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辸AHHK8HuMHH@MHL$ S>HL$ iBHHK8Hu_MHH@QMHL$ >HL$ 蔡BHHK8Hu*MHH@MHL$ =HL$ BHHK8HuMHH@MHL$ =HL$ QBHHK8HuMHH@MHL$ =HL$ 腣AHHK8HuMHH@}MHL$ J=HL$ wBHHK8HuVMHH@HMHL$ =HL$ kAHHK8Hu!MHH@MHL$ HHK8HuMHH@MHL$ vHHK8HuMHH@MHL$ :HL$ AHHK8HuMHH@MHL$ y:HL$ BHHK8HuMHH@wMHL$ D:HL$ *>HHK8HuPMHH@BMHL$ :HL$ 5>HHK8HuMHH@ MHL$ 9HL$ 0CHHK8HuMHH@MHL$ 9HL$ {CHHK8HuMHH@MHL$ p9HL$ >HHK8Hu|MHH@nMHL$ ;9HL$ AHHK8HuGMHH@9MHL$ 9HL$ ,CHHK8HuMHH@MHL$ 8HL$ 跻DHHK8HuMHH@MHL$ 8HL$ 袏BHHK8HuMHH@MHL$ g8HL$ =DHHK8HusMHH@eMHL$ 28HL$ DHHK8Hu>MHH@0MHL$ 7HL$ þDHHK8Hu MHH@MHL$ 7HL$ .DHHK8HuMHH@MHL$ 7HL$ ْAHHK8HuMHH@MHL$ ^7HL$ dBHHK8HujMHH@\MHL$ )7HL$ /QBHHK8Hu5MHH@'MHL$ 6HL$ DHHK8HuMHH@MHL$ 6HL$ E>HHK8HuMHH@MHL$ 6HL$ `AHHK8HuMHH@MHL$ U6HL$ >HHK8HuaMHH@SMHL$ 6HL$ AHHK8Hu,MHH@MHL$ 5HL$ !CHHK8HuMHH@MHL$ 5HL$ BHHK8HuMHH@MHL$ 5HL$ 'rBHHK8HuMHH@MHL$ L5HK8HtHH;H@ WMHC8H$(H]HL$SHHHL$ AHHK8Hu MHH@MHL$ 4HL$ BHHK8HuMHH@MHL$ 4HL$ lBHHK8HuMHH@MHL$ a4HL$ gAHHK8HumMHH@_MHL$ ,4HL$ AHHK8Hu8MHH@*MHL$ 3HL$ CHHK8HuMHH@MHL$ 3HL$ x:DHHK8HuMHH@MHL$ 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M?@@@@N A@D@@I@M@@I@M@V@V@PU@@U@@/@0@ 3@7@8@P;@7@8@P;@@?@0@@0@@@@@@p@2@З@@@@@P@@@@@@@NAAAA =APA@AA0 B0 BBBp B BBBp B BP B@ BpBBBBpBBpB`Bp B B B BpBBBBpPBPB@B0Bp B BP Bp Bp`B`BBBpBBBBpBBBBp0B0BBBpBBPBBpPBPB@B0Bp`B`BPB@BpPBPBB@BppBpBBpBppBpB`BPBp@B@B0B BpBBBBpBBBBp0B0B BBpBBBpBpBBpB`BpBBBBpBBBBpBBpB`Bp`B`B`BPBpBBPB@Bp B BBBp@B@BBBpBBBBpBBBBppBpB`BPBpBBBpBpBBBBpBBBBpBBPBBp0B0B BBp0dD0dDbDbDp^D^D^D^Dp]D]D]D]DpP`DP`DP_D@_Dp`D`DP_D`Dp@\D@\D0\D \Dpp\Dp\D`\DP\Dp]D]D]D]Dp^D^D^D]Dp^D^D^D^DpbDbD`D`Dp`dD`dDPdD@dDp]D]D\D\Dp`^D`^DP^D@^Dp`D`Dp`D``Dp^D^D^Dp^Dp0_D0_D _D_DpdDdDP_DpdDp0^D0^D;> ^Dp\D\D\D[Dp[D[D[D[DppEpE@E`EpEEEEppEpEpE`Epp Ep E@<` Ep E E E EpEEEEpEEEEp E E E Ep E E E Ep E E E EpEEEEpEE>Ep E EEEp@E@EEEpEEEEp`E`E>PEpEEEpEpPEPE@E0EpPEPE@E0EpP EP E0 E EpEEEpEp E EEEpEE<EpEEE EpP&EP&E@&E0&EpEE`<>EpEE =>Ep0oF0oF oFoFpoFoFBpoFpnFnFBmFpkFkFkFkFp lF lFlFlFplFlFPBlFpnFnFBpnFpoFoFPBoFp0pF0pFPB pFppFpFBpFpplFplF B`lFpPmFPmFpB@mFplFlFBlFp0mF0mFpB mFpkFkFkFkFpPpFPpFkF@pFp`kF`kFPkF@kFpoFoF BoFplFlFlFlFpkFkFkFpkFppFpFpFpFp`nF`nFBPnFp nF nFBnFpPlFPlF@lF0lFpnFnFBnFpnFnFBnFpqFqFpFpFpmFmFmFmFp@nF@nFB0nFppFpFpFoFpnFnF@BnFpmFmFmFmFpmFmFmFlFpoFoFPBoFpppFppFB`pFppFpF`BpFp qF qFBqFp`oF`oFPoF@oFpoFoFBnFpmFmFpmF`mFp F FFFpPFPF@F0FpFFpF`FpFFFFpGGP^DGpGGpG`GpGG0\DGpGG0G GpGGP_DGpGG0G GpGGGGpGGGGpPGPG@G0GpGG^DGpGGpGGpGGP_DGp G GGGpGGP_DGpGGpG`GpGG]DGpGGP_DGpGGP_DGpPGPG GGppGpGP^D`GpGGGGp@G@G0G GpGGGGpGG[DGppGpGP^D`GpGGGGpPGPGGGpGGGGpGGGGpGGP_DGpJHJHIHIHp0KH0KH KHKHpGHGHGHGHpKHKHJHJHpJHJHIHJHpIHIH HHpIHp`IH`IH HHHHpHHHHGHGHpJHJHJHJHp@]H@]H KH0]Hp\H\HZHZHp ]H ]H0\H \HpZHZHZHZHpHHEHpHHHHpHH<HpHH EHpHHHHpHH@EHpHHHHpHHHHp0H0H@E HpHH@H0HpHHHpHpHHHpHpHH@EpHp`H`H@H0Hp H HHHpHHEHpHHHHpHHHHp`H`HPH@HpP'IP'I@'I0'Ipp'Ip'I@F`'IpP&IP&I@F@&Ip &I &I&I&IpP)IP)I'I'Ip)I)I)Ip)Ip&I&I&I&Ip&I&Ip&I`&Ip&I&I&I&Ip 'I 'I'I'Ip5I5I5I5Ip5I5I5I5IpIIIIpIIIIpIIpI`IpIIIIpIIвIIp@I@IIIpIIIIpPIPI@I0Ip I IGIpIIIIpPIPIG@Ip0I0I0I IpIIpI`Ip I IIIppIpI =>`IpPIPI`<>@IpJJJJppJpJ`JPJp@J@J0J JpJJJJJJJJJ`8`KJJyK`[P=}K@}K@J`yK{K;KJJ@K`[P=KK@JKK`%;KpJ`J` K`[P= KPK@J K@ KJKJJK`[P=PKK@JKK3vKpJ`JaK`[P=@rKnK@JbKhK3WK0J JHK`[P=pTKQK@JIKNK0J[KPJ@JWK`[P=P[KZK@JXKYKJKJJ:K`[P=>K>K@JP;K0=KJp'KJJ K`[P=&K@%K@J0!K#K0J?KJJ:K`[P=>K>K@JP;K0=KpJKpJ`JK`[P=PKK@JKKpJ`EKJJ@K`[P=DKCK@J@KBK0JНKPJ@JK`[P=@KK@JKKJK0J JK`[P=K@K@JKpK@8ЭKJJK`[P=0KPK@J`KKpJKJJK`[P=0KKJ0KK@<KJJPK`[P=KpK@JKKJ`KJJK`[P=pKK@JPKK53 K0J JJ`[P=KpK@JJ JW9KJJ@+K`[P= 6K3K@J0,K0KZ3KKKK`[P=KpK@JKK JLHLHL HL M.L.LHL HL .L TLphLiLgLPhLyLvLwLwL M JL0LPL HLLpMMMMMM0MPM`cMpmMmMdMlM`cMuMuM3 M`cMuM`eMdMdM.L0yM{M HLUnknown exceptionbad array new lengthstring too longD:\a\_work\1\s\cmake\external\protobuf\src\google/protobuf/parse_context.h(cannot determine missing fields for lite message)D:\a\_work\1\s\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 longD:\a\_work\1\s\cmake\external\protobuf\src\google\protobuf\io\zero_copy_stream.ccThis ZeroCopyOutputStream doesn't support aliasing. Reaching here usually means a ZeroCopyOutputStream implementation bug.D:\a\_work\1\s\cmake\external\protobuf\src\google\protobuf\io\zero_copy_stream_impl.ccclose() failed: CHECK failed: !is_closed_: D:\a\_work\1\s\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 %I64u|TTTTCan't happenD:\a\_work\1\s\cmake\external\protobuf\src\google\protobuf\arena.ccCHECK failed: (min_bytes) <= (std::numeric_limits::max() - SerialArena::kBlockHeaderSize): falsetrue\\x%02xty10invalid string position...\\?\\/INVALID_ARGUMENT:, map/set too longunordered_map/set too longinvalid hash bucket count/\ was false.Stacktrace: Cpuai.onnx.trainingai.onnx.mlai.onnx.preview.trainingVIWEF!=D:\a\_work\1\s\include\onnxruntime\core/framework/data_types_internal.hD:\a\_work\1\s\include\onnxruntime\core/framework/tensor.h)IsTensor()Trying to get a Tensor, but got: D:\a\_work\1\s\include\onnxruntime\core/framework/ml_value.hOrtValue::GetTrying to get a TensorSeq, but got: OrtValue::GetMutableIsTensorSequence()CPUExecutionProviderORT_LOAD_CONFIG_FROM_MODELInteger overflowD:\a\_work\1\s\onnxruntime\core/common/safeint.hSafeIntExceptionHandler::SafeIntOnOverflowelem_type_ != nullptrTensor sequence must contain only primitive typesD:\a\_work\1\s\onnxruntime\core/framework/TensorSeq.honnxruntime::TensorSeq::SetTypeonnxruntime::TensorSeq::Geti < tensors_.size()size overflowtried creating tensor with negative value in shapenot enough space: expected , got type is not supported in this functionRegisterCustomOpsRegisterCustomOpsLibrary: Failed to load libraryonnxruntime_profile_RegisterCustomOpsLibrary: Entry point RegisterCustomOps not found in libraryoutput name cannot be emptyinput name cannot be emptystring buffer allocation failedlengths allocation failedinput array doesn't equal tensor sizeOutput buffer allocation failedshape is invalidelement index is out of boundsoutput buffer is too smalloffsets buffer is not equal to tensor sizeout of indexbuffer size is too small for stringindex out of rangeinternal errorInput is not of type sequence or map.Input is not of one of the supported sequence types.Unsupported tensor element type in the input: input array is too shortExpecting all elements to be tensors. Got: Input is not of one of the supported map types.OrtCreateValueImplSeqHelperin[idx]->IsTensor()Sequences must have tensors of the same data type. There was at least one tensor in the input that was different.D:\a\_work\1\s\onnxruntime\core\session\onnxruntime_c_api.ccAt least one element in the sequence is of a type different from others.Each element of the sequence should be either tensor or map.For map type num_values MUST be 2Unsupported input typeKey and value tensors have unequal number of elements.Either the key tensor or the value tensor has NumDimensions > 1Number of values should be at least 1.Key type is not supported yet.,opaque(ml_type != nullptrSpecified domain and type names combination does not refer to a registered opaque typeOpaque type is not a non_tensor type!!!OrtApis::CreateOpaqueValueOrtApis::GetOpaqueValuenon_tensor_base != nullptrlocation dimensions do not match shape sizethis API does not support stringsROCM execution provider is not enabled.invalid location rangearena_extend_strategymax_memmax_dead_bytes_per_chunkinitial_chunk_size_bytesInvalid key found: initial_growth_chunk_size_bytes1.8.0The given version [%u] is not supported, only version 1 to %u is supported in this build. Tensor type mismatch. T onnxruntime::Tensor::MutableDatautils::IsPrimitiveDataType(dtype_)Invalid index requested for map type.onnxruntime::Tensor::Datavalue_type != nullptrTensor must always contain primitive types. Found: Value type is not supported yet: OrtCreateValueImplMapHelper++index < c.size()Map is missing type entry for its valueonnxruntime::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 > > >::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 >,double,struct std::less,class std::allocator > >,class std::allocator,class std::allocator > const ,double> > > >::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 > > > > >::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::allocator,class std::allocator > > > > >::checkSequence is missing type entry for its elementinvalid vector subscriptonnxruntime::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> > > > > >::checkonnxruntime::DataTypeImpl::GetType() == type_ != OrtCreateMapMLValuelen >= 0 && static_cast(len) < std::numeric_limits::max()Encountered nullptr.not implementedexecution_mode is not validgraph_optimization_level is not valid is not implementedai.onnxonnxruntime::OpKernel::ComputeAsyncResult buffer is not large enoughonnxruntime::CustomOpKernel::CustomOpKernelD:\a\_work\1\s\onnxruntime\core\session\custom_ops.cc' in custom op 'Unsupported version 'Inputall typescustom op registered at runtimeTonnxruntime::CreateCustomRegistryT0There must be one (and only one) dynamic typed input to the custom op. Its type info at runtime will be used to infer the type info of this dynamic typed output which is required for the success of the model loading step. More than one dynamic typed inputs are currently not supported as differing types at runtime means the output type cannot be inferred without which model loading cannot proceed.type_id_counter == 1OutputNo requested allocator availableEnv is nullOrtMemoryInfo is nullD:\a\_work\1\s\onnxruntime\core\session\ort_env.ccenv_ptr == p_instance_OrtEnv::ReleaseD:\a\_work\1\s\onnxruntime\core\session\IOBinding.cconnxruntime::IOBinding::BindInputp_providerbad castAttempt to use DefaultLogger but none has been registered.D:\a\_work\1\s\include\onnxruntime\core/common/logging/logging.honnxruntime::logging::LoggingManager::DefaultLogger DeviceId:] OrtAllocatorType:Device:[DeviceType: MemoryType:OrtMemoryInfo:[name: id: OrtMemType:CUDAExecutionProviderTensorrtExecutionProviderDmlExecutionProviderProvider has already been registered.IsSparseTensor()Trying to get a SparseTensor, but got: D:\a\_work\1\s\onnxruntime\core/framework/execution_providers.honnxruntime::ExecutionProviders::Addonnxruntime::FeedsFetchesInfo::FeedsFetchesInfoRelease_State_Create_State_Compute_D:\a\_work\1\s\onnxruntime\core/framework/feeds_fetches_manager.hMemcpyTransformer] ORTM[json.exception.other_errorsession.save_model_formatsession.load_model_formatsession.use_env_allocatorssession.set_denormal_as_zeromemory.enable_memory_arena_shrinkagesession.intra_op.allow_spinningsession.inter_op.allow_spinning are '0' and '1'. The environment variable contained the value: 4%Y-%m-%d_%H-%M-%SD:\a\_work\1\s\onnxruntime\core\session\inference_session.cconnxruntime::FinalizeSessionOptionsReading the provided model for the ORT configThe only supported values for the environment variable offonis_model_proto_parsedModelProto needs to be parsed to check for ORT config within itstatus.IsOK()Could not finalize session options while constructing the inference session. Error Message: onnxruntime::InferenceSession::ConstructorCommon::::operator ()Flush-to-zero and denormal-as-zero are -Creating and using per session threadpools since use_per_session_threads_ is truegraph_transformation_mgr_.SetSteps(session_options_.max_num_graph_transformation_steps).IsOK()onnxruntime::InferenceSession::ConstructorCommonFailed to create the inter-op thread pool for the parallel executor, setting ExecutionMode to SEQUENTIAL-inter-opsession--intra-opCastFloat16Transformersession_env.EnvCreatedWithGlobalThreadPools()When 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 falsest.IsOK()Could not parse model successfully while constructing the inference sessiononnxruntime::InferenceSession::InferenceSessionGiven model could not be parsed while creating inference session. Error message: Unknown error during EndProfiling()onnxruntime::InferenceSession::{dtor}::::operator ()Error during EndProfiling(): resultonnxruntime::InferenceSession::RegisterExecutionProviderExecution providers must be registered before the session is initialized. Received nullptr for exec provideronnxruntime::InferenceSession::~InferenceSessionSo 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 disabling it for this session since it uses the DML Execution Provider.Execution providers must be registered before the session is initialized.Parallel execution mode does not support the CUDA 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. Received nullptr for custom registryonnxruntime::InferenceSession::AddCustomOpDomainsonnxruntime::InferenceSession::SaveToOrtFormatUnknown exception in Load()onnxruntime::InferenceSession::LoadThis session already contains a loaded model.Exception during loading: Failed to load model because protobuf parsing failed.ModelProto corresponding to the model to be loaded has already been parsed. Invoke Load().ORTEncountered unknown exception in Load()ModelProto 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.model_loading_array(Could not find an implementation for the node onnxruntime::InferenceSession::TransformGraphmodel_loading_from_saved_protoAll nodes have been placed on [].Node placements (onnxruntime::InferenceSession::PartitionOrtFormatModel Provider: [: [ORT model verification failed.This session has already been initialized.onnxruntime::InferenceSession::LoadOrtModelonnxruntime::InferenceSession::LoadOrtModel::::operator ()The ORT format model version [] is not supported this build Serialized version info is null. Invalid ORT format model.InferenceSession is null. Invalid ORT format model.The session already has a PrePackedWeightsContainer instanceThe provided PrePackedWeightsContainer instance to be added to the session is nullSessionState is null. Invalid ORT format model.Missing Model. Invalid ORT format model.Initializing session.onnxruntime::InferenceSession::Initialize::::operator ()onnxruntime::InferenceSession::Initialize::::operator ()Exception during initialization: Session has already been initialized.Model was not loaded.Model was not loadedonnxruntime::InferenceSession::InitializeNchwcTransformerUnable to serialize model as it contains compiled nodes. Please disable any execution providers which generate compiled nodes.This session will use the allocator registered with the environment.Adding default CPU execution provider.session_initializationEncountered unknown exception in Initialize()Session successfully initialized.Serializing optimized model with Graph Optimization level greater than ORT_ENABLE_EXTENDED and the NchwcTransformer enabled. The generated model may contain hardware specific optimizations, and should only be used in the same environment the model was optimized in.Invalid rank for input: Got: Expected: Please fix either the inputs or the model.Unexpected input data type. Actual: ( index: Got invalid dimensions for input: for the following indices elements, but feeds has elements.)))) , expected: (Input with name: is not expected to be of type tensor.Invalid Feed Input Name:Size mismatch: feed_names has sparse_tensor is not expected to be of type sparse tensor.onnxruntime::InferenceSession::ValidateInputstensorAt least one output should be requested.Output vector pointer is NULLseq is not expected to be of type tensor sequence.Session was not initializedInvalid Output Name:Output vector incorrectly sized: output_names.size(): p_fetches->size(): Encountered unknown exception in Run()Running with tag: Session not initialized.onnxruntime::InferenceSession::Runonnxruntime::InferenceSession::GetOverridableInitializersonnxruntime::InferenceSession::GetModelInputsonnxruntime::InferenceSession::GetModelMetadatamodel_runonnxruntime::InferenceSession::EndProfilingProfiler is disabled.onnxruntime::InferenceSession::NewIOBindingonnxruntime::InferenceSession::GetModelOutputsUnsupported device specified in the memory arena shrink list: gpucpuCould not write a profile because no model was loaded. combination is not an arena based allocator: Did not find an arena based allocator registered for device-id combination in the memory arena shrink list: Unsupported device id in the memory arena shrink list: onnxruntime::InferenceSession::ShrinkMemoryArenasUnable to shrink arena: error message: The registered allocator for device-id Invalid run log severity level. Not a valid onnxruntime::logging::Severity value: onnxruntime::InferenceSession::CreateLoggerForRunrun_options.run_log_severity_level >= 0 && run_options.run_log_severity_level <= static_cast(logging::Severity::kFATAL)onnxruntime::InferenceSession::InitLoggersession_options_.session_log_severity_level >= 0 && session_options_.session_log_severity_level <= static_cast(logging::Severity::kFATAL)Invalid session log severity level. Not a valid onnxruntime::logging::Severity value: /961c151d2e87f2686a955a9be24d316f1362bf21 3.7.1model_loading_uri_.json bytes were able to be read.onnxruntime::LoadOrtModelBytesLoad model from failed:list too long failed. Only onnxruntime::`anonymous-namespace'::GetCurrentTimeStringlocaltime_s(&local_tm, &in_time_t) == 0com.microsoft.nchwccom.microsoft.experimentalcom.microsoftcom.microsoft.mlfeaturizers[TypeInferenceError] unknownInput expected to have tensor type expected to have type but instead is null expected to have sequence typeOutput Element type of input Mismatch between source and target type. Source= Target=[ShapeInferenceError] unknowntensor(int64)tensor(int32)tensor(uint64)tensor(uint32)tensor(bfloat16)tensor(double)tensor(float)tensor(float16)tensor(int16)tensor(int8)tensor(uint16)tensor(uint8)tensor(string)tensor(complex128)tensor(complex64)tensor(bool)Schema error: version: ) from file , but it is already registered from file line , but its domain is not known by the checker.Trying to register schema with name (domain: ] (usually, this means you bumped the operator version but forgot to update the version range in DomainToVersionRange in onnx/defs/schema.h)., but its version is not in the inclusive range [Exception caught: Received invalid value for arena extend strategy. Valid values can be either 0, 1 or -1.Only CPU devices are supported for now.Allocator with this OrtMemoryInfo is already registered.MemcpyFromHostD:\a\_work\1\s\onnxruntime\core\session\environment.ccinter-opintra-opYoutputXinputMemcpyToHostConstrain to any tensor type. If the dtype attribute is not provided this must be a valid output type.: value'parse_errorparse errorinvalid_iterator at line , column session_optionsort_configout_of_rangetype_errorSetting intra_op_num_threads to D:\a\_work\1\s\onnxruntime\core\session\inference_session_utils.cconnxruntime::SetIntraOpNumThreadsUnsupported value for intra_op_num_threads: Unsupported execution_mode value in ORT config: Setting inter_op_num_threads to onnxruntime::SetInterOpNumThreadsUnsupported value for inter_op_num_threads: Setting execution_mode to Parallel modeSequential modeonnxruntime::SetExecutionModeSetting graph_optimization_level to ORT_ENABLE_EXTENDEDSetting graph_optimization_level to ORT_ENABLE_BASIConnxruntime::SetGraphOptimizationLevelSetting graph_optimization_level to ORT_DISABLE_ALLonnxruntime::SetEnableProfilingUnsupported value for enable_profiling option: Unsupported graph_optimization_level value in ORT config: Setting graph_optimization_level to ORT_ENABLE_ALLThe Model Proto has already been checked for the ORT config json.onnxruntime::inference_session_utils::JsonConfigParser::ParseOrtConfigJsonInModelProto::::operator ()Json stored in the `ort_config` key cannot be parsed. Error message: Setting enable_profiling to The Model Proto hasn't been checked for the ORT config json.ORT config json from the model: onnxruntime::inference_session_utils::JsonConfigParser::ParseOrtConfigJsonInModelProtoFound session/run/environment configuration in the model file to be used while running the modelintra_op_num_threads option in the model file must be an integerintra_op_num_threadsonnxruntime::inference_session_utils::JsonConfigParser::ParseSessionOptionsFromModelProtoDid not find session options in the model file to be used while running the modelexecution_mode option in the model file must be an integerexecution_modeinter_op_num_threads option in the model file must be an integerinter_op_num_threadsenable_profiling option in the model file must be an integerenable_profilinggraph_optimization_level option in the model file must be an integergraph_optimization_levelkey '' not foundIgnoring unsupported session option in ORT config: cannot use at() with cannot use key() for non-object iteratorsnullobjectinvalid map keydiscardedbooleanstringarraynumbercannot compare iterators of different containerscannot get value; last read: '- while parsing syntax error invalid BOM; must be 0xEF 0xBB 0xBF if given; expected unexpected false literaltrue literalinvalid literal'['number literalstring literalnull literal':''}'']''{''[', '{', or a literalend of input','invalid number; expected '+', '-', or digit after exponentinvalid number; expected digit after '.'invalid number; expected digit after '-'unknown tokeninvalid string: surrogate U+DC00..U+DFFF must be followed by U+DC00..U+DFFFinvalid string: '\u' must be followed by 4 hex digitsinvalid string: missing closing quoteinvalid number; expected digit after exponent signinvalid string: control character U+0001 (SOH) must be escaped to \u0001invalid string: control character U+0000 (NUL) must be escaped to \u0000invalid string: forbidden character after backslashinvalid string: surrogate U+DC00..U+DFFF must follow U+D800..U+DBFFinvalid 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+0003 (ETX) must be escaped to \u0003invalid string: control character U+0002 (STX) must be escaped to \u0002invalid 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+0007 (BEL) must be escaped to \u0007invalid string: control character U+0006 (ACK) must be escaped to \u0006invalid 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+000B (VT) must be escaped to \u000Binvalid string: control character U+000A (LF) must be escaped to \u000A or \ninvalid 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+000F (SI) must be escaped to \u000Finvalid string: control character U+000E (SO) must be escaped to \u000Einvalid 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+0013 (DC3) must be escaped to \u0013invalid string: control character U+0012 (DC2) must be escaped to \u0012invalid 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+0017 (ETB) must be escaped to \u0017invalid string: control character U+0016 (SYN) must be escaped to \u0016invalid 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+001B (ESC) must be escaped to \u001Binvalid string: control character U+001A (SUB) must be escaped to \u001Avector too longinvalid string: ill-formed UTF-8 byteinvalid string: control character U+001F (US) must be escaped to \u001Finvalid string: control character U+001E (RS) must be escaped to \u001Eobject keynumber overflow parsing 'object separatoriterator does not fit current valuetype must be number, but is cannot use erase() with iterator out of rangeValidating no unexpected access using an invalid node_index. Got: Max:D:\a\_work\1\s\include\onnxruntime\core/graph/graph.honnxruntime::Graph::NodeAtIndexImplnode_index < nodes_.size()Inserted_CastD:\a\_work\1\s\include\onnxruntime\core/optimizer/graph_transformer.honnxruntime::GraphTransformer::RecurseRemoveDuplicateCastTransformertoCastcast node to cast from float16 to float32 on cpudtypeonnxruntime::InsertCastTransformer::ApplyImplD:\a\_work\1\s\onnxruntime\core\optimizer\insert_cast_transformer.cconnxruntime::RemoveDuplicateCastTransformer::ApplyImpldtype_attribute->second.has_i()InsertCastTransformer works on the assumption that `dtype` attribute holds an integer.ArmNNExecutionProviderACLExecutionProviderROCMExecutionProviderBiasGeluFusionAttentionFusionCommonSubexpressionEliminationCastEliminationBiasSoftmaxFusionConvConvAddFusionConvActivationFusionDivDivMulFusionConvMulFusionConvBNFusionEmbedLayerNormFusionDynamicQuantizeMatMulFusionDropoutEliminateDropoutGeluApproximationFastGeluFusionExpandExpandEliminationGemmGemmTransposeFusionGemmActivationFusionGeluFusionSimplifiedLayerNormFusionLayerNormFusionIdentityEliminateIdentityNhwcTransformerMatMulScaleFusionMatMulIntegerToFloatFusionMatMulAddFusionWhereNotWhereFusionAddNoopEliminationShapeToInitializerReshapeFusionReluFuseReluClipSliceEliminateSliceSkipLayerNormFusionShapeQDQS8ToU8TransformerQDQPropagationTransformerUnsqueezeUnsqueezeEliminationsession.disable_quant_qdqReluQuantRewriteQDQTransformerBiasDropoutFusionMatmulTransposeFusionoptimization.enable_gelu_approximationonnxruntime::optimizer_utils::GenerateRewriteRulesUnsupported levelLevel_RuleBasedTransformeronnxruntime::optimizer_utils::GenerateTransformersUnsupported level D:\a\_work\1\s\onnxruntime\core\optimizer\graph_transformer_utils.cconnxruntime::Node::ForEachWithIndexVitisAIExecutionProvideronnxruntime::MemcpyTransformer::ApplyImplD:\a\_work\1\s\onnxruntime\core\optimizer\transformer_memcpy.ccCopy from/to host memorydup_replacements.find(&arg) == dup_replacements.end()onnxruntime::TransformerMemcpyImpl::ProcessInitializers::::operator ()onnxruntime::TransformerMemcpyImpl::ProcessInitializersonnxruntime::TransformerMemcpyImpl::ProcessDefs' doesn't support memcpy Execution type 'Memcpyonnxruntime::GraphTransformer::ApplyD:\a\_work\1\s\onnxruntime\core\optimizer\graph_transformer.cconnxruntime::GraphTransformerManager::ApplyTransformersD:\a\_work\1\s\onnxruntime\core\optimizer\graph_transformer_mgr.ccThis transformer is already registered unsupported data type: ReadExternalRawData() failed: onnxruntime::Initializer::InitializerD:\a\_work\1\s\onnxruntime\core/optimizer/initializer.hsize is differentsize_ == sizeCould not find OrtValue with name 'onnxruntime::ConstantFolding::ApplyImplD:\a\_work\1\s\onnxruntime\core\optimizer\constant_folding.cc node 'can't constant fold ConstantFoldingDequantizeLinearCould not find a CPU kernel and hence fetches.size() == node->OutputDefs().size(). Can't constant fold Unsupported output type of Unsupported data type: div_inputs.size() == 2D:\a\_work\1\s\onnxruntime\core\optimizer\matmul_scale_fusion.ccMulonnxruntime::`anonymous-namespace'::GetScaleFromNodeConstant initializer NodeArg shape should not be null. NodeArg: onnxruntime::`anonymous-namespace'::GetScalarConstantInitializershapeMatMulonnxruntime::`anonymous-namespace'::GetOutputNodeMergesalphaFusedMatMulinput_node.InputDefs().size() == 2 && scale_and_index.value().second < 2mul_inputs.size() == 2output_node.OutputDefs().size() == 1onnxruntime::`anonymous-namespace'::GetInputNodeMerges_FusedMatMulAndScaleFused MatMul and Scaleonnxruntime::MatMulScaleFusion::ApplyImplonnxruntime::`anonymous-namespace'::ExtractScalarAsFloatDispatchTarget::operator ()onnxruntime::`anonymous-namespace'::ExtractScalarAsFloatDispatchTarget::operator ()onnxruntime::`anonymous-namespace'::ExtractScalarAsFloatDispatchTarget::operator ()onnxruntime::`anonymous-namespace'::ExtractScalarAsFloatDispatchTarget<__int64>::operator ()onnxruntime::utils::mltype_dispatcher_internal::UnsupportedTypeDefaultPolicy::operator ()onnxruntime::`anonymous-namespace'::ExtractScalarAsFloatDispatchTarget::operator ()onnxruntime::`anonymous-namespace'::ExtractScalarAsFloatDispatchTarget::operator ()onnxruntime::`anonymous-namespace'::ExtractScalarAsFloatDispatchTarget::operator ()onnxruntime::`anonymous-namespace'::ExtractScalarAsFloatDispatchTarget::operator ()Expected value:Slice ends is less than INT_MAXStart MatchGemmSubgraphSlice parameter is not expected. Input index:onnxruntime::AttentionFusionHelper::CheckSliceParametersinput_indices.size() == expected_values.size() && input_indices.size() > 0Slice does not have enough number of inputsD:\a\_work\1\s\onnxruntime\core/optimizer/attention_fusion_helper.hInput 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 pathReshapeonnxruntime::AttentionFusionHelper::MatchGemmSubgraphSqueezeConcatconcat_after_gather does not have expected number of inputs or output edgesFaild to match concat node for Gather pathsGatherconcat_after_gather input 2 does not have expected valueGemm bias shape not expectedGemm bias is not constantconcat first input value is not -1CheckSliceParameters return falsegather axis value not expectedaxisPass MatchGemmSubgraphgather input 1 value is not expectedOutput edge count not expected for nodes in gemm gather pathFaild to match gemm gather pathunsqueeze_after_gather axes value not expectedaxesGemm weight shape is not expectedGemm weight is not constant initializerunidir mask is not constantPass ValidateGemmInitializeronnxruntime::AttentionFusionHelper::ValidateGemmInitializerStart ValidateGemmInitializerGemm bias shape is not expectedGemm bias is not constant initializerStart MatchUnidirMaskSubgraphExpect mask data type is uint8 or floatFaild to match the path (Div-->Where-->Add) for unidirectional maskonnxruntime::AttentionFusionHelper::MatchUnidirMaskSubgraphunidir mask shape not expectedonnxruntime::AttentionFusionHelper::ValidateUnidirMaskMask is neither unidirectional nor all onesThis optimizer does not support external data for unidirectional mask right nowCheckSliceParameters returns false for mask_sliceCheckSliceParameters returns false for last_sliceCheckSliceParameters returns false for slice1ValidateUnidirMask returns false for mask_sliceFaild to match path 1 for