s: ErrorNo: %u(%s) %sRequest[%lu] allocate block failed, ret = %uParam:sync_req_info is nullptr, check invalid[%s:%d]%lu %s: ErrorNo: %u(%s) %s[Check][Param:sync_req_info]null is invalid[%s:%d]%lu %s:Request[%lu] Feed push KV request start, prompt_cluster_id = %lu[%s:%d]%lu %s: ErrorNo: %u(%s) %sRequest [%lu] Failed to feed push kv request, prompt_cluster_id = %lu[%s:%d][EVENT]%lu %s:[LlmPerf] Request [%lu] Feed PushKV ended, prompt_cluster_id = %lu, elapsed_us = %ld[%s:%d]%lu %s:[LlmPerf] Request [%lu] Feed PushKV ended, prompt_cluster_id = %lu, elapsed_us = %ld[%s:%d]%lu %s:Request [%lu] Fetch PullKv output start, indices = %s[%s:%d]%lu %s: ErrorNo: %u(%s) %sRequest [%lu] Failed to fetch sync kv response[%s:%d][EVENT]%lu %s:[LlmPerf] Request [%lu] Fetch PullKV ended, elapsed_us = %ld[%s:%d]%lu %s:[LlmPerf] Request [%lu] Fetch PullKV ended, elapsed_us = %ldRequest [%lu] check param failed, batch id (%d) out of range [0, %d)[%s:%d]%lu %s: ErrorNo: %u(%s) %sRequest [%lu] check param failed, batch id (%d) out of range [0, %d)Request [%lu] check param failed, batch index (%d) out of range [0, %d)[%s:%d]%lu %s: ErrorNo: %u(%s) %sRequest [%lu] check param failed, batch index (%d) out of range [0, %d)Request [%lu] failed to merge KV, cache not exist[%s:%d]%lu %s: ErrorNo: %u(%s) %sRequest [%lu] failed to merge KV, cache not exist[%s:%d][EVENT]%lu %s:KV cache [%d] was freed[%s:%d]%lu %s:KV cache [%d] was freed[%s:%d]%lu %s:Batch merge kv start, number of requests = %zu, model id = %lu.Number of req_ids (%zu) mismatches that of batch_indices (%zu)[%s:%d]%lu %s: ErrorNo: %u(%s) %sNumber of req_ids (%zu) mismatches that of batch_indices (%zu)Error occurred while merging KV, failure/total = %d/%zu[%s:%d]%lu %s: ErrorNo: %u(%s) %sError occurred while merging KV, failure/total = %d/%zu[%s:%d]%lu %s:Batch merge kv ended successfully, number of requests = %zu, model id = %lu.[%s:%d][EVENT]%lu %s:Request [%lu] kv cache (%d) freed before merging[%s:%d]%lu %s:Request [%lu] kv cache (%d) freed before mergingNumber of request ids (%zu) mismatches batch size (%zu)[%s:%d]%lu %s: ErrorNo: %u(%s) %sNumber of request ids (%zu) mismatches batch size (%zu)Request [%lu] not in batch [%d:%zu], batch already occupied by Request [%ld], req_ids = %s, batch_state = %s[%s:%d]%lu %s: ErrorNo: %u(%s) %sRequest [%lu] not in batch [%d:%zu], batch already occupied by Request [%ld], req_ids = %s, batch_state = %sRequest [%lu] already pulled KV, occupying cache [%d][%s:%d]%lu %s: ErrorNo: %u(%s) %sRequest [%lu] already pulled KV, occupying cache [%d]Insufficient idle caches available, num_idle_caches = %zu, current requests = %s[%s:%d]%lu %s: ErrorNo: %u(%s) %sInsufficient idle caches available, num_idle_caches = %zu, current requests = %s[%s:%d][EVENT]%lu %s:Request [%lu] occupy KV cache id [%d][%s:%d]%lu %s:Request [%lu] occupy KV cache id [%d][%s:%d]%lu %s:Batch pull kv start, number of requests = %zu, model id = %lu.Error occurred while pulling KV, failure/total = %d/%zu[%s:%d]%lu %s: ErrorNo: %u(%s) %sError occurred while pulling KV, failure/total = %d/%zu[%s:%d]%lu %s:Batch pull kv ended successfully, number of requests = %zu, model id = %lu.[%s:%d]%lu %s:KvCacheManager initialized, timeout_in_ms = %d, num_batches = %sCheckAndGetBatchIdCheckRequestsReleaseAllCachesMergeKvWithCacheBatchMergeKvPullKvWithCacheBatchPullKvMergeKvAllocateBlockPullKvInitialize*ZN3llm14KvCacheManager6PullKvERKNS_6LLMReqEimEUlvE_ApB_llm_engine.ccUnlink clusters failedRole should be Prompt!PriorityScheduleSeparateSchedulege.graph_compiler_cache_dir[LLMEngine] not find %s.[%s:%d]%lu %s:[LLMEngine] cluster:%lu llm engine is not initializedParam:priority_schedule_manager_ is nullptr, check invalid[%s:%d]%lu %s: ErrorNo: %u(%s) %s[Check][Param:priority_schedule_manager_]null is invalidParam:prompt_manager_ is nullptr, check invalid[%s:%d]%lu %s: ErrorNo: %u(%s) %s[Check][Param:prompt_manager_]null is invalidParam:decoder_manager_ is nullptr, check invalid[%s:%d]%lu %s: ErrorNo: %u(%s) %s[Check][Param:decoder_manager_]null is invalid[%s:%d]%lu %s: ErrorNo: %u(%s) %s[LLMEngine] cluster:%lu finalize failed[%s:%d]%lu %s: ErrorNo: %u(%s) %s[LLMEngine] cluster:%lu GEFinalize failed[%s:%d]%lu %s:LLMEngineFinalize Finalize end.Llm engine of cluster:%lu is not initialized.[%s:%d]%lu %s: ErrorNo: %u(%s) %sLlm engine of cluster:%lu is not initialized.[LLMEngine] role is %s, linkClusters must be used Decoder[%s:%d]%lu %s: ErrorNo: %u(%s) %s[LLMEngine] role is %s, linkClusters must be used Decoder[%s:%d]%lu %s: ErrorNo: %u(%s) %sUnlink clusters failed[%s:%d]%lu %s: ErrorNo: %u(%s) %sThe cluster:%lu where the req_id:%lu is located is invalid, the valid cluster is:%lu[%s:%d]%lu %s: ErrorNo: %u(%s) %smodel id is over limit.[%s:%d]%lu %s:Begin to run prompt, model_id = %luLlm engine is not initialized, cluster_id:%lu.[%s:%d]%lu %s: ErrorNo: %u(%s) %sLlm engine is not initialized, cluster_id:%lu.Role is invalid, current cluster role:%s, cluster_id:%lu.[%s:%d]%lu %s: ErrorNo: %u(%s) %sRole is invalid, current cluster role:%s, cluster_id:%lu.