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C.H.kb>/.;.--k0.39.7.?`1.5.k^>p.0'.`-P-k`.3 %.#.?. .k_>pQ.PV. --kR.3U.@U.?S.PT.k_>;.>.--k<.30>.=.?0<. =.kc>l.l.--k(.3`l.).?@(. ).k]>..@-0-k@.3.0.?..kD_>0k.k.--k`.3c.b.? a.b.kb>'.*.-p-k(.3 *.).?@(. ).k,a>c.f.--kd.3pf.0f.?d.e.kX_> .0. --k .3. .?0 ..k]>+.@..--k`+.3-.@-.?+.,.k0_>u.x.@-0-k0v.3px.x.?`v.`w.ka>@g.j.--k h.3@j.i.?`h.@i.kTb>\._.`-P-k\.30_.^.?\.^.k_>p. .--k.3 . .?@.P.k^>`.c.-p-k`.3c.b.? a.b.k ^>-.--k-3.@.?--k`>Pm.pu. --kn.30t.ps.?o.q.k^>V.[.@-0-kW.3Z.Z.?X.@Y.khc>..@.0.k.3p..?.p.kc>0.. .0./c>.. .0..d>.....4d> ..../d>0.`..0..`H/H/H/H/H/I/ I/@I/e>/. .0./ e>///Њ/P/He>/.P/Њ//f>.p//0. f> С///|f> С///Unknown exceptionbad array new lengthC:\apilot\agent\_work\1\s\engine\lotus\cmake\external\protobuf\src\google/protobuf/parse_context.h(cannot determine missing fields for lite message)C:\apilot\agent\_work\1\s\engine\lotus\cmake\external\protobuf\src\google\protobuf\message_lite.cc.Can't message of type "" because it is missing required fields: parse exceeded maximum protobuf size of 2GB: vector too longstring too longC:\apilot\agent\_work\1\s\engine\lotus\cmake\external\protobuf\src\google\protobuf\io\zero_copy_stream.ccThis ZeroCopyOutputStream doesn't support aliasing. Reaching here usually means a ZeroCopyOutputStream implementation bug.: genericiostreamiostream stream errorbad castbad locale nameios_base::badbit setios_base::failbit setios_base::eofbit setC:\apilot\agent\_work\1\s\engine\lotus\cmake\external\protobuf\src\google\protobuf\io\zero_copy_stream_impl.ccclose() failed: CHECK failed: !is_closed_: C:\apilot\agent\_work\1\s\engine\lotus\cmake\external\protobuf\src\google\protobuf\io\zero_copy_stream_impl_lite.ccCHECK failed: (count) >= (0): CHECK failed: backup_bytes_ == 0 && buffer_.get() != NULL: BackUp() can only be called after Next().CHECK failed: (count) <= (buffer_used_): Can't back up over more bytes than were returned by the last call to Next(). Parameter to BackUp() can't be negative.CHECK failed: (backup_bytes_) == (0): CHECK failed: (buffer_used_) == (buffer_size_): INFOWARNINGERRORFATAL[libprotobuf %s %s:%d] %s %d%u%p!6!6 !6(!6PC:\apilot\agent\_work\1\s\engine\lotus\cmake\external\protobuf\src\google\protobuf\arena.ccCHECK failed: (min_bytes) <= (std::numeric_limits::max() - kBlockHeaderSize): Can't happenfalsetrue\\x%02xty10invalid string position...\\?\\/LuLdluldeEpPOKUNKNOWNINVALID_ARGUMENT:C:\apilot\agent\_work\1\s\engine\lotus\cmake\external\protobuf\src\google\protobuf\repeated_field.ccCHECK failed: (new_size) <= ((std::numeric_limits::max() - kRepHeaderSize) / sizeof(old_rep->elements[0])): Requested size is too large to fit into size_t., C:\apilot\agent\_work\1\s\engine\lotus\cmake\external\protobuf\src\google\protobuf\generated_message_util.ccCHECK failed: (scc->visit_status.load(std::memory_order_relaxed)) == (SCCInfoBase::kRunning): map/set too longunordered_map/set too longinvalid hash bucket countdevice or resource busyinvalid argumentno such processnot enough memoryoperation not permittedresource deadlock would occurresource unavailable try again $ %6,%6@%6P%6d%6|%6%6bad allocationbad function callsystemunknown error*Caddress family not supportedaddress in useaddress not availablealready connectedargument list too longargument out of domainbad addressbad file descriptorbad messagebroken pipeconnection abortedconnection already in progressconnection refusedconnection resetcross device linkdestination address requireddirectory not emptyexecutable format errorfile existsfile too largefilename too longfunction not supportedhost unreachableidentifier removedillegal byte sequenceinappropriate io control operationinterruptedinvalid seekio erroris a directorymessage sizenetwork downnetwork resetnetwork unreachableno buffer spaceno child processno linkno lock availableno message availableno messageno protocol optionno space on deviceno stream resourcesno such device or addressno such deviceno such file or directorynot a directorynot a socketnot a streamnot connectednot supportedoperation canceledoperation in progressoperation not supportedoperation would blockowner deadpermission deniedprotocol errorprotocol not supportedread only file systemresult out of rangestate not recoverablestream timeouttext file busytimed outtoo many files open in systemtoo many files opentoo many linkstoo many symbolic link levelsvalue too largewrong protocol type o&R  7d ) pP' ({W!''   2na i )   ' @'dA'e?'f5'g' E'jM'kF'l7'm'Q'n4'p'&'H'q('8'sO'&B'tD'uC'vG'w:'{I'~6'=';'9'L'3'f8&6dX&6eh&6q&6&6!&6&6 &6h&6 &6j&6g'6k0'6lD'6X'6ml'6%6)'6'6'6'6&'6('6n(6o(6*((6@(6d(6,%6p(6(6(6s(6t(6u(6v(6w(6 (6y)6' )6x )6z8)6{D)6X)6|l)6)6)6)6@%6)6)6})6~)6 P%6*6i*6p,*6d%6D*6\*6t*6 *6*6*6*6$|%6 %6"*6*6+6+6 +6,+6L+6`+6rp+6+6+6bad exception0123456789ABCDEFabcdef-+Xx0123456789ABCDEFabcdef-+XxPp0123456789-+Ee0123456789abcdefghijklmnopqrstuvwxyz! :Sun:Sunday:Mon:Monday:Tue:Tuesday:Wed:Wednesday:Thu:Thursday:Fri:Friday:Sat:Saturday:Jan:January:Feb:February:Mar:March:Apr:April:May:May:Jun:June:Jul:July:Aug:August:Sep:September:Oct:October:Nov:November:Dec:December:Sun:Sunday:Mon:Monday:Tue:Tuesday:Wed:Wednesday:Thu:Thursday:Fri:Friday:Sat:Saturday:Jan:January:Feb:February:Mar:March:Apr:April:May:May:Jun:June:Jul:July:Aug:August:Sep:September:Oct:October:Nov:November:Dec:December!%x%b %d %H : %M : %S %Y%m / %d / %y :AM:am:PM:pm%I : %M : %S %p%H : %M%H : %M : %S%d / %m / %y%.0Lf$+xv+v$x+v$xv$+xv+$xv$+x+$vx+$vx$v+x+$vx$+vx+v $+v $v $+v +$v $++$ v+$ v$ v++$ v$+ v+xv$+ v$v$ +v+ $v$ ++x$v+ $v$v ++ $v$ +v:AM:am:PM:pm-0123456789-!%x-0123456789abcdefghijklmnopqrstuvwxyzA)! /\ was false. Stacktrace: Cpuai.onnx.mlai.onnx.trainingai.onnx.preview.training!=C:\apilot\agent\_work\1\s\engine\lotus\include\onnxruntime\core/framework/tensor.h)Trying to get a Tensor, but got: IsTensor()OrtValue::GetC:\apilot\agent\_work\1\s\engine\lotus\include\onnxruntime\core/framework/ml_value.hOrtValue::GetMutableTrying to get a TensorSeq, but got: IsTensorSequence()VIWEFCPUExecutionProviderDmlExecutionProviderInteger overflowSafeIntExceptionHandler::SafeIntOnOverflowC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core/common/safeint.hORT_LOAD_CONFIG_FROM_MODELTensor sequence must contain only primitive typeselem_type_ != nullptronnxruntime::TensorSeq::SetTypeC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core/framework/TensorSeq.hi < tensors_.size()onnxruntime::TensorSeq::Gettried creating tensor with negative value in shapesize overflow, got not enough space: expected is not supported in this functiontype RegisterCustomOpsLibrary: Failed to load libraryRegisterCustomOpsRegisterCustomOpsLibrary: Entry point RegisterCustomOps not found in libraryonnxruntime_profile_input name cannot be emptyoutput name cannot be emptylengths allocation failedstring buffer allocation failedOutput buffer allocation failedinput array doesn't equal tensor sizeelement index is out of boundsshape is invalidoffsets buffer is not equal to tensor sizeoutput buffer is too smallbuffer size is too small for stringout of indexinternal errorindex out of rangeInput is not of one of the supported sequence types.Input is not of type sequence or map.input array is too shortUnsupported tensor element type in the input: Input is not of one of the supported map types.Expecting all elements to be tensors. Got: in[idx]->IsTensor()OrtCreateValueImplSeqHelperC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\session\onnxruntime_c_api.ccSequences must have tensors of the same data type. There was at least one tensor in the input that was different.Each element of the sequence should be either tensor or map.At least one element in the sequence is of a type different from others.Unsupported input typeFor map type num_values MUST be 2Either the key tensor or the value tensor has NumDimensions > 1Key and value tensors have unequal number of elements.Key type is not supported yet.Number of values should be at least 1.opaque(,Specified domain and type names combination does not refer to a registered opaque typeml_type != nullptrOrtApis::CreateOpaqueValueOpaque type is not a non_tensor type!!!non_tensor_base != nullptrOrtApis::GetOpaqueValuethis API does not support stringslocation dimensions do not match shape sizeinvalid location range1.5.2T Tensor type mismatch. utils::IsPrimitiveDataType(dtype_)onnxruntime::Tensor::MutableDataonnxruntime::Tensor::Datastatic_cast(num_kv_pairs) < std::numeric_limits::max()OrtGetValueImplMapHelperInvalid index requested for map type.Tensor must always contain primitive types. Found: value_type != nullptrOrtCreateValueImplMapHelperValue type is not supported yet: Map is missing type entry for its value++index < c.size()onnxruntime::utils::ContainerChecker::IsContainerOfType,class std::allocator > > >::checkC:\apilot\agent\_work\1\s\engine\lotus\include\onnxruntime\core/framework/data_types_internal.honnxruntime::utils::ContainerChecker::IsContainerOfType,class std::allocator > > >::checkonnxruntime::utils::ContainerChecker::IsContainerOfType,class std::allocator > > >::checkonnxruntime::utils::ContainerChecker::IsContainerOfType,class std::allocator >,struct std::less<__int64>,class std::allocator,class std::allocator > > > > >::checkonnxruntime::utils::ContainerChecker::IsContainerOfType,class std::allocator >,double,struct std::less,class std::allocator > >,class std::allocator,class std::allocator > const ,double> > > >::checkonnxruntime::utils::ContainerChecker::IsContainerOfType,class std::allocator >,float,struct std::less,class std::allocator > >,class std::allocator,class std::allocator > const ,float> > > >::checkonnxruntime::utils::ContainerChecker::IsContainerOfType,class std::allocator >,__int64,struct std::less,class std::allocator > >,class std::allocator,class std::allocator > const ,__int64> > > >::checkonnxruntime::utils::ContainerChecker::IsContainerOfType,class std::allocator >,class std::basic_string,class std::allocator >,struct std::less,class std::allocator > >,class std::allocator,class std::allocator > const ,class std::basic_string,class std::allocator > > > > >::checkCheck for duplicate types in MLTypeCallDispatcherRetcalled_ < 2onnxruntime::utils::mltype_dispatcher_internal::CallableDispatchableRetHelper::GetSequence is missing type entry for its elementonnxruntime::utils::ContainerChecker::IsContainerOfType,class std::allocator > >,class std::allocator,class std::allocator > > > > >::checkonnxruntime::utils::ContainerChecker::IsContainerOfType,class std::allocator >,float,struct std::less,class std::allocator > >,class std::allocator,class std::allocator > const ,float> > >,class std::allocator,class std::allocator >,float,struct std::less,class std::allocator > >,class std::allocator,class std::allocator > const ,float> > > > > >::checkinvalid vector subscript != onnxruntime::DataTypeImpl::GetType() == type_len >= 0 && static_cast(len) < std::numeric_limits::max()OrtCreateMapMLValueEncountered nullptr.L76d76not implementedexecution_mode is not validgraph_optimization_level is not validunknown is not implementedai.onnxonnxruntime::OpKernel::ComputeAsync' in custom op 'Result buffer is not large enoughonnxruntime::CustomOpKernel::CustomOpKernelUnsupported version 'C:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\session\custom_ops.cconnxruntime::CreateCustomRegistryANo requested allocator availableOrtMemoryInfo is nullEnv is nullOnly CPU devices are supported for now.env_ptr == p_instance_C:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\session\ort_env.ccOrtEnv::ReleaseC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\session\IOBinding.cconnxruntime::IOBinding::BindInputp_providerAttempt to use DefaultLogger but none has been registered.C:\apilot\agent\_work\1\s\engine\lotus\include\onnxruntime\core/common/logging/logging.honnxruntime::logging::LoggingManager::DefaultLoggersession.use_env_allocatorssession.save_model_formatsession.load_model_format]session.set_denormal_as_zeroCUDAExecutionProvider has already been registered.onnxruntime::ExecutionProviders::AddProvider Trying to get a SparseTensor, but got: C:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core/framework/execution_providers.honnxruntime::FeedsFetchesInfo::FeedsFetchesInfoIsSparseTensor()Compute_C:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core/framework/feeds_fetches_manager.hRelease_State_Create_State_MemcpyTransformer] ORTM[json.exception.other_error%Y-%m-%d_%H-%M-%SThe environment variable contained the value: 1.4.0The only supported values for the environment variable are '0' and '1'. onnxruntime::FinalizeSessionOptionsReading the provided model for the ORT configModelProto needs to be parsed to check for ORT config within itC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\session\inference_session.cconis_model_proto_parsedFlush-to-zero and denormal-as-zero are offCould not finalize session options while constructing the inference session. Error Message: onnxruntime::InferenceSession::ConstructorCommon::::operator ()onnxruntime::InferenceSession::ConstructorCommonstatus.IsOK()Creating and using per session threadpools since use_per_session_threads_ is trueFailed to create the inter-op thread pool for the parallel executor, setting ExecutionMode to SEQUENTIALintra-opWhen the session is not configured to use per session threadpools, the env must be created with the the CreateEnvWithGlobalThreadPools API.Using global/env threadpools since use_per_session_threads_ is falseCastFloat16Transformersession_env.EnvCreatedWithGlobalThreadPools()onnxruntime::InferenceSession::InferenceSessionGiven model could not be parsed while creating inference session. Error message: st.IsOK()Could not parse model successfully while constructing the inference sessionError during EndProfiling(): resultUnknown error during EndProfiling()onnxruntime::InferenceSession::{dtor}::::operator ()Received nullptr for exec provideronnxruntime::InferenceSession::~InferenceSessiononnxruntime::InferenceSession::RegisterExecutionProviderExecution providers must be registered before the session is initialized. So disabling it for this session since it uses the DML Execution Provider.Execution providers must be registered before the session is initialized.So making the execution mode sequential for this session since it uses the DML Execution Provider.Having memory pattern enabled is not supported while using the DML Execution Provider. So making the execution mode sequential for this session since it uses the CUDA Execution Provider.Parallel execution mode does not support the DML Execution Provider. Parallel execution mode does not support the CUDA Execution Provider. Received nullptr for graph transformeronnxruntime::InferenceSession::RegisterGraphTransformerGraph transformers must be registered before the session is initialized.Received nullptr for custom registryonnxruntime::InferenceSession::AddCustomOpDomainsonnxruntime::InferenceSession::SaveToOrtFormatException during loading: onnxruntime::InferenceSession::LoadThis session already contains a loaded model.Encountered unknown exception in Load()Unknown exception in Load()ModelProto corresponding to the model to be loaded has already been parsed. Invoke Load().ORTmodel_loading_arrayFailed to load model because protobuf parsing failed.model_loading_protomodel_loading_from_saved_protoModelProto corresponding to the model to be loaded has not been parsed yet. This API should be called in conjunction with a ctor that takes a model abstraction.Could not find an implementation for the node onnxruntime::InferenceSession::TransformGraph ((].Node placements: [All nodes have been placed on [ Provider: [onnxruntime::InferenceSession::LoadOrtModelonnxruntime::InferenceSession::LoadOrtModel::::operator ()Not satisfied: fbs::VerifyInferenceSessionBuffer(verifier) This session has already been initialized.Satisfied, but should not be: nullptr == fbs_session InferenceSession is null. Invalid ORT format model.Satisfied, but should not be: fbs_ort_model_version == nullptr Serialized version info is null. Invalid ORT format model.The ORT format model version [] is not supported this build Missing Model. Invalid ORT format model.Not satisfied: IsOrtModelVersionSupported(fbs_ort_model_version->str()) SessionState is null. Invalid ORT format model.Satisfied, but should not be: nullptr == fbs_model Exception during initialization: Satisfied, but should not be: nullptr == fbs_sess_state onnxruntime::InferenceSession::Initialize::::operator ()onnxruntime::InferenceSession::Initialize::::operator ()onnxruntime::InferenceSession::InitializeInitializing session.Model was not loaded.Model was not loadedAdding default CPU execution provider.Session has already been initialized.Session successfully initialized.Serializing optimized model with Graph Optimization level greater than ORT_ENABLE_EXTENDED. The generated model may contain hardware and execution provider specific optimizations, and should only be used in the same environment the model was optimized for.session_initializationEncountered unknown exception in Initialize() Expected: Please fix either the inputs or the model.Invalid rank for input: Got: Got invalid dimensions for input: for the following indices ) , expected: ( index: elements.Unexpected input data type. Actual: (Size mismatch: feed_names has elements, but feeds has is not expected to be of type tensor.Invalid Feed Input Name:onnxruntime::InferenceSession::ValidateInputsInput with name: is not expected to be of type tensor sequence. is not expected to be of type sparse tensor.At least one output should be requested.Output vector pointer is NULLOutput vector incorrectly sized: output_names.size(): p_fetches->size(): Session was not initializedInvalid Output Name:Session not initialized.onnxruntime::InferenceSession::RunEncountered unknown exception in Run()Running with tag: onnxruntime::InferenceSession::GetModelMetadatamodel_runonnxruntime::InferenceSession::GetOverridableInitializersonnxruntime::InferenceSession::GetModelInputsonnxruntime::InferenceSession::NewIOBindingonnxruntime::InferenceSession::GetModelOutputsonnxruntime::InferenceSession::EndProfilingProfiler is disabled.Could not write a profile because no model was loaded.run_options.run_log_severity_level >= 0 && run_options.run_log_severity_level <= static_cast(logging::Severity::kFATAL)Invalid run log severity level. Not a valid onnxruntime::logging::Severity value: onnxruntime::InferenceSession::CreateLoggerForRunInvalid session log severity level. Not a valid onnxruntime::logging::Severity value: onnxruntime::InferenceSession::InitLoggersession_options_.session_log_severity_level >= 0 && session_options_.session_log_severity_level <= static_cast(logging::Severity::kFATAL)graph_optimization_level <= TransformerLevel::MaxLevelExceeded max transformer level. Current level is set to onnxruntime::InferenceSession::AddPredefinedTransformers/961c151d2e87f2686a955a9be24d316f1362bf21 3.7.1_.json failed:model_loading_urionnxruntime::LoadOrtModelBytesLoad model from failed. Only bytes were able to be read.list too longlocaltime_s(&local_tm, &in_time_t) == 0onnxruntime::`anonymous-namespace'::GetCurrentTimeStringhpcom.microsoft.nchwccom.microsoftcom.microsoft.dmlcom.microsoft.mlfeaturizers[TypeInferenceError] expected to have tensor type unknownInput Output Element type of input expected to have type but instead is null expected to have sequence typetensor(uint64)tensor(uint32)tensor(int64)tensor(int32)tensor(float)tensor(float16)tensor(bfloat16)tensor(double)tensor(uint16)tensor(uint8)tensor(int16)tensor(int8)tensor(string)tensor(complex64)tensor(bool)tensor(complex128) line ) from file , but it is already registered from file (domain: version: known by the checker.Trying to register schema with name in onnx/defs/schema.h)., but it its domain is notbumped the operator version but forgot to update the version range in DomainToVersionRange in the inclusive range [] (usually, this means you Schema error: , but it its version is not Allocator with this OrtMemoryInfo is already registered.inter-opException caught: MemcpyFromHostC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\session\environment.ccinputToutputXConstrain to any tensor type. If the dtype attribute is not provided this must be a valid output type.YMemcpyToHostXvalue'parse errorparse_error at line , column type_errorinvalid_iteratorort_configout_of_rangeUnsupported value for intra_op_num_threads: session_optionsC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\session\inference_session_utils.cconnxruntime::SetIntraOpNumThreadsUnsupported value for inter_op_num_threads: Setting intra_op_num_threads to Setting inter_op_num_threads to onnxruntime::SetInterOpNumThreadsonnxruntime::SetExecutionModeUnsupported execution_mode value in ORT config: Parallel modeSequential modeSetting graph_optimization_level to ORT_DISABLE_ALLSetting execution_mode to Setting graph_optimization_level to ORT_ENABLE_BASIConnxruntime::SetGraphOptimizationLevelSetting graph_optimization_level to ORT_ENABLE_ALLSetting graph_optimization_level to ORT_ENABLE_EXTENDEDUnsupported value for enable_profiling option: Unsupported graph_optimization_level value in ORT config: Setting enable_profiling to onnxruntime::SetEnableProfilingonnxruntime::inference_session_utils::JsonConfigParser::ParseOrtConfigJsonInModelProto::::operator ()Json stored in the `ort_config` key cannot be parsed. Error message: Found session/run/environment configuration in the model file to be used while running the modelThe Model Proto has already been checked for the ORT config json.ORT config json from the model: onnxruntime::inference_session_utils::JsonConfigParser::ParseOrtConfigJsonInModelProtoDid not find session options in the model file to be used while running the modelThe Model Proto hasn't been checked for the ORT config json.intra_op_num_threadsonnxruntime::inference_session_utils::JsonConfigParser::ParseSessionOptionsFromModelProtointer_op_num_threadsintra_op_num_threads option in the model file must be an integerexecution_modeinter_op_num_threads option in the model file must be an integergraph_optimization_levelexecution_mode option in the model file must be an integerenable_profilinggraph_optimization_level option in the model file must be an integerIgnoring unsupported session option in ORT config: enable_profiling option in the model file must be an integer' not foundcannot use at() with key 'cannot use key() for non-object iteratorsnullobjectinvalid map keystringarraydiscardedbooleannumbercannot compare iterators of different containerscannot get valuewhile parsing syntax error ; last read: '- ; expected unexpected invalid BOM; must be 0xEF 0xBB 0xBF if giveninvalid literalfalse literaltrue literalstring literalnull literal'['number literal']''{'':''}'',''[', '{', or a literalend of inputinvalid number; expected digit after '-'unknown tokeninvalid number; expected '+', '-', or digit after exponentinvalid number; expected digit after '.'invalid string: missing closing quoteinvalid number; expected digit after exponent signinvalid string: surrogate U+DC00..U+DFFF must be followed by U+DC00..U+DFFFinvalid string: '\u' must be followed by 4 hex digitsinvalid string: forbidden character after backslashinvalid string: surrogate U+DC00..U+DFFF must follow U+D800..U+DBFFinvalid string: control character U+0001 (SOH) must be escaped to \u0001invalid string: control character U+0000 (NUL) must be escaped to \u0000invalid string: control character U+0003 (ETX) must be escaped to \u0003invalid string: control character U+0002 (STX) must be escaped to \u0002invalid string: control character U+0005 (ENQ) must be escaped to \u0005invalid string: control character U+0004 (EOT) must be escaped to \u0004invalid string: control character U+0007 (BEL) must be escaped to \u0007invalid string: control character U+0006 (ACK) must be escaped to \u0006invalid string: control character U+0009 (HT) must be escaped to \u0009 or \tinvalid string: control character U+0008 (BS) must be escaped to \u0008 or \binvalid string: control character U+000B (VT) must be escaped to \u000Binvalid string: control character U+000A (LF) must be escaped to \u000A or \ninvalid string: control character U+000D (CR) must be escaped to \u000D or \rinvalid string: control character U+000C (FF) must be escaped to \u000C or \finvalid string: control character U+000F (SI) must be escaped to \u000Finvalid string: control character U+000E (SO) must be escaped to \u000Einvalid string: control character U+0011 (DC1) must be escaped to \u0011invalid string: control character U+0010 (DLE) must be escaped to \u0010invalid string: control character U+0013 (DC3) must be escaped to \u0013invalid string: control character U+0012 (DC2) must be escaped to \u0012invalid string: control character U+0015 (NAK) must be escaped to \u0015invalid string: control character U+0014 (DC4) must be escaped to \u0014invalid string: control character U+0017 (ETB) must be escaped to \u0017invalid string: control character U+0016 (SYN) must be escaped to \u0016invalid string: control character U+0019 (EM) must be escaped to \u0019invalid string: control character U+0018 (CAN) must be escaped to \u0018invalid string: control character U+001B (ESC) must be escaped to \u001Binvalid string: control character U+001A (SUB) must be escaped to \u001Ainvalid string: control character U+001D (GS) must be escaped to \u001Dinvalid string: control character U+001C (FS) must be escaped to \u001Cinvalid string: control character U+001F (US) must be escaped to \u001Finvalid string: control character U+001E (RS) must be escaped to \u001Evector too longinvalid string: ill-formed UTF-8 byteobject keynumber overflow parsing 'object separatoriterator out of rangeiterator does not fit current valuetype must be number, but is cannot use erase() with ~6ExceptionLogHrReturnHr%hs(%u)\%hs!%p: FailFast(caller: %p) %hs!%p: %hs(%d) tid(%x) %08X %wsCallContext:[%hs] Msg:[%ws] [%hs] [%hs(%hs)] kernelbase.dll RaiseFailFastExceptionwil%wsstd::exception: %hsC:\apilot\agent\_work\1\s\engine\lotus\cmake\external\wil\include\wil\resource.h_p0WilError_03Local\SM0:%d:%d:%hsC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\providers\dml\dml_provider_factory.ccWKT!37dm?B^|*W?6OF !9Fp~]"L[vDML_OPERATOR_ELEMENT_WISE_ABSDML_OPERATOR_ELEMENT_WISE_IDENTITYDML_OPERATOR_ELEMENT_WISE_ADDDML_OPERATOR_ELEMENT_WISE_ACOSDML_OPERATOR_ELEMENT_WISE_ATANDML_OPERATOR_ELEMENT_WISE_ASINDML_OPERATOR_ELEMENT_WISE_CLIPDML_OPERATOR_ELEMENT_WISE_CEILDML_OPERATOR_ELEMENT_WISE_DIVIDEDML_OPERATOR_ELEMENT_WISE_COSDML_OPERATOR_ELEMENT_WISE_FLOORDML_OPERATOR_ELEMENT_WISE_EXPDML_OPERATOR_ELEMENT_WISE_LOGICAL_ANDDML_OPERATOR_ELEMENT_WISE_LOGDML_OPERATOR_ELEMENT_WISE_LOGICAL_GREATER_THANDML_OPERATOR_ELEMENT_WISE_LOGICAL_EQUALSDML_OPERATOR_ELEMENT_WISE_LOGICAL_GREATER_THAN_OR_EQUALDML_OPERATOR_ELEMENT_WISE_LOGICAL_LESS_THANDML_OPERATOR_ELEMENT_WISE_LOGICAL_NOTDML_OPERATOR_ELEMENT_WISE_LOGICAL_LESS_THAN_OR_EQUALDML_OPERATOR_ELEMENT_WISE_LOGICAL_XORDML_OPERATOR_ELEMENT_WISE_LOGICAL_ORDML_OPERATOR_ELEMENT_WISE_MEANDML_OPERATOR_ELEMENT_WISE_MAXDML_OPERATOR_ELEMENT_WISE_MULTIPLYDML_OPERATOR_ELEMENT_WISE_MINDML_OPERATOR_ELEMENT_WISE_CONSTANT_POWDML_OPERATOR_ELEMENT_WISE_POWDML_OPERATOR_ELEMENT_WISE_SINDML_OPERATOR_ELEMENT_WISE_RECIPDML_OPERATOR_ELEMENT_WISE_SUBTRACTDML_OPERATOR_ELEMENT_WISE_SQRTDML_OPERATOR_ELEMENT_WISE_THRESHOLDDML_OPERATOR_ELEMENT_WISE_TANDML_OPERATOR_ELEMENT_WISE_DEQUANTIZE_LINEARDML_OPERATOR_ELEMENT_WISE_QUANTIZE_LINEARDML_OPERATOR_GEMMDML_OPERATOR_CONVOLUTIONDML_OPERATOR_ARGMINDML_OPERATOR_REDUCEDML_OPERATOR_AVERAGE_POOLINGDML_OPERATOR_ARGMAXDML_OPERATOR_MAX_POOLINGDML_OPERATOR_LP_POOLINGDML_OPERATOR_ROI_POOLINGDML_OPERATOR_MAX_POOLING1DML_OPERATOR_CASTDML_OPERATOR_SLICEDML_OPERATOR_JOINDML_OPERATOR_SPLITDML_OPERATOR_VALUE_SCALE_2DDML_OPERATOR_PADDINGDML_OPERATOR_GATHERDML_OPERATOR_UPSAMPLE_2DDML_OPERATOR_DEPTH_TO_SPACEDML_OPERATOR_SPACE_TO_DEPTHDML_OPERATOR_TOP_KDML_OPERATOR_TILEDML_OPERATOR_MEAN_VARIANCE_NORMALIZATIONDML_OPERATOR_BATCH_NORMALIZATIONDML_OPERATOR_LP_NORMALIZATIONDML_OPERATOR_LOCAL_RESPONSE_NORMALIZATIONDML_OPERATOR_LSTMDML_OPERATOR_RNNDML_OPERATOR_ELEMENT_WISE_SIGNDML_OPERATOR_GRUDML_OPERATOR_ELEMENT_WISE_ERFDML_OPERATOR_ELEMENT_WISE_IS_NANDML_OPERATOR_ELEMENT_WISE_COSHDML_OPERATOR_ELEMENT_WISE_SINHDML_OPERATOR_ELEMENT_WISE_ASINHDML_OPERATOR_ELEMENT_WISE_TANHDML_OPERATOR_ELEMENT_WISE_ATANHDML_OPERATOR_ELEMENT_WISE_ACOSHDML_OPERATOR_ELEMENT_WISE_ADD1DML_OPERATOR_ELEMENT_WISE_IFDML_OPERATOR_DIAGONAL_MATRIXDML_OPERATOR_MAX_UNPOOLINGDML_OPERATOR_ONE_HOTDML_OPERATOR_SCATTERDML_OPERATOR_ELEMENT_WISE_BIT_SHIFT_LEFTDML_OPERATOR_RESAMPLEDML_OPERATOR_ELEMENT_WISE_ROUNDDML_OPERATOR_ELEMENT_WISE_BIT_SHIFT_RIGHTDML_OPERATOR_ELEMENT_WISE_MODULUS_TRUNCATEDML_OPERATOR_ELEMENT_WISE_IS_INFINITYDML_OPERATOR_FILL_VALUE_CONSTANTDML_OPERATOR_ELEMENT_WISE_MODULUS_FLOORDML_OPERATOR_CUMULATIVE_SUMMATIONDML_OPERATOR_FILL_VALUE_SEQUENCEDML_OPERATOR_GATHER_ELEMENTSDML_OPERATOR_REVERSE_SUBSEQUENCESDML_OPERATOR_SCATTER_NDDML_OPERATOR_GATHER_NDDML_OPERATOR_SLICE1DML_OPERATOR_MAX_POOLING2DML_OPERATOR_DEPTH_TO_SPACE1DML_OPERATOR_TOP_K1DML_OPERATOR_MEAN_VARIANCE_NORMALIZATION1DML_OPERATOR_SPACE_TO_DEPTH1DML_OPERATOR_MATRIX_MULTIPLY_INTEGERDML_OPERATOR_RESAMPLE1DML_OPERATOR_CONVOLUTION_INTEGERDML_OPERATOR_QUANTIZED_LINEAR_MATRIX_MULTIPLYDML_OPERATOR_ELEMENT_WISE_BIT_ANDDML_OPERATOR_QUANTIZED_LINEAR_CONVOLUTIONDML_OPERATOR_ELEMENT_WISE_BIT_XORDML_OPERATOR_ELEMENT_WISE_BIT_ORDML_OPERATOR_ELEMENT_WISE_BIT_COUNTDML_OPERATOR_ELEMENT_WISE_BIT_NOTDML_OPERATOR_AVERAGE_POOLING_GRADDML_OPERATOR_ACTIVATION_RELU_GRADDML_OPERATOR_RANDOM_GENERATORDML_OPERATOR_MAX_POOLING_GRADDML_OPERATOR_RESAMPLE_GRADDML_OPERATOR_NONZERO_COORDINATESDML_OPERATOR_ADAM_OPTIMIZERDML_OPERATOR_SLICE_GRADDML_OPERATOR_GATHER_ND1DML_OPERATOR_ROI_ALIGNOutputTensorInputTensorATensorScaleBiasMinBTensorExponentTensorMaxScaleTensorExponentFilterTensorZeroPointTensorModeBiasTensorDimensionCountDirectionDilationsStridesEndPaddingStartPaddingGroupCountOutputPaddingCTensorFusedActivationTransBTransABetaAlphaAxisCountFunctionAxisDirectionAxesIncludePaddingWindowSizeOutputIndicesTensorPSpatialScaleROITensorOffsetsPooledSizeOutputCountSizesAxisOutputTensorsInputTensorsInputCountPaddingValuePaddingModeChannelCountScaleScaleSizeBiasIndicesTensorInterpolationModeBlockSizeIndexDimensionsRepeatsRepeatsCountOutputIndexTensorOutputValueTensorMeanTensorKSpatialVarianceTensorCrossChannelEpsilonLocalSizeNormalizeVarianceRecurrenceTensorWeightTensorSequenceLengthsTensorHiddenInitTensorOutputSingleTensorOutputSequenceTensorActivationDescsActivationDescCountPeepholeTensorCellMemInitTensorClipThresholdOutputCellSingleTensorCoupleInputForgetUseClipThresholdConditionTensorLinearBeforeResetValueOffsetValuesTensorUpdatesTensorScalesScaleCountInfinityModeRoundingModeValueStartValueDataTypeHasExclusiveSumValueDeltaIndicesDimensionCountInputDimensionCountInputWindowSizesInputWindowOffsetsOrderInputWindowStridesOutputPixelOffsetsInputPixelOffsetsBZeroPointTensorAZeroPointTensorBScaleTensorAScaleTensorOutputZeroPointTensorOutputScaleTensorFilterZeroPointTensorInputZeroPointTensorFilterScaleTensorInputScaleTensorOutputGradientTensorInputGradientTensorOutputStateTensorInputStateTensorOutputCountTensorTypeInputParametersTensorOutputCoordinatesTensorInputSecondMomentTensorInputFirstMomentTensorTrainingStepTensorGradientTensorOutputFirstMomentTensorOutputParametersTensorLearningRateOutputSecondMomentTensorBeta2Beta1ReductionFunctionBatchIndicesTensorSpatialScaleYSpatialScaleXMinimumSamplesPerOutputOutOfBoundsInputValueBatchDimensionCountMaximumSamplesPerOutputDML_OPERATOR_ACTIVATION_CELUDML_OPERATOR_ACTIVATION_ELUDML_OPERATOR_ACTIVATION_HARD_SIGMOIDDML_OPERATOR_ACTIVATION_HARDMAXDML_OPERATOR_ACTIVATION_LEAKY_RELUDML_OPERATOR_ACTIVATION_IDENTITYDML_OPERATOR_ACTIVATION_LOG_SOFTMAXDML_OPERATOR_ACTIVATION_LINEARDML_OPERATOR_ACTIVATION_PARAMETERIZED_RELUSlopeTensorDML_OPERATOR_ACTIVATION_RELUDML_OPERATOR_ACTIVATION_PARAMETRIC_SOFTPLUSDML_OPERATOR_ACTIVATION_SCALED_ELUGammaDML_OPERATOR_ACTIVATION_SIGMOIDDML_OPERATOR_ACTIVATION_SCALED_TANHSteepnessDML_OPERATOR_ACTIVATION_SOFTMAXDML_OPERATOR_ACTIVATION_SOFTSIGNDML_OPERATOR_ACTIVATION_SOFTPLUSDML_OPERATOR_ACTIVATION_THRESHOLDED_RELUDML_OPERATOR_ACTIVATION_TANHDML_OPERATOR_ACTIVATION_SHRINKThresholdequationfused_activation_since_versionfused_activationfused_activation_domainDml::GraphTransformer::ApplyImplC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\providers\dml\DmlExecutionProvider\src\GraphTransformer.cppfused op ( ') + (C:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core/providers/dml/OperatorAuthorHelper/MLOperatorAuthorHelper.hC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\providers\dml\DmlExecutionProvider\src\GpuEvent.hC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\providers\dml\DmlExecutionProvider\src\ExecutionProvider.cppDml::ExecutionProviderImpl::CopyTensorsC:\apilot\agent\_work\1\s\engine\lotus\cmake\external\wil\include\wil/wrl.hێHG[[KBdi.Y@y[\[^Mc!biaslambdfused_betafused_gammafused_alphaLeakyReluThresholdedReluReluSigmoidHardSigmoidTanhScaledTanhSoftplusBILINEAREluSoftsignNNBatchNormalizationInstanceNormalizationConvConvTransposeMatMulAddMeanVarianceNormalizationGemmSeluParametricSoftplusSumPReluC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\providers\dml\DmlExecutionProvider\src\Operators\OperatorUtility.cppLinearDropoutFusednearestNEARESTShrinkfused_bilinearDCRnnlinearCRDC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\providers\dml\DmlExecutionProvider\src\GraphPartitioner.cppDmlFusedNode_DmlFusedNodeDomainauto_padaxisborderceil_modedilationskernel_shapeoutput_shapeoutput_paddingpadsscalesample_sizesplitstridesNOTSETSAME_UPPERVALIDC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\providers\dml\OperatorAuthorHelper\MLOperatorAuthorHelper.hC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\providers\dml\OperatorAuthorHelper\OperatorHelper.hC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\providers\dml\OperatorAuthorHelper\OperatorHelper.cpppermC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\providers\dml\DmlExecutionProvider\src\External/DirectMLHelpers/GeneratedSchemaHelpers.hC:\apilot\agent\_work\1\s\engine\lotus\include\onnxruntime\core/framework/op_kernel.htensor(complext64)tensor(complext128)C:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\providers\dml\DmlExecutionProvider\src\MLOperatorAuthorImpl.hC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\providers\dml\DmlExecutionProvider\src\MLOperatorAuthorImpl.cpponnxruntime::OpKernelContext::InputMissing Input: `6x6D6T6D6 h6T6D6x6x6T66D6 h6<66T6D6`6x6D6T6D66666\,6T6D6 h66f66866r`6T6D6x6`6x6D6T666(6066@p6 6|6{lAݣ1Q 6D66$686y86б606,6 p6626`6(66x666P686D6T6D6T6D6(6\66666z66j6T6D6 h6̘66`66x66D6`6x6D6T6D6 h6\6Y@6T6D6ı6T666ԧ6666Ԫ6666$6q6T6D666 6 ܬ6 Ȭ6T6D6 h6T66ԧ6D6L6p6\6P6T6D6 h6T6D6T6D6 66а6*6x6P6`6x6D666ܠ6U6L6c6L6=66kh6T6D6 h6`6x6D6P6T6ԭ666w 6T6D6 h6ܜ66T6D66666L 6|6X661646`6T666ԧ66466$66Ԫ6X6666H66p6T6D6ȩ66̤6t66H6̝66П6J6$6O66;(6`6x6D6̛66;8{Bu8T6D6 h6T66ԧ6D666p6\6P66(@6(606466T6D6T6D6 h6T6ԭ66ԭ66666 6T66D66̼6l6bX666t6?P66P6`6x6D6,6t6\666Ȯ66666 6\6T6D6666 66X6H66Ģ6d6T6|66d6D666666,66|Ľ6T6D6 h6T66 6T6D6T6D66%P6T6D66T666ԧ6666Ԫ66666T66D6p6X6T666(6066T6D6 h6t6T6D6 h6T6D6T66ԧ6D6̧666666686,6P66,06О6C66A6$6$66#6T666D66u6ԭ6666 6 ܬ6 Ȭ6T6D6ȩ66`6I@6)k[ThJY Z4`6x6D666T6D6`6x6D6D6'66:X6T6D6 h6`6x6D6`6x6D66Dp6T6D666 666x6T6D6666 6`6x6D66o66@6T6D6 h6`6x6D6y:KP d636T6D6 h6p6 66 66^ 6X6Q6D6N P6A0DTAmȹh65P6T6D6 h6T6D66E6T6D6 h6ؙ6 `6T6D6 h6666T6D6̨66666686i66K6T6D6x6p6T6D66=YTeFp1T6D6̨6666666`6п6L6F06h6&6T6D6YK@lF4LT6D6666666T66|666d6ԧ6P686D666666,6T6D6 h6t66T6D6x6p6T6D6 h66`6x6D66]66v `66`6<6V 66 p66W66H 6T6D6 h66.86(66T66D66 66n 66̫6D6(66a6Х6}6l6p6D6,6 6 L666T66D6(6T6$6<66ԧ6D646\6P6(6!6\6h6s!fM߈&T6606D666T6D6|6d66T66ܫ6D6(66X6(6+6@66676`6x6H6D6h6`6x6p6P6T6D6x6p6T66D6(6T6~p66R6`6x6D66p6,6 6P6@6D6(6666B66/6T6D6x6\6x@6T6D6x6D6,6 6T6D6ȩ66S6(jS"iG?'6@6T6606(6X6s 686GH68646T6D6666666T66{<6T6D66T6D666T66D6p6X66666>66h6T6D6ȩ6T66D66Z6T6D6l6\6666T6D6L6d6x6p66T6D6 h6T666D6T6D6x66)6T66D6T6D6x6p66g@6`6<(6T6D6`6x6D6T6D66x6 6`6x6D6`6x6D66(6T6D6(6<66T6D6 h6`6x6D6T6D6cCBL6e\6(;HBonFC666"6t66T66ܫ6D6p6X6ء6_66M6696T6D6T6D666 6P6 6D6p`6ģ6m6h6,6`6x6D6T6D6666666T66D6(6ԩ6ԭ66666666T6D66606[6`6x6D6`6x6D6T6-06T66D6ī66`6x6D66@6ܣ6l6C:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\providers\dml\DmlExecutionProvider\src\AbiCustomRegistry.cpp-*rFS =@{A4DML allocatorC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\providers\dml\DmlExecutionProvider\src\BucketizedBufferAllocator.cppDML CPUC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\providers\dml\DmlExecutionProvider\src\PooledUploadHeap.cppC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\providers\dml\DmlExecutionProvider\src\DmlCommon.cppalphabatch_dimsaxesbroadcastblocksizebetaendsgrouphidden_sizeselect_last_indexkeepdimskpooled_shapeoutput_widthoutput_heightscalesstartstransAtransBT1C:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core/providers/dml/OperatorAuthorHelper/OperatorHelper.hIT4T3T2GlobalAveragePoolAveragePoolBTindGlobalLpPoolLpPoolGlobalMaxPoolMaxPoolLpNormalizationLRNRoiAlignMaxRoiPoolConvTransposeWithDynamicPadsLSTMGRURNNSliceConcatTransposeSplitTileDepthToSpaceSpaceToDepthPadGatherElementsGatherConstantOfShapeExpandScatterNDScatterElementsScatterGatherNDSqueezeFlattenIdentityEyeLikeReciprocalSqrtReshapeUnsqueezeAbsLogExpPowSubClipFloorCeilCosMeanDivMulAsinAcosTanSinDequantizeLinearQuantizeLinearAffineAtanCoshSinhIsNanSignErfAtanhAcoshAsinhReduceMeanEinsumReduceSumWhereReduceSumSquareReduceLogSumExpReduceLogSumReduceProdReduceMinReduceMaxReduceL2ReduceL1GreaterNegArgMinArgMaxEqualLessOrEqualGreaterOrEqualLessXorOrAndNotResizeUpsampleImageScalerCropHardmaxLogSoftmaxSoftmaxCeluFusedConvOneHotTopKCastFusedMeanVarianceNormalizationFusedBatchNormalizationFusedInstanceNormalizationFusedConvTransposeFusedSumFusedAddFusedMatMulFusedGemmRoundBitShiftModIsInfMaxUnpoolRangeCumSumReverseSequenceConvIntegerMatMulIntegerQLinearMatMulQLinearConvC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\providers\dml\DmlExecutionProvider\src\Operators\OperatorRegistration.cppT9ҮKS Tɦ@Ҹ w$66j~~6~6|6B~B|66~|6L$66|666b @~06~~ @|6$6$6666|6$66~~|6||66B $66666o I5u C:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\providers\dml\DmlExecutionProvider\src\ReadbackHeap.cppC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\providers\dml\DmlExecutionProvider\src\ExecutionContext.cppGlaIO{cQqGh7 [ABUOUC]Y(aY KMs$jk\I:e ܇)t;Nh.O]+SC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\providers\dml\DmlExecutionProvider\src\FusedGraphKernel.cppbad variant accessC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\providers\dml\DmlExecutionProvider\src\External/DirectMLHelpers/ApiHelpers.hC:\apilot\agent\_work\1\s\engine\lotus\include\onnxruntime\core/graph/graph.honnxruntime::Graph::NodeAtIndexImplnode_index < nodes_.size()Validating no unexpected access using an invalid node_index. Got: Max:C:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\providers\dml\DmlExecutionProvider\src\GraphDescBuilder.cppinvalid unordered_map keyC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\providers\dml\DmlExecutionProvider\src\External/DirectMLHelpers/SchemaHelpers.hC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\providers\dml\DmlExecutionProvider\src\External/DirectMLHelpers/ApiTraits.hdeque too longz&03F"oWi]C:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\providers\dml\DmlExecutionProvider\src\Operators\DmlOperatorGather.cppgNayC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\providers\dml\DmlExecutionProvider\src\Operators\DmlOperatorMaxUnpool.cppC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\providers\dml\DmlExecutionProvider\src\Operators\DmlOperatorGemm.cppdirectiondetect_negativedetect_positivefmodminmaxLEFTC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\providers\dml\DmlExecutionProvider\src\Operators\DmlOperatorElementWise.cppRIGHTC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\providers\dml\DmlExecutionProvider\src\Operators\DmlOperatorExpand.cppC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\providers\dml\DmlExecutionProvider\src\Operators\DmlOperatorConvolution.cppC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\providers\dml\DmlExecutionProvider\src\Operators\DmlOperatorReduce.cppcount_include_padpstorage_orderC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\providers\dml\DmlExecutionProvider\src\Operators\DmlOperatorPooling.cppgammaC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\providers\dml\DmlExecutionProvider\src\Operators\DmlOperatorActivation.cppepsilonspatialC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\providers\dml\DmlExecutionProvider\src\Operators\DmlOperatorBatchNormalization.cppC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\providers\dml\DmlExecutionProvider\src\Operators\DmlOperatorScatter.cppC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\providers\dml\DmlExecutionProvider\src\Operators\DmlOperatorQLinearConv.cppextrapolation_valuecoordinate_transformation_modeexclude_outsidemodenearest_modehalf_pixelpytorch_half_pixelalign_cornerscubicround_prefer_floorasymmetrictf_half_pixel_for_nntf_crop_and_resizeC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\providers\dml\DmlExecutionProvider\src\Operators\DmlOperatorResize.cppC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\providers\dml\DmlExecutionProvider\src\Operators\DmlOperatorSplit.cppC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\providers\dml\DmlExecutionProvider\src\Operators\DmlOperatorConstantOfShape.cppconstantC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\providers\dml\DmlExecutionProvider\src\Operators\DmlOperatorPadding.cppedgereflectC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\providers\dml\DmlExecutionProvider\src\Operators\DmlOperatorMemcpy.cppspatial_scaleC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\providers\dml\DmlExecutionProvider\src\Operators\DmlOperatorRoiPooling.cppclipactivation_betaactivation_alphaactivationslinear_before_resetinput_forgetreverseforwardbidirectionalC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\providers\dml\DmlExecutionProvider\src\Operators\DmlOperatorRecurrentNeuralNetwork.cppC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\providers\dml\DmlExecutionProvider\src\Operators\DmlOperatorSlice.cppsampling_ratioC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\providers\dml\DmlExecutionProvider\src\Operators\DmlOperatorRoiAlign.cppavgbatch_axisC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\providers\dml\DmlExecutionProvider\src\Operators\DmlOperatorReverseSequence.cpptime_axisC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\providers\dml\DmlExecutionProvider\src\Operators\DmlOperatorLpNormalization.cpplargestC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\providers\dml\DmlExecutionProvider\src\Operators\DmlOperatorTopk.cppC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\providers\dml\DmlExecutionProvider\src\Operators\DmlOperatorCopy.cppC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\providers\dml\DmlExecutionProvider\src\Operators\DmlOperatorEyeLike.cppC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\providers\dml\DmlExecutionProvider\src\Operators\DmlOperatorInstanceNormalization.cppacross_channelsnormalize_varianceC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\providers\dml\DmlExecutionProvider\src\Operators\DmlOperatorMeanVarianceNormalization.cppC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\providers\dml\DmlExecutionProvider\src\Operators\DmlOperatorMatMul.cppC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\providers\dml\DmlExecutionProvider\src\Operators\DmlOperatorEinSum.cppexclusiveC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\providers\dml\DmlExecutionProvider\src\Operators\DmlOperatorCumSum.cppC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\providers\dml\DmlExecutionProvider\src\Operators\DmlOperatorAffine.cppC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\providers\dml\DmlExecutionProvider\src\Operators\DmlOperatorConvInteger.cppC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\providers\dml\DmlExecutionProvider\src\Operators\DmlOperatorDepthToSpace.cppC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\providers\dml\DmlExecutionProvider\src\Operators\DmlOperatorNeg.cppC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\providers\dml\DmlExecutionProvider\src\Operators\DmlOperatorOneHot.cpptoC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\providers\dml\DmlExecutionProvider\src\Operators\DmlOperatorCast.cppC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\providers\dml\DmlExecutionProvider\src\Operators\DmlOperatorMatMulInteger.cppC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\providers\dml\DmlExecutionProvider\src\Operators\DmlOperatorSpaceToDepth.cppC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\providers\dml\DmlExecutionProvider\src\Operators\DmlOperatorCrop.cppC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\providers\dml\DmlExecutionProvider\src\Operators\DmlOperatorTile.cppsizeC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\providers\dml\DmlExecutionProvider\src\Operators\DmlOperatorLocalResponseNormalization.cppC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\providers\dml\DmlExecutionProvider\src\Operators\DmlOperatorConcat.cppC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\providers\dml\DmlExecutionProvider\src\Operators\DmlOperatorQLinearMatMul.cppC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\providers\dml\DmlExecutionProvider\src\Operators\DmlOperatorRange.cppC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\providers\dml\DmlExecutionProvider\src\Operators\DmlOperatorTranspose.cppC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\providers\dml\DmlExecutionProvider\src\Operators\DmlOperatorValueScale2D.cppC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\providers\dml\DmlExecutionProvider\src\DmlCommandRecorder.cppC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\providers\dml\DmlExecutionProvider\src\CommandAllocatorRing.h|B\CFvM]rC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\providers\dml\DmlExecutionProvider\src\CommandQueue.cpp=u