unidirectional maskSubDiv and Shape1 does not have edgeOutput edge count not expected for nodes in path 1 of unidirectional maskDiv and Shape does not have edgeFaild to match path 4 for unidirectional maskCheckSliceParameters return false for slice2Output edge count not expected for squeeze_2/slices2/shape2 of unidirectional maskOutput edge count not expected for unsqueeze2 of unidirectional maskFaild to match path 2 for unidirectional maskOutput edge count not expected for unsqueeze3 of unidirectional maskFaild to match path 3 for unidirectional maskOutput edge count not expected for SoftmaxFailed to find Softmax nodeOutput edge count not expected for mask nodesFailed to find path for maskStart MatchInputMaskSubgraphPass MatchUnidirMaskSubgraphSoftmaxonnxruntime::AttentionFusionHelper::MatchInputMaskSubgraphPass MatchInputMaskSubgraphmask_mul const input not matchedEqualStart MatchInputMaskSubgraphDistilBertmask_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: 2Failed to find reshape shape path 2Failed to find reshape shape path 1Pass MatchInputMaskSubgraphDistilBertgather indices not matched.where const not matched.Failed to find mask pathequal const not matched.Failed to find shape pathFailed to find path for present_v and past_vFailed to find path for present_kpermFailed to match v_concatonnxruntime::AttentionFusionHelper::MatchPastSubgraphStart MatchPastSubgraphFailed to find path for past_kTransposepast_k_gather indices != 0past_v_gather indices != 1Output edge count not expected for nodes in past subgraphpast_v_gather and past_k_gather does not have same past inputpresent_k_transpose perm attribute not matchedpast_k_transpose perm attribute not matchedpresent_v_unsqueeze axes value not expectedpresent_k_unsqueeze axes value not expectedFailed in match Transpose attribute perm. Expected: 0, 2, 1, 3Output edge count not expected for nodes in path vhidden_size != num_heads * head_sizeFailed in match v_transpose attribute perm. Expected: 0, 2, 1, 3onnxruntime::AttentionFusionHelper::CheckDistilBertReshapeShapePass MatchPastSubgraphonnxruntime::AttentionFusionHelper::CheckNodesInPathVStart CheckNodesInPathVq_reshape const not matchedonnxruntime::AttentionFusionHelper::CheckNodesInPathQq_transpose perm attribute not matchedqk_div const not matched.Pass CheckNodesInPathVv_reshape initializer value is not expectedStart CheckNodesInPathQreshape initializer value is not expectedPass CheckNodesInPathKk_reshape const not matchedCast mask from int64 to int32Mask_Int32Start CheckNodesInPathKPass CheckNodesInPathQk_transpose perm attribute not matchedonnxruntime::AttentionFusionHelper::CheckNodesInPathKFaild to find path v to SplitSplitMatchInputMaskSubgraph returns falseCheckNodesInPathV return falseStart FuseGptAttentionFaild to find path to qkv_matmulonnxruntime::AttentionFusionHelper::FuseGptAttentionk and v are not from same Split nodeFailed to find path for kMatchPastSubgraph returns falseCheckNodesInPathK returns falseFailed to find path for qMatchUnidirMaskSubgraph returns NULLCheckNodesInPathQ returns falseq and v are not from same Split nodeqkv_weightsFused an attention node for GPT.Mask shape is unknown or not 2D, or data type unknownqkv_biasAttentionFused Attention subgraphs unidirectionalnum_headsshape of layer norm bias tensor not expectedLayerNormalizationonnxruntime::FuseSubGraphQKImplTotal fused Attention node count: D:\a\_work\1\s\onnxruntime\core\optimizer\attention_fusion.cconnxruntime::ConvertMaskToInt32onnxruntime::AttentionFusion::ApplyImplMask data type is not int32 or int64 or float32Failed 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 int32q_matmul and q_add shape not matchedq root should be layer normalizationk_matmul and k_add shape not matchedk root is not layer normFailed in match v_matmul and v_add input shapeOutput edge count not expected for Add or MatMul in path vFailed in match input mask subgraphonnxruntime::FuseSubGraphQKDistilBertFused an attention node.onnxruntime::AttentionFusion::FuseSubGraphFaild to find path vbn_scale_tensor_protoepsilononnxruntime::Initializer::ToProtodata type is not supportedconv_W_tensor_protobn_var_tensor_protoConvBnFusion_W_conv_B_tensor_protoD:\a\_work\1\s\onnxruntime\core\optimizer\conv_bn_fusion.cconnxruntime::ConvBNFusion::Applybn_mean_tensor_protobn_B_tensor_protoBatchNormalizationConvBnFusion_BN_B_D:\a\_work\1\s\onnxruntime\core\optimizer\qdq_transformer\qdq_s8_to_u8.cconnxruntime::QDQS8ToU8Transformer::ApplyImplqdq_s8_to_u8_zp_conversionQuantizeLinearqdq_s8_to_u8_quantD:\a\_work\1\s\onnxruntime\core\optimizer\qdq_transformer\qdq_transformer.cconnxruntime::QDQTransformer::ApplyImplonnxruntime::`anonymous-namespace'::GetClipConstantMinMax::::operator ()Unexpected data type for Clip input of ClipD:\a\_work\1\s\onnxruntime\core\optimizer\conv_activation_fusion.ccSigmoidactivationLeakyReluTanhmaxminFusedConvonnxruntime::ConvActivationFusion::ApplyImplfused Conv with activation activation_paramsfused UnsqueezeElimination cannot remove node UnsqueezeElimination_D:\a\_work\1\s\onnxruntime\core\optimizer\unsqueeze_elimination.cconnxruntime::UnsqueezeElimination::Applydata type is not supported.FuseReluClip_onnxruntime::FuseReluClip::ApplyUnexpected data type for Clip 'min' input of _min_zero_constantD:\a\_work\1\s\onnxruntime\core\optimizer\relu_clip_fusion.ccBiasGeluGeluTotal Gelu Approximation (FastGelu) node count: Gelu approximationFastGeluonnxruntime::GeluApproximation::ApplyImplD:\a\_work\1\s\onnxruntime\core\optimizer\gelu_approximation.ccUUUallowzeroFused reshape node: onnxruntime::ReshapeFusion::ApplyImplD:\a\_work\1\s\onnxruntime\core\optimizer\reshape_fusion.cconnxruntime::ReshapeFusion::Fuse_SubgraphendsCannot replace concat node with initializer:Total fused reshape node count: startsD:\a\_work\1\s\onnxruntime\core\optimizer\qdq_transformer\qdq_propagation.ccup_node should be parent of down_node and NodeArg slots of the edge between up_node and down_node should be (0, 0).optimizer_utils::CheckOutputEdges(graph, up_node, 1)onnxruntime::SwapAdjacentNodesMaxPoolup_node should have only one Edge that points to down_node and its output is not graph outputonnxruntime::QDQPropagationTransformer::ApplyImpledge_it->GetDstArgIndex() == 0 && edge_it->GetSrcArgIndex() == 0 && edge_it->GetNode().Index() == down_node.Index()SwapAdjacentNodes̼+Input shape is unknown or not 2D, or data type unknownonnxruntime::CheckInputCast Input from int64 to int32_Cast_Int32Failed to find path 2 of position shape.Output edge count not expected for nodes in path 2 of position shape.Output edge count not expected for nodes in path 1 of position shape.Second input of Gather in path 1 of position shape should be a constant with value 0.Failed to find path 1 of position shape.onnxruntime::MatchInputToConcatSubgraphD:\a\_work\1\s\onnxruntime\core\optimizer\embed_layer_norm_fusion.ccInput data type is not int32 or int64RangeOutput edge count not expected for nodes in path1.NonZeroConstantOfShapeThe parent of two shape nodes are expected to be input_ids.two paths share the same shapeGather node in path 2 is not linked to another subgraph.Second input of Gather in path 2 of position shape should be a constant with value 1.Optional position subgraph nodes number of outputs unexpected.Optional position subgraph nodes Where node is expected to be the parent of Reshape.Failed to match Shape node. The parent of shape nodes are expected to be input_ids.The third input of Range should be a constant with value 1.Second input of Gather should be a constant with value 1. onnxruntime::MatchPositionEmbeddingSubgraphsFromGatherThe first input of Range should be a constant with value 0.onnxruntime::FuseSubGraphInput is expected to have dim value in all dimensions.EmbedLayerNormalizationWord embedding shape not expected.mask_indexfused EmbedLayerNorm subgraphs Failed to match position subgraph.position_embeddingsInput id is not valid. Segment id is not valid. Failed to get position embedding weights.Position embedding shape is not expected.Position embedding data type shall be float or float16.Failed to match position embedding subgraph.Failed to get initializer tensor.Position embedding shape not matched.onnxruntime::EmbedLayerNormFusion::ApplyImplBeta should be of shape (hidden_size). onnxruntime::FuseSubGraphDistilBertInput_ids and segment id should have the same shape. Gamma should be of shape (hidden_size). YieldOpDynamicQuantizeMatMulMatMulIntegerToFloatDynamicQuantizeLinearonnxruntime::DynamicQuantizeMatMulFusion::ApplyImplD:\a\_work\1\s\onnxruntime\core\optimizer\dynamic_quantize_matmul_fusion.ccFreeDimensionOverrideTransformerThe model has input '' with a fixed dimension size which does not equal the specified override of Conflicting free dimension overrides.D:\a\_work\1\s\onnxruntime\core\optimizer\free_dim_override_transformer.cconnxruntime::FreeDimensionOverrideTransformer::FreeDimensionOverrideTransformerInvalid free dimension override.onnxruntime::FreeDimensionOverrideTransformer::ApplyImplfused Matmul and Add D:\a\_work\1\s\onnxruntime\core\optimizer\matmul_add_fusion.cconnxruntime::MatMulAddFusion::ApplyImplSoftplusSeluHardSigmoidEluFusedGemmfused Gemm D:\a\_work\1\s\onnxruntime\core\optimizer\gemm_activation_fusion.cconnxruntime::GemmActivationFusion::ApplyImplThresholdedReluParametricSoftplusScaledTanhSoftsignactivation_transBtransAinvalid unordered_map keybeta_transformedFused Gemm with Transpose!node_consumers.empty()D:\a\_work\1\s\onnxruntime\core\optimizer\matmul_transpose_fusion.cconnxruntime::GetTransposePermstranspose_node.InputDefs().size() == 1MatMul_With_Transposefused MatMul and Transpose onnxruntime::MatmulTransposeFusion::ApplyImplCreated a new Transpose node to interchange Cast and Transpose nodesCreated a new Cast node to interchange Cast and Transpose nodesonnxruntime::ReorderCastAndTransposecast != nullptronnxruntime::UpdateConsumerCountdeque too longConvMulFusion_W_mul_B_tensor_protoD:\a\_work\1\s\onnxruntime\core\optimizer\conv_mul_fusion.cconnxruntime::ConvMulFusion::ApplyConvMulFusion_Mul_B_SqrtReduceMeanD:\a\_work\1\s\onnxruntime\core\optimizer\layer_norm_fusion.cconnxruntime::LayerNormFusion::ApplyImplcast output of layer normlayer_norm_outSimplifiedLayerNormalizationcast scale of layer normCast_Scaleonnxruntime::SimplifiedLayerNormFusion::ApplyImplfused LayerNorm subgraphs Pow'7SkipLayerNormalizationfused SkipLayerNorm subgraphs D:\a\_work\1\s\onnxruntime\core\optimizer\skip_layer_norm_fusion.cconnxruntime::SkipLayerNormFusion::ApplyImpl̼+kernel_shapedilationsstridespadschannels_lastReorderInputreorderreshape_nchwcgroupVALIDSAME_LOWERSAME_UPPERNOTSETauto_padnearestmodechannelsReorderOutput_bn_nchwcspatialUpsamplefloornearest_modehalf_pixelalign_cornersasymmetriccoordinate_transformation_modelinearD:\a\_work\1\s\onnxruntime\core\optimizer\nchwc_transformer.cconnxruntime::NchwcTransformer::ApplyImplGlobalAveragePoolGlobalMaxPoolResizeSumAveragePoolscales and is not in valid range [-axis axis >= -tensor_rank && axis <= tensor_rank - 1onnxruntime::HandleNegativeAxisD:\a\_work\1\s\onnxruntime\core/providers/common.hD:\a\_work\1\s\onnxruntime\core\optimizer\bias_softmax_fusion.cc into softmax(input + bias)BiasSoftmaxsoftmax_axisbroadcast_axisonnxruntime::BiasSoftmaxFusion::ApplyImplNot eliminating output onnxruntime::CommonSubexpressionElimination::ApplyImplD:\a\_work\1\s\onnxruntime\core\optimizer\common_subexpression_elimination.ccrepresentative.output_index != kInvalidOutputIndex] because it's the graph's output.[ of node Not_nhwcQLinearConvNhwcMaxPoolstorage_ordernhwc_permutated_padsonnxruntime::NhwcTransformer::ApplyImplD:\a\_work\1\s\onnxruntime\core\optimizer\nhwc_transformer.ccQLinearAddQLinearMulQLinearLeakyReluQLinearSigmoidQLinearGlobalAveragePoolQLinearAveragePoolQLinearConcatPadseedonnxruntime::BiasDropoutFusion::ApplyImplD:\a\_work\1\s\onnxruntime\core\optimizer\bias_dropout_fusion.ccBiasDropoutfused Add and Dropoutonnxruntime::MatMulIntegerToFloatFusion::ApplyImplD:\a\_work\1\s\onnxruntime\core\optimizer\matmul_integer_to_float.ccMatMulIntegerD:\a\_work\1\s\onnxruntime\core\optimizer\rule_based_graph_transformer.cconnxruntime::RuleBasedGraphTransformer::ApplyRulesOnNodeonnxruntime::RuleBasedGraphTransformer::ApplyImplD:\a\_work\1\s\onnxruntime\core\optimizer\gelu_fusion.cconnxruntime::GeluFusion::ApplyImplfused Gelu subgraphs Erfonnxruntime::BiasGeluFusion::ApplyImplfused Add and GeluD:\a\_work\1\s\onnxruntime\core\optimizer\bias_gelu_fusion.ccD:\a\_work\1\s\onnxruntime\core\optimizer\fast_gelu_fusion.cconnxruntime::FastGeluFusion::ApplyImplfused GPT2Gelu subgraphs fast_gelu_outputD:\a\_work\1\s\onnxruntime\core\optimizer\conv_add_fusion.cconnxruntime::ConvAddFusion::ApplyConvAddFusion_B_add_B_tensor_protoConvAddFusion_Add_B_, external_data.length: model_path must not be empty. Ensure that a path is provided when the model is created or loaded.TensorProto external data size mismatch. Computed size: External data type must not be UNDEFINED or STRING.D:\a\_work\1\s\onnxruntime\core\optimizer\initializer.cconnxruntime::Initializer::ReadExternalRawDataRandomUniformLikeRandomNormalLikeRandomUniformRandomNormalMultinomialFailed to get allocator for optimizerallocator_ptr_onnxruntime::OptimizerExecutionFrame::Info::{ctor}::::operator ()D:\a\_work\1\s\onnxruntime\core\optimizer\optimizer_execution_frame.cconnxruntime::OptimizerExecutionFrame::Info::InfoTried to allocate without valid type information, ort_value index=35b188f7-c464-43e3-8692-97ac832bb14a855dd0fa-cd7b-4b10-ae5a-df64cabfe1f8QLinearMatMulQLinearNnapiExecutionProviderNupharExecutionProviderOpenVINOExecutionProviderDnnlExecutionProviderMIGraphXExecutionProviderRknpuExecutionProviderTT8U80UU UU5UЈU0U 0U TXUTonnxruntime::CPUExecutionProvider::GetKernelRegistryonnxruntime::RegisterCPUKernelsonnxruntime::ml::RegisterOnnxMLOperatorKernelsD:\a\_work\1\s\onnxruntime\core\providers\cpu\cpu_execution_provider.cconnxruntime::RegisterOnnxOperatorKernelsbad allocationD:\a\_work\1\s\include\onnxruntime\core/framework/op_kernel_context.hbidirectionalreverseforwarddirectionlayoutclipAffineallowed_directions.find(direction_) != allowed_directions.end()D:\a\_work\1\s\onnxruntime\core/providers/cpu/rnn/rnn.honnxruntime::RNN::RNNinfo.GetAttr("direction", &direction_).IsOK()D:\a\_work\1\s\onnxruntime\core\providers\cpu\rnn\rnn.cconnxruntime::RNN::ComputeRNNT1layout_ == 0Batchwise recurrent operations (layout == 1) are not supported. If you need support create a github issue with justification.allowed_activations.find(activations_[direction]) != allowed_activations.end()info.GetAttrs("activations", activations_).IsOK()activationsactivation_betaactivation_alphaRNN op: Invalid activation attribute - activations_.size() == static_cast(num_directions)info.GetAttr("hidden_size", &hidden_size_).IsOK()hidden_sizeonnxruntime::OpKernelContext::InputMissing Input: sigmoidinput_forgetclip_ > 0.finfo.GetAttr("hidden_size", &int64_value).IsOK() && int64_value > 0onnxruntime::DeepCpuLstmOp::PrePackLSTMactivation_func_names.size() == static_cast(num_directions_) * 3tanhonnxruntime::rnn::detail::MakeDirectionInvalid 'direction' argument of ''. Must be one of 'forward', 'reverse', or 'bidirectional'.D:\a\_work\1\s\onnxruntime\core\providers\cpu\rnn\lstm_base.honnxruntime::LSTMBase::LSTMBaseinfo.GetAttr("direction", &direction).IsOK()D:\a\_work\1\s\onnxruntime\core/providers/cpu/rnn/rnn_helpers.honnxruntime::DeepCpuLstmOp::ComputeInvalid data type for LSTM operator of LSTM operator does not support double yetD:\a\_work\1\s\onnxruntime\core\providers\cpu\rnn\deep_cpu_lstm.ccGRU operator does not support double yetGRUactivation_func_names.size() == static_cast(num_directions_) * 2info.GetAttr("linear_before_reset", &int64_value).IsOK()D:\a\_work\1\s\onnxruntime\core\providers\cpu\rnn\deep_cpu_gru.cconnxruntime::DeepCpuGruOp::ComputeInvalid data type for GRU operator of linear_before_resetD:\a\_work\1\s\onnxruntime\core/providers/cpu/rnn/deep_cpu_gru.honnxruntime::DeepCpuGruOp::DeepCpuGruOpB + (N * ldb - (ldb - K)) <= B_endA + (M * lda - (lda - K)) <= A_endonnxruntime::rnn::detail::ComputeGemmlda >= K && ldb >= K && ldc >= Noffset + size <= size_t(span.size())onnxruntime::rnn::detail::SafeRawConstPointercur + size <= endC + (M * ldc - (ldc - N)) <= C_endonnxruntime::DeepCpuGruOp::ComputeImplonnxruntime::Tensor::MutableDataAsSpanonnxruntime::Tensor::DataAsSpanonnxruntime::rnn::detail::SafeRawPointerInvalid input B: NumDimensions() != Invalid input B: 0th dimension != Invalid input B: Invalid input mean: NumDimensions() != Invalid input scale: NumDimensions() != Invalid input scale: 0th dimension != dimension != Invalid input scale: Invalid input X: The rank of input X must be atleast 2. Got rank: Training mode is not supported in this build.!is_train_onnxruntime::BatchNorm::Computeonnxruntime::BatchNorm::BatchNormInvalid input var: onnxruntime::BatchNorm::ComputeD:\a\_work\1\s\onnxruntime\core/providers/cpu/nn/batch_norm.honnxruntime::BatchNorm::BatchNormInvalid input mean: 0th dimension != Invalid input mean: Invalid input var: NumDimensions() != Invalid input var: 0th dimension != Tensor type mismatch.type == dtype_onnxruntime::Tensor::MutableDataRawonnxruntime::Tensor::DataRawX != nullptronnxruntime::IdentityOp<0>::ComputeD:\a\_work\1\s\onnxruntime\core/providers/cpu/tensor/identity_op.hUnable to get an allocatorVonnxruntime::IdentityOp<1>::ComputeA dimension cannot be less than -1, got requested_shape[i] >= -1onnxruntime::ReshapeHelper::ReshapeHelperD:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\reshape_helper.hgsl::narrow_cast(input_shape.Size()) == sizeunknown_dim == -1The dimension with value zero exceeds the dimension size of the input tensor.i < input_shape.NumDimensions()size != 0 && (input_shape.Size() % size) == 0, requested shape:The input tensor cannot be reshaped to the requested shape. Input shape:!allow_zeroAt most one dimension can be -1.onnxruntime::Reshape_1::Reshape_1shapeTensor->Shape().NumDimensions() == 1onnxruntime::Reshape::ComputeD:\a\_work\1\s\onnxruntime\core/providers/cpu/tensor/reshape.hAttribute shape is not set.A shape tensor must be a vector tensor.called_ == 1onnxruntime::utils::mltype_dispatcher_internal::CallableDispatchableHelper::CheckCalledOncePlease fetch output tensor with specified shape.p_ml_valueonnxruntime::OpKernelContext::Outputaxis == 1 || axis == largestonnxruntime::BroadcastIterator::InitD:\a\_work\1\s\onnxruntime\core/providers/cpu/math/element_wise_ops.honnxruntime::BroadcastIterator::Append by Attempting to broadcast an axis by a dimension other than 1. Broadcast Output range [start_offset % span_size == 0 && real_end % span_size == 0onnxruntime::TensorAllocator::TensorAllocatorInvalid start/ending offset [start_offset >= 0 && real_end >= 0 && start_offset <= real_end && real_end <= lenonnxruntime::OutputBroadcaster::OutputBroadcaster) are not at boundary of span with size:InputBroadcaster can only start at span boundary!offset % span_size_ == 0onnxruntime::InputBroadcaster::AdvanceBy) for tensor of length: is invalid.Can broadcast 0 by 0 or 1. largest <= 1onnxruntime::Broadcaster::BroadcasterAbsNegFloorAndOrXorUnsupported X type: LessOrEqualGreaterOrEqualMeanBitShiftMaxMinLessGreaterCeilReciprocalExpLogAsinAcosAtanSinhdata_1.Shape() == shapeSinCosTanAll inputs must have the same shapedata_n.Shape() == shapeonnxruntime::Max_6::Computeonnxruntime::Mean_6::ComputeMust have 1 or more inputsinputCount >= 1onnxruntime::Min_6::ComputeD:\a\_work\1\s\onnxruntime\core\providers\cpu\math\element_wise_ops.ccfmod must have value either 0 or 1(fmod == 0) || (fmod == 1)onnxruntime::Mod::ModModTensor with shape information must be 1 dimensional.shape_data_tensor.Shape().GetDims().size() == 1onnxruntime::UntypedExpandfmodPReluExpandBroadcastLooper should only have a shape for the second input.!helper.HaveTwoTensorInputs()onnxruntime::ExpandBroadcastLooperCoshAsinhAcoshAtanhonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ElementWiseKernelD:\a\_work\1\s\onnxruntime\core/providers/cpu/element_wise_ranged_transform.honnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ElementWiseKernelfmod attribute must be true for floating point typesonnxruntime::mod_internal::CallModImpl::operator ()input_size < std::numeric_limits::max()onnxruntime::ElementWiseKernel >::Computeonnxruntime::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 >::Computeonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ElementWiseKernelLEFTRIGHT'. Valid values are 'LEFT' or 'RIGHT'.Invalid direction value of 'input_count >= 1onnxruntime::Sum_6::Computeonnxruntime::Sum_6::Computeonnxruntime::BitShift::BitShiftonnxruntime::BroadcastLooperInvalid usage. Input 1 is a shape with no data.onnxruntime::Expand_8,class std::allocator > >::Compute::::operator ()BroadcastLooper requires two tensors as input.helper.HaveTwoTensorInputs()onnxruntime::BitShift::Compute::::operator ()onnxruntime::BitShift::BitShiftonnxruntime::BitShift::Compute::::operator ()Unsupported Y type: cur1 == end1onnxruntime::BitShift::Compute::::operator ()cur_out == end_outonnxruntime::BitShift::BitShiftonnxruntime::mod_internal::CallModImpl::operator ()onnxruntime::mod_internal::CallModImpl::operator ()((`(`(``JJ J J } M}7M}p7M}6p7M6p76pf6OfOfOfOupperonnxruntime::Trilu::Triluinfo.GetAttr("upper", &temp).IsOK()D:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\trilu.cconnxruntime::Trilu::ComputeUnsupported input data type of Input tensor should have a rank of at least 2TriluD:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\trilu.hIsScalarOr1ElementVector(k)k should be a 1-D or 0-D tensor.onnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ComputeNo attribute with name:''is defined.CeluAttribute name and type don't match for 'onnxruntime::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::functors::ScaledTanh::Initunknown kernel typeonnxruntime::functors::HardSigmoid::Initonnxruntime::functors::Selu::InitgammaD:\a\_work\1\s\onnxruntime\core/providers/cpu/activation/activations.honnxruntime::functors::ParametricSoftplus::InitD:\a\_work\1\s\onnxruntime\contrib_ops/cpu/activations.hdims.size() == extents.size() && dims.size() >= steps.size()D:\a\_work\1\s\onnxruntime\core/providers/cpu/tensor/utils.honnxruntime::SliceSkips::SliceSkipsCumSumAxis tensor should be 0D or 1DAxis tensor must be provided to the CumSum opT2Axis tensor should be of type `int32_t` or `int64_t`onnxruntime::SliceIteratorBase::Initdims.size() == starts.size() && dims.size() == extents_.size() && dims.size() >= steps.size()D:\a\_work\1\s\onnxruntime\core\providers\cpu\math\cumsum.ccexclusivedims.size()=starts.size()=onnxruntime::CumSum<__int64>::Computeonnxruntime::CumSum::Computeonnxruntime::CumSum::Computeonnxruntime::CumSum::ComputeCannot apply CumSum operator on a scalaronnxruntime::WritableSliceIterator::Initdims.size() == steps.size()steps.size()=onnxruntime::WritableSliceIterator::Initonnxruntime::WritableSliceIterator::Initonnxruntime::WritableSliceIterator<__int64>::Initdims.size() == starts.size()dims.size() == extents_.size()extents.size()=LogSoftmaxonnxruntime::Softmax::ComputeImplOpset13D:\a\_work\1\s\onnxruntime\core\providers\cpu\math\softmax.cconnxruntime::Softmax::ComputeImplOpset13onnxruntime::Hardmax::ComputeHardmax, D=D:\a\_work\1\s\onnxruntime\core\providers\cpu\math\hardmax.ccHardmax inputs N, D and N * D must be < . N=onnxruntime::If::Info::Infonum_subgraph_outputs == static_cast(num_outputs)then_branchD:\a\_work\1\s\onnxruntime\core\providers\cpu\controlflow\if.cc outputs.If'If' node has outputs which doesn't match the subgraph's Bonnxruntime::IfImpl::Initializesession_stateOnly tensors or sequence of tensors are suppportedFailed to create output tensor for onnxruntime::If::Computethen_feeds_fetches_manager_ && else_feeds_fetches_manager_Subgraph SessionState was not found for '' attribute.info == nullptrSetupSubgraphExecutionInfo should only be called once for each subgraph.CreateFeedsFetchesManager must be called prior to execution of graph.onnxruntime::If::SetupSubgraphExecutionInfoonnxruntime::If::Initinfo.GetAttr("then_branch", &proto).IsOK()info.GetAttr("else_branch", &proto).IsOK()else_branchonnxruntime::IfImpl::ExecuteFailed to create output tensor for If output outputs so the subgraph requires but has num_subgraph_outputs - 1 == num_outputs'Loop' node has static_cast(num_subgraph_inputs) == subgraph_inputs.size()Graph in 'body' attribute of Loop should have D:\a\_work\1\s\onnxruntime\core\providers\cpu\controlflow\loop.cconnxruntime::Loop::Info::InfoI inputs. Found:LoopLoop subgraph input 0 has unknown shape: 'Loop' input 'cond' should be a scalar tensor. Got shape of Loop subgraph input 1 has unknown shape: onnxruntime::LoopImpl::Initializeonnxruntime::Loop::ComputeSubgraph SessionState was not found for 'body' attribute.'Loop' input 'M' should be a scalar tensor. Got shape of feeds_fetches_manager_onnxruntime::Loop::Initinfo.GetAttr("body", &proto).IsOK()onnxruntime::Loop::SetupSubgraphExecutionInfoinfo_ == nullptr Expected: Got:bodyInconsistent shape in loop output for output. onnxruntime::LoopImpl::Executeonnxruntime::LoopImpl::Execute::::operator ()Loop had zero iterations and the shape of subgraph output was not found. Defaulting to a rank 1 shape of {0}.last_outputs[j + 1].IsTensor()All scan outputs MUST be tensorsonnxruntime::LoopImpl::ConcatenateLoopOutputonnxruntime::LoopImpl::SaveOutputsAndUpdateFeedsonnxruntime::UpsampleBase::UpsampleBaseD:\a\_work\1\s\onnxruntime\core/providers/cpu/tensor/upsample.hcubicinfo.GetAttr("mode", &mode).IsOK()pytorch_half_pixeltf_half_pixel_for_nnmode attribute is onnxruntime::UpsampleBase::StringToUpsampleMode(default) or . It can only be exclude_outside can be set to 1 only when mode is CUBIC. Current mode is set to or cubic_coeff_aexclude_outside`tf_half_pixel_for_nn` is deprecated since opset 13, round_prefer_floor model uses the deprecated attributeyet this opset info.GetAttrs("scales", scales_).IsOK()extrapolation_valueResize operatorUpsample operatorScale value should be greater than 0.scale > 0scale >= 1onnxruntime::UpsampleBase::ScalesValidationonnxruntime::UpsampleBase::StringToNearestModeScale value should be greater than or equal to 1.] is not supported!nearest_mode:[round_prefer_ceilceilcoordinate_transform_mode:[onnxruntime::UpsampleBase::StringToCoordinateTransformationModetf_crop_and_resize] is not supportted!onnxruntime::UpsampleBase::ParseScalesDatascales size should be greater than 0.scales_size > 0'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() == 2 || (scales.size() == 4 && 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 scales.size() == 2 || (scales.size() == 4 && scales[0] == 1 && scales[1] == 1) || scales.size() == 3 || (scales.size() == 5 && scales[0] == 1 && scales[1] == 1)ArgMaxArgMinReduceSumReduceSumSquareReduceMinReduceProdReduceLogSumExpReduceMaxReduceL2ReduceLogSumReduceL1fast_shape.size() == 3onnxruntime::ValidateFastReduceKRKonnxruntime::ValidateFastReduceRKfast_shape[1] == output.Shape().Size()Output size mismatch.fast_shape[0] == output.Shape().Size()fast_shape.size() == 2onnxruntime::ValidateFastReduceKRonnxruntime::ValidateMustBeOverloadedOnly works on matrices with two dimensions.projected_index.size() > 0must be overloaded.D:\a\_work\1\s\onnxruntime\core\providers\cpu\reduction\reduction_ops.cclast_loop_size > 0last_loop_red_size > 0onnxruntime::ResultsNoTransposePrepareForReduce::ValidateNotEmptykeepdimsonnxruntime::ValidateKeepDimsaxes_tensor->Shape().NumDimensions() == 1Can't reduce on dim with value of 0 if 'keepdims' is false. Invalid output shape would be produced. input_shape:onnxruntime::ValidateCommonFastReduceAn axes tensor must be a vector tensor.Axes input is nullaxes_tensor != nullptrcount == 1onnxruntime::ValidateNoTransposeReducefast_shape[0] * fast_shape[2] == output.Shape().Size()Reduction on all axes, output size should be 1.noop_with_empty_axesselect_last_indexonnxruntime::ReduceKernelBase<0>::ReduceKernelBaseD:\a\_work\1\s\onnxruntime\core/providers/cpu/reduction/reduction_ops.hinfo.GetAttr("keepdims", &keepdims).IsOK()onnxruntime::ReduceKernelBase<1>::ReduceKernelBaseD:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\padbase.hInvalid 'pads' attribute valueInvalid 'mode' attribute valueonnxruntime::PadBase::PadBasereflectedgeconstantPads 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::ComputeD:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\pad.ccPads tensor should be an INT64 tensorUnexpected mode of onnxruntime::PadValueFromFloatCannot use 'reflect' mode to pad dimension with a value of 0. Input shape:Value 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()data_rank > 0onnxruntime::PadImplInput tensor has no dimensionsbatch_dimsTindindices tensor data type not supportedD:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\gather_nd.cclast dimension of indices must not be larger than rank of input tensorinput_tensor != nullptr && indices_tensor != nullptronnxruntime::GatherND::ComputeGatherNDGatherNDBase PrepareForCompute: Input count mismatchinvalid index found, index = indices tensor must has rank larger than 0onnxruntime::NonZero::Computeonnxruntime::NonZero::ComputeD:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\nonzero_op.ccfailed to get first output!X input is required!onnxruntime::NonZero::Computeonnxruntime::NonZero<__int64>::Computeonnxruntime::NonZero::ComputeIsNaNNull