[%s:%d]%lu %s: ErrorNo: %u(%s) %s[LLMEngine] req is invalid[%s:%d]%lu %s: ErrorNo: %u(%s) %sFail to run prompt[%s:%d]%lu %s:Success to run prompt[%s:%d]%lu %s:Begin to run prompt, req_id:%lu, prefix_id:%lu, prompt_cluster_id:%lu, decoder_cluster_id:%lu, inputs tensor size:%zu.[%s:%d]%lu %s: ErrorNo: %u(%s) %sFail to run prompt, req_id:%lu, prefix_id:%lu, cluster_id:%lu.[%s:%d]%lu %s:Success to run prompt, req_id:%lu, prefix_id:%lu, prompt_cluster_id:%lu, decoder_cluster_id:%lu.[%s:%d]%lu %s:[LLMEngine] run decoder, req_id:%lu, prompt_cluster_id:%lu, decoder_cluster_id:%lu, inputs tensor size:%zu.[%s:%d]%lu %s: ErrorNo: %u(%s) %s[LLMEngine] cluster:%lu llm engine is not initialized[%s:%d]%lu %s: ErrorNo: %u(%s) %s[LLMEngine] role is %s, not match RunDecoderAsync[%s:%d]%lu %s:[LLMEngine] run decoder begin, req_id: %lu, input_size:%ld[%s:%d]%lu %s: ErrorNo: %u(%s) %s[LLMEngine] req id:%lu run decoder failed in cluster:%lu[%s:%d]%lu %s:[LLMEngine] req_id:%lu run decoder success in cluster:%lu[%s:%d]%lu %s:Begin to preload prompt prefix kv cache, prefix_id:%lu, cluster_id:%lu.[%s:%d]%lu %s: ErrorNo: %u(%s) %sRole should be Prompt!Prefix_id:%lu is invalid, valid range is [0, %lu).[%s:%d]%lu %s: ErrorNo: %u(%s) %sPrefix_id:%lu is invalid, valid range is [0, %lu).[%s:%d]%lu %s: ErrorNo: %u(%s) %sFail to preload prompt prefix kv cache, prefix_id:%lu, cluster_id:%lu.[%s:%d]%lu %s:Success to preload prompt prefix kv cache, prefix_id:%lu, cluster_id:%lu.[%s:%d]%lu %s:Begin to release prompt prefix kv cache, prefix_id:%lu, cluster_id:%lu.[%s:%d]%lu %s: ErrorNo: %u(%s) %sFail to release prompt prefix kv cache, prefix_id:%d, cluster_id:%lu.[%s:%d]%lu %s:Success to release prompt prefix kv cache, prefix_id:%lu, clustr_id:%lu.This API this only supported in decoder[%s:%d]%lu %s: ErrorNo: %u(%s) %sThis API this only supported in decoder[%s:%d]%lu %s: ErrorNo: %u(%s) %sRequest [%lu] is invalid[%s:%d]%lu %s:[LLMEngine] complete req_id:%lu, prompt_cluster_id:%lu, decoder_cluster_id:%lu.role should be Prompt or Decoder!((role_ == kDecoder) || (role_ == kPrompt))[%s:%d]%lu %s: ErrorNo: %u(%s) %s[LLMEngine] complete req_id:%lu failed in cluster:%luimpl is nullptr, check LLMEngine construct[%s:%d]%lu %s: ErrorNo: %u(%s) %simpl is nullptr, check LLMEngine construct[%s:%d]%lu %s:Begin to async run prompt, req_id:%lu, prefix_id:%lu, prompt_cluster_id:%lu, decoder_cluster_id:%lu, inputs tensor size:%zu.[%s:%d]%lu %s:[LLMEngine] run prompt async begin, req_id: %lu, input_size:%ld[%s:%d]%lu %s: ErrorNo: %u(%s) %s[LLMEngine] req id:%llu run prompt async failed in cluster:%lu[%s:%d]%lu %s:Success to async run prompt, req_id:%lu, prefix_id:%lu, prompt_cluster_id:%lu, decoder_cluster_id:%lu[%s:%d]%lu %s:req_id:%lu, prompt_cluster_id:%lu, decoder_cluster_id:%lu, inputs tensor size:%zu.[%s:%d]%lu %s:[LLMEngine] run decoder async begin, req_id: %lu, input_size:%ld[%s:%d]%lu %s: ErrorNo: %u(%s) %s[LLMEngine] req id:%lu run decoder async failed in cluster:%lu[%s:%d]%lu %s:[LLMEngine] req_id:%lu run decoder async success in cluster:%lu[%s:%d]%lu %s:Enter LLMEngine construct[%s:%d]%lu %s:Create LlmEngineImpl failed[%s:%d]%lu %s: ErrorNo: %u(%s) %sRequest %s Failed to run decoder model[%s:%d][EVENT]%lu %s:Request %s FetchOutput ended, elapsed_us = %ld[%s:%d]%lu %s:Request %s FetchOutput ended, elapsed_us = %ld[%s:%d]%lu %s: ErrorNo: %u(%s) %sFailed to run decoder model[%s:%d]%lu %s:Begin to init in PrioritySchedule run mode((priority_schedule_manager_) != nullptr)[%s:%d]%lu %s: ErrorNo: %u(%s) %sCan not call AddLLMModel after initialized.[%s:%d]%lu %s: ErrorNo: %u(%s) %sModel num is over limit.[%s:%d]%lu %s:cluster:%lu, model_buffer_datas size:%zu.[LLMEngine]Runmode is invalid.(run_mode_iter != run_mode_map.cend())[%s:%d]%lu %s: ErrorNo: %u(%s) %sAddLLMModel is already called, can not add model by initilize again.[%s:%d]%lu %s:LLMEngine role: %s.[%s:%d]%lu %s: ErrorNo: %u(%s) %s[Manager] initialize failed.[%s:%d]%lu %s:[LLMEngine] role:[%s] cluster:[%lu] llm engine init successAddLLMModelUnlinkClustersLinkClustersRunDecoderRunDecoderMergeKvPullKvReleasePromptPrefixPreloadPromptPrefixLLMEngineFinalizeLLMReqCompleteRunDecoderRunDecoderAsyncRunPromptRunPromptRunPromptAsyncLLMEngineInitializeV2LLMEngineCheckValidityOfRequestInitializePriorityScheduleAddLLMModelUnlinkClustersLinkClustersRunDecoderRunDecoderMergeKvPullKvReleasePromptPrefixPreloadPromptPrefixLLMReqCompleteRunDecoderRunDecoderAsyncRunPromptRunPromptRunPromptAsyncLLMEngineFinalizeLLMEngineInitializeV2llm.graph_compiler_cache_dirllm.RunModellm.RoleinferenceDecoderPromptSt15_Sp_counted_ptrIPN3llm21PagedAttentionOptionsELN9__gnu_cxx12_Lock_policyE2EESt15_Sp_counted_ptrIPN3llm17BlockSpaceManagerELN9__gnu_cxx12_Lock_policyE2EESt15_Sp_counted_ptrIPN3llm13PromptManagerELN9__gnu_cxx12_Lock_policyE2EESt15_Sp_counted_ptrIPN3llm14DecoderManagerELN9__gnu_cxx12_Lock_policyE2EESt15_Sp_counted_ptrIPN3llm23PriorityScheduleManagerELN9__gnu_cxx12_Lock_policyE2EEllm_engine_v2.cc[%s:%d]%lu %s:cluster:%luParam:llm_flow_service_ is nullptr, check invalid[%s:%d]%lu %s: ErrorNo: %u(%s) %s[Check][Param:llm_flow_service_]null is invalid[%s:%d]%lu %s: ErrorNo: %u(%s) %sFailed to initialize LLM flow serviceParam:cache_engine_ is nullptr, check invalid[%s:%d]%lu %s: ErrorNo: %u(%s) %s[Check][Param:cache_engine_]null is invalidParam:offline_model_manager_ is nullptr, check invalid[%s:%d]%lu %s: ErrorNo: %u(%s) %s[Check][Param:offline_model_manager_]null is invalid[%s:%d]%lu %s: ErrorNo: %u(%s) %sFailed to load model, model_index = %zu[%s:%d]%lu %s:offline models loaded successfully, model_index = %zu[%s:%d]%lu %s:Initialize LLM flow service successfullyRunModelDistributeGetCacheTensorsRemoveCacheKeyCopyCachePullCacheDeallocateCacheAllocateCacheAddLLMModelUnlinkClustersLinkClustersLLMEngineInitializeLLMEngineV2InitFlowServiceRunModelDistributeGetCacheTensorsRemoveCacheKeyCopyCachePullCacheDeallocateCacheAllocateCacheUnlinkClustersLinkClustersAddLLMModelLLMEngineFinalizeLLMEngineInitializellm.Rolellm.OmCachePathDecoderPromptpriority_schedule_manager.ccstolCall %s fail, ret: 0x%X(kv_shapes.size() == 1UL)(kv_dtypes.size() == 1UL)(ret)Gen tensor data failed.((ret) == ge::SUCCESS)complete req %lu failed(ret.first)Complete req:%lu failed.{tensor_desc[]=, shape:[], dtype:, place:} ; (insert_ret.first)PromptRunexecute prompt failedPromptBuildbuild prompt graph failedge.graph_key(data_size > 0L)rtSetDevice(device_id)DecoderBuildrun decoder graph failedDecoderRunbuild graph failedexecute decoder failed[%s:%d]%lu %s: ErrorNo: %u(%s) %sCall rt api failed, ret: 0x%X[%s:%d]%lu %s: ErrorNo: %u(%s) %sCall rtFree failed, error: %#x[%s:%d]%lu %s: ErrorNo: %u(%s) %sprompt and decoder interleaved step:%s is invaliddecoder inputs size:%zu, batch inputs size:%zu(dst.size() >= user_inputs.size())input size check invalid, inputs_batch_dim_index_:%zu, batch_input_tensors_:%zu, prompt_input_shapes:%zu, prompt_input_dtypes:%zu, decoder_input_shapes:%zu, decoder_input_dtypes:%zu(((decoder_input_shapes.size() == inputs_batch_dim_index_.size()) && (decoder_input_shapes.size() == batch_input_tensors_.size()) && (prompt_input_dtypes.size() == prompt_input_shapes.size()) && (decoder_input_dtypes.size() == decoder_input_shapes.size())))option[%s] should be set an non zero value(paged_attention_options_.max_seqs_num != 0UL)(paged_attention_options_.max_prompt_len != 0UL)(paged_attention_options_.max_seq_len != 0UL)[%s:%d]%lu %s:PriorityScheduleManager is already finalized.rtSetDevice(static_cast(device_id))((LLMUtils::InitMultiInputShapeAndDtype(options, LLM_OPTION_MODEL_INPUTS_SHAPES, LLM_OPTION_MODEL_INPUTS_DTYPES, input_shapes, input_dtypes)) == ge::SUCCESS)(input_shapes.size() == static_cast(RoleType::kRoleTypeEnd))(input_dtypes.size() == static_cast(RoleType::kRoleTypeEnd))((LLMUtils::InitMultiInputShapeAndDtype(options, LLM_OPTION_MODEL_KV_CACHE_SHAPES, LLM_OPTION_MODEL_KV_CACHE_DTYPES, kv_shapes, kv_dtypes)) == ge::SUCCESS)[%s:%d]%lu %s:actual_batch_size is:%zuthe size of decoder_user_inputs_[%zu] should be equal to batch_input_tensors_[%zu](decoder_user_inputs_.size() == batch_input_tensors_.size())((UpdateDecoderInputTensor(inputs_batch_dim_index_[i], actual_batch_size, batch_input_tensors_[i].GetData(), decoder_user_inputs_[i])) == ge::SUCCESS)(decoder_non_user_inputs_.size() == 3UL)((UpdateDecoderInputTensor( 0UL, actual_batch_size, reinterpret_cast(decoder_non_user_inputs_data_.indices.data()), decoder_non_user_inputs_[0UL])) == ge::SUCCESS)((UpdateDecoderInputTensor( 0UL, actual_batch_size, reinterpret_cast(decoder_non_user_inputs_data_.actual_seq_len.data()), decoder_non_user_inputs_[1UL])) == ge::SUCCESS)((UpdateDecoderInputTensor( 0UL, actual_batch_size, reinterpret_cast(decoder_non_user_inputs_data_.block_table.data()), decoder_non_user_inputs_[2UL])) == ge::SUCCESS)[%s:%d]%lu %s: ErrorNo: %u(%s) %sSeq len:%lu over limit: block_num:%lu, block_size:%lu[Call][Push] failed, req_id:%lu[%s:%d][EVENT]%lu %s:[PriorityScheduleManager] RunPriorityScheduleAsync end, req_id:%lu, role_type:%u.[%s:%d]%lu %s:[PriorityScheduleManager] RunPriorityScheduleAsync end, req_id:%lu, role_type:%u.((RunPriorityScheduleAsync(role_type, req, inputs, callback)) == ge::SUCCESS)[%s:%d][EVENT]%lu %s:[PriorityScheduleManager] RunPrioritySchedule end, req_id:%lu, role_type:%u.[%s:%d]%lu %s:[PriorityScheduleManager] RunPrioritySchedule end, req_id:%lu, role_type:%u.((LLMUtils::InitBatchDimIndex(options, decoder_input_shape, inputs_batch_dim_index_)) == ge::SUCCESS)((LLMUtils::InitBatchSize(decoder_input_shape, inputs_batch_dim_index_, batch_size_)) == ge::SUCCESS)((LLMUtils::InitBatchInputsPadding(options, decoder_input_shape, inputs_batch_padding_)) == ge::SUCCESS)The option:%s is invalid. The number of parsed option is incorrect, please check.size:%zu is not equal to inputs shape size:%zu.(inputs_batch_padding_.size() == decoder_input_shape.size())the size should be equal, while output size:%zu, output tensor desc size:%zu(outputs.size() == output_tensor_desc_to_size.size())(interleaved_step.size() == kInterleavedStepSize)((trans_func(interleaved_step[0UL], prompt_m_)) == ge::SUCCESS)((trans_func(interleaved_step[1UL], decoder_n_)) == ge::SUCCESS)[%s:%d]%lu %s:[InitOption]prompt step:%zu, decoder step:%zu.