đKZ`ZvC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\providers\dml\DmlExecutionProvider\src\GraphKernelHelper.cpp_DmlExecutionProvider__DmlExecutionProvider_token_Bad optional accessC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\providers\dml\DmlExecutionProvider\src\Operators\DmlOperator.cppC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\providers\dml\DmlExecutionProvider\src\TensorDesc.cppC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\providers\dml\DmlExecutionProvider\src\DescriptorPool.cppproviderop_nameC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core/providers/dml/OperatorAuthorHelper/SchemaInferenceOverrider.hSessionNode77Model file not found!Failed to parse model file!Failed to parse model stream!Engine failed to create a model!), but the current device does not support 16-bit float.The model contains a 16-bit input (C:\apilot\agent\_work\1\s\engine\lotus\winml\adapter\winml_adapter_dml.cppDirectML.dllDMLCreateDevice1Mem pattern should be disabled when using DML execution provider.Sequential execution should be enabled when using DML execution provider.Windows::AI::MachineLearning::Adapter::SessionRegisterCustomRegistryC:\apilot\agent\_work\1\s\engine\lotus\winml\adapter\winml_adapter_session.cppSession must be initialized to create session state.session_state_ != nullptronnxruntime::InferenceSession::GetSessionStateC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core/session/inference_session.hOut of memoryonnxruntime::GraphTransformer::RecurseC:\apilot\agent\_work\1\s\engine\lotus\include\onnxruntime\core/optimizer/graph_transformer.hInserted_Castcast node to cast from float16 to float32 on cpuRemoveDuplicateCastTransformeronnxruntime::RemoveDuplicateCastTransformer::ApplyImplC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\optimizer\insert_cast_transformer.cconnxruntime::InsertCastTransformer::ApplyImpldtypeInsertCastTransformer works on the assumption that `dtype` attribute holds an integer.dtype_attribute->second.has_i()ACLExecutionProviderBiasGeluFusionBiasSoftmaxFusionCastEliminationCommonSubexpressionEliminationAttentionFusionEliminateDropoutDynamicQuantizeMatMulFusionEmbedLayerNormFusionExpandEliminationFastGeluFusionGeluApproximationGeluFusionGemmActivationFusionConvActivationFusionConvAddFusionConvBNFusionConvMulFusionShapeToInitializerShapeSkipLayerNormFusionEliminateSliceUnsqueezeElimination_RuleBasedTransformerLevelUnsupported levelEliminateIdentityLayerNormFusionSimplifiedLayerNormFusionMatMulAddFusionMatMulScaleFusionFuseReluClipReshapeFusiononnxruntime::optimizer_utils::GenerateRewriteRulesC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\optimizer\graph_transformer_utils.ccUnsupported level onnxruntime::optimizer_utils::GenerateTransformersDnnlExecutionProviderNGRAPHExecutionProviderOpenVINOExecutionProviderNupharExecutionProvideronnxruntime::MemcpyTransformer::ApplyImplVitisAIExecutionProviderTensorrtExecutionProviderNnapiExecutionProviderArmNNExecutionProvideronnxruntime::Node::ForEachWithIndexonnxruntime::TransformerMemcpyImpl::ProcessInitializersC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\optimizer\transformer_memcpy.cconnxruntime::TransformerMemcpyImpl::ProcessDefs' doesn't support memcpy Execution type 'MemcpyCopy from/to host memorydup_replacements.find(&arg) == dup_replacements.end()onnxruntime::TransformerMemcpyImpl::ProcessInitializers::::operator ()C:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\optimizer\graph_transformer.cconnxruntime::GraphTransformer::ApplyC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\optimizer\graph_transformer_mgr.cconnxruntime::GraphTransformerManager::ApplyTransformersThis transformer is already registered C:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core/optimizer/initializer.honnxruntime::Initializer::Initializersize_ == sizesize is differentReadExternalRawData() failed: unsupported data type: Could not find OrtValue with name 'fetches.size() == node->OutputDefs().size()Could not find a CPU kernel and hence Unsupported output type of . Can't constant fold ConstantFoldingC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\optimizer\constant_folding.cconnxruntime::ConstantFolding::ApplyImplcan't constant fold node 'shapeConstant initializer NodeArg shape should not be null. NodeArg: C:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\optimizer\matmul_scale_fusion.cconnxruntime::`anonymous-namespace'::GetScalarConstantInitializer_FusedMatMulAndScaleFused MatMul and Scalehas_value_onnxruntime::MatMulScaleFusion::ApplyImplC:\apilot\agent\_work\1\s\engine\lotus\include\onnxruntime\core/common/optional.honnxruntime::optional >::valueonnxruntime::optional::valueonnxruntime::`anonymous-namespace'::GetScaleFromNodediv_inputs.size() == 2input_node.InputDefs().size() == 2 && scale_and_index.value().second < 2mul_inputs.size() == 2output_node.OutputDefs().size() == 1onnxruntime::`anonymous-namespace'::GetInputNodeMergesTransposeMatMulonnxruntime::`anonymous-namespace'::GetOutputNodeMergesonnxruntime::`anonymous-namespace'::ExtractScalarAsFloatDispatchTarget::operator ()onnxruntime::`anonymous-namespace'::ExtractScalarAsFloatDispatchTarget::operator ()onnxruntime::`anonymous-namespace'::ExtractScalarAsFloatDispatchTarget<__int64>::operator ()onnxruntime::`anonymous-namespace'::ExtractScalarAsFloatDispatchTarget::operator ()onnxruntime::`anonymous-namespace'::ExtractScalarAsFloatDispatchTarget::operator ()onnxruntime::`anonymous-namespace'::ExtractScalarAsFloatDispatchTarget::operator ()onnxruntime::`anonymous-namespace'::ExtractScalarAsFloatDispatchTarget::operator ()Unsupported data type: onnxruntime::utils::mltype_dispatcher_internal::CallableDispatchableRetHelper >::Getonnxruntime::`anonymous-namespace'::ExtractScalarAsFloatDispatchTarget::operator ()onnxruntime::utils::mltype_dispatcher_internal::UnsupportedTypeDefaultPolicy::operator ()Slice parameter is not expected. Input index:Expected value:onnxruntime::AttentionFusionHelper::MatchGemmSubgraphStart MatchGemmSubgraphInput of reshape_before_gemm is not the input of subgraphFaild to match gemm pathGemm does not have 3 inputsOutput edge count not expected for nodes in gemm pathinput_indices.size() == expected_values.size() && input_indices.size() > 0C:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core/optimizer/attention_fusion_helper.honnxruntime::AttentionFusionHelper::CheckSliceParametersSlice ends is less than INT_MAXSlice does not have enough number of inputsunsqueeze_after_gather axes value not expectedOutput edge count not expected for nodes in gemm gather pathgather input 1 value is not expectedgather axis value not expectedStart ValidateGemmInitializerPass MatchGemmSubgraphGemm bias is not constant initializeronnxruntime::AttentionFusionHelper::ValidateGemmInitializerGemm bias shape not expectedGemm bias is not constantconcat first input value is not -1CheckSliceParameters return falseconcat_after_gather does not have expected number of inputs or output edgesFaild to match concat node for Gather pathsFaild to match gemm gather pathconcat_after_gather input 2 does not have expected valueDiv and Shape1 does not have edgeOutput edge count not expected for nodes in path 1 of unidirectional maskCheckSliceParameters returns false for mask_sliceCheckSliceParameters returns false for last_sliceFaild to match path 2 for unidirectional maskCheckSliceParameters returns false for slice1Faild to match path 3 for unidirectional maskOutput edge count not expected for unsqueeze2 of unidirectional maskGemm weight is not constant initializerGemm bias shape is not expectedPass ValidateGemmInitializerGemm weight shape is not expectedonnxruntime::AttentionFusionHelper::MatchUnidirMaskSubgraphStart MatchUnidirMaskSubgraphFaild to match path 1 for unidirectional maskFaild to match the path (Div-->Where-->Add) for unidirectional maskOutput edge count not expected for SoftmaxFailed to find Softmax nodeOutput edge count not expected for mask nodesFailed to find path for maskmask_unsqueeze_1 axes not matched. Expect: 1Softmax attribute axis is expected to be 3mask_sub const input not matchedmask_unsqueeze_2 axes not matched. Expect: 2Faild to match path 4 for unidirectional maskOutput edge count not expected for unsqueeze3 of unidirectional maskOutput edge count not expected for squeeze_2/slices2/shape2 of unidirectional maskDiv and Shape does not have edgePass MatchUnidirMaskSubgraphCheckSliceParameters return false for slice2onnxruntime::AttentionFusionHelper::MatchInputMaskSubgraphStart MatchInputMaskSubgraphgather indices not matched.Failed to find reshape shape path 2Start MatchPastSubgraphPass MatchInputMaskSubgraphDistilBertFailed to find path for past_konnxruntime::AttentionFusionHelper::MatchPastSubgraphFailed to find path for present_v and past_vFailed to find path for present_kPass MatchInputMaskSubgraphmask_mul const input not matchedFailed to find mask pathStart MatchInputMaskSubgraphDistilBertFailed to find shape pathwhere const not matched.Failed to find reshape shape path 1equal const not matched.Pass MatchPastSubgraphOutput edge count not expected for nodes in past subgraphStart CheckNodesInPathVonnxruntime::AttentionFusionHelper::CheckDistilBertReshapeShapeOutput edge count not expected for nodes in path vonnxruntime::AttentionFusionHelper::CheckNodesInPathVFailed in match v_transpose attribute perm. Expected: 0, 2, 1, 3Failed in match Transpose attribute perm. Expected: 0, 2, 1, 3past_k_transpose perm attribute not matchedFailed to match v_concatpresent_k_unsqueeze axes value not expectedpresent_k_transpose perm attribute not matchedpast_v_gather indices != 1present_v_unsqueeze axes value not expectedpast_v_gather and past_k_gather does not have same past inputpast_k_gather indices != 0Pass CheckNodesInPathQq_transpose perm attribute not matchedonnxruntime::AttentionFusionHelper::CheckNodesInPathKStart CheckNodesInPathKk_reshape const not matchedk_transpose perm attribute not matchedMask_Int32Pass CheckNodesInPathKv_reshape initializer value is not expectedhidden_size != num_heads * head_sizereshape initializer value is not expectedPass CheckNodesInPathVonnxruntime::AttentionFusionHelper::CheckNodesInPathQStart CheckNodesInPathQqk_div const not matched.q_reshape const not matchedFailed to find path for qMatchUnidirMaskSubgraph returns NULLCheckNodesInPathQ returns falseq and v are not from same Split nodek and v are not from same Split nodeFailed to find path for kMatchPastSubgraph returns falseCheckNodesInPathK returns falseMaskCastCast mask from int64 to int32onnxruntime::AttentionFusionHelper::FuseGptAttentionStart FuseGptAttentionFaild to find path v to SplitFaild to find path to qkv_matmulMatchInputMaskSubgraph returns falseCheckNodesInPathV return falseC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\optimizer\attention_fusion.cconnxruntime::ConvertMaskToInt32onnxruntime::AttentionFusion::ApplyImplMask data type is not int32 or int64 or float32shape of layer norm bias tensor not expectedLayerNormalizationonnxruntime::FuseSubGraphQKImplTotal fused Attention node count: AttentionFused Attention subgraphs unidirectionalnum_headsqkv_weightsFused an attention node for GPT.Mask shape is unknown or not 2D, or data type unknownqkv_biasonnxruntime::FuseSubGraphQKDistilBertFused an attention node.onnxruntime::AttentionFusion::FuseSubGraphFaild to find path vFailed in match v_matmul and v_add input shapeOutput edge count not expected for Add or MatMul in path vFailed in match input mask subgraphq_matmul and q_add shape not matchedq root should be layer normalizationk_matmul and k_add shape not matchedk root is not layer normFailed to load Q, K and V bias tensors, or data type is not float or float16.Failed to load Q, K and V weights, or data type is not float or float16.onnxruntime::FuseSubGraphQKFailed to convert mask to int32bn_var_tensor_protobn_mean_tensor_protoconv_B_tensor_protoconv_W_tensor_protoConvBnFusion_BN_B_ConvBnFusion_W_onnxruntime::Initializer::ToProtodata type is not supportedonnxruntime::ConvBNFusion::Applybn_scale_tensor_protobn_B_tensor_protoC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\optimizer\conv_bn_fusion.cconnxruntime::`anonymous-namespace'::GetClipConstantMinMax::::operator ()Unexpected data type for Clip input of onnxruntime::ConvActivationFusion::ApplyImplC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\optimizer\conv_activation_fusion.ccfused Conv with activation activationfused activation_paramsUnsqueezeElimination cannot remove node UnsqueezeElimination_C:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\optimizer\unsqueeze_elimination.cconnxruntime::UnsqueezeElimination::Applyonnxruntime::FuseReluClip::ApplyUnexpected data type for Clip 'min' input of _min_zero_constantC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\optimizer\relu_clip_fusion.ccFuseReluClip_data type is not supported.FastGeluGelu approximationTotal Gelu Approximation (FastGelu) node count: GeluBiasGeluC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\optimizer\gelu_approximation.cconnxruntime::GeluApproximation::ApplyImplonnxruntime::ReshapeFusion::ApplyImplFused reshape node: C:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\optimizer\reshape_fusion.ccCannot replace concat node with initializer:Total fused reshape node count: onnxruntime::ReshapeFusion::Fuse_SubgraphInput data type is not int32 or int64C:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\optimizer\embed_layer_norm_fusion.cconnxruntime::MatchInputToConcatSubgraphFailed to find path 1 of position shape.Second input of Gather in path 1 of position shape should be a constant with value 0.Output edge count not expected for nodes in path 1 of position shape.Output edge count not expected for nodes in path 2 of position shape.Failed to find path 2 of position shape._Int32_CastCast Input from int64 to int32onnxruntime::CheckInputInput shape is unknown or not 2D, or data type unknownSecond input of Gather should be a constant with value 1. The third input of Range should be a constant with value 1.The parent of shape nodes are expected to be input_ids.Failed to match Shape node. Optional position subgraph nodes Where node is expected to be the parent of Reshape.Optional position subgraph nodes number of outputs unexpected.position_embeddingsFailed to match position subgraph.Second input of Gather in path 2 of position shape should be a constant with value 1.Gather node in path 2 is not linked to another subgraph.two paths share the same shapeThe parent of two shape nodes are expected to be input_ids.Output edge count not expected for nodes in path1.NonZeroThe first input of Range should be a constant with value 0.onnxruntime::MatchPositionEmbeddingSubgraphsFromGatherFailed to match position embedding subgraph.Position embedding data type shall be float or float16.Position embedding shape is not expected.Failed to get position embedding weights.Segment id is not valid. Input id is not valid. Gamma should be of shape (hidden_size). Input_ids and segment id should have the same shape. fused EmbedLayerNorm subgraphs mask_indexWord embedding shape not expected.EmbedLayerNormalizationInput is expected to have dim value in all dimensions.onnxruntime::FuseSubGraphPosition embedding shape not matched.Failed to get initializer tensor.onnxruntime::FuseSubGraphDistilBertBeta should be of shape (hidden_size). onnxruntime::EmbedLayerNormFusion::ApplyImpl̼+C:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\optimizer\dynamic_quantize_matmul_fusion.cconnxruntime::DynamicQuantizeMatMulFusion::ApplyImplDynamicQuantizeMatMulDynamicQuantizeLinearnullptr != fused_nodeMatMulIntegerToFloatConflicting free dimension overrides.C:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\optimizer\free_dim_override_transformer.ccwith a fixed dimension size which does not equal the specified override of The model has input '' onnxruntime::FreeDimensionOverrideTransformer::ApplyImplFreeDimensionOverrideTransformeronnxruntime::FreeDimensionOverrideTransformer::FreeDimensionOverrideTransformerInvalid free dimension override.fused Matmul and Add C:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\optimizer\matmul_add_fusion.ccgemmonnxruntime::MatMulAddFusion::ApplyImplC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\optimizer\gemm_activation_fusion.cconnxruntime::GemmActivationFusion::ApplyImplactivation_fused Gemm ConvMulFusion_Mul_B_mul_B_tensor_protoConvMulFusion_W_onnxruntime::ConvMulFusion::ApplyC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\optimizer\conv_mul_fusion.ccfused LayerNorm subgraphs onnxruntime::SimplifiedLayerNormFusion::ApplyImplonnxruntime::LayerNormFusion::ApplyImplC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\optimizer\layer_norm_fusion.ccSimplifiedLayerNormalization'7onnxruntime::SkipLayerNormFusion::ApplyImplC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\optimizer\skip_layer_norm_fusion.ccfused SkipLayerNorm subgraphs SkipLayerNormalization̼+floorSAME_LOWER and C:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core/providers/common.h into softmax(input + bias)BiasSoftmaxaxis >= -tensor_rank && axis <= tensor_rank - 1onnxruntime::HandleNegativeAxis is not in valid range [-axis onnxruntime::BiasSoftmaxFusion::ApplyImplC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\optimizer\bias_softmax_fusion.ccsoftmax_axisbroadcast_axis] because it's the graph's output.[C:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\optimizer\common_subexpression_elimination.ccrepresentative.output_index != kInvalidOutputIndex of node Not eliminating output onnxruntime::CommonSubexpressionElimination::ApplyImplonnxruntime::RuleBasedGraphTransformer::ApplyRulesOnNodeC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\optimizer\rule_based_graph_transformer.cconnxruntime::RuleBasedGraphTransformer::ApplyImplfused Gelu subgraphs onnxruntime::GeluFusion::ApplyImplC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\optimizer\gelu_fusion.cconnxruntime::BiasGeluFusion::ApplyImplC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\optimizer\bias_gelu_fusion.ccfused Add and Gelufast_gelu_outputfused GPT2Gelu subgraphs onnxruntime::FastGeluFusion::ApplyImplC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\optimizer\fast_gelu_fusion.ccGPT2Geluadd_B_tensor_protoConvAddFusion_B_onnxruntime::ConvAddFusion::ApplyC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\optimizer\conv_add_fusion.ccConvAddFusion_Add_B_TensorProto external data size mismatch. Not satisfied: external_data_length == 0 || external_data_length == actual_tensor_data_length , external_data.length: Computed size: External data type must not be UNDEFINED or STRING.onnxruntime::Initializer::ReadExternalRawDatamodel_path must not be empty. Ensure that a path is provided when the model is created or loaded.Satisfied, but should not be: model_path.IsEmpty() C:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\optimizer\initializer.ccNot satisfied: tensor_proto.data_type() != ONNX_NAMESPACE::TensorProto_DataType_UNDEFINED && tensor_proto.data_type() != ONNX_NAMESPACE::TensorProto_DataType_STRING RandomUniformRandomNormalLikeMultinomialRandomNormalRandomUniformLikeonnxruntime::OptimizerExecutionFrame::Info::{ctor}::::operator ()allocator_ptr_onnxruntime::OptimizerExecutionFrame::Info::InfoC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\optimizer\optimizer_execution_frame.ccFailed to get allocator for optimizerTried to allocate without valid type information, ort_value index=onnxruntime::RegisterOnnxOperatorKernelsonnxruntime::RegisterCPUKernelsonnxruntime::CPUExecutionProvider::GetKernelRegistryC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\providers\cpu\cpu_execution_provider.cconnxruntime::ml::RegisterOnnxMLOperatorKernelsC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core/providers/cpu/tensor/pad.hInvalid 'pads' attribute valueInvalid 'mode' attribute valueonnxruntime::PadBase::PadBaseCannot use 'reflect' mode to pad dimension with a value of 0. Input shape:Unexpected mode of C:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\providers\cpu\tensor\pad.ccPads tensor should be an INT64 tensorUnsupported input data type of onnxruntime::PadValueFromFloatdims.size() == extents.size() && dims.size() >= steps.size()dims.size() == starts.size() && dims.size() == extents_.size() && dims.size() >= steps.size()onnxruntime::SliceIteratorBase::Initonnxruntime::SliceSkips::SliceSkipsC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core/providers/cpu/tensor/utils.hdata_rank > 0onnxruntime::PadImplInput tensor has no dimensionsPads tensor should be a 1D tensor of shape [2 * input_rank] or a 2D tensor of shape [1, 2 * input_rank]pads_tensor_dims.size() == 1 || (pads_tensor_dims.size() == 2 && pads_tensor_dims[0] == 1)pads_tensor.IsDataType()onnxruntime::Pad::ComputeValue tensor should be a 1D tensor of size 1 with the same type as that of the input tensorvalue_tensor->DataType() == data_type && value_tensor->Shape().Size() == 1Pads tensor size should be equal to twice the input dimension count pads_size == 2 * data_rankonnxruntime::PadInputWithDimValueOfZero'pads' has wrong number of valuesdata_rank * 2 == pads.size()last dimension of indices must not be larger than rank of input tensorindices tensor data type not supportedonnxruntime::GatherND::ComputeC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\providers\cpu\tensor\gather_nd.ccGatherNDBase PrepareForCompute: Input count mismatchinput_tensor != nullptr && indices_tensor != nullptrinvalid index found, index = indices tensor must has rank larger than 0failed to get first output!C:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\providers\cpu\tensor\nonzero_op.cconnxruntime::NonZero::ComputeX input is required!onnxruntime::NonZero::Computeonnxruntime::NonZero<__int64>::Computeonnxruntime::NonZero::Computeonnxruntime::NonZero::ComputeNull input ptrIsNaNonnxruntime::Tensor::MutableDataRawtype == dtype_Tensor type mismatch.onnxruntime::Tensor::DataRawonnxruntime::IdentityOp<1>::ComputeC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core/providers/cpu/tensor/identity_op.honnxruntime::IdentityOp<0>::ComputeX != nullptrupdate_tensor != nullptrindice_tensor != nullptronnxruntime::ScatterNDBase::PrepareForComputeinput_tensor != nullptr, data shape: input tensor and indices tensor must has rank larger than 0. input shape: , indices shape: C:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\providers\cpu\tensor\scatter_nd.cconnxruntime::ScatterND::Computeinvalid indice found, indice = updates tensor should have shape equal to indices.shape[:-1] + data.shape[indices.shape[-1]:]. updates shape: Missing/Invalid 'axis' attribute valuedata type is different from updates typeC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\providers\cpu\tensor\scatter.cconnxruntime::Scatter::Scatterinfo.GetAttr("axis", &axis_).IsOK()Expecting indices to be either int32_t or int64_tonnxruntime::Scatter::ComputeUnknown tensor type of . Input rank=Indices vs updates dimensions differs at position= vs Indices and updates must have the same rankIndices dim= at pos= is greater than input dim=Indices must have the same rank as Input. Indices rank=indices element out of data bounds, idx= must be within the inclusive range [C:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core/providers/cpu/tensor/space_depth_ops.honnxruntime::SpaceDepthBase::SpaceDepthBaseinfo.GetAttr("blocksize", &blocksize_).IsOK()Attribute blocksize is not set.input_shape.NumDimensions() == 4input count mismatchinput_width % this->blocksize_ == 0input_height % this->blocksize_ == 0C:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\providers\cpu\tensor\space_depth_ops.cconnxruntime::SpaceToDepth::ComputeDepthToSpace op: only 'DCR' and 'CRD' modes are supportedinput_depth % (blocksize_ * blocksize_) == 0onnxruntime::DepthToSpace::Computeinput.Shape().NumDimensions() == 4onnxruntime::DepthToSpace::DepthToSpaceGatherElements op: Data type for 'indices' tensor must be 'int32_t' and 'int64_t'C:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\providers\cpu\tensor\gather_elements.honnxruntime::GatherElements::GatherElementsGatherElements op: Data type of input 'data' should match the data type of the output , ]. Actual value is C:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\providers\cpu\tensor\gather_elements.cconnxruntime::parse_and_validate_indices_tensorGatherElements op: 'indices' shape should have values within bounds of 'data' shape. Invalid value in indices shape is: GatherElements op: Rank of input 'data' needs to be equal to rank of input 'indices'GatherElements op: Cannot operate on scalar inputGatherElements op: Value in indices must be within bounds [onnxruntime::Gather::ComputeC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core/providers/cpu/tensor/gather.honnxruntime::GatherBase::GatherBaseType for Tind not supported yet in Gather.C:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\providers\cpu\tensor\gather.cc'repeat' input tensor must be 1 dimensionalthe tensor to be tiled using Tile OP must be atleast 1 dimensionalInput count of Tile OP mismatch, the second one is emptyInput count of Tile OP mismatch, the first one is emptyC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\providers\cpu\tensor\tile.cconnxruntime::Tile::ComputeTile doesn't have an implementation yet for the type: 'repeat' input tensor must have the same length as the 'input' tensorperm: does not align with rank of input data: C:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core/providers/cpu/tensor/transpose.honnxruntime::TransposeBase::TransposeBasev >= 0 && static_cast(v) <= std::numeric_limits::max()[ is repeated.Attribute perm of Transpose has an invalid value. Value is outside range.Mismatched data types between input and output Tensors. C:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\providers\cpu\tensor\transpose.cconnxruntime::Transpose::Computeinput_tensor_ptr != nullptrMissing or invalid starts and ends attributeInvalid axes attribute, axes attribute (if present) should have the same size as starts/ends attributesC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core/providers/cpu/tensor/slice.honnxruntime::SliceBase::SliceBasehas_starts && has_ends && attr_starts_.size() == attr_ends_.size()onnxruntime::SliceIteratorBase::CopyInnermostAxisNonSolitaryInnerStepUnexpected element size of !has_axes || attr_axes_.size() == attr_starts_.size()start_tensor.Shape() == ends_tensor.Shape()Starts and ends shape mismatchends_tensor.Shape().NumDimensions() == 1Ends must be a 1-D arraynullptr == steps_tensor || start_tensor.Shape() == steps_tensor->Shape()Starts and steps shape mismatchnullptr == axes_tensor || start_tensor.Shape() == axes_tensor->Shape()Starts and axes shape mismatch'step' value cannot be 0'axes' has duplicates'axes' has an axis outside of the tensor dimension countC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\providers\cpu\tensor\slice.cconnxruntime::SliceBase::FillVectorsFromInputstart_tensor.Shape().NumDimensions() == 1Starts must be a 1-D arrayonnxruntime::SliceImpl::::operator ()onnxruntime::SliceImpl::::operator ()output == output_endonnxruntime::SliceImpl::::operator ()onnxruntime::SliceImpl::::operator ()onnxruntime::SliceImpl::::operator ()Cannot slice scalarsonnxruntime::SliceBase::ComputeMissing input tensor to be processedData type for starts and ends inputs' need to be int32_t or int64_t, but instead got onnxruntime::OpKernelContext::Outputp_ml_valuePlease fetch output tensor with specified shape.onnxruntime::Broadcaster::Broadcasterlargest <= 1Can broadcast 0 by 0 or 1. is invalid.) for tensor of length:onnxruntime::InputBroadcaster::AdvanceByoffset % span_size_ == 0InputBroadcaster can only start at span boundary!Attempting to broadcast an axis by a dimension other than 1. by onnxruntime::BroadcastIterator::AppendC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core/providers/cpu/math/element_wise_ops.honnxruntime::BroadcastIterator::Initaxis == 1 || axis == largest) are not at boundary of span with size:onnxruntime::OutputBroadcaster::OutputBroadcasterstart_offset >= 0 && real_end >= 0 && start_offset <= real_end && real_end <= lenInvalid start/ending offset [onnxruntime::TensorAllocator::TensorAllocatorstart_offset % span_size == 0 && real_end % span_size == 0Broadcast Output range [onnxruntime::Mean_6::Computeonnxruntime::Max_6::Computeonnxruntime::Min_6::ComputeinputCount >= 1Tensor with shape information must be 1 dimensional.onnxruntime::ExpandBroadcastLooper!helper.HaveTwoTensorInputs()ExpandBroadcastLooper should only have a shape for the second input.onnxruntime::Sum_6::Computeinput_count >= 1Must have 1 or more inputsUnsupported X type: data_n.Shape() == shapedata_1.Shape() == shapeAll inputs must have the same shapeC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\providers\cpu\math\element_wise_ops.cconnxruntime::ElementWiseKernel >::ElementWiseKernelC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core/providers/cpu/element_wise_ranged_transform.honnxruntime::ElementWiseKernel >::Computeinput_size < std::numeric_limits::max()onnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::Compute(fmod == 0) || (fmod == 1)fmod must have value either 0 or 1onnxruntime::UntypedExpandshape_data_tensor.Shape().GetDims().size() == 1onnxruntime::Mod::Computefmod_fmod attribute must be true for float, float16 and double typesonnxruntime::Mod::Modonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ComputeInvalid direction value of ''. Valid values are 'LEFT' or 'RIGHT'.onnxruntime::BitShift::BitShiftonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::Computeonnxruntime::Expand_8::Compute::::operator ()onnxruntime::Expand_8::Compute::::operator ()onnxruntime::Expand_8::Compute::::operator ()Invalid usage. Input 1 is a shape with no data.onnxruntime::Expand_8::Compute::::operator ()onnxruntime::Expand_8<__int64>::Compute::::operator ()onnxruntime::Expand_8::Compute::::operator ()onnxruntime::Expand_8::Compute::::operator ()onnxruntime::BitShift::BitShiftcur_out == end_outonnxruntime::BitShift::Compute::::operator ()cur1 == end1Unsupported Y type: onnxruntime::BitShift::Compute::::operator ()onnxruntime::BitShift::BitShiftonnxruntime::BitShift::Compute::::operator ()onnxruntime::BroadcastLooperhelper.HaveTwoTensorInputs()BroadcastLooper requires two tensors as input.helper.called_ == 1onnxruntime::Expand_8::Compute::::operator ()onnxruntime::Expand_8::Compute::::operator ()onnxruntime::Expand_8::Compute::::operator ()onnxruntime::Expand_8::Compute::::operator ()onnxruntime::utils::MLTypeCallDispatcher::Invokehelper.called_ < 2Check for duplicate types in MLTypeCallDispatcheronnxruntime::Expand_8::Compute::::operator ()((`(`(``JJ J J } M}7M}p7M}6p7M6p76pf6OfOfOfO'is defined.onnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ComputeAttribute name and type don't match for 'No attribute with name:'onnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::functors::HardSigmoid::Initonnxruntime::functors::ScaledTanh::Initunknown kernel typeonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ComputeC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core/providers/cpu/activation/activations.honnxruntime::functors::Selu::Initonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::Computeonnxruntime::functors::ParametricSoftplus::InitC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\contrib_ops/cpu/activations.honnxruntime::ConcatBase::PrepareForComputeNot satisfied: inputs_0_rank > 0 Cannot concatenate scalarsNot satisfied: input_count >= 1 C:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\providers\cpu\tensor\concat.cconnxruntime::ConcatBase::ConcatBaseMust have valid 'axis' attributenew_axisC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core/providers/cpu/tensor/concat.h has mismatched dimensions of inputs_n_rank == inputs_0_rankRanks of input data are different, cannot concatenate them. expected rank: got: Not satisfied: data_n.DataType() == p.output_tensor->DataType() Not satisfied: dim_value == inputs_0_dims[axis_index] Non concat axis dimensions must match: Axis onnxruntime::ReshapeHelper::ReshapeHelperC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\providers\cpu\tensor\reshape_helper.hAt most one dimension can be -1.unknown_dim == -1A dimension cannot be less than -1, got requested_shape[i] >= -1size != 0 && (input_shape.Size() % size) == 0gsl::narrow_cast(input_shape.Size()) == sizeThe dimension with value zero exceeds the dimension size of the input tensor.i < input_shape.NumDimensions(), requested shape:The input tensor cannot be reshaped to the requested shape. Input shape:Attribute shape is not set.onnxruntime::Reshape_1::Reshape_1Reshape_1A shape tensor must be a vector tensor.shapeTensor->Shape().NumDimensions() == 1onnxruntime::Reshape::ComputeC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core/providers/cpu/tensor/reshape.hnoop_with_empty_axesonnxruntime::ReduceKernelBase<1>::ReduceKernelBaseonnxruntime::optional<__int64>::valueinput_tensor_ptr->Shape().Size() == input_shape_override->Size()info.GetAttr("keepdims", &keepdims).IsOK()onnxruntime::ReduceKernelBase<0>::ReduceKernelBaseC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core/providers/cpu/reduction/reduction_ops.hInput to be reduced is nullonnxruntime::SetupForReduceC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\providers\cpu\reduction\reduction_ops.ccThe input shape override's size does not match the input tensor's shape sizeonnxruntime::CommonReduceAn axes tensor must be a vector tensor.axes_tensor->Shape().NumDimensions() == 1keepdims_onnxruntime::ReduceSum<__int64>::Implonnxruntime::ReduceSum::ImplAxes input is nullaxes_tensor != nullptrCan't reduce on dim with value of 0 if 'keepdims' is false. Invalid output shape would be produced. input_shape:keep_dimsonnxruntime::ReduceSum::Implonnxruntime::ReduceSum::ImplReduction on all axes, output size should be 1.count == 1onnxruntime::NoTransposeReduceseedonnxruntime::Tensor::DataAsSpanratio input should have a single value.ratio_tensor->Shape().Size() == 1onnxruntime::`anonymous-namespace'::GetRatioOrDefaultC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core/providers/cpu/nn/dropout_op.honnxruntime::Dropout::Computeonnxruntime::Dropout::Computeonnxruntime::Dropout::ComputeX and mask should have the same shape!mask || mask->Shape() == X_shapeonnxruntime::Dropout::Computeratio must be in the range [0, 1)0.0f <= ratio_value && ratio_value < 1.0fonnxruntime::Tensor::MutableDataAsSpanC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core/providers/cpu/tensor/mean_variance_normalization.hinfo.GetAttr("normalize_variance", &normalize_variance_).IsOK()Input is expected to have four dimensions corresponding to [N,C,H,W]info.GetAttr("across_channels", &across_channels_).IsOK()onnxruntime::MeanVarianceNormalization_0::MeanVarianceNormalization_0alpha_ > 0.0finfo.GetAttr("beta", &beta_).IsOK()beta_ > 0.0fC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core/providers/cpu/nn/lrn.hsize_ > 0size_ % 2 == 1info.GetAttr("alpha", &alpha_).IsOK()onnxruntime::LRN::ComputeC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\providers\cpu\nn\lrn.ccinfo.GetAttr("size", &size).IsOK()onnxruntime::LRN::LRNX->Shape().NumDimensions() == 4C:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core/providers/cpu/nn/flatten.hThe rank of input tensor must be >= axisgsl::narrow_cast(X_shape.NumDimensions()) >= axisonnxruntime::Flatten::Computeonnxruntime::Flatten::Flattenx_zero_point must be null or a scalar or 1D tensor or size 1.zero_point_ptr == nullptr || IsScalarOr1ElementVector(zero_point_ptr)onnxruntime::PrepareForQDQzero_point_ptr == nullptr || (zero_point_ptr->Shape().NumDimensions() == 1 && zero_point_ptr->Shape()[0] == broadcast_dim)C:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\providers\cpu\tensor\quantize_linear.ccscale must be 1D tensor with size scale.Shape().NumDimensions() == 1 && scale.Shape()[0] == broadcast_dimx_zero_point must be null or 1D tensor with size Sizeonnxruntime::utils::MLTypeCallDispatcher::Invokeleft.NumDimensions() == 2 || left.NumDimensions() == 1c_shape is required if c_data is providedc_shape != nullptronnxruntime::Gemm::ComputeGemminfo.GetAttr("transA", &temp).IsOK()onnxruntime::Gemm::GemmC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core/providers/cpu/math/gemm.hinfo.GetAttr("transB", &temp).IsOK() K: GEMM: Dimension mismatch, W: Gemm: Invalid bias shape for broadcastM_ >= 0 && K_ > 0 && N_ >= 0onnxruntime::GemmHelper::GemmHelperC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\providers\cpu\math\gemm_helper.hright.NumDimensions() == 2 N:Not satisfied: num_dims_with_pad - 1 == num_output_dims Not satisfied: num_dims_with_pad - 2 == num_output_dims Not satisfied: K_ == right_shape[0] Not satisfied: right_padded_dims_[idx_dim] == 1 Not satisfied: K_ == right_shape[transb ? right_num_dims - 1 : right_num_dims - 2] Not satisfied: num_dims_with_pad == num_output_dims Not satisfied: M_ == 1 && N_ == 1 Not satisfied: K_ == right_shape[right_num_dims - 2] || transb && K_ == right_shape[right_num_dims - 1] left operand cannot broadcast on dim Not satisfied: left_padded_dims_[idx_dim] == 1 right operand cannot broadcast on dim onnxruntime::MatMulComputeHelper::ComputeC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core/providers/cpu/math/matmul_helper.hNot satisfied: left_num_dims >= 1 && right_num_dims >= 1 MatMul dimension mismatchonnxruntime::MatMul::ComputeC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\providers\cpu\math\matmul.cconnxruntime::MatMul::Computeonnxruntime::MatMul::Computeonnxruntime::MatMul<__int64>::ComputeC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core/providers/cpu/math/clip.hmin_ <= max_onnxruntime::clip_internal::Clip_6Base::Clip_6Baseonnxruntime::Clip::ComputeImpl::operator ()onnxruntime::Clip::ComputeImpl::operator ()max->Shape().NumDimensions() == 0onnxruntime::Clip::ComputeImpl<__int64>::operator ()onnxruntime::Clip::ComputeImpl::operator ()onnxruntime::Clip::ComputeImpl::operator ()min->Shape().NumDimensions() == 0onnxruntime::Clip::ComputeImpl::operator ()C:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\providers\cpu\math\clip.ccmax should be a scalar.onnxruntime::utils::MLTypeCallDispatcher::Invokemin should be a scalar.invalid stoi argumentstoi argument out of rangeinvalid stof argumentstof argument out of rangeinvalid stod argumentstod argument out of rangeinvalid stoll argumentstoll argument out of rangeinvalid stoull argumentstoull argument out of rangeinvalid stol argumentstol argument out of rangeinvalid stoul argumentstoul argument out of rangeonnxruntime::utils::MLTypeCallDispatcher::Invokeonnxruntime::utils::MLTypeCallDispatcher::Invokeonnxruntime::Cast::ComputeAttribute to is not set.onnxruntime::Cast::CastC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\providers\cpu\tensor\cast_op.cconnxruntime::`anonymous-namespace'::CastFromStringDataNaN-INFINFonnxruntime::utils::MLTypeCallDispatcherWithCarriedType::Invokeonnxruntime::utils::MLTypeCallDispatcherWithCarriedType::Invokeonnxruntime::utils::MLTypeCallDispatcherWithCarriedType::InvokeUnsupported type in cast op: from String to onnxruntime::utils::MLTypeCallDispatcherWithCarriedType::Invokeonnxruntime::utils::MLTypeCallDispatcherWithCarriedType::Invokeonnxruntime::utils::MLTypeCallDispatcherWithCarriedType::Invokeonnxruntime::utils::MLTypeCallDispatcherWithCarriedType::Invokeonnxruntime::utils::MLTypeCallDispatcherWithCarriedType::Invokeonnxruntime::utils::MLTypeCallDispatcherWithCarriedType::Invokeonnxruntime::utils::MLTypeCallDispatcherWithCarriedType<__int64,struct onnxruntime::Cast::Dispatcher,float,double,signed char,unsigned char,short,unsigned short,int,unsigned int,__int64,unsigned __int64,bool>::Invokeonnxruntime::utils::MLTypeCallDispatcherWithCarriedType::Invokeonnxruntime::UnsqueezeBase::UnsqueezeBaseC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core/providers/cpu/tensor/unsqueeze.hMissing/Invalid 'axes' attribute valueinfo.GetAttrs("axes", axes_).IsOK()'axes' has a duplicate axisnullptr != p.output_tensoronnxruntime::Unsqueeze::Computeonnxruntime::UnsqueezeBase::PrepareComputeC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\providers\cpu\tensor\unsqueeze.cc'axes' has an out of range axisonnxruntime::Einsum::DeviceComputeC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\providers\cpu\math\einsum.cc is not supported yetEinsum op: An implementation for the input type onnxruntime::Einsum::EinsumC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\providers\cpu\math\einsum.hEinsum op: There must be atleast one inputThere was a problem acquiring temporary memory allocator in Einsum op->Missing 'equation' attributeinfo.GetAttr("equation", &equation_).IsOK()Unknown AutoPadType Stringonnxruntime::StringToAutoPadTypestrides.size() == kernel_shape.size()Dilations dimensions should match kernel shapedilations.size() == kernel_shape.size()Invalid input shape. Only N can be zero. Got:info.GetAttr("storage_order", &storage_order).IsOK()kernel_shape[dim] > 0Pad should be smaller than kernel.pads[dim] < kernel_shape[dim] && pads[dim + kernel_shape.size()] < kernel_shape[dim]onnxruntime::PoolAttributes::PoolAttributesC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core/providers/cpu/nn/pool_attributes.hinfo.GetAttr("auto_pad", &auto_padding).IsOK()info.GetAttr("count_include_pad", &temp).IsOK()No kernel shape is set.info.GetAttrs("kernel_shape", kernel_shape).IsOK()Not satisfied: input_dims >= 3 Unsupported pooling size.kernel_shape num_dims is not compatible with X num_dims.Not satisfied: pooling_dims == pool_attrs_.kernel_shape.size() C:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core/providers/cpu/nn/pool_base.hInput dimension cannot be less than 3.onnxruntime::PoolBase::ComputeC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\providers\cpu\nn\pool.ccUnsupported AutoPad Type.onnxruntime::PoolAttributes::ComputeSizePadDilationsinfo.GetAttr("p", &p_).IsOK()onnxruntime::PoolProcessContext::initinput_shape.Size() > 0 || input_shape[0] == 0onnxruntime::PoolAttributes::SetOutputSizeinput_dims.size() >= 2onnxruntime::PoolAttributes::InferOutputSizeonnxruntime::MaxPoolV8::ComputeImplonnxruntime::Pool::ComputeNot satisfied: x_shape.NumDimensions() >= 3 Unsupported pooling size : onnxruntime::UpsampleBase::StringToCoordinateTransformationModecoordinate_transform_mode:[ceilround_prefer_ceilmode attribute is . It can only be ] is not supportted!onnxruntime::UpsampleBase::StringToUpsampleModeexclude_outside can be set to 1 only when mode is CUBIC. Current mode is set to cubic_coeff_a(default) or or onnxruntime::UpsampleBase::UpsampleBaseinfo.GetAttr("mode", &mode).IsOK()info.GetAttrs("scales", scales_).IsOK()C:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core/providers/cpu/tensor/upsample.hscales.size() == 2 || (scales.size() == 4 && scales[0] == 1 && scales[1] == 1)'Cubic' mode only support 2-D inputs ('Bicubic') or 4-D inputs with the corresponding outermost 2 scale values being 1 in the scales_size > 0scales size should be greater than 0.Upsample operatorResize operatorscales.size() == 2 || (scales.size() == 4 && scales[0] == 1 && scales[1] == 1) || scales.size() == 3 || (scales.size() == 5 && scales[0] == 1 && scales[1] == 1)'Linear' mode only support 2-D inputs or 3-D inputs ('Bilinear', 'Trilinear') or 4-D inputs or 5-D inputs with the corresponding outermost 2 scale values being 1 in the onnxruntime::UpsampleBase::ScalesValidationscale >= 1scale > 0Scale value should be greater than 0.nearest_mode:[] is not supported!Scale value should be greater than or equal to 1.onnxruntime::UpsampleBase::StringToNearestModeonnxruntime::UpsampleBase::ParseScalesDataInvalid argument for values; either it's rank is more than 1 or it has more than 2 elementsInvalid argument for depth; it's not a scalar.onnxruntime::OneHotOp<__int64,float,float>::Computeonnxruntime::OneHotOp<__int64,int,float>::Computeonnxruntime::OneHotOp<__int64,float,int>::Computeonnxruntime::OneHotOp::Computeonnxruntime::OneHotOp<__int64,float,__int64>::Computeonnxruntime::OneHotOp::Computeonnxruntime::OneHotOp::Computeonnxruntime::OneHotOp::ComputeDepth is negative.onnxruntime::OneHotOp,class std::allocator >,__int64>::Computeonnxruntime::OneHotOp<__int64,class std::basic_string,class std::allocator >,__int64>::ComputeC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\providers\cpu\tensor\onehot.cconnxruntime::OneHotOp<__int64,__int64,__int64>::ComputeC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\providers\cpu\math\top_k.cconnxruntime::TopK<9,float>::TopKop_kernel_info.GetAttr("axis", &axis_temp).IsOK()k_temp > 0op_kernel_info.GetAttr("k", &k_temp).IsOK()k argument [] should not be greater than specified axis dim value [op_kernel_info.GetAttr("sorted", &sorted_temp).IsOK()onnxruntime::TopkOpset11ConstructorCommonvalue of k must not be negativesortedop_kernel_info.GetAttr("largest", &largest_temp).IsOK()onnxruntime::TopK<10,float>::TopKinput count mismatch, expected 1 input - the tensor to be processedk tensor should be a 1D tensor of size 1input count mismatch, expected 2 inputs - the tensor to be processed and a tensor containing k valueoutput count mismatch, expected 2 outputs to be present for TopK