input ptrinput shape: , indices shape: ScatterNDonnxruntime::ScatterND::Computeinvalid indice found, indice = D:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\scatter_nd.cconnxruntime::ScatterNDBase::PrepareForComputeupdates tensor should have shape equal to indices.shape[:-1] + data.shape[indices.shape[-1]:]. updates shape: , data shape: input tensor and indices tensor must has rank larger than 0. D:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\scatter.cconnxruntime::Scatter,class std::allocator > > >::Scatterinfo.GetAttr("axis", &axis_).IsOK()Missing/Invalid 'axis' attribute valueScatterElementsScatter vs Indices and updates must have the same rankdata type is different from updates typeindices element out of data bounds, idx= must be within the inclusive range [Indices type is not supported.Indices dim= at pos= is greater than input dim=Indices must have the same rank as Input. Indices rank=. Input rank=Indices vs updates dimensions differs at position=onnxruntime::SpaceDepthBase::SpaceDepthBaseinfo.GetAttr("blocksize", &blocksize_).IsOK()Attribute blocksize is not set.blocksizeSpaceToDepthD:\a\_work\1\s\onnxruntime\core/providers/cpu/tensor/space_depth_ops.honnxruntime::DepthToSpace::DepthToSpaceDepthToSpace op: only 'DCR' and 'CRD' modes are supportedDCRCRDinput_depth % (blocksize_ * blocksize_) == 0onnxruntime::DepthToSpace::Computeinput.Shape().NumDimensions() == 4input_width % this->blocksize_ == 0input_height % this->blocksize_ == 0D:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\space_depth_ops.cconnxruntime::SpaceToDepth::Computeinput_shape.NumDimensions() == 4input count mismatchDepthToSpaceGatherElements 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 inputGatherElementsD:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\gather_elements.honnxruntime::GatherElements::GatherElementsGatherElements op: Data type of input 'data' should match the data type of the outputonnxruntime::core_impl::::operator ()onnxruntime::core_impl::::operator ()onnxruntime::core_impl::::operator ()D:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\gather_elements.cconnxruntime::core_impl::::operator ()GatherElements op: Value in indices must be within bounds [ , ]. Actual value is Gather Tind type not supported in this build.D:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\gather.cconnxruntime::Gather::ComputeD:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\gatherbase.honnxruntime::GatherBase::GatherBaseonnxruntime::Tile::Compute!input_tensor.IsDataType()Tile doesn't support string type yet'repeat' input tensor must have the same length as the 'input' tensor'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 emptyTileTile doesn't have an implementation yet for the type: D:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\tile.ccperm: does not align with rank of input data: [ is repeated.Attribute perm of Transpose has an invalid value. Value is outside range.D:\a\_work\1\s\onnxruntime\core/providers/cpu/tensor/transpose.honnxruntime::TransposeBase::TransposeBasev >= 0 && static_cast(v) <= std::numeric_limits::max()num_axes > 0Transpose not implemented for empty tensors.onnxruntime::DoTransposeImpl(local_source >= source) && (local_source < source + num_blocks * blocksize)D:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\transpose.cconnxruntime::IncrementIndexAndComputeOffsetSetupnaxes > 0Method IncrementIndexAndComputeOffset assumes this value is strictly positive.onnxruntime::Transpose::Computeinput_tensor_ptr != nullptrMismatched data types between input and output Tensors. (local_source >= source) && (local_source < source + num_blocks)onnxruntime::DoTransposeEltWiseTranspose of element size not supported in this build. Size=(local_source >= source) && (local_source < source + num_blocks * num_elts_in_block)onnxruntime::TypedDoTransposeEltWise(local_source >= source) && (local_source < source + sizeof(T) * num_blocks). shape= must be 1 instead of Dimension of input input_shape[i] == 1onnxruntime::SqueezeBase::ComputeOutputShapeD:\a\_work\1\s\onnxruntime\core/providers/cpu/tensor/squeeze.honnxruntime::Squeeze::ComputeMissing/Invalid 'axes' attribute valueinfo.GetAttrs("axes", axes_).IsOK()onnxruntime::UnsqueezeBase::UnsqueezeBaseD:\a\_work\1\s\onnxruntime\core/providers/cpu/tensor/unsqueeze.honnxruntime::UnsqueezeBase::PrepareComputeD:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\unsqueeze.ccAn axes tensor must be a scalar or a 1-D tensor.axes_tensor->Shape().NumDimensions() == 0 || axes_tensor->Shape().NumDimensions() == 1'axes' has an out of range axis'axes' has a duplicate axisnullptr != p.output_tensoronnxruntime::Unsqueeze::ComputesplitInvalid value in 'split' attribute. All values must be > 0std::all_of(split_sizes_.cbegin(), split_sizes_.cend(), [](int64_t value) { return value >= 0; })onnxruntime::SplitBase::SplitBaseD:\a\_work\1\s\onnxruntime\core/providers/cpu/tensor/split.hCannot split using values in 'split' attribute. Axis= yetSplit operator does not support onnxruntime::Split::ComputeD:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\split.cc NumOutputs= Axis=Input cannot be split evenly on selected axis. Input shape= Sum of sizes in 'split' (must equal size of selected axis) was Num entries in 'split' (must equal number of outputs) was Input shape=An split tensor must be a vector tensor.split_tensor->Shape().NumDimensions() == 1onnxruntime::Split::ComputeImplMissing or invalid starts and ends attributehas_starts && has_ends && attr_starts_.size() == attr_ends_.size()onnxruntime::SliceBase::SliceBaseD:\a\_work\1\s\onnxruntime\core/providers/cpu/tensor/slice.hInvalid axes attribute, axes attribute (if present) should have the same size as starts/ends attributes!has_axes || attr_axes_.size() == attr_starts_.size()Unexpected element size of onnxruntime::SliceIteratorBase::CopyInnermostAxisNonSolitaryInnerStep'axes' has an axis outside of the tensor dimension count'axes' has duplicatesoutput == output_endonnxruntime::SliceImpl::::operator ()'step' value cannot be 0Starts must be a 1-D arrayonnxruntime::SliceBase::FillVectorsFromInputD:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\slice.ccEnds must be a 1-D arrayStarts and ends shape mismatchStarts and axes shape mismatchStarts and steps shape mismatchData type for starts and ends inputs' is not supported in this build. Got Cannot slice scalarsonnxruntime::SliceBase::Computeonnxruntime::SliceImpl::::operator ()onnxruntime::SliceImpl::::operator ()onnxruntime::SliceImpl::::operator ()onnxruntime::SliceImpl::::operator ()Must have valid 'axis' attributeonnxruntime::ConcatBase::ConcatBaseD:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\concatbase.hnew_axisonnxruntime::ConcatBase::PrepareForComputeD:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\concat.ccCannot concatenate scalars got: Ranks of input data are different, cannot concatenate them. expected rank: inputs_n_rank == inputs_0_rank has mismatched dimensions of Non concat axis dimensions must match: Axis Data type mismatchonnxruntime::`anonymous-namespace'::GetRatioOrDefaultratio must be in the range [0, 1)0.0f <= ratio_value && ratio_value < 1.0fX and mask should have the same shape!mask || mask->Shape() == X_shapeonnxruntime::Dropout::ComputeD:\a\_work\1\s\onnxruntime\core/providers/cpu/nn/dropout_op.honnxruntime::Dropout::Computeonnxruntime::Dropout::Computeonnxruntime::Dropout::Computeratio input should have a single value.ratio_tensor->Shape().Size() == 1across_channelsinfo.GetAttr("across_channels", &across_channels_).IsOK()onnxruntime::MeanVarianceNormalization_0::MeanVarianceNormalization_0D:\a\_work\1\s\onnxruntime\core/providers/cpu/tensor/mean_variance_normalization.hnormalize_varianceinfo.GetAttr("normalize_variance", &normalize_variance_).IsOK()MeanVarianceNormalizationInput is expected to have four dimensions corresponding to [N,C,H,W]info.GetAttr("size", &size).IsOK()sizeD:\a\_work\1\s\onnxruntime\core/providers/cpu/nn/lrn.honnxruntime::LRN::LRNsize_ % 2 == 1size_ > 0alpha_ > 0.0finfo.GetAttr("alpha", &alpha_).IsOK()beta_ > 0.0finfo.GetAttr("beta", &beta_).IsOK()biasonnxruntime::LRN::ComputeX->Shape().NumDimensions() == 4LRND:\a\_work\1\s\onnxruntime\core\providers\cpu\nn\lrn.ccD:\a\_work\1\s\onnxruntime\core/providers/cpu/nn/flatten.honnxruntime::Flatten::Flattengsl::narrow_cast(X_shape.NumDimensions()) >= axisThe rank of input tensor must be >= axisFlattenonnxruntime::Flatten::Computex_zero_point must be null or a scalar or 1D tensor or size 1.onnxruntime::PrepareForQDQzero_point_ptr == nullptr || IsScalarOr1ElementVector(zero_point_ptr)scale must be 1D tensor with size D:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\quantize_linear.ccx_zero_point must be null or 1D tensor with size scale.Shape().NumDimensions() == 1 && scale.Shape()[0] == broadcast_dimzero_point_ptr == nullptr || (zero_point_ptr->Shape().NumDimensions() == 1 && zero_point_ptr->Shape()[0] == broadcast_dim)zero_point == nullptr || std::all_of(zero_point, zero_point + x_zero_point->Shape().Size(), [](int32_t zp) { return zp == 0; })onnxruntime::DequantizeLinear::ComputeDequantizeLinear with type int32 should have no zero point or all zero points should be 0SizeSignonnxruntime::GemmHelper::GemmHelperleft.NumDimensions() == 2 || left.NumDimensions() == 1right.NumDimensions() == 2D:\a\_work\1\s\onnxruntime\core\providers\cpu\math\gemm_helper.h K: N:Gemm: Invalid bias shape for broadcastGEMM: Dimension mismatch, W: info.GetAttr("transA", &temp).IsOK()M_ >= 0 && K_ > 0 && N_ >= 0D:\a\_work\1\s\onnxruntime\core/providers/cpu/math/gemm.honnxruntime::Gemm::Gemmonnxruntime::Gemm::Gemminfo.GetAttr("transB", &temp).IsOK()c_shape != nullptrc_shape is required if c_data is providedD:\a\_work\1\s\onnxruntime\core\providers\cpu\math\gemm.cconnxruntime::GemmBroadcastBiasD:\a\_work\1\s\onnxruntime\core\providers\cpu\math\matmul.cconnxruntime::MatMul::Computeleft_num_dims and right_num_dims must be >= 1D:\a\_work\1\s\onnxruntime\core/providers/cpu/math/matmul_helper.honnxruntime::MatMulComputeHelper::Computeleft operand cannot broadcast on dim MatMul dimension mismatchnum_dims_with_pad != num_output_dimsright operand cannot broadcast on dim num_dims_with_pad - 1 != num_output_dimsM_ == 1 && N_ == 1 was falsenum_dims_with_pad - 2 != num_output_dimsonnxruntime::MatMul::Computeonnxruntime::MatMul::Computeonnxruntime::MatMul<__int64>::Computemin should be a scalar.min->Shape().IsScalar()max should be a scalar.max->Shape().IsScalar()onnxruntime::Clip::ComputeImpl::operator ()D:\a\_work\1\s\onnxruntime\core\providers\cpu\math\clip.cconnxruntime::Clip::ComputeImpl::operator ()onnxruntime::Clip::ComputeImpl::operator ()onnxruntime::Clip::ComputeImpl<__int64>::operator ()onnxruntime::Clip::ComputeImpl::operator ()min_ <= max_onnxruntime::clip_internal::Clip_6Base::Clip_6BaseD:\a\_work\1\s\onnxruntime\core/providers/cpu/math/clip.honnxruntime::Clip::ComputeImpl::operator ()invalid stoull argumentstoull argument out of rangeinvalid stoll argumentstoll argument out of rangeinvalid stod argumentstod argument out of rangeonnxruntime::`anonymous-namespace'::GetIntermediateMLFloat16ToFloatTensoronnxruntime::`anonymous-namespace'::Cast::CastD:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\cast_op.ccAttribute to is not set.INF%.8gNaN-INFonnxruntime::`anonymous-namespace'::CastToStringFailed to write value with snprintf().snprintf() failed with return value: snprintf_result > 0snprintf_result > 0 && gsl::narrow_cast(snprintf_result) == buffer_span.size() - 1invalid expand shapeMissing 'equation' attributeinfo.GetAttr("equation", &equation_).IsOK()->equationEinsumEinsum op: There must be atleast one inputonnxruntime::Einsum::EinsumD:\a\_work\1\s\onnxruntime\core\providers\cpu\math\einsum.hD:\a\_work\1\s\onnxruntime\core\providers\cpu\math\einsum.cc is not supported yetThere was a problem acquiring temporary memory allocator in Einsum oponnxruntime::Einsum::DeviceComputeEinsum op: An implementation for the input type info.GetAttr("storage_order", &storage_order).IsOK()kernel_shape[dim] > 0count_include_padinfo.GetAttr("count_include_pad", &temp).IsOK()strides.size() == kernel_shape.size()Dilations dimensions should match kernel shapePad should be smaller than kernel.pads[dim] < kernel_shape[dim] && pads[dim + kernel_shape.size()] < kernel_shape[dim]input_shape.Size() > 0 || input_shape[0] == 0onnxruntime::PoolAttributes::SetOutputSizedilations.size() == kernel_shape.size()Invalid input shape. Only N can be zero. Got:Unsupported AutoPad Type.onnxruntime::PoolAttributes::ComputeSizePadDilationsinput_dims.size() >= 2onnxruntime::PoolAttributes::InferOutputSizeUnknown AutoPadType Stringonnxruntime::StringToAutoPadTypeNo kernel shape is set.info.GetAttrs("kernel_shape", kernel_shape).IsOK()GlobalLpPoolinfo.GetAttr("auto_pad", &auto_padding).IsOK()ceil_modeonnxruntime::PoolAttributes::PoolAttributesD:\a\_work\1\s\onnxruntime\core/providers/cpu/nn/pool_attributes.honnxruntime::Pool::ComputeUnsupported pooling size : onnxruntime::PoolProcessContext::initD:\a\_work\1\s\onnxruntime\core/providers/cpu/nn/pool_base.hpinfo.GetAttr("p", &p_).IsOK()D:\a\_work\1\s\onnxruntime\core\providers\cpu\nn\pool.ccUnsupported pooling size.Input dimension cannot be less than 3.onnxruntime::PoolBase::Computekernel_shape num_dims is not compatible with X num_dims.LpPoolonnxruntime::MaxPoolV8::ComputeImplInvalid argument for depth; it's not a scalar.Invalid argument for values; either it's rank is more than 1 or it has more than 2 elementsT3OneHotonnxruntime::OneHotOp<__int64,float,int>::ComputeDepth is negative.onnxruntime::OneHotOp::Computeonnxruntime::OneHotOp<__int64,__int64,__int64>::ComputeD:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\onehot.cconnxruntime::OneHotOp<__int64,float,__int64>::Computeonnxruntime::OneHotOp::Computeonnxruntime::OneHotOp<__int64,class std::basic_string,class std::allocator >,__int64>::Computeonnxruntime::OneHotOp,class std::allocator >,__int64>::Computeonnxruntime::OneHotOp<__int64,int,float>::Computeonnxruntime::OneHotOp<__int64,float,float>::Computeonnxruntime::OneHotOp::Computeonnxruntime::OneHotOp::Computeinput count mismatch, expected 1 input - the tensor to be processedD:\a\_work\1\s\onnxruntime\core\providers\cpu\math\top_k.cconnxruntime::TopkOpset9ConstructorCommonop_kernel_info.GetAttr("k", &k_temp).IsOK()konnxruntime::TopkOpset11ConstructorCommononnxruntime::TopkOpset10ConstructorCommonop_kernel_info.GetAttr("axis", &axis_temp).IsOK()k_temp > 0op_kernel_info.GetAttr("sorted", &sorted_temp).IsOK()sortedop_kernel_info.GetAttr("largest", &largest_temp).IsOK()largestTopKvalue of k must not be negativek tensor should be a 1D tensor of size 1input count mismatch, expected 2 inputs - the tensor to be processed and a tensor containing k valuek argument [] should not be greater than specified axis dim value [output count mismatch, expected 2 outputs to be present for TopK operatorUnsupported tensor type of Uniqueinput_ptrRequired input at index is not present.delta in Range operator should be scalar like tensor, yet got shape:limit in Range operator should be scalar like tensor, yet got shape:start in Range operator should be scalar like tensor, yet got shape:onnxruntime::OpKernelContext::RequiredInputdelta in Range operator can not be zero!onnxruntime::Tensor::Reshapeshape_.Size() == new_shape.Size()Tensor size () != new size (Unsupported 'dtype' value: onnxruntime::SequenceEmpty::ComputeSequenceEmpty: Got nullptr for output sequenceSequenceEmptyonnxruntime::SequenceInsert::ComputeS != nullptronnxruntime::CreateCopyAndAppendCpuTensorSequenceInsertSequenceInsert: Got nullptr for output sequenceData type of the input tensor MUST be same as that of the input sequence. Sequence data type (), input tensor data type (Got nullptr for input tensor.SequenceConstructSequenceErase: Got nullptr for output sequenceonnxruntime::SequenceErase::ComputeSequenceEraseGot nullptr for sequence input.SequenceLengthSY != nullptrSequenceLength: Got nullptr for output tensorD:\a\_work\1\s\onnxruntime\core\providers\cpu\sequence\sequence_ops.cconnxruntime::SequenceLength::ComputeGot nullptr input for index tensoronnxruntime::SequenceAt::Computeonnxruntime::GetSeqIdxSequenceAtSequenceAt: Got nullptr for output tensorInvalid sequence index () specified for sequence of size (I != nullptronnxruntime::SplitToSequence::ComputeImplsplit_scalar > 0Split should be > 0Violation of the requirment that all input tensors must have the same data type.SequenceConstruct: Got nullptr for output sequenceonnxruntime::SequenceConstruct::Computenum_inputs >= 1split_size_sum () != split_dim_size (SplitToSequence operator does not support SplitToSequenceonnxruntime::GetSplitSizesInputonnxruntime::GetScalarSplitInputInvalid data type for split tensor std::all_of(split_sizes.cbegin(), split_sizes.cend(), [](int64_t value) { return value >= 0; })Invalid value in 'split' input. All values must be >= 0D:\a\_work\1\s\onnxruntime\core\providers\cpu\sequence\concat_from_sequence.cconnxruntime::ConcatFromSequence::ComputeConcatFromSequenceD:\a\_work\1\s\onnxruntime\core/providers/cpu/nn/conv_attributes.honnxruntime::ConvAttributes::ConvAttributesauto_pad == AutoPadType::NOTSETA Conv/ConvTranspose node has both 'auto_pad' and 'pads' attributeskernel_shape is not compatible with W shape.kernel_shape num_dims is not compatible with W num_dims. kernel_shape: W: group: X num_dims does not match W num_dims. X: onnxruntime::ConvTransposeAttributes::ComputeTransposePadAndOutputShape*out_size >= 0onnxruntime::ConvTransposeAttributes::ComputePadsAndOutputShapedim_size > 0output_paddingInvalid input shape: filter_number: num_input_channels: group count is <= 0output_shapeD:\a\_work\1\s\onnxruntime\core/providers/cpu/nn/conv_transpose_attributes.honnxruntime::ConvTransposeAttributes::PrepareForComputeInput channels is not divisible by group.filter number not equal to input channel number.D:\a\_work\1\s\onnxruntime\core\providers\cpu\nn\conv_transpose.cconnxruntime::ConvTranspose::DoConvTransposeConvTransposeDilation not supported for AutoPadType::SAME_UPPER or AutoPadType::SAME_LOWER. C: kernel channels: onnxruntime::ComputePadAndOutputShapeComputePad: pad type not supported.Not enough elements in strides. Expected: Output channels M is not divisible by group. M: Input channels C is not equal to kernel channels * group.D:\a\_work\1\s\onnxruntime\core\providers\cpu\nn\conv.cconnxruntime::Conv::Computeonnxruntime::ConvAttributes::InferOutputShapeNot enough elements in pads. Expected: Not enough elements in dilations. Expected: Not enough elements in kernel shape. Expected: MaxUnpoolonnxruntime::MaxUnpool::Computekernel_shape_[dim] > 0pads_[dim] < kernel_shape_[dim] && pads_[dim + kernel_shape_.size()] < kernel_shape_[dim]strides_.size() == kernel_shape_.size()info.GetAttrs("kernel_shape", kernel_shape_).IsOK()onnxruntime::MaxUnpool::MaxUnpoolD:\a\_work\1\s\onnxruntime\core/providers/cpu/nn/unpool.houtput_shape is smaller than minimum required. output_shape:D:\a\_work\1\s\onnxruntime\core\providers\cpu\nn\Unpool.ccIndex tensor shape should be same as that of the input data tensor to unpool.Shape must be 1 dimensional as it's tensor data of a shape inferred output shape:scores_tensorboxes must be a 3D tensor.scores must be a 3D tensor.boxes and scores should have same num_batches.NonMaxSuppressionboxes_tensoronnxruntime::NonMaxSuppressionBase::PrepareComputeD:\a\_work\1\s\onnxruntime\core\providers\cpu\object_detection\non_max_suppression.cconnxruntime::NonMaxSuppression::Computeoutput != nullptrboxes and scores should have same spatial_dimension.The most inner dimension in boxes must have 4 data.iou_threshold must be in range [0, 1].onnxruntime::NonMaxSuppressionBase::GetThresholdsFromInputscenter_point_box only support 0 or 10 == center_point_box_ || 1 == center_point_box_onnxruntime::NonMaxSuppressionBase::NonMaxSuppressionBaseD:\a\_work\1\s\onnxruntime\core\providers\cpu\object_detection\non_max_suppression.hcenter_point_boxDetonnxruntime::Det::ComputeD:\a\_work\1\s\onnxruntime\core\providers\cpu\math\det.ccMatrix dimensions are not equal. Square matrix is expectedonnxruntime::Compress::ComputeD:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\compress.ccCompressaxes_right_stride >= 0 && static_cast(axes_right_stride) < std::numeric_limits::max()Attempt to retrieve final output before it was set.onnxruntime::Scan<9>::Initfinal_output_mlvalue_onnxruntime::scan::detail::OutputIterator::GetOutputD:\a\_work\1\s\onnxruntime\core/providers/cpu/controlflow/scan_utils.h' dimension but input 'Scan inputs have inconsistent sequence lengths. Previous value was . Input tensor rank was dimensions or more but input had shape of Expected Invalid scan input: has length of outputs but Scan expects Subgraph in 'body' produces onnxruntime::ScanImpl::AllocateOutputTensorsonnxruntime::ScanImpl::CreateLoopStateVariables of Invalid value in scan_input_axes for input onnxruntime::ScanImpl::ValidateInputonnxruntime::ScanImpl::SetupInputsscan_output_directionsscan_input_axes but expected Number of entries in 'scan_input_axes' was D:\a\_work\1\s\onnxruntime\core\providers\cpu\controlflow\scan_9.ccnum_scan_inputsinfo.GetAttr("num_scan_inputs", &num_scan_inputs_).IsOK()scan_input_directionsonnxruntime::Scan<9>::SetupSubgraphExecutionInfofeeds_fetches_manager_ && info_onnxruntime::Scan<9>::Computeonnxruntime::ScanImpl::Initializegsl::narrow_cast(input_axes_.size()) == num_scan_inputs_scan_output_axesNumber of entries in 'scan_output_axes' was gsl::narrow_cast(output_axes_.size()) == num_scan_outputsInvalid value in scan_output_axes for output Outputs from Scan are not optional and should never be null.onnxruntime::ScanImpl::TransposeOutputScanOutput OrtValue has not been created for loop state variable output output_mlvalueonnxruntime::ScanImpl::Execute. Output tensor rank was onnxruntime::DynamicQuantizeLinear::ComputeD:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\dynamicquantizelinear.ccx_ptr != nullptrRoundbatch_axis. Must be 0 or 1info.GetAttr("time_axis", &time_axis).IsOK()batch_axis < 2Invalid batch_axis of onnxruntime::ReverseSequenceOp::ReverseSequenceOpinfo.GetAttr("batch_axis", &batch_axis).IsOK()time_axisD:\a\_work\1\s\onnxruntime\core/providers/cpu/tensor/reverse_sequence.honnxruntime::ReverseSequenceOp::ComputeUnknown tensor type of D:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\reverse_sequence.ccReverseSequencesequence_lens shape must be {batch_size}. Got:. batch_size=time_axis < 2Invalid time_axis of batch_axis != time_axistime_axis and batch_axis must have different values but both are . Value must be in range [0,Invalid sequence length: specified. It should be either avg or maxavgsampling_ratiooutput_widthsampling_ratio_ >= 0Sampling ratio should be >=0, but it was onnxruntime::RoiAlignBase::RoiAlignBaseInvalid mode of value output_heightD:\a\_work\1\s\onnxruntime\core\providers\cpu\object_detection\roialign.hNumber of dimensions for rois should be exactly Number of dimensions for batch indices should be exactly 1First dimension (num_rois) of batch_indices and rois don't matchSecond dimension for rois should be exactly spatial_scaleNull input X ptrRoiAlignNull batch_indices_ptrNull rois_ptrIsInfFailed to obtain detect_negativedetect_negativeFailed to obtain detect_positivedetect_positiveD:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\isinf.cconnxruntime::IsInf::IsInfQLinearConv : filter zero point must be constantIsScalarOr1ElementVector(Y_zero_point)QLinearConv : input scale must be a scalar or 1D tensor of size 1QLinearConv : filter zero point shape invalidIsScalarOr1ElementVector(X_zero_point)QLinearConv : input zero point must be a scalar or 1D tensor of size 1QLinearConv : result zero point must be a scalar or 1D tensor of size 1onnxruntime::QLinearConv::ComputeQLinearConv : result scale must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(X_scale)QLinearConv : filter scale shape invalidIsScalarOr1ElementVector(Y_scale)prepacked_buffers[0].get() == nullptrT4D:\a\_work\1\s\onnxruntime\core\providers\cpu\nn\qlinearconv.cconnxruntime::QLinearConv::UseSharedPrePackedBuffersIsScalarOr1ElementVector(W_Zero_Point)Non per-tensor quantization is not supported now.IsScalarOr1ElementVector(X_Zero_Point)Must be a scalar or 1D tensor or size 1.D:\a\_work\1\s\onnxruntime\core\providers\cpu\nn\conv_integer.cconnxruntime::ConvInteger::ComputeConvIntegerIsBQuantParamSupported(b_zero_point->Shape(), b ? b->Shape() : b_shape_)MatmulInteger : B zero point is not validIsScalarOr1ElementVector(a_zero_point)MatmulInteger : input1 zero point must be a scalar or 1D tensor of size 1D:\a\_work\1\s\onnxruntime\core\providers\cpu\math\matmul_integer.cconnxruntime::MatMulInteger::Computeonnxruntime::MatMulComputeHelper::Compute::::operator ()Per-column quantization parameter of batched matrix should have same dimension as the matrix,and its size by K should be equal to the matrix's size.IsBQuantParamSupported(b_offset->Shape(), b ? b->Shape() : b_shape_)QLinearMatmul : weight zero point must be a scalar, 1D tensor of size 1, or last to second dimension is 1IsScalarOr1ElementVector(y_offset)QLinearMatmul : result zero point must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(a_offset)QLinearMatmul : input zero point must be a scalar or 1D tensor of size 1D:\a\_work\1\s\onnxruntime\core\providers\cpu\math\quantize_linear_matmul.cconnxruntime::QLinearMatMul::ComputeIsScalarOr1ElementVector(y_scale)QLinearMatmul : result scale must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(a_scale)QLinearMatmul : input scale must be a scalar or 1D tensor of size 1IsBQuantParamSupported(b_scale->Shape(), b ? b->Shape() : b_shape_)QLinearMatmul : weight scale must be a scalar, 1D tensor of size 1, or last to second dimension is 1*Conversion ErrorConversion ErrorFailed to construct locale with name::Please, install necessary language-pack-XX and configure localesStringNormalizeronnxruntime::StringNormalizer::StringNormalizerattribute is_case_sensitive is not setattribute case_change_action is not setcase_change_actionD:\a\_work\1\s\onnxruntime\core\providers\cpu\nn\string_normalizer.cconnxruntime::string_normalizer::Locale::Localeis_case_sensitiveen-USbad conversionstopwordslocale!sw.empty()Empty stopwords not allowedUPPERLOWERattribute case_change_action has invalid valueNONEInput dimensions are either[C > 0] or [1][C > 0] allowedSingle dimension value must be greater than 0Input contains invalid utf8 chars at: p.secondDuplicate stopwords not allowedwstr != wconv_errorStopword contains invalid utf8 charsonnxruntime::UpsampleBase::ParseScalesDataFromOutputSizeInput dim is zero but required output dim is non-zero. output_dims[i] == 0Dimension: Cannot scale 0 by any factor to generate a non-zero value. Output dim value: Input dim value: onnxruntime::Upsample::BaseComputeRank of input and output tensor should be same.onnxruntime::Upsample::ComputeX->Shape().GetDims().size() == output_dims.size()onnxruntime::Upsample::Computeonnxruntime::Upsample::BaseComputeonnxruntime::Upsample::BaseComputeResize: unexpected modeUpsample: unexpected modeUpsample: input tensor's dimension does not match the scales.Resize: size of roi array should be 2 * N where N is the rank of input tensor X.output_dims.size() == dims.size()Resize: input tensor's dimension does not match the scales.onnxruntime::Upsample::Computesizes != nullptr && sizes->Shape().Size() != 0Resize: input tensor's rank does not match the output tensor's rank.sizes == nullptrEither scales or sizes MUST be provided as input.roi_input_idx_ > 0Only one of scales or sizes must be provided as input.D:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\upsample.ccInvalid roi input index.Upsample: input shape needs to be at least a single dimension.Upsample: input/output value's dimension mismatchResize: input shape needs to be at least a single dimensionUpsample: input/output value is nullptrResize: input/output value's dimension mismatchResize: input/output value is nullptrmode is requiredonnxruntime::TfIdfVectorizer::TfIdfVectorizerTfIdfVectorizerimpl_->weighting_criteria_ != kNonemin_gram_length is unrecognized, acceptable values are TF,IDF,TFIDFmode: IDFTFIDFD:\a\_work\1\s\onnxruntime\core\providers\cpu\nn\tfidfvectorizer.ccTFNegative ngram_indexes values are not allowedstd::all_of(impl_->ngram_indexes_.cbegin(), impl_->ngram_indexes_.cend(), [](int64_t i) { return i >= 0; })Non-empty ngram_indexes is requiredstatus.IsOK() && !impl_->ngram_indexes_.empty()size_t(impl_->max_gram_length_) <= impl_->ngram_counts_.size()ngram_indexessize_t(impl_->min_gram_length_) <= impl_->ngram_counts_.size()max_gram_length must be inbounds of ngram_counts: pool_strings must not be empty if specified!pool_strings.empty()impl_->weights_.size() == impl_->ngram_indexes_.size()pool_strings but ngram_indexes size: Got weights of size: weights must be of equal sizeimpl_->max_gram_length_ >= impl_->min_gram_length_max_skip_count >= min_gram_length >= max_gram_length required: impl_->min_gram_length_ > 0max_gram_lengthmin_gram_length is requiredRequired min_gram_length must be positive: <= min_gram_length must be inbounds of ngram_counts: Non-empty ngram_counts is