(input_shapes_[static_cast(RoleType::kDecoder)].size() == inputs_batch_dim_index_.size())[%s:%d]%lu %s:Not support this Data type:%s, and will set zero[%s:%d]%lu %s:[InitOption]model inputs tensor[%zu] value:%zu.((InitShapesAndDtypes(options)) == ge::SUCCESS)((InitBatch(options)) == ge::SUCCESS)((InitPromptAndDecoderInterleavedStep(options)) == ge::SUCCESS)((LLMUtils::ParserWaitTimeInfo(options, wait_time_info_)) == ge::SUCCESS)((LLMUtils::InitPagedAttentionOptions(options, paged_attention_options_)) == ge::SUCCESS)option[%s] should not be set 0(paged_attention_options_.block_size != 0)(paged_attention_options_.blocks_num != 0)[%s:%d]%lu %s:cache_batch_size is :%zu, batch_size is:%ld((InitBatchInputTensors()) == ge::SUCCESS)((CheckOptions()) == ge::SUCCESS)[%s:%d]%lu %s:[LLMReqComplete] req_id:%lu not in user request list.[%s:%d]%lu %s:Can not find req id:%lu in cache infoInit Decoder non user inputs failed.((decoder_non_user_inputs_data_.InitBatchData(ret.second)) == ge::SUCCESS)[%s:%d][EVENT]%lu %s:[ReqComplete]req id:%lu is competed[%s:%d]%lu %s:[ReqComplete]req id:%lu is competed[%s:%d]%lu %s:[PriorityScheduleManager] complete req_id:%lu, log:%s, ret:%d.[%s:%d]%lu %s: ErrorNo: %u(%s) %s[PriorityScheduleManager] req_id:%lu, log:%s.[%s:%d]%lu %s:[PriorityScheduleManager] callback req_id:%lu, log:%s, ret:%d.Append Slot for req:%lu failed, ret=%u[%s:%d]%lu %s:req_id:%lu will be free this turn.[%s:%d]%lu %s:req_id:%lu will be free when callback arrive.[%s:%d][EVENT]%lu %s:[FreeBlock]req_id:%lu will be free as no free block, block_table info:%s.[%s:%d]%lu %s:[FreeBlock]req_id:%lu will be free as no free block, block_table info:%s.((LLMReqComplete(last_enqueue_req)) == ge::SUCCESS)[%s:%d]%lu %s:req_id:%lu is already freed.[PriorityScheduleManager] update batch index:%lu tensor failed.((LLMUtils::UpdateBatchInputs(run_args.input_tensor, batch_index, input_shapes_[static_cast(RoleType::kDecoder)], inputs_batch_dim_index_, input_dtypes_[static_cast(RoleType::kDecoder)], batch_input_tensors_)) == ge::SUCCESS)Append Slot for req:%lu failed((ConstructDecoderNonUserInputsPageAttention(active_batch_index_callback)) == ge::SUCCESS)((UpdateDecoderUserInput(decoder_user_inputs_, inputs.second)) == ge::SUCCESS)((UpdateDecoderUserInput(batch_input_tensors_, inputs.second)) == ge::SUCCESS)user_inputs_size:%zu + decoder_non_user_inputs_:%zu bigger than inputs.second:%zu((user_inputs_size + decoder_non_user_inputs_.size()) <= inputs.second.size())prompt_non_user_inputs_ is not be initialized.(!prompt_non_user_inputs_.empty())[%s:%d]%lu %s: ErrorNo: %u(%s) %sallocate block failed, ret = %u[%s:%d]%lu %s:Prompt slot size:%zu(batch_index_data_.size() == 2UL)(inputs.second.size() >= (run_args.input_tensor.size() + prompt_non_user_inputs_.size()))[%s:%d]%lu %s:[%s] tensor_desc info is: %s[%s:%d]%lu %s:[CacheInfo][Insert] Insert failed, as Cash has no idle index with capacity:%zuInsert req id:%lu to cache failed[%s:%d]%lu %s: ErrorNo: %u(%s) %sConstruct inputs for graph id:%lu with req_id:%lu failedRun graph id:%lu with req_id:%lu failed((GeApi().GetInstance().RunGraphDistribute(kPromptGraphId, prompt_device_to_inputs_, device_to_outputs)) == ge::SUCCESS)[%s:%d][EVENT]%lu %s:[LlmPerf]success to run prompt index:%zu [req_id,batch_index]:[%lu,%ld], cost time:%zu [%zu:%zu:%zu] us, BlockTable[%s][%s:%d]%lu %s:[LlmPerf]success to run prompt index:%zu [req_id,batch_index]:[%lu,%ld], cost time:%zu [%zu:%zu:%zu] us, BlockTable[%s][%s:%d]%lu %s:[PriorityScheduleInputThread][RunPrompt]prompt[%zu] can not pop from queue and will continue[%s:%d]%lu %s: ErrorNo: %u(%s) %s[PriorityScheduleInputThread][RunPrompt]execute prompt failed, prompt index:%zu, req_id:%ld((GeApi().GetInstance().BuildGraph(kPromptGraphId, prompt_inputs)) == ge::SUCCESS)[%s:%d][EVENT]%lu %s:[PriorityScheduleInputThread][BuildGraph]build prompt graph success, cost time:%zu us[%s:%d]%lu %s:[PriorityScheduleInputThread][BuildGraph]build prompt graph success, cost time:%zu usoptions LLM_OPTION_OM_CACHE_PATH must contain prompt & decoder om path.(om_list.size() == kGraphSize)[%s:%d]%lu %s:prompt graph path is %s, decode graph path is %s.((GeApi().GetInstance().LoadGraph(static_cast(i), options, om_list[i])) == ge::SUCCESS)((LLMUtils::InitInputNodeDeployment(options, input_node_deployment_)) == ge::SUCCESS)(cache_iter != options.cend())options LLM_OPTION_GRAPH_PATH must contain prompt & decoder graph.(graph_list.size() == kGraphSize)options LLM_OPTION_GRAPH_KEYS must contain init &prompt & decoder garph.(graph_keys.size() == kGraphSize)[%s:%d]%lu %s:prompt graph_key is %s, decode graph_key is %s.((graph.LoadFromFile(graph_list[i].c_str())) == ge::SUCCESS)((GeApi().GetInstance().AddGraph(static_cast(i), graph, graph_options)) == ge::SUCCESS)[%s:%d]%lu %s:[PriorityScheduleManager] option key:%s, value:%s((InitOptions(options)) == ge::SUCCESS)((InitGraphs(options)) == ge::SUCCESS)[%s:%d][EVENT]%lu %s:[PriorityScheduleManager][Initialize] success to initialize.