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REgc_T`.E@dGBRE`ocTG`BB{m!{G`8G`%@B@:A'B^^^hj!wgh&ozj%sEK@T`H9G@Ҡ^9o__zj s^` {GbBB;c^w@Dh#v!{GuvtТG`B<@%@A@7^ej"Godcwc.ERRE!@3`{ab`TA!`B@`BInitialize()) == 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 freeDeallocateRemoveCacheKeyPullCacheCopyCacheGetCacheTensorsAllocate, [cache_id:%ld][Deallocate] failed, cache_id not foundcache_engine.cc[%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]cache_idInvalid 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.DDK_versionge.feFlagop_debug_optionge.experiment.quant_bias_optimizege.op_dependency_in_omCaffeMindSporeTensorFlow3Android_NN4OnnxAI_COREAIVMIX_AICMIX_AIVAI_CPUDSAWRITE_BACKINVALIDTASK_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_CPUUDFPStnhb\LF@:4.("zRZtnhb\LF@:4.("zRZN3llm17LlmFlowModelSaverEN2ge8OperatorEN15ascend_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   000102030405060708091011121314151617181920212223242526272829303132333435363738394041424344454647484950515253545556575859606162636465666768697071727374757677787980818283848586878889909192939495969798990001020304050607080910111213141516171819202122232425262728293031323334353637383940414243444546474849505152535455565758596061626364656667686970717273747576777879808182838485868788899091929394959697989900010203040506070809101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778798081828384858687888990919293949596979899ƚpOܼw kAV<Ѝ,U1(\QFɦqaˋ#w"{mSx@I̮ Wζ]y<7VM6OH8oꖐ$:%˅t,φ4* 45<5*g8PD;?ȄkL'DŅT%Οk\b}$lۺd_ Xfl&ޓt |J|l_b%S04`?U&NZ~)p$wt帟ߦ}t_ϛpDk11eU%{?;+*\.Ӓsi$$Icd~̈Po ̼,eXѳ@ bx x9?{Η8$p\{2~S,h髤8m4E"&'O<'1cDȌ8eްLeǃTqB]\X,iM dpd'lJwmBtk}{x \|wyTwś[[=]S5Ȭ\*_F%94›\rξTSܷ1A"Lx\ӛ fS!{Z:0ܵ\S٨::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]ToNumberGetKvCacheShapeSizevoid 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]llm.InputShapesllm.InputDtypesParserInferenceModelIoInfollm.RefInputShapesllm.RefInputDtypesllm.OutputNumsllm.KvCacheCountsParsePipelineStageConfigToNumberascend_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*]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]ParseDeviceIndicesllm.PostProcessInputShapesllm.PostProcessInputDtypesParsePostProcessModelInputsllm.PostProcessOutputNumsParsePostProcessModelOutputsllm.OutputMappingpostprocessParserPostProcessModelIoInfoParserIoInfoAddUdfFuncInfoCreateFlowDataInfollm.PostProcessOmCachePathPreparePostProcessFlowModelpostprocess_flow_model_cluster_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&]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::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]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]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]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 = 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 = 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 = 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 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::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 = ascend_nlohmann::json_abi_v3_11_2::detail::value_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 = ascend_nlohmann::json_abi_v3_11_2::detail::value_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 = 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 = 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 = 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 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 = 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 = bool&; 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]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::json_sax_dom_callback_parser::end_array() [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]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]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)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)void ascend_nlohmann::json_abi_v3_11_2::detail::dtoa_impl::grisu2(char*, int&, int&, FloatType) [with FloatType = double]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)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_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)void ascend_nlohmann::json_abi_v3_11_2::detail::dtoa_impl::grisu2_round(char*, int, uint64_t, uint64_t, uint64_t, uint64_t)char* ascend_nlohmann::json_abi_v3_11_2::detail::to_chars(char*, const char*, FloatType) [with FloatType = double]char* ascend_nlohmann::json_abi_v3_11_2::detail::dtoa_impl::format_buffer(char*, int, int, int, int)char* ascend_nlohmann::json_abi_v3_11_2::detail::dtoa_impl::append_exponent(char*, int)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]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]func_listinput_numoutput_numbuilt_in_flow_funcCreateFuncPpCompileConfigfunc_nameinputs_indexoutputs_indexascend_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]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 > >]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]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]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] 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 > >] 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]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]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]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]falseBasicJsonType* 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::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::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 = 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 = 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 = 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 = 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 unsigned int&; 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]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::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 = 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 = 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 = 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 = 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 unsigned int&; 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 > >]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<>]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<>]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::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 > >]hcom_clusterllm.HcomClusterConfigSetHcomClusterDescByOptionstatic 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 > >]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<>&]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]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::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::basic_json::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]_dataflow_deploy_info.jsonbatch_deploy_infoflow_node_listlogic_device_list./GetDataFlowDeployInfoPathBuildFlowGraphge.experiment.data_flow_deploy_info_pathllm.OutputMaxSizeLlmGraphBuild_llm.ClusterInfoParserDeployInfostatic 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]logic_device_idvoid 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& ascend_nlohmann::json_abi_v3_11_2::detail::iter_impl::operator++() [with BasicJsonType = const ascend_nlohmann::json_abi_v3_11_2::basic_json]listen_ip_infoipportParserOmToFlowModelParserModelBufferDataToFlowModelParserOmToFlowModelllm.OmCachePathConvertInputModelsToOminferencellm.IsFlowModelPrepareFlowGraphInfo_ppConstructFlowGraphLlmGraph_ConstructFlowGraphincompatible ReferenceType for get_ref, actual type is m_type == val.type()iterator does not fit current valueiterator out of rangecannot use erase() with index < 400\u%04x\u%04x\u%04xinvalid UTF-8 byte at index : 0xfalseincomplete UTF-8 string; last byte: 0x\ufffd�n_chars < number_buffer.size() - 1{}{ , i != val.m_value.object->cend()std::next(i) == val.m_value.object->cend()":[][ !val.m_value.array->empty()"bytes": [], "subtype": {"bytes":[],"subtype":null}true0123456789ABCDEFtype_errorother_errorparse_error at line , column parse error: out_of_rangeinvalid_iterator!ref_stack.empty()type must be number, but is !keep_stack.empty()ref_stack.back()->is_array() || ref_stack.back()->is_object()!key_keep_stack.empty()object_elementcurrent == '\"'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 \u001Fcurrent == 'u'0x0000 <= codepoint && codepoint <= 0xFFFFranges.size() == 2 || ranges.size() == 4 || ranges.size() == 6invalid string: ill-formed UTF-8 byteinvalid BOM; must be 0xEF 0xBB 0xBF if giveninvalid comment; missing closing '*/'invalid comment; expecting '/' or '*' after '/'invalid literalinvalid number; expected digit after '-'invalid number; expected digit after '.'invalid number; expected '+', '-', or digit after exponentinvalid number; expected digit after exponent sign!token_string.empty()endptr == token_buffer.data() + token_buffer.size()object keyobject separatorref_stack.back()->is_array()number overflow parsing 'value!states.empty()true literalfalse literalnull literalstring literalnumber literal'[''{'']''}'':'','unknown tokenend of