requiredstatus.IsOK() && !impl_->ngram_counts_.empty()impl_->max_skip_count_ >= 0ngram_countsmax_skip_count is requiredmax_skip_count must be non-negative: onnxruntime::ngram_details::PopulateGramsDuplicate ngram detected, size: p.first->second->id_ == 0 id: Number of items must compose whole (items % ngram_size == 0)n-gram counts out of bounds for end_idx >= start_idx && end_idx <= total_itemsstatus.IsOK() && !pool_int64s.empty()-gramspool_int64snon-empty pool_int64s is required if pool_strings not providedInput shape must have either [C] or [B,C] dimensions with B > 0.onnxruntime::Shrink::ShrinkD:\a\_work\1\s\onnxruntime\core/providers/cpu/nn/shrink.hop_kernel_info.GetAttr("bias", &bias_temp).IsOK()lambdop_kernel_info.GetAttr("lambd", &lambd_temp).IsOK()Shrinkoutput_ptronnxruntime::OpKernelContext::RequiredOutputRequired output at index EyeLike : Input tensor dimension is not 2EyeLikeMust have a valid input shape.onnxruntime::ConstantOfShapeBase >::PrepareComputeD:\a\_work\1\s\onnxruntime\core\providers\cpu\generator\constant_of_shape.ccUnsupported output datatype with size: onnxruntime::`anonymous-namespace'::ConstantOfShape::Computeonnxruntime::ConstantOfShapeBase >::SetValueUnsupported value attribute datatype with size: !utils::HasExternalData(t_proto)Unsupported value attribute datatype: ONNX_NAMESPACE::TensorProto::DataType_IsValid(t_proto.data_type())Tensor proto with external data for value attribute is not supported.utils::HasDataType(t_proto)onnxruntime::ConstantOfShapeBase >::SetValueFromTensorProtoMust have a single dimension of 1t_proto_p->dims()[0] == 1t_proto_p->dims_size() == 1onnxruntime::ConstantOfShapeBase >::ConstantOfShapeBaseD:\a\_work\1\s\onnxruntime\core/providers/cpu/generator/constant_of_shape_base.hMust have a single dimensiononnxruntime::Scan<8>::SetupSubgraphExecutionInfoonnxruntime::Scan<8>::ComputeD:\a\_work\1\s\onnxruntime\core\providers\cpu\controlflow\scan_8.ccdirectionsScan<8> spec does not support transpose of output. This should never be called.onnxruntime::Scan<8>::Initsequence_lens length of Invalid entries in sequence_lens. Max sequence length was onnxruntime::Scan8Impl::ValidateInput did not match batch size of but Scan inputs have inconsistent batch size. Previous value was onnxruntime::Scan8Impl::Initialize has batch size of D:\a\_work\1\s\onnxruntime\core/framework/ort_value_tensor_slicer.hposition_ >= 0 && position_ < sequence_length_onnxruntime::OrtValueTensorSlicer::Iterator::operator *onnxruntime::Scan8Impl::CreateLoopStateVariablesonnxruntime::Scan8Impl::Executeonnxruntime::Scan8Impl::AllocateOutputTensorsp_mlvalueroi_batch_id >= 0D:\a\_work\1\s\onnxruntime\core\providers\cpu\nn\roi_pool.cconnxruntime::RoiPool::ComputeR->Shape()[1] == 5MaxRoiPoolpooled_width_ > 0pooled_height_ > 0pooled_shape.size() == 2D:\a\_work\1\s\onnxruntime\core/providers/cpu/nn/roi_pool.honnxruntime::RoiPool::RoiPoolinfo.GetAttrs("pooled_shape", pooled_shape).IsOK()pooled_shaperoi_batch_id < batch_sizespatial_scale_ > 0info.GetAttr("spatial_scale", &spatial_scale_).IsOK()p_ == 1 || p_ == 2op_kernel_info.GetAttr("p", &p_).IsOK()D:\a\_work\1\s\onnxruntime\core/providers/cpu/nn/lp_norm.honnxruntime::LpNorm::LpNormop_kernel_info.GetAttr("axis", &axis_).IsOK()LpNormalizationonnxruntime::LpNorm::LpNormMismatch 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 onnxruntime::InstanceNorm::ComputeInstanceNormalization vs. Invalid input scale: number of dimensions is not 1: Invalid input data: number of dimensions is less than 3: D:\a\_work\1\s\onnxruntime\core/providers/cpu/nn/instance_norm.honnxruntime::InstanceNorm::InstanceNormop_kernel_info.GetAttr("epsilon", &epsilon_).IsOK()D:\a\_work\1\s\onnxruntime\core\providers\cpu\nn\instance_norm.ccinfo.GetAttr("scale", &scale_).IsOK()scaleD:\a\_work\1\s\onnxruntime\core/providers/cpu/generator/random.honnxruntime::RandomNormal::RandomNormalinfo.GetAttr("mean", &mean_).IsOK()meanonnxruntime::RandomUniform::RandomUniforminfo.GetAttr("high", &high_).IsOK()highonnxruntime::RandomNormalLike::RandomNormalLikeinfo.GetAttrs("shape", shape).IsOK()ONNX_NAMESPACE::TensorProto::DataType_IsValid(dtype_) && dtype_ != ONNX_NAMESPACE::TensorProto::UNDEFINEDInvalid dtype of info.GetAttr("dtype", &dtype).IsOK()onnxruntime::RandomNormalLike::ComputeInvalid data type of num_samples is < 1num_classes is < 1batch_size is < 1Empty dimensions for input tensoronnxruntime::RandomUniformLike::ComputeCould not infer data type from input tensor with data type D:\a\_work\1\s\onnxruntime\core\providers\cpu\generator\random.ccONNX_NAMESPACE::TensorProto::DataType_IsValid(output_dtype_) && output_dtype_ != ONNX_NAMESPACE::TensorProto::UNDEFINEDonnxruntime::Multinomial::Multinomialinfo.GetAttr("sample_size", &num_samples_).IsOK()sample_sizeonnxruntime::RandomUniformLike::RandomUniformLikeinfo.GetAttr("low", &low_).IsOK()lowonnxruntime::MultinomialComputeOutput type not supported in this build: onnxruntime::contrib::RegisterCpuContribKernelsonnxruntime::contrib::RegisterQuantizationKernelsD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\cpu_contrib_kernels.cconnxruntime::contrib::RegisterNchwcKernelsonnxruntime::ml::LabelEncoder::LabelEncoderinfo.GetAttrs("classes_strings", string_classes).IsOK()classes_strings(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 >,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 Input 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_stringD:\a\_work\1\s\onnxruntime\core/providers/cpu/ml/label_encoder.hvalues_int64skeys_int64sdefault_floatvalues_floatskeys_strings_Unusedvalues_stringskeys_floatsonnxruntime::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 countD:\a\_work\1\s\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_int64sZipMapMINSUMAVERAGESOFTMAX_ZEROSOFTMAXLOGISTICBRANCH_EQBRANCH_GTBRANCH_GTEBRANCH_LTLEAFBRANCH_LEQtarget_weightsTreeEnsembleRegressornodes_falsenodeids.size() == nodes_featureids.size()D:\a\_work\1\s\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. 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_nodeidsnodes_treeidsnodes_truenodeidsnodes_valuespost_transformtarget_idstarget_nodeidstarget_treeidsaggregate_functionbase_valuesn_targetsnodes_falsenodeidsnodes_featureidsnodes_hitratesnodes_missing_value_tracks_truenodes_modesonnxruntime::ml::detail::TreeAggregatorMax::MergePredictiononnxruntime::ml::detail::TreeAggregator::FinalizeScorespredictions.size() == (size_t)n_targets_or_classes_onnxruntime::ml::detail::TreeAggregatorSum::ProcessTreeNodePredictionit->i < (int64_t)predictions.size()onnxruntime::ml::detail::TreeAggregatorSum::MergePredictiononnxruntime::ml::detail::TreeAggregatorAverage::FinalizeScoresthis->base_values_.size() == predictions.size()onnxruntime::ml::detail::TreeAggregator::FinalizeScoresonnxruntime::ml::detail::TreeAggregatorSum::ProcessTreeNodePredictiononnxruntime::ml::detail::TreeAggregatorSum::MergePredictiononnxruntime::ml::detail::TreeAggregatorAverage::FinalizeScoresonnxruntime::ml::detail::TreeAggregatorMin::MergePrediction (weights). (falsenode).One falsenode is pointing either to itself, either to another tree.Unable to find node - (truenode).Node in tree onnxruntime::ml::detail::TreeAggregatorMin::MergePredictionD:\a\_work\1\s\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.TreeEnsembleClassifieronnxruntime::ml::detail::TreeAggregatorClassifier::FinalizeScoresclasses.size() == 2 || classes.size() == 1onnxruntime::ml::detail::TreeAggregatorClassifier::_set_score_binaryonnxruntime::ml::detail::TreeAggregatorClassifier<__int64,float>::_set_score_binaryonnxruntime::ml::detail::TreeAggregatorClassifier::_set_score_binaryonnxruntime::ml::detail::TreeAggregatorClassifier::_set_score_binarypredictions.size() == 2onnxruntime::ml::detail::TreeAggregatorClassifier::FinalizeScoresonnxruntime::ml::detail::TreeAggregatorSum::MergePredictiononnxruntime::ml::detail::TreeAggregatorSum::ProcessTreeNodePredictiononnxruntime::ml::detail::TreeAggregatorClassifier<__int64,float>::FinalizeScoresonnxruntime::ml::detail::TreeAggregatorSum<__int64,float>::MergePredictiononnxruntime::ml::detail::TreeAggregatorSum<__int64,float>::ProcessTreeNodePredictiononnxruntime::ml::detail::TreeAggregatorClassifier::FinalizeScoresonnxruntime::ml::detail::TreeEnsembleCommonClassifier::computeonnxruntime::ml::detail::TreeEnsembleCommonClassifier::computeonnxruntime::ml::detail::TreeEnsembleCommonClassifier<__int64,float>::computeonnxruntime::ml::detail::TreeEnsembleCommonClassifier::computeonnxruntime::ml::detail::TreeEnsembleCommon<__int64,float>::TreeEnsembleCommononnxruntime::ml::detail::TreeEnsembleCommon::TreeEnsembleCommonclass_weightsclass_treeidsclass_nodeidsclass_idsX dims is empty.LINEARPOLYRBFn_supportssupport_vectorsrhoinfo.GetAttrs("rho", rho_).IsOK()onnxruntime::ml::SVMRegressor::SVMRegressorD:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\svmregressor.cccoefficientskernel_typekernel_paramsinfo.GetAttrs("kernel_params", kernel_params).IsOK()onnxruntime::ml::SVMCommon::SVMCommonD:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\svmclassifier.hSVMRegressorinfo.GetAttrs("coefficients", coefficients_).IsOK()!coefficients_.empty()one_classnum_features == feature_count_onnxruntime::ml::SVMRegressor::ComputeUnexpected mode:D:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\ml_common.honnxruntime::ml::SVMClassifier::ComputeUnsupported data type of prob_bonnxruntime::ml::SVMClassifier::SVMClassifierD:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\svmclassifier.ccproba_.size() == probb_.size()classlabels_intsinfo.GetAttrs("classlabels_strings", classlabels_strings_).IsOK() || info.GetAttrs("classlabels_ints", classlabels_ints_).IsOK()classlabels_strings_.size() > 0 || classlabels_ints_.size() > 0coefficients_.size() > 0SVMClassifiervectors_per_classprob_ascores.size() == static_cast(expected_num_scores)onnxruntime::ml::batched_update_scores_inplaceUnexpected value for 'add_second_class' of offsetEmpty scale in attributes!scale_.empty()Scaleronnxruntime::ml::ScalerOp::ScalerOponnxruntime::ml::ScalerOp<__int64>::ScalerOponnxruntime::ml::ScalerOp::ScalerOponnxruntime::ml::ScalerOp::ScalerOpD:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\scaler.cc) != (Scale size: (scale_.size() == offset_.size()Invalid argument: input has empty dimensions.) or 1Either both scale and offset can be of feature size (OneHotEncoderUnknown Category and zeros = 0.D:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\onehotencoder.ccnum_categories_ > 0onnxruntime::ml::OneHotEncoderOp::OneHotEncoderOponnxruntime::ml::OneHotEncoderOp::OneHotEncoderOponnxruntime::ml::OneHotEncoderOp,class std::allocator > >::OneHotEncoderOpzeroscats_int64scats_stringsOne and only one of the 'cats_*' attributes must be definedtmp_cats_int64s.empty() || tmp_cats_strings.empty()onnxruntime::ml::OneHotEncoderOp<__int64>::OneHotEncoderOpMAXL1L2Invalid normalize value of onnxruntime::ml::MakeNormalizenorminfo.GetAttr("norm", &norm).IsOK()onnxruntime::ml::Normalizer::NormalizerD:\a\_work\1\s\onnxruntime\core/providers/cpu/ml/ml_common.hUnexpected NORMALIZE value of Rank of input to Normalized must be less than 2. Got D:\a\_work\1\s\onnxruntime\core/providers/cpu/ml/normalizer.hNormalizeronnxruntime::ml::LinearRegressor::LinearRegressorD:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\linearregressor.ccInput shape had more than 2 dimension. Dims=LinearRegressorinterceptstargetsinfo.GetAttr("targets", &num_targets_).IsOK()Unsupported input element type of multi_classLinearClassifierD:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\linearclassifier.cconnxruntime::ml::LinearClassifier::LinearClassifierScores output is incorrect size. Expected: Found:onnxruntime::ml::LinearClassifier::ComputeImplscores_output_data.length() >= scores_output_sizeshape_size == out.length()onnxruntime::ml::CastInputToFloatreplaced_value_int64D:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\imputer.ccMust provide imputed_values_float_ or imputed_values_int64_ but not both.Expected 'replace_value_int64' attribute since 'imputed_values_int64' is specifiedonnxruntime::ml::ImputerOp::Computeimputed_values_float_.empty() ^ imputed_values_int64_.empty()Invalid typeimputed_value_floatsImputerreplaced_value_floatimputed_value_int64sonnxruntime::ml::ImputerOp::ImputerOpExpected 'replaced_value_float' attribute since 'imputed_value_floats' is specifiedEmpty value of imputed values.Empty input dimensions.).FeatureVectorizerNumber of inputs () does not match number of inputdimensions values (onnxruntime::ml::FeatureVectorizer::Computeinput_count >= 0 && static_cast(input_count) == input_dimensions_.size()Invalid input type:D:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\feature_vectorizer.ccinputdimensionsstatus.IsOK() && !input_dimensions_.empty()inputdimensions attribute must be providedD:\a\_work\1\s\onnxruntime\core/providers/cpu/ml/feature_vectorizer.honnxruntime::ml::FeatureVectorizer::FeatureVectorizeronnxruntime::ml::DictVectorizerOp,class std::allocator >,float>::DictVectorizerOponnxruntime::ml::DictVectorizerOp,class std::allocator >,double>::DictVectorizerOponnxruntime::ml::DictVectorizerOp,class std::allocator >,__int64>::DictVectorizerOpstring_vocabularyDictVectorizerinfo.GetAttrs(std::is_same::value ? "string_vocabulary" : "int64_vocabulary", vocabulary_).IsOK()int64_vocabularyD:\a\_work\1\s\onnxruntime\core/providers/cpu/ml/dictvectorizer.honnxruntime::ml::DictVectorizerOp<__int64,double>::DictVectorizerOponnxruntime::ml::DictVectorizerOp<__int64,class std::basic_string,class std::allocator > >::DictVectorizerOponnxruntime::ml::DictVectorizerOp<__int64,float>::DictVectorizerOponnxruntime::ml::CategoryMapper::CategoryMapperinfo.GetAttrs("cats_strings", string_categories).IsOK()info.GetAttrs("cats_int64s", int_categories).IsOK()D:\a\_work\1\s\onnxruntime\core/providers/cpu/ml/category_mapper.hCategoryMappernum_entries == int_categories.size()Input of int64 must have output of string Input of string must have output of int64invalid stof argumentstof argument out of range%fDENSEonnxruntime::ml::MakeCast Expected DENSE or SPARSESPARSEonnxruntime::ml::MakePackInvalid PACK_MAP value of info.GetAttr("cast_to", &attr).IsOK()cast_toTO_FLOATTO_INT64TO_STRINGInvalid CAST_TO value of Expected TO_FLOAT, TO_STRING or TO_INT64Negative index values are not permitted. First entry in map has index value of Expected std::map or std::mapCastMap0.fInvalid input type of value: Unexpected CAST_TO value of D:\a\_work\1\s\onnxruntime\core/providers/cpu/ml/cast_map.honnxruntime::ml::CastMap::CastMapinfo.GetAttr("map_form", &attr).IsOK()map_forminfo.GetAttr("max_map", &max_map_).IsOK()max_mapmap_form_ != PACK_MAP::SPARSE || max_map_ > 0max_map must be > 0 if map_form is SPARSEonnxruntime::ml::CastMap::ComputeImplcur_input == end_input || cur_input->first >= 0D:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\cast_map.ccthresholdBinarizerInput data with index: is NaNArrayFeatureExtractorInvalid Y argument: num_indices = 0Invalid argument: X input has empty dimensions.] () >=Invalid Y argument: index is out of range: Y[D:\a\_work\1\s\onnxruntime\core\providers\cpu\rnn\rnn_helpers.ccA + (M * K) <= A_endExpecting activation to be one of Affine, Relu, LeakyRelu, ThresholdedRelu, Tanh, ScaledTanh, Sigmoid, HardSigmoid, Elu, Softsign, Softplus. Got onnxruntime::rnn::detail::NormalizeActivationArgumentAndGetAlphaBetaCountalpha == 1.0f && (beta == 0.0f || beta == 1.0f)Invalid activation function of weights.quant_para_Quantized GEMM only support alpha equal to 1.0f and beta equal to 0.0f or 1.0fthresholdedreluscaledtanhreluleakyrelusoftsignsoftplushardsigmoideluInput R must have shape {Input B must have shape {}. Actual:Input W must have shape {Input initial_h must have shape {affineInput sequence_lens must have shape {Invalid value/s in sequence_lens. All values must be > 0 and < seq_length. seq_length=Input X must have 3 dimensions only. Actual:onnxruntime::rnn::detail::deepcpu::LstmMergeGatesFuncByNameInvalid GRU reset gate activation function: onnxruntime::rnn::detail::deepcpu::ActivationFuncByNameInvalid LSTM merge activation function of onnxruntime::rnn::detail::deepcpu::GruOutputGateFuncByNameonnxruntime::rnn::detail::deepcpu::GruResetGateFuncByNameInvalid GRU hidden gate activation function: onnxruntime::LSTMBase::ComputeImplD:\a\_work\1\s\onnxruntime\core\providers\cpu\rnn\lstm_base.ccInput initial_c must have shape {Input P must have shape {SoftmaxCPU inputs N, D and N * D must be < onnxruntime::EinsumTypedComputeProcessor<__int64>::PairwiseOperandProcessonnxruntime::EinsumTypedComputeProcessor<__int64>::FinalizeOutputonnxruntime::EinsumTypedComputeProcessor::PairwiseOperandProcessonnxruntime::EinsumTypedComputeProcessor::FinalizeOutputonnxruntime::EinsumTypedComputeProcessor::FinalizeOutputNot all dimensions to be reduced have been reduced in the candidate output. Candidate output dims: Einsum op: The candidate output cannot be reshaped into the op's outputcandidate_output.Shape().Size() == output_shape.Size()onnxruntime::EinsumTypedComputeProcessor::PairwiseOperandProcessonnxruntime::EinsumTypedComputeProcessor::FinalizeOutputcandidate_output_dims[iter] == 1Einsum op: Could not copy the intermediate output's buffer into the op's output buffer. Error: Right shape: Ranks of pair-wise operands must be equal. Right shape: right.Shape().Size() == right_shape_override.Size()left_dim == right_dimEinsum op: Input shapes do not alignleft_rank == right_rankEinsum op: Input dimensions must be equal along an axis to be reduced across all inputsLeft shape: The override dims are not compatible with given tensor's shape. Left shape override: D:\a\_work\1\s\onnxruntime\core\providers\cpu\math\einsum_utils\einsum_typed_compute_processor.cc Right shape override: left.Shape().Size() == left_shape_override.Size()onnxruntime::EinsumTypedComputeProcessor::PairwiseOperandProcessEllipsis must indicate a fixed number of dimensions across all inputsFound '.' not part of an ellipsis in input: Found a '.' not part of an ellipsis in input: Einsum subscripts string contains too many subscript labels when compared to the rank of the inputAnother operand has a dim value of . The shape is: The only subscript labels allowed are lower-cased letters (a-z) and upper-cased letters (A-Z) in the same dimensionD:\a\_work\1\s\onnxruntime\core\providers\cpu\math\einsum_utils\einsum_compute_preprocessor.ccNumber of subscripts in the input equation does not match number of input tensorsonnxruntime::EinsumComputePreprocessor::RunFound a '.' not part of an ellipsis in the output subscript providedFound '.' not part of an ellipsis in the output subscript provided...Inputs have ellipses in them but the provided output subscript does not contain an ellipsisRank of the input must match number of subscript labels corresponding to the inputOutput subscript contains repeated lettersOutput subscript contains letters not seen in the inputsEinsum 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 is incompatible in the dimension Einsum operands could not be broadcast together. Please check input shapes/equation provided.Input shape of operand The broadcasted dimensions of the inputs are incompatibledim_iter == ranknum_broadcasted_indices < num_of_ellipsis_dims_onnxruntime::EinsumComputePreprocessor::PostProcessBroadcastedDimsonnxruntime::EinsumOp::DeviceHelpers::CpuDeviceHelpers::DiagonalThe rank of the input must match permutation size for TransposeCannot parse the diagonal elements along dims rank >= 2 && dim_1 != dim_2 && input_dims[dim_1] == input_dims[dim_2]Length of permutation must match the rank of the input to be permutatedonnxruntime::EinsumOp::Transposeinput_rank == permutation.size()onnxruntime::EinsumOp::IsTransposeRequiredD:\a\_work\1\s\onnxruntime\core\providers\cpu\math\einsum_utils\einsum_auxiliary_ops.ccThe innermost dims should have the same dim value to parse the diagonal elementsoutput.SizeInBytes() == input.SizeInBytes()onnxruntime::EinsumOp::DeviceHelpers::CpuDeviceHelpers::DataCopyEinsum op: Unsupported data type for Diagonal for input shape input_dims[rank - 2] == input_dims[rank - 1]onnxruntime::EinsumOp::DeviceHelpers::CpuDeviceHelpers::DiagonalInnermostDimsEinsum op: The candidate output does not match the actual output's shapeIncompatible matrix dimensions for matMulinput_shape_1_override[2] == input_shape_2_override[1]Batch dimension should match for MatMul;input_shape_1_override[0] == input_shape_2_override[0]Einsum op: Exception during MatMul operation: Data types of the inputs must match for MatMulOnly 1 batch dimension is allowed for MatMulinput_shape_1_override.size() == 3 && input_shape_2_override.size() == 3input_1.DataType() == input_2.DataType()onnxruntime::EinsumOp::MatMulEinsum op: Transpose failed: onnxruntime::scan::detail::IterateSequence::::operator ()onnxruntime::scan::detail::IterateSequenceSubgraph must have the shape set for all outputs but onnxruntime::scan::detail::CreateFeedsFetchesManageronnxruntime::scan::detail::LoopStateVariable::Next is not compatible with Misuse of LoopStateVariable. Attempt to move beyond end of sequenceiteration_num_ < sequence_len_directions.size() == num_entriesonnxruntime::scan::detail::ReadDirections' was Number of entries in 'valid did not.'. 0 == forward. 1 == reverse.Invalid values in ' inputs but Scan was only given onnxruntime::scan::detail::Info::InfoD:\a\_work\1\s\onnxruntime\core\providers\cpu\controlflow\scan_utils.ccThe subgraph in 'body' requires num_variadic_inputs == num_subgraph_inputsis_concrete_shape_Expected AllocateFinalOutput to have been called to before we increment the iteratoronnxruntime::scan::detail::OutputIterator::operator *Expected AllocateFinalOutput to have been called to before we read the OrtValue from the iterator.onnxruntime::scan::detail::OutputIterator::operator ++onnxruntime::OrtValueTensorSlicer::Iterator::operator *Failed to create output tensor for output #onnxruntime::scan::detail::OutputIterator::AllocateFinalBufferMismatch between expected shape and shape from first outputonnxruntime::scan::detail::OutputIterator::Initializeonnxruntime::scan::detail::OutputIterator::AllocateFinalOutputcur_iteration_ < num_iterations_If shape was concrete we shouldn't be using a custom allocator!is_concrete_shape_Inversegamma is expected to have 1 dimension, got skip is expected to have same shape as inputinput is expected to have 3 dimensions, got epsilon_ >= 0bias is expected to have 1 dimension, got Last dimension of beta and input does not matchbeta is expected to have 1 dimension, got Last dimension of gamma and input does not matchD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\skip_layer_norm.cconnxruntime::contrib::SkipLayerNorm::SkipLayerNormonnxruntime::contrib::SkipLayerNorm::SkipLayerNormLast dimension of bias and input does not matchonnxruntime::contrib::LayerNorm::Computeonnxruntime::contrib::LayerNorm::LayerNormonnxruntime::contrib::LayerNorm::ComputeD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\layer_norm.cconnxruntime::contrib::LayerNorm::Computeonnxruntime::contrib::LayerNorm::LayerNormonnxruntime::contrib::LayerNorm::Computeonnxruntime::contrib::LayerNorm::LayerNormonnxruntime::contrib::LayerNorm::LayerNormop_kernel_info.GetAttr("axis", &axis_).IsOK()onnxruntime::contrib::Scale::Scaleinfo.GetAttr("scale", &scale_).IsOK()Scaleinfo.GetAttr("beta", &beta_).IsOK()onnxruntime::contrib::Affine::Affineinfo.GetAttr("alpha", &alpha_).IsOK()D:\a\_work\1\s\onnxruntime\contrib_ops\cpu\element_wise_ops.honnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ComputeImageScaleronnxruntime::contrib::ImageScaler::ImageScalerBias size () does not match the number of channels (Input is expected to have four dimensions corresponding to [N,C,H,W], got info.GetAttrs("bias", bias_).IsOK()D:\a\_work\1\s\onnxruntime\contrib_ops\cpu\image_scaler.hDynamicSliceCroponnxruntime::contrib::Crop::ComputeD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\crop.h) needs to be greater than or equal to the leftBorder (Input's width () + scale_[0] () + scale_[1] () + bottomBorder () needs to be greater than or equal to the topBorder (Input's height () + rightBorder (borderAttribute border needs to be specified with four border elements, got input dimensions insteadonnxruntime::contrib::BiasGelu::ComputeD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\bert\bias_gelu.ccuse_approximationonnxruntime::contrib::BiasGelu::ComputeD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\cdist.hsqeuclideaneuclideanonnxruntime::contrib::CDist::CDistCDistmetricinfo.GetAttr("metric", &metric).IsOK()onnxruntime::contrib::CDist::CDistInput shape dimensions mismatch:The first input of CDist kernel has wrong shape: The second input of CDist kernel has wrong shape: onnxruntime::contrib::CropAndResize::CropAndResizeD:\a\_work\1\s\onnxruntime\contrib_ops/cpu/crop_and_resize.hCropAndResizebilinear specified. It should be either bilinear or nearestNull crop_size_ptrNumber of dimensions for crop size should be exactly 1ConvTransposeWithDynamicPadsInput tensor to Unique op should be 1DMaxpoolWithMaskD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\maxpool_with_mask.honnxruntime::contrib::MaxpoolWithMask::ComputeTransposeMatMulinput_num_bytes % 4 == 0D:\a\_work\1\s\onnxruntime\contrib_ops\cpu\murmur_hash3.cconnxruntime::contrib::MurmurHash3::Computesizeof(uint32_t) == output_element_bytesInvalid assumption of output element sizepositivekeysMurmurHash3conv_window_sizeembedding_sizechar_embedding_sizeonnxruntime::contrib::WordConvEmbedding::ComputeChar embedding size does not match conv kernal size 2.WordConvEmbeddingD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\word_conv_embedding.cc char_embedding_size attribute: Char embedding size: Conv kernal size 2 : Char embedding size does not match char_embedding_size attribute. 