[%s:%d]%lu %s:[PriorityScheduleManager][Initialize] success to initialize.(rtMalloc(&dev_mem, static_cast(data_size), (0x2U), static_cast(GE)) == RT_ERROR_NONE)(rtMemset(dev_mem, static_cast(data_size), 0U, static_cast(data_size)) == RT_ERROR_NONE)((tensor.SetData(reinterpret_cast(dev_mem), static_cast(data_size), [](uint8_t *ptr) { (void)ptr; return; })) == ge::SUCCESS)[%s:%d]%lu %s:Construct refdata tensor devicd:%d, shape:%s size:%zu, dev_mem:0x%lx(kv_shapes_.size() == kv_dtypes_.size())[%s:%d]%lu %s:Refdata has not be slicedkv_shape size:%zu, refdata size:%zu(kv_shapes_.size() == refdata_device_to_dim_slices.size())(iter.second.size() == kv_shapes_.size())((GeApi().GetInstance().BuildGraph(kDecoderGraphId, decoder_inputs)) == ge::SUCCESS)[%s:%d][EVENT]%lu %s:[PriorityScheduleInputThread][BuildGraph]build decoder graph success, cost time:%zu us[%s:%d]%lu %s:[PriorityScheduleInputThread][BuildGraph]build decoder graph success, cost time:%zu us((compile_graph_summary) != nullptr)((compile_graph_summary->GetInputShardMethod(refdata_device_to_dim_slices)) == ge::SUCCESS)((InitDistributedRefDataTensors(refdata_device_to_dim_slices_shape)) == ge::SUCCESS)[%s:%d][EVENT]%lu %s:[PriorityScheduleInputThread][InitRefData]init refdata tensor success, cost time:%zu us[%s:%d]%lu %s:[PriorityScheduleInputThread][InitRefData]init refdata tensor success, cost time:%zu us((InitDeviceIdToRefDataTensors(-1, ge::Shape(kv_shapes_[i]), kv_dtypes_[i])) == ge::SUCCESS)((InitDeviceIdToRefDataTensors(device_id, ge::Shape(kv_shapes_[i]), kv_dtypes_[i])) == ge::SUCCESS)Construct non user input tensordesc failed.((ConstructNonUserInputTensorDesc()) == ge::SUCCESS)((BuildPromptGraphAndInitRefTensor()) == ge::SUCCESS)((BuildDecoderGraphAndInitRefTensor()) == ge::SUCCESS)[%s:%d]%lu %s:req_id:%lu is already free, ret:%d.((vector_future[i].get()) == ge::SUCCESS)((AssembleDecoderInputs(active_batch_index_callback, batch_occupied_info)) == ge::SUCCESS)[%s:%d]%lu %s:[PriorityScheduleInputThread][RunDecoder]Has no input for all batch and will continue((ConstructDecoderInputs(active_batch_index_callback)) == ge::SUCCESS)run decoder graph failed, decoder_index:%zu((GeApi().GetInstance().RunGraphDistribute(kDecoderGraphId, decoder_device_to_inputs_, device_to_outputs)) == ge::SUCCESS)((OutputDecoderResult(active_batch_index_callback, device_to_outputs[-1])) == ge::SUCCESS)[%s:%d][EVENT]%lu %s:[LlmPerf]success to run decoder:%zu, cost time:%ld [%ld:%ld:%ld] us, batch_occupied_info:[%s][%s:%d]%lu %s:[LlmPerf]success to run decoder:%zu, cost time:%ld [%ld:%ld:%ld] us, batch_occupied_info:[%s][%s:%d]%lu %s: ErrorNo: %u(%s) %s[PriorityScheduleInputThread][BuildGraph]build graph failed[%s:%d][EVENT]%lu %s:[PriorityScheduleInputThread][RunPrompt]begin to run prompt:%zu[%s:%d]%lu %s:[PriorityScheduleInputThread][RunPrompt]begin to run prompt:%zu[%s:%d]%lu %s:[PriorityScheduleInputThread] llm engine has been stopped.[%s:%d][EVENT]%lu %s:[PriorityScheduleInputThread][RunDecoder]begin to run decoder:%zu[%s:%d]%lu %s:[PriorityScheduleInputThread][RunDecoder]begin to run decoder:%zu[%s:%d]%lu %s: ErrorNo: %u(%s) %s[PriorityScheduleInputThread][RunDecoder]execute decoder failed, decoder index:%zu[%s:%d][EVENT]%lu %s:Warm up later, as idle size:%zu is larger than deocder_n:%zu or batch_size:%zu / 2.[%s:%d]%lu %s:Warm up later, as idle size:%zu is larger than deocder_n:%zu or batch_size:%zu / 2.batch_index:%zu * max_block_num_per_seq:%zu + max_block_num_per_seq bigger than block_table:%zu(batch_index * max_block_num_per_seq + max_block_num_per_seq <= block_table.size())[%s:%d]%lu %s:Init batch_index:%zuXp@`pPoperator()InitPromptAndDecoderInterleavedStepoperator()FinalizeInitBatchDataEraseLLMReqCompleteRunPriorityScheduleRunPriorityScheduleAsyncReturnPriorityScheduleErrorLlmPriorityScheduleInputThreadBuildGraphAndInitRefDataTensorBuildDecoderGraphAndInitRefTensorBuildPromptGraphAndInitRefTensorInitoperator()FrontShardInitRefDataTensoroperator()InitDistributedRefDataTensorsoperator()InitDeviceIdToRefDataTensorsSeparateOutputcommitOutputDecoderResultExecuteDecoderUpdateDecoderUserInputConstructDecoderInputsUpdateDecoderInputTensorConstructDecoderNonUserInputsPageAttentionAssembleDecoderInputsDecoderInputsCheckAppendSlotForReqPrintTensorDescInfoInsertDoExecutePromptConstructPromptInputsExecutePromptInitBatchInputTensorsInitShapesAndDtypesInitBatchInitGraphsInitOptionsCheckOptionsPrintOptionsInitialize*NSt13__future_base12_Task_setterISt10unique_ptrINS_7_ResultIjEENS_12_Result_base8_DeleterEEZNS_11_Task_stateISt5_BindIFZN3llm23PriorityScheduleManager19OutputDecoderResultERKSt3mapImSt4pairINS9_7RunArgsEjESt4lessImESaISC_IKmSE_EEERSt6vectorIN2ge6TensorESaISP_EEEUlvE_vEESaIiEFjvEE6_M_runEvEUlvE_jEE*NSt13__future_base12_Task_setterISt10unique_ptrINS_7_ResultIjEENS_12_Result_base8_DeleterEEZNS_11_Task_stateISt5_BindIFZN3llm23PriorityScheduleManager