operatorUniqueUnsupported tensor type of delta in Range operator can not be zero!start in Range operator should be scalar like tensor, yet got shape:delta in Range operator should be scalar like tensor, yet got shape:limit in Range operator should be scalar like tensor, yet got shape:onnxruntime::Tensor::Reshape) != new size (shape_.Size() == new_shape.Size()Tensor size (Invalid sequence index () specified for sequence of size (SequenceEmptySequenceAt: Got nullptr for output tensoronnxruntime::SequenceAt::Computeonnxruntime::GetSeqIdxI != nullptrGot nullptr input for index tensorSequenceLength: Got nullptr for output tensorC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\providers\cpu\sequence\sequence_ops.ccSequenceAtY != nullptrSequenceLengthSonnxruntime::SequenceLength::ComputeGot nullptr for sequence input.SequenceErase: Got nullptr for output sequenceonnxruntime::SequenceErase::Computenum_inputs >= 1SequenceConstructData type of the input tensor MUST be same as that of the input sequence. Sequence data type (), input tensor data type (SequenceEraseSequenceInsert: Got nullptr for output sequenceS != nullptronnxruntime::CreateCopyAndAppendCpuTensorGot nullptr for input tensor.onnxruntime::SequenceInsert::Computeonnxruntime::SequenceEmpty::ComputeSequenceEmpty: Got nullptr for output sequenceSequenceInsertUnsupported 'dtype' value: Invalid data type for split tensor split_size_sum (onnxruntime::GetSplitSizesInputonnxruntime::GetScalarSplitInputSplitToSequence operator does not support yet) != split_dim_size (onnxruntime::SplitToSequence::ComputeSequenceConstruct: Got nullptr for output sequenceonnxruntime::SequenceConstruct::ComputeSplitToSequenceViolation of the requirment that all input tensors must have the same data type.std::all_of(split_sizes.cbegin(), split_sizes.cend(), [](int64_t value) { return value >= 0; })Invalid value in 'split' input. All values must be >= 0Split should be > 0onnxruntime::SplitToSequence::ComputeImplsplit_scalar > 0ConcatFromSequenceC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\providers\cpu\sequence\concat_from_sequence.cconnxruntime::ConcatFromSequence::ComputeC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\providers\cpu\controlflow\if.cconnxruntime::If::Info::Infoinfo.GetAttr("then_branch", &proto).IsOK()then_branch outputs which doesn't match the subgraph's outputs.num_subgraph_outputs == static_cast(num_outputs)'If' node has IfVFailed to create output tensor for onnxruntime::IfImpl::Initializeonnxruntime::IfImpl::ExecuteFailed to create output tensor for If output ' attribute.onnxruntime::If::Computesession_stateSubgraph SessionState was not found for 'onnxruntime::If::SetupSubgraphExecutionInfoinfo == nullptrthen_feeds_fetches_manager_ && else_feeds_fetches_manager_CreateFeedsFetchesManager must be called prior to execution of graph.else_branchonnxruntime::If::IfSetupSubgraphExecutionInfo should only be called once for each subgraph.info.GetAttr("else_branch", &proto).IsOK()X num_dims does not match W num_dims. X: group: kernel_shape: W: kernel_shape is not compatible with W shape.kernel_shape num_dims is not compatible with W num_dims.onnxruntime::ConvTransposeAttributes::PrepareForComputeInput channels is not divisible by group.dim_size > 0C:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core/providers/cpu/nn/conv_transpose_attributes.h num_input_channels: group count is <= 0filter number not equal to input channel number. filter_number: Invalid input shape: *out_size >= 0onnxruntime::ConvTransposeAttributes::ComputePadsAndOutputShapepad type not supportedonnxruntime::ConvTransposeAttributes::ComputeTransposePadAndOutputShapeC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\providers\cpu\nn\conv_transpose.cconnxruntime::ConvTranspose::DoConvTransposeNot enough elements in kernel shape. Expected: Not enough elements in strides. Expected: Not enough elements in pads. Expected: Not enough elements in dilations. Expected: Input channels C is not equal to kernel channels * group. C: Output channels M is not divisible by group. M: ComputePad: pad type not supported.Dilation not supported for AutoPadType::SAME_UPPER or AutoPadType::SAME_LOWER. kernel channels: onnxruntime::ComputePadAndOutputShapeC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core/providers/cpu/nn/conv_attributes.honnxruntime::ConvAttributes::InferOutputShapeC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core/util/math.hC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\providers\cpu\nn\conv.cconnxruntime::Conv::Computeonnxruntime::math::Im2colNd::operator ()d_iter[d_i] < d_maxonnxruntime::MaxUnpool::MaxUnpoolinfo.GetAttrs("kernel_shape", kernel_shape_).IsOK()onnxruntime::MaxUnpool::ComputeNot satisfied: X_shape.NumDimensions() >= 3 C:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\providers\cpu\nn\Unpool.cckernel_shape_[dim] > 0C:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core/providers/cpu/nn/unpool.hstrides_.size() == kernel_shape_.size()pads_[dim] < kernel_shape_[dim] && pads_[dim + kernel_shape_.size()] < kernel_shape_[dim]output_shape is smaller than minimum required. output_shape: inferred output shape:Not satisfied: given_shape.Size() >= shape.Size() Not satisfied: I_shape == X_shape Index tensor shape should be same as that of the input data tensor to unpool.Not satisfied: tensor_shape->Shape().GetDims().size() == 1 Shape must be 1 dimensional as it's tensor data of a shapescores must be a 3D tensor.Not satisfied: boxes_shape.NumDimensions() == 3 boxes and scores should have same num_batches.Not satisfied: scores_shape.NumDimensions() == 3 C:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\providers\cpu\object_detection\non_max_suppression.cconnxruntime::NonMaxSuppressionBase::PrepareComputeboxes must be a 3D tensor.scores_tensorC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\providers\cpu\object_detection\non_max_suppression.honnxruntime::NonMaxSuppressionBase::NonMaxSuppressionBaseboxes_tensorNonMaxSuppressioncenter_point_box0 == center_point_box_ || 1 == center_point_box_center_point_box only support 0 or 1Not satisfied: ret.IsOK() onnxruntime::NonMaxSuppression::Computeoutput != nullptriou_threshold must be in range [0, 1].Not satisfied: boxes_dims[2] == 4 Not satisfied: (iou_threshold >= 0 && iou_threshold <= 1.f) onnxruntime::NonMaxSuppressionBase::GetThresholdsFromInputsboxes and scores should have same spatial_dimension.Not satisfied: boxes_dims[0] == scores_dims[0] The most inner dimension in boxes must have 4 data.Not satisfied: boxes_dims[1] == scores_dims[2] Input tensor should have a rank of at least 2C:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\providers\cpu\math\det.ccMatrix dimensions are not equal. Square matrix is expectedonnxruntime::Det::ComputeDetC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core/providers/cpu/tensor/squeeze.honnxruntime::SqueezeBase::ComputeOutputShape must be 1 instead of . shape=input_shape[i] == 1Dimension of input C:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core/providers/cpu/tensor/split.hInvalid value in 'split' attribute. All values must be > 0onnxruntime::SplitBase::SplitBasestd::all_of(split_sizes_.cbegin(), split_sizes_.cend(), [](int64_t value) { return value >= 0; }) Input shape= Num entries in 'split' (must equal number of outputs) was Split operator does not support Cannot split using values in 'split' attribute. Axis= Axis= NumOutputs= Sum of sizes in 'split' (must equal size of selected axis) was Input cannot be split evenly on selected axis. Input shape=onnxruntime::Split::ComputeImplC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\providers\cpu\tensor\split.cconnxruntime::Split::Computeonnxruntime::Compress::Computeaxes_right_stride >= 0 && static_cast(axes_right_stride) < std::numeric_limits::max()C:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\providers\cpu\tensor\compress.ccCompressC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core/providers/cpu/controlflow/scan_utils.hAttempt to retrieve final output before it was set.onnxruntime::scan::detail::OutputIterator::GetOutputfinal_output_mlvalue_scan_input_directionsinfo.GetAttr("num_scan_inputs", &num_scan_inputs_).IsOK()scan_input_axesscan_output_directionsonnxruntime::Scan<9>::Scaninfo.GetAttr("body", &proto).IsOK()num_scan_inputsC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\providers\cpu\controlflow\scan_9.ccbody Expected dimensions or more but input had shape of has length of Invalid scan input:onnxruntime::Scan<9>::Computefeeds_fetches_manager_ && info_onnxruntime::ScanImpl::InitializeSubgraph SessionState was not found for 'body' attribute.gsl::narrow_cast(output_axes_.size()) == num_scan_outputsNumber of entries in 'scan_output_axes' was onnxruntime::Scan<9>::SetupSubgraphExecutionInfoinfo_ == nullptrNumber of entries in 'scan_input_axes' was but expected scan_output_axesgsl::narrow_cast(input_axes_.size()) == num_scan_inputs_output_mlvalueOutput OrtValue has not been created for loop state variable output . Output tensor rank was onnxruntime::ScanImpl::ExecuteSubgraph in 'body' produces outputs but Scan expects onnxruntime::ScanImpl::CreateLoopStateVariablesonnxruntime::ScanImpl::AllocateOutputTensorsInvalid value in scan_input_axes for input of onnxruntime::ScanImpl::SetupInputsonnxruntime::ScanImpl::ValidateInput but input '' dimension . Input tensor rank was Scan inputs have inconsistent sequence lengths. Previous value was Outputs from Scan are not optional and should never be null.Invalid value in scan_output_axes for output Scanonnxruntime::ScanImpl::TransposeOutputonnxruntime::Loop::LoopInconsistent shape in loop output for output. onnxruntime::Loop::Computeonnxruntime::Loop::SetupSubgraphExecutionInfonum_subgraph_outputs - 1 == num_outputs'Loop' node has Expected: Got:C:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\providers\cpu\controlflow\loop.cconnxruntime::Loop::Info::Info outputs so the subgraph requires but has inputs. Found:Loopstatic_cast(num_subgraph_inputs) == subgraph_inputs.size()Graph in 'body' attribute of Loop should have was not found. Defaulting to a rank 1 shape of {0}.onnxruntime::LoopImpl::ExecuteLoop had zero iterations and the shape of subgraph output 'Loop' input 'M' should be a scalar tensor. Got shape of feeds_fetches_manager_onnxruntime::LoopImpl::ConcatenateLoopOutput'Loop' input 'cond' should be a scalar tensor. Got shape of . N=Hardmax inputs N, D and N * D must be < , D=onnxruntime::DynamicQuantizeLinear::ComputeC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\providers\cpu\tensor\dynamicquantizelinear.ccx_ptr != nullptrAxis tensor should be of type `int32_t` or `int64_t`Axis tensor must be provided to the CumSum opAxis tensor should be 0D or 1Ddims.size() == starts.size()onnxruntime::WritableSliceIterator<__int64>::Initstarts.size()=dims.size()=onnxruntime::CumSum<__int64>::Computeonnxruntime::CumSum::Computeonnxruntime::CumSum::ComputeCannot apply CumSum operator on a scalaronnxruntime::CumSum::ComputeC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\providers\cpu\math\cumsum.cconnxruntime::WritableSliceIterator::Initonnxruntime::WritableSliceIterator::Initonnxruntime::WritableSliceIterator::Initsteps.size()=dims.size() == steps.size()extents.size()=dims.size() == extents_.size()time_axis and batch_axis must have different values but both are batch_axis != time_axisInvalid time_axis of time_axis < 2Invalid batch_axis of batch_axis < 2info.GetAttr("time_axis", &time_axis).IsOK(). Must be 0 or 1onnxruntime::ReverseSequenceOp::ReverseSequenceOpC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\providers\cpu\tensor\reverse_sequence.hinfo.GetAttr("batch_axis", &batch_axis).IsOK()C:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\providers\cpu\tensor\reverse_sequence.ccsequence_lens shape must be {batch_size}. Got:onnxruntime::ReverseSequenceOp::Compute. batch_size= specified. It should be either avg or maxInvalid mode of value Null input X ptrSampling ratio should be >=0, but it was sampling_ratio_ >= 0onnxruntime::RoiAlignBase::RoiAlignBaseC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\providers\cpu\object_detection\roialign.hSecond dimension for rois should be exactly First dimension (num_rois) of batch_indices and rois don't matchNumber of dimensions for batch indices should be exactly 1Number of dimensions for rois should be exactly Null rois_ptrNull batch_indices_ptrFailed to obtain detect_positiveonnxruntime::IsInf::ComputeFailed to obtain detect_negativeData type X must be float or double, but instead got onnxruntime::IsInf::IsInfC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\providers\cpu\tensor\isinf.ccQLinearConv : input zero point must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(X_zero_point)onnxruntime::math::Im2colNd::operator ()QLinearConv : result scale must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(Y_scale)QLinearConv : filter scale must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(W_scale)QLinearConv : input scale must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(X_scale)QLinearConv : result zero point must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(Y_zero_point)QLinearConv : filter zero point must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(W_zero_point)onnxruntime::QLinearConv::ComputeC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\providers\cpu\nn\qlinearconv.ccIsScalarOr1ElementVector(W_Zero_Point)C:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\providers\cpu\nn\conv_integer.ccNon per-tensor quantization is not supported now.IsScalarOr1ElementVector(X_Zero_Point)onnxruntime::ConvInteger::ComputeMust be a scalar or 1D tensor or size 1.IsScalarOr1ElementVector(b_zero_point)IsScalarOr1ElementVector(a_zero_point)MatmulInteger : input2 zero point must be a scalar or 1D tensor of size 1C:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\providers\cpu\math\matmul_integer.ccMatmulInteger : input1 zero point must be a scalar or 1D tensor of size 1onnxruntime::MatMulInteger::ComputeIsScalarOr1ElementVector(b_scale)QLinearMatmul : result scale must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(a_scale)QLinearMatmul : weight scale must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(y_offset)QLinearMatmul : input scale must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(b_offset)QLinearMatmul : result zero point must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(a_offset)QLinearMatmul : weight zero point must be a scalar or 1D tensor of size 1C:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\providers\cpu\math\quantize_linear_matmul.ccQLinearMatmul : input zero point must be a scalar or 1D tensor of size 1onnxruntime::QLinearMatMul::ComputeIsScalarOr1ElementVector(y_scale)LOWERUPPERcase_change_actionattribute case_change_action is not setattribute is_case_sensitive is not setonnxruntime::StringNormalizer::StringNormalizeren-USis_case_sensitiveonnxruntime::string_normalizer::Locale::LocaleC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\providers\cpu\nn\string_normalizer.cc:Please, install necessary language-pack-XX and configure localesFailed to construct locale with name:Conversion ErrorConversion ErrorStringNormalizerInput contains invalid utf8 chars at: bad conversionSingle dimension value must be greater than 0Input dimensions are either[C > 0] or [1][C > 0] allowedStopword contains invalid utf8 charswstr != wconv_errorDuplicate stopwords not allowedp.secondEmpty stopwords not allowed!sw.empty()localestopwordsNONEattribute case_change_action has invalid valuemode: impl_->weighting_criteria_ != kNoneTFIDF is unrecognized, acceptable values are TF,IDF,TFIDFTFIDFonnxruntime::TfIdfVectorizer::TfIdfVectorizerC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\providers\cpu\nn\tfidfvectorizer.ccTfIdfVectorizermode is requiredstatus.IsOK() && !impl_->ngram_counts_.empty() <= ngram_countsNon-empty ngram_counts is requiredmax_skip_count must be non-negative: impl_->max_skip_count_ >= 0max_skip_countmax_skip_count is requiredmin_gram_length >= max_gram_length required: impl_->max_gram_length_ >= impl_->min_gram_length_max_gram_length >= Required min_gram_length must be positive: impl_->min_gram_length_ > 0min_gram_lengthmin_gram_length is requiredpool_stringspool_strings must not be empty if specifiedGot weights of size: impl_->weights_.size() == impl_->ngram_indexes_.size() must be of equal size but ngram_indexes size: std::all_of(impl_->ngram_indexes_.cbegin(), impl_->ngram_indexes_.cend(), [](int64_t i) { return i >= 0; })weightsstatus.IsOK() && !impl_->ngram_indexes_.empty()Negative ngram_indexes values are not allowedngram_indexesNon-empty ngram_indexes is requiredmax_gram_length must be inbounds of ngram_counts: size_t(impl_->max_gram_length_) <= impl_->ngram_counts_.size()min_gram_length must be inbounds of ngram_counts: size_t(impl_->min_gram_length_) <= impl_->ngram_counts_.size() id: (items % ngram_size == 0)Input shape must have either [C] or [B,C] dimensions with B > 0.end_idx >= start_idx && end_idx <= total_itemsNumber of items must compose whole -gramsn-gram counts out of bounds for non-empty pool_int64s is required if pool_strings not providedstatus.IsOK() && !pool_int64s.empty()!impl_->pool_strings_.empty()pool_int64sonnxruntime::ngram_details::PopulateGramsDuplicate ngram detected, size: p.first->second->id_ == 0op_kernel_info.GetAttr("lambd", &lambd_temp).IsOK()onnxruntime::Shrink::ShrinkC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core/providers/cpu/nn/shrink.hop_kernel_info.GetAttr("bias", &bias_temp).IsOK()EyeLike : Input tensor dimension is not 2onnxruntime::EyeLike::ComputeC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\providers\cpu\tensor\eye_like.ccT1 != nullptronnxruntime::ConstantOfShapeBase::SetValueFromTensorProtoC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\providers\cpu\generator\constant_of_shape.ccutils::HasDataType(t_proto)Unsupported value attribute datatype with sizeof=: onnxruntime::ConstantOfShape::Computeinput_shape.NumDimensions() > 0onnxruntime::ConstantOfShapeBase::PrepareComputet_proto.dims()[0] == 1Must have a valid input shape.onnxruntime::ConstantOfShapeBase::ConstantOfShapeBaseMust have a single dimension of 1Must have a single dimensiont_proto.dims_size() == 1unpack_status.IsOK()Unsupported value attribute datatype: TensorProto::DataType_IsValid(t_proto.data_type())Value attribute unpacking failed:Invalid entries in sequence_lens. Max sequence length was onnxruntime::Scan8Impl::AllocateOutputTensors did not match batch size of sequence_lens length of Scan inputs have inconsistent batch size. Previous value was onnxruntime::Scan8Impl::ValidateInput has batch size of but onnxruntime::Scan<8>::Computeonnxruntime::Scan8Impl::Initializedirectionsonnxruntime::Scan<8>::SetupSubgraphExecutionInfoonnxruntime::Scan<8>::ScanC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\providers\cpu\controlflow\scan_8.ccScan<8> spec does not support transpose of output. This should never be called.onnxruntime::OrtValueTensorSlicer::Iterator::operator *C:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core/framework/ort_value_tensor_slicer.honnxruntime::Scan8Impl::Executeposition_ >= 0 && position_ < sequence_length_p_mlvalueonnxruntime::Scan8Impl::CreateLoopStateVariablesonnxruntime::UpsampleBase::ParseScalesDataFromOutputSizeoutput_dims[i] == 0Input dim is zero but required output dim is non-zero. Cannot scale 0 by any factor to generate a non-zero value. Dimension: Input dim value: Output dim value: Resize: input tensor's rank does not match the output tensor's rank.sizes != nullptr && sizes->Shape().Size() != 0Either scales or sizes MUST be provided as input.sizes == nullptrOnly one of scales or sizes must be provided as input.scales != nullptrroi_input_idx_ > 0Invalid roi input index.C:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\providers\cpu\tensor\upsample.cconnxruntime::Upsample::ComputeResize: input/output value's dimension mismatchUpsample: input/output value is nullptrResize: input/output value is nullptronnxruntime::Upsample::BaseComputeonnxruntime::Upsample::BaseComputeUpsample: unexpected modeResize: unexpected modeResize: size of roi array should be 2 * N where N is the rank of input tensor X.Upsample: input tensor's dimension does not match the scales.Resize: input tensor's dimension does not match the scales.output_dims.size() == dims.size()Rank of input and output tensor should be same.onnxruntime::Upsample::BaseComputeonnxruntime::Upsample::Computeonnxruntime::Upsample::ComputeX->Shape().GetDims().size() == output_dims.size()Upsample: input shape needs to be at least a single dimension.Resize: input shape needs to be at least a single dimensionUpsample: input/output value's dimension mismatchallowed_activations.find(activations_[direction]) != allowed_activations.end()RNN op: Invalid activation attribute - activations_.size() == static_cast(num_directions)info.GetAttr("hidden_size", &hidden_size_).IsOK()info.GetAttrs("activations", activations_).IsOK()allowed_directions.find(direction_) != allowed_directions.end()C:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core/providers/cpu/rnn/rnn.honnxruntime::RNN::RNNinfo.GetAttr("direction", &direction_).IsOK()C:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\providers\cpu\rnn\rnn.cconnxruntime::RNN::ComputeInvalid 'direction' argument of ''. Must be one of 'forward', 'reverse', or 'bidirectional'.}. Actual:onnxruntime::DeepCpuLstmOp::ValidateInputsC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\providers\cpu\rnn\deep_cpu_lstm.cconnxruntime::DeepCpuLstmOp::ComputeInvalid data type for LSTM operator of LSTM operator does not support double yetactivation_func_names.size() == static_cast(num_directions_) * 3tanhsigmoidclip_ > 0.finfo.GetAttr("hidden_size", &int64_value).IsOK() && int64_value > 0C:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core/providers/cpu/rnn/deep_cpu_lstm.honnxruntime::DeepCpuLstmOp::DeepCpuLstmOpinfo.GetAttr("direction", &direction).IsOK()C:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core/providers/cpu/rnn/rnn_helpers.honnxruntime::rnn::detail::MakeDirectioncur + size <= endC + (M * ldc - (ldc - N)) <= C_endonnxruntime::rnn::detail::ComputeGemmA + (M * K) <= A_end rows[..onnxruntime::DeepCpuLstmOp::ComputeImplInput P must have shape {Input initial_c must have shape {onnxruntime::rnn::detail::SafeRawConstPointeroffset + size <= size_t(span.size())onnxruntime::rnn::detail::SafeRawPointerInvalid data type for GRU operator of GRU operator does not support double yetactivation_func_names.size() == static_cast(num_directions_) * 2info.GetAttr("linear_before_reset", &int64_value).IsOK()C:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core/providers/cpu/rnn/deep_cpu_gru.honnxruntime::DeepCpuGruOp::DeepCpuGruOpB + (N * ldb - (ldb - K)) <= B_endA + (M * lda - (lda - K)) <= A_endlda >= K && ldb >= K && ldc >= Nonnxruntime::DeepCpuGruOp::ComputeImplC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\providers\cpu\rnn\deep_cpu_gru.cconnxruntime::DeepCpuGruOp::Computeonnxruntime::RoiPool::ComputeR->Shape()[1] == 5spatial_scale_ > 0info.GetAttr("spatial_scale", &spatial_scale_).IsOK()pooled_width_ > 0pooled_height_ > 0pooled_shape.size() == 2C:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core/providers/cpu/nn/roi_pool.honnxruntime::RoiPool::RoiPoolinfo.GetAttrs("pooled_shape", pooled_shape).IsOK()roi_batch_id < batch_sizeroi_batch_id >= 0C:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\providers\cpu\nn\roi_pool.ccp_ == 1 || p_ == 2op_kernel_info.GetAttr("p", &p_).IsOK()C:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core/providers/cpu/nn/lp_norm.honnxruntime::LpNorm::LpNormop_kernel_info.GetAttr("axis", &axis_).IsOK()Mismatch between input data and B: size of B != input channel count Invalid input B: number of dimensions is not 1: Mismatch between input data and scale: size of scale != input channel count vs. Invalid input scale: number of dimensions is not 1: Invalid input data: number of dimensions is less than 3: C:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core/providers/cpu/nn/instance_norm.honnxruntime::InstanceNorm::InstanceNormop_kernel_info.GetAttr("epsilon", &epsilon_).IsOK()C:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\providers\cpu\nn\instance_norm.cconnxruntime::InstanceNorm::ComputeInvalid input var: 0th dimension != Invalid input var: NumDimensions() != Invalid input mean: Invalid input mean: 0th dimension != Invalid input mean: NumDimensions() != Invalid input B: Invalid input B: 0th dimension != Invalid input B: NumDimensions() != Invalid input scale: dimension != Invalid input scale: 0th dimension != Invalid input scale: NumDimensions() != Invalid input X: The rank of input X must be atleast 2. Got rank: onnxruntime::BatchNorm::BatchNormonnxruntime::BatchNorm::Computeonnxruntime::BatchNorm::BatchNormC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core/providers/cpu/nn/batch_norm.honnxruntime::BatchNorm::ComputeInvalid input var: Invalid dtype of info.GetAttr("dtype", &dtype).IsOK()info.GetAttr("scale", &scale_).IsOK()C:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core/providers/cpu/generator/random.honnxruntime::RandomNormal::RandomNormalinfo.GetAttr("mean", &mean_).IsOK()meanONNX_NAMESPACE::TensorProto::DataType_IsValid(output_dtype_) && output_dtype_ != ONNX_NAMESPACE::TensorProto::UNDEFINEDonnxruntime::Multinomial::Multinomialinfo.GetAttr("sample_size", &num_samples_).IsOK()onnxruntime::RandomUniformLike::RandomUniformLikeinfo.GetAttr("low", &low_).IsOK()lowonnxruntime::RandomUniform::RandomUniforminfo.GetAttr("high", &high_).IsOK()highonnxruntime::RandomNormalLike::RandomNormalLikeinfo.GetAttrs("shape", shape).IsOK()ONNX_NAMESPACE::TensorProto::DataType_IsValid(dtype_) && dtype_ != ONNX_NAMESPACE::TensorProto::UNDEFINEDonnxruntime::RandomUniformLike::ComputeCould not infer data type from input tensor with data type C:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\providers\cpu\generator\random.cconnxruntime::RandomNormalLike::Computeonnxruntime::MultinomialComputeonnxruntime::RandomUniformComputeonnxruntime::RandomNormalComputeFLOAT16 is not supportedInvalid data type of num_samples is < 1num_classes is < 1batch_size is < 1Empty dimensions for input tensoronnxruntime::contrib::RegisterCpuContribKernelsonnxruntime::contrib::RegisterQuantizationKernelsC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\contrib_ops\cpu\cpu_contrib_kernels.cconnxruntime::ml::LabelEncoder::LabelEncoderinfo.GetAttrs("classes_strings", string_classes).IsOK()classes_stringsvalues_int64skeys_int64sdefault_floatvalues_floatskeys_strings_Unusedvalues_stringskeys_floatsInput of tensor(int64) must have output of tensor(string)Input of tensor(string) must have output of tensor(int64)LabelEncoderinfo.GetAttr("default_int64", &default_int_).IsOK()default_int64info.GetAttr("default_string", &default_string_).IsOK()default_stringC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core/providers/cpu/ml/label_encoder.honnxruntime::ml::LabelEncoder_2,class std::allocator >,float>::LabelEncoder_2onnxruntime::ml::LabelEncoder_2<__int64,float>::LabelEncoder_2onnxruntime::ml::LabelEncoder_2::LabelEncoder_2onnxruntime::ml::LabelEncoder_2<__int64,class std::basic_string,class std::allocator > >::LabelEncoder_2onnxruntime::ml::LabelEncoder_2,class std::allocator >,__int64>::LabelEncoder_2num_keys == num_valuesThe attribtues in LabelEncoder (name: ) must have the same length. However, the number of key is and the number of values is info.GetAttrs(_value_field_name, values).IsOK()onnxruntime::ml::LabelEncoder_2<__int64,__int64>::LabelEncoder_2info.GetAttrs(_key_field_name, keys).IsOK()onnxruntime::ml::LabelEncoder_2,class std::allocator > >::LabelEncoder_2Input features_per_batch[] != number of classlabels[Zipmap only supports 1D or 2D input tensorsZipmap does not support empty dim countC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\providers\cpu\ml\zipmap.cconnxruntime::ml::ZipMapOp::ZipMapOpclasslabels_strings_.empty() ^ classlabels_int64s_.empty()Must provide classlabels_strings or classlabels_int64s but not both.classlabels_stringsclasslabels_int64sZipMapLOGISTICBRANCH_EQBRANCH_GTBRANCH_GTEBRANCH_LTLEAFBRANCH_LEQMINSUMAVERAGESOFTMAX_ZEROSOFTMAXtarget_weightsTreeEnsembleRegressoraggregate_functionbase_valuesn_targetsnodes_falsenodeidsnodes_featureidsnodes_hitratesnodes_missing_value_tracks_truenodes_modesnodes_nodeidsnodes_treeidsnodes_truenodeidsnodes_valuespost_transformtarget_idstarget_nodeidstarget_treeids (truenode).Node in tree is already there.target_class_ids.size() == target_class_treeids.size()target_class_ids.size() == target_class_nodeids.size()nodes_falsenodeids.size() == nodes_values.size()nodes_falsenodeids.size() == nodes_truenodeids.size()nodes_falsenodeids.size() == nodes_treeids.size()nodes_falsenodeids.size() == nodes_nodeids.size()nodes_falsenodeids.size() == nodes_modes.size()nodes_falsenodeids.size() == nodes_featureids.size()C:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\providers\cpu\ml\tree_ensemble_common.honnxruntime::ml::detail::TreeEnsembleCommon::TreeEnsembleCommonn_targets_or_classes > 0Input shape needs to be at least a single dimension.it->i < (int64_t)predictions.size()onnxruntime::ml::detail::TreeAggregatorSum::MergePredictiononnxruntime::ml::detail::TreeAggregatorAverage::FinalizeScoresthis->base_values_.size() == predictions.size()onnxruntime::ml::detail::TreeAggregatorMin::MergePredictionC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\providers\cpu\ml\tree_ensemble_aggregator.honnxruntime::ml::detail::TreeAggregatorMax::MergePredictionpredictions.size() == predictions2.size()onnxruntime::ml::detail::TreeEnsembleCommon::computeonnxruntime::ml::detail::TreeEnsembleCommon::TreeEnsembleCommononnxruntime::ml::detail::TreeEnsembleCommon::computeUnknown aggregation function in TreeEnsemble. (weights). (falsenode).One falsenode is pointing either to itself, either to another tree.Unable to find node onnxruntime::ml::detail::TreeAggregator::FinalizeScoresonnxruntime::ml::detail::TreeAggregatorSum::ProcessTreeNodePredictiononnxruntime::ml::detail::TreeAggregatorSum::MergePredictiononnxruntime::ml::detail::TreeAggregatorAverage::FinalizeScoresonnxruntime::ml::detail::TreeAggregatorMin::MergePredictiononnxruntime::ml::detail::TreeAggregatorMax::MergePredictiononnxruntime::ml::detail::TreeAggregator::FinalizeScorespredictions.size() == (size_t)n_targets_or_classes_onnxruntime::ml::detail::TreeAggregatorSum::ProcessTreeNodePredictionclass_weightsTreeEnsembleClassifieronnxruntime::ml::detail::TreeAggregatorSum<__int64,float>::MergePredictiononnxruntime::ml::detail::TreeAggregatorClassifier<__int64,float>::FinalizeScoresonnxruntime::ml::detail::TreeAggregatorSum::ProcessTreeNodePredictiononnxruntime::ml::detail::TreeAggregatorSum::MergePredictiononnxruntime::ml::detail::TreeAggregatorClassifier::FinalizeScorespredictions.size() == 2onnxruntime::ml::detail::TreeEnsembleCommon::TreeEnsembleCommononnxruntime::ml::detail::TreeEnsembleCommon<__int64,float>::TreeEnsembleCommononnxruntime::ml::detail::TreeEnsembleCommonClassifier::computeonnxruntime::ml::detail::TreeEnsembleCommonClassifier<__int64,float>::computeonnxruntime::ml::detail::TreeEnsembleCommonClassifier::computeonnxruntime::ml::detail::TreeEnsembleCommonClassifier::computeX dims is empty.class_idsclass_nodeidsclass_treeidsonnxruntime::ml::detail::TreeAggregatorClassifier::_set_score_binaryonnxruntime::ml::detail::TreeAggregatorClassifier::_set_score_binaryonnxruntime::ml::detail::TreeAggregatorClassifier<__int64,float>::_set_score_binaryonnxruntime::ml::detail::TreeAggregatorClassifier::_set_score_binaryclasses.size() == 2 || classes.size() == 1onnxruntime::ml::detail::TreeAggregatorClassifier::FinalizeScoresonnxruntime::ml::detail::TreeAggregatorClassifier::FinalizeScoresonnxruntime::ml::detail::TreeAggregatorSum<__int64,float>::ProcessTreeNodePredictionRBFPOLYLINEARSVMRegressorC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\providers\cpu\ml\svmclassifier.honnxruntime::ml::SVMCommon::SVMCommoninfo.GetAttrs("kernel_params", kernel_params).IsOK()kernel_paramskernel_typeUnexpected mode:onnxruntime::ml::SVMRegressor::Computenum_features == feature_count_one_class!coefficients_.empty()info.GetAttrs("coefficients", coefficients_).IsOK()coefficientsC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\providers\cpu\ml\svmregressor.cconnxruntime::ml::SVMRegressor::SVMRegressorinfo.GetAttrs("rho", rho_).IsOK()rhosupport_vectorsn_supportsC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\providers\cpu\ml\ml_common.honnxruntime::ml::SVMClassifier::SVMClassifierprob_bprob_avectors_per_classSVMClassifierUnexpected value for 'add_second_class' of onnxruntime::ml::batched_update_scores_inplacescores.size() == static_cast(expected_num_scores)Unsupported data type of onnxruntime::ml::SVMClassifier::Computecoefficients_.size() > 0classlabels_strings_.size() > 0 || classlabels_ints_.size() > 0info.GetAttrs("classlabels_strings", classlabels_strings_).IsOK() || info.GetAttrs("classlabels_ints", classlabels_ints_).IsOK()classlabels_intsproba_.size() == probb_.size()C:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\providers\cpu\ml\svmclassifier.ccScaleronnxruntime::ml::ScalerOp::ScalerOponnxruntime::ml::ScalerOp<__int64>::ScalerOponnxruntime::ml::ScalerOp::ScalerOpEither both scale and offset can be of feature size () or 1Invalid argument: input has empty dimensions.scale_.size() == offset_.size()Scale size: () != (C:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\providers\cpu\ml\scaler.cconnxruntime::ml::ScalerOp::ScalerOp!scale_.empty()Empty scale in attributesoffsetUnknown Category and zeros = 0.OneHotEncoderonnxruntime::ml::OneHotEncoderOp,class std::allocator > >::OneHotEncoderOponnxruntime::ml::OneHotEncoderOp::OneHotEncoderOponnxruntime::ml::OneHotEncoderOp::OneHotEncoderOpnum_categories_ > 0C:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\providers\cpu\ml\onehotencoder.cconnxruntime::ml::OneHotEncoderOp<__int64>::OneHotEncoderOptmp_cats_int64s.empty() || tmp_cats_strings.empty()One and only one of the 'cats_*' attributes must be definedcats_stringscats_int64szerosC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core/providers/cpu/ml/ml_common.hMAXL1L2Invalid normalize value of onnxruntime::ml::MakeNormalizenorminfo.GetAttr("norm", &norm).IsOK()onnxruntime::ml::Normalizer::NormalizerC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core/providers/cpu/ml/normalizer.hNormalizerRank of input to Normalized must be less than 2. Got Unexpected NORMALIZE value of C:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\providers\cpu\ml\linearregressor.ccInput shape had more than 2 dimension. Dims=LinearRegressorinterceptstargetsinfo.GetAttr("targets", &num_targets_).IsOK()onnxruntime::ml::LinearRegressor::LinearRegressorshape_size == out.length()onnxruntime::ml::CastInputToFloatLinearClassifiermulti_classonnxruntime::ml::LinearClassifier::LinearClassifierC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\providers\cpu\ml\linearclassifier.cc Found:Scores output is incorrect size. Expected:scores_output_data.length() >= scores_output_sizeonnxruntime::ml::LinearClassifier::ComputeImplUnsupported input element type of imputed_value_floatsimputed_value_int64sreplaced_value_floatExpected 'replaced_value_float' attribute since 'imputed_value_floats' is specifiedonnxruntime::ml::ImputerOp::ImputerOpC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\providers\cpu\ml\imputer.ccreplaced_value_int64Expected 'replace_value_int64' attribute since 'imputed_values_int64' is specifiedMust provide imputed_values_float_ or imputed_values_int64_ but not both.imputed_values_float_.empty() ^ imputed_values_int64_.empty()onnxruntime::ml::ImputerOp::ComputeInvalid typeImputerEmpty value of imputed values.Empty input dimensions.C:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\providers\cpu\ml\feature_vectorizer.ccInvalid input type:inputdimensionsinputdimensions attribute must be providedstatus.IsOK() && !input_dimensions_.empty()onnxruntime::ml::FeatureVectorizer::FeatureVectorizerC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core/providers/cpu/ml/feature_vectorizer.hFeatureVectorizer).) does not match number of inputdimensions values (Number of inputs (input_count >= 0 && static_cast(input_count) == input_dimensions_.size()onnxruntime::ml::FeatureVectorizer::ComputeDictVectorizerstring_vocabularyint64_vocabularyinfo.GetAttrs(std::is_same::value ? "string_vocabulary" : "int64_vocabulary", vocabulary_).IsOK()onnxruntime::ml::DictVectorizerOp<__int64,double>::DictVectorizerOpC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core/providers/cpu/ml/dictvectorizer.honnxruntime::ml::DictVectorizerOp<__int64,float>::DictVectorizerOponnxruntime::ml::DictVectorizerOp<__int64,class std::basic_string,class std::allocator > >::DictVectorizerOponnxruntime::ml::DictVectorizerOp,class std::allocator >,double>::DictVectorizerOponnxruntime::ml::DictVectorizerOp,class std::allocator >,float>::DictVectorizerOponnxruntime::ml::DictVectorizerOp,class std::allocator >,__int64>::DictVectorizerOpinfo.GetAttrs("cats_strings", string_categories).IsOK()onnxruntime::ml::CategoryMapper::CategoryMapperC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core/providers/cpu/ml/category_mapper.hinfo.GetAttrs("cats_int64s", int_categories).IsOK()num_entries == int_categories.size()CategoryMapperInput of string must have output of int64Input of int64 must have output of string %fTO_FLOATTO_STRINGTO_INT64 Expected TO_FLOAT, TO_STRING or TO_INT64Invalid CAST_TO value of CastMap Expected std::map or std::mapInvalid input type of value: 0.fUnexpected CAST_TO value of Negative index values are not permitted. First entry in map has index value of cur_input == end_input || cur_input->first >= 0onnxruntime::ml::CastMap::ComputeImplC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\providers\cpu\ml\cast_map.cconnxruntime::ml::MakeCastDENSESPARSE Expected DENSE or SPARSEInvalid PACK_MAP value of onnxruntime::ml::MakePackcast_toinfo.GetAttr("cast_to", &attr).IsOK()onnxruntime::ml::CastMap::CastMapC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core/providers/cpu/ml/cast_map.hmap_forminfo.GetAttr("map_form", &attr).IsOK()max_mapinfo.GetAttr("max_map", &max_map_).IsOK()max_map must be > 0 if map_form is SPARSEmap_form_ != PACK_MAP::SPARSE || max_map_ > 0Binarizerthreshold is NaNInput data with index: ArrayFeatureExtractorInvalid argument: X input has empty dimensions.Invalid Y argument: num_indices = 0) >=] (Invalid Y argument: index is out of range: Y[C:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\providers\cpu\math\einsum_utils\einsum_typed_compute_processor.cc Right shape override: Right shape: right.Shape().Size() == right_shape_override.Size() Right shape: Ranks of pair-wise operands must be equal. left_rank == right_rankEinsum op: Input dimensions must be equal along an axis to be reduced across all inputsleft_dim == right_dimEinsum op: Input shapes do not alignEinsum op: The candidate output cannot be reshaped into the op's outputcandidate_output.Shape().Size() == output_shape.Size()onnxruntime::EinsumTypedComputeProcessor::FinalizeOutputNot all dimensions to be reduced have been reduced in the candidate output. Candidate output dims: candidate_output_dims[iter] == 1Einsum op: Could not copy the intermediate output's buffer into the op's output buffer. Error: Left shape override: Left shape: The override dims are not compatible with given tensor's shape. left.Shape().Size() == left_shape_override.Size()onnxruntime::EinsumTypedComputeProcessor::PairwiseOperandProcessonnxruntime::EinsumTypedComputeProcessor::PairwiseOperandProcessonnxruntime::EinsumTypedComputeProcessor::FinalizeOutputonnxruntime::EinsumTypedComputeProcessor::PairwiseOperandProcessonnxruntime::EinsumTypedComputeProcessor::FinalizeOutputonnxruntime::EinsumTypedComputeProcessor<__int64>::PairwiseOperandProcessonnxruntime::EinsumTypedComputeProcessor<__int64>::FinalizeOutputFound '.' not part of an ellipsis in input: The only subscript labels allowed are lower-cased letters (a-z) and upper-cased letters (A-Z) in the same dimensionAnother operand has a dim value of . The shape is: is incompatible in the dimension Einsum operands could not be broadcast together. Please check input shapes/equation provided.Input shape of operand Einsum subscripts string contains too many subscript labels when compared to the rank of the input Einsum subscripts does not contain enough subscript labels and there is no ellipsis for input num_broadcasted_indices < num_of_ellipsis_dims_onnxruntime::EinsumComputePreprocessor::PostProcessBroadcastedDimsThe broadcasted dimensions of the inputs are incompatibledim_iter == rank...Inputs have ellipses in them but the provided output subscript does not contain an ellipsisFound a '.' not part of an ellipsis in the output subscript providedonnxruntime::EinsumComputePreprocessor::RunC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\providers\cpu\math\einsum_utils\einsum_compute_preprocessor.ccNumber of subscripts in the input equation does not match number of input tensorsFound a '.' not part of an ellipsis in input: Ellipsis must indicate a fixed number of dimensions across all inputsFound '.' not part of an ellipsis in the output subscript providedThe only subscript labels allowed in the output subscript are lowercase letters (a-z)Output subscript contains repeated lettersOutput subscript contains letters not seen in the inputsRank of the input must match number of subscript labels corresponding to the inputThe rank of the input must match permutation size for Transposeinput_rank == permutation.size()onnxruntime::EinsumOp::IsTransposeRequiredLength of permutation must match the rank of the input to be permutatedonnxruntime::EinsumOp::TransposeEinsum op: Transpose failed: Data types of the inputs must match for MatMulinput_1.DataType() == input_2.DataType()onnxruntime::EinsumOp::MatMulOnly 1 batch dimension is allowed for MatMulinput_shape_1_override.size() == 3 && input_shape_2_override.size() == 3Einsum op: The candidate output does not match the actual output's shapeoutput.SizeInBytes() == input.SizeInBytes()onnxruntime::EinsumOp::DeviceHelpers::CpuDeviceHelpers::DataCopyC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\providers\cpu\math\einsum_utils\einsum_auxiliary_ops.ccThe innermost dims should have the same dim value to parse the diagonal elementsinput_dims[rank - 2] == input_dims[rank - 1]onnxruntime::EinsumOp::DeviceHelpers::CpuDeviceHelpers::DiagonalInnermostDimsEinsum op: Unsupported data type for Diagonal for input shape Cannot parse the diagonal elements along dims rank >= 2 && dim_1 != dim_2 && input_dims[dim_1] == input_dims[dim_2]onnxruntime::EinsumOp::DeviceHelpers::CpuDeviceHelpers::DiagonalBatch dimension should match for MatMul;input_shape_1_override[0] == input_shape_2_override[0]Incompatible matrix dimensions for matMulinput_shape_1_override[2] == input_shape_2_override[1]Einsum op: Exception during MatMul operation: onnxruntime::scan::detail::Info::InfoC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\providers\cpu\controlflow\scan_utils.cc' was Number of entries in 'num_entries < 0 || gsl::narrow_cast(directions.size()) == num_entriesonnxruntime::scan::detail::ReadDirections'. 0 == forward. 