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: embedding_size attribute: conv filter size: mincharnum_ > 0attribute mincharnum must have a positive valuetokenexpseparatorsattribute pad_value is not setpad_valueattribute mincharnum is not setmincharnumattribute mark is not setmarkD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\tokenizer.cconnxruntime::contrib::Tokenizer::TokenizerTokenizerInput dimensions are either [C] or [N][C] allowedInput string contains invalid utf8 chars: Can not digest tokenexp: tensor(string) expected as inputMatch contains invalid utf8 chars: mincharnum is too big for char level tokenezation!separators.empty()Can not digest separators: !char_tokenezation_ || mincharnum_ < 2Expecting a non-empty tokenexpEither one of the separators OR tokenexp attributes required but none is setseparators must not be empty!tokenexp.empty()Attention query layer weight shape error! Expected:{}, Got: Attention v weight shape error! Expected:{}. Got: Attention mechanism memory sequence lengths value must in (0, ], while Attention memory layer weight shape error! Expected:{, am_attn_size}, Got:Attention mechanism memory shape error! Expected: {}, actural: found!Attention mechanism memory sequence lengths must have shape {AttnLSTMstatic_cast(activation_func_names.size()) == num_directions_ * 3D:\a\_work\1\s\onnxruntime\contrib_ops\cpu\attnlstm\deep_cpu_attn_lstm.cconnxruntime::contrib::DeepCpuAttnLstmOp::ComputeD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\attnlstm\deep_cpu_attn_lstm.honnxruntime::contrib::DeepCpuAttnLstmOp::DeepCpuAttnLstmOponnxruntime::contrib::DeepCpuAttnLstmOp::ComputeImplAttention layer weight shape error! Expected: {, aw_attn_size}. Got:onnxruntime::contrib::DeepCpuAttnLstmOp::ValidateInputsD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\fused_gemm.cconnxruntime::contrib::FusedGemm::FusedGemmD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\fused_conv.cconnxruntime::contrib::FusedConvFloat::FusedConvFloatGetFusedActivationAttr(info, activation_).IsOK()ExpandDimsAxis must be within range [axis <= X_NumDims && axis >= -X_NumDimsD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\expand_dims.h Axis is axis_tensor->Shape().IsScalar()onnxruntime::contrib::ExpandDims::ComputeAn axis tensor must be a scalar tensor.onnxruntime::contrib::EmbedLayerNorm::Computeinput index out of rangeonnxruntime::contrib::EmbedLayerNorm::EmbedLayerNormD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\bert\embed_layer_norm.ccInputs 'past' dimension 2 shall have length of num_headsInputs 'past' dimension 2 shall have length of Inputs 'past' dimension 0 shall have length of 2Inputs 'past' dimension 1 shall have same length as dimension 0 of input 0Input 'bias' dimension 0 should have same length as dimension 1 of input 'weights'Input 'past' is expected to have 5 dimension, got hidden_size should be divisiable by num_heads.Input 'bias' is expected to have 1 dimension, got Input 1 dimension 0 should have same length as dimension 2 of input 0Input 1 dimension 1 should be 3 times of hidden dimensionInput 'input' is expected to have 3 dimensions, got Input 'weights' is expected to have 2 dimensions, got D:\a\_work\1\s\onnxruntime\contrib_ops\cpu\bert\attention_base.hinfo.GetAttr("num_heads", &num_heads).IsOK() && num_heads > 0onnxruntime::contrib::AttentionBase::AttentionBaseonnxruntime::contrib::Attention::Computeonnxruntime::contrib::AttentionBase::GetPresentD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\bert\attention.ccExpect to have present state output when past state input is givenInputs 'mask_index' with 4D data shall have is_unidirectional_ set to falseInput 'mask_index' is expected to have 1, 2, 3 or 4 dimensions, got Inputs 'mask_index' with 3D data shall have shape batch_size x sequence_length x (past_sequence_length + sequence_length)Inputs 'mask_index' with 4D data shall have shape batch_size x 1 x max_sequence_length x max_sequence_length)Inputs 'mask_index' with 1D data shall have length of batch_size or 2 * batch_sizeInputs 'mask_index' with 2D data shall have shape batch_size x (past_sequence_length + sequence_length)4D mask in attention cpu kernel is not supportedonnxruntime::contrib::AttentionCPUBase::ApplyAttentionD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\bert\attention_cpu_base.hSampleOpinput_shape.Size() > 0 || N == 0onnxruntime::contrib::NhwcMaxPool::ComputeD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\quantization\nhwc_max_pool.cc} for per-tensor/layer quantization or shape { must have shape {} for per-channel quantization. Actual:, 4*onnxruntime::contrib::DynamicQuantizeLSTM::PrePackD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\quantization\dynamic_quantize_lstm.ccDynamicQuantizeLSTMWeight point must be constantRecurrentWeight zero point must be zeroDynamicQuantizeLSTM : W_scaleR_scaleW_zero_pointR_zero_pointonnxruntime::contrib::MatMulIntegerToFloatBase::ComputeCommonD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\quantization\dynamic_quantize_matmul.ccIsScalarOr1ElementVector(a_zero_point_tensor)onnxruntime::contrib::MatMulIntegerToFloat::Computeonnxruntime::contrib::DynamicQuantizeMatMul::ComputeMatMulIntegerToFloat : input a zero point must be a scalar or 1D tensor of size 1. Per-Channel is not supported yet.MatmulInteger : b zero point is not validIsBQuantParamSupported(b_zp_tensor->Shape(), b_tensor ? b_tensor->Shape() : b_shape_)QAttentioninput zero point must be a scalar or 1D tensor of size 1.D:\a\_work\1\s\onnxruntime\contrib_ops\cpu\quantization\attention_quant.ccinput scale must be a scalar or 1D tensor of size 1onnxruntime::contrib::QAttention::Computetensor_c_zero_point == nullptr || IsScalarOr1ElementVector(tensor_c_zero_point)MatmulInteger : input1 C_zero_point must be a scalar or 1D tensor of size 1 if givenIsScalarOr1ElementVector(tensor_c_scale)MatmulInteger : input1 C_scale must be a scalar or 1D tensor of size 1tensor_b_zero_point == nullptr || IsScalarOr1ElementVector(tensor_b_zero_point)MatmulInteger : input1 B_zero_point must be a scalar or 1D tensor of size 1 if givenIsScalarOr1ElementVector(tensor_b_scale)MatmulInteger : input1 B_scale must be a scalar or 1D tensor of size 1tensor_a_zero_point == nullptr || IsScalarOr1ElementVector(tensor_a_zero_point)MatmulInteger : input1 A_zero_point must be a scalar or 1D tensor of size 1 if givenD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\qlinear_binary_op.cconnxruntime::contrib::`anonymous-namespace'::QLinearImplIsScalarOr1ElementVector(tensor_a_scale)MatmulInteger : input1 A_scale must be a scalar or 1D tensor of size 1tensor_y_zero_point == nullptr || IsScalarOr1ElementVector(tensor_y_zero_point)QlinearBuildLookupTable : input Y_zero_point must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(tensor_y_scale)QlinearBuildLookupTable : input Y_scale must be a scalar or 1D tensor of size 1tensor_x_zero_point == nullptr || IsScalarOr1ElementVector(tensor_x_zero_point)QlinearBuildLookupTable : input X_zero_point must be a scalar or 1D tensor of size 1D:\a\_work\1\s\onnxruntime\contrib_ops\cpu\qlinear_lookup_table.cconnxruntime::contrib::QlinearBuildLookupTableIsScalarOr1ElementVector(tensor_x_scale)QlinearBuildLookupTable : input X_scale must be a scalar or 1D tensor of size 1onnxruntime::contrib::QLinearAveragePool::ComputeInput x_scale must be a scalar or 1D tensor of size 1QLinear Pooling unsupported pooling size!Unsupported 'dtype' in QLinear Pooling:input y_zero_point must be a scalar or 1D tensor of size 1 if giveninput y_scale must be a scalar or 1D tensor of size 1input x_zero_point must be a scalar or 1D tensor of size 1 if givenD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\qlinear_pool.cconnxruntime::contrib::QLinearConcat::Computeinput_count_x3 >= 6 && input_count_x3 % 3 == 0tensor_x_zero_point->GetElementType() == tensor_y_zero_point->GetElementType()Wrong input type encountered for zero point input def @tensor_x_scale->IsDataType()Input scale is not float for input def @D:\a\_work\1\s\onnxruntime\contrib_ops\cpu\qlinear_concat.cconnxruntime::contrib::QLinearConcat::QLinearConcatinput_def_count >= 8 && (input_def_count - 2) % 3 == 0At least two inputs are needed, and each input must be (tensor, scale, zero_point) tuple!Wrong input type encountered for zero point of quantized input @Input scale is not float for quantized input @IsScalarOr1ElementVector(tensor_y_zero_point)IsScalarOr1ElementVector(tensor_x_zero_point)D:\a\_work\1\s\onnxruntime\contrib_ops\cpu\qlinear_global_average_pool.cconnxruntime::contrib::QLinearGlobalAveragePool::ComputeMatMulInteger16D:\a\_work\1\s\onnxruntime\contrib_ops\cpu\matmul_integer16.cconnxruntime::contrib::MatMulInteger16::ComputeA != nullptr && B != nullptr(static_cast(X_shape[1]) < nchwc_block_size) || ((X_shape[1] % nchwc_block_size) == 0)Unsupported convolution size.output and sum shape must matchonnxruntime::contrib::NchwcPoolBase::NchwcPool(X_shape[1] % MlasNchwcGetBlockSize()) == 0X_shape.size() == 4onnxruntime::contrib::NchwcUpsample::Computeinfo.GetAttr("channels_last", &channels_last_).IsOK()onnxruntime::contrib::ReorderInput::ReorderInputD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\nchwc_ops.hinfo.GetAttr("channels", &channels_).IsOK()onnxruntime::contrib::ReorderOutput::ReorderOutputinvalid channel countchannels_ > 0onnxruntime::contrib::NchwcConv::NchwcConvpool_attrs_.kernel_shape.size() == 2onnxruntime::contrib::NchwcPoolBase::NchwcPoolBaseinfo.GetAttrs("scales", scales_).IsOK()onnxruntime::contrib::NchwcUpsample::NchwcUpsamplescales_.size() == 4scales_[0] == 1 && scales_[1] == 1 && scales_[2] >= 1 && scales_[3] >= 1info.GetAttr("coordinate_transformation_mode", &transformation_mode).IsOK()' for NCHWc UpsampleUnsupported transformation mode 'transformation_mode_ == TransformationMode::ASYMMETRICUnsupported mode 'X_rank == 4onnxruntime::contrib::ReorderInput::ComputeD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\nchwc_ops.cc(channels % 4) == 0onnxruntime::contrib::ReorderOutput::Computechannels_ <= X_shape[1]onnxruntime::contrib::NchwcConv::ComputeX_shape.NumDimensions() == 4.. rows[Input 0 is expected to have 1 or more dimensions, got Input 1 is expected to have 1 dimensions, got Input 1 dimension 0 should have same length as the last dimension of input 0 [seqno= row[not support normalize yet.!normalize_onnxruntime::contrib::BahdanauAttention::BahdanauAttentionD:\a\_work\1\s\onnxruntime\contrib_ops\cpu\attnlstm\bahdanau_attention.cc is not in (0, Real memory steps mem_steps <= max_memory_steps_ && mem_steps > 0onnxruntime::contrib::BahdanauAttention::PrepareMemoryunimplemented activation: activation_params count mismatchInput 0 and 1 shall have same shapeInput 0 and 7 (mask) shall have same shapeinput_ids is expected to have 2 dimensions, got word_embedding is expected to have 2 dimensions, got position_embedding is expected to have 2 dimensions, got segment_embedding is expected to have 2 dimensions, got word_embedding and segment_embedding shall have same dimension 1word_embedding and position_embedding shall have same dimension 1beta is expected to have 1 dimensions, got beta is expected to have size of gamma is expected to have 1 dimensions, got gamma is expected to have size of Received null OrtThreadingOptionsReceived invalid value for allow_spinning. Valid values are 0 or 1onnxruntime::math::NextPositiondims[d_i] < d_maxD:\a\_work\1\s\onnxruntime\core\util\math_cpu.ccTensor types should have been handled alreadyOrtValue is TensorSequence type but has no element Tensor DataType. is not supportedCudaPinnedCudaonnxruntime::IAllocator::CalcMemSizeForArrayWithAlignment::::operator ()Specified device is not supported.D:\a\_work\1\s\onnxruntime\core\framework\allocator.ccNot implementedtype != nullptrOrtValue is not a TensorD:\a\_work\1\s\onnxruntime\core\framework\tensor_type_and_shape.ccOrtApis::GetTensorTypeAndShapeArgument is not a tensortype_proto is not of type sequence!onnxruntime::ProviderSharedLibrary::Ensureonnxruntime_providers_shared.dllProvider_SetHostD:\a\_work\1\s\onnxruntime\core\framework\provider_bridge_ort.ccGetProvideronnxruntime::ProviderLibrary::Getonnxruntime_providers_dnnl.dllonnxruntime_providers_cuda.dllonnxruntime_providers_tensorrt.dllonnxruntime_providers_openvino.dllonnxruntime::GetProviderInfo_CUDAGetProviderInfo_CUDA called, returning nullptrCUDA Provider not available, can't get interface for itSessionOptionsAppendExecutionProvider_Tensorrt: Failed to load shared libraryCUDA execution provider is not enabled.SessionOptionsAppendExecutionProvider_OpenVINO: Failed to load shared libraryOrtSessionOptionsAppendExecutionProvider_Cuda: Failed to load shared librarytype_proto is not of type map!D:\a\_work\1\s\onnxruntime\core\framework\tensor.ccshape.Size() must >=0p_type != nullptronnxruntime::Tensor::Tensoronnxruntime::Tensor::SizeInBytesonnxruntime::Tensor::Inittensor failed memory size calculationtensor size overflowTensor is expected to contain one of the primitive data types. Got: dtype_ != nullptronnxruntime::SparseTensorTypeBase::GetElementTypeonnxruntime::SequenceTensorTypeBase::GetElementTypeonnxruntime::TensorTypeBase::GetElementTypeonnxruntime::data_types_internal::IsCompatibleD:\a\_work\1\s\onnxruntime\core\framework\data_types.cconnxruntime::data_types_internal::DataTypeRegistry::RegisterDataTypeWe do not expect duplicate registration of types for: Only ONNX MLDataType can be registeredproto != nullptr is not currently registered or supportedMLDataType for: tensor type Invalid DataTypeImpl TypeProto definitiononnxruntime::utils::ContainerChecker::ContainerCheckerexpected a registered ONNX typeD:\a\_work\1\s\include\onnxruntime\core/framework/data_types.honnxruntime::data_types_internal::SetMapTypes,class std::allocator >,__int64>::Setvalue_proto != nullptronnxruntime::data_types_internal::SetMapTypes,class std::allocator >,class std::basic_string,class std::allocator > >::Setonnxruntime::data_types_internal::SetMapTypes<__int64,class std::basic_string,class std::allocator > >::Setonnxruntime::data_types_internal::SetMapTypes<__int64,__int64>::Setonnxruntime::data_types_internal::SetMapTypes,class std::allocator >,float>::Setonnxruntime::data_types_internal::SetMapTypes,class std::allocator >,double>::Setelem_proto != nullptronnxruntime::data_types_internal::SetSequenceType::Setonnxruntime::data_types_internal::SetMapTypes<__int64,float>::Setonnxruntime::data_types_internal::SetMapTypes<__int64,double>::Setutils::HasElemType(thisProto->tensor_type())thisProto->value_case() == TypeProto::ValueCase::kSparseTensorTypethisProto->value_case() == TypeProto::ValueCase::kTensorTypeonnxruntime::TensorTypeBase::IsCompatiblethisProto->value_case() == TypeProto::ValueCase::kSequenceTypeonnxruntime::SequenceTensorTypeBase::IsCompatibleonnxruntime::SparseTensorTypeBase::IsCompatibleutils::HasElemType(thisProto->sparse_tensor_type())onnxruntime::NonTensorTypeBase::IsMapCompatibleutils::HasKeyType(thisProto->map_type())utils::HasElemType(thisProto->sequence_type())thisProto->value_case() == TypeProto::ValueCase::kMapTypeonnxruntime::NonTensorTypeBase::FromDataContaineronnxruntime::NonTensorTypeBase::IsSequenceCompatiblefloatboolonnxruntime::NonTensorTypeBase::ToDataContainer(null)uint8int16doubleint8uint32int64uint16int32bfloat16(unknown type)uint64float16onnxruntime::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,class std::allocator > >::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::Setonnxruntime::data_types_internal::SetSequenceType,class std::allocator >,float,struct std::less,class std::allocator > >,class std::allocator,class std::allocator > const ,float> > > >::Setdimstart <= dimend && dimend <= size()onnxruntime::TensorShape::Sliceonnxruntime::TensorShape::SizeFromDimensionInvalid tensor shape slice argument.{} dimensions. for SizeFromDimension. Tensor has onnxruntime::TensorShape::SizeToDimensionD:\a\_work\1\s\onnxruntime\core\framework\tensor_shape.ccInvalid dimension of dimension <= num_dimsConfig value is longer than maximum length 1024]. It will be overwrittenConfig key is empty or longer than maximum length 128onnxruntime::ConfigOptions::AddConfigEntryD:\a\_work\1\s\onnxruntime\core\framework\config_options.cc] already exists with value [Config with key [Received nullptr for name.Buffer containing the initializer must be owned by the user.An OrtValue for this name has already been added.Received nullptr for OrtValue.Received OrtValue is not a tensor. Only tensors are supported.Failed to find allocator for device allocator != nullptrinvalid allocatoronnxruntime::utils::FindMemoryInfoForValueexec_plan_ptronnxruntime::utils::BatchOrCopyMLValueD:\a\_work\1\s\onnxruntime\core\framework\utils.cconnxruntime::utils::InitializeFeedFetchCopyInfofeed_locations.size() == copy_info.size()onnxruntime::utils::CalculateStaticCopyInfoForFeedonnxruntime::utils::CalculateStaticCopyInfoForFeedsonnxruntime::utils::FinalizeCopyInfoForFetchescopy_info.size() == num_feedsonnxruntime::utils::FinalizeCopyInfoForFeedsfetch_alloc_info.size() == copy_info.size()onnxruntime::utils::CopyOutputsAcrossDevicesOnly one thread was configured for parallel execution. Hence will use sequential execution.onnxruntime::utils::CopyInputsAcrossDevicesonnxruntime::utils::CopyOneInputAcrossDevicesonnxruntime::utils::ExecuteGraphImplonnxruntime::utils::ExecuteGraphCoreMLExecutionProviderInternalTestingExecutionProviderRequested attribute: is expected to have type: Attibute name and type don't matchNo attribute with this name is defined. is defined.No attribute with name: Attribute: but is of type: expected to be of type: node_offsets_index < node_offsets_size_onnxruntime::NodeIndexInfo::GetNodeOffsetD:\a\_work\1\s\onnxruntime\core/framework/node_index_info.h. Num args is Invalid arg_num of kernel != nullptronnxruntime::OpKernelContext::OutputMLValueTempSpace allocator not foundonnxruntime::OpKernelContext::GetOrCreateOutputMLValuearg_num < arg_counts.size()onnxruntime::OpKernelContext::NumVariadicInputsExecution frame was nullD:\a\_work\1\s\onnxruntime\core\framework\op_kernel.ccOpKernel was nullframe != nullptronnxruntime::OpKernelContext::OpKernelContextD:\a\_work\1\s\onnxruntime\core\framework\kernel_def_builder.ccinput_offset >= 0 && output_offset >= 0onnxruntime::KernelDefBuilder::VariadicAliasCould not find OrtValue with idx '!op_type.empty()The op type of a node cannot be emptyweights_to_be_filled_in.buffers_.size() > 0 doesn't have an implementation that can cache computed pre-packed weightsUnable to write the provided PrePackedWeights instance into the containerUsing cached version of pre-packed weight for constant initializer: used in the node: which is of op type: Failed 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::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::GetSubGraphSessionStatesOrtFormatonnxruntime::SessionState::LoadFromOrtFormatKernel create info is null. Invalid ORT format model.onnxruntime::SessionState::LoadFromOrtFormat::::operator ()onnxruntime::SessionState::CreateSubgraphSessionStatesession.disable_prepackingD:\a\_work\1\s\onnxruntime\core\framework\session_state.cconnxruntime::SessionState::SetupAllocatorsAllocator already registered for . Ignoring allocator from onnxruntime::SessionState::CreateGraphInfoSaveMLValueNameIndexMappingonnxruntime::SessionState::GetNodeKernelCreateInfoentry != kernel_create_info_map_.cend()onnxruntime::SessionState::PopulateKernelCreateInfoDone saving OrtValue mappings. doesn't have an implementation that can consume provided pre-packed weightsonnxruntime::KernelUseSharedPrePackedBuffersduplicated ort_value index:. Do you have duplicated calls to SessionState::AddInitializedTensor function?onnxruntime::SessionState::PrepackConstantInitializedTensors::::operator ()allocator_for_caching.get() != nullptr+The kernel corresponding to the node . Invalid ORT format model.Size mismatch for kernel create info node indexes and hashes. Invalid ORT format model.Kernel create info hashes are null. Invalid ORT format model.Kernel create info node indices are null. Invalid ORT format model.SessionState for subgraphs is null. Invalid ORT format model.Unable to find compiled kernel hash for node ''.Can't find node with index Subgraph SessionState for is null. Invalid ORT format model.Subgraph SessionState entry for is missing. Invalid ORT format model. Index: Attribute:onnxruntime::SessionState::FinalizeSessionStateImplonnxruntime::SessionState::FinalizeSessionStatep_op_kernelentry != node_to_subgraph_ss.second.cend()Missing session state for subgraph. Node:'' OpType: in KernelRegistryManagerThe node is not placed on any Execution Provider. (node ). found duplicated provider Failed to find kernel for corrupted protobuf data: tensor shape size() does not match the data size() in protoTensorProtoToTensor() tensor shape mismatch!onnxruntime::utils::GetFileContentdata overflowUnpackTensor: the pre-allocate size does not match the size in protostring tensor can not have raw dataonnxruntime::utils::TensorProtoToTensorTensorProto type can not be writen into Tensor type string tensor can not use pre-allocated bufferTensorProtoToMLValue() must take a pre-allocated MemBuffer!Initialized tensor with unexpected type: tensor can't contain negative dimsThe preallocated buffer is too small. Requires , Got Unsupported sparse tensor data type of Unsupported attribute value type of in 'Constant' node 'onnxruntime::utils::ConstantNodeProtoToTensorProto BUG! Report to onnxruntime team. element_size of: is not supported. type: onnxruntime::utils::SparseTensorProtoToDenseTensorProto data_type: onnxruntime::utils::DenseTensorToSparseTensorProtoHasDataType(dense_proto)Must have a valid data typeSafeIntExceptionHandler::SafeIntOnDivZeroDivide by zeroUnpackTensor: the pre-allocated size does not match the raw data size, expected External data type cannot be UNDEFINED or STRING.D:\a\_work\1\s\onnxruntime\core\framework\tensorprotoutils.cc`anonymous-namespace'::GetExternalDataInfoTensor does not have external data to read from.onnxruntime::utils::UnpackTensorWithExternalDataImplnullptr == p_data`anonymous-namespace'::ReadExternalDataForTensorTensorProto external data size mismatch. Computed size: Unsupported type: onnxruntime::utils::UnpackInitializerDataSparse Indicies raw data size does not match expected.onnxruntime::utils::CopySparseDataIndicies expected to be INT64Invalid TensorProtoInvalid SparseTensor indices. Should be rank 0 or 1. Got:cur_index == &*indices_data.cend()rank == dims.size() && rank > 0Invalid SparseTensor indices. Should either have raw or int64 datanullptr != type_protoD:\a\_work\1\s\onnxruntime\core\framework\mldata_type_utils.cconnxruntime::utils::GetMLDataTypeonnxruntime::IExecutionProvider::GenerateMetaDefIdmetadef_id_generator_duplicated allocatoronnxruntime::IExecutionProvider::TryInsertAllocatorduplicated allocator: D:\a\_work\1\s\onnxruntime\core\framework\execution_provider.cconnxruntime::IExecutionProvider::InsertAllocatorIExecutionProvider constructor must be called with true for use_metadef_id_creatorIExecutionProvider::Compile with FusedNodeAndGraph is not implemented by IExecutionProvider::Compile with fused Node and dll path is not implemented by IExecutionProvider::Compile with fused Node is not implemented by onnxruntime::FeedsFetchesInfo::MapNamesToMLValueIdxsinput_copy_needed != DeviceCopyCheck::Unknown && output_copy_needed != DeviceCopyCheck::UnknownError mapping output names: Error mapping feeds: D:\a\_work\1\s\onnxruntime\core\framework\feeds_fetches_manager.cconnxruntime::FeedsFetchesManager::SetDeviceCopyChecksD:\a\_work\1\s\onnxruntime\core/framework/func_kernel.honnxruntime::FunctionKernel::FunctionKernelD:\a\_work\1\s\onnxruntime\core\framework\graph_partitioner.cconnxruntime::PlaceNode1 == capability.nodes.size()compute_info_->create_state_func(&context, &func_state_) == 0 did not return correct number of compiled functionsfusion_style == IExecutionProvider::FusionStyle::FunctionMust use Function based fusion when exporting compiled nodes to dll.onnxruntime::PartitionOnnxFormatModelImpl has Compile error: onnxruntime::PartitionOrtFormatModelImplonnxruntime::GraphPartitioner::PartitionOnnxFormatModelonnxruntime::InlineNodesExisting entry in compiled kernel hashes for . Execution Provider must generate unique names across the entire model.single_node_compute_func should have 1 elementsEP: Compiled kernel hashes must be providedonnxruntime::GraphPartitioner::PartitionNo provider specified.onnxruntime::GraphPartitioner::PartitionOrtFormatModelcompiled_kernel_hashes != nullptrReceived invalid value of arena_extend_strategy onnxruntime::CreateAllocatorD:\a\_work\1\s\onnxruntime\core\framework\allocatormgr.cconnxruntime::AllocatorManager::InsertAllocatordata_transfer registered is nullptr.Tensor size mismatch to There's no data transfer registered for copying tensors from onnxruntime::DataTransferManager::CopyTensorsD:\a\_work\1\s\onnxruntime\core\framework\data_transfer_manager.ccCaught exception while loading custom ops with message: has already been loaded.A dso with name onnxruntime::ExLibLoader::LoadExternalLibD:\a\_work\1\s\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 ()offset >= 0 && static_cast(offset) < node_values_size_onnxruntime::NodeIndexInfo::GetMLValueIndexort_value_index >= 0 && static_cast(ort_value_index) < all_values_size_onnxruntime::IExecutionFrame::GetMLValueD:\a\_work\1\s\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::ExecutionFrame::AllocateAsPerAllocationPlanWe don't expect custom allocators for non-tensor types, so a shape is mandatory here.Allocation of tensor types requires a shape.Invalid allocation kind: onnxruntime::ExecutionFrame::AllocateMLValueTensorPreAllocateBuffermlvalue.Fence() == nullptronnxruntime::AllocateSparseTensorort_value_index >= 0 && static_cast(ort_value_index) < alloc_plan.size()onnxruntime::ExecutionFrame::ReleaseMLValueImplort_value_idx >= 0 && static_cast(ort_value_idx) < alloc_plan.size()onnxruntime::ExecutionFrame::GetAllocationPlan failed: for output does not match actual shape of Expected shape from model of onnxruntime::ExecutionFrame::VerifyOutputSizesTraceFree for ort_value_idx=Memory pattern planner is not enabled on this execution framework. size=TraceAllocation for ort_value_idx=onnxruntime::ExecutionFrame::TraceAllocateonnxruntime::ExecutionFrame::TraceFreeshape && tensor.Shape() == *shapeonnxruntime::IExecutionFrame::GetOrCreateNodeOutputMLValueinvalid index feeds.size() == feed_mlvalue_idxs.size()onnxruntime::IExecutionFrame::IExecutionFrameD:\a\_work\1\s\onnxruntime\core\framework\execution_frame.cc Requested shape:OrtValue shape verification failed. Current shape: failed. Error:Allocation of memory pattern buffer for onnxruntime::ExecutionFrame::{ctor}::::operator ()buffers_.find(location) == buffers_.end()onnxruntime::IExecutionFrame::Initfetches.empty() || fetches.size() == fetch_mlvalue_idxs_.size() entries which doesn't match the number of fetches the frame was initialized with of Fetches vector passed to GetOutputs contains returned nullptrTrying to allocate memory for unused optional inputs/outputsTensor shape cannot contain any negative valueonnxruntime::ExecutionFrame::ExecutionFrame should only appear once.Memory type static_activation_memory_sizes_in_byte_.find(location.name) == static_activation_memory_sizes_in_byte_.end(), block in memory pattern size is: For ort_value with index: . 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. ort_value.Fence() == nullptronnxruntime::ExecutionFrame::AllocateMLValueTensorSelfOwnBufferHelper, fall back to default allocation behavior but the actually size is: checksummodel format error!model format error! Missing 'location'location failedparsing lengthmodel format error! Need a key for the external data infomodel format error! Need a value for the external data info 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: Encountered following errors: ( kernel is not supported in Kernel not foundkernel def can't be NULLonnxruntime::KernelRegistry::TryCreateKernelD:\a\_work\1\s\onnxruntime\core\framework\kernel_registry.cc No matching hash for kernel not found in : Conflicting with a registered kernel with op versions.Failed to add kernel for len <= op_schema.inputs().size()onnxruntime::`anonymous-namespace'::TraverseFormalParametersWithTypeProtosrc.SizeInBytes() == dst.SizeInBytes()onnxruntime::CPUDataTransfer::CopyTensoronnxruntime::IDataTransfer::CopyTensorsD:\a\_work\1\s\onnxruntime\core\framework\data_transfer.ccD:\a\_work\1\s\onnxruntime\core\framework\node_index_info.cconnxruntime::NodeIndexInfo::Init::::operator ()onnxruntime::NodeIndexInfo::Init::::operator ()cannot find allocatoronnxruntime::OpKernelInfo::GetMemoryInfoD:\a\_work\1\s\onnxruntime\core\framework\op_kernel_info.ccCan't slice a non-tensor OrtValue. Type was onnxruntime::OrtValueTensorSlicer::Createort_value.IsTensor()gsl::narrow_cast(tensor_shape.NumDimensions()) >= slice_dimensionInsufficient dimensions to slice on Invalid dim0_offset of . Dimension 0 is OrtValue has not been allocated so can't be sliced.D:\a\_work\1\s\onnxruntime\core\framework\ort_value_tensor_slicer.cc. Shape:ort_value.IsAllocated()onnxruntime::OrtValueTensorSlicer::Iterator::Iteratordim0_offset < dim0_sizeonnxruntime::OrtValueTensorSlicer::Iterator::Iteratoronnxruntime::OrtValueTensorSlicer::Createonnxruntime::GetCpuPreferredNodeskernel_info != nullptr because the CPU execution path is deemed faster than overhead involved with execution on other EPs capable of executing this nodeCandidate for fallback CPU execution: D:\a\_work\1\s\onnxruntime\core\framework\fallback_cpu_capability.cconnxruntime::GetCpuPreferredNodes::::operator ()ORT optimization- Force fallback to CPU execution for node: D:\a\_work\1\s\onnxruntime\core/framework/op_kernel_context_internal.honnxruntime::OpKernelContextInternal::OpKernelContextInternalonnxruntime::ParallelExecutor::ExecuteMultiple errors were found. does not.entry != nullptrAll implicit inputs should have OrtValue instances by now. onnxruntime::ParallelExecutor::RunNodeAsyncBegin executionGot nullptr from GetKernel for node: Exiting due to terminate flag being set to true.D:\a\_work\1\s\onnxruntime\core\framework\parallel_executor.ccParallelExecutor::ExecuteNon-zero status code returned while running node. Name:'thread_scheduling_statsprovider_fence_beforeop_name' Status Message: Exception running nodes starting at _fence_after_kernel_time'. Unknown exception was caught by catch-all handler.D:\a\_work\1\s\onnxruntime\core\framework\sequential_executor.cconnxruntime::SequentialExecutor::Executeoutput_sizeparameter_sizegraph_indexactivation_sizeexec_plan_indexonnxruntime::ReleaseNodeMLValues bytes for SequentialExecutor::Execute[Memory] ExecutionFrame dynamically allocates [Memory] ExecutionFrame statically allocates buffers_.size() == buffer_sizes_.size()D:\a\_work\1\s\onnxruntime\core\framework\prepacked_weights.cconnxruntime::PrePackedWeights::GetHash failed.Using user supplied initializer with name (Deserialize tensor ort_value_name_idx_map.MaxIdx() > -1OrtValue indexes should have been populated.