19OutputDecoderResultERKSt3mapImSt4pairINS9_7RunArgsEjESt4lessImESaISC_IKmSE_EEERSt6vectorIN2ge6TensorESaISP_EEEUlvE_vEESaIiEFjvEE14_M_run_delayedESt8weak_ptrINS_13_State_baseV2EEEUlvE_jEE*ZN3llm12_GLOBAL__N_114SeparateOutputERKSt6vectorIN2ge6TensorESaIS3_EERKS1_ISt4pairINS2_12GeTensorDescEmESaISA_EEmEUlPhE_*ZN3llm12_GLOBAL__N_124UpdateDecoderInputTensorEmlPhRN2ge6TensorEEUlS1_E_*ZN3llm23PriorityScheduleManager21ConstructPromptInputsERKNS_7RunArgsERKlEUlPhE_*ZN3llm23PriorityScheduleManager21ConstructPromptInputsERKNS_7RunArgsERKlEUlPhE0_*ZN3llm23PriorityScheduleManager28InitDeviceIdToRefDataTensorsEiN2ge5ShapeENS1_8DataTypeEEUlvE_*ZN3llm23PriorityScheduleManager28InitDeviceIdToRefDataTensorsEiN2ge5ShapeENS1_8DataTypeEEUlPhE0_*ZN3llm23PriorityScheduleManager29InitDistributedRefDataTensorsERKSt3mapISsS1_IiN2ge5ShapeESt4lessIiESaISt4pairIKiS3_EEES4_ISsESaIS6_IKSsSA_EEEEUlvE_*ZN3llm23PriorityScheduleManager27FrontShardInitRefDataTensorEvEUlvE_*ZN3llm23PriorityScheduleManager19RunPriorityScheduleENS_8RoleTypeERKNS_6LLMReqERKSt6vectorIN2ge6TensorESaIS7_EERS9_EUljSC_E0_*ZN3llm23PriorityScheduleManager8FinalizeEvEUlvE_*NSt13__future_base11_Task_stateISt5_BindIFZN3llm23PriorityScheduleManager19OutputDecoderResultERKSt3mapImSt4pairINS2_7RunArgsEjESt4lessImESaIS5_IKmS7_EEERSt6vectorIN2ge6TensorESaISI_EEEUlvE_vEESaIiEFjvEEE*ZN2ge10ThreadPool6commitIZN3llm23PriorityScheduleManager19OutputDecoderResultERKSt3mapImSt4pairINS2_7RunArgsEjESt4lessImESaIS5_IKmS7_EEERSt6vectorINS_6TensorESaISH_EEEUlvE_JEEESt6futureIDTclfp_spfp0_EEEOT_DpOT0_EUlvE_*St23_Sp_counted_ptr_inplaceINSt13__future_base11_Task_stateISt5_BindIFZN3llm23PriorityScheduleManager19OutputDecoderResultERKSt3mapImSt4pairINS3_7RunArgsEjESt4lessImESaIS6_IKmS8_EEERSt6vectorIN2ge6TensorESaISJ_EEEUlvE_vEESaIiEFjvEEESQ_LN9__gnu_cxx12_Lock_policyE2EEllm.PagedAttentionMaxPromptLenllm.PagedAttentionMaxSeqsNumllm.PagedAttentionMaxSeqLenllm.PagedAttentionBlocksNumllm.PagedAttentionBlockSizellm.GraphPathllm.graph_keysllm.InputsBatchPaddingllm.PromptAndDecoderInterleavedStepllm.RefInputDtypesllm.RefInputShapesllm.InputDtypesllm.InputShapesllm.OmCachePathNSt6thread11_State_implINS_8_InvokerISt5tupleIJMN3llm23PriorityScheduleManagerEFvvEPS4_EEEEEEprompt_batch_manager.ccUnsupported data type: %d((task) != nullptr)[%s:%d]%lu %s:%s = %s[%s:%d]%lu %s:already stoppedInvalid shape dims: %s[%s:%d]%lu %s: ErrorNo: %u(%s) %sFailed to calc tensor size, index = %zu, shape = [%s][%s:%d]%lu %s:input[%zu], shape = [%s], size = %ld[%s:%d]%lu %s:input[%zu], tensor need padding, init tensor with value: %s[%s:%d]%lu %s:Reallocate input tensor data, index = %zu, size = %ldindex (%zu) out of range [0, %zu)[%s:%d]%lu %s: ErrorNo: %u(%s) %sindex (%zu) out of range [0, %zu)[%s:%d]%lu %s: ErrorNo: %u(%s) %sUnsupported data type: %d[%s:%d]%lu %s:input_index = %zu, padding value = %s[%s:%d]%lu %s:push request, req_id = %lu, prefix_id = %lu[%s:%d]%lu %s:push task, request = %s[%s:%d]%lu %s:option %s not set, padding zero by defaultelement format should be ${index}:${value}, but value = %s, option_value = %s[%s:%d]%lu %s: ErrorNo: %u(%s) %selement format should be ${index}:${value}, but value = %s, option_value = %s[%s:%d]%lu %s: ErrorNo: %u(%s) %sindex not a number, value = %s, option_value = %s[%s:%d]%lu %s: ErrorNo: %u(%s) %sFailed to parse padding value, value = %s, option_value = %sParam:dst_base_addr is nullptr, check invalid[%s:%d]%lu %s: ErrorNo: %u(%s) %s[Check][Param:dst_base_addr]null is invalid[%s:%d]%lu %s:input_index = %zu, shape = %s, batch_dim_index = %ld, copy_times = %zu, dst_batch_size = %zu[%s:%d]%lu %s:batch_index = %zu, shape = %s, src_batch_size = %zuFailed to copy input, input_index = %zu, batch_index = %zu, dst_max = %zu, copy_size = %zu, dst_shape = %s, src_shape = %s[%s:%d]%lu %s: ErrorNo: %u(%s) %sFailed to copy input, input_index = %zu, batch_index = %zu, dst_max = %zu, copy_size = %zu, dst_shape = %s, src_shape = %s[%s:%d]%lu %s:sub_copy_times = %zu, sub_copy_stride = %zu, block_size = %zu[%s:%d]%lu %s:start to merge inputs, request_count = %zu[%s:%d]%lu %s:merge input ended successfully[%s:%d]%lu %s: ErrorNo: %u(%s) %salready stopped[%s:%d]%lu %s: ErrorNo: %u(%s) %sFailed to prepare inputs[%s:%d]%lu %s: ErrorNo: %u(%s) %sFailed to create inference task[%s:%d]%lu %s:batch is full, start to submit requests, size = %zu[%s:%d]%lu %s: ErrorNo: %u(%s) %sFailed to submit batch[%s:%d]%lu %s:start to submit requests, size = %zu[%s:%d]%lu %s: ErrorNo: %u(%s) %sFailed to submit current batch[%s:%d]%lu %s: ErrorNo: %u(%s) %sInvalid shape dims: %sDynamic dims are not supported before batch_dim, batch_dim_index = %zu, shape = %s[%s:%d]%lu %s: ErrorNo: %u(%s) %sDynamic dims are not supported before batch_dim, batch_dim_index = %zu, shape = %s[%s:%d]%lu %s: ErrorNo: %u(%s) %sFailed to collect dynamic dims, input_index = %zu[%s:%d]%lu %s:Input[%zu] is dynamic shaped %s, but batch_dim_index = -1Input[%zu] batch dim index (%ld) out of range [0, %zu)[%s:%d]%lu %s: ErrorNo: %u(%s) %sInput[%zu] batch dim index (%ld) out of