input'[', '{', or a literalsyntax error while parsing - ; last read: 'unexpected ; expected last - 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 < 1000std::isfinite(value)value > 0delta >= 0((x.f << delta) >> delta) == x.fx.f != 0m_plus.e == m_minus.em_plus.e == v.ee >= -1500e <= 1500index >= 0static_cast(index) < kCachedPowers.size()kAlpha <= cached.e + e + 64kGamma >= cached.e + e + 64p1 > 0d <= 9len >= 1dist <= deltaten_k > 0buf[len - 1] != '0'p2 > deltap2 <= (std::numeric_limits::max)() / 10[json.exception.] m_object != nullptrFailed to convert [%s] to number[%s:%d]%lu %s: ErrorNo: %u(%s) %sFailed to convert [%s] to numberit != m_value.object->end()cannot use operator[] with a string argument with objectarraystringbooleanbinarydiscardednumbercannot use at() with key '' not foundm_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 containersvector::_M_default_appendtype must be string, but is vector::_M_insert_auxvector::_M_realloc_insertflow_graph_manager.ccx.f >= y.fm_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 optiondo not get the %s option[%s:%d]%lu %s: ErrorNo: %u(%s) %sdo not get the %s option%s is not a int64 value.[%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.init batch size 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 = %s(count > 0)option %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 = %s(pipeline_stage_num_ > 1)option %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 = %sge.exec.rankId[%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 = %u[%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 modelinvalid output indices: %s[%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.flow_model__index__sub_graph_[%s:%d]%lu %s: ErrorNo: %u(%s) %sload flow model from om failed..om[%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.DSAGenBitMaskDSARandomNormalDSARandomTruncatedNormalDSARandomUniform[%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:%ucannot use push_back() with [%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 = %sloc != nullptrcomm_groupgroup_namegroup_rank_list[%s:%d]%lu %s:group name = [%s], rank_id_list = %sInvalid HcomClusterConfig: %s[%s:%d]%lu %s: ErrorNo: %u(%s) %sInvalid HcomClusterConfig: %srank_table[%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 must be array.[%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 is invalid.[%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:om path is:%sflow_model size should be %zu(om_list.size() == 1U)[%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 failedparser nn io 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)x[%s:%d]%lu %s:Add node:%s to device:%s[%s:%d]%lu %s:link node:[%s:%u] to node:[%s:%u]map::atParam:flow_graph_ is nullptr, check invalid[%s:%d]%lu %s: ErrorNo: %u(%s) %s[Check][Param:flow_graph_]null is invalid./invoke_decoder_func_.jsoncreate 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=%sN3llm11LinkManagerEParseOutputRetStatusLinkAsyncCheckClusterInfoPrepareLinkInfoTensorsGetUnLinkResultUnlinkClustersGetLinkResultLinkClusters*ZN3llm11LinkManager22PrepareLinkInfoTensorsERKSt6vectorINS_11ClusterInfoESaIS2_EENS_12LinkOperatorEmiEUlvE_The output contains invalid return valueslink_manager.cc[%s:%d]%lu %s: ErrorNo: %u(%s) %sThe output contains invalid return values[%s:%d]%lu %s:link_input_indices_ size:%zu, tensor size:%zulink or unlink feed data failed.[%s:%d]%lu %s: ErrorNo: %u(%s) %slink or unlink feed data failed.[%s:%d]%lu %s:link or unlink feed inputs successfully[ip:, port:clusters info is empty[%s:%d]%lu %s: ErrorNo: %u(%s) %sclusters info is empty[%s:%d]%lu %s:Link remote cluster:%lu， remote role type:%dlocal ip infos size:%zu, remote ip infos:%zu[%s:%d]%lu %s: ErrorNo: %u(%s) %slocal ip infos size:%zu, remote ip infos:%zucluster ip info size:%zu not match sliced_num:%zu[%s:%d]%lu %s: ErrorNo: %u(%s) %scluster ip info size:%zu not match sliced_num:%zulocal_ip_infos should be same[%s:%d]%lu %s: ErrorNo: %u(%s) %slocal_ip_infos should be same[%s:%d]%lu %s:Link remote ip:%u[%s:%d]%lu %s:Link local ip:%ucheck cluster info failed[%s:%d]%lu %s: ErrorNo: %u(%s) %scheck cluster info failedlink or unlink size:%zu exceeds the upper limit:%u[%s:%d]%lu %s: ErrorNo: %u(%s) %slink or unlink size:%zu exceeds the upper limit:%uallocate link info failed[%s:%d]%lu %s: ErrorNo: %u(%s) %sallocate link info faileddecoder graph fetch data failed.[%s:%d]%lu %s: ErrorNo: %u(%s) %sdecoder graph fetch data failed.parser output ret status failed[%s:%d]%lu %s: ErrorNo: %u(%s) %sparser output ret status failed[%s:%d][EVENT]%lu %s:Link is currently processing. Try again later.