1 == reverse.Invalid values in 'valid did not.Subgraph must have the shape set for all outputs but onnxruntime::scan::detail::CreateFeedsFetchesManageronnxruntime::scan::detail::IterateSequence::::operator ()onnxruntime::scan::detail::IterateSequenceMisuse of LoopStateVariable. Attempt to move beyond end of sequenceiteration_num_ < sequence_len_ inputs but Scan was only given The subgraph in 'body' requires num_variadic_inputs == num_subgraph_inputsonnxruntime::scan::detail::LoopStateVariable::Next is not compatible with Mismatch between expected shape and shape from first outputonnxruntime::scan::detail::OutputIterator::InitializeFailed to create output tensor for output #onnxruntime::scan::detail::OutputIterator::AllocateFinalBufferIf shape was concrete we shouldn't be using a custom allocator!is_concrete_shape_onnxruntime::scan::detail::OutputIterator::AllocateFinalOutputcur_iteration_ < num_iterations_onnxruntime::scan::detail::OutputIterator::operator *Expected AllocateFinalOutput to have been called to before we read the OrtValue from the iterator.is_concrete_shape_Expected AllocateFinalOutput to have been called to before we increment the iteratoronnxruntime::scan::detail::OutputIterator::operator ++onnxruntime::OrtValueTensorSlicer::Iterator::operator *SoftmaxCPU inputs N, D and N * D must be < Input X must have 3 dimensions only. Actual:Input W must have shape {Input R must have shape {Invalid activation function of onnxruntime::rnn::detail::deepcpu::ActivationFuncByNameInvalid LSTM merge activation function of onnxruntime::rnn::detail::deepcpu::LstmMergeGatesFuncByNameInvalid GRU reset gate activation function: onnxruntime::rnn::detail::deepcpu::GruResetGateFuncByNameInvalid GRU hidden gate activation function: onnxruntime::rnn::detail::deepcpu::GruOutputGateFuncByNameInput B must have shape {Input sequence_lens must have shape {Invalid value/s in sequence_lens. All values must be > 0 and < seq_length. seq_length=Input initial_h must have shape {affinereluleakyreluthresholdedreluscaledtanhhardsigmoidelusoftsignsoftplusExpecting activation to be one of Affine, Relu, LeakyRelu, ThresholdedRelu, Tanh, ScaledTanh, Sigmoid, HardSigmoid, Elu, Softsign, Softplus. Got onnxruntime::rnn::detail::NormalizeActivationArgumentAndGetAlphaBetaCountC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\providers\cpu\rnn\rnn_helpers.ccC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\contrib_ops\cpu\trilu.ccupperinfo.GetAttr("upper", &temp).IsOK()onnxruntime::contrib::Trilu::TriluC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\contrib_ops\cpu\trilu.hTriluk should be a 1-D or 0-D tensor.IsScalarOr1ElementVector(k)onnxruntime::contrib::Trilu::ComputeInverseonnxruntime::utils::MLTypeCallDispatcher::Invokeonnxruntime::contrib::SkipLayerNorm::SkipLayerNormC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\contrib_ops\cpu\skip_layer_norm.ccepsilon_ >= 0input is expected to have 3 dimensions, got skip is expected to have same shape as inputgamma is expected to have 1 dimension, got Last dimension of gamma and input does not matchbeta is expected to have 1 dimension, got Last dimension of beta and input does not matchbias is expected to have 1 dimension, got Last dimension of bias and input does not matchonnxruntime::contrib::SkipLayerNorm::SkipLayerNormop_kernel_info.GetAttr("axis", &axis_).IsOK()C:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\contrib_ops\cpu\layer_norm.cconnxruntime::contrib::LayerNorm::LayerNormonnxruntime::contrib::LayerNorm::LayerNormonnxruntime::contrib::LayerNorm::Computeonnxruntime::contrib::LayerNorm::LayerNormonnxruntime::contrib::LayerNorm::Computeonnxruntime::contrib::LayerNorm::LayerNormonnxruntime::contrib::LayerNorm::Computeonnxruntime::contrib::LayerNorm::Computeonnxruntime::contrib::Scale::Scaleinfo.GetAttr("scale", &scale_).IsOK()info.GetAttr("alpha", &alpha_).IsOK()C:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\contrib_ops\cpu\element_wise_ops.hinfo.GetAttr("beta", &beta_).IsOK()onnxruntime::contrib::Affine::Affineonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::Compute) does not match the number of channels (Input is expected to have four dimensions corresponding to [N,C,H,W], got onnxruntime::contrib::ImageScaler::ImageScalerBias size (info.GetAttrs("bias", bias_).IsOK()C:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\contrib_ops\cpu\image_scaler.hDynamicSliceAttribute border needs to be specified with four border elements, got ) + bottomBorder ( input dimensions insteadInput's height () needs to be greater than or equal to the topBorder () needs to be greater than or equal to the leftBorder () + rightBorder () + scale_[0] (Input's width (onnxruntime::contrib::Crop::Compute) + scale_[1] (C:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\contrib_ops\cpu\crop.honnxruntime::contrib::BiasGelu::Computeuse_approximationC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\contrib_ops\cpu\bert\bias_gelu.cconnxruntime::contrib::BiasGelu::ComputeThe first input of CDist kernel has wrong shape: Input shape dimensions mismatch:The second input of CDist kernel has wrong shape: metricCDistonnxruntime::contrib::CDist::CDistinfo.GetAttr("metric", &metric).IsOK()sqeuclideanC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\contrib_ops\cpu\cdist.honnxruntime::contrib::CDist::CDisteuclideanCropAndResizeonnxruntime::contrib::CropAndResize::CropAndResize specified. It should be either bilinear or nearestC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\contrib_ops/cpu/crop_and_resize.hNumber of dimensions for crop size should be exactly 1Null crop_size_ptrInput tensor to Unique op should be 1Donnxruntime::contrib::MaxpoolWithMask::ComputeMaxpoolWithMaskC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\contrib_ops\cpu\maxpool_with_mask.hInvalid assumption of output element sizeC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\contrib_ops\cpu\murmur_hash3.ccinput_num_bytes % 4 == 0sizeof(uint32_t) == output_element_bytesMurmurHash3positiveonnxruntime::contrib::MurmurHash3::Computekeysconv_window_sizeembedding_sizechar_embedding_sizeonnxruntime::contrib::WordConvEmbedding::ComputeChar embedding size does not match conv kernal size 2.WordConvEmbeddingC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\contrib_ops\cpu\word_conv_embedding.cc embedding_size attribute: conv filter size: conv kernal size 1: Conv filter size does not match embedding_size attribute.Conv kernal size 1 does not match conv_window_size attribute . conv_window_size attribute: char_embedding_size attribute: Char embedding size: Conv kernal size 2 : Char embedding size does not match char_embedding_size attribute.Tokenizerattribute mark is not setmarkC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\contrib_ops\cpu\tokenizer.cconnxruntime::contrib::Tokenizer::Tokenizerattribute pad_value is not setpad_valueattribute mincharnum is not setmincharnummincharnum_ > 0attribute mincharnum must have a positive valuetokenexpseparatorsExpecting a non-empty tokenexpEither one of the separators OR tokenexp attributes required but none is setseparators must not be empty!tokenexp.empty()mincharnum is too big for char level tokenezation!separators.empty()Can not digest separators: !char_tokenezation_ || mincharnum_ < 2Input string contains invalid utf8 chars: Can not digest tokenexp: tensor(string) expected as inputMatch contains invalid utf8 chars: Input dimensions are either [C] or [N][C] allowedonnxruntime::contrib::DeepCpuAttnLstmOp::DeepCpuAttnLstmOpstatic_cast(activation_func_names.size()) == num_directions_ * 3C:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\contrib_ops\cpu\attnlstm\deep_cpu_attn_lstm.honnxruntime::contrib::DeepCpuAttnLstmOp::ComputeAttnLSTM}, actural: C:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\contrib_ops\cpu\attnlstm\deep_cpu_attn_lstm.cconnxruntime::contrib::DeepCpuAttnLstmOp::ComputeImplAttention mechanism memory sequence lengths must have shape {Attention mechanism memory shape error! Expected: {], while found!, am_attn_size}, Got:Attention mechanism memory sequence lengths value must in (0, }, Got: Attention memory layer weight shape error! Expected:{}. Got: Attention query layer weight shape error! Expected:{, aw_attn_size}. Got:Attention v weight shape error! Expected:{onnxruntime::contrib::DeepCpuAttnLstmOp::ValidateInputsAttention layer weight shape error! Expected: {onnxruntime::contrib::FusedGemm::FusedGemmC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\contrib_ops\cpu\fused_gemm.ccGetFusedActivationAttr(info, activation_).IsOK()onnxruntime::contrib::FusedConvFloat::FusedConvFloatC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\contrib_ops\cpu\fused_conv.ccAxis must be within range [axis <= X_NumDims && axis >= -X_NumDimsC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\contrib_ops\cpu\expand_dims.h Axis is ExpandDimsaxis_tensor->Shape().IsScalar()onnxruntime::contrib::ExpandDims::ComputeAn axis tensor must be a scalar tensor.C:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\contrib_ops\cpu\bert\embed_layer_norm.cconnxruntime::contrib::EmbedLayerNorm::Computeonnxruntime::contrib::EmbedLayerNorm::EmbedLayerNorminput index out of rangeinfo.GetAttr("num_heads", &num_heads).IsOK() && num_heads > 0Expect to have present state output when past state input is givenonnxruntime::contrib::AttentionBase::GetPresentInputs 'mask_index' with raw attention mask shall have shape batch_size x (past_sequence_length + sequence_length)Input 'mask_index' is expected to have 1 or 2 dimensions, got onnxruntime::contrib::AttentionCPUBase::ApplyAttentionC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\contrib_ops\cpu\bert\attention_cpu_base.honnxruntime::contrib::Attention::ComputeInput 'input' is expected to have 3 dimensions, got Input 0 dimension 2 should be divisiable by value of the num_heads attribute.onnxruntime::contrib::AttentionBase::AttentionBaseC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\contrib_ops\cpu\bert\attention.ccInput 'weights' dimension 1 should be 3 times of dimension 0Input 'bias' is expected to have 1 dimension, got Input 'weights' is expected to have 2 dimensions, got Input 1 dimension 0 should have same length as dimension 2 of input 0Input 'past' is expected to have 5 dimension, got Inputs 'past' dimension 0 shall have length of 2Input 'bias' dimension 0 should have same length as dimension 1 of input 'weights'Input 'past' is only allowed for unidirectionalInputs 'past' dimension 2 shall have length of Inputs 'mask_index' dimension 0 shall have length of batch_size or 2 * batch_sizeInputs 'past' dimension 1 shall have same length as dimension 0 of input 0Inputs 'past' dimension 2 shall have length of num_headsSampleOponnxruntime::contrib::MatMulIntegerToFloatBase::ComputeCommonIsScalarOr1ElementVector(b_scale_tensor)onnxruntime::contrib::DynamicQuantizeMatMul::ComputeC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\contrib_ops\cpu\quantization\dynamic_quantize_matmul.ccDynamicQuantizeMatMul : input B scale must be a scalar or 1D tensor of size 1. Per-Channel is not supported yet.MatMulIntegerToFloat : input A scale must be a scalar or 1D tensor of size 1. Per-Channel is not supported yet.IsScalarOr1ElementVector(a_scale_tensor)DynamicQuantizeMatMul : input B zero point must be a scalar or 1D tensor of size 1. Per-Channel is not supported yet.IsScalarOr1ElementVector(b_zero_point_tensor)MatMulIntegerToFloat : input A zero point must be a scalar or 1D tensor of size 1. Per-Channel is not supported yet.IsScalarOr1ElementVector(a_zero_point_tensor)onnxruntime::contrib::MatMulIntegerToFloat::ComputeMatMulIntegerToFloat : input B scale must be a scalar or 1D tensor of size 1. Per-Channel is not supported yet.MatMulIntegerToFloat : input B zero point must be a scalar or 1D tensor of size 1. Per-Channel is not supported yet.weight zero point must be a scalar or 1D tensor of size 1.Not satisfied: IsScalarOr1ElementVector(w_zp_tensor) QAttentioninput scale must be a scalar or 1D tensor of size 1Not satisfied: IsScalarOr1ElementVector(input_scale_tensor) onnxruntime::contrib::QAttention::ComputeC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\contrib_ops\cpu\quantization\attention_quant.ccinput zero point must be a scalar or 1D tensor of size 1.Not satisfied: IsScalarOr1ElementVector(i_zp_tensor) weight must be a scalar or 1D tensor of size 1Not satisfied: IsScalarOr1ElementVector(weight_scale_tensor) QLinearMulQLinearAddonnxruntime::contrib::`anonymous-namespace'::QLinearImplC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\contrib_ops\cpu\qlinear_binary_op.ccMatmulInteger : input1 A_scale must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(tensor_a_scale)MatmulInteger : input1 B_scale must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(tensor_b_scale)MatmulInteger : input1 A_zero_point must be a scalar or 1D tensor of size 1 if giventensor_a_zero_point == nullptr || IsScalarOr1ElementVector(tensor_a_zero_point)MatmulInteger : input1 C_scale must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(tensor_c_scale)MatmulInteger : input1 B_zero_point must be a scalar or 1D tensor of size 1 if giventensor_b_zero_point == nullptr || IsScalarOr1ElementVector(tensor_b_zero_point)MatmulInteger : input1 C_zero_point must be a scalar or 1D tensor of size 1 if giventensor_c_zero_point == nullptr || IsScalarOr1ElementVector(tensor_c_zero_point)onnxruntime::contrib::QlinearBuildLookupTableC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\contrib_ops\cpu\qlinear_lookup_table.ccQLinearLeakyRelu : input X_scale must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(tensor_x_scale)QLinearLeakyRelu : input Y_scale must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(tensor_y_scale)QLinearLeakyRelu : input X_zero_point must be a scalar or 1D tensor of size 1tensor_x_zero_point == nullptr || IsScalarOr1ElementVector(tensor_x_zero_point)QLinearLeakyRelu : input Y_zero_point must be a scalar or 1D tensor of size 1tensor_y_zero_point == nullptr || IsScalarOr1ElementVector(tensor_y_zero_point)QLinearSigmoidQLinearLeakyReluonnxruntime::contrib::MatMulInteger16::ComputeC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\contrib_ops\cpu\matmul_integer16.ccMatMulInteger16A != nullptr && B != nullptrInput 1 dimension 0 should have same length as the last dimension of input 0Input 0 is expected to have 1 or more dimensions, got Input 1 is expected to have 1 dimensions, got [seqno= row[not support normalize yet.!normalize_ is not in (0, Real memory steps onnxruntime::contrib::BahdanauAttention::BahdanauAttentionC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\contrib_ops\cpu\attnlstm\bahdanau_attention.ccmem_steps <= max_memory_steps_ && mem_steps > 0onnxruntime::contrib::BahdanauAttention::PrepareMemoryactivation_params count mismatchunimplemented activation: Input 0 and 7 (mask) shall have same shapeinput_ids is expected to have 2 dimensions, got Input 0 and 1 shall have same shapesegment_embedding is expected to have 2 dimensions, got word_embedding and segment_embedding shall have same dimension 1word_embedding is expected to have 2 dimensions, got position_embedding is expected to have 2 dimensions, got beta is expected to have size of gamma is expected to have 1 dimensions, got word_embedding and position_embedding shall have same dimension 1beta is expected to have 1 dimensions, got gamma is expected to have size of Received invalid value for allow_spinning. Valid values are 0 or 1Received null OrtThreadingOptionsTensor types should have been handled alreadyOrtValue is TensorSequence type but has no element Tensor DataType. is not supportedCudaPinnedCuda DeviceId: OrtAllocatorType:Device:[DeviceType: MemoryType:OrtMemoryInfo:[name: id: OrtMemType:C:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\framework\allocator.cconnxruntime::IAllocator::CalcMemSizeForArrayWithAlignment::::operator ()Specified device is not supported.Argument is not a tensorC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\framework\tensor_type_and_shape.ccOrtApis::GetTensorTypeAndShapetype != nullptrOrtValue is not a TensorNot implementedtype_proto is not of type sequence!type_proto is not of type map!shape.Size() must >=0C:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\framework\tensor.cconnxruntime::Tensor::Tensorp_type != nullptronnxruntime::Tensor::Initonnxruntime::Tensor::SizeInBytestensor size overflowtensor failed memory size calculationdtype_ != nullptrTensor is expected to contain one of the primitive data types. Got: onnxruntime::SparseTensorTypeBase::GetElementTypeonnxruntime::TensorTypeBase::GetElementTypeonnxruntime::SequenceTensorTypeBase::GetElementTypeproto != nullptrOnly ONNX MLDataType can be registeredC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\framework\data_types.cconnxruntime::data_types_internal::IsCompatibleonnxruntime::TensorTypeBase::IsCompatiblethisProto->value_case() == TypeProto::ValueCase::kTensorTypeWe do not expect duplicate registration of types for: onnxruntime::data_types_internal::DataTypeRegistry::RegisterDataTypeMLDataType for: is not currently registered or supportedtensor type value_proto != nullptr expected to be a registered ONNX typeonnxruntime::utils::ContainerChecker::ContainerCheckerInvalid DataTypeImpl TypeProto definitiononnxruntime::data_types_internal::SetMapTypes,class std::allocator >,float>::Setonnxruntime::data_types_internal::SetMapTypes,class std::allocator >,__int64>::SetC:\apilot\agent\_work\1\s\engine\lotus\include\onnxruntime\core/framework/data_types.honnxruntime::data_types_internal::SetMapTypes,class std::allocator >,class std::basic_string,class std::allocator > >::Setonnxruntime::data_types_internal::SetMapTypes<__int64,float>::Setonnxruntime::data_types_internal::SetMapTypes<__int64,__int64>::Setonnxruntime::data_types_internal::SetMapTypes<__int64,class std::basic_string,class std::allocator > >::Setonnxruntime::data_types_internal::SetMapTypes,class std::allocator >,double>::Setutils::HasElemType(thisProto->sparse_tensor_type())onnxruntime::SparseTensorTypeBase::IsCompatiblethisProto->value_case() == TypeProto::ValueCase::kSparseTensorTypeutils::HasElemType(thisProto->tensor_type())thisProto->value_case() == TypeProto::ValueCase::kMapTypeutils::HasElemType(thisProto->sequence_type())onnxruntime::SequenceTensorTypeBase::IsCompatiblethisProto->value_case() == TypeProto::ValueCase::kSequenceTypeonnxruntime::NonTensorTypeBase::IsSequenceCompatibleutils::HasKeyType(thisProto->map_type())onnxruntime::NonTensorTypeBase::IsMapCompatible(null)onnxruntime::NonTensorTypeBase::ToDataContaineronnxruntime::NonTensorTypeBase::FromDataContaineronnxruntime::data_types_internal::SetSequenceType,class std::allocator > > >::Setonnxruntime::data_types_internal::SetSequenceType,class std::allocator >,float,struct std::less,class std::allocator > >,class std::allocator,class std::allocator > const ,float> > > >::Setonnxruntime::data_types_internal::SetSequenceType::Setonnxruntime::data_types_internal::SetSequenceType::Setelem_proto != nullptronnxruntime::data_types_internal::SetMapTypes<__int64,double>::Setonnxruntime::data_types_internal::SetSequenceType::Setonnxruntime::data_types_internal::SetSequenceType::Setonnxruntime::data_types_internal::SetSequenceType::Setonnxruntime::data_types_internal::SetSequenceType::Setonnxruntime::data_types_internal::SetSequenceType::Setonnxruntime::data_types_internal::SetSequenceType<__int64>::Setonnxruntime::data_types_internal::SetSequenceType::Setonnxruntime::data_types_internal::SetSequenceType::Setonnxruntime::data_types_internal::SetSequenceType::Setonnxruntime::data_types_internal::SetSequenceType::Setonnxruntime::data_types_internal::SetSequenceType,class std::allocator > >::Setonnxruntime::data_types_internal::SetSequenceType::Set for SizeFromDimension. Tensor has dimensions.C:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\framework\tensor_shape.cconnxruntime::TensorShape::SizeToDimensiondimension <= num_dimsInvalid dimension of onnxruntime::TensorShape::Slicedimstart <= dimend && dimend <= size()Invalid tensor shape slice argument.onnxruntime::TensorShape::SizeFromDimension}{An OrtValue for this name has already been added.Buffer containing the initializer must be owned by the user.Received OrtValue is not a tensor. Only tensors are supported.Received nullptr for OrtValue.] already exists with value []. It will be overwrittenConfig value is longer than maximum length 1024Config key is empty or longer than maximum length 128Received nullptr for name.C:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\framework\session_options.cconnxruntime::SessionOptions::AddConfigEntrySession Config with key [RknpuExecutionProvideronnxruntime::utils::CopyInputsAcrossDevicescopy_info.size() == num_feedsonnxruntime::utils::FinalizeCopyInfoForFetchesfetch_alloc_info.size() == copy_info.size()onnxruntime::utils::ExecuteGraphImplOnly one thread was configured for parallel execution. Hence will use sequential execution.onnxruntime::utils::CopyOutputsAcrossDevicesonnxruntime::utils::CopyOneInputAcrossDevicesonnxruntime::utils::ExecuteGraphonnxruntime::utils::BatchOrCopyMLValueallocator != nullptrFailed to find allocator for device invalid allocatoronnxruntime::utils::CalculateStaticCopyInfoForFeedexec_plan_ptronnxruntime::utils::FindMemoryInfoForValueC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\framework\utils.cconnxruntime::utils::FinalizeCopyInfoForFeedsfeed_locations.size() == copy_info.size()onnxruntime::utils::InitializeFeedFetchCopyInfoonnxruntime::utils::CalculateStaticCopyInfoForFeedsonnxruntime::OpNodeProtoHelper::GetAttrsvalues.size() == static_cast(attr->floats_size())No attribute with this name is defined.Attibute name and type don't matchvalues.size() == static_cast(attr->ints_size())onnxruntime::OpNodeProtoHelper::GetAttrsC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\framework\op_node_proto_helper.ccC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\framework\op_kernel_info.cconnxruntime::OpKernelInfo::GetMemoryInfocannot find allocatornode_offsets_index < node_offsets_size_C:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core/framework/node_index_info.honnxruntime::NodeIndexInfo::GetNodeOffsetonnxruntime::OpKernelContext::NumVariadicInputsarg_num < arg_counts.size()Invalid arg_num of . Num args is onnxruntime::OpKernelContext::GetOrCreateOutputMLValueTempSpace allocator not foundonnxruntime::OpKernelContext::OpKernelContextframe != nullptrExecution frame was nullonnxruntime::OpKernelContext::OutputMLValuekernel != nullptrOpKernel was nullC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\framework\op_kernel.ccC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\framework\allocatormgr.cconnxruntime::CreateAllocatorReceived invalid value of arena_extend_strategy onnxruntime_providers_tensorrt.dllonnxruntime_providers_dnnl.dllsession.disable_prepackingCould not find OrtValue with idx 'entry != kernel_create_info_map_.cend()onnxruntime::SessionState::PopulateKernelCreateInfoDone saving OrtValue mappings.onnxruntime::SessionState::PrepackInitializedConstantTensorsduplicated ort_value index:. Do you have duplicated calls to SessionState::AddInitializedTensor function?onnxruntime::SessionState::GetNodeKernelCreateInfoFailed to find input name in the mapping: Using an input in multiple nodes on different devices is not supported currently. Input: is used by node ) and node onnxruntime::SessionState::AddOutputNameToNodeInfoMappingoutput_names_to_nodeinfo.empty()Only one node should produce an output. Existing entry for onnxruntime::SessionState::SetupAllocatorsAllocator already registered for . Ignoring allocator from onnxruntime::SessionState::CreateGraphInfoSaveMLValueNameIndexMappingC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\framework\session_state.ccSatisfied, but should not be: nullptr == kernel_def_hashes Kernel create info hashes are null. Invalid ORT format model.Satisfied, but should not be: nullptr == node_indices Kernel create info node indices are null. Invalid ORT format model.Can't find node with index . Invalid ORT format model.Not satisfied: node_indices->size() == kernel_def_hashes->size() Size mismatch for kernel create info node indexes and hashes. Invalid ORT format model. is missing. Invalid ORT format model.Satisfied, but should not be: nullptr == fbs_sub_graph_session_states SessionState for subgraphs is null. Invalid ORT format model.Satisfied, but should not be: node == nullptr Subgraph SessionState for is null. Invalid ORT format model.Satisfied, but should not be: nullptr == fbs_sub_graph_ss Subgraph SessionState entry for onnxruntime::SessionState::AddSubgraphSessionStateexisting_entries.find(attribute_name) == existing_entries.cend()Entry exists in node for attribute onnxruntime::SessionState::UpdateToBeExecutedNodesonnxruntime::SessionState::GetNodeIndexInfonode_index_info_SetGraphAndCreateKernels must be called prior to GetExecutionInfo.subgraphMain Graph instance should have populated all subgraphs when being resolved.onnxruntime::SessionState::SaveToOrtFormatonnxruntime::GetSubGraphSessionStatesOrtFormatSatisfied, but should not be: nullptr == fbs_kcis onnxruntime::SessionState::LoadFromOrtFormatKernel create info is null. Invalid ORT format model.onnxruntime::SessionState::CreateSubgraphSessionState Attribute:onnxruntime::SessionState::FinalizeSessionStateImplonnxruntime::SessionState::FinalizeSessionStateSatisfied, but should not be: nullptr == fbs_sub_session_state entry != node_to_subgraph_ss.second.cend()Missing session state for subgraph. Node:'' OpType: Index:p_op_kernel). found duplicated provider in KernelRegistryManagerFailed to find kernel for The node is not placed on any Execution Provider. (node corrupted protobuf data: tensor shape size() does not match the data size() in protoUnsupported type: Unsupported sparse tensor data type of UnpackTensor: the pre-allocated size does not match the raw data size, expected onnxruntime::utils::CopySparseDatarank == dims.size() && rank > 0Invalid TensorProto`anonymous-namespace'::UnpackTensorWithRawDataonnxruntime::utils::ReadLittleEndiansource and destination buffer size mismatchInvalid SparseTensor indices. Should be rank 0 or 1. Got:cur_index == &*indices_data.cend()C:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\framework\tensorprotoutils.cconnxruntime::utils::GetFileContentdata overflowUnpackTensor: the pre-allocate size does not match the size in protoThe buffer planner is not consistent with tensor buffer size, expected tensor can't contain negative dimsonnxruntime::utils::TensorProtoToMLValuestring tensor can not have raw dataInitialized tensor with unexpected type: initialize preallocated buffer failedonnxruntime::utils::SparseTensorProtoToDenseTensorProtoUnsupported attribute value type of in 'Constant' node 'onnxruntime::utils::ConstantNodeProtoToTensorProtoNot satisfied: source_bytes.size_bytes() == destination.size_bytes() C:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core/framework/endian_utils.hC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\framework\mldata_type_utils.cconnxruntime::utils::GetMLDataTypenullptr != type_protoC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\framework\execution_provider.ccduplicated allocatoronnxruntime::IExecutionProvider::InsertAllocatorinput_copy_needed != DeviceCopyCheck::Unknown && output_copy_needed != DeviceCopyCheck::Unknownonnxruntime::FeedsFetchesManager::SetDeviceCopyChecksonnxruntime::FeedsFetchesInfo::MapNamesToMLValueIdxsC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\framework\feeds_fetches_manager.ccError mapping feeds: Error mapping output names: 1 == capability->nodes.size()onnxruntime::PlaceNodeC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\framework\graph_partitioner.cconnxruntime::FunctionKernel::FunctionKernelC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core/framework/func_kernel.hcreate_func(&context, &func_state_) == 0No provider specified.onnxruntime::GraphPartitioner::PartitionProvider doesn't return correct number of compiled functionsnode_compute_funcs.size() == nodes_need_compile.size()data_transfer registered is nullptr.Tensor size mismatch to There's no data transfer registered for copying tensors from onnxruntime::DataTransferManager::CopyTensorsC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\framework\data_transfer_manager.ccCaught exception while loading custom ops with message: has already been loaded.A dso with name onnxruntime::ExLibLoader::LoadExternalLibC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\framework\ex_lib_loader.ccUnloading DSO onnxruntime::ExLibLoader::~ExLibLoaderFailed to unload DSO: Caught exception while destructing CustomOpsLoader with message: onnxruntime::ExLibLoader::{dtor}::::operator () kernel start version: node_version: Version mismatch. and type ( kernel_end_version: in the supported version rangeFound kernel for Op with name (), but the node in the model has the following type (Op with name ( This op has been implemented only for the following types ( However the types are incompatible. (node_version: onnxruntime::`anonymous-namespace'::TraverseFormalParametersWithTypeProto Encountered following errors: ( kernel is not supported in Kernel not foundkernel def can't be NULLonnxruntime::KernelRegistry::TryCreateKernelC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\framework\kernel_registry.cc No matching hash for kernel not found in len <= op_schema.inputs().size(): Conflicting with a registered kernel with op versions.Failed to add kernel for onnxruntime::CPUDataTransfer::CopyTensoronnxruntime::IDataTransfer::CopyTensorsC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\framework\data_transfer.ccsrc.SizeInBytes() == dst.SizeInBytes()ort_value_index >= 0 && static_cast(ort_value_index) < all_values_size_onnxruntime::IExecutionFrame::GetMLValueC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core/framework/execution_frame.hnode_index_info and ort_value_idx_map are out of sync and cannot be usednode_index_info_.GetMaxMLValueIdx() == ort_value_idx_map.MaxIdx()onnxruntime::IExecutionFrame::IExecutionFrameC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\framework\execution_frame.ccoffset >= 0 && static_cast(offset) < node_values_size_onnxruntime::NodeIndexInfo::GetMLValueIndexTensor shape cannot contain any negative valueTensor shape is too largeort_value.Fence() == nullptronnxruntime::ExecutionFrame::AllocateMLValueTensorSelfOwnBufferHelperMemory type static_activation_memory_sizes_in_byte_.find(location.name) == static_activation_memory_sizes_in_byte_.end() returned nullptrTrying to allocate memory for unused optional inputs/outputs. Validate usage of dim_value (values should be > 0) and dim_param (all values with the same string should equate to the same size) in shapes in the model.Shape mismatch attempting to re-use buffer. onnxruntime::ExecutionFrame::AllocateMLValueTensorPreAllocateBuffermlvalue.Fence() == nullptr, fall back to default allocation behavior but the actually size is: , block in memory pattern size is: For ort_value with index: invalid index feeds.size() == feed_mlvalue_idxs.size()onnxruntime::IExecutionFrame::Initfetches.empty() || fetches.size() == fetch_mlvalue_idxs_.size() Requested shape:OrtValue shape verification failed. Current shape:shape && tensor.Shape() == *shapeonnxruntime::IExecutionFrame::GetOrCreateNodeOutputMLValueonnxruntime::ExecutionFrame::{ctor}::::operator ()buffers_.find(location) == buffers_.end()onnxruntime::ExecutionFrame::ExecutionFrame should only appear once. entries which doesn't match the number of fetches the frame was initialized with of Fetches vector passed to GetOutputs contains failed. Error:Allocation of memory pattern buffer for Allocation of tensor types requires a shape.Invalid allocation kind: onnxruntime::ExecutionFrame::ReleaseMLValueImplort_value_idx >= 0 && static_cast(ort_value_idx) < alloc_plan.size()onnxruntime::AllocateSparseTensorort_value_index >= 0 && static_cast(ort_value_index) < alloc_plan.size()onnxruntime::ExecutionFrame::AllocateAsPerAllocationPlanWe don't expect custom allocators for non-tensor types, so a shape is mandatory here.onnxruntime::ExecutionFrame::TraceAllocateonnxruntime::ExecutionFrame::TraceFreeTraceFree for ort_value_idx=Memory pattern planner is not enabled on this execution framework.onnxruntime::ExecutionFrame::GetAllocationPlan failed: size=TraceAllocation for ort_value_idx=model format error! Need a key for the external data infolengthchecksummodel format error!model format error! Missing 'location'model format error! Need a value for the external data infolocation failedparsing onnxruntime::NodeIndexInfo::Init::::operator ()C:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\framework\node_index_info.cconnxruntime::NodeIndexInfo::Init::::operator ()dim0_offset < dim0_sizeonnxruntime::OrtValueTensorSlicer::Iterator::Iteratoronnxruntime::OrtValueTensorSlicer::Createonnxruntime::OrtValueTensorSlicer::Iterator::IteratorInsufficient dimensions to slice on gsl::narrow_cast(tensor_shape.NumDimensions()) >= slice_dimension. Dimension 0 is Invalid dim0_offset of C:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\framework\ort_value_tensor_slicer.ccOrtValue has not been allocated so can't be sliced.ort_value.IsAllocated(). Shape:Can't slice a non-tensor OrtValue. Type was ort_value.IsTensor()onnxruntime::OrtValueTensorSlicer::Create does not.All implicit inputs should have OrtValue instances by now. entry != nullptronnxruntime::OpKernelContextInternal::OpKernelContextInternalParallelExecutor::ExecuteBegin executionC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core/framework/op_kernel_context_internal.hMultiple errors were found.onnxruntime::ParallelExecutor::ExecuteC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\framework\parallel_executor.cc' Status Message: node. Name:'Non-zero status code returned while running onnxruntime::ParallelExecutor::RunNodeAsyncExiting due to terminate flag being set to true.Got nullptr from GetKernel for node: _fence_beforeException running nodes starting at _kernel_time_fence_afterUnknown exception was caught by catch-all handler.'. SequentialExecutor::Execute bytes for [Memory] ExecutionFrame statically allocates activation_sizeparameter_sizeoutput_size[Memory] ExecutionFrame dynamically allocates onnxruntime::ReleaseNodeMLValuesC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\framework\sequential_executor.ccgraph_indexexec_plan_indexonnxruntime::SequentialExecutor::Executefunc info for node: Can't use func with null ptr not found.onnxruntime::FuncManager::GetFuncs already exist.C:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\framework\fuse_nodes_funcs.cc0 == memory_size % kMinAllocationSizeonnxruntime::BFCArena::AllocationRegion::AllocationRegionC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core/framework/bfc_arena.hcudaMallocIncorrect arena extend strategy.onnxruntime::BFCArena::Extend::::operator () is smaller than requested bytes of BinForSize(bin_size * 2 - 1) == BinFromIndex(b)BinForSize(bin_size * 2) != BinFromIndex(b)h < chunks_.size()onnxruntime::BFCArena::ChunkFromHandleonnxruntime::BFCArena::ExtendTotal allocated bytes: Allocated memory at Available memory of Failed to allocate memory for requested buffer of size bytes.Extended allocation by onnxruntime::BFCArena::RegionManager::RegionFor arena_extend_strategy memory limit: max_dead_bytes_per_chunk: p_int >= base_intonnxruntime::BFCArena::AllocationRegion::IndexForp_int < base_int + memory_size_Could not find Region for bins of max chunk size Creating BinForSize(bin_size) == BinFromIndex(b)BinForSize(bin_size + 255) == BinFromIndex(b) with following configs: initial_chunk_size_bytes: Creating BFCArena for onnxruntime::BFCArena::BFCArenaC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\framework\bfc_arena.cconnxruntime::BFCArena::Mergec2->prev == h1onnxruntime::BFCArena::InsertFreeChunkIntoBin!c->in_use() && (c->bin_num != kInvalidBinNum)!c->in_use() && (c->bin_num == kInvalidBinNum)onnxruntime::BFCArena::SplitChunkonnxruntime::BFCArena::DeallocateRawInternal!c1->in_use() && !c2->in_use()c->in_use() && (c->bin_num == kInvalidBinNum)onnxruntime::BFCArena::FreeAndMaybeCoalesceonnxruntime::BFCArena::RemoveFreeChunkIterFromBinonnxruntime::BFCArena::RemoveFreeChunkFromBinCould not find chunk in binBinFromIndex(c->bin_num)->free_chunks.erase(h) > 0tried to allocate 0 bytesonnxruntime::BFCArena::AllocateRawInternalreserved_chunks_.find(ptr) == reserved_chunks_.end()onnxruntime::BFCArena::Reserveh != kInvalidChunkHandle!chunk->in_use()onnxruntime::BFCArena::FindChunkPtr rounded_bytes:. bin_num:Extending BFCArena for Failed to find a free memory block despite calling Extend. rounded_bytes=. string tensor is not supported for copying between allocatorsonnxruntime::session_state_utils::DeserializeTensorProto ) is different from what is supplied () because the ORT planned memory location device Cannot use user supplied initializer with name: (onnxruntime::session_state_utils::SaveInitializedTensors::::operator ()C:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\framework\session_state_utils.cc, Got Internal error. The preallocated buffer is too small. Requires Failed to copy tensor to SubgraphGraph[Memory] SessionStateInitializer statically allocates Using user supplied initializer with name ( failed.Deserialize tensor Saving initialized tensors.onnxruntime::session_state_utils::SaveInitializedTensorsOrtValue indexes should have been populated.ort_value_name_idx_map.MaxIdx() > -1onnxruntime::session_state_utils::SaveInputOutputNamesToNodeMapping::::operator ()onnxruntime::session_state_utils::SaveInputOutputNamesToNodeMapping is not used by any node. input with name Done saving initialized tensorsonnxruntime::session_state_utils::SaveInputOutputNamesToNodeMapping::::operator ()' is not foundWeight buffer for initializer '' failedGet preallocated buffer for initializer 'C:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\framework\tensor_allocator_with_mem_pattern.hInternal error. is not foundMem pattern for initializer onnxruntime::TensorAllocatorWithMemPattern::TraceFailed to get allocator for location: duplicated locationonnxruntime::TensorAllocatorWithMemPattern::FinalizePlan : ) p_kernel_defonnxruntime::PlannerImpl::ComputeUseCountsCan not find the execution provider onnxruntime::PlannerImpl::GetLocationForNodeInputnullptr != tensor_type_baseonnxruntime::PlannerImpl::GetElementSizeCan not find the node Should not have entry in kernel create info with nullptr for kernel_defonnxruntime::PlannerImpl::GeneratePlanForWeightsonnxruntime::PlannerImpl::ComputeReusePlanonnxruntime::PlannerImpl::CreatePlanentry != kernel_create_info_map.cend()onnxruntime::GetKernelCreateInfoonnxruntime::PlannerImpl::Indexn >= 0 && static_cast(n) < ort_value_info_.size()C:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\framework\allocation_planner.ccSessionState should have saved the KernelCreateInfo prior to this running. NodeIndex:id >= 0 && static_cast(id) < ort_value_info_.size()onnxruntime::PlannerImpl::ProcessDefreused != reused_foronnxruntime::PlannerImpl::Reuseonnxruntime::PlannerImpl::UseCountonnxruntime::PlannerImpl::Buffern >= 0 && static_cast(n) < plan_.allocation_plan.size()onnxruntime::PlannerImpl::AllocPlanDivide by zeroSafeIntExceptionHandler::SafeIntOnDivZeroinvalid ort_value_index:onnxruntime::SimpleTensorAllocator::GetPreallocatedBufferC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\framework\simple_tensor_allocator.cc', location: Failed to get allocator for initializer '[ShapeInferenceError] Can't merge shape info. Both source and target dimension have values but they differ. Source=Mismatch between number of source and target dimensions. Source= Dimension= Target=onnxruntime::model_load_utils::IsAllowReleasedONNXOpsetsOnlySetC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core/graph/model_load_utils.h target:' source:Error merging shape info for output. 'onnxruntime::MergeShapeInfoOutput:. Falling back to lenient merge.ConstantNoOpALLOW_RELEASED_ONNX_OPSET_ONLY are '0' and '1'. The environment variable contained the value: onnxruntime::Node::LoadFromOrtFormatfbs_attr cannot be nullSatisfied, but should not be: nullptr == fbs_attr Serialization error. Graph attribute was serialized without Graph instanceSatisfied, but should not be: nullptr == node_arg_name ], could not find NodeArg LoadNodeArgsFromOrtFormat: Node [Satisfied, but should not be: nullptr == node_arg onnxruntime::Node::LoadEdgesFromOrtFormat::::operator ()Satisfied, but should not be: nullptr == fbs_edge is not the same as this node's index:input index: Not satisfied: subgraph Node::LoadFromOrtFormat, input_arg_counts is missingSatisfied, but should not be: nullptr == fbs_input_arg_counts Node::LoadEdgesFromOrtFormat, edge is missing for onnxruntime::NodeArg::UpdateTypeAndShapeSparseTensor element type mismatch. ] op_type [Node [C:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\graph\graph.cc Input=Type mismatch. Current=Tensor element type mismatch. fbs_node_arg_names cannot be nullonnxruntime::Node::LoadFromOrtFormat::::operator ()Satisfied, but should not be: nullptr == fbs_node_arg_names node_arg_name cannot be nullSerialization of fused function body is not currently supported, does not have the graph for key onnxruntime::Node::SaveToOrtFormatNot satisfied: it != attr_to_subgraph_map_.cend() ) does not exist in the graph.This is an invalid model. Graph output (This is an invalid model. Error: two nodes with same node name (This is an invalid model. Error: Duplicate definition of name (graph_inputs_excluding_initializers_.empty() && graph_inputs_including_initializers_.empty() && value_info_.empty() && graph_outputs_.empty()onnxruntime::Graph::InitializeStateFromModelFileGraphProtoGraph ctor should have created NodeArg for initializer. Missing:node_argInvalid destination node arg slot specified when adding edge.Argument type mismatch when adding edge.Invalid node indexes specified when removing edge.onnxruntime::Graph::RemoveEdgeonnxruntime::Graph::SetOuterScopeNodeArgsInvalid node indexes specified when adding edge.onnxruntime::Graph::AddEdgeInvalid source node arg slot specified when adding edge.This is an invalid model. The sum of input arg count is not equal to size of input defs in node (graph_proto cannot be nullgraph_proto != nullptronnxruntime::Graph::Graphonnxruntime::Node::LoadEdgesFromOrtFormatSatisfied, but should not be: fbs_node_edges.node_index() != index_ input edgesoutput edgesThis may prevent some of the graph optimizations, like const folding. appears in graph inputs and will not be treated as constant value/weight. Initializer Graph state to be loaded into must be empty.This is an invalid model. Tensor does not have type information.or with the tool onnxruntime/tools/python/remove_initializer_from_input.py.by either re-generating the model with latest exporter/converter Move it out of graph inputs if there is no need to override it, UpdateTypeShapeInference is not intended to be used with control flow nodes containing subgraphsnode.GetAttributeNameToMutableSubgraphMap().empty()onnxruntime::Graph::UpdateShapeInference) Op (Size mismatch validating subgraph inputs. Got Node:Subgraph input missing type.onnxruntime::Graph::InferAndVerifySubgraphTypes) is invalid.) in node () of operator (' of input parameter (Node () does not have type information set by parent node.) input arg (This is an invalid model. Node ( Graph may not conform to the ONNX spec and contain initializers that are not graph inputs.This is an invalid model. At top level graph without matching NodeArg that subgraph consumes. Name=This is an invalid model. Failed to find NodeArg in all parent graphs. Name=Some nodes are not included in the topological sort, graph have a cycle.Invalid source node arg slot specified when removing edge.Invalid destination node arg slot specified when removing edge.Argument mismatch when removing edge.onnxruntime::Graph::BuildConnectionsNo Graph instance was found for attribute inputs. Either provide all subgraph inputs, or just the required inputs. inputs and requires inputs but subgraph has onnxruntime::Graph::KahnsTopologicalSortThis is an invalid model. Error: the graph is not acyclic.Graph attribute inferencing failed: in node is not a registered function/opFatal error: ) is required but not specified.) attribute ( does not match. Type Error: Shape of initializer This is an invalid model. Error in Node:onnxruntime::Graph::VerifyNodeAndOpMatchonnxruntime::Graph::ResolveShouldn't be possible to have NodeArgs that haven't been handled already.outer_scope_node_args_consumed.empty() but different TensorProto.Error: Duplicate definition-site for (onnxruntime::Graph::InitInputsInitializersOutputsonnxruntime::Graph::PerformTypeAndShapeInferencingonnxruntime::Graph::ForThisAndAllSubgraphsonnxruntime::Graph::InferAndVerifyTypeMatch) type inference failed) output arg () does not match expected type (This is an invalid model. Type Error: Type ' in node () bound to different types (Type Error: Type parameter (This is an invalid model. Model input ( but usage of initializer in graph expects ' has element type Type Error: Data in initializer ') of node () of output arg (Type Error: Type () does not have type information.Can't remove node node->GetOutputEdgesCount() == 0onnxruntime::Graph::RemoveNode'was added but does not exist. onnxruntime::Graph::SaveToOrtFormat as it still has output edges.onnxruntime::Graph::CleanUnusedInitializers'. It is no longer used by any node. must be either specified in graph inputs or graph initializers.nodes_.size() < static_cast(std::numeric_limits::max())Outer scope node arg name 'onnxruntime::Graph::ToGraphProtoInternal'. It is not used by any node and should be removed from the model.Removing initializer '!foundonnxruntime::Graph::RemoveInitializedTensorAddInitializedTensor already has tensor with name existing->second == &tensoronnxruntime::Graph::AddInitializedTensorgraph_proto_ is not in sync with name_to_initial_tensor_.Initializer tensor is missing. Invalid ORT format model.Satisfied, but should not be: nullptr == fbs_tensor NodeArg is missing. Invalid ORT format model.Satisfied, but should not be: nullptr == fbs_value_info onnxruntime::Graph::LoadFromOrtFormatNodeArg Name is missing. Invalid ORT format model.onnxruntime::Graph::LoadFromOrtFormat::::operator ()Satisfied, but should not be: nullptr == fbs_node_arg_name index < data_.size()onnxruntime::ConstPointerContainer > >::atC:\apilot\agent\_work\1\s\engine\lotus\include\onnxruntime\core/common/const_pointer_container.hNode is missing. Invalid ORT format model.Satisfied, but should not be: nullptr == fbs_node NodeEdge is missing. Invalid ORT format model.Satisfied, but should not be: nullptr == fbs_node_edge 's number of inputs is different from function body graph's number of input.'s number of outputs is different from function body graph's number of outputs.node.MutableOutputDefs().size() == subgraph.GetOutputs().size()onnxruntime::Graph::InlineFunctiononnxruntime::Graph::AllocateNodenullptr != sub_graph && nullptr != sub_graph->GetMetaDef()onnxruntime::Graph::FuseSubGraphInput to set must exist.input->Exists()onnxruntime::Graph::SetInputso9known by the checker.onnxruntime::OnnxRuntimeOpSchemaRegistry::RegisterOpSchemaInternal, but it its version is higherthan the operator set version Domain already set in registryC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\graph\schema_registry.cconnxruntime::OnnxRuntimeOpSchemaRegistry::RegisterOpSetNo graph was found in the protobuf.Failed to load model with error: Protobuf parsing failed.onnxruntime::Model::LoadONNX Runtime only *guarantees* support for models stamped with opset version 7 or above for opset domain 'ai.onnx'. Please upgrade your model to opset 7 or higher. For now, this opset model may run depending upon legacy support of some older opset version operators.onnxruntime::Model::SaveToOrtFormatProtobuf serialization failed.Graph is null. Invalid ORT format model.onnxruntime::Model::LoadFromOrtFormat less than 0.onnxruntime::Model::Save is less than 0. is under development and support for this is limited. The operator schemas and or other functionality may change before next ONNX release and in this case ONNX Runtime will not guarantee backward compatibility. Current official support for domain is till opset C:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\graph\model.cconnxruntime::Model::ModelUnknown model file format version.Missing opset in the model. All ModelProtos MUST have at least one entry that specifies which version of the ONNX OperatorSet is being imported.onnxruntime::model_load_utils::ValidateOpsetForDomainONNX Runtime only *guarantees* support for models stamped with official released onnx opset versions. Opset ModelProto does not have a graph. is under development and support for this is limited. The operator schemas and or other functionality could possibly change before next ONNX release and in this case ONNX Runtime will not guarantee backward compatibility. Current official support for domain failed. File doesn't existonnxruntime::SaveModelSatisfied, but should not be: nullptr == fbs_graph system error number Load model Incompatible dimensionsSecond input does not have rank 2First input does not have rank 2Incompatible dimensions for matrix multiplicationInput tensors of wrong rank (0).fused_ratiosumvarmomentumC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\graph\dml_ops\dml_defs.ccWdata_0saved_varsaved_meanAttribute Negative values are not allowed in a shape specification should specify a shapeInput data type does not match the expected data type. Current data type is Input data type does not match the expected data typeScale and Zero-point must be a scalarPads has incorrect number of values'pads' input must be a 1D (shape: [2 * n_input_dims]) tensor of type int64First operand.