range [0, %zu)No more than 2 dims are supported after batch_dim, input_index = %zu, batch_dim_index = %zu, shape = %s[%s:%d]%lu %s: ErrorNo: %u(%s) %sNo more than 2 dims are supported after batch_dim, input_index = %zu, batch_dim_index = %zu, shape = %s[%s:%d]%lu %s:input_index = %zu, shape = %s, batch_dim_index = %ld, copy_times = %zu[%s:%d]%lu %s: ErrorNo: %u(%s) %sFailed to init batch statePushPushPushSubmitBatchEnsureNotEmptyBatchThreadInitializeMergeInputMergeInputsAllocateInputTensorToNumberToNumberParsePaddingValueToNumberParsePaddingValuesCollectDynamicDimsParseCopyArgs*ZN3llm18PromptBatchManager11SubmitBatchERSt10unique_ptrINS_13InferenceTaskESt14default_deleteIS2_EEEUlvE_llm.InputPaddingsN3llm19PromptTaskQueueBaseEN3llm15PromptTaskQueueEN3llm23BatchingPromptTaskQueueENSt6thread11_State_implINS_8_InvokerISt5tupleIJMN3llm18PromptBatchManagerEFvvEPS4_EEEEEEprompt_manager.ccprompt unlink clusters failed_BuiltIn_prompt_execute_ssm_BuiltIn_prompt_unlink_func./invoke_prompt_func_PromptExecute_parser wait time info failedparser flow graph info failedprepare execute node failedconstruct flow graph failedinvoke_prompt_modelpp_devicecheck_nn_output_indicesclean_func_output_indicesunlink_func_output_indices[%s:%d][EVENT]%lu %s:[LlmPerf] A new loop start, req_id:%lu, prefix_id:%lu, cluster_id:%lu, queue_wait_time_us: %ld.[%s:%d]%lu %s:[LlmPerf] A new loop start, req_id:%lu, prefix_id:%lu, cluster_id:%lu, queue_wait_time_us: %ld.[%s:%d]%lu %s:inference task executed successfully, request = %s[%s:%d]%lu %s:Prompt inference thread exit[%s:%d]%lu %s: ErrorNo: %u(%s) %sCan not find kv cache of prefix_id:%lu in cluster:%lu, req_id:%lu. Please check![%s:%d]%lu %s:PromptManager is already finalized.[%s:%d]%lu %s: ErrorNo: %u(%s) %sprompt unlink clusters failed[%s:%d]%lu %s:Begin to release prompt request kv cache, req_id:%lu, cluster_id:%lu.Invalid outputs size! outputs.size is %zu, expected size is %zu.[%s:%d]%lu %s: ErrorNo: %u(%s) %sInvalid outputs size! outputs.size is %zu, expected size is %zu.Fail to release prompt request kv cache, req_id:%lu, cluster_id:%lu, result:%d, index:%zu.[%s:%d]%lu %s: ErrorNo: %u(%s) %sFail to release prompt request kv cache, req_id:%lu, cluster_id:%lu, result:%d, index:%zu.[%s:%d][EVENT]%lu %s:[LlmPerf] Success to release prompt request kv cache, req_info size:%zu, req_id:%lu, clustr_id:%lu.[%s:%d]%lu %s:[LlmPerf] Success to release prompt request kv cache, req_info size:%zu, req_id:%lu, clustr_id:%lu.[%s:%d][EVENT]%lu %s:Prefix id:%lu is not exist in current prompt cluster:%lu.[%s:%d]%lu %s:Prefix id:%lu is not exist in current prompt cluster:%lu.(flow_graph_io_info_.prompt_clean_kv_indices_.size() == 1UL)Fail to release prompt prefix, prefix_id:%lu, cluster_id:%lu, result:%d, index:%zu.[%s:%d]%lu %s: ErrorNo: %u(%s) %sFail to release prompt prefix, prefix_id:%lu, cluster_id:%lu, result:%d, index:%zu.[%s:%d]%lu %s:Success to release prompt prefix kv cache, prefix_id:%lu, cluster_id:%lu.[PromptManager] batch dim index:%ld is out of range, dim size:%zu[%s:%d]%lu %s: ErrorNo: %u(%s) %s%zu input shape is not equal to the expected. %s != %s, option %s = %s[%s:%d]%lu %s: ErrorNo: %u(%s) %s%zu input dtype is not equal to the %s option given before. %d != %d[%s:%d]%lu %s:Begin to run prompt async, inputs size:%zu, req_id:%lu, prefix_id:%lu, model_id:%lu.[%s:%d][EVENT]%lu %s:[LlmPerf] after enqueue req_id:%lu, prefix_id:%lu, args_q size:%u.[%s:%d]%lu %s:[LlmPerf] after enqueue req_id:%lu, prefix_id:%lu, args_q size:%u.Fail to push input args, req_id:%lu, cluster:%lu.[%s:%d]%lu %s: ErrorNo: %u(%s) %sFail to push input args, req_id:%lu, cluster:%lu.[%s:%d]%lu %s:Success to run prompt async, req_id:%lu, prefix_id:%lu, cluster:%lu.[%s:%d]%lu %s:[PromptManager] RunPrompt begin, model id:%lu.[%s:%d]%lu %s:[PromptManager] RunPrompt end, ret:%d.[%s:%d][EVENT]%lu %s:execute async thread exit[%s:%d]%lu %s:execute async thread exit[%s:%d][EVENT]%lu %s:fetch output thread exit[%s:%d]%lu %s:fetch output thread exitNumber of reqs (%zu) should <= batch_size (%d)[%s:%d]%lu %s: ErrorNo: %u(%s) %sNumber of reqs (%zu) should <= batch_size (%d)Param:inference_task is nullptr, check invalid[%s:%d]%lu %s: ErrorNo: %u(%s) %s[Check][Param:inference_task]null is invalid[%s:%d][EVENT]%lu %s:[LlmPerf] after enqueue request: %s, args_q size:%u.[%s:%d]%lu %s:[LlmPerf] after enqueue request: %s, args_q size:%u.[%s:%d]%lu %s:Success to run prompt async, requests = %s[%s:%d]%lu %s:[PromptManager] RunPrompt begin model id:%lu.[%s:%d]%lu %s:[PromptManager] RunPrompt end, ret:%u.[%s:%d]%lu %s:Prompt nn_inputs_indices_:%s, clean_kv_indices_:%s, unlink_input_indices:%s[%s:%d]%lu %s:Prompt out clean_kv_indices:%s, unlink_indices:%s, check_nn_indices:%s, nn_indices:%s, prefix_indices:%sParam:task_queue is nullptr, check invalid[%s:%d]%lu %s: ErrorNo: %u(%s) %s[Check][Param:task_queue]null is invalid[%s:%d]%lu %s: ErrorNo: %u(%s) %sFailed to initialize BatchingPromptTaskQueue[%s:%d]%lu %s:BatchingPromptTaskQueue created successfullyParam:task_queues_.back() is nullptr, check invalid[%s:%d]%lu %s: ErrorNo: %u(%s) %s[Check][Param:task_queues_.back()]null is invalid[%s:%d]%lu %s:PromptTaskQueue created successfully[%s:%d]%lu %s: ErrorNo: %u(%s) %sPrefix kv cache is exist, prefix_id:%lu, cluster_id:%lu._BuiltIn_prompt_execute_nn_func_BuiltIn_prompt_kv_data_sync_func_BuiltIn_prompt_kv_data_clean_func[%s:%d]%lu %s:[PromptManager] CreatePromptFlowNodes begin.