[Memory] SessionStateInitializer statically allocates entry != initialized_tensors_to_allocate.end() && entry->second->data_type() != ONNX_NAMESPACE::TensorProto_DataType_STRING is not used by any node.onnxruntime::session_state_utils::SaveInputOutputNamesToNodeMappingSubgraph input with name Done saving initialized tensorsonnxruntime::session_state_utils::SaveInputOutputNamesToNodeMapping::::operator ()onnxruntime::session_state_utils::SaveInputOutputNamesToNodeMapping::::operator ()GraphInternal error. The preallocated buffer is too small. Requires DeserializeTensorProto() takes either pre-allocated buffer or an allocator!D:\a\_work\1\s\onnxruntime\core\framework\session_state_utils.cconnxruntime::session_state_utils::DeserializeTensorProtoFailed memory size calculationsession.use_device_allocator_for_initializersonnxruntime::session_state_utils::SaveInitializedTensors::::operator ()Cannot use user supplied initializer with name: (onnxruntime::session_state_utils::SaveInitializedTensorsSaving initialized tensors.Failed to copy tensor to string tensor is not supported for copying between allocators) because the ORT planned memory location device ) is different from what is supplied (onnxruntime::PrepackedWeightsContainer::GetOrCreateAllocatorUnsupported device allocator in the context of pre-packed weights caching: D:\a\_work\1\s\onnxruntime\core\framework\prepacked_weights_container.ccInvalid 'start'. Value is smaller than previous 'end'.D:\a\_work\1\s\onnxruntime\core/framework/sequential_execution_plan.hNo matching 'start' entry.starts_.empty() || start > ends_.back()Previous entry was not terminated.onnxruntime::AllocPlanPerValue::ProgramCounter::AddStartstarts_.size() == ends_.size()onnxruntime::PlannerImpl::ProcessDefid >= 0 && static_cast(id) < ort_value_info_.size()onnxruntime::PlannerImpl::Reusereused != reused_foronnxruntime::PlannerImpl::Bufferonnxruntime::PlannerImpl::UseCountonnxruntime::PlannerImpl::AllocPlann >= 0 && static_cast(n) < plan_.allocation_plan.size()onnxruntime::PlannerImpl::ComputeUseCountsp_kernel_defallocatorCan not find the execution provider onnxruntime::PlannerImpl::GetElementSizenullptr != tensor_type_baseShould not have entry in kernel create info with nullptr for kernel_defCan not find the node Invalid program_counter entries at index !IsNonTensor(*node_output)onnxruntime::PlannerImpl::VerifyMemoryTimeScheduleentry.program_counter.HasValidEntries()onnxruntime::PlannerImpl::GeneratePlanForWeightsonnxruntime::PlannerImpl::GetLocationForNodeInputOnly tensors are supported for external outputs for now.onnxruntime::PlannerImpl::ComputeReusePlanonnxruntime::PlannerImpl::GenerateDeallocationPlanAllocPlan(ml_value_idx).program_counter.Ends().back() == program_counteronnxruntime::PlannerImpl::CreatePlanonnxruntime::AllocPlanPerValue::ProgramCounter::AddEndstarts_.size() == ends_.size() + 1end >= starts_.back()Invalid 'end'. Value is larger than 'start'. : ) onnxruntime::GetKernelCreateInfoentry != kernel_create_info_map.cend()n >= 0 && static_cast(n) < ort_value_info_.size()onnxruntime::PlannerImpl::IndexD:\a\_work\1\s\onnxruntime\core\framework\allocation_planner.ccSessionState should have saved the KernelCreateInfo prior to this running. NodeIndex:onnxruntime::utils::detail::CopyLittleEndiansource and destination buffer size mismatchD:\a\_work\1\s\onnxruntime\core\framework\endian_utils.ccCan't use func with null ptrfunc info for node: onnxruntime::FuncManager::GetFuncs not found. already exist.D:\a\_work\1\s\onnxruntime\core\framework\fuse_nodes_funcs.cconnxruntime::BFCArena::AllocationRegion::AllocationRegionD:\a\_work\1\s\onnxruntime\core/framework/bfc_arena.h0 == memory_size % kMinAllocationSizeCould not find Region for onnxruntime::BFCArena::RegionManager::RegionForentry != regions_.end()onnxruntime::BFCArena::RegionManager::RemoveAllocationRegionp_int < base_int + memory_size_Could not find Region for: p_int >= base_intonnxruntime::BFCArena::AllocationRegion::IndexForonnxruntime::BFCArena::BFCArenaD:\a\_work\1\s\onnxruntime\core\framework\bfc_arena.cc with following configs: initial_chunk_size_bytes: Creating BFCArena for initial_growth_chunk_size_bytes: max_dead_bytes_per_chunk: arena_extend_strategy: memory limit: h < chunks_.size()onnxruntime::BFCArena::ChunkFromHandleBinForSize(bin_size * 2 - 1) == BinFromIndex(b)BinForSize(bin_size * 2) != BinFromIndex(b)BinForSize(bin_size) == BinFromIndex(b)BinForSize(bin_size + 255) == BinFromIndex(b) bins of max chunk size Creating onnxruntime::BFCArena::DeallocateRawInternal!c1->in_use() && !c2->in_use() BFC Arena shrunk by onnxruntime::BFCArena::Shrink The total allocated bytes is now bytes. !c->in_use() && (c->bin_num == kInvalidBinNum)onnxruntime::BFCArena::SplitChunkCould not find chunk in binBinFromIndex(c->bin_num)->free_chunks.erase(h) > 0onnxruntime::BFCArena::RemoveFreeChunkIterFromBinonnxruntime::BFCArena::RemoveFreeChunkFromBinonnxruntime::BFCArena::InsertFreeChunkIntoBin!c->in_use() && (c->bin_num != kInvalidBinNum)onnxruntime::BFCArena::Mergec2->prev == h1c->in_use() && (c->bin_num == kInvalidBinNum)onnxruntime::BFCArena::FreeAndMaybeCoalesce. Failed to allocate memory for requested buffer of size bytes. is smaller than requested bytes of Available memory of Incorrect arena extend strategy.onnxruntime::BFCArena::Extend::::operator ()cudaMallochipMalloc size: Reserving memory in BFCArena for Total allocated bytes: Allocated memory at Extended allocation by onnxruntime::BFCArena::Extendonnxruntime::BFCArena::AllocateRawInternal (actual) rounded_bytes:tried to allocate 0 bytesh != kInvalidChunkHandleonnxruntime::BFCArena::Reservereserved_chunks_.find(ptr) == reserved_chunks_.end()!chunk->in_use()onnxruntime::BFCArena::FindChunkPtrExtending BFCArena for Failed to find a free memory block despite calling Extend. rounded_bytes= (requested) num_bytes: . bin_num:!using_counters_onnxruntime::MemPatternPlanner::TraceAllocationD:\a\_work\1\s\onnxruntime\core/framework/mem_pattern_planner.hcurrent <= buffer_size_ConstantNoOponnxruntime::model_load_utils::IsAllowReleasedONNXOpsetsOnlySetD:\a\_work\1\s\onnxruntime\core/graph/model_load_utils.hALLOW_RELEASED_ONNX_OPSET_ONLY are '0' and '1'. The environment variable contained the value: 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=' Model is invalid.Duplicate constant node sparse initializer name: 'graph_proto != nullptronnxruntime::Graph::GraphThis is an invalid model. The sum of input arg count is not equal to size of input defs in node (graph_proto cannot be nullinput edgesoutput edgesThis is an invalid model. Tensor does not have type information.or with the tool onnxruntime/tools/python/remove_initializer_from_input.py.' the model will use the latest encountered initializerDuplicate initializer (dense, sparse or ConstantNode): 'Duplicate sparse_tensor_initializer: '. Please, fix your model.Sparse initializer must have a name. This model is invalidutils::HasName(sparse_tensor)graph_inputs_excluding_initializers_.empty() && graph_inputs_including_initializers_.empty() && value_info_.empty() && graph_outputs_.empty()onnxruntime::Graph::InitializeStateFromModelFileGraphProtoInitializer Graph state to be loaded into must be empty.This may prevent some of the graph optimizations, like const folding. appears in graph inputs and will not be treated as constant value/weight. by either re-generating the model with latest exporter/converter Move it out of graph inputs if there is no need to override it, onnxruntime::Graph::SetOuterScopeNodeArgsInvalid node indexes specified when adding edge.This is an invalid model. Error: two nodes with same node name (This is an invalid model. Error: Duplicate definition of name () does not exist in the graph.This is an invalid model. Graph output (Graph ctor should have created NodeArg for initializer. Missing:node_argonnxruntime::MergeShapeInfoD:\a\_work\1\s\onnxruntime\core\graph\graph.cc' source:Error merging shape info for output. '. Falling back to lenient merge. target:Output:Node [Serialization of fused function body is not currently supported, SparseTensor element type mismatch. ] op_type [Tensor element type mismatch. onnxruntime::NodeArg::UpdateTypeAndShape Input=Type mismatch. Current=LoadNodeArgsFromOrtFormat: Node [onnxruntime::Node::LoadFromOrtFormatnode_arg_name cannot be null], could not find NodeArg fbs_node_arg_names cannot be nullonnxruntime::Node::LoadFromOrtFormat::::operator ()does not have the graph for key onnxruntime::Node::SaveToOrtFormatinput index: onnxruntime::Node::LoadEdgesFromOrtFormatonnxruntime::Node::LoadEdgesFromOrtFormat::::operator () is not the same as this node's index:Node::LoadFromOrtFormat, input_arg_counts is missingNode::LoadEdgesFromOrtFormat, edge is missing for fbs_attr cannot be nullSerialization error. Graph attribute was serialized without Graph instance) of operator (' of input parameter () is invalid.) in node () input arg (This is an invalid model. Node (Node () does not have type information set by parent node.) type inference failed) output arg (Type Error: Type parameter (onnxruntime::Graph::InferAndVerifyTypeMatch) bound to different types () of Optype (This is an invalid model. Type Error: Type ' in node ( but usage of initializer in graph expects ' has element type ) does not have type information.This is an invalid model. Model input () of output arg (Type Error: Type () does not match expected type () of node (Fatal error: ) is required but not specified.onnxruntime::Graph::VerifyNodeAndOpMatch is not a registered function/opType Error: Shape of initializer This is an invalid model. Error in Node:Type Error: Data in initializer ' does not match. Invalid source node arg slot specified when removing edge.Invalid destination node arg slot specified when removing edge.Invalid node indexes specified when removing edge.onnxruntime::Graph::RemoveEdgeInvalid destination node arg slot specified when adding edge.Argument type mismatch when adding edge.onnxruntime::Graph::AddEdgeInvalid source node arg slot specified when adding edge.Invalid model. Node input 'Some nodes are not included in the topological sort, graph have a cycle.This is an invalid model. Failed to find NodeArg in all parent graphs. Name=' is not a graph input, initializer, or output of a previous 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=Argument mismatch when removing edge.onnxruntime::Graph::BuildConnections inputs and requires inputs but subgraph has No Graph instance was found for attribute inputs. Either provide all subgraph inputs, or just the required inputs.Graph attribute inferencing failed: in node onnxruntime::Graph::KahnsTopologicalSortThis is an invalid model. Error: the graph is not acyclic.onnxruntime::Graph::UpdateShapeInference) Op (UpdateTypeShapeInference is not intended to be used with control flow nodes containing subgraphsnode.GetAttributeNameToMutableSubgraphMap().empty()Subgraph input missing type.onnxruntime::Graph::InferAndVerifySubgraphTypesSize mismatch validating subgraph inputs. Got Node:Outer scope node arg name 'onnxruntime::Graph::ToGraphProtoInternal'was added but does not exist. onnxruntime::Graph::ToGraphProtoFailed to convert dense initializer to sparseonnxruntime::Graph::AllocateNodenullptr != func_meta_def must be either specified in graph inputs or graph initializers.nodes_.size() < static_cast(std::numeric_limits::max())onnxruntime::Graph::CleanUnusedInitializers'. It is no longer used by any node.'. It is not used by any node and should be removed from the model.Removing initializer 'onnxruntime::Graph::InlineFunctionInput to set must exist.'s number of outputs is different from function body graph's number of outputs.node.MutableOutputDefs().size() == subgraph.GetOutputs().size()dst_implicit_input_idx < (int)node->ImplicitInputDefs().size()'s number of inputs is different from function body graph's number of input.onnxruntime::Graph::CreateFusedSubGraphNodeonnxruntime::Graph::FinalizeFuseSubGraphonnxruntime::Graph::LoadFromOrtFormatinput->Exists()onnxruntime::Graph::SetInputsShouldn't be possible to have NodeArgs that haven't been handled already.outer_scope_node_args_consumed.empty()onnxruntime::Graph::ForThisAndAllSubgraphsonnxruntime::Graph::Resolveonnxruntime::Graph::InitInputsInitializersOutputsonnxruntime::Graph::PerformTypeAndShapeInferencing) attribute (Error: Duplicate definition-site for (onnxruntime::Graph::RemoveInitializedTensorgraph_proto_ is not in sync with name_to_initial_tensor_.sparse_tensor_names_ not in sync with name_to_initial_tensor_sparse_tensor_names_.count(tensor_name) == 0existing->second == &tensoronnxruntime::Graph::AddInitializedTensor but different TensorProto.AddInitializedTensor already has tensor with name !foundnode->GetOutputEdgesCount() == 0onnxruntime::Graph::RemoveNode as it still has output edges.Can't remove node _token_onnxruntime::Graph::SaveToOrtFormatNode is missing. Invalid ORT format model.Node index is out of rangeSparse Initializer tensor is missing. Invalid ORT format model.NodeArg is missing. Invalid ORT format model.Initializer tensor is missing. Invalid ORT format model.Duplicate initializer (dense or ConstantNode): 'NodeArg Name is missing. Invalid ORT format model.onnxruntime::Graph::LoadFromOrtFormat::::operator ()onnxruntime::ConstPointerContainer > >::atD:\a\_work\1\s\include\onnxruntime\core/common/const_pointer_container.hNodeEdge is missing. Invalid ORT format model.index < data_.size()@W, but it its domain is notthan the operator set version onnxruntime::OnnxRuntimeOpSchemaRegistry::RegisterOpSchemaInternalknown by the checker.onnxruntime::OnnxRuntimeOpSchemaRegistry::RegisterOpSetD:\a\_work\1\s\onnxruntime\core\graph\schema_registry.ccDomain already set in registry, but it its version is higheronnxruntime::Model::LoadFromOrtFormatGraph is null. Invalid ORT format model.onnxruntime::SaveModelsystem error number failed. File doesn't existLoad model ONNX Runtime only *guarantees* support for models stamped with official released onnx opset versions. Opset onnxruntime::model_load_utils::ValidateOpsetForDomain is till opset 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 model may run depending upon legacy support of some older opset version operators.ONNX 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 Missing opset in the model. All ModelProtos MUST have at least one entry that specifies which version of the ONNX OperatorSet is being imported.Unknown model file format version.onnxruntime::Model::ModelD:\a\_work\1\s\onnxruntime\core\graph\model.cc 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 ModelProto does not have a graph.Protobuf parsing failed.No graph was found in the protobuf.onnxruntime::Model::LoadFailed to load model with error: onnxruntime::Model::SaveToOrtFormatProtobuf serialization failed. is less than 0.onnxruntime::Model::Save less than 0.CDimension mismatch in unification between Incompatible dimensions but has rank should specify a shapeNegative values are not allowed in a shape specificationAttribute expected to have rank >oneT5bX_biasaword_embedding should have 2 dimensions and dimension size is known.weightWhether every token can only attend to previous tokens. Default value is 0.D:\a\_work\1\s\onnxruntime\core\graph\contrib_ops\contrib_defs.ccNumber of attention headsT6T7presentpastMweight_scaleinput_scaleConstrain input and output types to float tensors.Constrain mask index to integer typesPads has incorrect number of valuesInput tensors of wrong rank (0).'pads' input must be a 1D (shape: [2 * n_input_dims]) tensor of type int64Inputs 0 shall be 2 dimensionsInvalid bias shapeInputs 4 shall be 5 dimensionsIncompatible dimensions for matrix multiplicationInputs 0 shall be 3 dimensionsoutput tensorConstrain input and output types to float or half tensors.input tensorConstrain input and output integer tensors typesConstrain input and output float tensors types.UskipScale and bias the input image. Bias values are stored in the same ordering as the image pixel format.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. 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. 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. inv_std_varConstrain mean and inv_std_var to float tensors.Saved mean used during training to speed up gradient computationSaved inverse standard variance used during training to speed up gradient computation.Input's shape must be 4-D'Border' attribute must be present and must contain exactly 4 values - (left_border, top_border, right_border, bottom_border) Scale takes one input data (Tensor) and produces one output data (Tensor) whose value is the input data tensor scaled element-wise. 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]. input_as_shapeextra_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], ] Constrain input and output types to int8 tensors.LongformerAttentionweight_zero_pointinput_zero_pointglobal_weightglobal_biasmaskOne sided attention windows length W, or half of total window lengthwindowinput_idsThe epsilon value to use to avoid division by zero.globalConstrain to integer typesGposition_embeddingsegment_embeddingword_embeddingsegment_idsVarPlusEpsilonStdDevSquareOfMeanVarSquareMeanOfSquareXUMean2DInvStdDevGaussian 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.BiasedInvStdDev2DNormalizedTScaledDeviationNormalizedBias Gelu. It's an extension of Gelu. It takes the sum of input A and bias input B as the input of Gelu activation. Input rank must be >= 2.ERFCXPlus1PhiXERFCXOneBoth attributes isinf_only and isnan_only cannot be set. Unset both to check for both conditions.!(isinf_only && isnan_only)isinf_onlyisnan_only Based on Torch operator Embedding, creates a lookup table of embedding vectors of fixed size, for a dictionary of fixed size. 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. != mat_h:The inner-most 2 dimensions must have the same size (mat_w:) + scale[1] (First input does not have rank 2) needs to be greater than or equal to the left_border () + scale[0] () needs to be greater than or equal to the top_border () + right_border ('Scale' must contain exactly 2 values - (height, width)) + bottom_border (both data and indices tensor need to have rank larger than zero.last dimension of indices must not be larger and rank of data tensor 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 Matrix product that behaves like numpy.matmul: https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.matmul.html 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]. inputs are expected to have tensor type and output type should not be null.Second input does not have rank 2Input axis is invalid: EpsilonXShapecrop_size shape input tensor has wrong dimensionstash_typerois input tensor has wrong dimensionbatch_indices shape input tensor has wrong dimension'pads' input must be a 1D (shape: [input_rank]) or 2D tensor (shape: [1, input_rank]) of type int64first input tensor has wrong dimensionReducedShapeX2DNumReducedAxesSuffixShapeAxis1DPrefixShapeRankZero1DThe timestep for this operation.The new GRU hidden state calculated by this op.Array of sequence lengths. len(seq_lengths) should equal batch size N.seq_lengthsUnactivated gate outputs from forget, update, and output gates, pre-activation.gatesThe previous GRU hidden state.hidden_prev Sample echo operator.For internal use.Scaling valueThe scaled hyperbolic tangent values of the input tensor computed element-wiseIf 1, mean and variance are computed across channels. Default is 0.If 0, normalize the mean only. Default is 1.hiddenPerform mean variance normalization.ComplexMulinput_0onesidedIrfftsignal_ndimnormalizedConstrain input0 and output types to float tensorsRfft The fused convolution operator schema is the same as Conv besides it includes an attribute activation.ZPadsConstrain input and output types to float tensorsComplexMulConjWAinput_1The scale to apply.Input tensor of shape [N,C,H,W]1D output tensorBias applied to each channel, same size as C.Value of beta1D input tensoronnxruntime::contrib::RegisterContribSchemas::::operator ()Value of alphaOutput tensorTensor of data to extract slices from.Threshold valueInput tensorA 1-D values of (height, width).Result, has same type as input, with H and W dimensions reduced.Result, has same shape and type as inputA 1-D values of (leftBorder, topBorder, rightBorder, bottomBorder).Constrain indices to integer typesGivenTensorFillSliced data tensor.Constrain input and output types to all tensor types.1-D tensor of ending indices (exclusive) of corresponding axis in axes1-D tensor of axes that `starts` and `ends` apply to.data1-D tensor of starting indices of corresponding axis in `axes`Bool to determine if hidden state is zeroes or passed along for timesteps past the given sequence_length.drop_statesOutput data after scalingGRUUnitvaluesInput data to be scaledThe shape of filled tensorThe filled tensorIf value is 1, output type is uint32_t, else int32_t. Default value is 1.Tensor of rank r >= 1.Constrain output type to unsigned and signed 32-bit integer tensor.Seed for the hashing algorithm, unsigned 32-bit integer, default to 0.32-bit hash value.Constrain input type to unsigned or signed 32-bit integer tensor, or string tensor. It should be utf-8 encoded if using unicode.The underlying implementation is MurmurHash3_x86_32 generating low latency 32bits hash suitable for implementing lookup tables, Bloom filters, count min sketch or feature hashing.An input tensor to hash.Integer representing the embedding vector size for each word.If not provide, use the fileter size of conv weightThis 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.Constrain indice type to int32 or int64 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]]] Tensor of rank q-1+r-indices[-1].Constrain input and output types to any tensor type.Tensor of rank q >= 1.indicesConstrain to tensor(int32).Constrain to tensor(float).Specify bias of convSpecify embedding vector of charSequenceSpecify weights of convInteger representing the embedding vector size for each char.If not provide, use the char embedding size of embedding vector.Specify batchs of sequence words to embedding 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], ], ] A 1-D input tensor that is to be processed.(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 after padding.Input tensor.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`.The WordConvEmbedding takes in a batch of sequence words and embed each word to a vector.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 arrayWhether A should be transposedWhether B should be transposedOutput tensor of shape (M, N).Constrain input and output types to float/int tensors.Input tensor B. The shape of B should be (K, N) if transB is 0, or (N, K) if transB is non-zero.Input tensor C. The shape of C should be unidirectional broadcastable to (M, N). The FusedGemm operator schema is the same as Gemm besides it includes attributes activation and leaky_relu_alpha.Input tensor A. The shape of A should be (M, K) if transA is 0, or (K, M) if transA is non-zero.Tokenized stringsInput/Output is a string tensorExpandDims echo operator.Strings to tokenizeactivation_gammaSpecified axis to insert a dimensionScalar multiplier for the product of input tensors A * B.Scalar multiplier for input tensor C.N-dimensional matrix AN-dimensional matrix BMinimum number of characters allowed in the output. For example, if mincharnum is 2, tokens such as "A" and "B" would be ignored 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.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.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.Boolean whether to mark the beginning/end character with start of text character (0x02)/end of text character (0x03).The 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.Whether B should be transposed on the last two dimensions before doing multiplicationMatrix multiply resultsScalar multiplier for the product of the input tensors.Whether A should be transposed on the last two dimensions before doing multiplicationConstrain input B data types as 16-bit integer tensorConstrain 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).Matrix multiply results from A * BConstrain input A data types as 16-bit integer tensorThe output.The normal input data.Constrain mean and inv_std_var to be float tensors.The input data as Tensor.Type of Mean and InvStdDev tensors.Constrain input and output types (except mean and inv_std_var) to float tensors.Saved inverse standard deviation used during training to speed up gradient computation.Constrain input types and output Y type to float tensors.A 0-D scalar tensor. If specified, the entries at `padding_idx` do not contribute to the gradient; therefore, the embedding vector at `padding_idx` is not updated during training, i.e. it remains as a fixed pad.padding_idxThe embedding matrix of size N x M. 'N' is equal to the maximum possible index + 1, and 'M' is equal to the embedding sizeLong tensor containing the indices to extract from embedding matrix.Output tensor of the same type and shape as the input tensor.TorchEmbeddingThe bias input data that is a 1D tensor.Input tensor. Every matrix in the batch must be invertible.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.Constrain input and output types to all numeric tensors and bool tensors.Boolean. Indicates whether upper or lower part of matrix is retained. Default is true.Input tensor of rank 2 or higher.Output tensor of the same type as the input tensor. Shape of the output is * x M, where '*' is the shape of input indices, and 'M' is the embedding size.Constrain input and output types to all numeric tensors.A 0-D bool tensor. If given, this will scale gradients by the inverse of frequency of the indices (words) in the mini-batch. Default is ``False``scale_grad_by_freqThe bias input, a vector with the same shape as last dim of dataThe residual input, must have the same shape as dataoutput, dropout_mask = Dropout(data + bias, ratio) + residual, Intended to specialize the dropout pattern commonly found in transformer models.(Optional) Seed to the random generator, if not specified we will auto generate one.broadcast bias across input for dimensions broadcast_axis to softmax_axis-1The bias (or mask) as Tensor.Y = softmax(scores + bias)) with simple broadcast on bias. Intended to specialize softmax(scores + additive_mask) commonly found in transformer models.apply softmax to elements for dimensions softmax_axis or higherInput can be of any tensor type. 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] A 1-D INT64 tensor containing the the count of each element of 'uniques' in the input 'x'countsA 1-D INT64 tensor of the same size as 'x' containing the indices for each value in 'x' in the output 'uniques'idxxA 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'Value used for extrapolation, when applicable. Default is 0.0f. 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.Constrains input to only numeric types.The pooling method. Two modes are supported: 'bilinear' and 'nearest'. Default is 'bilinear'.2D matrix with shape (K,N)A 2D Matrix that represents the distance between each pair of the two collections of inputs.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".2D matrix with shape (M,N)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].Constrain types to float tensors.1-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.crop_size1-D tensor of shape (num_rois,) with each element denoting the index of the corresponding image in the batch.batch_indicesRoIs (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.roisBias tensor.Output data tensor.Input data tensor from the previous layer.Scale tensor.The first normalization dimension: normalization will be performed along dimensions axis : rank(inputs).type used for stash mean/inv_std_varConstrain types to int tensors. 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 input 'ratio' types to float tensors.Constrain output 'mask' types to boolean tensors.training_modeThe output mask of dropout.ratioIf set to true then it indicates dropout is being used for training. It is an optional value hence unless specified explicitly, it is false. If it is false, ratio is ignored and the operation mimics inference mode where nothing will be dropped from the input data and if mask is requested as output it will contain all ones.residualThe ratio of random dropout, with value in [0, 1). If this input was not set, or if it was set to 0, the output would be a simple copy of the input. If it's non-zero, output will be a random dropout of input, which is typically the case during training.Input tensors to check.The output scalar. Its value is true if all input tensors are finite. Otherwise, the output value would be false.If true, check only for NaN.Constrain the output to a boolean tensor.IsAllFiniteIf true, check only for Inf, -Inf.̼+̼+ in one of the subgraphs.D:\a\_work\1\s\onnxruntime\core\graph\graph_utils.cconnxruntime::graph_utils::UpdateImplicitInputNameInSubgraphstd::count_if(subgraph_node.InputEdgesBegin(), subgraph_node.InputEdgesEnd(), [input_slot_index](const Node::EdgeEnd& entry) { return entry.GetDstArgIndex() == input_slot_index; }) == 0Attempting to get index by a name which does not exist:for node: onnxruntime::graph_utils::RemoveNodeWithSingleNodeInSingleUsedOutputstd::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::CanUpdateImplicitInputNameInSubgraphs Implicit input name cannot be safely updated to onnxruntime::graph_utils::GetNodeOutputNameindex >= 0 && static_cast(index) < outputs.size()Attempting to get an output that does not exist.onnxruntime::graph_utils::GetNodeInputNameindex >= 0 && static_cast(index) < inputs.size()Attempting to get an input that does not exist.onnxruntime::graph_utils::GetIndexFromNameitr != node_args.end(). Index: ExplicitInputs: ImplicitInputs:onnxruntime::graph_utils::AddInitializer!graph.GetInitializedTensor(new_initializer.name(), existing)Initializer with same name exists. Name:onnxruntime::graph_utils::RemoveNodeShould be unreachable if CanRemoveNodeAndMergeEdges is in sync with the logic here.onnxruntime::graph_utils::FindPathFailed since multiple edges matched:onnxruntime::graph_utils::AddNodeInputnum_explicit_inputs == static_cast(target_input_idx)Can only add a new input at the end of the current ones.onnxruntime::graph_utils::ReplaceNodeInputInvalid input index for node Mismatch between Graph and IndexedSubGraph. Output not found:nodeargMismatch between Graph and IndexedSubGraph. Input not found:D:\a\_work\1\s\onnxruntime\core\graph\graph_viewer.cconnxruntime::GraphViewer::GraphViewergraph_->GetNode(idx) != nullptrIndexedSubGraph contains values not present in the Graphonnxruntime::GraphViewer::GetRootNodesfilter_info_ == nullptrNot supported with filtered graph.onnxruntime::GraphViewer::GetNodesInTopologicalOrderInvalid ExecutionOrdernodeMismatch between Graph and IndexedSubGraph. Node not found: TENSORonnxruntime::experimental::utils::SaveAttributeOrtFormatonnxruntime::experimental::utils::SaveSparseInitializerOrtFormatD:\a\_work\1\s\onnxruntime\core\graph\graph_flatbuffers_utils.cconnxruntime::experimental::utils::SaveInitializerOrtFormatonnxruntime::experimental::utils::LoadSparseInitializerOrtFormatMissing values for sparse initializer. Invalid ORT format model.Missing raw data for initializer. Invalid ORT format model.Missing string data for initializer. Invalid ORT format model.onnxruntime::experimental::utils::LoadInitializerOrtFormatMissing dimensions for initializer. Invalid ORT format model.SaveAttributeOrtFormat: Unsupported attribute type: Graph attribute value was null. Invalid ORT format model.Null graph attribute. Invalid ORT format model.Null tensor attribute. Invalid ORT format model.onnxruntime::experimental::utils::LoadAttributeOrtFormatNull string attribute. Invalid ORT format model.Missing dims for sparse initializer: Missing indicies for sparse initializer: Invalid ORT format model.Missing name for SparseTensor initializer. Invalid ORT format model.INTSFLOATSGRAPHSTRINGINTFLOATUNDEFINEDSPARSE_TENSORSSPARSE_TENSORGRAPHSTENSORSSTRINGSNull tensor in tensors attribute. Invalid ORT format model.Null tensors attribute. Invalid ORT format model.Null string in strings attribute. Invalid ORT format model.Null strings attribute. Invalid ORT format model.Null ints attribute. Invalid ORT format model.Null floats attribute. Invalid ORT format model.Empty graph proto from deserialization of ORT format modelZXZXZXZXUXxZXpZXhZXZXZXZXZXZXonnxruntime::ViewerFunctionImpl::BodyNot supportedoutinD:\a\_work\1\s\onnxruntime\core/graph/function_impl.h for domain and function is trying to import opset version for the same domainonnxruntime::CreateSchemainput_arg->Type() != nullptrD:\a\_work\1\s\onnxruntime\core\graph\function.cconnxruntime::update_subgraphs_within_function_bodyA node with a function body within a subgraph within another function body is currently not supported in ORTResolve subgraph failed:_dummyonnxruntime::FunctionImpl::FunctionImplfused_function_subgraphonnxruntime::CreateOpsetImportsForFunction(result.first->second == opset_version)ONNX model does not support multiple opset versions for a domain. Model imports opset version {name} Performs element-wise binary {name} on 8 bit data types (with Numpy-style broadcasting support). {additionalDocumentation} inputs are expected to have tensor type.Scale and Zero-point must be a scalarInput data type does not match the expected data type. Current data type is Input data type does not match the expected data typeA_zero_pointA_scale{additionalDocumentation}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.reducedReduceSumIntegerConstrain 'y', 'x_scale' to float tensors.Constrain output types to 32 bit tensors.Constrain input types to 8 bit signed and unsigned tensors.MulIntegerKeep 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.a_scaleConstrain input B data type to 8-bit integer tensor.Constrain input A, b_scale and output Y data type as float tensor.b_zero_pointb_scalemultiplicationC = ((A - A_zero_point) * (B - B_zero_point)) * (A_scale * B_scale)/C_scale + C_zero_pointadditionC = (A_scale * (A - A_zero_point) + B_scale * (B - B_zero_point))/C_scale + C_zero_pointConstrain input a_scale, b_scale and output Y data type as float tensor.Constrain input A data type to 8-bit integer tensor.a_zero_pointC_zero_pointC_scaleB_zero_pointB_scaleD:\a\_work\1\s\onnxruntime\core\graph\contrib_ops\quantization_defs.ccAll inputs to Concat must have same rankaxis must be in [-rank, rank)Required attribute axis is missingaxis must be in [-rank, rank-1]. input rank was Constrain input and output types to 8 bit signed and unsigned tensors.Constrain 'x', 'y_scale' to float tensors.y_zero_pointy_scaleThe 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.Constrain 'x' and 'x_zero_point' to 8-bit integer tensors.x_zero_pointx_scaleConstrain 'y_zero_point' and 'y' to 8-bit integer tensors.Pinitial_cinitial_hsequence_lensY_hT8Which axis to concat onConstrain weights types to 8 bit tensors.Constrain seq_lens to integer tensor.Y_cSequence of (Tensor, Scale, ZeroPoint) tuples. The type is sequence of (T8, TF, T8).Constrain scale types to any float tensor type.inputsTVreduced_zero_pointreduced_scaledata_zero_pointdata_scaleQLinearReduceMeanY_scaleX_zero_pointX_scaleCoefficient of leakage.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.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.Number of neurons in the hidden layerSpecify if the RNN is forward, reverse, or bidirectional. Must be one of forward (default), reverse, or bidirectional.Constrain input and output types to 8 bit tensors.Y_zero_pointRCouple the input and forget gates if 1.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.limitstartConstrain input and output types.Unsupported type:Can not get shape initializer data!deltaUnsupported non-raw-data data type!w_zero_pointw_scalewWhether to use ceil or floor (default) to compute the output shape.Stride along each spatial axis. If not present, the stride defaults to 1 along each spatial axis.The size of the kernel along each axis.Whether include pad pixels when calculating values for the edges. Default is 0, doesn't count include pad.Constrain input and output types to singed/unsigned int8 tensors.Works on NHWC layout or not? Default not.D:\a\_work\1\s\onnxruntime\core\graph\contrib_ops\nhwc_schema_defs.ccauto_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.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.weight and zero_point pair is expected to have same type.input and zero_point pair is expected to have be same type.Output tensor must have at least 2 dimensionsInput tensor must have at least 2 dimensionsD:\a\_work\1\s\onnxruntime\core\graph\contrib_ops\nchwc_schema_defs.ccinvalid scales valueinvalid scales dimensiontensor rank too smallConstrain seq_lens to integral tensors.Number of neurons in the hidden layer.Couple the input and forget gates if 1, default 0.memory_seq_lensMWQWAWReturnHrExceptionSetFilePointerEx length > buffer.size()offset < 0onnxruntime::`anonymous-namespace'::WindowsEnv::ReadFileIntoBufferfile_path == nullptrReceived negative size from stat callInvalid fd was supplied: RemoveDirectory() failed - path: onnxruntime::`anonymous-namespace'::WindowsEnv::DeleteFolderDeleteFile() failed - path: , error code: \/MapFileIntoMemory is not implemented on Windows. fail: unexpected endReadFile onnxruntime::`anonymous-namespace'::WindowsEnv::FormatLibraryFileNameFailed to find symbol in library, error code: Failed to unload library, error code: Failed to load library, error code: UNC\onnxruntime::`anonymous-namespace'::WindowsEnv::GetCanonicalPathGetFinalPathNameByHandle() failed: Msg:[%ws] %hs(%d) tid(%x) %08X %ws(caller: %p) %hs!%p: %hs(%u)\%hs!%p: FailFastLogHr%wsRaiseFailFastExceptionkernelbase.dll [%hs] [%hs(%hs)] CallContext:[%hs] SetThreadDescriptionkernel32.dllD:\a\_work\1\s\onnxruntime\core\platform\windows\env.ccGetFileSizeEx open file fail, errcode = Fatal error: 0 count processors from GetLogicalProcessorInformationonnxruntime::`anonymous-namespace'::WindowsEnv::GetNumCpuCoresFatal error: 0 count processors from GetSystemInfoonnxruntimeonnxruntime::LoopDir\*D:\a\_work\1\s\onnxruntime\core/platform/path_lib.hcode != static_cast(common::OK)onnxruntime::common::Status::StatusD:\a\_work\1\s\onnxruntime\core\common\status.ccSystemError[ONNXRuntimeError]ENGINE_ERRORRUNTIME_EXCEPTIONINVALID_PROTOBUFMODEL_LOADEDNOT_IMPLEMENTEDINVALID_GRAPHEP_FAILGENERAL ERRORSUCCESSFAILNO_SUCHFILENO_MODEL [ is not a valid year is not a valid day is not a valid dateampmonnxruntimeISink must be provided.onnxruntime::logging::LoggingManager::LoggingManagerD:\a\_work\1\s\onnxruntime\core\common\logging\logging.ccdefault_logger_id must be provided if instance_type is InstanceType::DefaultOnly one instance of LoggingManager created with InstanceType::Default can exist at any point in time.Default logger already set. onnxruntime::logging::LoggingManager::CreateDefaultLoggerunnamed_thread_poolProfiler not started yetenabled_onnxruntime::concurrency::ThreadPoolProfiler::Stoponnxruntime::concurrency::ThreadPoolProfiler::MainThreadStat::LogEndonnxruntime::concurrency::ThreadPoolProfiler::MainThreadStat::LogEndAndStartpoints_.empty()onnxruntime::concurrency::ThreadPoolProfiler::MainThreadStat::Reset", "block_size": ["thread_id": "], "core": ": D:\a\_work\1\s\onnxruntime\core\common\threadpool.cc}}}, "sub_threads": {", "thread_pool_name": "{"main_thread": {LogStart must pair with LogEnd!points_.empty()"num_run": ": {Nested parallelism not supported!current_parallel_sectiononnxruntime::concurrency::ThreadPool::ParallelSection::ParallelSectionn >= 0onnxruntime::concurrency::ThreadPool::ParallelFor"DistributionDistributionEnqueueRunWaitWaitRevokeUnknownEvent"core": length overflowonnxruntime::ToMBStringD:\a\_work\1\s\onnxruntime\core\common\str_helper.cconnxruntime::ToWideStringsession_logger != nullptronnxruntime::profiling::Profiler::InitializeMaximum number of events reached, could not record profile event.onnxruntime::profiling::Profiler::EndTimeAndRecordEventWriting profiler data to file onnxruntime::profiling::Profiler::EndProfilingSessionNodeonnxruntime::profiling::Profiler::StartTimeD:\a\_work\1\s\onnxruntime\core\common\profiler.cc"name" :""args" : {" : " : "} }, ] [ ",{"cat" : ""pid" :"tid" :"dur" :"ts" :"ph" : "X",XȈXFailed to parse path root: onnxruntime::`anonymous-namespace'::ParsePathRootD:\a\_work\1\s\onnxruntime\core\common\path.cconnxruntime::Path::Parse/\unexpected failureillegal input path:api-ms-win-eventing-provider-l1-1-0.dlladvapi32.dllEventSetInformationkernel32.dllGetSystemTimePreciseAsFileTimerB¥B@K;?r1:n/ <*=(*>>??33{@l?A ;Eۼ# =pu>7fȹ~;ƼX=P"?>?B;?r1mL:e9 <*=*>>??@KAQ5@.N3;A8X} <2?~>++{16?:>=4?~??A%~3a*7A\3JCy7 ':Y;Ӡ5֕8;Y;bad dimensions?@@@    QLinearGlobalAveragePool ImageSize too large!ElementSize must be power of 2 and less or equal than 16!QLinearGlobalAveragePool parameter out of computation range!??tensor_typemap_typesequence_typeonnxruntime::experimental::utils::SaveValueInfoOrtFormatWe do not support type [SaveValueInfoOrtFormat: value_info_proto for is missing type info.onnxruntime::experimental::utils::LoadTensorDimensionOrtFormatdim_param value with no name. Invalid ORT format model.onnxruntime::experimental::utils::LoadTensorShapeOrtFormatNull entry in dimensions. Invalid ORT format model.D:\a\_work\1\s\onnxruntime\core\flatbuffers\flatbuffers_utils.cconnxruntime::experimental::utils::SaveSequenceTypeOrtFormatonnxruntime::experimental::utils::SaveTensorTypeAndShapeOrtFormatonnxruntime::experimental::utils::SaveMapTypeOrtFormat] for nowonnxruntime::experimental::utils::SaveTypeInfoOrtFormat is not supported currentlyNull map type info. Invalid ORT format model.Null type info for Type:Model must have opset imports. Invalid ORT format model.onnxruntime::experimental::utils::LoadValueInfoOrtFormatopset id is null. Invalid ORT format model.onnxruntime::experimental::utils::LoadOpsetImportOrtFormatNull value type info in fbs::SequenceType. Invalid ORT format model.onnxruntime::experimental::utils::LoadTensorTypeAndShapeOrtFormatNull value type info in fbs::MapType. Invalid ORT format model.onnxruntime::experimental::utils::LoadSequenceTypeOrtFormatNull tensor type info. Invalid ORT format model.onnxruntime::experimental::utils::LoadMapTypeOrtFormatNull sequence type info. Invalid ORT format model.onnxruntime::experimental::utils::LoadTypeInfoOrtFormatopset import domain is null. Invalid ORT format model.0pV X@X0X' has been deprecated since version , max=Operator ') has input size not in range [min=) has more outputs ()'s input ' appeared multiple times.)'s output for operator Attribute 'Mismatched attribute type in 'Unrecognized attribute: ) has output size not in allowed input sizes.has output size not in allowed output sizes.) than declared () in op definition. is marked single but has an empty string in the graph) has more inputs (' is missing. has unknown expected typeRequired attribute '(inputs_.size() - 1) == iDuplicate type constraint nameONNX Schema : failed validating the check: ' is expected to have field 'sparse_tensor'' is expected to have field 't'' is expected to have field 'floats'' is expected to have field 'g'' is expected to have field 'strings'' is expected to have field 'ints'' is expected to have field 'graphs'' is expected to have field 'tensors'!(it.GetName().empty())(outputs_.size() - 1) == iA 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.The size of each input in the input listThe input type must be a tensor of a numeric type.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.map(int64, double)map(string, int64)map(string, double)map(string, float)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.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.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.A value that needs replacing.Value(s) to change toValue(s) to change to.The input must be a tensor of a numeric type. The output will be of the same tensor type.Values greater than this are mapped to 1, others to 0.D:\a\_work\1\s\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.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].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.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)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.If true and category is not present, will return all zeros; if false and a category if not found, the operator will fail.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.One of 'MAX,' 'L1,' 'L2'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.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].Flag indicating whether the regression is a one-class SVM or not.Chosen support vectorsThe number of support vectors.Support vector coefficients.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 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'A float.An integer.Input's shape should be 1D or 2DThe 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]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.A list of floats.A list of ints.A string.A list of strings. One and only one of 'value_*'s should be set.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.Weights of the intercepts, if used.Weights of the model(s).The total number of regression targets, 1 if not defined.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.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'The keys when using int keys.
One and only one of the 'classlabels_*' attributes must be defined.Defines how to aggregate leaf values within a target.
One of 'AVERAGE,' 'SUM,' 'MIN,' 'MAX.'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))Thresholds 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.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.Indicates the transform to apply to the score.
One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT.'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.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.The index of the target that each weight is forThe node id of each weightThe total number of targets.The weight for each targetThe 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.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.The input type must be a tensor of integers or strings, of any shape.A list of labels.The output type will be a tensor of strings or integers, and will have the same shape as the input.D:\a\_work\1\s\cmake\external\onnx\onnx\defs\traditionalml\old.cc does not specify a valid type.Value of attribute should be of integer type and specify a type. not specifiedInput tensor can be of arbitrary type.reshapedConstrain output to int64 tensor, which should be a scalar though.Constrain output to int64 tensor.Invalid dimension value: Invalid Target shape product of 0. Product cannot be 0 in combination with -1Target shape may not have multiple -1 dimensions.Invalid position of 0.Dimension could not be inferred: incompatible shapes(Optional) By default, when any value in the 'shape' input is equal to zero the corresponding dimension value is copied from the input tensor dynamically. allowzero=1 indicates that if any value in the 'shape' input is set to zero, the zero value is honored, similar to NumPy.Mismatch between number of splits () and the split dimension of the input (Invalid value of attribute 'axis'. Rank=) and outputs (Mismatch between the sum of 'split' (The input is not evenly splittableaxis must be in [-rank, rank-1].Which axis to concat on. A negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(inputs).. Value=concat_resultConstrain output types to any tensor type.seq(tensor(int32))seq(tensor(int64))seq(tensor(int8))seq(tensor(int16))seq(tensor(double))seq(tensor(string))seq(tensor(float16))seq(tensor(float))seq(tensor(uint32))seq(tensor(uint64))seq(tensor(uint8))seq(tensor(uint16))The data type to which the elements of the input tensor are cast. Strictly must be one of the types from DataType enum in TensorProtoD:\a\_work\1\s\cmake\external\onnx\onnx\defs\tensor\defs.ccConstrain input types. Casting from complex is not supported.Constrain output types. Casting to complex is not supported.seq(tensor(complex128))seq(tensor(bool))seq(tensor(complex64))List 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).'axes' attribute must not contain any duplicatesvalues in 'axes' are beyond the bounds of the computed output shapesqueezedWhich 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 >= 1axis must be in [-r, r-1]DCR (default) for depth-column-row order re-arrangement. Use CRD for column-row-depth order.'Repeats' input has incorrect number of values. The number of values in 'repeats' must be equal to the number of input dimensions.'Repeats' input must be 1D tensor of type int64expandedList 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).Blocks of [blocksize, blocksize] are moved.Blocksize must be positiveInput tensor must be 4-dimensional'step' cannot be 0Input steps has incorrect lengthInput axes has invalid datastepsWhich axis to split on. A negative value means counting dimensions from the back. Accepted range is [-rank, rank-1] where r = rank(input).outputsIncorrect or missing input value for starts and endsInput axes has incorrect lengthOnly supports `int32_t` or `int64_t` inputs for starts/ends/axes/stepsSlice op must have either three, four or five inputs.Input and output types can be of any tensor type.updatesA list of integers. By default, reverse the dimensions, otherwise permute the axes according to the values given.Invalid attribute perm {}, input shape = {Which axis to scatter on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data).transposeddepth(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).Constrain to any tensor type.OneHot node must have three inputs.Constrains to boolean tensors.Input '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.Constrain to boolean tensors.'input' must have rank >= 2'sequence_lens' must have rank of 1Constrain to all tensor types.(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.(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. 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_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).Constrain input 'X' and output 'Y' to all tensor types.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.Four 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".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).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".Constrain repeat's type to int64 tensors.repeatsThe 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'.Two interpolation modes: nearest (default), and linear (including bilinear, trilinear, etc)Indices tensor must have rank >= 1Constrain input and output types to all tensor and sequence types.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).roiWhen 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.Constrain roi type to float or double.sizes(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).inverse_indices(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.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).constant_valueThe 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` tensorSupported modes: `constant`(default), `reflect`, `edge`'pads' input must be a 1D (shape: [2 * input_rank]) tensor of type int64]. Its actual value is: 'axis' must be in [D:\a\_work\1\s\cmake\external\onnx\onnx\defs\math\defs.ccConstrain input and output types to signed numeric tensors.Whether the operator should behave like fmod (default=0 meaning it will do integer mods); Set this to 1 to force fmod treatmentConstrain input and output types to high-precision numeric tensors.X_ReduceMaxX_ReduceSumX_SubX_Expdata_0HardSwishList of tensors for Constrain input and output types to numeric tensors.sumAxis has less than the requested k elements.K input must be a one-dimensional tensor of size 1.K input must be of type int64.X_LogConstrain 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.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).X_alphaCoefficient of ELU.slopeValue 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. Constrain input X and output types to float/int tensors.Constrain input Y types to float/int tensors.input and zero_point pair is expected to have same type.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.If set to 1 will perform the sums in reverse direction.Constrain output Y data type as 32-bit integer tensor.axis tensor can be int32 or int64 onlyConstrain 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 output y and its zero point data type to 8-bit integer tensor.Whether to return the top-K largest or smallest elements.Whether to return the elements in sorted order.ValuesKConstrain index tensor to int64Dimension on which to do the sort. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).IndicesConstrain input and output types to all tensors.'shape' input must be 1D tensor of type INT64Constrain target to integer typesNegativeLogLikelihoodLossSpecifies a target value that is ignored and does not contribute to the input gradient. It's an optional value.Constrain input, weight, and output types to floating-point tensors. does not match the equation indices.Rank of input Number of input tensors does not match the operands in the equation.Ellipsis represents incompatible dimensions.Weight rank must be 1.Target rank must be 1 less than the input rank.Input and target dimension value mismatch.Type 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.X_NDCX_LogSMscoresX_NCDlog_problabelsX_LogSM_NCDX_shapeInputsEinsum expression string.Shape3DConstrain 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.loss_NCddloss_N1ddignore_indexinput_gather_elementlossloss_NddnonereductionConstrain input and output types to floating-point tensors.targetexpanded_targetconst_zeroconst_oneinput_gather_element_transformsqueeze_maskconst_zero_floatconst_zero_castedweight_gather_tempweight_gather_temp_1const_one_floatconst_one_castedloss_sumweight_gather_sumweight_gatherloss_unweightedexpanded_target_int64transform_targetsconst_ignore_indexconst_zero_target_typedSoftmaxCrossEntropyLossauto_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 `output_shape[i] = ceil(input_shape[i] / strides[i])` for each axis `i`. The padding is split between the two sides equally or almost equally (depending on whether it is even or odd). In case the padding is an odd number, the extra padding is added at the end for SAME_UPPER and at the beginning for SAME_LOWER.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 `output_shape[i] = input_shape[i] * strides[i]` for each axis `i`. The padding is split between the two sides equally or almost equally (depending on whether it is even or odd). In case the padding is an odd number, the extra padding is added at the end for SAME_UPPER and at the beginning for SAME_LOWER.Attribute strides has incorrect sizeAttribute kernel_shape has incorrect sizeInput tensor must have atleast 2 dimensionsAttribute dilations has incorrect sizeThis 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. MaxUnpool op must have either two or three inputs.The storage order of the tensor. 0 is row major, and 1 is column major.Dilation value along each spatial axis of filter. If not present, the dilation defaults to 1 along each spatial axis.Attribute strides has incorrect size.Attribute kernel_shape has incorrect size.Input tensor X must have atleast 2 dimensions.Attribute pads has incorrect size.Second input tensor has wrong dimensionConstrain input and output types to float and 8 bit tensors.Attribute kernel_shape must be specifiedAttribute pads has incorrect sizeD:\a\_work\1\s\cmake\external\onnx\onnx\defs\nn\defs.ccnumber of groups input channels and output channels are divided into. default is 1.dilation value along each spatial axis of the filter. If not present, the dilation defaults to 1 along each spatial axis.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.Constrain input x and its zero point data type to 8-bit integer tensor.Constrain input w and its zero point data type to 8-bit integer tensor.Constrain bias type to 32-bit integer tensor.The shape of the convolution kernel. If not present, should be inferred from input 'w'.Constrain filter type to 8-bit integer tensor.Constrain output type to 8-bit integer tensor. 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 five required inputs 'X', 'scale', 'B', 'input_mean' and 'input_var'. Note that 'input_mean' and 'input_var' are expected to be the estimated statistics in inference mode (training_mode=False, default), and the running statistics in training mode (training_mode=True). There are multiple cases for the number of outputs, which we list below: Output case #1: Y, running_mean, running_var (training_mode=True) Output case #2: Y (training_mode=False) When training_mode=False, extra outputs are invalid. The outputs are updated as follows when training_mode=True: ``` running_mean = input_mean * momentum + current_mean * (1 - momentum) running_var = input_var * momentum + current_var * (1 - momentum) Y = (X - current_mean) / sqrt(current_var + epsilon) * scale + B where: current_mean = ReduceMean(X, axis=all_except_channel_index) current_var = ReduceVar(X, axis=all_except_channel_index) Notice that ReduceVar refers to the population variance, and it equals to sum(sqrd(x_i - x_avg)) / N where N is the population size (this formula does not use sample size N - 1). ``` When training_mode=False: ``` Y = (X - input_mean) / sqrt(input_var + epsilon) * scale + B ``` 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.Factor used in computing the running mean and variance.e.g., running_mean = running_mean * momentum + mean * (1 - momentum).momentumThis number of op outputs should be 3 when Training_mode = True, but it is not.This number of op outputs should be 1 when Training_mode = False, but it is not.Stride along each spatial axis. If not present, the stride defaults to 1 along each axis.The pads attribute cannot be used simultaneously with auto_pad attributeConstrain output y data type to 32-bit integer tensor.dilation value along each spatial axis of the filter. If not present, the dilation defaults to 1 along each axis.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 padsRoIs tensor must have 2 dimensionsp value of the Lp norm used to pool over the input data.ROI 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 specified'output_shape' must have same number of elements as the shape of input tensor X.Attribute kernel_shape must be specified.'output_shape' must be rank 1 tensor.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.The shape of the convolution kernel. If not present, should be inferred from input W.Constrain input and output types to float tensors.Scaling parameter.The exponent.Input tensor must have rank 1 or 2ngram_indexes must be non-empty with no negative values) for attribute 'axis'Invalid value(The number of channels to sum overConstrain input and output to all tensor types.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). 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.The weighting criteria. It can be one of "TF" (term frequency), "IDF" (inverse document frequency), and "TFIDF" (the combination of TF and IDF)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.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.Input shape must have either [C] or [1,C] dimensions where C > 01-D tensor of floatsMaximum n-gram length. If this value is 3, 3-grams will be used to generate the output.Input is ether string UTF-8 or int32/int64List 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.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.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.