[%s:%d]%lu %s:Link is currently processing. Try again later.Input clusters info is invalid[%s:%d]%lu %s: ErrorNo: %u(%s) %sInput clusters info is invalid[%s:%d][EVENT]%lu %s:[LlmPerf] prepare unlink input tensor end, elapsed_us = %ld[%s:%d]%lu %s:[LlmPerf] prepare unlink input tensor end, elapsed_us = %ldUnlink feed input failed[%s:%d]%lu %s: ErrorNo: %u(%s) %sUnlink feed input failed[%s:%d][EVENT]%lu %s:[LlmPerf] feed unlink input tensor end, elapsed_us = %ld[%s:%d]%lu %s:[LlmPerf] feed unlink input tensor end, elapsed_us = %ldUnlink fetch output failed[%s:%d]%lu %s: ErrorNo: %u(%s) %sUnlink fetch output failed[%s:%d][EVENT]%lu %s:[LlmPerf] fetch unlink result end, elapsed_us = %ld[%s:%d]%lu %s:[LlmPerf] fetch unlink result end, elapsed_us = %ld[%s:%d]%lu %s: ErrorNo: %u(%s) %sParse output ret status failed.[%s:%d][EVENT]%lu %s:[LlmPerf] prepare link input tensor end, elapsed_us = %ld[%s:%d]%lu %s:[LlmPerf] prepare link input tensor end, elapsed_us = %ldlink feed input failed[%s:%d]%lu %s: ErrorNo: %u(%s) %slink feed input failed[%s:%d][EVENT]%lu %s:[LlmPerf] feed link input tensor end, elapsed_us = %ld[%s:%d]%lu %s:[LlmPerf] feed link input tensor end, elapsed_us = %ldlink fetch output failed[%s:%d]%lu %s: ErrorNo: %u(%s) %slink fetch output failed[%s:%d][EVENT]%lu %s:[LlmPerf] fetch link result end, elapsed_us = %ld[%s:%d]%lu %s:[LlmPerf] fetch link result end, elapsed_us = %ldrollback clusters failed[%s:%d]%lu %s: ErrorNo: %u(%s) %srollback clusters failed[%s:%d][EVENT]%lu %s:[LlmPerf] rollback link failed clusters end, elapsed_us = %ld[%s:%d]%lu %s:[LlmPerf] rollback link failed clusters end, elapsed_us = %ldPPPPPPPP P P P P PPPPPPPN2ge8PneModelEN3llm27LlmFlowModelSaverFromOmListEN3llm30LlmFlowModelSaverFromFlowModelEN3llm32LlmFlowModelSaverFromModelBufferENSt13__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_policyE2EEflow_model_cluster_GetFlowModelGetFlowModelRenameFileConstantpostprocess_flow_model_cluster_SaveFlowModelToOmPrepareConstructSubFlowModelAddSubModelGetFlowModelSetHcomInfoToFlowModeloperator()SaveFlowModelsToOmcommitSaveToOm*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_policyE2EEstd::future_error: llm_flow_model_saver.cc[%s:%d]%lu %s: ErrorNo: %u(%s) %sGetFolderPath failedParam:sub_pne_model is nullptr, check invalid[%s:%d]%lu %s: ErrorNo: %u(%s) %s[Check][Param:sub_pne_model]null is invalidParam:pne_root_graph is nullptr, check invalid[%s:%d]%lu %s: ErrorNo: %u(%s) %s[Check][Param:pne_root_graph]null is invalidParam:op_desc is nullptr, check invalid[%s:%d]%lu %s: ErrorNo: %u(%s) %s[Check][Param:op_desc]null is invalid[%s:%d]%lu %s:set file constant name from %s to %s.%s submodules is empty.(!flow_model->GetSubmodels().empty())[%s:%d]%lu %s:flow_model_name: %s.Param:flow_model is nullptr, check invalidflow model is nullptr[%s:%d]%lu %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 invalidsub_flow_model_[%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 endsave flow model to om failed.[%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]FjmRKSt6vectorIN2ge6TensorESaIS1_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_Result_base8_DeleterEEZNS_11_Task_stateISt5_BindIFZN3llm14LlmFlowService8RunMultiINS9_19CacheAllocateResultEEEjmRKSt6vectorIN2ge6TensorESaISF_EERKSt8functionIFjmSJ_RT_EERSD_ISL_SaISL_EEEUlvE_vEESaIiEFjvEE14_M_run_delayedESt8weak_ptrINS_13_State_baseV2EEEUlvE_jEENSt13__future_base7_ResultIvEEN3llm15SpmdLinkManagerENSt13__future_base16_Task_state_baseIFvvEEEPFjmRKSt6vectorIN2ge6TensorESaIS1_EERN3llm19CacheAllocateResultEEPFjmRKSt6vectorIN2ge6TensorESaIS1_EERjEPFjmRKSt6vectorIN2ge6TensorESaIS1_EERiEPFjmRKSt6vectorIN2ge6TensorESaIS1_EERS1_ESt15_Sp_counted_ptrIPSt13packaged_taskIFvvEELN9__gnu_cxx12_Lock_policyE2EENSt13__future_base11_Task_stateISt5_BindIFZN3llm14LlmFlowService8RunMultiINS2_19CacheAllocateResultEEEjmRKSt6vectorIN2ge6TensorESaIS8_EERKSt8functionIFjmSC_RT_EERS6_ISE_SaISE_EEEUlvE_vEESaIiEFjvEEENSt13__future_base11_Task_stateISt5_BindIFZN3llm14LlmFlowService8RunMultiIjEEjmRKSt6vectorIN2ge6TensorESaIS7_EERKSt8functionIFjmSB_RT_EERS5_ISD_SaISD_EEEUlvE_vEESaIiEFjvEEENSt13__future_base11_Task_stateISt5_BindIFZN3llm14LlmFlowService8RunMultiIiEEjmRKSt6vectorIN2ge6TensorESaIS7_EERKSt8functionIFjmSB_RT_EERS5_ISD_SaISD_EEEUlvE_vEESaIiEFjvEEENSt13__future_base11_Task_stateISt5_BindIFZN3llm14LlmFlowService8RunMultiIN2ge6TensorEEEjmRKSt6vectorIS6_SaIS6_EERKSt8functionIFjmSB_RT_EERS7_ISD_SaISD_EEEUlvE_vEESaIiEFjvEEEZN2ge10ThreadPool6commitIZN3llm14LlmFlowService8RunMultiINS2_19CacheAllocateResultEEEjmRKSt6vectorINS_6TensorESaIS7_EERKSt8functionIFjmSB_RT_EERS6_ISD_SaISD_EEEUlvE_JEEESt6futureIDTclfp_spfp0_EEEOSD_DpOT0_EUlvE_ZN2ge10ThreadPool6commitIZN3llm14LlmFlowService8RunMultiIjEEjmRKSt6vectorINS_6TensorESaIS6_EERKSt8functionIFjmSA_RT_EERS5_ISC_SaISC_EEEUlvE_JEEESt6futureIDTclfp_spfp0_EEEOSC_DpOT0_EUlvE_ZN2ge10ThreadPool6commitIZN3llm14LlmFlowService8RunMultiIiEEjmRKSt6vectorINS_6TensorESaIS6_EERKSt8functionIFjmSA_RT_EERS5_ISC_SaISC_EEEUlvE_JEEESt6futureIDTclfp_spfp0_EEEOSC_DpOT0_EUlvE_ZN2ge10ThreadPool6commitIZN3llm14LlmFlowService8RunMultiINS_6TensorEEEjmRKSt6vectorIS5_SaIS5_EERKSt8functionIFjmSA_RT_EERS6_ISC_SaISC_EEEUlvE_JEEESt6futureIDTclfp_spfp0_EEEOSC_DpOT0_EUlvE_St23_Sp_counted_ptr_inplaceINSt13__future_base11_Task_stateISt5_BindIFZN3llm14LlmFlowService8RunMultiINS3_19CacheAllocateResultEEEjmRKSt6vectorIN2ge6TensorESaIS9_EERKSt8functionIFjmSD_RT_EERS7_ISF_SaISF_EEEUlvE_vEESaIiEFjvEEESR_LN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceINSt13__future_base11_Task_stateISt5_BindIFZN3llm14LlmFlowService8RunMultiIjEEjmRKSt6vectorIN2ge6TensorESaIS8_EERKSt8functionIFjmSC_RT_EERS6_ISE_SaISE_EEEUlvE_vEESaIiEFjvEEESQ_LN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceINSt13__future_base11_Task_stateISt5_BindIFZN3llm14LlmFlowService8RunMultiIiEEjmRKSt6vectorIN2ge6TensorESaIS8_EERKSt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ce8FinalizeEvEUlvE0_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_policyE2EE[%s:%d]%lu %s: ErrorNo: %u(%s) %sFailed to run flow func in all devices[%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 = %sexpect at least 4 bytes, but got %zu, device_index = %zullm_flow_service.cc[%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 outputsdevice_indexis_promptipport[%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/%zusizeof(int32_t)output_size[%s:%d]%lu %s: ErrorNo: %u(%s) %sAllocate cache return code = %u, device_index = %zu_args__BuiltIn_cluster_unlink_func_BuiltIn_cluster_update_link_func_BuiltIn_cache_allocate_func_BuiltIn_cache_deallocate_func_BuiltIn_cache_get_func_BuiltIn_cache_copy_func_BuiltIn_cache_remove_index_func_BuiltIn_cluster_kv_data_sync_func_BuiltIn_cache_pull_func[%s:%d]%lu %s:Load flow funcs start, device_index = %zuGetCacheTensors[%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 = %sge.exec.modelDeployModeMultiMasterge.exec.modelDeployDevicelist[%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 failedN3llm5GeApiESt15_Sp_counted_ptrIPN2ge7SessionELN9__gnu_cxx12_Lock_policyE2EENewSessionInitialize((new_ge_api) != 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 invalidPopllm_req_queue.cc[%s:%d]%lu %s:Wait failed, is_stopped = %dSt15_Sp_counted_ptrIPN2ge10AlignedPtrELN9__gnu_cxx12_Lock_policyE2EEIsPositiveIntegerInitBatchSizeCheckOutputsRetCodeGetFolderPathUpdateBatchInputsUpdateInputTensorInputShapeSplitAnalyInputDtypeSplitAnalyautollm.batch_modemanualParseBatchModellm.InputWaitTimeParserWaitTimeInfollm.SyncKvCacheWaitTimellm.NnExecuteWaitTimellm.ProcessRequestWaitTimeoperator()ParserInputsShapeAndDtypeInitMultiInputShapeAndDtypellm.InputNodeDeploymentInitInputNodeDeploymentllm.InputsBatchPaddingInitBatchInputsPaddingllm.PipelineInputIndicesParsePipelineInputIndicesToNumberllm.EnablePagedAttention01InitPagedAttentionOptionsllm.PagedAttentionBlockSizellm.PagedAttentionBlocksNumllm.PagedAttentionMaxSeqLenllm.PagedAttentionMaxSeqsNumllm.PagedAttentionMaxPromptLenllm.InputsBatchSizeDimIndexInitBatchDimIndexSeparateRequestOutputenable:, block_size:, blocks_num:, max_seq_len, max_seqs_num, max_prompt_lenInput string is empty.(!input_string.empty())llm_utils.ccInput string contains non-numeric characters.(std::isdigit(c) != 0)(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 emptyOutput [%zu] ret is failed[%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:Option: %s = %s[%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)option %s can not be negative(tmp >= 0)options of llm engine Initialize must contain: %s.(iter != options.end())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.(%s option is invalid.(!inputs_padding.empty())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)))convert [%s] to int failed.