[%s:%d][EVENT]%lu %s:[PromptManager] cluster: %lu begin initialize, model_defines size: %zu[%s:%d]%lu %s:[PromptManager] cluster: %lu begin initialize, model_defines size: %zu[%s:%d]%lu %s: ErrorNo: %u(%s) %sparser flow graph info failed[%s:%d]%lu %s: ErrorNo: %u(%s) %sprepare IO indices for stages failed[%s:%d]%lu %s: ErrorNo: %u(%s) %sprepare execute node failed[%s:%d]%lu %s:input_shapes size: %zu. indices_size: %zu[%s:%d]%lu %s: ErrorNo: %u(%s) %s[PromptManager] parse batch mode failed.[%s:%d]%lu %s: ErrorNo: %u(%s) %sFailed to initialize task queue[%s:%d][EVENT]%lu %s:[PromptManager] cluster:%lu end initialize, cost time:%ld.[%s:%d]%lu %s:[PromptManager] cluster:%lu end initialize, cost time:%ld.PrepareIoIndicesForStagesUnlinkClustersReleasePromptPrefixPreloadPromptPrefixLlmPromptOutputThreadLlmPromptInputThreadForMultiStagesLlmPromptInputThreadFinalizeLLMReqCompleteCheckPrefixCheckInputsRunPromptAsyncRunPromptAsyncRunPromptRunPromptInitializeTaskQueueInitializePrepareExecuteNodeInfoBuildUdfFlowFuncsPrepareIoIndices*ZN3llm13PromptManager22PrepareExecuteNodeInfoERKSt6vectorINS_12FlowDataInfoESaIS2_EERS1_INS_12FlowNodeInfoESaIS7_EEEUlN2ge5dflow10FunctionPpEE_*ZN3llm13PromptManager9RunPromptERKNS_6LLMReqERKSt6vectorIN2ge6TensorESaIS6_EERS8_mEUljSB_E0_*ZN3llm13PromptManager9RunPromptERKSt6vectorINS_6LLMReqESaIS2_EERKS1_IN2ge6TensorESaIS8_EERSA_mEUljSD_E_UnlinkFlowData_CleanFlowData_ReqFlowData_NnFlowData_llm.InputPaddingsllm.InputDtypesllm.InputShapesNSt6thread11_State_implINS_8_InvokerISt5tupleIJMN3llm13PromptManagerEFvmEPS4_mEEEEEEprompt_model_executor.cc{req_id:, prefix_id:, prompt_length:fetch check nn result failed, ret = %u, output num = %zu[%s:%d]%lu %s: ErrorNo: %u(%s) %sfetch check nn result failed, ret = %u, output num = %zunn func check failed when execute nn[%s:%d]%lu %s: ErrorNo: %u(%s) %snn func check failed when execute nn[%s:%d]%lu %s:[LlmPerf] check nn func result success, execute_time_us: %ld[%s:%d]%lu %s: ErrorNo: %u(%s) %sCallback on error, ret:%d, req_id:%lu, prefix_id:%lu[%s:%d][EVENT]%lu %s:[LlmPerf] callback ended, ret = %u, req_id:%lu, prefix_id:%lu, prompt_length:%lu[%s:%d]%lu %s:[LlmPerf] callback ended, ret = %u, req_id:%lu, prefix_id:%lu, prompt_length:%lu[%s:%d]%lu %s:Copy output tensors start, batch_size = %zu[%s:%d]%lu %s:Output[%zu] shape = [%s]insufficient output batch size, index = %zu, shape = [%s], batch_size = %zu[%s:%d]%lu %s: ErrorNo: %u(%s) %sinsufficient output batch size, index = %zu, shape = [%s], batch_size = %zuFailed to calc output tensor size, index = %zu, shape = %s[%s:%d]%lu %s: ErrorNo: %u(%s) %sFailed to calc output tensor size, index = %zu, shape = %sParam:output_buffer is nullptr, check invalidFailed to allocate output buffer, output index = %zu, shape = %s[%s:%d]%lu %s: ErrorNo: %u(%s) %s[Check][Param:output_buffer]null is invalidFailed to allocate output buffer, output index = %zu, shape = %sFailed to copy output, size = %ld[%s:%d]%lu %s: ErrorNo: %u(%s) %sFailed to copy output, size = %ld[%s:%d]%lu %s:Copy output[%zu] to batch_index[%zu] successfully[%s:%d]%lu %s:Copy output tensors ended[%s:%d]%lu %s: ErrorNo: %u(%s) %sFailed to copy outputs[%s:%d][EVENT]%lu %s:[LlmPerf] callback ended, req_id:%lu, prefix_id:%lu, prompt_length:%lu, execute_time_us: %ld, total_time_us: %ld[%s:%d]%lu %s:[LlmPerf] callback ended, req_id:%lu, prefix_id:%lu, prompt_length:%lu, execute_time_us: %ld, total_time_us: %ld[%s:%d][EVENT]%lu %s:[LlmPerf] Success to execute prompt graph, requests = %s, execute_time_us: %ld, total_time_us: %ld[%s:%d]%lu %s:[LlmPerf] Success to execute prompt graph, requests = %s, execute_time_us: %ld, total_time_us: %ld[%s:%d]%lu %s: ErrorNo: %u(%s) %sCallback on error, ret:%d, requests = %s[%s:%d][EVENT]%lu %s:[LlmPerf] callback ended, ret = %u, requests = %s[%s:%d]%lu %s:[LlmPerf] callback ended, ret = %u, requests = %s[%s:%d]%lu %s: ErrorNo: %u(%s) %sCheckNnFuncResultBeforeNn failed[%s:%d]%lu %s:Begin to fetch data, output_indices:%s[%s:%d]%lu %s: ErrorNo: %u(%s) %sFetch NN result failed[%s:%d]%lu %s:fetch nn result successfully[%s:%d]%lu %s: ErrorNo: %u(%s) %sCheck prefix result failed[%s:%d]%lu %s: ErrorNo: %u(%s) %sFailed to fetch result[%s:%d]%lu %s:Begin to feed data, inputs_indices:%s[%s:%d]%lu %s: ErrorNo: %u(%s) %sFailed to feed data, inputs_indices:%s[%s:%d]%lu %s:Feed data successfully[%s:%d][EVENT]%lu %s:execute graph async ended, ret = %u, requests = %s, feed_time_us = %ld[%s:%d]%lu %s:execute graph async ended, ret = %u, requests = %s, feed_time_us = %ld[%s:%d]%lu %s: ErrorNo: %u(%s) %sFail to feed data, requests = 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