{additionalDocumentation}A_scaleinputs are expected to have tensor type.{name} Performs element-wise binary {name} on 8 bit data types (with Numpy-style broadcasting support). {additionalDocumentation} word_embedding should have 2 dimensions and dimension size is known.Number of attention headsC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\graph\contrib_ops\contrib_defs.ccInputs 4 shall be 5 dimensionsInputs 0 shall be 3 dimensionsInputs 0 shall be 2 dimensionsMWhether every token can only attend to previous tokens. Default value is 0.weightInput B zero point. Default value is 0 if it's not specified. It's a scalar, which means a per-tensor/layer quantization.B_scaleOutput scale. It's a scalar, which means a per-tensor/layer quantization.B_zero_pointA_zero_pointInput A zero point. Default value is 0 if it's not specified. It's a scalar, which means a per-tensor/layer quantization.Second operand.Padding for the beginning and ending along each spatial axis, it can take any value greater than or equal to 0. The value represent the number of pixels added to the beginning and end part of the corresponding axis. `pads` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number of pixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`. This attribute cannot be used simultaneously with auto_pad attribute. If not present, the padding defaults to 0 along start and end of each spatial axis.Constrain input and output types to 8 bit signed and unsigned tensors.auto_pad must be either NOTSET, SAME_UPPER, SAME_LOWER or VALID. Where default value is NOTSET, which means explicit padding is used. SAME_UPPER or SAME_LOWER mean pad the input so that the output spatial size match the input.In case of odd number add the extra padding at the end for SAME_UPPER and at the beginning for SAME_LOWER. VALID mean no padding.Output zero point. Default value is 0 if it's not specified. It's a scalar, which means a per-tensor/layer quantization.C_scaleC_zero_pointinput_idssegment_idsConstrain input and output types to int8 tensors.The epsilon value to use to avoid division by zero.maskword_embeddingsegment_embeddingposition_embeddingConstrain mask index to integer typesConstrain input and output types to float tensors.pastpresentinput_zero_pointweight_zero_pointinput_scaleweight_scaleScale and bias the input image. Bias values are stored in the same ordering as the image pixel format. ParametricSoftplus takes one input data (Tensor) and produces one output data (Tensor) where the softplus function, y = alpha * ln(exp(beta * x) + 1), is applied to the tensor elementwise. Crop and image to the specified spatial dimensions. If scale is given, then optionally start the crop offset by the left/top border amounts. If scale is not provided, crop the borders as provided.inv_std_varSaved inverse standard variance used during training to speed up gradient computation. Affine takes one input data (Tensor) and produces one output data (Tensor) where the affine function, y = alpha * x + beta, is applied to the tensor elementwise. Constrain mean and inv_std_var to float tensors.Input's shape must be 4-D GRUUnit computes the activations of a standard GRU, in a sequence-length aware fashion. Concretely, given the (fused) inputs X (TxNxD), the previous hidden state (NxD), and the sequence lengths (N), computes the GRU activations, avoiding computation if the input is invalid (as in, the value at X[t][n] >= seqLengths[n]. 'Scale' must contain exactly 2 values - (height, width)'Border' attribute must be present and must contain exactly 4 values - (left_border, top_border, right_border, bottom_border)input_as_shape Produces a slice of the input tensor along multiple axes. Similar to numpy: https://docs.scipy.org/doc/numpy/reference/arrays.indexing.html Slices uses `axes`, `starts` and `ends` inputs to specify the start and end dimension for each axis in the list of axes, it uses this information to slice the input `data` tensor. If a negative value is passed for any of the start or end indices, it represent number of elements before the end of that dimension. If the value passed to start or end is larger than the `n` (the number of elements in this dimension), it represents `n`. For slicing to the end of a dimension with unknown size, it is recommended to pass in `INT_MAX`. If `axes` are omitted, they are set to `[0, ..., ndim-1]`. Example 1: data = [ [1, 2, 3, 4], [5, 6, 7, 8], ] axes = [0, 1] starts = [1, 0] ends = [2, 3] result = [ [5, 6, 7], ] Example 2: data = [ [1, 2, 3, 4], [5, 6, 7, 8], ] starts = [0, 1] ends = [-1, 1000] result = [ [2, 3, 4], ] Scale takes one input data (Tensor) and produces one output data (Tensor) whose value is the input data tensor scaled element-wise. extra_shapeoutput tensorConstrain input and output types to float or half tensors.Constrain input and output integer tensors typesinput tensorConstrain input and output float tensors types.Saved mean used during training to speed up gradient computationUskipstash_typecrop_size shape input tensor has wrong dimensionBias Gelu. It's an extension of Gelu. It takes the sum of input A and bias input B as the input of Gelu activation. Gaussian Error Linear Unit. A high-performing neural network activation function.The GELU nonlinearity is the expected transformation of a stochastic regularizer which randomly applies the identity or zero map to a neuron's input. The GELU nonlinearity weights inputs by their magnitude, rather than gates inputs by their sign as in ReLUs.first input tensor has wrong dimension'pads' input must be a 1D (shape: [input_rank]) or 2D tensor (shape: [1, input_rank]) of type int64batch_indices shape input tensor has wrong dimensionrois input tensor has wrong dimensionValue of betaValue of alpha1D output tensor1D input tensor != mat_h:Input rank must be >= 2. Returns the upper or lower triangular part of a 2-D matrix, or batches of 2-D matrices. If the attribute "upper" is set to true, the upper triangular matrix is retained. Lower triangular matrix is retained otherwise. Default value for upper is true. Trilu takes one input tensor of shape [*, N, M], where * is zero or more batch dimensions. The upper triangular part consists of the elements on and above the given diagonal (k). The lower triangular part consists of elements on and below the diagonal. All other elements in the matrix are set to zero. If k = 0, the triangular part on and above/below the main diagonal is retained. If upper is set to true, a positive k retains the upper triangular matrix excluding k diagonals above the main diagonal. A negative k value includes as many diagonals below the main diagonal. If upper is set to false, a positive k retains the lower triangular matrix including k diagonals above the main diagonal. A negative k value excludes as many diagonals below the main diagonal. The inner-most 2 dimensions must have the same size (mat_w:) + scale[1] () + scale[0] ( The linear quantization operator. It consumes a full precision data, a scale, a zero point to compute the low precision / quantized tensor. The quantization formula is y = saturate ((x / y_scale) + y_zero_point).For saturation, it saturates to [0, 255] if it's uint8, or [-128, 127] if it's int8. For (x / y_scale), it's rounding to nearest ties to even. Refer to https://en.wikipedia.org/wiki/Rounding for details. Scale and zero point must have same shape. They must be either scalar (per tensor) or 1-D tensor (per 'axis').Input axis is invalid: ) needs to be greater than or equal to the top_border () + bottom_border () needs to be greater than or equal to the left_border () + right_border (axis must be in [-rank, rank-1]. input rank was Matrix product that behaves like numpy.matmul: https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.matmul.html last dimension of indices must not be larger and rank of data tensorboth data and indices tensor need to have rank larger than zero. Tokenizer divides each string in X into a vector of strings along the last axis. Allowed input shapes are [C] and [N, C]. If the maximum number of tokens found per input string is D, the output shape would be [N, C, D] when input shape is [N, C]. Similarly, if input shape is [C] then the output should be [C, D]. Tokenizer has two different operation modes. The first mode is selected when "tokenexp" is not set and "separators" is set. If "tokenexp" is set and "separators" is not set, the second mode will be used. The first mode breaks each input string into tokens by matching and removing separators. "separators" is a list of strings which are regular expressions. "tokenexp" is a single regular expression. Let's assume "separators" is [" "] and consider an example. If input is ["Hello World", "I love computer science !"] whose shape is [2], then the output would be [["Hello", "World", padvalue, padvalue, padvalue], ["I", "love", "computer", "science", "!"]] whose shape is [2, 5] because you can find at most 5 tokens per input string. Note that the input at most can have two axes, so 3-D and higher dimension are not supported. If "separators" contains a single empty string, the Tokenizer will enter into character tokenezation mode. This means all strings will be broken part into individual characters. For each input string, the second mode searches matches of "tokenexp" and each match will be a token in Y. The matching of "tokenexp" is conducted greedily (i.e., a match should be as long as possible). This operator searches for the first match starting from the beginning of the considered string, and then launches another search starting from the first remained character after the first matched token. If no match found, this operator will remove the first character from the remained string and do another search. This procedure will be repeated until reaching the end of the considered string. Let's consider another example to illustrate the effect of setting "mark" to true. If input is ["Hello", "World"], then the corresponding output would be [0x02, "Hello", "World", 0x03]. This implies that if mark is true, [C]/[N, C] - input's output shape becomes [C, D+2]/[N, C, D+2]. If tokenizer removes the entire content of [C]-input, it will produce [[]]. I.e. the output shape should be [C][0] or [N][C][0] if input shape was [N][C]. If the tokenizer receives empty input of [0] then the output is [0] if empty input of [N, 0] then [N, 0]. The linear dequantization operator. It consumes a quantized data, a scale, a zero point and computes the full precision data. The dequantization formula is y = (x - x_zero_point) * x_scale. Scale and zero point must have same shape. They must be either scalar (per tensor) or 1-D tensor (per 'axis'). Duplicate of FusedMatMul. Going forward FusedMatMul should be used. This OP will be supported for backward compatibility. Matrix product that behaves like numpy.matmul: https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.matmul.html inputs are expected to have tensor type and output type should not be null.valuesThe filled tensorOutput data after scalingInput data to be scaledConstrain indices to integer typesConstrain input and output types to all tensor types.The shape of filled tensorGivenTensorFillUnactivated gate outputs from forget, update, and output gates, pre-activation.hidden_prevArray of sequence lengths. len(seq_lengths) should equal batch size N.gatesBool to determine if hidden state is zeroes or passed along for timesteps past the given sequence_length.GRUUnitThe previous GRU hidden state.drop_statesA 1-D values of (height, width).A 1-D values of (leftBorder, topBorder, rightBorder, bottomBorder).Threshold valueResult, has same type as input, with H and W dimensions reduced.The scale to apply.Bias applied to each channel, same size as C.Result, has same shape and type as inputInput tensor of shape [N,C,H,W]1-D tensor of ending indices (exclusive) of corresponding axis in axes1-D tensor of starting indices of corresponding axis in `axes`Sliced data tensor.1-D tensor of axes that `starts` and `ends` apply to.Output tensorInput tensordataTensor of data to extract slices from.PadsComplexMulConj The fused convolution operator schema is the same as Conv besides it includes an attribute activation.Constrain input and output types to float tensorsComplexMulIrfftinput_1input_0Constrain input and output types to float/int tensors.Output tensor of shape (M, N).Whether B should be transposedWhether A should be transposedInput tensor A. The shape of A should be (M, K) if transA is 0, or (K, M) if transA is non-zero. The FusedGemm operator schema is the same as Gemm besides it includes attributes activation and leaky_relu_alpha.Input tensor C. The shape of C should be unidirectional broadcastable to (M, N).Input tensor B. The shape of B should be (K, N) if transB is 0, or (N, K) if transB is non-zero.If 1, mean and variance are computed across channels. Default is 0.Perform mean variance normalization.Scaling valueIf 0, normalize the mean only. Default is 1.The timestep for this operation.seq_lengthshiddenThe new GRU hidden state calculated by this op.signal_ndimRfftonesidednormalized Sample echo operator.The scaled hyperbolic tangent values of the input tensor computed element-wiseConstrain input0 and output types to float tensorsFor internal use.Constrain 'x' and 'x_zero_point' to 8-bit integer tensors.N-D full precision output tensor. It has same shape as input 'x'.Strings to tokenizeConstrain 'y', 'x_scale' to float tensors.x_scaleScale for input 'x'. It could be a scalar or a 1-D tensor, which means a per-tensor or per-axis quantization.If it's a 1-D tensor, its number of elements should be equal to the dimension value of 'axis' dimension of input 'x'.x_zero_pointZero point for input 'x'. It could be a scalar or a 1-D tensor, which means a per-tensor or per-axis quantization.If it's a 1-D tensor, its number of elements should be equal to the dimension value of 'axis' dimension of input 'x'.an optional list of strings attribute that contains a list of separators - regular expressions to match separators Two consecutive segments in X connected by a separator would be divided into two tokens. For example, if the input is "Hello World!" and this attribute contains only one space character, the corresponding output would be ["Hello", "World!"]. To achieve character-level tokenization, one should set the 'separators' to [""], which contains an empty string.An optional string. Token's regular expression in basic POSIX format (pubs.opengroup.org/onlinepubs/9699919799/basedefs/V1_chap09.html#tag_09_03). If set, tokenizer may produce tokens matching the specified pattern. Note that one and only of 'tokenexp' and 'separators' should be set. Matrix product that behaves like numpy.matmul: https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.matmul.html. The production MUST never overflow. The accumulation may overflow if and only if in 32 bits.Minimum number of characters allowed in the output. For example, if mincharnum is 2, tokens such as "A" and "B" would be ignoredInput/Output is a string tensorTokenized stringsThe string used to pad output tensors when the tokens extracted doesn't match the maximum number of tokens found. If start/end markers are needed, padding will appear outside the markers.Boolean whether to mark the beginning/end character with start of text character (0x02)/end of text character (0x03).The axis along which same quantization parameters are applied. It's optional.If it's not specified, it means per-tensor quantization and input 'x_scale' and 'x_zero_point' must be scalars.If it's specified, it means per 'axis' quantization and input 'x_scale' and 'x_zero_point' must be 1-D tensors.ExpandDims echo operator.xN-D full precision Input tensor to be quantized.Scalar multiplier for input tensor C.Scalar multiplier for the product of input tensors A * B.Specified axis to insert a dimensionactivation_gammaConstrain 'x', 'y_scale' to float tensors.N-D quantized output tensor. It has same shape as input 'x'.N-D quantized Input tensor to be de-quantized.Constrain 'y_zero_point' and 'y' to 8-bit integer tensors.y_scaleScale for doing quantization to get 'y'. It could be a scalar or a 1-D tensor,which means a per-tensor or per-axis quantization. If it's a 1-D tensor, its number of elements should be equal to the dimension value of 'axis' dimension of input 'x'.y_zero_pointZero point for doing quantization to get 'y'. It could be a scalar or a 1-D tensor, which means a per-tensoror per-axis quantization. If it's a 1-D tensor, its number of elements should be equal to the dimension value of 'axis' dimension of input 'x'.Whether B should be transposed on the last two dimensions before doing multiplicationWhether A should be transposed on the last two dimensions before doing multiplicationReduceSumIntegerMatrix multiply resultsConstrain input A data type to 8-bit integer tensor.a_zero_pointScalar multiplier for the product of the input tensors.Constrain input a_scale, b_scale and output Y data type as float tensor.Constrain output data type to 32-bit integer tensor.T2 must be tensor(uint32) when T1 is tensor(uint8),or must be tensor(int32) when T1 is tensor(int8).Constrain input type to 8-bit integer tensor.Keep the reduced dimension or not, default 1 mean keep reduced dimension.A list of integers, along which to reduce. The default is to reduce over all the dimensions of the input tensor.An input tensor. Computes the sum of the low-precision input tensor's element along the provided axes. The resulting tensor has the same rank as the input if keepdims equal 1. If keepdims equal 0, then the resulting tensor have the reduced dimension pruned. The above behavior is similar to numpy, with the exception that numpy default keepdims to False instead of True.reducedReduced output tensor.Constrain output Y data types as 32-bit integer tensor.T3 must be tensor(uint32) when both T1 and T2 are tensor(uint16),or must be tensor(int32) when either T1 or T2 is tensor(int16).Constrain input B data types as 16-bit integer tensorb_scaleScale of quantized input 'B'. It could be a scalar or a 1-D tensor, which means a per-tensor or per-column quantization. If it's a 1-D tensor, its number of elements should be equal to the number of columns of input 'B'.N-dimensional matrix BN-dimensional matrix AConstrain input A data types as 16-bit integer tensorMatrix multiply results from A * BScale of quantized input 'A'. It could be a scalar or a 1-D tensor, which means a per-tensor or per-column quantization. If it's a 1-D tensor, its number of elements should be equal to the number of columns of input 'A'.Constrain input B data type to 8-bit integer tensor.Zero point tensor for input 'A'. It's optional and default value is 0. It could be a scalar or a 1-D tensor, which means a per-tensor or per-column quantization. If it's a 1-D tensor, its number of elements should be equal to the number of columns of input 'A'.a_scaleb_zero_pointZero point tensor for input 'B'. It's optional and default value is 0. It could be a scalar or a 1-D tensor, which means a per-tensor or per-column quantization. If it's a 1-D tensor, its number of elements should be equal to the number of columns of input 'B'.Constrain input A, b_scale and output Y data type as float tensor.1D input tensor, whose dimension is same as B's last dimensionStride along each spatial axis. If not present, the stride defaults to 1 along each spatial axis.The size of the kernel along each axis.Input data tensor from the previous operator; dimensions for image case are (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data. For non image case, the dimensions are in the form of (N x C x D1 x D2 ... Dn), where N is the batch size. Optionally, if dimension denotation is in effect, the operation expects the input data tensor to arrive with the dimension denotation of [DATA_BATCH, DATA_CHANNEL, DATA_FEATURE, DATA_FEATURE ...].Whether to use ceil or floor (default) to compute the output shape. QLinearAveragePool consumes an input tensor X and applies average pooling across the tensor according to kernel sizes, stride sizes, and pad lengths. average pooling consisting of computing the average on all values of a subset of the input tensor according to the kernel size and downsampling the data into the output tensor Y for further processing. The output spatial shape will be following: ``` output_spatial_shape[i] = floor((input_spatial_shape[i] + pad_shape[i] - kernel_spatial_shape[i]) / strides_spatial_shape[i] + 1) ``` or ``` output_spatial_shape[i] = ceil((input_spatial_shape[i] + pad_shape[i] - kernel_spatial_shape[i]) / strides_spatial_shape[i] + 1) ``` if ceil_mode is enabled ``` * pad_shape[i] is sum of pads along axis i ``` `auto_pad` is a DEPRECATED attribute. If you are using them currently, the output spatial shape will be following: ``` VALID: output_spatial_shape[i] = ceil((input_spatial_shape[i] - kernel_spatial_shape[i] + 1) / strides_spatial_shape[i]) SAME_UPPER or SAME_LOWER: output_spatial_shape[i] = ceil(input_spatial_shape[i] / strides_spatial_shape[i]) ``` And pad shape will be following if `SAME_UPPER` or `SAME_LOWER`: ``` pad_shape[i] = (output_spatial_shape[i] - 1) * strides_spatial_shape[i] + kernel_spatial_shape[i] - input_spatial_shape[i] ``` The output of each pooling window is divided by the number of elements (exclude pad when attribute count_include_pad is zero). Input and output scales and zero points are used to convert the output to a new quantization range. Output = Dequantize(Input) -> AveragePool on fp32 data -> Quantize(output) Constrain output types to 32 bit tensors.Whether include pad pixels when calculating values for the edges. Default is 0, doesn't count include pad.QLinearAveragePoolX_scaleInput X's scale. It's a scalar, which means a per-tensor/layer quantization.X_zero_pointInput X's zero point. Default value is 0 if it's not specified. It's a scalar, which means a per-tensor/layer quantization.Constrain input and output types to 8 bit tensors.Output data tensor from average or max pooling across the input tensor. Dimensions will vary based on various kernel, stride, and pad sizes. Floor value of the dimension is usedCoefficient of leakage. QLinearLeakyRelu takes quantized input data (Tensor), an argument alpha, and quantize parameter for output, and produces one output data (Tensor) where the function `f(x) = quantize(alpha * dequantize(x)) for dequantize(x) < 0`, `f(x) = quantize(dequantize(x)) for dequantize(x) >= 0`, is applied to the data tensor elementwise. Computes the mean of the low-precision input tensor's element along the provided axes. The resulting tensor has the same rank as the input if keepdims equal 1. If keepdims equal 0, then the resulting tensor have the reduced dimension pruned. The above behavior is similar to numpy, with the exception that numpy default keepdims to False instead of True. Input and Output scales and zero points are used to requantize the output in a new range. This helps to improve accuracy as after ReduceMean operation the range of the output is expected to decrease. ``` "Output = Dequantize(Input) -> ReduceMean on fp32 data -> Quantize(output)", ``` QLinearReduceMeandata_scaleInput scale. It's a scalar, which means a per-tensor/layer quantization.additionC = (A_scale * (A - A_zero_point) + B_scale * (B - B_zero_point))/C_scale + C_zero_pointmultiplicationC = ((A - A_zero_point) * (B - B_zero_point)) * (A_scale * B_scale)/C_scale + C_zero_pointMulIntegerConstrain input types to 8 bit signed and unsigned tensors.Constrain output to 32 bit tensorPerforms element-wise binary quantized multiplication (with Numpy-style broadcasting support). "This operator supports **multidirectional (i.e., Numpy-style) broadcasting**" The output of this op is the int32 accumulated result of the mul operation ``` C (int32) = (A - A_zero_point) * (B - B_zero_point) ``` data_zero_pointInput zero point. Default value is 0 if it's not specified. It's a scalar, which means a per-tensor/layer quantization.reduced_zero_pointreduced_scaleInteger representing the embedding vector size for each char.If not provide, use the char embedding size of embedding vector.This operator applies convolution to word from left to right with window equal to conv_window_size and stride to 1.Take word 'example' for example, with conv_window_size equal to 2, conv is applied to [ex],[xa], [am], [mp]...If not provide, use the first dimension of conv kernal shape.SequenceSpecify batchs of sequence words to embeddingConstrain indice type to int32 or int64Constrain input and output types to any tensor type.Integer representing the embedding vector size for each word.If not provide, use the fileter size of conv weight Given `data` tensor of rank r >= 1, and `indices` tensor of rank q >= 1, gather slices of `data` into an output tensor of rank q - 1 + r - indices[-1]. Example 1: data = [[0,1],[2,3]] indices = [[0,0],[1,1]] output = [0,3] Example 2: data = [[0,1],[2,3]] indices = [[1],[0]] output = [[2,3],[0,1]] Example 3: data = [[[0,1],[2,3]],[[4,5],[6,7]]] indices = [[0,1],[1,0]] output = [[2,3],[4,5]] Example 4: data = [[[0,1],[2,3]],[[4,5],[6,7]]] indices = [[[0,1]],[[1,0]]] output = [[[2,3]],[[4,5]]] The WordConvEmbedding takes in a batch of sequence words and embed each word to a vector.Constrain to tensor(float).Input tensor.Three modes: `constant`(default) - pads with a given constant value, `reflect` - pads with the reflection of the vector mirrored on the first and last values of the vector along each axis, `edge` - pads with the edge values of arraySpecify bias of convSpecify weights of convConstrain to tensor(int32).Specify embedding vector of charThe underlying implementation is MurmurHash3_x86_32 generating low latency 32bits hash suitable for implementing lookup tables, Bloom filters, count min sketch or feature hashing. QLinearSigmoid takes quantized input data (Tensor), and quantize parameter for output, and produces one output data (Tensor) where the function `f(x) = quantize(Sigmoid(dequantize(x)))`, is applied to the data tensor elementwise. Wwhere the function `Sigmoid(x) = 1 / (1 + exp(-x))` 32-bit hash value.An input tensor to hash.Y_scaleOutput Y's scale. It's a scalar, which means a per-tensor/layer quantization.Y_zero_pointOutput Y's zero point. Default value is 0 if it's not specified. It's a scalar, which means a per-tensor/layer quantization.Tensor of rank q >= 1.Tensor of rank r >= 1.Tensor of rank q-1+r-indices[-1].indicesConstrain output type to unsigned and signed 32-bit integer tensor.Constrain input type to unsigned or signed 32-bit integer tensor, or string tensor. It should be utf-8 encoded if using unicode.If value is 1, output type is uint32_t, else int32_t. Default value is 1.Seed for the hashing algorithm, unsigned 32-bit integer, default to 0.roisRoIs (Regions of Interest) to pool over; rois is 2-D input of shape (num_rois, 4) given as [[y1, x1, y2, x2], ...]. The RoIs' coordinates are normalized in the coordinate system of the input image. Each coordinate set has a 1:1 correspondence with the 'batch_indices' input.batch_indices1-D tensor of shape (num_rois,) with each element denoting the index of the corresponding image in the batch.The pooling method. Two modes are supported: 'bilinear' and 'nearest'. Default is 'bilinear'.Constrains input to only numeric types.Input data tensor from the previous operator; 4-D feature map of shape (N, C, H, W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data.Value used for extrapolation, when applicable. Default is 0.0f. Extracts crops from the input image tensor and resizes them using bilinear sampling or nearest neighbor sampling (possibly with aspect ratio change) to a common output size specified by crop_height and crop_width. Returns a tensor with crops from the input image at positions defined at the bounding box locations in boxes. The cropped boxes are all resized (with bilinear or nearest neighbor interpolation) to a fixed size = [crop_height, crop_width]. The result is a 4-D tensor [num_boxes, crop_height, crop_width, depth]. The resizing is corner aligned.Constrain types to int tensors.type used for stash mean/inv_std_varThe first normalization dimension: normalization will be performed along dimensions axis : rank(inputs).crop_size1-D tensor of 2 elements: [crop_height, crop_width]. All cropped image patches are resized to this size. Both crop_height and crop_width need to be positive.Constrain types to float tensors.RoI pooled output, 4-D tensor of shape (num_rois, C, crop_height, crop_width). The r-th batch element Y[r-1] is a pooled feature map corresponding to the r-th RoI X[r-1].A 1-D tensor of the same type as 'x' containing all the unique values in 'x' sorted in the same order that they occur in the input 'x'A 1-D input tensor that is to be processed.idxA 1-D INT64 tensor of the same size as 'x' containing the indices for each value in 'x' in the output 'uniques'(Optional) A scalar or rank 1 tensor containing a single value to be filled if the mode chosen is `constant` (by default it is 0.0).Tensor of integers indicating the number of padding elements to add or remove (if negative) at the beginning and end of each axis. For 2D input tensor, it is the number of pixels. `pads` should be a 1D tensor of shape [2 * input_rank] or a 2D tensor of shape [1, 2 * input_rank]. `pads` format (1D example) should be as follow [x1_begin, x2_begin,...,x1_end, x2_end,...], where xi_begin is the number of pixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`. Given `data` tensor, pads, mode, and value. Example: Insert 0 pads to the beginning of the second dimension. data = [ [1.0, 1.2], [2.3, 3.4], [4.5, 5.7], ] pads = [0, 2, 0, 0] output = [ [ [0.0, 0.0, 1.0, 1.2], [0.0, 0.0, 2.3, 3.4], [0.0, 0.0, 4.5, 5.7], ], ] Tensor after padding.2D matrix with shape (M,N)The distance metric to use. If a string, the distance function can be "braycurtis", "canberra", "chebyshev", "cityblock", "correlation", "cosine", "dice", "euclidean", "hamming", "jaccard", "jensenshannon", "kulsinski", "mahalanobis", "matching", "minkowski", "rogerstanimoto", "russellrao", "seuclidean", "sokalmichener", "sokalsneath", "sqeuclidean", "wminkowski", "yule".A 2D Matrix that represents the distance between each pair of the two collections of inputs.2D matrix with shape (K,N)countsA 1-D INT64 tensor containing the the count of each element of 'uniques' in the input 'x' Finds all the unique values (deduped list) present in the given input tensor. This operator returns 3 outputs. The first output tensor 'uniques' contains all of the unique elements of the input, sorted in the same order that they occur in the input. The second output tensor 'idx' is the same size as the input and it contains the index of each value of the input in 'uniques'. The third output tensor 'counts' contains the count of each element of 'uniques' in the input. Example: input_x = [2, 1, 1, 3, 4, 3] output_uniques = [2, 1, 3, 4] output_idx = [0, 1, 1, 2, 3, 2] output_counts = [1, 2, 2, 1] Input can be of any tensor type.apply softmax to elements for dimensions softmax_axis or higherY = softmax(scores + bias)) with simple broadcast on bias. Intended to specialize softmax(scores + additive_mask) commonly found in transformer models.The bias (or mask) as Tensor.broadcast bias across input for dimensions broadcast_axis to softmax_axis-1Constrain mean and inv_std_var to be float tensors.Constrain input and output types (except mean and inv_std_var) to float tensors.The output.The input data as Tensor.Scale tensor.Input data tensor from the previous layer.Output data tensor.Bias tensor.Input tensor of rank 2 or higher.Boolean. Indicates whether upper or lower part of matrix is retained. Default is true.Constrain input and output types to all numeric tensors and bool tensors.A 0-D tensor containing a single value corresponding to the number diagonals above or the main diagonal to exclude or include.Default value is 0 if it's not specified.The bias input data that is a 1D tensor.The normal input data.Output tensor of the same type and shape as the input tensor.Input tensor. Every matrix in the batch must be invertible.̼+̼+C:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\graph\graph_utils.cconnxruntime::graph_utils::UpdateImplicitInputNameInSubgraph cannot be safely updated to in one of the subgraphs.std::count_if(subgraph_node.InputEdgesBegin(), subgraph_node.InputEdgesEnd(), [input_slot_index](const Node::EdgeEnd& entry) { return entry.GetDstArgIndex() == input_slot_index; }) == 0. Index: ExplicitInputs:onnxruntime::graph_utils::ReplaceNodeInputInvalid input index for node !graph.GetInitializedTensor(new_initializer.name(), existing)Initializer with same name exists. Name: ImplicitInputs:onnxruntime::graph_utils::AddInitializeronnxruntime::graph_utils::FindPathFailed since multiple edges matched:num_explicit_inputs == static_cast(target_input_idx)Can only add a new input at the end of the current ones.onnxruntime::graph_utils::AddNodeInputAttempting to get index for an input which does not exist.onnxruntime::graph_utils::RemoveNodeWithSingleNodeInSingleUsedOutputonnxruntime::graph_utils::GetNodeInputIndexFromInputNameitr != node.InputDefs().end()onnxruntime::graph_utils::CanUpdateImplicitInputNameInSubgraphs Implicit input name std::all_of(output_edges.cbegin(), output_edges.cend(), [&src_idx](const GraphEdge& edge) { return edge.src_arg_index == src_idx; })Node must only have one used outputonnxruntime::graph_utils::GetNodeOutputNameindex >= 0 && static_cast(index) < outputs.size()onnxruntime::graph_utils::RemoveNodeShould be unreachable if CanRemoveNodeAndMergeEdges is in sync with the logic here.index >= 0 && static_cast(index) < inputs.size()Attempting to get an input that does not exist.Attempting to get an output that does not exist.onnxruntime::graph_utils::GetNodeInputNameInvalide ExecutionOrderC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\graph\graph_viewer.cconnxruntime::GraphViewer::GetNodesInTopologicalOrderTENSORSPARSE_TENSORSSPARSE_TENSORSTRINGSINTSGRAPHSTENSORSSTRINGINTFLOATSGRAPHFLOATUNDEFINEDMissing string data for initializer. Invalid ORT format model.Satisfied, but should not be: nullptr == fbs_dims Missing raw data for initializer. Invalid ORT format model.Satisfied, but should not be: nullptr == fbs_str_data SaveAttributeOrtFormat: Unsupported attribute type: Satisfied, but should not be: nullptr == graph onnxruntime::experimental::utils::LoadInitializerOrtFormatMissing dimensions for initializer. Invalid ORT format model.Null graph attribute. Invalid ORT format model.Null tensor attribute. Invalid ORT format model.Null floats attribute. Invalid ORT format model.Empty graph proto from deserialization of ORT format modelNull string attribute. Invalid ORT format model.Satisfied, but should not be: nullptr == fbs_raw_data Satisfied, but should not be: nullptr == fbs_str onnxruntime::experimental::utils::LoadAttributeOrtFormatC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\graph\graph_flatbuffers_utils.cconnxruntime::experimental::utils::SaveInitializerOrtFormatGraph attribute value was null. Invalid ORT format model.onnxruntime::experimental::utils::SaveAttributeOrtFormatNull string in strings attribute. Invalid ORT format model.Satisfied, but should not be: nullptr == fbs_strings Satisfied, but should not be: nullptr == fbs_tensors Null tensors attribute. Invalid ORT format model.Null ints attribute. Invalid ORT format model.Satisfied, but should not be: nullptr == fbs_floats Null strings attribute. Invalid ORT format model.Satisfied, but should not be: nullptr == fbs_ints Null tensor in tensors attribute. Invalid ORT format model.(:(:(:(:H(:(:(:x(:p(:(:(:`(:P(:ininput_arg->Type() != nullptrfused_function_subgraphResolve subgraph failed:onnxruntime::FunctionImpl::FunctionImplA node with a function body within a subgraph within another function body is currently not supported in ORToutC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\graph\function.cconnxruntime::update_subgraphs_within_function_bodyConstrain input and output types.startUnsupported type:Can not get shape initializer data!limitdeltaUnsupported non-raw-data data type!Number of neurons in the hidden layer.Couple the input and forget gates if 1, default 0.Constrain seq_lens to integral tensors.Specify if the RNN is forward, reverse, or bidirectional. Must be one of forward (default), reverse, or bidirectional.Optional scaling values used by some activation functions. The values are consumed in the order of activation functions, for example (f, g, h) in LSTM. Default values are the same as of corresponding ONNX operators.For example with LeakyRelu, the default alpha is 0.01.A list of 3 (or 6 if bidirectional) activation functions for input, output, forget, cell, and hidden. The activation functions must be one of the activation functions specified above. Optional: See the equations for default if not specified.Cell clip threshold. Clipping bounds the elements of a tensor in the range of [-threshold, +threshold] and is applied to the input of activations. No clip if not specified.Optional scaling values used by some activation functions. The values are consumed in the order of activation functions, for example (f, g, h) in LSTM. Default values are the same as of corresponding ONNX operators.sequence_lensRY_hY_cQWMWinitial_hinitial_cAWmemory_seq_lensC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\platform\windows\env.ccopen file fail, errcode = GetFileLength: File is too largeGetFileSizeEx Fatal error: 0 count processors from GetSystemInfoonnxruntimeFatal error: 0 count processors from GetLogicalProcessorInformationonnxruntime::`anonymous-namespace'::WindowsEnv::GetNumCpuCoresFailed to unload library, error code: Failed to load library, error code: onnxruntime::`anonymous-namespace'::WindowsEnv::FormatLibraryFileNameFailed to find symbol in library, error code: onnxruntime::`anonymous-namespace'::WindowsEnv::GetCanonicalPathGetFinalPathNameByHandle() failed: UNC\Not satisfied: result_length > 0 onnxruntime::LoopDir\*C:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core/platform/path_lib.hNot satisfied: offset >= 0 Not satisfied: file_path SetFilePointerEx Not satisfied: length <= buffer.size() Received negative size from stat callInvalid fd was supplied: onnxruntime::`anonymous-namespace'::WindowsEnv::ReadFileIntoBufferFile is too large.DeleteFile() failed - path: , error code: RemoveDirectory() failed - path: onnxruntime::`anonymous-namespace'::WindowsEnv::DeleteFolder fail: unexpected endReadFile \/MapFileIntoMemory is not implemented on Windows.SUCCESSNO_SUCHFILEFAILNOT_IMPLEMENTEDMODEL_LOADEDEP_FAILINVALID_GRAPHENGINE_ERRORNO_MODELINVALID_PROTOBUFRUNTIME_EXCEPTIONSystemErrorC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\common\status.cc[ONNXRuntimeError]GENERAL ERRORonnxruntime::common::Status::Statuscode != static_cast(common::OK) is not a valid daypm is not a valid year [am is not a valid dateC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\common\logging\logging.cconnxruntime::logging::LoggingManager::LoggingManagerOnly one instance of LoggingManager created with InstanceType::Default can exist at any point in time.default_logger_id must be provided if instance_type is InstanceType::DefaultonnxruntimeISink must be provided.onnxruntime::logging::LoggingManager::CreateDefaultLoggerDefault logger already set. onnxruntime::concurrency::ThreadPool::ParallelForn >= 0fn != nullptrC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\common\threadpool.cconnxruntime::concurrency::ThreadPool::RunInParallelonnxruntime::concurrency::ThreadPool::Scheduleonnxruntime::concurrency::ThreadPool::ThreadPooldegree_of_parallelism >= 1onnxruntime::ToWideStringC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\common\str_helper.cconnxruntime::ToMBStringlength overflow"pid" :{"cat" : ""dur" :"tid" :onnxruntime::profiling::Profiler::EndProfilingWriting profiler data to file ",[ " : ""}, } "ph" : "X","ts" :"args" : {"name" :"enabled_onnxruntime::profiling::Profiler::StartProfilingcustom_logger != nullptronnxruntime::profiling::Profiler::EndTimeAndRecordEventMaximum number of events reached, could not record profile event.C:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\common\profiler.cconnxruntime::profiling::Profiler::StartTimeonnxruntime::profiling::Profiler::Initializesession_logger != nullptr] onnxruntime::Path::Parseonnxruntime::`anonymous-namespace'::ParsePathRootFailed to parse path root: Not satisfied: has_trailing_separator C:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\common\path.cc/\unexpected failureillegal input path:bad dimensions?@@@sequence_typetensor_typemap_typedim_param value with no name. Invalid ORT format model.SaveValueInfoOrtFormat: value_info_proto for Satisfied, but should not be: nullptr == fbs_dim_param onnxruntime::experimental::utils::LoadTensorDimensionOrtFormatWe do not support type [] for now is missing type info.onnxruntime::experimental::utils::SaveValueInfoOrtFormatSatisfied, but should not be: nullptr == fbs_type_info onnxruntime::experimental::utils::LoadSequenceTypeOrtFormatonnxruntime::experimental::utils::LoadMapTypeOrtFormatNull value type info in fbs::MapType. Invalid ORT format model.onnxruntime::experimental::utils::LoadTensorTypeAndShapeOrtFormatNull entry in dimensions. Invalid ORT format model.Null value type info in fbs::SequenceType. Invalid ORT format model.Satisfied, but should not be: nullptr == fbs_dim onnxruntime::experimental::utils::SaveMapTypeOrtFormatC:\apilot\agent\_work\1\s\engine\lotus\onnxruntime\core\flatbuffers\flatbuffers_utils.cconnxruntime::experimental::utils::SaveTypeInfoOrtFormatonnxruntime::experimental::utils::SaveTensorTypeAndShapeOrtFormatonnxruntime::experimental::utils::SaveSequenceTypeOrtFormatopset import domain is null. Invalid ORT format model.Satisfied, but should not be: nullptr == fbs_op_set_id Satisfied, but should not be: nullptr == fbs_domain Null map type info. Invalid ORT format model.Satisfied, but should not be: nullptr == fbs_sequence_type is not supported currentlySatisfied, but should not be: nullptr == fbs_map_type onnxruntime::experimental::utils::LoadTypeInfoOrtFormatNull tensor type info. Invalid ORT format model.Null sequence type info. Invalid ORT format model.Satisfied, but should not be: nullptr == fbs_tensor_type onnxruntime::experimental::utils::LoadOpsetImportOrtFormatModel must have opset imports. Invalid ORT format model.opset id is null. Invalid ORT format model.Satisfied, but should not be: nullptr == fbs_op_set_ids Null type info for Type:Satisfied, but should not be: !value_info_proto.name().empty() onnxruntime::experimental::utils::LoadValueInfoOrtFormatxC8D:D:D: not in allowed input sizes.) has input size not in allowed output sizes.) has output size Operator '' has been deprecated since version not in range [min=, max= has unknown expected type' is expected to have field 'graphs'Required attribute '' is missing.' is expected to have field 'ints'' is expected to have field 'floats'' is expected to have field 'tensors'' is expected to have field 'strings'(outputs_.size() - 1) == iONNX Schema !(it.GetName().empty())Duplicate type constraint nameAttribute specification type mismatch.: failed validating the check: (inputs_.size() - 1) == i)'s input is marked single but has an empty string in the graph)'s output ) has more outputs () in op definition.has output size ) has more inputs () than declared (' is expected to have field 't'Mismatched attribute type in '' is expected to have field 'g'' is expected to have field 'sparse_tensor'Attribute '' appeared multiple times.Unrecognized attribute: for operator The output is a tensor of strings or integers. Its shape will be the same as the input shape.The input must be a tensor of strings or integers, either [N,C] or [C].Indicates whether to only output as many values as are in the input (dense), or position the input based on using the key of the map as the index of the output (sparse).