((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(!dim_indexes.empty())((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.Ascend910AAscend910BAscend910PremiumAAscend910ProAAscend910ProBAscend910B1Ascend910B2Ascend910B3Ascend910B4Ascend910B2CAscend910C1Ascend910C2Ascend910C3Ascend920AAscend310P1Ascend310P3Ascend310Ascend310B1Ascend310B2Ascend310B3Ascend310B4Ascend310M1Ascend320Ascend610Ascend610LiteAscend615Ascend710Vir01Ascend710Vir02Ascend710Vir04Ascend710Vir08BS9SX1AAHi3796CV300CSHi3796CV300ESOPTGSD3403TsnsCTsnsEAscend031Ascend035ddr_rateubblock_sizeAICPUSyncBySWTSCPUSyncBySWCounterWorkspaceSizeai_core_cntjit_compile_default_valuejit_compile_modevec_freqIntrinsic_mmadAICoreMemoryRatesAICoreSpecCPUCacheDSARandomSoCInfoSoftwareSpecVectorCoreMemoryRatesVectorCoreSpecversionAICoreintrinsicDtypeMapVectorCoreintrinsicDtypeMapALL_LABEL_END[%s:%d]%lu %s: ErrorNo: %u(%s) %scopy output_data failed.[%s:%d]%lu %s:SeparateRequestOutput success, size:%zuExecuteModelDistributeinferencellm.InputShapesllm.InputDtypesLoadModeloffline_model_manager.cc[%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 omexpect 1 om_path, but got %zu(om_paths.size() == 1U)[%s:%d]%lu %s: ErrorNo: %u(%s) %sFailed to load model[%s:%d]%lu %s:[Load][Model] success, model_id = %uCheckModelDescAssembleInputPopAllTimeoutRequestsUpdateBatchOccupyBatchCheckTensorSizeParseCopyArgsCollectCurrentBatchCompleteRequestInitBatchInputTensorsInitialize[DecoderManager] input shape size:%zu mismatches input dtypes size:%zu(model_desc.input_shapes.size() == model_desc.input_dtypes.size())batch_manager.cc[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]Invalid tensor shape: %s[%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 is empty[%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]Failed to push to idle queue[%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 argsFinalizePullKvToCacheLinkClustersUnlinkClustersCheckInputsllm.InputShapesllm.InputDtypesRunDecoderBuildUdfFlowFuncsPrepareIoIndicesInitGeneratePagedAttentionInputCreateSingleBatchPagedAttentionInputPrepareExecuteNodeInfoRunDecoderAsyncRunDecoderRunDecoderMergeKvFromCacheToBatchParsePipelineExecutionllm.PipelineExecutionenabledisablePrepareIoIndicesForStagesInitialize*ZN3llm14DecoderManager22PrepareExecuteNodeInfoERKSt6vectorINS_12FlowDataInfoESaIS2_EERS1_INS_12FlowNodeInfoESaIS7_EEEUlN2ge5dflow10FunctionPpEE_*ZN3llm14DecoderManager10RunDecoderERKNS_6LLMReqERKSt6vectorIN2ge6TensorESaIS6_EERS8_EUljSB_E0_*ZN3llm14DecoderManager10RunDecoderERKSt6vectorImSaImEERKS1_IN2ge6TensorESaIS7_EERS9_mEUljSC_E_vector::_M_range_insertvector::_M_fill_insertdecoder_manager.cc[%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 modedecoder link clusters failed[%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 != %dFailed to check inputs[%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_BuiltIn_sync_kv_cache_func_BuiltIn_merge_kv_cache_func_BuiltIn_update_link_func[%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.build udf flow funcs failed[%s:%d]%lu %s: ErrorNo: %u(%s) %sbuild udf flow funcs failedDecoderExecute_[%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 = %sNnFlowData_ReqFlowData_BatchIndexFlowData_LinkInfoFlowData_[%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:%zuinvoke_postprocess_modelpp__modelpostprocess_depend_keypostprocess_output_indicespostprocess_external_input_numinvoke_decoder_modelpp_devicedepend_keynn_need_set_outputkv_in_data_shapekv_in_data_typecluster_idkv_sizecached_kv_max_tensor_sizebatch_numnn_input_numenable_paged_attentionnn_output_indicessync_kv_output_indicesmerge_kv_output_indiceslink_func_output_indicesNSt6thread11_State_implINS_8_InvokerISt5tupleIJMN3llm20DecoderModelExecutorEFvlEPS4_mEEEEEEGetModelOutputCheckOutputTensorSizesFinalizeInitializeExecuteModelAsyncPrepareOutputTensorsTryPrepareOutputTensorsCacheOutputTensorDescsCallbackThreaddecoder_model_executor.ccOutput [%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((output_buffer) != nullptr)[%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 %zuOperator graph failed[%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 exitNSt6thread11_State_implINS_8_InvokerISt5tupleIJMN3llm16DecoderSchedulerEFvvEPS4_EEEEEESt15_Sp_counted_ptrIPSt11unique_lockISt5mutexELN9__gnu_cxx12_Lock_policyE2EEMergeKvGetOutputTensorsCollectCurrentBatchDispatchRequestWaitIdleBatchIndexSyncKvInitExecuteModelAsyncPPCallbackOnErrorFinalizeTimeoutRequestEvictThreadCollectCurrentBatchSyncKvThreadCallbackOutputsoperator()PipelineInferenceThreadApendSlotForReqUpdatePagedAttentionInputTensorExecuteModelInferenceThreadInitialize*ZN3llm16DecoderScheduler17ExecuteModelAsyncERKNS_12BatchManagerEiRSt6vectorINS_14DecoderRequestESaIS5_EEOSt11unique_lockISt5mutexEEUljRS4_IN2ge6TensorESaISE_EEE_decoder_scheduler.cc[%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 batchcopy 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][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 = %ld[%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((defer_lock) != nullptr)[%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]%lu %s:No new request[%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 initializedpBAllocateBlockCheckAndGetBatchIdMergeKvMergeKvWithCacheBatchMergeKvReleaseAllCachesPullKvCheckRequestsPullKvWithCacheBatchPullKvInitialize*ZN3llm14KvCacheManager6PullKvERKNS_6LLMReqEimEUlvE_kv_cache_manager.cc[%s:%d]%lu %s: ErrorNo: %u(%s) %sReq seq len %llu is bigger than max seq len %llu, req id:%lluAll requests are invalid[%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 = %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 merging[%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 = %ld;Number 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 = %sSt15_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_policyE2EELLMEngineFinalizePromptLinkClustersDecoderUnlinkClustersCheckValidityOfRequestRunPromptRunPromptRunDecoderPreloadPromptPrefixReleasePromptPrefixPullKvLLMReqCompleteMergeKvRunPromptRunPromptRunDecoderLLMReqCompleteLLMEngineFinalizePreloadPromptPrefixReleasePromptPrefixPullKvMergeKvLinkClustersUnlinkClustersRunPromptAsyncRunPromptAsyncRunDecoderAsyncRunDecoderAsyncLLMEngineRunDecoderRunDecoderRunDecoderRunDecoderInitializePrioritySchedulellm.graph_compiler_cache_dirAddLLMModelAddLLMModelLLMEngineInitializeV2llm.RunModellm.RoleLLMEngineInitializeV2inferencellm_engine.cc[%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 DecoderUnlink clusters failed[%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.Role should be Prompt![%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 modelPriorityScheduleSeparateSchedule[%s:%d]%lu %s:Begin to init in PrioritySchedule run modege.graph_compiler_cache_dir((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.[LLMEngine] not find %s.[%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 successLLMEngineFinalizeLinkClustersDecoderUnlinkClustersAllocateCacheDeallocateCachePullCacheCopyCacheRemoveCacheKeyGetCacheTensorsRunModelDistributeLinkClustersUnlinkClustersAllocateCacheDeallocateCachePullCacheCopyCacheRemoveCacheKeyGetCacheTensorsRunModelDistributeLLMEngineV2PromptInitFlowServicellm.OmCachePathLLMEngineInitializellm.RoleLLMEngineInitializeAddLLMModelAddLLMModelllm_engine_v2.ccParam: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 successfully[%s:%d]%lu %s:cluster:%luNSt6thread11_State_implINS_8_InvokerISt5tupleIJMN3llm23PriorityScheduleManagerEFvvEPS4_EEEEEEoperator()operator()operator()operator()operator()UpdateDecoderUserInputInitBatchDataCheckOptionsllm.PagedAttentionMaxSeqsNumllm.PagedAttentionMaxPromptLenllm.PagedAttentionMaxSeqLenFinalizellm.InputShapesllm.InputDtypesInitShapesAndDtypesllm.RefInputShapesllm.RefInputDtypesUpdateDecoderInputTensorConstructDecoderNonUserInputsPageAttentionRunPriorityScheduleAsyncRunPriorityScheduleInitBatchllm.InputsBatchPaddingSeparateOutputllm.PromptAndDecoderInterleavedStepInitPromptAndDecoderInterleavedStepInitBatchInputTensorsInitOptionsllm.PagedAttentionBlockSizellm.PagedAttentionBlocksNumInitLLMReqCompleteEraseReturnPriorityScheduleErrorAppendSlotForReqDecoderInputsCheckAssembleDecoderInputsConstructDecoderInputsConstructPromptInputsPrintTensorDescInfoInsertDoExecutePromptExecutePromptBuildPromptGraphAndInitRefTensorllm.OmCachePathInitGraphsllm.GraphPathllm.graph_keysPrintOptionsInitializeInitDeviceIdToRefDataTensorsInitDistributedRefDataTensorsBuildDecoderGraphAndInitRefTensorFrontShardInitRefDataTensorBuildGraphAndInitRefDataTensorOutputDecoderResultcommitExecuteDecoderLlmPriorityScheduleInputThread*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_policyE2EEbatch_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:%zupriority_schedule_manager.cc[%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: %#xstol[%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))Call %s fail, ret: 0x%X((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)(kv_shapes.size() == 1UL)(kv_dtypes.size() == 1UL)[%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(ret)[%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 zeroGen tensor data failed.((ret) == ge::SUCCESS)[%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 infocomplete req %lu failed(ret.first)Init 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.Complete req:%lu failed.((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())){tensor_desc[]=, shape:[], dtype:, place:} ; [%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(insert_ret.first)[%s:%d]%lu %s: ErrorNo: %u(%s) %sConstruct inputs for graph id:%lu with req_id:%lu failedPromptRunRun 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:%ldexecute prompt failedPromptBuildbuild prompt graph failed((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)ge.graph_key((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.