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=1running_meanrunning_varConstrain mean and variance types to float tensors. It allows all float type for U.If set to true, it indicates BatchNormalization is being used for training, and outputs 1, 2, 3, and 4 would be populated.input_varinput_meanRatio of Dropout must be a scalar.training_mode of Dropout must be a scalar.The bias value added to output. Default is 0.The lambd value for the Shrink formulation. Default is 0.5.The order of the normalization, only 1 or 2 are supported.The axis on which to apply normalization, -1 mean last axis.E_XsquaredVarianceEX_squaredX_squaredProcessed_STDSTDX_varianceList of stop words. If not set, no word would be removed from X.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.string enum that cases output to be lowercased/uppercases/unchanged. Valid values are "LOWER", "UPPER", "NONE". Default is "NONE"Boolean. Whether the identification of stop words in X is case-sensitive. Default is falseX_RMA 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.First input tensor must have rank 3One (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.The shape format of inputs X, initial_h and outputs Y, Y_h. If 0, the following shapes are expected: X.shape = [seq_length, batch_size, input_size], Y.shape = [seq_length, num_directions, batch_size, hidden_size], initial_h.shape = Y_h.shape = [num_directions, batch_size, hidden_size]. If 1, the following shapes are expected: X.shape = [batch_size, seq_length, input_size], Y.shape = [batch_size, seq_length, num_directions, hidden_size], initial_h.shape = Y_h.shape = [batch_size, num_directions, hidden_size]. Computes an one-layer simple RNN. This operator is usually supported via some custom implementation such as CuDNN. Notations: `X` - input tensor `i` - input gate `t` - time step (t-1 means previous time step) `Wi` - W parameter weight matrix for input gate `Ri` - R recurrence weight matrix for input gate `Wbi` - W parameter bias vector for input gate `Rbi` - R parameter bias vector for input gate `WBi` - W parameter weight matrix for backward input gate `RBi` - R recurrence weight matrix for backward input gate `WBbi` - WR bias vectors for backward input gate `RBbi` - RR bias vectors for backward input gate `H` - Hidden state `num_directions` - 2 if direction == bidirectional else 1 Activation functions: Relu(x) - max(0, x) Tanh(x) - (1 - e^{-2x})/(1 + e^{-2x}) Sigmoid(x) - 1/(1 + e^{-x}) (NOTE: Below are optional) Affine(x) - alpha*x + beta LeakyRelu(x) - x if x >= 0 else alpha * x ThresholdedRelu(x) - x if x >= alpha else 0 ScaledTanh(x) - alpha*Tanh(beta*x) HardSigmoid(x) - min(max(alpha*x + beta, 0), 1) Elu(x) - x if x >= 0 else alpha*(e^x - 1) Softsign(x) - x/(1 + |x|) Softplus(x) - log(1 + e^x) Equations (Default: f=Tanh): - Ht = f(Xt*(Wi^T) + Ht-1*(Ri^T) + Wbi + Rbi) The shape format of inputs X, initial_h, initial_c and outputs Y, Y_h, Y_c. If 0, the following shapes are expected: X.shape = [seq_length, batch_size, input_size], Y.shape = [seq_length, num_directions, batch_size, hidden_size], initial_h.shape = Y_h.shape = initial_c.shape = Y_c.shape = [num_directions, batch_size, hidden_size]. If 1, the following shapes are expected: X.shape = [batch_size, seq_length, input_size], Y.shape = [batch_size, seq_length, num_directions, hidden_size], initial_h.shape = Y_h.shape = initial_c.shape = Y_c.shape = [num_directions, batch_size, hidden_size]. Computes an one-layer LSTM. This operator is usually supported via some custom implementation such as CuDNN. Notations: `X` - input tensor `i` - input gate `o` - output gate `f` - forget gate `c` - cell gate `t` - time step (t-1 means previous time step) `W[iofc]` - W parameter weight matrix for input, output, forget, and cell gates `R[iofc]` - R recurrence weight matrix for input, output, forget, and cell gates `Wb[iofc]` - W bias vectors for input, output, forget, and cell gates `Rb[iofc]` - R bias vectors for input, output, forget, and cell gates `P[iof]` - P peephole weight vector for input, output, and forget gates `WB[iofc]` - W parameter weight matrix for backward input, output, forget, and cell gates `RB[iofc]` - R recurrence weight matrix for backward input, output, forget, and cell gates `WBb[iofc]` - W bias vectors for backward input, output, forget, and cell gates `RBb[iofc]` - R bias vectors for backward input, output, forget, and cell gates `PB[iof]` - P peephole weight vector for backward input, output, and forget gates `H` - Hidden state `num_directions` - 2 if direction == bidirectional else 1 Activation functions: Relu(x) - max(0, x) Tanh(x) - (1 - e^{-2x})/(1 + e^{-2x}) Sigmoid(x) - 1/(1 + e^{-x}) (NOTE: Below are optional) Affine(x) - alpha*x + beta LeakyRelu(x) - x if x >= 0 else alpha * x ThresholdedRelu(x) - x if x >= alpha else 0 ScaledTanh(x) - alpha*Tanh(beta*x) HardSigmoid(x) - min(max(alpha*x + beta, 0), 1) Elu(x) - x if x >= 0 else alpha*(e^x - 1) Softsign(x) - x/(1 + |x|) Softplus(x) - log(1 + e^x) Equations (Default: f=Sigmoid, g=Tanh, h=Tanh): - it = f(Xt*(Wi^T) + Ht-1*(Ri^T) + Pi (.) Ct-1 + Wbi + Rbi) - ft = f(Xt*(Wf^T) + Ht-1*(Rf^T) + Pf (.) Ct-1 + Wbf + Rbf) - ct = g(Xt*(Wc^T) + Ht-1*(Rc^T) + Wbc + Rbc) - Ct = ft (.) Ct-1 + it (.) ct - ot = f(Xt*(Wo^T) + Ht-1*(Ro^T) + Po (.) Ct + Wbo + Rbo) - Ht = ot (.) h(Ct) D:\a\_work\1\s\cmake\external\onnx\onnx\defs\rnn\defs.cc Computes an one-layer GRU. This operator is usually supported via some custom implementation such as CuDNN. Notations: `X` - input tensor `z` - update gate `r` - reset gate `h` - hidden gate `t` - time step (t-1 means previous time step) `W[zrh]` - W parameter weight matrix for update, reset, and hidden gates `R[zrh]` - R recurrence weight matrix for update, reset, and hidden gates `Wb[zrh]` - W bias vectors for update, reset, and hidden gates `Rb[zrh]` - R bias vectors for update, reset, and hidden gates `WB[zrh]` - W parameter weight matrix for backward update, reset, and hidden gates `RB[zrh]` - R recurrence weight matrix for backward update, reset, and hidden gates `WBb[zrh]` - W bias vectors for backward update, reset, and hidden gates `RBb[zrh]` - R bias vectors for backward update, reset, and hidden gates `H` - Hidden state `num_directions` - 2 if direction == bidirectional else 1 Activation functions: Relu(x) - max(0, x) Tanh(x) - (1 - e^{-2x})/(1 + e^{-2x}) Sigmoid(x) - 1/(1 + e^{-x}) (NOTE: Below are optional) Affine(x) - alpha*x + beta LeakyRelu(x) - x if x >= 0 else alpha * x ThresholdedRelu(x) - x if x >= alpha else 0 ScaledTanh(x) - alpha*Tanh(beta*x) HardSigmoid(x) - min(max(alpha*x + beta, 0), 1) Elu(x) - x if x >= 0 else alpha*(e^x - 1) Softsign(x) - x/(1 + |x|) Softplus(x) - log(1 + e^x) Equations (Default: f=Sigmoid, g=Tanh): - zt = f(Xt*(Wz^T) + Ht-1*(Rz^T) + Wbz + Rbz) - rt = f(Xt*(Wr^T) + Ht-1*(Rr^T) + Wbr + Rbr) - ht = g(Xt*(Wh^T) + (rt (.) Ht-1)*(Rh^T) + Rbh + Wbh) # default, when linear_before_reset = 0 - ht = g(Xt*(Wh^T) + (rt (.) (Ht-1*(Rh^T) + Rbh)) + Wbh) # when linear_before_reset != 0 - Ht = (1 - zt) (.) ht + zt (.) Ht-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. was Loop 'body' subgraph outputs should all be tensors or sequences but output but subgraphs produce If node has condLoop 'body' subgraph scan outputs should all be tensors but output Scan input outputs. Expected Number of scan output axes specified ( was not a tensor.Scan 'body' subgraph outputs should all be tensors but output then_branch and else_branch produce different number of outputs. Graph attribute inferencing returned type information for was notv_initialtensor of int64, which should be a scalar.tensor of bool, which should be a scalar.v_final_and_scan_outputsThe 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.Graph 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.All Tensor and Sequence typesGraph 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.Only boolD:\a\_work\1\s\cmake\external\onnx\onnx\defs\controlflow\defs.ccInput was expected to have either tensor or sequence type. Got source=Mismatched type: target=Input type was nullInput was expected to have tensor type. Got Input element type of Output was expected to have tensor type. Got Element type of input was unknown does not match existing output type of is invalid for a tensor of rank Number of scan input axes specified () is not equal to number of scan outputs ( axis value ) is not equal to number of scan inputs (target sequence type missing element type.Mismatched tensor element type:source sequence type missing element type.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].Int64 tensorAn 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 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).All Tensor typesinitial_state_and_scan_inputsAn attribute specifying the number of scan_inputs M. 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.final_state_and_scan_outputsThe 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.Constrain 'y_zero_point' and 'y' to 8-bit integer tensor.D:\a\_work\1\s\cmake\external\onnx\onnx\defs\quantization\defs.cc(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 or int32 tensor.X_Min_AdjustedX_MaxQ_MaxX_MinMin_ScaledInitial_ZeroPoint_FPX_Max_AdjustedX_Range(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)Constrain 'x_zero_point' and 'x' to 8-bit/32-bit integer tensor.Constrain 'y_zero_point' and 'y' to 8-bit unsigned integer tensor.Q_MinConstrain 'x' to float tensor.ZeropointClipped_ZeroPoint_FPRounded_ZeroPoint_FPD:\a\_work\1\s\cmake\external\onnx\onnx\defs\tensor\old.cc 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).Constrain input types. Casting from strings and complex are not supported.Constrain output types. Casting to strings and complex are not supported.Constrains input and output to only numeric types.Invalid Target shape product of 0Target shape may not have multiple -1 dimensionsInvalid position of 0length of each output. Values should be >= 0.Attribute axes has incorrect lengthAxes that `starts` and `ends` apply to. It's optional. If not present, will be treated as [0, 1, ..., len(`starts`) - 1].Incorrect or missing attribute value for starts and endsStarting indices of corresponding axis in `axes`Ending indices (exclusive) of corresponding axis in axes`Ranks inferred (Number of elements of attribute 'scales' must be same as rank of input 'X'Constrain output types to bool, int32, int64, float16, float, double tensors.) is not equal to the existing rank value (Attribute 'scales' must have floats type.Attribute 'scales' is required.'axis' must be in [-rank(indices)-1, rank(indices)]List of non-negative integers, indicate the dimensions to be inserted(Optional) Axis along which to take slices. If not specified, input is flattened before elements being selected.(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.Which axis to scatter on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1]List of non-negative integers, indicate the dimensions to squeeze.Which axis to gather on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1]List 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`.paddingslength of each outputNew shapeThree modes: constant(default), reflect, edgeOne float, indicates the value to be filled, default is 0Constrain output types to float tensors.rank must be greater than axisWhich axis to concat on. Default value is 1.outputs...Which axis to split onwidth_scaleThe scale along height dimension. It takes value greater than or equal to 1.Constrain tiles and axis's type to int64 tensors.The scale along width dimension. It takes value greater than or equal to 1.height_scaleTwo interpolation modes: nearest(default), bilinearlegacy optimization attribute.consumed_inputstilesAttribute value for pads is requiredWhich axis to split on. One float, indicates the value to be filled.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`.axes as an input and attribute cannot be specified at the same time.The axis in which to compute the arg indices. Accepted range is [-r, r-1] where r = rank(data).Whether to select the last index or the first index if the {name} appears in multiple indices, default is False (first index).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).Constrain input and output types to high-precision and 8 bit numeric tensors.Defines behaviour if 'axes' is empty. Default behaviour with 'false' is to reduce all axes. When axes is empty and this attribute is set to true, input tensor will not be reduced,and the output tensor would be equivalent to input tensor.D:\a\_work\1\s\cmake\external\onnx\onnx\defs\reduction\defs.ccD:\a\_work\1\s\cmake\external\onnx\onnx\defs\math\old.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).This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md).Coefficient of leakage default to 0.01.Coefficient of SELU default to 1.6732.Coefficient of SELU default to 1.0507.Coefficient of ELU default to 1.0.Pass 1 to enable broadcastingbroadcastDescribes 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. If set, defines the broadcast dimensions. See doc for details.Maximum value, above which element is replaced by maxMinimum value, under which element is replaced by minValue of beta default to 0.5Value of alpha default to 0.2Dimension on which to do the sort.Invalid value for attribute kNumber of top elements to retrieveWhether C should be broadcastedScalar multiplier for the product of input tensors A * B, the default value is 1.0.Scalar multiplier for input tensor C, the default value is 1.0.Constrains output to boolean tensor.D:\a\_work\1\s\cmake\external\onnx\onnx\defs\logical\defs.ccConstrains input types to all numeric tensors.Constrains input to boolean tensor.Constrains input/output to boolean tensors.Direction of moving bits. It can be either "RIGHT" (for right shift) or "LEFT" (for left shift).Constrain input and output types to integer tensors.Attribute 'value_float' expect a float.Attribute 'value_floats' expect a list of floats.Attribute 'value_int' expect an integer.Attribute 'value_ints' expect a list of integers.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.value_intsvalue_floatsparse_valuevalue_intvalue_stringsOne and only one of the attributes 'value', 'value_*' or 'sparse_value' must be specified for a Constant node.value_floatsvalue_stringShape input must be a one-dimensional tensor.Invalid shape value: D:\a\_work\1\s\cmake\external\onnx\onnx\defs\generator\defs.ccConstrain 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 float32The values for the elements for the 1D, int64, output tensor.The value for the sole element for the scalar, float32, output tensor.The value for the elements of the output tensor in sparse format.The value for the sole element for the scalar, int64, output tensor.The 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.Attribute expected to have a one-dim tensorAttribute expected to have tensor typediv_resultceil_resultsub_result_casteddelta_castedceil_result_relu_boolvariadic_outputceil_result_reluceil_result_relu_intiprevloop_body_attributerangesub_resultcond_outcurrentInput to 'Range' op should be scalars (Tensor with only one element and shape empty)All inputs to 'Range' op must be of the same typeConstrain input types to common numeric type tensors.Lower boundary of the output values.Upper boundary of the output values.Constrain output types. Strings and complex are not supported.The data type for the elements of the output tensor. If not specified, default is TensorProto::FLOAT.The shape of the output tensor.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.Constrain output types to be numerics.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 have rank 2Number of times to sample.Output type must be int32 or int64Constrain output types to integral tensors.(Optional) The data type for the elements of the output tensor, if not specified, we will use int32.The data type for the elements of the output tensor. Default is TensorProto::FLOAT.The mean of the normal distribution.The standard deviation of the normal distribution.(Optional) The data type for the elements of the output tensor, if not specified, we will use the data type of the input tensor.Attribute 'value' of Constant node must exist with 'Tensor' data.D:\a\_work\1\s\cmake\external\onnx\onnx\defs\generator\old.ccOne of the attributes 'value' or 'sparse_value' must be specified for a Constant node.Only one of the attributes 'value' or 'sparse_value' must be specified for a Constant node.dilation value along each spatial axis of the filter.The zero-padding added to one side of the output. This is also called adjs/adjustment in some frameworks.If true, compute the mean and variance across all spatial elements If false, compute the mean and variance across per feature.Default is 1.If set to nonzero, run spatial batch normalization in test mode, default is 0.Dilation value along each spatial axis of filter.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.Stride along each axis.p value of the Lp norm used to pool over the input data, default is 2.0.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.varsaved_varsaved_meanFactor used in computing the running mean and variance.e.g., running_mean = running_mean * momentum + mean * (1 - momentum), default is 0.9f.is_testThe epsilon value to use to avoid division by zero, default is 1e-5f.D:\a\_work\1\s\cmake\external\onnx\onnx\defs\nn\old.ccStride along each spatial axis.Constrain output mask types to boolean tensors.The ratio of random dropoutIndicate 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). 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. (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 per activation. If false, compute the mean and variance across per feature over each mini-batch.The 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.D:\a\_work\1\s\cmake\external\onnx\onnx\defs\reduction\old.ccThe pooling method. Two modes are supported: 'avg' and 'max'. Default is 'avg'.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.default 1; Pooled output Y's width.default 1; Pooled output Y's height.D:\a\_work\1\s\cmake\external\onnx\onnx\defs\object_detection\defs.ccMultiplicative 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. 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_thresholdboxesiou_thresholdmax_output_boxes_per_class expected to have rank Loop 'body' subgraph outputs should all be tensors but output D:\a\_work\1\s\cmake\external\onnx\onnx\defs\controlflow\old.ccAn 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.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.Mismatched tensor element type for output Mismatched type for output then= else=Constrain input types to any tensor type.output_sequenceInput Sequence and Tensor are expected to have the same elem type. Sequence= Tensor=Input Sequence and Tensor are expected to have type info. Current type is null.SequenceConstruct is expected to have at least 1 input.D:\a\_work\1\s\cmake\external\onnx\onnx\defs\sequence\defs.ccElement type of inputs are expected to be the same.Input type for input at index is null. Type info is expected.Constrain output to integral tensor. It must be a scalar(tensor of empty shape).positioninput_sequenceInput type for input at index 0 is null. Type info is expected.Constrain position to integral tensor. It must be a scalar(tensor of empty shape).(Optional) The data type of the tensors in the output sequence. The default type is 'float'.Attribute dtype should be of integer type and specify a type.Keep the split dimension or not. Default 1, which means we keep split dimension. If input 'split' is specified, this attribute is ignored.Which axis to split on. A negative value means counting dimensions from the back. Accepted range is [-rank, rank-1].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]`. Invalid value of attribute 'axis'. Accepted range=[], Value=new_axis must be either 0 or 1Sum of split values not equal to 'input' dim size on 'axis'. 'axis' dim size= sum of split values=Input 'split' can not be empty.Only supports `int32_t` or `int64_t` inputs for splitConstrain split size to integral tensor.Constrain input types to all tensor types.Insert and concatenate on a new axis or not, default 0 means do not insert new axis.If set, defines the broadcast dimensions.Enable broadcastingConstrains input to integral tensors.Constrains input to float tensors.D:\a\_work\1\s\cmake\external\onnx\onnx\defs\logical\old.ccD:\a\_work\1\s\cmake\external\onnx\onnx\defs\object_detection\old.ccD:\a\_work\1\s\cmake\external\onnx\onnx\defs\quantization\old.ccOptional scaling values used by some activation functions. The values are consumed in the order of activation functions, for example (f, g, h) in LSTM.fowardD:\a\_work\1\s\cmake\external\onnx\onnx\defs\rnn\old.ccThe sequence output for the hidden is optional if 0. Default 0.complex128complex64DataTypeUtils::FromDataTypeString - Received invalid data type string opaquemap(seq(tensor(Invalid data type mapInvalid tensor data type Unsuported type proto value case.sparse_tensor(float_datasetting data_type field (tensor name: ) to UNDEFINED is not alloweddata_typeraw_dataint64_datastring_dataint32_dataelem_typetype is required but missing.Field 'Unrecognized type value case (value_info name: ): value_typekey_type) should not be stored in raw_data field) should contain one and only one value field.TensorProto (tensor name: ) is 0-element but contains data!values of data_type '' should be stored in field '' instead of 'STRING data (tensor name: Data of TensorProto ( tensor name: ) is stored externally and should not have data field.uint64_datadouble_dataTensorProto ( tensor name: ) is stored externally but doesn't have a location.) should be stored in , but it doesn't exist or is not accessible.name' of value_info is required to be non-empty. ==> Context: is deprecated in domain_version of No Op registered for with domain_version of No opset import for domain 'graphBad node spec for node. Name: OpType: Op registered for op_type) should refer to attribute in parent node.Attribute (name: ) should not contain more than one value field.Warning: Checker does not support models with experimental ops: NodeProto (name: , type: ) has zero input and zero output.Nodes in a graph must be topologically sorted, however input '' of node: name: is not output of any previous nodes. where as the model imports opset version ' has been used as output names multiple times.Tensor initializers must have a non-empty nameinitGraph must be in single static assignment (SSA) form, however '' has been used as graph input names multiple times. sparse initializer name is not unique across initializers and sparse_initializersSparse tensor initializers must have a non-empty name in initializer but not in graph input initializer name is not unique values, but NNZ is ) index value at position [] out of range [0, Sparse tensor indices () has Unrecognized data_type (tensor name: ) must have INT64 type.) dimensions are not positive.) must have a dense-rank > 0Sparse tensor values (type field and data field mismatch in attribute attr) has no index values.) must have rank 1 or 2.) second dimension size does not match rank of tensor.) first dimension size does not equal NNZ.] not in sorted order.Sparse tensor () must have rank 1.sparse_tensor_proto] not in lexicographic sorted order.] out of range. imports opset version ConstantFill does not contain a graph.GraphProto attribute inferencing is not enabled in this InferenceContextImpl instance.) vs (NOT_SETsparse_tensor_typeopaque_typeoutput input is out of boundsONNX models don't support multiple opset version imports for a domain. Function Warning: Unsupported operator . No schema registered for this operator.Warning: Shape inference does not support inputs but were providedInferred shape and existing shape differ in dimension : (Inferred shape and existing shape differ in rank: (Inferred elem type differs from existing elem type: ( models with experimental operators: type case unsupported. existing=type case mismatch. existing= inferred=(op_type:, node name: Graph has Input 'sizes' must have int64 element type.Number of elements of input 'sizes' must be same as rank of input 'X'Dimension value inferred () is not equal to the existing dim value (Input 'scales' must have float element type.Number of elements of input 'scales' must be same as rank of input 'X'onnx.ValueInfoProtoonnx.AttributeProtoonnx.StringStringEntryProtoonnx.ModelProtoonnx.TrainingInfoProtoonnx.NodeProtoonnx.TypeProto.Opaqueonnx.TypeProto.SparseTensoronnx.TypeProto.Maponnx.TypeProto.Sequenceonnx.OperatorSetIdProtoonnx.TypeProtoonnx.TensorProtoonnx.TensorProto.Segmentonnx.GraphProtoonnx.TensorAnnotationonnx.TypeProto.Tensoronnx.TensorShapeProtoonnx.TensorShapeProto.Dimensiononnx.SparseTensorProto   FLOATFLOATSGRAPHGRAPHSINTINTSSPARSE_TENSORSPARSE_TENSORSSTRINGSTRINGSTENSORTENSORSUNDEFINEDonnx.FunctionPrototext size: endpos: startpos: RE2: invalid startpos, endpos pair. [Error reverse compiling 'pattern too large - reverse compile failedInvalid RE2: ': Error parsing 'Error compiling 'pattern too large - compile failedUnknown encoding D:\a\_work\1\s\cmake\external\re2\re2\re2.ccSearchOnePass inconsistencySearchBitState inconsistencySearchNFA inconsistencylist count bytemap range DFA out of memory: size SearchDFA inconsistencyWalk NULLmissing ]missing )trailing \no argument for repetition operatorunexpected errorinvalid escape sequenceinvalid character classinvalid character class rangeD:\a\_work\1\s\cmake\external\re2\re2\regexp.ccBad reference count Unexpected op in Regexp::Equal: no errorRegexp not destroyed.Stack not empty.D:\a\_work\1\s\cmake\external\re2\re2/walker-inl.hinvalid named capture groupNumCapturesWalker::ShortVisit calledinvalid repetition sizebad repetition operatorinvalid perl operatorinvalid UTF-8YYYY0YYYYYYYYȻY8YCompiler::Copy called!No ranges in char classMissing case in Compiler: should never happenD:\a\_work\1\s\cmake\external\re2\re2\compile.ccBad hex digit AnyBad call to ParseState::ParsePerlFlagsRepetitionWalker::ShortVisit calledConcat of unknown round: RE2: unexpected op: AddFoldedRange recurses too much.D:\a\_work\1\s\cmake\external\re2\re2\parse.ccUnexpected opcode in short circuit: context does not contain textBad args: nsubmatch= in stepUnhandled in AddToThreadqunhandled D:\a\_work\1\s\cmake\external\re2\re2\nfa.cc in ComputeFirstByteunhandled opcode: D:\a\_work\1\s\cmake\external\re2\re2\dfa.ccDeadState in RunStateOnByte|RunStateOnByteUnlocked failed after ResetFailed to analyze start state.RunStateOnByteUnlocked failed after ResetCacheNULL state in RunStateOnByteUnexpected special state in RunStateOnByteStateSaver failed to restore state. arg Unexpected opcode: Bad arg in kInstAltMatch: Bad arg in kInstCapture: job_.size() = njob_ = GrowStack() failed: D:\a\_work\1\s\cmake\external\re2\re2\bitstate.ccCannot use SearchOnePass for unanchored matches.D:\a\_work\1\s\cmake\external\re2\re2\onepass.ccUnexpected opcode in IsMatch: D:\a\_work\1\s\cmake\external\re2\re2\prog.ccSimplifyWalker::ShortVisit calledSimplify case not handled: Malformed repeat Case not handled in ComputeSimple: CoalesceWalker::ShortVisit calledDoCoalesce failed: r1->op() is DoCoalesce failed: r2->op() is D:\a\_work\1\s\cmake\external\re2\re2\simplify.cc[:xdigit:][:^xdigit:][:upper:][:^upper:][:word:][:^word:][:alpha:][:^alpha:][:ascii:][:^ascii:]\w\W[:alnum:][:^alnum:]\d\D\s\S[:punct:][:^punct:][:space:][:^space:][:lower:][:^lower:][:print:][:^print:][:digit:][:^digit:][:graph:][:^graph:][:blank:][:^blank:][:cntrl:][:^cntrl:]az09 09 !/:@[`{~09AZaz09AZ__az ~09AZ__az!~AZ09AFaf AZazYYYYYYYYY0YY0YYYYYPY`Y`Y`YpYYYYYXYYXYYYYYHYYXYYhY@YxY@YYYYY(Y(Y8Y(YYYYYYYYYYDY YDY0YY@YYYHYYHYGujaratiGunjala_GondiGurmukhiHanGlagoliticGothicGranthaGreekEgyptian_HieroglyphsElbasanEthiopicGeorgianDeseretDevanagariDograDuployanKannadaKatakanaKayah_LiKharoshthiInscriptional_PahlaviInscriptional_ParthianJavaneseKaithiHebrewHiraganaImperial_AramaicInheritedHangulHanifi_RohingyaHanunooHatranBopomofoBrahmiBrailleBugineseBassa_VahBatakBengaliBhaiksukiArmenianAvestanBalineseBamumAdlamAhomAnatolian_HieroglyphsArabicCsCuneiformCypriotCyrillicCherokeeCoCommonCopticCcCfChakmaChamBuhidCanadian_AboriginalCarianCaucasian_AlbanianNkoNlNoNushuNabataeanNdNew_Tai_LueNewaMroMultaniMyanmarNMiaoMnModiMongolianOsmanyaPahawh_HmongPalmyrenePau_Cin_HauOld_South_ArabianOld_TurkicOriyaOsageOld_North_ArabianOld_PermicOld_PersianOld_SogdianOghamOl_ChikiOld_HungarianOld_ItalicLmLoLtLuLinear_ALinear_BLisuLlLaoLatinLepchaLimbuKhmerKhojkiKhudawadiLMeetei_MayekMende_KikakuiMeroitic_CursiveMeroitic_HieroglyphsMasaram_GondiMcMeMedefaidrinMalayalamMandaicManichaeanMarchenLycianLydianMahajaniMakasarVaiWarang_CitiYiZanabazar_SquareTibetanTifinaghTirhutaUgariticTangutTeluguThaanaThaiTai_ThamTai_VietTakriTamilZlZpZsSamaritanSaurashtraScSharadaPsPsalter_PahlaviRejangRunicPhags_PaPhoenicianPiPoPcPdPePfSyriacTagalogTagbanwaTai_LeSora_SompengSoyomboSundaneseSyloti_NagriSkSmSoSogdianShavianSiddhamSignWritingSinhalaY[[]^0coo    : : .... . . . ...!.!.@kCkoooopsuwz}&*]afj EHMPWYY[[]]_}&!&!ee3@x #AAJJX_y        @ H } ~     X _ x      0 9 `~&QTRo6?PYPY0;PlPY$n$`jijPkYk[kaknn`xPYq @E-0>      ( * 0 2 3 5 9 < D G H K M V W \ ] _ c f w 0.4AZ      ""$$&&((**,,..0022446699;;==??AACCEEGGJJLLNNPPRRTTVVXXZZ\\^^``bbddffhhjjllnnpprrttvvxy{{}}      ""$$&&((**,,..0022:;=>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""$$&&((**,,..22446688::<<>>@@BBDDFFHHJJLLNNPPRRTTVVXXZZ\\^^``bbddffhhjjllnnyy{{}~!:.0/011122`2~2`|װנP    ª۪ߪ$$   ?? 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