One of 'DENSE', 'SPARSE'.A string indicating the desired element type of the output tensor, one of 'TO_FLOAT', 'TO_STRING', 'TO_INT64'.If the value of map_form is 'SPARSE,' this attribute indicates the total length of the output tensor.The input must be a map from strings or integers to either strings or a numeric type. The key and value types cannot be the same.The integers of the map. This sequence must be the same length as the 'cats_strings' sequence.The strings of the map. This sequence must be the same length as the 'cats_int64s' sequenceAn integer to use when an input string value is not found in the map.
One and only one of the 'default_*' attributes must be defined.A string to use when an input integer value is not found in the map.
One and only one of the 'default_*' attributes must be defined.The input must be a tensor of a numeric type. The output will be of the same tensor type.ZC:\apilot\agent\_work\1\s\engine\lotus\cmake\external\onnx\onnx\defs\traditionalml\defs.ccThe input must be a tensor of a numeric type or string. The output will be of the same tensor type.map(int64, string)The input must be an integer map to either string or float.The output is a 1-D tensor of string, float, or integer.map(int64, float)Values greater than this are mapped to 1, others to 0.Label encoder has only one output.Label encoder has only one input.Input type is not string tensor but key_strings is setOnly one of keys_*'s can be set in label encoder.A value that needs replacing.Value(s) to change toValue(s) to change to.The input type is a tensor of any shape.A list of strings. One and only one of 'keys_*'s should be set.Output type is determined by the specified 'values_*' attribute.Input type is not float tensor but keys_floats is setInput type is not int64 tensor but keys_int64s is setOnly one of values_*'s can be set in label encoder.A string vocabulary array.
One and only one of the vocabularies must be defined.The output will be a tensor of the value type of the input map. It's shape will be [1,C], where C is the length of the input dictionary.An integer vocabulary array.
One and only one of the vocabularies must be defined.map(int64, double)map(string, int64)map(string, double)map(string, float)The input type must be a tensor of a numeric type, either [N,C] or [C]. The output type will be of the same tensor type and shape.The size of each input in the input listThe input type must be a tensor of a numeric type.Indicates the transform to apply to the regression output vector.
One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT'The input must be a tensor of a numeric type.Class labels when using integer labels. One and only one 'classlabels' attribute must be defined.Class labels when using string labels. One and only one 'classlabels' attribute must be defined.Indicates the transform to apply to the scores vector.
One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT'One of 'MAX,' 'L1,' 'L2'Weights of the intercepts, if used.Weights of the model(s).The total number of regression targets, 1 if not defined.A float.An integer.Input's shape should be 1D or 2DA list of floats.A list of ints.A string.A list of strings. One and only one of 'value_*'s should be set.A collection of weights of the model(s).The output will be a tensor of strings or integers.Indicates whether to do OvR or multinomial (0=OvR is the default).A collection of intercepts.The input must be a tensor of a numeric type, and of of shape [N,C] or [C]. In the latter case, it will be treated as [1,C]Class labels if using integer labels.
One and only one of the 'classlabels_*' attributes must be defined.The input type must be a tensor of a numeric type, either [C] or [N,C].Second set of probability coefficients. This array must be same size as prob_a.
If these are provided then output Z are probability estimates, otherwise they are raw scores.First set of probability coefficients.Class labels if using string labels.
One and only one of the 'classlabels_*' attributes must be defined.Indicates the transform to apply to the score.
One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT'Indicates the transform to apply to the score.
One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT.'Flag indicating whether the regression is a one-class SVM or not.Chosen support vectorsThe number of support vectors.Support vector coefficients.If true and category is not present, will return all zeros; if false and a category if not found, the operator will fail.List of categories, strings.
One and only one of the 'cats_*' attributes must be defined.List of categories, ints.
One and only one of the 'cats_*' attributes must be defined.The output type will be a tensor of strings or integers, depending on which of the the classlabels_* attributes is used. Its size will match the bactch size of the input.The input must be a tensor of a numeric type, either [C] or [N,C].List of 3 elements containing gamma, coef0, and degree, in that order. Zero if unused for the kernel.The kernel type, one of 'LINEAR,' 'POLY,' 'RBF,' 'SIGMOID'.Second, multiply by this.
Can be length of features in an [N,F] tensor or length 1, in which case it applies to all features, regardless of dimension count.
Must be same length as 'offset'First, offset by this.
Can be length of features in an [N,F] tensor or length 1, in which case it applies to all features, regardless of dimension count.Child node if expression is falseChild node if expression is trueThe id of the tree that each node is in.For each node, define what to do in the presence of a NaN: use the 'true' (if the attribute value is 1) or 'false' (if the attribute value is 0) branch based on the value in this array.
This attribute may be left undefined and the defalt value is false (0) for all nodes.Base values for classification, added to final class score; the size must be the same as the classes or can be left unassigned (assumed 0)Node id for each node. Node ids must restart at zero for each tree and increase sequentially.Defines how to aggregate leaf values within a target.
One of 'AVERAGE,' 'SUM,' 'MIN,' 'MAX.'The index of the target that each weight is forThe node id of each weightThe total number of targets.The weight for each targetThresholds to do the splitting on for each node.Feature id for each node.The node kind, that is, the comparison to make at the node. There is no comparison to make at a leaf node.
One of 'BRANCH_LEQ', 'BRANCH_LT', 'BRANCH_GTE', 'BRANCH_GT', 'BRANCH_EQ', 'BRANCH_NEQ', 'LEAF'Popularity of each node, used for performance and may be omitted.The output type will be a tensor of strings or integers, depending on which of the the classlabels_* attributes is used.Node id for each node. Ids may restart at zero for each tree, but it not required to.Tree id for each node.The index of the class list that each weight is for.node id that this weight is for.Indicates the transform to apply to the score.
One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT.'The weight for the class in class_id.Child node if expression is false.Child node if expression is true.The id of the tree that this node is in.For each node, define what to do in the presence of a missing value: if a value is missing (NaN), use the 'true' or 'false' branch based on the value in this array.
This attribute may be left undefined, and the defalt value is false (0) for all nodes.The keys when using int keys.
One and only one of the 'classlabels_*' attributes must be defined.seq(map(string, float))The output will be a sequence of string or integer maps to float.The keys when using string keys.
One and only one of the 'classlabels_*' attributes must be defined.seq(map(int64, float))The output type will be a tensor of strings or integers, and will have the same shape as the input.The input type must be a tensor of integers or strings, of any shape.C:\apilot\agent\_work\1\s\engine\lotus\cmake\external\onnx\onnx\defs\traditionalml\old.ccA list of labels.Input was expected to have sequence type. Got Output was expected to have tensor type. Got Input was expected to have either tensor or sequence type. Got Element type of input was unknownInput was expected to have tensor type. Got Input element type of does not match existing output type of Input type was nullseq(tensor(double))seq(tensor(float))seq(tensor(bool))seq(tensor(string))seq(tensor(int32))seq(tensor(int16))seq(tensor(float16))seq(tensor(int64)) is invalid for a tensor of rank seq(tensor(complex128))seq(tensor(complex64))source sequence type missing element type.Mismatched tensor element type:target sequence type missing element type. target=Mismatched type: source=seq(tensor(uint32))seq(tensor(uint16))seq(tensor(int8))seq(tensor(uint64))seq(tensor(uint8))outputsGraph to run if condition is false. Has N outputs: values you wish to be live-out to the enclosing scope. The number of outputs must match the number of outputs in the then_branch.Graph to run if condition is true. Has N outputs: values you wish to be live-out to the enclosing scope. The number of outputs must match the number of outputs in the else_branch.Loop 'body' subgraph scan outputs should all be tensors but output condv_initialOnly boolAll Tensor and Sequence typesC:\apilot\agent\_work\1\s\engine\lotus\cmake\external\onnx\onnx\defs\controlflow\defs.cc was not a tensor.Number of scan output axes specified ( outputs. Expected Scan input ) is not equal to number of scan inputs ( axis value ) is not equal to number of scan outputs (Number of scan input axes specified (If node has but subgraphs produce Loop 'body' subgraph outputs should all be tensors or sequences but output was was notGraph attribute inferencing returned type information for then_branch and else_branch produce different number of outputs. Scan 'body' subgraph outputs should all be tensors but output All Tensor typesInt64 tensortensor of bool, which should be a scalar.initial_state_and_scan_inputsv_final_and_scan_outputstensor of int64, which should be a scalar.The graph run each iteration. It has 2+N inputs: (iteration_num, condition, loop carried dependencies...). It has 1+N+K outputs: (condition, loop carried dependencies..., scan_outputs...). Each scan_output is created by concatenating the value of the specified output value at the end of each iteration of the loop. It is an error if the dimensions or data type of these scan_outputs change across loop iterations.An optional list of K flags, one for each scan_output. The i-th element of the list specifies whether the i-th scan_output should be constructed by appending or prepending a new value in each iteration: 0 indicates appending and 1 indicates prepending. If omitted, all scan_output tensors will be produced by appending a value in each iteration.An optional list of M flags. The i-th element of the list specifies the direction to be scanned for the i-th scan_input tensor: 0 indicates forward direction and 1 indicates reverse direction. If omitted, all scan_input tensors will be scanned in the forward direction.An optional list of K flags. The i-th element of the list specifies the axis for the i-th scan_output. The scan outputs are accumulated along the specified axis. If omitted, 0 will be used as the scan axis for every scan_output. Negative value for an axis means counting dimensions from the back. Accepted range is [-r, r-1].An optional list of M flags. The i-th element of the list specifies the axis to be scanned (the sequence axis) for the i-th scan_input. If omitted, 0 will be used as the scan axis for every scan_input. Negative value for an axis means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).final_state_and_scan_outputsAn attribute specifying the number of scan_inputs M. The graph run each iteration. It has N+M inputs: (loop state variables..., scan_input_elts...). It has N+K outputs: (loop state variables..., scan_output_elts...). Each scan_output is created by concatenating the value of the specified scan_output_elt value at the end of each iteration of the loop. It is an error if the dimensions of these values change across loop iterations.X_RangeMin_ScaledX_MaxX_Max_AdjustedX_MinX_Min_AdjustedQ_MinQ_MaxRounded_ZeroPoint_FPZeropointInitial_ZeroPoint_FPClipped_ZeroPoint_FPC:\apilot\agent\_work\1\s\engine\lotus\cmake\external\onnx\onnx\defs\quantization\defs.ccConstrain 'x' to float or int32 tensor.Constrain 'y_zero_point' and 'y' to 8-bit integer tensor.(Optional) The axis of the quantization dimension of the input tensor. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input)Constrain 'x' to float tensor.Constrain 'y_zero_point' and 'y' to 8-bit unsigned integer tensor.Constrain 'x_zero_point' and 'x' to 8-bit/32-bit integer tensor.(Optional) The axis of the dequantizing dimension of the input tensor. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input) not specifiedValue of attribute Target shape may not have multiple -1 dimensionsConstrain output types. Casting to complex is not supported.C:\apilot\agent\_work\1\s\engine\lotus\cmake\external\onnx\onnx\defs\tensor\defs.ccConstrain input types. Casting from complex is not supported.The data type to which the elements of the input tensor are cast. Strictly must be one of the types from DataType enum in TensorProtoreshapedInvalid Target shape product of 0Dimension could not be inferred: incompatible shapesInvalid position of 0Invalid dimension value: should be of integer type and specify a type. does not specify a valid type.Mismatch between the sum of 'split' (The input is not evenly splittableMismatch between number of splits () and the split dimension of the input (Invalid value of attribute 'axis'. Rank=) and outputs ( Value=Slice op must have either three, four or five inputs.Incorrect or missing input value for starts and endsOnly supports `int32_t` or `int64_t` inputs for starts/ends/axes/stepsWhich axis to split on. A negative value means counting dimensions from the back. Accepted range is [-rank, rank-1] where r = rank(input).length of each output. Values should be >= 0.Required attribute axis is missingaxis must be in [-rank, rank-1].Constrain output to int64 tensor, which should be a scalar though.Constrain output to int64 tensor.Input tensor can be of arbitrary type.concat_resultConstrain output types to any tensor type.inputsAll inputs to Concat must have same rankWhich axis to concat on. A negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(inputs)..Input and output types can be of any tensor type.updatesaxis must be in [-r, r-1]Which axis to gather on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data).data tensor must have rank >= 1stepsInput axes has invalid data'step' cannot be 0Input axes has incorrect lengthInput steps has incorrect lengthWhich axis to scatter on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data).transposed}, input shape = {A list of integers. By default, reverse the dimensions, otherwise permute the axes according to the values given.Invalid attribute perm {'Repeats' input must be 1D tensor of type int64'Repeats' input has incorrect number of values. The number of values in 'repeats' must be equal to the number of input dimensions.DCR (default) for depth-column-row order re-arrangement. Use CRD for column-row-depth order.Two interpolation modes: nearest (default), and linear (including bilinear, trilinear, etc)Constrain repeat's type to int64 tensors.repeats'axes' attribute must not contain any duplicatessqueezedList of integers indicating the dimensions to squeeze. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data).Input tensor must be 4-dimensionalBlocks of [blocksize, blocksize] are moved.Blocksize must be positiveexpandedvalues in 'axes' are beyond the bounds of the computed output shapeList of integers indicating the dimensions to be inserted. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(expanded).condition'axis' must be in [-rank(indices), rank(indices)-1](Optional) Axis along which to take slices. If not specified, input is flattened before elements being selected. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).Indices tensor must have rank >= 1Input 'values' must be rank 1 tensor.Input 'values' must have exactly two elements.Input 'depth' must be a scalar or rank 1 tensor.Input 'depth' must have exactly one element.OneHot node must have three inputs.Constrains to boolean tensors.The coefficient 'a' used in cubic interpolation. Two common choice are -0.5 (in some cases of TensorFlow) and -0.75 (in PyTorch). Check out Equation (4) in https://ieeexplore.ieee.org/document/1163711 for the details. This attribute is valid only if "mode" is "cubic".If set to 1, the weight of sampling locations outside the tensor will be set to 0 and the weight will be renormalized so that their sum is 1.0. The default value is 0. This attribute describes how to transform the coordinate in the resized tensor to the coordinate in the original tensor.
The coordinate of each dimension is transformed individually. Let's describe a case using axis x as an example. Denote x_resized as the coordinate of axis x in the resized tensor, x_original as the coordinate of axis x in the original tensor, length_original as the length of the original tensor in axis x, length_resized as the length of the resized tensor in axis x, roi_x = (start_x, end_x) of the axis x in input "roi", scale = length_resized / length_original,
if coordinate_transformation_mode is "half_pixel",
x_original = (x_resized + 0.5) / scale - 0.5,
if coordinate_transformation_mode is "pytorch_half_pixel",
x_original = length_resized > 1 ? (x_resized + 0.5) / scale - 0.5 : 0,
if coordinate_transformation_mode is "align_corners",
x_original = x_resized * (length_original - 1) / (length_resized - 1),
if coordinate_transformation_mode is "asymmetric",
x_original = x_resized / scale,
if coordinate_transformation_mode is "tf_half_pixel_for_nn",
x_original = (x_resized + 0.5) / scale,
if coordinate_transformation_mode is "tf_crop_and_resize",
x_original = length_resized > 1 ? start_x * (length_original - 1) + x_resized * (end_x - start_x) * (length_original - 1) / (length_resized - 1) : 0.5 * (start_x + end_x) * (length_original - 1).Three interpolation modes: nearest (default), linear and cubic. The "linear" mode includes linear interpolation for 1D tensor and N-linear interpolation for N-D tensor (for example, bilinear interpolation for 2D tensor). The "cubic" mode includes cubic interpolation for 1D tensor and N-cubic interpolation for N-D tensor (for example, bicubic interpolation for 2D tensor).Constrain input 'X' and output 'Y' to all tensor types.The scale array along each dimension. It takes value greater than or equal to 1. The number of elements of 'scales' should be the same as the rank of input 'X'.sizesConstrain roi type to float or double.roiFour modes: round_prefer_floor (default, as known as round half down), round_prefer_ceil (as known as round half up), floor, ceil. Only used by nearest interpolation. It indicates how to get "nearest" pixel in input tensor from x_original, so this attribute is valid only if "mode" is "nearest".When coordinate_transformation_mode is "tf_crop_and_resize" and x_original is outside the range [0, length_original - 1], this value is used as the corresponding output value. Default is 0.0f.'input' must have rank >= 2'sequence_lens' must have rank of 1Constrain to all tensor types.Constrain to boolean tensors.Invalid value for attribute axis(Optional) Whether to sort the unique elements in ascending order before returning as output. Must be one of 0, or 1 (default).(Optional) Specify which axis is time axis. Must be one of 0 (default), or 1.(Optional) Specify which axis is batch axis. Must be one of 1 (default), or 0.Constrain to any tensor type.depth(Optional) Axis along which one-hot representation in added. Default: axis=-1. axis=-1 means that the additional dimension will be inserted as the innermost/last dimension in the output tensor. Negative value means counting dimensions from the back. Accepted range is [-r-1, r] where r = rank(indices).(Optional) Whether map negative infinity to true. Default to 1 so that negative infinity induces true. Set this attribute to 0 if negative infinity should be mapped to false.(Optional) Whether map positive infinity to true. Default to 1 so that positive infinity induces true. Set this attribute to 0 if positive infinity should be mapped to false.Constrain input types to float tensors.Constrain output types to boolean tensors.Constrains input and output to only numeric types.constant_valueinverse_indices(Optional) The dimension to apply unique. If not specified, the unique elements of the flattened input are returned. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).Supported modes: `constant`(default), `reflect`, `edge`'pads' input must be a 1D (shape: [2 * input_rank]) tensor of type int64The number of batch dimensions. The gather of indexing starts from dimension of data[batch_dims:]Both `data` and `indices` input tensors in GatherND op need to have rank larger than 0.Last dimension of `indices` input tensor in GatherND op must not be larger than the rank of `data` tensorConstrain input and output types to all numeric tensors.Whether to select the last index or the first index if the {name} appears in multiple indices, default is False (first index).The axis in which to compute the arg indices. Accepted range is [-r, r-1] where r = rank(data).C:\apilot\agent\_work\1\s\engine\lotus\cmake\external\onnx\onnx\defs\reduction\defs.ccConstrain input and output types to high-precision and 8 bit numeric tensors.Constrain input and output types to high-precision numeric tensors.A list of integers, along which to reduce. The default is to reduce over all the dimensions of the input tensor. Accepted range is [-r, r-1] where r = rank(data).This operator has **optional** inputs/outputs. See [the doc](IR.md) for more details about the representation of optional arguments. An empty string may be used in the place of an actual argument's name to indicate a missing argument. Trailing optional arguments (those not followed by an argument that is present) may also be simply omitted. Attribute kernel_shape has incorrect size.Attribute kernel_shape must be specified.Attribute pads has incorrect size.Attribute strides has incorrect size.MaxUnpool op must have either two or three inputs.Input tensor X must have atleast 2 dimensions.IndicesConstrain index tensor to int64p value of the Lp norm used to pool over the input data.'output_shape' must be rank 1 tensor.'output_shape' must have same number of elements as the shape of input tensor X.Attribute pads has incorrect sizeSecond input tensor has wrong dimensionAttribute kernel_shape has incorrect sizeAttribute kernel_shape must be specifiedAttribute dilations has incorrect sizeAttribute strides has incorrect sizeInput tensor must have atleast 2 dimensionsDilation value along each spatial axis of filter. If not present, the dilation defaults to 1 along each spatial axis.The storage order of the tensor. 0 is row major, and 1 is column major.C:\apilot\agent\_work\1\s\engine\lotus\cmake\external\onnx\onnx\defs\nn\defs.ccConstrain input and output types to float and 8 bit tensors.weight and zero_point pair is expected to have same type.input and zero_point pair is expected to have be same type.number of groups input channels and output channels are divided into.dilation value along each spatial axis of the filter. If not present, the dilation defaults is 1 along each spatial axis.Stride along each spatial axis. If not present, the stride defaults is 1 along each spatial axis.w_zero_pointw_scalewROI pool output shape (height, width).Multiplicative spatial scale factor to translate ROI coordinates from their input scale to the scale used when pooling.Attribute pooled_shape has incorrect lengthAttribute pooled_shape must be specifiedInput tensor must have at least 2 dimensionsRoIs tensor must have 2 dimensionsThe shape of the convolution kernel. If not present, should be inferred from input W. Carries out batch normalization as described in the paper https://arxiv.org/abs/1502.03167. Depending on the mode it is being run, there are multiple cases for the number of outputs, which we list below: Output case #1: Y, mean, var, saved_mean, saved_var (training mode) Output case #2: Y (test mode) For previous (depreciated) non-spatial cases, implementors are suggested to flatten the input shape to (N x C*D1*D2 ..*Dn) before a BatchNormalization Op. Additional elements added to the side with higher coordinate indices in the output. Each padding value in "output_padding" must be less than the corresponding stride/dilation dimension. By default, this attribute is a zero vector. Note that this attribute doesn't directly affect the computed output values. It only controls the selection of the computed values, so changing this attribute only adds or removes output elements. If "output_shape" is explicitly provided, "output_padding" does not contribute additional size to "output_shape" but participates in the computation of the needed padding amount. This is also called adjs or adjustment in some frameworks.The shape of the output can be explicitly set which will cause pads values to be auto generated. If output_shape is specified pads values are ignored. See doc for details for equations to generate padsFactor used in computing the running mean and variance.e.g., running_mean = running_mean * momentum + mean * (1 - momentum).Padding for the beginning and ending along each spatial axis, it can take any value greater than or equal to 0.The value represent the number of pixels added to the beginning and end part of the corresponding axis.`pads` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number ofpixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`.This attribute cannot be used simultaneously with auto_pad attribute. If not present, the padding defaultsto 0 along start and end of each spatial axis.number of groups input channels and output channels are divided into. default is 1.The shape of the convolution kernel. If not present, should be inferred from input 'w'.dilation value along each spatial axis of the filter. If not present, the dilation defaults to 1 along each spatial axis.Constrain output type to 8-bit integer tensor.Constrain bias type to 32-bit integer tensor.Constrain filter type to 8-bit integer tensor.dilation value along each spatial axis of the filter. If not present, the dilation defaults to 1 along each axis.Stride along each spatial axis. If not present, the stride defaults to 1 along each axis.Constrain input w and its zero point data type to 8-bit integer tensor.Constrain output y data type to 32-bit integer tensor.Constrain input x and its zero point data type to 8-bit integer tensor.Constrain output 'mask' types to boolean tensors.Constrain input 'ratio' types to float tensors.training_moderatioConstrain input and output to all tensor types.Invalid value() for attribute 'axis'The lambd value for the Shrink formulation. Default is 0.5.The bias value added to output. Default is 0.training_mode of Dropout must be a scalar.(Optional) Seed to the random generator, if not specified we will auto generate one.Ratio of Dropout must be a scalar.The axis on which to apply normalization, -1 mean last axis.The order of the normalization, only 1 or 2 are supported.The weighting criteria. It can be one of "TF" (term frequency), "IDF" (inverse document frequency), and "TFIDF" (the combination of TF and IDF)list of int64s (type: AttributeProto::INTS). This list is parallel to the specified 'pool_*' attribute. The i-th element in ngram_indexes indicate the coordinate of the i-th n-gram in the output tensor.list of floats. This attribute stores the weight of each n-gram in pool. The i-th element in weights is the weight of the i-th n-gram in pool. Its length equals to the size of ngram_indexes. By default, weights is an all-one tensor.This attribute is used when mode is "IDF" or "TFIDF" to scale the associated word counts.List of int64 n-grams learned from the training set. Either this or pool_strings attributes must be present but not both. It's an 1-D tensor starting with the collections of all 1-grams and ending with the collections of n-grams. The i-th element in pool stores the n-gram that should be mapped to coordinate ngram_indexes[i] in the output vector.The starting indexes of 1-grams, 2-grams, and so on in pool. It is useful when determining the boundary between two consecutive collections of n-grams. For example, if ngram_counts is [0, 17, 36], the first index (zero-based) of 1-gram/2-gram/3-gram in pool are 0/17/36. This format is essentially identical to CSR (or CSC) sparse matrix format, and we choose to use this due to its popularity.Maximum number of items (integers/strings) to be skipped when constructing an n-gram from X. If max_skip_count=1, min_gram_length=2, max_gram_length=3, this operator may generate 2-grams with skip_count=0 and skip_count=1, and 3-grams with skip_count=0 and skip_count=1List of strings n-grams learned from the training set. Either this or pool_int64s attributes must be present but not both. It's an 1-D tensor starting with the collections of all 1-grams and ending with the collections of n-grams. The i-th element in pool stores the n-gram that should be mapped to coordinate ngram_indexes[i] in the output vector.Environment dependent string that denotes the locale according to which output strings needs to be upper/lowercased.Default en_US or platform specific equivalent as decided by the implementation.Boolean. Whether the identification of stop words in X is case-sensitive. Default is falseList of stop words. If not set, no word would be removed from X.string enum that cases output to be lowercased/uppercases/unchanged. Valid values are "LOWER", "UPPER", "NONE". Default is "NONE"Input shape must have either [C] or [1,C] dimensions where C > 0Constrain input and output types to float tensors.The exponent.The number of channels to sum overScaling parameter.Indicate up to which input dimensions (exclusive) should be flattened to the outer dimension of the output. The value for axis must be in the range [-r, r], where r is the rank of the input tensor. Negative value means counting dimensions from the back. When axis = 0, the shape of the output tensor is (1, (d_0 X d_1 ... d_n), where the shape of the input tensor is (d_0, d_1, ... d_n). Maximum n-gram length. If this value is 3, 3-grams will be used to generate the output.Minimum n-gram length. If this value is 2 and max_gram_length is 3, output may contain counts of 2-grams and 3-grams.Input is ether string UTF-8 or int32/int641-D tensor of floatsngram_indexes must be non-empty with no negative valuesInput tensor must have rank 1 or 2STDX_varianceE_XsquaredVarianceEX_squaredX_squaredA list of integers, along which to reduce. The default is to caculate along axes [0,2,3] for calculating mean and variance along each channel. Two variables with the same C-coordinate are associated with the same mean and variance.X_RMProcessed_STD]. Its actual value is: 'axis' must be in [Coefficient of SELU default to 1.67326319217681884765625 (i.e., float32 approximation of 1.6732632423543772848170429916717).Coefficient of SELU default to 1.05070102214813232421875 (i.e., float32 approximation of 1.0507009873554804934193349852946).Constrain input and output types to float32 tensors.Elu_ResultThe Alpha value in Celu formula which control the shape of the unit. The default value is 1.0.X_alphaCoefficient of ELU.Whether the operator should behave like fmod (default=0 meaning it will do integer mods); Set this to 1 to force fmod treatmentC:\apilot\agent\_work\1\s\engine\lotus\cmake\external\onnx\onnx\defs\math\defs.ccDescribes the axis of the inputs when coerced to 2D; defaults to one because the 0th axis most likely describes the batch_size. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).Constrain input and output types to signed numeric tensors.Constrain input and output types to numeric tensors.List of tensors for Value of alpha.Value of beta. Pow takes input data (Tensor) and exponent Tensor, and produces one output data (Tensor) where the function `f(x) = x^exponent`, is applied to the data tensor elementwise. slopeConstrain input Y types to float/int tensors.Constrain input X and output types to float/int tensors.Whether to return the elements in sorted order.'shape' input must be 1D tensor of type INT64K input must be a one-dimensional tensor of size 1.K input must be of type int64.Dimension on which to do the sort. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).Whether to return the top-K largest or smallest elements.ValuesAxis has less than the requested k elements.bainput and zero_point pair is expected to have same type.Constrain output y and its zero point data type to 8-bit integer tensor.Constrain input a and its zero point data type to 8-bit integer tensor.Constrain input b and its zero point data type to 8-bit integer tensor.Constrain input and output types to all tensors.reductionlossloss_N1ddnoneinput_gather_elementloss_NCddexpanded_targetignore_indexconst_zero_target_typedexpanded_target_int64weight_gather_sumconst_ignore_indexloss_unweightedloss_sumloss_Nddweight_gatherIf set to 1 will perform the sums in reverse direction.If set to 1 will return exclusive sum in which the top element is not included. In other terms, if set to 1, the j-th output element would be the sum of the first (j-1) elements. Otherwise, it would be the sum of the first j elements.Constrain output Y data type as 32-bit integer tensor.const_onetargetconst_zeroConstrain input and output types to floating-point tensors.axis tensor can be int32 or int64 onlyRank of input Ellipsis represents incompatible dimensions. does not match the equation indices.NegativeLogLikelihoodLossNumber of input tensors does not match the operands in the equation.Constrain input, weight, and output types to floating-point tensors.Constrain target to integer typesscoresConstrain input and output types to all numerical tensor types.Type of reduction to apply to loss: none, sum, mean(default). 'none': no reduction will be applied, 'sum': the output will be summed. 'mean': the sum of the output will be divided by the number of elements in the output.InputsOutputEinsum expression string.weight_gather_temp_1Target rank must be 1 less than the input rank.const_one_floatweight_gather_tempinput_gather_element_transformsqueeze_masktransform_targetsconst_zero_floatType of reduction to apply to loss: none, sum, mean (default). 'none': the output is the loss for each sample. 'sum': the output will be summed. 'mean': the sum of the output will be divided by the sum of applied weights.Specifies a target value that is ignored and does not contribute to the input gradient. It's an optional value.Input and target dimension value mismatch.Weight rank must be 1.labelsX_Loglog_probX_RSX_DivX_SubX_ExpSoftmaxCrossEntropyLossC:\apilot\agent\_work\1\s\engine\lotus\cmake\external\onnx\onnx\defs\logical\defs.ccConstrains input to boolean tensor.Constrains output to boolean tensor.Constrains input/output to boolean tensors.Constrains input types to all numeric tensors.O2O1Constrain input and output types to integer tensors.Direction of moving bits. It can be either "RIGHT" (for right shift) or "LEFT" (for left shift).Attribute expected to have a one-dim tensorAttribute expected to have tensor typeThe value for the elements of the output tensor in sparse format.The value for the sole element for the scalar, int64, output tensor.TypeAndShapeInferenceFunction implementation incomplete: this line should never be reached.The value for the elements of the output tensor.Attribute 'value_string' expect a string.Attribute 'value_strings' expect a list of strings.Attribute 'value_float' expect a float.Attribute 'value_floats' expect a list of floats.C:\apilot\agent\_work\1\s\engine\lotus\cmake\external\onnx\onnx\defs\generator\defs.ccThe values for the elements for the 1D, UTF-8 string, output tensor.The values for the elements for the 1D, float32, output tensor.The value for the sole element for the scalar, UTF-8 string, output tensor.The values for the elements for the 1D, int64, output tensor.The value for the sole element for the scalar, float32, output tensor.sparse_valuevalue_intAttribute 'value_int' expect an integer.Attribute 'value_ints' expect a list of integers.value_stringsOne and only one of the attributes 'value', 'value_*' or 'sparse_value' must be specified for a Constant node.value_floatsvalue_stringvalue_intsvalue_floatThe mean of the normal distribution.Constrain output types to float tensors.The data type for the elements of the output tensor. If not specified, default is TensorProto::FLOAT.The shape of the output tensor.Lower boundary of the output values.Upper boundary of the output values.(Optional) The data type for the elements of the output tensor, if not specified, we will use the data type of the input tensor.The standard deviation of the normal distribution.The data type for the elements of the output tensor. Default is TensorProto::FLOAT.Constrain output types to be numerics.Constrain input types.(Optional) The value of the output elements.Should be a one-element tensor. If not specified, it defaults to a tensor of value 0 and datatype float32Shape input must be a one-dimensional tensor.Invalid shape value: Constrain output types. Strings and complex are not supported.Constrain input types. Strings and complex are not supported.(Optional) The data type for the elements of the output tensor. If not specified,the data type of the input tensor T1 is used. If input tensor T1 is also notspecified, then type defaults to 'float'.Input tensor must be 2-dimensional(Optional) Index of the diagonal to be populated with ones. Default is 0. If T2 is the output, this op sets T2[i, i+k] = 1. k = 0 populates the main diagonal, k > 0 populates an upper diagonal, and k < 0 populates a lower diagonal.variadic_outputAll inputs to 'Range' op must be of the same typeceil_result_relu_intceil_result_relu_boolceil_resultceil_result_reludelta_casteddiv_resultConstrain input types to common numeric type tensors.Constrain output types to integral tensors.Number of times to sample.(Optional) The data type for the elements of the output tensor, if not specified, we will use int32.Output type must be int32 or int64Input tensor must have rank 2sub_resultsub_result_castedcurrentrangeprevcond_outloop_body_attributeiInput to 'Range' op should be scalars (Tensor with only one element and shape empty)Coefficient of ELU default to 1.0.Coefficient of SELU default to 1.6732.Coefficient of SELU default to 1.0507.Coefficient of leakage default to 0.01.Value of alpha default to 0.2Value of beta default to 0.5Describes the axis of the inputs when coerced to 2D; defaults to one because the 0th axis most likely describes the batch_size If necessary the right-hand-side argument will be broadcasted to match the shape of left-hand-side argument. When broadcasting is specified, the second tensor can either be of element size 1 (including a scalar tensor and any tensor with rank equal to or smaller than the first tensor), or having its shape as a contiguous subset of the first tensor's shape. The starting of the mutually equal shape is specified by the argument "axis", and if it is not set, suffix matching is assumed. 1-dim expansion doesn't work yet. For example, the following tensor shapes are supported (with broadcast=1): shape(A) = (2, 3, 4, 5), shape(B) = (,), i.e. B is a scalar tensor shape(A) = (2, 3, 4, 5), shape(B) = (1, 1), i.e. B is an 1-element tensor shape(A) = (2, 3, 4, 5), shape(B) = (5,) shape(A) = (2, 3, 4, 5), shape(B) = (4, 5) shape(A) = (2, 3, 4, 5), shape(B) = (3, 4), with axis=1 shape(A) = (2, 3, 4, 5), shape(B) = (2), with axis=0 Attribute `broadcast=1` needs to be passed to enable broadcasting. C:\apilot\agent\_work\1\s\engine\lotus\cmake\external\onnx\onnx\defs\math\old.ccThis operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md).consumed_inputsIf set, defines the broadcast dimensions. See doc for details.Pass 1 to enable broadcastinglegacy optimization attribute.Number of top elements to retrieveDimension on which to do the sort.Minimum value, under which element is replaced by minMaximum value, above which element is replaced by maxInvalid value for attribute kScalar multiplier for input tensor C, the default value is 1.0.Whether C should be broadcastedScalar multiplier for the product of input tensors A * B, the default value is 1.0.Attribute 'value' of Constant node must exist with 'Tensor' data.C:\apilot\agent\_work\1\s\engine\lotus\cmake\external\onnx\onnx\defs\generator\old.ccOnly one of the attributes 'value' or 'sparse_value' must be specified for a Constant node.One of the attributes 'value' or 'sparse_value' must be specified for a Constant node.C:\apilot\agent\_work\1\s\engine\lotus\cmake\external\onnx\onnx\defs\nn\old.ccStride along each axis.Padding for the beginning and ending along each axis, it can take any value greater than or equal to 0. The value represent the number of pixels added to the beginning and end part of the corresponding axis. `pads` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number of pixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`. This attribute cannot be used simultaneously with auto_pad attribute.auto_pad must be either NOTSET, SAME_UPPER, SAME_LOWER or VALID. Where default value is NOTSET, which means explicit padding is used. SAME_UPPER or SAME_LOWER mean pad the input so that the output size match the input.In case of odd number add the extra padding at the end for SAME_UPPER and at the beginning for SAME_LOWER. VALID mean no padding. DEPRECATION NOTE: auto_pad is only intended to support legacy uses, and for framework authors, one is explicitly encouraged to use explicit padding specified in the pads attribute.Dilation value along each spatial axis of filter.Stride along each spatial axis.Constrain output mask types to boolean tensors.The ratio of random dropout(float, default 0.5) the ratio of random dropout(int, default 0) if nonzero, run dropout in test mode where the output is simply Y = X.If true, compute the mean and variance across all spatial elements If false, compute the mean and variance across per feature.Default is 1.The zero-padding added to one side of the output. This is also called adjs/adjustment in some frameworks.dilation value along each spatial axis of the filter.p value of the Lp norm used to pool over the input data, default is 2.0.The epsilon value to use to avoid division by zero, default is 1e-5f.Factor used in computing the running mean and variance.e.g., running_mean = running_mean * momentum + mean * (1 - momentum), default is 0.9f.If set to nonzero, run spatial batch normalization in test mode, default is 0.is_testIf true, compute the mean and variance across per activation. If false, compute the mean and variance across per feature over each mini-batch.Indicate up to which input dimensions (exclusive) should be flattened to the outer dimension of the output. The value for axis must be in the range [0, R], where R is the rank of the input tensor. When axis = 0, the shape of the output tensor is (1, (d_0 X d_1 ... d_n), where the shape of the input tensor is (d_0, d_1, ... d_n). C:\apilot\agent\_work\1\s\engine\lotus\cmake\external\onnx\onnx\defs\tensor\old.ccWhich axis to concat onWhich axis to concat on. Default value is 1.rank must be greater than axisConstrain output types. Casting to strings and complex are not supported.Constrain input types. Casting from strings and complex are not supported.Two interpolation modes: nearest(default), bilinearThe scale along height dimension. It takes value greater than or equal to 1.height_scaleThe scale along width dimension. It takes value greater than or equal to 1.width_scaleConstrain tiles and axis's type to int64 tensors.Attribute 'scales' is required.Number of elements of attribute 'scales' must be same as rank of input 'X'Attribute 'scales' must have floats type.) is not equal to the existing rank value (Ranks inferred (Constrain output types to bool, int32, int64, float16, float, double tensors.paddingsThree modes: constant(default), reflect, edgeList of integers indicate the padding element count at the beginning and end of each axis, for 2D it is the number of pixel. `paddings` rank should be double of the input's rank. `paddings` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number of pixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`.Which axis to split onlength of each outputoutputs...tilesNew shapeOne float, indicates the value to be filled, default is 0'axis' must be in [-rank(indices)-1, rank(indices)](Optional) Axis along which one-hot representation in added. Default: axis=-1. axis=-1 means that the additional dimension will be inserted as the innermost/last dimension in the output tensor.List of non-negative integers, indicate the dimensions to be insertedList of non-negative integers, indicate the dimensions to squeeze.Attribute pads has incorrect lengthList of integers indicating the number of padding elements to add or remove (if negative) at the beginning and end of each axis. For 2D it is the number of pixels. `pads` rank should be double of the input's rank. `pads` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number of pixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`.Attribute value for pads is requiredInvalid value of attribute 'axis'Which axis to split on. (Optional) Axis along which to take slices. If not specified, input is flattened before elements being selected.Ending indices (exclusive) of corresponding axis in axes`Axes that `starts` and `ends` apply to. It's optional. If not present, will be treated as [0, 1, ..., len(`starts`) - 1].Starting indices of corresponding axis in `axes`Incorrect or missing attribute value for starts and endsAttribute axes has incorrect lengthWhich axis to gather on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1]Which axis to scatter on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1]One float, indicates the value to be filled.C:\apilot\agent\_work\1\s\engine\lotus\cmake\external\onnx\onnx\defs\reduction\old.ccThe axis in which to compute the arg indices. Computes the indices of the {name} elements of the input tensor's element along the provided axis. The resulting tensor has the same rank as the input if keepdims equal 1. If keepdims equal 0, then the resulting tensor have the reduced dimension pruned. The type of the output tensor is integer.C:\apilot\agent\_work\1\s\engine\lotus\cmake\external\onnx\onnx\defs\object_detection\defs.ccThe pooling method. Two modes are supported: 'avg' and 'max'. Default is 'avg'.default 1; Pooled output Y's width.Number of sampling points in the interpolation grid used to compute the output value of each pooled output bin. If > 0, then exactly sampling_ratio x sampling_ratio grid points are used. If == 0, then an adaptive number of grid points are used (computed as ceil(roi_width / output_width), and likewise for height). Default is 0.Multiplicative spatial scale factor to translate ROI coordinates from their input spatial scale to the scale used when pooling, i.e., spatial scale of the input feature map X relative to the input image. E.g.; default is 1.0f. default 1; Pooled output Y's height.iou_thresholdmax_output_boxes_per_classboxesDimension mismatch in unification between expected to have rank > but has rank expected to have rank Integer indicate the format of the box data. The default is 0. 0 - the box data is supplied as [y1, x1, y2, x2] where (y1, x1) and (y2, x2) are the coordinates of any diagonal pair of box corners and the coordinates can be provided as normalized (i.e., lying in the interval [0, 1]) or absolute. Mostly used for TF models. 1 - the box data is supplied as [x_center, y_center, width, height]. Mostly used for Pytorch models.selected_indicesscore_threshold else= then=An optional list of M flags. The i-th element of the list specifies the axis to be scanned (the sequence axis) for the i-th scan_input. If omitted, 0 will be used as the scan axis for every scan_input.An optional list of K flags. The i-th element of the list specifies the axis for the i-th scan_output. The scan outputs are accumulated along the specified axis. If omitted, 0 will be used as the scan axis for every scan_output.Loop 'body' subgraph outputs should all be tensors but output Mismatched type for output Mismatched tensor element type for output C:\apilot\agent\_work\1\s\engine\lotus\cmake\external\onnx\onnx\defs\controlflow\old.ccAttribute dtype should be of integer type and specify a type.(Optional) The data type of the tensors in the output sequence. The default type is 'float'.Element type of inputs are expected to be the same. is null. Type info is expected.Input type for input at index SequenceConstruct is expected to have at least 1 input.C:\apilot\agent\_work\1\s\engine\lotus\cmake\external\onnx\onnx\defs\sequence\defs.ccConstrain output to integral tensor. It must be a scalar(tensor of empty shape).Input type for input at index 0 is null. Type info is expected.Constrain input types to all tensor types.Sum of split values not equal to 'input' dim size on 'axis'. 'axis' dim size=Input 'split' can not be empty. sum of split values=Only supports `int32_t` or `int64_t` inputs for splitInput Sequence and Tensor are expected to have the same elem type. Sequence=Input Sequence and Tensor are expected to have type info. Current type is null. Tensor=Constrain input types to any tensor type.output_sequenceConstrain position to integral tensor. It must be a scalar(tensor of empty shape).positiontensorinput_sequence], Value=Invalid value of attribute 'axis'. Accepted range=[new_axis must be either 0 or 1Which axis to split on. A negative value means counting dimensions from the back. Accepted range is [-rank, rank-1].Keep the split dimension or not. Default 1, which means we keep split dimension. If input 'split' is specified, this attribute is ignored.Constrain split size to integral tensor.Constrain output types to all tensor types.Which axis to concat on. Accepted range in `[-r, r - 1]`, where `r` is the rank of input tensors. When `new_axis` is 1, accepted range is `[-r - 1, r]`. Insert and concatenate on a new axis or not, default 0 means do not insert new axis.Constrains input to float tensors.Constrains input to integral tensors.Enable broadcastingIf set, defines the broadcast dimensions.C:\apilot\agent\_work\1\s\engine\lotus\cmake\external\onnx\onnx\defs\logical\old.ccC:\apilot\agent\_work\1\s\engine\lotus\cmake\external\onnx\onnx\defs\object_detection\old.ccC:\apilot\agent\_work\1\s\engine\lotus\cmake\external\onnx\onnx\defs\quantization\old.ccC:\apilot\agent\_work\1\s\engine\lotus\cmake\external\onnx\onnx\defs\rnn\defs.ccOne (or two if bidirectional) activation function for input gate. The activation function must be one of the activation functions specified above. Optional: Default `Tanh` if not specified.Constrain seq_lens to integer tensor.Couple the input and forget gates if 1.A list of 2 (or 4 if bidirectional) activation functions for update, reset, and hidden gates. The activation functions must be one of the activation functions specified above. Optional: See the equations for default if not specified.When computing the output of the hidden gate, apply the linear transformation before multiplying by the output of the reset gate.First input tensor must have rank 3Number of neurons in the hidden layerThe sequence output for the hidden is optional if 0. Default 0.fowardOptional scaling values used by some activation functions. The values are consumed in the order of activation functions, for example (f, g, h) in LSTM.C:\apilot\agent\_work\1\s\engine\lotus\cmake\external\onnx\onnx\defs\rnn\old.cctensor(complex128booluint64complex64uint16uint32sparse_tensorfloatmapopaquesparse_tensor(seqseq(map(int64uint8int16int32doubleint8float16bfloat16typeelem_typeField ' is required but missing.' of name is required to be non-empty.value_infosetting data_type field (tensor name: float_datadata_type) to UNDEFINED is not allowed): Unrecognized type value case (value_info name: key_typevalue_type ==> Context: Unrecognized data_type (tensor name: ' should be stored in field 'values of data_type 'STRING data (tensor name: ' instead of ' values, but NNZ is ) is stored externally and should not have data field.Data of TensorProto ( tensor name: double_datauint64_dataint64_dataraw_dataint32_datastring_data) should contain one and only one value field.) should not be stored in raw_data field) is 0-element but contains data!TensorProto (tensor name: ) is stored externally but doesn't have a location.TensorProto ( tensor name: , but it doesn't exist or is not accessible.) should be stored in ) should refer to attribute in parent node.node) should not contain more than one value field.Attribute (name: attrtype field and data field mismatch in attribute ) must have rank 1 or 2.) has no index values. with domain_version of No Op registered for Warning: Checker does not support models with experimental ops: No opset import for domain ', type: NodeProto (name: op_type) has zero input and zero output.) first dimension size does not equal NNZ.) second dimension size does not match rank of tensor.Sparse tensor (] not in sorted order.] out of range [0, ) index value at position [) has Sparse tensor indices () dimensions are not positive.) must have INT64 type.Sparse tensor values () must have a dense-rank > 0sparse_tensor_proto) must have rank 1.] out of range.] not in lexicographic sorted order.ConstantFillATen is not output of any previous nodes.' of node: Graph must be in single static assignment (SSA) form, however ' in initializer but not in graph inputgraph' has been used as graph input names multiple times. is deprecated in domain_version of Op registered for ' has been used as output names multiple times.Nodes in a graph must be topologically sorted, however input 'Bad node spec: NOT_SET) vs (opaque_typesparse_tensor_typeGraphProto attribute inferencing is not enabled in this InferenceContextImpl instance. does not contain a graph.type case unsupported. existing= models with experimental operators: inferred=type case mismatch. existing=: (Inferred shape and existing shape differ in dimension Inferred elem type differs from existing elem type: (Inferred shape and existing shape differ in rank: (input output is out of boundsGraph has , node name: were provided inputs but Warning: Unsupported operator Shape inference error(s): Warning: Shape inference does not support. No schema registered for this operator.(op_type:Number of elements of input 'scales' must be same as rank of input 'X'Input 'scales' must have float element type.Number of elements of input 'sizes' must be same as rank of input 'X'Input 'sizes' must have int64 element type.) is not equal to the existing dim value (Dimension value inferred (onnx.AttributeProtoonnx.OperatorSetIdProtoonnx.TypeProto.Opaqueonnx.TypeProtoonnx.GraphProtoonnx.TensorProto.Segmentonnx.StringStringEntryProtoonnx.TensorAnnotationonnx.TrainingInfoProtoonnx.ModelProtoonnx.ValueInfoProtoonnx.NodeProtoonnx.TypeProto.Maponnx.TypeProto.SparseTensoronnx.TypeProto.Tensoronnx.TypeProto.Sequenceonnx.TensorShapeProto.Dimensiononnx.TensorShapeProtoonnx.TensorProtoonnx.SparseTensorProtoonnx.FunctionProtoendpos: startpos: Invalid RE2: text size: pattern too large - reverse compile failedpattern too large - compile failedError reverse compiling 'SearchBitState inconsistencySearchNFA inconsistencySearchDFA inconsistencySearchOnePass inconsistencybytemap range DFA out of memory: size RE2: invalid startpos, endpos pair. [list count Error parsing 'Error compiling 'C:\apilot\agent\_work\1\s\engine\lotus\cmake\external\re2\re2\re2.cc': Unknown encoding C:\apilot\agent\_work\1\s\engine\lotus\cmake\external\re2\re2\regexp.ccBad reference count Regexp not destroyed.missing ]missing )invalid character classinvalid character class rangeunexpected errorinvalid escape sequenceUnexpected op in Regexp::Equal: no errorinvalid named capture groupNumCapturesWalker::ShortVisit calledinvalid perl operatorinvalid UTF-8invalid repetition sizebad repetition operatortrailing \no argument for repetition operatorWalk NULLStack not empty.C:\apilot\agent\_work\1\s\engine\lotus\cmake\external\re2\re2/walker-inl.h>;>;>;X>;p>;@>;L>;?;?;X?;p?;0?;H?;>;No ranges in char classMissing case in Compiler: C:\apilot\agent\_work\1\s\engine\lotus\cmake\external\re2\re2\compile.ccCompiler::Copy called!should never happenRE2: unexpected op: Bad hex digit Concat of unknown round: C:\apilot\agent\_work\1\s\engine\lotus\cmake\external\re2\re2\parse.ccRepetitionWalker::ShortVisit calledAddFoldedRange recurses too much.Bad call to ParseState::ParsePerlFlagsAny in stepunhandled C:\apilot\agent\_work\1\s\engine\lotus\cmake\external\re2\re2\nfa.cc in AddToThreadqcontext does not contain textBad args: nsubmatch=Unhandled in ComputeFirstByteUnexpected opcode in short circuit: unhandled opcode: ||Unexpected special state in RunStateOnByteStateSaver failed to restore state.DeadState in RunStateOnByteNULL state in RunStateOnByteC:\apilot\agent\_work\1\s\engine\lotus\cmake\external\re2\re2\dfa.cc|Failed to analyze start state.RunStateOnByteUnlocked failed after ResetRunStateOnByteUnlocked failed after ResetCacheBad arg in kInstAltMatch: Bad arg in kInstCapture: arg Unexpected opcode: GrowStack() failed: C:\apilot\agent\_work\1\s\engine\lotus\cmake\external\re2\re2\bitstate.ccjob_.size() = njob_ = Cannot use SearchOnePass for unanchored matches.C:\apilot\agent\_work\1\s\engine\lotus\cmake\external\re2\re2\onepass.ccC:\apilot\agent\_work\1\s\engine\lotus\cmake\external\re2\re2\prog.ccUnexpected opcode in IsMatch: DoCoalesce failed: r1->op() is CoalesceWalker::ShortVisit calledCase not handled in ComputeSimple: C:\apilot\agent\_work\1\s\engine\lotus\cmake\external\re2\re2\simplify.ccMalformed repeat Simplify case not handled: SimplifyWalker::ShortVisit calledDoCoalesce failed: r2->op() is `K;L;K;L;K;PL;K;PL;K;`L;K;`L;|K; L;pK; L;dK;HL;J;HL;J;L;J;L;J;L;J;L;J; c;;b;;c;Ȓ;|c;;lc;;dc;s;\c;(;Pc;;Hc;;AACFHHJJLLNNpprrvv/``bbddffhhjjllnnpprrttvvxxzz||~~      ""$$&&((**,,..1V      ""$$&&((**,,..0022446688::<<>>@@BBDDFFHHJJLLNNPPRRTTVVXXZZ\\^^``bbddffhhjjllnnpprrttvvxxzz||~~(/8?HMYY[[]]__ho!!!! ! !!!!!!!$!$!&!&!(!(!*!-!0!3!>!?!E!E!!!,.,`,`,b,d,g,g,i,i,k,k,m,p,r,r,u,u,~,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,@@BBDDFFHHJJLLNNPPRRTTVVXXZZ\\^^``bbddffhhjjll""$$&&((**,,..22446688::<<>>@@BBDDFFHHJJLLNNPPRRTTVVXXZZ\\^^``bbddffhhjjllnnyy{{}~!:0.4      ( * 0 2 3 5 9 < D G H K M V W \ ] _ c f w -0>    P.0/011122`2~2`|װנ@j^j`jijnjoj@kCkoooo    : : .... . . . ...!.!.0cooY[[]^@E3@x #AAJJX_y        @ H } ~     X _ x      0 9 `~&QTRo6?PYPY0;PlPY$n$`jijPkYk[kaknn`xPYq psuwz}&*]afj EHMPWYY[[]]_}&!&!ee`l'  @n_n4Mh  89;>@DFFJPl!<Up4Vn!0J JMO`jooWW  ? ? P X     9 ?   UYGM@Ctu8=KO[[]]AC`l<>;;?FAEpqp$t$njojjj7k;kDkDknn^_DPY   ?AJVoqPP=Pptvsz AZazptvwz}/1VYY` Jnoq/M$$((@X`j 9 = = P P X a q     ( * 0 2 3 5 6 8 9 Y \ ^ ^ r t     ( * 0 2 3 5 9 = = \ ] _ a q q     ( * 9 = = X Z ` a     : = = N N T V _ a z  023@F@GIl*??PUZ]aaefnpuHJMPVXXZ]`Zlo  1@Q`lnp xPmpt T3EK#MOZ} EHMPWYY[[]]_}q q   !!!! !!!!!!$!$!&!&!(!(!*!-!/!9!>@ACDF=Pptv!:AZf@ U X _ A0000:@[`!!!!00!0)080:0Pv55OOoo__? @ T T 34MO??  --  ....:.;.@.@.0000000012XXcc 3ux #X_y        @ H } ~     X _ x      `~&QTRe:;Zl[kaknn`xq @[`{tt~~  @@d e ??56  d f p t ~ !%!'!)!,!1!3!M!O!_!!!!&$@$J$`$')s+v++++++.N.//00000 00070<0?00000001111 2_222X33MM!09..ϩϩ[[>?0RTfhk ;@[epp$$   ?? 88iiª۪ߪ,.,0,^,`~!"$$'')24799;;BBGGIIKKMOQRTTWWYY[[]]__abddgjlrtwy|~~( ( Pmpt      9 9 .... . . . ... . .OJPY^_AZaz%,\bekwyq q   *!+!2!2!N!N!`!!`,,"0Z\d!:AZ(([[{{::<<    E E } } ## # #)#)#h'h'j'j'l'l'n'n'p'p'r'r't't'''''''''''''))))))))))))))))))))))))))))".".$.$.&.&.(.(.B.B.00 0 0 0 000000000000000??557799;;==??AACCGGYY[[]];;[[__bb  ( ) / / _ _ 00;;gi{))]]}};;==F F ~ ~ # # # #*#*#i'i'k'k'm'm'o'o'q'q's's'u'u'''''''''''''))))))))))))))))))))))))))))#.#.%.%.'.'.).). 0 0 0 0 0 000000000000000>>6688::<<>>@@BBDDHHZZ\\^^ ==]]``cc@w$$++<>^^``||~~uu   p p   O O y y ??446688@@ajt|D D R R z | !!!!! !!!!!!#!%!%!'!'!)!)!.!.!:!;!@!D!J!M!O!O!!!!# #(#+#&$@$J$$$%g''''''))))))s+v++++++,,....////0000 0 06070>0?00011111122*2G2P2P2`22222233MMƤ !(+69wy[[))bbdfii >>@@\\^^jjjjPzh8j      @nn(O  `nn3NTVg7Rk";Vo6NPUp"C p p   O O y y 446688@@ajt|!!!!! !!!!!!#!%!%!'!'!)!)!.!.!:!;!J!J!L!M!O!O!!!!!!!!!!!!!!!!!!!!!## ##"#(#+#{#}#####&$@$J$$$%%%%%%&n&p&g'''((+/+E+F+M+s+v++++++,,....////0000 0 06070>0?011111122*2G2P2P2`22222233MMƤ(+6799wy  &(:<=?MP]-@BIPuP'0c6@U`g 578<<?U`v   9            5 ` |        5 @ U ` r    H  # ''0E7&DDPrvv+ (*02359==PP]a4GJ/DD+ 2::PP\ .@@r  0FF`eghj #$C%0.4DFFh8j@j^jjjk/kckwk}kkoDoPoPoppjp|!"$$'')24799;;BBGGIIKKMOQRTTWWYY[[]]__abddgjlrtwy|~~֦4@ vz      8 : ? ?   $ ' FP8F'+-4ss/14467>>;<@@flpt8?BDFF^^3:==?@"%'+/79: 38;>GGQVY[068=??16::<=?EGGjj0k6koogi{BD6;luu!#$&*DJP68<>>@ACDFO0Y`eghj000011223W3foq0 9 fo6?PYPY09PYPY`jijPkYkPY ooWW  ? ? 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MRo,,EF,.2355>?ADGHKMWWbc57@AEE02;<>> !&&,.8899WX//>>Qo~oefmr7?ywx  ??@psvzz|`m@tAAJJ$n$09`if o f o f o f o f o PY )@IFOPY@IPY )Ш٨ Щ٩PY....//0000!0)080;04MNmp) ) !#%*,/:;?@[]__{{}}~~Z_    jm 0>^^d e p p v v OOZ[:=JO`hmn56 DEZ`;?~ ' 0 C E Q S ^ } ~ # #)#*#h'u''''')))))),,,,p-p-...0.N.0000000000=0=00000 ss~~twΨϨ./__ͩީߩ\_ުߪ>?0RTacchhjk  ;=??[[]]_eoDoPo~ooo 4PHJMPVXXZ]`Z]|------------------  &(.-%-'-'-----6@U`g P S c f  1/111GIlqjp|^^``uu00 ![[>>@@GoKUppQ R  *0-000 -!#%'**,,./:;?@\\~~Z_    jm 0>^^d e p p v v OOZ[JO`hmn56 DEZ`;?~  ' 0 8 ; > A C G Q S S U ^ ,,,,p-p-.... . .........*...0.9.<.?.A.A.C.N.00=0=000 ss~~twΨϨ./__ͩީߩ\_ުߪ00EFILPRTW_ahhjk  < @ I L N O   > @   > > @ @ G H K L W W   A D   > @ F H J L W W >?+,1188;<VWbdgm#&)+0138UUWWaacdmr55;;=ACD$+45.0/0#$''èRS/034MM{{}}6@MPY\_`   ?AJnoq/M@X`j 9 = = P P X a r     ( * 0 2 3 5 6 8 9 Y \ ^ ^ r t     ( * 0 2 3 5 9 = = \ ] _ a q q     ( * 9 = = X Z ` a     : = = N N T V _ a z  023@E@GIl*??PUZ]aaefnpuHJMPVXXZ]`Zlo  1@Q`lnp BDxPmpt T3EK#MOZw5!8!0-g-------------------00<0<0A000000001/111111114MNФ *+nn妏 "@s %0F`|(@BDK`oqvzz~ªª۪ܪ  &(.װmp(*68<>>@ACDF=Pptvfoq`lnprs  &(:<=?MP]az      !!##%%''))++--//11335578::<<>>@@BBDDFFHIKKMMOOQQSSUUWWYY[[]]__aacceeggiikkmmooqqssuuwwzz||~      !!##%%''))++--//1139<<?@BBGGIIKKMMOqqssww{}0_aacceeggiikkmmooqqssuuwwyy{{}}      !!##%%''))++--//`+kwy      !!##%%''))++--//1133557799;;==??AACCEEGGIIKKMMOOQQSSUUWWYY[[]]__aacceeggiikkmmooqqssuuwwyy{{}} '07@EPW`gp} ! !!!!!/!/!4!4!9!9!@DFFJPR46NPnpC!"$$'')24799;;BBGGIIKKMOQRTTWWYY[[]]__abddgjlrtwy|~~֦4@  68EPlp(('-// (*02359<DGHKMPPWW]cflpt  6::<=?GPY xKP| ^`|09`if o f o f o r w f o x ~ X ^ f x PY 3@Ii|FOPY@IPYp p t y P!!!!`$$$$v'',,00!0)080:011 2)2H2O2Q2_22222 )05Ш٨ Щ٩PY`46F r w x ~ X ^ p x *3i|p p t y P!_!!!`$$$$v'',,11 2)2H2O2Q2_2222205ŨΨ٨         5 8 : ? H P X  1VY>?P<C:\apilot\agent\_work\1\s\engine\lotus\cmake\external\re2\re2\tostring.cckRegexpCapture cap() == 0(?: [truncated]{%d}{%d,}+?Bad final char: (?:)[^\x00-\x{10ffff}](){}[]*+?|.^$\\b\z(?-m:$)(?-m:^)$^\C{%d,%d}\x{%x}\f\n\t\r[]^-\(?HaveMatch:%d)\BAZ ajkk lrss tz F y/279HJwxxy~Oa8  ~"3::+*;<==]>>(*?@?*ABCC=DDEEEGFOPP*QQ*RR*SS.TT2VW3YY6[[5\\O``3aaKcc1ee(ffDhh/ii-jjDkk)llAoo-qq)rr+uu*}})&&*&'%EETpsvw{}t&%@?  @60]#~P/ 0122N3344M5=>>D?@ABBCIJJ<KOP_`bcc$d/1V0a```  З‰@@yy}}`aa:bA '(/078?@EHMQQSSUUWWYY[[]]__`ghopqJruVvwdxyz{p|}~   &!&!*!*!!+!+!2!2!N!N!`!o!p!!!!$$$$,.,00,^,`,a,b,b, c,c,d,d,e,e,f,f,g,l,m,m,n,n,o,o,p,p,r,s,u,v,~,,,,,,,,-%-'-'-----@JKK=vLm"/2oy|}}u~ZZZZZZZZZSS`p0h!: AZ'((O(  @   @n_n `nn!""Cr+abwbrbawra+ba+bw+br+ba+w+rw+br+ba+w+r+abwbrb !"2* #3+;D:D::;;; ;;;;;;;;;;;;;;;;;;;;;WinmlRuleTransformerBatchNormalizationAddFusionBatchNormalizationMulFusionInternal error in BatchNormalizationMulFusion. 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5.?AV?$MLOperatorKernel@V?$DmlOperatorElementwiseBinary@UDML_ELEMENT_WISE_LOGICAL_LESS_THAN_OR_EQUAL_OPERATOR_DESC@@@Dml@@@@ 5.?AV?$MLOperatorKernel@V?$DmlOperatorElementwiseBinary@UDML_ELEMENT_WISE_MULTIPLY_OPERATOR_DESC@@@Dml@@@@ 5.?AV?$DmlOperatorElementwiseBinary@UDML_ELEMENT_WISE_LOGICAL_LESS_THAN_OR_EQUAL_OPERATOR_DESC@@@Dml@@ 5.?AVDmlOperatorElementwiseClip7@Dml@@ 5.?AV?$DmlOperatorElementwiseUnary@UDML_ELEMENT_WISE_TAN_OPERATOR_DESC@@@Dml@@ 5.?AV?$DmlOperatorElementwiseBinary@UDML_ELEMENT_WISE_LOGICAL_EQUALS_OPERATOR_DESC@@@Dml@@ 5.?AV?$DmlOperatorElementwiseUnary@UDML_ELEMENT_WISE_RECIP_OPERATOR_DESC@@@Dml@@ 5.?AV?$MLOperatorKernel@V?$DmlOperatorElementwiseBinary@UDML_ELEMENT_WISE_LOGICAL_XOR_OPERATOR_DESC@@@Dml@@@@ 5.?AV?$DmlOperatorElementwiseBinaryLoop@UDML_ELEMENT_WISE_ADD1_OPERATOR_DESC@@@Dml@@ 5.?AVDmlOperatorElementwiseBitShift@Dml@@ 5.?AV?$MLOperatorKernel@V?$DmlOperatorElementwiseUnary@UDML_ELEMENT_WISE_ASIN_OPERATOR_DESC@@@Dml@@@@ 5.?AV?$DmlOperatorElementwiseUnary@UDML_ELEMENT_WISE_ATANH_OPERATOR_DESC@@@Dml@@ 5.?AV?$MLOperatorKernel@V?$DmlOperatorElementwiseUnary@UDML_ELEMENT_WISE_ACOSH_OPERATOR_DESC@@@Dml@@@@ 5.?AVDmlOperatorElementwiseIsInf@Dml@@ 5.?AV?$MLOperatorKernel@V?$DmlOperatorElementwiseUnary@UDML_ELEMENT_WISE_ACOS_OPERATOR_DESC@@@Dml@@@@ 5.?AV?$MLOperatorKernel@V?$DmlOperatorElementwiseBinary@UDML_ELEMENT_WISE_LOGICAL_GREATER_THAN_OPERATOR_DESC@@@Dml@@@@ 5.?AV?