(data_size > 0L)(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())rtSetDevice(device_id)DecoderBuildrun decoder graph failed((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)DecoderRunrun 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 failedbuild 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:%zuexecute decoder failed[%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.N3llm19PromptTaskQueueBaseEN3llm15PromptTaskQueueEN3llm23BatchingPromptTaskQueueENSt6thread11_State_implINS_8_InvokerISt5tupleIJMN3llm18PromptBatchManagerEFvvEPS4_EEEEEEAllocateInputTensorToNumberToNumberParsePaddingValuePushPushPushllm.InputPaddingsParsePaddingValuesToNumberMergeInputMergeInputsSubmitBatchBatchThreadEnsureNotEmptyCollectDynamicDimsParseCopyArgsInitialize*ZN3llm18PromptBatchManager11SubmitBatchERSt10unique_ptrINS_13InferenceTaskESt14default_deleteIS2_EEEUlvE_prompt_batch_manager.cc[%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)Unsupported data type: %d[%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((task) != nullptr)[%s:%d]%lu %s:push task, request = %s[%s:%d]%lu %s:option %s not set, padding zero by default[%s:%d]%lu %s:%s = %selement 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 successfullyalready stopped[%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:already stopped[%s:%d]%lu %s:start to submit requests, size = %zu[%s:%d]%lu %s: ErrorNo: %u(%s) %sFailed to submit current batchInvalid shape dims: %s[%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 stateNSt6thread11_State_implINS_8_InvokerISt5tupleIJMN3llm13PromptManagerEFvmEPS4_mEEEEEELlmPromptInputThreadCheckPrefixFinalizeUnlinkClustersNnFlowData_ReqFlowData_CleanFlowData_UnlinkFlowData_LLMReqCompleteReleasePromptPrefixCheckInputsllm.InputShapesllm.InputDtypesRunPromptAsyncRunPromptLlmPromptInputThreadForMultiStagesLlmPromptOutputThreadRunPromptAsyncRunPromptPrepareIoIndicesllm.InputPaddingsInitializeTaskQueuePreloadPromptPrefixPrepareIoIndicesForStagesBuildUdfFlowFuncsPrepareExecuteNodeInfoInitialize*ZN3llm13PromptManager22PrepareExecuteNodeInfoERKSt6vectorINS_12FlowDataInfoESaIS2_EERS1_INS_12FlowNodeInfoESaIS7_EEEUlN2ge5dflow10FunctionPpEE_*ZN3llm13PromptManager9RunPromptERKNS_6LLMReqERKSt6vectorIN2ge6TensorESaIS6_EERS8_mEUljSB_E0_*ZN3llm13PromptManager9RunPromptERKSt6vectorINS_6LLMReqESaIS2_EERKS1_IN2ge6TensorESaIS8_EERSA_mEUljSD_E_prompt_manager.cc[%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.prompt unlink clusters failed[%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_execute_ssm_BuiltIn_prompt_kv_data_sync_func_BuiltIn_prompt_kv_data_clean_func_BuiltIn_prompt_unlink_func[%s:%d]%lu %s:[PromptManager] CreatePromptFlowNodes begin../invoke_prompt_func_PromptExecute_[%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: %zuparser wait time info failedparser flow graph info failed[%s:%d]%lu %s: ErrorNo: %u(%s) %sparser flow graph info failed[%s:%d]%lu %s: ErrorNo: %u(%s) %sprepare IO indices for stages failedprepare execute node failed[%s:%d]%lu %s: ErrorNo: %u(%s) %sprepare execute node failedconstruct flow graph 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.invoke_prompt_modelpp_devicecheck_nn_output_indicesclean_func_output_indicesunlink_func_output_indicesN3llm13InferenceTaskEN3llm24SingleBatchInferenceTaskEN3llm22AutoBatchInferenceTaskEN3llm24ManualBatchInferenceTaskECheckNnFuncResultBeforeNnCallbackOnErrorCopyOutputsCallbackOutputsCallbackOutputsCallbackOnErrorFetchNnResultFetchPrefixResultFetchAndCallbackExecuteModelAsyncExecuteAsyncfetch check nn result failed, ret = %u, output num = %zuprompt_model_executor.cc[%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{req_id:, prefix_id:, prompt_length:}[%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 = %s;P1)9L,9lt:,D<T*@d-pA:A<@BtD1|D1D1D2D2D02DD2DX2Dl2D2D2D2TE03I34J4L6L6Pd7Q7S@8ST8TT8\T8W9$Y9\Y<:Z:Z:L[:\;_4<`h<a =b@=$d=d>f$?i?4k@lB\mpC\sCu$D~tDDLFLB8C,8EܢEE,F@GdGdpHDL4MNIJ0N̲DNԲXNܲlN@OTOVp[[t]p_h``l4abdPghTh,lxT @4/ \NȪhnTz~̉P$Ȯܖ\ܮTIhI|IIIII$I\J0J̯XJJm?0nBnB,G\Kt \|T\Dl\4ttXl $`4Tд4İ @DT`t @\|$\lpl@T T|$plpl$@ \ *d$*x,*4*<*D*ȺL*ܺ\****0*X*l***л*** * *44+++ ,D,Dt,l|,-l-\-1,9h:@HCdE0EETFGGIxDJAč,BĖBTB`C DdD\EԸF|FF;|<<4,== 0> ?d@??0@@ ALD(ETdGGG,@HlhHHHI_\B`HaSb_cq(d|xd$dDe fHl I$DIt$LԯhLd@|DNԟQ<R]<\^t_e TjmpswLz|}Ā$DĎ ė<$ltxȯTح<\$,8\L`tlرdlt(t|ԲH|<\ȴ\H xķ|<<$ 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