$DmlOperatorElementwiseBinary@UDML_ELEMENT_WISE_LOGICAL_XOR_OPERATOR_DESC@@@Dml@@ 5.?AV?$DmlOperatorElementwiseBinary@UDML_ELEMENT_WISE_LOGICAL_GREATER_THAN_OR_EQUAL_OPERATOR_DESC@@@Dml@@ 5.?AV?$MLOperatorKernel@V?$DmlOperatorElementwiseUnary@UDML_ELEMENT_WISE_CEIL_OPERATOR_DESC@@@Dml@@@@ 5.?AV?$MLOperatorKernel@V?$DmlOperatorElementwiseBinary@UDML_ELEMENT_WISE_DIVIDE_OPERATOR_DESC@@@Dml@@@@ 5.?AV?$MLOperatorKernel@V?$DmlOperatorElementwiseUnary@UDML_ELEMENT_WISE_RECIP_OPERATOR_DESC@@@Dml@@@@ 5.?AV?$MLOperatorKernel@V?$DmlOperatorElementwiseUnary@UDML_ELEMENT_WISE_SIN_OPERATOR_DESC@@@Dml@@@@ 5.?AV?$MLOperatorKernel@V?$DmlOperatorElementwiseUnary@UDML_ELEMENT_WISE_COS_OPERATOR_DESC@@@Dml@@@@ 5.?AV?$MLOperatorKernel@V?$DmlOperatorElementwiseUnary@UDML_ELEMENT_WISE_FLOOR_OPERATOR_DESC@@@Dml@@@@ 5.?AV?$MLOperatorKernel@V?$DmlOperatorElementwiseUnary@UDML_ELEMENT_WISE_SINH_OPERATOR_DESC@@@Dml@@@@ 5.?AV?$DmlOperatorElementwiseBinary@UDML_ELEMENT_WISE_LOGICAL_AND_OPERATOR_DESC@@@Dml@@ 5.?AVDmlOperatorElementwiseClip11@Dml@@ 5.?AV?$MLOperatorKernel@VDmlOperatorElementwiseIf@Dml@@@@ 5.?AV?$DmlOperatorElementwiseUnary@UDML_ELEMENT_WISE_ABS_OPERATOR_DESC@@@Dml@@ 5.?AV?$MLOperatorKernel@VDmlOperatorExpand@Dml@@@@ 5.?AVDmlOperatorExpand@Dml@@ 5.?AVExpandHelper@OperatorHelper@@ 5.?AV?$DmlOperatorConvolutionTemplate@$00$00$00@Dml@@ 5.?AV?$DmlOperatorConvolutionTemplate@$00$0A@$0A@@Dml@@ 5.?AVConvolutionHelperBase@OperatorHelper@@ 5.?AV?$MLOperatorKernel@V?$DmlOperatorConvolutionTemplate@$00$00$0A@@Dml@@@@ 5.?AV?$MLOperatorKernel@V?$DmlOperatorConvolutionTemplate@$00$00$00@Dml@@@@ 5.?AV?$DmlOperatorConvolutionTemplate@$00$00$0A@@Dml@@ 5.?AVDmlOperatorConvolution@Dml@@ 5.?AV?$MLOperatorKernel@V?$DmlOperatorConvolutionTemplate@$00$0A@$0A@@Dml@@@@ 5.?AV?$MLOperatorKernel@V?$DmlOperatorReduceTemplate@$09@Dml@@@@ 5.?AV?$MLOperatorKernel@V?$DmlOperatorReduceTemplate@$03@Dml@@@@ 5.?AV?$DmlOperatorReduceTemplate@$00@Dml@@ 5.?AV?$MLOperatorKernel@V?$DmlOperatorReduceTemplate@$01@Dml@@@@ 5.?AV?$DmlOperatorReduceTemplate@$01@Dml@@ 5.?AV?$MLOperatorKernel@V?$DmlOperatorReduceTemplate@$00@Dml@@@@ 5.?AV?$DmlOperatorReduceTemplate@$02@Dml@@ 5.?AV?$DmlOperatorReduceTemplate@$0L@@Dml@@ 5.?AV?$MLOperatorKernel@V?$DmlOperatorReduceTemplate@$0A@@Dml@@@@ 5.?AV?$DmlOperatorReduceTemplate@$05@Dml@@ 5.?AV?$MLOperatorKernel@V?$DmlOperatorReduceTemplate@$0L@@Dml@@@@ 5.?AV?$DmlOperatorReduceTemplate@$03@Dml@@ 5.?AV?$DmlOperatorReduceTemplate@$08@Dml@@ 5.?AVReduceHelperBase@OperatorHelper@@ 5.?AV?$DmlOperatorReduceTemplate@$04@Dml@@ 5.?AV?$DmlOperatorReduceTemplate@$07@Dml@@ 5.?AV?$DmlOperatorReduceTemplate@$09@Dml@@ 5.?AVDmlOperatorReduce@Dml@@ 5.?AV?$MLOperatorKernel@V?$DmlOperatorReduceTemplate@$02@Dml@@@@ 5.?AV?$MLOperatorKernel@V?$DmlOperatorReduceTemplate@$07@Dml@@@@ 5.?AV?$DmlOperatorReduceTemplate@$06@Dml@@ 5.?AV?$MLOperatorKernel@V?$DmlOperatorReduceTemplate@$06@Dml@@@@ 5.?AV?$MLOperatorKernel@V?$DmlOperatorReduceTemplate@$08@Dml@@@@ 5.?AV?$MLOperatorKernel@V?$DmlOperatorReduceTemplate@$05@Dml@@@@ 5.?AV?$MLOperatorKernel@V?$DmlOperatorReduceTemplate@$04@Dml@@@@ 5.?AV?$DmlOperatorReduceTemplate@$0A@@Dml@@ 5.?AV?$DmlOperatorPoolingTemplate@$0GO@$0A@@Dml@@ 5.?AVPoolingHelperBase@OperatorHelper@@ 5.?AV?$DmlOperatorPoolingTemplate@$0DI@$0A@@Dml@@ 5.?AV?$MLOperatorKernel@V?$DmlOperatorPoolingTemplate@$0DJ@$00@Dml@@@@ 5.?AV?$MLOperatorKernel@V?$DmlOperatorPoolingTemplate@$0GO@$0A@@Dml@@@@ 5.?AV?$DmlOperatorPoolingTemplate@$0DJ@$0A@@Dml@@ 5.?AV?$MLOperatorKernel@V?$DmlOperatorPoolingTemplate@$0DI@$0A@@Dml@@@@ 5.?AV?$MLOperatorKernel@V?$DmlOperatorPoolingTemplate@$0DK@$00@Dml@@@@ 5.?AV?$DmlOperatorPoolingTemplate@$0DK@$00@Dml@@ 5.?AV?$MLOperatorKernel@V?$DmlOperatorPoolingTemplate@$0DI@$00@Dml@@@@ 5.?AVDmlOperatorPooling@Dml@@ 5.?AV?$DmlOperatorPoolingTemplate@$0DI@$00@Dml@@ 5.?AV?$MLOperatorKernel@V?$DmlOperatorPoolingTemplate@$0DJ@$0A@@Dml@@@@ 5.?AV?$DmlOperatorPoolingTemplate@$0DJ@$00@Dml@@ 5.?AV?$DmlOperatorActivationTemplate@$0DD@@Dml@@ 5.?AV?$MLOperatorKernel@V?$DmlOperatorActivationTemplate@$0FK@@Dml@@@@ 5.?AV?$DmlOperatorActivationTemplate@$0DB@@Dml@@ 5.?AV?$MLOperatorKernel@V?$DmlOperatorActivationTemplate@$0CM@@Dml@@@@ 5.?AV?$DmlOperatorActivationTemplate@$0CH@@Dml@@ 5.?AV?$MLOperatorKernel@V?$DmlOperatorActivationTemplate@$0DC@@Dml@@@@ 5.?AV?$MLOperatorKernel@V?$DmlOperatorActivationTemplate@$0CD@@Dml@@@@ 5.?AV?$DmlOperatorActivationTemplate@$0IA@@Dml@@ 5.?AV?$DmlOperatorActivationTemplate@$0CK@@Dml@@ 5.?AV?$MLOperatorKernel@V?$DmlOperatorActivationTemplate@$0CN@@Dml@@@@ 5.?AV?$MLOperatorKernel@V?$DmlOperatorActivationTemplate@$0CE@@Dml@@@@ 5.?AV?$MLOperatorKernel@V?$DmlOperatorActivationTemplate@$0CO@@Dml@@@@ 5.?AV?$MLOperatorKernel@V?$DmlOperatorActivationTemplate@$0DA@@Dml@@@@ 5.?AV?$DmlOperatorActivationTemplate@$0CD@@Dml@@ 5.?AV?$DmlOperatorActivationTemplate@$0CG@@Dml@@ 5.?AV?$MLOperatorKernel@V?$DmlOperatorActivationTemplate@$0CP@@Dml@@@@ 5.?AV?$DmlOperatorActivationTemplate@$0FK@@Dml@@ 5.?AV?$MLOperatorKernel@V?$DmlOperatorActivationTemplate@$0IA@@Dml@@@@ 5.?AV?$DmlOperatorActivationTemplate@$0CN@@Dml@@ 5.?AV?$MLOperatorKernel@V?$DmlOperatorActivationTemplate@$0CK@@Dml@@@@ 5.?AV?$MLOperatorKernel@V?$DmlOperatorActivationTemplate@$0DD@@Dml@@@@ 5.?AV?$MLOperatorKernel@V?$DmlOperatorActivationTemplate@$0CH@@Dml@@@@ 5.?AV?$MLOperatorKernel@V?$DmlOperatorActivationTemplate@$0DE@@Dml@@@@ 5.?AV?$DmlOperatorActivationTemplate@$0CO@@Dml@@ 5.?AV?$MLOperatorKernel@V?$DmlOperatorActivationTemplate@$0CG@@Dml@@@@ 5.?AV?$DmlOperatorActivationTemplate@$0CF@@Dml@@ 5.?AVDmlOperatorActivation@Dml@@ 5.?AV?$MLOperatorKernel@V?$DmlOperatorActivationTemplate@$0CL@@Dml@@@@ 5.?AV?$MLOperatorKernel@V?$DmlOperatorActivationTemplate@$0CJ@@Dml@@@@ 5.?AV?$DmlOperatorActivationTemplate@$0DC@@Dml@@ 5.?AV?$DmlOperatorActivationTemplate@$0CJ@@Dml@@ 5.?AV?$MLOperatorKernel@V?$DmlOperatorActivationTemplate@$0DB@@Dml@@@@ 5.?AV?$DmlOperatorActivationTemplate@$0CL@@Dml@@ 5.?AV?$DmlOperatorActivationTemplate@$0DA@@Dml@@ 5.?AV?$DmlOperatorActivationTemplate@$0CE@@Dml@@ 5.?AV?$DmlOperatorActivationTemplate@$0CM@@Dml@@ 5.?AV?$DmlOperatorActivationTemplate@$0CP@@Dml@@ 5.?AV?$MLOperatorKernel@V?$DmlOperatorActivationTemplate@$0CF@@Dml@@@@ 5.?AV?$DmlOperatorActivationTemplate@$0DE@@Dml@@ 5.?AVDmlOperatorBatchNormalization@Dml@@ 5.?AV?$MLOperatorKernel@VDmlOperatorBatchNormalization@Dml@@@@ 5.?AVDmlOperatorScatterNd@Dml@@ 5.?AV?$MLOperatorKernel@VDmlOperatorScatter@Dml@@@@ 5.?AVDmlOperatorScatter@Dml@@ 5.?AV?$MLOperatorKernel@VDmlOperatorScatterNd@Dml@@@@ 5.?AV?$MLOperatorKernel@VDmlOperatorQLinearConv@Dml@@@@ 5.?AVDmlOperatorQLinearConv@Dml@@ 5.?AV?$VersionedKernel@VDmlOperatorResize@Dml@@$09@@ 5.?AVDmlOperatorResize@Dml@@ 5.?AV?$VersionedKernel@VDmlOperatorResize@Dml@@$06@@ 5.?AV?$MLOperatorKernel@V?$VersionedKernel@VDmlOperatorResize@Dml@@$0L@@@@@ 5.?AV?$MLOperatorKernel@V?$VersionedKernel@VDmlOperatorResize@Dml@@$08@@@@ 5.?AV?$VersionedKernel@VDmlOperatorResize@Dml@@$08@@ 5.?AV?$MLOperatorKernel@V?$VersionedKernel@VDmlOperatorResize@Dml@@$06@@@@ 5.?AV?$VersionedKernel@VDmlOperatorResize@Dml@@$0L@@@ 5.?AV?$MLOperatorKernel@V?$VersionedKernel@VDmlOperatorResize@Dml@@$09@@@@ 5.?AVResizeHelper@OperatorHelper@@ 5.?AVSplitHelper@OperatorHelper@@ 5.?AV?$MLOperatorKernel@VDmlOperatorSplit@Dml@@@@ 5.?AVDmlOperatorSplit@Dml@@ 5.?AVConstantOfShapeHelper@OperatorHelper@@ 5.?AV?$MLOperatorKernel@VDmlOperatorConstantOfShape@Dml@@@@ 5.?AVDmlOperatorConstantOfShape@Dml@@ 5.?AV?$MLOperatorKernel@V?$VersionedKernel@VDmlOperatorPadding@Dml@@$06@@@@ 5.?AV?$VersionedKernel@VDmlOperatorPadding@Dml@@$06@@ 5.?AVPaddingHelper@OperatorHelper@@ 5.?AV?$MLOperatorKernel@V?$VersionedKernel@VDmlOperatorPadding@Dml@@$0L@@@@@ 5.?AV?$VersionedKernel@VDmlOperatorPadding@Dml@@$0L@@@ 5.?AVDmlOperatorPadding@Dml@@ 5.?AV?$MLOperatorKernel@VDmlOperatorMemcpy@Dml@@@@ 5.?AVDmlOperatorMemcpy@Dml@@ 5.?AV?$MLOperatorKernel@VDmlOperatorRegionOfInterestPooling@Dml@@@@ 5.?AVDmlOperatorRegionOfInterestPooling@Dml@@ 5.?AVRoiPoolingHelper@OperatorHelper@@ 5.?AVRoiPoolingHelperBase@OperatorHelper@@ 5.?AVRecurrentHelper@OperatorHelper@@ 5.?AV?$MLOperatorKernel@VDmlOperatorRecurrentNeuralNetwork@Dml@@@@ 5.?AVDmlOperatorRecurrentBase@Dml@@ 5.?AVDmlOperatorLongShortTermUnit@Dml@@ 5.?AVDmlOperatorGatedRecurrentUnit@Dml@@ 5.?AV?$MLOperatorKernel@VDmlOperatorGatedRecurrentUnit@Dml@@@@ 5.?AVDmlOperatorRecurrentNeuralNetwork@Dml@@ 5.?AV?$MLOperatorKernel@VDmlOperatorLongShortTermUnit@Dml@@@@ 5.?AV?$MLOperatorKernel@V?$VersionedKernel@VDmlOperatorSlice@Dml@@$09@@@@ 5.?AVSliceHelper@OperatorHelper@@ 5.?AV?$MLOperatorKernel@V?$VersionedKernel@VDmlOperatorSlice@Dml@@$06@@@@ 5.?AV?$VersionedKernel@VDmlOperatorSlice@Dml@@$0L@@@ 5.?AV?$VersionedKernel@VDmlOperatorSlice@Dml@@$09@@ 5.?AVDmlOperatorSlice@Dml@@ 5.?AV?$MLOperatorKernel@V?$VersionedKernel@VDmlOperatorSlice@Dml@@$0L@@@@@ 5.?AV?$VersionedKernel@VDmlOperatorSlice@Dml@@$06@@ 5.?AVRoiAlignHelper@OperatorHelper@@ 5.?AV?$MLOperatorKernel@V?$VersionedKernel@VDmlOperatorRegionOfInterestAlign@Dml@@$09@@@@ 5.?AVDmlOperatorRegionOfInterestAlign@Dml@@ 5.?AV?$VersionedKernel@VDmlOperatorRegionOfInterestAlign@Dml@@$09@@ 5.?AVDmlOperatorReverseSequence@Dml@@ 5.?AV?$MLOperatorKernel@VDmlOperatorReverseSequence@Dml@@@@ 5.?AV?$MLOperatorKernel@VDmlOperatorLpNormalization@Dml@@@@ 5.?AVDmlOperatorLpNormalization@Dml@@ 5.?AV?$VersionedKernel@VDmlOperatorTopK@Dml@@$0L@@@ 5.?AV?$MLOperatorKernel@V?$VersionedKernel@VDmlOperatorTopK@Dml@@$06@@@@ 5.?AV?$VersionedKernel@VDmlOperatorTopK@Dml@@$06@@ 5.?AVDmlOperatorTopK@Dml@@ 5.?AV?$VersionedKernel@VDmlOperatorTopK@Dml@@$09@@ 5.?AV?$MLOperatorKernel@V?$VersionedKernel@VDmlOperatorTopK@Dml@@$09@@@@ 5.?AV?$MLOperatorKernel@V?$VersionedKernel@VDmlOperatorTopK@Dml@@$0L@@@@@ 5.?AVTopKHelper@OperatorHelper@@ 5.?AV?$MLOperatorKernel@VDmlOperatorCopy@Dml@@@@ 5.?AVDmlOperatorCopy@Dml@@ 5.?AVDmlOperatorEyeLike@Dml@@ 5.?AV?$MLOperatorKernel@VDmlOperatorEyeLike@Dml@@@@ 5.?AVDmlOperatorInstanceNormalization@Dml@@ 5.?AV?$MLOperatorKernel@VDmlOperatorInstanceNormalization@Dml@@@@ 5.?AVDmlOperatorMeanVarNormalization@Dml@@ 5.?AV?$MLOperatorKernel@VDmlOperatorMeanVarNormalization@Dml@@@@ 5.?AV?$MLOperatorKernel@VDmlOperatorMatMul@Dml@@@@ 5.?AVDmlOperatorMatMul@Dml@@ 5.?AV?$MLOperatorKernel@V?$VersionedKernel@VDmlOperatorEinSum@Dml@@$0M@@@@@ 5.?AV?$VersionedKernel@VDmlOperatorEinSum@Dml@@$0M@@@ 5.?AVEinSumHelper@OperatorHelper@@ 5.?AVDmlOperatorEinSum@Dml@@ 5.?AVDmlOperatorCumSum@Dml@@ 5.?AV?$MLOperatorKernel@VDmlOperatorCumSum@Dml@@@@ 5.?AVDmlOperatorAffine@Dml@@ 5.?AV?$MLOperatorKernel@VDmlOperatorAffine@Dml@@@@ 5.?AVDmlOperatorConvInteger@Dml@@ 5.?AV?$MLOperatorKernel@VDmlOperatorConvInteger@Dml@@@@ 5.?AV?$MLOperatorKernel@VDmlOperatorDepthToSpace@Dml@@@@ 5.?AVDepthToSpaceHelper@OperatorHelper@@ 5.?AVDmlOperatorDepthToSpace@Dml@@ 5.?AVDmlOperatorNeg@Dml@@ 5.?AV?$MLOperatorKernel@VDmlOperatorNeg@Dml@@@@ 5.?AVOneHotHelper@OperatorHelper@@ 5.?AV?$MLOperatorKernel@VDmlOperatorOneHot@Dml@@@@ 5.?AVDmlOperatorOneHot@Dml@@ 5.?AV?$MLOperatorKernel@VDmlOperatorCast@Dml@@@@ 5.?AVDmlOperatorCast@Dml@@ 5.?AV?$MLOperatorKernel@VDmlOperatorMatMulInteger@Dml@@@@ 5.?AVDmlOperatorMatMulInteger@Dml@@ 5.?AVDmlOperatorSpaceToDepth@Dml@@ 5.?AVSpaceToDepthHelper@OperatorHelper@@ 5.?AV?$MLOperatorKernel@VDmlOperatorSpaceToDepth@Dml@@@@ 5.?AVDmlOperatorCrop@Dml@@ 5.?AVCropHelper@OperatorHelper@@ 5.?AV?$MLOperatorKernel@VDmlOperatorCrop@Dml@@@@ 5.?AV?$MLOperatorKernel@VDmlOperatorTile@Dml@@@@ 5.?AVDmlOperatorTile@Dml@@ 5.?AVTileHelper@OperatorHelper@@ 5.?AV?$MLOperatorKernel@VDmlOperatorLocalResponseNormalization@Dml@@@@ 5.?AVDmlOperatorLocalResponseNormalization@Dml@@ 5.?AV?$MLOperatorKernel@VDmlOperatorConcat@Dml@@@@ 5.?AVDmlOperatorConcat@Dml@@ 5.?AVConcatHelper@OperatorHelper@@ 5.?AVDmlOperatorQLinearMatMul@Dml@@ 5.?AV?$MLOperatorKernel@VDmlOperatorQLinearMatMul@Dml@@@@ 5.?AVRangeHelper@OperatorHelper@@ 5.?AV?$MLOperatorKernel@VDmlOperatorRange@Dml@@@@ 5.?AVDmlOperatorRange@Dml@@ 5.?AVDmlOperatorTranspose@Dml@@ 5.?AVTransposeHelper@OperatorHelper@@ 5.?AV?$MLOperatorKernel@VDmlOperatorTranspose@Dml@@@@ 5.?AVDmlOperatorValueScale2d@Dml@@ 5.?AV?$MLOperatorKernel@VDmlOperatorValueScale2d@Dml@@@@ 5.?AVICommandRecorder@Dml@@ 5.?AVDmlCommandRecorder@Dml@@ 5.?AVbad_optional_access@std@@ 5.?AV?$_Func_impl_no_alloc@V@@XAAUInferenceContext@onnx@@@std@@ 5.?AV?$_Func_impl_no_alloc@V@@XAAUInferenceContext@onnx@@@std@@ 5.?AV?$_Func_impl_no_alloc@V@@XAAUInferenceContext@onnx@@@std@@ 5.?AV?$_Func_impl_no_alloc@V@@XAAUInferenceContext@onnx@@@std@@ 5.?AV?$_Func_impl_no_alloc@V@@XAAUInferenceContext@onnx@@@std@@ 5.?AV?$_Func_impl_no_alloc@V@@XAAUInferenceContext@onnx@@@std@@ 5.?AV?$_Func_impl_no_alloc@V@@XAAUInferenceContext@onnx@@@std@@ 5.?AV?$_Func_impl_no_alloc@V@@XAAUInferenceContext@onnx@@@std@@ 5.?AV?$_Func_impl_no_alloc@V@@XAAUInferenceContext@onnx@@@std@@ 5.?AV?$_Func_impl_no_alloc@V@@XAAUInferenceContext@onnx@@@std@@ 5.?AV?$_Func_impl_no_alloc@V@@XAAUInferenceContext@onnx@@@std@@ 5.?AV?$_Func_impl_no_alloc@V@@XAAUInferenceContext@onnx@@@std@@ 5.?AVWinmlAdapterLoggingWrapper@@ 5.?AV@@ 5.?AV@@ 5.?AV@@ 5.?AV@@ 5.?AV@@ 5.?AV@@ 5.?AV@@ 5.?AV@@ 5.?AV@@ 5.?AV@@ 5.?AV@@ 5.?AV@@ 5.?AVAbiCustomRegistryImpl@Adapter@MachineLearning@AI@Windows@@ 5.?AVRemoveDuplicateCastTransformer@onnxruntime@@ 5.?AVFastGeluFusion@onnxruntime@@ 5.?AVAttentionFusion@onnxruntime@@ 5.?AVMatMulAddFusion@onnxruntime@@ 5.?AVFuseReluClip@onnxruntime@@ 5.?AVRuleBasedGraphTransformer@onnxruntime@@ 5.?AVRewriteRule@onnxruntime@@ 5.?AVCastElimination@onnxruntime@@ 5.?AVReshapeFusion@onnxruntime@@ 5.?AVEliminateDropout@onnxruntime@@ 5.?AVEliminateIdentity@onnxruntime@@ 5.?AVMatMulScaleFusion@onnxruntime@@ 5.?AVEliminateSlice@onnxruntime@@ 5.?AVGeluFusion@onnxruntime@@ 5.?AVGeluApproximation@onnxruntime@@ 5.?AVBiasGeluFusion@onnxruntime@@ 5.?AVGemmActivationFusion@onnxruntime@@ 5.?AVExpandElimination@onnxruntime@@ 5.?AVSimplifiedLayerNormFusion@onnxruntime@@ 5.?AVSkipLayerNormFusion@onnxruntime@@ 5.?AVConvAddFusion@onnxruntime@@ 5.?AVConvBNFusion@onnxruntime@@ 5.?AVUnsqueezeElimination@onnxruntime@@ 5.?AVDynamicQuantizeMatMulFusion@onnxruntime@@ 5.?AVConvActivationFusion@onnxruntime@@ 5.?AVBiasSoftmaxFusion@onnxruntime@@ 5.?AVShapeToInitializer@onnxruntime@@ 5.?AVEmbedLayerNormFusion@onnxruntime@@ 5.?AVLayerNormFusion@onnxruntime@@ 5.?AVConvMulFusion@onnxruntime@@ 5.?AVCommonSubexpressionElimination@onnxruntime@@ 5.?AV?$_Func_base@VStatus@common@onnxruntime@@ABVNodeArg@3@I@std@@ 5.?AV@@ 5.?AV?$_Func_impl_no_alloc@V@@VStatus@common@onnxruntime@@ABVNodeArg@4@I@std@@ 5.?AV@@ 5.?AV@@ 5.?AV?$_Func_impl_no_alloc@V@@VStatus@common@onnxruntime@@ABVNodeArg@4@I@std@@ 5.?AV?$_Func_impl_no_alloc@V@@VStatus@common@onnxruntime@@ABVNodeArg@4@I@std@@ 5.?AVConstantFolding@onnxruntime@@ 5.?AVFreeDimensionOverrideTransformer@onnxruntime@@ 5.?AVCPUDataTransfer@onnxruntime@@ 5.?AV@@ 5.?AVIExecutionFrame@onnxruntime@@ 5.?AV?$_Func_impl_no_alloc@V@@VStatus@common@onnxruntime@@ABVNodeArg@4@I@std@@ 5.?AVOptimizerExecutionFrame@onnxruntime@@ 5.?AV?$_Ref_count_obj2@UCpuProviderFactory@onnxruntime@@@std@@ 5.?AUCpuProviderFactory@onnxruntime@@ 5.P6APAVOpKernel@onnxruntime@@ABVOpKernelInfo@1@@Z 5.?AVPadBase@onnxruntime@@ 5.?AV?$_Func_impl_no_alloc@P6APAVOpKernel@onnxruntime@@ABVOpKernelInfo@2@@ZPAV12@ABV32@@std@@ 5.?AUPad@onnxruntime@@ 5.?AVGatherNDBase@onnxruntime@@ 5.?AVGatherND@onnxruntime@@ 5.?AV?$_Func_impl_no_alloc@V@@XHH@std@@ 5.?AV?$_Func_impl_no_alloc@V@@XHH@std@@ 5.?AV@@ 5.?AV@@ 5.?AV@@ 5.?AV?$_Func_impl_no_alloc@V@@XHH@std@@ 5.?AV?$_Func_impl_no_alloc@V@@XHH@std@@ 5.?AV@@ 5.?AV?$_Func_base@XHH@std@@ 5.?AV?$NonZero@H@onnxruntime@@ 5.?AV?$NonZero@_J@onnxruntime@@ 5.?AV?$NonZero@M@onnxruntime@@ 5.?AV?$NonZero@_N@onnxruntime@@ 5.?AV?$NonZero@E@onnxruntime@@ 5.?AV?$IsNaN@TMLFloat16@onnxruntime@@@onnxruntime@@ 5.?AV?$IsNaN@M@onnxruntime@@ 5.?AV?$IdentityOp@$00@onnxruntime@@ 5.?AV?$IdentityOp@$0A@@onnxruntime@@ 5.?AV?$_Func_impl_no_alloc@V@@XHH@std@@ 5.?AV@@ 5.?AV@@ 5.?AVScatterNDBase@onnxruntime@@ 5.?AV?$_Func_impl_no_alloc@V@@XHH@std@@ 5.?AVScatterND@onnxruntime@@ 5.?AVScatter@onnxruntime@@ 5.?AV?$SpaceToDepth@M@onnxruntime@@ 5.?AV?$DepthToSpace@M@onnxruntime@@ 5.?AVSpaceDepthBase@onnxruntime@@ 5.?AVGatherElements@onnxruntime@@ 5.?AV@@ 5.?AV@@ 5.?AV?$_Func_impl_no_alloc@V@@XHH@std@@ 5.?AV?$_Func_impl_no_alloc@V@@XHH@std@@ 5.?AVGather@onnxruntime@@ 5.?AVGatherBase@onnxruntime@@ 5.?AUTile@onnxruntime@@ 5.?AVTransposeBase@onnxruntime@@ 5.?AVTranspose@onnxruntime@@ 5.?AUSlice1@onnxruntime@@ 5.?AUSlice10@onnxruntime@@ 5.?AVSliceBase@onnxruntime@@ 5.?AV?$ElementWiseKernel@U?$Abs@H@functors@onnxruntime@@@onnxruntime@@ 5.?AV?$Expand_8@_N@onnxruntime@@ 5.?AV?$_Func_impl_no_alloc@U?$Exp@N@functors@onnxruntime@@XHH@std@@ 5.?AVOr@onnxruntime@@ 5.?AV?$Equal@_N@onnxruntime@@ 5.?AV?$Sinh@M@onnxruntime@@ 5.?AV?$Acosh@M@onnxruntime@@ 5.?AU?$Exp@N@functors@onnxruntime@@ 5.?AV?$Expand_8@G@onnxruntime@@ 5.?AU?$ElementWiseRangedTransform@M@functors@onnxruntime@@ 5.?AV?$Greater@_J@onnxruntime@@ 5.?AV?$_Func_impl_no_alloc@U?$Floor@M@functors@onnxruntime@@XHH@std@@ 5.?AV?$ElementWiseKernel@U?$Exp@N@functors@onnxruntime@@@onnxruntime@@ 5.?AU?$ElementWiseRangedTransform@I@functors@onnxruntime@@ 5.?AV?$Mul@_J@onnxruntime@@ 5.?AV?$Greater@N@onnxruntime@@ 5.?AV?$Expand_8@C@onnxruntime@@ 5.?AVXor@onnxruntime@@ 5.?AV?$ElementWiseKernel@U?$Abs@M@functors@onnxruntime@@@onnxruntime@@ 5.?AV?$ElementWiseKernel@U?$Floor@M@functors@onnxruntime@@@onnxruntime@@ 5.?AV?$Equal@H@onnxruntime@@ 5.?AU?$Abs@N@functors@onnxruntime@@ 5.?AU?$Abs@E@functors@onnxruntime@@ 5.?AV?$ElementWiseKernel@U?$Neg@M@functors@onnxruntime@@@onnxruntime@@ 5.?AU?$Floor@M@functors@onnxruntime@@ 5.?AV?$ElementWiseKernel@U?$Abs@E@functors@onnxruntime@@@onnxruntime@@ 5.?AU?$ElementWiseRangedTransform@G@functors@onnxruntime@@ 5.?AV?$Sum_8@M@onnxruntime@@ 5.?AV?$ElementWiseKernel@U?$Neg@C@functors@onnxruntime@@@onnxruntime@@ 5.?AV?$ElementWiseKernel@U?$Neg@H@functors@onnxruntime@@@onnxruntime@@ 5.?AV?$_Func_impl_no_alloc@U?$Reciprocal@M@functors@onnxruntime@@XHH@std@@ 5.?AV@@ 5.?AV?$ElementWiseKernel@U?$Abs@I@functors@onnxruntime@@@onnxruntime@@ 5.?AV?$_Func_impl_no_alloc@U?$Abs@C@functors@onnxruntime@@XHH@std@@ 5.?AV?$Max_6@M@onnxruntime@@ 5.?AV?$ElementWiseKernel@U?$Ceil@M@functors@onnxruntime@@@onnxruntime@@ 5.?AV?$_Func_impl_no_alloc@U?$Neg@N@functors@onnxruntime@@XHH@std@@ 5.?AU?$Neg@H@functors@onnxruntime@@ 5.?AV?$Asinh@M@onnxruntime@@ 5.?AV?$ElementWiseKernel@U?$Sqrt@M@functors@onnxruntime@@@onnxruntime@@ 5.?AV?$Greater@M@onnxruntime@@ 5.?AU?$Abs@I@functors@onnxruntime@@ 5.?AU?$Abs@_J@functors@onnxruntime@@ 5.?AV?$Mul@M@onnxruntime@@ 5.?AU?$Sqrt@N@functors@onnxruntime@@ 5.?AV?$Less@M@onnxruntime@@ 5.?AV?$Mul@N@onnxruntime@@ 5.?AV?$Atanh@M@onnxruntime@@ 5.?AU?$ElementWiseRangedTransform@_J@functors@onnxruntime@@ 5.?AV?$Expand_8@M@onnxruntime@@ 5.?AV?$_Func_impl_no_alloc@U?$Neg@_J@functors@onnxruntime@@XHH@std@@ 5.?AV?$Tan@M@onnxruntime@@ 5.?AV?$ElementWiseKernel@U?$Abs@_J@functors@onnxruntime@@@onnxruntime@@ 5.?AV?$_Func_impl_no_alloc@V@@XHH@std@@ 5.?AV?$Cosh@M@onnxruntime@@ 5.?AV?$_Func_impl_no_alloc@U?$Abs@M@functors@onnxruntime@@XHH@std@@ 5.?AV?$Div@H@onnxruntime@@ 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5.?AV?$_Func_impl_no_alloc@U?$MaxPool2DTask@N@onnxruntime@@XHH@std@@ 5.?AV?$_Func_impl_no_alloc@U?$MaxPool3DTask@C@onnxruntime@@XHH@std@@ 5.?AU?$MaxPool1DTask@C@onnxruntime@@ 5.?AU?$MaxPool3DTask@C@onnxruntime@@ 5.?AV?$_Func_impl_no_alloc@U?$MaxPool1DTask@C@onnxruntime@@XHH@std@@ 5.?AU?$MaxPool2DTask@N@onnxruntime@@ 5.?AU?$MaxPool2DTask@M@onnxruntime@@ 5.?AU?$MaxPool3DTask@E@onnxruntime@@ 5.?AV?$_Func_impl_no_alloc@U?$MaxPool2DTask@M@onnxruntime@@XHH@std@@ 5.?AU?$MaxPool1DTask@M@onnxruntime@@ 5.?AU?$Pool3DTask@MVLpPool@onnxruntime@@@onnxruntime@@ 5.?AU?$MaxPool1DTask@E@onnxruntime@@ 5.?AV?$_Func_impl_no_alloc@U?$MaxPool3DTask@E@onnxruntime@@XHH@std@@ 5.?AU?$MaxPool3DTask@M@onnxruntime@@ 5.?AU?$MaxPool2DTask@E@onnxruntime@@ 5.?AV?$_Func_impl_no_alloc@U?$Pool2DTask@MVLpPool@onnxruntime@@@onnxruntime@@XHH@std@@ 5.?AV?$_Func_impl_no_alloc@U?$Pool3DTask@MVLpPool@onnxruntime@@@onnxruntime@@XHH@std@@ 5.?AV?$Pool@MVAveragePool@onnxruntime@@@onnxruntime@@ 5.?AU?$MaxPool3DTask@N@onnxruntime@@ 5.?AV?$_Func_impl_no_alloc@U?$MaxPool3DTask@N@onnxruntime@@XHH@std@@ 5.?AU?$MaxPool2DTask@C@onnxruntime@@ 5.?AU?$Pool2DTask@MVLpPool@onnxruntime@@@onnxruntime@@ 5.?AVMaxPoolV8@onnxruntime@@ 5.?AV?$_Func_impl_no_alloc@U?$MaxPool2DTask@E@onnxruntime@@XHH@std@@ 5.?AU?$MaxPool1DTask@N@onnxruntime@@ 5.?AV?$_Func_impl_no_alloc@U?$MaxPool2DTask@C@onnxruntime@@XHH@std@@ 5.?AV?$_Func_impl_no_alloc@U?$MaxPool1DTask@M@onnxruntime@@XHH@std@@ 5.?AVPoolBase@onnxruntime@@ 5.?AU?$Pool1DTask@MVLpPool@onnxruntime@@@onnxruntime@@ 5.?AV?$Pool@MVLpPool@onnxruntime@@@onnxruntime@@ 5.?AV?$_Func_impl_no_alloc@U?$MaxPool1DTask@N@onnxruntime@@XHH@std@@ 5.?AV?$Resize@H@onnxruntime@@ 5.?AV@@ 5.?AV?$_Func_impl_no_alloc@V@@MMMMMMM@std@@ 5.?AV@@ 5.?AV?$_Func_impl_no_alloc@V@@_JM_N@std@@ 5.?AV?$_Func_impl_no_alloc@V@@MMMMMMM@std@@ 5.?AV?$Resize@M@onnxruntime@@ 5.?AV?$_Func_base@_JM_N@std@@ 5.?AV?$_Func_impl_no_alloc@V@@MMMMMMM@std@@ 5.?AV@@ 5.?AV@@ 5.?AV?$_Func_impl_no_alloc@V@@_JM_N@std@@ 5.?AV?$Upsample@E@onnxruntime@@ 5.?AV?$_Func_impl_no_alloc@V@@_JM_N@std@@ 5.?AV@@ 5.?AV?$_Func_impl_no_alloc@V@@MMMMMMM@std@@ 5.?AV@@ 5.?AV@@ 5.?AV@@ 5.?AV@@ 5.?AV?$Resize@E@onnxruntime@@ 5.?AV?$Upsample@H@onnxruntime@@ 5.?AV?$_Func_impl_no_alloc@V@@_JM_N@std@@ 5.?AV?$Upsample@M@onnxruntime@@ 5.?AV?$_Func_base@MMMMMMM@std@@ 5.?AV@@ 5.?AVUpsampleBase@onnxruntime@@ 5.?AV?$_Func_impl_no_alloc@V@@MMMMMMM@std@@ 5.?AV?$_Func_impl_no_alloc@V@@MMMMMMM@std@@ 5.?AV@@ 5.?AV?$_Func_impl_no_alloc@V@@_JM_N@std@@ 5.?AV?$OneHotOp@_JMM@onnxruntime@@ 5.?AV?$OneHotOp@MV?$basic_string@DU?$char_traits@D@std@@V?$allocator@D@2@@std@@_J@onnxruntime@@ 5.?AV?$OneHotOp@_JM_J@onnxruntime@@ 5.?AV?$OneHotOp@_JV?$basic_string@DU?$char_traits@D@std@@V?$allocator@D@2@@std@@_J@onnxruntime@@ 5.?AV?$OneHotOp@M_J_J@onnxruntime@@ 5.?AV?$OneHotOp@HMH@onnxruntime@@ 5.?AV?$OneHotOp@_JMH@onnxruntime@@ 5.?AV?$OneHotOp@_JHM@onnxruntime@@ 5.?AV?$OneHotOp@_J_J_J@onnxruntime@@ 5.?AV?$OneHotOp@MMM@onnxruntime@@ 5.?AV?$OneHotOp@HMM@onnxruntime@@ 5.?AV?$_Func_base@XH@std@@ 5.?AV@@ 5.?AV?$_Func_impl_no_alloc@V@@XH@std@@ 5.?AV?$_Func_impl_no_alloc@V@@XH@std@@ 5.?AV?$TopK@$09M@onnxruntime@@ 5.?AV?$_Func_impl_no_alloc@V@@XH@std@@ 5.?AV@@ 5.?AV?$_Func_impl_no_alloc@V@@XH@std@@ 5.?AV@@ 5.?AV?$_Func_impl_no_alloc@V@@XH@std@@ 5.?AV@@ 5.?AV@@ 5.?AV@@ 5.?AV?$TopK@$0L@M@onnxruntime@@ 5.?AV?$TopK@$08M@onnxruntime@@ 5.?AV?$_Func_impl_no_alloc@V@@XH@std@@ 5.?AV@@ 5.?AV?$_Func_impl_no_alloc@V@@XH@std@@ 5.?AV?$TopK@$0L@_J@onnxruntime@@ 5.?AV?$_Func_impl_no_alloc@V@@XH@std@@ 5.?AV@@ 5.?AV?$_Func_impl_no_alloc@V@@XH@std@@ 5.?AV?$_Func_impl_no_alloc@V@@XH@std@@ 5.?AV@@ 5.?AV@@ 5.?AV?$_Func_impl_no_alloc@V@@XH@std@@ 5.?AV@@ 5.?AV?$_Func_impl_no_alloc@V@@XH@std@@ 5.?AV@@ 5.?AVUnique@onnxruntime@@ 5.?AVRange@onnxruntime@@ 5.?AVSplitToSequence@onnxruntime@@ 5.?AVSequenceInsert@onnxruntime@@ 5.?AVSequenceAt@onnxruntime@@ 5.?AVSequenceLength@onnxruntime@@ 5.?AVSequenceEmpty@onnxruntime@@ 5.?AVSequenceErase@onnxruntime@@ 5.?AVSequenceConstruct@onnxruntime@@ 5.?AVConcatFromSequence@onnxruntime@@ 5.?AVIf@onnxruntime@@ 5.?AV?$_Func_impl_no_alloc@V@@VStatus@common@onnxruntime@@ABVTensorShape@4@ABUOrtMemoryInfo@@AAUOrtValue@@AA_N@std@@ 5.?AV@@ 5.?AVIControlFlowKernel@controlflow@onnxruntime@@ 5.?AV?$_Func_base@VStatus@common@onnxruntime@@ABVTensorShape@3@ABUOrtMemoryInfo@@AAUOrtValue@@AA_N@std@@ 5.?AV?$ConvTranspose@M@onnxruntime@@ 5.?AV?$Conv@M@onnxruntime@@ 5.?AVMaxUnpool@onnxruntime@@ 5.?AVNonMaxSuppression@onnxruntime@@ 5.?AVNonMaxSuppressionBase@onnxruntime@@ 5.?AV?$Det@M@onnxruntime@@ 5.?AVSqueezeBase@onnxruntime@@ 5.?AVSqueeze@onnxruntime@@ 5.?AVSplit@onnxruntime@@ 5.?AVSplitBase@onnxruntime@@ 5.?AVCompress@onnxruntime@@ 5.?AV@@ 5.?AV@@ 5.?AV?$_Func_base@VStatus@common@onnxruntime@@ABV?$vector@IV?$allocator@I@std@@@std@@ABVTensor@3@AAV63@@std@@ 5.?AV?$_Func_base@VStatus@common@onnxruntime@@PAXI@std@@ 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5.?AV?$_Func_base@MMMM@std@@ 5.P6AMMMM@Z 5.?AV?$RNN@M@onnxruntime@@ 5.?AV?$_Func_impl_no_alloc@P6AMMMM@ZMMMM@std@@ 5.?AVDeepCpuLstmOp@onnxruntime@@ 5.?AV?$_Func_impl_no_alloc@V@@XHH@std@@ 5.?AV?$_Func_base@XPAH@std@@ 5.?AV@@ 5.?AV?$_Func_impl_no_alloc@V@@XPAH@std@@ 5.?AV@@ 5.?AVDeepCpuGruOp@onnxruntime@@ 5.?AV?$RoiPool@M@onnxruntime@@ 5.?AV?$LpNorm@M@onnxruntime@@ 5.?AV?$InstanceNorm@M@onnxruntime@@ 5.?AV?$BatchNorm@N@onnxruntime@@ 5.?AV?$BatchNorm@M@onnxruntime@@ 5.?AVRandomNormal@onnxruntime@@ 5.?AVMultinomial@onnxruntime@@ 5.?AVRandomNormalLike@onnxruntime@@ 5.?AVRandomUniform@onnxruntime@@ 5.?AVRandomUniformLike@onnxruntime@@ 5.?AV?$LabelEncoder_2@V?$basic_string@DU?$char_traits@D@std@@V?$allocator@D@2@@std@@_J@ml@onnxruntime@@ 5.?AV?$LabelEncoder_2@_JM@ml@onnxruntime@@ 5.?AV?$LabelEncoder_2@V?$basic_string@DU?$char_traits@D@std@@V?$allocator@D@2@@std@@M@ml@onnxruntime@@ 5.?AV?$LabelEncoder_2@M_J@ml@onnxruntime@@ 5.?AV?$LabelEncoder_2@MV?$basic_string@DU?$char_traits@D@std@@V?$allocator@D@2@@std@@@ml@onnxruntime@@ 5.?AV?$LabelEncoder_2@_JV?$basic_string@DU?$char_traits@D@std@@V?$allocator@D@2@@std@@@ml@onnxruntime@@ 5.?AVLabelEncoder@ml@onnxruntime@@ 5.?AV?$LabelEncoder_2@_J_J@ml@onnxruntime@@ 5.?AVZipMapOp@ml@onnxruntime@@ 5.?AV?$_Func_impl_no_alloc@V@@XH@std@@ 5.?AV@@ 5.?AV?$_Func_impl_no_alloc@V@@XH@std@@ 5.?AV?$_Func_impl_no_alloc@V@@XH@std@@ 5.?AV?$_Func_impl_no_alloc@V@@XH@std@@ 5.?AV@@ 5.?AV@@ 5.?AV@@ 5.?AV@@ 5.?AV?$_Func_impl_no_alloc@V@@XH@std@@ 5.?AV@@ 5.?AV@@ 5.?AV@@ 5.?AV@@ 5.?AV@@ 5.?AV@@ 5.?AV@@ 5.?AV?$_Func_impl_no_alloc@V@@XH@std@@ 5.?AV?$_Func_impl_no_alloc@V@@XH@std@@ 5.?AV@@ 5.?AV@@ 5.?AV@@ 5.?AV?$_Func_impl_no_alloc@V@@XH@std@@ 5.?AV?$_Func_impl_no_alloc@V@@XH@std@@ 5.?AV@@ 5.?AV@@ 5.?AV?$_Func_impl_no_alloc@V@@XH@std@@ 5.?AV?$_Func_impl_no_alloc@V@@XH@std@@ 5.?AV?$_Func_impl_no_alloc@V@@XH@std@@ 5.?AV@@ 5.?AV@@ 5.?AV@@ 5.?AV?$_Func_impl_no_alloc@V@@XH@std@@ 5.?AV?$_Func_impl_no_alloc@V@@XH@std@@ 5.?AV?$_Func_impl_no_alloc@V@@XH@std@@ 5.?AV@@ 5.?AV?$_Func_impl_no_alloc@V@@XH@std@@ 5.?AV?$TreeEnsembleRegressor@M@ml@onnxruntime@@ 5.?AV?$_Func_impl_no_alloc@V@@XH@std@@ 5.?AV@@ 5.?AV?$_Func_impl_no_alloc@V@@XH@std@@ 5.?AV?$_Func_impl_no_alloc@V@@XH@std@@ 5.?AV@@ 5.?AV?$_Func_impl_no_alloc@V@@XH@std@@ 5.?AV?$_Func_impl_no_alloc@V@@XH@std@@ 5.?AV@@ 5.?AV@@ 5.?AV@@ 5.?AV@@ 5.?AV?$_Func_impl_no_alloc@V@@XH@std@@ 5.?AV@@ 5.?AV?$_Func_impl_no_alloc@V@@XH@std@@ 5.?AV?$_Func_impl_no_alloc@V@@XH@std@@ 5.?AV?$_Func_impl_no_alloc@V@@XH@std@@ 5.?AV@@ 5.?AV?$_Func_impl_no_alloc@V@@XH@std@@ 5.?AV?$_Func_impl_no_alloc@V@@XH@std@@ 5.?AV?$_Func_impl_no_alloc@V@@XH@std@@ 5.?AV?$_Func_impl_no_alloc@V@@XH@std@@ 5.?AV?$TreeEnsembleRegressor@N@ml@onnxruntime@@ 5.?AV@@ 5.?AV?$_Func_impl_no_alloc@V@@XH@std@@ 5.?AV?$_Func_impl_no_alloc@V@@XH@std@@ 5.?AV?$_Func_impl_no_alloc@V@@XH@std@@ 5.?AV@@ 5.?AV@@ 5.?AV?$_Func_impl_no_alloc@V@@XH@std@@ 5.?AV@@ 5.?AV?$_Func_impl_no_alloc@V@@XH@std@@ 5.?AV@@ 5.?AV@@ 5.?AV?$_Func_impl_no_alloc@V@@XH@std@@ 5.?AV@@ 5.?AV@@ 5.?AV?$_Func_impl_no_alloc@V@@XH@std@@ 5.?AV@@ 5.?AV@@ 5.?AV@@ 5.?AV?$_Func_impl_no_alloc@V@@XH@std@@ 5.?AV?$_Func_impl_no_alloc@V@@XH@std@@ 5.?AV?$_Func_impl_no_alloc@V@@XH@std@@ 5.?AV?$_Func_impl_no_alloc@V@@XH@std@@ 5.?AV?$_Func_impl_no_alloc@V@@XH@std@@ 5.?AV?$_Func_impl_no_alloc@V@@XH@std@@ 5.?AV@@ 5.?AV@@ 5.?AV@@ 5.?AV@@ 5.?AV?$TreeEnsembleClassifier@N@ml@onnxruntime@@ 5.?AV@@ 5.?AV?$_Func_impl_no_alloc@V@@XH@std@@ 5.?AV?$_Func_impl_no_alloc@V@@XH@std@@ 5.?AV?$_Func_impl_no_alloc@V@@XH@std@@ 5.?AV?$_Func_impl_no_alloc@V@@XH@std@@ 5.?AV@@ 5.?AV?$TreeEnsembleClassifier@_J@ml@onnxruntime@@ 5.?AV@@ 5.?AV?$_Func_impl_no_alloc@V@@XH@std@@ 5.?AV?$_Func_impl_no_alloc@V@@XH@std@@ 5.?AV?$TreeEnsembleClassifier@M@ml@onnxruntime@@ 5.?AV?$TreeEnsembleClassifier@H@ml@onnxruntime@@ 5.?AV@@ 5.?AVSVMCommon@ml@onnxruntime@@ 5.?AV?$SVMRegressor@M@ml@onnxruntime@@ 5.?AV?$_Func_impl_no_alloc@V@@XMPAM@std@@ 5.?AV?$_Func_impl_no_alloc@V@@XMPAM@std@@ 5.?AV@@ 5.?AV@@ 5.?AV?$_Func_impl_no_alloc@V@@XMPAM@std@@ 5.?AV@@ 5.?AV?$_Func_impl_no_alloc@V@@XH@std@@ 5.?AV@@ 5.?AVSVMClassifier@ml@onnxruntime@@ 5.?AV?$_Func_base@XMPAM@std@@ 5.?AV?$ScalerOp@N@ml@onnxruntime@@ 5.?AV?$ScalerOp@M@ml@onnxruntime@@ 5.?AV?$ScalerOp@_J@ml@onnxruntime@@ 5.?AV?$ScalerOp@H@ml@onnxruntime@@ 5.?AV?$OneHotEncoderOp@V?$basic_string@DU?$char_traits@D@std@@V?$allocator@D@2@@std@@@ml@onnxruntime@@ 5.?AV?$OneHotEncoderOp@N@ml@onnxruntime@@ 5.?AV?$OneHotEncoderOp@_J@ml@onnxruntime@@ 5.?AV?$OneHotEncoderOp@M@ml@onnxruntime@@ 5.?AVNormalizer@ml@onnxruntime@@ 5.?AVLinearRegressor@ml@onnxruntime@@ 5.?AVLinearClassifier@ml@onnxruntime@@ 5.?AVImputerOp@ml@onnxruntime@@ 5.?AVFeatureVectorizer@ml@onnxruntime@@ 5.?AV?$DictVectorizerOp@V?$basic_string@DU?$char_traits@D@std@@V?$allocator@D@2@@std@@N@ml@onnxruntime@@ 5.?AV?$DictVectorizerOp@V?$basic_string@DU?$char_traits@D@std@@V?$allocator@D@2@@std@@_J@ml@onnxruntime@@ 5.?AV?$DictVectorizerOp@_JV?$basic_string@DU?$char_traits@D@std@@V?$allocator@D@2@@std@@@ml@onnxruntime@@ 5.?AV?$DictVectorizerOp@_JN@ml@onnxruntime@@ 5.?AV?$DictVectorizerOp@V?$basic_string@DU?$char_traits@D@std@@V?$allocator@D@2@@std@@M@ml@onnxruntime@@ 5.?AV?$DictVectorizerOp@_JM@ml@onnxruntime@@ 5.?AVCategoryMapper@ml@onnxruntime@@ 5.?AVCastMap@ml@onnxruntime@@ 5.?AV?$BinarizerOp@M@ml@onnxruntime@@ 5.?AV?$ArrayFeatureExtractorOp@_J@ml@onnxruntime@@ 5.?AV?$ArrayFeatureExtractorOp@V?$basic_string@DU?$char_traits@D@std@@V?$allocator@D@2@@std@@@ml@onnxruntime@@ 5.?AV?$ArrayFeatureExtractorOp@H@ml@onnxruntime@@ 5.?AV?$ArrayFeatureExtractorOp@M@ml@onnxruntime@@ 5.?AV?$ArrayFeatureExtractorOp@N@ml@onnxruntime@@ 5.?AV@@ 5.?AVIterator@?$OrtValueTensorSlicer@UOrtValue@@@onnxruntime@@ 5.?AV?$_Func_impl_no_alloc@V@@VStatus@common@onnxruntime@@ABVTensorShape@4@ABUOrtMemoryInfo@@AAUOrtValue@@AA_N@std@@ 5.?AVTrilu@contrib@onnxruntime@@ 5.?AVInverse@contrib@onnxruntime@@ 5.?AV@@ 5.?AV?$_Func_impl_no_alloc@V@@XH@std@@ 5.?AV?$SkipLayerNorm@N@contrib@onnxruntime@@ 5.?AV?$_Func_impl_no_alloc@V@@XH@std@@ 5.?AV@@ 5.?AV@@ 5.?AV?$SkipLayerNorm@M@contrib@onnxruntime@@ 5.?AV?$_Func_impl_no_alloc@V@@XH@std@@ 5.?AV?$LayerNorm@N$0A@@contrib@onnxruntime@@ 5.?AV?$_Func_impl_no_alloc@V@@XH@std@@ 5.?AV@@ 5.?AV@@ 5.?AV@@ 5.?AV@@ 5.?AV?$LayerNorm@N$00@contrib@onnxruntime@@ 5.?AV?$_Func_impl_no_alloc@V@@XH@std@@ 5.?AV?$LayerNorm@M$0A@@contrib@onnxruntime@@ 5.?AV?$_Func_impl_no_alloc@V@@XH@std@@ 5.?AV?$_Func_impl_no_alloc@V@@XH@std@@ 5.?AV?$LayerNorm@M$00@contrib@onnxruntime@@ 5.?AV?$Affine@M@contrib@onnxruntime@@ 5.?AV?$Scale@M@contrib@onnxruntime@@ 5.?AV@@ 5.?AV?$ElementWiseKernel@U?$ParametricSoftplus@M@functors@onnxruntime@@@onnxruntime@@ 5.?AV?$Gelu@M@contrib@onnxruntime@@ 5.?AV?$_Func_impl_no_alloc@U?$ParametricSoftplus@M@functors@onnxruntime@@XHH@std@@ 5.?AV?$ElementWiseKernel@U?$ScaledTanh@M@functors@onnxruntime@@@onnxruntime@@ 5.?AV?$_Func_impl_no_alloc@V@@XH@std@@ 5.?AV?$_Func_impl_no_alloc@U?$ScaledTanh@M@functors@onnxruntime@@XHH@std@@ 5.?AV?$ImageScaler@M@contrib@onnxruntime@@ 5.?AV?$Crop@M@contrib@onnxruntime@@ 5.?AVCropBase@contrib@onnxruntime@@ 5.?AV?$BiasGelu@M$00@contrib@onnxruntime@@ 5.?AV?$BiasGelu@M$0A@@contrib@onnxruntime@@ 5.?AV?$_Func_impl_no_alloc@V@@XH@std@@ 5.?AV@@ 5.?AV?$_Func_impl_no_alloc@V@@XH@std@@ 5.?AV@@ 5.?AV@@ 5.?AV?$_Func_impl_no_alloc@V@@XH@std@@ 5.?AV?$CDist@N@contrib@onnxruntime@@ 5.?AV?$CDist@M@contrib@onnxruntime@@ 5.?AV?$_Func_impl_no_alloc@V@@XH@std@@ 5.?AV@@ 5.?AV?$CropAndResize@M@contrib@onnxruntime@@ 5.?AV?$ConvTransposeWithDynamicPads@M@contrib@onnxruntime@@ 5.?AV?$Unique@M@contrib@onnxruntime@@ 5.?AV?$_Func_impl_no_alloc@U?$MaxpoolWithMask1DTask@M@contrib@onnxruntime@@XHH@std@@ 5.?AVMaxpoolWithMask@contrib@onnxruntime@@ 5.?AV?$_Func_impl_no_alloc@U?$MaxpoolWithMask3DTask@M@contrib@onnxruntime@@XHH@std@@ 5.?AU?$MaxpoolWithMask1DTask@M@contrib@onnxruntime@@ 5.?AU?$MaxpoolWithMask3DTask@M@contrib@onnxruntime@@ 5.?AU?$MaxpoolWithMask2DTask@M@contrib@onnxruntime@@ 5.?AV?$_Func_impl_no_alloc@U?$MaxpoolWithMask2DTask@M@contrib@onnxruntime@@XHH@std@@ 5.?AVMurmurHash3@contrib@onnxruntime@@ 5.?AVWordConvEmbedding@contrib@onnxruntime@@ 5.?AVTokenizer@contrib@onnxruntime@@ 5.?AVDeepCpuAttnLstmOp@contrib@onnxruntime@@ 5.?AV?$FusedGemm@M@contrib@onnxruntime@@ 5.?AVFusedConvFloat@contrib@onnxruntime@@ 5.?AVExpandDims@contrib@onnxruntime@@ 5.?AV?$EmbedLayerNorm@M@contrib@onnxruntime@@ 5.?AV?$_Func_impl_no_alloc@V@@XH@std@@ 5.?AV@@ 5.?AV?$_Func_impl_no_alloc@V@@XHH@std@@ 5.?AV@@ 5.?AVAttentionCPUBase@contrib@onnxruntime@@ 5.?AV@@ 5.?AV@@ 5.?AV?$_Func_impl_no_alloc@V@@XHH@std@@ 5.?AV?$Attention@M@contrib@onnxruntime@@ 5.?AV?$_Func_impl_no_alloc@V@@XHH@std@@ 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